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

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/ED5i6AF April 2, 2026 at 01:16AM

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

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/eIWVA2D April 1, 2026 at 11:58PM

Show HN: Modern AI assisted goals and performance management https://ift.tt/Eva62Uc

Show HN: Modern AI assisted goals and performance management Hey hey I'm launching this on product hunt and I did a show many months back but prfrm is way better now prfrm - by ArchitectFWD, is a performance management platform. It is a platform for Teams, Startups & Organizations and also Individuals, Solo Founders & Families to organise and track goals, set plans and review periods to stay on top of development plans and set out the path for success. Typically uses for Review periods, performance plans, goals Also just added Team OKR The Goals AI assistant can create meaningful goals linked to the OKR or to individual goals and plan outcomes I included a journal to track progress The AI assistant can go over the journal for next steps, talking points for the next meeting or check in An a Kanban style schedule tracking --- I built prfrm by ArchitectFWD because I was tired of traditional performance management Spreadsheet.. blank cell ..what is next.. No more. I can set myself up, set a period, set the plan and outcome and use the AI assistant to help generate meaningful goals. I can track how I’m going and plot my path to success. With the addition of team OKR (objectives and key results) the goals can be mapped to team objectives as well, strengthening goals to real business goals https://prfrmhq.com goes to https://ift.tt/i04wAbn There's a silent video on the landing of how it works in mobile view If you want to comment on product hunt that's welcome too at https://ift.tt/qOifck0... Lastly, want to see video's? They're on https://www.youtube.com/playlist?list=PLBYzijBKDTJVrBzOlYuU0... https://ift.tt/i04wAbn April 2, 2026 at 12:34AM

Show HN: How This Graybeard Built the Fastest and Freest Postgres BM25 Search https://ift.tt/HtEFZM8

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/1b5TGhO 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/8wTo60m... 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/1b5TGhO March 31, 2026 at 09:59PM

Show HN: PhAIL – Real-robot benchmark for AI models https://ift.tt/RiBwNOM

Show HN: PhAIL – Real-robot benchmark for AI models I built this because I couldn't find honest numbers on how well VLA models [1] actually work on commercial tasks. I come from search ranking at Google where you measure everything, and in robotics nobody seemed to know. PhAIL runs four models (OpenPI/pi0.5, GR00T, ACT, SmolVLA) on bin-to-bin order picking – one of the most common warehouse operations. Same robot (Franka FR3), same objects, hundreds of blind runs. The operator doesn't know which model is running. Best model: 64 UPH. Human teleoperating the same robot: 330. Human by hand: 1,300+. Everything is public – every run with synced video and telemetry, the fine-tuning dataset, training scripts. The leaderboard is open for submissions. Happy to answer questions about methodology, the models, or what we observed. [1] Vision-Language-Action: https://ift.tt/YjLrA6W https://phail.ai March 31, 2026 at 09:55PM

Show HN: Rusdantic https://ift.tt/isnh3m9

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/8zx7v3s March 31, 2026 at 03:27AM

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

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/9NS08Wm Demo: https://deepanmpc.github.io/SMART-SEARCH/ March 30, 2026 at 08:43PM

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

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/edTYhpv March 30, 2026 at 10:39PM

Show HN: I made my fitness dashboard public and Apple Health needs an API https://ift.tt/sGATgCB

Show HN: I made my fitness dashboard public and Apple Health needs an API https://ift.tt/fHt09hc March 30, 2026 at 11:09PM

Show HN: Pglens – 27 read-only PostgreSQL tools for AI agents via MCP https://ift.tt/PvT39t2

Show HN: Pglens – 27 read-only PostgreSQL tools for AI agents via MCP https://ift.tt/hXLQwy8 March 29, 2026 at 10:00PM

Show HN: I built an OS that is pure AI https://ift.tt/318CzrR

Show HN: I built an OS that is pure AI I've been building Pneuma, a desktop computing environment where software doesn't need to exist before you need it. There are no pre-installed applications. You boot to a blank screen with a prompt. You describe what you want: a CPU monitor, a game, a notes app, a data visualizer and a working program materializes in seconds. Once generated, agents persist. You can reuse them, they can communicate with each other through IPC, and you can share them through a community agent store. The idea isn't that everything is disposable. It's that creation is instant and the barrier to having exactly the tool you need is just describing it. Under the hood: your input goes to an LLM, which generates a self-contained Rust module. That gets compiled to WebAssembly in under a second, then JIT-compiled and executed in a sandboxed Wasmtime instance. Everything is GPU-rendered via wgpu (Vulkan/Metal/DX12). If compilation fails, the error is automatically fed back for correction. ~90% first-attempt success rate. The architecture is a microkernel: agents run in isolated WASM sandboxes with a typed ABI for drawing, input, storage, and networking. An agent crash can't bring down the system. Agents can run side by side, persist to a local store, and be shared or downloaded from the community store. Currently it runs as a desktop app on Linux, macOS, and Windows. The longer-term goal is to run on bare metal and support existing ARM64 binaries alongside generated agents. A full computing environment where AI-generated software and traditional applications coexist. Built entirely in Rust. I built this because I think the traditional software model of find an app, install it, learn it, configure it; is unnecessary friction. If a computer can generate exactly the tool you need in the moment you need it, and then keep it around when it's useful, why maintain a library of pre-built software at all? Free tier available (no credit card). There's a video on the landing page showing it in action. Interested in feedback on the concept, the UX, and whether this is something you'd actually use. https://pneuma.computer March 29, 2026 at 12:08AM

Show HN: Octopus, Open-source alternative to CodeRabbit and Greptile https://ift.tt/ulLVD50

Show HN: Octopus, Open-source alternative to CodeRabbit and Greptile Hey HN, we built Octopus an open-source, self-hostable AI code reviewer for GitHub and Bitbucket. It uses RAG with vector search (Qdrant) to understand your full codebase, not just the diff, and posts inline findings on PRs with severity ratings. Works with Claude and OpenAI, and you can bring your own API keys. Video: https://www.youtube.com/watch?v=HP1kaKTOdXw | GitHub: https://ift.tt/pjcEKaJ https://ift.tt/VZ9Eiln March 28, 2026 at 06:50PM

Show HN: GitHub Copilot Technical Writing Skill https://ift.tt/qecXoLk

Show HN: GitHub Copilot Technical Writing Skill Its not super fancy, but I have found it useful from small emails to larger design docs so thought I would share. https://ift.tt/wsOTSWJ March 29, 2026 at 12:03AM

Show HN: AgentGuard – A high-performance Go proxy for AI agent guardrails https://ift.tt/uTSZiYf

Show HN: AgentGuard – A high-performance Go proxy for AI agent guardrails https://ift.tt/UG7K3MY March 27, 2026 at 10:09PM

Show HN: Burn Room – End-to-End Encrypted Ephemeral SSH Chat https://ift.tt/kiBhft5

Show HN: Burn Room – End-to-End Encrypted Ephemeral SSH Chat Burn Room is a simple, disposable chat built on SSH. There are no accounts to create and nothing to install. There’s no database behind it, no logs, no cookies, and no tracking. Messages exist only in memory, encrypted end-to-end, and disappear on their own. When a room’s timer runs out, everything in it is gone for good. You can jump in right away: ssh guest@burnroom.chat -p 2323 password: burnroom Or just open https://burnroom.chat in your browser. It runs in a web terminal and works on mobile too. How it handles encryption Private, password-protected rooms are fully end-to-end encrypted. The server never has access to readable messages — it only ever sees encrypted data. Keys are derived from the room password using scrypt, with a unique salt for each room. Every message is encrypted with XChaCha20-Poly1305 using a fresh random nonce, following the same general approach used in tools like Signal and WireGuard. When you join a room, you’re shown a fingerprint so you can confirm everyone is using the same key. When you leave, the encryption keys are wiped from memory. Designed to disappear Everything in Burn Room is temporary by design. Messages are never written to disk, never logged, and never backed up. By default, they’re cleared from memory after an hour. Room creators can set a burn timer — 30 minutes, 1 hour, 6 hours, or 24 hours. When time runs out, the room and everything in it are destroyed. If a room sits idle, it closes on its own. Creators can also destroy a room instantly at any time. If the server restarts, everything is wiped. The only thing briefly stored for recovery is minimal room metadata, and even then, encrypted rooms remain unreadable. Privacy first There are no accounts, no identities, and no tracking of any kind. IP addresses are only used briefly for rate limiting and are kept in memory, not stored. Usernames are temporary and get recycled. The platform is built to minimize what exists in the first place, rather than trying to protect stored data later. Language support Burn Room adapts to your system or browser language automatically. The interface is translated across menus, prompts, and messages. Chat itself can be translated per user, so people speaking different languages can talk in the same room and each see messages in their own language. In encrypted rooms, translation happens locally after decryption — the server never sees the original text. Features you’ll notice There are a few always-available public rooms like Politics, Gaming, Tech, and Lobby, along with the option to create private, password-protected rooms. You can mention others, navigate message history, and use simple command shortcuts. Rooms show a live countdown so you always know when they’ll disappear. You can also share direct links to rooms to bring others in instantly. It works the same whether you connect through SSH or the browser. Under the hood Burn Room is built with Node.js and TypeScript, using SSH for direct connections and a terminal interface in the browser. Encryption relies on audited native libraries, not custom implementations. It’s lightweight but designed to handle a large number of users at once, with built-in protections against abuse like rate limiting and connection throttling. Enter, say what you need to say, and let it disappear. Enter.Chat.Burn https://burnroom.chat March 27, 2026 at 12:42AM

Show HN: Orloj – agent infrastructure as code (YAML and GitOps) https://ift.tt/zjgADh8

Show HN: Orloj – agent infrastructure as code (YAML and GitOps) Hey HN, we're Jon and Kristiane, and we're building Orloj ( https://orloj.dev ), an open-source (Apache 2.0) orchestration runtime for multi-agent AI systems. You define agents, tools, policies, and workflows in declarative YAML manifests, and Orloj handles scheduling, execution, governance, and reliability. We built this because running AI agents in production today looks a lot like running containers before Kubernetes: ad-hoc scripts, no governance, no observability, no standard way to manage the lifecycle of an agent fleet. Everyone we talked to was writing the same messy glue code to wire agents together, and nobody had a good answer for "which agent called which tool, and was it supposed to?" Orloj treats agents the way infrastructure-as-code treats cloud resources. You write a manifest that declares an agent's model, tools, permissions, and execution limits. You compose agents into directed graphs — pipelines, hierarchies, or swarm loops. The part we're most excited about is governance. AgentPolicy, AgentRole, and ToolPermission are evaluated inline during execution, before every agent turn and tool call. Instead of prompt instructions that the model might ignore, these policies are a runtime gate. Unauthorized actions fail closed with structured errors and full audit trails. You can set token budgets per run, whitelist models, block specific tools, and scope policies to individual agent systems. For reliability, we built lease-based task ownership (so crashed workers don't leave orphan tasks), capped exponential retry with jitter, idempotent replay, and dead-letter handling. The scheduler supports cron triggers and webhook-driven task creation. The architecture is a server/worker split. orlojd hosts the API, resource store (in-memory for dev, Postgres for production), and task scheduler. orlojworker instances claim and execute tasks, route model requests through a gateway (OpenAI, Anthropic, Ollama, etc.), and run tools in configurable isolation — direct, sandboxed, container, or WASM. For local development, you can run everything in a single process with orlojd --embedded-worker --storage-backend=memory. Tool isolation was important to us. A web search tool probably doesn't need sandboxing, but a code execution tool should run in a container with no network, a read-only filesystem, and a memory cap. You configure this per tool based on risk level, and the runtime enforces it. We also added native MCP support. You register an MCP server (stdio or HTTP), Orloj auto-discovers its tools, and they become first-class resources with governance applied. So you can connect something like the GitHub MCP server and still have policy enforcement over what agents are allowed to do with it. Three starter blueprints are included (pipeline, hierarchical, swarm-loop). Docs: https://docs.orloj.dev We're also building out starter templates for operational workflows where governance really matters. First on the roadmap: 1. Incident response triage, 2. Compliance evidence collector, 3. CVE investigation pipeline, and 4. Secret rotation auditor. We have 20 templates in mind and community contributions are welcome. We're a small team and this is v0.1.0, so there's a lot still on the roadmap — hosted cloud, compliance packaging, and more. But the full runtime is open source today and we'd love feedback on what we've built so far. What would you use this for? What's missing? https://ift.tt/iymsxEC March 26, 2026 at 10:37AM