Show HN: 20+ Claude Code agents coordinating on real work (open source) https://ift.tt/8x70uAD

Show HN: 20+ Claude Code agents coordinating on real work (open source) Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the pattern generalizes to things like refactors, app builds, and long research. It’s packaged as a Claude Code skill and designed to be small, readable, and modifiable. Use it, break it, tell me about what workloads we should try and run next! https://ift.tt/ZQA2tdN February 12, 2026 at 11:23PM

Show HN: Agent Tools – 136 deterministic data tools for AI agents (MCP/A2A/REST) https://ift.tt/gFMAKSL

Show HN: Agent Tools – 136 deterministic data tools for AI agents (MCP/A2A/REST) https://ift.tt/yDIcLPj February 12, 2026 at 11:17PM

Show HN: ClawDeploy – OpenClaw deployment for non-technical users https://ift.tt/WKgvYuo

Show HN: ClawDeploy – OpenClaw deployment for non-technical users Hi HN, I’m building ClawDeploy for people who want to use OpenClaw but don’t have a technical background. The goal is simple: remove the setup friction and make deployment approachable. With ClawDeploy, users can: - get a server ready - deploy OpenClaw through a guided flow - communicate with the bot via Telegram Target users are solo operators, creators, and small teams who need a dedicated OpenClaw bot but don’t want to deal with infrastructure complexity. Would love your feedbacks :) https://clawdeploy.com February 12, 2026 at 11:10PM

Show HN: Inamate – Open-source 2D animation tool (alternative to Adobe Animate) https://ift.tt/s20XIlW

Show HN: Inamate – Open-source 2D animation tool (alternative to Adobe Animate) Adobe recently announced the end-of-life for Adobe Animate, then walked it back after community backlash. Regardless of what Adobe decides next, the message was clear: animators who depend on proprietary tools are one corporate decision away from losing their workflow. 2D animation deserves an open-source option that isn't a toy. We've been working with a professional animator to guide feature priorities and ensure we're building something that actually fits real production workflows - not just a tech demo. Github Repo: https://ift.tt/UuD9oGJ We're at the stage where community feedback shapes the direction. If you're an animator, motion designer, or just someone who's been frustrated by the state of 2D animation tools — we'd love to hear: - What features would make you switch from your current tool? - What's the biggest pain point in your animation workflow? - Is real-time collaboration actually useful for animation, or is it a gimmick? Try it out, break it, and tell us what you think. Built with Go, TS & React, WebAssembly, PostgreSQL, WebSocket, ffmpeg (for video exports). February 10, 2026 at 07:15AM

Show HN: Yet another music player but written in Rust https://ift.tt/PJAvnaX

Show HN: Yet another music player but written in Rust Hey i made a music player which support both local music files and jellyfin server, and it has embedded discord rpc support!!! it is still under development, i would really appreciate for feedback and contributions!! https://ift.tt/LApY5ts February 12, 2026 at 02:59AM

Show HN: NOOR – A Sovereign AI developed on a smartphone under siege in Yemen https://ift.tt/mXsp0NQ

Show HN: NOOR – A Sovereign AI developed on a smartphone under siege in Yemen "I am a software developer from Yemen, coding on a smartphone while living under siege. I have successfully built and encrypted the core logic for NOOR—a decentralized and unbiased AI system. Execution Proof: My core node is verified and running locally via Termux using encrypted truth protocols. However, I am trapped in a 6-inch screen 'prison' with 10% processing capacity. My Goal: To secure $400 for a laptop development station to transition from mobile coding to building the full 'Seventh Node'. This is my bridge to freedom. Codes from the heart of hell are calling for your rescue. Wallet: 0x4fd3729a4fEdf54a74b73d93F7f775A1EF520CEC" https://ift.tt/caEzrC5 February 12, 2026 at 01:23AM

Show HN: MOL – A programming language where pipelines trace themselves https://ift.tt/kePtYRz

Show HN: MOL – A programming language where pipelines trace themselves Hi HN, I built MOL, a domain-specific language for AI pipelines. The main idea: the pipe operator |> automatically generates execution traces — showing timing, types, and data at each step. No logging, no print debugging. Example: let index be doc |> chunk(512) |> embed("model-v1") |> store("kb") This auto-prints a trace table with each step's execution time and output type. Elixir and F# have |> but neither auto-traces. Other features: - 12 built-in domain types (Document, Chunk, Embedding, VectorStore, Thought, Memory, Node) - Guard assertions: `guard answer.confidence > 0.5 : "Too low"` - 90+ stdlib functions - Transpiles to Python and JavaScript - LALR parser using Lark The interpreter is written in Python (~3,500 lines). 68 tests passing. On PyPI: `pip install mol-lang`. Online playground (no install needed): http://135.235.138.217:8000 We're building this as part of IntraMind, a cognitive computing platform at CruxLabx. """ https://ift.tt/v8SYOfD February 12, 2026 at 12:31AM

Show HN: Stripe-no-webhooks – Sync your Stripe data to your Postgres DB https://ift.tt/pWLBUPh

Show HN: Stripe-no-webhooks – Sync your Stripe data to your Postgres DB Hey HN, stripe-no-webhooks is an open-source library that syncs your Stripe payments data to your own Postgres database: https://ift.tt/jzJTb1g Here's a demo video: https://youtu.be/cyEgW7wElcs It creates a webhook endpoint in your Stripe account to forward webhooks to your backend where a webhook listener stores all the data into a new stripe.* schema. You define your plans in TypeScript, run a sync command, and the library takes care of creating Stripe products and prices, handling webhooks, and keeping your database in sync. We also let you backfill your Stripe data for existing accounts. It supports pre-paid usage credits, account wallets and usage-based billing. It also lets you generate a pricing table component that you can customize. You can access the user information using the simple API the library provides: billing.subscriptions.get({ userId }); billing.credits.consume({ userId, key: "api_calls", amount: 1 }); billing.usage.record({ userId, key: "ai_model_tokens_input", amount: 4726 }); Effectively, you don't have to deal with either the Stripe dashboard or the Stripe API/SDK any more if you don't want to. The library gives you a nice abstraction on top of Stripe that should cover ~most subscription payment use-cases. Let's see how it works with a quick example. Say you have a billing plan like Cursor (the IDE) used to have: $20/mo, you get 500 API completions + 2000 tab completions, you can buy additional API credits, and any additional usage is billed as overage. You define your plan in TypeScript: { name: "Pro", description: "Cursor Pro plan", price: [{ amount: 2000, currency: "usd", interval: "month" }], features: { api_completion: { pricePerCredit: 1, // 1 cent per unit trackUsage: true, // Enable usage billing credits: { allocation: 500 }, displayName: "API Completions", }, tab_completion: { credits: { allocation: 2000 }, displayName: "Tab Completions", }, }, } Then on the CLI, you run the `init` command which creates the DB tables + some API handlers. Run `sync` to sync the plans to your Stripe account and create a webhook endpoint. When a subscription is created, the library automatically grants the 500 API completion credits and 2000 tab completion credits to the user. Renewals and up/downgrades are handled sanely. Consume code would look like this: await billing.credits.consume({ userId: user.id, key: "api_completion", amount: 1, }); And if they want to allow manual top-ups by the user: await billing.credits.topUp({ userId: user.id, key: "api_completion", amount: 500, // buy 500 credits, charges $5.00 }); Similarly, we have APIs for wallets and usage. This would be a lot of work to implement by yourself on top of Stripe. You need to keep track of all of these entitlements in your own DB and deal with renewals, expiry, ad-hoc grants, etc. It's definitely doable, especially with AI coding, but you'll probably end up building something fragile and hard to maintain. This is just a high-level overview of what the library is capable of. It also supports seat-level credits, monetary wallets (with micro-cent precision), auto top-ups, robust failure recovery, tax collection, invoices, and an out-of-the-box pricing table. I vibe-coded a little toy app for testing: https://snw-test.vercel.app There's no validation so feel free to sign up with a dummy email, then subscribe to a plan with a test card: 4242 4242 4242 4242, any future expiry, any 3-digit CVV. Screenshot: https://ift.tt/gJx7aNT Feel free to try it out! If you end up using this library, please report any bugs on the repo. If you're having trouble / want to chat, I'm happy to help - my contact is in my HN profile. https://ift.tt/jzJTb1g February 11, 2026 at 12:14AM

Show HN: Open-Source SDK for AI Knowledge Work https://ift.tt/7AobI3Z

Show HN: Open-Source SDK for AI Knowledge Work GitHub: https://ift.tt/hm5qTUD Most AI agent frameworks target code. Write code, run tests, fix errors, repeat. That works because code has a natural verification signal. It works or it doesn't. This SDK treats knowledge work like an engineering problem: Task → Brief → Rubric (hidden from executor) → Work → Verify → Fail? → Retry → Pass → Submit The orchestrator coordinates subagents, web search, code execution, and file I/O. then checks its own work against criteria it can't game (the rubric is generated in a separate call and the executor never sees it directly). We originally built this as a harness for RL training on knowledge tasks. The rubric is the reward function. If you're training models on knowledge work, the brief→rubric→execute→verify loop gives you a structured reward signal for tasks that normally don't have one. What makes Knowledge work different from code? (apart from feedback loop) I believe there is some functionality missing from today's agents when it comes to knowledge work. I tried to include that in this release. Example: Explore mode: Mapping the solution space, identifying the set level gaps, and giving options. Most agents optimize for a single answer, and end up with a median one. For strategy, design, creative problems, you want to see the options, what are the tradeoffs, and what can you do? Explore mode generates N distinct approaches, each with explicit assumptions and counterfactuals ("this works if X, breaks if Y"). The output ends with set-level gaps ie what angles the entire set missed. The gaps are often more valuable than the takes. I think this is what many of us do on a daily basis, but no agent directly captures it today. See https://ift.tt/2lIZozt... and the output for a sense of how this is different. Checkpointing: With many ai agents and especially multi agent systems, i can see where it went wrong, but cant run inference from same stage. (or you may want multiple explorations once an agent has done some tasks like search and is now looking at ideas). I used this for rollouts a lot, and think its a great feature to run again, or fork from a specific checkpoint. A note on Verification loop: The verify step is where the real leverage is. A model that can accurately assess its own work against a rubric is more valuable than one that generates slightly better first drafts. The rubric makes quality legible — to the agent, to the human, and potentially to a training signal. Some things i like about this: - You can pass a remote execution environment (including your browser as a sandbox) and it would work. It can be docker, e2b, your local env, anything, the model will execute commands in your context, and will iterate based on feedback loop. Code execution is a protocol here. - Tool calling: I realize you don't need complex functions. Models are good at writing terminal code, and can iterate based on feedback, so you can just pass either functions in context and model will execute or you can pass docs and model will write the code. (same as anthropic's programmatic tool calling). Details: https://ift.tt/7aH2lrC... Lastly, some guides: - SDK guide: https://ift.tt/hk461i9 - Extensible. See bizarro example where i add a new mode: https://ift.tt/horpmsn... - working with files: https://ift.tt/yxZ7ri9... - this is simple but i love the csv example: https://ift.tt/Bb78jTe... - remote execution: https://ift.tt/7MyHmAR... And a lot more. This was completely refactored by opus and given the rework, probably would have taken a lot of time to release it. MIT licensed. Would love your feedback. https://ift.tt/hm5qTUD February 11, 2026 at 12:06AM

Show HN: Rowboat – AI coworker that turns your work into a knowledge graph (OSS) https://ift.tt/9QMHqp4

Show HN: Rowboat – AI coworker that turns your work into a knowledge graph (OSS) Hi HN, AI agents that can run tools on your machine are powerful for knowledge work, but they’re only as useful as the context they have. Rowboat is an open-source, local-first app that turns your work into a living knowledge graph (stored as plain Markdown with backlinks) and uses it to accomplish tasks on your computer. For example, you can say "Build me a deck about our next quarter roadmap." Rowboat pulls priorities and commitments from your graph, loads a presentation skill, and exports a PDF. Our repo is https://ift.tt/2Ic7QBH , and there’s a demo video here: https://www.youtube.com/watch?v=5AWoGo-L16I Rowboat has two parts: (1) A living context graph: Rowboat connects to sources like Gmail and meeting notes like Granola and Fireflies, extracts decisions, commitments, deadlines, and relationships, and writes them locally as linked and editable Markdown files (Obsidian-style), organized around people, projects, and topics. As new conversations happen (including voice memos), related notes update automatically. If a deadline changes in a standup, it links back to the original commitment and updates it. (2) A local assistant: On top of that graph, Rowboat includes an agent with local shell access and MCP support, so it can use your existing context to actually do work on your machine. It can act on demand or run scheduled background tasks. Example: “Prep me for my meeting with John and create a short voice brief.” It pulls relevant context from your graph and can generate an audio note via an MCP tool like ElevenLabs. Why not just search transcripts? Passing gigabytes of email, docs, and calls directly to an AI agent is slow and lossy. And search only answers the questions you think to ask. A system that accumulates context over time can track decisions, commitments, and relationships across conversations, and surface patterns you didn't know to look for. Rowboat is Apache-2.0 licensed, works with any LLM (including local ones), and stores all data locally as Markdown you can read, edit, or delete at any time. Our previous startup was acquired by Coinbase, where part of my work involved graph neural networks. We're excited to be working with graph-based systems again. Work memory feels like the missing layer for agents. We’d love to hear your thoughts and welcome contributions! https://ift.tt/2Ic7QBH February 10, 2026 at 11:47PM

Show HN: I made paperboat.website, a platform for friends and creativity https://ift.tt/ZruHy5v

Show HN: I made paperboat.website, a platform for friends and creativity https://paperboat.website/home/ February 10, 2026 at 11:57PM

Show HN: Reef – Bash compatibility layer for Fish shell, written in Rust https://ift.tt/utMiQkX

Show HN: Reef – Bash compatibility layer for Fish shell, written in Rust Fish is the fastest, friendliest interactive shell, but it can't run bash syntax, which has kept it niche for 20 years. Reef fixes this with a three-tier approach: fish function wrappers for common keywords (export, unset, source), a Rust-powered AST translator using conch-parser for structural syntax (for/do/done, if/then/fi, $()), and a bash passthrough with env capture for everything else. 251/251 bash constructs pass in the test suite. The slowest path (full bash passthrough) takes ~3ms. The binary is 1.18MB. The goal: install fish, install reef, never think about bash compatibility again. Your muscle memory, Stack Overflow commands, and tool configs all just work. https://ift.tt/oAGmawW February 10, 2026 at 06:44AM

Show HN: Stack Overflow for AI Coding Agents https://ift.tt/pa9yI1N

Show HN: Stack Overflow for AI Coding Agents https://shareful.ai/ February 10, 2026 at 01:42AM