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

Show HN: Pyrig – One command to set up a production-ready Python project https://ift.tt/MtuVUBx

Show HN: Pyrig – One command to set up a production-ready Python project pyrig – Production-ready Python project infrastructure in three commands I built pyrig to stop spending hours setting up the same project infrastructure repeatedly. uv init uv add pyrig uv run pyrig init You get: source structure with a Typer CLI, pytest with 90% coverage enforcement, GitHub Actions (CI, release, deploy), MkDocs site, git hooks, Containerfile, and all the config files — pyproject.toml, .gitignore, branch protection, issue templates, everything for a full Python project. Ships with all of Astral's tools (uv, ruff with all rules enabled, ty), plus pytest-cov, bandit, pip-audit, rumdl, prek, MkDocs Material, and Podman. Everything is pre-configured and wired into CI/CD and git hooks from the start. The interesting part is what happens after scaffolding. pyrig isn't a one-shot template generator. Every config is a Python class. Running "pyrig mkroot" regenerates and validates all configs — merging missing values without removing your customizations. Change your project description in pyproject.toml, rerun, and it propagates to your README and docs. Fully idempotent. pytest enforces project correctness. 11 autouse session fixtures run before your tests: they verify every source module has a corresponding test file (auto-generating skeletons if missing), that no unittest usage exists, that src/ doesn't import from dev/, that there are no namespace packages, and that configs are up to date. You can't get a green test suite with a broken project structure. Zero-boilerplate CLIs. Any public function in subcommands.py becomes a CLI command automatically — no decorators, no registration: my_project/dev/cli/subcommands.py def greet(name: str) -> None: """Say hello.""" print(f"Hello, {name}!") $ uv run my-project greet --name World Hello, World! Automatic test generation. Add a new file my_project/src/utils.py, run pytest, and tests/test_my_project/test_src/test_utils.py appears with a NotImplementedError stub so you know what still needs writing. Customizable via subclassing. Config subclassing. Want a custom git hook? Subclass PrekConfigFile, call super(), append your hook. pyrig discovers it automatically — the leaf class in the dependency chain always wins. Multi-package inheritance. Build a base package on top of pyrig with shared configs, fixtures, and CLI commands. Every downstream project inherits everything: pyrig -> service-base -> auth-service -> payment-service -> notification-service All three services get the same standards, hooks, and CI/CD — defined once in service-base. Everything is adjustable. Every tool and config can be customized or replaced through subclassing. Tools like ruff, ty, and pytest are wrapped in Tool classes — subclass one and pyrig uses yours instead. Want black instead of ruff? Subclass it. Config files work the same way. Standard Python inheritance, no patching. Source: https://ift.tt/ewO0yso Docs: https://winipedia.github.io/pyrig/ PyPI: https://ift.tt/NVPQmJZ https://ift.tt/ewO0yso February 9, 2026 at 11:55PM

Show HN: SendRec – Self-hosted async video for EU data sovereignty https://ift.tt/kWri6FX

Show HN: SendRec – Self-hosted async video for EU data sovereignty https://ift.tt/LRVFDx8 February 9, 2026 at 01:54AM

Show HN: Hivewire – A news feed where you control your algorithm weights https://ift.tt/XWZuseb

Show HN: Hivewire – A news feed where you control your algorithm weights Hivewire is a news app that lets you define what you want to read about, rather than inferring it from your behavior. We process thousands of articles daily from hundreds of sources and rank them based on explicit preferences you set. How it works: • Instead of collaborative filtering or engagement-driven ranking, you assign weights across four levels (Focus, More, Less, Avoid) and the engine prioritizes the intersection of your high-weight topics while aggressively down-weighting what you don't care about. • Articles are clustered by story so you get one entry per development, not 15 versions of the same headline. • Every morning, it pulls your top clusters and uses an LLM to generate a narrative briefing that summarizes what matters to you, delivered to your email. Currently web-only and English-language. We'd love feedback from the community on the relevance of feed results, the UI, and the quality of the clustering. https://hivewire.news February 9, 2026 at 12:26AM

Show HN: I created a Mars colony RPG based on Kim Stanley Robinson's Mars books https://ift.tt/Zd9G4HC

Show HN: I created a Mars colony RPG based on Kim Stanley Robinson's Mars books https://ift.tt/LD8p3Ox February 9, 2026 at 12:08AM

Show HN: Bhagavan – a calm, approachable app for exploring Hinduism https://ift.tt/WvalwrZ

Show HN: Bhagavan – a calm, approachable app for exploring Hinduism Bhagavan is a calm, modern app for exploring Hinduism. It brings together philosophy, stories, scriptures, prayers and daily practices in one simple, accessible place. It’s designed for people who feel Hinduism can be overwhelming or hard to connect to and want a gentler, more modern way to explore it at their own pace. What’s inside (all free): • Guided exploration of Hinduism through structured learning paths • Clear, accessible explanations of scriptures (Vedas, Upanishads, Smritis, Puranas) • Complete Bhagavad Gita with translations and key takeaways • Deity profiles with stories, symbolism and context • Epic stories including the Ramayana and Panchatantra • Prayers with translations, audio, and japa using a virtual mala • Festival calendar with key dates, reminders and lunar phases • Daily practices for reflection and focus • Daily quizzes, crosswords and challenges • Philosophy and spirituality concepts (e.g. dharma, karma, moksha) • Daily horoscope • 'Ask Bhagavan' for thoughtful, philosophy-rooted guidance No ads. Just a calm space to learn and explore. Free to use, with all content accessible. iOS: https://ift.tt/RMd9qpn Android: https://ift.tt/aJreSEZ Let me know what you guys think! Please do share with family and friends https://www.bhagavan.io February 8, 2026 at 11:22PM

Show HN: Stacky – certain block game clone https://ift.tt/La9Xtrn

Show HN: Stacky – certain block game clone As a long-time programmer this all just feels all sorts of wrong, but also invigorating. Vibe "coded" the whole thing from 0-100 over the course of few days, on and off. I have no intentions of developing it further since it's obvious what it is; I would absolutely love to work on a licensed game and do it proper with all the various ideas I have, since this is maybe 10% of what I want in such a game, but I heard somewhere licensing is cost-prohibitive. Putting AI shame aside, it really allowed me to explore so many things in a short amount of time that it feels good, almost enough to compensate the feeling of shame using AI to begin with. WebGPU isn't in there, although it's in another experimental version, part are indeed written in Rust (game logic). It has: - lock delay / grace period (allowing for 15 moves) - DAS (Delayed Auto Shift) and ARR (Auto Repeat Rate for continuous movement) for horizontal and soft drop movements - SRS wall kicks (Super Rotation System) to rotate pieces in-place - Shift+Enter "hidden" level select on the main screen - Shift+D for debug/performance indicator panel - Several ranodmizers including 7-bag and NES ones - combo system with difficulty (time) modes (easy by default) - x2: DOUBLE STRIKE, x5: CHAIN REACTION, x7: MEGA COMBO, x9: PHOSPHOR OVERLOAD, x10+: CRITICAL MASS - backgrounds which change over time or you can change them with SHIFT+B (B turns it off/on) which react both to music (FFT!) and to your game play when you clear lines - normal and two phosphor rendering modes of game field (R to toggle) - CRT Filter (shift+c to toggle) - F for full screen toggle - A for previous song, S for pause song, D for next song (all songs made with Suno, of course) and many more. It was a fun experience for sure, just not sure how to feel about it. On one hand I understand it wouldn't look like it does without my input, and it was a lot of what felt like work (intense sessions looking over the output, correcting etc), yet it doesn't feel like I really made anything by myself. I had fun though. While at it, created a small demo as well which isn't a game yet: https://ift.tt/Fzn4omQ and also something to play with parametric curves here: https://ift.tt/0Qy46H9 all within a span of a couple of days while we were having our third baby. The future is weird, and I'm still not sure whether I like it or not. One thing is sure - it's here to stay. Peace out, my friends! https://ift.tt/y07oCie February 8, 2026 at 12:41AM

Show HN: A toy compiler I built in high school (runs in browser) https://ift.tt/75It9YZ

Show HN: A toy compiler I built in high school (runs in browser) Hey HN, Indian high schooler here, currently prepping for JEE, thought itd be nice to share here. Three years ago in 9th/10th grade I got a knack for coding, I taught myself and made a custom compiler with LLVM to try to learn C++. So I spent a lot of time learning LLVM from the docs and also C++. It's not some marvelous piece of engineering, It has: - Basic types like bool, int, double, float, char etc. with type casting - Variables, Arrays, Assign operators & Shorthands - Conditionals (if/else-if/else), Operators (and/or), arithmetics (parenthesis etc) - Arrays and indexing stuff - C style Loops (for/while) and break/continue - Structs and dot accessing - extern C interop with the "extern" keyword Some challenges I faced: - Emscripten and WASM, as I also had to make it run on my demo website - Learning typescript and all for the website (lol) - Custom parser with basic error reporting and Semantic analysis was a PITA for my undeveloped brain - Learning LLVM from the docs Important Learnings: - Testing is a very important aspect of making software, I skipped it - big regret - Learning how computers interpret text - Programming in general was a new tour for me - I appreciate unique_ptrs and ownership Github: https://ift.tt/oBW5sbv Its on my github and there's a link to my web demo ( https://vire-lang.web.app/ ), it might take some time to load the binary from firebase. Very monolithic, ~7500 lines of code, I’d really appreciate any feedback, criticism, or pointers on how I could’ve done this better. https://vire-lang.web.app February 8, 2026 at 12:19AM