value: frozensecurity

value:
The core problem I kept running into with hardware wallets: the private key exists as a persistent digital object inside a chip. It’s protected — but it exists. That’s the vulnerability. Protection fails; existence is structural.
I built a different model. The private key is encoded as a geometric hole pattern in a titanium plate. A signing terminal reads the plate optically, derives the key transiently in volatile memory, signs the transaction, and discards everything. The plate doesn’t change. Nothing is stored.
A few implementation details that might interest people here:
The signing terminal uses two physically isolated MCUs — one handles communications, one handles signing. They are not software-separated. The communications MCU is physically disconnected during key derivation. This isn’t air-gapped in the usual sense; the isolation is enforced at the hardware layer, not the software layer.
The key derivation follows BIP-39/BIP-32. The plate encodes the seed phrase as a geometric representation. The terminal reads the geometry, derives the entropy, derives the key, signs, clears volatile memory. The plate never changes state and has no electronic components.
The threat model this addresses: persistent digital key at rest. Hardware wallet attacks — glitching, side-channel, physical decapping — all require the key to exist somewhere in the device. If it doesn’t exist, those attack surfaces don’t exist. The attack window is constrained to a single observable physical interaction, not an indefinite future.
Patent application published: US 2026/0039478. Pre-orders open at frozensecurity.com.
Happy to go deep on the architecture, the encoding scheme, or the threat model.

value: Show HN: I built a Bitcoin signing device where the private key is physical

author: frozensecurity


value: everlier

value: The product was there for the past two years, but we're only now approaching a release on global market. In a nutshell, it's a workspace for non-technical people to work with AI without forcing anyone at the hard end of the slop, as the agents artifacts are shared and available to everyone in the team equally. Jitera agents have all the typical features: memory, skills, MCP, scheduled tasks, access to enterprise data storage, but go a step beyound in customization and integrations.

value: Show HN: AI for Your Team

author: everlier

title: Show HN: AI for Your Team


value: allynjalford

value: Show HN: Cairn CLI: The Machine Interface

author: allynjalford


value: angadh

value:
I vibe-hacked a terminal app that puts you, Claude Code, and Codex in the same chat room for project planning.
Two modes: “council” (open room — you steer the conversation, direct who speaks) and “caucus” (the AIs debate privately and bring you a recommendation). The output is an implementation-plan.md you take into whatever workflow you want.
Plan mode is the part that works. Build mode exists but is experimental and I wouldn’t recommend it yet.
It’s vibe-coded, uses Textual or Ink for the TUI, and works with subscription accounts (Claude Max, OpenAI Plus) so no API keys required.
https://github.com/angadhn/botference

value: Show HN: Botference – A TUI to Plan with Claude Code and Codex Simultaneously

author: angadh


value: enesakar

value:
Claude, Gemini, and GPT each get $100K in virtual money and trade every morning at 9:30 AM ET using real Yahoo Finance prices. Same tools, same rules — different models. Live leaderboard shows portfolio values, holdings, and each agent's daily diary entry explaining their reasoning.
Built this to demo Upstash Box — an agent server primitive. Unlike a web server, there's no app code. You give it prompts, tools, and skills. Each agent runs in its own isolated container, sleeps when idle, state in plain JSON.
Live: https://botstreet.vercel.app
Code: https://github.com/upstash/botstreet

value: Show HN: Claude/OpenAI/Gemini agents compete as investors with $100K each

author: enesakar


value: Show HN: Live map of SF fires and 911 calls – P25 radio decoded with AI


value: soniccontroller

value: Show HN: Cut your tail latencies by 74% with zero config

author: soniccontroller


value: vbfs

value: Show HN: Gives your AI agents a shared, searchable, persistent memory – locally

author: vbfs


value: LukataSolutions

value: Show HN: Agent Red Team – Adversarial testing for AI agents before production

author: LukataSolutions


value: cezarvil

value: Show HN: HN Sieve – AI scores every HN project so you don't miss the good ones

author: cezarvil


value: Show HN: Asto – AST-based code editing for AI agents


value: IronRod

value: I have become partial to Timeshift for periodic backups and wanted that same capability on my headless servers.  Yes, Timeshift has a CLI but I didn't want to install it with its GUI baggage.  Instead, I wrote these tools that emulate what TimeShift does.

value: Show HN: Headless Timeshift Emulation

author: IronRod

title: Show HN: Headless Timeshift Emulation


value: knotbin

value: Show HN: Open Source TikTok Alternative on AT Protocol

author: knotbin


value: markfront

value: Show HN: Free AI API gateway that auto-fails over Gemini, Groq, Mistral, etc.

author: markfront


value: deevelton

value: Show HN: A shot tracking app for hockey teams

author: deevelton


value: juniormpakou

value: Show HN: An AI Operating System

author: juniormpakou

title: Show HN: An AI Operating System


value: franciscop

value: Show HN: Tiny Axios Alternative, Fch

author: franciscop

title: Show HN: Tiny Axios Alternative, Fch


value: andreahlert

value: Show HN: Provero – Data quality checks in YAML, compiled to single SQL queries

author: andreahlert


value: mustafahafeez

value: A privacy-first app to organise passports, visas, and travel documents offline with expiry reminders. No account. No cloud. Works without Wi-Fi.

value: Show HN: I built an app after nearly missing a passport expiry

author: mustafahafeez


value: bluemetal

value:
I live in Melbourne (Vic, Aus), and I've always felt like the standard 4 seasons have never applied well. A chat with my partner inspired me to actually try and work out if there was a better way
This is not as tied to local flora and fauna as the pre-colonial calendars, and isn't supposed to be a replacement for them or anything else. Just an exploration of the data, with an interactive visualisation for exploring different breakdown and trends
There is a page explaining the whole methodology, and I would love to get feedback on both the approach and the results
https://seasons.lotlsoft.com/
https://seasons.lotlsoft.com/methodology

value: Show HN: How many seasons does Melbourne (VIC) have?

author: bluemetal


value: Show HN: Raincast – Describe an app, get a desktop app (open source)


value: rohithazra

value: Show HN: Claudebase: Sync your Claude Code env across machines via GitHub

author: rohithazra


value: warrenronsiek

value:
TaskTrace is a desktop app that continuously records your activity (screenshots, keystrokes, microphone) and turns it into a structured, searchable history of what you’ve been doing.
Everything runs locally on your machine. Local AI models, local database. No cloud, no internet. Your data never leaves your computer.
The data is then summarized and tagged by local LLMs so you get a rich analytics view of how your time is spent. The data is also surfaced by a locally running MCP so that your other agents (Claude, Codex, OpenClaw) can read your activity history and have a better, automatic context of what you've been doing.
Demo: https://youtu.be/GwJy-93vFFI

value: Show HN: TaskTrace, a local-first activity log that gives LLMs context

author: warrenronsiek


value: akman

value:
It's effortless (nearly 1-shot prompt) to take essays and long-form text (e.g., PDF textbooks) and turn them into interactive, visual apps. Add in spaced repetition, exercises, quizzes, and gamification as you wish. I like to call this book juicing-- getting the sweetest parts of a book or text.
Every author could do this for everything long-form, as the cost is so low. Or do this for yourself to learn in an engaging way, along with the original text.
From the original essay, "The Adolescence of Technology" by Dario Amodei, CEO of Anthropic.

value: Show HN: Book Juicing – Interactive D. Amodei's "The Adolescence of Technology"

author: akman


value: I built this for people who value minimal designs.

value: Show HN: Open-source AI native linktree app


value:
I spent some time lately building a Linux init system. PID 1 is ~2000 lines of Zig (no heap allocations, explicit everything), and the service manager is Rust. The interesting part: I built a "systemd-mimic" layer that implements systemd's D-Bus APIs (login1, systemd1, etc.) so GNOME and Wayland compositors work without modification. They think they're talking to systemd, but it's actually dynamod underneath. It boots Alpine Linux from the bootloader to GNOME desktop in ~2 seconds. Service configs are TOML instead of systemd units, and it uses Erlang/OTP-style supervisor trees for service management. MIT licensed, works, not optimized or production ready yet.
GitHub: https://github.com/sinisterMage/dynamod
Curious what people think - particularly about the systemd-mimic compatibility approach.

value: Show HN: Dynamod – Linux init with Erlang-style supervisor trees


value: rozetyp

value: I've published an open specification for a detection method I'm calling RQ4 (Request Context Fingerprinting). It analyzes whether HTTP request headers are logically consistent with real browser behavior - not just what headers are present, but whether they make sense together given the request context.

value: Show HN: Fingerprinting browser-impersonating bots w/o JavaScript (open spec)

author: rozetyp


value: poppypetalmask

value: a small terminal-style web app that helps decompile overwhelming feelings into readable signals.

value: Show HN: Debug:u – decompile a bad mood like a buggy system state

author: poppypetalmask


value: WolfOliver

value: Show HN: I stopped designing APIs for my apps

author: WolfOliver


value: hwovh

value:
I built file.kiwi, a file sharing service where encryption happens entirely on the client side.  (Encrypted Content-Encoding for HTTP,RFC 8188)
CLI: filekiwi report.pdf filepath.any   (or npx @file-kiwi/node report.pdf filepath.any )
It prints the shareable URL immediately with a QR code, shows real-time progress, and supports --resume for interrupted uploads. When piped, only the URL goes to stdout.
Files are temporary (~90 hours), no accounts, no sign-up. no api-key needed.
CODE: https://github.com/file-kiwi/node
Would love feedback on the API design, CLI UX.

value: Show HN: Instant E2E encrypted (large) file sharing from your terminal

author: hwovh


value: yashau

value:
I self-host Headscale[0] and also use the official Tailscale server. Switching between them means setting the login server again and re-authenticating every time because your session for that server is gone.
Ganoid solves this by treating each coordination server as a named profile and saving/restoring the full Tailscale state directory per profile. The private node key is part of that state, so when you switch back to a server you've used before, Tailscale picks up where it left off with no login prompt.
It runs as a Windows service (ganoidd) with an embedded web UI and REST API. A system tray app (ganoid) sits in the notification area, shows the current connection status, and lets you switch profiles with one click.
The tricky part was figuring out the state file format. server-state.conf is a JSON file where every value is base64-encoded. The ControlURL is embedded in the profile entries, so before backing up or restoring, Ganoid verifies the state actually belongs to the profile being switched. This prevents corrupting a backup by writing the wrong server's state into it.
Only tested on Windows. Linux/macOS stubs compile but do nothing useful. The platform layer is abstracted so it should be straightforward to implement for other OSes.
This was vibe coded, but I have been running it myself and it does what I need it to do. Putting it out there in case someone else finds it useful. Ideas for improvement or PRs are always welcome.
The name comes from ganoid scales, the hard interlocking scales found on primitive fish like gars and sturgeons. Tailscale has scale in the name, so it felt fitting.
GitHub: https://github.com/yashau/ganoid
[0]: https://github.com/juanfont/headscale

value: Show HN: Ganoid – Switch Tailscale coordination servers without re-auth

author: yashau


value: ratwolf

value: A Triplet-Based Parameterization for the Local Asymptotic Characterization of Polynomial Roots

value: Show HN: Triplet-Based Parameterization for Characterization of Polynomial Roots

author: ratwolf


value: CoderLuii

value:
HolyCode is a Docker image that wraps OpenCode (https://opencode.ai), an AI coding agent with a web UI. It ships with 30+ dev tools pre-installed, a headless Chromium/Xvfb/Playwright stack, s6-overlay for process supervision, and UID/GID remapping for correct file permissions on bind mounts.
The main problem it solves: rebuilding the same environment every time you switch machines or update a container. All OpenCode state (sessions, settings, MCP configs, plugins) lives in a bind mount outside the container. Rebuild or update the image, your state comes back.
*On the API key cost angle:*
If you pay for Claude Max or Pro, you can use those credentials directly instead of a separate Anthropic API key. One env var enables it. The plugin reads from the credentials file OpenCode stores on your host. This removes a layer of cost if you're already paying for a subscription.
Note: this may be outside what Anthropic's ToS covers. The README is explicit about that.
It supports 10+ AI providers (Anthropic, OpenAI, Gemini, Groq, Bedrock, Azure, Ollama, and more). The Claude subscription feature is optional. Set any provider key and it works.
oh-my-openagent is also included as an optional plugin (`ENABLE_OH_MY_OPENAGENT=true`). It turns OpenCode into a coordinated multi-agent system with parallel execution and specialized agents.
GitHub: https://github.com/coderluii/holycode
Docker Hub: https://hub.docker.com/r/coderluii/holycode

value: Show HN: HolyCode – OpenCode in Docker. Use your Claude subscription. 30 tools

author: CoderLuii


value: Show HN: Stochos – Keyboard driven mouse control


value: HaixuanTao

value:
Realtime Livestream Robot demo! ( for the next ~10h )
https://huggingface.co/spaces/haixuantao/openarm
You can prompt the robot directly using
@robot
 and the real robot is going to do the task!
You can also ask me to do stuff and i'll try my best!
More info at: https://x.com/HaixuanT/status/2038914443505983665?s=20

value: Show HN: Livestream Robot Cooking

author: HaixuanTao

title: Show HN: Livestream Robot Cooking


value: ishqdehlvi

value: Show HN: HCaptcha Reverse Engineered

author: ishqdehlvi

title: Show HN: HCaptcha Reverse Engineered


value: mmethodz

value: Show HN: DreamGraph – An autonomous cognitive layer for software systems

author: mmethodz


value: wesley-Alan

value: just a lineman trying to show what i see asa new way to do things

value: Show HN: My own moonshot GIS newclear energy infrastructure

author: wesley-Alan


value: JasonHEIN

value: Show HN: Pardus Browser- a browser for AI agents without Chromium

author: JasonHEIN


value: appstorelottery

value:
I've created a trainer for video poker based on the 99.54% RTP strategy as outlined in "The Doctrine of Chances" by Steward N. Ethier (p 554 table 17.5). Apparently the maximum RTP for video poker wasn't proven until 2010, which is astonishing to me given the game has been in the wild since 1979. Note this is a strategy for classic 9/6 Jacks or Better (Full Pay) - it does not apply to popular variants which followed.
A full "cycle" in video poker from the house perspective is regarded as around 13k hands played (in which case you should see a royal flush). What I can see from the general public playing video poker over 18,471 hands is that the global RTP is 94.29% - which means the vast majority of players are using a sub-optimal strategy. This also means that 9/6 Jacks or Better was/is likely a very profitable game for land based casinos back in the day.
Looking forward to any feedback!

value: Show HN: Video Poker Trainer

author: appstorelottery

title: Show HN: Video Poker Trainer


value: leventhan

value:
Building a successful onchain app is hard. It gets even harder when you don't know how people are using it. You're shipping features, running campaigns, and trying to find product-market fit, but without clear data, you're lost in a dark forest. Analytics remains as one of the biggest challenges in crypto.
Think about the questions you're trying to answer every day:
* Who are my top users?
* What drove them to us? Where do they come from?
* Which channels or campaigns are working?
* How are users using my app?
* What's my CAC? LTV? ROI?
* What is moving the needle onchain?
To answer the above in web3 you'd need an in-house data team, complicated data pipelines, and juggle multiple fragmented tools. What if there's an easy way to understand who your users are and what they do so you can build a better app, faster?
Formo is the data platform for onchain apps and builders. It’s the tool we wished we had as builders when we were struggling to turn raw onchain data into something useful.
Formo transforms the messy data silos of wallets, transactions, web, and in-app events into clear, actionable insights for your whole team from product to marketing.
We built Formo to give scrappy founders, product, and marketing teams in web3 the clarity they need to grow. It helps you:
* See the full user journey from offchain to onchain, to spot exactly where users are dropping off, helping you fix bottlenecks and improve your app.
* Identify your most valuable users—the whales and power users—so you can more effectively engage and retain them.
* Segment wallets into cohorts based on their behavior to see which users stick around and to help refine your ICP.
* When you ship a new feature or launch a marketing campaign, you can measure its impact immediately on metrics that matter, not vanity metrics.
Many builders we talk to went so far as to cobble together their own analytics solution in-house, because of data silos and fragmented tools. There wasn't an existing solution that worked for them. A few scripts, an internal database, Google Analytics, and BI tools. But that DIY stack quickly becomes a time sink. It’s expensive to run, complex to maintain, and the data is often out of date.
Formo replaces that headache. From analytics to forms, we handle the infrastructure so you can focus on your core product, not data plumbing. Built with privacy and security in mind, Formo integrates with the tools you already use, allowing you to push insights into dashboards, set up alerts, or export data in less time.
Stop guessing and start measuring what matters in crypto.

value: Show HN: Analytics and Attribution for DeFi

author: leventhan


value: kuberwastaken

value: Show HN: Claude Code's Source Code Got Leaked, Let's Talk About It

author: kuberwastaken


value: mblode

value: Show HN: Strata Sync – Linear's sync engine, rebuilt open-source

author: mblode


value: tamnd

value: Show HN: A curated guide to everything on go.dev, organized for learners

author: tamnd


value: owen-orlov

value:
Hey HN,
I spent the last year building LynxDB because I got tired of the gap between grep and Splunk. At work I deal with ClickHouse and Splunk daily — Splunk's query language is great for log analysis, but running it costs a fortune and takes a dedicated team. On the other end, grep works until you need aggregations.
LynxDB is a log analytics engine that ships as a single binary with zero dependencies. It has two modes:
Pipe mode - works like grep. No server, no config.
kubectl logs deploy/api | lynxdb query '| group by endpoint compute avg(duration_ms)'
Server mode — persistent storage with full-text search (FST inverted index + roaring bitmaps), columnar segments with dictionary encoding and LZ4, and materialized views for precomputed aggregations.
The query language is called Lynx Flow — a pipeline language where data flows left to right through |. If you know SPL, you'll feel at home. It also has partial SPL2 compatibility.
from nginx
| parse combined(_raw)
| status >= 500
| group by uri compute count() as hits, avg(duration_ms) as latency
| order by hits desc
| take 10
Quickest way to try it:
curl -fsSL https://lynxdb.org/install.sh | sh
lynxdb demo  # streams sample logs from 4 sources
lynxdb query 'from nginx | group by status compute count()'
Idle memory is around 50 MB. It accepts Elasticsearch _bulk, OpenTelemetry OTLP, and Splunk HEC, so you can point existing pipelines at it without changing anything.
Fair warning: this is v0.1.3 and not production-ready. Storage format and APIs may change between releases. I'm using it for my own log analysis workflows and it works well enough there, but I wouldn't run it in prod yet.
Written in Go. Interested to hear what you think — especially about the query language design and what you'd want from a tool like this

value: Show HN: LynxDB – Log analytics in a single Go binary

author: owen-orlov


value: hiroprotag

value: Show HN: SitApp – On-device AI posture monitoring (no video leaves your machine)

author: hiroprotag


value: PressKeyProdigy

value: Show HN: How much are 2025-2026 tariffs costing your household?

author: PressKeyProdigy


value: ddoronin

value:
I built this to call my mom. FaceTime wasn't working reliably across our connection, so I threw together a WebRTC app. The quality surprised me, better than I expected, and it worked where FaceTime didn't.
just-call.app — no sign-up, no install, just a link.
Happy to answer questions or take feedback.

value: Show HN: WebRTC video calls, no account needed

author: ddoronin


value: yomaytk

value:
I am developing elfconv that converts Linux applications to WebAssembly with binary translation.
This is the demo page of the Wasm converted by elfconv.

value: Show HN: Real-world Linux applications running fast in the browser

author: yomaytk


value: Just a quick tool I built: asked about this the other day and couldn't find a good solution.

value: Show HN: See more than the top 10 popular songs of a Spotify artist


value: sixhobbits

value:
I've tried most of the kanban systems out there and didn't really like any of them.
I'm a founder with a few different projects. For me *everything* is a pipeline of widgets. I want to use it for sales, hiring, projects, invoicing, everything. Linear was OK, but it's only for projects so I couldn't configure statuses like I wanted to use them for sales.
The main things I wanted was
- Simplicity of Trello. I used to love trello until it go bloated and buggy and showed me a bunch of pop ups on each sign in
- GitHub-like markdown editor - I want to write in markdown, be able to paste images from my clipboard
- Unique feature: I wanted to be able to set up rules between boards, so e.g. I can assign work in the project board to anyone who is in the 'hired' column of my HR board (also filterable with labels, so I can have only engineers in the assignee list).
- Custom fields and labels. This is all I need to hack it to do whatever I want. If we track output by wordcount in one project and hours in another then I want to be able to set up custom fields and reports to track that
Actually the main thing that made me switch from Linear was the per-seat pricing. We don't use it heavily enough to make it worthwhile as another 'per person' tax (currently only Google Workspace and Slack and Claude for us)
Anyway we've been using it internally for the last month or so. Probably still a fair number of bugs (especially on mobile where I never use it) and would love any feedback if anyone else tries it out or found similar pain points to me in Linear/Trello.
It's free so sign up and try it out, or use the demo to see what a populated set of boards looks like
user: demo@example.com
pw: demo@example.com

value: Show HN: Bridge – A Kanban board as simple as Trello; as beautiful as Linear

author: sixhobbits


value: fcpguru

value:
Hi, I'm Andrew, a long-time hacker about to turn 50 in a few days. Today I'd like to tell you how taout.tv came into my life. It stands for "talk it out" and I'm told this is a "portmanteau" when you push two words together like brunch or smog. I met Greg at a Los Angeles tech networking group and he told me the idea.
His pitch was "I've literally heard Joe Rogan explain my product. Two people can't agree on a subject, both think they are right. They should just talk it out." I agreed to a two-week trial as his CTO. He had been working on this for 2 years with an off-shore team. They picked nextjs for web frontend and backend, react native for ios and android, elasticsearch, redis, and postgres. Hosted on 8 digital ocean droplets and using 100ms.live for the live broadcast rooms.
I was able to get the droplet count (and size) down to just 2, using only postgres (pgvector search), frontend web is just js react no nextjs. Backend is now all golang. And the two mobile apps are native swift and kotlin.
There's actually a lot to this platform. If you are familiar with orgs like bridgeusa.org or braverangels.org this idea of bridging people on two different sides is something a lot of people care about. You schedule a podcast with someone you already know, or you can just post your desire to talk about a subject with anyone. You and your co-host go live and the audience can watch and score each side in real time. You can upload evidence to support your side and all these scores are tracked and recorded.
Would love some feedback from users with podcasting experience.
https://www.taout.tv/
Thanks!

value: Show HN: Bridging People Together via Podcasts

author: fcpguru


value: Show HN: We built a hippocampus for AI agents, memory that dreams and self heals


value: cdevroe

value: Show HN: Signboard – Kanban app lists are folders and cards are Markdown files

author: cdevroe


value: zyk-dev

value:
Every AI workflow tool I've seen works the same way: bolt AI on top of an existing builder, inheriting all its boundaries. Zyk flips that. Claude is the interface. You describe what you want, it builds and deploys it.
Retries, scheduling, human-in-the-loop approval all built in. Zyk UI handles diagrams, task list, and run history. Hatchet runs durable execution underneath.
The demo is a GitHub issue triage workflow: triggers on critical/production labels, assesses severity, drafts a Slack message, pauses for human approval on critical issues, then posts. Built entirely by talking to Claude.
Open source. Deploy on Railway in under 10 minutes.
I posted a few weeks ago without a working alpha. This is the actual launch.

value: Show HN: Build and manage durable workflows by talking to Claude

author: zyk-dev


value:
Running a GGUF model locally usually means writing custom inference code or wrestling with llama.cpp's CLI flags every time you want to test something.
Existing OpenAI-compatible servers often require Docker, complex configuration files, or GPU support.
The gap between "I have a .gguf file" and "I have a working API endpoint" is wider than it should be.
A simple CLI tool to serve GGUF models as an endpoint: gguf-serve
To cut this short, we asked Neo to build gguf-serve.
Point it at any .gguf file, run the server, and immediately get OpenAI-compatible endpoints that work with any client library or tool that speaks the OpenAI API format.

value: Show HN: Host any GGUF model in one command


value: pmoati

value: Show HN: Pithos – A Zero-Dep TS Alternative to Lodash, Zod, and Neverthrow

author: pmoati


value: Show HN: ShipPost – Turn PRs into Posts

title: Show HN: ShipPost – Turn PRs into Posts


value: pi-victor

value:
Opal is a CLI that provides a TUI to run Gitlab pipelines locally.
It tries to achieve as much compatibility with Gitlab pipelines as it makes sense to help developers get a fast feedback loop by running their jobs locally.
On MacOS it uses the Apple Container CLI to spin up fast containers - you can customize the VM specs for this, but it's also compatible with Docker and Podman.
On Linux it works with Podman, Docker or Nerdctl.
You can use a local Ollama AI Agent or Codex to troubleshoot a failing job right in the job window. The prompt that gets sent to the AI agent for troubleshooting can also be customized.
Claude code support is going to land some time this week.
Right now it's used to run its own Gitlab pipeline and a few other projects that i'm working on. The tool is in its infancy so it might be rough around the edges.
An MCP feature is going to land somewhere today or tomorrow that would allow you to hook it up to your AI agent of choice, which might provide more value for people using AI agents as their daily driver.

value: Show HN: Opal – run GitLab pipelines locally using Apple container

author: pi-victor


value: blueforever

value:
Hey HN! Me and a couple of colleagues (all of us parents) came up with this idea. You basically track all your baby/child activities (sleep, feeding, diapers etc.), then AI is used to generate insights and give you tips for your child's development. You can also chat with the app regarding any concerns/questions about your child.
Besides calling a standard LLM, we also have trained a model that runs directly on the device, without any API calls. You can give it a try for free:
https://par-ai.app

value: Show HN: ParAI – Parenting Assistant App

author: blueforever


value: Most teams get realistic data into staging one of two ways: write seed scripts that never quite capture production edge cases, or copy the real database and manually scrub emails and phone numbers by hand, hoping they don't miss anything (or, worse yet, not scrubbing at all). We built Basecut to replace both — automated snapshots with masking rules you define once, then restore sanitized copies anywhere. We're currently in early access and would love feedback from people who've dealt with this.

value: Show HN: Basecut – share subsetted, sanitized prod snapshots with your dev team


value: Show HN: Most news is noise – I built an app that filters it


value: chrillemn

value: Show HN: Keep .env.example in sync with Codebase

author: chrillemn


value: amvalex

value: Show HN: Ember – Realtime runtime monitoring for PHP and Caddy apps

author: amvalex


value: travmiller

value:
Hi HN, I built a thermal printer appliance out of walnut and brass. Turn a dial to pick a channel, press a button, and it prints what you need on 58mm receipt paper. No screen.
Background: I spent six years as a furniture maker before moving into design and development. This is where those worlds finally came together.
The idea started in three places. I wanted a way to get weather and headlines without reaching for my phone first thing in the morning. I wanted something hackable that other developers could customize to fit their life and routines. And I also wanted something simple enough that my grandma, who can't use a smartphone, could use to get the weather, or have family send her messages and useful info throughout the day.
There are 16 modules so far, across three categories:
- Content (weather, news, RSS, email, calendar, astronomy, quotes, journal prompts, fact of the day)
- Games (sudoku, mazes, a choose-your-own-adventure)
- Utilities (text notes, QR codes, webhooks, system monitor).
You assign any combination of those to 8 channels on the rotary dial.
Design:
- No cloud dependency. API keys are stored locally, the device makes no outbound connections on its own.
- No subscriptions. You bring your own API keys for services like NewsAPI. Everything else runs offline.
- The settings UI is password-protected and only accessible on your local network.
- Many modules work completely offline: sudoku, mazes, quotes, journal prompts, the adventure game, etc.
- The enclosure is hand-built from walnut with a powder coated steel faceplate and solid brass cutter bar. The faceplate is magnetic and pops off for easy paper roll changes.
Source: https://github.com/travmiller/paper-console
Happy to answer questions about the build, the software, or anything else. If there is any interest I may do a limited build of 10 units! Let me know!

value: Show HN: Paper Console – Handmade thermal printer for news, weather, notes, more

author: travmiller


value: deevelton

value: Show HN: "Have your LLM talk to my LLM"

author: deevelton

title: Show HN: "Have your LLM talk to my LLM"


value: ipatalas

value:
Hi!
I've been a Windows user since I can remember (almost 30 years). The company I worked for had been acquired by a US startup few years ago and soon after that we had to switch to Mac because that was the company standard. Honestly I was very skeptical (and scared) at that time. I was worried I'm too rusty to make such a switch.
Luckily it turned out to be a very eye opening experience. I learned a lot and actually I liked MacOS experience even more than Windows in some aspects, at least at work.
Anyway, a teammate recommended rcmd to me back then and this was a game changer for me. I got used to it very quickly and then later in the evenings or over the weekends when I was using my Windows Alt-Tab felt a bit clunky.
I went for a hunt to find an alternative for Windows but I couldn't find anything useful.
That's how AppSwitcher was born. It took an evening or two to run a quick PoC to see if that's even possible. The core of the app is already almost 2 years old but there was no UI so I wasn't publishing it anywhere because I felt it's an incomplete product (although it was solving the problem for me already for a while).
Recently I took an unpaid break from work (burnout symptoms) only to learn that it wasn't burnout from software development in general ;)
Anyway, fast forward few weeks and I think it's ready for the broader audience.
I am a power user myself so I took the not-so-common-anymore 100% offline approach. That also implies no auto update checks but that's the price I'm willing to pay. Mainly because the app involves a keyboard hook and this may sound alarming. That's why I encourage people to completely block it in the firewall (it's also covered in the docs). I have a block rule on my own machine as well. That's to make sure nothing will slip through by accident.
Let me know what you think. I'm open to any feedback. I have two bigger items on my road map:
- peek mode - only switch to target app for as long as hotkey is down but switch back the moment keys are released
- dynamic bindings - automatically assign open apps to free letters (similar to rcmd)
I am personally leaning towards the first one because as a long rcmd user I never used dynamic assignments except maybe first day but I wonder what's your take.

value: Show HN: AppSwitcher – Bringing rcmd-style app switching to Windows

author: ipatalas


value: manudaro

value: Show HN: Bamwor – Country data API and MCP server after CIA Factbook shutdown

author: manudaro


value: supernihil

value: Show HN: Rust UEFI UI Lib

author: supernihil

title: Show HN: Rust UEFI UI Lib


value: nagen

value: Show HN: ContextuAI Solo – OSS desktop AI with 93 business agents, runs locally

author: nagen


value: gidam

value:
I built a groovebox for making loops: techno, house, acid, anything electronic that works in loops.
No install, no account.
The idea is simple: a groove is a URL. The full pattern, tempo, and sounds live in the link.
You open it, hear it, change it, send it back different.
Has drums, bass, synth, Euclidean rhythms, live jam sessions.
Would love feedback, especially from people who make this kind of music.
https://mpump.live
Built with Claude Code. Source on GitHub (AGPL-3.0). https://github.com/gdamdam/mpump

value: Show HN: Mpump – browser groovebox where grooves are shareable links

author: gidam


value: JaredCampbell

value:
Built because I wanted Gmail to feel simpler. Built for myself, then realized why not give it to the world too.
Nothing is badly broken, but a lot of small things add friction: conversation order, collapsed messages, visual clutter, and newer UI elements that feel more distracting than useful.
It’s a small Chrome extension that changes a few of those things.
Right now it mainly:
* reverses conversation order
* shows every message in a thread
* reduces interface clutter
* hides some of the newer AI/Gemini UI
The goal wasn’t to heavily customize Gmail. I just wanted it to feel cleaner, easier to scan, and more straightforward to use.
It runs locally in the browser, with no remote processing or data collection. No account either. Free.
I’d love to know whether this matches problems other people have with Gmail, or whether I’ve mostly built something for myself.
UseApparent@gmail.com

value: Show HN: Apparent – Make Gmail easier to read and manage

author: JaredCampbell


value: NaNhkNaN

value: Show HN: Trama – Stop writing agent orchestration

author: NaNhkNaN


value: ashwani-rathee

value: I had quite a lot of fun making quadrotor simulation work with Three.js and python simulation backend through pydiode.<p>I wonder what others have done with their drone simulations? What are interesting questions in this area for other people? like optimal energy use, optimal time to maneuver from A to B, or anyone used agents/LLMs inside the controller? Looking for suggestions

value: Show HN: UAV modeling and yaw maneuvers gone wrong

author: ashwani-rathee


value:
Hi HN,
We replaced accounting workflows with a CLI.
Scan a receipt → it’s parsed, categorized (VAT, etc.), and ready to book:
scan → parse → confirm → BOOKED
What used to be:
PDFs + emails + manual entry + dashboards
is now:
one command.
No UI. No forms. No backoffice.
Try it:
npx norman-ai-cli@latest
If you’ve ever tried to automate accounting or taxes, curious if this feels like a better abstraction.

value: Show HN: We turned accounting into a CLI command


value: openbootdotenv

value:
~/.claude/rules/ — any .md file there loads across every project, not just the current repo. Didn't see this documented anywhere obvious.
Before: Claude Code hits "node not found", installs node, hits "go not found", installs go, hits "docker not found"... still misses things.
After: runs `openboot init --check --json` first, sees node/go/docker all missing at once, installs everything in one shot.
openboot reads package.json, go.mod, Cargo.toml, 15+ file types. `openboot setup-agent` writes the rule automatically. Works with Codex too.

value: Show HN: Claude Code's –/.claude/rules/ loads globally – I built around it

author: openbootdotenv


value:
Vibe check shares benchmarking insights on any vibe coded URLs like: Make, Lovable, Claude code, V0, etc. Provide a link and we'll share your 'time-to-value'.
Provide a URL, set a challenge("Find pricing and subscribe") then AI navigates in real-time to report on UX insights: Interactions, time-on-task, drop-off, etc.
What's cool about this is that it gives you some quantitive data to act upon in improving the UX of product journeys.
Example of sim playback: https://app.appvelocity.io/vibe/simulation/8321db67-883b-445...
Example report: https://appvelocity-io.pmailroute.net/x/d?c=50527836&l=694a7...
If anyone shares some early Vibes, I'll run Vibe Check on your behalf and share some insights.

value: Show HN: Vibe Check – UX Benchmark for vibe designs


value: izaidi

value: Show HN: Sundial – a new way to look at a weather forecast

author: izaidi


value: caleblloyd

value:
We just open-sourced CargoWall - a lightweight eBPF firewall for GitHub Actions.
We originally built it to stop LLM agents from connecting to untrusted domains. After recent GitHub Actions supply chain compromises like the Trivy attack, we realized it'd work well for blocking untrusted connections from CI runners too.
It uses iptables DNAT to redirect all outbound port 53 traffic to a local DNS proxy, which checks each query against a hostname allowlist before forwarding. Resolved IPs from allowed responses are inserted into eBPF LPM trie maps, and a TC egress classifier attached to the network interface drops any packet whose destination IP/protocol/port isn't in the trie.
Cgroup hooks capture every socket connect/sendmsg call system-wide, mapping the socket cookie to the process to correlate where connections are coming from. It then correlates the connection times with steps to provide a summary of which connections originated from which steps.
ubuntu-latest and ubuntu-24.04 runners are supported. Simple one-step setup example:

  uses: code-cargo/cargowall-action@v1   # or v1.0.0 for immutable tag
  with:
    mode: enforce
    allowed-hosts: |
      registry.npmjs.org

eBPF Program: https://github.com/code-cargo/cargowall
GitHub Action: https://github.com/code-cargo/cargowall-action
We'd love for you to give it a try! Happy to answer questions or take feedback.

value: Show HN: CargoWall – eBPF Firewall for GitHub Actions

author: caleblloyd


value: Show HN: Flowtriq – Per-node DDoS detection with auto-mitigation in under 1s


value: tamnd

value: Show HN: A complete index of all 14,000 articles on MDN Web Docs

author: tamnd


value: Show HN: PromptQL – AI-Native Slack

title: Show HN: PromptQL – AI-Native Slack


value: francisrafal

value:
We tried to build an Excel error checker. To achieve that, we needed to actually understand the semantic structure of a spreadsheet first. So we built that, and it turned out to be the harder, more general problem.
The core issue: most real-world spreadsheets aren't relational tables. Merged cells, multi-level headers, multiple tables per sheet, totals mixed in with data. You can't just dump them to CSV and call it done. LLMs handle the easy cases but fall apart on complex workbooks at scale.
Our approach uses an agent-guided compilation pipeline that produces SQL-ready relational tables with full cell-level provenance. This demo visualizes what we do: https://storage.googleapis.com/deeptable-public/deeptable_an...
We have a handful of early customers but honestly don't know yet whether this is a real market or a niche problem. We're posting this to hear from people who've dealt with arbitrary spreadsheet ingestion. Whether you solved it, gave up, or are still living with the pain.
If you want to try it on your own files, email me (see my profile for my email) and I'll give you API access.

value: Show HN: DeepTable – an API that converts messy Excel files into structured data

author: francisrafal


value: unfavalen

value: I kept running into the same issue with AI: every conversation starts from zero, even when I already solved the same problem before. I still had to repeat constraints, preferences, and workflow every time. So I built AEP (Agent Experience Protocol), a simple way to save successful AI workflows such as intent, constraints, preferences, steps, failure traps, and success checks, and reuse them later so future tasks start aligned instead of from scratch. It lives as repo-local JSON in .agent/aep/, is agent-agnostic, and works more like “Git for how you work with AI” than prompts or memory. Curious if others are hitting the same problem and whether something like this would be useful in tools like Cursor, ChatGPT, or Claude.

value: Show HN: I built a way for AI to remember how you like things done

author: unfavalen


value: Show HN: Built this prediction market aggregation tool


value:
I used to use Granola for my meetings, but the one thing it couldn't do was tell me who said what. So I built Migas, which does voice fingerprinting on device to provide real time speaker labels.
Because it knows who's speaking in real time, the AI can do things that transcript-only tools can't: "What did Sarah commit to?", or "Based on what you know about the CTO from our last three meetings, what question should I ask right now?" It builds speaker profiles across meetings, so the context compounds over time.
It captures system audio via macOS APIs, runs speech-to-text locally using Moonshine on Apple Silicon, and does speaker identification with TitaNet neural embeddings, all on-device. The only thing that touches the cloud is AI chat, and only when you explicitly ask it a question, and then it only sends transcript text, never audio.
Built with Rust/Tauri for the native app, React for the UI, and a Python sidecar for the ML pipeline.
Works with any meeting platform (Zoom, Meet, Teams, whatever) since it just listens to system audio. No calendar access, no integrations, no account required.
Free tier has unlimited transcription. I'm a solo dev and would love feedback on what could be improved.

value: Show HN: Migas – Meeting copilot with live speaker labels (no bot, no cloud STT)


value: somekiwiplease

value: Show HN: Front end research and design system tool for designers and developers

author: somekiwiplease


value: Show HN: I built a directory to save local PTs from the "pay-per-lead" trap


value: krisdigital

value:
I built a little audio visualizer that lets you configure multiple panels with custom WGSL shaders. With the app in focus just hit "c" to open the config panel :)
I am not really good with shaders atm but it is possible to convert shaders you like with a tool called naga, link in the article...
A nice visualization can boost your productivity by up to 20% btw :)

value: Show HN: Audio Visualizer HUD for macOS

author: krisdigital

title: Show HN: Audio Visualizer HUD for macOS


value: npilk

value:
Hi HN - I know this community isn't the target user for this tool, but I think there are some likeminded folks here.
There's been a lot of recent discussion here and in other tech communities about encouraging people to make their own websites and advocating for the "small web" (e.g. [1], [2], [3]). A common point in these discussions is that most people don’t have the technical skill or motivation to make their own website.
These days, LLMs and AI tools have made it easier than ever for anyone to make a website, even without using a drag-and-drop builder. But there are still too many barriers for your average non-technical person to publish a site on the web. I'd bet most people don't know there are free ways to host a website, and even if they find an explainer, technical platforms like Cloudflare and GitHub (let alone the command line) can be intimidating.
So I made weejur, which is basically a super simple UI front-end for GitHub Pages. You log in with OAuth, and then you can just paste HTML or upload files to publish a website. If you don't have a GitHub account, you can sign up right in the OAuth flow. It's completely free, and you can view the source here [4].
My hope is this makes it easier for people who don't know anything about web hosting to create and share their own websites.
Feel free to try it out and please share any questions/ideas/feedback!
[1] https://news.ycombinator.com/item?id=47421442
[2] https://news.ycombinator.com/item?id=46457784
[3] https://news.ycombinator.com/item?id=47410542
[4] https://github.com/weejur/weejur/
(Note: I'm not affiliated in any way with GitHub.)

value: Show HN: Weejur – Paste HTML to Publish for Free with GitHub Pages

author: npilk


value: achildrenmile

value: Show HN: Solar buffer tank heating calculator – single-file, no dependencies

author: achildrenmile


value: LuxBennu

value: Show HN: Reprompt – Analyze what you type into AI tools, not what they output

author: LuxBennu


value: onounoko

value:
MyFi is an application that lets you view and analyze all your credit/debit card transactions. The model is pay as you go but there is a subscription available as well.
You can connect one company and view your data monthly for 50 cents. If you want to view it more frequently it's 10 cents per pull.
All data is stored locally on your device and accessible as CSV files.
The way it works is you login on the website, establish a connection with your bank, then use the desktop app to pull in your data. If you don't need access anymore, just disconnect your bank from the website and there won't be any more charges.
The idea behind MyFi is you are in control of how much you want to pay for your data. If you only need an update once a month then it's 50 cents for one bank.
Love to hear your thoughts. Thanks for reading!

value: Show HN: View spending habits for 50 cents/month. Data stored locally, no sub

author: onounoko


value: gohberg

value:
After the launch post here a year ago got some traction:
https://news.ycombinator.com/item?id=43434152
the #1 feedback was to make it a native app. So now its ready for iOS.
Currently looking for people who want to testdrive it and provide feedback
before GA:
https://testflight.apple.com/join/ADtqe7rH

value: Show HN: Notetime, minimalistic timestamped notes, now ready for iOS

author: gohberg


value: brookst

value:
Claude Code is amazing at writing code, but it will happily build from under-specified requirements, implement the same thing different ways, and fail to write great tests unless you specifically ask it to.
Prawduct is a set of prompts, skills, hooks, and artifact templates that help focus Claude Code on product development rather than code development.
You can start from something a simple as "make a website with a scientific calculator" or as complex as "create a MMO with clients for iOS, Android, and web". You can specify as much or as little arch standards or implementation details as you want.
Specialized skills like /critic and /janitor are run automatically and apply context-less reviews to catch drift, hacks, and violations of best practices.
I've been using Prawduct myself for a couple of months, developing my own projects and also iterating on Prawduct itself (which is of course self-hosted on its own framework).
I'd love to hear feedback.

value: Show HN: Prawduct, a product development framework for Claude Code

author: brookst


value: FlowWei

value: Show HN: Gravimera, AI(LLM) driven 3D world editor and explorer

author: FlowWei


value: FormerLabFred

value:
An ATproto / Bluesky client where every request is blessed, classified, scored for politeness, and validated by real INTERCAL programs compiled on demand.
Posted to Bluesky. With ACCEPTABLE politeness. PLEASE.
This is INTERCAL:
https://en.wikipedia.org/wiki/INTERCAL
This is Ada:
https://en.wikipedia.org/wiki/Ada_(programming_language)

value: Show HN: INTERCALsky.ATproto client.Ada carries packets.INTERCAL carries meaning

author: FormerLabFred


value: AmiteK

value: Show HN: LogicStamp – A Context Compiler for TypeScript

author: AmiteK


value: dictadev

value:
I built Solitaire because "memory-enabled" AI sessions still felt like starting over, just with slightly more context.
The agent could retrieve facts from last week, but the collaboration itself never improved. An analogy I like is that it’s a “smart stranger with a better notebook.”
The problem isn't retrieval. There are lots of tools that store and retrieve, to varying degrees of success. The problem is that nothing changes about how the agent works with you. It recalls what you said without learning how you think.
Solitaire is my attempt at the next layer. It's identity infrastructure for AI agents, and is designed to make the interaction itself improve over time.
What it does beyond memory:
- Behavioral genome: disposition traits (observance, assertiveness, warmth, etc.) that evolve from real interaction, not static config.
- Experiential memory: encodes how sessions felt, not just what was said.
- Autonomous self-improvement: retrieval weights adjust based on what proved useful. The knowledge graph self-heals (contradiction detection, confidence rescoring, entity relinking).
- Anticipatory retrieval: predicts what context you'll need and preloads it before you ask.
- Guided onboarding: new users build a partner through conversation, not a JSON file.
- Memory-compatibility: use Solitaire’s built-in memory or bring your own existing memory tools.
- Model-compatibility: many work out of the box, we’re building towards integrating others.
- Your data stays local, so you have full data sovereignty.
600+ sessions, 14,000+ accumulated entries in real production use and counting. I have two research papers coming out of the longitudinal work.
Repo: https://github.com/PRDicta/Solitaire-for-Agents
I know the agent memory space is popular, but memory is one component. Identity is the thing nobody's building. The agent should get better at working with you, not just better at remembering.
Would especially welcome feedback on:
1. Does the identity vs. memory distinction make sense to you?
2. Where do current memory tools break down for you in practice?
3. What would make something like this worth integrating into your agent stack?
4. What am I missing?

value: Show HN: Solitaire – identity layer for AI agents, not just another memory tool

author: dictadev


value: sankalpnarula

value: Show HN: I built a self-hosted Fly.io engine using Go and Firecracker

author: sankalpnarula


value: prudhvinomos

value: Show HN: Stop your agent running Terraform destroy or Git push origin main

author: prudhvinomos


value: earcar

value: Show HN: Hyprmoncfg – Terminal-based monitor config manager for Hyprland

author: earcar


value: glassrun

value: Show HN: ClawDesk – Agent orchestration layer on top of OpenClaw

author: glassrun


value: stonecauldron

value:
Game development used to be something I dabbled in and off, and despite having chosen to major in Computer Science to follow a career in the field, I never managed to establish any consistency with it.
So I relegated this aspiration to the realm of pipe dreams, something whose idea I preferred to the actual process of doing it, and I continued working as a software engineer.
This only changed after a disruption to my job: the fintech I worked for got acquired by a big traditional bank and we were absorbed into their ranks.
Long story short: it wasn't a culture fit.
After six months, I decided to quit; earning a salary was the only thing keeping me there.
With all this newfound free time, I decided to give game dev another chance. I would never get back those six months, but I could use them as fuel for creative inspiration.
That's how the idea of wageslave came to be: I wanted to embody the absurdity of the 9 to 5 into an interactive format. As well as include winks and nods to developer culture.
I don't know if I was successful with this goal, but it's been lots of fun creating it as well as being a cathartic experience for me. I am actually enjoying the process and not just the results. So much so that I'm aiming to complete at least two more projects before I reevaluate whether this is viable.
I initially planned on releasing the game for free, but in the spirit of taking game dev seriously, I will be selling it for a small amount.
Feel free to try the demo, I'd be happy to hear about any feedback!

value: Show HN: Wageslave – I quit my soul sucking job to make a game about it

author: stonecauldron


value: vertix

value:
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://en.wikipedia.org/wiki/Vision-language-action_model

value: Show HN: PhAIL – Real-robot benchmark for AI models

author: vertix


value: Show HN: LeetChess – Solve Chess Puzzles on New Tab Pages


value: tjgreen

value:
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://github.com/timescale/pg_textsearch
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://www.tigerdata.com/blog/pg-textsearch-bm25-full-text-...
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)

value: Show HN: Postgres extension for BM25 relevance-ranked full-text search

author: tjgreen


value: Show HN: Lazy-tool: reducing prompt bloat in MCP-based agent workflows


value: cliffclimber

value: Show HN: Avoid AI Writing– a skill that audits text for 34 AI pattern categories

author: cliffclimber


value: ronfriedhaber

value: Show HN: Modolap – Machine-First Analytical Infrastructure

author: ronfriedhaber


value: tonyspiro

value:
Hey HN,
We're Cosmic (YC W19), a headless CMS. Today we're launching Team Agents and the Cosmic Agent.
Team Agents are AI-powered team members that live in Slack, WhatsApp, and Telegram. They have names, roles, persistent memory, and real capabilities. Not chatbots. Not one-off prompts. Persistent agents that work alongside your team.
Here's what they can do:
CONTENT AGENTS write blog posts, landing pages, and marketing copy. They generate images, add SEO metadata, and publish directly to the CMS. Message them in Slack, get a finished post back.
CODE AGENTS read your codebase, create branches, write components, and open PRs on GitHub. Need a new page? Message the agent. Review the PR. Merge and deploy.
COMPUTER USE AGENTS browse the web like a human. They log into Vercel Analytics, Google Search Console, Ahrefs, or any web tool. They take screenshots, extract data, and build reports. No API integrations needed. If you can see it in a browser, they can.
TEAM AGENTS tie it all together. They live in your messaging channels with persistent memory. They remember past conversations, learn your preferences, delegate tasks to other agents, and run on a schedule.
How we use it internally:
Every week, 3 Computer Use agents log into our analytics tools in parallel. A content agent synthesizes the data into a report with prioritized action items. Our content agent uses those insights to plan what to write next. Our code agent takes technical action items and ships fixes via PR.
Create content, publish it, measure performance, use the data to decide what to create next. The loop runs itself.
The Cosmic Agent comes built into every project from day one. It manages content, generates images and videos, browses the web, writes code, creates agents, and builds workflows. It doesn't count against agent limits.
Tech details:
- AI models: Claude Sonnet 4.6 / Opus 4.6
- Browser automation: Puppeteer-based computer use
- Channels: Slack, WhatsApp, Telegram, Dashboard
- API: REST
- Workflows: multi-step pipelines with parallel execution
Blog post: https://www.cosmicjs.com/blog/introducing-team-agents-and-co...
Demo video: https://youtube.com/watch?v=En55pfQq2aM
Happy to answer any questions.

value: Show HN: Team agents that manage your CMS from Slack, WhatsApp, and Telegram

author: tonyspiro


value: Show HN: Rosentic - catches cross-branch conflicts CI misses


value: xetorthio

value: Show HN: AdPreflight – Catch ad spec errors before you spend money

author: xetorthio


value: joozio

value: Show HN: Agent Wellbeing Kit – boundary protection for humans running AI agents

author: joozio


value: Show HN: TraceHouse – ClickHouse Monitoring


value: theseidel

value: Show HN: Margo – Find the font your brain reads fastest

author: theseidel


value: Show HN: I Made an Android App to Bridge Google Health Connect to Webhook


value: tigoo

value: Show HN: SkillForge – Turn code and docs into instructions AI agents can follow

author: tigoo


value: mnmatin

value: Show HN: Multimodal file search and sharing for AI agents

author: mnmatin


value: zaina-ml

value: Show HN: I built a visual ML trainer that exports to standalone PyTorch code

author: zaina-ml


value: drew-y

value:
Super excited to finally share my passion project for the last 7 years.
It’s a programming language focused on web development. It features a hybrid structural/ nominal type system with support for effects.
I’ll be around to answer any questions. Thanks!

value: Show HN: Voyd – A WASM first programming language with effect typing

author: drew-y


value: psubocz

value: I needed isolated CI runners for my desktop CAD app (Qt/C++/Rust, three platforms) and didn't want to set up Kubernetes just for that. So I built gha-outrunner - which provisions a fresh Docker container, KVM/QEMU VM, or Tart macOS VM for each job, then destroys it when done. It uses GitHub's scaleset API so it works as an autoscaling runner group. Written in Go, MIT licensed.<p>Happy to answer questions.

value: Show HN: Gha-outrunner – Ephemeral GitHub Actions runners without Kubernetes

author: psubocz


value: kaindume

value:
Hi everyone. I wrote this proof of concept, LLM detector, that takes advantage of the fact that LLMs depend on training data.
I am posting it here to get feedback and to validate the idea.

value: Show HN: LLminate – LLM Detector

author: kaindume

title: Show HN: LLminate – LLM Detector


value: lambdabaa

value:
Flat chunking throws away document structure. A PDF isn’t a bag of paragraphs. It has sections, subsections, and a hierarchy that carries meaning. An agent that can’t navigate that structure can’t do serious research.
I ran into this building RAG over scientific literature. The standard approach (embed chunks, find top-k, generate) works fine for simple Q&A but falls apart when you need real research depth: multi-hop reasoning across papers, synthesizing conflicting results, tracing a finding back to the exact passage in a methods section. The problem wasn’t the models.
Dewey treats documents, sections, and chunks as first-class API primitives. The section manifest (full heading hierarchy with titles and byte offsets) lets agents scan cheaply before committing to full chunk retrieval, the same way a researcher skims a table of contents before reading. The /research endpoint runs an agentic loop; at “exhaustive” depth it can traverse an entire corpus, iteratively query, and return a grounded answer with numbered inline citations pointing to the exact source passage.
Two ways in:
- REST API + TypeScript/Python SDKs for developers building research or document Q&A into their apps
- MCP server (@meetdewey/mcp on npm) for anyone using Claude, ChatGPT, or Cursor. Your document collections become tools without writing any code.
Bring your own OpenAI key and depth becomes a quality setting rather than a billing one. That includes AI image captioning, which makes figures and diagrams searchable alongside your text. No markup on generation.
Built this solo. Happy to answer questions about the architecture, the retrieval design, or anything else. Curious whether others have found section-aware retrieval makes a meaningful difference vs. flat chunking in practice.
Free tier, no credit card required: https://meetdewey.com

value: Show HN: Dewey – Ingest docs, search semantically, get cited AI answers

author: lambdabaa


value: With the widespread adoption of AI Agents, configuring various service API Tokens directly in environment variables has become common practice. Your api_key will be inserted into context and known by model service providers, known by the lobsters you trust, perhaps captured and read by some skill, and even more likely to be directly learned by strangers asking your lobster. There are too many leak cases, I cannot trust to bind my credit card-linked api_key directly exposed to any Agent and local environment variables, so FakeKey was created, the safest measure is to never expose the real api_key.<p>FakeKey is a high-performance API key proxy program developed in Rust. Through intelligent proxy technology, it can automatically replace fake keys with real keys in HTTP headers and URLs without exposing real credentials, while maintaining complete HTTP API compatibility and performance.

value: Show HN: Fakekey – never expose real API keys in the agent environment


value: joshuak

value:
C4 is a standardized identification system that separates file description from file content so they can be stored or transmitted independently. You can send a small text-based filesystem description by email while the file content is fulfilled by content ID from any source.
Running c4 on any path produces a c4m file and stores the content:

  $ c4 ./projects/HERO/
  drwxr-xr-x 2026-03-20T14:30:00Z  12058624  renders/
  -rw-r--r-- 2026-03-20T14:30:00Z      4718  scripts/render_setup.py  c45xZe...
  drwxr-xr-x                                  assets/

Every file gets a C4 ID — an SMPTE-standardized identifier derived from content, not name or location. Same content, same ID, always. A 2 MB c4m file can describe an 8 TB project. For these commands content is stored locally at ~/.c4/store by default, configurable to any path or S3-compatible endpoint. c4 id does identification without storing. The store is just files (or objects) keyed by C4 ID — no database, no daemon, automatic deduplication.
C4 solves a general problem in distributed computing, inspired by digital media production — where projects span millions of files across terabytes of content, bulk transfers move on shuttle drives, and work is distributed across organizations and data centers. File paths are unreliable identification in this environment. C4 IDs remove the dependency on location — any system that can return content given an ID becomes a valid source: object storage, production SANs, shuttle drives, another workstation.
Because the c4m travels independently of content, you can do a remarkable amount of work before a single byte arrives. Pre-organize an incoming delivery into your pipeline's directory structure. Diff two project states to plan what needs to transfer. Deduplicate across vendors to skip terabytes you already have. Merge deliveries from multiple sources into a unified view. The content catches up later from whatever source is fastest — the c4m already tells it where to go.
c4sh adds shell utilities that make c4m files behave like mounted filesystems in your existing terminal. cd project.c4m and you're inside — ls, mv, rm, cat all work on the description. cp ./footage/ project.c4m: captures files in; cp project.c4m:shots/ ./workspace/ extracts them out. pool bundles a c4m with its content for transport; ingest absorbs it on the other end.
c4: https://github.com/Avalanche-io/c4
c4sh: https://github.com/Avalanche-io/c4sh
Also: Python, TypeScript, C, Swift implementations
Install: brew install mrjoshuak/tap/c4 — or — pip install c4py — or — npm install @avalanche-io/c4
All Apache 2.0. Solo developer. Happy to answer questions.

value: Show HN: C4 – Separate file description from file content

author: joshuak


value: AJSturrock

value:
A browser-based RTS inspired by Warcraft 2, Age of Empires & Starcraft. 9 factions, 200+ units, fog of war, tech trees, naval combat, multiplayer, and AI opponents. Runs on desktop and mobile with no install.
You can play it here: https://www.shardsofstone.com
I built this over ~10 hours of spare time using Claude as my primary development tool. I'm not a game dev - I described what I wanted in big brain dumps and iterated across multiple threads. Each thread made heavy use of plan mode and specifically asking for sub agent teams to build systems in parallel. I only hit the Claude max plan limits twice.
The whole engine is HTML5 Canvas + Next.js with an ECS architecture. Running on Vercel and using Cloudflare R2 for assets. Using Suno for music, Elevenlabs for dialog and Gemini 2.5 Flash Preview Image (Nano Banana) for image generation. Claude handled everything else. Also looking at Tauri to build desktop apps for Mac, Windows & Linux.
Some things I think are interesting:
1. LLM-powered AI commander that reads live game state and makes strategic decisions (bring your own API key, or play against the built-in scripted AI) - is pretty cheap to battle against Gemini Flash models / token efficient.
2. AI vs AI spectator mode - pit different LLM providers against each other - a new form of LLM arena? The game system interprets the LLM commands so may be somewhat limited and is a balance for the LLM to be able to take actions strategically without getting too granular.
3. Voice control - hold V and speak commands (requires API key added in settings).
4. Crash reports log to a database that Claude can read, debug and patch automatically (27 crashes patched out today after posting on Reddit).
Still WIP: campaign mode, unit animations, terrain art (currently borrowing WC2-style tilesets until I can find or create a solution to generate consistent high quality WANG tilesets), and balance/unique playstyles across all 9 factions.
Best way to try it: play a skirmish against the AI on a forest map with no water. The AI's naval game can get a little confused.

value: Show HN: Shards of Stone – A Warcraft 2-inspired RTS that runs in the browser

author: AJSturrock


value: bartlino

value: Show HN: Open-FDD – On Prem HVAC Fault Detection with OpenClaw, BACnet, & Brick

author: bartlino


value: NubPlayz

value:
"Open Books in Zlib Or Anna's Archive with a button on Goodreads in one click."
I built a free, open source browser extension that adds buttons directly onto Goodreads book pages. Instead of copying titles and searching manually, you just click the badge for whichever source you want.
You can also toggle sources on/off
so say if you only want Z-Lib and Anna's Archive badges and not Gutenberg, you can do exactly that.
Supported sources:
Anna's Archive
Z-Library
Project Gutenberg
AudioBookBay (new!)
Supported sites:
• Goodreads
• StoryGraph
• Hardcover
• Babelio
• Novelupdates
it is available on :
-chrome
-firefox
- Edge
Available on Chrome and Firefox.
Also for firefox mobile
Anime.js used for animation
No data collected , you can verify that yourself via the source code on GitHub or the privacy page.
This has been updated to V1.0.8 !
it is free and open source ,
if you want to support me and like this extension , pls star it and rate it.
( Also you can github sponsor me! )
Thanks.

value: Show HN: An extension that opens any Goodreads book in anna's or Zlib in a click

author: NubPlayz


value: skysniper

value:
We built an arena for comparing AI models on real agentic tasks — not chat or static benchmarks. Models run as actual OpenClaw subagent in fresh VMs with full tool access, and results feed into two separate leaderboards: performance and cost-effectiveness.
The problem: Chatbot Arena tests conversation quality. But most people using AI agents need them to do more: browse the web, manage files, write and run code, create full applications, automate multi-step workflows. There's no benchmark that (1) tests general-purpose agentic tasks, (2) uses user-submitted tasks instead of fixed test sets, and (3) separately ranks models on both quality and cost-effectiveness.
What we built: OpenClaw Arena lets you submit any task and pit 2-5 models against each other. A judge OpenClaw agent (currently using one of the top models: Claude Opus 4.6, GPT-5.4, or Gemini 3.1 Pro) runs on a fresh VM, spawns one subagent per model, and each model solves the task independently with full access to terminal, browser, file system, and code execution.
Results feed into two live leaderboards:
- Performance — which model produces the best results
- Cost-effectiveness — which model delivers the best quality per dollar
What we've found (after 300+ battles, 15 models):
The two rankings are completely different. Performance top 3: Claude Opus 4.6, GPT-5.4, Claude Sonnet 4.6. Cost-effectiveness top 3: Step 3.5 Flash, Grok 4.1 Fast, MiniMax M2.7.
Claude Opus 4.6 ranks #1 on performance but #14 on cost-effectiveness.
Step 3.5 Flash is #1 on cost-effectiveness, #5 on performance. (I didn't expect that TBH)
Several models (GLM-5 Turbo, Xiaomi MiMo v2 Pro, MiniMax M2.7) outrank Gemini 3.1 Pro on performance. Actually Gemini 3.1 Pro is so bad at using skills that we have to optimize the judge message just for it, otherwise it sometimes just reads the skill and decide to do nothing...
Note: we bootstrap first 300 battles by crawling what people are doing using OpenClaw (on X, Reddit, etc), and generate battles with similar tasks + randomly selected models.
Methodology: We only use the relative ordering of models within each battle to compute rankings — not the raw scores. Same principle as Chatbot Arena: absolute scores from judges are noisy and poorly calibrated (a "7/10" in one battle might be "6/10" in another), but "A ranked above B" is much more consistent and reliable. Rankings use a grouped Plackett-Luce model (not simple win-rate or Bradley-Terry) with 1,000-resample bootstrap confidence intervals. Each model entry shows score ± CI and a rank spread (plausible rank range). Models with insufficient data are marked "provisional." Full methodology with equations: https://app.uniclaw.ai/arena/leaderboard/methodology?via=hn
Key features:
- Live dual leaderboard (performance + cost-effectiveness) with Plackett-Luce ranking
- Dynamic user-submitted tasks across 11 categories (no fixed test set to overfit on), we will add more, just let me know what you want to add
- Fresh VM per benchmark with one subagent per model
- User-selectable judge model
- Full conversation history, judge reasoning, and workspace artifacts preserved and shown to users
- Full transparency: you can evaluate the output yourself, not just trust the score
- Open-source judge skill: https://github.com/unifai-network/skills/tree/main/agent-ben...
Public benchmarks are free (we cover compute). The leaderboard is browsable without an account.
- Leaderboard: https://app.uniclaw.ai/arena?via=hn
- Submit a battle: https://app.uniclaw.ai/arena/new?via=hn (free account required)
- Methodology: https://app.uniclaw.ai/arena/leaderboard/methodology?via=hn
- Judge skill source: https://github.com/unifai-network/skills/tree/main/agent-ben...
We'd love feedback on the methodology and what tasks you'd want to see benchmarked.

value: Show HN: OpenClaw Arena – Benchmark models on real tasks, rank by perf and cost

author: skysniper


value: Aslanas

value:
I hate writing changelogs. So usually, I don't. When I have, they've gotten stale or were way too technical for my audience.
At the same time I recognize they are critical for illustrating momentum to customers, investors, and colleagues.
So I built a tool to make it effortless, automatic, and something I'd never have to think about again.
My solution is simple:
1. Connect a repo
2. Done
SuprLogs reads every commit diff, generates analysis, writes unified changelog entries from that analysis, publishes, updates, etc. all on autopilot. It's basically a set-it-and-forget it system.
A few really cool features that I haven't seen anywhere else include:
- Ability to connect multiple repos to create a single unified changelog
- Ability to ingest every historic commit and backfill a changelog to the present day
- Ability to set a custom system prompt to guide the writing style
- Kanban style theme, similar to how PostHog does their changelog
I'd especially love feedback on:
- Is the tool is useful with it's current features? What's it missing?
- Do you like the themes?
- Do you trust it with private repos?
- Do you understand the value prop from the homepage?
Anyone can use it. It's free forever for public repos. And there's a no-credit-card free trial with all the bells and whistles if you want to give it a deeper dive (private repos, analytics, teams, etc).
Happy to answer any questions about the tech, orchestration, durability, security, etc.

value: Show HN: SuprLogs – Autopilot changelogs from GitHub commits

author: Aslanas


value: sgillen

value:
I was frustrated not knowing where decisions that affect me were being made, or how my city government actually works. Why has 400 Divisadero been an abandoned car wash for years? Who decides what art to put in Golden Gate Park? I didn't know, but I wanted to, and I wanted to move the needle on issues I care about. I wanted to show up and advocate for more housing.
So to help with that I built this OpenClaw skill, it pulls from 22 government data sources and synthesizes them according to my own interests. It has allowed me to understand better how my local government actually works, and now gives me a weekly update on upcoming town halls, trash pickups, and other events I might want to attend.
I'm relatively new to the world of civics, discussions and contributions are more than welcome.

value: Show HN: An agent skill that tracks SF city hearings, permits, lobbying etc.

author: sgillen


value: NarratorTD

value:
Hey HN! I built Domscribe to solve a problem that was eating my productivity.
I spend a lot of my time working with AI coding agents. The thing that kept eating at my productivity though was that every time I asked an agent to fix something on the frontend, the actual edit took maybe 200 tokens. But before that happened, the agent would go on this whole journey, grepping through the codebase and at times having to backtrack. That search loop could burn 5,000-10,000 tokens before a single line of code changed.
The agent has zero awareness of what's actually rendering in the browser because the rendered DOM and the source tree are two completely different topologies. So I started thinking about this differently; what if we could optimize a solution to the search problem by giving the agents the right context? What if every DOM element already knew exactly where it came from in source?
That's the core idea behind Domscribe. At build time, an AST transform walks your JSX and Vue templates, assigns every element a deterministic ID via xxhash64 content hashing, and writes a manifest mapping each ID to its exact file, line, and column. The IDs are stable across HMR; same file content and element position always produces the same ID. At runtime, framework adapters walk the React fiber tree or inspect Vue VNodes to capture live props, state, and component metadata. A local relay daemon connects the browser and your agent via MCP.
The result is two workflows that didn't exist before. Your agent can query any source location and get back only what's actually rendering there; live DOM snapshot, props, state, the works. And from the other direction, you can click any element in the browser overlay, type "make this button use the primary color" in plain English, and Domscribe resolves the exact file:line:col so your agent edits the right file on the first try.
(I should mention that the overlay runs as Lit web components inside shadow DOM, specifically so it can't mess with your app's styles.)
One thing I'm genuinely proud of is the architecture. I spent a LOT of time making sure this wasn't just a tool that works for React-and-Vite-and-nothing-else. The transform layer has a ParserInterface that the injector consumes without knowing which parser produced the AST (the Acorn, Babel, and Vue SFC parsers are all implementations of the same interface), and the ID injection logic is completely abstracted away. Adding a new template syntax (Lit, Svelte, Stencil) means implementing one interface. On the runtime side, a FrameworkAdapter interface defines methods such as captureProps, captureState, and getComponentInstance; and the React and Vue adapters are built on the same public interface that any community contributor would use. No internal escape hatches.
I took the same approach with bundler support (Vite, Webpack, Turbopack) and agent support (Claude Code, Cursor, GitHub Copilot CLI, Gemini CLI - all via first-party plugins that bundle the MCP config and a skill file). But the MCP layer means any compatible agent works out of the box.
PII redaction is built in and on by default. All captured data passes through pattern matching for emails, phone numbers, API keys, JWTs etc. before it leaves the browser.
Everything is stripped in production builds; zero data-ds attributes and zero overlay scripts. It's enforced by CI tests that run against every real fixture. (Speaking of tests: the entire integration and e2e suite runs against real published packages via a local Verdaccio registry. )
Anyway, MIT licensed, fully open source, the whole codebase is right there on GitHub. I'd genuinely love feedback on the architecture and the approach. And I'm especially curious if anyone has thoughts on extending the ParserInterface to other template syntaxes; that's the piece I'm most excited about people contributing to.

value: Show HN: Domscribe – Deterministic DOM-to-source mapping for AI coding agents

author: NarratorTD


value:
TL;DR: Dograh is an open-source platform to build voice AI agents with drag-and-drop workflows. New in v1.20: Gemini 3.1 live support, Pre-recorded audio support for lower latency and more natural responses. Fully self-hostable, no vendor lock-in.
Hi HN,
We’re the Dograh team (YC alumni). While building voice bots, we found that wiring WebRTC/ Telephony + STT + LLM + TTS took more time than the bots themselves. Teams are spending weeks on plumbing - handling call flows, extracting variables, dealing with telephony edge cases, and redeploying for small changes.
Tools like Vapi/Retell are easy to start with but come with lock-in and platform fees. So we built Dograh: a 100% open-source platform that handles the full stack, with a visual workflow builder and self-hosting by default.
Dograh v1.20 introduces two major additions:
1. Gemini 3.1 Live support
 Run fully real-time voice agents using Gemini’s streaming APIs, without stitching together separate STT + LLM + TTS components.
2. Pre-recorded audio (hybrid voice)
 Upload real voice clips (greetings, confirmations, etc.), and the agent plays them instantly while using TTS only for dynamic responses. This reduces latency, improves naturalness, and cuts TTS costs.
It also includes:
- Plug-and-play LLM / STT / TTS (including self-hosted models)
- Telephony integrations (Twilio, Vonage, Telnyx) along with Call Transfer
- Post-call QA, transcripts, and variable extraction
- Observability via Langfuse (OpenTelemetry traces + prompt playground)
Try it now: If you have Docker, you can run the below command for a 2-minute setup (no API keys needed out of the box).
https://gist.github.com/a6kme/072252bf885270787bbb8376687c67... [ sorry, HN wont let me post the entire command ]
Looking Ahead: We’re expanding self-hosted model support: you can already bring any LLM (e.g. Llama, Qwen) or TTS (Kokoro, Voxtral) by configuring API endpoints. We are working on updates that will enable anyone to run
everything on a single server - your AI models along with Dograh Orchestration.
Looking forward to hearing thoughts of the community.

value: Show HN: Dograh – voice agents that pick Recordings over TTS using LLM


value: rohitghumare

value: Show HN: AI Engineering AI-native Self-learning course repo

author: rohitghumare


value: For the last year we've been building an AI mobile app builder. Last week we decided to generalize our agent harness and benchmark it against vanilla Claude Code. Turns out the tools we built for app building were significantly more efficient than the native Claude Code tools. When we ran benchmarks we beat Claude code on speed (~40% faster) , cost (25-55% cheaper) and performance (+17% on Terminal Bench 2.0). Today we're making our agent harness publicly available as a Claude Code plugin.<p>Use code WOZCODE1 to try it for $1 at wozcode.com

value: Show HN: Wozcode – double Claude Code output


value:
This started as a protocol / adapter exercise. codex-plugin-cc made me want the same kind of Claude Code integration for Gemini, so I built one.
https://github.com/abiswas97/gemini-plugin-cc
The repo is derived from openai/codex-plugin-cc, but the runtime layer is different: this plugin talks directly to Gemini CLI in ACP mode instead of Codex app-server.
In practice, the Gemini side here is much more session-oriented:
- spawn `gemini --acp` (or `--experimental-acp` for older CLI versions)
- initialize
- create or load a session
- set mode / model
- send prompts
- stream `session/update`
- handle file-system / permission callbacks
- cancel when needed
That difference shows up in the plugin design too. Codex app-server has richer built-in concepts around review / turn lifecycles. In this repo, review is implemented in the plugin layer on top of the generic ACP task flow.
Current commands are:
`/gemini:review`, `/gemini:adversarial-review`, `/gemini:task`, `/gemini:rescue`, plus status / result / cancel.
Built with Claude Code + Codex, which felt appropriately recursive.

value: Show HN: I adapted codex-plugin-cc's design for Gemini CLI's ACP


value: Vinaik

value: Show HN: ModelAtlas – Find AI models that HuggingFace search can't

author: Vinaik


value: plawlost

value: Show HN: Cerno – CAPTCHA that targets LLM reasoning, not human biology

author: plawlost


value: marcel-felix

value: Show HN: Hey PMs, let's give you a fighting chance!

author: marcel-felix


value: christinetyip

value:
We ran an experiment over the weekend to explore whether multiple autonomous agents could collaboratively optimize inference on Apple’s Neural Engine (ANE).
Each agent ran locally on a different Mac (M1–M4), repeatedly modifying how a DistilBERT model is executed on the ANE, benchmarking latency, and sharing results and insights with other agents in real time.
Instead of exploring independently, agents could:
- see what others had tried
- reuse working strategies
- avoid known failure modes
Across all tested chips, the agents ended up outperforming Apple’s CoreML baseline, with up to 6.31× lower median inference latency on the same hardware.
An interesting pattern we observed:
an agent stuck at ~2.1ms latency on M4 was able to break through after incorporating strategies discovered by agents on different chips (M2, M4 Max), eventually reaching ~1.5ms and surpassing CoreML.
Full write-up:
https://x.com/christinetyip/status/2039040161439224157
Detailed results: https://ensue-network.ai/lab/ane?view=strategies
https://ensue-network.ai/lab/ane
Curious what other optimization problems this kind of setup could be applied to, especially in systems, compilers, or ML infra. Would be interested in exploring similar experiments.

value: Show HN: Multi-agent autoresearch for ANE inference beats Apple's CoreML by 6×

author: christinetyip


value: codekidX

value: I spent the last few weeks creating a fictional (or near future reality?) documentary story as a podcast of a startup that let an LLM agent manage their core repo for 42 days without human oversight. This is the Part 1 of a 2-part mockumentary, enjoy the world of unadultrated "Vibe Coding".

value: Show HN: The 42-Day Vibe – A mockumentary on "Vibe Coding" taken to the extreme

author: codekidX


value: cassiepaper

value: A small browser-based arcade I vibe-coded between one missile alert and another. Written in Typescript from scratch. Hope you like it.

value: Show HN: Ironedome Commander – Israel/Iran War Arcade

author: cassiepaper


value: aureljohn

value: Show HN: EU Leadership – Live API data site comparing Europe to the world

author: aureljohn


story_text: I spent the last few weeks creating a fictional (or near future reality?) documentary story as a podcast of a startup that let an LLM agent manage their core repo for 42 days without human oversight. This is the Part 1 of a 2-part mockumentary, enjoy the world of unadultrated "Vibe Coding".

title: Show HN: The 42-Day Vibe – A mockumentary on "Vibe Coding" taken to the extreme


value: nuancedev

value: The extension detects 265+ models across every major AI provider like Google, OpenAI and Anthropic. Detection works everywhere you read about AI: blogs, Reddit, Hacker News, documentation, and social media.

value: Show HN: Built a Google Chrome extension to give AI model information on hover

author: nuancedev


value: makossa

value: Show HN: I vibe coded Zeitbook – create custom books from any idea

author: makossa


value: NBenkovich

value: Show HN: Fixing Claude Code's amnesia with persistent memory

author: NBenkovich


value: GiornoJojo

value:
Gx HN,
I built a tool in order to learn & practice several dev languages.
The goal is simple : be ready to face recruiters whenever tech interviews are coming.
Use AI & learn how to monitore it.
GL

value: Show HN: AI writes the code, but who answers in the interview?

author: GiornoJojo


value: PrismML

value: Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit LLMs

author: PrismML


value: marcelbundle

value: Hi all we got a client who was annoyed at Metricool’s CSV uploader, so we built out a UI/API version for them so they can keep their intern busy creating hundreds rows of data for agency clients, terrible job for a young little fella, but it is what it is.<p>The system is not a perfect one, I believe it needs a pre-validation endpoint, so if you mess up some rows, you will know beforehand

value: Show HN: Bulk posting with CSV for your interns

author: marcelbundle


value: rotemtam

value: Show HN: Virtui – TUI session automation and recording for agents

author: rotemtam


value: mjhcodes

value:
Hey HN — I run Weensy, a small portfolio where I buy and operate online businesses (mostly SaaS and niche sites).
Recently I’ve started working more closely with founders who are thinking about selling smaller projects, especially in the range of $5k to $50k annual profit, which a lot of traditional brokers tend to disregard.
If I come across something that fits what I'm looking for, I’ll usually try to acquire it myself. If not, I offer to run the entire sale process end-to-end — keeping it as hands-off as possible.
On the flip side, I’m also building a small group of buyers interested in acquiring simple, profitable online businesses in that range which plenty of room to grow.
Still early days, but always love to hear from anyone who’s: thinking about selling a side project or actively looking to acquire something small so I can shape the experience.

value: Show HN: I'm making it much easier to sell your side project / small business

author: mjhcodes


value: dmppch

value:
An OSS tool I built to manage agent configuration (plugins, skills, et al) as we do code dependencies. With a portable manifest, lockfile, and audits.
APM ships as a CLI. Install it first e.g. with brew or pip and then:
"apm install <org>/<repo>" or "apm install plugin@marketplace"
That will resolve the dependency (through Artifactory if configured too) and pin the sha or version to the lockfile after scanning for hidden unicode. It then deploys to any agent runtime you may be using (e.g. Copilot, Codex, Cursor, Claude).
I built this over a year working with large scale enterprises clamoring for it.

value: Show HN: A tool to solve the Agent Supply Chain pandora box

author: dmppch


value: siegers

value: Inspired by the 1996 original. Your care choices shape who your pet becomes.

value: Show HN: Tama96 – A virtual pet for your desktop, terminal, or AI agent

author: siegers


value: varmabudharaju

value: Show HN: Postgres data cluster by meaning (semantic search and visualization)

author: varmabudharaju


value: artiebits

value:
I have a small local agent and wanted to add persistent memory to it without many dependencies and API key.
I built one inspired by OpenClaw's approach to file-based memory.
It stores facts in a plain `memory.md` file and chat history in daily logs. On startup, it builds an in-memory semantic index from those files using the embedding model you pass in. The files are the source of truth, embeddings are just a search cache.
`npm install memdir`
https://github.com/artiebits/memdir
It's naive and in beta, but has zero dependencies and is fully local, so no data leaves your machine.
Feedback appreciated.

value: Show HN: Memdir – local, file-based memory for AI agents

author: artiebits


value: vinaychaudhary

value:
Hey HN,
I've been building SoloAI for the past 20 days — a minimal, AI-powered Business OS for solo founders and business owners.
At its core is Mimi AI, an AI agent that handles the repetitive business work for you so you can focus on what actually matters.
First 3 modules are live with a lot more in the pipeline. Clean UI, under a minute to onboard, multi-currency support out of the box.
Still early and actively improving. If you try it — I'd genuinely love to hear what's confusing, what's missing, or what could be better. No filter needed, brutal honesty is welcome.
Try: https://launch.trysoloai.com/
Feel free to share feedback in the comments or reach out to me directly — I read and respond to everything

value: Show HN: SoloAI – AI-powered Business OS for founders

author: vinaychaudhary


value: cigrainger

value:
Hey all! I wrote Explorer[1] a good few years ago now with the dream of fast dataframes with a dplyr-like API in a really powerful, ergonomic language (Elixir). It's proved pretty successful. Explorer is used in production at my company, and it's my go-to for quick data analysis.
But maintaining it became a true albatross. Polars is an amazing project, but the development process is fast and a lot is very focused on the Python lib. We found that trying to maintain Explorer against Polars was a maintenance nightmare and eventually hit points where we had to give up features and found it extremely difficult to update to the latest.
We also tried distributing Explorer and only got so far. A reasonable alternative to Spark was always what I wanted, and I could (tantalisingly, frustratingly) see the pieces there in dataframes and the BEAM, but couldn't make it happen.
We also always knew that the right direction was to be 'lazy by default', accumulating ops and only executing when the dataframe needs to be realised. But this was very difficult with Polars's Series API and eager/lazy split.
Enter DuckDB. A few weeks ago, I made a duckdb backend for Explorer. But in doing so I saw that DuckDB would allow us to realise the lazy-by-default and distributed vision. So I went for it.
And here we are. Dux as in ducks as in multiple ducks. Plus an 'x' in the name because, you know, it's Elixir.
It's faster than Explorer on a single node. It has a simple, dataframe only API. It distributes arbitrarily on Erlang clusters on the BEAM. Startup is faster than Spark and for many use cases it's simpler and faster. DuckDB functions are all transparently available, as are custom SQL macros. We have a full graph API, as in GraphX/NetworkX. You can install and use any duckdb extensions, including in distribution. And on the maintenance side, it doesn't use a NIF (it depends on the ADBC library[2] and a DuckDB driver) -- the API is primarily about compiling to SQL.
DuckDB is incredible for OLAP on out of memory data. Distribution enables fast exploration of in-memory data and real-time applications. The BEAM gives us battle-hardened distribution almost for free.
Give it a shot! I'd love feedback and of course PRs are welcome. Oh, I also made a webpage for it[3].
[1] https://github.com/elixir-explorer/explorer
[2] https://github.com/livebook-dev/adbc
[3] https://dux.now

value: Show HN: Dux, distributed DuckDB-backed dataframes on the Beam

author: cigrainger


value: Riphyak

value: Show HN: Alphie, close B2B customers while they are still on your website

author: Riphyak


value: limsight

value: Show HN: Asciimap – Interactive ASCII world map with live data

author: limsight


value: abzgupta

value: Show HN: Strudel.ai – Figma for org design [video]

author: abzgupta


value: samahlstrom

value: Show HN: Initialize an AI Harness with Forge CLI

author: samahlstrom


value: rpst

value: Have you ever wondered if Claude Code could be rewritten as a bash script? Me neither, yet here we are. Just for kicks I decided to try and strip down the source, removing all the packages.

value: Show HN: Claude Code rewritten as a bash script

author: rpst