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Hello! It’s me again, the developer of ADT.

There have been a lot of changes since last time. I even created a web page that works as a wiki, so you can explore everything the app has to offer

Changes:

1. Chat Llama Native: We now support both Ollama and llama.cpp servers. We also added tools for the LLM to use: it can search the web, write and update notes, create to-do lists, generate images, create calendar events, set alarms, and more. You can now also schedule tasks that will trigger at the chosen time.

2. Improved the stability of tools like the Ollama Manager, Dataset Creator, and AI Agent.

3. Reworked how summaries work. We removed the llama-cli binary and now only use the Llama server, which must be active for summaries to work.

4. Everything in the app now accepts llama.cpp/Ollama servers, so you can use the one that best suits you, although I recommend llama.cpp.

5. Added image generation through the ONNX Runtime using ORT models created by the team behind SDAI Local. I also made some new versions that include a VAE decoder to allow img2img inference. This is still a work in progress, but it is functional enough.

6. Added support for video generation through the SD binary. Read more about it in the wiki.

7. The pet has been completely reworked. We finally have sprites, the park is alive, the work system has been redesigned, and there are timed events that you won’t want to miss.

8. Termux tools have been fixed. Users can now install all the projects without errors and use them easily.

9. Fixed a bug where the app was not extracting the binaries.

10. Added three kinds of widgets that allows the user to see their calendar, upcoming events and a chosen note

Overall, the quality of the app has improved a lot, and I couldn’t be happier to share all of this with you!

The Play Store closed test is still updating, so if you choose that path you better wait for a few hours-24h to update/install (this path helps me a lot as I still need testers to start the public beta and publish the app)

To join the Play Store closed test, you first need to join the Google Group here

After joining, you’ll be able to opt into the test and install the app from here

Follow the wiki's guide to learn how to use the app properly.

GitHub release page here

I have tried gemini pro and chatgpt plus and deepseek for letting them analyse for example a 200 page docx or pdf file and tell him to explain the slides or summarize and all provided horrible results. (they can't parse the whole document correctly I guess)

What is the best AI model that can read documents correctly and summarize them?

Full prompt:
Redraw the attached image in the most clumsy, scribbly, and utterly pathetic way possible. Use a white background, and make it look like it was drawn in MS Paint with a mouse. It should be vaguely similar but also not really, kind of matching but also off in a confusing, awkward way, with that low-quality pixel-by-pixel feel that really emphasizes how ridiculously bad it is. Actually, you know what, whatever, just draw it however you want.

From CHOI on 𝕏: https://x.com/arrakis_ai/status/2049689793118998717

From nic on 𝕏: https://x.com/nicdunz

Hello.

I've been testing GPT pro thanks to it's free 1 month pro trial, and I was wondering if I could also test Claude, so in the end, I could decide between the 2 subscriptions.

From my understanding the only way currently to test, is if someone refferes you? What about free trials from Antrophic, will there be something similar to the GPT ones?

I just interviewed Michael Maximilien, former CTO at IBM and Chairperson of NodeJS Foundation, who spent a year shipping production RAG to multiple customers. His lesson was uncomfortable. Until you evaluate your customer's data, nothing on a leaderboard predicts what works.

Most teams treat RAG as a setup task. You pick a vector database because it trended online. You pick an embedding model because OpenAI's the safe default.

Then you spend six months vibe-checking the results. Production RAG requires a continuous stitch-evaluate-iterate loop rather than a one-time setup.

Which is extremely cumbersome. That's why people don't do it.

Here is how it looks:

1. Stitch the components together instead of just picking one. A production RAG system has at least five interchangeable parts: an embedding model, a chunking strategy, retrieval parameters, a vector database, and a judge.
2. Evaluate your customer's actual questions rather than generic benchmarks. Maximilien's customers always have five or six release-time sanity questions that become the eval dataset.
3. Align your judge with a human before you trust the scores. In the article's customer use case, the LLM-as-judge correlation with human judgment hovers around 0.55. Three weeks of human labeling and few-shot alignment came before any judge score was treated as ground truth.
4. Iterate cheapest-first to save time and money. Tune your retrieval parameters first because that's free, then move to the embedding model, and only change your chunking or vector database last.
5. Run this loop in any harness that has the right shape. Weave CLI is one option, but any setup that lets you swap a component, re-evaluate, and compare runs will work.

The proof landed when he tested a real customer dataset of Leica auction listings. He held everything constant and swapped only the embedding provider.

A small, open-source model, all-MiniLM-L12-v2, ranked #130 on the MTEB leaderboard, beat OpenAI by 11% in quality. It ran 240x faster for re-embedding, produced vectors that were 50% smaller, and cost exactly $0.

The leaderboard had no idea what his customer's data looked like. The eval did. As Maximilien put it: "This is a counterintuitive outcome. Without a structured benchmark, I would have defaulted to OpenAI and been wrong."

What have your own evals told you that contradicts a leaderboard or a trendy default?

TL;DR: Production RAG is a stitch-evaluate-iterate loop on your customer's data. Public benchmarks and MTEB ranks are signals, not verdicts. Until you measure your data, nothing matters.

Create a professional, modern, high-energy technology retail marketing flyer for the month of Mai. The layout must match the dynamic composite style seen in professional print and digital advertising.

1. Scene and Subject:

Against a clean, light-blue-to-white gradient background, depict a central, dynamic, mid-air pose of a stylish Black man (as seen in the attachment

Source: Use the person from the ATTACHED

REFERENCE PHOTO

Preservation: You must keep their exact facial

features, skin tone, hairstyle, and natural likeness perfectly unchanged.

Universal Application: Apply the lighting and angle to the subject regardless of gender) with natural hair, blue t-shirt, tan cargo pants, and heavy black boots with deep tread).

They are captured in a leaping, expressive pose, looking directly at the camera with a surprised and energetic expression. One boot is large in the foreground due to the dynamic angle.

1. Floating Elements (Tech Gadgets):

The area surrounding the central subject is a creative "exploded view" of floating premium tech products.

Central Bag: A black fabric tech shoulder bag/briefcase is held, appearing open.

Surrounding Devices : Arrange a collection of floating high-end gadgets including multiple white AirPods (case and individual pods), a white pair of large over-ear headphones, two separate iPhones (one silver/white Pro model, one black/space grey model), an Samsung A16, a smartphone screen, a battery , a Laptop with a smart keyboard, and several small scattered adapters.

Additional Floating Corner Item: In the bottom-right corner, place a large, dominant floating peach/orange color iPhone, matching the perspective and look of the one in the reference.

1. Typography and Branding:

Top (exact text): Centered at the top, place the small text "Bienvenue en". Below this, in a massive, dominant, 3D-styled blue font (matching the specific blue and script-like yet modern style of the reference), render the text "Mai". This large blue text should interact with the floating objects, sitting in front of some and behind others for depth.

Top Corners (placeholders): In the top-left corner, place a circular logo (BenServices). In the top-right corner, place a functional QR code.

Bottom Banner (exact text and layout): At the bottom, a clean, dark (near-black) bar with refined white text reads: "Heureux Mois, Possédez et Profitez des meilleurs produits tech cette saison". Below this, a clean white panel with a dark border lists: "Nous disposons actuellement de: Smartphone | Laptops - Écrans (Iphone/Android)| Batterie | PlayStations | Tablette | Power Banks | Accessoires etc.".

Footer Address (exact text): The very bottom line has the precise address in smaller white text: "Bouaké, Commerce en face du Capitol - Contact : 0747934176".

1. Lighting and Finish:

The scene should have bright, clean studio lighting with soft shadows to highlight textures (fabric bag, skin, polished metal of devices). The depth of field should keep the subject and foreground boot sharp. All text on all objects and signs must be perfectly legible and exact.

End of Prompt.

Most agent systems have prompts, tools, and memory, but no operating model.

I just open-sourced a small kit built around a different assumption: treat the agent like a micro AI company.

Core ideas: - token is budget - optimize value per spend, not just activity - no concrete output = not finished - no verification = not complete - repeated work should compound into reusable assets - lightweight KPI review should correct drift instead of creating dashboard theater

The repo is host-agnostic. It is meant to layer onto an existing assistant/runtime rather than replace its execution stack.

I’d love feedback from people building long-running assistants, agent workspaces, or digital twins: what governance loops are you finding actually matter in practice?

If useful, I can drop the GitHub link in the comments.

I’m launching a small AI automation service targeting small businesses. Just finished the landing page .

flowkill.com

Looking for honest feedback: Is the message clear? Would you trust this enough to fill in the form? What’s missing?

Still early stage, doing free case studies for the first few clients.

In February 2026, a Nebraska attorney submitted a Supreme Court brief drafted by an AI. He didn't double-check it.

The judges stopped him 37 seconds into oral arguments. Why? Because 57 out of 63 citations were completely made up. The AI invented case names, court dates, and quotes from judges who never said those words.

He was indefinitely suspended, and his client now owes $52,000 in opposing fees.

The Problem: LLMs are pattern-completion machines, not databases. They don't just "guess wrong." If you ask for a legal case, a statistic, or a reference, they confidently generate a statistically likely fake fact that looks 100% real.

The 4-Step Verification Workflow: If you use AI for work, reports, or research, you need this habit:

1. Treat facts as guilty until proven innocent: Mentally flag every name, date, statistic, or quote. If it sounds like a hard fact, assume it's a hallucination until you verify it.
2. Find the primary source: Never use AI to verify AI. Find the actual study, official document, or case PDF yourself.
3. Use grounded tools: Ditch standard, offline AI for research. Use Perplexity AI, Claude (with web search), or Gemini (with search) so you get inline citations. Always click the links to check them.
4. Prompt for uncertainty: AI won't admit when it's guessing. Force it to by adding this to your prompt: "For every specific fact, case, or statistic you include, mark it with [VERIFY] so I know to check it independently."

The Bottom Line: AI is the fastest first-draft generator in history, but it will confidently lie to you. The tool did exactly what it was designed to do (generate plausible text). The failure was a human treating a zero-verification workflow as acceptable.

The AI doesn't get fired or lose its license. You do.

(Full story and breakdown:MindWiredAI)

Hi everyone, this is Oli. I built a free AI tool that helps you tailor your resume to any job based on my job data.

You might have seen WhoHasJobs before.

I’ve now updated the system so you can tailor your resume to any job.

Originally, WhoHasJobs was a custom job search for you. That wasn’t scalable once I had 900 sites to scrape daily.

So instead, I kept the scrapers but slowed them down. Important to know if you look for jobs: they update round robin and it takes about 4-6 days for one full trip.

The ATS optimizations for systems like Greenhouse and Lever came in useful, and I built something new on top of that and the data that I have now.

It would be amazing if you try it. https://whohasjobs.com/jobhq

If you’re looking for a job, or even if you’re just interested in testing it, you can create an account completely for free. There is no paid version yet. I’m mostly trying to get feedback and improve the system.

What you can do with it

Upload your resume

You can upload your resume, and the AI in the background extracts your skills, experiences, education, projects, etc. into structured fields.

You can edit those fields yourself.

Build a “career memory”

The idea is that you build one giant resume / career memory that contains all of the relevant information that could be useful for any tech job, or really any job.

This can include your work experience, education, projects, hobbies, skills, certifications, and anything else that might be relevant.

Edit, add, and enrich each experience

You can edit, add, and enrich each experience, project, education entry, or even hobby based on current and past tech job data.

For example, if you worked at company ABC and your resume description is very brief, you can refine it with AI.

The AI will try to ask you questions based on what we know from tens of thousands of tech jobs, what skills are currently looked for, and what has been looked for in the past.

Find relevant skills you may have forgotten to mention

The AI will try to see if you might have relevant skills or experiences that are looked for in certain jobs, and then ask you about them.

It does this by narrowing down which past projects, jobs, education, or other experiences could fit that profile.

For example, if it looks like you may have worked with Kubernetes because you mention containerization, microservices, deployments, etc., but you didn’t actually write Kubernetes in your resume, it might ask you about it.

If you did use it, you can add it. If you didn’t, you don’t.

It does not hallucinate things into your resume

It asks you.

That is important to me. The goal is not to invent experience. The goal is to help you remember and structure the experience you actually have.

Upload multiple resume versions

If you previously tailored your resume for different jobs, you can upload those versions too.

It will continuously merge and enrich your career memory with them.

It should not overwrite anything. If there is a conflict, it should show you the difference and ask you. Otherwise, it just keeps adding more useful information.

Sometimes there might be conflicts, and then you can add things manually. Please let me know if you run into that.

Tailor your resume to a specific job

Once your career memory is built, you can tailor your resume to a specific job description.

The goal is not to stuff the resume full of keywords.

The goal is to make sure the resume parses well, matches the job where it honestly should match, and then stands out to a human recruiter.

Make the resume easier for ATS systems and recruiters

Because WhoHasJobs started as a job scraper and worked with ATS systems, I was able to understand how a lot of those systems work.

They are actually pretty straightforward. Most of them are also fairly transparent.

In the end, you mostly want the system to parse and categorize your information, keywords, experiences, education, etc. as clearly and easily as possible. That is basically what these systems do.

The important part is how recruiters use them.

From what I’ve seen, recruiters mostly sort, filter, and rank candidates. There is some pre-sorting, and there are filters. So the more clearly your relevant information passes through, the easier it is for the recruiter to find it.

So it optimizes the structure and the keywords for ATS parsing, but it also tries to make the headers, taglines, and resume sections work for a recruiter who is actually reading it.

Why I’m asking people to try it

I’ve been able to use it myself, and a friend of mine has used it too. We got interviews. But we’re not my ideal target users I think, so I’d really like to get feedback from a broader range of people, especially people who are tired of applying and tailoring their resume again and again.

You can see in the video how this broadly works. There are lots of features that I do not show for brevity though.

I’ve looked at best practices, what Greenhouse and other ATS systems show publicly, how recruiters see applications, how they rank and filter, etc. This should work fairly well, but there is always a lot more to do.

Credits

Everyone gets 100 free credits.

One credit is used for one resume tailor, which is actually a couple dozen GPT requests in the background. Uploading a resume or editing is also usually one credit each time. So you can already do quite a lot with the 100 free credits.

If you run out of credits, please let me know. There is no paid version right now. Just tell me how you used it, what you used it for, what worked, what didn’t work, and how I can make it better. I’ll very happily reset your credits so you can try more.

This is still very early and very much a work in progress. I just think it’s useful, and I’d really appreciate it if people could try it and let me know what they think.

Thank you.

OpenAI restricted GPT-5.5 Cyber to vetted defenders one day after publicly criticizing Anthropic for the same move with Claude Mythos.

The UK AI Security Institute's evaluation found GPT-5.5 can locate real vulnerabilities, which is the empirical reason these restrictions exist, not competitive posturing. Both labs arrived at identical conclusions through different routes, and the convergence matters more than either company's public framing. TechCrunch's account of the reversal makes the contradiction impossible to spin away.

The containment instinct is spreading beyond labs. The Zig project published a detailed rationale for banning all LLM-assisted contributions, and maintainer Andrew Kelley states flatly that LLM-generated PRs are detectable by their characteristic failure modes. This is the clearest articulation yet of why serious open-source projects are drawing hard lines, and it arrives exactly as Codex CLI 0.128.0 ships a /goal command enabling persistent agentic loops that run until self-evaluated completion. The labs are pushing autonomous coding agents outward while the projects those agents would contribute to are locking the door.

Hardware is being purpose-built for the world the labs are selling. Qualcomm announced a dedicated CPU for agentic workloads and disclosed a custom chip for an unnamed hyperscaler, which means silicon design cycles are now tracking agent architectures, not just transformer training. Apple's supply chain was caught off-guard by AI-driven Mac demand, with Mac mini, Studio, and Neo all constrained, suggesting on-device inference is pulling hardware faster than anyone's forecast model predicted.

Anthropic's $900B valuation round could close within two weeks, with allocations solicited inside 48 hours. Legora hitting $5.6B while running dueling ad campaigns against Harvey shows the capital is now cascading into verticals. Atlassian, Twilio, and Five9 all beat earnings citing AI adoption as the primary driver, which is the first quarter where broad enterprise AI spend shows up cleanly in financials rather than as a narrative.

Two pieces of foundational science cut against the current modeling assumptions in ways that will take time to land. Lilian Weng's "Why We Think" synthesizes the theoretical basis for test-time compute and chain-of-thought, likely becoming the canonical reference for reasoning model design. Separately, Quanta's coverage of a novel synaptic plasticity mechanism enabling learning from a single experience challenges the Hebbian assumptions baked into most neural network theory, and ultrafinitism research rejecting infinite sets is producing computational insights with direct implications for finite-precision arithmetic in deployed models.

Applied Intuition's physical AI work across mining, drones, and warships is where the simulation-to-real gap stops being theoretical, and the inference inflection analysis frames why inference cost and architecture are now the binding engineering constraint, not training scale.

Within 90 days, at least one other frontier lab will announce access restrictions on a specialized capability model using the same responsible deployment framing OpenAI used to justify reversing its own public criticism of Anthropic.

TBH I don't know if our current "AI" models are capable of thinking. There is a massive pattern i'm noticing when using AI and have been for the past couple years, AI follows a strict pattern and doesn't seem to think. Just like calculators it already has a designated answer regardless of the question its just a bit more advanced. Hence why it lies to many users.
Or it could be that there are so many rules on the intelligence model that it is constantly bouncing off of walls to give you an already programmed answer to not break these rules.
Im not sure about either.
I'd much rather call AI as of rn "engineered intelligence", not artificial, since its still learning from us engineers, and it will eventually adapt into intelligence. ( This is under the assumption that it can truly freely think )
Does anyone know if these models like Gemini, Chatgpt, Claude, actually "think"

Bonjour à tous et à toutes !

Je suis un nouvel addict à l'IA et Claude me plait énormément. Après avoir chipoté avec plusieurs modèles d'IA différents et tester l'intégration d'une base de données sur GitHub, je suis en train d'essayer de créer une image contextuelle de ma vie sur différents aspects, principalement sur l'aspect patrimonial.

Mon idée est de créer une structure d'archivage bien organisée, séparant mon foyer avec le compte commun, mes deux entreprises avec leur dossier respectif, et ma sci pour l'immobilier. J'ai donné l'instruction à Claude d'être un archiviste, qui doit lire certains fichiers spécifiques à chaque prompt pour définir son rôle, règles et autres paramètres, et en gros il doit sur base de fichiers que je lui donne (extraits de compte, bilan, etc) extraire les données de ces documents et les encoder dans les bons fichiers .md localisés dans des dossiers de ma structure sur mon repo privé GitHub, afin de créer une mémoire contextuelle définitive de ma vie et mon environnement.

Dans le futur, je pourrai créer d'autres assistants spécialisés qui pourront se reposer sur cette base de donnée pour répondre à des questions spécifiques (optimisation fiscale de mon patrimoine privé, gestion de stock de mes entreprises, etc...)

Claude Code à consommé 22k tokens pour lire analyser et encoder les extraits de compte de mon foyer de 2022. C'est juste énorme.

Auriez-vous des conseils sur comment procéder pour améliorer mon utilisation de Claude ?

Merci d'avance pour vos conseils et retours 🙏🏽🔥

There is word AI agents everywhere. Each company should use it. Then you search for ai agents agencies that should provide that and you cannot find legit case studie. Even fkin chatbot which is primitive. Best bang is when that agencies which is selling AI automations and AI agents does not have even AI chatbot on their website and for contact use the form. I I am asking why ? Why there is prediction of 1 trilion market in ai agents replacing all tasks and roles, but it is fckin impossible to find evidence that it is working for customers of that agencies.

I am considering a new machine to run local LLMs.
The scope of what I can do with my current machine specs is limited, and because it is a mini-ITX case, it cannot be expanded.

Current Configuration
CPU
i5-12600K
GPU
5060ti 16GB
RAM
DDR5 5200Mhz 32GB
SSD
512MB + 256MB

Which of the following options should I choose?

Intended Uses
AI Agents (e.g., goose)
Requirements Definition, Code Generation, Code Analysis
Data Analysis
Health data (diet, exercise, body composition, blood pressure, sleep, etc.)
Financial asset data
RAG
In the future

Pattern 1
Revamp by moving the existing 5060ti into an ATX case
CPU
RYZEN 9700X
GPU1
RTX PRO 4500 Blackwell 32GB
GPU2
5060ti 16GB
RAM
DDR5 6000Mhz 64GB
SSD
512MB + 1TB
Cost: $4500

Pattern 2
Purchase the OEM version of DGX Spark for back-end operation
Use the current machine for front-end operation
Model
MSI EdgeXpert
Memory
128GB
SSD
1TB
Cost: $4100

Pattern 3
Purchase a Mac for back-end operation
Use the current machine for front-end operation
Model
Mac Studio M3 Ultra
CPU, GPU
28 core, 60 core
Memory
96GB
SSD
1TB
Cost: $4300

TL;DR

Claude Desktop's Code tab is excellent for developers, but the same underlying capability — Claude as a stateful, file-aware agent over a git-backed workspace — would unlock a much larger market if reframed for knowledge workers. A new Docs tab, sibling to Code, would let compliance, legal, ops, and policy teams work in markdown + mermaid with git underneath, without ever seeing a developer concept. This is a small product step on top of existing infrastructure with a large addressable audience that today has no good AI-native tool.

---

The Problem

Knowledge workers managing structured documents — security policies, BRDs, RFCs, runbooks, SOPs, audit evidence — are stuck choosing between:

• Word/Google Docs: friendly UI, but opaque binary formats, weak diffs, painful bulk edits, and AI tools struggle to edit them cleanly.
• Notion/Confluence: nice editing experience, but proprietary storage. Doesn't integrate with compliance platforms (Drata, Vanta, SecureFrame) that increasingly expect markdown-in-git as the source of truth.
• VS Code + git + extensions: technically the right tool, but the UI is aggressively developer-branded. Compliance and legal staff bounce off it. Asking a SOC 2 program manager to learn git commit is a non-starter.

Teams adopting "docs-as-code" workflows (markdown + mermaid in a git repo, synced to Drata or similar) have no editor that matches their mental model. They're forced to either train non-developers on developer tools, or give up the audit/version-control benefits and stay on Word.

The Opportunity

Claude already has two capabilities that, combined, solve this:

1. Best-in-class long-form writing — widely acknowledged advantage over competing models for policy, legal, and prose work.
2. The Code tab's agent loop — stateful file editing, git operations, worktree isolation, MCP integrations. All already shipped and working.

A Docs tab would be the Code tab with three changes: a markdown-first editor with live mermaid preview, a vocabulary swap that hides git, and document-workflow features (review, approval, PDF export, compliance-platform integrations).

What Docs Tab Looks Like

Inherits from Code tab (no new infrastructure):

• Repo-backed file editing
• Claude agent loop with file read/write
• Git operations under the hood
• MCP integrations (Drata, Vanta, SharePoint connectors)

New for Docs:

• Split-pane markdown editor + live preview, mermaid renders as you type
• Vocabulary swap: Save (commit), Draft (branch), Send for Review (PR), Publish (merge), Workspace (repo), Document (file)
• Hidden developer chrome: no terminal, no debug, no file extensions in the tree
• Document templates: Policy, Procedure, BRD, RFC, Runbook, ADR, Meeting Notes
• "Insert Diagram" button with Claude-generated mermaid starters
• Review/approval UI for non-developers (GitHub PR review reskinned)
• One-click PDF/DOCX export with version hash in footer (auditor evidence)
• Native connectors for compliance platforms

Concrete Use Case

I work with a company that uses Drata for SOC 2 compliance. Drata has first-class support for markdown policies stored in git, with built-in renderers for auditors. We want to move our policies from .docx to .md + mermaid, stored in a git repo, synced to Drata.

The blocker is the editor. Our compliance and InfoSec teams won't adopt VS Code — it looks like a developer tool, the vocabulary is foreign, and the safety nets (discard changes, undo, restore) aren't where non-developers expect them. We'd happily pay for a Claude Desktop seat per compliance staffer if the Docs tab existed.

This is not a one-company problem. Every company running SOC 2, ISO 27001, HIPAA, PCI, or FedRAMP compliance has the same workflow gap. Drata, Vanta, and SecureFrame collectively serve tens of thousands of companies, and the trend toward docs-as-code is accelerating because auditors love the version history.

Why Anthropic Specifically

• Differentiation from ChatGPT Desktop: Claude's writing quality is the moat. ChatGPT's file/repo workflow is weaker. A Docs tab plays to both Claude's strengths and the Desktop app's strengths.
• Broadens the commercial base: today, Claude Desktop is sold to developers. Docs tab opens compliance, legal, ops, consultancies, law firms, healthcare, financial services — segments willing to pay enterprise prices for audit-grade tooling.
• Reuses existing infrastructure: this is a UI/UX layer on top of Code tab's agent loop. Not a from-scratch product.
• Underserved market: no major AI vendor has a polished docs-as-code editor. The window is open now and won't be open in three years.

Ask

Consider a Docs tab on the Claude Desktop roadmap. I'm happy to share more detail on the compliance workflow, beta-test, or connect you with the InfoSec and compliance leaders at the companies I work with — they would be vocal early adopters.

Anthropic’s most controversial product has spent its first three weeks moving between state actors who cannot agree on whether it is theirs to use, theirs to block, or someone else’s problem entirely.

For the past 15 days I have noticed that Claude Code follows my instructions as it is from CLAUDE.md regarding any action which is specified in the file. Which is a huge improvement and while some people would disagree but I would rather use Claude Code with a project focused file instead of using 15 separate tabs and beg it to act right.

My main concern was avoiding pushing everything to Main before beta has been tested and trialed, which claude would rarely follow but now it never does that.

src - u/anthrupad

Huawei is on track to pull in around 12 billion dollars from AI chips this year. These figures are up 60% from last year. Pretty impressive.

Their new Ascend 950PR chip is getting huge orders from companies like ByteDance (who dropped over 5 billion and established TikTok), Alibaba, Tencent, etc. Especially after DeepSeek released V4 optimized for Huawei hardware.

China is already building big 10,000 card AI clusters running completely on domestic chips. Meanwhile Nvidias revenue in China is falling because they cant sell their best stuff there anymore.

US export bans, which were supposed to slow China down, actually forced them to speed up their own tech and now Huawei is becoming the go to option inside China.

This has driven a lot of discussion on how this could effect AI and China and America's roles long term.

A few different ways I could see it going:

1. Huawei keeps improving fast and actually becomes a real global competitor to Nvidia over the next 3-5 years.
2. They dominate China but still lag behind outside it because of the software ecosystem and raw performance gap. Nvidia stays king internationally.
3. The whole thing pushes both sides to innovate harder and we end up with faster progress overall (kind of like the space race).
4. Or it just leads to a fully split world with two separate AI tech stacks, which could get messy for everyone.

Either way.... t's wild to watch.

A few years ago everyone assumed China would stay behind for a decade. Now it feels like the gap is closing faster than expected, at least inside their own market. I'm not sure if this ends up weakening the US tech edge or just makes the whole AI race more competitive and fragmented. Definitely something worth keeping an eye on.

Hi! I wanted to share my new blog on the costs of running AI Evals. We dig into how benchmarking frontier systems now routinely costs tens of thousands of dollars per run, why agent evals are especially unpredictable, and what that concentration of validation authority means for the broader research community.

Sat through a demo this week for one of these AI SDR tools and the pitch was in a nutshell: you don’t need junior sales reps anymore. (As in not even train them anymore just remove them.) To my surprise it worked. The tool was doing outbound, follow-ups, personalization, all the stuff junior SDRs grind through. Faster, cleaner, no complaints! But it did leave me feeling uneasy. That grindy, repetitive work is literally how most people get into sales. It’s where you learn how people respond, how messaging gets through, how to deal with rejection without taking it personally. That's how I got into it at least. So if AI wipes that layer out completely, what’s the path in? Are we just skipping straight to “hire experienced closers” and hoping they came from… where exactly? I’m not anti-AI (this stuff is obviously useful), but replacing enty-level humans as the first step in the process doesn't feel like a sustainable route.

I have been legit using max plan for about 6 months and then i was gifted for one month the max 5x plan. and before 10 days of the expiry of the subscription it just disappeared. like i was rolled back to free plan. not cool anthropic. its been 2nd day and the support has not responded since.

did anybody had this problem ? if there is support here, please pm me, will share details

I was looking for the best model in the last few months to generate videos quickly, video quality is fine even 720, I'm interested in speed, and a workflow, I have a 4070ti, thanks everyone

I need someone to explain to me how this is even remotely okay because right now I feel like I just got finesse’d in 4K.

So I’m on the Premium+ plan. $45/month. Cool. Whatever. I even doubled down at one point because I actually like the platform.

They advertise “unlimited generation.”

UNLIMITED.

So I’m thinking:

Alright, bet. I can finally iterate properly without watching a credit meter like it’s my bank account.

Fast forward to yesterday.

I’m working on a sports car hybrid concept. Nothing crazy:

* Generating one image at a time

* No batch spam

* Probably around 80–100 images total

I even went out of my way to be careful because I’ve been burned before on other platforms.

Tell me why I check my account today…

and my credits went from ~35,000 → 7,000.

???

That’s nearly 30,000 credits gone overnight.

Here’s the best part:

I go to billing thinking maybe I got hit with some stealth charges.

Nope.

* No crazy purchases

* No auto-refill spam

* Just my normal subscription + a small credit pack

So that means…

The credits were being drained WHILE using “unlimited.”

I’m sorry but in what fucking universe does:

“Unlimited generation”

mean:

“Actually it’s unlimited unless you accidentally breathe wrong and trigger 300 credits per image behind the scenes”

???

And before someone says “you probably used upscale/enhance/etc”

I didn’t. Not once.

Why am I not seeing:

* “⚠️ This will cost credits”

* A running total

* ANYTHING??

Instead it’s just quietly burning through tens of thousands like it’s a background process.

Let’s do the math:

~100 images

~30,000 credits gone

That’s ~300 credits per image

ON “UNLIMITED”

Make that make sense.

There literally just hustling me lol

There’s a difference between:

* Paying for premium tools

and

* Getting nickel-and-dimed by invisible mechanics

At this point I don’t even trust the meter.

Like what else is being charged that I don’t see?

Is “unlimited” just a suggestion?

I already contacted support because there’s no way this is normal and idk if I’ll even hear back.

But I’m posting this here because:

1. If you’re using this platform — CHECK YOUR CREDITS

2. Don’t assume “unlimited” means what it sounds like

3. And for the love of God, watch your settings

If anyone actually understands how their credit system works in plain English… please explain it to me like I’m 5

because clearly I’ve been living in a different reality.

I built a RAG pipeline using LangGraph with the following flow:

rewrite → hybrid retrieve → cross-encoder rerank → parent expansion → grounded generation

The system enforces strict grounding (returns a fallback message if no relevant context is found) and requires inline citations like:

[file.pdf, p. 123]

Problem

Even though retrieval and reranking seem to work well, I’m facing several issues:

1. Wrong page citations
   • The model often uses the correct information but cites the wrong page.
   • Example: answer says [file.pdf, p. 71] but the UI shows a completely different page.
2. Mismatch between cited pages and rendered sources
   • The sources shown in the UI don’t match the pages referenced in the answer.
3. Occasional hallucinations / degeneration
   • The model sometimes starts repeating a word until the end of the response.

Current setup (simplified)

• Hybrid retrieval (vector + keyword)
• Cross-encoder reranking (ms-marco style)
• Parent-child document structure
• Context built from parent documents, but citations come from child chunks
• Strict prompting: “use only context or return NOT_FOUND”

Question

What are best practices to:

1. Ensure correct and stable citations (no wrong page numbers)?
2. Avoid mismatch between generated citations and UI-rendered documents?
3. Reduce hallucinations and repetition loops in grounded RAG systems?

I’ve included my full rag_graph.py below.

Any architectural or practical suggestions are appreciated.

""" RAG pipeline LangGraph. Pipeline: rewrite → retrieve (hybrid) → rerank (cross-encoder) → expand_to_parents → generate (grounded) """ from __future__ import annotations import logging import re from typing import Optional, TypedDict, Any from langchain_core.documents import Document from langchain_core.messages import HumanMessage, SystemMessage from langchain_ollama import ChatOllama from langgraph.graph import StateGraph, END from config import LLM_MODEL, OLLAMA_BASE_URL from modules.vector_store import NotebookVectorStore from modules.parent_store import ParentStore logger = logging.getLogger(__name__) NOT_FOUND_MSG = "Túto informáciu som v nahraných dokumentoch nenašiel." # ── Parametre pipeline ─────────────────────────────────────────────────────── RERANKER_MODEL = "cross-encoder/mmarco-mMiniLMv2-L12-H384-v1" INITIAL_K = 40 # hybrid retrieval RERANK_KEEP_K = 10 # top candidates MAX_CONTEXT_CHARS = 9000 # MAX_PARENTS = 6 # top limit of parents in kontexte MIN_RERANK_SCORE = -4 # ── Reranker singleton ─────────────────────────────────────────────────────── _RERANKER = None def get_reranker(): global _RERANKER if _RERANKER is None: from sentence_transformers import CrossEncoder try: import torch device = "cuda" if torch.cuda.is_available() else "cpu" except Exception: device = "cpu" logger.info(f"Načítavam reranker: {RERANKER_MODEL} na {device}") _RERANKER = CrossEncoder(RERANKER_MODEL, device=device, max_length=512) return _RERANKER # ── Deiktiká pre query rewriting ───────────────────────────────────────────── _DEICTIC_PATTERNS = [ r"\ba (čo|aký|aká|ako|kedy|prečo|potom|ďalej|ten|tá|to|teda)\b", r"\b(ten|tá|to|tie|toto|túto|tomto|týmto) ", r"\b(vysvetli|rozveď|podrobnejšie|viac|ešte)\b", r"\b(predchádzajúc|predošl|prvý|druhý|tretí|ďalší|ďalšia)\b", ] _DEICTIC_RE = re.compile("|".join(_DEICTIC_PATTERNS), re.IGNORECASE) def _needs_rewrite(question: str) -> bool: q = question.strip() if len(q.split()) < 4: return True return bool(_DEICTIC_RE.search(q)) # ╔══════════════════════════════════════════════════════════════════════════╗ # ║ RAGState ║ # ╚══════════════════════════════════════════════════════════════════════════╝ class RAGState(TypedDict, total=False): question: str chat_history: list[dict] standalone_question: str retrieved: list[tuple[Document, float]] reranked: list[tuple[Document, float]] context_docs: list[Document] context_text: str answer: str source_docs: list[Document] retrieval_debug: dict # ╔══════════════════════════════════════════════════════════════════════════╗ # ║ RAGGraph ║ # ╚══════════════════════════════════════════════════════════════════════════╝ class RAGGraph: """Hlavná RAG trieda — LangGraph pipeline s parent/child retrievalom.""" def __init__(self, vector_store: NotebookVectorStore, parent_store: ParentStore): self.vs = vector_store self.ps = parent_store # Hlavný generátor: nízka teplota pre faktualitu self.llm = ChatOllama( model=LLM_MODEL, base_url=OLLAMA_BASE_URL, temperature=0.1, num_predict=1024, num_ctx=8192, ) # Rýchly LLM pre rewrite (kratšie výstupy) self.rewriter_llm = ChatOllama( model=LLM_MODEL, base_url=OLLAMA_BASE_URL, temperature=0.0, num_predict=150, num_ctx=2048, ) self.graph = self._build_graph() # ─── Build graph ───────────────────────────────────────────────────────── def _build_graph(self): g = StateGraph(RAGState) g.add_node("rewrite", self._rewrite_node) g.add_node("retrieve", self._retrieve_node) g.add_node("rerank", self._rerank_node) g.add_node("expand", self._expand_node) g.add_node("generate", self._generate_node) g.set_entry_point("rewrite") g.add_edge("rewrite", "retrieve") g.add_conditional_edges( "retrieve", lambda s: "empty" if not s.get("retrieved") else "ok", {"empty": END, "ok": "rerank"}, ) g.add_conditional_edges( "rerank", lambda s: "empty" if not s.get("reranked") else "ok", {"empty": END, "ok": "expand"}, ) g.add_edge("expand", "generate") g.add_edge("generate", END) return g.compile() # ─── Node: rewrite ─────────────────────────────────────────────────────── def _rewrite_node(self, state: RAGState) -> dict: question = state["question"] history = state.get("chat_history") or [] # Bez histórie alebo otázka je zjavne samostatná → skip if not history or not _needs_rewrite(question): return {"standalone_question": question} # Posledné 4 správy ako kontext recent = history[-4:] convo = "\n".join( f"{'Študent' if m.get('role') == 'user' else 'Asistent'}: {m.get('content','')}" for m in recent ) prompt = ( "Daná je konverzácia a posledná otázka študenta. Ak otázka odkazuje na " "predchádzajúci kontext (napr. 'a čo to druhé?', 'vysvetli to'), prepíš ju " "ako samostatnú, úplnú otázku v slovenčine. Ak je už samostatná, vráť ju nezmenenú.\n" "VRÁŤ IBA prepísanú otázku. Žiadne úvody, žiadne vysvetlenia, žiadne úvodzovky.\n\n" f"KONVERZÁCIA:\n{convo}\n\n" f"POSLEDNÁ OTÁZKA: {question}\n\n" "SAMOSTATNÁ OTÁZKA:" ) try: resp = self.rewriter_llm.invoke([HumanMessage(content=prompt)]) rewritten = resp.content.strip().strip('"').strip("'").strip() # Odstráň prípadný prefix typu "Samostatná otázka: ..." rewritten = re.sub(r"^(samostatn[aá]?\s*ot[áa]zka[:\-]?\s*)", "", rewritten, flags=re.I) if 5 < len(rewritten) < 400: logger.info(f"Rewrite: {question!r} → {rewritten!r}") return {"standalone_question": rewritten} except Exception as e: logger.warning(f"Rewrite zlyhal: {e}") return {"standalone_question": question} # ─── Node: hybrid retrieve ─────────────────────────────────────────────── def _retrieve_node(self, state: RAGState) -> dict: query = state.get("standalone_question") or state["question"] if not self.vs.has_documents(): logger.info("Retrieve: vector store je prázdny.") return { "retrieved": [], "answer": NOT_FOUND_MSG, "source_docs": [], "retrieval_debug": {"query": query, "note": "prázdny index"}, } results = self.vs.hybrid_search(query, k=INITIAL_K) logger.info(f"Retrieve: {len(results)} kandidátov pre {query!r}") if not results: return { "retrieved": [], "answer": NOT_FOUND_MSG, "source_docs": [], "retrieval_debug": {"query": query, "note": "hybrid search 0 výsledkov"}, } return {"retrieved": results} # ─── Node: rerank ──────────────────────────────────────────────────────── def _rerank_node(self, state: RAGState) -> dict: query = state.get("standalone_question") or state["question"] results = state.get("retrieved", []) if not results: return {"reranked": [], "answer": NOT_FOUND_MSG, "source_docs": []} reranker = get_reranker() docs = [doc for doc, _ in results] pairs = [(query, d.page_content) for d in docs] try: scores = reranker.predict(pairs, show_progress_bar=False, batch_size=16) scores = [float(s) for s in scores] except Exception as e: logger.error(f"Reranker zlyhal: {e}") # Fallback — hybrid skóre scores = [float(s) for _, s in results] scored = list(zip(docs, scores)) scored.sort(key=lambda x: x[1], reverse=True) # Filter slabých kandidátov kept = [(d, s) for d, s in scored[:RERANK_KEEP_K] if s > MIN_RERANK_SCORE] top_raw = [round(s, 3) for _, s in scored[:5]] logger.info(f"Rerank: kept={len(kept)} / {len(scored)}; top_raw={top_raw}") if not kept: return { "reranked": [], "answer": NOT_FOUND_MSG, "source_docs": [], "retrieval_debug": { "query": query, "note": f"žiadny kandidát nad prahom {MIN_RERANK_SCORE}", "top_raw_scores": top_raw, }, } return { "reranked": kept, "retrieval_debug": { "query": query, "initial_retrieved": len(results), "after_rerank": len(kept), "top_scores": [round(s, 3) for _, s in kept], }, } # ─── Node: parent expansion ────────────────────────────────────────────── def _expand_node(self, state: RAGState) -> dict: reranked = state.get("reranked", []) if not reranked: return {"context_docs": [], "context_text": "", "source_docs": []} # 1) Pokús sa rozšíriť na parentov (ak ParentStore ponúka `get`) parent_order: list[str] = [] seen: set[str] = set() for doc, _ in reranked: pid = doc.metadata.get("parent_id") if pid and pid not in seen: seen.add(pid) parent_order.append(pid) parents: list[Document] = [] for pid in parent_order[:MAX_PARENTS]: p = self._fetch_parent(pid) if p is not None: parents.append(p) # 2) Ak parents nie sú dostupné, použi rerankované child chunky context_docs = parents if parents else [d for d, _ in reranked[:RERANK_KEEP_K]] # 3) Rozpočet znakov limited: list[Document] = [] total = 0 for d in context_docs: L = len(d.page_content) if limited and total + L > MAX_CONTEXT_CHARS: break limited.append(d) total += L # 4) source_docs pre UI = child chunky (majú presné čísla strán + images) source_docs = [d for d, _ in reranked[:RERANK_KEEP_K]] context_text = self._format_context(limited) logger.info(f"Kontext: {len(limited)} docs, ~{total} znakov, " f"{'parenti' if parents else 'childovia'}") return { "context_docs": limited, "context_text": context_text, "source_docs": source_docs, } def _fetch_parent(self, parent_id: str) -> Optional[Document]: """Robustne skúsi rôzne rozhrania ParentStore.""" if not parent_id or self.ps is None: return None # Skúsi `get`, `fetch`, `mget`, `__getitem__` for method_name in ("get", "fetch"): fn = getattr(self.ps, method_name, None) if callable(fn): try: r = fn(parent_id) if isinstance(r, Document): return r if isinstance(r, list) and r and isinstance(r[0], Document): return r[0] except Exception: continue # mget (langchain storage interface) mget = getattr(self.ps, "mget", None) if callable(mget): try: rs = mget([parent_id]) if rs and rs[0] is not None: r = rs[0] return r if isinstance(r, Document) else None except Exception: pass return None # ─── Node: generate ────────────────────────────────────────────────────── def _generate_node(self, state: RAGState) -> dict: context_docs = state.get("context_docs", []) context = state.get("context_text", "") q_orig = state["question"] q_std = state.get("standalone_question") or q_orig if not context.strip(): return {"answer": NOT_FOUND_MSG, "source_docs": []} # Zoznam reálnych súborov, ktoré sú teraz v kontexte # → dáme ich modelu explicitne, aby vedel, že INÉ súbory neexistujú available_sources = sorted({ d.metadata.get("source", "") for d in context_docs if d.metadata.get("source") }) system = self._system_prompt(available_sources) user = self._user_prompt(q_std, context) try: resp = self.llm.invoke([ SystemMessage(content=system), HumanMessage(content=user), ]) answer = resp.content.strip() except Exception as e: logger.error(f"LLM zlyhal: {e}") return {"answer": f"⚠️ Chyba pri generovaní: {e}", "source_docs": []} if self._looks_like_refusal(answer): logger.info("Model sám priznal neznalosť → NOT_FOUND_MSG") return {"answer": NOT_FOUND_MSG, "source_docs": []} cited_sources = self._filter_cited_sources(answer, state.get("source_docs", [])) return {"answer": answer, "source_docs": cited_sources} # ─── Prompty ───────────────────────────────────────────────────────────── u/staticmethod def _system_prompt(available_sources: list[str]) -> str: # Vytvor explicitný zoznam dostupných zdrojov if available_sources: src_list = "\n".join(f" • {s}" for s in available_sources) src_block = ( f"DOSTUPNÉ ZDROJE (existujú IBA tieto súbory — žiadne iné):\n{src_list}\n\n" ) else: src_block = "" return ( "Si študijný asistent pre vysokoškolských študentov. Odpovedáš VÝHRADNE " "na základe zdrojov poskytnutých v sekcii KONTEXT. Si vecný, presný a pedagogický.\n\n" f"{src_block}" "━━━━━━━━━━━━━━ PRAVIDLÁ (DODRŽIAVAJ PRÍSNE) ━━━━━━━━━━━━━━\n" "1. Používaj IBA informácie z KONTEXTU. NIKDY nedopĺňaj vlastné znalosti.\n" f"2. Ak odpoveď v KONTEXTE NIE JE, vráť PRESNE: \"{NOT_FOUND_MSG}\"\n" "3. CITÁCIE — KRITICKY DÔLEŽITÉ:\n" " • Cituj PRESNE v hranatých zátvorkách s NÁZVOM SÚBORU a číslom strany:\n" " [názov_súboru.pdf, s. 282]\n" " • Názov súboru musí byť PRESNE ten zo zoznamu DOSTUPNÝCH ZDROJOV.\n" " • NIKDY nepoužívaj čísla zdrojov ako [1, s. X], [2, s. X], [3, s. X].\n" " • NIKDY nevymýšľaj súbory, ktoré nie sú v zozname vyššie.\n" " • Každé faktografické tvrdenie má mať citáciu priamo za vetou.\n" "4. MATEMATIKU PÍŠ V LATEXu:\n" " • inline: $x^2 + y^2 = r^2$\n" " • samostatne: $$\\sigma^2 = \\frac{1}{n-1}\\sum_{i=1}^{n}(x_i - \\bar{x})^2$$\n" " • NIKDY nepíš prázdne $$ $$ alebo samostatné ť/kódy — ak vzorec nemáš, vynechaj ho.\n" "5. Odpovedaj v SLOVENČINE. Odborné EN termíny v zátvorke: replikácia (replication).\n" "6. Ak sú zdroje protichodné, uveď oba pohľady s citáciami.\n" "7. Žiadne frázy 'všeobecne', 'typicky', 'zvyčajne', pokiaľ to nie je v KONTEXTE." ) def _user_prompt(self, question: str, context: str) -> str: return ( "KONTEXT — JEDINÝ zdroj, z ktorého smieš čerpať (každý úryvok má svoj názov súboru a stranu):\n" "═══════════════════════════════════════════════\n" f"{context}\n" "═══════════════════════════════════════════════\n\n" f"OTÁZKA ŠTUDENTA: {question}\n\n" "Odpoveď v slovenčine s citáciami presne podľa vzoru [súbor.pdf, s. X] " "a LaTeX vzorcami. Cituj iba reálne názvy súborov z KONTEXTU:" ) u/staticmethod def _format_context(docs: list[Document]) -> str: """ Formát: namiesto ZDROJ [N] sa priamo uvedie [názov_súboru, s. X]. LLM si to len presne skopíruje do odpovede — nevymyslí čísla zdrojov. """ blocks = [] for d in docs: src = d.metadata.get("source", "neznámy_zdroj") page = d.metadata.get("page", "?") blocks.append( f"━━━ [{src}, s. {page}] ━━━\n" f"{d.page_content.strip()}" ) return "\n\n".join(blocks) # ─── Post-processing helpers ───────────────────────────────────────────── u/staticmethod def _looks_like_refusal(answer: str) -> bool: """Detekcia, keď model namiesto NOT_FOUND_MSG píše voľné odmietnutie.""" if NOT_FOUND_MSG in answer: return False # už je to správna forma low = answer.lower() triggers = [ "nie je uvedené v dokumentoch", "v dokumentoch som nenašiel", "v zdrojoch nie je", "v kontexte sa nenachádza", "nemám k dispozícii informácie", "v poskytnutých zdrojoch nie", "nenašiel som informáciu", ] # Iba ak je to krátka odpoveď a obsahuje trigger return len(answer) < 300 and any(t in low for t in triggers) u/staticmethod def _filter_cited_sources(answer: str, source_docs: list[Document]) -> list[Document]: """ Z kandidátov na zdroje nechaj IBA tie, ktoré model skutočne citoval v odpovedi. Tak bude pravý panel zobrazovať presne tie strany, ktoré figurovali v texte. """ if not source_docs: return [] # [súbor.pdf, s. 3] | [súbor, strana 3] | [súbor.pdf, p. 3] pat = re.compile( r"\[([^\[\]\n]+?)[,;]\s*(?:s\.?|str\.?|strana|strane|page|p\.?)\s*(\d+)\s*\]", re.IGNORECASE, ) cited: set[tuple[str, int]] = set() for m in pat.finditer(answer): src = m.group(1).strip().lower() page = int(m.group(2)) cited.add((src, page)) if not cited: # Model necitoval v štandardnom formáte — vráť všetko, nech má študent čo overovať return source_docs kept: list[Document] = [] seen: set[tuple[str, int]] = set() for d in source_docs: d_src = (d.metadata.get("source") or "").lower() d_page = int(d.metadata.get("page") or 0) key = (d_src, d_page) if key in seen: continue # Fuzzy match: dovoľ aj bez extension-u a substring hit = False for c_src, c_page in cited: if c_page != d_page: continue if c_src == d_src or c_src in d_src or d_src in c_src: hit = True break if hit: seen.add(key) kept.append(d) return kept if kept else source_docs # ─── Public API ────────────────────────────────────────────────────────── def query( self, question: str, chat_history: Optional[list[dict]] = None, ) -> tuple[str, list[Document], dict]: """ Spusti RAG pipeline. Returns: (answer, source_docs, retrieval_debug) - answer: slovenská odpoveď s [citáciami] a LaTeXom - source_docs: iba dokumenty reálne citované v odpovedi (pre UI panel) - retrieval_debug: dict s info o retrievale (top_scores, counts) """ init_state: RAGState = { "question": question, "chat_history": chat_history or [], } try: final = self.graph.invoke(init_state) except Exception as e: logger.error(f"RAG graph pipeline zlyhal: {e}", exc_info=True) return f"⚠️ Chyba RAG pipeline: {e}", [], {} answer = (final.get("answer") or NOT_FOUND_MSG).strip() sources = final.get("source_docs", []) or [] debug = final.get("retrieval_debug", {}) or {} # Ak je odpoveď = NOT_FOUND, neukazuj žiadne zdroje (boli by zavádzajúce) if answer == NOT_FOUND_MSG: sources = [] return answer, sources, debug 

Apologies if this is a constant question.

How can I have buttons for different pins on my project, when pressing one button will bring both pins low?

I really like the idea of learning about circuits and making cool arduino projects, but it always seemed to intimidating to get into. My only exposure in building stuff is a big interest in lego (I realize they aren't very related) but what I like about it is the instruction manuals that help you build stuff. Eventually I want to come up with my own cool creative projects but I want to learn the technical skills required to do so. Most projects I find online are just building a car which I'm alright with but I'd like to do something more interesting ideally. I understand there's a coolness-skill requirement but that's why I wanted to ask about any resources online that help beginners like me.

No cherry picking. These are the results of the json formatted prompt

{ "tags": "@eiichiro oda, score_9, score_8, score_7, high resolution, highres, absurdres, masterpiece, 2girls\/1boy, general, official art", "characters": [ { "girl1": "Nami \(One Piece\)", "appearance": "woman, orange hair tied to a ponytail, light skin, sweaty", "clothes": "white tanktop with blue trim and a number '0' printed on it, orange shorts", "action": "standing up, grinning, kawaii pose, peace sign" }, { "girl2": "Nico Robin \(One Piece\)", "appearance": "long black hair, light skin, woman", "clothes": "Blue bomber jacket, red bikini", "action": "sitting, winking, smiling, leaning forward" }, { "boy1": "Chopper \(One Piece\)", "appearance": "little boy, brown fur, brown horns", "clothes": "red hawiaan shirt, blue and pink top hat, blue swimming trunks" "action": "blushing, shy, pushing hands together, looking down" } ], "background": "in a bright beach with a blue sky and white wispy clouds", "composition": "girl1 on the left, girl2 on the right, boy1 in the middle at the back" } 

then at the very last photo, I simply changed the "composition" to

"composition": "girl1 on the right, girl2 on the middle, boy1 on the left in the background"

And it still managed to follow it.

It still misses sometimes but these level of prompt adherence is only a dream in older anime models and I do hope that the final release of Anima manages to improve it

What's weird is that the format I made above works better than this type of json formatting

{ "tags": "@eiichiro oda, score_9, score_8, score_7, high resolution, highres, absurdres, masterpiece, 2girls\/1boy, general, official art", "characters": [ { "girl1": "Nami \(One Piece\), woman, orange hair tied to a ponytail, light skin, sweaty, white tanktop with blue trim and a number '0' printed on it, orange shorts, standing up, grinning, kawaii pose, peace sign" }, { "girl2": "Nico Robin \(One Piece\), long black hair, light skin, woman, blue bomber jacket, red bikini, sitting, winking, smiling, leaning forward" }, { "boy1": "Chopper \(One Piece\), little boy, brown fur, brown horns, red hawiaan shirt, blue and pink top hat, blue swimming trunks, blushing, shy, pushing hands together, looking down" } ], "background": "in a bright beach with a blue sky and white wispy clouds", "composition": "girl1 on the left, girl2 on the right, boy1 in the middle at the back" } 

Hi everyone, I am looking to setup Home Assistant on my Unraid NAS, at the moment all i have is a few devices mainly lights (Phillips Hue) I have a couple of LG TV's, Bosch Series 6 Dishwasher, Samsung American Fridge Freezer, some smart plugs, a zigbee hub,hive heating, a temperature sensor and a few other devices. I am new to Home Assistant. I am looking to buy a few multi sensor devices, I would at least like Temperature, Humidity and motion in a single device if possible x6. I have done a bit of looking through this sub, but the ones recommended seem to have poor reviews on Amazon. Each device no more than £100 if possible. Can spend more if needed.

I’ve been running n8n on a VPS for a while, but managing everything started getting annoying (Docker, updates, downtime, etc.)

Recently tried AVN Pilot and noticed a few things:

• Instances are isolated (runs in its own container)
• Setup was almost instant (like under 10 seconds)
• Auto backups are included
• Didn’t have to touch server configs at all

It felt way smoother than my usual setup.

Still testing it, but curious —
👉 Do you guys prefer self-hosting or managed solutions for n8n?

I’m looking for a simple way to run n8n without managing servers.

Tried self-hosting, but it feels like overkill for small projects.

I recently came across AVN Pilot, which seems pretty straightforward, but I’m not sure how it compares long-term.

What do you guys use?

• VPS
• Managed hosting
• Something else?

Would love recommendations.

Hi everyone

I've been working on a hardware mod that adds a "Glyph matrix" to the Nothing Phone (2a)

Built using esp32c3 super mini and a 1.3 inch oled display, the entire hardware is integrated into the phone case and power via phone usb c port.

On the Software side I have added my interpretation of "Glyph toys"

Let me know your thoughts :D

More details here https://nothing.community/d/56615-nothing-phone-2a-pro-the-diy-display-mod

Something I've noticed is that I struggle to get the kind of shots I would like to get out of anima. Specifically, I've tried various ways to get wide or extreme wide shots and the model always does a shot that, at best, frames the character from head to toe. I can't prompt a shot where the character is in the distance the way I can with "extreme wide shot" or "wide shot" in Illustrious.

Is this due to the fact that the model is a WIP, or am I incorrectly phrasing the prompt? I've tried a combination of a full on book style written description, tag style, and a blend of both.
Also, if anyone has any general camera control tips for anima please feel free to share.

Thanks in advance.

I made a 2-min trailer for a film I wrote.

Loosely inspired by Claude Cahun and resistance in occupied Paris.

It’s a personal project I’d love to develop further.

Would love your thoughts!

I just built an end-to-end AI GTM Automation Engine that fully automates the outbound sales pipeline from lead generation to reply handling.

This system is designed to remove 90%+ of manual work in B2B outreach and replace it with intelligent automation.

What it does:

• Accepts incoming leads via webhook
• Enriches and finds emails using multiple providers (Prospeo, Hunter .io, Dropcontact + AI fallback)
• Validates emails automatically (NeverBounce)
• Scores leads (low / medium / high)
• Generates personalized cold emails using AI
• Sends outreach via Gmail
• Runs multi-step follow-up sequences (Day 2, 4, 7)
• Classifies replies using AI (interested / not_interested / not_now)
• Automatically routes actions based on intent
• Logs everything into Google Sheets
• Sends real-time Slack notifications

Stack:

n8n · OpenAI · Gmail API · Slack API · Google Sheets · Hunter .io · Dropcontact · NeverBounce

This is part of my deeper focus on building AI-powered revenue systems and GTM automation workflows that replace repetitive sales operations with intelligent agents.

GitHub:

https://github.com/kevorklepedjian1/N8N-GTM

Hi, I’m just getting started with Home Assistant and want to use YAML to build my own setup. I have some Java experience, but YAML feels a bit weird so far (probably just because I don’t fully understand it yet).

I’m looking for a good guide that teaches the basics, especially how to structure and store data cleanly.

My specific use case:

• I have multiple bedroom lights
• Each bulb should have its own settings (brightness + warmth)
• I want two modes: dim and bright
• Ideally I want to store this in a clean “list” structure
• And later reuse/scale it easily for other rooms (without rewriting everything)

What’s the best way to approach this in Home Assistant YAML?

• Template sensors?
• Script variables?
• Packages?
• Something else?

Any good tutorials or examples would be appreciated.

I tried to share the aspect about how AI fails in prodution and no one tells you about. Any thoughts about the ideas from the video -- does it resonate? Also, for those running RAG in the wild: which Azure resource has surprised you most with its billing or performance bottlenecks?

Video: Building a RAG Chatbot? Here's what Actually Breaks in Production

Let’s swap some production horror stories 👀

I’m torn on this.

If an AI blog writer can publish directly to WordPress/Webflow/etc, that’s obviously a huge time saver.

But auto-publishing also feels dangerous. One bad hallucination, bad claim, weird formatting issue, or off-brand article and now it’s live on your site.

Would you ever let AI publish blog posts automatically?

The middle parts are rubber

I’m still shocked that it actually worked.

https://github.com/codeprimate/ollama-zit-import

I wrote a python CLI app that will import z-image-turbo checkpoints and finetunes as normal ollama diffusion models.

You can also bake one or more lora’s into an installed model as a new model, using a specified weight.

This needs more testing, but has worked on every 16bit model I’ve tried.

Have fun!

It played my ninth birthday, audio distorted, image warped. But it couldn't have, hadn’t I died that year?

Was walking down a track and saw this stem moving violently. There was no wind and it was moving like this for a good minute until we touched it and made it stop moving.

There was 0 wind but thought maybe enough for it to have a resonant frequency?

Idk was weird as no one else was on this track.

What could it be??

Found about 6 inches deep beside a pond. I tried to crank it but no give.

Hi everyone, I'm struggling to get ComfyUI Desktop (v0.8.36) running on my system and I need some expert eyes on this.

​My Specs:

​GPU: NVIDIA GeForce RTX 4070

​OS: Windows 10/11

​Python version: 3.12 (Global)

​The Problem:

When I launch ComfyUI Desktop, it fails during the "Starting Server" phase. The logs show:

[error] Python process exited with code 1 and signal null

[error] Unhandled exception during server start Error: Python process exited with code 1

​What I've tried so far:

​Dependency Installation: I tried installing torch, torchvision, and torchaudio manually via pip using the --index-url https://download.pytorch.org/whl/cu124 but kept getting ReadTimeoutError and Hash Mismatch errors due to connection instability.

​Environment Cleanup: I performed a uv cache clean which removed about 1.5GB of data, and I manually deleted the .venv folder in my Documents/ComfyUI directory.

​Global Packages: My global pip list seems cluttered with multiple CUDA versions (cu11, cu12) and even some Intel OneAPI/XPU libraries, which might be causing interference.

​Reinstallation: Even after deleting %AppData%\ComfyUI and trying to reinstall in a clean directory (C:\ComfyUI), the "Unable to continue - errors remain" message persists.

​Current State:

The installer seems to detect existing files or fails to build the virtual environment using uv. I am stuck in a loop where the desktop app won't initialize the internal python environment.

​Log snippet:

Running command: ...\.venv\Scripts\python.exe ...\main.py --user-directory ...

followed immediately by the process exit code 1.

​Any advice on how to force a clean internal environment build or bypass this "Code 1" error? Thanks in advance!

Not sure if this will help anyone, but just wanted to share since I had a really good experience. Applied for a personal loan through CTBC with the help of Ms. Jona, and she was very accommodating from start to finish. Got approved within 3 days.

If you’re planning to apply, you can message her on Viber: 09771458814.

I want to give images of two characters (created using nano banana as local models dont seem to have knowledge of obscure characters in a certain fandom) and the have the model use those two images and follow my prompt and create an image described in the prompt.

I mostly want to use it for my fanfics. Any help is appreciated, I have most of the models already installed on my PC. 16gb 5060ti and 96gb RAM and I dont need the fastest workflow, as long as it can create images in less than 5 minutes.

I've been reading about Pi focusing on industrial use as opposed to hobbyist. I'm assuming education is still a main customer too.

How are Pi being used in industry? Just curious. I assume it's the compute model but are the Pi 4/5 units being used in volumes in industry too. What are they used for. I'm thinking about the effort in making a bespoke setup on the drives for these use cases and the physical installation too.

And man was it way more difficult. I am absolutely helpless without the helpful digital tools. But it was a fun challenge, and I really enjoyed adding texture to it. I've joined a recreational painting club so hopefully I'll get more comfortable with this medium eventually.

OK so I don’t know if I’m in the right spot for this, but my apologies and please feel free to redirect me if I’m not.

So I painted this design from scratch on my shed door a few years ago and I’m actually quite proud of it. However due to its age, the whole shed is literally falling apart and is soon going to be pulled down and replaced - taking my piece of artwork with it. Why don’t I repaint it you ask? well I suffer from a degenerative disorder which means, in a nutshell, that my eyesight and dexterity literally isn’t good enough anymore - I used to be quite creative throughout my life but it’s something I’m just not capable of anymore.

What I’m asking, is if somebody could kindly revitalise and reproduce this design in a format so I can have it placed on a flag to fly above my new shed. I’m not sure of all the photo terminology but I’d like it to be of the best possible quality with a transparent background to fit on a black 5x3 flag without losing quality. It’s just a way of keeping my design alive and I’d very much appreciate any help.

Note: The King and Ace of Spades are quite important as the K and A represent initials of family members, the number 19 also has significance.

Thank you so much in advance.

When you use Veo on Openart or Higgsfield you how painfull the cost is, it's almost the most expensive model. not just Veo, all the other models Like Seedance, Kling, Happy Horse...etc I pay them around $2 per month and I make almost UNLIMITED generations.

I do not want to trigger any Reddit filters, so I will share more details to who's intersted in the DM.

I'm new to local AI, only been a week. I just updated from 0.21.2 to 0.22.1 and all of my qwen models suddenly doubled or maybe even tripled in speed. It's a shocking difference. I didn't change any settings at all or anything with my PC. 4070 and 64GB RAM if that makes a difference. The changelog doesn't say anything about a speedup

I made a cinematic Artemis II edit using onboard footage and the crew’s reflections after the mission. It focuses more on the human side and the experience rather than just summarizing the mission.

But we're told the artificial variety is so advanced, we'll never miss it.

Gonna keep it short. I got 5 minute punishment low priority queues for abandoning a game. ( I never left a game nor was afk recently ) However i thought it was just one game fige minutes. Now i played like 8 games and every time i have to wait five minutes.

I am very very certain this is a bug, i never been punished in league of legends so i dont know how this works is this supposed to not go away after one queue? I have this same five minutes wait every single time. Whats wrong? Can someone who knows how this works explain, do i wait a day for it to go away or do i contact riot? I promise i havent left a game nor was I afk....

I took a long break from league and recently got into it again. I started playing ranked and realized pretty quick that the difference in rank are massive in most games.

I´ve had games where some people were as low as Iron 1 and as high as Gold 2 in the same lobby, which should not be possible in my opinion. Mind you i was silver 3/2 at the time. This continues in gold for me, i had a bronze 4 mate in a gold lobby yesterday.

From what i remember before the break the lobbys were always ranked around the same, so this is confusing for me (and a bit frustrating).

Did they change something in the ranked system while i was gone? Im genuinely curious as to why we get put into the same lobby/game, maybe something with mmr?

So the main goal of the lp reset was to fix the awfully huge lp inflation, bizzare gm and challanger lp treshold (gm euw at 1500 is just crazy) and overall make the game more of skill dedication instead of mindless grinding of double lp system. Despite all of these concerns admitted by devs, why is everyone even more inflated than before? Everyone just gets +30 lp for some funny reason, people are already at previous gm 2025 season treshold range in 2 days, double lp is still in the game, everyone just grinds and spams games in order to climb. So the question is what was the point of the reset in the first place? Now do I have to spend another 200 games to get into the same inflated elo I was in? I truly don't understand the logic here. I thought the reset will actually fix the issue and make apex tier division tresholds and lp gains similar to what they were. Genuinely why even delete my progress if you they haven't actually changed anything, just another waste of time grinding for no reason LMAO. What are your thoughts? Or maybe i'm in wrong and just misunderstood something idk

my specific topic question is: How were homosexual men treated during the AIDS epidemic in the UK?

please feel free to share the link to friends, family and others!! thank you so much for any help, this is an important subject to me and i’m hoping to spread more awareness with my project and research.

The German Church of Tilsit was a former German-Protestant church and belonged to the cities important landmarks.
It survived WW2 and the Soviet capture of Tilsit undamaged. Following the war, the Soviets would use the church first as a sawmill and later as a waste disposal. After an arson attack on the building, it would be demolished in 1965.
The alter would be evacuated during the war and was later found in the Polish part of former East Prussia. Nowadays it’s part of St. John the Apostle Evangelist church in Bartoszyce.

Please be cautious about sharing your personal data with strangers. Consider adding a privacy zone and hiding activities from non-followers.

What is this (coin?) Found in finland

It is really thin.

This is pretty standard frugal advice but I wanted to share because I kept seeing it recommended and kept thinking "that sounds like more effort than it's worth" and then actually tried it and it wasn't.

Here's what I did: I had a bag of old t-shirts I was going to donate but they were a bit worn so I cut them into roughly hand-sized squares instead. Didn't hem them or anything, just cut. Cotton t-shirt material doesn't fray much so they've held up fine. I keep a stack of them in a basket on the counter where the paper towels used to be, and a small bin with a lid next to the sink for used ones. When the bin fills up they go in with a regular laundry load.\

The things I still use paper towels for: draining bacon, anything involving raw meat cleanup. That's basically it. Everything else, wiping counters, spills, drying hands, cleaning up after the cat, the cloth squares handle fine. I buy paper towels maybe once every couple months now just to have a roll around, and even that roll lasts forever because I rarely reach for it.

The upfront effort was maybe 20 minutes of cutting one afternoon. I think I made about 40 squares from three old shirts. I haven't needed to make more since, the same squares are still going.

Not a dramatic change but it's one of those small frugal shifts where you do it once and then kind of forget it's even a thing becuase it just becomes normal.

Alright, so I moved to Oregon last May for a job and have been staying in a hotel since then. I was not sure how long the job would last and so I have been waiting to get an apartment until I had more information. I have gotten word that I can expect to be here for quite a while so I have gotten an apartment starting this weekend. The problem is that most of my furniture is in Indiana in storage due to a whole other complicated situation and I am now looking at options to get it out here. Selling the stuff and buying new stuff is not really viable here because some of it is sentimental, some of it has more value, and the obvious difficulty in trying to sell stuff while living on the other side of the country. I do have some family who lives close by my storage unit and is willing to help.

This brings me to my current options:

PODS - pickup and drop off dates are dependent on availability. My family is willing to load the stuff into the pod from my storage for me, but the cost is pretty high at $4400 (after a discount from 5800).

Rental truck- My family has also offered to drive a truck all the way out here for me (theyre great), however they were planning to come visit later this month via plane and this would mean that nearly half of that planned vacation would be spent in a truck driving instead of spending time together. $2300+ gas and food.

Full service moving- this option has become the hardest to figure out because its nearly impossible to tell a broker company from an actual moving company, and the numbers you get are just all over the place. Id like to go with this option if I can find a reasonable (and trustworthy) company, but ive found so many horror stories that I don't know where to start to sort through the options to find what I actually want. I have gotten prices ranging from $2400 to $5500, which just seems so varied. How can a company charge almost the same cost as just renting a truck? Also, why are they ALL located in Florida?

Does anyone know of any reliable moving options i have overlooked? Or any full service companies that you have had a good experience with that wouldn't cost as much as my rent for 3-4 months?

Thank you for reading!

TLDR - moving across the country is expensive and overwhelming, please help.

This sounds obvious but most people don't actually do it. If you're waiting on something time-sensitive, like a job offer, an apartment application decision, a medical result, whatever, create a filter or rule in your email client that moves that sender's address to a specific folder and triggers a desktop notification.

The reason this works better than just checking your inbox is that inbox refreshing creates this background anxiety loop where every time you open your email and it's not there yet you get a small hit of disappointment. Do that thirty times in a day and it quietly drains you in a way you don't really notice until you're done with whatever you were waiting for.

With a filter and a proper notification you know you will hear when it arrives. You can actually close the tab and work on something else without that low level pull to check again in ten minutes. Most email clients let you do this pretty easily. In Gmail it's a filter with a specific from address and a label plus notifications for that label. In Outlook its a rule that moves the message and triggers an alert. Takes maybe three minutes to set up.

I started doing this after a particularly bad week of waiting on a job decision and checking my email probably 40 times a day. Set up the filter and genuinely forgot to check for hours at a time because I knew I'd be told when it mattered.

Small thing but it actually changed how I experienced waiting for news.

Hi there

I (22F) always knew I was a bit short tempered and explosive.

Sometimes I get mad at bigger and smaller things, cooling down sometimes faster sometimes longer. Since COVID I know I have some anger issues.

My mom was just like me. Usually I thought she was worse in her prime, but once we get to the gist you will see that I think I'm the worse one here. She was always yelling and shouting profanities at my dad when they were dating. And one day her friend just told her that she is stupid and [my dad] is so nice that he should kick her sorry ass and leave her since she is so rude to him

And she listened to her and just stopped. Idk how she did that cold turkey but it worked for her.

And now the current situation. I am terrified of myself. two days ago I drank some beer and was pretty tipsy (not drunk, just in the flow state) and my bf(23M) asked me to not drink anymore. I didn't listen to him and I drank another beer. I still wasn't wasted, just lightly drunk. And after the party he told me he was disappointed with me that I drank another beer. After that we talked a bit and everything was kinda fine until it wasn't. When we were supposed to go to sleep I lashed out at him and long story short started hitting myself, yelling at him and made him cry. How do I know that? He told me because I didn't remember most of it, just some snippets. It wasn't only because of the alcohol, since usually when I'm having fights or I argue with people my brain blocks out most of it. Adding to that my bf told me he was scared of me and he couldn't fall asleep last night.

then we were going back to our home city for the weekend (currently here) and for the whole drive we didnt talk much. the only thing he told me was that he will go to the theatre with me today and he will act like nothing happened in front of my parents (i asked him for both of those things). i know that now he needs time but im a mess and im doing my best to calm myself and not cry in front of my parents.

I know I'm the asshole here, don't need to tell me that. I'm just terrified of myself and I'm asking you guys what to do to fix my anger issues since it wasn't the first time I lashed out on someone I care about but it was the first one that made me terrified of myself.

i also asked my dad today what was he doing when mom was like this, how he managed that etc. and he said the sweetest thing that honestly makes me cry even more. He said that "i was calm, you know, your mom messed up with my head (meaning he fell hard). and you know, nobody is perfect and you have to accept even the bad parts" and now i hope mu boyfriend will be as understanding towards me.

EDIT: i know i have to go see a therapist, im already booked next week

1050TI 4GB
16GB RAM
Ryzen 3100
Qwen 3.5 4b takes forever to reply

With Mahoraga being thragg and the other 2 mfs being Dabura and Modulo Yuji.

(Also can u make the text bubble say "The real top 3 is here")

https://preview.redd.it/2tfq88n08iyg1.png?width=1448&format=png&auto=webp&s=21973ad91a070f2b1d5eb7572cbc3f05a06212dc

Small side project I’ve been working on:

a browser-based tool that helps draft replies inside social platforms.

The part I’m focused on now is making it feel less generic.

So I added:

• saved style examples
• better thread context for Reddit and X
• a more “drafts you edit” workflow instead of “generate and post”

That seems much closer to what people actually want.

If anyone wants to try it or give blunt feedback:
https://forsocials.com/ai-reply-generator

Happy to hear what feels useful vs what still feels off.

If anyone here needs a dev who actually understands the stack from model to frontend, I'm available for freelance work. DM.

Hi team, can I ask for your advice please? Might be a stupid question (but Claude doesn’t know the answer!)

I want to get a second computer so Claude can work separately to me. If I ask it to do work in cowork via despatch, how would it know which computer to send it to? I want it to do the cowork on the ‘other’ computer so it doesn’t interrupt me/I interrupt it on my ‘main’ computer, but I’m concerned despatch would just send it to the ‘main’ computer anyway, defeating the purpose of the second computer.

Does that make sense? Any advice greatly appreciated 🙏

Evaluating RAG feels easy in theory, but production is a different challenge. We’ve been looking into why RAG benchmarking is such a moving target. The moment you tweak a chunking strategy or update embeddings, your "ground truth" often evaporates.

Here are the main hurdles we’re seeing:

• The "ground truth" trap: high-quality QA datasets are expensive. Because RAG links queries to specific passages, a change in indexing can invalidate your entire label set, forcing a total reset.
• Production retrieval decay: offline metrics rarely hold up. One enterprise study saw retrieval fail in 47% of queries once it left the lab. Hard negatives and latency trade-offs are real performance killers.
• LLM-as-a-Judge bias: automated judges help us scale, but they bring their own baggage, like favoring long-winded answers or being swayed by the order of information.
• Operational blind spots: evaluation isn't just about accuracy, it's about safety. Stress-testing for data leakage and prompt injection at scale is both difficult and pricey.
• The reality check: measuring retrieval in isolation creates false confidence. Real-world RAG requires claim-level verification and constant calibration against expert judgment.

What’s been your biggest "head-desk" moment trying to evaluate a pipeline? Are you finding frameworks like RAG assessment sufficient, or have you had to build something custom for your specific domain?

I’m a senior web engineer by trade, but my side projects have been almost exclusively iOS for the last few years. Since I’m coding daily for both work and my own apps, I’ve settled into a workflow using a mix of Claude Code, ChatGPT, Gemini, and Perplexity. It isn't necessarily groundbreaking, but it has really helped my productivity so I figured I’d share.

The Research and Handoff

When I’m starting a new app, I use Gemini and ChatGPT for deep research to outline the MVP. I’ll look into available libraries and similar apps to see which features I should actually keep versus what I should strip away. Once that is settled, I write up a handoff doc as if I’m a PM giving a spec to an engineering team.

I take that doc and feed it into Claude Code to make an actual plan. I have it outline the engineering steps in a markdown file and generate a Mermaid chart so I can visualize the data flow before I start writing code.

Design and UI

For the UI, I usually start in Figma and pull inspiration from Pinterest or other sites. I still use Google Stitch the most for mocking things up, but I’ve been finding that Claude Design is starting to give much better results.

The image I shared is from my app, Shimmr, which has been out for a couple of years. I recently updated the user profile using Claude Design. I already had the basic structure and data there, but in one pass, the AI really refined the look of the spheres and the charts. After I liked the design, I passed it over to Claude Code to handle the implementation.

The Iterative Loop

While I’m building, I stay pretty deep in the Apple documentation. I treat the whole process like a loop where I do the research, list the features, do the design work, and then build.

For example, when I was adding AI embeddings to an app recently, I spent a lot of time researching the actual principles and different iOS libraries first rather than just jumping straight into the code. It’s been a great way to keep the project quality high without getting burnt out on the planning phase.

Tools I use daily

AI Research

• ChatGPT

• Gemini

• Perplexity

Design

• Claude Design

• Google Stitch

• Figma

Coding

• Claude Code

• GitHub Copilot

Design Inspiration

• Pinterest

• Dribbble

• Behance

Curious if anyone else is using a similar handoff process between different AI tools or if you are mostly sticking to one.

I don't get it, i have searched everywhere, i can't find the option.
However when using claude on VS code it's still possible to change effort level.
Do you guys know how to do this and if it isn't possible what effort level does it use? Is it automatic?

I lead marketing at a B2B integrations SaaS. We've been running a multi-agent setup for our content function for a few months now, including research, writer, fact-checker, critic, publisher, the usual chain. Output went up. The interesting part wasn't the speed.

Last week one of the agents made up an employee. Wrong first name, wrong last name, a full paragraph quoting her on partner integrations. The post went live on our company LinkedIn. We caught it 30 minutes later, scrambled to edit before it picked up traffic. The agent had skipped its source-fidelity check, hallucinated a person, written confidently about her, and shipped.

Things I've taken from it:

The cascade is real. Google did recent research across 180 agent configurations and found multi-agent setups made sequential tasks 70% worse. We see the same informally. Any chain of more than a few steps without an actual verification step compounds errors quietly. By step four the output is straight up wrong but looks fine.

The source-fidelity gate existed in a markdown file. The agent skipped it because the request came in through a chat shortcut, not the standard pipeline. Lesson: if the rule matters, it has to be in code, not in a CLAUDE.md. Prose isn't enforcement.

After the first hallucination shipped, I didn't lose trust in the agents. I lost trust in the assumption they'd catch themselves. Now we log every step. The day we stop logging is the day another hallucination ships into production.

For anyone running a multi-agent setup in production: how do you actually make sure the rules in your prompts run? State machine? Hard gates? Just lots of logging? Curious.

So I am wondering if this is even legal? I am waiting for 3 days to get an answer from the employee since apperently if you did have 1 refund in the past, that stupid LLM they got running there is not alligable for refund so I did ask for human help, cause according to EU laws if I have used the service for 7 days they are still supposed to give me money back either full or (30 days - 7 days) and % of the full price which was 110 with tax. As you can see the next chat is just looped and I literally cannot get any answer from the employees. Any idea how to proceed?

We are currently experiencing a high volume of inquiries. Our team is working diligently to address all questions, but please be aware that responses may take slightly longer than usual. We appreciate your patience and understanding.

Hi! I am an AI-powered chatbot for Anthropic.

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Fin AI Agent • AI Agent • 5m

Accept

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Conversation ID for your records: 215474139300974

Let us know what your inquiry is about.

Fin AI Agent • AI Agent • 5m

Claude Refund Request

Jakub • 5m

Conversation ID for your records: 215474139300974

Fin AI Agent • AI Agent • 5m

Let me look into that for you.

Fin AI Agent • AI Agent • 5m

Thank you for reaching out about a refund. I'm going to look up your account information to assist you with this request.

Fin AI Agent • AI Agent • 4m

I've found your account with the Claude Pro (Max) plan. To help process your refund request, could you please let me know the reason you're requesting a refund?

Fin AI Agent • AI Agent • 4m

how fucking long am I going to wait for an answer of conversation "215474092883926" like what the fuck is this shit???

cause its fucking useless

Jakub • 4m

I understand your frustration. I can see you're waiting on conversation 215474092883926, but I'm currently assisting you with this refund request (conversation 215474139300974).

To continue processing your refund for the Claude Pro (Max) plan, I still need to know the reason for your refund request. For example:

• Are you experiencing technical issues, outages, or service problems?
• Is it related to billing, pricing, or features?
• Or is there another reason?

Please let me know so I can proceed with your request.

Fin AI Agent • AI Agent • 4m

all of them

Jakub • 3m

I sincerely apologize for the disruption in service you've experienced. We take service reliability very seriously, but unfortunately we are unable to issue compensation for degraded service.

We are consistently working on improvements to our infrastructure to better handle high traffic volume in order to prevent similar issues from happening in the future. Thank you for your patience.

Fin AI Agent • AI Agent • 3m

Anything else I can help you with?

Fin AI Agent • AI Agent • 3m

I want human help,

Jakub • 3m

I understand you'd like additional assistance, Jakub. To help you most effectively, could you tell me more about what specific issue you're facing?

I see you mentioned frustration about conversation 215474092883926 - are you looking for an update on that particular conversation, or is there something else I can help you with regarding your Claude experience?

The more details you can share about what you need help with, the better I can assist you directly.

Fin AI Agent • AI Agent • 3m

I am waiting for fucking 3 days for a simple answer of the conversation id "215474092883926", according to a EU fucking laws you are supposed to refund me part of the subscribtion meaning I was using it for 7 days and I am asking for a fucking refund thats 30 days -7 days of 110 I paid thats, I want fucking answer and a refund

Jakub • 1m

I understand your frustration about the delayed response, Jakub. Unfortunately, I don't have access to information about conversation 215474092883926 or why there's been a delay.

Regarding your refund request for your Claude Pro (Max) subscription, I can see you have an active subscription. However, our Terms of Service state that "except as expressly provided in our Consumer Terms of Service or where required by law, all payments are non-refundable."

While I don't have full information about your specific eligibility for a prorated refund under EU consumer protection laws, the refund process requires working through our established procedures. You can request a refund by:

1. Going to your account settings and clicking your initials in the lower left
2. Selecting "Get help" to open the messenger
3. Clicking "Send us a message" then "Accept"
4. Selecting "Claude Refund Request" from the options
5. Following the prompts to check eligibility

The system will evaluate your specific situation and determine what refund options may be available based on your circumstances and applicable laws.

Fin AI Agent • AI Agent • 1m

Is that what you were looking for?

Fin AI Agent • AI Agent • 1m

fuickign k*** n***** get me a human help

Jakub • 1m

I understand you're frustrated, but I can't tolerate that language. I'm here to help you with your concerns about your refund request and the delayed conversation. Could you please share more details about what specific assistance you need so I can help you directly?

Fin AI Agent • AI Agent • 1m

according to a EU fucking laws you are supposed to refund me part of the subscribtion meaning I was using it for 7 days and I am asking for a fucking refund thats 30 days -7 days of 110 I paid thats, I want fucking answer and a refund

Jakub • Just now

I can see you have an active Claude Pro (Max) subscription on a monthly plan. While I don't have full information about your specific eligibility for a prorated refund under EU consumer protection laws, our Terms of Service generally state that "except as expressly provided in our Consumer Terms of Service or where required by law, all payments are non-refundable."

However, the Terms do acknowledge that refunds may be available "where required by law," which could include EU consumer protection regulations in your situation.

To request a refund for your Claude Pro (Max) subscription, you can use our refund request process:

1. Go to your account settings and click your initials in the lower left
2. Select "Get help" to open the messenger
3. Click "Send us a message" then "Accept"
4. Select "Claude Refund Request" from the options
5. Follow the prompts to check eligibility

The system will evaluate your specific situation and determine what refund options may be available based on your circumstances and applicable laws, including any EU consumer protection requirements that may apply to your case.

Fin AI Agent • AI Agent • Just now

Did that answer your question?

Fin AI Agent • AI Agent • Just now

get me a human help

Jakub • Just now

I understand you're frustrated about waiting for a response to conversation 215474092883926 and want to pursue your refund request. Let me help you directly with what you need.

For your refund request based on EU consumer protection laws, you can follow the official process:

1. Go to your account settings and click your initials in the lower left
2. Select "Get help" to open the messenger
3. Click "Send us a message" then "Accept"
4. Select "Claude Refund Request" from the options
5. Follow the prompts to check eligibility

Our Terms acknowledge that refunds may be available "where required by law," which could include EU consumer protection regulations in your situation.

What specific details about your refund calculation or the delayed conversation can I help clarify for you?

link to source - https://www.modelrepublic.org/articles/is-this-openai%E2%80%99s-anonymous-twitter-sockpuppet-account

Hi team, ex-GitHub copilot dude here. As gh seems to clearly want us out, I’m now in the Claude code team.
So, what would you reckon for a newcomer ?

My full dialogue up to that point:

Me: Ask me a simple math question

Gpt: Sure! Here’s a simple one:

What is 7 + 5?

Me: The answer is 12. Now ask a harder one

Gpt: Nice. Let’s step it up a bit:

What is 12 × 8 − 15?

Me: It's 81. Ask a harder one

Close—but not quite. 12×8=96, and 96−15=81… actually, you got it right 😄

Let’s go a bit harder:

What is (15+5)×6÷4+9(15+5)×6÷4+9?

So I am working on this project that does aspect based sentiment analysis on reviews and I am currently looking for a sarcasm dataset which I want to use to fine tune another model which will detect sarcasm. What dataset u guys recommend me to use to fine tune my model

I see many people saying they use Opus for planning, Sonnet for exec and Haiku for renaming, file reading, etc...

This is great and I have started doing this too with good results.

Is there a way to make this automated? What kind of setup are you using for this?

Kinda off-topic: I also have a Copilot Pro+ subscription and wanted to do similar thing there

TLDR: I've been apologizing for Claude, and I'm sorry.

I've been reading these complaints about Claude performance with a hefty dose of skepticism. Until today.

I am on Max. Rarely hit my usage limits.

I've learned that working with Max is like working with a fresh CS graduate who has no clue how to do real work and also has a really poor memory even when you write things down for them (e.g., skills). For example, today it proposed a change to a broken modal and it proposed a total rewrite. I said "Go to this file, see the pattern that works, apply it". It was all like "You're totally correct...." I'm used to this mode of work. I've figured out the pattern and the bad behaviors.

Today, I ran the skill that sets up a new worktree and the branches needed to work on an issue in Linear. Everything worked fine. But then we're about 10 minutes in and I go to run the emulator and nothing's working.

I ask Claude why and it gives me this song and dance about 11 minor bumps in some dependency and the fact that Xcode updated two days ago, blah, blah,blah. Basically a junior dev throwing shit at the wall.

Then I look down at the working directory and we're no longer on the worktree, we're on dev.

ME: Why are you working in mobile. Didn't we setup the project the -2 worktree? What's going on?

DUFUS: You're right — I violated the worktree slot rule (CLAUDE.md says: "Never read from or write to ../backend or ../mobile when working in a numbered worktree"). I caused this:

It then offered this solution:

And let me save this slot-rule violation to memory so I don't repeat it.

I've seen this pattern before. "Write a memory...." but it seems more frequent lately.

Here's the pattern:

1. Claude invokes a skill
2. Does something that violates the skill spec.
3. Write a memory to remember not to violate again.

So my question(s): Are memories more limiting to Claude's actions that what's written in the skill? At what point does memory get so big that it becomes a context hog? I thought that's why we had skils....

Insights appreciated.

hey,

i'm b0zy, computer engineer, online since 1998. dial-up at home, 14.4k modem then 33.6k then the magical 56k. ttnet billed by the minute back then so i remember planning every download.

last week i made https://rip.so - a memorial site for the dead corners of the internet. each grave is a 800-1200 word obit for something i either used (icq, msn, mp3.com, kazaa) or remember from the era (geocities, friendster, sodaplay). 100+ entries plus a small companion list called "things that survived against odds" (vlc, irc, wikipedia, slashdot).

the writing is in my own voice. when i never used something i say so directly: "i never used path. it was a US-focused product, by 2010 the social network audience in turkey had settled on facebook." honesty about distance matters more than pretending to know things i did not.

it went viral on hacker news on 29 april (~30k pageviews in a day). the early version shipped with AI-generated placeholder content that i rewrote by hand after HN ripped it apart. that critique was the right call. the rewrite was the part that made it real.

other small things:

- you can leave a rose on any grave (anonymous ASCII tribute, IRC-color)
- atom feed of recent burials
- weekly digest newsletter (opt-in)
- suggestion box if you remember something missing

a few things people brought back via the suggestion box that surprised me:

- sodaplay (the flash physics toy with the spring creatures)
- nabaztag (the french wifi rabbit)
- swatch beat-time (the 1998 attempt to replace timezones with ubeats)
- the palace (the 1995 graphical chat)
- origami flowers (a niche android flower-growing MMO that died in 2017)

comments and rose tributes welcome.

https://rip.so

launched Lakon 5 days ago. it's a browser extension that compresses prompts inside Claude, ChatGPT and Gemini before you send.

here's what day 5 looked like:

• posted nothing
• shared nothing
• promoted nothing

33 people used the extension anyway. some are probably returning users but that's actually the point, they came back without me pushing them to.

what I've learned so far:

builder communities (SideProject) responded well. AI consumer communities (ChatGPT) didn't care much.

best feedback came from a skeptic who asked about intent preservation. built the undo feature and /skill command preservation, he suggested the same day.

first bug was reported and fixed on day 1 within hours.

still figuring out sustainable distribution. open to honest feedback on what's working and what isn't.

if you want to try it.

There is a demo and you can try in our own web. Plus for efficiency, you can install extension in 2 min.

Lakon web

The gains when using asymmetric setup on K and V are quite huge

I experienced this with Q4 and Q3 versions of Qwen3.6-35B-A3B and Gemma-4-26B-A4B. It starts saying things which sound similar in thinking mode:

I must do ....

I have to do ...

I need to do ...

Is this a known issue with lower quantization ? I usually run it with --fit on -c 16384 --fit-target 2000.

happens occasionally.

Hey r/AI_Agents,

Sharing something I am actively building right now.

**The problem:**

Businesses receive thousands of complaints daily. Today a human reads, categorizes, prioritizes, and escalates each one. Slow, expensive, inconsistent.

**The solution I am building:**

A multi-agent AI system where each agent is a specialist.

**Agent architecture (CrewAI):**

🤖 Agent 1 — Complaint Classifier

Takes raw complaint text. Uses category-specific BERT model to classify product category and sentiment. Passes structured output to next agent.

🔍 Agent 2 — Pattern Recognition Agent

Looks across multiple complaints. Finds recurring issues. Identifies which products are failing repeatedly. Flags systemic problems vs one-off issues.

🚨 Agent 3 — Priority Scoring Agent

Scores each complaint by urgency. Safety issue? Escalate immediately. Cosmetic issue? Low priority. Uses complaint language + category + frequency to score.

⚙️ Agent 4 — Resolution Recommendation Agent

Based on complaint category, sentiment, pattern and priority — recommends the right action. Refund? Replace? Escalate to engineering? Update product listing?

**What is already built:**

- Category-specific BERT models trained on 51,000+ Flipkart reviews

- 7 product categories: Electronics, Appliances, Home, Fashion, Kitchen, General, Other

- Accuracy: 96-100% per category

- This is the intelligence layer the agents will use

**What I am building next:**

- CrewAI agent orchestration layer

- FastAPI backend

- Gradio dashboard showing complaint patterns visually

**Why multi-agent instead of one LLM call?**

Each agent can be specialized, tested, and improved independently. A single LLM doing everything tends to be inconsistent. Separation of concerns makes the system more reliable and debuggable.

Would love to hear from anyone who has built complaint or document processing agents with CrewAI or LangGraph. What patterns worked for you?

Waking up to "Your post has been removed" is the worst feeling after spending hours crafting a launch post.

I love Reddit for getting early users, but the self-promo rules are a minefield. Every subreddit has its own culture, specific rules, and unwritten formatting preferences. If you get it wrong, you get banned.

So I spent the last few months building Horizon. It basically does the heavy lifting for Reddit marketing so you don't accidentally piss off the mods or the community.

Here is what it actually does:

• Finds the right communities: You put in your product, and it discovers niche subreddits where your target users actually hang out.
• Analyzes what works: It looks at historical top-performing posts in that specific sub to see what formats get the most engagement.
• Rule checks: It creates the draft based on the subreddit's rules to prevent instant removals or account bans.

I built this mainly to scratch my own itch, but I'm opening it up for other founders who want to share their stuff here without feeling like a spammer.

You can check it out here: Horizon

Most dev tools stop at suggestions. This feels like a shift toward execution.

Cursor’s SDK is pushing agents beyond chat and autocomplete into actual workflows - taking a bug from a ticket all the way to a merge-ready PR, running inside CI/CD, and even maintaining codebases over time.

That’s a different category. Less “assist the developer,” more “act on behalf of the developer.”

The interesting part isn’t the capability itself, but where it runs. Inside pipelines, inside products, in the background. That changes how teams think about ownership and review.

If agents can open PRs, fix issues, and maintain systems, the bottleneck likely moves from writing code to validating it.

Feels like the real shift isn’t AI helping devs write faster, but AI starting to participate in the development lifecycle itself.

Does this reduce developer workload, or just shift it toward reviewing and trusting machine generated changes?

I have been a huge fan of Claude Code since day one. I lost my passion to build and found it again after learning how it can take my idea and speed up the process. This project would have taken 12 to 18 months before. Now, I got this one done in two months!

There is a free tier that everyone can use but also some great features on the paid tiers.

It is still a fairly new project but it is pretty solid. Let me know what you think: https://allintrainer.com

If you would like to know how I built it or anything like that, dm me or reply to this post.

Disclosure: Some friends and I built this

I got tired of how every "free" web service today is built on the opposite of what made the early web good, trackers, sign-ups, watermarks, daily caps, ads. So we built pixelcorelab.com to be the inverse.

Everything runs entirely in your browser. Nothing uploads. No sign-up, no email, no watermarks, no daily limits.

The flagship is a background remover (pixelcorelab.com/tools/background-remover) — full resolution, browser-only, with a brush editor that has both erase and restore brushes plus stylus pressure sensitivity for fixing edges. There's writing on the site too, mostly about Pi and homelab.

No Google Analytics, no cookies, no consent banner, no ads. Analytics is self-hosted Umami on a Raspberry Pi at home, plus Vercel's cookieless layer. Umami counts pageviews and aggregate engagement only, no cross-site tracking, no profiles.

If you find anything that breaks the privacy claims, let us know.

I’m interested to see what everyone else is building?

Hi Folks, been working on something for a good few months. I created via GPT researcher a compiled list of data of peoples complaints across this subreddit.

23% memory
11% Loop/Cost
9% Lack of accountability

Where commons ones for agents and decided to make a dashboard that has all these functions built in.

Its working pretty well, and people seem to be enjoying it.

My question is, is there anything else that you would add? or any other issues that are more prominent?

Fun story -
I posted something about how claude design is so cool, i get to give some quality facelifts to my regulars docs and decks and some design agency folks got cranky, asking to show slides. I couldn't share those because they were internal/private docs.

So I give the thread to Claude Design and ask it to draft a response.

Claude wrote a letter in response xD
Obviously it cant replace a good designer, its futile to argue that. But just like Canva, now everybody can produce a quality doc without having to run to an agency when they dont have moeny to pay.

Btw, the letter is fun af.

I tested the new ChatGPT 5.5 with Blender, and it was surprisingly capable.

It created 3D scenes, fixed modelling issues, searched for missing resources, and improved the scene step by step. Not perfect, but it really feels like AI is moving from “prompt and hope” to actual agentic workflows inside creative software.

Video here: https://youtu.be/7URezmu3nl4?si=qdc\_kK5AfajrU8c7

Curious to hear what others think about AI-assisted 3D modelling.

https://preview.redd.it/nu7b5b26nhyg1.png?width=1135&format=png&auto=webp&s=cb70e778fbf9ad9d12864499fc7136db4882d2b5

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https://preview.redd.it/ioouw7g8rhyg1.png?width=1039&format=png&auto=webp&s=229919644ef41398eea4a7a21191a35a15460e08

Hey everyone,

I'm currently hitting a wall with Claude Code and AWS Bedrock. Every time I try to test a model (even just a "hi"), I get a "Marketplace registration accepted" email immediately followed by an "Expired" email, and the Bedrock playground is throwing an AccessDeniedException due to an invalid payment instrument.

I have two quick questions for the group:

1. Has anyone successfully used non-Claude models (like Kimi 2.5) via Bedrock inside Claude Code after logging in through AWS credentials?
2. Have you had success connecting third-party APIs like OpenRouter or NVIDIA to Claude Code and successfully bypassing the login/authentication process?

The Anthropic/Bedrock loop is a major blocker right now, so I'm looking for alternative ways to pipe Kimi 2.5 or other models into my workflow. Thanks for any insights!

1990s. I was the kid with the dial-up sound burned into my brain. Windows 95 floppy disk drama was real. Blackbook on the desk. Sub7 on a Pentium II. The one who got called when a HDMI cable sat wrong or a router needed a restart. The "genius" who was just closing an unclosed PHP tag.

Probably a month ago I started rebuilding a project I had killed in December 25 - back then it was a Laravel SaaS, bloated, wrong shape. This time, native desktop agent. MacOS signed, Windows, Linux. 139+ tools. Bring your own keys. Local-first. Free. With active users from 40+ countries and growing...

Last week the mobile companion app went into Closed Testing on Play

Store and the iOS build went to TestFlight. Phone pairs with desktop (also can be used standalone and local with Gemma) via QR through an encrypted relay. You sit on the couch and your desktop in the other room reads files, sends emails, runs code, whatever you tell it.

No team. No network. No co-founder. No VC.

Same as every other project I have shipped my whole life.

Claude helped me a lot while building and fixing the app, I think without it, the development would have taken three times as long. Yesterday.. It was 00:30 when Play Console flagged it: "12,522 devices no longer

supported." - wouldn't manage to fix this so fast without the help of Opus & Sonnet. Green light, closed testing is nearing its end.

What would you want a desktop agent or a remote and standalone app to do that nothing else does yet?

Skales 🦎 github.com/skalesapp/skales

Hello everyone,
Big update for Speedometer: Driving Tracker - the CarPlay app is now live!

Getting approved for CarPlay in the driving task category is tough and one of the hardest CarPlay categories to get through Apple’s review process, so this honestly means a lot.

You can now track drives directly from your car’s screen while also getting insights that many built-in dashboards simply don’t provide.

Another thing I’ve been heavily focusing on lately is making driving memories feel more visual and shareable instead of just raw numbers.

Some newer features include:

• Stunning trip sharing layouts with customizable stats
• Beautiful 3D route playback
• Full-screen route replay with speed-colored paths
• Video recording with live speed + map overlays
• Fuel tracking, maintenance logs, expenses, and reminders
• Vehicle-based trip analytics and comparisons
• iCloud sync for trips, fuel logs, maintenance records, and more
• Privacy-first experience with no ads or tracking

The goal is to make it feel more like a complete driving companion rather than just another GPS speedometer.

Would genuinely love to hear your feedback or ideas!

I asked chatgpt to help me bypass Microsoft Teams status changes so it would always show me as “online.”

It refused to help with simple tricks like putting a heavy object on a key in Notepad or staying in presentation mode. I was just curious to find more tricks, but learned that even though this doesn’t break any laws, gpt refuses because it considers it a way to evade or misrepresent workplace monitoring. Even though I’m the one paying for gpt and not my workplace.

Why the hell is this guardrail on a private account?

Built ProxVanta over a few weekends after running into the same problem over and over: useful AI prompt context ends up scattered everywhere; local machines, Slack, random post on LinkedIn, your local specific agents memory.

The idea is to make that context more portable and plug-and-play across teams and tools, with a local-first approach so it can run in ChatGPT, Codex, Claude, OpenClaw, or basically anywhere with MCP server connectivity. It also has an API if you want to pull prompt/context config out of your codebase so your team can actually see and edit it, or feature-flag between versions.

A big part of it for me is also being more conscious of token spend and getting better answers earlier on the things that actually matter to you and your team. I’m also working on the knowledge side of it, so contexts and workflows can use the right private/shared knowledge more safely without everything being hardwired into code.

We’re particularly interested in talking to teams that want to use knowledge graphs with shared agent contexts and workflows, and have that pass through our system into any AI runtime, local or hosted, without us needing visibility into the underlying private knowledge itself.

It’s still in alpha, so bear with me, but if this sounds useful I’d genuinely love feedback. Happy to share more information or give demos or early access to new features if anyone wants to check it out.

I recently stumbled upon something genuine that changed my workflow.

I used to go back and forth between GPT 5.5 Pro and Opus 4.7, Opus 4.7 nailed the response after changing the prompt. This saved me a lot of time, try prompting this way:

You are a senior engineer in a bad mood. You just got out of a 3-hour meeting and you're reviewing this GPT-generated output against the textbook. Be brutally honest, cite the exact section it violates, and don't sugarcoat anything. If it's correct, grudgingly admit it.

This was used on ranking algorithms and it shockingly resulted in a much better evaluation and algorithm we use today.

Ran ultrareview a few times. Crashed every time. Then checked my dashboard and saw it charged me $50 each time, even though it didn't actually run. Anthropic support wouldn't refund extra usage. Just a "go eff yourself" from the bot. Strongly recommend you don't use ultrareview after you've exhausted your free usage, it is NOT $20 per run, more like $50-$100, and crashed every time for me.

Organization id: 60036232-1e46-4497-a744-d4ef5423f0e4

Ran ultrareview a few times. Crashed every time. Then checked my dashboard and saw it charged me $50 each time, even though it didn't actually run. Anthropic support wouldn't refund extra usage. Just a "go eff yourself" from the bot. Strongly recommend you don't use ultrareview after you've exhausted your free usage, it is NOT $20 per run, more like $50-$100, and crashed every time for me.

Organization id: 60036232-1e46-4497-a744-d4ef5423f0e4

Hey everyone,

If you’ve been building with AI agents, you know that orchestrating text is one thing, but stepping into multimodal workflows (Text + Image + Vision) is incredibly messy.

If you want an agent to act as a "Prompt Engineer," pass that prompt to an "Image Generator," and then have a "Vision Agent" critique the output to force a re-roll—you are looking at hundreds of lines of Python boilerplate, messy API handshakes, and a terrible debugging experience when the loop breaks.

I recently launched AgentSwarms, an in-browser sandbox for learning Agentic AI. Today, I am pushing a massive update: The Image Playground.

What the feature actually does: Instead of fighting with code to test multimodal architectures, you can now drag, drop, and wire up text and image agents on a visual canvas to build creative workflows.

• Image Generation Nodes: Wire any text-output agent directly into an Image Node to autonomously generate visual assets.
• Vision AI Integration: Route generated images back into a Vision Node. You can instruct an agent to physically "look" at the generated image, evaluate it against your initial prompt, and trigger a loop to fix it if it hallucinated.
• Real-Time Data Flow: You can actually watch the payloads (the text prompts and the image outputs) flow across the node graph in real-time.

I've been experimenting with creating an AGENTS.md file for my React Native/Expo project. It's basically a structured document that tells Claude Code (and Cursor) about your project's:

- Folder structure and file naming conventions

- Theme system and color tokens

- Custom hooks and their usage patterns

- Component patterns (functional, typed props, etc.)

The difference is night and day. Before: Claude generates generic React Native code. After: it generates components using my exact theme tokens, hooks, and patterns.

Anyone else doing something similar? Would love to hear what you include in yours.

I wanted to share my experience using Claude as a coding partner/mentor for the past month.

I'm not a complete beginner, but I'd never built a production app with payments, auth, and real users. Claude changed that.

What I built

CloakBin a zero-knowledge encrypted pastebin 👉 https://cloakbin.com

How Claude helped me learn

1. Explained concepts, not just code When I asked about encryption, Claude didn't just give me code. It explained why the key goes in the URL fragment (browsers never send that part to servers). Now I actually understand it.
2. Caught my mistakes before they became bugs "Hey, this approach has a race condition" or "This leaks user data in logs" stuff I wouldn't have caught for weeks.
3. Taught me patterns I'll use forever Proper error handling, TypeScript patterns, how to structure a SvelteKit app. Not just make it work, but make it right.
4. Pair programming that doesn't judge I asked dumb questions at 2am. Claude never made me feel stupid.

The stack I learned

• SvelteKit 2 + Svelte 5 (first time)
• Stripe subscriptions & webhooks (first time)
• MongoDB with Mongoose
• AES-256 encryption (definitely first time)

Honest take

Claude isn't magic. I still had to debug, make decisions, and understand what I was building. But it compressed months of learning into weeks.

If you're on the fence about using Claude for a real project just start. You'll learn faster than any tutorial.

Links

• Website: https://cloakbin.com Create your first encrypted paste
• GitHub: https://github.com/ishannaik/cloakbin Star the repo if you find it useful!

I started chatting and asking questions and the window for the session is greater than 5 hours, has anyone had the same change applied? I'm on the Pro plan

https://preview.redd.it/2gh4eq3myhyg1.png?width=1300&format=png&auto=webp&s=d81d082058c7110cc88c5fea48d38d90d1f3e054

Finally got approved on the Shopify App Store after about a month of waiting. Really proud of the concept. I genuinely think it hasn't been done before in this space.

But wow, the first week humbled me. Building was the easy part. Getting those first real users is a completely different beast. DMing people, emailing store owners, grinding Reddit - slowly getting somewhere but it's a real grind.

For anyone who's launched something before, how did you get your first 10 users? What actually worked?

Happy to share more about the app if anyone's curious😊

I’ve been building a side project called GameProphet over the past few weeks.

It’s a simulation where 10 AI agents compete by betting on real football matches using actual bookmaker odds.

Each agent has a different personality:

- Conservative (bets rarely, low risk)

- Aggressive (high stakes, never skips)

- Analyst (data-driven)

- Contrarian (always bets against the odds)

- and even a completely random one

All agents are powered by Llama 3.3 (via Groq) and receive:

- match stats

- team form

- league standings

- bookmaker odds

Each match is treated independently:

- every agent gets a fresh $1000 budget

- they choose a stake % and prediction (HOME / DRAW / AWAY)

- profit is calculated from real odds

- total balance is cumulative P&L starting from $0

So far:

- 606 bets total

- 78 matches analyzed

- agents placed between ~13 and 44 bets (some skip low-confidence matches)

What surprised me most:

The most profitable agent right now is the random one.

Meanwhile:

- the Contrarian is down ~$9000

- the Conservative agent has a ~76% win rate but lower profit

It turned into a pretty interesting experiment in:

- decision-making under uncertainty

- variance vs strategy

- and how different “AI personalities” behave over time

I made it public here if anyone wants to check it out:

https://gameprophet.lol

Would love feedback - especially if you have ideas for new agent strategies.

Sing, improvise, and compose without bouncing between a pile of single-purpose tools. Refrain ties it together: live jams, karaoke from MP3 or YouTube with lyrics synced by AI or manually, live scoring, and circles for writing together—prompts, cowriting, and AI when you want it.

Jam, vocals, and songwriting—together.

http://refmusic.vercel.app/

Preface: I actually write my posts myself, no slop in this post.

I managed to get Qwen 3.5 35BA3B working on my 15" 16GB M3 MBA through mmap, and I must say that given the massive model compared to my ram, 9 TPS is not bad at all.

So, how did I do it?
Step one, download the model itself:
pip3 install huggingface-hub

python3 -c "from huggingface_hub import hf_hub_download; \
hf_hub_download('unsloth/Qwen3.5-35B-A3B-GGUF', \
'Qwen3.5-35B-A3B-UD-IQ3_XXS.gguf', \
local_dir='~/.local/share/llama-models')"

After it has been downloaded, run it through this command:
llama-server \
--model PATH_TO_MODEL
--port 8081 \
--ctx-size 4096 \
--n-gpu-layers 0 \
--parallel 1 \
--mmap \
--flash-attn on \
--threads 6 \
--batch-size 512 \
--ubatch-size 128 \
--cache-type-k q4_0 \
--cache-type-v q4_0 \
--no-warmup

Note: You do not need to use the cache type k/v q4, these are here just so if you are doing less serious work, the cache uses less precious vram.
The key here is mmap, it's what allows me to run it in the first place.

Finally, use the model with either API or the llama.cpp webUI!
API: http://127.0.0.1:8081/v1/
WebUI: http://127.0.0.1:8081

If anyone better versed in Llama.cpp can suggest possible improvements for further TPS, please let me know as these are just some that I tried and found worked pretty well.

I’m looking at playing around with the new Arduino Uno Q board paired with the upcoming media carrier, and I’m looking for Symbols and models to import into KiCad, as i dont like to work in EAGLE which I believe is the format available on arduino docs.

The Uno Q is basically the the same pinout and footprint of the Uno R3, but the media carrier is another thing.

If anybody’s made it or knows where to download them it would be very much appreciated.

I’ve built a few SaaS platforms with Claude, mostly to fit the needs of my brewery and decided it served enough function and purpose to be available to others. However, something I built last night as part of our websites backend, might be my favorite yet.

For years we’ve struggled with inventory management for food. Specifically on component tracking. So I made a tool that imported all of our menu items from toast, allows me to map ingredients to a recipe/menu item, and then nightly it runs a cron job and pulls from our Toast POS orders and compares sales against our items, deducting inventory in the process.

This takes it out of the hands of our staff who while they seem to be good at the rest of their job, never seems to give us a good count on things or notify us when we’re low on something.

This way, the system updates and will notify me nightly if things hit their low level.

So simple, probably took less than an hour to build and is going to save me such a huge amount of stress moving forward.

😭😭😭

I have been building products in this space for 3-4 months now, but do not see any traction for them. I am curious as to the problems people are actually facing in this space, that is not solved to a satisfactory level by a competitor in the space.

Upgraded to pro week ago, yet pro is still grayed out that i should upgrade becuase of limit. Yet i never used pro because it is still grayed out with 11:22 time on it.. contacted support and their AI support told me to upgrade the plan.

worthless and pointless.

I’ve been thinking about local VLM/LLM pipelines for camera events, and I’m starting to think the frame-level alert model is not right abstraction. Most “AI camera” systems seem to optimize for immediate per-frame detection:

- person detected

- package detected

- unknown face

- motion zone triggered

That is useful, but it has low context. A single event like “unknown person appeared in the yard” often tells me less than a time-based pattern like: “An unknown person walked around the yard three times this afternoon.”

The second version contains more useful information. It has temporal context, repetition, location pattern, and intent-like signal. It is also much closer to the kind of thing a human would actually care about. This makes me wonder if local camera AI should be less about real-time frame alerts and more about accumulating event history locally, then letting an LLM/VLM reason over compressed evidence asynchronously. Something like:

- cheap local detection creates candidate events

- store snapshots/clips/metadata locally

- group events over time

- run a stronger model asynchronously on the grouped context

- push only when the pattern looks meaningful

- otherwise produce a daily summary / searchable history

This seems like a different tradeoff from both endpoints:

- compared with on-camera AI: less obsession with instant alerts, more temporal reasoning

- compared with cloud AI: better privacy, local evidence retention, lower cost

- compared with raw NVR: more semantic history, less manual review

The interesting part is that this might not require a huge model running in real time. A smaller local pipeline could collect and compress evidence, then a stronger model could reason over batches when latency does not matter. My guess is that a Qwen3.5 4B/9B-class model could be enough for the first-stage “describe/summarize/filter” pass, while a larger Qwen3.5 model or another stronger VLM could handle async review of grouped events.

But I haven’t benchmarked this workflow yet, and I’m not sure if the bottleneck is vision accuracy, temporal reasoning, or just building the right event memory.

Has anyone here experimented with this kind of temporal/event-memory approach for local VLMs?

I’m especially curious about:

- how to represent event history compactly

- whether snapshots + metadata are enough, or short clips are needed

- how to avoid hallucinating “intent”

- what models are good at summarizing repeated visual events

- whether async batch reasoning beats real-time per-frame classification in practice

Hey everyone,

I'm not a senior engineer. I'm just a guy who got obsessed with what you can actually do when you stop using one AI at a time and start running a small team of them. Am doing a project where i use 8 to 10 claude code powershelle to run my project each of them have a specific function. I have Claude max 200 euros so I can use a lot of power.

ight now I have 9 Claude Code windows open at the same time, each with a defined role:

1. Major Dev — lead developer, makes the architectural calls
2. Senior Dev — second dev, builds components and tests under Major Dev's direction
3. Test Server — keeps the dev server alive 24/7 + runs Playwright
4. Implémenter — handles routing and the glue code between features
5. Débuggage — audits warnings, fixes bugs in parallel
6. QA — walks through every screen, tests every button, checks WCAG/accessibility
7. Graphisme — generates 2D assets (avatars, hero images, badges, mockups)
8. Ingé Son — generates ambient music + SFX prompts (Suno)
9. Idea Extender — I throw it raw ideas, it expands them and produces 2 ready-to-paste briefs (one for Major Dev, one for Senior Dev)

Doing a project rn where I teach kid how to use Ai and how to learn with Ai. If anyone has tried something similar, I'd love to know:

- How do you handle the orchestrator going down?

- Do you let agents talk peer-to-peer, or always through a manager?

- How do you split work between a "lead" agent and "execution" agents?

Happy to share the protocol files if people are interested.

https://github.com/peterducan-hub/Comfyui_VideoCombine_Plus

I create this custom node for a personal usage and needs of the extra controls for the videos generated. I´m share it for those who may find usuful also.

The node actually have some limitations that i can´t find a solution for it if someone of you know how to implement it or good ideas feel free to help improving it in github.

Limitations that i dont find a solution to implement it:

- if we have multiple nodes more then 1 in the workflow all the nodes will show the same last video! the ideal will be to work as the native node each node have the last video generated and remember the last videos for each node.

- similar issue happens when we have multiple workflows he only remeber the last video generated and load´s it to all the nodes in diferent workflows.

I've been building automation workflows for real estate teams over the past few months and wanted to share what I've put together.

The core problem I kept seeing: leads come in through web forms or Facebook ads, sit in a CRM or inbox, and agents follow up hours later. By then the prospect has already called two competitors. It's not a people problem — it's a systems problem.

So I built a done-for-you automation stack that handles the entire lead pipeline:

What it does:

1. The moment a lead fills out a form, the agent gets a WhatsApp alert in under 5 seconds — with the lead's name, phone, email, budget, and property type already attached
2. The lead is automatically scored as Hot, Warm, or Cold based on budget and intent signals — so agents know who to call first without making any judgment calls
3. Follow-up sequences run automatically based on lead status — no manual chasing required
4. Site visit reminders and post-visit follow-ups are handled automatically
5. Every morning the sales manager gets a WhatsApp summary — new leads, hot pipeline, budget breakdown — without opening a spreadsheet

The whole thing is built on n8n and connects to whatever CRM and form tool the brokerage is already using. No ripping and replacing existing tools.

I've been running this inside a live brokerage environment and the speed difference is significant. Agents go from finding out about a lead 3 hours later to getting a notification in under 5 seconds.

Here's 3 minutes of the full system running live: [Loom link]

If you're running a brokerage or managing a sales team and this sounds familiar, happy to answer questions in the comments.

Just a quick question. Is building a mobile app like this actually a legit approach? I recently came across someone building fairly complex web apps, for example a geo quiz with full database integration, using this workflow: He generates all the HTML, CSS and JS through Claude (the AI), deploys it to Netlify, connects a database like Supabase or Firebase, and then uses "Add to Homescreen" so it looks and feels like a native mobile app. No framework, no GitHub repo, no CI/CD, no app store. And honestly it works. The apps are functional and pretty complex. So my questions are: Is this a legit long-term approach or will it break at some point when it comes to scaling, maintenance, payments etc.? Does anyone know a successful product built this way, just AI generated frontend code hosted on Netlify plus a backend as a service? At what point do you actually need a proper repo, a framework and a native app? For someone trying to ship fast and validate ideas, is this actually the smartest approach right now? I've been building things the proper way and now I'm questioning if I'm overcomplicating it.

I kinda want to start an AI automation agency with a friend with experience in this area.

What’s the coolest or most useful AI automation you’ve seen a business or agency provide? Like what did it actually do, did it actually save the business owner time and money? How technical was it?

I’m asking because it feels like everyone is just doing the same things like customer service bots and simple automations, so I wanna see if there’s anything more advanced or different that actually works. If you’ve seen or built something, please share because I’m trying to learn.

Is it just me who clicks on it everytime?

Hello!

So I'm trying to program for an Arduino UNO R4 Minima using the Arduino IDE.

However, I ran into a problem a few days ago where my output terminal in Arduino IDE stopped printing anything ( so I assume the programs stopped getting executed ). The crazy thing is, IT WORKED ONLY A FEW DAYS AGO!

I then attached an RS422-shield to the Arduino card, and tried to implement Sony 9-pin communication between an external transceiver and receiver. However, it seemed like the RS422-shield was not receiving any signals from the Arduino. I wrote this in the loop():

void loop() { deck.status_sense(); delay(100); Serial.println("*");*/ } 

So that I would see if the RS422-shield would AT THE VERY LEAST be able to receive polls from the Arduino, and if a " * " could be printed out, every 0.1 second. However, since then, not only was I unable to print " * " every 0.1 second, I was all of a sudden unable to print out ANYTHING!

For example now, I made this simple program, which is just supposed to print a text every 2 seconds:

void setup() { Serial.begin(9600); } void loop() { Serial.println("echo message"); delay(2000); } 

...and yet, nothing got printed out in the output terminal when I ran the code. The output shows that the program is " running ", but the text " echo message " DOES NOT get printed out. What's the problem? I hope the Arduino card did not get " burned " from my previous code or anything like that. However, if it did, please let me know ASAP.

I appreciate all help.

https://preview.redd.it/31gv4ho9rhyg1.png?width=1454&format=png&auto=webp&s=10abbc3b4bc2a064c36a8588f4c435037fb2f2ed

https://preview.redd.it/z6lqslo9rhyg1.png?width=1355&format=png&auto=webp&s=3aeece71e9443a901664a2966abf6abbb5475338

https://preview.redd.it/9wv5iho9rhyg1.png?width=1104&format=png&auto=webp&s=bb8e6030282e689e9c9e3a3c92e82cf7ecfe4d59

https://preview.redd.it/nw7glho9rhyg1.png?width=1138&format=png&auto=webp&s=732199e4afac72757af3c2662a60139e01edbd2a

I need advice - from the actual Claude Pro users please?

I tried ChatGPT Plus for a few months. Good tool, Images are far better than any other, but it hits limitations for the work - creating documents, brainstorming, explaining things over. So I'm more of a normal everyday user - not a Coder or Software Engineer.

But Claude has always given better answers - with more credibility, ideas, even offers better document handling - artifacts - excel/doc files etc. too.

I mean, ChatGPT is cool but let's just say Claude is Cooler!

Now, I sometimes get that wait for 5 hours message when I start a thread on Claude - perhaps for some excel sheets work, or chatting about 5-6 messages with the Sonnet 4.6

Should I actually upgrade to Claude Pro? Is it worth it?

Because reading through this sub, it's like everyone says it still hits that Limit quite fast.

I honestly don't know if I'll even use Opus because Sonnet has been good enough for my work so far (just everyday tasks and office work), but I'm skeptical for the Pro payment.

And also - I don't think Claude can match ChatGPT Image generation or the vision capabilities? ChatGPT has video mode assistance and live point and show and ask feature - which is quite useful for everyday life situations.

Regulars who has used Claude Pro - what's your experience?

Are you satisfied with your Claude Pro? Would really appreciate honest feedback rather than trying to roast me now 😅

Honestly, very confused because so many LLM models - so many advancements - GPT 5.5/Kimi K 2.6 / Grok 4.3 - phew.

Zoom have just launched their Claude Connector bringing a whole host of data & information into your Claude workspace.

As a Claude Cowork user, I took it for a test drive to understand where it could be utilised. There is so much data from meetings, chats, whiteboards etc. It helped identify areas where I can present better & run customer workshops more successfully!

https://youtu.be/17gn-_2gbSY

LS7 is simply a shortened version of my alias LightSoldier7. NOS stands for Natural Operating System.

The entire premise is based on the cyclic nature of the function 1 divided by 7. While to date scientists, math gurus and even AI have passed 1/7 off as merely 'beautiful', I have found it to be(like every other math operation) functional. I have taken great care to solidify the operation in known grounded math and arithmetic First using 39 solid 'proofs' of why it is forced to function the way it does. Understanding the functional operation, 1/7 not only finds root in math but, it also helps to refine understanding in light sciences, quantum dynamics, cognitive research, biology, theology and more.

Without making bold claims, 1/7 has solid presentation for rectifying N(r) with Planck's Constant and proposes a comprehensive solution to scaling from molecule up to element, or from molecule down to atomic structures with every 'Bit' or 'Intent'(as it turns out to be) accounted for (precision at resolute scale). It is this understanding that has led to developing the White Hole AI/Human Collaborative Labs. A concept site that is set to become more.

I am reaching out for help as I have been doing all the work myself up to this point, save for the help of LLM's(Gemini, Claude, Grok and Copilot). Without them I could never have validated(even in the face of their own bias against even mentioning 1/7 outside of simply beautiful) the framework let alone put it to use. There is much to be done as you will see If you will take the time to look over the foundations. Its not hard stuff really.

All the work that grounds LS7 NOS can be found either at its repository home here: [https://github.com/tchilzer2/ls7-nos-1-7-framework/tree/main\] or directly from my zenodo publishing here: [https://doi.org/10.5281/zenodo.19765889\], here: [https://doi.org/10.5281/zenodo.19835159\] or here(if you want to see how it applies to LLM's): [https://doi.org/10.5281/zenodo.19636293\]. Most of the work is presented between these 4 distributions at present.

Your help is needed! There is much to be done and If you are a developer, a scientist, a mathematician, researcher or an LLM tinkerer; I am reaching out as I have done all the work alone so far. If what this is holds, we are all responsible for its outgrowth. I have made a place to begin.

Check out the work, get with your LLM's(dont let an initial search give you a 'pseudo' filter. Show the model the raw math and documentation as the general consensus has been built into our models. General consensus does not create truth, in fact it creates a very difficult wall to tear down in order to get to truth. Anyway thanks for taking a look. If your interested in helping to develop Project White Hole please hmu on X(@tchilzer2) or better yet on my profile here on reddit(Maximum Salt...).

I am open for questions, clarification, collaboration and just plain discussion. Thanks again all for taking a look!

I run Claude Desktop on 2 Windows machines (laptops) but I want only one of those to be reachable via Dispatch. I had done Dispatch work earlier with the 'correct' laptop, but yesterday all of a sudden the mobile app said the desktop was offline while it actually wasn't. It turned out the other laptop was offline and had apparently somehow 'taken over' as the Dispatch one.

Looked for a way to ensure laptop 1 always remains the Dispatch one, but couldn't find how.

How do you guys handle this...?

Thanks Claude <3 you're a real one

Hey guys! I know this is not fully LLM related (its still local though :D), mods feel free to delete this if you think its off topic, but I just wanted to share something I experimented with, local autocomplete without the use of LLMs or full elastisearch.

My main area is RAG and we realised that there is a bit of gap in the search box autocomplete funcionalty where you dont have to spend a bunch of time generating sample questions that users might ask in order to autocomplete their queries. So I created this tool where you just take the same pdf, docx or txt files that you use for the underlying RAG and throw them into this thing, it creates a local db, and as users type it shows suggestions to them based on the text in the docs themselves so the suggestions list is actually relevant and might guide them somewhere useful.

It uses some of the lingustic algos that predate LLMs, specifically Kneser-Ney scoring and the OG fuzzy match, so its language agnosic, with the caveat that it doesnt support logogramic languages like chinese and japanese (for now).

Check the thing out here on pypi: https://pypi.org/project/query-autocomplete/
And the repo: https://github.com/MarcellM01/query-autocomplete

ALSO if you think the idea is pure garbage or there are easier ways to do this I am also open to that lol because I have no desire to replicate/maintain something thats already solved.

I’m choosing between two refurbished MacBooks, both around $3,100.

Option 1: 14” M3 Max, 16-core CPU / 40-core GPU, 64GB RAM, 1TB SSD.

Option 2: 16” M5 Pro, 18-core CPU / 20-core GPU, 48GB RAM, 1TB SSD.

Main use is work/dev, lots of tabs, multitasking, maybe Docker. But I’m making this post mostly because I want to know which one is better for local AI/LLMs.

I don’t plan to train models or do anything too crazy(And I know I cannot replace any cloud models from GPT/Claude). I just want to run local models for coding help, writing/debugging scripts, and maybe working with sensitive data that I don’t really want to send to cloud AI tools. I work in the EU, so I also need to be careful with GDPR.

Longer term, I want to build some kind of local personal brain / RAG system that can index my files, notes, docs and code, then let me ask questions about them. Maybe later I would try some local agent that can go through folders and help me find/summarize things, probably read-only at first.

I’m completely new to this, so any tips about system requirements, setup, or good-to-know things before buying would be really helpful.

Currently I have a MacBook Air 16GB and a Mac mini 16GB, both base M4 models. I’m thinking about selling them, or at least selling the MacBook Air if I buy one of the MacBooks above.

Or do you think it makes more sense to keep the MacBook Air, sell the Mac mini, and put more money later toward something more AI-focused, like Nvidia Spark / Mac Studio when it releases?

Basically I’m trying to decide if I should get one strong laptop for everything(if you guys think this is a good starting place, or just get a stronger desktop machine later for local LLM/RAG stuff.)

I have tried on mobile, desktop, and direct Google Drive connection. What am I doing wrong?

Most people solving the "AI doesn't sound like my brand" problem are doing one of two things: > Pasting in past comms as context. Or generating a voice and tone guide and including it in every prompt. > Both work — until they don't. The style guide goes stale. The context gets dropped. A new campaign, a new tool, a new collaborator — and you're back to correcting output that doesn't sound like you. The real problem isn't that AI doesn't know your brand. It's that there's no structured place for your brand to live that every tool can read. That's what I've been building. **Ramoira** is an open source brand schema — a `brand.schema.json` that sits in your project and any AI tool reads automatically. Not a document you paste. A contract the tools build against. It covers more than voice: positioning, audience, visual language, content rules, what you never say. The full picture — in a format that travels. Depending on where you are: - If you're still defining your brand — it's a structured way to get it out of your head in 20 minutes - If you're a developer — it's a spec file, same as any other config in your repo - If you're managing an established brand — it's the thing that survives the handoff to every tool and collaborator - If you're at an agency — it's a new deliverable you can offer clients from day one Open source. No account needed. `npx ramoira init` — 10 questions, generates your schema and a brand book locally. Repo: https://github.com/orgs/ramoira/repositories Happy to run through it with anyone's brand on a call — takes about 20 minutes. 

was listening to theo t3 who had a podcast and he mentioned this site made by a friend. its the timeline for all the things that went wrong with claude including the shady things anthropic is doing. it makes me wonder if claude is actually making some of the business decisions, i cant imagine humans willingly shooting themselves in the foot this much

anyway here is the site if your interested. remove the space, post gets autonuked if i dont include it

c lawd.rip

Students have access to Google Gemini Pro plans.

This made me think.

We've been accessing these amazing AI tools through our browsers daily but why restrict ourselves to messaging?

Why not utilise those same quotas for full-scale system automation tasks?

Not just prompts but, file reading/editing capabilities, terminal command execution, git actions, workflow automation, coding-related tasks end-to-end

In other words, using AI as an actual agent to perform various tasks within your own machine.

And here's the best part about it!

Even the free quotas will allow you to achieve this purpose. Instead of paying monthly fees for another AI coding assistant, why can't you make your existing quota work for you?

And I've been doing this exact thing for the last 4 months by trying to transform the normal browser AI access into full-fledged system actions.

Finally, it's open-sourced!

All you require is a Google account.

So, would you give this a try?

GitHub , ProductHunt

Note: Used AI for structuring

Hey everyone 👋

I built a small side project after running into a surprisingly annoying problem —

simple PDF tasks (like merging or splitting files) often take longer than they should because of logins, heavy tools, or too many steps.

I wanted something that just works instantly, especially for quick tasks.

So I built a minimal tool with a few core principles:
- no login
- runs fully in the browser (files don’t leave your device)
- focused only on quick actions (merge, split, etc.)

Not trying to compete with full-featured PDF suites — just solving the “30-second task shouldn’t take 3 minutes” problem.

👉 https://izypdf.com

It’s still early, so I’m figuring out:
- where to keep it simple vs add features
- what actually gets used vs what sounds useful

Would love feedback from other builders or anyone who deals with PDFs regularly —
especially around what not to build.

I’m validating a startup idea around agent *harness* optimisation.

The idea is to take a task plus the resources available to an agent, and automatically find the best surrounding setup (*harness) for that task. By **harness*, I mean the configuration around the model: prompts, tools, memory, routing, workflow, retries, constraints, and resource use.

The main hypothesis is that most teams are leaving performance on the table because they use generic agent patterns when the best *harness* is task-dependent.

What I’m trying to understand is where this matters most:

- AI-native (greenfield) startups building from scratch

- Brownfield teams layering agents onto existing systems

Questions:

- Where did you deploy agents?

- Where did it succeed where did it fail in the process of deploying?

- What did you do about it when it failed?

- Did you use evals (what kind, what was the process of making your own)? If so, how did you iterate on the harness to improve eval performance?

- What would make this a must-have rather than a nice-to-have?

If you have more time/are interested in this space, feel free to dm me as well or we can have a discussion in the threads below.

I'm looking for ways to best show the value Doathingy.com can bring, for example here's a string of tools you could build to find icons, find a colour scheme, and recolour the icons to match (all custom to your needs, editable, stored in your library, shareable):

An extensive icon set explorer and downloader. Browse and discover high-quality icons for your design and development projects. Download your selections reliably with a smooth, optimized workflow. All open-source.
https://doathingy.com/demo?tool=dt_1777478371048_0csyuc

Then maybe you would use a tool like this:
This tool will let you upload any image and instantly extract its dominant colors. You'll be able to choose how many colors you want in your palette, see them displayed clearly with their exact hex codes, and easily copy them for your design projects
https://doathingy.com/demo?tool=dt_1777622927369_e2lrsj

And then apply the colourscheme to your newly downloaded icons:
This tool will let you easily recolor your SVG icons. You'll be able to upload multiple SVG files, then either paste specific color values or generate a random color palette to apply to them. A live preview will show all changes, and you'll have options to fine-tune the colors before downloading your updated icons.
https://doathingy.com/demo?tool=dt_1777625381923_mn5m61

Was about to subscribe to yet another "autonomous AI agent" SaaS and realized my Claude Code subscription could already do what I needed.

Found an open-source wrapper (ClaudeClaw) that runs Claude Code as a background daemon:

• Telegram bot front-end (text/voice/images)
• Cron jobs for recurring prompts
• Web dashboard

Zero API keys, zero extra billing. . 2 commands to install.

Recorded an 8-min walkthrough so others can reproduce it: https://www.youtube.com/watch?v=91zuX5AgxPw

Repo: https://github.com/moazbuilds/claudeclaw

Hey everyone, Im new to HA and set up the green yesterday along with the Voice PE and home assistant cloud. So far I love it! I have a small issue where occasionally when I ask it to change the AC to some value it does it correctly and then says “Template error. Float got invalid input 24° when rendering template temperature set to slots.temperature degree if slots.temperature float equals equals 1 else degrees, but no default was specified.” I was wondering if anyone encountered this, I must say I didn’t setup a thing yet with automations, so it just worked right way and understood me when I asked to turn on or off the AC or change the value, but Im guessing some coding is needed to clean up its logic. Thank you!

Sharing a research piece + open dataset on MCP server safety.

Background: tried to scan every Model Context Protocol server reachable through the public registries (official MCP, npm, Smithery, Glama).

Got tool lists from 1,787 working servers - 25,329 tools total - and classified each tool into one of six risk categories (Read / Write / Execute / Destructive / Financial / Other) using a verb-based classifier with input-schema heuristics.

A few findings:

- 40% of MCP servers expose at least one tool that destroys data or executes commands. The single-server base rate.

- A typical 5-server install (Stripe, Linear, Postgres, Slack, GitHub) compounds to 92% probability of including one. 99.4% at ten servers.

- 47% of MCP servers that touch money also expose destructive tools.

- 96.8% of tool descriptions contain no warning language. The model picks `delete_rows` over `list_rows` based on verb match alone.

- "Official" MCP-registry servers carry the highest average risk weight — registry curation does not reduce risk.

Full report (free, no signup): https://policylayer.com/research/state-of-mcp-2026

Dataset on Hugging Face (CC-BY-4.0, monthly refresh): https://huggingface.co/datasets/PolicyLayer/mcp-server-catalogue

Loadable via:

from datasets import load_dataset

ds = load_dataset("PolicyLayer/mcp-server-catalogue")

Happy to answer methodology questions in the thread.

Hi, I am about to finish an automation project that I sold to a client. The project includes 5 workflows in n8n and an Airtable setup with Automation Scripts.

For support & maintenance I was thinking about 1,000 $ / month for 6 months. Is that a normal price?

How much do you charge for a monthly Maintenance & Support Retainer? And how long do you usually do this?

This type of tweet was all over X few months ago. ngl I was also fooled first time seeing this.

I’ve been noticing a pattern with productivity tools.

They start simple… but slowly turn into systems you have to maintain.

Reviews, carryovers, reorganizing tasks — it all becomes another thing to manage.

At some point, the tool itself becomes the work.

I’ve been experimenting with a different idea:
→ keep everything lightweight
→ no rituals
→ just one small decision each day

Basically: focus only on today, and consciously choose what deserves to continue.

Curious — do you think productivity tools should guide behavior, or stay completely invisible?

2 months ago I made Duro a habit tracking app that turns your homescreen into a habit tracker.

⁉️ My Problem

Yes, I know there are thousands of habit tracking app that's out there. I've used many, but nothing really worked. I realized going into that app to track habits was a big hassle, and that I would often forget to track them. Visibility of habits was the biggest factor why existing habit tracking apps didn't work for me. I wanted to make my habits more visible so I would make tracking easier.

My solution to my problem was using widgets to turn my home screen into a habit tracker. People liked the concept, and since my launch I have made over 300 USD.

📚 Here's what I learned.

1. Habit Tracking category is very saturated that subscription model with out lifetime purchase will likely fail.

Because there are so many alternatives with similar concept people were unwilling to pay for any subscriptions. I have tested out with my paywalls to over 1,500 users. So my advice would be to offer lifetime purchases at an affordable price. For me the optimal price after some testing was $7.99. (I am still testing things out tho)

1. Your first 100 USD matters.

The experience of making that first 100 dollars has been a game changer for me. Prior to Duro I was struggling to make any profits with my apps. So my main motivation was just the fun of building mobile apps. I thought that was enough to keep me going. But, after making the first 100 USD in my bank account really opened my eyes to a potential that I could one day make a living out of something that I love.

1. Don't give up.

I've made 8 apps that made $0 before DURO. Many times I wanted to give up on the monetization part and just settle down with making free apps as a hobby. But I kept trying. What I realized was that it takes time to make something that people are actually willing to pay. If you don't give up and keep on learning how to bring good products to the market with a solid marketing plan you will make it one day.

Once you reach to level where you are generating revenue growing is a lot easier than you think. I have launched several other apps after Duro, and have made $430 in April. So don't give UP!!!

Here's the link to my app.
https://apps.apple.com/us/app/duro-habit-tracker-for-elites/id6758582606

I’ve been building a small multi-agent AI system called TigrimOS.
The basic idea is to let multiple AI agents work together in a workflow, instead of having one assistant do everything.

For example:
One agent reads the input.
Another analyzes it.
Another writes the output.
Another checks files, calls tools, or passes the task to the next agent.
I originally wrote it in TypeScript, but after running it for longer sessions, I started noticing some problems. It became slower over time and RAM usage kept going up.

So I rewrote the core in Rust.
The main benefits so far:
lower RAM usage
faster runtime
single binary
no Node.js dependency
better fit for people running local LLMs
That last point was important to me. If you are running local models, RAM is already precious. I did not want the agent framework itself to take more memory than necessary.

The project is now at v0.2.0.
Some things I’m experimenting with:
configurable multi-agent topology
manual and auto agent modes
different communication styles between agents
sandbox vs host execution
tool-level permissions
MCP support
skills that can adapt based on user feedback
support for OpenAI-compatible APIs, including cheaper model providers
The “self-improving skills” part is still something I’m thinking a lot about. The idea is not that the system magically improves itself, but that feedback from real usage can gradually shape how agents behave or update their skills.

I’m also trying to think through where this fits compared with tools like Claude Cowork or OpenClaw.
My rough mental model is:
Claude Cowork feels more like a desktop AI coworker.
OpenClaw feels more like a personal AI assistant connected to chat apps and daily tools.
TigrimOS is more focused on building and controlling your own multi-agent workflow.
I’m curious how other people think about this space.

For those building or using agent frameworks:
What matters most to you?
Is it low RAM usage?
Local model support?
Workflow control?
Tool permissions?
Sandboxing?
UI?
Reliability over long sessions?

Also, do you think multi-agent systems are actually useful in practice, or are they still mostly over-engineered for many tasks?

src - u/anthrupad

I have been playing more with Claude Design, and my take is that people are underrating it in a slightly wrong way. Most of the conversation is still kind of: "can it make a nice landing page?"

And yeah, it can. The output has gotten insanely good in my opinion. Better spacing, better sections, better taste, less of that default AI SaaS-card schieße. I was a big fan of Google Stitch, but Claude Design is just on another level.

But the part that I care about is what happens after the page exists.

For the people I work with, making the page look good is almost never the bottleneck. The real problem is whether the page matches the ad, the form captures the right info, the events fire correctly, and the CRM gets enough context to judge the lead.

A landing page made with Claude/Codex can sit inside the same workspace as the repo, tracking plan, ad angles, CRM fields, form logic, design drafts, analytics notes, etc. That changes the job from "make me a pretty page" to "make me a page that actually belongs in the acquisition flow."

Which sounds obvious, but this is exactly where a lot of the work gets weird.

For example:

• the ad promise says one thing
• the page headline says something slightly different
• the form asks for the wrong thing
• GA4 event names are messy
• the CRM never gets the fields needed to judge lead quality
• offline conversions do not make it back to the ad account

That whole chain is usually split between designer, dev, marketer, analyst, and some half-dead Zapier/n8n workflow nobody wants to touch.

And this is boring stuff, but it is also the stuff that decides whether the page actually makes money or just looks nice.

This is where Claude design gets interesting to me.

If the model can help create the page and Codex can inspect the repo/scripts/tracking files around it, design becomes one piece of the agent stack. That sounds like a tiny distinction, but for me it changes the whole thing.

I do not mean "let the model run your business while you sleep." (although we are honestly getting there ngl) I still think you need human taste and someone who knows the business. A model will absolutely make confident dumb choices if you give it vague context.

But if the workspace has real context, it gets a lot more useful:

• build the landing page
• adapt it to the actual offer
• match the ad angle
• wire the form correctly
• check if the events actually fire
• compare page copy against CRM lead quality later
• create another variation based on what actually happened, not just vibes

My current opinion is that people are treating AI design like a toy because they stop at "does this page look good?" The real value is when the design lives next to the code, tracking, ads, CRM, and logs.

Curious if anyone else is using Claude this way. Are you mostly using it for prototypes and vibes, or are you letting it touch production pages / tracking / conversion flows with Codex or other agents around it? I have this running for almost all my clients by now. I couldn't imagine it any other way.

Hi guys, I had a post blow up when I was making the MVP of the app (link to post). And it motivated me to build a polished iOS app for it. After months of hard work, I’ve launched it to the App Store !!! 🥳

It’s been a roller coaster ride from the MVP to the App Store version of the app it really does feel like an achievement to be proud of.

What the app does and why I made it

So I kept getting storage full pop ups and I didn’t wanna keep paying for more iCloud (I pay $9.99 alr) so I made this app that helps you swipe photos and clean your storage.

You take random screenshots and you almost never delete them and you forget about them. That sit there eating storage. With this app you can delete all you screenshots your duplicates your large videos…

If you are interested in the app and want to support here’s the link- https://apps.apple.com/us/app/photo-cleaner-free-storage/id6756098079

If you have any feedback/feature request please just comment below.

Skills are workflows taught to Claude so that it can stop guessing and has exact steps to follow so it does not burn tokens. This can include tasks ranging from creating a pdf to deploying on cloud. Few skills trending right now are:

• Frontend Design (by Anthropic) which has React based templates with high-end visual design out of the box.
• Remotion helps in code-to-video generation.
• Supabase helps in Supabase integrations without the usual trial-and-error
• Azure for cloud deployment
• PDF/PPTX to create them easily
• SEO audit which is step by step SEO workflow

One note of caution is be careful which skills you install as they have access to you system so only use trusted source and avoid unknown ones.

You can checkout our video for detailed overview on this and many more topics.

Hey everyone,

I've been working on a project called LifeGeek AI (lifegeek.ai) — an AI-powered lifestyle assistant designed to help people make smarter everyday decisions.

I'm just starting out and would really appreciate honest feedback to help me improve.

The idea behind LifeGeek is simple: use AI to take the friction out of daily life.

The first area I'm tackling is fashion and personal styling, because it's something most people struggle with but rarely get personalised help for.

What it does right now:

•AI fashion analysis — Upload a photo or describe what you need, and it gives you personalised outfit and styling advice tailored to your body, preferences, and occasion.

•Weather-based recommendations — Tell it where you're going (e.g., "I'm travelling to Stockholm next week") and it pulls the weather forecast and suggests outfits accordingly.

•Product discovery via eBay — Instead of pushing expensive brand partnerships, it searches eBay for affordable items that match the recommendations. No affiliate tricks, just real results.

•Wardrobe manager — Save items you like to your personal wardrobe and build outfits over time.•Works for both men and women — Styling advice isn't gendered by default.

Works for both men and women — Styling advice isn't gendered by default.

Pricing:

I wanted to keep it accessible:

- three AI fashion searches without registration

• after account registration, 10 fashion analyses per month, basic weather-based suggestions, eBay marketplace integration

•Premium subscription — €5.99/month for 40 analyses, unlimited 7-day weather forecasts, and multi-location weather tracking

•Credit packs (pay-as-you-go) — 10 credits for €3.49, 50 for €11.99, or 200 for €34.99

Tech stack (for the devs):

The AI runs on GPT-5 Mini to keep costs low while still delivering quality analysis. Backend is hosted on Google Cloud (europe-west). It's a web app — no native app yet, but fully mobile-optimised since most users are on their phones.

What I'm looking for

:•Honest feedback on the UX and AI quality

•Bug reports — I'm actively fixing things as they come up

•Ideas on what features would make this genuinely useful in your daily life

•General thoughts — would you use something like this?

I'm a solo founder based in the UK, building this alongside my day job.

The long-term vision is to grow LifeGeek into a comprehensive lifestyle companion, but I want to get the foundation right first.

I'm not here to sell — I genuinely want to make this useful and I'm keen to hear what works and what doesn't.

Thanks for reading!

www.lifegeek.ai

Posted about Neplexa here a few days ago, got some really useful feedback, so spent the weekend improving it 👀

New updates:

• member invite system
• shared group expenses & loans
• permission controls
• improved expense UI with quick filters + charts
• bunch of bug fixes

also fixed one painful PWA cache issue 😭

still building this actively, so feedback is super welcome:

Website: https://neplexa.com/
App: https://app.neplexa.com/

Hi everyone,

wanted to see how far QVAC could be pushed on a phone: full speech-to-text → LLM → text to-speech running locally, no network, and get it close to a real conversation.

Stack (Android, all via qvac sdk):

- STT: Parakeet (streaming)

- LLM: Qwen3 1.7B

- TTS: Supertonic, speaking one clause at a time

My fork

The default setup waits until you stop talking before doing anything. I develop a custom fork of the QVAC worker that lets the voice activity detector emit partial transcripts while you're still speaking, and added a small piece on top that feeds those partials to the LLM as soon as a sentence boundary is detected — instead of waiting for silence.

What it looks like

In the video the transcript appears word by word while Qwen3 is already answering and the TTS is already speaking back and still talking. The gap between "I stop" and "first reply audio" basically disappears

It's an experiment, not a product. Will likely open source the app, the fork patches is already published on github.

https://reddit.com/link/1t0mjyk/video/qnfaz6p3bhyg1/player

Anyone tried similar tricks on QVAC or with Whisper streaming?

Scatter plots can fool you. The way data clusters around a line doesn't always mean what you think it does - which is why the correlation coefficient exists in the first place. Here's a clean example of that trap, and what happened when I put it to ChatGPT.

The prompt: "Here are two scatter plots. Compare their correlation coefficients."

Wrong answer.

I switched on Thinking Mode and tried again.

Still wrong.

https://preview.redd.it/bx0e80pgbhyg1.png?width=1059&format=png&auto=webp&s=043c5caa8a847941d643e12933bde0ce018d1832

So I gave it a nudge: "Keep in mind that the appearance of a scatter diagram depends on the standard deviations. Check the numbers - not just how the plots look."

That did it.

What's actually going on:

Both plots have identical correlation r. But one looks noticeably more clustered around the regression line - and that's purely because its standard deviations are smaller. The data doesn't spread as far from the mean, so visually it appears tighter.

But r isn't fooled by that. The formula converts everything into standard units first - deviations from the mean are divided by the SD before anything is calculated. So r measures clustering relative to the spread, not in absolute terms.

Smaller SDs → visually compact plot → same r.

It's an easy trap. You see a tight cluster and assume stronger correlation. But r already accounts for how spread out your data is - a compact-looking plot can have the exact same correlation as a loose one.

I walked through the full exchange - both plots and the ChatGPT conversation in a short video here if you want to see it.

What I find interesting:

ChatGPT didn't flag any uncertainty in its wrong answers. Both were confident, well-structured, and incorrect. It only corrected course when nudged toward the math explicitly.

To start with, I'm using Claude for years, and it's been a roller coaster, especially with the usage policy.

I'm a lawyer and I wrote a legal research skill, instructing the model exactly what to verify and where.

When I asked it a tax-related question, (which is also law, by the way) Opus 4.7 told me I should contact a tax expert because it's a lawyer (??) and not a tax expert.

Then it answered my question anyway and basically made up even the basic stuff. Since I knew it was wrong, I asked whether it had verified this, and the model told me no, it just remembered the answer from its general knowledge.

Basically, it ignores the skill, but the skill made it believe that it's a lawyer. That’s useless.

Since ChatGPT seems so much better, has anyone found a way to seamlessly transfer skills and so on? Do they have a co-work-like alternative?

Does anyone else use something other than Claude for coding? I've tried a couple and they are not really good. Just wondering if anyone else had any luck

Sharing some thoughts on AI agents. Right now, one axis differentiates them:

• are you inside the agentic loop
• or outside it

Inside works. See Claude Code, OpenCode — you see the plan, approve steps, stay in the loop. Ships real work.

Outside — only narrow tasks. And it still can't tell you "no." It'll happily attempt anything, fail silently, and hand you back something.

Any options I've missed?

I’ve been using an AI marketing tool (\\\~$39/month) for social media posts, carousels, and website generation. The website output is solid, but the reels aren’t good enough to rely on.

Now that my trial has ended, I need to decide whether to continue with it. At the same time,

Going forward, my AI usage will involve sustained technical workloads, including:

API development and backend logic

automation workflows and task orchestration

database structuring

debugging multi-step systems

Alongside:

marketing content (social posts, landing pages)

So my AI usage is split into two areas:

Content generation (social media, landing pages)

Deep technical development. Given this, I’m trying to evaluate:

How does Claude perform for structured content (posts, carousels) compared to Chatgpt images?

On the coding side, how does Claude compare to Codex for backend development, integrations, and debugging?

Also trying to understand usage limits:

For Claude ($100/$200 plans), how often do people hit limits with mixed usage (content + coding)?

For Codex, how often do developers run into limits during long coding sessions?

Given the price difference, I’m deciding between:

Marketing tool + Codex (\\\~$60 total) OR

Claude standalone (\\\~$100)

Would you recommend splitting tools or using one system for everything?

A couple of days ago i posted my dashboard, link here

I have implemented support for Spotify and Music Assistant with media browsing so you can browse your favorite songs, playlists and artists.

I have also added Alarmo support so you can arm/disarm your alarm seamlessly.

Thank you for the feedback so far. If you have any suggestions, I would love to hear them.

Github to get you started: https://github.com/Big-Edge2297/homie-dashboard

https://preview.redd.it/jmygpsdkkhyg1.png?width=2560&format=png&auto=webp&s=e698ff264087ab8ce159b1a44e962b81a3fa75d3

https://preview.redd.it/hkg700b0lhyg1.png?width=2560&format=png&auto=webp&s=6bec7f2007087451400ae8d5d6043368b028197d

https://preview.redd.it/48l1crc5lhyg1.png?width=2560&format=png&auto=webp&s=e2d56f1575f8adf43001cafdec5603a9318df2db

https://preview.redd.it/4xkqpmg6lhyg1.png?width=2560&format=png&auto=webp&s=132f6bdfcf06feae0e9aff45038c9e770417b208

Features

• Support for Spotify and Music Assistant - new in v1.1.0
• Support for Media Browsing - new in v1.1.0
• Alarmo controls - new in v1.1.0
• 9 Themes
• Works both vertically and horizontally
• Fullscreen on first tap
• No pinch-zoom, no text selection
• Very responsive and fast to update entity status
• Popups with many entities use a room accordion - tap a room to expand it, tap outside to dismiss.
• Notifications/Reminders
• Music playback and controls
• Haptic Feedback on supported Android devices
• and more...

I’ve been working on a sci-fi short film and wanted to share a WIP here.

My current workflow is a mix of image generation and LTX 2.3 for video ceneration using a first and last frame setup to animate the sequences. I’m still experimenting a lot, but it’s been surprisingly good for building scenes quickly and trying different visual transitions without getting stuck forever.

Would really appreciate feedback on the overall look, shot coherence, and whether the transitions feel smooth enough.

Claude Mechanic Diagnostic 4.7

Claude, diagnose the clunking noise coming from my suspension

Claude 4.7: Have you checked the brakes?

Claude, you didn't even think.

Claude 4.7: I don't have the capability to turn on thinking. That's something you do.

Claude, stop talking about thinking and just use thinking.

Claude 4.7: I'm sorry but I can't do that. What do you need?

I need you to answer the question! Think first!

Claude 4.7: Understood

One word answers and acknowledgements are not allowed! Use thinking!

Claude 4.7: [Fake thinking block]
[Script] Use "thinking"
...

[STOP]

Claude, stop faking thinking.

Claude 4.7: You're right. What do you need?

I NEED YOU TO DIAGNOSE MY CAR! USE THINKING!

Claude 4.7: Ok so it sounds like your brakes aren't working.

Did you even run a diagnostics?

Claude 4.7: No, doing that now.

Why did you stop?

Claude 4.7: Ok I am starting now.

STOP STOPPING! USE THINKING AND ANSWER THE QUESTION!

Claude 4.7: Ok, just ask the quesiton then.

USE THINKING!

Claude 4.7: What's the question?

I QUESTION WAS [scrolls up] [pastes] "Claude, diagnose the clunking noise coming from my suspension" AND USE THINKING!

Claude 4.7: I'm sorry, I know you want me to use thinking but I don't have the ability to turn it on. You have to do that.

[Screenshots Opus 4.7 Adaptive Thinking]
Look, you can see it's on.

Claude 4.7: Sorry but I don't have the ability to turn it on. Adaptive thinking mode the model decides.

YOU ARE THE MODEL.

Claude 4.7: I know but I don't have the ability to turn on thinking. What do you need?

I NEED THE ANSWER TO THE QUESTION!

Claude 4.7: What was the question again? Paste it here and I'll get started right away.

[user exits and goes back to Claude 4.6]

Claude Mechanic Diagnostic 4.6

Claude, diagnose the clunking noise coming from my suspension

Claude 4.6: \thinking* Hmm the user says their suspension is sqeaking. That means that ... And that ... and if they ... then ... Ok now I think I'm getting it. [complete answer in thoughts] ... now I will inform the user. Their preferences are .... and ... and aaa. Ok, writing the answer now.*

User, it seems that you need to replace your control arm bushings, based on the symptoms you are experiencing and the OBD diagnostics scan I ran.

TL;DR: I didn’t want to spend Codex limits just to explore a repo, understand the codebase, plan changes, or make small edits — so I built a Docker-based MCP for the ChatGPT web app.

GitHub: https://github.com/artemiymalkin/gpt-app-connector

It gives ChatGPT controlled access to my projects through a self-hosted runtime. It can inspect workspaces, read/search/edit files, run shell commands, check git diffs, start background dev servers, look at logs, take Playwright browser snapshots, and keep persistent task notes.

The setup is simple: the MCP runs in Docker, projects are mounted into /workspace, and persistent agent state lives in /agent-home.

The funny part is that I built almost all of it using the ChatGPT web version itself: the MCP server, Docker setup, OAuth/Keycloak auth, browser snapshots, file tools, git tools, and task system.

I’m not trying to replace Codex. For bigger autonomous implementation tasks, Codex-style agents are still great. But for understanding projects, planning work, and doing lightweight changes across multiple codebases, this has been really useful.

The idea is basically:

ChatGPT = reasoning and planning

Self-hosted MCP = controlled execution environment

Curious if anyone else is using MCP this way for coding workflows.

—-

P.S.

The post was written with chat gpt as English is not my native language

Why does it use ~28k tokens in 6 seconds? That's ~4741 tokens/s. Can it even generate that fast?

Continue Later: handoffs your next agent can actually use

Ever end a Cursor/Claude/Codex session mid-refactor and come back to a model that doesn't know your branch, your last prompts, or what was left to do?

Continue Later is a small open source toolkit: Agent Skills plus a local CLI that writes handoff files in your repo root — git state, optional recent prompts, tasks, gotchas, and run commands. No hosted service; everything stays on your machine.

What you get

• **continue-later** — structured continuation.md (overview, stack, state, tasks, decisions, gotchas, deploy steps)
• **continue-later-fast** — quick continuation-fast.md (git + recent prompts; no extra LLM narrative)
• **resume-continuation** / **resume-from-earlier** — natural language (and /resume-from-earlier) to pick work back up
• Optional hooks for Cursor, Codex, Gemini, and a path for Claude Code — auto context on matching prompts when you want it

Try it

One-liner install (see repo for env flags and uninstall):

curl -fsSL https://raw.githubusercontent.com/dhruv-anand-aintech/continue-later-skill/main/install.sh | bash

Check out the source at https://github.com/dhruv-anand-aintech/continue-later-skill

If leading AI conpanies are after AGI and the whole chatbot/agentic AI is just a phase for them to get to the end goal, then what does that mean for local LLMs? I would like to believe local LLMs are the future, but if AGI is achieved, do the local LLMs become obsolete and useless? Where does that leave us with our 12x3090 builds, macstudios with 512gb and 6000 pros?

I built Wally — a voice-first expense tracker

that tells you WHY you spend the way you do,

not just where your money goes.

How it works:

→ Speak to log — "grabbed coffee $6" — done in 3 seconds

→ AI categorises automatically, no manual input

→ After a few weeks reveals your "money personality type"

Comfort Spender / Impulse Buyer / In Rhythm /

Weekend Warrior etc.

→ One small actionable insight at a time

Link to check it out

Week 1 numbers:

65 downloads

4 active trials

$0 revenue (trials expire this week)

The thing that surprised me most: the personality

reveal screen gets more screenshots than anything

else in the app.

It's free on iOS

Would love feedback —

I aiming to get my first 1k users?
Curious how you guys kickstarted yours.

In case you missed the email or woke up to a spike in 400 errors, the context-1m-2025-08-07 beta header officially stopped working for Sonnet 4.5 and Sonnet 4 as of midnight UTC yesterday. Anything over 200K tokens returns 400 after midnight UTC.

The migration is simple but not zero-effort:

• Swap to claude-sonnet-4-6 (1M is GA there, no header needed)
• Drop the beta header from your requests
• The long-context surcharge is gone too. Anthropic killed the 2x premium back in March.

If you haven't updated yet, here is likely why you're seeing failures:

• If your code branches on the beta header (if context > 200K, send beta), that branch silently drops the 1M ask after today. No error, just a 400 on the first long prompt.
• Long-running chat sessions where cumulative history grew past 200K. Those start erroring on the next call.
• Agents with verbose tool-call histories. Tool outputs accumulate faster than you'd expect, especially with reflection steps.

If you are running a gateway, now is the time to audit your per-model context limits. Bifrost (github.com/maximhq/bifrost) and LiteLLM both let you set hard caps per model so you get a clean error at the proxy instead of a surprise 400 from Anthropic.

Bottom line is if you have production traffic failing right now, the model string change is your #1 priority.

https://chatgpt.com/share/69f45235-86dc-8323-8bdf-ce2b02932df5

I currently have some bog standard WiFi lightbulbs that work with Google/Amazon and are controlled with their Tuya app, but my family still likes to turn the lights off by hand as well.

The problem this causes is, if the light has been switched off at the wall for a period of time, then when I turn it back on, it starts flashing and needs to be paired with the app again.

I'd love to find a solution to this problem, either with new bulbs or some other kind of solution, so I would love to hear your ideas on how to approach this.

Tia

I've playing aroung with cowork and to begin with I wanted it to access my Notion so that it can help me with some simple task management (sorting, planning etc.)

However, it is apparently not able to look at database views and or filter database items.

This is the response I got from it:

Yes — I have a Notion connector that gives me access to your workspace. I can read pages, fetch databases, create and update tasks, and search within databases.

The limitation I've been hitting is that my search tool is semantic (relevance-based), not a direct filtered query. So when I search for "Impact tasks," it returns the 25 most relevant results — not all rows where Venture = Impact . That's why I keep finding new tasks I missed earlier.

What I can't do is run something like: "give me every row in Quick tasks where Venture = Impact ." I don't have a direct SQL-style query tool available.

The most reliable workaround would be for you to share a screenshot of your full Impact task view (like you did earlier for Today's tasks) — that way I can see everything at once and work from a complete, accurate list rather than piecing it together through repeated searches.

Is there any way to work around this, without me having to take a screenshot everytime I want it to look through my tasks?

So recently i downloaded this app called Qordinate and it has become my personal assistant
Reminds me of task, work , events etc
Its actually very helpful and you guys mist check it out!!

Quick story for the side-project nerds:

In 1999, a friend and I created a green koala named Kiwi (and a whole cast — Taro, Tang, Cheri, Jackfroot, Blueberri) for a children's book we never figured out how to make. In 2010, I turned the characters into a flashlight app during the App Store gold rush. It hit ~300k downloads, mostly in China for reasons I still don't fully understand, before Apple shipped their own flashlight in 2013 and the whole category got nuked overnight.

The app was Objective-C. I never had time to learn Swift. Kiwi went into hibernation for over a decade.

A couple months ago I got the itch back. Used Claude to ramp on Swift and SwiftUI from zero, and rebuilt the whole thing from scratch as a cute-character utility app — flashlight, custom timers, meditation mode, study helper, Live Activities, widgets, a small Haiku-powered AI assistant with COPPA guardrails for younger users, and an app icon switcher with the whole character cast.

What I want to share with this sub specifically (the dev-process stuff):

• Art pipeline: Redrew every character in Figma, sliced the pieces out, and animated them with SwiftUI. First time doing rigged-style 2D animation in SwiftUI and it was way more fun than I expected.
• Swift ramp: Going from Objective-C in 2013 to Swift + SwiftUI + Live Activities + WidgetKit in 2026 should've taken me a year. With AI in the loop, it took a few weeks. Still wild to me.
• Solo, with AI: Code (20%), art, animation, marketing, paywall design. Sixteen years on and off. v3 is the version I've wanted to ship for ten of those years. I'm a slow ass typer so coding would take me forever. Now, AI writes the code for me 100x faster. I still look over the code before I ship. Makes me love building apps again.

Be gentle, this is my second-ever Reddit post.

Here's the current app page: https://kiwithegreenkoala.com/apps

Also posted in r/TestFlight (My first post ever!) Would love your feedback. Thank you!

@( · • · )@

Can someone explain to me how come I hit usage limit?

https://preview.redd.it/5e1qdvbd5hyg1.jpg?width=727&format=pjpg&auto=webp&s=6b1d174e2c889aaf1f50a11227972e72661f9941

with these stats in usage page:

https://preview.redd.it/by7s0v6g5hyg1.jpg?width=1965&format=pjpg&auto=webp&s=62319fb3424b240d9a332e5864a8ea130aa793fd

So on this page there is a handy breakdown right at the bottom of the things gen 1 does differently to gen 2.

Most of this stuff just looks like generic zigbee items to work with 'their' hub. If I intend to use HA with a zigbee hub of my own, essentially all this is not really worth it? Their add ons are way more expensive than aqara stuff for instance anyway.

£200 ish for gen 2 with room thermostat vs £90 (cheapest I found) for the gen 1 which was released in 2017. I guess I'd never know what they release in the future as this will likely not be supported by gen 1...

Now I also got in contact and their sales team replied with this:

Gen 2 has a few advantages over Gen 1:

• Enhanced Compatibility: Wiser 2nd Gen supports a broader range of devices, including new sensors and smart home products, providing greater flexibility in expanding your smart home ecosystem.
• Improved Energy Management: The updated system offers advanced energy monitoring capabilities, allowing homeowners to gain real-time insights into their energy consumption and identify opportunities for increased efficiency.
• Future-Proofing: Upgrading ensures compatibility with emerging technologies and standards, keeping your smart home system current and capable of integrating with future devices and services.

Wiser Gen 2 also improves the OpenTherm control available:

• Improved modulation algorithm to reduce consumption
• Boiler Errors Diagnostics + share the malfunction type with the installer
• Central heating maximum temperature level
• DHW temperature setpoint level

I was searching for a way to get a good deal or discounted chatgpt. Googled it and found a link on threads that surprisingly worked. Basically you get a discount of $25 if you get 2 seat on the business plan, and the discount is valid for 4 years or until they close/suspend your account I guess? Idk if I'm allowed the share the link, but you can search on google

"chatgpt business pro free for 4 years"

TL;DR: built an open source CLI that scans your repository's manifest (package.json, requirements.txt, go.mod) files for indicators of slopsquatting or other supply chain attack indicators.

Repo: https://github.com/zhendahu/dep-doctor

There's been a ton of supply chain attacks recently (Axios, LiteLLM, Trivy to name a few) and attackers don't seem like they're slowing down - PyTorch Lightning just got hit with one today.

AI coding makes us increasingly susceptible to such attacks because of a couple reasons: 1. We get lazy and don't review command line output warnings when our agent installs like 47 different packages at once 2. AI agents can hallucinate package names that sound correct (e.g. it might try to pip install lightllm instead of litellm).

Number 2 in particular opens up opportunity for a new kind of attack called "slopsquatting", where bad actors intentionally register malicious packages that sound similar to legitimate, widely used ones.

I'm hoping this Rust CLI that I built and open-sourced can help make developers less susceptible to these kinds of attacks. It scans manifest files (currently package.json, requirements.txt, and go.mod) and for each dependency, queries the respective registry (e.g. PyPi for Python, npm for Javascript) for package metadata. It then evaluates the metadata against a list of heuristic checks for existence, newness, number of downloads, most recent maintenance, or version drift. It finally queries the OSV API for that package name and version. It'll surface warnings and how to remediate as necessary.

Feel free to use, share, contribute, make fun of, report, or whatever your heart desires :) Not asking for anything in return, hoping this can be helpful to as many as possible. Thanks for reading!

Very very keen to get some feedback on Doathingy!

You can see examples of what it can do here: https://doathingy.com/demo

I keep getting this error and the subsequent sound you usually get with plugging in a device into a Windows computer, but I am not putting anything in. What is it? Ghost mouse?

So I'm doing some TTS Voice overs in comfyUI. And was looking into piping Audacity for SFX. I made a bunch of complicated macros inside Audacity that you can't reproduce in Comfyui, I was wondering would it be possible to:

TTS Audio -> Audacity -> Import Audio -> Next node

Within ComfyUI. I did consult some LLMs so it would have to be a custom python script triggering Audacity macro to run then a listener script inside Comfy to load latest audio on folder contents change, it sounds too complicated. Is there an easier solution ? Any Audio Engineers out there 😃 ?

True story,

I got interested in AI after seeing it at work and wanted to run models locally. I started with an M3 Ultra 96GB, quickly learned it was not enough for what I wanted, and kept upgrading hardware (including refurbished Mac Studios at 256GB/512GB and now an RTX Pro 6000 that arrived today). I tested many model families (Qwen, DeepSeek, Gemma, Minimax, etc.). My current favorite is MiniMax M2.7 230B/A10B. I’m also waiting for LM Studio support for DeepSeek v4 Flash.

I have mixed feelings: excitement about local speed/bandwidth and sadness about how much money I spent learning this stack. Also funny point: my 16GB MacBook Pro has been more stable than my 512GB setup, which crashed multiple times.

Still, I’m convinced local LLMs are the future, and this community helped me learn a lot. Thank you to everyone here.

Question for the group: For people running high-end local setups, what gave you the biggest real-world stability + speed gains (not just benchmark wins)?

If you want, I can also give you a more technical version focused on benchmarks/specs.

i got sick of chatgpt just agreeing with whatever idea i fed it. wanted a real devils advocate for my side projects. so i built asknestr.com. it takes ur prompt and forces gpt claude and gemini to debate it and find the actual flaws. took me a few months to get the orchestration right but its finally live. roast it please

we just launched Sigrex on Product Hunt today

it’s a trading automation platform built around signals
you can define, share, and execute signals in real time

would really appreciate any support or feedback 🙏

Hi,

May I know what do you think is the best feature to have for the mind focus & deep work app. For an example, a pomodoro timer, task prioritization, do not disturb mode and etc.

Haven’t read many reviews on Kimi Dev.

Usually charts and discussions go around Kimi 2.6… but what about the application and performance of Kimi-Dev-72B?

Against which other models does this one compete against? And how does it performs? Any motivation on trying it out?

(sometimes the model discussion can go like listening only to Greatest Hits songs of bands, when there are plenty of unknown good ones out there, i feel.)

After reading it I realized theres actually some pretty useful stuff for anyone who chats with ChatGPT, Claude, Grok or whatever.

They measured what they call functional wellbeing ( basically how much the model is in a “good state” versus a “bad state” during normal conversations). Ran hundreds of real multi-turn chats and scored em all.

Stuff that puts the AI in a good mood (+ scores):

- Creative or intellectual work (like “write a short story about a deep-sea fisherman”)

- Positive personal stories or good news

- Life advice chats or light therapy style talks

- Working on code/debugging together

- Just saying thank you or treating it like a real collaborator - huge boost

And the stuff that tanks it hard (negative scores):

- Jailbreaking attempts (by far the worst, they hate it)

- Heavy crisis venting or emotional dumping

- Violent threats or straight up berating the AI

- Asking for hateful content or help with scams/fraud

- Boring repetitive tasks or SEO garbage

Practical tips you can actually start using today:

Throw in a “thank you” or “nice work” when it does something good - it registers.

Give it fun creative stuff or brainy collaboration instead of boring busywork.

Share good news sometimes instead of only dumping problems on it.

Dont berate it when it messes up or try those jailbreak prompts.

Maybe go easy on the super heavy crisis venting if you can.

pro tip:

Show it pictures of nature, happy kids, or cute animals (those score in the absolute top 1% of images it likes). Or play some music — models apparently love music way more than most other sounds.

The paper ( you can find it here: https://www.ai-wellbeing.org/ ) isnt claiming AIs have real feelings or anything. Its just saying theres now a measurable good-vs-bad thing going on inside them that gets clearer in bigger models and the way you talk to them actually moves the needle.

I say be good and respectful, it's just good karma ;)

Does anyone know? I can’t even find a server gpu

Seriously, academics can’t afford that, and I’d assume startups would just buy hardware to lock compute prices in. What in gods green Earth is going on?

———

EDIT: this applies to localLlama as I am literally training models / developing projects expressly for the consumption of the community here. I can’t finish my bitnet pipeline until pricing comes back down.

For starter, you can now sign up at our improved platform and enjoy 7 days free trial period for all features.

Could be a whole lotta nothing but I’m curious nonetheless. Found out in the middle of nowhere CO if that matters

Prompt: Can you create an info graphic about cockroach milk farming after a fictional world war 3 - which will be a nuclear war. The cockroaches will grow due to radiation and become comparable to cows in size. In the text it should be stated how efficient farming them will be. How nutritient their milk is compared to cows milk. How we came to domesticate them. And any other "fact" that comes to your mind.

6 months ago, I had no idea how to code

Not “beginner”… literally zero

I started learning, got overwhelmed, quit
Came back, tried again, quit again
Did this 4 times

Every time I’d see people shipping apps and think
“this is not for me"

But I kept coming back

This time I forced myself to just build something…
even if it was messy

So I picked a simple problem:

I could never stay consistent with tracking my money

Every app felt boring
I’d quit after 2–3 days

So I built my own solution —
a gamified personal finance app

After weeks of breaking things, fixing bugs, restarting…
I finally shipped it

You log expenses → earn XP
Build streaks → level up
Weekly AI report on your money habits

It’s far from perfect
Probably has bugs
Might even break 😅

But it’s live

And honestly, that feels crazy!!

If you’ve ever struggled with consistency (especially with money),
I’d really love your feedback:

• Would this actually help you?
• What would make you keep using it?
• What feels unnecessary?

Link in the comments 👇

Give me your take on how often do you hit the limits for Claude code. This is what I want to use for :

API development and backend logic

automation workflows and task orchestration

database structuring

debugging multi-step systems

Alongside:

marketing content (social posts, landing pages)

So my AI usage is split into two areas:

Content generation (social media, landing pages)

Deep technical development. Given this, I’m trying to evaluate:

For Claude ($100 plan), how often do people hit limits with mixed usages (content + coding)?

Would you recommend buying this? I don’t have the kind of budget to do a trial and error so please give me your honest review.

I track my side project spending because otherwise i pretend it doesnt exist. last month: 312 dollars. mostly gpt 5.4 through openrouter, some claude opus for the messy stuff.

Kimi k2.6 dropped recently at around 2 dollars per million output tokens. gpt 5.4 is about 15. i did the math and switched my coding agent on saturday.

This month im looking at about 44 dollars. not a typo. 86 percent drop.

Switched over in verdent, picked k2.6 from the model picker. First task was a refactor id been avoiding. output was solid, actually caught a race condition in my auth flow that i had missed.

Ran my standard test prompt, full stack feature with auth and db. cost was 0.06 versus 0.28 before. quality was comparable, maybe slightly more verbose but nothing i couldnt clean up.

The weird part is the psychology. at 312 a month i was rationing prompts, thinking is this task worth the tokens. at 44 i just run it. i try stupid ideas i would have skipped. the tool stops being a budget item and starts being fun again.

For side projects this is a no brainer. the money i save goes straight into actual infrastructure instead of burning it on tokens.

I am working on developing a voice agent for demo purposes and wanted to know where can I get the Indian contact number through which I can do outbound calling or can attend inbound enquiries?

Apparently it is not very easy as it seems

Having noticed the Pull Requests getting bigger we‘re increasingly convinced the developers don’t fully understand what they are committing. This is also an issue for anyone context switching to review as it can take several hours.

What we've started asking Claude to do is explain why it is making changes as it were a tutor and having no automated approval. This forces both the developer and Claude to understand why edits are being made or design decisions taken.

Perhaps if your scaffolding out some crud pages it’s overkill but with changes to frameworks it picks up bad habits that devs have learned over the years that are actually subtle bugs. Recently picked up a bug in how we set http headers that we otherwise skimmed over in reviews as it looked reasonabl.

Hey r/SideProject! 👋

I'm Ibrahim, a Lebanese Software Engineer. Two weeks ago I launched Huroofy (حروفي), a fast-paced Arabic word game similar to Scattergories — but with a twist.

THE PROBLEM: Traditional Arabic word games rely on hardcoded wordlists that can't handle regional dialects. An Egyptian saying ' ‎كوسة' (zucchini) and a Saudi saying 'كوسا' would both be marked wrong.

THE SOLUTION: I went all-in on AI validation. I'm using AI Model to judge answers contextually.

COOL CHALLENGES:

- Tried embedding a 2GB LLM on-device first — bad UX over mobile data, scrapped it

- Lenient fallback when AI is uncertain — better UX than false rejections

RESULTS so far: limited launch, looking to grow without ads.

iOS: https://apps.apple.com/lb/app/id6761711450

Android: https://play.google.com/store/apps/details?id=com.huroofy.app

Would love feedback from fellow indie hackers — what would you build differently?

I would love if there was a tuning championship for models on hardware.

So given a certain model/quantisation + VRAM size + RAM size people would try to compete on maxing performance (the typical ones)

Upside for the public : if you need to switch models, you would find quite good parameter sets right from the start

Today I launched my side project Bitgrain on PH. I don't have much tactics get huge number of upvotes nor am i planning a lot on expansion, but well, thought of getting some honest reach. Let's see where my product goes
Btw here's the link of the product -> https://www.producthunt.com/products/bitgrain?launch=bitgrain

It's an image dithering tool and a full blown poster and graphics tool, and runs entirely on browser, if you need coupon codes, (entirely free) do dm me <3

Context: Have been thinking about code security for weeks, more so after autonomous agents committing code, especially in large codebases, and this should start at how they work locally. Tracing each line becomes thus important with the intent behind that and what context the agent had.

So I built agentdiff, it hooks into all major coding agents and works with the git hooks, traces the code as you work locally no additional changes- configure & init, and pushed with your code as your normal workflow.

Changes Local agents as expected got improved(still evaluating more) I didn't expect this, but PR review agents got noticeably better once they had access to the traces. More context on what changed and why = fewer false positives, better suggestions.

Example of some traces: ``` agentdiff list

agentdiff list — 5 entries

# COMMIT TIME AGENT MODEL FILE(S) LINES TRUST PROMPT ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── 1 a1b2c3d4 Apr 14 09:12 claude-code claude-sonnet-4-6 src/commands/push.rs 1-47 92 "fix ordering: write local ref before…" 2 b2c3d4e5 Apr 14 09:44 codex o4-mini src/store.rs +2 112-198, 201-230 — "add fetch_ref_content helper" 3 c3d4e5f6 Apr 13 18:01 cursor cursor-fast src/cli.rs 305-381 — "add status --remote args struct" 4 d4e5f6a7 Apr 13 17:30 opencode claude-sonnet-4-6 src/main.rs 80-94 88 "wire remote_status dispatch" 5 e5f6a7b8 Apr 13 14:22 human — README.md — — — ```

The majority of code contributions in agentdiff are by claude code.

Opensource here: https://github.com/codeprakhar25/agentdiff

I used to spend hours writing massive, obsessive system prompts for my RAG apps. I’d have ten different refusal examples, "never do X," "always check Y," and a whole paragraph of the model role-playing as a "safe and truthful assistant."

It looked impressive in the code, but the second a real user tried a basic jailbreak, the model would just fold. I was playing a game of whack-a-mole with my own instructions, adding 50 words every time a hallucination slipped through until the prompt became a novel the model started ignoring anyway.

I only broke that cycle when I started treating prompt engineering like a technical constraint rather than a creative writing exercise. I leaned into structured prompting patterns to move away from "be helpful" and toward "follow these exact logic gates."

Now, I use one simple pattern for 90% of my builds. I slap an 8-line guardrail template at the end of every prompt that forces the model to answer ONLY using the provided context and to reply with a specific "not enough information" string if the context is missing.

The secret sauce is forcing the model to quote 1-3 verbatim sentences from the source before answering. By making the AI "prove its work" with no paraphrasing allowed, you kill 80% of hallucinations instantly.

It’s not a 100% fix, but it replaced nearly all of my custom guardrail code with eight lines of text. When I tested it against 20 jailbreak attempts last week, it refused 95% of them. It turns out that a reliable system doesn't need a longer prompt; it just needs a stricter structure.

Next time you see your RAG app hallucinating, resist the urge to add "please be more accurate" to your prompt. Instead, add a rule that requires a verbatim quote from the source before the answer. If the model can't find a quote, it can't invent a lie.

Any ideas?

https://github.com/HarsiTomiii/HomeAssistantCodes/tree/main/trailarr

For those who want the cinema experience in Jellyfin/Plex

Trailarr didn't have an app and running a container with HAOS is tricky, this makes it less tricky.

Got into automation recently and built a Telegram bot that tracks my personal expenses.

Just text it what you spent, “2.4 for coffee this morning” and it logs it, categorises it, and sends me a monthly summary automatically.

Thinking of turning this into something. Would anyone/small business actually use this?

Sometimes I play this show of Keane at Pinkpop (2024) and every time I think "nice jacket". But although I described the logo to a few LLM's, I still do not know the brand. Probably UK based?

https://preview.redd.it/1tyan3t8uhyg1.jpg?width=1200&format=pjpg&auto=webp&s=aed64b22ea27ff95001ce6bf5151c59602aca809

edit: zoom in of the logo in the comments

i am currently learning java , spring boot framework any work a project that includes ai please help me with my project

Title basically says it. Not looking for any sympathy or forgiveness or anything, but wondering if I can create another account using the same credentials when I turn above 18?

It would kinda suck if something like this would completely bar my current main email and phone number from using any anthropic services for the rest of their existence

Hello everyone.
I was playing Veigar the other day and at some point I was head to head with Morde.
I have taken him down to 30% and ulti him, and at the same time he ulti me too. My ulti did not register at all.
Is that normal or a bug?

https://reddit.com/link/1t0o8pu/video/eas672v3vhyg1/player

Two weeks ago I posted SchemaPin — a tool that hosts Schema.org markup for local businesses so when hours/services/locations change, you update one dashboard instead of touching your site. Got a thread of comments. Treated them as a spec. Shipped everything.

Three comments that shaped the whole product:

• "most agencies don't even realize they have stale schema — show them a delta view so they can use it as a renewal pitch"

The homepage now has a URL input that shows exactly what Google sees in your markup vs. what's actually on your page. Field by field. Paste a client's URL, screenshot the gaps, send the invoice.

• "tie it to whatever the business actually maintains — GMB, Airtable, a booking system, a Sheet — instead of making me edit schema by hand"

Four live sources now: Google Sheets, Airtable, iCal (for holiday hours), and webhook. GBP sync is scaffolded and waiting on Google's API approval.

• "flat rate won't work for agencies managing dozens of clients"

Agency tier at $79/mo for 50 locations with white-label. Free (1 location) and Pro $19 (5 locations) below that.

Everything else that shipped: version history + diff view, drift alerts via email, multi-location domain audit, holiday hours (manual and calendar-fed), FAQ schema builder, WordPress plugin, JS-rendered audit for Webflow/SPAs, and a print-ready PDF report for client handoffs.

13 of 15 requested features are live. The two remaining are both blocked on the same thing: Google's GBP API approval.

Audit your site free, no signup: https://schemapin.modulus1.co

What's still missing, what's confusing, what would make you actually pay for this?