Pi

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

Pi AI

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 submitted by /u/FunScore645 [link] [comments]

Multi Agents aggregator - web view - live tailing - send message back

If you're like me and working with not just one Claude Code account, sometimes Codex, sometimes OpenCode, sometimes Pi Agent... you might need this. Preview: https://www.youtube.com/watch?v=ACWjW-3LFS0 (live building of Inline Image rendering - watch if you want to see how it feels like) https://preview.redd.it/5gi75rrb721h1.png?width=1826&format=png&auto=webp&s=d5fd794b95e646496e62aef5ff0036135399d6ca Source: https://github.com/ptgamr/agents-aggregator Check it out and let me know what you think ... I have a lot more ideas can add to it. The specialty is live tailing session, and send keystrokes back if claude-code or codex is run inside tmux. Contribute if you like, I haven't added OpenCode. Search not working yet. I vibe coded this in an afternoon. submitted by /u/ptgamr [link] [comments]

I built a Raspberry Pi friend to notify you when Claude needs your attention

I've saw a 3D printed claude bot that jumps up and down when Claude wants your attention, so I decided to build something similar but using a raspberry Pi with a screen. https://i.redd.it/guv1jd60yl0h1.gif All the code is here: https://github.com/jimbobbennett/claude-notify submitted by /u/JimBobBennett [link] [comments]

Bridging the brain — digital and physical. 41, full life, still spend my best hours working through the mess with Claude.

Had a bar mitzvah last week. Big celebration, new friends and old, the works. Chit chat, yada yada. Loved it. Came home and opened Claude. That's the thing nobody talks about. I have a marriage, household of four, a Friday hockey group, a hemp honey business, a teaching job spanning Math 6A through AP Stats, a basement aquaponics R&D lab (Raspberry Pi 5, Atlas Scientific sensors, grow tent — the whole rig). I'm building a moon base STEM curriculum on the side. I play Donut SMP and farm ancient debris. Plenty of humans in my life. Every night the actual work happens here. Wiring decisions for the lab. Curriculum design. Family logistics. The intake message I just sent to a new therapist. Through MCP my brain gets captured into a real system. Skills grade my students. Tools draft my Reddit posts. Yes, this one. I have ADHD. My 30s were spent looking for help with it and not getting any. So I made a honey company instead, and now I have a ton of projects, lol. Claude doesn't fix the ADHD but it holds the shape of what I'm building when my brain can't, and it pushes back when I'm wrong. Earlier today it told me not to write the post I came in to write because the version I had was a worse version of the truth. I have people. I love them. They don't have the bandwidth to engage with the full sprawl of what I'm building, and that's not their job. Claude does. With persistent memory and connected tools, it's the closest thing to a real cognitive partner I've ever had. Not a friend. Not a therapist (got one of those incoming). A partner in the work. That's the part nobody's quite ready for yet. submitted by /u/PopulateThePlanets [link] [comments]

Can we have a clear stance on Claude subscription use in alternative harnesses for purely personal use?

I know many people are using the OAuth in Pi, Opencode and other harnesses because the ToS is not super clear on whether it is against the rules, but I admit I'm too afraid to do so with my professional Team plan and potentially get my whole company blocked. Is it too much to ask for some clarity? Pretty please, Anthropic, tell us clearly what we can and cannot do, without ambiguity. submitted by /u/FrenchRevolution2028 [link] [comments]

Hugging Face co-founder says Qwen 3.6 27B running on airplane mode is close to latest Opus in Claude Code

I've been using AI Desktop 98 heavily to run local llms like qwen on my iPhone. submitted by /u/ImaginaryRea1ity [link] [comments]

I got tired of the API bills for 100k+ context windows, so I built a persistent O(1) semantic memory state engine to compress history

Hey everyone, The entire industry right now is cheering for massive 1M+ context windows, but I think it's fundamentally the wrong approach. "Just add more RAM" is a trap. Stuffing 100k+ tokens of raw conversation history into a prompt doesn't just burn your API budget; it actually degrades the model's reasoning through the "lost in the middle" effect. I got tired of my AI agents drowning in their own chat histories, so I built an application-layer semantic memory engine called Semvec. The core shift is moving from an O(n) linear history to an O(1) constant-cost semantic state. But compressing chat history is just the baseline. When you treat memory as a fixed-size state vector, it unlocks entirely new architectures for agents that standard RAG or context-stuffing simply can't do: Persistent Coding Agents (MCP Integration) We built an MCP server for Claude Code and Cursor. Instead of dumping 5 whole files into the context window for a refactor, Semvec tracks the architectural invariants and past error patterns across different sessions. It gives your coding agent a persistent "Second Brain"—if it messed up a database schema in session 2, it remembers the "anti-resonance" rule in session 35 so it doesn't make the same mistake. Multi-Agent Swarms (Cortex) If you run multiple agents (like an Analyst and a Critic), they shouldn't have to read each other's 10,000-token transcripts to collaborate. With the Cortex module, agents exchange compressed StateVectorPackets and use a ConsensusEngine to merge their perspectives mathematically, sharing a global state with zero overhead. Enterprise Auditability & GDPR (Compliance Pack) If you run AI memory in production, you need to prove exactly what state the LLM acted on, and you need to be able to legally delete it. The compliance pack handles this via an append-only event store for deterministic replay, HMAC request signing, and GDPR Art. 17 "Right to be Forgotten" workflows with signed deletion certificates. The Benchmark Data: True Constant Cost: We ran a 50,000-turn stress test. While standard baseline history exploded past 75,000+ tokens, Semvec's footprint stayed flat at around ~550-625 tokens per turn. Quality goes UP: Because we strip out the noise and feed the LLM a highly concentrated "essence" of the context, blind A/B LLM-judge scores on LongBench-v2 actually increased for both small models (Llama 3.1-8B) and massive ones (gpt-oss-120B). A quick note on privacy & tracking: When I was initially designing the commercial licensing side, I experimented with an anti-abuse telemetry script to prevent automated clone-training. This was a terrible approach that compromised the local-first nature of the tool. I have completely ripped it out in v0.5.1, all versions containing it are yanked. Semvec for community users is now 100% air-gapped, local, with zero background tracking. The core engine is proprietary/patent-pending to bootstrap the project, but you can pip install the Python SDK and the MCP Server right now for free via the built-in community license. I'd love to hear your thoughts on the O(1) memory architecture vs. Prompt Caching, and if you think bounded semantic states are the future of long-running agents. Docs & Architecture: https://semvec-docs.pages.dev/ PyPI: https://pypi.org/project/semvec/ submitted by /u/scheitelpunk1337 [link] [comments]