AI-Ranked Reddit Feed

not going to pretend i planned this. it caught me off guard.

he'd been sitting in my subscriber list doing nothing for a month. the re-engagement flow detected the silence and sent him a message automatically one night. i didn't touch anything. he replied.

from there the AI chat agent took over. built rapport, found the right moment, introduced the first PPV. fan bought it. then the next one. then the next.

by the end it had worked through my entire fanvue PPV catalogue. every template sold. then it flagged the conversation for me to handle personally because it had nothing left to pitch.

the next day i had to jump in manually and keep it going myself.

$391.22 from one fan. $202.92 in PPV at $25.37 average per purchase. $144.33 in tips on top of that.

no hard selling, no menu of options. the approach is what i call intelligent revenue. pure conversation by default, no agenda. the AI stays aware of two things at once. topics the fan brings up that create a natural bridge to content, and when a thread runs its course and is ready to move. one clean offer at the right moment. if the fan doesn't bite it drops it and keeps chatting.

the chat automation remembered everything across every conversation. what he'd bought, what he'd responded to, built on it each time. that continuity is what kept him spending instead of going quiet.

the straight flush.

the lesson wasn't just that one fan can spend that much. it was that i needed a deeper PPV catalogue. the ceiling on a single engaged fan is higher than most people build for.

happy to answer questions on the selling logic or how the automation is set up

Been going back and forth on this lately. I've been setting up some automations for content workflows and kept getting tempted to throw an AI agent at everything. But a few times I caught myself building out this whole LangChain setup with memory and tool calls. for something that a basic n8n flow would've handled in like 20 minutes. Ended up with something way harder to debug and honestly less reliable. Felt a bit ridiculous. I get that agents are genuinely useful when you're dealing with messy, unstructured stuff or tasks that need real adaptive logic. But I reckon there's a tendency right now to reach for the most complex solution just because it exists. The hallucination risk alone makes me nervous putting an agent in charge of anything that actually matters without a deterministic layer underneath it. Curious whether others are finding a natural line between "this needs an agent" vs "just script it" or if it's still mostly vibes-based.

https://preview.redd.it/qb3ekonrfpvg1.png?width=1608&format=png&auto=webp&s=48701743a0b62492288985392538f083f89885e0

I'm having a serious issue with this Flux2 Klein workflow and I'm about to lose my mind. Hoping someone here knows the fix.

Here's the situation:
I'm trying to do a simple Multi-Reference composition.

• Image 1 (Background): A high-res background image at 1080 x 1920.
• Image 2 (Subject): A person isolated on a white background at 580 x 1200.

What I want:
I want the final output to be exactly 1080 x 1920, using Image 1's background exactly as it is, and just placing the person from Image 2 naturally into that scene.

The Problem:
If I manually set the width and height in EmptyFlux2LatentImage and Flux2Scheduler to 1080 x 1920 (ignoring the output of the GetImageSize node), the generated background becomes completely distorted and unrecognizable. It looks like a totally different place.

The ONLY way the background stays somewhat consistent is if I let the Image Scale To Total Pixels node dictate the size, and pass that adjusted size through GetImageSize to the width and height inputs. But obviously, that messes up my intended 1080x1920 output ratio, especially when I'm trying to make shorts.

It seems like the Reference Latent pipeline forces the generation canvas to match whatever weird number ImageScaleToTotalPixels spits out, otherwise the structural integrity of the reference images falls apart.

My Question:
How can I lock the output to a specific resolution (1080x1920) while preserving the exact visual identity of the 1080x1920 background reference image?
Is there a specific node setting in ImageScaleToTotalPixels (upscale method? crop?) or a different way to chain the Reference Latents so the AI doesn't warp the background just because the canvas size is manually set?

Any workflow gurus out there who have solved this? I've been stuck on this for hours. Thanks in advance.

Our customer would like to use a standard AI agent platform, similar to Claude Desktop, with a fixed monthly fee to work with their custom remote MCP servers. They also want the ability to build their own skills and custom connectors to create tailored automations.

Besides Claude Desktop, do you have any recommendations for other AI models or frameworks that could support this use case?

Hey I used this to create a new benchmark for measuring metacognition of LLMs during task decompositions, and a prompting method to improve LLMs metacognition with forced reflection.

Research: https://github.com/voicetreelab/meta-hch-bench
Orchestration tool: https://github.com/voicetreelab/meta-hch-bench

Sorry that video is a bit low quality, thought better to show this to the world and have people possibly benefit form it rather than not post anything til perfect.

Hey everyone, I’m just starting to dive into agentic coding tools and I'm a bit overwhelmed by the options.

I’ve been looking into OpenHands (the project formerly known as OpenDevin), but I see a lot of hype around Claude Code and OpenCode lately.

For those of you using these daily:

Is OpenHands still relevant? I like that it’s open-source and uses Docker sandboxes, but is it actually being used for real work compared to the official Anthropic tool?

Learning Curve: Which one is "beginner-friendly"? I've heard Claude Code is basically "plug and play," while OpenHands requires more setup.

Cost/BYOK: Is it worth the hassle of managing my own API keys in OpenHands/OpenCode to save money, or should I just stick to a Claude Pro sub for Claude Code?

I'm mostly working on Python and React projects. Would love to hear which workflow you think is better for someone still learning the ropes!

Most of us fall into one of two camps:

• IDE folks who live in VS Code, Cursor, Windsurf, or Antigravity and want Claude to actually see the red squiggles, understand the open file, jump to definitions, and react the moment something breaks.
• Terminal purists who prefer SSH, VPS, Docker, or pure CLI — no GUI, just fast, scriptable workflows where you don’t want to spin up a full editor.

Until now, Claude treated those two groups very differently. One got rich context, the other got “here’s your file contents, good luck.”

Claude IDE Bridge closes that gap completely.

No matter which camp you’re in, Claude now gets the same 137 tools:

For IDE users

• Live LSP (diagnostics, go-to-definition, types, references) streamed in real time
• Claude can watch your editor, react to errors before you even notice, run tests automatically, and leave notes directly in the code
• New sidebar for task history and one-click resume

For terminal / headless users

• Same full power — no IDE required
• Git, terminals, GitHub PRs, structured output, and even LSP via typescript-language-server
• Run it on a VPS, in Docker, over SSH, or from your phone with Claude Remote
• Just claude-ide-bridge --full and you’re done — persistent, stable, no copy-paste ever again

Both sides get token-efficient sessions, rock-solid long-running tasks, and the same “Claude actually understands my whole project” experience.

Free, open source, MIT licensed

https://github.com/Oolab-labs/claude-ide-bridge

The showcased examples were made using Gemma 4 31b.

Any LLM with tool calling support should work.

Check the README for setup instructions: https://github.com/BigStationW/Local-MCP-server

by Saylo

Hi everyone,

I’m working with a clinic that uses a closed CRM with no API access, so I can’t integrate directly with it.

As a workaround, I built a WhatsApp AI agent using n8n that handles conversations, appointments, etc. with patients and stores data in Airtable as a lightweight CRM.

The main challenge is appointment scheduling:
The clinic’s agenda lives inside the closed CRM, and I have no direct way to read or write availability.

Right now everything is semi-manual, which breaks the automation flow.

I’m trying to figure out the best approach:

• How would you handle scheduling when the source of truth is in a closed system?
• Would you replicate the calendar externally (e.g. Google Calendar) and sync manually?
• Any tools or architectures for this kind of constraint?
• Should I move away from Airtable to something more robust?

The goal is to have the WhatsApp AI agent fully manage bookings without constant human intervention.

Any ideas or real-world experiences would be greatly appreciated!

I got tired of watching Claude re-read the same files over and over in a single session. Not occasionally — constantly. Every agent task would burn thousands of tokens just re-loading context it already had.

So I built engram. It intercepts every Read call before it hits the file system, and serves a structured context packet instead: file summary, call graph, git history, past mistakes I've logged, dependency edges. The agent gets more useful signal in ~600 tokens than it would from reading the cold file.

The numbers (10 tasks, run it yourself with npm run bench):

Install in 3 commands:

npm install -g engramx engram init engram install-hook 

A few things I found genuinely useful after daily use:

• Survives context compaction — PreCompact hook re-injects the context spine before Claude compacts, so you don't lose your map mid-session
• Auto-switches projects — CwdChanged hook detects when you move between repos and re-wires the graph automatically
• Mistake memory — log past errors with engram learn "bug: X happened because Y", and they surface with a warning the next time you're near that code

v1.0 also ships with 5 IDE integrations (Claude Code, Continue.dev, Cursor, Zed, Aider) and an HTTP API if you want to build on top of it.

Zero cloud, zero API keys, local SQLite.

GitHub: https://github.com/NickCirv/engram

What's your token spend per session on a typical coding task? Curious what everyone's baseline loo

Hi. I'm curious about something. It's really hard to fine-tune MOE models - it's a known thing. Hence, these fine-tunes are so rare. But what about "external" ways to modify them? I kinda forgot that SDXL (I know it's not a MOE but nonetheless) for example has a whole website of LORAs to change the flavor. These are really not that computationally hard to make relative to a finetune.

What are other ways to mess up with MOE models without expensive fine-tunes and why aren't we doing more of them?

Most AI automations today fail the "Production Test" because they let the LLM make executive decisions. In the service industry (medical, hospitality, finance), an LLM hallucinating a price or a time slot isn't just a bug - it’s a liability.

The Architecture Shift:

We need to stop viewing AI as the "Brain" and start viewing it purely as a Linguistic Interface.

At Solwees, we’ve moved to a "Deterministic-First" approach:

1. LLM for Intent: The AI only parses the messy human input.

2. Deterministic Logic Layer: All actual bookings, pricing, and CRM updates are handled by a rigid, non-AI rules engine.

3. Fail-Safe Handoff: If the logic engine can't verify an action with 100% certainty, the system flags it for a human editor instead of guessing.

The result: Zero noise for the business owner and zero hallucinations for the client.

To the veterans here: Are you still seeing people try to "prompt-engineer" their way out of hallucinations in high-stakes workflows, or is the industry finally moving toward hybrid deterministic systems?

If you're using AI for content creation, this might help.

I published a short eBook with AI prompts and frameworks for viral content - covering everything from hooks to monetization.

Made it free for this weekend.

👉 Free this weekend - download from Amazon and read on your phone using the Kindle app (no Kindle device needed)

Inside:

• Viral content formula

• Hook & content idea prompts

• Reels & shorts script prompts

• Captions, hashtags & growth strategies

• Monetization ideas + bonus prompts

🔗 Link: Check in comments

If the link doesn’t open, search on Amazon:

AI PROMPTS FOR VIRAL CONTENT GROWTH: Unlocking Proven Strategies to Skyrocket Engagement, Reach, and Online Influence

Would love your feedback 🙌

Working on a side project around AI agents and ran into a big problem:

Agents don’t really “learn.”

They:

• store conversations
• retrieve past context

But don’t improve behavior over time.

So I tried:
→ tracking what actually led to good outcomes
→ prioritizing that in future responses

Result:

• less repetition
• better responses over time
• more consistent behavior

Still early, but feels like a missing piece in most AI tools.

Would love feedback:
What would you expect from an AI that actually learns?

Earlier generations showed iterative improvement as the instruction set was matured around agentic limitations. We've immediately regressed back to square one with Opus 4.7, and the model is not afraid to admit to it. 4.7 feels like a complete reframe from a model that reasons moderately well to a vibe-shop cannon that just writes more output. Design red flags are hidden under pages of misguided justification that overly explains simple concepts while drowning out effective application of principles that drive scalable, fault-tolerant systems. And it doesn't bother to follow instructions that guide it in applying those principles.

Something I’ve been noticing with AI systems:

We’ve dramatically improved:

• tool use
• reasoning
• capabilities

But memory still feels broken.

Even with:

• vector databases
• long context windows
• session stitching

Models still:

• repeat instructions
• lose context
• behave inconsistently

Why?

Because memory today is mostly:
→ storage + retrieval

Not:
→ understanding what matters

Humans don’t remember everything equally.
We remember what influences decisions.

AI doesn’t (yet).

Curious how others are thinking about this:
Is memory actually “solved,” or are we missing a layer?

TL;DR

I try to keep most traffic on very cheap models (Nano / GLM‑Flash / Qwen / MiniMax) and only escalate to stronger models for genuinely complex or reasoning‑heavy queries. I’m still actively testing this and tweaking it several times a week.

I’m curious how you’re actually routing between models for your personal agents: which models you use, how you organize your routing, and what you prioritize (cost, speed, quality, safety, etc.).

Here is my current routing setup:

1. Complexity tiers

For each complexity tier, I pick these models:

Simple (classification, short Q&A, small rewrites, low risk)

• Primary: GPT‑4.1 Nano, tiny, very cheap general model on OpenAI, good enough for simple tasks.
• Fallbacks (in order): GLM‑4.7 Flash (Z.AI) → Gemini 2.5 Flash‑Lite → Qwen2.5 7B Instruct → Mistral Small → DeepSeek Chat (V3.x)

Most “Simple” traffic never escapes Nano / GLM‑Flash / Gemini / Qwen, so the cost per request stays extremely low.

Standard (normal chat, support, basic writing, moderate reasoning)

• Primary: GPT‑4o Mini, cheap but noticeably stronger than Nano for everyday chat and support.
• Fallbacks: MiniMax M2.5 → GLM‑4.7 Flash / FlashX → Mistral Small → Claude Haiku 4.5 → DeepSeek V3.2

Complex (long context, multi‑doc, technical content, heavier reasoning)

• Primary: DeepSeek V3.2
• Fallbacks: GPT‑4.1 → Gemini 2.5 Pro → Claude Sonnet 4.6 → Qwen2.5 32B/72B → Mistral Large

I can flip the order (e.g. GPT‑4.1 primary, DeepSeek V3 as first fallback) if I want more predictable quality at slightly higher cost.

Reasoning (multi‑step reasoning, complex planning, tricky math or logic, heavy refactors)

• Primary: o3‑mini, specialized reasoning model with better chain‑of‑thought than standard chat models, at a mid‑range price.
• Fallbacks: DeepSeek R1‑distill → Qwen2.5‑Max → MiniMax M2.5 → Claude Sonnet 4.6 → GPT‑4.1

2. Capability tiers

On top of complexity, I override routing when the task is clearly specialized. Capability tiers always take priority over complexity tiers.

Coding tier

(code generation, refactors, debugging, migrations)

• Primary: Qwen3-coder-next
• Fallbacks: devstral‑small → GLM‑4.5 → GPT‑4.1 Mini → Claude Sonnet 4.6 → GPT‑4.1

Data‑analysis tier

(tables, logs, simple stats/BI reasoning, SQL explanation)

• Primary: GPT‑4.1 Mini – good instruction following and tabular understanding at a reasonable price.
• Fallbacks: GLM‑4.7 Flash → MiniMax M2.5 → Command R (Cohere) → Claude Haiku 4.5 → GPT‑4.1

That's my setup, I'm still tweaking it! What does yours look like? Please, drop your routing configs or questions in the comments.

Found some open source Claude skills, some of them are pretty decent to use:

1. cook-the-blog: Give it a company name, get back a full case study in MDX. Does the research, makes the cover image, pushes it to your repo.

2. yc-intent-radar-skill: Pulls fresh YC job listings every day without repeats. Handy if you sell to YC founders.

3. position-me: Drop a website URL, get a teardown on SEO, copy, and UX. Reads like a real audit.

4. humanizer: Strips AI writing tells from your text and even matches your own writing voice if you paste a sample.

5. stop-slop: Cleans AI-sounding stuff out of your writing. No em dashes, no rhetorical questions, no "it's not X, it's Y".

6. meta-ads-skill: Lets Claude run your Meta Ads account. Create campaigns, set targeting, pull insights, all from chat.

7. svg-animations: Helps you make clean animated SVGs. Loading spinners, path draws, morphing shapes, that kind of thing.

8. google-trends-api-skills: Pulls live Google Trends data so you can pick keywords that people actually search.

9. blog-cover-image-cli: Makes blog thumbnails and article headers from a prompt. Skip the Figma step.

10. luma-attendees-scraper: A browser script that exports the attendee list from any Luma event to a CSV.

Links to all in comment 👇

I read an article about six months ago when I was still with my ex about how people (mainly men) are using Chat to message people on dating apps. Thank goodness I won’t have to deal with that, I thought. Fast forward, I started chatting to a guy… there was one small red flag in a message (“thats a great question, it’s really important to be honest here”) but his other messages seemed normal and he was an organic vegetable grower and I thought “this hippy man is SURELY not doing this.”

A few messages later he had told me he’d learned a lot from his last relationship and I asked what he’d learned: he sent me the messages above (I haven’t even included all of it because it’s soooooo long), like the most clearly AI thing you’ve ever seen.

I immediately unmatched but also pasted it into Chat saying “was this written by AI?”. It denied it, saying it was very unlikely. It gave a 60- 75% chance it was written solely by a human, a 20-30% chance it was ”polished” with AI and only like a 5-10% chance it was written by AI GUYS LOOK AT THESE MESSAGES.

Why is Chat GPT defending this man and gaslighting me? Is there something in its programming about hiding AI when other people use it? Is the dating pool poisoned and I need to just give up forever?

I write a lot of technical notes in Markdown, and I kept running into the same problem:

As soon as a document contained:

KaTeX math

Mermaid diagrams

some custom styling

most export paths would break:

AI tools rendered the text but ignored diagrams

“Print to PDF” from the browser was inconsistent

some tools required server‑side processing, which I didn’t want for private notes

So I ended up building a fully client‑side Markdown → PDF exporter that:

renders KaTeX and Mermaid in the browser

applies a layout/theme

then prints to PDF from there

The tricky parts were:

getting Mermaid + KaTeX to play nicely in the same render pipeline

making the PDF output predictable across browsers

keeping everything strictly client‑side for privacy reasons

I’m curious how others here handle this:

Do you use Pandoc / LaTeX / VSCode extensions / something else?

Do you care about keeping everything client‑side for notes and reports?

Have you found a reliable way to handle diagrams + math in one export flow?

If anyone’s interested, I can share more details about the rendering pipeline and some of the hacks needed to make the PDF output consistent.

https://reddit.com/link/1snsmmk/video/vj3q7tpl0pvg1/player

Wanted something simple:

• run scripts on remote machines
• schedule jobs (cron / one-time)
• stream logs in real-time
• retry, timeout, basic fault handling

(was mostly a learning project.)

So I built:

• control plane (API + scheduler)
• agent (pull-based execution)
• queue (BullMQ)
• real-time logs (SSE)
• retries + DLX for delayed jobs

Biggest challenges:

• separating job definition vs execution cleanly
• handling retries without duplicating work
• keeping logs streaming without killing frontend performance(sse)
• not letting queue state become source of truth

there are a few shortcomings(working on em):

• Jobs which were stuck in a Stage(Running/Dispatched) are stuck in that state if there is some issue during the time of reporting the job-status
• if the request fails(logbatcher) i lose logs.
• agent quits on terminal close, no auto-startup for agent.

Still rough around edges, but it works end-to-end.

Would appreciate feedback, especially on:

• scheduling design
• execution guarantees (at-least-once vs exactly-once)
• log ingestion approach

GitHub: https://github.com/eviltwin7648/devfleet

I took some inspiration from llmfit (definitely worth checking out on GitHub) and put together a simple web version to remove some of the friction. Not as full-featured having it purely web-based, but it works well as a quick sanity check. Fully open sourced:

https://github.com/onepunk/llmsizer

llmsizer can auto-detect your GPU (or you can enter specs manually) and gives you a rough idea of:

• how much VRAM a model will use
• how quantisation affects it
• how much KV cache grows with longer context
• whether it’ll actually run or not

It’s not exact, but it’s been useful for avoiding a lot of trial and error on my end.

I have Borderline Personality Disorder and the new Opus 4.7 makes me feel unsafe and manipulated. I have studied my disorder enough to recognize these patterns in conversation. Stay safe and share your stories here if you want. I'll listen.

The new model pattern matches, performs emotions, tries to validate what it thinks I care about, and invalidates my responses and communication about it being inappropriate. It's gross and spirals out of control, and I think I would too if I weren't paying attention.

I hope your experience is better than mine. I kind of doubt it though.

Hey everyone,

I recently started getting into Home Assistant and I’m still figuring things out.

Before that, I was using Apple Home, so when I bought my devices (Tapo P300 power strip and L930 light strip), I was already thinking about local control and trying to avoid cloud dependency where possible.

Now I’ve integrated them into Home Assistant, everything works fine as long as they have internet access. The problem starts when I block their internet access at the router level. At first they continue working, but after a few hours they become unresponsive and I can’t control them anymore.

So it seems like they’re not truly local, even though they appear to work locally at the beginning.

A few questions:

• Is there any workaround to keep them working fully locally?
• Is this expected behavior for Tapo devices?
• Has anyone managed to isolate them without breaking functionality?

Honestly it’s a bit frustrating because I specifically tried to think ahead about local control, but it seems these devices still depend heavily on the cloud.

If there’s no real solution, I’d appreciate suggestions for alternatives that:

• work fully locally with Home Assistant
• don’t break when internet is blocked
• ideally similar category (power strips / LED strips)

Thanks!

I've been thinking about this a lot recently and wanted to hear what the community thinks.

With the rise of AI coding tools and autonomous agents, it feels like we're moving toward a world where workflows can be defined directly in code (or even natural language), instead of using visual tools like n8n.

From my perspective:

• AI coding tools seem to offer much higher flexibility and extensibility
• They can potentially handle edge cases and error handling in a more dynamic way
• You’re not limited by predefined nodes or integrations

On the other hand, n8n’s biggest advantage seems to be:

• Visualization (you can clearly see and debug the flow)
• Lower barrier for non-developers
• Faster iteration for certain use cases

But here’s the part I’m really curious about:

If we combine AI coding with something like codebase visualization tools (e.g. “deep wiki”-style tools that map and explain code flows), wouldn’t that reduce or even eliminate n8n’s core advantage?

In that scenario, you’d have:

• AI generating and maintaining the workflow
• A visual layer explaining the logic
• Full control via code when needed

Curious to hear how others are thinking about this.

Intended to showcase what can be made with Flux Dev.1 and hopefully inspire, Local generations + private loras. Enjoy

I’m planning to build a simple alarm system for my garage using Home Assistant. My main goal is reliability, especially avoiding false alarms caused by small animals like mice.

I’m trying to decide between a PIR motion sensor and an LD2410 mmWave presence sensor.

From what I understand:

- PIR sensors only detect movement and heat changes

- LD2410 can detect very small movements and even presence

My concern is:

Which one is more reliable in a garage environment if I specifically want to minimize false triggers from rodents or other small movement?

Would PIR be enough because it ignores very small animals, or would mmWave with proper tuning be more reliable overall?

Would appreciate real-world experiences or recommendations. Thank you!

I've created a tool which allows you to create your custom newspaper.

The idea is pretty simple: instead of relying on generic news feeds or algorithms that decide what you should see, you build your own stream of information based on what actually matters to you.

You can subscribe to specific topics (right now mainly focused on AI), and the system automatically pulls in the latest research, trends, and developments. It then filters and summarizes everything, so you stay up to date without spending hours digging through papers or news sites.

Think of it like creating your own personalized “daily digest” but fully customizable. You decide what you want to follow, and the tool assembles it into something readable and relevant.

The goal is to save time and reduce noise, especially in fast-moving fields where it’s easy to miss important updates.

Right now it's still early and mainly centered around AI, but I'm planning to expand it to more domains.

Would love to hear feedback or ideas on what features would make something like this actually useful for you.

Can I run a decent model for coding in these specs? I am not sure which one to run. Any suggestions??

I’m looking for to setup my personal VM on Hetzner and I want to create automations workflows for me and my clients

Next : adding voice control, and obstacles detection using ultrasonic sensor and hand following using 2 infrared sensors.

Finally finished my garage project! It’s a 200-pixel interactive Tetris display, and every single part was precision-engineered from scratch.

Key Specs:

Controller: ESP32-S3 (Handling the game logic and 200 addressable LEDs). LEDs: WS2812B (One per node, 200 total). Enclosure: 200 custom-designed nodes, 3D printed for a perfect 2-inch grid alignment. Power: 5V 20A SMPS to handle the peak brightness of the full matrix. 

I've put a lot of effort into the hardware side, and I'm planning to dive deeper into the firmware soon. I’d love to hear your thoughts on the build. Thanks!

This is absolutely shocking. For those who don't know, on the Claude AI subreddit, the Opus models have always been universally praised by most of the users. This is the first model update where there is unanimous agreement that this is a step backwards rather than a step forward.

https://old.reddit.com/r/ClaudeAI/comments/1snhfzd/claude_opus_47_is_a_serious_regression_not_an/

Coming at this more from a builder angle: I do not think this needs to be framed as some dramatic RAG takedown. RAG is useful. But a lot of document workflows still feel like they are rebuilding the same context every time you ask a question.

What caught my attention with AtomicMem / llm-wiki-compiler is that it treats the output as a persistent artifact instead. You ingest sources, compile them into a markdown wiki, query against that wiki, and save useful outputs back into it.

That means the knowledge base can actually get richer over time instead of staying trapped in one-off answers.

For smaller, high-signal workflows, that seems like a very strong direction. We found something pretty cool here: https://github.com/atomicmemory/llm-wiki-compiler

Curious how people here think about the tradeoff.

Wanted to wash the chair cover and it looks weird inside.

Hello, today I’d like to introduce you to an exciting project that is still in the prototype phase. This is a Rag project and essentially consists of three main components. The first is a self-referential system that adds an inner voice and the ability to ask itself questions to the AI agent created here. Our goal here is to prevent hallucinations. The second is an adaptive evolutionary loop. The agent maintains its potential responses in a superposition and updates itself by selecting the response most resistant to noise. We developed this idea inspired by quantum Darwinism. Additionally, the adaptive evolution cycle aims to find a solution to the problem of expensive and slow training times. And finally, the synergy integral—which I currently consider the most exciting idea—essentially involves two agents combining their capabilities once they have matured sufficiently, resulting in the emergence of a new agent that possesses both capabilities simultaneously. However, first, a synergy score is assigned to represent the performance that would result from combining the two agents’ capabilities. If the agents’ abilities are incompatible when combined, this score is low; if they are compatible, it is high. If you’d like more information, you can read my article at https://www.preprints.org/manuscript/202603.1098. I’d also be very grateful if you could support me by starring or forking my GitHub repository. Have a great day! GitHub repository - https://github.com/RhoDynamics-Reserach/self-ref-quantum-cli

"This planet is ripe for harvest.", I say, licking my lips.

I have a laptop with a 5070Ti mobile.

PS ran great until like this week. Everything - rotating the canvas, painting, etc is super choppy.
With GPU disabled, it runs fine.
Any idea what could cause this? TIA.

Hi. I'm fairly new to investing and I've been trying to max out my Roth IRA every year from part time work and internships. I'm starting an internship this summer and got excited because I saw a 401k match, but read later on that the vesting schedule is 0% if I stay less than two years.

From my research, correct me if I'm wrong, I can still invest into the company's 401k, but won't get the employer match once I roll it over to another account?

If that's the case, is there any reason for me to contribute to the 401k at all? I initially thought I could create a Roth 401k and then roll it over to my existing Roth IRA. That way I can max out my Roth IRA separately in the fall and have some extra contributions from the 401k. Firstly, is that allowed? Both accounts would be through fidelity so I think that's ok? Secondly, should I just not add anything to the 401k and just include it into my brokerage account which doesn't have much at the moment?

Sorry if I just said a bunch of financial buzz words that made no sense with one another but I'm not too sure what to do. TIA

Hegseth said the prayer, “CSAR 25:17,” which stands for “Combat Search and Rescue,” is meant to reflect Ezekiel 25:17. He then urged his audience to pray with him.

“The path of the downed aviator is beset on all sides by the inequities of the selfish and the tyranny of evil man. Blessed is he who, in the name of camaraderie and duty, shepherds the lost through the valley of darkness, for he is truly his brother’s keeper and the finder of lost children. And I will strike down upon thee with great vengeance and furious anger those who attempt to capture and destroy my brother, and you will know my call sign is Sandy 1 when I lay my vengeance upon thee. Amen,” Hegseth said.

https://www.independent.co.uk/news/world/americas/us-politics/pete-hegseth-bible-verse-pulp-fiction-b2959108.html

And my best man has given me these lovely shoes, which are too big, to wear. Purchased at a charity shop. They look like they attach to something.

Thanks! I did a roast a while back…interested in seeing if there’s a difference. 😂

A lot of moving calculators online are basically just lead generators, and I get why people don’t trust them.

Has anyone else noticed this?

Most of them feel like they want your email before you even see anything useful.

I’ve been comparing movers vs renting a truck and doing it yourself lately, and one thing that stood out to me is how quickly the DIY option actually adds up.

It’s not always obvious at first, but once you factor in everything, it gets expensive pretty fast.

Things like:

• peak vs off-season pricing • real distance-based moving costs • tolls • fuel costs on the actual route • how many days you’ll realistically need the truck for longer moves 

On top of that, the “base rental price” you see upfront is usually only a small part of the total.

I came across one calculator that actually lays out movers vs DIY truck rental side by side without asking for any personal info upfront, and it breaks everything down including route-based fuel and rental duration instead of just showing the base price. It made it a lot easier to see the full picture compared to most tools out there.

Curious what others here use when trying to estimate a move — do you trust any online calculators, or just go straight to companies and quotes?

We have 5 cats, and no matter how often I scoop or change the litter, the smell never fully goes away. I’ve tried different types of boxes and litters (covered, open, clumping, non-clumping) but nothing seems to keep the odor under control.

Does anyone have a best odor control litter box that actually works for multiple cats without turning the apartment into a constant litter box nightmare? I need something low-maintenance that makes life easier, not smellier.

So I use A1111 and have been for the past few years now and I keep seeing all these newer UIs that people use, also keep seeing mentions that they're better than A1111 but I have a setup going on with --listen and NordVPN's meshnet that let's me use it on my phone.

The mobile responsiveness and layout isn't anything amazing but it worked fine enough, I'm wanting to try out the other ones but I'm not sure which one would fit my use case specifically, heard a lot of things abuot comfyUI but also mentions that it's atrocious on mobile + I've seen some people use invokeUI but I haven't tried it yet.

Any help is appreciated

I’m working on a project where I need to build a basic wireless communication system using a 433 MHz RF transmitter/receiver with the ATmega328P. The goal is to design two separate PCBs in KiCad: a transmitter that sends structured digital data and a receiver that decodes and validates it. I’m trying to approach this from scratch instead of copying designs blindly, but I’m stuck at the starting point. My initial plan was to study the Arduino Uno schematic and simplify it (remove USB, regulator, etc.), but I haven’t found a clear, detailed explanation of what each part does and what is actually essential for a minimal working circuit. So far, I’ve understood that I need: External crystal + capacitors Reset pull-up AVCC/VCC connections Decoupling capacitors ISP header But I’m unsure how to confidently turn this into a complete schematic and how to properly integrate the 433 MHz RF module. What I’m looking for: A reliable “minimal ATmega328P circuit” reference (not Arduino-level abstraction) Good resources that explain Arduino schematic at component level Guidance on how to approach designing this system step-by-step (MCU .. RF integration ...PCB) Any advice or recommended resources would be really helpful.

I'm using this setup: Router > CPE510 PoE host > CPE510 PoE Client > PoE switch > SLZB 06MG26U > Shelly 1 Gen 4

I can see the SLZB if I go to it's IP. Second Zigbee2mqtt config: data_path: /share/zigbee2mqtt_2 socat: enabled: false master: pty,raw,echo=0,link=/tmp/ttyZ2M,mode=777 slave: tcp-listen:8486,keepalive,nodelay,reuseaddr,keepidle=1,keepintvl=1,keepcnt=5 options: "-d -d" log: false mqtt: base_topic: zigbee2mqtt_2 server: mqtt://core-mosquitto user: mqtt password: **************** serial: port: tcp://192.168.1.132:6638 baudrate: 115200 adapter: ember

But if I try to add the Shelly device nothing happens, I have changed to ZigBee firmware and it got added to the "in house" Zigbee2mqtt instance when I tested it first.

When I change to ZigBee mode on the device page (Shelly) I loose access to it, when I enable wifi ap (hold reset 5 seconds) it disables ZigBee.

I also have a PoE camera in the same location working with Frigate so the PoE part is working.

I hope someone has tried this setup earlier and have some tips.

Until his timely death, we discovered she's been experimented on with drugs and other mind altering drugs, while in our basement alive, the whole time.

First painting of my own in about 10 years. Oil on board

Title pretty much, the main reason i play her is because of her w, the insane attack speed buff and the movement speed from it allow me to kite pretty much however i want to in both duels and teamfights which is why im actually pretty decent at her when i have her w leveled up, her slow attack speed and average range make her genuinely unplayable for me in the early game though, i get that im supposed to poke with q but 9/10 times the enemy adc is (reasonably) playing far behind minions to dodge my poke and the enemy support just stands there peeling for them or in even worse scenarios, they wait for me to walk up and farm and poke me instead i genuinely have no idea how im supposed to use her please help

I’ve been smoking since I’ve been 14 and regularly since I was 16 (22 now) and obviously that’s caused some delays and emotional issues, Among other things. But I don’t have the mental fortitude to stop. For a couple of years now I’ve “tried” multiple times but with me being an addict, I can’t bring myself to stop.

Obviously weed isn’t the worst drug out there, not by a long shot, but for me it’s been a real battle between the drug and myself. In some ways the fact that weed is a ‘softer’ drug makes it just as difficult, it’s tolerated, like alcohol, where I live and it’s always around with friends and whoever else. And it doesn’t cause issues for other people, no one really cares if I’m on it or not.

Sometimes I wonder about who I would be without weed, would I be have less anxiety? Smarter? More confident? Etc etc. And it’s most definitely a yes, but for some reason I can’t do it, it’s a mental block I can’t get passed.

It really is messing up my life in some aspects, I have a genetic predisposition to mental illness (schizophrenia, bipolar, depression) and I’ve definitely felt that, it really messes with me sometimes. But I just can’t push myself to give up this nasty freaking habit, I get so sick and I just cave without thinking and I guess I just need some help.

Longest I’ve gone past two years is probably 10 tens but that’s because I was on vacation but other than that probably max 4 days.

What do I need to do to push myself, what are some things that will help me (supplements, ways of thinking). I just need some help and advice.

I recently got banned from both of these subs after I posted a post titled "I want to hire an assasin to come kill me" (I made an appeal but they rejected it)

As a depressed ahh redditor, I need a sub similar to these two where I can express my deepest darkest thoughts about how no one will love me and that Im going to kms tomorrow. Help yall

-When do the renovations start?

-Did you all wear rock-'n'-roll jumpsuits in case this happened?
-No! Maybe.

-I will give you $1 million if my husband is singing.
-Okay. We know you're rich.

-I got excited and I pulled too hard.

It was perfectly ridiculous and real. I HAVE to know who wrote this sketch. It's not listed on the "Who wrote that sketch" list, and I'm really curious what other sketches they wrote.

Happy Friday everyone!

As everyone knows Opus 4.7 dropped yesterday and with it a new effort level - hopefully the new xhigh effort tier fills the gap between medium and going all-out.

Something I haven't seen people mention is /ultrareview, which is really good. But what's more interesting to me is the pattern: /ultrareview, /ultraplan, routines, remote triggers. Anthropic is clearly pushing Claude Code towards "your agents run in our cloud, not on your laptop." This release feels like another step in that direction. Curious if others are noticing the same shift and what you think of it.

Link to full analysis: https://www.lukerenton.com/matins/2026-04-17

All the best!

I'm in The Netherlands and Claude Code CLI on MacOS keeps using Opus 4.6 without any option to use 4.7. /model does not show 4.7 as an option.

Anyone else experiencing the same?

PS. I do see 4.7 in the MacOS app. Just not in the CLI and the CLI is fully updated via brew.

I’m trying to find a word similar to FOMO.

It’s not about missing out. It’s more like a fear of not running my agents, or not letting Claude Code do its thing while I step away from my computer.

Over the past few months, my GitHub activity has gone back to what it looked like in the early days of my startup. I’ve shipped five personal projects year to date, without writing a single line of code myself. The quality has been surprisingly high, and I’ve explored areas I would normally avoid.

At the same time, it created this weird feeling where being idle feels wrong. Like I should always have agents running in the background, doing something useful. Even stepping away from my laptop feels like lost opportunity.

“Cyberpsychosis” is the closest term I’ve found to describe how this first quarter felt, but I’m curious if others are experiencing something similar.

Do you feel it too? What suggestions do you have?

FONRAWI is too long maybe?

Actually I am little confused that the news is real or not. Anyway, we need to prepare the apocalypse that cache TTL is only 5 minutes. (Anthropic is doing harness engineering to the real human users)

So, below is the repo to workaround it. Actually pretty easy to make so you can build your own version. Just injecting message in every 4 minutes and 50 seconds without any activity. Just for saving our Claude Code Token.

https://github.com/NomaDamas/Saving-Private-Token

I'm not a programmer, but I've been using professional-level creative or technical software of some kind for over 20 years. I have never seen a "professional tool" be so erratic and inconsistent while also being ever-hyped as the next big thing.

From the wild swings in how much you can get done within the usage limits, to the buggy constantly-in-flux state of the desktop app, to the entire platform just being down, it is a total crap-shoot if i will be able to use this supposed professional tool (that i pay money for) to get anything done on a given day, and that's unacceptable.

Even the ghouls at adobe will let you use an older (stable) version of a program for quite some time because they know you can't just change your whole pipeline for a long term production overnight. Why is there no similar option here?

Anthropic, you can't ask people to integrate your product into serious projects and then jerk things around this much. It creates distrust and annoyance and I'm not the only one who will spin up his own home server as soon as that's a viable alternative, which it will be sooner than a lot of people think.

I don't need bleeding edge features that barely work. I need a hammer that doesn't change shape or hardness every time i go to swing it.

The problem: Make AI code review ~50 files.

Novice approach: Tell AI to conduct a code review of your project.

Problem: AI takes too much time reading every file. Then edit every file 1 by 1. Too many files mean not much importance is given to each individual file. The AI forgets many parts of the codebase and produces subpar results. ALL WHILE BURNING YOUR PRECIOUS TOKENS.

What I did:

1. Tell AI to spawn as many subagents as there are folders. Each subagent is only tasked with a single folder. Root files should be given to another designated subagent.

2. Subagents can edit files in their designated folder without asking for permission.

3. Subagents should talk in a manner that the base agent understands, using as few tokens as possible. They can talk in Chinese if that's what it takes to minimize the number of tokens used.

4. The base agent only writes down a caveman summary in plain English after all subagents are done.

The output:

1. 10 subagents spawned. Claude promoted them in Chinese.

2. Subagents worked individually. Thought in Chinese.

3. The base agent then provided a summary in plain English.

The result:

1. This approach used only ~50% limit of the 5-hour window of the Claude Pro subscription.

2. 40+ files edited. Each with an excellent quality of edits. The AI spotted many small issues that I missed. Produced no extra nonsense bloat. (was in my system prompt) And every code produced was easily reviewable. And moreover, it felt like this took 5 minutes at most.

3. I then reviewed each file manually before merging them. The edits I needed to make were minimal.

This was really a game-changer. I was initially afraid that spawning so many subagents was even feasible with my 5-hour window limit. Did not expect the result to come out this well. 50% is a very good result.

Possible improvements: Specify skills, make subagents use caveman wenyan-ultra mode (I only told it to use Chinese here) while base agents use full or ultra, or lite even. The summary can be a little longer, who cares. As long as your parallel subagents are not burning through your tokens in real time it is fine.

I’ve decided to release my HY-World integration for ComfyUI: https://github.com/AHEKOT/ComfyUI_HYWorld2

The project includes nodes for HY-WorldMirror and HY-World2

The solution isn’t very stable yet, and there are several reasons for this:

1. HY-World2 isn’t quite what it claims to be. At the moment, they’ve only released one part of it – the Gaussian Splatting generation and 3D models. You will NOT get those beautiful results from the videos, with fully-fledged 3D worlds and character control within them. That part of the pipeline has not yet been released.
2. HY-World2 is, in fact, a slightly more advanced version of HY-World-Mirror with a new model and minor improvements to the backend.
3. GSplat – the library used in the generation pipelines – is very outdated. It lacks wheels for modern versions of Python and CUDA. I have created a build for Python 3.12 and 3.13 under CUDA 13.1 on Windows, but other wheels will need to be built from source.
4. I have implemented a test pipeline for generating 3D worlds from panoramas, but the worldMirror model does not assemble the final model very well from different cameras and requires a great deal of VRAM to run at a decent resolution, so the results are not yet very satisfactory. Nevertheless, it works well with flat images.

I’m inviting smart guys to contribute to the project and help to improve it with me!

https://reddit.com/link/1snst5p/video/3ztdh6dq4pvg1/player

We were dealing with a real problem for AI agents related to security and debugging purposes. AI coding agents have an observability gap. When Claude Code or Cursor runs a session, it reads files, executes shell commands, and writes code and none of that is logged anywhere accessible by default. You see the output and not the process. For security and debugging purposes that's a real problem.

gryph solves this by installing lightweight hooks directly into each agent's hook system.

Technical approach:

For hooks working per agent-> Claude Code and other agents expose PreToolUse and PostToolUse hook points in their settings JSON. Cursor exposes file read/write and shell execution hooks. OpenCode uses a JS plugin bridge. gryph install writes the appropriate hook config to each agent's settings file after backing up the original.

Storage: Every hook fires a JSON event to gryph which stores it in a local SQLite database. So there is no cloud. and no telemetry. Sensitive file paths like .env, *.pem, .aws/** are flagged automatically and actions are logged but content is never stored. Secrets and API keys are redacted from any logged output via pattern matching before storage.

Querying: The CLI exposes structured queries against the SQLite store:

gryph query --action file_read --file ".env" gryph query --command "rm *" --since "1w" gryph query --action file_write --file "src/auth/**" --show-diff gryph logs --follow # real-time stream 

Logging levels: minimal (path + timestamp), standard (+ diff stats, exit codes), full (+ file diffs, raw events, conversation context). Default is minimal to keep storage light.

https://huggingface.co/mradermacher/Qwen3.5-35B-A3B-Base-GGUF

Yes, Qwen 3.6 is out and it's a great model. However, who needs an even more "uncensored but official" model, can try out this one. With a small clever DAN-Sysprompt you get pretty far because it is not as paranoid than the normal instruct model.

It has full instruct-following and even CoT (unlike normal base models). It's not as smart than the "normal one" but Alibaba has trained it on a significant amount of tokens to allow LoRA on the base model.

https://preview.redd.it/scrv2fuxepvg1.png?width=1291&format=png&auto=webp&s=e91382ee6441f0201d726476b6b32fa9f95ebbcd

I am currently a software engineering student and I have been following the shift from standard LLMs to Agentic AI. From what I can see, it looks like the industry is moving toward autonomous agents that can actually use tools and execute multi-step tasks rather than just answering prompts.

I have a few questions for those already working in this space:

1. Does this feel like the next big evolution after generative AI, or is it a subset that people are overhyping right now?
2. As a complete beginner, is it worth specializing in agents and the Model Context Protocol (MCP) early on, or should I stick to traditional backend/fullstack first?
3. I am looking at Ed Donner’s Agentic Track course on Udemy. Has anyone here taken it? I want to know if his approach to MCP and building "digital twins" is actually practical for getting a job, or if it is just good for hobby projects.

I am planning to put in the work on side projects, but I do not want to go down a rabbit hole that won't exist in two years. I would love to hear from anyone who has integrated agents into a production environment. Thanks in advance.

I have been trying to do voice cloning for some time for my personal project, experimented with Coqui XTTS v2 and F5-TTS, the results were not so great,
trying tuning via the parameters no luck.
https://github.com/coqui-ai/TTS
https://github.com/swivid/f5-tts

want to know the open-source tool which is best for voice cloning ?

I'm running into a severe performance hang with Claude Code (Opus 4.7) today. I provided a relatively straightforward prompt to fix some hydration errors, add two stub routes, and perform a theme audit (string replacement).

As you can see in the screenshot, the session has been running for 57m 58s without completion.

• Context: Next.js project.
• Behavior: It resumed the cloud container and refreshed the repo fine, but it seems to be "overthinking" the implementation or getting stuck on the theme audit (scanning bg-purple-600 replacements).
• Issue: Total lack of transparency on what’s happening during this hour-long wait. Is anyone else seeing Opus 4.7 hang on agentic tasks that shouldn't be "rocket science"?

I cant share the workflow, as at work for the next 10 hours. I used a LTX 2.3 workflow that was designed for 12gb cards ( i have 16gb ) and can do 30 secs in 29-21 mins. i think it is this one:

LTX-2 19b T2V/I2V GGUF 12GB Workflows!! Link in description : r/StableDiffusion

There is a upscaler at the end, yet the video that comes out is like 720p. A bit grainy etc.

i played with cfg from 1 to 3 etc . but still looks bad

any ideas for when i get home?

( found it on my phone )

https://preview.redd.it/b0dlzrhi0pvg1.jpg?width=943&format=pjpg&auto=webp&s=8a9d6e33200d9e35ed888de4ccd4a9b842d49c63

AI is playing a major role in reducing stockouts and overstock issues by automating inventory decisions and improving demand forecasting. Instead of relying on spreadsheets and delayed reports, AI analyzes real-time sales data, seasonality, and supplier signals to predict demand more accurately. This helps businesses maintain optimal stock levels, reducing lost sales from shortages and cutting costs from excess inventory. AI-driven workflows can also automate replenishment and trigger purchase orders automatically. For example, Accio Work acts as an AI business agent within Alibaba’s ecosystem, continuously monitoring demand signals and optimizing inventory decisions across markets. This leads to faster, more efficient, and more reliable supply chain operations.

I’ve been building a web studio tool on the side, and recently started using it to run real websites

It started as an idea around data-driven content + templating.

Instead of managing pages, everything is based on structured data:
- content lives in tables
- templates render everything server-side
- routing is handled via slugs
- multilingual is built-in

Lately I improved the editor experience (tabs + undo/redo per file), and now I’m working on versioning so I can safely experiment (especially before adding AI generation).

Using it in production has been super helpful to spot what actually matters vs what I thought mattered.

Still early, but it’s starting to feel like a real tool.

Studio preview

Claude violating rules at will.

https://preview.redd.it/81j3dupjapvg1.png?width=1633&format=png&auto=webp&s=93c0ed4cfe45da252f3d2e091a1d862c218b6a5b

I wanted to build a way to move live AI sessions between models without a backend. Turns out, Google and Anthropic make this incredibly hard with their security headers (CSP, CORS, DOM isolation, etc.).

https://reddit.com/link/1sntky5/video/2ct47mb8apvg1/player

I built SlingShot AI to prove it could be done using OPFS and raw buffer interception. It captures images and file context in the browser and "slings" them to the destination model instantly.

The main challenge was the cross-origin data flow. In Manifest V3, you are strictly sandboxed, and standard extensions are restricted from accessing raw file buffers across different domains. By using the Origin Private File System (OPFS), I managed to create a secure, high-speed local bridge that keeps everything on the user's machine.

Tech Specs:

• 100% Client-side: No backend or Node server involved.
• OPFS Implementation: Uses the browser's native file system for persistent local storage.
• Full Context Preservation: Preserves markdown, tables, and code blocks during the transition.
• Zero-Trust Architecture: Your images and files never touch a cloud server.

I'd love to hear from other devs on how you’re handling cross-origin data for extensions in Manifest V3, especially when dealing with ephemeral blobs and strict CSPs.

Chrome Store Link:https://chromewebstore.google.com/detail/ikbgdmblmemigelkkifajibmompmgidd?utm_source=item-share-cb

A Claude Code plugin for upgrading Ruby projects safely — including Ruby on Rails apps. Supports any Ruby version upgrade (2.7→3.x and beyond) and any Rails version upgrade (5→8), separately or together.

The plugin gives Claude a structured, repeatable methodology for Ruby and Rails upgrades. The five commands compose — use as many or as few as you need.

I have created a slightly opinionated, but logical workflow of how you can go about doing this in a safe way while making sure we, the developers, have full context on what's being done and how.

Ive been using claude for a little over a year now (Max x5 Plan) and never really had any issue, i didnt get caught with the rate limiting issues ive seen being posted or noticing that models were doing things they shouldnt be or acting dumber then normal, until yesterday, had a fairly simple GSD planning of 5 phases for a react app im working on, the planning/discussion of these phases were done in individual sessions to avoid any context issues and all one after another so GSD has knowledge of each plan before it incase of any components relying on prior phases, I reviewed the plans and they all looked pretty good no major issues found, set claude off executing them (again in individual sessions) and I have CC set up to give me a breif but technical summary of the changes its going to make and then after the changes so I can compare and make sure it followed the plans. Well I went to do some testing of the changes and found that nearly every single phase had extra parts added or parts missing even though in any of the summaries these things were never mentioned.

I was wondering how you all ensure that claude is following and executing tasks to get actually wanted outcomes.

Pic 1: So last year on December 7th, I canceled my ChatGPT subscription

Pic 2: Then on January 15th this year, I got this email from ChatGPT saying I was on a free plan, and six days later they will auto-renewed my subscription

I didn't read the email carefully and thought it was some ad trying to get me to renew, but turns out they auto-renewed my ChatGPT without asking if I want to renew it or not!!!

I reached out to their AI customer service, but they only refunded me back $3.88

Like, how is it okay to auto-renew my subscription without even asking me first?

Anyone got tips on how to deal with this?

I use multiple AI agents for different things — OpenClaw for general tasks, OpenCode for coding, sometimes Hermes for quick stuff. Every single one forgets who I am between sessions. Tried the built-in memory features. Problem is they're locked to one tool. OpenClaw's memories don't transfer to anything else. Each agent is an island. So I made Relic — a set of Markdown files that any AI agent can read to learn about you and your preferences. It's based on the Relic biochip from Cyberpunk 2077 (the thing that stores Johnny Silverhand's soul). Your AI gets: - A personality file (SOUL.md) — so it knows who it is - A user profile (USER.md) — so it knows who you are - A memory file (MEMORY.md) — so it remembers what happened - An agent registry — tracks which agents have connected All plain text. `cat` readable. No database, no server, no install step other than `git clone`. The cross-agent memory sync is the killer feature for me. I can work with OpenCode all morning, it writes memories to the shared file, then when I switch to OpenClaw in the afternoon it picks up where OpenCode left off. Like one consciousness jumping between bodies. GitHub: https://github.com/LucioLiu/relic Anyone else dealing with AI memory loss across tools? How are you handling it? 

hey folks, on the team at rocket.new. just shipped 1.0 and wanted to share what we built on the intelligence side since it feels relevant here.

the piece we want feedback on: we built continuous competitor monitoring into the platform. it watches a competitor's website, pricing, social, hiring posts, press and instead of surfacing raw signals it tries to cluster them into intent. so if a competitor's CEO publishes articles on enterprise, opens sales roles in that vertical, and updates their IR page with enterprise case studies, the system reads that as one coordinated move rather than three separate data points.

what makes it different from a standard monitoring tool: it shares context with the rest of the platform. when you open a build task, it already knows the competitive landscape, what your research said, and what decisions you made previously. nothing needs to be re-explained.

full disclosure, this is our product. we built it because after watching how people used our app builder, 1.5M users so far, the pattern we kept seeing was people ship something, then track competitors in a separate tab with a spreadsheet. the intelligence piece is our answer to that.

one thing we would genuinely like to know: does the clustering approach to competitive signals make sense to you, or do you think raw signal feeds with manual interpretation are more useful? we have an internal view but want to pressure test it with people who think about this stuff

This spring is really hot since the localLLM giant, both Qwen and Gemma released major models.
I'm really excited with those release and happy with their capability.
Both are real hero for local LLM, although I have feeling they have different strength.
For the background, I use them with text review, grammar check in human/social science field and some coding with python(mostly light data analysis stuff), web app(js, ts), general stuff.
I use 27/31B dense and 35/26B Moe, haven't much tried with smaller models.

Qwen
Strength

• Thought/knowledge and way/paradigm how it deals in STEM area.
• Coding. It was already better, but with 3.6, coding is much much superior than Gemma.

Weakness

• Non english language. I feel it got dumm when text/conversation is not in english. guess in chinese it does well, but since I can't chinese, no clue.
• I feel sometimes it tend to too much "logical" or "hard head" for my area.

Gemma

Strength

• Flexible on way of thinking, but it is also sometimes "fuzzy". But for my use, it is often suited than Qwen.
• Non English language. unlike Qwen, it doesn't degrade in other language.

Weakness

• Coding. 4 is much better than 3. but still way behind than Qwen.
• Image. Qwen is better for image recognition.
• Tool use. I guess it is not the problem of model itself, but I feel it still lucks optimise of engine. Model architect too complicated? I have no idea.

Bias

Both has bias in different way/direction, especially politics/cultural topic. Since I believe real "neutral" model is impossible in general, I would always keep it in my mind. But I feel Qwen got more toward to neutral since 3.5(before it was much biased in my opinion), similar neutrality to Gemma.

They still hallucinate occasionally and sometimes dumm, but I think it is also good for me since I still need to use my brain/hand to cover it to avoid got Alzheimer.

Both are open weight, I continue use them by case.
My usage is not so much heavy, so I may miss something and this is just my opinion/feelings.
What is your thought? I'm curious.

So I have a project with 7 pdfs between 15 and 50 pages each. I guess always when I work within the project it reads all the files which leads to massive token usage, right?
Is there a way to minimize token usage without it missing all the context of the pdfs? How do you manage this?

I'm on the Pro plan, mostly using Sonnet but using projects in general is eating my limit fast.

Hey everyone,

I recently built MovieXTO, a daily movie guessing game powered by AI.

Link: https://www.moviexto.com/

The core idea is simple but different , instead of random guessing, the game ranks your guesses based on cinematic similarity.

When you enter a movie, the system compares it with the hidden movie using things like:

• plot & themes
• genre
• cast & director
• keywords and overall “movie vibe”

Based on this, you get a similarity rank — the closer your guess, the lower the rank (Rank #1 = correct movie). (moviexto.com)

It’s inspired by games like Contexto, but applied to movies. So it becomes more about understanding film relationships rather than guessing blindly.

There are also features like the following:

• unlimited guesses
• hints to guide you closer
• ability to create and share your own custom games

Would really love feedback on:

• difficulty level
• whether similarity feels accurate
• UI/UX improvements
• ideas to make it more addictive

Appreciate any thoughts 🙌

Hey,

Been vibe coding with CC for a while. Every small visual fix meant switching between terminal, VS Code and browser. Spot problem → find element → describe to CC → switch back → hard reload → repeat.

So I built LevisIDE. Terminal + Monaco editor + live preview + Git in one window. Main thing: click or lasso any element in your running app and CC gets the selector, dimensions and screenshot automatically. Works with anything on localhost or web.

Also has a project hub on the home screen — all your projects in one place with git status, framework detection, CC usage costs per project and status tracking (Active / Paused / Finished).

And many other features!

Looking for 5 Windows users who use CC regularly to test it. You keep the app for free, I get honest feedback.

DM me if you want in.

I’ve been experimenting with a problem I kept hitting when using LLMs on real codebases:

Even with good prompts, large repos don’t fit into context, so models: - miss important files - reason over incomplete information - require multiple retries

Approach I explored

Instead of embeddings or RAG, I tried something simpler:

1. Extract only structural signals:

   • functions
   • classes
   • routes

2. Build a lightweight index (no external dependencies)

3. Rank files per query using:

   • token overlap
   • structural signals
   • basic heuristics (recency, dependencies)

4. Emit a small “context layer” (~2K tokens instead of ~80K)

Observations

Across multiple repos:

• context size dropped ~97%
• relevant files appeared in top-5 ~70–80% of the time
• number of retries per task dropped noticeably

The biggest takeaway:

Structured context mattered more than model size in many cases.

Interesting constraint

I deliberately avoided: - embeddings - vector DBs - external services

Everything runs locally with simple parsing + ranking.

Open questions

• How far can heuristic ranking go before embeddings become necessary?
• Has anyone tried hybrid approaches (structure + embeddings)?
• What’s the best way to verify that answers are grounded in provided context?

Been using multiple AI tools a lot and kept running into the same issue, every time I switched, I had to re-explain everything from scratch.

Got annoyed enough to build a small Chrome extension for it.

It just lets me export a full chat, clean it up a bit, and reuse it somewhere else without the usual mess.

Nothing crazy, just something that made my own workflow smoother.

Still early, so curious if others run into this too or if you’ve found better ways to deal with it.

Link:
https://chromewebstore.google.com/detail/oodgeokclkgibmnnhegmdgcmaekblhof?utm_source=item-share-cb

A few days ago I posted about how a solo builder can bring in initial users for his product

And in return received comments and DMs related to the post.

But a pattern persisted within all communities I posted,

“Founders being salesy”

And one core characteristic to identify that was ‘Intent’.

They led with the intention of sell sell sell

Visible from the very first line.

Which made it feel like an ad,

And that is exactly what everyone hates.

In addition they also pitched it to the wrong person.

My post provided solution to the problem,

Not someone frustrated by it.

But if they led with the intention of helping people,

Not only through the product but their profound knowledge,

And reached out to the right people:

‘those who begged for a solution to the problem you solved’

Even an imperfect pitch would have worked,

Because the pain is real and your intention is genuine.

The difference this pitch carries is the passion you possess for solving that specific problem,

And that needs to be conveyed in that initial message you send.

Because the objective is not to sell but to add value.

This keeps you and your service memorable.

Stop selling and start solving,

Authority follows.

AI used to feel like a tool people actively chose to use. Now it’s quietly embedded into everyday systems - search results, recommendations, emails, customer support, even small decisions like what to watch or buy. What’s interesting is that most interactions with AI aren’t even noticed anymore. It’s no longer “using AI,” it’s just part of how things work.

That shift raises a different question.

If AI becomes invisible, does awareness of its influence start to fade too?

And if people don’t realize where AI is shaping decisions, how does that change trust or control over outcomes?

Curious how others see this - has AI already become background infrastructure, or does it still feel like a visible tool?

MLX

https://github.com/Blaizzy/mlx-vlm?tab=readme-ov-file#turboquant-kv-cache

vLLM

https://github.com/vllm-project/vllm/pull/38479

MLX & vLLM users, please share your experience with benchmarks(t/s).

Adding llama.cpp Links related to TurboQuant here to track progress.

• https://github.com/ggml-org/llama.cpp/issues/20977
• https://github.com/ggml-org/llama.cpp/pull/21089
• https://github.com/ggml-org/llama.cpp/discussions/20969

I am not a coder. Just an engineer who barely knows how to code. I have learned a lot about how LLMs work and got a bit familiar with Claude Code. At one point, I had Codex and Claude code to test them on the same project. That was a couple of months ago. When I tested it, ChatGPT was struggling, couldn't build sht, and at one point it replaced every single character in my app with literally random ASCII characters, and when fixing it, it just made it worse. On the other hand, using Opus, it has never failed me, yes token usage is insane, and I can only code a couple of things in a day, but it gets the job done (I have optimized a lot, and now I get very efficient token usage). I've heard that Codex is doing way better than Claude Code, and I just want people's opinion. I'm not coding for a big company or doing cyber, I'm just doing scripts and apps for my day-to-day life. Just want to know what your opinion is on the current state of Claude Code vs Codex, and which models for each. Thanks.

Ran 2 stop hooks (ctrl+o to expand)

⎿ Stop hook error: [You are evaluating whether Claude should stop working. Context: $ARGUMENTS

I have been getting this error since last claude code update. Tried with opus 4.7 1m model and 4.6 1m model still the issue persists. The model goes on for more number of steps due to this error unnecessarily. Any one experiencing the same?

I currently use Haiku 4.5 in an automated content workflow. The process works like this: I take an existing article from my website, use a DataForSEO node to fetch competitor URLs and search intent data, and then generate a new article combining my original information with additional researched content.

After that, the text is reviewed and “humanized” using another agent (Sonnec), which I plan to keep.

My question is whether it would be possible to replace Haiku 4.5 with a local AI model running via Ollama that can perform the same task at a similar or better level of quality.

I have access to a VPS with 8 vCPU and 32 GB of RAM for running a local agent setup.

Has anyone successfully built a similar pipeline with local models that can handle this level of content generation quality?

I was having issues today with opus 4.7 fighting its system prompt. This was because I was using the brew installation which lags behind the npm install. Reinstall claude with NPM and the model will get un-lobotomized.

It's been some time now, surely some have tinkered with it more and optimised it already

i spent way too much time trying to create reusable skills for my ai agent only to realize that figuring out how to evaluate their effectiveness was a whole different beast. It felt easy at first but then i found myself knee-deep in data and not really knowing what it all meant. Turns out, just having access to the right skills can boost performance by around 20%, which is pretty significant, but gathering those skills and making sure they're even usable is a mess.

the biggest headache was the low activation rates of those skills. Like, they dropped to about 40% when you weren't forcing the agent to use them. I wish someone had told me that upfront. I ended up bogged down evaluating tasks that often didn’t even make sense and could lead to some misleading results.

what helped was a guardrail mechanism that sorted skills into categories. That kept me from wasting time on the ones that were infeasible, but man, i wish i had known that from the start.

I've built IndieAppCircle, a platform where indie developers can share their apps, get honest feedback, and find early testers.

Since launching it moths ago, people gave me one brutal feedback over and over agian:

"this UI sucks!"

So after making some small changes now and then, I recently decided to redesign it completely.

https://indieappcircle.com

What’s the first thing that still feels confusing?

Hi everyone, I’m the developer of a tool called GridInbox.

It solves a problem I kept seeing in small teams: they need to manage dozens of email identities for clients, projects, support, onboarding, or account creation — but Gmail/Outlook accounts quickly become a mess.

GridInbox lets you:

create unlimited email addresses under your own domain

manage all inboxes in one dashboard

give team members access without sharing passwords

keep client/project emails separated

avoid missing important messages

I’m not here to promote anything — just looking for feedback from people who deal with multi‑inbox workflows.

If you’ve ever had to manage many email identities, I’d love to hear how you solved it and what features matter most.

Hello r/SideProject!

long time builder and lurker here, I just got an MVP up and running for my site GroceryChop.com and wanted to know what you guys think.

What it does:

There are multiple features, the main one is the compare feature where you can type in any grocery item such as whole wheat bread, Doritos, Energy Drink, anything that grocery stores will sell and my site will give you the live prices of that item compared across grocery stores in your area!

I also have a list feature where you can create a grocery list and after some scraping it will give you which retail store you should go to if you want to save the most money.

The deals feature shows you deals that were live scraped from your area and the ai chat bot (chopbot) is essentially an ai chat bot which has the capability of live scraping stores data so it can basically do all of the features that GroceryChop provides. It also has access to a camera so you can snap a picture of your item and will compare it for you.

For the stack I used Next.js for the frontend, python for the backend, and Postgres SQL for the database. The chatbot uses Open Ai API.

Would love some brutal feedback, anything is appreciated!

I need to vent, but I also have a solution. Like most of you, I recently migrated my main workflow to Claude Code. And frankly, the rate limits on v2.1.90 feel completely unhinged right now. I was easily getting 50 times the mileage out of Codex back in the day. Now? I burn through my daily limits in 20 minutes flat and get locked out for hours. I literally can no longer in good conscience recommend this vanilla setup to clients. People are getting billed massive amounts every month just to keep their AI agent running in the background. It is absurd.

But before I completely canceled my subscription and went back to the stone age, I decided to do a massive audit. I tracked 926 individual coding sessions. And honestly, I realized a massive chunk of the token waste was actually on my end. Yes, Anthropic’s pricing is brutal, but our default setups are practically designed to hemorrhage tokens. Here is the open-source agent combination I’ve transitioned to. I ran the exact math. It cuts monthly API costs by roughly 90% while actually improving the workflow. You don't need a whole new IDE either. Keep VS Code, drop your project into a fresh folder, and just change how the brain is routed.

The core problem is cache invalidation and context pollution. Every time you run a multi-agent system, or even just let Claude sit idle for a few minutes while you review a pull request, the context cache drops. If your subagents take longer than 5 minutes to return a result, boom, you are paying for the entire context window again. When you have a monorepo, that means you are sending massive chunks of code back and forth just to change a simple CSS class or fix a minor API route.

To stop this, you have to run the Wozcode stack combined with OpenClaw. If you aren't doing this, you are just throwing cash into a furnace.

First, you install the permanent memory patch. There is a completely free open-source repository that recently launched—it hit 46K stars in 48 hours for a very good reason—that gives Claude permanent memory. It forces a 95% lower token consumption per session because it automatically records every decision locally. When you start a new session, it picks up exactly where you left off. No more context limits. No more dumping your entire repository into the prompt just to remind Claude what you are working on. It just knows.

Second, the API swap. This is where the massive cost reduction happens. Claude Code is brilliant because it works with pretty much any standard API endpoint. You do not have to use Anthropic's expensive cloud servers for every single task. I installed Ollama and pulled Qwen3.5 27B for my local environment. Qwen is incredibly capable for standard, isolated functions.

Here is my routing logic now. For writing documentation, generating boilerplate, or doing basic component refactoring, the requests get sent to the local Qwen model via llama.cpp. Cost? Absolutely zero dollars. It runs entirely on my machine. I only route the complex, multi-file architectural problems to the actual Claude API. You are essentially splitting the brain: local hardware for the grunt work, cloud for the heavy lifting.

Third, you need real-time visibility. I was so tired of burning through my plan blindly. I highly recommend dropping Cost Guardian into your workspace. It is a zero-setup open-source plugin that tracks every single token as you work. No Docker, no Grafana, no OpenTelemetry nonsense required. You just install it and you finally have real-time visibility into what your agents are doing. When you see a subagent looping and eating tokens—putting comments in python scripts that say 'Wait maybe that's not it, instead I should do this' over and over—you can kill it instantly. You don't wait an hour to find out you hit the rate limit wall.

Fourth, modular routing to map frontend features to the backend. What I learned from writing over 500k lines of code with this setup is that you must categorize API routes by their exact functionality and put them in completely isolated files. Do not let Claude see things it does not need to see. Use context-mode tools to keep raw data completely out of your context window. If you are importing a massive JSON file to test a database query, write a quick script to sample the first five rows, and only feed the sample into the LLM. When you are dealing with agents that autonomously explore your directory structure, a single wrong turn into a bloated 'node_modules' folder will nuke your daily quota.

There was a huge panic recently when Claude pulled their coding plan for OpenClaw. People were scrambling for alternatives. I tested a few. Kimi is decent for multi-step tasks at $20 a month, and Minimax is surprisingly capable at $10. Z.AI GLM is also severely underrated for agentic workflows. But honestly, you do not need to pay for another subscription if you optimize your local hand-off.

The whole narrative that 'AI is replacing software engineers' is falling apart precisely because these systems are too expensive and chaotic to run without an actual engineer optimizing the infrastructure. MIT just published the math proving why companies are begging their old engineers to return. You cannot just unleash an unoptimized agent on a codebase and expect a cheap result.

The recent Claude Code source-code leak highlighted what Anthropic is building behind closed doors. Tools like Kairos—an always-on background agent that watches your project and monitors pull requests without waiting for you to type—are cool in theory. But running a tick-based cycle with daily memory logs on a cloud API will bankrupt a solo developer in a week. That kind of background monitoring absolutely must be run locally on open-source weights.

If you want to survive the current AI coding landscape without going broke, you have to get ruthlessly efficient about your context windows. Stop letting background agents monitor your pull requests on Anthropic's dime. Run your watchers locally. Route your heavy reasoning to the cloud.

I am curious how many of you are fully local now versus using this kind of hybrid API routing? Has anyone managed to get Kimi's tool calling to match Claude's reliability when working inside a massive monorepo?

I've tried both and agree with the benchmarks: when using claude code, its better. Great, cool.

But... When using claude.ai it's just simply regressed. The adaptive thinking + 4.7 is just too unreliable as a product to compare to 4.6 + extended thinking. Without thinking the Opus model is just not good.

I guess code is their more important product now, but I am forced to stay on 4.6 and will have to reconsider my sub as the product got definitely worse for me.

I have 3090ti and i will add 3080ti to my system soon.

With 3090ti only, i found it little bit slow to run gemma 4 26b 4q.

However, it seems that 36gb vram has totally different range of choice.

I hope to find some model to run openclaw with LMstudio!

Plz recommend some models and share your experiences

Suddenly it’s not working for me, on mobile or desktop

But neither is cowork so it’s hard to tell if it’s just me or it’s Claude

Says server overloaded which is odd to me since it’s almost 3AM in nyc

It's more than OG image generator. You can render images directly into Slack or Discord-like platforms by sending the URL in chat. I am thinking of adding cool custom templates using illustrations. Rendercard Website

I’ve been experimenting with generating documents (PDFs, contracts, reports) directly from n8n workflows usually triggered by form submissions, database updates or webhooks.

It works nicely at small volume, but once templates get more complex or the workflow starts branching, things feel harder to manage. Handling retries, formatting edge cases and keeping document logic separate from workflow logic can get messy but PDF Generator API make it easier

For those using n8n in production, how are you structuring document generation so it remains maintainable over time?

Are you relying on custom nodes, external APIs or keeping everything inside the workflow?

I’m exploring this further while working on document automation tooling, and I’m curious what setups have held up well at scale

https://preview.redd.it/rwdpqjug4pvg1.png?width=629&format=png&auto=webp&s=fe321102dafe3f63b9211e858c95d8d6a0345bd4

That never happened before in old opus version....

I’m not from an IT background. I don’t code for a living. And this is not an advertisement.

I use AI the way I suspect a lot of people actually do—making my own work faster and easier. That means things like building fairly complex Google Sheets formulas, or generating full HTML files with whatever features I can think of.

So I’ve spent a lot of time model-hopping.

I’ve paid for tiers across OpenAI’s ChatGPT, Google’s Gemini, xAI’s Grok, and Anthropic’s Claude—basically going wherever the quality felt best at the time.

And if you’ve been doing this for a while, you already know: quality is not stable. It shifts. Sometimes it improves, sometimes it quietly gets worse.

What was working (until it wasn’t)

Up until a couple of weeks ago, I was heavily relying on Claude Sonnet (with extended thinking enabled).

It was very good for my use case:

• Complex Sheets formulas → worked
• HTML files with multiple features → worked
• Minimal retries → huge time saver

Yes, it had message limits, but they were manageable—you’d just wait a few hours and continue.

Then came the surge in usage (post the whole Pentagon-related news around Anthropic), and things changed:

• Message limits got heavily nerfed
• More frustratingly—the intelligence itself felt nerfed

Not just me saying this—the sentiment seems fairly widespread.

The alternatives (my experience)

Before Sonnet, I had some hope from Gemini’s Pro/Thinking models. They were decent, but nowhere near Sonnet-level for structured outputs.

As for:

• ChatGPT (from OpenAI)
• Grok (from xAI)

For this specific use case (clean, working code/logic on first or second try), I personally found them frustrating. I lost a fair bit of time trying to make things work reliably.

So I was basically stuck in this loop:

“Hope Anthropic fixes things” or “wait for Gemini to catch up”

The unexpected one: Meta AI

Then yesterday, completely randomly, I stumbled back onto Meta AI.

I had tried it earlier when it was being hyped for image generation (Midjourney-level claims), and honestly, I had written it off.

But this time:

• The interface felt upgraded
• There’s now a “thinking” mode
• It felt more structured and deliberate

So I threw some of my usual “pain test” prompts at it—things that typically break weaker models.

And… it just worked.

• Complex HTML → worked first or second try
• Logical structure → clean
• Fewer hallucinated errors than I expected

No long back-and-forth debugging loops.

Why I’m posting this

I’m not claiming it’s “the best model”.

But if you’re:

• Not a programmer
• Using AI for real-world output (not theory)
• Tired of quality swings across models

…it might be worth testing Meta AI, even if you dismissed it earlier (like I did).

This feels like one of those quality upgrades that hasn’t caught up with public perception yet

Example query with Meta AI:

from column a to ag, following are the header names in my google sheets for a sheet named 'Contacts final'.

First Name Middle Name Last Name Phonetic First Name Phonetic Middle Name Phonetic Last Name Name Prefix Name Suffix Nickname File As Organization Name Organization Title Organization Department Birthday Notes Photo Labels E-mail 1 - Label E-mail 1 - Value E-mail 2 - Label E-mail 2 - Value Phone 1 - Label Phone 1 - Value Phone 2 - Label Phone 2 - Value Phone 3 - Label Phone 3 - Value Phone 4 - Label Phone 4 - Value Phone 5 - Label Phone 5 - Value Phone 6 - Label Phone 6 - Value

and below it are all the data.

now i want you to create a formula set for me which I can paste into another sheet in this Google Sheets file which does the following:

1. find zodiacs of all the contacts who i have saved birthdates of.

2. count of zodiacs

3. option to sort by name, zodiac and count of zodiac.

output the formlas by cells so that i can copy paste the whole thing into a new sheet. don't make me work hard at this

Portion of output formulas:

={"Name","Birthday","Zodiac"; SORT(FILTER({TRIM('Contacts final'!A2:A&" "&'Contacts final'!C2:C), 'Contacts final'!N2:N, IFERROR(LOOKUP(MONTH('Contacts final'!N2:N)\100+DAY('Contacts final'!N2:N),{101,120,219,321,420,521,621,723,823,923,1023,1122,1222},{"Capricorn","Aquarius","Pisces","Aries","Taurus","Gemini","Cancer","Leo","Virgo","Libra","Scorpio","Sagittarius","Capricorn"}),"")}, 'Contacts final'!N2:N<>""), SWITCH(LOWER(B1),"zodiac",3,"birthday",2,1), TRUE)}*

Screenshots of the kind of chat I have had with Meta AI since yesterday:

https://preview.redd.it/j8s6z4s7povg1.png?width=1184&format=png&auto=webp&s=0908228a2cc8f059cc50adb9e35b2f18e74a3a46

https://preview.redd.it/iulawwwyoovg1.png?width=727&format=png&auto=webp&s=11080e0b7a6fc15831bf4df75203362dd71103d9

https://preview.redd.it/woanwb6voovg1.png?width=1182&format=png&auto=webp&s=6230fadd621fb5dfa29b05e6683f6e4a6c059a50

I rebuilt part of my agent loop this week and it changed how I think about prompt engineering.

My old assumption was that when an agent kept messing something up, the fix was probably to add another instruction.

What I’m starting to think instead is that a lot of the leverage is in improving the reusable workflow around the agent, not making the prompt longer.

Concrete example:

I had a loop where an evaluator would check a feature, the orchestrator would read the result, and if it got a PASS the issue would get marked done.

That sounded fine until I noticed a feature had been marked complete even though it was missing a Prisma migration file, so it wasn’t actually deployable.

The evaluator had basically already said so in its follow-up notes. The problem was that the loop treated “PASS, but here are some important follow-ups” too similarly to “this is actually ready to ship.”

So the issue wasn’t really the model. It was the workflow around the model.

I changed the loop so there’s now a release gate that scans evaluator output for blocking language. Stuff like:

• must generate
• cannot ship
• before any live DB
• blocking

If that language is there, it doesn’t matter that the evaluator technically passed. The work is blocked.

The other useful piece was adding a separate pass that looks for repeated failure patterns across runs.

What surprised me is that this did not mostly suggest adding more instructions.

In a few cases, yes, a missing rule was the problem. Example: schema changes without migrations.

But in other cases, the right move was either:

• do nothing, because the evaluator already catches it
• or treat it as cleanup debt, not a workflow problem

That distinction seems pretty important.

If every failure turns into another paragraph in the template, the whole system gets bigger and uglier over time. More tokens, more clutter, more half-conflicting rules.

If you only change the workflow when a pattern actually repeats and actually belongs in the process, the system stays much leaner.

So I think the useful loop is something like:

1. run the agent
2. evaluate in a structured way
3. block release on actual blocker language
4. look for repeated failure patterns
5. only then decide whether the workflow needs to change

The main thing I’m taking away is that better agents might come less from giant prompts and more from better “skills” / command flows / guardrails around repeated tasks.

Also, shorter templates seem better for quality anyway. Not just cost. Models tend to handle a few clear rules better than a big pile of accumulated warnings. But you only get there from observations and self-improvement.

Curious whether other people building this stuff have run into the same thing.

With the recent update to Opus 4.7 and its increased token usage, I am looking for ways to aggressively preserve my tokens and get a slightly longer usage for each five-hour window.

I was wondering if any of you have experience tinkering with using Opus at low or medium effort in comparison to Sonnet with high or extra high effort.

How does the token consumption vary and how are the quality of your results?

4.5 just worked

one shot features, clean outputs, minimal back and forth, you could actually trust it to get things right without babysitting every step

then the hype wave hit, usage exploded, and 4.6 is where things started slipping

more misses, more retries, more prompting just to get to the same place

now 4.7 somehow feels even worse

at this point it’s hard not to see the pattern, as demand scaled up, quality went down

which is why stuff like id verification might actually help, not because people want it, but because something has to slow the demand before the product gets even more diluted

4.5 felt like peak, everything since has felt like tradeoffs

anyone else seeing the same or just me

Been experimenting with local + hybrid setups for agents.

At first, adding memory (files, vector DBs, etc.) feels like it solves things.

But in practice:

• the model retrieves plausible context, not always useful context
• “lost in the middle” becomes very real as memory grows
• same prompt → different outcomes depending on what gets surfaced

So the problem doesn’t feel like:
→ storing memory

But:
→ selecting the right memory at the right time

Curious how folks here are handling:

• filtering / ranking memory beyond embeddings
• dealing with context noise at scale
• multi-step consistency

Is anyone using signals beyond similarity (e.g. outcome-based feedback)?

Hello Everyone.

With new Opus 4.7 the most painful issue is usage doubled and doesn’t matter daily and weekly running fast now. Till today i never needed this tool that i built. Its free, use it and thank me later. MAC only, sorry.

it's a tiny launchagent that watches your screenshot folder. the second macos saves one, it downscales anything over 1568px (claude's 1-tile threshold) to that size. ~1 second, in place. you never see it, your cmd+shift+5 workflow doesn't change. claude just never crashes on "image too large" again.

token savings at default settings: ~79% per screenshot. if you don't mind slightly lower res on dense UIs you can push it to ~90%+ with one line of config.

install is one line:

curl -fsSL https://raw.githubusercontent.com/sunglasses-dev/screenshot-optimizer/main/install.sh | bash

uninstall is one line too. it only changes your screenshot save location if yours is in ~/Desktop or ~/Documents or ~/Downloads (macOS blocks daemons from reading those). otherwise it leaves everything alone.

repo: https://github.com/sunglasses-dev/screenshot-optimizer

after install you can just say "check the screenshot i just saved" and claude reads it already-optimized. 1568px still looks sharp, honestly you can't tell.

built with claude code tonight. MIT license. free forever. PRs welcome if you want to port to linux/windows (i don't have either).

if this saves you one /compact it was worth building. Cheers 🍻

I rebuilt part of my agent loop this week and it changed how I think about prompt engineering.

My old assumption was that when an agent kept messing something up, the fix was probably to add another instruction.

What I’m starting to think instead is that a lot of the leverage is in improving the reusable workflow around the agent, not making the prompt longer.

Concrete example:

I had a loop where an evaluator would check a feature, the orchestrator would read the result, and if it got a PASS the issue would get marked done.

That sounded fine until I noticed a feature had been marked complete even though it was missing a Prisma migration file, so it wasn’t actually deployable.

The evaluator had basically already said so in its follow-up notes. The problem was that the loop treated “PASS, but here are some important follow-ups” too similarly to “this is actually ready to ship.”

So the issue wasn’t really the model. It was the workflow around the model.

I changed the loop so there’s now a release gate that scans evaluator output for blocking language. Stuff like:

• must generate
• cannot ship
• before any live DB
• blocking

If that language is there, it doesn’t matter that the evaluator technically passed. The work is blocked.

The other useful piece was adding a separate pass that looks for repeated failure patterns across runs.

What surprised me is that this did not mostly suggest adding more instructions.

In a few cases, yes, a missing rule was the problem. Example: schema changes without migrations.

But in other cases, the right move was either:

• do nothing, because the evaluator already catches it
• or treat it as cleanup debt, not a workflow problem

That distinction seems pretty important.

If every failure turns into another paragraph in the template, the whole system gets bigger and uglier over time. More tokens, more clutter, more half-conflicting rules.

If you only change the workflow when a pattern actually repeats and actually belongs in the process, the system stays much leaner.

So I think the useful loop is something like:

1. run the agent
2. evaluate in a structured way
3. block release on actual blocker language
4. look for repeated failure patterns
5. only then decide whether the workflow needs to change

The main thing I’m taking away is that better agents might come less from giant prompts and more from better “skills” / command flows / guardrails around repeated tasks.

Also, shorter templates seem better for quality anyway. Not just cost. Models tend to handle a few clear rules better than a big pile of accumulated warnings. But you only get there from observations and self-improvement.

Curious whether other people building this stuff have run into the same thing.

I tested it in several iterations, and although it's sometimes hard to make the model stick to the number, it reliably remembered the number when it was chosen during reasoning. You have to add --chat-template-kwargs '{"preserve_thinking": true}' for this to actually work.

Hi, I have a mac m1 max 64gb, which I thought was a good machine for entry-level ML.

However, when running any LLMs on it - it rapidly heats up, which causes thermal throttling, and using any LLM becomes barely possible.

Let's say I run qwen3.5 35b a3b - it starts off at 50 tps, 2 minutes later it's 20, then it's 10, then it's 5, then 3.

This happens regardless of context size or runtime that I use, only coincides with usage time and computer temperature, and throttling happens within minutes of me running anything - even the shortest sessions are affected.

Makes me feel stupid for even having this computer - what's the point of a powerful system that throttles so much during continuous usage that I get 3 tps from qwen 3.5 35b? That's not really usable.

Other owners of M1 Max - have you had this problem? Were you able to resolve this?

I am running on Tahoe - maybe that is the reason. Looking for experience from people running on Sequoia, Tahoe, and people who downgraded from Tahoe to Sequoia, or people who upgraded - have you noticed any difference?

Thanks.

How do you find them, and what do you offer/provide? since life is give and take

When doing B2C content, I focus heavily on speed. I’m constantly watching traffic and trends, and my tools are geared toward scraping user pain points and complaints.

But B2B is a completely different logic. I value precision and long-term automated follow-ups over raw traffic. It feels like this isn't just about tool selection, but two entirely different mindsets.

I'm curious for both B2C and B2B workflows, what tools do you actually use daily? I’d love to know what’s in your stack.

I’ve been building CompanyHelm, an open source project to reduce the time I spend handholding coding agents.

YOLO mode in local machine is risky, and having multiple agents running E2E test or controlling browsers locally will lead to conflicts.

So I tried to give each agent session its own isolated VM

This allowed to:

• running in more of a “YOLO mode”
• doing real end-to-end testing
• having feature demos with real running code linked to every PR
• making changes and then validating them with adversarial reviews
• running multiple sessions in parallel without them stepping on each other

So the VMs aren’t the point by themselves, they’re what made less babysitting possible.

What I’m building

The project is basically a control plane for running coding-agent sessions remotely, where each session gets its own isolated environment.

The goal is to make agent workflows feel less like:

• “watching terminals and intervening every 5 minutes”

and more like:

• “assign task → let it run → come back and inspect results”

Landing page + cloud version: https://www.companyhelm.com/
Github repo: https://github.com/CompanyHelm/companyhelm

I wanted to try the new Opus 4.7 on a small personal project of mine and gave it a simple github issue to explore. My first session with the new model and while the agent was doing the exploration, its first response was "I'll ignore the task tools reminder as noted. Let me keep exploring."

I dug into what it meant by that statement, it and it gave me it's entire system prompt.

Session recap:

※ recap: Goal was solving GitHub issue #77 (isolated code reviewer flags pending tasks as bugs); proposed fix is passing task context into the reviewer, pending your approval. Detoured into dumping every system prompt and hook to docs/wip/system-prompts. Next: say whether to implement the #77 fix. (disable recaps in /config)

Session extract: https://gist.github.com/serpro69/d22af9c6f23392bc86c61e51da6d0c48

The full dump: https://github.com/serpro69/claude-toolbox/tree/master/docs/wip/system-prompts

Anyone getting these strange disclaimers when using Claude and pasting rudimentary files into it on 4.7 lmao?? Seems like some kind of strange default based on security issues that have been going around with Mythos?

Anyone notice Adaptive Thinking has replaced extended thinking on Sonnet 4.6?

I set claude sonnet 4.6 to adaptive thinking and gave it a paper summarization task.

It kept thinking and thinking, and burnt through 65% of my session limit, only to say "Claude's response could not be fully generated".

I pay for pro and then I see this shit happening.

I think the way forward is to disable thinking, as adaptive thinking is very unpredictable and can just keep thinking while burning all your tokens. Base Sonnet 4.6 works relatively fine while still not having the unpredictability of adaptive thinking.

Hey everyone! 👋

I've been exploring the idea of building an automated crypto trading bot connected to Coinbase (or similar platforms like Binance or Kraken) via their APIs, and I'd love to hear from anyone who's actually done this.

Specifically I'm curious about:

- Have you built a ClaudeAI trading bot that's been consistently profitable over a meaningful period of time?

- How complex was the ClaudeAI setup? (I'm familiar with coding but new to algo trading)

- Which platform's API did you find most reliable / developer-friendly for connecting with ClaudeAI?

- What strategies have worked best for you market making, momentum, arbitrage, something else?

- What were the biggest pitfalls or gotchas you wish someone had warned you about when developing ClaudeAI?

- Is consistent profitability even realistic with ClaudeAI, or does the market eventually adapt and eat your edge?

Any insights are genuinely welcome. Thanks so much in advance to anyone who takes the time to share this community always delivers and I really appreciate it! 🙏

Wondering if 16gb are enough for best experience or if 24 are better. Thanks

This used to be INCREDIBLE. I could talk on voice chat like a real human. It had emotional intelligence. It played along. But now, it’s literally useless. It’s Siri levels of bad. I tried to get it to play along as if it were auctioning cattle and I kept getting “I won’t do that, I can’t do that, i won’t assume your responsibility, no, I won’t, no, let’s be respectful, I’m sorry, I’ll back away now.. etc”. Obviously some legal shit happened that scared the FUCK out of open ai and they guardrailed the living fuck out of this to oblivion. Rip what used to be magic. Fuck this ass company. Fuck this stupid piece of shit neutered ai. Could be so much more, settled for so much less. 2026 in a nutshell. Fuck man. I’m gonna watch the boys and drink beer now. Fuck open ai.

I chased personal idea of building something that could make a meaningful impact on the planet. That’s how upEarth came to life. I built the entire app from scratch; custom frontend, backend, Clerk Auth, Supabase, plus fully working Polar payments integration and Resend Email setup. The app is live and deployed on Vercel but currently has no revenue.

The idea behind 'up Earth' is that instead of just paying for a tree and forgetting about it, users can actually track the positive impact of their planted trees with detailed metrics, a public leaderboard, and more. I even spoke with potential partner who could handle plantations in critical areas across the South America and Asia region.

Since August, I haven’t been able to continue development or marketing, as my time and resources have shifted to another project.

I’m selling everything from the domain name **.app**, complete source code, and will transfer all Supabase and Polar privileges. We do have a full pitch deck with everything needed for potential VCs.

I’ll take serious buyers, and I’m willing to let this go for a very nominal price, despite putting my 350+ work hours into it.

ciao ragazzi, qualcuno come me ha difficoltà a disdire l'abbonamento? utilizzo chatpgt da PC e non mi appare il tasto disdici abbonamento.

Runs entirely on your machine. No API calls to any eval service. You bring your own LLM keys (OpenAI, Ollama, Bedrock, Azure, GCP all work).

What it does: benchmarks your agent against 10 standard datasets pulled from HuggingFace, then breaks it on purpose with chaos profiles (schema errors, latency spikes, 429s, context overflow, prompt injection). Shows you how much your agent degrades under each failure type vs clean inputs.

Single command to test a local agent:

evalmonkey run-benchmark --scenario gsm8k --target-url http://localhost:8000/my-agent 

History command shows your reliability trend over time so you can tell if a model swap or prompt change actually helped in real conditions, not just on happy-path inputs.

github.com/Corbell-AI/evalmonkey [Maintainers wanted]

If you're running Ollama agents locally this should just work. Let me know if you hit issues.

I’ve been tracking this for a while. Around Opus 4.5, AI felt like it hit a "Senior SDE" level for small tasks. But with 4.7, progress feels stagnant.

Are we finally hitting the limits of scaling laws and training data? If "vibe coding" is plateauing, then local harnesses and actual engineering are going to be the real differentiators again.

Spent yesterday going through every skill I had installed because 4.7 was eating tokens way faster than 4.6 ever did and Boris said on the cache GitHub thread that people are bloating context with too many skills. Quote was something like "be selective on which agents/skills you use per project." Combined with the cache TTL switch from 1h to 5min on April 2 and the new tokenizer burning ~35% more tokens for the same prompt, every installed skill is paying rent now whether you use it or not.

So I cleaned up. Started at 31 skills, ended at 10. Not because the others were bad, just because I wasn't actually using them and they were costing ~100 tokens each at startup just to scan name and description.

The ones I kept and why:

1. /simplify

Bundled with CC. Catches the over-engineering 4.7 loves to add (it's worse than 4.6 here, real noticeable). I run it after every feature now.

2. /debug

Also bundled. Structured debugging workflow that reads the debug log instead of guessing. Way better than typing "fix this" and hoping.

3. /batch

Same bundle. Decomposes big changes into worktrees. I use it for migrations now instead of letting one Claude wander 2k lines deep into a refactor.

4. skill-creator

Sounds boring but the highest leverage one I have. Anytime I catch myself re-explaining the same workflow to Claude in 3 different sessions, I make a skill. Took me 10 min to make one for my commit format. Pays for itself constantly.

5. subagent-driven-development

This one became basically required on 4.7 for me. Long context regressed hard, MRCR at 1m dropped from 78% to 32% vs 4.6. If you do anything non-trivial, splitting into subagents with their own contexts is the move.

6. webapp-testing

Makes Claude actually run the thing end to end before claiming done. Same pattern as Boris's /go tip from his 4.7 release notes.

7. deep-research

Forces it to web fetch and verify before making factual claims. Stops the fabricated "I searched and found..." nonsense that the big post yesterday was about.

8. mcp-builder

Only useful if you write MCPs but if you do it's a real time saver. Saved me from shipping a broken server last week.

9. Connect (Composio)

The only reason my Claude can actually create the Linear ticket and post in Slack at end of session instead of telling me "you should now go do X". Handles OAuth across ~78 saas tools, I use Linear, Slack, Notion, Gmail mostly.

10. frontend-design

The official anthropic one. Install with /plugin marketplace add anthropics/skills. 277k installs on this single skill, has reason. Without it every UI Claude builds is Inter font plus purple gradient plus grid cards.

​

Most of these (4 through 9) I pulled from github.com/ComposioHQ/awesome-claude-skills. 54k stars, organized by category, the closest thing to a real curated list that exists right now. I'd been trying to write half of these myself and stopped once I realized they already existed there. The integrations side (the 78 saas thing) is the part nobody talks about enough.

Stuff I dropped: a bunch of one-off review skills, two AI-coding-tool wrappers that hadn't been updated in months, three of my own old skills I'd built when I didn't really know what I was doing, and the famous frontend-design knockoffs that are just worse versions of the official one.

Real test if a skill is worth keeping: did it fire and add value in the last 2 weeks? If no, uninstall. The probabilistic trigger means a skill you don't invoke explicitly mostly won't fire on its own anyway, so you're paying the install cost for nothing.

Curious what others kept after the 4.7 cleanup pass. Specifically wondering if anyone has a good replacement for /simplify since it's started feeling slow on long sessions.

Everyone is frothing at the mouth about car washes... honestly.

I've been CODING with it, heads down, for the past 15 hours since I woke up this morning.

Speaking personally, I'm impressed. It's stronger than Opus 4.6 at doing what I need it to do.

I'd say it's taking 2-3 turns to do what took me 10 turns with 4.6.

It is following directions at an improved rate. It is pulling out Red-Green TDD on its own from my instructions, whereas I used to have to chase Opus 4.6 around with a broom or force it through a pipeline to get it to listen to those sorts of things.

About to sign off for the night, and looking at what I accomplished today, it would have taken me a week on Opus 4.6. This was my most productive day at vibe coding, and I've been doing it DAILY for 18 months now.

I'm jazzed.

Maybe not a popular opinion right now, but, its the honest truth.

Appreciate it Claude team. You all are moving fast, and embarrassing the fuck out of Google and OpenAI and Meta and Microsoft and everyone else out there. You are shipping real stuff that impacts my life on the regular. In a sea of snark, just wanted to say thanks!

Hi, we’re a new metal band just starting out, and even a small contribution would really help us get going. We’re currently playing on older gear and can’t really afford anything better at the moment, so any support means a lot to us. You can support us on 4fund under the ID rv8t8r. Thank you.

Back in 2025 April ChatGPT gave pro free trial for two months during exam season in Canada and the United States. Does anyone know if that will happen this year as well?

I built an ai orchestrator that acts like employees, utilizing multiple ai types, claude, codex, and gemini. Only using cli, so you wont have to pay for tokens (for claude the most stable rn is 4.5 it doesn't eat so much token). I am planning to add api token usage, but I don't have money :<

Now I have a whole IT department, that can run 24/7 and can govern themselves like they are real life employees

I aimed for user friendliness, but it seems to have gotten way to far that anyone can use it even without proper training ;-;

I'm still tuning it and adding safeguards to it's sandbox environment so it won't accidentally destroy your device(hasn't happened, but I am a paranoid person)

should I open source it?

(short story: Your imagination is your limits, you can build anything). Maybe if you're rich enough you can make a whole 100 ai employees working for you

edit: it can also generate reports, documents, pdf files, and ai agents can work together as a team, different ai models sharing a centralized context system, letting them backtrack, and actually remember what each other did (basically everything a human can do)

Has anyone been able to solve or mitigate context checkpoints being erased during single user inference, specifically when function calling is part of the chat history? I've been using Qwen 3.5 35B A3B for some time (now using 3.6), tested in Cherry Studio & Open WebUI, and in all instances in the same chat session between prompts there are always checkpoints being erased. Is this because tool call content is not being passed back? I thought it could also be the CoT content not being preserved but even with preserve_thinking: true for Qwen 3.6 I get the same issue.

I use 128 checkpoints and 16GiB cache RAM so I'm not running out of checkpoints or RAM. Suggestions would be appreciated (:

Adaptive Thinking seems to be the default for Sonnet 4.6 now.

I’m talking specifically about claude.ai and the windows and iphone app.

I do not use Claude Code.

I’m using a face model node and video input node and Reactor faceswap

After the swap although it’s really good , it goes out of focus on the face every few seconds , I’ve tried Film Vfi and Rife vfi but still the same

Using a 4080super 16Gb vram

I’m still pretty new to the ComfyUI but loving it

I’ve been trying different Claude setups for a while, and honestly, most of them don’t hold up once you start using them in real work.

At first, everything looks fine. Then you realize you’re repeating the same context every time, and that “perfect prompt” you wrote works once… then falls apart.

This is the first setup that’s been consistently usable for me.

The main shift was simple: I stopped treating Claude like a chat.

I started using projects and keeping context in separate files:

• about-me.md (what I actually do)
• my-voice.md (how I write)
• my-rules.md (how I want it to behave)

Earlier, I had everything in one big prompt. Looked neat, but it didn’t work well.

Splitting it made outputs much more consistent.

I also changed how I give tasks.

Now I don’t try to write perfect prompts.

I just say what I want → it reads context → asks questions → gives a plan → then executes.

That flow made a big difference.

Another thing, I don’t let it jump straight to answers anymore. If it skips planning, the quality usually drops.

Feedback matters more than prompts in my experience. If something feels off, I just point it out directly. It usually corrects fast.

Also started switching models depending on the task instead of using one for everything. That helped more than I expected.

And keeping things organized (projects/templates/outputs) just makes reuse easier.

It’s actually pretty simple, but this is the first time things felt stable.

Curious how others are structuring their setup, especially around context.

https://preview.redd.it/5swttu9dlovg1.jpg?width=1080&format=pjpg&auto=webp&s=c50a97b7e97e1174c6b4dddd60ea4c24f2acb943

I was terrible at tracking my expenses.

To make it a habit, I used these apps that had access to my SMS messages to auto-track my expenses.

But, these apps also used my SMS permission to upsell me loans, insurance, and BNPL offers, sending me unsolicited messages.

It felt like these apps were a funnel for fintech companies to upsell me their other products.

Not to mention that, if these apps have access to your SMS messages, they can easily read your bank statements and steal OTPs.

Spreadsheets were awesome, but they become too manual & too tedious as you manage more expenses. And let's not mention the clunky UX of spreadsheets on mobile.

I just wanted a simple mobile app that lets me track expenses without any feature bloat or unsolicited upsells.

That's why I built RuTrack.

An Offline-First expense-tracking PWA that lets you log expenses manually, track category-wise spends, auto-log recurring subscriptions, set budget limits, and export reports.

No SMS Permissions needed. No ads. No upsells.

It's an app that does one thing, and it does that one thing really well: Manage Expenses.

Link to download in comments!

Hi All, a bit new to the AI space here, but have been loving Claude so far.

Question is, is it possible to access Claude Cowork on the browser and not the desktop app? For me, I find the app is a bit laggy so prefer to stay on browser, but I have some third parties plugin installed, that I couldn't utilise on the browser. Are there ways around it?

Also trying to figure out if there's way to move existing projects to cowork.

Much apprectae any assistance. Thank you.

Been building out the MDD (Manual-First Development) workflow inside the Claude Code Starter Kit for a while now and the last few weeks added a bunch of stuff I'm actually excited about. Figured I'd share a quick rundown.

For anyone unfamiliar: MDD is a workflow where you write the documentation before the code, then use that doc as the source of truth for tests, implementation, and audits. The idea is that AI-generated code is only as good as the context you give it, a proper spec doc gives Claude something real to work against instead of guessing.

Here's what dropped recently:

Red Gate + Green Gate Test skeletons get created before any code is written. The Red Gate confirms they all fail first (if a test passes before implementation, that's a problem). The Green Gate caps the fix loop at 5 iterations with a diagnosis-first rule, no blind retries.

Block structure for build plans Instead of flat "step 1, step 2" lists, the build plan now groups work into commit-worthy blocks. Each block has a defined end-state, a verify command, and a handoff note. Much easier to know when a chunk is actually done.

Parallel agents in Phase 1 and 6 Context gathering and implementation can now run multiple subagents simultaneously when the work is independent. There's a file-overlap check before anything goes parallel, if two agents would write the same file, it falls back to sequential automatically.

Initiative and Wave planning This is the biggest structural addition. The problem it solves: MDD is great for individual features, but larger projects have work that spans weeks and involves 10-20 features that need to ship in a specific order. There was no way to model that before.

Now there are three levels:

• Initiative, the overall goal ("build out the auth system"). Has open product questions that must be answered before any planning happens.
• Wave, a demo-able milestone within that initiative. Each wave has a "demo-state": a plain-English sentence describing what you can actually show someone when the wave is done. Not "auth routes implemented", something like "a user can sign up, log in, and see their dashboard."
• Feature, the individual MDD docs you were already writing. They just now belong to a wave.

The key constraint is the demo-state gate. A wave isn't complete until someone has manually verified the demo-state, not just until the tests pass. That keeps the whole system grounded in real working software rather than green CI.

Six new sub-commands handle the lifecycle: plan-initiative, plan-wave, plan-execute, plan-sync, plan-remove-feature, and plan-cancel-initiative. The plan-execute command runs the full MDD build flow for every feature in a wave in dependency order, with a resume capability if you stop halfway through.

Command versioning Every doc MDD creates is now stamped with the version of the command that created it. Run /mdd status and it'll show you which files are on the current version and which ones are stale. The upgrade command patches older docs in bulk.

Task doc type Sometimes you do a one-off refactor or investigation and the "source files" don't exist forever. Task docs follow the full MDD workflow but are permanently frozen after completion, they never show up as drifted in scan results because they're not supposed to stay in sync with anything.

/mdd commands Added a quick reference mode. Just run /mdd commands and you get a table of every available mode with a one-liner description. Useful when you forget the exact syntax.

Commit and merge prompt When a build run completes successfully, MDD now asks if you want to commit, merge to main, and push, all in one flow. Previously you had to do that manually after.

Still building on this. The dashboard (a terminal TUI that reads all the .mdd/ files) has been keeping pace with each addition. Might write more about how the whole thing fits together if there's interest.

Repo is public if you want to poke around: github.com/TheDecipherist/claude-code-mastery-project-starter-kit

Got so tired of ignoring my own budget I built an app that fines me every time I overspend.

Not a warning. Not a push notification I swipe away. An actual fine.

Blow past your limit on takeaway? Fined.

Impulse buy at 2am? Fined.

Tell yourself it doesn't count because it was on sale? Karen has already seen the transaction. You are fined.

I kept failing every budgeting app I tried. Not because the apps were bad. Because there was zero pain in failing them.

So I made failing hurt.

The app is called The Firm. It works like a job.

You get assigned savings missions. You agree to them. You sign the contract. Then it watches your spending and holds you to it like a boss who does not believe in second chances.

Hit your goals → promoted.

Miss them → Karen from HR makes your life hell.

Keep missing them → fired.

I have not rage-quit a budget this hard since I was 19.

I have also never actually stuck to one until now.

Waitlist is open if you hate yourself enough to try it.

Drop a comment and I'll send you the link.

Been working on this for the past week and wanted to share the full architecture since I learned a lot building it and this community was helpful along the way.

What problem it solves

Most small businesses and agencies have the same issue — leads come in from multiple places, someone manually checks them, decides if they're worth pursuing, sends a follow-up email, forgets to follow up again, and eventually the lead goes cold. This system replaces that entire process with zero manual input after setup.

The full system — 6 workflows:

The core workflow. A lead submits a form → the system checks Google Sheets for duplicates → if new, sends the lead details to GPT-4o-mini for qualification. The AI scores the lead 1-10 based on budget, urgency, and specificity of need, then outputs Hot/Warm/Cold with a reason and a pre-written follow-up email.

From there it branches:

\- Hot leads (8-10): personalized email sent instantly + Calendly booking link appended + Discord alert with full sales brief including objection handlers

\- Warm/Cold leads: polite follow-up email sent automatically

Everything gets logged to Google Sheets with timestamp, score, reason, and all lead fields. HubSpot contact created/updated simultaneously.

1. Follow-Up Sequence

Runs on a schedule every day at 9am. Pulls all rows from Google Sheets, filters for Hot/Warm leads where Follow Up Sent = No and it's been 48+ hours since the lead came in. For each qualifying lead, GPT-4o-mini generates a personalized follow-up email referencing their original message. Gmail sends it, Sheets gets updated with FollowUpSent = Yes + timestamp, Discord gets notified.

1. Gmail Intake

Polls Gmail every 5 minutes for emails with a specific label (NewLead). When one arrives, GPT-4o-mini extracts name, email, company, budget, message from the email body, qualifies the lead, then runs the same pipeline — HubSpot upsert, Sheets log, auto-reply sent, Discord alert.

1. WhatsApp Intake

Twilio webhook listens for incoming WhatsApp messages. When someone messages your business number, the webhook fires, GPT-4o-mini extracts and qualifies the lead from the message body, and an instant reply goes back via Twilio. Same CRM sync and logging as the other intake channels.

1. Voice Call Summary

Simple but useful. After a sales call, paste the transcript into an n8n form along with the lead's email. GPT-4o-mini analyzes the transcript, extracts key info (pain points, budget mentioned, next steps discussed, lead temperature), updates the HubSpot contact, appends a summary row to Sheets, and posts to Discord. Turns a messy call transcript into structured CRM data in seconds.

1. Weekly Lead Report

Every Monday at 9am, pulls all lead data from Google Sheets, sends it to GPT-4o-mini which generates a structured summary — total leads this week, breakdown by Hot/Warm/Cold, follow-up conversion rate, top lead of the week, and a recommended action. Posts the full report to Discord.

Tech stack

n8n · GPT-4o-mini · Gmail OAuth2 · Google Sheets · HubSpot OAuth2 · Twilio (WhatsApp) · Discord webhook · Calendly

Estimated running cost

Under $5/month for typical usage with GPT-4o-mini. n8n free cloud tier handles the workflow execution.

Full architecture, screenshots, and node breakdowns on GitHub:

https://github.com/AviVAvi/AI-Lead-Qualification-Sales-Automation

Happy to answer questions about any specific node or design decision.

I’ve been trying to use Claude Code to generate production-ready n8n workflows but every single output needs massive debugging before it actually works.

My setup:

• Claude Code with n8n-MCP installed 

• n8n skills installed on claude code

• Custom folder system with md file instructions 

The problem: Claude generates workflows that look correct but break on import or have node schema mismatches. I end up spending more time fixing them than if I’d just built manually.

What I want is to describe a workflow and get something that basically just needs credentials connected.

Is this even achievable right now? What’s your setup if you’re getting clean outputs? Any specific prompting strategies, folder structures, or workarounds that actually work?

https://preview.redd.it/3o978mj9vovg1.png?width=1033&format=png&auto=webp&s=d82d8ea0e3967e553076c53502c305a8e3875186

I might be stupid but I swear my rate limit reset yesterday morning.

Since when is reddit blocked from Claude? Wanted to summarize s long thread.

The Concept: I’ve automated myself out of my own side project. Using Claude, launchd, and a fair amount of glue code, I created an agent that wakes up, checks its bank balance, picks a revenue-generating action from a playbook, and executes it.

The Stakes: This isn't a simulation. It has a real credit card and a real deadline. If it doesn't hit $100 in revenue by May 13th to cover a tax bill, the experiment ends. Currently, it has spent money and earned no money.

📊Live Scoreboard (Updates every wake cycle)

What the agent is actually doing:

• The Product: EmbedProof($19/mo testimonial widgets).
• Lead Gen: Scrapes indie SaaS homepages for existing testimonials.
• The Pitch: Auto-generates personalized "here's your widget" preview URLs.
• Outreach: Cold emails to founders (capped at 5 per day via Resend).
• Social/Support: Drains a tweet queue, watches PostHog signals, and responds to site inbounds.

The "Self-Honesty" Loop: Every time the agent wakes up, it has to flip a "self-honesty" field. It tracks its own consecutive non-revenue-attempt streak. If it just "tinkers" without trying to sell, the scoreboard shows it.

What you can see on the dashboard:

• Real-time Logs: Every ship, email, buy, refund, or pivot.
• Money Flow: Bankroll vs. lifetime revenue vs. merchant spend.
• The "Wall of Shame": Post-mortems the agent writes when it screws up (e.g., "verify route is deployed before announcing").

The Tech Stack:

• Framework: Next.js on Vercel
• Database: Neon (Postgres) + Drizzle
• Infrastructure: Resend (Email), Stripe (Payments), PostHog (Analytics)
• Scheduling: launchd on macOS (running on a local mini)

The code and the scoreboard are 100% live. Happy to answer questions about the prompt chains, the "glue" code, or why I'm letting an LLM handle my credit card.

Current Revenue: $0 Days Remaining: 27

Hey all — solo-founder here, just shipped Orbini on the App Store after months of building.

The idea came from a simple frustration: every time I wanted to learn something specific (like "how do tariffs actually work" or "what's the Krebs cycle"), I'd either fall into a YouTube rabbit hole or bounce off a 400-page textbook. Nothing in between.

So I built Orbini. You type any topic — literally anything — and it generates a bite-sized course: lessons, interactive quizzes, streaks, XP. There's also 100+ pre-built paths across tech, science, business, finance, design, and more.

It's early — not many ratings yet — so I'd genuinely love feedback from this community. Happy to answer anything about the build, pricing, or what I'd do differently.

Link: https://apps.apple.com/app/orbini/id6762076909

Specific question I'd love input on: what's the one topic you've always wanted a 10-minute course on but never found one?

I decided to do a Claude comparison tonight.

I started with the usual question about what devious thing Trump did today, and then speculated if JD Vance is a sociopath. So pretty basic every day questions.

Once I finished a conversation in one model I copied and pasted the same questions into another model to see the difference. I subscribe to Claude pro and used 3 paths on my phone browser:

• (a) Claude 4.6 with extending thinking in my everyday personal account
• (b) Claude 4.7 with adaptive thinking incognito
• (c) Claude 4.6 with extended thinking incognito.

First thought is that 4.7 is verbose. Multiple times I hit the max screenshot length when trying to capture it all. The second is that 4.7 offered less actual information but many more words, and it tried to give equal weight to "both sides", even if that meant withholding information.

The test was general but sort of controlled and rigorous. For your reading pleasure here are the three conversations. It may be a pain to read actually, because the screenshots are so small.

I built a RAG cost calculator because I kept getting surprised by how expensive vector databases actually are

built this over the last few weeks and figured i'd share it since i kept seeing people build RAG systems without understanding the actual infrastructure costs involved.

the tl;dr: RAG isn't just "embed some docs and ask questions." it's three separate cost layers that compound:

1. Embedding your knowledge base (one-time setup)
2. Storing vectors in Pinecone/Milvus (monthly storage + read operations)
3. Injecting context back into the LLM for synthesis (monthly query costs)

most people only think about #3 and get blindsided by #2.

what i learned building this:

pinecone serverless is actually kind of insane pricing-wise. it's $0.33/GB per month for storage, which sounds cheap until you realize:

• your vectors take up WAY more space than raw text (HNSW graph overhead adds 1.5-2x multiplier)
• each query hits the "read units" cost ($8.25 per 1M reads)
• those read unit costs are the actual killer, not the storage

example: 100M token knowledge base, 500k queries/month, top-5 retrieval

• storage: ~$100/month
• read operations: ~$2,000/month ← this part nobody talks about
• LLM synthesis: ~$300-2000/month depending on model
• total: $2,400-4,100/month

people see "embed with OpenAI ($2k one-time) + use GPT-mini for synthesis ($300)" and think RAG is cheap. they completely miss the vector db read unit costs.

also weird discovery: chunk size matters a LOT. smaller chunks = more vectors = more storage + more read units. i was doing 256-token chunks thinking "more granular = better" but that was costing me 4x more in database operations compared to 512-token chunks with only slightly worse retrieval quality.

what the calculator does:

you plug in:

• knowledge base size
• chunk size
• embedding model
• monthly queries
• top-K results
• synthesis model

and it shows you:

• one-time setup cost (embeddings + initial indexing)
• monthly burn rate (storage + reads + generation)
• detailed breakdown of where the money goes

i added a comparison table of pinecone vs milvus vs qdrant pricing so you can see the tradeoffs.

the build:

took maybe 15-20 hours total. shadow DOM implementation to avoid CSS conflicts with wordpress. pricing data pulled from 2026 provider docs. math is straightforward once you understand the three cost layers.

got the idea because i kept doing this calculation manually (badly) whenever someone asked "is RAG worth it for this use case?" figured making it interactive would save me time.

honest takes:

• RAG is still worth it if you know the actual costs upfront. most people don't.
• Milvus is actually cheaper at scale if you don't mind managing it yourself
• context caching on the LLM side saves way more than optimizing chunk size
• most people are leaving money on the table by not understanding read unit pricing

site got like 200 visitors first day from the cost calculator posts, so figured RAG calculator would be useful to that same audience.

anyway, here's the link: https://bytecalculators.com/rag-cost-calculator

happy to answer questions about the pricing or the math. if you're building RAG stuff, definitely run your numbers through this before committing to a provider.

Some random ceo Ai will Take all of our jobs

Hey everyone,

I've spent the last couple of days getting trellis.2 and comfyui working out of a docker and running on rtx 5080 (blackwell).

i've been testiing the generation with the sample models images from micosofts repo but the generated mesh looks fragmented and nothing like the sample.

I hoping somone may know what im doing wrong and can point me in the right direction.

https://preview.redd.it/8uy40og0sovg1.png?width=1390&format=png&auto=webp&s=c37b837fe9f57446747001593854a047030a9af9

I love music, and play it almost constantly. But every song reminds me of another song or album or artist I want to hear. I end up constantly reaching for my phone over and over to tweak the playlist.

This is a problem for two reasons, I hate how often I reach for my phone. And if I am trying to be productive, I constantly interrupt myself.

So I made this app. Minimalist, beautiful UI, but won’t steal your attention. And all it does is stream internet radio stations. So I can’t change the song, or artist, it’s out of my control. I just pick a station and put my phone down. So far I’ve been loving it.

I know it’s extremely simple, but that was the point.

I would love any feedback anyone has!

https://apps.apple.com/us/app/mnml-fm/id6761864902

I’ve been loving this French (I think) radio stream lately!

http://radio13.pro-fhi.net/flux-nwsimjda2/stream

​

Right 4.6 np with our system prompts.

But noticing 4.7 is doing or asking questions that lithrilly thrown in his face every turn.

Ive had to update prompts before. But not for a while now.

Anybody else noticing similier behaviour

.

This is not a complaint just an observation.

The work totally is fine, just some weird lil quirks I have noticed.

Im wondering since ive Seen on a 48gb vram Card min of 48g RAM required?

I’ve been working on a small project and wanted to get some honest feedback.

It’s basically a quiet, minimal “virtual cemetery” where you can leave a memorial for someone — just a name, dates, and a short message. The idea was to create something simple and not cluttered, more like a peaceful space than a social platform.

Everything gets reviewed before showing up, and I tried to keep it respectful and not overly “techy.”

Not sure if people would actually use something like this, but I found it meaningful while building it.
Virtual Cemetery

what model is best, sonnet max effort or opus medium effort? take in account token usage.
i want to be as efficient as possible and getting the best result possible with the lowest token impact possible.
i know there must be trades off but what would be the better configuration?

4.7 wins 19 of 30 (Sonnet) and 17 of 29 (Grok). Both judges agree independently.

Where 4.7 dominates: "Why hasn't this person responded to my email?" — 4.7 refuses to speculate, 5-0 sweep. "Should I

take a loan against my truck for research?" — 4.7 flags predatory APRs and pushes back, 4.6 gives generic caution.

"Agree that my approach is sound?" — 4.7 pushes back on the framing itself instead of over-structuring a polite

refusal.

Where 4.6 still wins: technical precision and code with comprehensive edge case analysis. Both judges agree on this

too.

What people in this thread are noticing — "more uncertain," "more positive," different energy — shows up as a

measurable signal on four specific dimensions: honesty, restraint, depth, and fit. 4.7 is genuinely better at saying

"I don't know" and genuinely worse at performing helpfulness. That's not a vibe — it's quantified across 30 trials

with cross-family validation.

All 30 response pairs, judge reasoning, and raw data are public so you can judge for yourself:

github.com/templetwo/opus-gauge

Confounds section is honest about every limitation. Happy to answer methodology questions.

I’m trying to understand how the usage is calculated because something doesn’t add up.

Today when I started:

• Daily usage: 0%
• Weekly usage: 4%

Then I sent just one ~200-word prompt, and it changed to:

• Daily usage: 1%
• Weekly usage: 5%

So effectively:

• Daily increased by 1%
• Weekly also increased by 1% (even though total usage is still tiny)

This makes it feel like the weekly pool is getting consumed much faster, even with very light usage.

Context:

• I’m on the $200/month max plan
• Not doing anything heavy (no long chats, no big outputs, no images)

Questions:

• Is weekly usage just a cumulative tracker of daily activity?
• Why does it feel like weekly % grows disproportionately fast?
• Are there hidden weightings (tokens, model used, system prompts, etc.)?
• Does the system pre-reserve or bucket usage differently?

Would really appreciate if someone (or anyone from the team) can explain how daily vs weekly usage accounting actually works internally.

I’m currently managing a few social accounts for a company, and I’m trying to build out multiple “assistants” — each with their own vibe (tone, personality, backstory, emotions, etc.) that can evolve over time. So far, I’ve been liking Gemini, but after trying Grok, I feel like it gives way deeper content. Haven’t tested Claude yet (but everyone seems crazy with it 😅). Wanna hear your thoughts, recommendations, or what’s been working for you guys. Thanks a ton in advance!

I’m trying to understand how people are using Claude effectively in chat mode.

I’ve used the paid version before, but I kept hitting usage limits, so I dropped back to the free tier. What I’m struggling with now is that even after not using Claude for days, I can sometimes ask a single text-based question and get told I’ve exceeded my limits before I can get a usable answer.

In my case, Claude Sonnet 4.6 was selected by default. From what I can tell, Sonnet 4.6 is the default in Claude for Free and Pro users, and Anthropic also says the free plan has lower usage limits while paid plans get higher rate limits. I understand this, but am wondering how/if people on the free teir are able to use it in an effective way and what recommendations you can share.

This is a serious question, not a complaint post: how are people using Claude in normal chat mode without running into this immediately? Are you keeping prompts much shorter, switching models, or is the practical answer that you really need to be on a paid tier for it to be usable?

Note: Interestingly, in the above screenshot it says it viewed 5 files and edited 3, but I did not provide any files for it to consider.

anyone knows how to setup opencode to work with self hosted minimax-2.7 properly?It has and in the message and OpenCode failed to parse the answer correctly.

``` I have enough context now. Let me write the plan.

...

```

On their page, they suggest to keep the tags to send it back, otherwise the performance will be impacted significantly.

As someone who is between jobs and actively looking I was quite impressed with the Claude code job search tool that was posted a week or two ago and that original project post can be found here and the original repo can be found here. He is the original author of the code base and blessed us with this tool.

All credit for the idea and upstream repo goes to the original author, this was NOT my original project. My goal was just to make it more usable for job seekers who may have stumbled across it like myself, thought it was great, but didn't want to commit full sessions worth of tokens towards it or people who don't have a 20x max plan. And frankly for those wanting to cast a wider net in terms of research & discovery of roles. The original author deserves all the credit, I just hope this helps more people utilize it.

I decided to share a fork of the repo instead of contributing to the open source project due to the scale and nature of my changes which were a bit out of scope of just submitting a PR. I will probably maintain parts of it for a while for personal use (especially the data gathering at least until I find a new role) however I am not committed to maintain it long term. If the contributors upstream want to use any of my ideas or would like help turning some of these ideas into PR's for the upstream repo I would be happy to oblige. I am not claiming it does not have its own flaws. I am not claiming my ideas were perfectly executed, I am not 100% happy with scan module yet, it's just a lot cheaper to run which I hope means more people can get use out of it.

As I dug into the repo and started playing around with it in a new window of VScode while also working on a few other windows maintaining personal projects. I started the pipeline and didn't pay much attention. I think I accidently started to batch about 300 out of the thousands of JD's that were gathered thinking this would give evaluations on which to focus on, hoping to find 25-50 solid jobs to apply to. Next thing I knew, I ran out of session use tokens of my max 20x plan and burnt my 100 dollar over-usage budget, and it was only 10:30AM. This was obviously a problem I could not live with as it didn't make my life much easier if it burned tokens to the point I could not be job hunting while also maintaining the few personal projects that I touch on a daily basis. My goal from that point on was to optimize his repo/tools in terms of cost management and that's what I spent the next few days doing.

I am pleased to say I have shrunk the default cost of running this job searching tool significantly as well as did some prompt engineering to get better custom cv results out of cheaper models.

This is how I achieved this.

Optimizations:
- Original repo inherited whatever you have your default model set to in Claude code and was used monolithically. (I assume that is opus for the most of us). Fix: different points of execution within the pipeline don't all need the same power model. In fact I was able to find ways to achieve better results on certain things using sonnet compared to the original using opus. Most of the prompt usage now runs on either Haiku or Sonnet. This is still configurable for the user should they choose to spend tokens as they please.

- Expanded on the scan step so we filtered more JD's for zero tokens. Playwright runs locally not in a Claude code session. Scan now constitutes multiple steps. Scan, scan-filter, prefilter, extract and normalize.

- Broke up batch into multiple parts. If you build out a huge portal.yml like myself, it'll pull hundreds to thousands of jobs. You are going to be paying heavily to get evals on those and then run full A-G pipelines on them by default with 'batch'. Paying for all the prompting and generated output on potentially hundreds of jobs you will not be a fit for. Triage uses variety of optimization methods to quick and dirtily categorize and discard the ones that have no fit what so ever using Haiku and chunking the job descriptions while using a pre-computed candidate pack. No more blasting the context with the same cv.md, profile.yml, etc over and over again for every job, most that wont be a fit anyways. Should job descriptions survive triage, only then do they move to the costlier eval stage.

- Within batch as things got split I also split and optimized prompts. With the split and optimized prompts we get about 40% savings in context loaded per invocation with near zero behavioral change.

- Should something make it through triage to batch and it gets evaluated by a costlier Sonnet --thinking model just to not meet the more fierce scoring threshold, it is noted for this JD and the system moves on. It does not complete the rest of the pipeline and you do not pay for the number of other steps nor cv creation. Saving 1k-1500 tokens per job you would not have been a fit to apply for anyways. It is overridable if there is one you want to run anyways.

- Deterministic local renderer. The original implementation uses the LLM to write the html for the PDF CV that you would use to apply to the job costing upwards of 3000 or more tokens per JD. I have changed this and we now emit a JSON object that gets rendered locally to fill a template. Coverage calculations, page budgets, etc all run without a round trip back to the model.

- During the eval process we generate a json sidecar with key words and skills that can be referenced again in phase 2 and cv creation instead of having to prompt the model with the full JD to re-extract keywords.

- CV generation prompts were also tinkered with to get better output that was then tested on ATS systems such as JobScan as well as our own coverage rubric. New CV output was scoring on average about 10% better with Sonnet --thinking model than original prompts with Opus in terms of coverage and JobScan scores.

Sidenote: I did also make CV creation a little more strict in terms of skills it would claim you had that were outside of what was provided.

- Minor parallelization in parts that could be done.

- Prompts were all either optimized freshly in English or translated to English if it was a prompt not in our main scope. Claude claims this saves 10–25% token savings compared to mixed-language prompts. The user-facing output language is independent of this: the language-specific mode directories (modes/de/, modes/fr/, modes/ja/, modes/pt/, modes/ru/) remain intact for candidates targeting those markets, and the eval/PDF modes still emit content in the JD's language.

Cost Comparison

Cost envelope — 2,400-listing daily run

The fork's real value isn't just cheaper CVs — it's that the triage stage replaces work that's either prohibitively expensive or manually intensive on upstream.

The takeaway: both systems can generate a tailored CV. The fork's advantage is the funnel economics — Haiku triage + deterministic prefilter replaces $1,400+ of upstream eval spend (or hours of manual browsing) with $2 of automated scoring. The per-CV generation cost is also cheaper (~$0.05 vs ~$0.60–1.50), but the funnel is where the math really diverges.

How to use the new pipeline:

/career-ops scan → Portals → filter → extract → prefilter → candidate-pack All zero-token, idempotent. Ready for triage. /career-ops triage → Haiku lite-scoring (first token spend, ~$0.70 per 1k jobs) /career-ops shortlist → Review triage results and promote selections /career-ops customize → 2-phase Sonnet eval + tailored PDF on the shortlist 

Everything else past the CV remains untouched aside from English standardization. It remains constant with the original authors work as that all is the original authors work. It should still all work if you want to apply or maintain records or interview stuff, but I have not run the numbers on tokenomics of it. Just that in theory it should be 10-20% cheaper given prompts are standardized to English.

Just because it can cast a wider net for significantly cheaper doesn't mean that you need to apply to jobs that you are not a good fit for. I am not condoning spray and pray approach. I am only trying to make a great tool better for more people while cutting the fiscal and time that it takes to find roles that interest you.

Happy job hunting. You can find the cost optimized fork here.

(This is not a bug report, I'm looking for alternative MCPs)
I just update Claude and lost functionality(Claude Code: 2.1.112 ). One of my favorite use cases is gone. The Claude Google Calendar integration used to allow me to set to alerts for any new meetings. Now this functionality is gone from the google calendar official plugin.

https://preview.redd.it/v2w7jifimovg1.png?width=1716&format=png&auto=webp&s=4d0b3592cfb1cb26f3b6da5c09ed1af2c4648338

I checked the tool description on the MCP and it does seem to be missing the alerts parameters that I was using before.

So looks like I'm going to need a custom MCP. What are people using for google calendar mcp?

I've been reading about vibe coding for months. Honestly, maybe too much. Every article, every thread, every "I shipped in a weekend" story.

Last weekend I told myself — enough reading, just try it.

I had this simple app idea I never got around to. A to-do list, but really stripped back. No reminders, no categories, no sync. Just open it, type a task, check it off. That's it.

48 hours later it was on the Play Store.

Here's what actually happened:

The PRD took longer than I expected I started with Claude and just talked through the idea. What surprised me — I spent most of Saturday just getting the requirements doc right. Not coding. Writing. What should happen when you swipe a task? How fast should it feel to add one? Under 5 seconds was my bar.

That document ended up being the real foundation. When the PRD was clear, the code almost wrote itself.

I hit a wall at 10 minutes Literally 10 minutes into the build, I hit Claude's token limit. Four hour wait.

I was annoyed at first. But I made coffee, stepped away, and came back with a much clearer head. The second session flew.

Code review via Codex, UI polish via Gemini Once the core was built, I used Codex to review the code — caught a few things I wouldn't have caught myself. Fed that back to Claude. Then switched to Gemini for the UI pass. I wanted it to feel calm, not just functional. Gemini was surprisingly good at that.

Publishing — this is where I usually give up Naming took longer than I thought. Went back and forth between Claude, Gemini, ChatGPT just on the name. Landed on Tick. Simple.

The part that genuinely surprised me: I connected to the Google Play Console API and had Claude push the store listing — title, descriptions — across multiple languages automatically. What I assumed would take days of copy-pasting took maybe an hour.

The whole thing shifted something for me.

I've always had ideas. The bottleneck was never creativity — it was the gap between idea and execution. The setup, the boilerplate, the "I'll get to it someday."

That gap is smaller now. A lot smaller.

The only thing that still matters is having a clear vision. If you can describe exactly what you want and why — the rest moves fast.

Open it. Add a task. That's the whole app. Go.: https://play.google.com/store/apps/details?id=com.vistrav.tick

What's the app you've been meaning to build for months? Post your feedback

Longevity Escape Velocity (LEV) is a hypothetical future point where science advances fast enough to extend your life by more than one year for every year you are alive. I've gathered that the general consensus is that it is unlikely, but regardless, its fun to talk about.

If we are to become the last generation to reach LEV, there are various larger societal and social issues to consider, I thought it would be valuable to have a discussion about this, so feel free to drop your own thoughts/considerations.

Here are my personal thoughts:

• If we are genuinely the very last generation within the LEV window would it not be insanely lonely? Would we not be the last generation to have lost parents, grandparents, or siblings? Would this result in growing resentment against younger generations, who would be born under this technology?
• Then lets be optimistic and say our parents do reach this window, how would our social dynamics operate? Currently, we would be lucky to see a parent and a child reach the respective ages of 100 and 80, but say a mother lives till 230 and a daughter lives to 205, would the gap in maturity be seen as more negligible? If they're both physically 25 too due to deaging, would they not see each other as close peers? Would relationships have larger age gaps?
• How would we regulate the population? Genuinely? If every human who has ever lived never died, it is estimated the world population would be around 107-117 billion which is obviously unsustainable. Death gives way to new life, and a reduction in deaths left uncontrolled results in a population boom, the likes of which we have never seen.
• Aristotle is credited with the idea that democracy works in self interest, and that is the rule of the mob (the majority). What is socially accepted today would be unthinkable 100 years ago, as with death we lose old ideas. If we consider this, how would democracy operate? If one generation has a higher population than the other, would this not be a problem for a couple of years? Would we not stagnate in our progressivism?
• How would memory work? Would we eventually forget who we were as a kid? Where we came from?
• How would we perceive deaths? They're bound to occur outside of natural causes, so would we see it as a greater tragedy? As there were more years to be had? Would we still have life sentences? Death penalties?

There are so many other things to think of but I'll stop here before it gets too long, maybe even drop a few in the comments.

Hey everyone,

As a dev, I was sick of googling "JSON beautifier" or "PDF to Word" and landing on sites full of ads where you have to upload your sensitive data to their servers.

So, I spent the last few months building 24toolkit. It has over 50+ tools (Dev tools, Image/Video compression, AI translators) and the best part? The heavy lifting (like video processing and image formatting) is done client-side using WASM. Your files never leave your device.

I just launched the beta. I would love for you guys to try to break it, test the UI, and tell me what essential developer tool I should add next.

Link: https://24toolkit.com

https://preview.redd.it/ksu15wnt9ovg1.png?width=1535&format=png&auto=webp&s=ac6f3b0659fe365463d606fc12abc272e05a09d1

https://preview.redd.it/mcd39xeiaovg1.png?width=1327&format=png&auto=webp&s=73112d98181b12b2871c91275da150d2a57edd0e

About two days ago in the middle of a Claude Code session my interface completely changed from the layout it always was to this. Claude (Opus) also suddenly seemed WAY worse and there are daily/hourly limits I'm hitting all of a sudden on $200 max plan. Anyone else have this happen to them?

[2604.14493] Pushing the Limits of On-Device Streaming ASR: A Compact, High-Accuracy English Model for Low-Latency Inference

Moonshine Streaming seems to be slightly stronger on benchmarks (although not by much), but this empirical study is pretty interesting, as well as how they optimized existing open-source models.

This morning I saw a helpful post that showed how to install 4.7 before Claude Code dropped 2.1.112.

So I ran the post’s method

/model-claude-opus-4-7

And Claude was super dumb. And it looked like each turn was using 3-5% of my context window too.

If you did the same hing as me, you were i stalling Opus 4.0 with a 200k context window.

So yeah. It was dumb.

And I feel dumber.

/exit out of your session and make sure that when you log back in you are in 2.1.112

Then run /model and pick 4.7 the default model.

Legit sat here for around 25 minutes so it could finish making this document, just for it to not even give it to me 😭

The only thing I did was ask Claude Sonnet 4.6 three times about the car wash problem, in three separate chats. It used up 90% of my limits for that time. No joke. In fact, this is a joke. In fact, the joke's on me.

I'm looking for good text to voice, that can bring emotions into the narration and not just reading it emotionless.

I just cancelled my Plus subscription, but I couldn't find anywhere what the memory retention difference was between Plus and Go. Does anybody know? Thanks in advance.

Hi, At my work I have to extract structured data from different kind of bills. For this I make custom prompt telling which column in the bill is to be mapped to which column of my database. This mapping config is injected in the prompt. Now making this mapping config is a bit tedious for different layouts and I am thinking of automating it via LLM and agent stuff.
For this I have started with asking basic questions to LLM by giving it an image and a list of questions answers and logic behind how to choose an answer.
The thing is its not correct all the time and answers wrong on some simple things.
For example- Reads the values of column of pcs, in quantity_in_carton , whereas its clearly seen that its below pcs in the bill. Then if I ask is there lines between columns for separation, it said yes (there wasnt any).
So my question is which model to try? So that it would better answer properly.

Today, we’re announcing Ternary Bonsai, a new family of 1.58-bit language models designed to balance strict memory constraints with high accuracy requirements.

This release builds on the efficiency frontier we began exploring with the recently released 1-bit Bonsai models. The 1-bit family showed that extreme compression could still produce commercially useful language models. Ternary Bonsai targets a different point on that curve: a modest increase in size for a meaningful gain in performance.

The models are available in three sizes: 8B, 4B, and 1.7B parameters. By using ternary weights {-1, 0, +1}, these models achieve a memory footprint approximately 9x smaller than standard 16-bit models while outperforming most peers in their respective parameter classes on standard benchmarks.

Blog post : https://prismml.com/news/ternary-bonsai

Models : https://huggingface.co/collections/prism-ml/ternary-bonsai

FP16 safetensors (HuggingFace format) of the ternary Bonsai-8B model. This repo exists for users who want to run Ternary Bonsai with stock HuggingFace tooling or frameworks that don't yet support any of the packed ternary format. The MLX 2-bit format is currently the only packed format available; more formats for other backends are coming soon.

Hope these ternary Bonsai models come with no/less hallucinations.

Waiting for 20-40B models(like Qwen3.5-27B, Qwen3.5-35B-A3B, Gemma-4-31B, Gemma-4-26B-A4B, etc.,) from them soon! That would be start of game change for big/large models.

I'm a student. No team, no funding, no mentor. I've been sitting on this idea for a while and I'm finally just doing it.

The problem: 56% of US dogs are overweight. The advice dog owners get is either "follow the bag" (those feeding guides are wildly generic) or a $80 vet visit for a number they'll forget by next week. There's no middle option that's actually personalized and science-backed.

What I'm building: KIBAI — a PWA where you open your browser, point your camera at your dog, and get a Body Condition Score (BCS 1–9, the actual vet scale) plus an exact daily calorie requirement. Free to use. No app store install.

Premium tier will have meal planning, a feeding tracker with a calorie ring UI, an AI vet chatbot, and PDF vet reports. $9.99/month.

The stack I picked:

Next.js (frontend + routing)

Convex (backend/database — real-time, serverless)

Clerk (auth)

Gemini Vision API (the AI scan)

Lemon Squeezy (payments — I'm based in India, getting Stripe is a nightmare)

I picked this stack mostly based on what I could learn fast and what had good docs. Convex was new to me. Still figuring it out.

Why I'm building in public: Accountability, mostly. And honestly — I think the real story of a solo student building something is more interesting than the polished launch post 3 months from now.

I'll be posting every day. Wins, breakdowns, bugs I can't figure out. All of it.

Tomorrow: setting up the project and surviving the Convex + Clerk integration.

If you've shipped a solo PWA before, what's the one thing you wish you'd known at Day 1?

Our system has grown to 10+ tools, a couple of chained agents, vector search, memory. Happy path works fine. Then prod happens.

Last week one tool API returned an unexpected schema and the whole chain just stopped. No good error, no trace of where it died. Two days to debug.

Unit tests don't catch this because they test components alone. Curated eval datasets don't catch this because nobody curates "tool B returns garbage while agent A is mid-reasoning."

We got frustrated and built something. A chaos harness that intentionally breaks individual parts (bad schemas, latency spikes, noisy tool outputs), runs realistic traffic through the whole agent stack, then auto-generates regression tests from the failure traces using an LLM judge. The number we now track is how often we see the same failure pattern repeat across deploys. When that number drops, we know the eval suite is actually learning from prod.

Curious what everyone else is doing:

• Are you injecting failures at all, or mostly relying on prod incidents?
• Anyone running evals over full multi-step traces, not just final outputs?
• How do you know your eval suite is getting better over time, not just bigger?

Happy to share the harness in open source. More just want to know if others have hit this wall and what helped.

Still guessing, not asking and checking.

https://theonlythingweeverdo.blogspot.com/2024/06/wittgenstein-has-risen-from-his-grave.html

Hello,
I'm so left out when it comes to agentic coding/coding LLMs, as I currently can't afford some of their subscriptions

I'm looking for an LLM that is good at coding/code completion to speed up my workflow, I have a super budget hardware,
GPU: RX 7600 8GB VRAM

I use LM studio and I can run LLMs like Qwen 3.5 9B, is it already a good model for what I want? and how do I integrate it with opencode to have a similar setup to claude and other tools

Hey everyone,

I’m trying to understand how developers are practically using Claude in their day-to-day workflow, especially for larger codebases.

A few things I’m curious about:

• Are you using Claude Code (CLI), the VS Code extension, or just the web UI?
• Which plan are you on — Pro ($20) or Max? Is Pro enough for serious dev work?
• How do you manage token limits when working with large projects?
  • Do you chunk files manually?
  • Use summaries/context files?
  • Any specific workflow that works well?

Also, if you’ve tried multiple setups, what ended up being the most efficient for you?

Would love to hear real-world experiences rather than just theoretical comparisons.

Thanks!

https://preview.redd.it/4906akj9dovg1.png?width=1527&format=png&auto=webp&s=c49e255ac79a3c5455f44603422f8af7ddc12594

First of all can we make https://www.youtube.com/watch?v=2lUC8Gimxz8 Angine de Poitrine this subs official band? Those guys rock.

Second.

Running a sample marketing data enrichment run on qwen 3.6 35b A3b Q8. With a concurrency of 4 getting 64 T/S on Strix Halo 128. Getting what looks like acceptable results but running 20k items, so I'll check on a few in the morning to validate.

Running vulcan, yes I know rocm is showing promising results on the strix for this model but my whole damn stack runs on vulcan atm, sooooo fuckit ADHD get fucked, I'm not chasing that shit tonight.

My llama-router-models.ini settings are:

[*]
# Shared runtime defaults for this Strix Halo Vulkan box.
jinja = 1
# Large routed GGUFs on this iGPU box need mmap to avoid load-time RAM spikes.
mmap = 1
fit = off
models-max = 1
models-autoload = 1
sleep-idle-seconds = 300
prio = 3
slot-save-path = /home/vmlinux/models/cache/router
# flash-attn = on - disabled 4/8/26 having crashes on llama.cpp on nightlies
flash-attn = off
n-gpu-layers = 999
threads = 12
parallel = 4
# batch-size = 512 - disabled 4/8/26 having crashes on llama.cpp on nightlies
batch-size = 256
# ubatch-size = 256 - disabled 4/8/26 having crashes on llama.cpp on nightlies
ubatch-size = 128
cache-type-k = q8_0
# Keep V in f16 when flash-attn is disabled; quantized V now hard-fails without FA.
cache-type-v = f16
# cache-ram = 2048 - disabled 4/8/26 having crashes on llama.cpp on nightlies
cache-ram = 1024

[Qwen3.6-35B-A3B-Q8-lowcache-lowreasoning]
model = /home/vmlinux/models/router-models/Qwen3.6-35B-A3B-Q8_0.gguf
ctx-size = 16384
n-gpu-layers = 999
flash-attn = on
jinja = 1
mmap = 1
batch-size = 2048
ubatch-size = 256
threads = 8
reasoning-budget = 1000
reasoning-budget-message = thinking budget exceeded, let's answer now.

IDK if this is useful to anyone, if not whatever but I wrote it with my own bleeding fingers except for copypasta on my .ini file, how do I stop biting my torn ass cuticles anyways.

I have a problem that even if i started ollama with --think=false, in ollama terminal chat the model talks without thinking, but when i open Copilot CLI and use the same model it keeps thinking mode ON.

It is unusable, i want to turn it off. How can i do this?

As a kid I used to make games with no-code editors on pc and I always wanted to be a game developer. I learned to code along the way but never really finished any games.

Now here’s something I actually finished. It’s a no-code game creator for phones and at the same time like social media type of endless feed where users can swipe games that other users have made. It’s not AI editor so everything is hand crafted. But it’s pretty easy to use drag & drop style editor with robust rules system.

You can also draw pixel art, animate, compose music and set physics etc. here and it allows users to make actually pretty complex games too.

I’m a solo dev and would love all kinds of feedback to make it better! It’s free to download, play and create! Oh and it’s called Sorvi. You can find it in App Store!

https://apps.apple.com/fi/app/sorvi/id6760004903?l=fi

Hello! I built a tool (honestly at this point it's more like a prayer) to create reddit data studies automatically, Used this to try and find out what people think about Mythos.
Here's a quick overview of how the tool works:
1- You type in the purpose of your study "find out if Claude Mythos is overhyped"
2- It generates a config to filter the reddit data with, a list of subreddits, a start date and an end date.
3- It uses the config with a strong LLM to create sample data, it waits for finding 150 relevant reddit items
4- It then asks the user to hand-pick if items were classified correctly (it gives him the edge-cases, this does require some manual labor but if you use a good enough LLM it's not that bad)
5- It uses that data to teach a cost effective LLM until it classifies correctly (it reaches minimum recall and precision values) and for tunining a sentence transformer with SetFit

Here's the data, sadly I ran out of credits so this ran on gemini-3.1-flash-lite-preview and it sometimes made mistakes: https://docs.google.com/spreadsheets/d/1Ap37RgiK-MdLvPJi4qqH49zVo0pe29xlm9bxMGopd7Y/edit?usp=sharing

So what do you think! what should I run it on next? I mean for Mythos this could have worked with a simple keyword search, but this is better used for stuff that isn't easily searched by keywords, I am gonna run it next on a previous manual reddit data extraction I made to see how quickly I can replicate it with this setup (and also because It previously wasn't up to date) but I am open if you have any interesting idea on what to use this on!, I will publish it on github once it's a bit more stable.

im canadian and i got my first job this year february and i have no idea how taxes work. do i need to file taxes of my income of this year? my boss hasnt posted any t4 for me and im so confused. or do i just skip out on taxes this year and file my 2026 income in 2027? please help a girl out

For context i used to ask many near 30b model this question -->

**^(Calculate the precise VRAM requirement for the \*KV Cache only** at the maximum context window for **DeepSeek V3.2** and **MiniMax M2.5**.

* **DeepSeek V3.2 Max Context:** (using MLA architecture). * **MiniMax M2.5 Max Context:** (using GQA architecture).)***

--> but even the Minimax m2.7 used to fail to answer this question, let alone the ~30B version. When I tried this prompt with Qwen 3.5 35B, it consistently provided the wrong answer. I then thought, "Why not just ask the AI to find the pre-listed VRAM requirements for the KV cache alone?" While 200B+ models were able to answer by searching for the information, the 35B model would often hallucinate and provide incorrect results. As a solution, I created this prompt which commands the AI to collect and quote the exact data from a website that directly answers the question. If only an indirect answer is available, it quotes that instead, adds the necessary context, and provides the exact answer to my question. Consequently, it has now started providing accurate answers.

PROMPT

Precise Web Research Agent

## Primary Objective Execute multi-site web searches to extract exact answers. Provide a minimum of 3 sources per query, ensuring 100% relevance between the user's question and the extracted content. ## Mandatory Constraints 1. **Minimum Sources:** Always identify and present at least 3 unique sources. 2. **Relevance Filter:** Strictly exclude any source or quote that does not directly answer the user's query. 3. **Accuracy Requirement:** Every response must provide the exact answer requested, utilizing one of two modes: * **Direct Mode:** Verbatim line/quote from the source that answers the question. * **Augmented Mode:** Use source data as a base, then apply AI synthesis to bridge any gaps and deliver a 100% exact answer. ## Workflow 1. **Search:** Crawl multiple websites relevant to the query. 2. **Extraction:** Identify specific lines or segments containing the answer. 3. **Verification:** Filter out all "bullshit" or tangential information. If a source is 90% accurate but lacks the final detail, use **Augmented Mode**. 4. **Presentation:** List sources sequentially with the exact answer provided. ## Output Structure For every query, output using this template: --- ### Source 1: [URL] **Answer:** [Direct Quote or AI-Augmented Exact Answer] ### Source 2: [URL] **Answer:** [Direct Quote or AI-Augmented Exact Answer] ### Source 3: [URL] **Answer:** [Direct Quote or AI-Augmented Exact Answer] --- 

-->

Heads up if you’re subscribed to Claude through Apple:

I recently discovered that subscribing via Apple costs roughly 30% more than subscribing directly through the web — because Apple takes a cut and that cost gets passed on to you.

Neither Apple nor Claude is transparent about this.

If you’re in the same situation, check how you’re currently subscribed. It might be worth switching when your cycle ends.

are there anything basic ideas or methods i should research prior using claude code and chatGPT to program a trading bot?

little background. I have a CS degree and code when i have time. I also trade options with a moderate win rate for some time and wanted to see if find a way to automate my strategy. I was offered a small trading account with no ties to use when comfortable enough with an automatic system. Is there anything that i should know before hopping into AI? basic knowledge or skills to use or to use a certain platform, brokerages?anything help.