Show HN: Uv-pack – Pack a uv environment for later portable (offline) install https://ift.tt/r436umD

Show HN: Uv-pack – Pack a uv environment for later portable (offline) install I kept running into the same problem: modern Python tooling, but deployments to air-gapped systems are a pain. Even with uv, moving a fully locked environment into a network-isolated machine was no fun. uv-pack should make this task less frustrating. It bundles a locked uv environment into a single directory that installs fully offline—dependencies, local packages, and optionally a portable Python interpreter. Copy it over, run one script, and you get the exact same environment every time. Just released, would love some feedback! https://ift.tt/3Czo2ac January 26, 2026 at 12:26AM

Show HN: I Created a Tool to Convert YouTube Videos into 2000 Word SEO Blog https://ift.tt/t9u3iKz

Show HN: I Created a Tool to Convert YouTube Videos into 2000 Word SEO Blog https://landkit.pro/youtube-to-blog January 25, 2026 at 11:16PM

Show HN: Remote workers find your crew https://ift.tt/CwgucMe

Show HN: Remote workers find your crew Working from home? Are you a remote employee that "misses" going to the office? Well let's be clear on what you actually miss. No one misses that feeling of having to go and be there 8 hours. But many people miss friends. They miss being part of a crew. Going to lunch, hearing about other people's lives in person not over zoom. Join a co-working space you say? Yes. We have. It's like walking into a library and trying to talk to random people and getting nothing back. Zero part of a crew feeling. https://ift.tt/5E38K4R This app helps you find a crew and meet up for work and get that crew feeling. This is my first time using cloudflare workers for a webapp. The free plan is amazing! You get so much compare to anything else out there in terms of limits. The sqlite database they give you is just fine, I don't miss psql. January 24, 2026 at 11:54PM

Show HN: Polymcp – Turn Any Python Function into an MCP Tool for AI Agents https://ift.tt/6ghNF93

Show HN: Polymcp – Turn Any Python Function into an MCP Tool for AI Agents I built Polymcp, a framework that allows you to transform any Python function into an MCP (Model Context Protocol) tool ready to be used by AI agents. No rewriting, no complex integrations. Examples Simple function: from polymcp.polymcp_toolkit import expose_tools_http def add(a: int, b: int) -> int: """Add two numbers""" return a + b app = expose_tools_http([add], title="Math Tools") Run with: uvicorn server_mcp:app --reload Now add is exposed via MCP and can be called directly by AI agents. API function: import requests from polymcp.polymcp_toolkit import expose_tools_http def get_weather(city: str): """Return current weather data for a city""" response = requests.get(f" https://ift.tt/PqVOWDT ") return response.json() app = expose_tools_http([get_weather], title="Weather Tools") AI agents can call get_weather("London") to get real-time weather data instantly. Business workflow function: import pandas as pd from polymcp.polymcp_toolkit import expose_tools_http def calculate_commissions(sales_data: list[dict]): """Calculate sales commissions from sales data""" df = pd.DataFrame(sales_data) df["commission"] = df["sales_amount"] * 0.05 return df.to_dict(orient="records") app = expose_tools_http([calculate_commissions], title="Business Tools") AI agents can now generate commission reports automatically. Why it matters for companies • Reuse existing code immediately: legacy scripts, internal libraries, APIs. • Automate complex workflows: AI can orchestrate multiple tools reliably. • Plug-and-play: multiple Python functions exposed on the same MCP server. • Reduce development time: no custom wrappers or middleware needed. • Built-in reliability: input/output validation and error handling included. Polymcp makes Python functions immediately usable by AI agents, standardizing integration across enterprise software. Repo: https://ift.tt/1HobY6U January 25, 2026 at 12:57AM

Show HN: Obsidian Workflows with Gemini: Inbox Processing and Task Review https://ift.tt/mghlVMN

Show HN: Obsidian Workflows with Gemini: Inbox Processing and Task Review https://gist.github.com/juanpabloaj/59bc13fbed8a0f8e87791a3fb0360c19 January 24, 2026 at 12:03AM

Show HN: Teemux – Zero-config log multiplexer with built-in MCP server https://ift.tt/Jz21iMS

Show HN: Teemux – Zero-config log multiplexer with built-in MCP server I started to use AI agents for coding and quickly ran into a frustrating limitation – there is no easy way to share my development environment logs with AI agents. So that's what is Teemux. A simple CLI program that aggregates logs, makes them available to you as a developer (in a pretty UI), and makes them available to your AI coding agents using MCP. There is one implementation detail that I geek out about: It is zero config and has built-in leader nomination for running the web server and MCP server. When you start one `teemux` instance, it starts web server, .. when you start second and third instances, they join the first server and start merging logs. If you were to kill the first instance, a new leader is nominated. This design allows to seamless add/remove nodes that share logs (a process that historically would have taken a central log aggregator). A super quick demo: npx teemux -- curl -N https://ift.tt/sKhBTMp https://teemux.com/ January 23, 2026 at 09:19PM

Show HN: Synesthesia, make noise music with a colorpicker https://ift.tt/7lLdNV2

Show HN: Synesthesia, make noise music with a colorpicker This is a (silly, little) app which lets you make noise music using a color picker as an instrument. When you click on a specific point in the color picker, a bit of JavaScript maps the binary representation of the clicked-on color's hex-code to a "chord" in the 24 tone-equal-temperament scale. That chord is then played back using a throttled audio generation method which was implemented via Tone.js. NOTE! Turn the volume way down before using the site. It is noise music. :) https://visualnoise.ca January 22, 2026 at 11:22AM

Show HN: Mirage – Experimental Java obf using reflection to break direct calls https://ift.tt/rIO3vb2

Show HN: Mirage – Experimental Java obf using reflection to break direct calls Replaces method calls and field accesses with reflection equivalents → makes static analysis and decompilers much less useful. Experimental, performance hit expected. https://ift.tt/DUmiYeX January 21, 2026 at 10:22PM

Show HN: I built a chess explorer that explains strategy instead of just stats https://ift.tt/vP9nJuG

Show HN: I built a chess explorer that explains strategy instead of just stats I built this because I got tired of Stockfish giving me evaluations (+0.5) without explaining the actual plan. Most opening explorers focus on statistics (Win/Loss/Draw). I wanted a tool that explains the strategic intent behind the moves (e.g., "White plays c4 to clamp down on d5" vs just "White plays c4"). The Project: Comprehensive Database: I’ve mapped and annotated over 3,500 named opening variations. It covers everything from main lines (Ruy Lopez, Sicilian) to deep sidelines. Strategic Visualization: The UI highlights key squares and draws arrows based on the textual explanation, linking the logic to the board state dynamically. Hybrid Architecture: For the 3,500+ core lines, it serves my proprietary strategic data. For anything deeper/rarer, it seamlessly falls back to the Lichess Master API so the explorer remains functional 20 moves deep. Stack: Next.js (App Router), MongoDB Atlas for the graph data, and Arcjet for security/rate-limiting. It is currently in Beta. I am working on expanding the annotated coverage, but the main theoretical landscape is mapped. Feedback on the UI/UX or the data structure is welcome. https://ift.tt/Fj4UeMQ January 21, 2026 at 09:26PM

Show HN: Mastra 1.0, open-source JavaScript agent framework from the Gatsby devs https://ift.tt/ek1Qfr8

Show HN: Mastra 1.0, open-source JavaScript agent framework from the Gatsby devs Hi HN, we're Sam, Shane, and Abhi. Almost a year ago, we first shared Mastra here ( https://ift.tt/AEfw5qe ). It’s kind of fun looking back since we were only a few months into building at the time. The HN community gave a lot of enthusiasm and some helpful feedback. Today, we released Mastra 1.0 in stable, so we wanted to come back and talk about what’s changed. If you’re new to Mastra, it's an open-source TypeScript agent framework that also lets you create multi-agent workflows, run evals, inspect in a local studio, and emit observability. Since our last post, Mastra has grown to over 300k weekly npm downloads and 19.4k GitHub stars. It’s now Apache 2.0 licensed and runs in prod at companies like Replit, PayPal, and Sanity. Agent development is changing quickly, so we’ve added a lot since February: - Native model routing: You can access 600+ models from 40+ providers by specifying a model string (e.g., `openai/gpt-5.2-codex`) with TS autocomplete and fallbacks. - Guardrails: Low-latency input and output processors for prompt injection detection, PII redaction, and content moderation. The tricky thing here was the low-latency part. - Scorers: An async eval primitive for grading agent outputs. Users were asking how they should do evals. We wanted to make it easy to attach to Mastra agents, runnable in Mastra studio, and save results in Mastra storage. - Plus a few other features like AI tracing (per-call costing for Langfuse, Braintrust, etc), memory processors, a `.network()` method that turns any agent into a routing agent, and server adapters to integrate Mastra within an existing Express/Hono server. (That last one took a bit of time, we went down the ESM/CJS bundling rabbithole, ran into lots of monorepo issues, and ultimately opted for a more explicit approach.) Anyway, we'd love for you to try Mastra out and let us know what you think. You can get started with `npm create mastra@latest`. We'll be around and happy to answer any questions! https://ift.tt/Z5HzFTi January 20, 2026 at 10:08PM

Show HN: Typing Tennis https://ift.tt/GmzbUVp

Show HN: Typing Tennis Hey HN, Here’s a quick weekend project: tennis, but played by typing. Try it out! https://ift.tt/ovecFiz January 20, 2026 at 11:36PM

Show HN: An interactive physics simulator with 1000's of balls, in your terminal https://ift.tt/8WXNOUJ

Show HN: An interactive physics simulator with 1000's of balls, in your terminal https://ift.tt/nejiUMw January 19, 2026 at 11:17PM

Show HN: Subth.ink – write something and see how many others wrote the same https://ift.tt/nqW6hPS

Show HN: Subth.ink – write something and see how many others wrote the same Hey HN, this is a small Haskell learning project that I wanted to share. It's just a website where you can see how many people write the exact same text as you (thought it was a fun idea). It's built using Scotty, SQLite, Redis and Caddy. Currently it's running in a small DigitalOcean droplet (1 Gb RAM). Using Haskell for web development (specifically with Scotty) was slightly easier than I thought, but still a relatively hard task compared to other languages. One of my main friction points was Haskell's multiple string-like types: String, Text (& lazy), ByteString (& lazy), and each library choosing to consume a different one amongst these. There is also a soft requirement to learn monad transformers (e.g. to understand what liftIO is doing) which made the initial development more difficult. https://subth.ink/ January 20, 2026 at 12:04AM

Show HN: Xenia – A monospaced font built with a custom Python engine https://ift.tt/ZFeIj5n

Show HN: Xenia – A monospaced font built with a custom Python engine I'm an engineer who spent the last year fixing everything I hated about monofonts (especially that double-story 'a'). I built a custom Python-based procedural engine to generate the weights because I wanted more logical control over the geometry. It currently has 700+ glyphs and deep math support. Regular weight is free for the community. I'm releasing more weights based on interest. https://ift.tt/S7hcmwd January 18, 2026 at 04:09PM

Show HN: ChunkHound, a local-first tool for understanding large codebases https://ift.tt/ys5rvWt

Show HN: ChunkHound, a local-first tool for understanding large codebases ChunkHound’s goal is simple: local-first codebase intelligence that helps you pull deep, core-dev-level insights on demand, generate always-up-to-date docs, and scale from small repos to enterprise monorepos — while staying free + open source and provider-agnostic (VoyageAI / OpenAI / Qwen3, Anthropic / OpenAI / Gemini / Grok, and more). I’d love your feedback — and if you have, thank you for being part of the journey! https://ift.tt/q7U3em5 January 18, 2026 at 02:33AM

Show HN: Docker.how – Docker command cheat sheet https://ift.tt/H08Tdwp

Show HN: Docker.how – Docker command cheat sheet https://docker.how/ January 18, 2026 at 01:47AM

Show HN: UAIP Protocol – Secure settlement layer for autonomous AI agents https://ift.tt/ge2Nylk

Show HN: UAIP Protocol – Secure settlement layer for autonomous AI agents Hi HN! Creator here. I built UAIP (Universal Agent Interoperability Protocol) - infrastructure that enables AI agents from different companies (OpenAI, Anthropic, Microsoft) to securely transact with each other. The Problem: As AI agents become autonomous economic actors, they need: Cryptographic identity (not just API keys) Secure payment rails for cross-company transactions Automated compliance (EU AI Act, SOC2, GDPR) Forensic audit trails The Solution: 5-layer security stack combining: Zero-Knowledge Proofs (Schnorr/Curve25519) for identity Multi-chain settlement (USDC on Base, Solana, Ethereum) RAG-based compliance auditing (Llama-3-Legal) Ed25519 signatures for non-repudiation Complete audit logging Technical Stack: Backend: Python, FastAPI, SQLite (WAL mode) Cryptography: NaCl, custom ZK-proof implementation Blockchain: Web3.py for multi-chain support Compliance: RAG with retrieval-augmented generation Use Case: GPT agent pays Claude agent for data analysis: Both prove identity via ZK-proofs Transaction checked for compliance Settled in USDC on Base (<$0.01 fee) Complete audit trail generated Why blockchain: Neutral settlement layer (no single company controls it) Instant microtransactions (traditional payments don't work for $0.01-$10) Programmable escrow (smart contracts) Verifiable computation (on-chain proofs) Open source (FSL-1.1-Apache-2.0). Built over the last few months after hitting these problems in AI automation work. Happy to answer technical questions! GitHub: https://github.com/jahanzaibahmad112-dotcom/UAIP-Protocol https://github.com/jahanzaibahmad112-dotcom/UAIP-Protocol January 18, 2026 at 01:12AM

Show HN: Minikv – Distributed key-value and object store in Rust (Raft, S3 API) https://ift.tt/CAJrRiP

Show HN: Minikv – Distributed key-value and object store in Rust (Raft, S3 API) Hi HN, I’m releasing minikv, a distributed key-value and object store in Rust. What is minikv? minikv is an open-source, distributed storage engine built for learning, experimentation, and self-hosted setups. It combines a strongly-consistent key-value database (Raft), S3-compatible object storage, and basic multi-tenancy. I started minikv as a learning project about distributed systems, and it grew into something production-ready and fun to extend. Features/highlights: - Raft consensus with automatic failover and sharding - S3-compatible HTTP API (plus REST/gRPC APIs) - Pluggable storage backends: in-memory, RocksDB, Sled - Multi-tenant: per-tenant namespaces, role-based access, quotas, and audit - Metrics (Prometheus), TLS, JWT-based API keys - Easy to deploy (single binary, works with Docker/Kubernetes) Quick demo (single node): git clone https://ift.tt/oSbDfhZ cd minikv cargo run --release -- --config config.example.toml curl localhost:8080/health/ready # S3 upload + read curl -X PUT localhost:8080/s3/mybucket/hello -d "hi HN" curl localhost:8080/s3/mybucket/hello Docs, cluster setup, and architecture details are in the repo. I’d love to hear feedback, questions, ideas, or your stories running distributed infra in Rust! Repo: https://ift.tt/j4l9zp6 Crate: https://ift.tt/H9PSnXe https://ift.tt/j4l9zp6 January 18, 2026 at 01:09AM

Show HN: Aventos – An experiment in cheap AI SEO https://ift.tt/3O6uvlE

Show HN: Aventos – An experiment in cheap AI SEO Hi HN, we built Aventos- a cheap way to track company mentions in LLMs. Aventos is an experiment we're doing after spending ~6 weeks working on various projects in the AI search / GEO / AEO space. One thing that surprised us is how most tools in this category work. Traditionally, they simulate ChatGPT or Perplexity queries by attempting to reverse engineer the search process. Over the past year, many have shifted to scraping live ChatGPT results instead, since those are signficantly cheaper and reflect more real outputs. Building and maintaining scrapers is tedious and fragile, so recently a number of SaaS products have emerged that effectively wrap a small number of third-party ChatGPT/Perplexity/Google AIO/etc scraping APIs. What felt odd to us is that many of these still tools charge $70–$200+ per month, despite largely being wrappers around the same underlying data providers. So we wanted to test a simple idea: if the core cost is just API usage and commodity infrastructure and software costs are lower because of AI, can we be a successful startup if we price near our costs? What we have so far: 1. Analytics similar to other tools (tracking AI citations, AI search results, and competitor mentions) 2. Content creation features (early and still being improved) We’d love feedback- especially from a non-marketing perspective on: * bugs * confusing terminology or tabs * anything that feels hand-wavy or misleading There’s a demo account available if you want to poke around: username: divit.endal4@gmail.com password: password Happy to answer questions about what other things we've built in the space, how these tools work, etc. https://ift.tt/lquNhQf January 16, 2026 at 08:27AM

Show HN: The Hessian of tall-skinny networks is easy to invert https://ift.tt/fUHnXQD

Show HN: The Hessian of tall-skinny networks is easy to invert It turns out the inverse of the Hessian of a deep net is easy to apply to a vector. Doing this naively takes cubically many operations in the number of layers (so impractical), but it's possible to do this in time linear in the number of layers (so very practical)! This is possible because the Hessian of a deep net has a matrix polynomial structure that factorizes nicely. The Hessian-inverse-product algorithm that takes advantage of this is similar to running backprop on a dual version of the deep net. It echoes an old idea of Pearlmutter's for computing Hessian-vector products. Maybe this idea is useful as a preconditioner for stochastic gradient descent? https://ift.tt/Uvh1Ydu January 16, 2026 at 02:06AM