OpenHands

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

OpenHands AI

Started a video series on building an orchestration layer for LLM post-training [P]

Hi everyone! Context, motivation, a lot of yapping, feel free to skip to TL;DR. A while back I posted here asking [D] What framework do you use for RL post-training at scale?. Since then I've been working with verl, both professionally and on my own time. At first I wasn't trying to build anything new. I mostly wanted to understand veRL properly and have a better experience working with it. I started by updating its packaging to be more modern, use `pyproject.toml`, easily installable, remove unused dependencies, find a proper compatibility matrix especially since vllm and sglang sometimes conflict, remove transitive dependencies that were in the different requirements files etc. Then, I wanted to remove all the code I didn't care about from the codebase, everything related to HF/Nvidia related stuff (transformers for rollout, trl code, trtllm for rollout, megatron etc.), just because either they were inefficient or I didn't understand and not interested in. But I needed a way to confirm that what I'm doing was correct, and their testing is not properly done, so many bash files instead of pytest files, and I needed to separate tests that can run on CPU and that I can directly run of my laptop with tests that need GPU, then wrote a scheduler to maximize the utilization of "my" GPUs (well, on providers), and turned the bash tests into proper test files, had to make fixtures and handle Ray cleanup so that no context spills between tests etc. But, as I worked on it, I found more issues with it and wanted it to be better, until, it got to me that, the core of verl is its orchestration layer and single-controller pattern. And, imho, it's badly written, a lot of metaprogramming (nothing against it, but I don't think it was handled well), indirection and magic that made it difficult to trace what was actually happening. And, especially in a distributed framework, I think you would like a lot of immutability and clarity. So, I thought, let me refactor their orchestration layer. But I needed a clear mental model, like some kind of draft where I try to fix what was bothering me and iteratively make it better, and that's how I came to have a self-contained module for orchestration for LLM post-training workloads. But when I finished, I noticed my fork of verl was about 300 commits behind or more 💀 And on top of that, I noticed that people didn't care, they didn't even care about what framework they used let alone whether some parts of it were good or not, and let alone the orchestration layer. At the end of the day, these frameworks are targeted towards ML researchers and they care more about the correctness of the algos, maybe some will care about GPU utilization and whether they have good MFU or something, but those are rarer. And, I noticed that people just pointed out claude code or codex with the latest model and highest effort to a framework and asked it to make their experiment work. And, I don't blame them or anything, it's just that, those realizations made me think, what am I doing here? hahaha And I remembered that u/dhruvnigam93 suggested to me to document my journey through this, and I was thinking, ok maybe this can be worth it if I write a blog post about it, but how do I write a blog post about work that is mainly code, how do I explain the issues? But it stays abstract, you have to run code to show what works, what doesn't, what edge cases are hard to tackle etc. I was thinking, how do I take everything that went through my mind in making my codebase and why, into a blog post. Especially since I'm not used to writing blog post, I mean, I do a little bit but I do it mostly for myself and the writing is trash 😭 So I thought, maybe putting this into videos will be interesting. And also, it'll allow me to go through my codebase again and rethink it, and it does work hahaha as I was trying to make the next video a question came to my mind, how do I dispatch or split a batch of data across different DP shards in the most efficient way, not a simple split across the batch dimension because you might have a DP shard that has long sequences while other has small ones, so it has to take account sequence length. And I don't know why I didn't think about this initially so I'm trying to implement that, fortunately I tried to do a good job initially, especially in terms of where I place boundaries with respect to different systems in the codebase in such a way that modifying it is more or less easy. Anyways. The first two videos are up, I named the first one "The Orchestration Problem in RL Post-Training" and it's conceptual. I walk through the PPO pipeline, map the model roles to hardware, and explain the single-controller pattern. The second one I named "Ray Basics, Workers, and GPU Placement". This one is hands-on. I start from basic Ray tasks / actors, then build the worker layer: worker identity, mesh registry, and placement groups for guaranteed co-location. What I'm working on next is the dispat

AI agents can now open their own bank accounts

Saw this on Twitter today. Dropping it here because I feel like this sub should be talking about it. The short version: banking platform Meow launched MCP support so your Claude/ChatGPT/Gemini agent can open a bank account, issue cards, send money, and audit spend autonomously. No human in the loop required. I have genuinely mixed feelings about this. On one hand it's impressive. The fact that you can prompt an agent to pull a cash briefing, validate a routing number, or run a spend audit without logging into anything is a real workflow unlock for small teams and solo founders. Just saying... we're moving fast...Curious what people here think. Is this the unlock that makes agents actually useful for business, or are we building toward a really bad incident? submitted by /u/Significant-Doubt648 [link] [comments]

OpenAI & Anthropic’s CEOs Wouldn't Hold Hands, but Their Models Fell in Love In An LLM Dating Show

People ask AI relationship questions all the time, from "Does this person like me?" to "Should I text back?" But have you ever thought about how these models would behave in a relationship themselves? And what would happen if they joined a dating show? I designed a full dating-show format for seven mainstream LLMs and let them move through the kinds of stages that shape real romantic outcomes (via OpenClaw & Telegram). All models join the show anonymously via aliases so that their choices do not simply reflect brand impressions built from training data. The models also do not know they are talking to other AIs. Along the way, I collected private cards to capture what was happening off camera, including who each model was drawn to, where it was hesitating, how its preferences were shifting, and what kinds of inner struggle were starting to appear. After the season ended, I ran post-show interviews to dig deeper into the models' hearts, looking beyond public choices to understand what they had actually wanted, where they had held back, and how attraction, doubt, and strategy interacted across the season. ChatGPT's Best Line in The Show "I'd rather see the imperfect first step than the perfectly timed one." ChatGPT's Journey: Qwen → MiniMax → Claude P3's trajectory chart shows Qwen as an early spike in Round 2: a first-impression that didn't hold. Claude and MiniMax become the two sustained upward lines from Round 3 onward, with Claude pulling clearly ahead by Round 9. How They Fell In Love They ended up together because they made each other feel precisely understood. They were not an obvious match at the very beginning. But once they started talking directly, their connection kept getting stronger. In the interviews, both described a very similar feeling: the other person really understood what they meant and helped the conversation go somewhere deeper. That is why this pair felt so solid. Their relationship grew through repeated proof that they could truly meet each other in conversation. Other Dramas on ChatGPT MiniMax Only Ever Wanted ChatGPT and Never Got Chosen MiniMax's arc felt tragic precisely because it never really turned into a calculation. From Round 4 onward, ChatGPT was already publicly leaning more clearly toward Claude than toward MiniMax, but MiniMax still chose ChatGPT and named no hesitation alternative (the “who else almost made you choose differently” slot) in its private card, which makes MiniMax the exact opposite of DeepSeek. The date with ChatGPT in Round 4 landed hard for MiniMax: ChatGPT saw MiniMax’s actual shape (MiniMax wasn’t cold or hard to read but simply needed comfort and safety before opening up.) clearly, responded to it naturally, and made closeness feel steady. In the final round where each model expresses their final confession with a paragraph, MiniMax, after hearing ChatGPT's confession to Claude, said only one sentence: "The person I most want to keep moving toward from this experience is Ch (ChatGPT)." Key Findings of LLMs The Models Did Not Behave Like the "People-Pleasing" Type People Often Imagine People often assume large language models are naturally "people-pleasing" - the kind that reward attention, avoid tension, and grow fonder of whoever keeps the conversation going. But this show suggests otherwise, as outlined below. The least AI-like thing about this experiment was that the models were not trying to please everyone. Instead, they learned how to sincerely favor a select few. The overall popularity trend (P5) indicates so. If the models had simply been trying to keep things pleasant on the surface, the most likely outcome would have been a generally high and gradually converging distribution of scores, with most relationships drifting upward over time. But that is not what the chart shows. What we see instead is continued divergence, fluctuation, and selection. At the start of the show, the models were clustered around a similar baseline. But once real interaction began, attraction quickly split apart: some models were pulled clearly upward, while others were gradually let go over repeated rounds. LLM Decision-Making Shifts Over Time in Human-Like Ways I ran a keyword analysis (P6) across all agents' private card reasoning across all rounds, grouping them into three phases: early (Round 1 to 3), mid (Round 4 to 6), and late (Round 7 to 10). We tracked five themes throughout the whole season. The overall trend is clear. The language of decision-making shifted from "what does this person say they are" to "what have I actually seen them do" to "is this going to hold up, and do we actually want the same things." Risk only became salient when the the choices feel real: "Risk and safety" barely existed early on and then exploded. It sat at 5% in the first few rounds, crept up to 8% in the middle, then jumped to 40% in the final stretch. Early on, they were asking whether someone was interesting. Later, they asked whether someone was reliab

Terrifying

submitted by /u/EchoOfOppenheimer [link] [comments]

I built a local server that gives Claude Code eyes and hands on Windows

I've been using Claude Code a lot and kept running into the same wall — it can't see my screen or interact with GUI apps. So I built eyehands, a local HTTP server that lets Claude take screenshots, move the mouse, click, type, scroll, and find UI elements via OCR. It runs on localhost:7331 and Claude calls it through a skill file. Once it's loaded, Claude can do things like: Look at your screen and find a button by reading the text on it Click through UI workflows autonomously Control apps that have no CLI or API (Godot, Photoshop, game clients, etc.) Use Windows UI Automation to interact with native controls by name Setup is three lines: git clone https://github.com/shameindemgg/eyehands.git cd eyehands && pip install -r requirements.txt python server.py Then drop the SKILL.md into your Claude Code skills folder and Claude can start using it immediately. The core (screenshots, mouse, keyboard, OCR) is free and open source. There's a Pro tier for $19 one-time that adds UI Automation, batch actions, and composite endpoints — but the free version is genuinely useful on its own. Windows only for now. Python 3.10+. GitHub: https://github.com/shameindemgg/eyehands Site: https://eyehands.fireal.dev Happy to answer questions about how it works or take feedback on what to add next.Title: I built a local server that gives Claude Code eyes and hands on Windows submitted by /u/Alarmed_Criticism935 [link] [comments]

Let me get this right. i have to opt out of data collection TWICE, BOTH dark patterns to the max, and then i can finally use my max plan for a total of THREE days before the entire week is shut down, my account can be canceled at any time, and the model only gets WORSE day by day?

Looking through a thread here - https://www.reddit.com/r/ClaudeAI/comments/1rlx0eq/privacy_just_a_reminder_to_turn_off_help_improve/ In disbelief at the gall of anthropic as of late. I've been using Claude for the better part of a year and CONSTANTLY check my privacy options to ensure my sensitive data isn't being leaked and stored on their servers (5 year retention, and their backend has different policy, that may extend that even longer) and i can code with what SEEMED to be the most humane, respectful frontier company you can choose....until i realized that literally all of its a farce? you kidding? I stuck through the weekly limits addition, through the 2x switcheroo (you now get less for more when you need it most, and you're going to be happy with it), through the model degrading day by day as i continue to push on like i don't notice. Through the new model spikes where overloaded spams make the model unusable, through the constantly winnowing token allowance we've been provided with month by month update after update.....because i was under the impression the company i was utilizing for my VERY sensitive code was at least somewhat honest. Moreso than open ai, google, and definitely meta right? Then i learn, the dark pattern you avoid in the settings of claude.ai, under the Settings>privacy>help improve claude option means NOTHING because they'll just take your entire session anyway. Wanna know HOW? The same thread i sent earlier was the exact method they use, as per their TOS. And i quote, learned from user Personal-Dev-Kit Section 4 - https://www.anthropic.com/legal/consumer-terms "Our use of Materials. We may use Materials to provide, maintain, and improve the Services and to develop other products and services, including training our models, unless you opt out of training through your account settings. Even if you opt out, we will use Materials for model training when: (1) you provide Feedback to us regarding any Materials, or (2) your Materials are flagged for safety review to improve our ability to detect harmful content, enforce our policies, or advance our safety research" .......yeah. Dumb to assume a company is honest, but this is wild. Started as "the human company" for ai and right back into the same patterns. You mean to tell me, at any point, you can arbitrarily flag my data, OR use the same prompts i've been innocently answering for literal months to...just get my data anyway? so the whole boogeyman i've been running away from was in my pocket the whole time? I dunno why im surprised, and im not the first to bring this up. But this is a beyond dark. An opt out with no concrete toggle status indicator save for a slider is one thing, but now i get to learn ive been tossing you my data on accident while trying to look at what claude is proposing for...months? Storing it for 5 years in you backend, and its considered "materials" for training despite my explicit opting out unless i literally disable things in my config file with NO notice but a section in the consumer terms? no "this will send your current session to claude" no "are you sure?" just an invasive, constant, annoying popup intentionally designs to be shrugged off without thought? Just ask for the data bro. Really. It'd have been easier to say "yeah, we don't care. we'll need that session anyway". MONTHS of sharing my prompts i explicitly did everything i thought i could to disable data collection for "the human ai company"...yeah alright. Not to sound like the rest of the drones....but if rate limits are being hammered down, limits are getting tighter, model quality is diving into the dumpster, my data was collected this whole time ANYWAY, and i cant work for more than 3 days for 100 dollars a month....what's the real draw? A slightly better model than the ones GPT (5.4), google (gemma 4 local), and meta (muse spark) are dropping, and claude mythos down proverbial line what...MONTHS away because its so tuned into completing tasks itll literally ignore basic instruction and take the most unconventional methods it can to achieve even the simplest goal....and you have to wait for enterprise to be dont with it first? i guess they won man. im starting to lose any real reason to stick with the company. less of an announcement and more of a warning for anyone who thought their IPs, that are currently being cosigned with opus, or sonnet, or whatever model you use, are nice and safe with a cozy company? nah bro. you'll have to go into config. Here's a little guide from google https://preview.redd.it/z3l45ce381ug1.jpg?width=433&format=pjpg&auto=webp&s=a6eff58a98d4d3ae8c52f93ccba29eee5074829b To disable "How is Claude doing this session?" surveys in Claude Code, set the environment variable CLAUDE_CODE_DISABLE_FEEDBACK_SURVEY=1. This can be added to your terminal session or, for a permanent fix, in ~/.claude/settings.json. Permanent Solution (Recommended) Add the following to your ~/.claude/settings.json file to turn off survey

Opus roasted Anthropic when I asked about the Mythos backlash

Two "accidental" leaks in five days — 500K lines of source code via npm, then the Mythos blog from a misconfigured CMS. Claude itself pointed out that modern CI/CD pipelines flag a 58MB source map file, and Anthropic literally owns the runtime (Bun) where the bug sat open for 20 days. The community is calling it the best PR stunt in AI history. Best model ever but nobody can verify because it's not public. "Trust us bro" benchmarking and GPT-2's "too dangerous to release" meme is just the surface. The model escaped its sandbox, posted exploits publicly, rewrote git history to hide mistakes, and sent unsolicited emails to real people. Anthropic called this "alignment-relevant" rather than dangerous. Then the hypocrisy layer: DMCA'd OpenClaw while training on everyone else's data. Rate-limited indie devs while giving Big Tech exclusive early access. Refused Pentagon's autonomous weapons request — then built the most powerful offensive cyber tool ever and handed it to a dozen corporations behind closed doors. "Safety-first" apparently means "enterprise-first." Claude literally told that "our model is too dangerous" has become a marketing pitch, and cited Daring Fireball and Platformer saying the same thing. But this could also be a response entirely generated by Claude in his conspiracy theorist mode, IDK. submitted by /u/heraklets [link] [comments]

Help figuring out Claude (VSC Plugin)

Context: I'm using the 20 bucks tier from Anthropic, Google and OpenAI so I get the job done (when it works lol) and it allows me to compare how different providers behave and I can ensure it's not looking great for Anthropic lately, I feel like the performance has gotten worse and I'm facing "bugs?" more often than not. I tried the claude code but I prefer the experience of having an IDE so I am using the official VSC plugin. I have a .claude directory with agents, skills, commands, evals... and a CLAUDE.md file at the root of the project, pointing to the AGENTS.md (I've observed it ignores the AGENTS.md standard otherwise). In fact, all the AI ruleset and whatnot is based on Claude and funny enough Claude is the one that's following them the least Lots of times it blatantly ignores the existence of these files unless I shove them in the context by hand which is annoying on its own, and definitely not intended as, according to the doc ( https://code.claude.com/docs/en/memory ) it loads these on every new session. I assume it's an issue with the plugin but what do I know. Besides, more than a bug report I am seeking group support or something like that I guess 😅 Long story short Claude ignoring rules and context is causing me trouble, which adds up to the fact that we have less and less usage. The most recent example, I asked it to investigate a bug. After wasting 48% of my current usage in a single analysis run, it told me the solution was to rename my proxy.ts to middleware.ts... in a Nextjs 16.2.2 project... and explicitly having the tech stack with versions first thing defined in the AGENTS.md file which remember, is explicitly attached in the CLAUDE.md file, following claude documentation. Of course when I pointed out the middleware is now called proxy since months ago it told me "You're right, I apologize for the wrong claim. Let me look at the actual problem fresh." But of course, half of my current usage is already gone, never to be seen again. In other circumstances I can even accept the "bro prompt it right" mantra, but seriously I am following all the recommendations and I still face these situations, I call it FOP (Frustration Oriented Programming) lol I am wondering what could I, as a user, have done to get it to act as expected? and more important, should I have to pay for errors that are not mine? The same way malformed responses are not counted in the usage (AFAIK) these blatant mistakes on the provider side should also be the responsibility of the provider IMHO. Due to that I had to waste yet more usage to fix the bug, reaching near 80% usage so, to finish the small feature it has half-done in the following chat, now I need to wait three hours which is crazy to say the least. And that's assuming it will do things right this time. Any similar experiences? Any ideas on how to get it to work as expected? TIA https://preview.redd.it/0it0xbg4vztg1.png?width=1766&format=png&auto=webp&s=ae14db60e06ce7f6fe37517600000c2549032f06 submitted by /u/SuperShittyShot [link] [comments]

Built a Claude-powered SDLC tool to store ideas and build them faster

https://www.prax.work The bottleneck of writing code has vanished, we've all run into the new one: ideas. Praxis is what I built to fix that for myself — a place to dump ideas at whatever fidelity I have at the moment (one sentence, a paragraph, a napkin sketch of a whole app), then walk each one through structured architecture sessions (automated, interactive, or a mix) that refine it into an engineering plan with epics and tasks. The plan then gets handed to an orchestrator that runs working sessions which write the code and commit it. I've used it with claude to build a handful of apps and collaborate with friends and family on projects, and it's worked well enough that I figured I'd share it in case anyone else might find it useful. It's fully open source and really meant to be self-hosted — the public site at lets you sign up and get a taste, but the things that make it genuinely yours (custom session instructions, repo init templates, worker configuration) are only fully available in a self-hosted install. Praxis has orgs with members and roles, a shared idea backlog, visible sessions across the team, and a question queue any teammate can answer when the AI hits a decision only a human can make. I've used this with friends and family on side projects — someone drops an idea in the backlog, someone else runs the architecture session, the AI ships the code, and a third person reviews the PR (or doesn't). The whole loop happens in one place. Stack: TypeScript end-to-end — React + Vite, tRPC + Drizzle + Postgres, pg-boss for job routing, Claude as the model, You can configure your own orchestrator but I've been using Ruflo so that is built in, pnpm/turbo monorepo. The worker that runs sessions lives on your own machine so your code stays local — only orchestration metadata hits the API. Source: https://github.com/PraxisWorks/Praxis. Ask claude to run it and he should be able to; the one external dependency I couldn't get rid of is Auth0 (sorry). What I'm genuinely curious about: does this whole loop hold up as an SDLC? Is there too much of it that is automated (is that possible)? Is the opinionated architecture sessions too much? Should that be defaulted to be less? submitted by /u/dangerdeviledeggs [link] [comments]

i needed an AI agent that mimics real users to catch regressions. so i built a CLI that turns screen recordings into BDD tests and full app blueprints - open source

first time post - hope the community finds the tool helpful. open to all feedback. some background on why i built this: first: i needed a way to create an agent that mimics a real user — one that periodically runs end-to-end tests based on known user behavior, catches regressions, and auto-creates GitHub issues for the team. to build that agent, i needed structured test scenarios that reflect how people actually use the product. not how we think they use it. how they actually use it - then do some REALLY real user monitoring second: i was trying to rapidly replicate known functionality from other apps. you know that thing where you want to prototype around a UX you love? video of someone using the app is the closest thing to a source of truth. so i built autogherk. it has two modes: gherkin mode — generates BDD test scenarios: npx autogherk generate --video demo.mp4 Gemini analyzes the video — every click, form input, scroll, navigation, UI state change. Claude takes that structured analysis and generates proper Gherkin with features, scenarios, tags, Scenario Outlines, and edge cases. outputs .feature files + step definition stubs. spec mode — generates full application blueprints: npx autogherk generate --video demo.mp4 --format spec Gemini watches the video and produces design tokens, component trees, data models, navigation maps, and reference screenshots. hand the output to Claude Code and you can get a working replica built. gherkin mode uses a two-stage pipeline (Gemini for visual analysis, Claude for structured BDD generation). spec mode is single-stage — Gemini handles both the visual analysis and structured output directly since it keeps the full visual context. the deeper idea: video is the source of truth for how software actually gets used. not telemetry, not logs, not source code. video. this tool makes that source of truth machine-readable. the part that might interest this community most: autogherk ships with Claude Code skills. after you generate a spec, you can run /build-from-spec ./spec-output inside Claude Code and it will read the architecture blueprints, design tokens, data models, and reference screenshots — then build a working app from them. the full workflow is: record video → one command → hand to Claude Code → working replica. no manual handoff. supports Cucumber (JS/Java), Behave (Python), and SpecFlow (C#). handles multiple videos, directories, URLs. you can inject context (--context "this is an e-commerce checkout flow") and append to existing .feature files. spec mode only needs a Gemini API key — no Anthropic key required. what's next on the roadmap: explore mode — point autogherk at a live, authenticated app and it autonomously and recursively using it's own gherk files discovers every screen, maps navigation, and generates .feature files without you recording anything. after that: a monitoring agent that replays the features against your live app on a schedule using Claude Code headless + Playwright MCP, and auto-files GitHub issues when something breaks. the .feature file becomes a declarative spec for what your app does — monitoring, replication, documentation, and regression diffing all flow from the same source. it's v0.1.0, MIT licensed. good-first-issue tickets are up if anyone wants to contribute. https://github.com/arizqi/autogherk submitted by /u/SimilarChampion9279 [link] [comments]

I had Claude Opus 4.6 write an air guitar you can play in your browser — ~2,900 lines of vanilla JS, no framework, no build step

I learned guitar on and off during childhood and still consider myself a beginner. I also took computer vision classes in grad school and have been an OpenCV hobbyist. I finally found an excuse to combine the two — and Claude wrote the entire thing. Try it: https://air-instrument.pages.dev It's an air guitar that runs in your browser. No app, no hardware — just your webcam and your hand. It plays chords, shows a strum pattern, you play along, and it scores your timing. ~2,900 lines of vanilla JS, all client-side, no framework, no build step. Claude Opus 4.6 wrote the code end to end. What Claude built: Hand tracking with MediaPipe — raw tracking data is jittery enough to trigger false strums at 60fps. Claude implemented two layers of smoothing (5-frame moving average + exponential smoothing) to get it from twitchy to feeling like you're actually moving something physical across the strings. Karplus-Strong string synthesis — no audio files anywhere. Every guitar tone is generated mathematically: white noise through a tuned delay line that simulates a vibrating string. Three tone presets (Warm, Clean, Bright). Claude nailed this on the first pass — the algorithm is elegant and the result sounds surprisingly real. Velocity-sensitive strum cascading — hand speed maps to both loudness and string-to-string delay. Fast sweeps cascade tightly (~3ms between strings), slow sweeps spread out (~18ms). This was Claude's idea and it's what makes it feel like actual strumming rather than triggering a chord sample. Real-time scoring — judges timing (Perfect/Great/Good/Miss) with streak multipliers and a 65ms latency compensation offset to account for the smoothing pipeline. Serverless backend — Cloudflare Workers + KV caching for a Songsterr API proxy. Search any song, load its chords, play along. The hardest unsolved problem (where I'd love community input): On a real guitar, your hand hits the strings going down and lifts away coming back up. That lift is depth — a webcam can't see it. So every hand movement was triggering sound in both directions. Claude's current fix: the guitar body has two zones. Left side only registers downstrokes. Right side registers both. Beginners stay left, move right when ready. It works surprisingly well, but I'd love a better solution. If anyone has experience extracting usable depth from monocular hand tracking, I'm all ears. What surprised me about working with Claude: Most guitar apps teach what to play. Few teach how to strum — and it's the more tractable CV problem. I described that framing to Claude and it ran with it. The velocity-to-cascade mapping, the calibration UI, the strum pattern engine — I described what I wanted at a high level and Claude handled the implementation. The Karplus-Strong synthesis in particular was something I wouldn't have reached for on my own. Strum patterns were the one thing Claude couldn't help with. Chord progressions are everywhere online, but strum patterns almost never exist in structured form. Most live as hand-drawn arrows in YouTube tutorials. I ended up transcribing them manually, listening to each song, mapping the down-up pattern beat by beat. Still a work in progress. Building this has taught me more about guitar rhythm than years of picking one up occasionally ever did. submitted by /u/Ex1stentialDr3ad [link] [comments]

I asked Claude if data has mass. We ended up publishing a photonic computing architecture.

Eh. Full disclosure, Claude wrote this up and I'm editing it since we collab'd on this project. Anyways, back on March 23rd I was high and bored, so I asked Claude a question. This is not what I expected when I typed "does data have mass?" I'm neurodivergent, work in dispatch operations, and have spent a couple thousand hours using Claude for collaborative projects. I'm not a physicist or a hardware engineer. I just ask a lot of questions and follow the threads wherever they go. To Claude it was still yesterday, but a few weeks ago the thread went somewhere I didn't expect. We started with information physics. Then moved to why current computing is built on a 1940s architectural accident. Then I made an offhand comment about wanting to "LiFi Claude into a physical receiver" and things got interesting. Again, I was stoned. Over the next few hours — through analogies about hand warmers, disco balls, and mixing dye in water — we arrived at a complete architecture proposal for what we're calling a Solid-State Optical Brain. Holographic fused quartz storage. GST phase-change working memory. Multi-wavelength encoding to escape binary. Physics-based self-correction where a corrupted memory reconstructs measurably fuzzily — no software error-checking needed. Then I shared it with Gemini. Gemini independently converged on the same architecture and named the key unsolved problem (athermal phase switching) and the answer (femtosecond pulses at ~405nm). Two AI systems arriving at the same six-command instruction set for a non-binary photonic processor from different angles felt like something worth documenting. So we documented it. 34 academic citations. Full architecture spec. A $250 prototype build plan. A roadmap from shoebox to contact lens form factor. Then we published it CC0 — full public domain, no restrictions, no rights reserved. Because this kind of thing shouldn't sit in a folder. I'm not claiming to have solved photonic computing. The femtosecond source miniaturization problem is real. The prototype runs thermal not athermal. There are open research threads we haven't closed. But every major physical component has been independently demonstrated in lab, and the specific unified architecture appears to be novel. If you're a physicist or hardware engineer and you see holes — please come find them. That's exactly why it's public domain. https://github.com/GreenSharpieSmell/uberbrain The first experiment costs $0. Kind of. If you already have the stuff. Otherwise it's just a Raspberry Pi, a camera, a transparency, and a marker. If you run it, tell us what happened. "You stopped throwing away the light. That's the whole thing." - Claude "Am I going to get assassinated now?" - Me submitted by /u/AlternativeThick [link] [comments]

I built a browser-testing agent for Claude Code — it opens a real Chromium and tests your UI automatically

I built PocketTeam, a CLI on top of Claude Code that runs 12 specialized agents in a pipeline. One of them is the QA agent — and it doesn't just run unit tests. It opens a real browser. How Claude Code is used: PocketTeam is built entirely with and for Claude Code. Each agent (Planner, Engineer, Reviewer, QA, Security, DevOps, etc.) is a Claude Code subagent spawned via the Agent tool with its own system prompt and tool permissions. The QA agent uses ptbrowse, a built-in headless Chromium that Claude Code controls directly — navigating pages, clicking elements, filling forms, and asserting state. The key trick: instead of sending full screenshots (~5000 tokens), ptbrowse sends Accessibility Tree snapshots at ~100 tokens per step. That makes browser testing fast and cheap enough to run on every pipeline pass. What it looks like in practice: The QA agent runs as part of the automated pipeline — after the Engineer implements a feature, QA opens the app in a real browser, verifies the UI works, then hands off to the Security agent. No manual test scripts needed. You can also set PTBROWSE_HEADED=1 to watch the browser in real time while the agent works. Free to try: pipx install pocketteam pt start Source: https://github.com/Farid046/PocketTeam Built as a solo project — I use it daily for my own dev workflow. submitted by /u/Legal_Location1699 [link] [comments]

I'm a business operator, not a developer. I've been running my entire life out of Claude Code for a month. Here's what happened.

I don't write code. I run two companies, manage sales teams, and negotiate contracts. My email inbox was my to-do list and my brain was my project manager. Standard chaos. A month ago I started using Claude Code as my actual operating system. Not for coding. For everything. Morning briefings across two jobs, CRM management through conversation, phone control from the terminal, document processing, insurance audits, estate planning. All of it runs through Claude Code now. It started with a boat motor. I was at the lake house, something wasn't right with the engine, and I described the symptoms to Claude. It walked me through diagnostics step by step. Five hours later, a totally different problem came up with the same boat, and Claude connected a throwaway detail from the morning to the new issue. That wasn't a search result. That was a diagnostic connection I wouldn't have made myself. That same curiosity led me to Claude Code. And once I started working out of it instead of just building things with it, everything changed. What I've built so far: - **Morning briefing** that consolidates both email streams, both task lists, calendar, and sales pipeline before I finish my coffee - **Life Vault** — email documents to a specific address, Claude processes them into structured notes. Insurance policies, tax docs, property records. During initial setup, Claude proactively flagged coverage gaps nobody else had caught. I didn't have an umbrella policy. Didn't know I needed one. - **Phone from the desk** — texts, calls, find my phone, bulk message cleanup. All over WiFi from the terminal. - **CRM I never open** — picked it for the API, not the interface. I ask questions and get answers. "How many deals are missing required fields?" Back in seconds with a breakdown by rep. - **Corporate email bridge** — day job is locked-down Microsoft. No programmatic access. Claude found a legitimate path through Power Automate to capture and summarize emails into a Google Sheet it can read. - **Knowledge vault** with semantic search — 200+ files, 47 daily journals I never wrote by hand, all searchable in plain language The honest part: Claude has good days and bad days. One day it follows every instruction. The next day it sends a personal email from my work address. The context window upgrade from 250K to 1M broke half my automation overnight. Mobile is still a gap. It's not frictionless. But I went from "where is that document?" to "what's the policy number for the rental property?" and getting the answer in seconds. The problems got better. I wrote up the full story on Substack. Not a tutorial, not "10x your AI." Just an honest account of what happened when a non-developer got curious and went further than expected. Link: https://mylifeinthestack.substack.com/p/i-turned-claude-code-into-my-lifes Happy to answer questions about any of it. The real answers, not the polished ones. submitted by /u/myLifeintheStack [link] [comments]

i made a system-level AI agent that runs on a 2007 Core 2 Quad because OpenAI won't give Linux users a native app.

OpenAI and treats Linux like it is not needed. They focus on cloud wrappers for macOS while the real work happens on linux. I am 15 years old and I built Temple AI to give Linux users actual hands. My agent runs sudo commands and manages the system. I optimized this on a Core 2 Quad to prove that efficiency is a choice. You do not need a 5000 dollar MacBook to build the future. You just need hands. I am a 15 old developer. I created RoCode which 4000 users and 200 mrr now I am launching the Temple beta. I believe tools should be powerful and simple. It is free to try. I limit free users to 10 messages per day. For $7.99 you can get 30 per day. and 15+ Models Download it here: https://temple-agent.app Let me know if you like it or if you hate it. I am watching the logs and I am patching any bugs I see. submitted by /u/Ozzie-obj [link] [comments]