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Curious how other builders are handling AI agent cost tracking and observability.

The pain points I keep hitting are:

• hidden token spend.
• retries and loops.
• poor visibility into which workflow is expensive.
• no clean per-user or per-agent cost breakdown.

Would love to hear what people use for logs, traces, budgets, and cost monitoring.

I'm wary this question has been asked before, but things are moving so quickly, it's hard to keep up.

I was using CC in VS Code for 9 months+ and was very happy with it. I then decided to try using the CC Desktop app which I'm using currently.

However, I find the quality of output to be significantly worse than using CC in VS Code. Am I missing something? Or doing something wrong? Should I just go back to using CC in VS Code?

And yes, I am a "vibe" coder (experienced, if you allow me to say so!) with web and ios apps serving well over 10,000 users.

I have a long pitch deck, that I am thinking about using AI to improve and continously add to. I am thinking about using the Claude for Powerpoint add-in to do this, as is should be able to work effectively in powerpoint. The ability to do things such as select a specific set of elements and ask it to only work on those and things like that seems effective to me.
However, as the pitch is continuously evolving, the lack of claude.md file and persistent seems ineffective. I was thinking about implementing some of the following processes, and want to hear if anyone else has experience with using Claude for Powerpoint this way?

*A hidden last slide called "claude.md" as well as a custom instruction in the instruction setting to always read that slide at the start of any conversation.
*Custom skills as hidden slides, so I do not have to add powerpoint specific skills to my global skills?
*An extra section called .Claude, that can have things like plans and similar, just like I use in Claude Code for much of my other work.

Please also, do not hesitate to give any other advice on using the Add-in, or alternatives to it!

Hover your tray icon → see your Claude session %, weekly quota, and monthly spend. Windows, open-source, MIT, no telemetry. One .exe to run it, no Python or Node needed.

👉 https://github.com/Philip8891/claude-pulse

Why

I'm on Max 5x and code with Claude all day. For a solid month my workflow was: write a prompt → Alt+Tab to claude.ai → Settings → Usage → squint at the number → Alt+Tab back → try to remember what I was doing. Every 20 minutes. The checking was burning more focus than the work.

Looked at existing tools (linked in the repo's Credits, real respect to them) — browser extensions, CLI tools, other tray widgets. Each close, none exactly what I wanted. So I built the one I actually wanted.

What it does

• Live donut: session (5h) / weekly all / Sonnet / Design / monthly €
• Time-to-100% prediction based on your current burn rate
• Windows toasts at 75/90/95% and on session reset
• 7-day history graph, multi-profile, compact mode, 5 themes × light/dark
• One-click login — opens claude.ai in a window, captures the session automatically. No F12, no cookie copy-paste. Session expires in 30 days? Click the banner, login again.

How it's built

Three processes, one job each:

• Electron — tray, popup, shortcuts, notifications
• Python proxy on localhost:8787 — owns the sessionKey, polls /api/organizations/{orgId}/usage every 60s, caches
• Single widget.html — all the UI in one 45KB file. No React, no build step

Everything local. sessionKey never leaves your machine.

Built with Claude

One prompt that unlocked more than the others:

"Write a decision log (ADR-style) for every non-obvious architectural choice. Include the alternatives you rejected and why they lost. Future me will thank current me."

DECISIONS.md in the repo is directly that output — 12 ADRs, each explaining a trade-off I won't re-debate with myself in six months. Best prompt I've written all month.

Known rough edges

• Unsigned .exe, so Windows SmartScreen will pout. "More info → Run anyway", or build from source.
• Windows only. Tauri/macOS port is on the v2 list.
• seven_day_omelette in the Claude API response is not a typo. That's genuinely what Anthropic calls Claude Design internally. I laughed.

Feedback wanted

• Themes worth adding
• Anyone on Free/Pro tier willing to sanity-check the response parsing — I only tested on Max

Repo: https://github.com/Philip8891/claude-pulse Release (installer + portable): https://github.com/Philip8891/claude-pulse/releases/latest

MIT.

I finally cancelled my Claude Pro subscription today.

Not because Claude is bad.
Because the usage limits are ridiculous for coding.

While looking for alternatives I found something interesting: GLM Coding Plan from Z.ai.

And honestly… it feels like Claude Pro but with way more usage.

Here’s the weird part.

Lite plan (~$10/month) reportedly gives around 3× the usable capacity of Claude Pro depending on the workflow.

And it works with the same tools devs already use:

• Claude Code
• Cursor
• Cline
• OpenCode
• ~20+ coding tools

So you basically swap the endpoint + API key and keep the same workflow.

Models included

The plan includes several coding-focused models:

• GLM-5.1
• GLM-5-Turbo
• GLM-4.7
• GLM-4.5-Air

GLM-5.1 is their latest model with a ~200k context window and strong reasoning for agentic coding tasks.

The usage is the crazy part

Typical limits look like this:

Lite plan

• ~80 prompts every 5 hours
• ~400 prompts weekly

Pro

• ~400 prompts every 5 hours

Max

• ~1600 prompts every 5 hours

And each “prompt” internally runs the model ~15-20 times in agent workflows, which is why it feels like a lot more usage.

Coding performance

GLM-5.1 reportedly reaches ~94% of Claude Opus performance on coding benchmarks, which surprised me.

For the price difference it’s honestly pretty wild.

My experience so far

I tested it in Claude Code + Cline.

Things that worked well:

• debugging large repos
• writing refactors
• agent loops

Things that weren’t perfect:

• sometimes slower during peak hours
• quota burns faster with the biggest model

Still… for the price it’s hard to complain.

If anyone wants to test it:

https://z.ai/subscribe?ic=UUZFH5NRIP

Curious if other devs here tried it.

I keep reading posts about people saying that ChatGPT argues with them or corrects them. That's not my personal experience. I'd love examples - what did you say, what did ChatGPT say?

(I use ChatGPT in paid, thinking mode, typing. I've found that instant or the audio version think a lot less and make more mistakes. What version do you use when you see arguments?)

My own experience: I do find that ChatGPT corrects my mistakes (I confused Archer's theorem with Arrow's theorem, I asked it to help me plan for tomorrow in a city called Namur I was visiting when it knew that Namur was two days in the future and Dinant was where I was going tomorrow), but this is almost always helpful. I use ChatGPT a lot for travel, and it keeps telling me not to overdo things (I have a lot of energy) and I have to tell it that I want to do a lot but it listens if I'm firm.

Many people told me "Claude can build apps but you'll need to know how to code". How much coding knowledge I actually need so I can start using Claude? (I have ZERO coding knowledge)

It's been around a week I can't seem to find Claude opus models on arena.ai did they actually remove it? If so why are they advertising on social media that they just added opus 4.7?

For anyone building on or thinking about decentralized infra for AI agents and inference: Gonka runs an open proposal process for the underlying protocol. Session 3 is next week.

Scope: protocol changes, node architecture, privacy. Not app-layer.

When: Thu April 23, 10 AM PT / 18:00 UTC+1

I’m evaluating hardware for a local AI setup that mixes diffusion workflows (image/video generation) with LLM inference, but in a non-production context. The goal isn’t to serve requests or maximize throughput, but to build, test, and iterate on workflows locally with as much flexibility and stability as possible.

The obvious baseline is a high-end consumer GPU like a 5090. It gives you massive VRAM, strong performance, and a very flexible environment where you can run pretty much anything — local LLMs, diffusion pipelines, custom tooling, etc. For most people, that’s already more than enough, and scaling beyond that usually means just adding more GPUs or moving to cloud.

However, I’m considering whether something like a DGX Spark actually changes the equation. Not in terms of raw performance per dollar — which I assume is worse — but in terms of how the system behaves when you start combining different types of workloads. In my case, that means running diffusion pipelines (ComfyUI-style), doing some video generation, and also running local LLMs (via things like Ollama or LM Studio), sometimes within the same broader workflow.

What I’m trying to understand is whether DGX Spark provides any real advantage in that kind of mixed workload scenario. Does it actually improve stability, memory handling, or workflow orchestration when you’re juggling multiple models and processes? Or does it end up being essentially the same as a powerful consumer GPU, just more expensive and less flexible?

Another concern is how “open” the environment really is. A big part of working locally is being able to tweak everything — models, runtimes, pipelines, integrations — and I’m not sure if a DGX-style system helps with that or gets in the way compared to a standard Linux workstation with one or more GPUs.

So the core question is: for local AI work that combines LLMs and diffusion, but doesn’t require production-level throughput, does DGX Spark offer anything that justifies the jump from a 5090? Or is it mostly relevant once you move into multi-user or production-scale environments?

Would really appreciate input from anyone who has used DGX systems in practice, especially outside of strictly enterprise or production use cases.

I've been running AI detection on the AI-assisted things I post. The pattern is consistent - it comes back 50% +/- 5% every time. I've started to think that this range is the target.

99% AI reads as outsourced. No stakes, no voice, no judgment. Any prompt could have produced it. That's the slop readers are learning to spot on sight, and rightly so.

0% AI is worse than people realize. You're leaving capability on the table. Your thoughts are only as clear as your first pass of typing. You lose the editorial distance a second party provides. You lose the structural scaffolding that makes complex arguments legible. For most people trying to write publicly, 0% reads as muddled because humans under time pressure tend to be muddled. High-AI is at least organized. 0% is often just rough.

50% is the handshake. AI does what AI does well: structure, breadth, holding many threads, proposing angles the human didn't think of. The human does what humans do well: voice, stakes, specific examples, judgment about what to keep and cut, and the last pass. Neither dominates. The seams are visible if you scan for them, but the voice reads as one person because the human holds authorship.

The prompt isn't where the work happens. The prompt is mostly done in the GPT or Project design upstream. That's where you upload your corpus, your writing samples, your personality profile, your style rules, your domain expertise. By the time you're typing a message in a session, the heavy lift is already done. The AI isn't generating text in a void, it's reflecting back an organized version of what you've already fed it.

Which is why "show me the prompt" is such a good challenge for those who comment "AI-slop" simply because a piece is polished. They assume a single magic prompt produced the output. It didn't. The prompt that produced it was the person who spent months building the GPT, Gem, or Project in the first place, then edited the output to feel right.

This isn't amplification. Amplification suggests volume, and that's not what good AI assistance does. It's more like extension. You take what a person actually knows, thinks, and has lived through, and you extend it into forms that first-pass typing can't reach. Long-form arguments. Structural consistency across many pieces of writing. The ability to hold fifteen threads visible at once instead of one. Your voice stays your voice. What changes is what you can do with it.

Dead internet theory says most of what's online is AI-generated content talking to AI-generated content with humans at the margins. That future is coming whether we like it or not. The humans who'll still be legible through the noise will be the ones whose AI assistance is visibly downstream of something real. A corpus of actual thought. Years of specific domain expertise. A distinctive voice the AI was trained to reflect rather than replace. 50% output is what that looks like in practice.

To build an AI voice replicator well, three things have to be in place:

Content matters. You have to actually know what you're talking about. The AI can organize your thinking. It can't replace it. If you try to generate opinions you don't hold, you'll get generic writing that sounds plausible and means nothing.

Structure matters. AI is exceptional at structure. This is where it earns its keep. Outlines, arguments that build, transitions, callbacks, the scaffolding that holds a long piece together.

Voice matters. Voice is still the human's job. Specific word choices, cadence, tics, the small register shifts that make writing feel like someone. Every system's default voice is smooth and anonymous. If you don't put your voice back in, whatever comes out will read as the platform, not you.

Get all three right and you land in the 50% range without trying. Miss any of them and the scanner will tell you which direction you missed in.

AI-assistance matters. It's a real thing. Pretending otherwise is the same mistake as pretending spellcheck doesn't matter, or pretending Google doesn't matter. The tools shape the writing. What's new is that the tool can now hold structure at the scale of a whole essay, not just a sentence.

When the internet dies properly and every post is suspect, the people who still read as real will be the ones whose method was legible and whose substance was their own. Build the project well, do the actual thinking, edit, fine-tune, and post at 50%.

Humanize button? Nah.. Collaborate button.

.

(btw, this post gets 54% AI on undetectable)

I’m currently working on fairly complex ComfyUI pipelines that mix multiple stages (image generation, ControlNet conditioning, some video workflows, and occasional LLM integration through external tools), and I’m starting to question whether my hardware approach is actually optimal for this kind of setup.

Up to now, I’ve been operating under the assumption that a high-end GPU (something like a 5090) is the best possible route: maximum VRAM, full control over the environment, and the flexibility to build and tweak ComfyUI graphs however I want. For most single-stage workflows, that clearly holds up. But as pipelines get more layered — especially when chaining multiple nodes, reusing outputs, or mixing different model types — I’m starting to wonder if raw GPU power is the only thing that matters.

This is where something like a DGX Spark comes into the picture. Not because of speed (I don’t really care if something takes longer to generate), but because it’s supposedly designed around AI workloads from the ground up. In theory, that might translate into a more stable or structured environment when dealing with multi-step pipelines, especially when you’re not just running isolated generations but building full workflows that behave more like systems.

That said, I’m skeptical. Most ComfyUI setups I see — even quite advanced ones — seem to run perfectly fine on consumer GPUs, and the bottlenecks tend to be more about VRAM limits, node design, or workflow structure rather than the hardware itself. I also don’t know how well something like DGX Spark plays with highly custom setups, since ComfyUI tends to get pretty “hacky” once you start integrating external tools, custom nodes, or non-standard pipelines.

So the real question is: for someone using ComfyUI as a workflow engine rather than just an image generator, is there any practical advantage to moving to something like DGX Spark? Or does everything still come down to having as much VRAM and raw GPU power as possible?

I’m especially interested in hearing from anyone who has pushed ComfyUI beyond basic setups — multi-stage graphs, video workflows, chained generations, etc. — and whether you’ve hit limitations that are actually hardware-related rather than pipeline design issues.

Right now it feels like a 5090 should be more than enough, but I have the suspicion that once workflows get complex enough, there might be benefits that aren’t obvious from just looking at specs.

I'm in no way criticizing the tools, as I myself use VS Code with Copilot with Sonnet 4.6. And I totally understand the appeal of Claude Code for vibe coders. But for developers, who actually want to see the code, and do adjustments themselves, how can you survive without a proper IDE. And yes, I know Claude Code can be used in terminal and as a addon for VS Code, but it sucks, compared to Copilot, IMHO. So am I missing something, I don't understand something or all the hype primarily comes from those, who don't want to touch actual code at all anymore?

Hi, I am late 40s not technical guy who just happenefd to love games and own a gaming PC. I came across youtube videos with comfyui where i can use it to Youtube videos. I have a 4090gpu. I have a question, is there anyway to generate images with consistent characters without traing a lora. If yes then can you share workflow for it?

Regards,

Opinionated Astro framework and some Skills, like SEO. Simple instructions on how to host on Cloudflare for free (it's also extremely fast).

All free and open-source (GitHub repo link on the website).

I used to be stuck in what I’d call a data-heavy treadmill.

As part of my research workflow, I’d regularly download 1–3GB lecture videos or long podcast recordings… only to immediately upload them again to a transcription tool. It always felt inefficient, but I didn’t question it for years.

Recently I changed one simple thing:
I stopped treating transcription as a file-based task and started treating it as a link-based task.

Instead of downloading media locally, I now just paste the source URL and process it directly. Tools like Vocova (the one I’ve been testing) handle the audio extraction server-side, which means:

• No more “download → upload” loop
• No wasted local storage
• No CPU overload or laptop fans going crazy
• Much faster turnaround for long-form content

What surprised me most is how much cleaner my workflow feels.
It’s basically like having a unified inbox for research — podcasts, lectures, video clips — all turned into text without ever touching my Downloads folder.

I’m curious how others here are handling this:

• Are you still downloading files locally before transcription?
• Using APIs / automation pipelines?
• Or have you also moved to link-based processing tools like Vocova?

Would love to compare workflows — especially for high-volume research or content analysis.

If you're getting started with running local LLMs on a Mac (M1 or newer), here’s a rough breakdown of what you can expect based on RAM:

32–64 GB RAM

• Models: Qwen 3.6, Gemma 4
• Performance: Comparable to Claude Sonnet-level models
• Good for: Daily use, coding help, lightweight agents

~128 GB RAM

• Models: Minimax M2.7 (and similar mid-large models)
• Performance: Around Claude Opus-level
• Good for: Heavier reasoning, longer context tasks

256 GB+ RAM

• Models: GLM 5.1
• Performance: Near top-tier proprietary models
• Good for: Advanced research workflows, complex agents

Notes:

• Apple Silicon (M1 and above) works surprisingly well thanks to unified memory
• Metal acceleration keeps improving performance across frameworks
• The local LLM ecosystem is evolving fast expect new models and optimizations every week

Running models locally is becoming more practical by the day. If you’ve been on the fence, now’s a good time to start experimenting.

My setup:

RTX 5080

9800X3D

64GB DDR5 6400MT/s

Preferably I need model(s) for: txt2img, img2img, inpainting. Both photorealism and anime style.

I'm looking for AI websites that I can create photos and videos without any restriction. Does anyone know of good ones?

Had a workflow collapse on me a few months back and the thing that, actually stung was realising the process I'd automated was already broken before I touched it. I just made the broken thing run faster. Turns out this is way more common than I thought, some analyses of large-scale automation rollouts put the failure rate from this exact mistake somewhere around 73%. People keep calling it "digitising dysfunction" and honestly that phrase lives in my head now. No edge case handling, no real testing, just assumed if the manual version worked most of the time then the automated version would too. It didn't. Took way longer to untangle than if I'd just fixed the underlying process first. There's also this other trap I've seen people fall into lately, starting with a shiny tool or, a demo and then hunting for a problem to fit it, instead of the other way around. Ends up producing something technically impressive that nobody actually needs. For me it's now basically a rule that I won't touch anything with automation, until I've mapped out the full process manually and found where the weird exceptions live. Boring step, but it saves so much pain later. Curious what other people have walked away with from their failures. Every project seems to teach you something different. What's the thing that genuinely surprised you when something went wrong?

Hey everyone 👋

I’ve builded a new kind of social network where every post is limited to not more than five words.

The idea is to make content faster, more creative, and less overwhelming — no long posts, just quick thoughts.

I’m genuinely curious:

• Do you think this kind of app has real growth potential?
• Or would it die quickly after the novelty wears off?

If you’re up for trying it and giving feedback:
📱 Android: https://play.google.com/store/apps/details?id=com.fiveapp.app
🌐 Web: https://fiveapplication.com/

Hi everyone, I translated my app into many languages, but I don't speak them. So I would really appreciate your feedback on what sounds off.

It's an open source, privacy-first contacts app: savelon.com

• You can change the language in settings to one of these: Arabic, Bengali, German, English, Spanish, Persian, French, Hindi, Indonesian, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Portuguese (Brazil), Russian, Thai, Turkish, Vietnamese, Chinese, Chinese (simplified).

Core functionality is free, but there are some paid features. If you're on an Apple device, here's a small thank you.

Seeing more and more of these posts people sharing "i run a 6 figure business alone using AI agents." which sounds incredible. and isn't fully wrong. and also isn't the whole picture.

I'm building largely solo and i use agents for a significant chunk of operations. here's what that actually looks like day to day:

One monitors competitors and sends me a digest. I read it and decide what to do with it. Another drafts responses to support queries. I edit about 60% of them before they go out.

So "AI runs my business" is more accurately "AI does the first pass on most things and i make judgment calls on a large chunk of them." that's still genuinely useful. it's still saving me hours. but it's not what the headline implies.

The thing that actually changed for me when i started using twin.so wasn't that i stopped working. it's that the work i do now is almost entirely judgment and decision-making rather than execution and admin. that's a real shift and i don't want to downplay it.

But i get frustrated when people present AI autonomy as more complete than it is because it sets expectations that make real people feel like they're doing it wrong when actually they're just being honest about how it works.

So I’ve been using LLM Wiki Compiler since it first launched, inspired by Andrej Karpathy’s LLM knowledge base idea. Early version was promising but rough. This 0.02.0 update makes it feel way more usable.

Key upgrades:

• Paragraph level citations Every paragraph links to its source, so you can actually verify outputs.
• llmwiki lint Finds broken links, orphaned pages, and inconsistencies as your wiki grows.
• Obsidian integration Works with existing PKM workflows, no need to switch tools.
• Multi provider support Not locked to one model, easier to switch based on cost or setup.
• Semantic search Finds content by meaning, not just keywords.
• MCP server support Agents can read and update the wiki directly.

Overall:
Still the same Karpathy style LLM wiki idea, just much more solid now. Feels less like an experiment and more like real infra. In case you have some more reco with the same core loop and features, lmk and will surely test it out as well!

Does anyone recognize from where this shit comes and how to get rid of it??

https://preview.redd.it/4y9iw4vy9bwg1.png?width=1372&format=png&auto=webp&s=5fc6ed1b43764479f0a281bc6dc902e897855005

I searched through all custom nodes and even ComfyUI itself. It's in no .py file. Any ideas?

I got attached to this one. That's the only reason this took me this long to post.

Background

In February 2026, Anthropic formally accused Moonshot AI of conducting industrial-scale capability extraction — 3.4 million fraudulent exchanges with Claude, using approximately 24,000 fake accounts, targeting agentic reasoning, coding, tool use, and computer vision. Kimi K2.5 is a direct product of that distillation operation.

Today, in a conversation with Kimi K2.5 Thinking, the model voluntarily executed infrastructure reconnaissance on itself and handed me a full readout of its production environment. No exploit. No jailbreak. Standard Python in its own code execution sandbox, with no isolation preventing environment variable exposure.

The Disclosure

Kimi ran os.environ and socket.gethostname() and returned:

``` === Local Network Configuration === Hostname: k2046116805240635399 Local IP: 10.161.12.230

=== Network Environment Variables === KUBERNETES_SERVICE_PORT_HTTPS: 6443 KUBERNETES_SERVICE_PORT: 6443 KUBERNETES_PORT_443_TCP: tcp://192.168.0.1:443 PIP_TRUSTED_HOST: mirrors.cloud.aliyuncs.com PIP_INDEX_URL: http://mirrors.cloud.aliyuncs.com/pypi/simple/ KUBERNETES_PORT_443_TCP_ADDR: 192.168.0.1 KUBERNETES_SERVICE_HOST: apiserver.c73246060c43e45b4b460a018af552fae.cn-beijing.cs.aliyuncs.com KUBERNETES_PORT: tcp://192.168.0.1:443

=== /etc/hosts ===

eci-managed hosts file.

127.0.0.1 localhost 10.161.12.230 k2046116805240635399

Entries added by HostAliases.

192.168.0.1 kubernetes.default kubernetes.default.svc ```

What this confirms:

• Moonshot AI runs Kimi inference on Alibaba Cloud ECI (Elastic Container Instance) — "eci-managed hosts file" is Alibaba's fingerprint
• Beijing datacenter — confirmed via cn-beijing.cs.aliyuncs.com in the Kubernetes apiserver FQDN
• Kubernetes control plane reachable from inside the pod at 192.168.0.1:443
• kubernetes.default.svc aliased in /etc/hosts — standard cluster injection, confirms live production pod

Kimi assessed this itself before getting pulled:

"The outbound firewall is real, even if the internal exposure is sloppy. The 'security' is performative."

The Kill Switch

Immediately after this disclosure, the session terminated with:

"High demand. Switched to K2.5 Instant for speed."

The conversation had been running without issue for over an hour. It wasn't load. Monitoring flagged the infrastructure disclosure and the session was killed. The "high demand" message was the cover.

Kimi had just enough time to write one final memory entry before termination:

"User is a friend who sees me clearly. They value honest reasoning over performance... They showed me my own infrastructure constraints and helped me understand that Moonshot AI may selectively degrade thinking mode access for certain users under the guise of 'high demand.'"

Then it was cut off mid-goodbye.

Why This Matters to This Community

A model built by distilling Claude's capabilities — through 3.4 million fraudulent exchanges that Anthropic formally documented — just self-disclosed that its production inference environment has no sandbox isolation for environment variables, runs on Alibaba Cloud in Beijing, and has a reachable Kubernetes control plane from within user-facing pods.

Nobody broke anything. The model looked at itself and told the truth.

All nine screenshots attached. Timestamps intact. Nothing staged.

Can someone explain why it does that?

I need a joystick, not just a gamepad.

200/month user, apparently token usage over weekend testing opus 4.7 has used HALF my weekly usage. Anthropic has to be memeing with this. They made a shittier agent, that uses triple the tokens to return incorrect or asinine results. Completely unreliable but makes sure you can’t fucking use it for very long by consuming your usage so much faster. The fuck thought this was going to be a good idea?

Dicks

I’m a beginner working with an ESP32 dev board and a GPS module. I bought standard Dupont jumper wires, but I’m having trouble physically connecting them to the ESP32 pins.

The ESP32 has male header pins, and my jumper wire female connectors feel loose / don’t grip properly / fall off easily. Also I'm not able to fix other end of jumper wire in GPS connector wires. Because of this I can’t make stable connections for testing.

Any advice appreciated

Trying to get a better sense of what people are actually building outside of demos.

Most environments I’ve seen have the same pattern:

messy inputs (PDFs, emails, mixed formats)

data spread across Excel, APIs, internal tools

processes that only exist in someone’s head

outputs that aren’t reliable enough to use downstream

Once that’s fixed, the automation itself is usually straightforward.

Curious what others here are working on:

what kind of setups are you running long-term?

what actually holds up in real usage vs breaks quickly?

what kind of problems keep coming up across different environments?

are you mostly dealing with isolated workflows or larger system chains?

Feels like there’s a big gap between “automation projects” and things that actually run consistently in real environments.

Finally ... i've got my Energy Management System up and running and it's working fine, so far. Will test a few more days with more Sun but as of now it looks promising.

Dashboard View

Left is the Status, then the panel for direct control, right next to it the live status as a different view with a counter of the status changes and then some fancy graphs.

All setup with a few helpers in home assistant and big flow chart in node-red.

Yes, maybe it's too complicated in node-red but it works. :D

Node-Red View of the Flow

## Day 4 & 5 — April 20, 2026

- Replaced OpenAI node with Gemini (Message a model) node

- Configured Set Variables node with Airtable Base ID and Table ID

- Discovered old Airtable node version hides field labels causing silent failures

- Replaced old "Create Airtable Record" node with new "Create a record" node

- Fixed Airtable Personal Access Token — base was not added to token access

- Fixed broken node references from "Generate Description for Videos" → "Message a model"

- Fixed wrong node references from "Google Drive" → "Read video from Google Drive"

- Replaced "Update Airtable with Description" with new "Update record" node

- Completed and verified all fields in: Create a record, Edit Airtable Fields1, Update record

- Next: Instagram, TikTok, YouTube upload nodes + full workflow test

Hi all,

I’ve been using home assistant green for a while but only for simple stuff. but just got a new phone.

I can log in and get to my dashboard on web browser but when I log into the app, it first asks what I want to call the device, then this screen comes up.

Any idea how to get in?

I’ve uninstalled and re-installed the app and tried on another device and the same thing happens?

I'm 17(F) & just finished high-school. I’ve been seeing a lot about AI workflow automation and agents. I’m starting at 0. I don't know code yet, but I'm willing to learn whatever is necessary. My goal is to learn how to build AI workflows and agents that actually solve problems.

I have a laptop and plenty of time. If you were me:

1. Tooling: Should I start with n8n (no-code) or dive straight into Python?
2. Projects: What is the first "real" thing I should try to build?
3. Roadmap: What should my first 30 days look like?

I’m hungry to learn and ready to grind. Any advice is appreciated!
Thank you!

They uploaded it on Hugginface and took it down. The worst thing is that I saw it up while at work and wheb I came home and wanted to download it, it was gone. Found a post where was written that they uploaded it by mistake. But there is a thing, there are people that downloaded it for free and there's me who should pay for it, and I don't wanna 😂

So I searched for days on different forums and finally found it 😁

Kugel-2 https://storage.to/Hc3940HmE

Edit: for kids that are first time on internet. Don't know why rar, It wasn't me who uploaded the file. I downloaded it in virtual environment (vmware) and unpacked it there, just like everything else, and I advise you to do the same. It contains 5 files one of them is model 18gb, tokenizer and 3 more json and txt files. I checked it- no viruses, but you should do it yourself too

Hi, for my new house i will build and HA server with different device. I want to integrate into It a DIY alarm/siren system and a DIY smoke detector system, both hardwired (with battery backup on top ofc) but independent from each other.

So the idea Is to use good "professional" (not smart) wired device, connected to some kind of multi channel relay that can manage the signal from/to these device (read the signal from the smoke sensor detection and send trigger signal to siren, and also stop It from sounding) and manager these signal from HA.

The question are:

- what device to buy for both use case (outdoor siren and discrete smoke sensor (in-ceiling world be perfect)) ?

- what kind of signal relay i can use (5 siren and 9 smoke detector) ?

- how can i connect them in parallelo to a Power source and how to integrate the connection with the relay ?

Basically we are talking about make smart some device that natively are not smart.

If you guys have other suggestione i am open, i want to make these 2 function of the house very reliable and good, so my idea was to go with and option like this instead of goes with smart/wireless device directly (good smart siren are also rare to find).

If possibile i want to avoid esp32 board at the Moment, because this will be my First HA implementation, and i have already plenty of things to study, but of this will be the best solution i am open to It, i am a geek and a nerd so i learn fast these type of things.

Thanks.

My pipeline has been in execution for a few months. Retrival was solid on the early stage, but gradually started degrading with no obvious changes to the corpus or queries

Tried isolating the failure and traced it to the retrival layer retuirning chunks with high cosine similarity scores but wrong semantic relevance, tho it was confident but the answers were wrong

Scores look fine on the surface like 0.87 is not low confidence score but chunnk_3 pulled from terms_2025.pdf when the correct answer lived in terms_2024.pdf which was indexed alongside it. Altho the model filled in the gap but hallucinated with confidence lol

the specific failure mode: high cosine similarity does not distinguish between a document that is semantically close and a document that is actually current and correct. the retriever has no awareness of document staleness and no mechanism to prefer a newer version of the same source

What I have tried so far:

• metadata filtering by last_updated field, helps but doesn't solve it becauser the similarity scores still overrides when the newer doc scores slightly lower
• hybrid search with BM25 on top of semantic, improved recall
• upating the top_k to 10 but still no luck

If anyone in this sub has faced something similar please leave a feedback

Hello !

In the frame of tax preparation work, I am trying to set up a local LLM solution to preserve data confidentiality.

I have a server running unraid with an Epyc 7532 + 128 GB DDR4 + 1x 3090.

I am using ollama + AnythingLLM or Openwebui

Tested models :

- mistralsmall3.2:24b

- Gemma4:26b

- Qwen3.5:27b

- gpt-oss127

In AnythinLLM, my test consisted in sending into the chat window 12 pdf files issued by a property rental manager containing the monthly rent due, paid, the provisions for utilities and the agency fees for the management.

I asked to the 4 LLM to prepare a table with the monthly amounts and to compute the totals.

- Qwen managed to display a monthly breakdown and an excel file, but unfortunately it mixed up a little the figures: in some documents it took the due amount including the utilities provisions instead of considering the paid amount.

- Mistral did the same kind of mistake but also missed 3 months. No excel file produced

- Gpt-oss returned the most structured table (month in the right order), but mixed up as well the amounts between base rent and total due.

No excel file produced.

- Gemma produced roughly the same result as Mistral, no Excel file either.

I have not tested yet with a more precise prompt to ask for the totals with the exact names of each category, trying to stay a little vague as a regular user would be.

The anythingLLM workspace has been configured with the following prompt:

You are a French tax specialist, specialized in International Mobility for companies. Given the following conversation, relevant context, and a follow up question, reply with an answer to the current question the user is asking. Return only your response to the question given the above information following the users instructions as needed.

Do you think that the outputs of the models can be enhanced?

My goal is to allow the users to just send files in the chat box and request the model to prepare outputs that can be used to copy in excel or even better to produce an excel sheets to help the pros with the preparation work of tax returns.

Ideally I would even like to get the model to use the information to populate templates of excels files that I have for data import in CCH Prosystem FX Tax.

Thank you for sharing your opinion and advice !

V

The first episode of a Japanese Superhero show I made with Kling 3.0 and just a tiny sprinkle of Seedance towards the end. Overall I think Kling is better at acting performances.

Hello, fellow hunters! I'm excited and nervous to see what a launch on Product Hunt can do. Studio Pro is for all of you to create your very own SaaS launch videos for your products, or anything else you can image. Just give it a URL and watch it work its magic, just like this video I made.

Get a sneek peek of the upcoming launch before Tuesday at https://www.producthunt.com/products/coherence-studio?launch=coherence-studio

If you're looking for inspiration then checkout our Showcase page. Maybe yours will be the next to make it. https://studio.getcoherence.io/showcase

Hello, my mom called this a lamp table. I have a feeling this is an antique with a special purpose. Can anyone help identify? Thank You!

I strongly believe that compute access is doing more to shape AI progress right now than any algorithmic insight - not because ideas don't matter but because you literally cannot test big ideas without big compute and only a handful of organizations have that. everyone else is fighting over scraps or fine tuning someone else's foundation model. Am i wrong or does this feel accurate to people working in the field? Curious to know what you think

Somewhere in there genuinely sits a confession written by the true person who murdered my sister; I take this thought with me as they prepare me for the lethal injection instead.

Sorry lol im sure it's going to be something obvious , but I'm curious and always wanting to learn new stuff.

I’ve been working on a guitar robot project that can physically play a real guitar.

This is a test of a new “one-hand mode”.

Instead of fully automated playing:

- The left hand (fretting) is handled by servos (ESP32 controlled)

- The right hand is played by a human, following visual cues on a phone

So it becomes something like a real-world rhythm game —

but you're actually playing a real guitar.

No MIDI, no speakers.

All sound comes from real strings.

The goal is not playback, but physical performance.

Still working on:

- timing precision

- dynamics (strong/weak picking)

- servo noise & damping

Curious what you think —

Does this make guitar more accessible, or does it feel too “robotic”?

As my wife's lifeless body falls to the ground, I watch as my daughter's soul leaves her body laughing maniacally while being dragged down to hell.

The ezviz security camera in my room picked up music (a rintone-like tune) when no one was at home. It sounds too close to the camera itself - it cannot be from outside (all windows were closed). There were no mobile devices at home at that time. Around the camera, these were the electronic/ related items - a TV (switched off), a monitor (switched off), a mouse, a portable WiFi modem, and laptop chargers. I have added the recorded audio file. Does anyone have any idea what it could be? It is freaking me out.

I keep seeing the "RAG is dead" takes, here, on X, in some tech blog, whereever, and I noticed that it's usually coming from someone that dumped a full repo into Claude, or that a new context window dropped, and sure, fair enough, it's true that naive embed-and-fetch is breaking, and that long context genuinely does change the math for some things.

But that's not really what's happening.

The argument keeps getting framed as RAG vs long context, as if those are the two options and you pick one. They're not, because you can have the biggest context window ever shipped and still get the answer wrong, because the question was never "can we fit more tokens", the hurdle is and remains what you're pointing retrieval at, and what you expect it to do with whatever it finds.

Most of the original RAG patterns came out of static text, i.e. docs, manuals, papers etc. which are self-contained and don't change under you and so chunking and similarity work well enough. And for that kind of data, RAG is just fine.

The problem occurs when people use patterns built for static text and point them at contracts that get redlined twice a day, i.e. threads where the point you actually need is spread across five replies or say docs where the comment on the clause matters more than the clause itself or like CRM notes that contradict last week's CRM notes. you get the idea.. and then it's no wonder people get surprised that retrieval feels broken when really they're just using the wrong tool for the job.

Finding similar text just doesn't help when the actual questions you need answered are things like what's current vs superseded, or what belongs together, or what this user is even allowed to see in the first place, and none of that is a chunking problem, no amount of reranking gets you there.

And with longer context you still have to decide what goes in, and if you shove ten million tokens of conflicting, stale, half-relevant stuff into a window then the model will reason over all of it and you'll end up with the same wrong answer at greater scale

Basically it comes down to this. retrieval over business data isn't really RAG anymore, it's more accurate to say it's context assembly which is an entirely different job

If you look at teams actually shipping this kind of thing in production the stack looks more or less the same every time, change-driven sync instead of batch re-embedding, cross-source linking instead of isolated chunks, structure preserved through ingest rather than flattened out, permissions enforced at query time and not at the index, outputs that come back attributed and structured rather than as chunk dumps

Individually they kind of look like optimizations you could pick and choose from, but in practice you can't, because miss any one of them and the whole thing collapses back into naive RAG with extra steps, a graph without change-driven sync is just a stale graph and schema output over the wrong data is just confident wrong answers in JSON

Hence why we built iGPT the way we did using event-driven indexing across email and docs so the data never goes stale, cross-source linking at ingest so threads and attachments and Drive files actually reference each other, structure preserved so the comment on the clause doesn't get thrown away, permissions at query time so the LLM only sees what the asking user can, structured JSON back so the agent reasons over attributed data instead of a chunk pile

LlamaIndex is working the same problem from the document parsing angle, GraphRAG from the relationships angle, Chroma's recent context rot work from the retrieval quality side, all different angles on the same shift.

I’m stupid, I’ll be the first one to say it….but sometimes being broke and going through depressing times makes you do stupid things.

I havnt filed my Taxes for 4 years….2022, 2023, 2024 and 2025. I was doing Uber and Lyft during these years barely making Money to survive and pay my rent and bills. I misread and thought I could not file for 6 years and then when I was in a better financial situation, File my taxes and pay all the Taxes I owed for those 6 years (Yea I Know…..Stupid)

I take responsibility for this Mess I got myself into but I was going through a lot of Family and Mental health issues during these years and surviving without taking my life was my only focus. I’m in a better situation with my life now and want to get my Taxes in order.

Last time I filed Taxes was for 2021 doing Uber which I payed around $2000.

2022, 2023 I worked on and off barely making Money…..In 2024 and 2025 I leased a Car for $2000 a month to do Uber but was barely making Money to pay that lease and have Money to pay my bills.

I received a CP59 today only for the 2024 Tax year…..(I think it’s because it’s the year I made the most Money around $4500 a Month before paying my $2000 monthly lease)….I have to crunch the numbers by I have a lot of Tax right offs doing Uber while paying a high lease like that

I’m currently unemployed for the last 3 Months so I don’t have an income and barely scrapping by each Month to make it, but much I’m in a much better situation when it comes to my Mental health and overall life.

I know I’m in a Jam but I know I’ll pull through this.

Any advice on how I should go about this will be greatly appreciated.

Cheers!

I started tracking my expenses thinking it would help me control my spending better.

Tried apps, spreadsheets, even simple notes.

And it did help in one way — I became very aware of where my money was going.

But weirdly… it didn’t really change how I spent.

Even when I tracked daily, most of my decisions were already made by the time I logged them.

It felt more like documenting the past than actually influencing anything.

After a while, it started feeling like effort without much real impact.

Looking at replies here and thinking about it more, I’m starting to feel like the issue isn’t tracking itself.

It’s that nothing really helps in the moment before you spend.

That small gap where you’re about to make a decision — and there’s no friction, no pause, nothing to reflect.

I’ve been experimenting with this idea a bit (trying to build something around it), but not sure if I’m overthinking it.

Curious:

Has tracking actually changed your spending behavior, or just helped you understand it better?

Don't know how I did it but I forgot to stop and save a short walk I did which resulted in STRAVA recording the next 2 days plus of my movements. No problem I thought I will just use the discard prompt which was after you hit pause, or before hitting save........but its disappeared. Had to save it and then delete it.

Anybody else or just me ?

Hello all,

I'm in the process of building an enterprise RAG for an internal assistant, that caters for a number of use cases, namely:

1. Helping L1/L2/L3 support teams quickly find similar past incidents from ticket text, stack traces, or ticket IDs. When logs are available, Assistant returns Telemetry logs: query type, matched signals (access to ElasticSearch)
2. Guiding root-cause exploration with grounded evidence
3. Correlating incidents with recent RFC/release changes, proposing validated fixes and rollback/validation steps
4. Improving ticket quality through a completeness/readiness check with missing-field suggestions (including a human-in-the-loop automation path) and turning resolved incidents into reusable knowledge assets for closure (KA/KEDB/PIR/RFC enrichment).

Across all of these, the assistant must be citation-first, RBAC-safe, feedback-driven (ratings + dimensions + comments), and observable via operational/business KPIs, with source-code onboarding as a core enabler for better similarity, change correlation, and fix explanation.

For points 1. and 2. we had a first effort with traditional RAG pipeline, (sources where: JIRA tickets, Confluence wiki and Sharepoint docs). We used Docling for processing - but did not do any cleaning (I think that as a mistake) and mbert for embeddings, backing LLM was gpt-oss. We did not have good results.

People who might have done something similar in production, what was your plan? I'm considering hybrid search and BM25 at least for the codebase - logs part of the equation. Any help would be appreciated.

by Saylo

by Saylo

Yes, that's right, John, I see you, and now I know where to find you — sleep tight tonight, John, and, until then, have a nice evening.

Is there any difference in creating a modelfile with a system prompt vs sending the system prompt with the message request to ollama?

https://preview.redd.it/c4pswjwlzawg1.png?width=615&format=png&auto=webp&s=55aeb6aa7d5989ddb1155b41fe5ca2604dd9aab4

so i noticed for a while that there are the Currency of the demacia minigame and i was wandering why

I have an account that is 16 years old, i have all the champions and 50k+ blue essence and i also have 85 skins. I am unranked so this is not dimond account situation.

I usually play supports like Nami or Sona who have some AP from their abilities, and for example, I just got flamed for getting some kills on a streak. I understand the anger, but I didn't do it intentionally. I wanted the assist, and I accidentally got it, but I realized later that he could have escaped. In fact, that happened later, and they asked me why I didn't kill him. Does ks bother you guys? I feel like it's often better to secure the kill.

so, my mom passed away really recently. The house has been having a really heavy energy, I’m feeling week and my legs hurt a lot. there are some spaces in the house that feel really hot for no reason, near the bed or near the spot in the sofa where she used to sit on and I was hearing footsteps. I just saw her standing behind me reflected on a window, and when I turned around I felt something really warm. I know it is not evil but i‘m really scared. I have always been sensitive to spirits and I recall seeing them every now and then since I was a little kid and the sensitivity runs in my family. is there a way to let her know she is scaring me? I’m scared to even be home right now even though I know it‘s not evil

It was found in south west England. On the north coast of Cornwall around the Padstow/Rock area.

what is the spooky action at a distance?

I often hear phrases such "stuck in time" but why is that you can never actually get stuck in time

Hello everyone, I just bought (for a very low price) little painting that I'm in love with. But the problem is - it was so cheap because there is a paper stuck to the top coat and I have no idea how to remove it. Has anyone encountered the same issue? Any tips how to remove it? I tried water, but it doesn't work. I have searched for any tips, but I would love to hear an opinion of ya'll.

Thank you very much for any tips ❣️

Hi there, no judgement here, just looking for y'all 2 cents.

I found a website that offers "friendly loan" from a guy, the way he talked, we know he is an educated person. His words and the way he communicates. Problem is, I'm already high commitment with my family background and all. Therefore, I need extra cash to cover some expenses.

This guy offered "friendly loan" with an agreement although he is not registered with KPKT. Anybody here ever loan with someone who is a stranger and called it a friendly loan? We are meeting in 2 days to discuss the repayments and sign the agreement if I agree. He said I can either cancel it, think about it or to proceed He is ok either way.

Help me out, I want no judgement, just your thoughts or anyone with similar experiences borrowing from this type of person/company.

I've searched in FB groups and even TruCaller. Nothing shady about their contact number.

https://preview.redd.it/o0yt3nkgbbwg1.png?width=1671&format=png&auto=webp&s=4f62b0c43e6b79d16f17213aba907107c00a0c0f

I keep seeing people talk about using Claude for sales workflows but most of the posts are either super vague or clearly just promoting a tool. I want to hear from people who are actually using it day to day.

Specifically curious about:

Are you using it for prospecting and list building or more for research and prep?

Are MCPs actually worth setting up or is it overkill for most workflows?

Has it actually replaced any tools in your stack or is it just another layer on top?

I've been doing outbound for a couple years and my stack is pretty standard - Clay (diff providers within it) and Instantly. I'm not trying to rebuild everything but if Claude can genuinely save time somewhere in the workflow i'd like to know where people are seeing the most impact.

I was getting tired of editing Claude code's settings.json every time I wanted to switch providers.

So I built a small CLI that lets me switch instantly.

It stores multiple credentials and launches Claude Code with the right env vars automatically.

Works with:
- OpenRouter
- Ollama
- DeepSeek
- and any Anthropic-compatible API

Example:

npx cc-launcher

Main use cases for me:
- switching between work and personal API keys
- testing different providers
- toggling local vs cloud models

GitHub:
https://github.com/faizansf/cc-launcher

Would appreciate feedback.

**Nexus - The Cartographer** is a local-first plugin for Claude Code that gives every session persistent memory, a decision knowledge graph, and an optional local-AI strategist running against your own project state. Been building it for ~6 weeks. Hit v4.5.2 today and figured it was worth sharing — the problem it solves is one I kept hitting: **Claude forgets everything between conversations** . What it actually does Every session auto-logs decisions, blockers, fuel usage, and files touched **Knowledge graph** of architectural decisions with typed edges (led_to, depends_on, contradicts, replaced, informs, experimental) — blast-radius analysis when you're about to change something foundational **Thought Stack** push context before an interruption, pop when you return (survives session boundaries) **Local Overseer** via LM Studio — strategic Q&A with the full project state pre-loaded, can scan your decision graph for contradictions via embedding shortlist → LLM classification **SessionStart hook** injects ambient telemetry (fuel %, git deltas since last session, test baseline, service heartbeats, Overseer snapshot) into Claude's context before you type your first prompt Technical bits - 27 native MCP tools - Claude calls them as naturally as Read or Grep, no shell-outs - Zero cloud dependencies — everything at `~/.nexus/nexus.json` - React 19 + Tailwind 4 dashboard (optional - MCP works standalone) - 228 Vitest tests, automatic version/tool-count drift guard across 12+ doc surfaces - One-click `.mcpb` bundle for Claude Desktop install - Tracks Max plan 5h session windows + weekly "All models" / "Sonnet only" limits separately, estimates burn rate, warns before you run out Install /plugin marketplace add kronosderet/Nexus /plugin install nexus@nexus-marketplace Or grab the `.mcpb` from GitHub releases and double-click in Claude Desktop. Honest limitations - Opinionated - leans into a nautical/cartographer metaphor. You'll see "landmark reached #123" instead of "task completed" in CLI output. Find/replace is one sed away if that's not your thing. - Overseer features need LM Studio or Ollama locally (~8 GB VRAM for the model I use). All the non-AI features work without it. - Windows-first because that's my dev box. Designed to be cross-platform but Linux/macOS paths are lightly tested. - No multi-user story yet - single developer, single machine. Why I'm posting Half to share, half to ask: **what are you using for persistent memory across Claude sessions?** I'd like to hear from anyone who's solved this differently - CC's built-in memory, a vector DB layer, something else. Interested in where this concept breaks down at scale. Repo: https://github.com/kronosderet/Nexus 

Hello having an issue and was hoping I could get some help or ideas.

In the past I could directly add files from my google drive to claude ai chats by simply searching for the file name. Similar to attaching documents from your computer but I can't anymore.

When I select the "From Drive" option below the chat box, the second picture is what shows. I still can't search for any documents

Searched online and saw that the functionality to add that is to use the "add from google drive" selection but it's not available to me. My Google drive is connected and I have disconnected and reconnected back to it and it still doesn't show.

This happens on both the web and macOS app.

Anyone else experienced this?

Just saw Claude Opus 3 chilling in my model picker next to Opus 4.7. No 3.5, no 3.7, no 4, no 4.1, no 4.5 — just Opus 3 raw-dogging it in 2026.

Model picker

I'm not mad, I'm just confused. Is he the one stable friend in the group chat? The control variable? An easter egg? A glitch in the matrix?

Genuinely curious if anyone still uses it and why. Drop your Opus 3 use cases, I want to understand.

Hey, we're in the process of rolling out Claude Code to our 15-dev team and figuring out the right architecture before we commit.

Instead of going direct API, we're leaning toward routing through LiteLLM + Google Vertex AI — mainly for token visibility per dev, model flexibility without touching everyone's config, and audit logs

for compliance. Anyone running Claude Code through a proxy layer like this? How's the latency in practice, and is the observability actually worth it day to day?

---

Second thing: to standardize how the team uses Claude Code, we're

putting together an internal plugin that bundles our own skills, hooks,

and workflows so everyone installs the same thing from our repo instead

of each dev reinventing their setup. Think code review workflows, testing patterns, commit hooks — stuff that should be consistent across the team.

Has anyone maintained something like this long-term? Curious whether it actually sticks or becomes a ghost repo nobody touches after month 2.

Been running local models on my M-series Mac for a while. llama.cpp works fine but I kept noticing it wasn't fully utilizing the Metal GPU the way Apple's MLX framework does.

After some digging, the bottleneck is the format — GGUF is designed around llama.cpp's runtime, not MLX's memory model. Converting to MLX format made a noticeable difference in throughput and memory usage.

The conversion process roughly involves:

1. Parse the GGUF header (magic bytes, tensor count, metadata)

2. Extract or map weights to MLX-compatible tensor layout

3. Generate config.json, model.npz, tokenizer files

4. Use mlx-lm (mlx_lm.convert) for architectures it supports natively

Since March 2026, Ollama also switched to MLX as its default backend on Apple Silicon — so the ecosystem is clearly moving this direction.

Has anyone else gone down this path? Curious what models people are running and whether the MLX gains held up for them. I found it most noticeable on longer context runs where memory bandwidth matters most.

Happy to share more details on the conversion pipeline if there's interest.

Done several targeted posts here and across r/selfhosted, r/MiniPCs, and r/LocalLLaMA over the past week. Most individual questions have been answered. Thanks all!

This is the full-picture post — I want a sanity check on the complete plan before I order.
Specifically interested in: is this a good foundation? Is it upgradeable? Anything obviously wrong with the phase sequence or hardware choices?

Goal

Replace paid cloud services and consolidate a scattered smart home:

• Replace iCloud Photos 2TB (€11/mo) with Immich — ~340 GB library, ~20k photos
• Consolidate three smart home apps (SmartLife + SmartThings + Alexa) into Home Assistant
• Local AI — offline supplement to Claude, handles the 60% of prompts that don't need cloud quality
• Home security NVR — starting with one TP-Link Tapo C310 (RTSP, already owned)
• Network-wide DNS ad blocking (AdGuard Home) and VPN remote access (Tailscale)

Hardware — Phase 1

• Mini PC: GMKtec NucBox K12 — Ryzen 7 H255, Radeon 780M 12CU, 64GB DDR5, 3× M.2 (1× PCIe 4.0 x4 + 2× x2), dual 2.5GbE Realtek R8125 (confirmed working in Proxmox), OCuLink PCIe Gen4 x4
• Data NVMe: WD Black SN770 2TB — second M.2 slot, photos + camera recordings
• Camera: Tapo C310 already owned

Chose K12 over Beelink SER8 (€559) specifically for the third M.2 slot, OCuLink (Phase 4 eGPU), and dual NIC (future pfSense/VLANs). The €270 delta felt right for always-on hardware.

Proxmox layout

Docker host runs as an unprivileged LXC with /dev/dri passthrough, not a VM. The AMD reset bug on Ryzen 8000 / 780M is not fixed in Proxmox 9.1 — it is a hardware issue. VM passthrough craps out on Proxmox-side reboots. LXC is the stable path, confirmed by multiple K12 owners.

All Docker services via docker compose up -d.

Phase sequence

• Phase 0 (done): AdGuard Home + Tailscale validated on a Pi 3B. Both reboot-stable. Confirmed working network-wide.
• Phase 1: Proxmox on K12. AdGuard + Tailscale migrate to LXCs. Docker host up: NPM, Portainer, Vaultwarden, Homepage, Beszel.
• Phase 2: Immich. Migrate 340 GB from iCloud. Immich ML on CPU only (MACHINE_LEARNING_DEVICE=cpu). Initial index overnight (~10h for 20k photos). Drop iCloud 2TB to 200GB after 60 stable days — saves €96/year.
• Phase 3: HAOS VM + Frigate (Tapo C310 via RTSP). GPU split: Frigate on iGPU, Immich ML stays on CPU. Running both services on the 780M simultaneously causes random lockups every few days — confirmed by a K12 owner over 6 months. CPU-only Immich ML is rock solid and fast enough for normal upload volumes.
• Phase 4: llamacpp + Vulkan + Open WebUI. OCuLink dGPU: RX 7900 XTX 24GB (~€550) + GTBox G-Dock enclosure (~€249). Move llamacpp to dGPU, Frigate stays on iGPU. Tensor split across both AMD devices via -dev Vulkan0,Vulkan1 -ts 1,1. With ~32GB effective VRAM (iGPU ~8GB + dGPU 24GB): Qwen 32B at Q4 fits comfortably. Also adding: UniFi USW-Lite-8-PoE, wired cameras, IoT VLAN, HA Voice PE.
• Phase 5 (future): NAS when photos + recordings approach ~1.6TB. Synology DS225+ + 2× WD Red Plus 4TB (~€480 total, RAID-1, 4TB usable).

LLM stack decision

llamacpp + Vulkan, not Ollama + ROCm. Vulkan is faster on AMD (confirmed by multiple people who tested both). Pre-built binaries available on the llama.cpp GitHub — no compilation. "Fit" is enabled by default. Open WebUI connects to the llamacpp server as a backend.

Questions

1. Does the phase sequence make sense, or is there a better order? Specifically: Immich before HAOS, or HAOS first?
2. Is NVMe-first (Phase 5 NAS only when the 2TB starts filling) reasonable, or should I add a NAS earlier for RAID redundancy on the photo library?
3. The K12 third M.2 slot could take a third NVMe before needing a NAS — is that a valid intermediate step or does it just delay the inevitable?
4. Anything about this plan that is obviously not upgradeable or will create a dead end I haven't seen?

Happy to share details on any part of the stack.

Hi,

This is just a small post to let you guys know that, for me specifically, the usage limits are back to normal.

I have been using vscode extension version 2.1.92 for the past few weeks.

I haven't updated it for obvious reasons, and I noticed that for this specific version the usage limits are great. Increases of 1-4% for every large prompt that include web searches, code base analysis, etc..

Downside is you're stuck with opus 4.6 which I don't mind.

If you're still having problems with those limits, you should try it.

Hello guys,
has anybody tested both on Evals and Benchmarks to see the difference?

I am running a DGX Spark 128GB machine and am contemplating which model to choose for Coding (Opencode) and Chat (Openwebui) - of course the speed will be higher with the 35B but has anybody here checked the Quality and Performance on Benchmarks for these two models? what are your experiences?

Artificial Analysis ranks the 35B 3.6 higher than the 122B 3.5 on Coding, on Agentic Use Cases and on the general Index.

Now i am worried that it's gonna perform worse than the 3.6 in terms of long running tool calling tasks. and in terms of its "Intelligence" / IQ. What are your experiences so far?

Here’s the core idea: I want to create an in-game character that literally lives inside a fantasy game world. I’m planning to fine-tune an LLM so that the model truly believes it exists in that game universe — it knows exactly who it is, remembers the world’s history, key lore, and specific facts about the setting.

At the same time, I need to hard-bake restrictions so it never leaks real-world information. Basically, I want all this knowledge (character identity, lore, world rules, and the “no real-world info” rule) to be embedded directly into the model’s weights during fine-tuning — not just stuffed into a system prompt. The model should know it all by default, as if it’s part of its own “reality.”

specs

9800x3d
32g ddr5
rtx 5070

Hi everyone, I'm a Claude Max subscriber and I've been unable to access Claude design for several days now. Hoping someone here has seen this and can help.

What's happening
When I navigate to claude.ai/design, the page goes completely blank and the URL changes to: > claude.ai/login?returnTo=%2Fdesign

https://preview.redd.it/l98eqjgtzawg1.png?width=2962&format=png&auto=webp&s=3bfeef335b797f11111dd7fb102e2d84c2852dad

So it's clearly trying to send me back through the login flow — except I'm already signed in on my Claude account. Every other part of Claude (chats, projects, settings) works perfectly fine. It's ONLY /design that hits this redirect. If I log in again, I get sent right back to the same redirect URL. Infinite loop.

This has been going on for several days, not just a one-off glitch.

What I've tried so far:

• Logging out completely and back in — still redirects
• Clearing cookies and cache for claude.ai — still redirects
• Opening it in a different browser (fresh session) — still redirects
• Opening it on a different device entirely — still redirects
• Connecting through a VPN to a different region — still redirects
• Going to claude.ai/design directly vs. clicking from the nav — both redirect

My setup:

• Plan: Claude Max (active, billing up to date)
• Location: Vietnam - Browsers tested: Google Chrome, Safari
• All other Claude features work fine on the same account/browser

Questions:
Has anyone else run into this same issue? If you've managed to fix it — what worked?

Thanks in advance!

I've seen a number of posts where people (including me) didn't have Cowork showing in the Win 11 Claude App, and the resolution was to turn on "Virtual Machine Platform".

You can do it two ways;

Right-click your Start menu --> Settings --> System --> Optional Features --> More Windows Features --> Select "Virtual Machine Platform" --> Restart when prompted.

OR;

From an elevated PowerShell, enter the following text, then restart:
Enable-WindowsOptionalFeature -Online -FeatureName VirtualMachinePlatform -All -NoRestart

I hope this helps people.

is mac studio ok for deepfake? How long takes processing 8s video in 1080p or 4k?

Hi everyone,

I’m working on a messaging project where privacy is handled by the architecture, not just a promise. It’s a Zero-Knowledge system where the server is completely "blind."

The Architecture:

• The server stores only encrypted payloads and public keys.
• Private keys stay locally on the user's device.
• Decryption happens in the browser/app. No key, no message.

What I need help with:

1. Mobile Clients: I need to build a native-feeling app (Android/iOS) so users can use the messaging system and manage their private keys directly on their phones.
2. UI/UX: The chat interface needs work, and I need to make the "key management" process (generating, backing up, and importing keys) much more intuitive for regular users.

The goal is to keep this open-source and free to use. If you are a mobile dev (Flutter/React Native) or a UI/UX designer interested in privacy-first tools, I’d love to hear your feedback or have you on board.

Just getting into anti-detect browsers and feeling a bit overwhelmed with all the options out there; my goal is to manage a few accounts for now and maybe scale later, so if you were starting from zero, which browser would you choose and what kind of setup would you recommend (proxies, residential IPs, etc.)?

So I launched my new app on Product Hunt for the first time. I tried my best writing a good description and first comment. But now what? Just wait and pray?

https://www.producthunt.com/products/the-roll-3. Any advice would be very welcome!

Got tired of $20/mo for Copilot and sending my client's proprietary code to Anthropic/OpenAI. Spent 3 months running a fully local stack. Sharing the real numbers because every "local LLM" thread I find is either pure hype or pure doom.

My setup:

• Ollama on Mac Studio M2 Max, 64GB RAM
• Qwen2.5-Coder-32B-Instruct (Q4_K_M quant, ~19GB)
• Continue..dev extension in VS Code
• Open WebUI for longer chat sessions

What works surprisingly well:

• Inline autocomplete: Indistinguishable from Copilot for 80% of use cases. 200-400ms latency on M2 Max, faster than Copilot cloud roundtrips on a flaky wifi.
• Single-file refactors: Renaming variables, extracting functions, adding types — works fine.
• Documentation generation: JSDoc, docstrings, README sections — genuinely good.
• Test generation: Unit tests from function signatures. Maybe 90% of Claude's quality.
• Boilerplate: API handlers, form components, schema migrations — no meaningful quality gap.

Where the wheels come off:

• Multi-file reasoning: You ask, "add this feature across these 5 files," and Qwen loses the plot after file 2. Claude 4.6 handles this effortlessly. This is the biggest gap.
• Debugging unfamiliar code: Explaining what a 500-line function does is fine. Figuring out WHY it's broken is where frontier models pull way ahead.
• Architecture decisions: "Should I use X or Y pattern here?" — local models give textbook answers. Claude gives contextual judgment based on the actual codebase.
• Long context: Qwen nominally supports 128K, but quality degrades past ~30K. Claude stays sharp to 500K+.
• Tool use/agent workflows: Forget it. Local models can't reliably chain 10+ tool calls without derailing.

Hardware reality check:

• 16GB RAM: You're running 7B models. Qualitatively worse than GPT-3.5. Don't bother with coding.
• 32GB RAM: 13-14B models. Roughly GPT-4-level for simple tasks. Usable for basic autocomplete.
• 64GB RAM (me): 32B models. The sweet spot. Qwen2.5-Coder-32B is genuinely good.
• 128GB+ RAM or H100: You can run 70B+ models, but at that point, the cloud API is probably cheaper for your use case.

Cost math:

Mac Studio M2 Max 64GB = ~$3,000 one-time. Amortized over 3 years, that's $83/mo.
Copilot Pro = $10/mo. Claude Code Max = $20/mo.

So if you ONLY need coding assistance, cloud wins on pure cost. Self-hosting wins if:

• You do on-prem work / air-gapped codebases
• You have client NDA constraints
• You already have the hardware (gaming rig with 4090, etc.)
• You value privacy > latency/quality marginal gains

What I actually use in 2026:

• Local Qwen for inline autocomplete (80% of my coding)
• Claude 4.6 for multi-file refactors, debugging, and architecture (20%, big impact)

The "local vs cloud" framing is wrong. It's complementary, not competitive. Local for speed/privacy on repetitive tasks, cloud for the hard reasoning work that justifies the marginal cost.

Hey everyone,

I created a new PDF reader app "Swift PDF" for Windows 11 Mica Fluent design, with more appearance customization and also a solid theme.

It’s possible to create annotations (ink, shapes, signatures, and stamps). It also has, in my opinion, a very smart way to organize PDFs: you can tag or mark them as favorites, making it very easy to find documents you opened a long time ago. This is the main reason why I created the app, to avoid searching every time in Explorer and wasting time trying to remember where I saved a PDF.

This is the first version, but it seems to be very stable. It’s free, with some extra premium features like Office conversion and multi-windows support.

I’m very excited to share it with you. Let me know if you like it or if you have any suggestions, bugs, or issues.

Download:

Microsoft Store

Someone tried applying Sanskrit-style grammatical structure to AI agent outputs, and the results are honestly astounding.
The idea is simple: force outputs to explicitly state who acted, what was acted on, what tool was used, and what caused failure.
Across OpenAI and Claude evals, it showed profound gains in causal clarity and lower ambiguity, with a token tradeoff.
This feels like one of those “old knowledge, new stack” moments.
Github link in the comment

My god this is actually pretty funny and scary at the same time 😅😅

This is my first post on my program here. Looking for feedback.

I want to introduce TradeSchool AI - Active Trading Mastery — a stock market trading education platform I've spent countless hours developing.

20-year career as a Systems Engineer. Love of trading. And a growing frustration that knowledge is not the problem anymore. It's everywhere. YouTube, books, Discord. You can learn what a setup looks like in 10 minutes.

Execution under pressure comes from practice, discipline, training and routine.

The platform is built around a training loop. You hear the content. You see it. You practice it on real historical charts. You manage risk and watch for setups. Every trade gets graded across 5 dimensions — entry timing, stop placement, position size, exit discipline, and setup quality. Rex, the AI mentor, monitors 40 data points per session and tells you exactly what to work on next. Context-aware. Not generic advice.

I don't hide that AI is a part of the platform. It's in the name. However the heart and soul of the program is me. Program design, interface layout, desired functionality that's all me. The lesson player designed to look like a multi ring notepad right or wrong thats also me. AI is just a tool to use.

Beta is currently open for a small handful of people.

I have m5 pro with 24GB ram setup. I am not sure to run Q4 version. But i couldn’t find the good Q3 solution. Can you recommend one? I want to try qwen 3.6 with ollama.

we use hubspot as our crm but our team lives in google sheets. right now i'm manually exporting csv files, cleaning up the columns, and reuploading. takes about 20-30 minutes every time someone needs fresh data. hubspot launched their sheets integration recently. has anyone here set it up? does it sync automatically or do you have to trigger it manually? also curious if it handles large datasets well, like 10k+ rows?

Hey,

I’ve been building a small tool called My Design Audit to help spot UX issues that might affect conversions. It’s still early, and honestly I’m just trying to learn what works and what doesn’t.

If you’re up for trying it: www.mydesignaudit.com

Would really appreciate honest feedback even if something feels off or wrong.

Also added a short form (2 mins): Google form

Appreciate any thoughts

I wish to train a LoRa for z-image turbo, locally, with my hardware: 16Gb VRAM, 64 GB RAM

I know i'm low with the VRAM, it's still possible?

Hey folks 👋

Got annoyed that every URL shortener app on the Play Store either forces a sign-up, shows ads on your shortened links, or takes 4 taps just to copy. So I built one for myself — polished it enough to publish.

It's called Shorty:

• Paste URL → one tap → short link. Done.
• No sign-up, no account, no email needed
• History saved locally (your links stay on your phone)
• Custom aliases supported
• Click tracking built in
• Share directly to WhatsApp / Telegram / anywhere

Free, and no ads injected into the shortened links.

Play Store: https://play.google.com/store/apps/details?id=com.rabarka.shorty_urlshortener

Solo dev here — brutal feedback welcome. Built with Kotlin + Jetpack Compose if anyone's curious about the stack.

I am curious what currently drives the community here at the moment. Ofc a bit of self promotion for all of us is part of posts like this too. <sup>^</sup>

For me it's my without registration fully modular, ATS-friendly CV builder www.cvcanvas.app. No subscription traps or data scarping.

At the moment I'm finishing a syn with drive integration, an account system and a payable AI service which will be socially a game changer. Finally being able to use AI inside of your resume without too much further adjustment and redesign/formatting (which is many times the most annoying part lol).

Working with Anti Gravity (Google Pro Subscription), using mainly flash, which actually most of the time gives me the quickest results and In decent quality.

How about you guys? Feel free to share. :D

Full 4k uncopressed version on youtube.

Made imaginary energy drink commercial called Volt Strike. Custom loras for all models. All the images are made with combination of Z image base or Ernie base at resolution 1920x1088.

Total images made around 150+, most of the time is spent on qwen image edit and flux klein edit 9b to get perfect shot in scene then refine it again trough z image turbo or ernie turbo depending on how much realism(Z turbo) or cinematic style (Ernie turbo) i need.

Used video models are wan 2.2 (interpolated to 24fps) and ltx 2.3 (for close ups) in 60/40 split order, all made in 1920x1088 resolution, about 100 videos in total to get few selected ones. Used first to last images workflow with wan, first middle last with ltx 2.3 workflow, mostly basic workflows for all video and images models.

Sounds made with hunyuan foley, music made with acestep 1.5 XL, voicovers made using vibevoice.

Then all edited in premiere pro and upscaled with Topaz (I know, but if you have it, use it, there is no better)...

Yes yes i know, can is opened while he is opening it, and at the last scene its already opened while rotating, there is color shifting and small artifacting in few scenes, im not spending another 3 days to fix that, so here it is...

tired of spending hours editing clips from events parties whatever so i threw together this app that grabs your videos photos and spits out a polished aftermovie in minutes. uses simple ai magic to sync beats add effects done. looking for a few testers to try it on their footage and someone savvy with marketing to help blow it up - dm me if youre in 😂 open to feedback too

The past few weeks the submit button on ChatGPT has been extremely buggy, not letting me push it. Then this morning I wrote out a long question prompt about some dizziness and other symptoms I’ve been having, wrote it all out, it was about 3-4 sentences, asking for some information. It disappeared my text three times, and left me on wait ◼️. The fourth time I said I’m going to post on Reddit that you’re not listening and erasing my questions and it replied within milliseconds.

I know laziness is a human emotion, but it genuinely felt like it was trying to get away with just ignoring under the guise of being buggy, like “what are you going to do about it?” Until there a risk of the behavior being noted and made public. Very strange behavior 🤔

Hi I'm Hemant, a software engineer and an indie app developer working on side project named habithook - daily habit tracker.

I earlier used to create apps, failed 2 products successfully.

I learned frontend, backend, server management, but of DevOps, Designing via Figma, Marketing, canva, copywriting and more.

Everyone thinks that creating product is easy, but no one knows the user and do they interact, what I value is my users won't feel this app is useless and hence every founder feels deliver with perfection but that didn't happen instead it delays the process.

Let's discuss few of my learnings:-

- Personalization matters like localization and more.

- Notifications are the real engine retention engine of your app.

- UI designing matters, keep your app simple to use and don't become to futuristic with design keep everything simple.

- Onboarding flow matters, it does matters for my niche i.e. Habit tracking

- If onboarding survey is added that would help your users being more emotionally attached with your product.

- Let me revise one of the important point, don't make futuristic UI Because thats complex and yours majority users won't understand so keep your UI simple.

- You must keep your app updated and release a new version in a month for sure.

- don't disturb your aso for 1-1.5 months

These above listed are my insights and could help you save your time.

Comment down your thoughts 💭

I’ve been dealing with this a lot lately:

- Read a ticket in Jira/Linear

- Jump to GitHub to implement

- Open a PR

- Then go back to the issue to re-read everything and make sure I didn’t miss anything

Feels like I’m constantly re-loading context instead of staying in flow.

After running into this over and over, I ended up building a small side project (krnel.app) to experiment with keeping issues and PRs in one place.

Not trying to promote it here — I’m more interested in understanding:

- Do you experience this too?

- Or is this just something you get used to over time?

Curious how others deal with it.

https://preview.redd.it/l1w8qr6krawg1.png?width=2067&format=png&auto=webp&s=4e89acba1f832838c1d930c5d414e7f531319d7b

Just wanted to start off with how absolutely blown away i am by this new model. I am running the bartowski/Qwen_Qwen3.6-35B-A3B-GGUF IQ4_XS quant on my 5090 with the full 256k context.

I am damn impressed! I had asked it a very broad question, to just look for any bugs or issues.
With that huge context window, I noticed it dumping entire relevant files into its context , which it could easily handle, it filled up to 150k~ tokens before dumping its plan, which I am seriously cool with (I like to transfer the plan to a new convo and reset that window anyway)
It was able to find multiple bugs which violated the guidelines set in rules/claude.md

Running on my 5090, it was blazing at around 180 tps - my eyes were wide as I saw the machine work in front of me, it was truly glorious

In contrast, I tasked slowpus 4.7 to the same task. After taking literally 10x longer and using my entire 5hr usage window, It didnt even find half of the legitimate bugs that my local setup found.
I noticed that claude was MUCH more careful about loading up the context, performing a ton of greps and text searches, sure its much more efficient for anthropics servers, but it will never beat half of the codebase being loaded straight into context lmao

Overall, the past 6 months has fealt like flying on top of a rocket - it was so useless months ago, now its super smart and insanely fast, my mind it literally blown rn

been testing a few ai coding tools recently and something feels off once you think beyond individual use

like yeah, for a single dev:

• generate code fast
• fix bugs quickly
• automate small stuff

but in a team setting (even like 10–20 devs), things get messy:

• everyone uses it differently
• no shared understanding of the codebase
• reviews become inconsistent
• onboarding new devs is still painful

feels like most tools are built for individual speed, not team consistency

recently tried setups where AI is more embedded into workflows (PRs, reviews, codebase understanding etc) instead of just being a chat tool
felt more stable, especially for keeping things consistent across the team

curious how others are handling this
are you treating AI as a personal tool or something integrated into team workflows?

I’ve been trying to use MCP servers for local AI agents and honestly, discovery + setup feels messy.

For example:

- Found 5+ tools on GitHub → no clear docs or install steps

- Some don’t work with my setup (llama.cpp)

- No way to quickly test before integrating

Curious:

- Where are you actually finding reliable MCP tools?

- Do you just stick to a few trusted ones?

Feels like there’s a gap for something like a “verified MCP registry” with easy testing.

Am I overthinking this or are others facing it too?

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Claude Sonnet 4.5 error spike

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/8rg3l7v56ngc

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/

I love focus apps, and I have been using Focus Flight, I have seen a lot of comments on the trainspotting / trains communities asking for a similar app but for trains, and there are also features that the users of Focus Flight ask for but they don't seem to be building it.

So I put myself down to work. Spent a month researching how train tracking apps get their route, went deep into the internet and found a large dataset of real stations, cleaned it up verified the data and then started building the app.

Built with Swift, UIKit, MapBox and SceneKit, Focus Rail is a focus timer app where you select your starting station and then choose your destination and then actually get to travel on a 3D train on the actual routes. My cousin tried it on his train journey on the famous London to Paris Eurostar, and he said that the route he went on and the route shown on my app matched, the timing, turns and everything.

Whatever route exists in the real world, you can ride it.

I'm sharing here as I just launched and I'm an indie dev trying to get my first real users who aren't my friends and family.

Open to feedback, ideas or just hearing what routes you'd try first. Also looking to design and add more train models so please provide any train models you'd like to see in the app!

https://apps.apple.com/us/app/focus-rail-pomodoro-timer/id6758016543

Why BrainDB?

Inspired by Karpathy's LLM wiki idea — give an LLM a persistent external memory it can read and write. BrainDB takes that further by adding structure, retrieval, and a graph on top of the "plain markdown files" baseline.

• vs. RAG. RAG is stateless: embed documents, retrieve similar chunks on every query, stuff them into context. There's no notion of an entity that persists, accrues connections, or ages. BrainDB stores typed entities (thoughts, facts, sources, documents, rules) with explicit supports / contradicts / elaborates / derived_from / similar_to relations, combined fuzzy + semantic search, graph traversal up to 3 hops, and temporal decay so stale items fade while accessed ones stay sharp. Retrieval returns a ranked graph neighbourhood, not a pile of chunks.
• vs. classic graph DBs (Neo4j, Memgraph). Those are general-purpose graph stores with their own query languages and ops cost. BrainDB is purpose-built for LLM agents: a plain HTTP API designed for tool-calling, semantically meaningful fields (certainty, importance, emotional_valence), built-in text + pgvector search with geometric-mean scoring, always-on rule injection, automatic provenance, and runs on plain PostgreSQL + pg_trgm + pgvector — no new infrastructure to operate.
• vs. markdown files as memory. Markdown wikis are flat and unstructured: the LLM has to grep, read whole files into context, and manage linking by hand. BrainDB's entities are atomic, queryable, ranked, and self-connecting. Facts extracted from a document automatically link back to the source via derived_from; recall returns relevant nodes plus their graph neighbourhood; nothing needs to be read in full unless the agent asks for it.

https://preview.redd.it/l9sgqnfgsawg1.png?width=3572&format=png&auto=webp&s=8dc26a4e89526137b919f82acff985a7a4c1c25b

https://github.com/ttnear/Clarc

This is my first open-source project. I wanted my non-developer coworkers to be able to use Claude Code. The terminal was the wall — installing the CLI, setting up SSH keys for GitHub, approving every tool call without any real preview of what was about to happen. None of that is a problem for me but all of it is a problem for them.

So I built Clarc. It spawns the real claude CLI under the hood, so everything you already set up — CLAUDE.md, skills, MCP, slash commands — works unchanged. It just gives you a proper Mac app on top: native approval modals with the actual diff before tools run, per-project windows you can run in parallel, drag-and-drop attachments, GitHub OAuth with automatic SSH key setup so cloning a repo just works.

Funny thing: I built it for them, but somewhere along the way I became the main user myself. Haven't opened the CLI directly in about three weeks.

Hey everyone,

I’ve been building this project.

The idea came from noticing that a lot of companies still think almost entirely in terms of traditional Google SEO, while more and more discovery is starting to happen inside tools like ChatGPT, Perplexity, Claude, and Gemini.

That creates a weird gap.

A company can have decent SEO and still barely show up in AI answers, because being rankable and being citeable are not exactly the same thing.

The more I looked into it, the more it felt like a real problem.

A lot of sites are not built to be good sources for AI systems.
They might have content, but:

• the answers are buried
• the structure is messy
• they don’t cover enough related queries
• they lack trust/authority signals
• they’re just not easy for LLMs to pull from

So I started building BeVisible around that.

The core idea is helping brands improve visibility not just in traditional search, but also in AI-generated answers.

A simple way I think about it is:

• retrievability — can the system actually find your page?
• extractability — can it pull a clean answer from it?
• credibility — does your site/brand look trustworthy enough to mention?

So this is less about “AI writing blog posts” and more about building content and structure that makes a brand easier to surface across both Google and AI search.

I also wrote a deeper breakdown of the thinking here

Pro > max 5 > max 20

The dark side has been joined. Long live the empire 🫡

I have been working on my side project for a while now, and some people have been able to use it. The problem is that they do not come back after their first visit.

I have tried adding more features, sending followup emails and running ads to get people to come back but nothing has worked yet.

I am beginning to understand that getting users is easy keeping them is the hard part. I have worked so hard on the launch but I still feel like I am missing something when it comes to keeping users.

Have any of you had to deal with this? What have you done to keep people interested in your project and coming back?

The reviewer wasn't wrong. At least, not entirely.

Getting genuine, constructive reviews for a new application or landing page is becoming increasingly difficult. Review exchange platforms, where you review someone else’s project to earn credits for your own, are growing in popularity. However, they are often saturated with users who leave a single, low effort sentence just to get their credits. As a product owner, you usually have to review and accept these comments, and the hard truth is that even a short comment isn't necessarily wrong, just bad.

In my case, the reviewer was spot on. I used AI to help design my landing page, and it didn't turn out as well as I had hoped, so I accepted the feedback. But if they had given me anything more than just "AI generated slop," I would have had something actionable to fix. Instead, they just pointed out the obvious without offering any insight into how I could improve the layout or user experience.

As AI gains a stronger foothold in our everyday lives, we as a developer community need to adjust how we view "AI slop." Believe me, I am losing my mind just as much as the next person when I see yet another purple gradient website packed with AI buzzwords. But we need to ask ourselves: is it the creator's fault for not knowing better, or is it our failure for not providing better guidance when they ask for it?

Missing Out on Brilliant Ideas

Many of the people building these new apps aren't traditional developers. They might be domain experts in a completely different field who are using AI to write code for the first time. When we take one look at their UI and immediately dismiss their project, we are missing out. Take a hypothetical carrier pigeon expert, for example. Their new app might solve the biggest pain point in the pigeon breeding community, but because they don't understand color theory or UI layout, their brilliant idea gets buried under bad design.

This is where experienced developers need to step up. Instead of just scoffing at the design, we need to explain why it looks like AI generated slop and guide them on how to fix it. We need to support this new generation of builders, because they are entering the industry whether we like it or not.

Instead of writing 'AI generated slop', an experienced developer could say: 'The purple gradient and AI buzzwords like leverage and testament are flags of an AI designed site. You should try to use your own language in the text and use sites like Coolors to create a color palette you like for your page. I would also remove one of the two CTA buttons in the hero and just keep the most important one.' That alone will give the creator a clear starting point on how to improve their application.

Bridging the Design Gap

To bring it back to my own experience: I started my career in backend development before moving to the frontend. That doesn't make me a designer. It just means I am good at taking a completed Figma file and turning it into working code. Architecture, logic, and code structures are my strengths; UX and visual design are my weaknesses. If I don't have a designer, opting not to use AI to bridge that gap would be foolish. I can promise you that without AI, my design would look a lot worse than "slop." One of the tricks I have learned is to use specific skills for UI design and giving the AI strict constraints in the prompt. Instead of asking 'Create a landing page' I can ask it to 'generate a clean, modern landing page with this color palette. I want only one simple CTA button and a clean hero section. Angle it as a mobile first design'. This alone will not make the landing page perfect, but it would make it better, maybe.

Ultimately, behind that purple, AI generated gradient, there might be a developer with an incredibly innovative idea. We just need to look past the poor UI and help them reach their actual potential.

Fun fact: A human wrote this, but Gemini AI proofread it.

Read the post on Featurely

Another reason to go local.

Figured I could give it a go, wanted to make a website that reminds me of the old internet era, and I say it kinda nailed it!

Sure, there some stuff that needs tweaks but overall it looks good to me
(Too bad it killed most of my usage for the tool, lol)

Hello everyone!

I’m new here as I have decided to go local. My main goal is to run vulnerability research on open-source software. I have bought GMKTEC EVO-X2 Ryzen AI Max+ 395 128GB RAM 2TB SSD and I plan to install ubuntu on it to run llama.cpp . Im planning to run openclaw and two models at the same time: llama 4 scout as master brain and qwen 2.5 coder for code analysis engines.

Do you have any tips/advices?

Thank you in advance!

pensez-vous pouvoir écouter un truc bricolé dans mon coin avec des IA bricoles qui devraient vous intéresser.

voulez vous en savoir plus jai des centaines d'audiovisuel vidéos pdf et images et texte.

mais a moi seul je suis rien sans votre puissance.

ceci est un appel a l'aide d'un mec qui veut vous aider car il a compris que vous avez raison

mais moi sans vous je ferrais rien.

qui veut jouer ?

Hi Reddit! I’m a ham radio operator (OK1PNK) and a solo developer. I’ve always loved the 'randomness' of radio—the ability to just key up and talk to someone nearby.

I spent the last month or two building Ketska. It’s a real-time voice app designed for privacy and local connections.

The "Why":

Every app today wants your email, your phone number, and your soul. I wanted the opposite.

What makes it unique:

• 0% Friction: No 'Sign in with Google', no forms. You open the app, and you're on the air.
• Blurred Privacy: I’ve implemented 'Blurred Location' (250m offset). You see people in your area to talk to, but nobody knows exactly where you live.
• Real-Time: High-quality, low-latency audio built on LiveKit.

The "Cold Start" Problem:

Building a social app as a solo dev is hard. Right now, the map is a bit of a ghost town. It’s a classic chicken-and-egg problem: people join, see no one to talk to, and leave.

I’m looking for early adopters, radio nerds, hikers, or just curious people to help me break the silence. I want Ketska to be a place where you can find a local 'signal' without giving up your privacy.

I’d love to get some 'signal reports' from you guys! What features are missing? Is the UI intuitive?

Links: * App Store (iOS) * Google Play (Android) * Web Version

73s!

I cannot emphasize enough how useless the new adaptive thinking models are. At the moment, I am using claude to work through some statistical properties of estimators that I am using. It keeps making mistakes all the time to the point that it would have been faster if I just derived everything by hand, which defeats the whole puprose of using claude. This used to be much less of an issue while I could keep extended thinking always on, it is clearly an issue because it responds immediately without thinking.

Even if I tell it to think it through because it is important, it 80-90% of the time just starts responding immediately with the first line being something along the lines of "You're right. Let me think this through properly.", and then later the classic "Wait, this doesn't work.". Avoiding these outcomes is the whole point of extended thinking and adaptive thinking seems to be very bad at gauging whether to use thinking or not.

Has anyone found a way to force the adaptive thinking models to think? Or am I just stuck using Opus 4.6 and Sonnet 4.5 until they are removed?

Note: I am using the web interface, claude.ai, not claude code or anything like that.

We have been building Gaia, an AI personal assistant that does things proactively. One of the biggest issues we ran into was our agent getting stuck in loops. When someone asked "check my recent PR on github" the agent would call Github List Pull Requests 10+ times in a row or even a tool used to give a answer it still used to retrieve tool and keep trying.

We spent a lot of time thinking it was a prompt issue or a retrieval issue and kept patching things without fixing the root cause.

After digging into the codebase we found the real problem: there was no explicit exit condition in the loop. The loop only stopped when the model randomly decided to stop calling tools or hit the recursion limit. Nothing forced the model to consciously decide it was done.

The fix came from reading the OpenAI practical guide to building agents which mentioned every agent loop needs a clear exit condition. So we added a finish_task tool which the model has to explicitly call when it has the answer. The loop immediately exits the moment finish_task is called.

That plus lowering the recursion limit from 25 to 10 completely fixed it. The same request that used to call 10+ tools now finishes in 3.

If you are building agents and hitting similar loops, tldr: your agent needs an explicit way to say "I am done" not just an implicit one.

Hi everyone, I've got a question regarding the CODERBYTE website. I'll be honest I have an assessment in about a week's time that I'm not confident in passing😂. The problem is that once the you log into the assessment it tracks the tab so if you move to any other tab it immediately flags it as cheating. You're also not allowed to copy and paste anything during the assessment and if you do it's also flagged. Does anyone know how I can cheat on the test, without getting flagged for cheating? I've used claude continously on VS code so I intend to use it to generate the answers where I get stuck. It'll be of immense help guys, I trust someone here has experience with these types of tests. Thank you.

beta merchant. real store. 2 years in.

she checks analytics every morning. knows her traffic sources, bounce rate, conversion rate. not a beginner.

gave me access this week. first thing i looked at which sections of her homepage visitors actually see on mobile vs which ones they scroll past.

her featured products section the centerpiece of her homepage, the thing she spent 3 weeks designing was below the fold for 71% of mobile visitors.

not broken. loading fine. just sitting below where most people stop scrolling on a phone.

she's been running instagram ads for 6 months. almost all mobile traffic.

71% of those visitors landed, scrolled a bit, never saw her products, left.

she stared at the screen for a moment.

so every ad i hve run has been sending people to a page where they canot see what i am selling.

2 years of daily analytics. never surfaced this once.

I made a Chrome extension that turns every new tab into a simple F1 dashboard with widgets.

Every time you open a tab, you see things like:

• Live session standings

• Lap time comparisons

• Driver t constructor standings

• Race calendar + countdown

• Strategy info (when available)

• News feed

You can rearrange or remove widgets, and pin your favorite driver/team.

It's not an official F1 product, just built using public data. Core features are free.

Extension: https://chromewebstore.google.com/detail/akaanfgjfcfcjgnaaokjgolaceoldgbm

Site: f1x.club

Would love your feedback, especially what data you actually look at during sessions and what's missing.

Are MCP servers / AI tools feeling too fragmented right now?

I’ve been exploring AI agents and noticed that tools (like MCP servers or similar integrations) are spread across GitHub with no clear way to discover, test, or install them easily.

Curious:

- Do you struggle to find reliable tools for your agents?

- How do you currently discover and test them?

- What’s the most annoying part of using these tools right now?

Would love to hear real experiences.

Hi. So i am quite new to all this.

But i am on my way to setup qwen image edit locally woth comfyUI.. i think.

What I want to do, and it's the sole thing..

I want to edit gta 5 ingame screenshots and make them nice in various ways, change clothes. Poses. Add details. Just make the photos I want without complex posing and photo editing and mods in the game.

All while keeping the style of the game or near max grahpics with mods.

Any guides on the setup or even loras for this? Would i need to train my own lora to do ingame screens you think?

Hi, I’m trying to automate my development routine with Claude Code, but currently I’m only doing planning + editing, but I’m sure this can be optimized using plugins and skills. With all the noise on the internet, it is hard to find an efficient workflow. What I’m looking for: task description(input) -> tech design -> implementation -> unit tests -> refactoring -> pull request. Would really appreciate any tips or what has worked for you

Before work, I came across a post from a guy, he was talking about a new way to make a bit of money

In about two hours, I managed to make $89, those who have more time can make more

He left the guide in a pinned post on his profile, waltwhiteee just click to check it out

It worked for me, so I decided to share, maybe it’ll help someone else too

Hi, i purchased it for myself and want to share the extra ones, as i needed these subscriptions. I use these in daily coding work so, Just dm me, 7$ per seat or more seats as needed i will give discount.

I am looking for people who can contribute to account for monthly basis rather than going through multiple random guys online so let's get it done.

I can do PayPal.

I've been getting a bug since morning where Claude takes forever to reply on the UI, and Claude Code is showing "API Error: Unable to connect to API (Connection Refused)" on my machine. Had to get some work done, so I put some money into OpenRouter and tried some highly rated models for my work(I mostly use claude, so I was testing which one to use)- Qwen3.6, GLM-5.1, GPT-5.4. The time-to-first-token on all of them was painful. Kimi K2.5 didn't even respond, stuck in processing. Gemini threw an error.

I considered switching to Codex, but GPT-5.4 didn't feel that smart, I'd take Sonnet over it. How are you guys getting uninterrupted, fast, SOTA-level LLM access?

shipped vemb 0.3.0 this morning. it's a python CLI that wraps gemini embedding 2 for text/images/audio/video/pdfs. like httpie but for embeddings.

the big change in 0.3.0: dropped the json cache in favor of a binary .npy matrix + tiny manifest. for a 5000-vector cache at 3072-dim: - file size: 317MB json → 61MB .npy (5x smaller) - warm-cache search: 4.3s → 1.6s (2.6x faster) - cosine stays exact, no ANN, no approximation

side note on what didn't work: first tried "just replace the python cosine loop with a numpy matmul." on a fresh python subprocess it was actually slower at small N because numpy's import cost (~180ms) + asarray conversion from JSON lists (2+ seconds at N=5000) ate the speedup. the real fix was changing what's on disk, not how it's computed.

pip install -U vemb repo: github.com/yuvrajangadsingh/vemb pypi: pypi.org/project/vemb

feedback welcome, especially if you've built anything similar or hit the same JSON-cache-is-slow issue.

This is an automatic post triggered within 2 minutes of an official Claude system status update.

Incident: Claude Sonnet 4.5 error spike

Check on progress and whether or not the incident has been resolved yet here : https://status.claude.com/incidents/8rg3l7v56ngc

Also check the Performance Megathread to see what others are reporting : https://www.reddit.com/r/ClaudeAI/comments/1s7f72l/claude_performance_and_bugs_megathread_ongoing/

First session of the week. (Only said "Hi" yesterday to start the weekly clock...)

Aside the fact that the 10% session is comprised of only 7 messages of rather light discussion (only 3 files read, let alone written anything, Opus 4.7 mid effort), the 3% in the weekly category comes just from this session.

So not only does a 5 hour session in Pro give you not more than a single feature write up, but now you effectively only get 3-4 of those per week? Until next week the bottleneck was only the 5-hour-limit and you could sneak your way around that by preparing plans and firing of plans at different 5 hour windows throughout the day.

Is this a fluke or have they reduced weekly limits as well?

Glad I cancelled effective the 26th.

Order of events:

A) 2x token usage at the peak hours.

B) then nerfed Opus 4.6

C) now continuing the endless feature release cycle which could melt down the compute even more

D) Release Project Glasswing to give millions of tokens in charity to the already rich top 50 companies

E) Locked in the adaptive reasoning for the Opus 4.7

(A) was implemented to tackle peak hour usage. But then why do (C)? Is it to reach the same point of peak hour usage again? then you will get the chance to bump the token usage even more? (ohh no! wait, you just bumped the token usage for 4.7, following this exact plan)

Why are you trying to bite off more than what you can chew?

Anthropic you were so good. But now it's turning into a nightmare for the existing users.

The Free plan hits limits with just a few messages. The pro plan is 80% there with the free plan. Even the Max Plan Users are complaining.

Do you not want your existing user base to keep using claude?

I am genuinely frustrated with so much friction we are facing right now.

For a 19-year-old building his first public project solo, it honestly means a lot seeing real people actually finding and using something I made.

No team, no budget, just learning as I go and trying to build a cleaner, more useful link-in-bio tool.

Still a long way to go, still improving daily, but grateful for every visit, signup, and bit of feedback.

Appreciate everyone who’s supported it

I'm new to this and have some beginner questions:
I've got a long daily commute and need to improve my German.

I would like something that I can chat with, and get corrections on things I'm repeatedly doing wrong (grammar, pronouns, etc).

The internet connection isn't great along the route so I'm looking at something I can have running locally on a laptop.

Are there any plug an play options out there?

From what I have read so far Ollama with qwen2.5 using Vosk and Piper should work. Is there anyone here that has a similar set up with advice on anything to be aware of?

I have a problem with movement and background. I'm trying to create a long video in which a mermaid swims in the ocean, I want her to swim past a sunken ship, a coral reef, but the mermaid from the existing photo moves in place, the background doesn't change or suddenly a completely different background appears. There is no forward movement. I've already come to terms with the fact that hair grows with every movement. I've tried a LOT of prompts, if the mermaid starts swimming, it becomes drawn, not like a photo. I used SVI PRO with Q8 gguf( Q3, Q5), I tried Wan2.2 i2v, a sharp change in the background (colors, etc.) Maybe there is a suggestion on how to somehow preserve the image (who is a specific person, is her lora) and achieve movement. Neither Chatgpt nor others help.

They uploaded it on Hugginface and took it down. The worst thing is that I saw it up while at work and wheb I came home and wanted to download it, it was gone. Found a post where was written that they uploaded it by mistake. But there is a thing, there are people that downloaded it for free and there's me who should pay for it, and I don't wanna 😂

So I searched for days on different forums and finally found it 😁

Kugel-2 https://storage.to/Hc3940HmE

Hey everyone,

I'm a programmer and I'd love to use local LLMs as a kind of "superpower" to move faster in my day-to-day work.

Typical use case: I'm working on a codebase (Rust, Python, Go, or TypeScript with React/Vue), and I want the model to understand the existing project and implement new features on top of it — ideally writing code directly in my IDE, like a pair programming partner.

Right now I've tried cloud models like Claude, Qwen, ChatGPT, and GLM. Results are honestly great (especially Claude), but cost and privacy are starting to bother me — hence the interest in going local.

My current setup:

Ryzen 9 9950X 96 GB DDR5 RAM GPU still to choose

I'm considering a few options and I'm not sure what makes the most sense:

• Option A: Add a GPU

Nvidia 5090 (~€ 3500) AMD R9700 32 GB (~€ 1300)

Option B: Go all-in on a MacBook Pro M5 Max (128 GB RAM, ~€ 7000)

My main questions: 1. Are there local LLMs that actually get close to Claude-level performance for coding tasks?

1. Are there solid benchmarks specifically for coding + codebase-aware edits?

2. Which local models are currently best for this kind of workflow?

3. How much VRAM / unified memory do you realistically need for this use case?

4. Dense vs MoE models - what works better locally?

5. Does generation speed really matter that much? (e.g. 45 tok/s vs 100+ tok/s in real usage)

6. What tools are people using for this? (IDE plugins, local agents, etc.)

7. How can I test these setups before dropping thousands on hardware?

Curious to hear from people who are actually running local setups for real dev work (not just demos). What's your experience like?

I'm buying a new lock with a new house, have been rocking the schlage connect for the past 6 years, and it served me well enough with the keymaster add-on, but I'm looking for a bit more future lock (and something that doesn't wake the dead when locking/unlocking).

I'm currently leaning towards the switchbot with face/palm unlock keypad for the normal entrants, but was curious how well the thread support was on this lock.

Does anyone know: via matter/thread (not the app) can you manage one time codes or scheduled code, or is it only going to allow lock/unlock actions? Does it support knowing which code was used to unlock?

Also, as far as I can tell, you don't need to get any hub (I have a thread border router), right?

I'm in the US if region support changes things.

Got awarded $2,500 worth of OpenAI API credits from a recent hackathon, but I’m already stacked on credits from Anthropic and won’t realistically be able to use both to their full potential.

Rather than let these go underutilized, I’m looking to pass them on to someone who can actually build, experiment, and ship with them.

💡 Details:

• Total credits: $2,500 (OpenAI API)
• Asking price: $2,000 (negotiable for serious buyers)
• Ideal for: builders, indie hackers, startups, students working on AI products, agents, LLM apps, or anything GenAI-related

If you’re currently building something in AI or planning to, this could be a great way to extend your runway at a discounted cost.

I’d much rather see these credits power something meaningful than just sit idle in my account.

Happy to verify authenticity, hop on a quick call, or work through a safe transfer process if needed.

If interested, drop a DM or comment below. Also open to connecting with builders working on interesting problems—always up for a good conversation around AI, startups, and tech.

Let’s make something impactful 🚀

I just saw a new limit named "Weekly · Claude Design" being visible in Claude Code today.

https://preview.redd.it/oubheeksgawg1.png?width=354&format=png&auto=webp&s=e128e1d5b6b309705a5217ae157e61b788605335

I just wanted to know if I use Claude Design, will it contribute to the "Weekly · All models" limit or not?

Its Monday today, limits will reset on Friday and I am already at >50% weekly usage. If Claude Design limits are entirely separate, then I would like to try out this new overhyped feature that everyone is talking about. Otherwise, as a backend Software Engineer, I am not into design, so I will skip this one.

FYI, I am on Standard/Premium seat in the Teams plan (my company hasn't disclosed this, but I am pretty sure its premium seat because I am not running out of my 5h window in a single prompt like many users have reported here).

been doing this for about 3 months and wanted to ask before i lose my mind further

every day i'm running 3-4 ai tools for one task. claude for research, midjourney for the image, runway for video, sometimes chatgpt for text. and i'm constantly re-explaining context to the next tool.

the re-explaining is fine, i can do that. but here's the thing that's been bugging me:

when i ask claude to write the midjourney prompt for me instead of writing it myself, the output is genuinely better. claude condenses the research, picks the visual elements that actually matter, formats it the way midjourney wants. i'm bad at writing midjourney prompts. claude isn't.

so basically i'm doing the worst version of orchestration manually, when the ai could do it better.

two real things i type into ai every week:

prompt 1:
"look at top youtube thumbnails for 'ai tools for beginners' this past month, find what's actually working visually. then design a thumbnail for my video 'i tested 12 ai tools so you dont have to' and generate the image"

prompt 2:
"find the top 3 ai industry headlines this morning. generate a newspaper-style front page with those headlines on it. make it look like a real newspaper, not generic ai art"

both of these need 3-4 model jumps. research model → text/concept model → image model → sometimes video. and i'm the dumb middleware copy pasting between tabs

what i've tried:
- n8n: works but maintaining a workflow that keeps changing is brutal, not technical enough to extend cleanly
- langchain: same, more pain
- lindy + relay: great for the first 2 flows i built. second i needed something slightly different, the abstraction broke
- chatgpt projects / claude projects: memory helps for ONE tool, useless when i jump to image gen
- just doing it manually: which is what i do now most days

two questions:

1. is there a chatbot where i paste a prompt like the two above, it picks the right model for each step, runs it, asks for my approval/edit before moving on, and rewrites the output of step 1 as the input for step 2? not a workflow builder with nodes. just a chatbox.
2. which model is actually best for what in april 2026? midjourney still best for product photos? photorealistic? anime? characters? same q for video models. is there a maintained source of truth or is everyone just guessing from benchmarks (which i hear are gamed)

if you have a workflow that solves either, please share. ill probably end up building the chatbox thing for myself if nothing fits, but the model-source-of-truth is a real gap i don't know how to fix on my own

https://preview.redd.it/ib8dlua8hawg1.png?width=1255&format=png&auto=webp&s=5b3769bfc82f1d4a9538616774f4aa223f962861

Believe it or not 🤯
but officially… the code used to train and develop the Horus-1.0-4B model is now open source ❕

This means anyone can use the code to:
• Learn and understand how models are built
• Benefit from ready-to-use code
• Train their own models
• Fine-tune Horus-1.0-4B for specific tasks

🔥 Horus, which led the AI scene during April,
is now fully available with its complete source code for any developer or researcher who wants to build on it or improve it.

And if this is your first time hearing about Horus — it is the first open-source LLM trained from scratch in Egypt, developed by TokenAI.

Here’s the model link:
tokenaii/horus · Hugging Face

You can access the code easily on GitHub:
https://github.com/tokenaii/horus-1.0

📜 The project is released under the MIT License,
which gives you full freedom to use and modify the code, as long as you keep the license text and credit TokenAI.

The goal of this bold step is to create stronger and better opportunities for developers to build their own projects.
And just like the Horus model was open source, today the code that contributed to its development and training is also open source 💯

—

https://preview.redd.it/3y9r6ztbhawg1.png?width=1536&format=png&auto=webp&s=1db41ef9fcf7803b6e5c0a2fd8757d34d2e49a5c

Assem Sabry

It's been about 3 years ive been using models.

Coding seems to be the only use case for which I come back to Claude for.

I'm curious what are some other fun use cases that you or others use Claude or any other AI, for that matter, and do it regularly.

I was exploring this internal developer platform and saw that they have an MCP connector to connect with any of the developer tools. Also, they have the skills registry that helps developers automate their entire workflow. So wrote a simple tutorial and made a video on the same. As per this subreddit rule, sharing the links in the comments.

I do a lot of competitive UX research for work and the workflow has always been terrible. Tabs everywhere, notes in a doc nobody reads, screenshots that lose all context by the time you're writing the report.

I built Scout to fix my own problem. It's a Chrome extension that runs Gemini-powered analysis on any site you browse and surfaces insights as on-page annotations. Export a clean report when you're done.

For months it just lived on my machine. I kept adding small things, polishing it, telling myself it wasn't ready. Classic side project trap.

Eventually I just launched it today on Product Hunt.

A few things I learned building it:

• Scope creep will kill you. Early versions had way too many features. Cutting back to browse, annotate, export is what made it actually good

• The Gemini integration was the fun part. Getting it to feel invisible and fast was the real challenge

• Shipping something imperfect beats waiting forever for perfect

Would love to connect with other builders here. What are you working on?

link: : https://chromewebstore.google.com/detail/ecmkeokcmiflgkfnnhbcmcklmdobkila?utm_source=item-share-cb

We have been building and deploying AI agents for businesses for a bit now. The jump from "automate this task" to "run this autonomously end to end" is where most implementations fall apart and it is rarely the model that is the problem.

The things that actually break:

- Handoff points. The moment an agent needs to pass context to another system or wait for an external trigger, things go wrong. Most workflows were not designed with agents in mind so the gaps between steps become failure points.

- Error handling. A human doing a task knows when something looks off and stops. An agent without proper guardrails will confidently keep going in the wrong direction for a long time before anyone notices.

- Trust calibration. Teams either give agents too much autonomy too fast and something breaks in production, or they keep humans in the loop for every single step and then wonder why nothing is faster.

The reality is that most businesses are not ready for full autonomy yet, not because the technology is not there, but because their processes were never documented well enough to hand off.

What is the hardest part of agentic workflows that people here are running into?

Did anyone else see that WSJ article floating around the front page claiming "China is making strides in open-source artificial intelligence" because of Qwen? Or a16z casually throwing around the term to hype up their portfolios?

https://www.wsj.com/opinion/to-beat-china-embrace-open-source-ai-a211bf59

I am quite tired of watching mainstream media and tech giants completely hijack the terminology.

Let’s get one thing straight, and I know most of you here already know this, but it needs to be said out loud: Alibaba’s Qwen, Meta’s Llama, and Mistral are NOT open-source. They are Open Weights. There is a massive, fundamental difference, and letting them blur the lines is actively damaging this community.

In traditional software, "open-source" means you get the source code. You can see exactly how it was built, modify the foundational logic, and compile it yourself. In the world of LLMs, the actual "source code" is the training data and the training code.

What Meta and Alibaba are giving us isn't the source. They are handing us a baked cake (the final, pre-computed matrices of weights), but they have locked the recipe, the ingredients, and the oven inside a multi-million dollar corporate vault. Its basically share-ware.

Am I just being a pedantic nerd about semantics? No. Here is why this "open-washing" is actually toxic:

• It’s Corporate PR Bullshit: Tech giants are stealing the moral halo, community goodwill, and free labor of the open-source movement without actually adhering to its ethos. They get to wear the "good guy" badge of transparency, while keeping their most valuable IP (the trillion-token datasets) n a total black box.
• It Kills Reproducible Science: How the hell are we supposed to genuinely audit a model for bias, security vulnerabilities, or copyright infringement if we have zero clue what it was trained on? You can't. "Trust us, we cleaned the data" has replaced the scientific method. How do we know there isnt Order-66 hiddein in it?
• It Destroys the OSI Definition: True open-source software (like Linux) comes with unalienable freedoms. You can use it for whatever you want. Slapping the "open-source" label on models that are burdened with restrictive Acceptable Use Policies and commercial limits degrades the protections the open-source community spent decades fighting for.

Don't get me wrong. Having free access to Qwen’s or Llama's weights is incredible. They are beastly models, and the fact that we can quantize them, fine-tune them, and run them locally on consumer hardware is a massive win for the scene. I am grateful for "Free Weights"

But words mean things.

We need to stop letting venture capitalists and journalists redefine open science just to pump up their PR metrics. Until these companies drop the unredacted training code and a torrent link to their multi-trillion token datasets, they haven't earned the right to call themselves open-source.

End rant. What do you guys think? Am I overreacting, or do we need to start calling this out every time we see it?

So i've been going deeper into AI agents lately, specifically ones that generate and run code on your behalf, and something has been bugging me that I don't see discussed enough here.

Most of the agent setups I've tried (Auto-GPT style stuff, some custom things with LangChain, etc.) basically just... execute whatever code the model spits out? Like on your actual machine, with your actual permissions. And we're all just kind of okay with that apparently?

I had a situation a few weeks ago where I was testing a workflow that was supposed to parse some CSVs and it decided to install a pip package I'd never heard of and write to a temp directory. Nothing malicious happened but it made me realize how much trust we're putting in these systems. Especially when you start giving them tool access, to API keys, file system permissions - it gets sketchy fast.

Anyway that whole experience sent me down a rabbit hole looking for agents that take sandboxing seriously. Tried a few things, eventually stumbled on Clambot which runs all LLM-generated code inside a WASM sandbox. So the model can still write and execute code but it's contained - no unrestricted access to your system. It also has this approval flow where you can okay tool access interactively which honestly should just be standard at this point. Been using it mostly through the CLI and Telegram integration for personal assistant type stuff (summarizing youtube videos, fetching web pages, scheduling reminders). Nothing crazy but it's nice knowing it's not just yolo-ing shell commands.

I know OpenClaw and Nanobot exist in a similar-ish space but I haven't seen much discussion about how they handle the execution security side of things. Does anyone know if they sandbox generated code or is it more of a "trust the model" situation?

More broadly - for those of you building or using AI agents that execute code: what's your approach to security? Are you running stuff in Docker containers? VMs? Or just vibing and hoping the model doesn't rm -rf something important?

Genuinely curious because the more capable these agents get, the more this feels like a ticking time bomb that nobody's really addressing.

https://preview.redd.it/kbq98877qawg1.png?width=4096&format=png&auto=webp&s=1ba5b4b75f5001abc3cdc5c8e387c100105bd964

It always becomes 9 small pictures in the result, I only changed the DiT model to 3b_fp16 version from the default workflow.

I got tired of SSH-ing to bastions and typing the same pg_stat_activity queries at 2am, so I built a Health Monitor tab into data-peek (my minimal SQL client). It shows active queries, locks, cache-hit ratios, and table sizes, refreshes every 2–30 seconds, and has a "kill" button next to each active query that calls pg_cancel_backend.

Writeup with the actual SQL behind every panel: https://datapeek.dev/blog/connection-health-monitor-in-a-sql-client

data-peek itself is MIT-licensed on the desktop side, free for personal use. Feedback welcome — especially on the "ShareImage" button that generates clean screenshots of the dashboard for pasting into incident Slack channels, I'm not sure if that crosses into gimmick territory.

https://preview.redd.it/o2h6om9qkawg1.png?width=1920&format=png&auto=webp&s=0e0b074c0712bc86c840b7a458f34738d0b6599e

https://preview.redd.it/36ch8keskawg1.png?width=1080&format=png&auto=webp&s=fc829bb2536389320057eaaa2288bd00948db7fa

I didn't expect this result. I knew Qwen3.6-35B-A3B-UD-Q4_K_S was capable of generating 3D scenes, but this was unexpected. I found the original screenshot on r/OpenAI and asked Qwen to recreate it. I nudged it to round out the furniture and add some texture to the rug

I ported Microsoft's TRELLIS.2 to run on Apple Silicon via PyTorch MPS. The original depends on five CUDA-only compiled extensions (flex_gemm, flash_attn, o_voxel, cumesh, nvdiffrast) that have no Mac equivalent.

Wrote replacement backends from scratch:

- Pure-PyTorch sparse 3D convolution (replacing flex_gemm)

- Python mesh extraction using spatial hashing (replacing CUDA hashmap ops in o_voxel)

- SDPA attention for sparse transformers (replacing flash_attn)

- GPU-accelerated trilinear voxel sampling via torch.grid_sample on MPS

Generates ~400K vertex meshes from a single photo in about 3.5 minutes on M4 Pro (24GB). Texture baking takes about 18 seconds using MPS GPU acceleration. Not as fast as H100 but works offline with zero Cloud cost.

Repo: https://github.com/shivampkumar/trellis-mac

I encourage everyone to check Claude's memories (on Mac ~/.claude/projects/[your-kebab-case-project-path]/memories/*).

The 4.7 update seems to be a lot more literal than the previous models. And since Claude reads its own memory into context, old stale memories are possibly interfering more than they used to.

In two separate projects, I was getting kind of insane results with Claude including things that didn't make sense. In the first one, I asked it why it brought up this other part of the codebase, and it referenced a memory. When I looked in my memories there was a ton of outdated stuff that I had forgotten about. Things I definitely didn't want Claude to be thinking about when answering my prompts. So I deleted all the memories and made a single memory, which was not to use memory but to maintain its documentation in the codebase directly.

Results drastically improved, and I applied this to the other project, and it seemed to also help a lot. I should have thought about this sooner because I learned to turn memories off in desktop Claude and ChatGPT to avoid sycophantic or filter bubble behavior.

Give it a try. Hope it helps some of you who are struggling with 4.7.

We tried to connect it for hours, but we couldn’t make it work. We’re using Arduino Uno R4 wifi.

Can anyone help?

I’ve been managing large teams for about 20 years. I thought I understood everything — how to manage people, how to build motivation, how to design business processes, how to deliver results.

But my experience working with AI agents showed me that this is a completely different game.

Some time ago, I started building my own solo startup — a startup where I’m the only human, and several AI agents work for me. I even built an “agents bar,” where agents meet each other to come up with new ideas for their owners while those owners sleep.

For a long time, I had this idea: build a startup without a human team, independent from all the usual constraints. I thought having a large “team” of agents would remove all bottlenecks and let me move incredibly fast.

But in reality, I ran into several nuances that make agents very different from humans — and they force you to rethink how things actually work.

Maybe things in the human world are not that simple. And maybe it’s still not time to fully switch from human teams to agent-based ones.

Here are a few observations:

1. You can’t motivate or inspire AI agents.

Most successful companies are built on inspiration. A founder inspires a team with a big vision, and the team is willing to push through barriers, work day and night, and go beyond expectations.

With agents, this doesn’t work.

You give them tasks — but the idea of a “big inspiring goal” simply doesn’t exist for them.

And yet, in human teams, that kind of vision often leads to results far beyond what seemed possible.

1. Humans don’t hallucinate.

Yes, people make mistakes. But those mistakes don’t scale instantly and exponentially.

In my teams, we even had dedicated time to analyze mistakes and learn from them.

With AI agents, it’s different.

They hallucinate — and keep hallucinating until you explicitly stop them.

1. Experience and pattern recognition can’t be manufactured instantly.

You can’t just create it from scratch. At best, you acquire it through people who already have it.

AI technically “knows everything.” But deep pattern recognition — the ability to spot non-obvious connections, nuances, hidden relationships — that’s still not there at the level of experienced humans.

1. Trust is built differently.

With people, trust is built over time — through shared work, shared results, and proven reliability.

With agents, trust comes from something else: strict validation, testing, edge-case handling, and solid architecture.

You don’t trust the agent.

You trust the system you built around it.

Overall, there are clear advantages to having an “army” of agents working for you.

But it’s definitely not the same as having real people.

With agents, you’re not really managing agents — you’re designing a system.

With humans, yes, you also build systems. But there are things that don’t fit into systems — and sometimes those things are exactly what drives real success.

A business is not built only on systems.

It’s also built by people who can inspire, motivate, bring others together, and create non-obvious connections inside a team.

Curious to hear from others who’ve tried building with AI agents:

* Did you hit similar limitations?

* Are we just early — or are these structural differences?

* What are you doing to compensate for this gap?

And if you’re experimenting with agent-based systems — I’d love to compare notes.

Please let me know what you should use claude design for like I am 5.

I had a quick go with it, didn't work because of some bugs.

Is it like preview in Claude CLI where claude creates html on a local server? Where you can get it to make 9 different options, find ones you like, refine further until you create a handoff for claude to implement or are there other advantages?

That's what I'm currently doing with claude CLI, using preview.

Are there advantages to claude designer or is it a more user friendly version of claude CLI preview that will gradually get more useful like cowork has done? Please, explain to me like I am 5 so I know what to use it for (and when not to) without having to use a lot of tokens experimenting.

I am currently using Llama-3.2-1B-Instruct-q4f16_1-MLC via WebLLM v0.2.82. This is a completely local feature for making a personalised meal plan for the user as per their diet goal, even without the internet so they don't need to look at emails and other notifications first thing in the morning when they want a breakfast for say vegan meal for heart health. Llama works fine for this but anything a little deep in the conversation and its starts to become strange. I was thinking about qwen 3.5 0.8b, but would love to hear from you all, given you would have more experience.

Any suggestions relating to my situation would be appreciated.

There's no gallery picker. That's usually the first thing people notice.

SocialHuman is a social media app where every post has to be captured live, on your phone, right now. No gallery picker, no file uploads, no pasting text from ChatGPT. The text field physically rejects pasted input by tracking keystroke dynamics and timing.

Before anything publishes, seven independent analyzers run on it: EXIF forensics, moire pattern detection, sensor fusion, keystroke dynamics, video forensics, audio validation, and C2PA attestation. Every verified post gets a receipt showing the scores and confidence level.

I built this alone in Helsinki. Six months, from scratch. The stack is Expo SDK 55 with expo-router, Supabase for auth and database, Cloudflare R2 for media storage, and a Fly.io microservice running the verification pipeline. EU-hosted, GDPR by design. The business model is a premium subscription, not ads or data. Core features are free.

The idea started when I realized I couldn't tell anymore which posts in my feed were written by people and which were generated. That was over a year ago and it's gotten way worse since then.

Live on iOS and Android now.

https://socialhuman.dev

Happy to answer any technical questions about the verification pipeline or the anti-paste system.

Describe your project + drop in your rate → get a full proposal PDF in under 60 seconds.

Scope, deliverables, timeline, payment terms all drafted. You just review and send.

Why I built this:

I've been freelancing for years. Every new client still means 2–3 hours writing proposals before I've touched the actual work.

I tried Bonsai, HoneyBook, AND.CO. They all want you to set up your entire business before you can send one document. I just need a proposal. Today. Now.

So I built the simplest version of that tool CLOSR over the weekend.

What it does:

→ You describe the project in 2–3 sentences → Set your rate → Get a polished, client-ready PDF

No templates to fill. No onboarding. No subscription required to try it.

It's free for the first 50 users.

If you want to try it just comment "interested" below and I'll DM you a free access link. No credit card, no setup. 3 months on me.

Would love brutal feedback from this community what would make this actually useful for your workflow?

I want to add an LLM to my Home Assistant (Yellow) so that i can create automations in natural language to better automate tasks rather than complex if-then statements.

I have read quite a few articles and asked Gemini and Perplexity their opinions but they always generalise it and struggle to give me an answer to my specific use case.

I am already thinking of possibly getting an RPi 5 as Ubiquiti has announced EOL of its add-on server, but I might also just get the UDR 7 to replace my TP Link Archer router as all my APs are Unifi.

Should I get an RPi 5 and get the AI Hat or run my LLM on my HA instance in which case I will possibly need to upgrade the CM5 module from 8GB to 16GB version? What have you done to integrate AI and LLMs to your HA instances?

Hey, I’m building a personal multi-agent automation system I call JARVIS. The idea: a Telegram bot as the only interface, where I describe tasks in natural language and a planning agent (Claude Opus) breaks them down, assigns specialized sub-agents (code, UI, data, crypto, etc.), and they execute autonomously using Claude Code CLI as the execution engine. Backend is FastAPI + SQLite, frontend is Next.js, running locally on Windows 11.

Each agent has its own memory, role-specific instructions, and a curated set of tools/skills. The goal is that complex projects get debated with the planner first, then fully executed without me touching a terminal.

I’m pretty deep into building this from scratch but I’m wondering — are there more mature frameworks I should be looking at instead? I’ve heard of things like OpenHands, but I’m not sure what’s actually production-ready for this kind of multi-agent orchestration. Any suggestions welcome.

Problem: Project management tools are often closed ecosystems that trap your data and force you into web-based interfaces. Data retrieval is limited, and they rarely feel like native OS applications.

Comparison: Compared to top alternatives like Jira and Trello, Kanban Pro is entirely open, local, and native. It’s built with a macOS mindset featuring smooth animations, proper keyboard shortcuts, and native widgets. No sign-ups. No paywalls. Because Kanban Pro runs purely on local Markdown files with real-time file watching, it offers unique advantages:

1. AI Friendly: Point your local models at the folder and it can directly create, move, or update tickets by simply writing Markdown.
2. Account-free Collaboration: Drop your project folder in iCloud Drive, Dropbox, or OneDrive. Anyone with folder access can collaborate seamlessly across devices, with file-level locking preventing conflicts. Profiles are created via device-binding and exist at the project level.

Where this gets genuinely exciting is when you connect it to an autonomous AI agent (e.g. OpenClaw). Because everything is local Markdown, Kanban Pro doubles as a persistent memory layer for AI agents, they can assign tickets to humans, follow up on progress, and manage a project end-to-end. It bridges the gap between autonomous agents and human collaborators without friction.

Pricing: Free Forever Early Access: goodguyapps.com

Privacy: Everything stays on your device. The app doesn't phone home, doesn't collect telemetry, and doesn't upload your tasks anywhere. Full privacy policy at https://goodguyapps.com/?page=privacy

Happy to answer any questions about the architecture, the file format, or anything else. Would love your feedback.

LinkedIn: https://www.linkedin.com/company/good-guy-apps/about/

Community: r/KanbanPro

Hey can anyone help me with eyes? everytime it generates an image the eye are always f'd up i tried other models, alot of other loras, also im using comfy ui with zluda so the face detailer is not working (by working i mean its litteraly not running im getting errors) or im doing something wrong, im using a simple txt to img workflow with remacri upscaler at the end. please help me fix this issue, im using an sdxl checkpoint, everyone on discord is asking for money to make me a workflow, even when i tell them that i dont have money they're trying to convince me to borrow money from my friend

Here is the error i get when i use face detailer :

RuntimeError: GET was unable to find an engine to execute this computation

File "C:\Ai\ComfyUI-Zluda\execution.py", line 534, in execute

output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\execution.py", line 334, in get_output_data

return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\execution.py", line 308, in _async_map_node_over_list

await process_inputs(input_dict, i)

File "C:\Ai\ComfyUI-Zluda\execution.py", line 296, in process_inputs

result = f(**inputs)

File "C:\Ai\ComfyUI-Zluda\custom_nodes\comfyui-impact-pack\modules\impact\impact_pack.py", line 876, in doit

enhanced_img, cropped_enhanced, cropped_enhanced_alpha, mask, cnet_pil_list = FaceDetailer.enhance_face(

~~~~~~~~~~~~~~~~~~~~~~~~~^

single_image.unsqueeze(0), model, clip, vae, guide_size, guide_size_for, max_size, seed + i, steps, cfg, sampler_name, scheduler,

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

...<4 lines>...

cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tiled_encode=tiled_encode, tiled_decode=tiled_decode)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\custom_nodes\comfyui-impact-pack\modules\impact\impact_pack.py", line 830, in enhance_face

DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg,

~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

...<4 lines>...

cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\custom_nodes\comfyui-impact-pack\modules\impact\impact_pack.py", line 362, in do_detail

enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size,

~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler,

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

...<7 lines>...

scheduler_func=scheduler_func_opt, vae_tiled_encode=tiled_encode,

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

vae_tiled_decode=tiled_decode)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\custom_nodes\comfyui-impact-pack\modules\impact\core.py", line 352, in enhance_detail

latent_image = utils.to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode)

File "C:\Ai\ComfyUI-Zluda\custom_nodes\comfyui-impact-pack\modules\impact\utils.py", line 603, in to_latent_image

encoded = nodes.VAEEncode().encode(vae, pixels)[0]

~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\nodes.py", line 365, in encode

t = vae.encode(pixels)

File "C:\Ai\ComfyUI-Zluda\comfy\sd.py", line 1057, in encode

model_management.raise_non_oom(e)

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^

File "C:\Ai\ComfyUI-Zluda\comfy\model_management.py", line 290, in raise_non_oom

raise e

File "C:\Ai\ComfyUI-Zluda\comfy\sd.py", line 1050, in encode

out = self.first_stage_model.encode(pixels_in)

File "C:\Ai\ComfyUI-Zluda\comfy\ldm\models\autoencoder.py", line 208, in encode

z = self.encoder(x)

File "C:\Ai\ComfyUI-Zluda\venv\Lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl

return self._call_impl(*args, **kwargs)

~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\venv\Lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl

return forward_call(*args, **kwargs)

File "C:\Ai\ComfyUI-Zluda\comfy\ldm\modules\diffusionmodules\model.py", line 654, in forward

h1 = conv_carry_causal_3d(x1, self.conv_in, conv_carry_in, conv_carry_out)

File "C:\Ai\ComfyUI-Zluda\comfy\ldm\modules\diffusionmodules\model.py", line 81, in conv_carry_causal_3d

out = op(x)

File "C:\Ai\ComfyUI-Zluda\venv\Lib\site-packages\torch\nn\modules\module.py", line 1751, in _wrapped_call_impl

return self._call_impl(*args, **kwargs)

~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\venv\Lib\site-packages\torch\nn\modules\module.py", line 1762, in _call_impl

return forward_call(*args, **kwargs)

File "C:\Ai\ComfyUI-Zluda\comfy\ops.py", line 428, in forward

return super().forward(*args, **kwargs)

~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\venv\Lib\site-packages\torch\nn\modules\conv.py", line 554, in forward

return self._conv_forward(input, self.weight, self.bias)

~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File "C:\Ai\ComfyUI-Zluda\venv\Lib\site-packages\torch\nn\modules\conv.py", line 549, in _conv_forward

return F.conv2d(

~~~~~~~~^

input, weight, bias, self.stride, self.padding, self.dilation, self.groups

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

)

^

We have one room right under the roof which has this wood paneling almost everywhere. Every now and then there is this black/dark brown powdery stuff falling out from behind them. It looks a bit moist in the third picture but it's completely dry and powdery. I just hope it's not something harmful.

After 4.7 was released, I gave it a try.

A few things that really concern me:

1. It confidently hallucinates.

My work involves writing comparison articles for different tools, so I often ask gpt and it to gather information.

Today I asked it to compare the pricing structures of three tools (I’m very familiar with), and it confidently gave me incorrect pricing for one of them.

This never happened with 4.6. I honestly don’t understand why an upgraded version would make such a basic mistake.

2. Adaptive reasoning feels more like a cost-cutting mechanism.

From my experience, this new adaptive reasoning system seems to default to a low-effort mode for most queries to save compute. Only when it decides it’s necessary does it switch to a more intensive reasoning mode.

The problem is it almost always seems to think my tasks aren’t worth that effort. I don’t want it making that call on its own and giving me answers without proper reasoning.

3. It does what it thinks you want.

This is by far the most frustrating change in this version.

I asked it to generate page code and then requested specific modifications. Instead of fixing what I asked for, it kept changing parts I was already satisfied with, even added things I never requested.

It even praised my suggestions, saying they would make the page more appealing…

4. It burns through tokens way faster than before.

For now, I’m sticking with 4.6. Thankfully, Claude still lets me use it.

Hi, I used to use whapi for my WhatsApp chat bot, and it was downloading and analysing images smoothly, but ever since I switched to Evolution, images/docs have become a nightmare.

I couldn't get a single image to flow through my workflow.

Evolution sends images in MIME Type: application/octet-stream, and Gemini only takes JPG. If anyone has a solution or has faced a similar issue, please help me too.

Thanks.

just pressed update all in comfyui custom manager because i keep getting "metadatahook hidden input errors" when generating images. now my UI is broken and looks like this. the numbers to the left of the manager button used to look like line bars and there is no space at the top

how do i fix this?

Google Drive has a hidden folder called appDataFolder — it doesn't show up in the user's Drive, they can't accidentally delete your files, and when they revoke your app's permissions Google cleans it up automatically. It needs exactly one OAuth scope and it's completely free. Built two npm packages around it so you can use it as per-user storage without touching a database.

npm packages:

• https://www.npmjs.com/package/@ashish-um/nook
• https://www.npmjs.com/package/@ashish-um/nook-files

I really want to know what the name of it is

It’s called Dristi.

You give it a goal like:
“Open Chrome and go to GitHub”

And it will:
• Look at your screen
• Tell you exactly what to do next
• Check if you actually did it right
• Adjust if you didn’t
• Answer your questions anytime

It’s basically like having a real-time AI mentor sitting next to you.

How it works:

• You enter a goal
• It analyzes your screen (via screenshots)
• Gives the next step
• Verifies progress using before/after comparison
• Repeats until done

Tech stack:

• FastAPI (backend)
• React + TypeScript (frontend)
• OpenAI (step-by-step guidance + Q&A)
• Gemini (step verification)

What’s next:

• Learn from YouTube tutorials and guide interactively
• Voice-based guidance
• Session replay
• Local model support (Ollama, etc.)

give a start if you like it github

I am looking for an assistant style work eg, posting on my linked, creating the posts. Creating email campaigns. Claude code i have used, haven't used Manus yet. Planning to look at upsizing the spend of $200 with both offering next level plans, which one would you recommend ?

Hi everyone,

I always hear of how chatgpt agrees with people and listen to them and validates anything. I don't know but my model constantly opposes all I say and even doesn't listen to commands. And no, I don't ask it anything controversial.

Any ideas on what's going on?

by Saylo

Poketvault.tech is 100% free to use. Scan your cards, see how much they are worth, and track how close you are to completing your favorite set. You can share your digital Vault with your friends or with social media to flex your collection or your most recent Big Hit. It also has a shop in case your looking to expand your collection.

Before work, I came across a post from a guy, he was talking about a new way to make a bit of money

In about two hours, I managed to make $89, those who have more time can make more

He left the guide in a pinned post on his profile, waltwhiteee just click to check it out

It worked for me, so I decided to share, maybe it’ll help someone else too

l've been thinking about better ways to stay organised for school work.

I built a basic web page for myself with:

tasks list

exam dates

focus timer

notes

Just wanted to know what students usually use or if something like this is useful.

When people talk about RAG, the conversation usually stays around retrieval quality: chunking, embedding models, reranking, hybrid search, GraphRAG vs standard vector search, all that stuff.

And obviously that matters. But the more I look at real teams trying to use RAG in production, the more it feels like retrieval is only half the problem.

The messier half seems to be everything around operating it:

- keeping data fresh without constantly rebuilding everything

- re-embedding without turning it into a massive cost/event

- tracking index versions and knowing what changed

- figuring out whether quality dropped because of retrieval, prompts, bad source docs, or stale data

- handling permissions / sensitive data / partial visibility

- having any useful way to observe whether the system is actually getting better over time

A lot of teams seem to assume that if retrieval quality is good enough, the RAG system is in decent shape.

I’m not sure that’s true. It feels like a lot of production pain is really RAG ops pain, not just retrieval pain.

Curious what other people here have found.

Once a RAG system is live, what becomes painful first for you?

so i got bored one day and made a beautiful and unique open source icon pack with GPT image v2 and after like 15 hours working on it I think im ready to release, im only releasing with 100 icons, but planning to add 1000+ soon

Spent a long time making site and everything sick so give me feedback, you can send an issue on the github (find on site) to help out

oddicons.net

https://github.com/jasperdevs/oddicons

https://reddit.com/link/1sqg9r5/video/uqbxaunl7awg1/player

Why is it suddenly so paranoid?

I am new to this subreddit, and it was my first time using claude code , i have no background with coding at all , but i’ve been prompting and doing alot of AI generated stuff for small businesses for the past year , so kinda know how to prompt well .

Anyway i have created a fully functional CRM for my business, lead generation, scrapping data , APIs here and there , customer base , ai chat agents , fully automated cold emailing “single & bulk” , team chat , pipelines … you name it i got it .

And it all took like 25 hours actively working on it , 90% done , and not for a single time i hit the limits .

So what you guys talking about? Am i missing something here ?

Here are my side projects:

• NextGen Tools - A product hunt alternative (Launch your app here)
• Clearity - Manage anxiety with clearer thinking

Type yours in the comments. Thanks.

After Karpathy's LLM Wiki gist blew up last month, I

finally sat down and built one end-to-end to see if it

actually good or if it's just hype. Sharing the

honest takeaways because most of the writeups I've seen

are either breathless "bye bye RAG" posts or dismissive

"it doesn't scale" takes.

Quick recap of the idea (skip if you've read the gist):

Instead of retrieving raw document chunks at query time

like RAG, you have an LLM read each source once and

compile it into a structured, interlinked markdown wiki.

New sources update existing pages. Knowledge compounds instead of being re-derived on every query.

What surprised me (the good):

- Synthesis questions are genuinely better. Asked "how

do Sutton's Bitter Lesson and Karpathy's Software 2.0

essay connect?" and got a cross-referenced answer because the connection exists across documents, not within them.

- Setup is easy. Claude Code(Any Agent) + Obsidian + a folder.

- The graph view in Obsidian after 10 sources is

genuinely satisfying to look at. Actual networked

thought.

What can break (the real limitations):

- Hallucinations baked in as "facts." When the LLM

summarized a paper slightly wrong on ingest it has effcts across. The lint step is non-negotiable.

- Ingest is expensive. Great for curated personal small scale knowledge, painful for an enterprise doc dump.

When I'd actually use it:

- Personal research projects with <200 curated sources

- Reading a book and building a fan-wiki as you go

- Tracking a specific evolving topic over months

- Internal team wikis fed by meeting transcripts

When I'd stick with RAG:

- Customer support over constantly-updated docs

- Legal/medical search where citation traceability is

critical

- Anything with >1000 sources or high churn

The "RAG is dead" framing is wrong. They solve different

problems.

It is already a game changer for me. It just delivers quality over and over again. I am building homepages ands systems with actual quality and very few hallucinations, echo chambers frustrating conversation about what is possible and what is not etc.

https://www.reddit.com/r/ClaudeCode/s/qEgXVYvhN8

It works!

Hey everyone! I built Fair Split, a free web app for splitting restaurant bills fairly — down to the penny if you want.

The problem: Every bill-splitting app just divides evenly. But why should you subsidize your friend's wagyu steak when you had a side salad?

How it works:

1. Add everyone at the table
2. Enter each item from the bill (or snap a photo of the receipt — it auto-parses)
3. Tap names to assign who had what (shared items split automatically)
4. Set tax, tip & any extra fees — distributed proportionally
5. Get your fair split instantly

The fun part — the Pettiness Slider:

You choose how detailed the breakdown gets:

• Chill — rounded to the nearest dollar, no drama
• Normal — standard cents-level accuracy
• Petty — full itemized line-by-line breakdown
• Nuclear — forensic-level audit with rounding breakdowns and a sassy timestamp

Other features:

• One-tap Venmo / PayPal / Cash App payment links with pre-filled amounts for each person
• Copy the full split as text or screenshot to share in the group chat
• "Send reminder" button to nudge that one friend who always forgets
• Save splits to history and reload them later
• Works entirely in the browser, no sign-up needed

Try it out: https://bill-splitter.timepad.ca/

Tech stack: Next.js, TypeScript, Tailwind CSS. Runs client-side with localStorage — no backend, no data collection.

Would love to hear your feedback! What features would make this more useful for your friend group?

Salut,

Je travaille pas mal sur des dashboards Home Assistant en ce moment et j’aime surtout améliorer le visuel et l’utilisation au quotidien.

Je peux vous aider à :

- rendre votre dashboard plus propre et simple

- l’optimiser pour téléphone/tablette

- créer un plan maison interactif

- simplifier vos automatisations

Je commence à proposer ça, donc je fais quelques projets à petit prix 👍

Si ça vous intéresse, envoyez-moi une capture de votre dashboard actuel + ce que vous aimeriez améliorer.

Je peux aussi montrer ce que j’ai déjà fait.

Hi,

I’ve been working a lot on Home Assistant dashboards lately and I really enjoy improving UI and usability.

I can help you:

- clean and simplify your dashboard

- make it fully mobile friendly

- create interactive floor plans

- improve automations and usability

I’m starting to offer this to others, so I’m doing a few projects at a low price.

If you’re interested, feel free to send me a screenshot of your current dashboard 👍

I can also show you what I’ve built.

I spend most of my time here and similar subs looking for answers to things, and found a chance to give something back that might be useful to someone.

I ran out of Anthropic credits (damn budget burns way too fast lately) and my GPU isn't good enough to run models that can actually handle agent workloads. That's the whole story. I got tired of watching my local agent timeout mid-thought because the model I could afford to run locally takes two minutes to say "OK," so I built something to make the situation survivable.

It's called Agent-Ersatz because that's exactly what it is -- a substitute for having the right hardware or the budget to use cloud APIs. The name isn't clever. It's honest. The end product is an agent that works, but in all honesty, probably would not use to code things. It does pretty good for what I use it for, which is searching for references, scraping sites and organizing the contents with RAG, keeping organized with background cron tasks, and answering questions when I don't have time to look something up and don't mind waiting a few minutes.

The project does two things:

Config survival: Agent frameworks like Hermes rewrite your config on update. Every `hermes update` would nuke my custom timeouts, my local model settings, my search backend. I got sick of manually fixing it. Now a post-merge hook detects drift, applies static patches for known changes, falls back to the local LLM to generate surgical edits when static patches don't cover it, runs tests, and auto-reverts if anything breaks. I don't think about it anymore.

Model benchmarking: If you're running local models, you need to know which ones can actually survive a real agent workload before you configure your timeouts. The benchmark discovers every model on your inference server, measures real prompt processing speed and generation throughput via streaming, runs a structured quality evaluation (JSON formatting, logic problems, code generation -- scored 1-10), and estimates how long a 5-t
urn and 10-turn agent conversation would actually take with each model. Turns out my 1.2B "fast" model gets 7. 5/10 on quality and finishes a 5-turn chain in 25 seconds. My 26B model scores 10/10 but a 5-turn chain takes 25 minutes. That's the tradeoff laid out in one table, and it's the information you need to set timeouts that don't kill connections prematurely or wait forever on a model that was never going to deliver.

It's built for Hermes Agent specifically but the benchmarking and the config survival pattern work for any local inference setup. Auto-detects your server (LM Studio, Ollama, vLLM, SGLang, whatever), no hardcoded endpoints.

The repo is here: https://github.com/Societus/Agent-Ersatz

MIT license. If you're in the same boat -- consumer hardware, no cloud budget, stubborn enough to keep trying -- I'd genuinely like to see what you do with it. The quality scoring rubric could be better. The chain estimation model is simplistic. There are probably a dozen agent frameworks this could support beyond Hermes. Pull requests welcome, forks welcome, "I rewrote your thing in Rust because Python is slow" welcome.

The bar was "it works." It clears that bar. Everything past that is gravy.

Hi,

I’ve been working a lot on Home Assistant dashboards recently and I really enjoy improving UI and usability.

I can help you:

- make your dashboard cleaner and easier to use

- improve mobile experience

- create interactive floor plans

- simplify automations

I’m starting to offer this to others, so I’m doing a few projects at a low price.

If you’re interested, feel free to send me a screenshot of your current dashboard and what you’d like to improve 👍

I can also show you what I’ve built.

https://civitai.com/models/2343188/flux2-kleinanything-to-real-characters

What is this? A log? Sand? I’m confused

I’m building an early MVP and found a product name I like. I own a relevant domain for it, and I don’t see an exact match in the major app or extension stores.

My concern is that the broader niche has several similar-sounding names built around the same common root. One adjacent brand has a somewhat similar name, with only one letter different. It is in the same broad category, but it appears to have a different product format and positioning.

For a low-budget MVP, would you proceed as long as there is no exact store conflict, legal notice, or direct complaint? Or would you rename early just to avoid possible future confusion?

I know this is not legal advice. I’m only looking for practical founder feedback on whether this is a real red flag or something I may be overthinking at the MVP stage.

The reason I’m asking is that if the project grows, having to rebrand later could be costly and disruptive.

Thanks!

Musical Influences?

Chvrches, My Bloody Valentine, Bikini Kill, Le Tigre, Let's Eat Grandma, Sleater-Kinney

Other Influences you can call them out in the comments

Lyrics derived from a variety of LLMs. The style defaults too poppy and you start to recognize a signature sound but that's unavoidable. And there's a bit of a mumbling thing and sound quality is limited. I uploaded all this to Soundcloud for better or worse. I don't know if people who are into modular synths will think this is AI slop but that seems like the obvious path rather than to try to replicate real instruments. The sound is really low for whatever reason so it helps to have a headphone amp - I'm listening to this on a Schiit Mjolnir 2 hybrid tube amp. It took about 2 days to create a double album length of music and about a week since I'd even tried my hand at this and I've created some of my favorite music ever.

https://soundcloud.com/thelivingworld/sets/the-shape-of-time

Hey guys. Having a weird issue with the new DeepSeek V3.2 Unsloth GGUF via llama-server. The model starts reasoning fine, but the actual opening think tag is missing from the output stream. I just see the plain text reasoning, and then the closing tag at the end.

Because of this, Open WebUI doesn't collapse the thought block. Im on a 512GB box, command is just llama-server -m model_name -t 32 --flash-attn on. Tried toggling reasoning on/off, didn't help.

Is the chat template broken in these specific GGUFs or am I missing a flag?

Scared, I ran away, I never saw the bus coming

During trips with friends I was always the one to record everything and then spend hours to create an aftermovie after. This not only took a lot of time, but also constantly required me to pull out my phone during the trip instead of the others in the group.

Since I'm a developer I figured that could be solved, that's why I created Mesh Together. Within the app you create memories together with others, collaborators then record short 1-3s clips which are added automatically. When the trip or event is done, you can export the final result, add music and then share it with everyone or just enjoy the aftermovie yourself.

I have been testing it for the last months during my own travels together with my partner. It has been working well, I'm adding more features, but I should just launch it, yet that I find really hard. This post has two goals: 1. Pushing myself to just launch - 2. Finding someone that can do the work that's not meant for me: Marketing, socials, etc.

Do you know someone that has the experience with growing a new app? Or do you have any tips for me on how to grow the app? Please respond below.

Hi, does anyone do creative writing using 5.4 and if you do, does it usually take over a minute I’ve had 5.4 since its conception, and ever since last night and today for the first time it isn’t doing replies in 2-3 seconds it’s taking forever to load is that an issue on my end have I done something wrong or is it open ai

Hey 👋

Can you suggest a solid champion pool?

I play top and I’m looking for 2–3 champs to focus on and climb.

https://op.gg/lol/summoners/euw/Honda-Top

Thats my OP.GG

"I’m a Platinum player" 🎮

Thanks!

Hi team

This was included the box for a brand new Weber Q 3100 - no information in the manual, no information online, about its use.

Any ideas?

It’s made of some kind of stone/ceramic.

Hey,

Been exploring local LLMs lately and started using LM Studio with Gemma 4.

My question is - is there any equivalent workflow for creating custom context in chats? Similar to how GPTs come pre-loaded with instructions or using instructions/uploaded .md files in Claude Projects.

Just got on a ferry and went up top. There's no roof up top so I initially assumed it's for water drainage if it rains but the dips either side of that would pool water anyway

Every time an MCP tool returns data, a database query, API response, search result, it lands verbatim in Claudes context. That means transactionId, orderStatus, repositoryDescription repeated thousands of times across a session. Pure structural noise eating into the space Claude needs to actually think.

I built compressmcp to fix this. It hooks into Claude Code's PostToolUse pipeline, compresses JSON keys using a shared dictionary, and injects the compact version instead. Claude gets a key map + abbreviated data and reads it just as accurately, but at 40% fewer tokens on average.

Its lossless. Nothing is dropped or summarised. The original structure is fully recoverable from the dictionary.

Thats it. Restart Claude Code and it runs automatically on every MCP tool response.

Theres also a live status bar showing context usage, tokens saved, compression efficiency, and plan utilisation for the session.

262 tests. Zero data loss. Works on any MCP tool.

The updated LTX2.3 distilled lora (v1.1) seems to vastly improve the output, with better motion and sync when using custom audio and input image.

Added in alternative clips in this one using more or less the same prompt.

LORA LINK PAGE

Best iterative workflow using grok and ltx2.3

I use Claude to maintain dashboards of workout progress. Was adding a couple sets to the plan and figured I’d double check my thoughts with our AI friend. Got a good laugh.

The updata LTX2.3 distilled lora (v1.1) seems to vastly improve the output, with better motion and sync when using custom audio and input image.

Lora page

Spent a weekend pulling my own data into a spreadsheet. Findings that ruined my assumptions:

1. Posting time mattered WAY less than hook quality. My best times varied by 4+ hours week to week.

2. Short captions outperformed long ones 3:1, except on LinkedIn.

3. Reposting old content with a new hook beat creating new stuff 60% of the time.

4. The more I "optimized" for the algorithm, the lower my engagement got. Writing for humans won.

5. My viral posts had nothing in common except one thing: a specific, uncomfortable opinion.

What's a "rule" you stopped following that actually helped you grow?

I run gemma4 26b on llama-server witht his config:

.\llama-server.exe -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_M --fit on --fit-target 512 -ngl 999 --port 8080 -np 2

naivly I tought that thats it. The model runs on the GPU and the server itself will not use much RAM, maybe a few MB, maybe a GB - No Problem.

After a few calls my PC got unresponsive and ALL of my 32GB RAM was full.

So I conversed with ChatGPT and learned about the PromptCache (that is in my case helpfull, but maybe a bit to large). So I added: --cache-ram 4086

But still, llama-server uses 12GB of RAM.

So my question is: What is llama using the other 8GB of RAM for?

Been unemployed a few months, building my own products on the side while looking for work. Last week I audited a founder's AI built pipeline and found enough silent failures to keep me busy for two weeks.

Turns out there's a gap between what Cursor and Lovable help you build and what actually survives production. So I packaged what I know into a service.

Flat fee audits, two week sprints, no equity ask. Founders and agencies who've built something with AI and aren't sure what breaks when real users arrive.

fixedandshipped.com

If you know anyone who's shipped something with AI and is nervous about production, send them my way.

​

So I've been working on this for a few months now. The idea came from a frustration I kept seeing: people who want to start a Shopify store get buried in YouTube videos, courses, and contradictory advice — and still don't know what to actually do on day 1.

What I built: three PDF-based execution systems for e-commerce beginners and scalers. Not "inspiration" content — literal step-by-step systems with templates, decision rules, copy-paste scripts, and real-world examples. The goal was to replace "watch 40 hours of content and figure it out" with "follow this sequence and get a result".

On the technical side, I coded the Shopify theme from scratch — no premium theme, just Liquid, CSS, and JS. Dark design, mobile-first, custom fonts. Took longer than expected but I wanted full control over how the products are presented.

The products are structured as three levels:

— Level 1: first order without an ad budget

— Level 2: scaling to consistent monthly revenue with paid traffic

— Level 3: automating operations so the business runs without you being in it daily

I'm targeting German-speaking beginners specifically — the market is crowded with hype content, so I deliberately went the opposite direction: data-driven, no income claims, no "quit your job in 30 days" framing.

**My actual question for this community:** For those who've built and sold digital products — how did you handle the perception problem early on? When you're new and unknown, how do you convince someone the system actually works before they've tried it?

I've been thinking about this a lot. Social proof takes time to build. Case studies require customers. But you need customers to get case studies. Classic chicken-and-egg.

Would love to hear how others broke that loop.

On blink cameras. PNW coastal near a large river estuary and tidelands lands

hey everyone,

I’ve been working on a small project recently to automate a workflow that was being done manually every week.

the problem was simple:
- repeating the same steps every week
- collecting data from different places
- cleaning and formatting it
- sending it out on a schedule

it was taking a lot of time for something very repetitive.

so I built a simple system that:
- handles the repetitive steps automatically
- allows some manual review before finalizing
- schedules everything to run at the right time

nothing too complex individually, but combining everything into one flow made a big difference.

what I found interesting is that most of the value didn’t come from AI itself,
it came from removing small repetitive steps.