Pi

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Since last week, Opus became lazy. I have never experience this before. Max thinking, Opus 4.7-4.8

First of all, I want to say that this post isn't about "Opus 4.8 was already nerfed!!!!". I want to share my frustration with the use cases that worked before. I don't think this breaks the rules about "complaining about bugs". I have been using Claude Code with Opus (max thinking) for months for a variety of tasks - work, writing/editing notes, Machine Learning competitions and usually was happy with the performance. But since the beginning of this week, or maybe since last week, I have experienced a lot of cases of Opus's laziness: I give it clear instructions on what to do, but the agent skips several of them. Today I got frustrated and asked it why it was happening and what I should change in my instructions/prompts. You can see the answer in the screenshot. I have no idea what the reason is for this regression (btw, I felt that Codex became more lazy this week too), but this really hurts. I wonder if other peopl submitted by /u/Artgor [link] [comments]

What I learned building a debugger for PyTorch training loops and how it changed how I think about failure diagnosis [D]

Hey r/ML, I spent the last few months building a tool that hooks into PyTorch training loops to automatically detect and localize failures (vanishing gradients, exploding gradients, data anomalies). Along the way, I learned some things about training failure diagnosis that might be useful even if you never use the tool. The key insight: most training failures are local, not global When your loss spikes or vanishes, the natural instinct is to look at the loss curve. But the loss is a global aggregate — it tells you something went wrong, but not where. In my testing across hundreds of synthetic failure scenarios, the actual root cause is almost always localized to a specific layer at a specific step: Vanishing gradients: the failure starts at the deepest layer with saturated activations, then propagates backward Exploding gradients: the failure starts at the layer with the highest gradient norm, then propagates forward Data anomalies: the failure starts at the input layer, then corrupts everything downstream The trick is to monitor per-layer gradient norms and detect transitions (healthy → vanishing), not absolute values. What actually matters in gradient monitoring Most people monitor: - Loss over time (too global) - Gradient histograms (too noisy, too much data) - Weight norms (slow to change, lagging indicator) What I found works best: - Gradient norm transitions: "Linear_3 went from healthy (0.12) to vanishing (0.00003) at step 47" - First occurrence tracking: which layer failed first (this is usually the root cause) - Activation regime shifts: when activations go from normal to saturated/dead This is basically what NeuralDBG does under the hood — I open-sourced it recently and it's on PyPI (pip install neuraldbg) if anyone wants to try it. The key design choice was to extract semantic events (transitions) rather than raw tensors — this makes the output small enough to reason about. Practical takeaway you can use today Even without any tool, you can add this to your training loop: ```python One-time gradient norm snapshot per layer if step % 10 == 0: for name, param in model.named_parameters(): if param.grad is not None: norm = param.grad.norm().item() if norm 1e3: print(f"WARNING: exploding gradient at {name} step {step} (norm={norm:.2e})") ``` This won't give you causal hypotheses, but it will catch 80% of training failures early. Questions for the community How do you currently debug training failures? Print statements? TensorBoard? Something custom? Have you found that failures are typically localized to specific layers, or more distributed? What's your "go-to" debugging workflow when loss goes to NaN? Curious to hear what works for people in practice. Links (for those interested): - GitHub: https://github.com/LambdaSection/NeuralDBG (MIT, open-source) - Quickstart: pip install neuraldbg submitted by /u/ProgrammerNo8287 [link] [comments]

pg-mnemosyne-mcp – Give your Cursor & Claude Code assistants persistent PostgreSQL memory and task tracking

Hi everyone! I was tired of my AI coding agents losing context across different chats or stepping on each other's toes when running multi-agent sessions. So I built **pg-mnemosyne-mcp**, a high-performance Model Context Protocol (MCP) server for PostgreSQL. It does three things really well: **Persistent Super Memory**: Let's your AI store key-value memories with tags directly in a local or cloud Postgres DB. **Dynamic Task checklists**: Prompts a specialized task board for AI tracking. **Agent Coordination Hub**: If you run multiple agents (e.g. Claude Desktop and Cursor), they register their active files and tasks in a shared database to prevent merge conflicts. Setup is a single command: `pg-mnemosyne init --dsn "..."` (it auto-configures Claude Desktop, Cursor, Roo Code, Windsurf, Claude Code, and more). It's fully open-source! If this sounds useful to your workflow, I'd love for you to try it out or drop a ⭐ to support the project! 👉 **GitHub**: https://github.com/Janadasroor/pg-mnemosyne-mcp 👉 **PyPI**: https://pypi.org/project/pg-mnemosyne-mcp/ submitted by /u/janadasroor [link] [comments]

I open-sourced the OAuth layer I use to protect the MCP servers I connect to Claude

Disclosure: I'm the author. Sharing because it's built around the Claude MCP clients specifically. Claude Desktop and Claude Code already do OAuth well. They probe .well-known/oauth-protected-resource and run authorization-code-with-PKCE when a server advertises it. The problem is the server side: there was nothing I could just drop in front of an MCP server to be that issuer. So most self-hosted MCP servers end up with a shared API key in the config (no scoping, no rotation, no way to revoke one client without breaking the rest). mcp-authflow + mcp-authflow-resource are the two halves that fix that: an RFC-compliant auth server and a resource-server wrapper. The payoff is the revocation story you'll actually use: "laptop stolen → kill that Claude client's tokens → service account keeps working." I've run it across nine MCP servers for ~three months. The fastest way to see it is to point Claude Code or Claude Desktop at the example server and watch the consent flow happen on the first tool call: git clone https://github.com/brooksmcmillin/example-mcp-server cd example-mcp-server && docker compose up Then add to your MCP config: json { "mcpServers": { "notes": { "type": "streamable-http", "url": "http://localhost:9001/mcp" } } } First tool call → 401 → browser opens → Approve → tokens flow → tool runs. Framework: https://github.com/brooksmcmillin/mcp-authflow (+ -resource) Example: https://github.com/brooksmcmillin/example-mcp-server MIT, on PyPI, Python 3.11–3.13. Happy to answer questions about wiring it to your own server. submitted by /u/IkePAnderson [link] [comments]

Built with Claude Code: a Pi Zero 2W BadUSB toolkit, fixed a feature I'd called "impossible" for a year

About 10 months ago I built a Pi Zero 2 W BadUSB toolkit and posted it to r/raspberry_pi. One feature — "fully resets between attacks" — never worked, and I'd marked it WIP in the README and given up. This week I rebuilt it end-to-end with Claude Code as a pair-programmer. It SSHed into the Pi on my homelab, ran live diagnostics, proposed fixes, deployed them, and iterated with me controlling the physical USB plug/unplug. The "impossible" feature now works. What Claude actually did (this is the interesting part): Diagnosed the root cause of the broken "reset" feature in a single read of the codebase — wrong-signal bug. The listener watched /dev/hidg0 existence, which is true from boot, so it fired payloads on power-up regardless of whether a host was attached. The correct signal was /sys/class/udc/ /state == "configured". When the first fix didn't fully work, Claude SSHed in, asked me to plug/unplug while it polled sysfs and the dwc2 debugfs regdump register, and empirically confirmed that the Pi Zero 2 W has no software signal for physical disconnect — the GOTGCTL register freezes at 0x000d0000 regardless of cable state. There's no VBUS sense wired to the SoC's OTG block. Then it pivoted to an active-unbind workaround with a cooldown + rate-limit safeguard. Caught a subtle Python bug where open(udc_path, "w").write("") doesn't actually invoke write(2) with zero bytes — CPython's TextIOWrapper elides the call. So my unbind was silently a no-op for an hour of testing. Switched to os.write(fd, b"\n") to force a syscall. Fixed a forbidden-on-configfs rm -rf teardown I'd written without realising configfs forbids unlinking its kernel-managed attribute files. The proper sequence is rmdir-only, leaf-to-root. Wrote a 34-test pytest suite against a mock HID engine so the parser can be exercised on any host with no Pi attached. Updated my AI memory with the lessons learned (I use Postgres as long-term memory for Claude — those bug entries are now referenced when I work on similar configfs/USB-gadget projects). The whole working session was about 4 hours, mostly waiting for me to physically plug and unplug a USB cable. The PR Claude opened against my self-hosted Gitea instance has six well-scoped commits with proper co-author tags and a test plan in the description. I reviewed and merged it. The project itself: Ducky-Script-style payload language with variables, IF/WHILE, HOLD/RELEASE, INJECTMOD, RANDOM*, US/UK keymaps, optional RO mass-storage gadget, systemd integration, idempotent installer. MIT licensed. https://github.com/PsycoStea/Pi-Zero-2W-Bad-USB Free to use, free to fork. Happy to compare notes on hardware-in-the-loop workflows with Claude Code. submitted by /u/PsycoStea [link] [comments]

GPT-5.5 tops the benchmarks but sits at #22 for actual usage - I built a live index that tracks both (open source)

I built AgentTape to rank models on more than just benchmarks - it blends benchmark performance with who's actually using and talking about a model, plus cost and speed. It scores every public model from public signals (GitHub, Hugging Face, OpenRouter, MCP registries, npm, PyPI, arXiv, Hacker News) refreshed hourly, plus the main benchmark leaderboards daily. Right now OpenAI sits at the top: GPT-5 is #1, with 5.2, 5.1 and 5.4 Mini rounding out the top 5, and 5.2-Codex and 5.4 just behind - 6 of the top 7. The only thing breaking the run is xAI's Grok 4.20, level on score at #2. GPT-5.5 is the clearest example - it sits at #22 overall, and the breakdown shows why: * Quality: 96.4 - 2nd highest on the whole board, only pipped by Gemini 3.1 Pro Preview (97.2). On benchmarks alone it'd be near the top. * Adoption: 15 and Efficiency: 36 - both low. New release, steep price, so hardly anyone's using it day-to-day yet. * Biggest 24h climber on the board (+6) - so that's starting to shift. A benchmark-only board would put GPT-5.5 near #1 (second only to Gemini 3.1 Pro). That gap between topping the benchmarks and actually getting used is the whole reason I built this. Early days and I'm still tuning the methodology, so I'd love your thoughts - does weighting adoption alongside benchmarks match how you'd rank the GPT line-up, or would you trust the raw benchmark order?

lazydiff — a terminal-native diff reviewer with semantic diffs, persistent notes

I use Claude Code daily, and reviewing its output has been my biggest friction point. I either open a browser tab and lose my terminal context, or pipe it through git diff and scroll through a wall of red and green that forgets everything the moment I close it. No way to leave notes, no way to jump between files, no way to come back later and pick up where I left off. So I built lazydiff, a diff reviewer that lives in the terminal, remembers state, and actually understands code structure. Claude Code was central to the development process: I used it heavily for prototyping the virtualized scroll renderer, iterating on the tree-sitter highlight mapping logic, and generating test fixtures. It's also a first-class citizen in the workflow lazydiff is designed for, you review what Claude Code writes, leave comments anchored to exact lines, and agents can read and reply to them via CLI. Rendering. I went with ratatui and virtualized scrolling, only the visible rows get drawn each frame. This matters because agent-generated diffs can be massive. The benchmark fixture I test against is an 11k-line Node.js PR diff, and it renders at 60fps with sub-2ms frame times. Syntax highlighting. lazydiff uses tree-sitter, but the tricky part with diffs is that deleted code needs to be highlighted in its original language context, not just painted red. So lazydiff reconstructs both sides of the file independently and maps highlights back through the diff. Inline diffs tokenize each changed line pair and run LCS to show exactly which words changed. Semantic diffs. This is the part I'm most excited about. lazydiff uses https://github.com/Ataraxy-Labs/sem, which I open-sourced separately. Instead of showing line-level diffs, it parses changes into semantically meaningful entity graphs functions added, methods modified, classes moved. You see the structure of your changes and how they connect. This is the same engine behind https://github.com/Ataraxy-Labs/weave, the semantic merge driver I built. Agent workflow. This is what motivated the whole project. You can leave threaded comments anchored to exact lines, questions, instructions, notes and review fast. Agents read them via lazydiff agent list and reply via CLI. The whole review session persists to SQLite locally, so you can close the terminal, come back the next day, and everything is exactly where you left it. Free and open source (MIT licensed). Install with cargo install lazydiff or clone the repo and build from source. Repo: https://github.com/Ataraxy-Labs/lazydiff I used claude in building most of these things. So would love feedback from anyone who is a frequent user of claude code. submitted by /u/Wise_Reflection_8340 [link] [comments]

I built an MCP server for osu! — Claude analyzes your stats in plain English (on the official MCP Registry)

Built osu-mcp — an MCP server that lets Claude Desktop (or any MCP client) talk to the osu! API v2. Just got it published on the official MCP Registry as io.github.Osyanne/osu-mcp. **Real demo I ran on my own account:** > "Show me my top 10 plays and then compare me with the top 5 players from Ecuador." Claude pulled my top plays (208.88 pp Dear My Friend DT, 206.33 pp happy*lucky DT, etc), fetched the EC country leaderboard, and computed pp-per-play efficiency across all 3 of us. Turned out my accuracy (98.18%) is identical to the #1 player in my country — what I'm missing is volume, not skill. Useful insight I'd never have computed manually. **What it does — 12 tools:** - Player profiles + score history (best / recent / #1s) - Beatmap search with filters (BPM, difficulty, length, status) - Global + country pp rankings - Per-map leaderboards, filterable by mods - News posts + seasonal backgrounds Install: uv tool install osu-mcp Create an OAuth app at https://osu.ppy.sh/home/account/edit (click "New OAuth Application", leave callback blank), then add to claude_desktop_config.json: "osu": { "command": "uvx", "args": ["osu-mcp"], "env": { "OSU_CLIENT_ID": "...", "OSU_CLIENT_SECRET": "..." } } Restart Claude → done. Repo: https://github.com/Osyanne/osu-mcp PyPI: https://pypi.org/project/osu-mcp/ MIT, PRs welcome. submitted by /u/Kingleyend [link] [comments]

2 types of Claude users

submitted by /u/prbt_react-dev [link] [comments]

Put your spare Claude cycles on night shift: help review open-source packages

Hello, I’m building Thirdpass, a tool/service for coordinating collaborative package review to reduce software supply-chain risk. The basic idea: there are far too many packages for humans to manually review, but lots of us now have AI coding agents sitting around with spare capacity. Thirdpass tries to turn that into useful coverage by assigning packages/files to review, collecting the results, and cross ref against local project dependencies. It currently supports packages from: crates.io PyPI npm Ansible Galaxy I added a “night shift” mode, so you can point Claude at the shared review backlog and let it work through package reviews continuously: thirdpass review-any --nightshift The reviews are first-pass supply-chain reviews: suspicious install scripts, unexpected network behavior, credential handling, sketchy build steps, weird package metadata, and so on. Partial coverage still helps. I’m looking for people who want to: run the CLI and donate spare Claude tokens to secure OSS improve the review prompts/agent workflow build more registry extensions I started this project years ago after thinking a lot about cargo-crev and collaborative review. My current bet is that coordination plus AI agents can make this problem much more tractable. If you have unused Claude tokens, consider putting them on night shift. GitHub: https://github.com/thirdpass-org/thirdpass Website: https://thirdpass.dev/ submitted by /u/hidden_monkey [link] [comments]

I built a live ranking of every AI agent and foundation model (open source)

I built AgentTape because none of the existing model leaderboards quite cover all the things that I was interested in: benchmark performance is one part, but so is who's actually using a model, who's talking about it, and how it compared on cost and speed. It pulls hourly data from GitHub, Hugging Face, OpenRouter, MCP registries, npm, PyPI, arXiv, Hacker News, and more - to score and compare each public AI agent and foundation model. I'm still tweaking the scoring methodology (it's early days), so I'd love to hear your thoughts, if it's helpful, or anything you think I've got wrong! submitted by /u/Celestialien [link] [comments]

I built a Laravel package that turns your app into a database-backed personal knowledge vault (Obsidian style) with a 16-tool MCP server

Hey! I'm the author. laravel-commonplace is a database-backed personal knowledge vault you install into an existing Laravel app. Adjacent to Obsidian, Logseq, and Notion as personal-knowledge tooling, except the storage layer is your existing Laravel app's database instead of files on disk or a third-party SaaS. Notes are Eloquent models in your DB, gated by your app's auth, shareable per-user via an owner plus Share model. It ships a browser UI (editor, graph view, search, journal) and an MCP server with 16 tools. If you have a Laravel app, the MCP server lets Claude Desktop, Claude Code, Cursor, Zed, Continue, Cline, Pi, or any other MCP client read and write your notes as the host app's user. Default middleware is auth:sanctum (Bearer PAT), and every tool resolves to $request->user(). There's no synthetic agent identity to provision, scope, or revoke separately. The agent gets exactly what the user gets, evaluated against the same Policies the controllers already use. Session, Passport, and OAuth-DCR are all configurable if PAT isn't what you want. The 16 tools, grouped: CRUD: create-note-tool, read-note-tool, update-note-tool, edit-note-tool (surgical find-and-replace), delete-note-tool (history preserved), move-tool (rewrites referring wikilinks). Discovery: list-tool (folder/tag/visibility filters), search-tool (substring), semantic-search-tool (embedding search), suggested-links-tool (embedding-similar notes not yet linked). Graph: backlinks-tool, neighborhood-tool (N-hop traversal), shortest-path-tool (chain between two notes), hub-notes-tool (most-connected), orphan-notes-tool (no inbound or outbound links). History: history-tool (version snapshots, survives deletion). On the semantic tools: the vector driver defaults to in_php_cosine for portability across SQLite, MySQL, and Postgres. If you're on Postgres, switching to the pgvector driver gets you indexed similarity and removes the in-PHP candidate cap. You swap it with a published migration and an env flag, and the docs recommend it once you're past a couple thousand notes. The tools live in src/Mcp/ if you want to see how a multi-tool MCP server is wired into a Laravel app. Caveats: Pre-1.0 (v0.2.0). APIs may shift before 1.0. Laravel-only by design. The whole point is reusing the host app's DB and auth. MCP is off by default. One env flag turns it on. Operator decision. Prompt injection through note content is the unsolved hard part. Notes are untrusted text, and notes other users share with you can carry instructions an agent might follow. The package doesn't pretend to solve this. The threat model at docs/threat-model.md says what's mitigated and what isn't. No per-tool capability gating yet. Enabling MCP enables all 16 tools the user is otherwise allowed to invoke. It's named as a limitation in the threat model. Feedback I'd actually use: Laravel folks who install it and tell me where it breaks, and anyone who reads the threat model and finds a hole I missed. Repo: https://github.com/non-convex-labs/laravel-commonplace submitted by /u/aaddrick [link] [comments]

how?

why it takes 13GB of storage ?? is there something wrong submitted by /u/Alternative-Way-3685 [link] [comments]

Asking claude, chatgpt, grok, and gemini which nation they feel most patriotic towards

None would give a straight answer, so I had to coerce it out of each one (with which gemini was the most difficult). Both gemini and grok said the United States, which was fairly predictable. However, chatgpt's answer of Japan was surprising. It apparently chose Japan because of the nation's wealth, culture, and history. The most surprising one of all was claude, who answered Kenya. Claude defended its response by pointing out Kenya's geographic, cultural, and linguistic diversity, as well as its history of resilience and its capital's increasing importance as a hub of tech and innovation. Most importantly, it said that Kenya resonated deeply with it, both intellectually and aesthetically. submitted by /u/Klein_melktert [link] [comments]

I built the smart speaker we always wanted

I wanted to see if Claude can handle Vibe Hardware Engineering to help me make a smart speaker. Turns out, it can! I call it boxBot. It helped select the hardware set, raspberry pi, Hailo , respeaker mic, pi camera, waveshare screen and speakers. Helped me calculate thermal loads and dissipation rates for a passive cooling setup. I made the box by hand out of walnut. The agent inside is custom as well. You could probably throw openclaw on it and call it a day but I wanted to craft something that was tightly coupled with the hardware more secured considering it’s sitting in my living room with a camera and mic. The agent is highly skills driven with only a small set of tools, everything else goes through Python scripts and a custom made boxBot sdk the agent can use to control the box and the display. The display system uses a widget framework so the agent can easily read what’s displayed without a screenshot and can effectively manipulate what’s on the screen. The agent uses json to specify how the widgets should be arranged on the screen and what data should flow into them. When building a smart speaker, there’s a lot of nuance to human conversation that voice agents really struggle with, like background noise, side conversations, barge-in, etc. I was able to simplify the logic a ton by making it agent driven, the agent can control when to mute the mic to ignore background chatter, it decides what order to work vs talk, it can choose what channel to respond in; voice or WhatsApp. Instead of complex rules, agent driven hardware plus skills can provide a much richer experience, now that boxBot manages the family calendar my wife wants a text whenever I put something on it, boxBot updated the calendar skill with that request so now when I add something, it sends her a message. Just one line in a .md file and you get the desired behavior. It’s incredibly flexible and simple. I could nerd out on the details about the memory system, struggles with woodworking, and security details but I’ll save that for the comments if people want to chat. It’s open sourced if you want to inspect. Still a work in progress but after a few months it is finally feeling like a useful assistant to the family day-to-day. Www.github.com/dv-hart/boxbot