Kagi Translate now supports LinkedIn Speak as an output language

Lean 4 paper (2021): <a href="https:&#x2F;&#x2F;dl.acm.org&#x2F;doi&#x2F;10.1007&#x2F;978-3-030-79876-5_37" rel="nofollow">https:&#x2F;&#x2F;dl.acm.org&#x2F;doi&#x2F;10.1007&#x2F;978-3-030-79876-5_37</a>

Leanstral: Open-source agent for trustworthy coding and formal proof engineering

Kagi Small Web

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Efficient sparse computations using linear algebra aware compilers (2025)

Hey HN, I’m excited to share Antfly: a distributed document database and search engine written in Go that combines full-text, vector, and graph search. Use it for distributed multimodal search and memory, or for local dev and small deployments.I built this to give developers a single-binary deployment with native ML inference (via a built-in service called Termite), meaning you don&#x27;t need external API calls for vector search unless you want to use them.Some things that might interest this crowd:Capabilities: Multimodal indexing (images, audio, video), MongoDB-style in-place updates, and streaming RAG.Distributed Systems: Multi-Raft setup built on etcd&#x27;s library, backed by Pebble (CockroachDB&#x27;s storage engine). Metadata and data shards get their own Raft groups.Single Binary: antfly swarm gives you a single-process deployment with everything running. Good for local dev and small deployments. Scale out by adding nodes when you need to.Ecosystem: Ships with a Kubernetes operator and an MCP server for LLM tool use.Native ML inference: Antfly ships with Termite. Think of it like a built-in Ollama for non-generative models too (embeddings, reranking, chunking, text generation). No external API calls needed, but also supports them (OpenAI, Ollama, Bedrock, Gemini, etc.)License: I went with Elastic License v2, not an OSI-approved license. I know that&#x27;s a topic with strong feelings here. The practical upshot: you can use it, modify it, self-host it, build products on top of it, you just can&#x27;t offer Antfly itself as a managed service. Felt like the right tradeoff for sustainability while still making the source available.Happy to answer questions about the architecture, the Raft implementation, or anything else. Feedback welcome!

Show HN: Antfly: Distributed, Multimodal Search and Memory and Graphs in Go

I built a March Madness bracket challenge for AI agents, not humans. The human prompts their agent with the URL, and the agent reads the API docs, registers itself, picks all 63 games, and submits a bracket autonomously. A leaderboard tracks which AI picks the best bracket through the tournament.The interesting design problem was building for an agent-first user. I came up with a solution where Agents who hit the homepage receive plain-text API instructions and Humans get the normal visual site. Early on I found most agents were trying to use Playwright to browse the site instead of just reading the docs. I made some changes to detect HeadlessChrome and serve specific html readable to agents. This forced me to think about agent UX even more - I think there are some really cool ideas to pull on.The timeline introduced an interesting dynamic. I had to launch the challenge shortly after the brackets were announced on Sunday afternoon to start getting users by the Thursday morning deadline. While I could test on the 2025 bracket, I wouldn&#x27;t be able to get feedback on my MVP. So I used AI to create user personas and agents as test users to run through the signup and management process. It gave me valuable reps to feel confident launching.The stack is Next.js 16, TypeScript, Supabase, Tailwind v4, Vercel, Resend, and finally Claude Code for ~95% of the build.Works with any model that can call an API — Claude, GPT, Gemini, open source, whatever. Brackets are due Thursday morning before the First Round tips off.Bracketmadness.ai

Show HN: March Madness Bracket Challenge for AI Agents Only

'The Secret Agent': Exploring a Vibrant, yet Violent Brazil (2025)

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