Show HN: Roadie – An open-source KVM that lets AI control your phone https://ift.tt/R4fw0MI

Show HN: Roadie – An open-source KVM that lets AI control your phone Roadie is an open-source hardware KVM controlled via HTTP. HDMI capture in, USB keyboard/mouse/touch out, all from a browser. Hardware KVMs with web UIs have existed for years (PiKVM, TinyPilot, JetKVM, etc.). Roadie adds two things they don't generally have: multi-touch support (so it works with phones and tablets) and a focus on agent-driven use: any browser automation tool can drive the /view page directly, or connect to the WebSocket endpoint for lower-level programmatic control. ~$86 in parts, including two CircuitPython boards, an HDMI-to-USB dongle, and a Go server running on the host. No software needed on the target. https://ift.tt/6l93oez April 1, 2026 at 11:46PM

Show HN: Canon PIXMA G3010 macOS driver, reverse-engineered with Claude https://ift.tt/vWF7uQ1

Show HN: Canon PIXMA G3010 macOS driver, reverse-engineered with Claude Canon doesn't provide a working macOS driver for the PIXMA G3010. I was stuck using Canon's iPhone app for all printing and scanning. I pointed Claude Code at a packet capture from the iPhone app and it reverse-engineered Canon's proprietary CHMP protocol, wrote a pure Rust eSCL-to-CHMP bridge daemon, and built a .pkg installer. My role was the physical parts: capturing packets, testing on the printer, confirming Image Capture worked. The protocol docs in docs/ are probably the first public documentation of Canon's CHMP protocol. https://ift.tt/VKwZlcQ April 1, 2026 at 10:28PM

Show HN: How This Graybeard Built the Fastest and Freest Postgres BM25 Search https://ift.tt/qDHprdl

Show HN: How This Graybeard Built the Fastest and Freest Postgres BM25 Search Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too. The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy. Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window. I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows. It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you: https://ift.tt/s4KoTzP In the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale: https://ift.tt/cS6aA2W... It's exciting (and, to be honest, a little unnerving) to see a field I've spent so much time toiling in change so quickly in ways that enable us to be more ambitious in our technical objectives. Technical moats are moats no longer. The benchmark scripts and methodology are available in the github repo. Happy to answer any questions in the thread. Thanks, TJ (tj@tigerdata.com) https://ift.tt/s4KoTzP March 31, 2026 at 08:29PM

Show HN: Rusdantic https://ift.tt/sDR3KI7

Show HN: Rusdantic A unified, high-performance data validation and serialization framework for Rust, inspired by Pydantic's ergonomics and powered by Serde. https://ift.tt/I61KxVg March 31, 2026 at 01:57AM

Show HN: AI Spotlight for Your Computer (natural language search for files) https://ift.tt/gzxYvSk

Show HN: AI Spotlight for Your Computer (natural language search for files) Hi HN, I built SEARCH WIZARD — a tool that lets you search your computer using natural language. Traditional file search only works if you remember the filename. But most of the time we remember things like: "the screenshot where I was in a meeting" "the PDF about transformers" "notes about machine learning" Smart Search indexes your files and lets you search by meaning instead of filename. Currently supports: - Images - Videos - Audio - Documents Example query: "old photo where a man is looking at a monitor" The system retrieves the correct file instantly. Everything runs locally except embeddings. I'm looking for feedback on: - indexing approaches - privacy concerns - features you'd want in a tool like this GitHub: https://ift.tt/TjCGQfq Demo: https://deepanmpc.github.io/SMART-SEARCH/ March 30, 2026 at 07:13PM

Show HN: Memv – Memory for AI Agents https://ift.tt/I78rWjx

Show HN: Memv – Memory for AI Agents memv is an open-source Python library that gives AI agents persistent memory. Feed it conversations; it extracts knowledge. The extraction mechanism is predict-calibrate (Nemori paper): given existing knowledge, it predicts what a new conversation should contain, then extracts only what the prediction missed. v0.1.2 adds the production path: - PostgreSQL backend (pgvector for vectors, tsvector for text search, asyncpg pooling). Single db_url parameter — file path for SQLite, connection string for Postgres. - Embedding adapters: OpenAI, Voyage, Cohere, fastembed (local ONNX). Other things it does: - Bi-temporal validity: event time (when was the fact true) + transaction time (when did we learn it), following Graphiti's model. - Hybrid retrieval: vector similarity + BM25 merged with Reciprocal Rank Fusion. - Episode segmentation: groups messages before extraction. - Contradiction handling: new facts invalidate old ones, with full audit trail. Procedural memory (agents learning from past runs) is next, deferred until there's usage data. https://ift.tt/F0uVy1g March 30, 2026 at 09:09PM

Show HN: React-Rewrite – Figma for localhost that directly edits your codebase https://ift.tt/ne7AMJj

Show HN: React-Rewrite – Figma for localhost that directly edits your codebase https://ift.tt/odbjiRz March 30, 2026 at 06:59AM