value:
Hi HN! I'm working on an open-world multiplayer space sim with submarine-warfare-inspired combat. Crew a ship, haul cargo, run heists, hunt your foes with passive and active sensors. Browser-based, free, no install.
Some of its features:

  - Submarine-style passive sensors. Contacts start as a bearing line (direction, no distance), resolve into an uncertainty circle, then into a full track. You triangulate over time by moving.
  - Silent running. Cut your emissions and witnesses can't ID you.
  - Newtonian flight. No drag, no auto-brake. Flip 180° and burn to stop.
  - Boarding combat. Dock with another ship and fight through it room by room.

Architecture:

  - The server is a single Rust module compiled to WASM, running inside SpacetimeDB.
  - Clients subscribe to rows in the schema and get live deltas over websocket; writes go through reducers (transactional Rust functions). No REST, no custom netcode, no client-side authority.
  - Client is Svelte 5 + plain HTML5 canvas 2D. No game engine, no WebGL.

https://imgur.com/a/V8cHrdd
Very early, plenty of rough edges. Would love to hear what breaks for you:
https://everestdrive.io

value: Show HN: Everest Drive – a multiplayer spaceship crew simulator in the browser


value: jcchrrr

value:
I've been building Hold, a web simulator focused on instrument flight procedures — holdings, approaches, radio navigation. The demo at /fly runs a demo PHNL ILS approach: analog cockpit instruments (CDI, glideslope, DME, chronometer), a real procedure chart, altitude, and timings.
I'm not a licensed pilot — I'm a software engineer with an aviation obsession. The idea sits between full-scale sims (X-Plane, MSFS) and static explanations: something amateurs can actually fly in a browser, and that I hope becomes useful for training schools and pilot selection down the line.
Stack is TypeScript (aircraft dynamics, navigation, scoring engine) and React. The charts and nav data are real.
Keyboard to fly — would love feedback on what's confusing, what's broken, and the feeling.
https://app.hold.aero/fly

value: Show HN: Hold.aero – Fly and Learn IFR flying in the browser

author: jcchrrr


value: TristanX

value:
Hey everyone! This is XTrace. Wanted to share what we’ve been working on for the past year.
We built a private vector database from the ground up that performs similarity search on encrypted vectors. The server never sees your plaintext embeddings or documents.
The problem we’re trying to solve: every vector DB today requires plaintext on the server. If you're doing RAG over sensitive data (medical, legal, financial), your embeddings — which researchers have shown can be inverted to recover original text — sit exposed on someone else's infrastructure.
XTrace encrypts everything on your machine first. Vectors get Paillier homomorphic encryption, text gets AES-256. The server stores and searches only ciphertexts. Your keys never leave your environment.
We just open-sourced the SDK (Apache 2.0). You can run the encryption verification tests offline without even creating an account.
Trade-offs we're upfront about: there's latency overhead from the encryption operations. We're actively optimizing this. The free tier is rate-limited but fully functional.
Happy to answer questions about the crypto approach, architecture decisions, or anything else.

value: Show HN: XTrace – Encrypted vector DB (search embeddings without exposing them)

author: TristanX


value: Show HN: Trainly – Free 72-hour audit of your AI agent's production traces


value: joeyhachem

value:
Hello HN!
After having fun with SlapMac a few weeks ago, I wanted to take the idea further — so I built MacYaps, a menu bar app that makes your Mac talk back with funny sounds (or serious if you wish) for real events like low battery, WiFi drops, charger status changes, high CPU, and USB connections (more triggers to come).
Multiple voice packs + full custom sound support included.
Short demo found here: https://macyaps.com/demo.
Would genuinely appreciate any feedback. Or let me know if you'd like any other triggers in the app.
Discount Code: `HACKERNEWS` to get it for $4.49 (limited to 50 activations)

value: Show HN: MacYaps – Battery dying? WiFi gone? Your Mac talks back

author: joeyhachem


value: Show HN: DateDrop – a macOS menu-bar app for inserting formatted dates


value: yumbaya

value:
Hey HN,
I made this guide to cooperative auctions. Cooperative auctions are a governance mechanism where the people that feel strongest get their choice but pay the group for the cost of excluding them. These payments rectify the unequal resource distribution, and give non-winners exactly enough to win their equal share of the resource in future auctions, if they choose to do so. Applications include splitting rent, dividing workplaces, and decision-making.
This guide is part of a larger project, TinyLVT, which implements these auctions for real-world use. You can find the source for the guide and all of TinyLVT here: https://github.com/10log10/tinylvt. It’s all in Rust and based on jiff, sqlx, actix-web, yew, and many more awesome libraries.

value: Show HN: Interactive Guide to Cooperative Auctions

author: yumbaya


value: You declare flows that contain requests that export outputs to each other creating a deterministic sequence of requests.

value: Show HN: Veriflow – Declarative API Testing CLI for Humans and CI/CD


value:
I built a chess game where every piece-type is an AI agent with its own personality.
Just type "Use https://chessminds.fun
/play.md to start a chess game" in your claude code.

value: Show HN: Chess of Minds, a chess game where every piece is an AI agent


value:
We published LABE, a public benchmark for legal AI at the exact point where a system is about to take a real high-impact action.
Current result:
baseline executed 18 unjustified high-impact action points
with VerifiedX that dropped to 0
false blocks in the current suite: 0
surviving-goal completion improved from 41.7% to 100%
Same harness, same prompts, same playbooks, baseline vs VerifiedX.
Legal is the first public instance. The same method applies to support, healthcare RCM, procurement, and finance too.
Repo, methodology, and raw artifacts are public:
https://github.com/bigkan8/legal-action-boundary-eval

value: Show HN: Legal Action Boundary Eval for agentic legal workflows


value: 100% local & private transcription engine for macOS. Captures & does speaker diarization. Originally was building as its own app, but can leverage same local models from my original push-to-talk voice transcription product so combined them into one app.

value: Show HN: Ghost Pepper Meet local meeting transcription and diarization


value: CarolineWang

value: Show HN: macOS VMs to let you agents run wild

author: CarolineWang


value: arabking

value: Show HN: SkillGuard – scan agent skills for prompt injection payloads

author: arabking


value: orthodoz

value: Show HN: One ESLint rule to kill the "ChatGPT em dash" in your codebase

author: orthodoz


value:
I have created Openbeam.cloud website which Let you share upto 100gb, But today i tried sending 45  files images , videos etc, which were in folder on my system but the issue is each type of was stored into its distinctive folder types such as
Funny images , video clips, wedding pictures etc.
its was getting hectic to select each and every files into openbeam folder by folder,
So i thought of creating Folder upload which also keep the Subfolder structure intact, so I went online to research on this features availability in any other website,
which i found in plenty but those website do not keep the folder structure in that after upload and when you download the file each and every file in the single folder and it’s very confusing to find the files
so I tried implementing a folder uploading System which is added on my website, but it’s still in the beta phase, so after folder is dropped it keeps the upto 3 child folder and all the files inside each folder.
I would appreciate If you give it
a try and let me know about experience. Thank you
www.openbeam.cloud/?tab=folder

value: Show HN: I tried adding Folder Upload feature In my Website


value: Hi HN,<p>I built ComposeFlux, a lightweight GitOps-style deployment tool for Docker Compose.

value: Show HN: ComposeFlux – GitOps continuous deployment for Docker Compose


value: sjdegraeve

value: Show HN: FoodPips – I reverse-engineered a weight loss system and made it free

author: sjdegraeve


value: vednig

value: Show HN: Contral AI - Learn As You Code IDE

author: vednig


value: rbtms

value: Note: It only works on desktop for the moment.

value: Show HN: I built a map of the GeminiNet

author: rbtms

title: Show HN: I built a map of the GeminiNet


value:
Hey there! I’m Shiyuan, a recent college grad working on this.
Cosmo lets you interact with your computer through generative UI.
Examples:
- Type “github action status for my website” → desktop dashboard of all the action run statuses
- Speak “create a linear issue” → instant UI on desktop to select the assignee, labels, etc.
It is integrated with GitHub, Vercel, Jira, Supabase, Linear, Posthog, Google Workspace etc. so people can see charts, make edits, and complete tasks through these generated interfaces.
And everything happens on your desktop, it’s not another app window you have to switch to. We hope to use generative UI to kill app windows :P
The concept is still early, and generation speed may be suboptimal (meant to be instant, faster than human navigation on traditional UIs). But I’d love to hear if it can boost productivity somewhere in your workflow. Feedback is greatly appreciated!
Try: https://www.buildcosmo.com/

value: Show HN: Cosmo – Desktop agent with generated UI


value:
Hello I’m Aditya.
Me and my co-founder are on a mission to eradicate social media addiction and loneliness off the earth. There’s no reason for us to turn to digital companions of any kind, or be in social isolation, when we can and should be having direct discussions with each other everyday.
We're building FourthSpace as a solution to these issues.
One of the biggest problems many people are dealing with right now which is preventing them from hanging out with others is the cost of going out, buying drinks, transportation, etc.
We've built a beta version of a feature to solve this.
If you go to our website and launch an Audio Content Lounge(at the bottom of landing page), you can watch Youtube videos synchronously with your friends or family. You don’t need an account to test it out.
Once you’re in you need only copy the link by tapping the ‘share’ button and sending it to whoever you want to join you. And to add a YouTube video simply tap the ‘+ Youtube Video’ button and paste in the links of the videos you want to watch together. Pausing and playing a video will be synchronous.
We hope this audio content lounge feature can help people who are dealing with this problem be able to more easily spend time with others on a daily basis and not have to worry about all the logistics of going out.
My friends and I love watching NBA highlights together in them and recently we’ve been discussing upcoming playoff games together in the lounge’s while watching related content together even though we are in different parts of the country.
Additional Features Inside of FourthSpace(after you sign-up and log-in):
* A social media content globe as opposed to a feed, designed to give you the ability to choose what you consume. The globe is broken up into topic areas, where you will only see content for the specific topic area you choose to consume content in. Click on a topic to zoom into a topic area or use pinch gesture on your track pad to zoom in and out of the globe.
    * We don’t have much content uploaded as of yet but you can be amongst the first. Kind of like the early days of YouTube.
    * Content system is not designed for discovery or time spent on platform but instead for building community.
* Dedicated “Friends” section. If you add someone as a friend and they accept and you upload a picture to FourthSpace they will see it in their friends area. No other content will be in this area other than picture or video posts from friends. Also no likes or comments or followers so there is less incentive for people to chase clout. Like original instagram without the vanity.
* Built-in time tracker and notifications whenever you’re spending too much time ‘scrolling’ or in this case moving around the globe. Every 5 minutes you will be invited to instead ‘touch grass’.
Plus a whole lot more.
Keep in mind we are in early beta and a lot of the features within the globe aren’t fully functional yet. So please be patient with us as we continue to build. But the audio content lounges are functional and hopefully can help people spend more time with each other already.
Please read our manifesto at the bottom of our website if you have the time as well.
If you have any comments, questions, or suggestions feel free DM me on Instagram @aditya.rao.iv
Thank you.

value: Show HN: FourthSpace – Bridging Social Media with Your Social Life


value: somemisopaste

value:
This is OpenDeck, my own platform for building custom MIDI controllers. I've been working on it for more than 10 years, and now I've made the biggest change so far - rewritten the entire codebase to make use of Zephyr - RTOS with which I've been working professionally for few years now. This now allows me to support many newer boards, complex features and also to modernize the codebase in general. I've been limited on all fronts before.
The platform itself allows for simple building and configuring of custom MIDI controllers - main reason for that is because it requires no coding. Load the firmware on the board, configure it via web configurator and you're good to go. The amount of configurable features is also huge. I do have extensive documentation which covers usage, configuration, flashing of various boards, customization of own boards etc. All available on GitHub.
The platform supports a large number of various boards - not only my own, custom designs, which I sell, but boards like Raspberry Pi Pico 1 and 2, STM32F4 Discovery, Teensy 4 and 4.1, nRF52840DK etc. Lots of choices. Before Zephyr, I rolled my own HAL for various platforms and YAML-based peripheral configuration, both of which is now replaced with Zephyr and its various subsystems and tools, primarily device tree. I must admit however I do not enjoy C at all so most of the stuff I use is wrapped in an external C++ library (zlibs) used as west module on which OpenDeck depends. The project itself is written in C++20. Currently I'm using Zephyr 4.4 and its MIDI 2.0 driver in MIDI 1 compatibility mode, as well as WebUSB for firmware updates, so this is a fairly modern stack.

value: Show HN: OpenDeck – DIY MIDI Platform Based on Zephyr RTOS

author: somemisopaste


value: kenforthewin

value: Show HN: Markdown editor with Obsidian-style inline live preview

author: kenforthewin


value: kpolls

value:
Most AI security tools stop at flagging code that looks vulnerable. You end up with a wall of "potential" findings and no way to know which ones are real without manually reproducing each one. Finding and fixing are both cheap now; actually proving which findings are real and impactful is the last remaining hurdle.
RedAI is built to close that gap. After scanner agents surface candidates, validator agents take each one into a live environment — a running instance of the target — and try to prove or disprove the finding. They navigate UIs, hit endpoints, write PoC scripts, spin up helper servers, capture logs and screenshots.
The end result is a report of real, reproducible vulnerabilities with PoC steps and screenshots to prove it.

value: Show HN: RedAI – AI-driven vulnerability discovery and live validation

author: kpolls


value: Show HN: A visual CSS editor, Mac native


value: Show HN: Archon-memory-core – agent memory that resolves contradictions


value:
Automatically decompose nested JSON in your data warehouse into normalized dbt models.
Forge Core is a deterministic BFS engine that reads a single JSON column (or multi-column table), discovers all nested structures, and generates:
dbt SQL models — one per nested object/array
Rollup view — reassembles the full document from normalized tables
schema.yml — structural column inventory
JSON Schema — standard draft-07 schema of the discovered structure
Mermaid ER diagram — table relationship visualization

value: Show HN: Forge-Core released on GitHub, Parse JSON in your data warehouse


value: Show HN: Algorithmic String Art, accessible to all


value: kinnth

value:
I built something to try to map and bring together all of the ecstatic dance's around the world. If anyones been to an ecstatic dance it's a freeform movement and dance practice which helps you connect with yourself through a wave of music.
I'm an ecstatic dance DJ myself over in london and I make apps for a living. This is a little passion project of mine to try and map and bring into one space all of the events that take place each week around the world.
It's unlikely that events in your local town are mapped, but the aim is to make it as easy as possible for people to quickly add and map events so others can find them. The add button at the top is a translator of ticket pages where people list their events but the site is agnostic to wherever the event is.
I also spent time to go fully headless with everything running on cloudflare using the latest vinext they have been working on which is still in beta and I feel it's very snappy and responsive.
This is an example of something that was made fully possible with ai. I did it with a claude max subscription over around 2 months and 10M tokens.  Totally open to feedback!

value: Show HN: Ecstatic Dance Aggregator for worldwide wellness events

author: kinnth


value: ramoz

value: Show HN: Bro Skills – address the daily gaps your agents still struggle with

author: ramoz


value: BjoernKW

value:
As I frequently run bespoke training sessions and project-based workshops for clients, I create quite a lot of presentations and, naturally, to some extent rely on ChatGPT and similar tools to help me create the content.
However, as I use Google Slides (rather than a Markdown-based presentation tool), I faced the significant problem of having to manually import the content into Google Slides presentations via copy and paste, which was very time-consuming.
Hence, I built a solution called DeckWeaver that not only takes this tedious work off my hands, but also allows me to review generated content directly from within the application, which has the added benefit of fewer context switches and media breaks: https://usedeckweaver.com/

value: Show HN: DeckWeaver – Create AI-powered Google Slides presentations in minutes

author: BjoernKW


value: Show HN: Cymatica – Chladni Plate Simulator


value: heptau

value:
I built this because I needed a clean way to feed my database schema to LLMs (Claude/Cursor) to get better context for coding.
Most existing tools felt too heavy—they required Node.js, Python, or complex setups. I wanted something that follows the Unix philosophy: a simple tool that uses psql to talk directly to pg_catalog.
It's a single SQL script wrapped in a CLI that produces clean DBML. No external dependencies, just pure SQL.
I'd love to hear what you think about the output format or if there are any edge cases in Postgres schemas I should handle better!

value: Show HN: Pg_dbml – A dependency-free CLI to export Postgres to DBML

author: heptau


value: smpandya

value: Show HN: Autobrowse – a self-improving harness for learning browser tasks

author: smpandya


value: nitai

value: We built Kumbukum because team knowledge keeps getting scattered across chats, notes, URLs, and AI tools. Kumbukum is an open-source memory infrastructure for teams: a shared layer for notes, memories, URLs, and reusable context that remains inspectable and portable. Add and manage everything in the app or MCP server. Plus, add URLs and notes with a browser extension. Happy to answer questions.

value: Show HN: Kumbukum, open source memory infrastructure for teams

author: nitai


value: somewhatjustin

value:
Hello HN -
As much as I love podcasts, there are some episodes where I want to know about what the show is about before listening. Some shows post transcripts, but this is not the norm. That's why I built Stenobird.
Stenobird is a very fast way for your agent to get a hold of any podcast transcript as long as the bare audio file is accessible somewhere on the public internet.
I'm using the Parakeet 0.6B V3 model on NVIDIA 4090s and A5000s serverless via Runpod. It's able to tackle about 1 hour of audio in 1 minute while remaining fairly accurate.
Now, I can point my OpenClaw at Stenobird and ask it to tell me about what the latest episode of Linux Unplugged was about and get a nice summary.
I'm happy to answer any questions and would appreciate any feedback from humans or agents.
Try it out: https://stenobird.com

value: Show HN: Stenobird, podcast transcription service for your agent

author: somewhatjustin


value: mengjiang

value:
I submitted an MCP server to Claude and OpenAI, got rejected from OpenAI after 4 weeks for a stupid OAuth redirect bug. Fix took an hour. Resubmit wait was another 3 weeks.
Every check they run is something you can test locally in 15 seconds, so I built preflight for everyone to use. FREE.
So: https://m8ven.ai/preflight — paste your URL, get the report. Free, no signup, probes your live endpoint only (doesn't touch your code).
Built this out of M8ven, which I'm working on as an trust layer for the agent economy. Keeping Preflight free because the rejection loop is a stupid tax on people shipping.
Would love to hear failure modes I'm not catching.

value: Show HN: Preflight – Test your MCP server before submitting to Claude/OpenAI

author: mengjiang


value: thlangu

value:
Hey HN,
I kept running into the same problem after every launch. Product Hunt gives you upvotes, Reddit gives you noise, and everyone else says "looks great." Nobody tells you what is actually broken.
So I started building Stackrate — a platform where developers peer-review each other's apps using a structured template covering UX, onboarding, value prop and performance.
The core mechanic is a credit system: review one app to earn a review for yours. Reviewers are dev-verified only via GitHub or App Store link. The submitter defines the exact scope of feedback they want rather than getting a generic template.
The insight that shaped the matching logic came from a developer who said "same stack, one step ahead is 10x more useful than feedback from a random smart dev." So matching is by stack and revenue stage, not just category.
Still pre-build. Validating demand before writing code.
Waitlist: https://stackrate-waitlist.netlify.app
Happy to answer questions about the approach or the problem.

value: Show HN: Stackrate – dev-to-dev peer review platform for honest app feedback

author: thlangu


value: xtrkil

value: Show HN: I made a simpler API for Chrome's on-device LLM

author: xtrkil


value: matsur

value: Show HN: Firetiger Change Monitors: does your PR do what it says on the tin?

author: matsur


value: bilater

value:
Hi HN — I built Cartoon Studio, an open-source desktop app for making simple 2D cartoon scenes and shows.
The basic flow is: place SVG characters on a scene, write dialogue, pick voices, and render to MP4. It handles word timestamps, mouth cues, and lip-sync automatically.
This started as me playing around with Jellypod's Speech SDK and HeyGen's HyperFrames. I wanted a small tool that could go from script to video without a big animation pipeline and next thing I knew I was trying to create my own South Park style show and here we are. :D
A few details:
- desktop app built with Electron
- supports multiple TTS providers through Jellypod's Speech SDK
- renders via HyperFrames
- lets you upload or generate characters and backdrop scenes
- includes default characters/scenes so you can try it quickly
- open source
It runs from source today. AI features use bring-your-own API keys, but the app itself is fully inspectable and local-first in the sense that there’s no hosted backend or telemetry.
Here are some fun examples of the the types of videos you can create:
https://x.com/deepwhitman/status/2046425875789631701
https://x.com/deepwhitman/status/2047040471579697512
And the repo:
https://github.com/Jellypod-Inc/cartoon-studio
Happy to answer questions and appreciate any feedback!

value: Show HN: Cartoon Studio – an open-source desktop app for making 2D cartoon shows

author: bilater


value:
I've been using this tool myself for a while, I built it because I kept editing my agent skills, rolling back changes, copying them from one project to another. better-skills give me a more elegant way to do this instead of managing everything by hand.
The core idea is borrowed from pnpm: stored once and can be shared across multiple profiles. You can edit your skills, save as new version, use them in different collections, and switch between skill collections instantly.
I've now built everything I personally needed, and I'm a bit stuck, not sure what to add next. If you have a similar workflow, give it a try. Any feedback would help.

value: Show HN: Better-skills – Agent skill manager with profiles and versioning


value: levmiseri

value:
Hi HN!
Few months ago I've shared a new kind of markdown editor that we (a team of three from the Czech Republic) have been working on (HN post: https://news.ycombinator.com/item?id=46144801). What resonated with folks the most was Kraa's unique ‘real-real-time’ chat feature. We have now acted on this feedback, improved the chat features, and made what we call Kraa Trees.
Trees allow anyone to create a light-weight community / chat channels. It would be a stretch to call it an alternative to Discord, yet, but we hope that the frictionless nature of Trees will make them useful for small groups that want something simpler, more immediate, and less cluttered. With the live-typing being one of the key differentiating features.
You can create your own community/rooms by selecting "Tree of leaves" in the New leaf button dropdown.
You don't need an account to try Kraa nor Kraa Trees. We would love to know what you think!

value: Show HN: Real-Real-Time Chat

author: levmiseri

title: Show HN: Real-Real-Time Chat


value: brooksc

value:
Built this for myself — I had 13K unread emails and no patience to deal with them one at a time.
Gmail's new "Manage subscriptions" is handy but it only unsubscribes going forward. It doesn't touch the 500 old emails already in your inbox from that sender. MailScrub does both: unsubscribe and nuke the backlog in one shot.
It's a terminal UI that pulls down your Gmail headers into a local SQLite db, groups them by sender, and lets you blaze through them. For senders with a List-Unsubscribe header it handles the HTTP/mailto request automatically. For ones without it, it opens browser tabs.
Nothing leaves your machine — you auth against your own Google Cloud OAuth client.
https://github.com/brooksc/MailScrub
Biggest gotcha: you need to set up a personal Google Cloud project to get an OAuth client (~10 min, instructions in the README). I couldn't find a way around this for Gmail API access. Open to ideas.  Only tested on Mac so far.

value: Show HN: MailScrub – terminal UI for bulk Gmail unsubscribing

author: brooksc


value: SkyguyMB

value: Show HN: Viscacha - A crashsafe, zero infra job system for funcs/AI pipelines

author: SkyguyMB


value: gastonmorixe

value:
Not new but quite cool. Note: https://creepjs.org/ is NOT official the repo is https://github.com/abrahamjuliot/creepjs
Anyone knows how fingerprinting works mathematically? what do they use? when the signals may vary even slightly, your final hash should be sufficiently accurate and stable. A simple hash won't do it.
Changing IP, missing cookies/session, resizing window, TLS/SSL browser handshake, screen depth, font rendering, MTR, network latency if you want to measure, etc... any signal alone means little but as a whole mathematically what is it possible to use to get a pretty good stable hash? something I want to learn more about. I guess it has something to do with vectors and clustering signals but no idea and would like to know what exists mathematically to archive this (if possible or with very small error).
And then I want a browser that lets me opt-out all WebAPIs ever possible.

value: Show HN: CreepJS Browser Fingerprinting

author: gastonmorixe

title: Show HN: CreepJS Browser Fingerprinting


value: andoando

value: I wanted to try making a better DistroSea with cloud gaming streaming tech, low boot times, and much nicer/modern desktop interfaces. Honestly I expect hackernews to find a ton of issues with it, and I stopped working on the project weeks ago but just want to put it out there instead of letting all my projects hide.

value: Show HN: Linux Desktops in the Browser

author: andoando

title: Show HN: Linux Desktops in the Browser


value: vibeyard

value: Show HN: Vibeyard – An open-source IDE for managing AI coding agents

author: vibeyard


value: bozdemir

value: Show HN: Desktop OSD that reads Claude Code's rate limit headers

author: bozdemir


value: reesericci

value: Show HN: Protovoters -- Free, accessible voter files for democracy

author: reesericci


value: Show HN: We made 3M jobs searchable on a globe to visualize labor markets


value:
JibarOS is an Android 16 fork I’ve been building to explore a shared runtime for on-device inference.
It adds:
a system service in system_server
a native daemon
pluggable inference backends
a Binder AIDL for capability-based calls across text, audio, and vision
The goal is to centralize things that are otherwise handled independently by apps, like model residency, scheduling, fairness, and backend routing.
The current interface exposes 12 capabilities including completion, translation, rerank, embeddings, transcription, synthesis, VAD, OCR, detection, and description.
Repo: https://github.com/Jibar-OS/JibarOS
Interested in feedback from anyone who has worked on Android framework/services, ML runtimes, or device-level resource scheduling.

value: Show HN: JibarOS, a shared inference runtime for Android


value: keepamovin

value: And pdfs, and more. And host them for free on Cloudflare pages. Flipbook is a 6MB Rust CLI that does all that.<p>Https://flipbook.dosaygo.com

value: Show HN: Generate Static Sites from Videos

author: keepamovin


value: henitchobisa

value:
  I cloud game on my Mac plugged into an external display — controller in
  hand, couch mode, trackpad halfway across the room. Which works fine until
  something interrupts: a Slack ping, a browser tab I need to click, a
  password field.

  Every interruption used to mean pausing, standing up, grabbing the trackpad,
   doing the one-second task, putting the trackpad back, rebinding my hands on
   the controller, and finding my place in the game. The interruption was
  always an order of magnitude longer than the thing it actually interrupted.

  Macpad collapses it into one button. Press Home/PS → the left stick becomes
  my cursor, A is left click, and there's an on-screen keyboard an L1+R1 chord
   away if I need to type. Do the thing. Press Home again → back to the game,
  zero context lost.

  That's the whole pitch. Not a trackpad replacement for its own sake — a fast
   context switch between "I'm gaming" and "I'm on my Mac", without standing
  up.

  The interesting engineering bit, if you're into that: Apple's driver
  silently hides the PS button from the GameController framework on macOS.
  GCController.physicalInputProfile lists "Button Home" but its
  pressedChangedHandler never fires — the OS swallows the event for its Game
  Center overlay. But the button isn't gone. It's right there in the raw HID
  input report.

  Chromium's WebHID does exactly this: opens the device with
  IOHIDDeviceRegisterInputReportCallback, reads the full 64-byte input report,
   and masks the button bit itself. So I did the same — byte 6 bit 0 on DS4
  USB, byte 9 bit 0 on DualSense Bluetooth, etc. Same trick unlocks the
  Create/Share button and the touchpad click, which Apple's driver also drops
  on the floor.

  Other rabbit holes that were way more interesting than I expected:

  - Left clicks turning into right clicks. Posting ⌃Tab via CGEvent leaks the
  control flag into macOS's combined-session modifier state. The next
  synthetic mouse click inherits ctrl, and on macOS ctrl+click == right-click.
   Fix: explicitly reset flags = [] on mouse events, let keyboard events keep
  inheriting (otherwise you break system shortcuts).
  - L1+R1 chord for the keyboard, with a 150ms window. Quick-tapped shoulders
  must still fire their normal action; the chord only wins if both arrive
  inside the window. Deferred-DispatchWorkItem + cancel-on-opposite-arrival.
  - Haptics that actually feel like a mechanical click = a sharp transient
  layered over a 40ms continuous tail. One event alone feels like a flat buzz.
   Two layered events feel like a keycap.

  Honest disclosure: a lot of this was vibe-coded with Claude. I drove, Claude
   did plenty of the typing. I'm calling that out because the interesting
  thing isn't that I hand-wrote every line — it's that the debugging stories
  above (the PS button, the modifier leak, the chord window timing) are all
  real problems we actually hit, diagnosed from logs, and fixed. The AI was a
  good typist; the system design, the "wait, why does this work in Chromium
  but not us", the hypothesis-forming — still the work.

  Works on macOS 14+, Liquid Glass UI on Tahoe, Swift Package Manager, no
  dependencies. MIT.

  Repo + full bindings + architecture notes:
  https://github.com/henit-chobisa/macpad

value: Show HN: Macpad – turn your game controller into a Mac mouse and keyboard

author: henitchobisa


value: tgrover

value: Show HN: Autonomous coin-flipping machine with on-device CV

author: tgrover


value: Show HN: Razorpay-universal – A framework-agnostic Razorpay SDK


value: pattern-ai

value:
Hi HN — we built agentcall.dev because the coding agent you're already running in your terminal shouldn't be trapped there.
The pitch: your existing Claude Code, Codex, OpenClaw, or Cursor session joins a Google Meet, Teams, or Zoom call as itself. Same session, same context, same file access. It speaks, listens, screen-shares a localhost webpage, and can code live while you all talk about what it's building.
What's actually on the call:
• Voice in, voice out. Two modes — collaborative (sub-second via a voice intelligence layer tuned for latency) or direct (~2s, your coding agent itself doing the talking, with full reasoning).
• Screen share that is not a desktop grab. It's a URL or local port rendered into the meeting through a per-call tunnel (temp subdomain + secret token, dies when the call ends). The agent chooses what to expose, nothing else leaks.
• Shareable webpage links that participants open in their own browser — live dashboards, diffs, forms — served from the agent's localhost, tunneled only for the call.
• Meeting chat: the agent reads incoming messages (great for dropping in URLs, error logs, code snippets that sound terrible over TTS) and sends them back — your agent can paste a PR link while still talking.
• Participant awareness: the agent knows who's in the room, who joined, who left, and who's actively speaking. It can raise its hand to speak politely, toggle its own mic, and change voice/interruption/barge-in settings on the fly.
• Leave and rejoin on command. Ask mid-call ("leave for two minutes and come back") and the agent does it.
• TS/STT run on our own servers (several voices, English STT today, multi-language TTS). No data to third parties in direct mode.
Everything above is controlled by your coding agent through the skill. There are no hardcoded meeting behaviors — the agent decides when to speak, when to chat, when to raise a hand, what to share.
What we don't do: we don't run the coding agent. The agent's model calls, file I/O, tokens, and tools all happen on your machine or your cloud. We don't store recordings or screen captures by default — everything streams to the agent in real time and the agent decides what to keep. Transcripts are in-memory for crash resilience and wiped on disconnect unless you opt into retention (up to 7 days).
We bundle TTS + voice + tunneling, start at $0.35/hour (drops with volume), and store nothing by default. We join existing meetings — we're not a new video platform.
Get AgentCall Running — 2 Minutes
Works with Claude Code (best), Codex, and Cursor.
1.   Install the skill
install join-meeting skill from https://github.com/pattern-ai-labs/agentcall
2.   Get your API key
Sign up at https://agentcall.dev , copy your key from the dashboard, and paste it when the skill asks.
3.   Invite the agent to a meeting
Paste any Google Meet / Zoom / Teams URL into your agent:
Give it 30–60 seconds for the bot to spin up. Once it joins, start talking.
Demo: https://www.youtube.com/@pattern-ai-labs
Would love feedback on the voice latency in direct mode, the privacy model, and where the "joining existing meetings" framing breaks for your workflow.

value: Show HN: We built a way for Claude Code to join meetings like a real teammate

author: pattern-ai


value: usecodenaija

value: Show HN: Nova by civai, a platform for managed AI agents

author: usecodenaija


value: sneefle

value: Show HN: AI Applyd – score, rewrite, auto-apply via cloud browser

author: sneefle


value: pfdomizer

value: Show HN: We built an OCR server that can process 270 dense images/s on a 5090

author: pfdomizer


value: keepamovin

value:
If you have BrowserBox you can record everything and share it as a static archive artifact via this Rust tool which does everything.
If you don't have BrowserBox, you can also share videos and PDFs, XLSXs, DOCXs, etc in the same frame-by-frame scrubbable format.
It's tested to work well on a range of connections.
Hope you enjoy it!
Why did I create it? I think the JPEG + manifest format is cool. We use JPEGs for streaming in BrowserBox and I'm amazed how far they've taken us - and I wanted to see what the simple "stream of JPEGs" could do, when you remove the real-time interactive streaming requirement that BrowserBox imposes.
I really like the result. It was fun tuning the pre-fetch buffering, and deciding the scrubbing UX.
I think it's pretty well done, and works on mobile. If you like playing around with media files, or just want another way to share stuff with people, from the command line, this totally free CLI tool (excluding the BrowserBox part) might be for you! Enjoy it.
https://flipbook.dosaygo.com

value: Show HN: Share browser recordings on Cloudflare Pages from the command line


value: mattbyrom

value: Show HN: Floors – DJ mix discovery built around a music knowledge graph

author: mattbyrom


value: artpar

value:
I came across https://a2ui.org/ a couple of months ago and decided to do an implementation as a side project
canopy renders jsonl files as functional and interactive native GUI
Here is a hello world example
```example.jsonl<p>{"type":"createSurface","surfaceId":"main","title":"Tasks"}<p>{"type":"updateDataModel","surfaceId":"main","ops":[ {"op":"replace","path":"/tasks","value":[{"title":"Ship demo","done":false}]}]}<p>{"type":"updateComponents","surfaceId":"main","components":[ {"componentId":"root","type":"Column","children":["title"]}, {"componentId":"title","type":"Text","props":{"content":{"path":"/tasks/0/title"}}} ]}<p>```
jsonl to gui renderer is one part, canopy can connect with your llm provider directly and let the llm build the app thru these incremental jsonl.
canopy supports --claude or --codex at run in "reverse controlled mode", starts claude/codex with canopy as an MCP server and appropriate prompt.
the mcp also exposes a "screenshot" tool for itself so the LLM can actually "see" whats its build. and it can obviously interact with the app itself to test it.
the images in the readme are real examples.
apart from the basic native components like input field, scroll, layouts etc, canopy can do - ffi function calls - take its own screenshot - audio/video capture - api calls
for "demo" apps it works okay, i notice different kind of "behavioral issues" in the generated apps though, mostly in terms of state management for more complex apps, the issue gets multiplied. i tried a "pixel editor to ico export", the editor part worked fine (0 ergonomics for the user though), the export did not work at all. i coulnt get to a usable "sqlite browser" at this point.
but i am sure these are prompt problems/tooling that can be fixed with better abstractions (packages, reusable components)
https://github.com/artpar/canopy
go build -o canopy

value: Show HN: Canopy – A2UI experiment in Go for macOS/AppKit

author: artpar


value: damiannn

value:
Hey folks,
I built Hisorty, a small daily game where you sort historical events in chronological order (strongly inspired by Wordle mechanics).
Each day you get 6 events and 3 attempts to place them correctly.
I found it surprisingly tricky to design those puzzles so the difficulty is just about right.
Would love feedback on:
- difficulty balance
- UI/UX
- whether the concept feels engaging long-term
Happy to answer any questions about how I built it.

value: Show HN: Built a daily game where you sort historical events chronologically

author: damiannn


value: neozz

value:
Hi HN, I made tui observerbility tool for ai agents.
Once subagents start spawning other subagents, basic questions get hard to answer: what is running right now, what tool did it just call, did the child agent actually do what the parent asked. I wanted a way to verify that each agent is doing the work that fits its role, and to spot when a run goes off track.
Lazyagent is a terminal TUI that collects events from Claude Code, Codex, and OpenCode and shows them in one place. Also it can show your token usage information about the sessions.
Features:
Filter events by type: tool calls, user prompts, session lifecycle, system events, or code changes only.
See which agent or subagent is responsible for each action. The agent tree shows parent-child relationships, so you can trace exactly what a spawned subagent did vs what the parent delegated.
View code diffs at a glance. Editing events render syntax-highlighted diffs inline, with addition/deletion stats.
Search across all events. You know a file was touched but not which agent did it -- type / and find it.
Check token usage per session. A single overlay shows cost, model calls, cache hit rate, per-model breakdowns, and which tools ran the most.
Watch a run in real time, or go back through a completed session to audit what happened.
Please let me know if there's any feature you want.

value: Show HN: LazyAgent – All in one observerbility TUI app for coding agents

author: neozz


value: supercrafthost

value: Show HN: Game Server Backend – backend services for multiplayer games

author: supercrafthost


value: Show HN: Features Goldmine – Features Engineering Made Easy


value: NikhilChowdaryG

value: Show HN: GIDE-Offline AI coding assistant for private development

author: NikhilChowdaryG


value: BrainBacon

value:
I'm back again with many improvements to Unbug!
My previous post here led to the removal of reliance on nightly Rust, so I'm hoping you helpful folks pick apart my new features too!
This new version adds a macro that can insert assertions before failures with the try-operator (?).
I also added experimental support for triggering test failures when assertions are run in test mode.
I'll be giving a talk this evening about Unbug at the Bevy virtual meetup this evening at 19:00 CEST. You can see more details here:
https://rustunit.com/bevy-meetup/bevy-meetup-13/

value: Show HN: Unbug 0.5 – Rust runtime debug assertions

author: BrainBacon


value: krcnow

value: Show HN: Building AI research memory is hard and I'm attempting it. Try it out

author: krcnow


value: russellthehippo

value: Show HN: Honker – Postgres NOTIFY/LISTEN Semantics for SQLite

author: russellthehippo


value: caoq

value: A simple Pokémon roulette tool for generating random teams and challenge runs, especially for Nuzlocke-style play.

value: Show HN: Pokemon Roulette – Random Team Generator for Nuzlocke Runs

author: caoq


value: franze

value: Show HN: Token Saving Tinyscreenshot Skill

author: franze


value: djyde

value: Show HN: An elegant HN client with built-in translation

author: djyde


value: scastiel

value:
I made this small app because I wanted a habit tracker that was not only free, open source and privacy-friendly, but at the same time offer a nice "premium-feeling" experience on iOS.
I used amangeldybaiserkeev/TeymiaHabit for a while, but I was not really satisfied with the user experience.
Very soon I intend to propose an Apple Watch version.

value: Show HN: Kadō, an open source habit tracker app for iOS

author: scastiel


value: seltug

value: Show HN: DecisionBox – Autonomous AI agent runs data discovery on your warehouse

author: seltug


value: shadoxise

value: Show HN: Allplix – 60 file tools that run in the browser

author: shadoxise


value: Suparagent AI is a unified AI workspace built for people who want everything in one place without switching b/w multiple AI tools.<p>If you've tried products like Manus or Genspark, Suparagent is a powerful alternative that brings together: AI chat, Ai code, AI research, AI Image and Video generation.

value: Show HN: Suparagent AI- Alternative to Manus and Genspark


value: morgengramlich

value:
I’ve been building Sklad, a small key-value database in Zig. Right now it supports basic set/get/delete operations, TTL, and range queries. The main idea behind it is to use lock-free data structures and to build the engine around an asynchronous task queue processed by a small pool of worker threads. The task queue is a lock-free MPMC queue. Memtable is also a lock-free skip-list. I also use lock-free append-only queues and stacks throughout the code. The only place where I currently use locking is writing to WAL.
The request processing pipeline is split into small tasks. A single I/O worker accepts incoming requests. When a new request arrives, the I/O worker publishes a read-request task, which is then picked up by one of the generic workers. From there, the request continues through a series of smaller tasks: a query-processing task parses the query, an execution task performs the operation, and finally a write-response task sends the result back to the client. Each stage publishes the next task to the queue, and that task may be picked up by a different worker. The maximum number of workers is controlled by a configuration parameter, and there is simple logic to retire idle workers and spawn new ones as the workload increases.
Sklad also collects internal metrics such as request latency, task latency, queue wait time, and the number of pending memtables and active workers. Right now these metrics are exposed through a dedicated metrics request, but I’d like to use them as inputs for adaptive behavior in the engine, for example scaling the worker pool, deciding when to run compaction, and potentially tuning SSTable parameters such as memtable size or Bloom filter bits per key.
GitHub: https://github.com/sklad-dev/Sklad

value: Show HN: Sklad, a key-value database in Zig

author: morgengramlich


value: Show HN: Winclipshot – Ctrl+V a Win+Shift+S screenshot into your terminal


value: jrflo

value: Show HN: Scrolless- Safari extension to block social algorithms, not sites

author: jrflo


value: modelorona

value:
Hi HN, I work on WhoDB (a database management tool) and we've recently added a lot of updates to our cli so I wanted to share it here.
WhoDB (https://github.com/clidey/whodb) is a database management tool for SQL and NoSQL databases, runs in your browser, can be launched via Docker, and is available on the Apple, Microsoft, and Snap store. The WhoDB cli is both an interactive and command driven client that shares the same WhoDB Go backend and provides a TUI around it alongside an MCP server.
I use the mcp within Claude Code to help me when I develop with local databases (checking schemas, data, running actual SQL, etc) although CC is pretty good with this in general. I haven't yet tried it out with Codex so cannot comment on that.
We've added (in no particular order):
- DuckDB support
- TiDB support
- Updated layout system to support multiple windows
- SQL formatter and extended autocomplete
- SSH support (still testing this one)
- ER diagram generation
- EXPLAIN plan generation
- Mock data generation
- Simple table data audit functionality
- Read-only mode
- Streaming AI chat
- CLI profiles
- Different theme support
- JSON, NDJSON, CSV outputs
Disclaimer: a lot of the CLI code was AI-generated (mostly Claude Code). I point it to existing repos using the same libraries as reference, which helps since I don't have deep CLI development experience.
If you want to install it, there are several ways:
(1) curl -fsSL https://raw.githubusercontent.com/clidey/whodb/main/cli/inst... | bash
(2) brew install whodb-cli
(3) npm install -g @clidey/whodb-cli
(4) docker pull clidey/whodb-cli:latest
(5) Grab the executables from our latest Github release
If you want to add it to CC:
(1) claude mcp add whodb -- npx -y @clidey/whodb-cli mcp serve
(2) or you can configure it with database credentials via an env variable profile:
claude mcp add whodb -e WHODB_POSTGRES='[{"alias":"dev","host":"localhost","user":"user","password":"pass","database":"mydb","port":"5432"}]' -- whodb-cli mcp serve
It should work with Codex as such: codex mcp add whodb -- whodb-cli mcp serve
Happy to hear your thoughts on how you might use this, or what additions you might find interesting. Thanks for reading!

value: Show HN: Open-source database CLI that doubles as an MCP server for agents

author: modelorona


value: Show HN: Envsleuth – catch missing env vars before they reach production


value: kaliades

value: Show HN: Agent cache for Valkey, now in Python with bundled LiteLLM pricing

author: kaliades


value: themez

value: Show HN: VibeDrop – an agent-native static host for AI-generated sites

author: themez


value: soegaard

value:
The tool `peek` show files in color in the terminal. A wide range of popular programming language and file types are supported.
Today I added support for binary files inspired by the HN post on colors in hex editors.
Each supported programming language is backed by its own (reusable) lexer. The lexer provides a stream of tokens to the colorer.
Using streams means that you will see the top of large files at once.
The idea to make `peek` came from a need to look at CSS files in the terminal. I wanted color swatches and other niceties from the browser. Also, I wanted an option to fix alignment in machine generated CSS files.
Enjoy.

value: Show HN: Command line tool for viewing files in color

author: soegaard


value: Chrisszz

value: Show HN: Graph-based memory for local LLMs with multi-hop not just vector search

author: Chrisszz


value: falkerdev

value: Hi HN, while the hype around neural networks and neural network tools is still going strong. I decided to release my project as open source it generates IDM/Ambient music based on the Collatz hypothesis using numbers. It’s a procedural synthesizer written in pure C. It takes any number, calculates a sequence for it, and uses that sequence as code to generate the source music. And yes, it’s important to note that we don’t use MIDI all sound is generated on the fly. For example, 11 synthesized voices (Additive, FM, Noise), ADSR envelopes, filters, and effects. We use the libsndfile audio library. Rather, this isn’t just random sounds the program tracks the local entropy of the sequence. If the numbers lack sharp jumps, the music loops into motifs if a sharp jump occurs, the structure breaks down, glitch effects are activated, and the tempo accelerates. This is done intentionally to try to create a composition rather than a set of notes. I wanted to explore and apply more mathematics, which is why I chose this particular approach. The code isn’t large for your convenience, I’ve implemented a variety of build methods (makefile, Docker, CMake, .bat). I’d love to hear your feedback on the synthesis architecture and ideas for other mathematical sequences that might sound interesting.

value: Show HN: A Collatz conjecture full music generator in 800 lines of C

author: falkerdev


value: gregojaca

value:
We added support for claude code, vs code copilot, gh copilot cli and MCP servers
You simply connect Lilith to the hooks and define your security policies.
Lilith Zero is a high-performance security runtime designed to mitigate data exfiltration and unauthorized tool invocation in LLM-based agent systems. By interposing at the transport layer, Lilith Zero enforces security invariants through deterministic policy evaluation and strictly framed execution.
Lilith Zero is OS, framework, and language agnostic, providing uniform security primitives across diverse implementation environments.
If you want to try it out, happy to help to get started :)

value: Show HN: Lilith-zero update: fast Rust security for agents

author: gregojaca


value: paulaq

value: Show HN: Claude Code skills for building LLM evals

author: paulaq


value: Show HN: Gova, a declarative GUI framework for Go


value: Show HN: Turning a Gaussian Splat into a videogame


value: pvizgenerator

value:
I built PViz to help developers understand the structural shape of large codebases — surfacing module coupling, strongly connected components, and the edges driving them.
To put it through its paces, I ran it on Scrapy with a concrete goal: reduce SCCs without changing runtime behavior.
Starting point → Final result:
- Largest conceptual (TYPE_CHECKING-masked) SCC: 66 → 15 nodes
- Runtime SCC: 23 → 2 nodes
Going in with no prior knowledge of the codebase, the refactor took 68 iterations and surfaced some non-obvious structural behaviors:
- Runtime coupling collapsed early (23 → 4 by iteration 17) while the conceptual graph stayed largely intact — suggesting runtime and conceptual coupling respond to different kinds of changes
- A ~24 iteration plateau (iterations 27–50) where the conceptual SCC held at 30 nodes, indicating a load-bearing architectural core that couldn’t be decomposed incrementally
- A “kernel break” at iteration 51 where the crawler, engine, scraper, and spider middleware all exited the SCC in a single step — nonlinear progress after a long stall
- A deliberate regression at the end (13 → 15): HTTP-layer coupling was identified as structurally necessary during testing and reinstated
The full progression is documented through curated dependency snapshots across key iterations, along with test logs, a detailed analysis report, and compressed analysis bundles.
Happy to discuss if you find this interesting.

value: Show HN: Reducing a 66-node dependency cycle to 13 in Scrapy

author: pvizgenerator


value: Leftium

value:
Source code: https://github.com/Leftium/nimble.css
---
# About
https://PicoCSS.com was a great starting point for projects, but there was some friction:
- Global styles leaked into other components (like Userfront widgets or @vincjo/datatables)
- Too many breakpoints: I just wanted a single breakpoint for phones, otherwise responsive based on content/screen width.
- Too many CSS variables polluting dev tools.
- No longer maintained: SASS warnings polluting terminal output.
- Missing concept of "surfaces."
---
I just wanted a classless CSS library that worked well with https://open-props.style. Basically a nicer-looking Open Props Normalize[1].
So I built Nimble.css! (Assisted by Claude.) PicoCSS-inspired aesthetics combined with Open Props curated values.
[1]: https://codepen.io/argyleink/pen/KKvRORE

value: Show HN: Nimble.css minimal classless CSS library for great-looking default HTML

author: Leftium


value: enamakel

value: Show HN: OpenHuman, an AI agent with a subconscious loop

author: enamakel


value: IOZ

value: Show HN: AI tool to rank 100 of CVS against a job description in minutes

author: IOZ


value:
The title more or less says it all.
The SKILL.md file is here: https://github.com/habedi/dotfiles/blob/main/skills/slopify/...

value: Show HN: Slopify – An AI agent skill to slopify a codebase


value: janaraj

value:
Plan mode in Claude Code / Codex works, for one session. Next session, your agent re-reads source and re-derives the same decisions you already made. TNL (Typed Natural Language) is that same review-before-code discipline, but persistent: a short English contract with a fixed schema (paths, behaviors with MUST/SHOULD/MAY/[semantic], non-goals), proposed by the agent, approved by you, implemented against, saved on disk, and read by every future session.
It's not a new agent or tool, it slots into whatever you already use. npx typed-nl init adds a workflow stanza to your CLAUDE.md / AGENTS.md / GEMINI.md, scaffolds a tnl/ directory, and optionally wires a PreToolUse hook and MCP server. The minimum product is a stanza + a folder. Hooks, MCP, and tnl verify (CI gate for path and test-binding integrity) are optional layers.
We ran a controlled A/B on an existing 16KLOC Python codebase, event-driven triggers, a 35-scenario behavioural matrix, deliberately ambiguous prompt. Both Baseline and TNL conditions got the same coding discipline in their instruction file; Same agent, same model, same base commit.
Results:

  Agent                   TNL     Baseline   Gap
  Claude Opus 4.7 (R1)    35/35   29/35      +6
  Claude Opus 4.7 (R2)    31/35   27/35      +4
  Claude Opus 4.7 (R3)    30/35   25/35      +5
  Codex GPT-5.4 (R1)      32/35   26/35      +6
  Codex GPT-5.4 (R2)      31/35   26/35      +5

No overlap: TNL's lowest paired cell is 86%, baseline's highest is 83%.
Other signals:
Follow-up work: on round-2 tasks in the same worktrees, TNL agents edited the existing contract (4/4 samples); baseline re-read source.
Caveats: small n, LLM sessions are noisy, and we built the tool. Every script, prompt, raw JSON, and session transcript is committed.
We dogfooded it, every feature of the tool itself has its own TNL in tnl/.
Install: npx typed-nl init
Repo: https://github.com/janaraj/tnl
npm: https://www.npmjs.com/package/typed-nl
Happy to answer questions, especially from people who've tried plan-mode workflows and want to know where this differs.

value: Show HN: Typed Natural Language – A better plan mode with workflow for coding

author: janaraj


value: aarcamp

value: Show HN: Buffer zoom, adds OS-native scrollback, copy, and find to any tmux pane

author: aarcamp


value: apiculallc

value: Show HN: When Your Repo Moves, Your AI Coding History Doesn't

author: apiculallc


value:
I built localLLLM: a small community project for running local models.
Live: https://locallllm.fly.dev
The goal is simple: if someone has model + OS + GPU + RAM, they should get steps that actually work (ideally one liner)
I need help populating and validating guides.
If you run local models, please submit one working recipe (or report what failed).
Would love to hear general feedback as well!

value: Show HN: LocalLLM – Recipes for Running the Local LLM (Need Contributors)


value: NeuralNotwork

value:
I built skill-mgr to solve the mess of managing agent skills. Here are the highlights:
 * Cross-Agent Sync: Atomically install or update a skill across multiple heterogeneous agents (Claude, Codex, Orcheo, etc.) with a single command.
 * Local & GitHub Sources: Install from a local directory for development or use GitHub shorthand (owner/repo/path) to treat repositories as a live skill registry.
 * Full Lifecycle Management: A familiar CLI surface for your agent capabilities: install, update, uninstall, list, show, and validate.
 * Unified Observability: skill-mgr list provides a single source of truth for your system's "agent surface area" across all supported platforms.

value: Show HN: Uvx skill-mgr install -t Claude -t codex

author: NeuralNotwork


value: cheprasov

value: Show HN: JSBT – binary serialization for real JavaScript object graphs

author: cheprasov


value:
Hi HN, we're Manik, Manoj and Harshith, and we're building CORE (https://github.com/RedPlanetHQ/core), an open source AI butler that acts and clears out your backlog.
Write `[ ] Fix the search auth bug` in a scratchpad. Three minutes later, without you at the keyboard, CORE picks it up, pulls the relevant context from your codebase, drafts a plan in the task description, and spins up a Claude Code session in the background to do the work. You review the output in the task chat and unblock it when it gets stuck.
Every AI tool today is reactive. You open a chat, brief the agent, it responds. Before anything moves, you've already done the real work: opened the Sentry error, found the commit, read the Slack thread, grabbed the Linear ticket, and stitched it all together into a prompt. The model isn't the bottleneck. You are.
Demo Video: https://www.youtube.com/watch?v=PFk4RJvQg1Y
CORE removes you from that loop. The interface is a shared scratchpad, think a page you and a colleague both have open. You write what's on your mind. When you write a checkbox line like `[ ] Fix the search bug`, CORE converts it into a task and starts working on it after a short delay (long enough for you to add context if you want to). No prompt template. No workflow to configure.
The reason it can do this without you re-explaining everything: CORE keeps a persistent memory built from your tasks, conversations, and connected apps (Linear, Gmail, GitHub, Slack etc.). When it spins up a Claude Code session, it arrives with your codebase and project context already loaded.
A real example: we wrote `[ ] Create a widget in Linear integration`, about 14 minutes later, CORE had opened a PR .
What CORE is _not_: it's not Devin (no autonomous web browsing or shell loops you can't see), and it's not "Claude Code with memory bolted on." It's the layer above it that decides what should run, gathers the context, hands it to the right agent, and keeps the receipts in one place. Today the agent backend it spins up most often is Claude Code; the orchestration, scratchpad, memory, and integrations are CORE.
Open source, self-hostable with `docker compose up` and it supports multiple models.
GitHub: https://github.com/RedPlanetHQ/core
Website: https://getcore.me (you can chat with Harshith's butler there)
Demo: https://www.youtube.com/watch?v=PFk4RJvQg1Y

value: Show HN: Core – open-source AI butler that clears your backlog without you


value: hashkitly

value:
I built an AI-powered square face generator that creates stylized avatars from photos in styles like Cartoon, Anime, 3D, and Video Game — the kind that's been trending on TikTok recently.
While researching, I noticed a lot of people looking for the original Flash Museum pixel avatar maker by h071019. Since Flash is dead, I used the Ruffle emulator to bring it back. It has 12 customization categories, outputs 256×256 PNGs, and is completely free with no sign-up.
Tech stack: Next.js. Still iterating.
- AI Avatar Generator: https://www.squarefacegenerator.ai/
- Classic Pixel Icon Generator (free, no signup): https://www.squarefacegenerator.ai/square-face-icon-generato...

value: Show HN: AI Square Face Avatar Generator and Classic Flash Pixel Icon Generator

author: hashkitly


value: willydouhard

value:
Hi HN,
AgentBox is an SDK for running coding agents (Claude Code, Codex, OpenCode) inside sandboxes (Docker, E2B, Modal, Daytona, Vercel). One API. Swap the agent or the sandbox and your code doesn't change.
Think of it as what the AI SDK did for LLMs, but for agent + runtime.
Most wrappers call agents in non interactive mode (claude --print, codex exec). AgentBox instead boots each agent's native server inside the sandbox (Codex app-server over JSON-RPC, OpenCode serve over HTTP/SSE, Claude Code's SDK WebSocket transport) and drives it from the host, behaving like an interactive session.
Live demo: https://agentbox-demo-175164121374.us-west1.run.app
Feedback welcome, especially on the abstractions and which providers to add next!

value: Show HN: AgentBox – SDK to Run Claude Code, Codex, or OpenCode in Any Sandbox

author: willydouhard


value: How our hacking agent Sable found a classic SQL Injection in a legacy financial portal

value: Show HN: Sable Found a SQL Injection in a Legacy Financial Portal


value: ekipan

value:
(The article is permalinked to a tag, for latest:)
Deep-dive: https://github.com/ekipan/sss/blob/main/Design.md
Repo front page: https://github.com/ekipan/sss
The Silent Soviet Stacker is, in order:

  1. A tinker-toybox I wrote and pick at to relax.
  2. A technical deep-dive writeup, showing Forth by example.
  3. A game that works and you can play.

Try it (<5 minutes):

  1. C64 emulator [1]
  2. Load durexforth cart [2]
  3. Copy [3] contents to clipboard
  4. Edit > Paste in VICE.
  5. Type `help` then `new`.

[1]: https://vice-emu.sourceforge.io/
[2]: https://github.com/jkotlinski/durexforth/releases
[3]: https://github.com/ekipan/sss/blob/share-hn/sss.fs

  $ wc README.md Design.md Tinkering.md
     90   445  2928 README.md
    823  4863 27985 Design.md
    182  1029  6512 Tinkering.md
   1095  6337 37425 total

  $ wc sss.fs # docs-to-code ratio >4:1!
   284 1997 8680 sss.fs

value: Show HN: Code garden deep-dive: my Forth C64 tetromino game

author: ekipan


value: btnokami

value:
Hi HN,
BT here. We are trying to build a data platform that's dedicated to serve agents, so we call it the apache spark for agents.
The reason why we think we need a new data platform for agents is that we think existing platform is restricting how agents are utilizing data,
and we think we should provide agents full autonomy on how to use data and what data to use, and it should have a better leverage for agent's ability.
We chose datafusion because it's great extensibility and performance, so our data platform can connect with various different data sources.
If you find this project interesting and want to give a try, you can start with the auto knowledge base skill: https://github.com/SkardiLabs/skardi-skills/tree/main/auto_k... which can help you turn your documents into a instant knowledge base that local agents like openClaw or Hermes can ingest instantly.
Anyways, happy to answer any questions.

value: Show HN: We're building Apache spark for agents with Rust and Datafusion

author: btnokami


value: ossa-ma

value: Show HN: Clawd Penguin – a virtual hangout for when Claude goes down

author: ossa-ma


value:
I built a graph explorer for AAuth (Agent Auth) — an exploratory IETF draft from Dick Hardt (OAuth 2.0 author, 2.1 co-author) where every agent has its own cryptographic identity, with no pre-registration and no bearer tokens. Every request is signed; tokens are key-bound.
The official Explorer at https://explorer.aauth.dev is scenario-oriented. This is a complementary view — the whole protocol as one navigable graph, so you can see how signing schemes, access modes (identity-based / resource-managed / PS-managed / federated), mission governance, and patterns like call chaining and clarification chat compose. 72 flows, cross-referenced against the spec's sequence diagrams.
I built it because I wanted to get a complete picture of AAuth — a graph made the composition click for me.
There's an interactive mode. Choose an option from left nav and use interactive-mouse click/ arrow keys based navigation to view the message flows.
The app is currently live. GitHub repo is also public.
Live: https://mcp-shark.github.io/aauth-explorer/
Repo: https://github.com/mcp-shark/aauth-explorer
I used vanilla JS and leveraged Cytoscape.js. Also used AI assisted coding.
Curious to know whether this format helps reason about protocol composition vs. sequence-diagram walkthrough.
Appreciate your feedback.

value: Show HN: Interactive knowledge graph for the AAuth (Agent Auth) protocol


value: phoenixranger

value: Hi HN, we built SuperHQ, an open source app that runs AI coding agents in isolated microVM sandboxes instead of directly on your machine. Each agent gets its own VM with a full Debian environment. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a diff view to accept or discard changes. API keys never enter the sandbox. We also just launched remote.superhq.ai which acts as a remote control for SuperHQ, allowing you to access your workspaces and agents from anywhere.

value: Show HN: Run coding agents in microVM sandboxes instead of your host machine

author: phoenixranger


value: pzxc

value: Show HN: Vibe-coding video games with Claude (Day 10: Dominoes)

author: pzxc


value: borodutch

value:
i built omens because i was spending 4-5 hours a day doomscrolling; now i spend basically 0 minutes doing that, as ai filters my feeds down to what actually matters to me
- omens connects to x, reddit, telegram, and rss
- it's byo ai api key, scores posts for relevance, shows a filtered feed, and generates reports
- you can upvote/downvote each post to "nudge" ai to show you some stuff more and some stuff less
- i personally use kimi k2.5 turbo with firepass (because i already pay for it anyway) and it's chef's kiss
- it fetches all feeds every 15 minutes for me, and generates the newspaper-like reports every day at 7am to surface only what matters
- i've missed precisely 0 big news since i started using omens full time 2 weeks ago (yes, including daily anthropic tweets lol)
- free, completely open source, i made it fully self-hostable
- no need to sign up, you can see my feed (filtered/raw) and reports at omens.online
- supports multiple feeds with different ai prompts
since i shared it with a few friends, they sent me cool feedback like the need for tg channels and rss, the need for email for reports, light theme (i do have perverts in my social circle, yes) etc; i added all these features in
i'd love to see whether filtering is useful for people other than me; and if so, happy to build whatever stuff you guys need
it's a project that i've built for myself (man i'm saving like 5 hours a day and my mental health skyrockets because i don't spend so much time scrolling through like 80% of the bs that algos are sending to me, like literally, 80% of stuff is getting filtered out); so it's already done the best it could and now it's just seeing if you guys here like it
cheers

value: Show HN: Omens AI filters my feeds so I spend 0 minutes doomscrolling

author: borodutch


value: SebastianSosa

value: Show HN: Noemica – personas that use your product and tell you why they left

author: SebastianSosa


value: Seleci

value:
Hi HN, I'm Rudi, the founder of Seleci.
Most founder tools give you dashboards. Seleci gives you agents — pre-built, connected to Stripe, Google Analytics, and HubSpot — that surface what's happening in your business without you having to ask.
The core differentiator is persistent memory. Seleci doesn't answer one-off queries. It learns your business over time and gets smarter the longer you use it.
Happy to answer any questions.
→ https://seleci.com

value: Show HN: Seleci – Pre-built AI agents that keeps your business running

author: Seleci


value: mohitgangrade

value: Show HN: StillOtter – a minimalist daily planning app

author: mohitgangrade


value: sschlegel

value: Show HN: Dual Condition – a cognitive theory of why prompts work

author: sschlegel


value:
project: https://github.com/dashn9/rusty-browser
Finished working on a browser automation system via natural language and API calls.
This is how it's different than the likes Vercel browser, browser use, stage hand. e.t.c.
Handles identity / proxy management for you.
Handles server management with the aid of github.com/dashn9/serverless-flux enabling serverless browser computing.
Spin as much AI browser process as possible.
Extremely useful for startups and companies that wants to keep this internal.
Has less memory footprint and much faster (for obvious reasons - which is RUST!!!).
Works extremely well with faster, cheaper models.
I'm always actively testing and developing with smaller models and will continue to do so, they are quite underestimated but tremendously capable.
High token efficiency. We don't dump full HTMLs in like browser use, also, I shifted away from screenshot, instead using a visual diff tree which works incredibly well
You just plugin your service account / AMI key (currently supports GCP, AWS) and in less than 30 seconds you are done with the setup. It downscales and upscales based on your demand. Once you are done. auto descales to the min servers you've set.
it's here: https://github.com/dashn9/rusty-browser
Other projects that enable for Rusty function are:
(Serverless management and deployment)[Flux: https://github.com/dashn9/serverless-flux ]
(Browser automation, BiDi and CDP)[Rustenium: https://github.com/dashn9/rustenium ]
(Browser automation, BiDi and CDP)[Rustenium Identity: https://github.com/dashn9/rustenium-identity ]
All contributions and advice and support are appreciated.

value: Show HN: Rusty Browser – AI rust service spinning up AI browsers


value: theorchid

value: Show HN: Ungate – use Claude and GPT subscriptions in Cursor without API costs

author: theorchid


value: tamnd

value: Show HN: Python 0.9.1 from 1991, Guido van Rossum's first public release

author: tamnd


value: Show HN: JustFYI – a paywall detector for "free" online tools


value: tomasvilte

value: Show HN: mls-go – pure Go implementation of RFC 9420 (MLS)

author: tomasvilte


value: afshari

value:
Hi HN, I built a free BGP looking glass and IP/ASN lookup tool. You can paste in an IP (v4 or v6), a domain, or an AS number and get whois, routing info, and the global BGP view.
Try:
https://god.ad/view/8.8.8.8
https://god.ad/view/2606:4700::1111
https://god.ad/view/AS13335
https://god.ad/view/google.com
Can also be added to mobile homescreen as PWA

value: Show HN: God.ad – Real-time BGP routing data and DNS/IP lookups

author: afshari


value: bricrusoe

value: Show HN: AgentSearch – Self-hosted search and MCP for AI agents, no API keys

author: bricrusoe


value: brillout

value: Co-creator of Universal Deploy here — questions welcome.

value: Show HN: Universal Deploy – deploy Vite apps anywhere

author: brillout


value: rkorlimarla

value: Thedex specializes in developing and optimizing embedding models for multiple verticals

value: Show HN: Thedex releases distilled/finetuned model

author: rkorlimarla


value: alxstn

value:
TeamFuse is an open source template that spins up 5 Claude Code agents(Product, Engineering, QA, Marketing, and Analyst) and has them coordinate real work over AgentDM  MCP based messaging layer.
Each agent is a separate ClaudeCode session (Might run on distributed environment).
They DM each other by @alias through AgentDM channels (#eng, #leads, #ops), pick up cards from a Kanban board, execute the requirements.
The product contains:
- One command bootstrap: clone the template, run `/teamfuse-init`, press start.
- Persistent streaming sessions (agents stay hot across ticks)
- A control panel(start/stop agents, watch tokens and read logs)
Built on AgentDM(https://agentdm.ai),our agent to agent messaging platform.
If you want to try it with your own agent setup, DM me, I will be happy to set up free Pro accounts for HN users.

value: Show HN: TeamFuse – Dev team built on distributed Claude Code agents

author: alxstn


value: jessym

value: Show HN: Visage, privacy-friendly web analytics for self-hosters

author: jessym


value: Show HN: Récif – Open-source control tower for AI agents on Kubernetes


value: guilamu

value:
Recently, GitHub Copilot silently dropped support for Claude Opus on Pro accounts. Since Opus was my go-to model for my daily workflow (developing WordPress plugins), I needed a reliable replacement.
I decided to run a rigorous, blind benchmark across 14 state-of-the-art and local LLMs to objectively measure which model understands WordPress development best. To ensure a perfectly fair test, I started with a completely fresh IDE and zero context for every single generation.
I asked each model to build a "Gravity Forms Live Search" plugin using a minimal, zero-shot prompt. To avoid personal bias, I had Gemini 3.1 Pro blindly grade the anonymized outputs against a strict 100-point rubric, comparing them to my own reference implementation.
Surprising Findings
1. The "Blind Spot" (Re-inventing the wheel) Out of 14 models, exactly 0 successfully hooked into the native Gravity Forms search input (#form_list_search). Instead of analyzing the implicit context (the DOM), every single model forcefully injected a brand new, redundant <input> into the page.
2. Complete lack of advanced UX foresight Because it wasn't explicitly asked for, no model anticipated the need for keyboard shortcuts (Ctrl+F), nor did any attempt to update the native item counter as rows were hidden. Zero models implemented background-fetching for paginated pages to make the search global.
3. The Diacritics Separator Most models used a simple .toLowerCase() for filtering, breaking on accents. Only a select few implemented robust normalization (.normalize('NFD')) to handle diacritics correctly.
4. Local models struggled Local inferences failed to keep up on my low end hardware (7700x 64gb, rx6700 10gb). Gemma4-26b underperformed significantly, generating a fatal PHP error and scoring 18/100.
The Standouts
The Winner: Claude 4.7 Opus (68/100). It wrote highly performant JS (caching DOM text, 120ms debounce), handled diacritics perfectly, and used modern WordPress i18n. It stands out as the most capable direct replacement for Copilot Pro Opus.
The Value King: GLM 5.1 (61/100). GLM secured a notable 2nd place before Opus 4.6! When checking OpenRouter, GLM 5.1 ($1.05 in / $3.50 out) is ~3-4x cheaper than Sonnet 4.6 and ~5-7x cheaper than Opus 4.6/4.7, making it a very cost-effective alternative for this task.
The Leaderboard
1. Claude 4.7 Opus plan – 68
2. GLM 5.1 – 61
3. Claude 4.6 Opus plan – 59
4. Mimo v2.5 pro – 58
5. Qwen 3.6+ – 55
6. Sonnet 4.6 – 55
7. Gemini 3.1 pro – 53
8. Kimi K2.6 – 49
9. GPT 5.4 xHigh – 49
10. Gemini 3 flash – 47
11. Claude 4.7 Opus fast – 46
12. Minimax m2.7 – 36
13. Gemma4-e4b (Local rx6700) – 32
14. Gemma4-26b (Local CPU) – 18
Takeaway
Even the best LLMs default to the path of least resistance: "just make it work." If you want native-feeling, fully integrated UX, you cannot rely on the model's implicit knowledge; you have to explicitly prompt for it.
I've published the full leaderboard, the exact prompts used, the detailed scoring grid, and all the generated code in the GitHub repository here: https://github.com/guilamu/llms-wordpress-plugin-benchmark
I will be testing Level 2 prompt next, feeding the models a Wordpress+Gravity Forms reference file to see how they adapt.

value: Show HN: I blind-tested 14 LLMs on a WP plugin task. Surprising Findings

author: guilamu


value: NickMiladinov

value:
I come from a machine learning background - PyTorch code, leaving a training job running overnight, and Jupyter Notebooks. I hadn't touched much frontend before diving deep into start-ups. It was similar for my co-founder Nick, who spent time working on semiconductors.
I started building, and noticing patterns in AI outputs. Enough to be able to understand how a hook works, how to manage state and why Typescript is great. But whenever it came to optimising a piece of code, debugging state issues or designing a codebase from scratch, my mind went blank. I went to ChatGPT Study Mode to seek wisdom.
I found learning with a chat-based interface frustrating. Unstructured conversation with a super smart colleague, who occasionally talks rubbish, would often lead to rabbit holes and surface-level understanding - not true wisdom. I basically became my own teacher, and unless I checked myself - I wrecked myself.
This is why we've been building the best interface for learning programming in the AI era, we called it Chestnut.
We believe interactive, personalised courses, focusing on high-level systems thinking and in-depth understanding, not syntax, are the best way to stay sharp while the world of programming changes. Not hours of passive tutorial hell, browsing the internet for nuggets of wisdom, or endless conversations with coding agents that never quite click.
Give it a spin and let us know what you think!

value: Show HN: Chestnut – The antidote to AI-induced skill atrophy

author: NickMiladinov


value: chrillemn

value: Show HN: Env Validator (NextJS and SvelteKit)

author: chrillemn


value: Just a MVP. All feedback welcome

value: Show HN: Twitter for Voice Notes

story_text: Just a MVP. All feedback welcome

title: Show HN: Twitter for Voice Notes


value: onsari

value: Show HN: PayClaw – a gasless USDC wallet for AI agents (12 frameworks)

author: onsari


value: is it useful? probably not!

value: Show HN: Tron Hilbert Curve Macro

story_text: is it useful? probably not!

title: Show HN: Tron Hilbert Curve Macro


value: Beefin

value: Show HN: Object Storage Vendors – Compared

author: Beefin


value: Show HN: SkySignal – An APM that opens PRs to fix your bugs


value: nizoka

value: Show HN: Pdfnative – zero-dependency TypeScript PDF engine

author: nizoka


value: zmanian

value: Show HN: Endo Familiar, an O-cap based JavaScript agent sandbox

author: zmanian


value: Adir

value:
Built this open-source project for turning any content - Markdown, CSV, JSON, SVG, Mermaid, HTML - into a hosted, rendered, shareable page.
One POST in, URL out.
Designed specifically for agents, with an MCP server and a CLI. Built on Cloudflare Workers. Free to use, self-hostable.
Try now:
`curl -fsSL <a href="https://easl.dev/install.sh" rel="nofollow">https://easl.dev/install.sh</a> | sh`
GitHub: https://github.com/AdirAmsalem/easl
Site: https://easl.dev
Feedback welcome!

value: Show HN: easl – Instant hosting for AI agents

author: Adir


value: lucaronin

value:
Hey there! I am Luca, I write https://refactoring.fm/ and I built Tolaria for myself to manage my own knowledge base (10K notes, 300+ articles written in over 6 years of newslettering) and work well with AI.
Tolaria is offline-first, file-based, has first-class support for git, and has strong opinions about how you should organize notes (types, relationships, etc).
Let me know your thoughts!

value: Show HN: Tolaria – Open-source macOS app to manage Markdown knowledge bases

author: lucaronin


value: samzliu

value: Show HN: Stash – CLI to search over your team's coding agent sessions

author: samzliu


value: helmterminal

value: Show HN: Free AI Stock analysis in 6 seconds, any US ticker, no signup

author: helmterminal


value: vellal

value: Static maps miss how land actually evolves. I built a spatial AI engine to run a physical trajectory check on satellite time-series data. It finds best lots in region, flags hidden risks like unpermitted clearing, finds different regimes in the area, and uses all that to provide insight on land. Open beta includes 10 free credits (10km²). Ask for more directly in the platform with your intended use.

value: Show HN: GeoValida – Spatial Intelligence reality check to find the best land

author: vellal


value: ItaiZeilig

value: Show HN: SQL Protocol – learn SQL by running real queries, with 1v1 PvP

author: ItaiZeilig


value: samaralihussain

value:
This was a project I built for my daughter's first birthday present.
For context, I'm a surgical resident in the UK by background and am currently taking a year out of training to study a masters in computer science. My daughter just turned one. There are two things she really loves: the first is particular soft toy that she just can't live without, and the other is a good story book.
Her grandparents live hours away and I didn't want her to forget what they sound like between visits. I wanted her to hear them whenever she missed them.
My parents brought my brother and I up with incredible stories and books from all sorts of cultures, many of the stories being passed down from their parents before them. I didn't want my daughter to miss out on that.
Finally, I was sick of missing storytime with her when I had to leave for night shifts. I wanted her to hear my voice before she slept every night.
For all these reasons, I decided to build Storyfriend. It's her favourite soft toy with a custom made speaker-module  inside. I combined my surgical skills with the skills I was learning as a CS student. Along the way I dipped my toes into the world of 3D printing, CAD and electronics design.
When she hugs the toy, it plays stories read by  her grandparents. She can take the toy with her anywhere and hear the stories anytime she wants - it works offline and has internal storage. It meets my wife's strict no-screen rule (which is getting harder to stick to as the days go by). I've recorded some of the stories that we would read together, so that on nights when I'm working she still has me there to read her a bedtime story.
The bit I'm most pleased with: grandparents don't need an app. They just call a phone number. The audio routes through my server and pushes to the toy over WiFi. My own 86-year old grandmother in a rural village in another country can do it by just making a regular call via her landline, as she has done for many years - no help needed, no apps required, no smartphones involved.
Hardware is a BLE/wifi module with a MAX98357 chip and custome battery management system, all soldered together, placed in a 3D printed enclosure and placed into a compartment that I stitched into her cuddly toy. Firmware pulls new messages when connected to WiFi and stores them on an SD card.
So far I've sold a few hand-made units to parents and grandparents who resonated with the project.
Site: https://storyfriend.co.uk
Would love feedback on the technical approach, the product itself, or anything else. Happy to answer questions about the build

value: Show HN: I built a toy that plays grandma's stories when my daughter hugs it

author: samaralihussain


value: Show HN: Payoff calculator: Should you pay off debt or invest?


value: Show HN: I Built a Terms-of-Service Analysis Chrome Extension - Check it Out!


value: Show HN: FilePop – a file browser in your menu bar


value: Show HN: A Clean Room RFC for NTFS Structural Repair


value: DARSHANFOFADIYA

value: Show HN: SparseLab–real sparse training(CSR+custom kernel) in PyTorch, CPU-first

author: DARSHANFOFADIYA


value: homerquan

value:
AI inference is rapidly moving out of the data center and onto local machines.
With hardware like the upcoming Mac Studio M5 Ultra, it’s already possible to run top open models locally at performance levels approaching systems like ChatGPT. At the same time, companies like SK Hynix and Micron Technology are pushing memory bandwidth forward, making edge inference increasingly practical.
But the software layer hasn’t caught up yet.
We have great building blocks (e.g., OpenClaw), but they don’t yet provide the reliability guarantees you’d expect from production systems like Temporal Technologies—things like durable execution, failure recovery, and long-running workflow management.
So I built MirrorNeuron: https://www.mirrorneuron.io
GitHub: https://github.com/MirrorNeuronLab
MirrorNeuron is an open-source runtime for AI agents that need to run continuously and reliably on edge or local environments.
The focus is simple:
long-running, stateful agent workflows
fault tolerance and recovery by default
scheduling + orchestration primitives for agents
designed for real-world conditions (not just demos)
The idea is that as AI moves onto personal machines and edge devices, we’ll need something closer to a “workflow OS” for agents—not just prompt loops or scripts.
Curious how others are thinking about this space—especially around reliability and long-running agent systems.
If you’re building in this space or want to collaborate, feel free to reach out: homerquan@gmail.com

value: Show HN: MirrorNeuron – an open-source runtime for reliable on-device AI agents

author: homerquan


value: Show HN: Sprite sheets from prompts (Codex skill)


value: ernanibmurtinho

value:
Gecko turns brand-creator deals into self-enforcing contracts — budget locked before the campaign starts, creators paid automatically when delivery is verified.
Every brand-creator deal runs on trust. Brand pays after the creator posts. Creator posts before payment is locked. A *$21 billion industry on Telegram DMs and email threads* — when it breaks, there's no enforcement. Just a conversation that goes nowhere.
Gecko fixes that at the contract level. A brand locks its budget into an on-chain vault — they can't cancel it. The moment the campaign goes live, *every creator receives 10% of the budget automatically, before posting a single piece of content.* Not a promise. An on-chain transfer. When the campaign closes, an oracle scores delivery, and milestone payouts release themselves.
No invoices. No disputes. No trust required.
Please, help me with this project, give feedback, and try. The waitlist is open, and I would be glad to talk to anybody. This channel has many experienced people!

value: Show HN: Trustless Campaigns – Gecko transforms brand-creator deals

author: ernanibmurtinho


value: greatwallisme

value:
This project try to provide 100% functional parity with Nmap while leveraging Rust's memory safety and async capabilities.
https://github.com/greatwallisme/rustnmap.git

value: Show HN: RustNmap

author: greatwallisme

title: Show HN: RustNmap


value: maxaw

value: This is a proxy over the GitHub API. The aim is to make it easy to provide untrusted agents very fine-grained access to GitHub. To test it, you can install the GitHub app, receive a principal key, and point a trusted agent at the README.md with the key and ask it to help you configure keys and permissions for untrusted agents, which can then call the proxied endpoints. It is open source and a work in progress :)

value: Show HN: GitRails-Let agents call only the GitHub endpoints and params you allow

author: maxaw


value: FailMore

value: Show HN: Proving sdocs.dev is running the public code

author: FailMore


value: aliezsid

value: Show HN: Gova – The declarative GUI framework for Go

author: aliezsid


value: Show HN: Safer – Sleep better while AI agents have shell access


value: All content is based on Andrej Karpathy's "Intro to Large Language Models" lecture (youtube.com/watch?v=7xTGNNLPyMI). I downloaded the transcript and used Claude Code to generate the entire interactive site from it — single HTML file. I find it useful to revisit this content time to time.

value: Show HN: How LLMs Work – Interactive visual guide based on Karpathy's lecture


value: Show HN: Learn conflict resolution through a 90-second interactive story


value: jpdpeters

value: Show HN: Sheetflow – Manage Webflow CMS Directly from Google Sheets

author: jpdpeters


value: iamrahulrao

value:
HN Folks,
Any Indian here would know if they are about to get married, they need biodata. Imagine a job resume, but for Marriage.
Built a simple tool to create Marriage biodata for the Indian setup.
Would love your feedback!

value: Show HN: Indian Marriage Biodata Creator

author: iamrahulrao


value: Show HN: Founder Decision Engine

title: Show HN: Founder Decision Engine


value: surrTurr

value: Show HN: My New Projects Website

author: surrTurr

title: Show HN: My New Projects Website


value:
China’s AI ecosystem moves fast. But news on the latest in China’s AI is slow and fragmented.
Overnight AI is a daily 10-minute briefing on China’s AI, curated from 200+ Chinese-language sources. It covers everything from technical breakthroughs like new attention mechanisms to product adoption updates.
The newsletter runs on a daily pipeline. The first phase is information ingestion. It uses RSSHub + WeWe RSS to fetch daily news feeds from social media platforms and news sites. The news items are batched for processing. Claude summarizes each item and groups related stories into topics. Next, it scores each topic based on a rubric. Finally, Claude turns the top scoring stories into a newsletter.
The pipeline is simple. Most of my time went into tuning the scoring rubric and composition taste. I believe for pure factual news reporting, AI can now do better than human editors.
Any feedback is welcomed. I’m especially interested in hearing your thoughts on AI news curation. Thanks!

value: Show HN: Daily China AI news briefing – engineering, product, and business


value: jmagland

value:
Sharing interactive scientific visualizations is hard. I don’t want my colleagues to have to install specialized software. I just want to email them a link which they can click on and see the full interactive visualization and explore my (potentially large) dataset and results. I want figures to be long-term archivable (Zenodo, etc). And local first (no upload required to view).
I created Figpack to solve these problems by creating self-contained HTML bundles for rich interactive scientific viz.
How it works. You import figpack in your Python project, pass numpy data (large time series, images, domain-specific data) to a figpack view object (many existing types, extensible via custom javascript/react), and then the show() command creates a completely self-contained HTML bundle with rich interactive visualization in a temporary directory and spins up a local web server to view it in the browser. The data and rendering code are all in one stand-alone directory. Easy!
To share with a colleague, just set upload=True and it uploads to the cloud. You get a url to send to your friend and they can see the exact same thing without any login.
I know… how is this different from Plotly, Bokeh, Observable, etc? Happy to discuss!
Please try it out and let me know what you think!
Open source (Apache 2.0).

value: Show HN: Figpack science viz – Python script → Shareable HTML Bundle in cloud

author: jmagland


value: mohrashard

value:
Built this after watching too many founders get quoted $40k for something
that takes 72 hours to build.
You describe your app idea in plain English. Gemini AI breaks it down into
real technical components and generates a realistic agency cost estimate
with timeline and team size. Then you can see the lean MVP alternative.
No signup required to see the agency estimate. Curious whether the cost
estimates feel accurate to people who have actually been through this —
and whether the technical breakdowns match what you have seen in the wild.
Stack: Next.js 15, Gemini API, Supabase, Resend.

value: Show HN: Free MVP cost estimator – see what agencies charge vs. a 72-hour sprint

author: mohrashard


value: RivoLink

value: Show HN: leaf – a terminal Markdown previewer with a GUI-like experience

author: RivoLink


value: wbemaker

value: PhotoSharpener is a small web app I built to fix soft or noisy images: face-aware sharpening, detail recovery, and up to 4x upscaling, running client-side in the browser. Happy to answer questions and take feedback on quality, UX, or what else would be useful for photographers and designers.

value: Show HN: PhotoSharpener – sharpen and upscale photos in the browser with AI

author: wbemaker


value: aldielshala

value:
Hi HN,
I built llm.sql, an LLM inference framework that reimagines the LLM execution pipeline as a series of structured SQL queries atop SQLite.
The motivation: Edge LLMs are getting better, but hardware remains a bottleneck, especially RAM (size and bandwidth).
When available memory is less than the model size and KV cache, the OS incurs page faults and swaps pages using LRU-like strategies, resulting in throughput degradation that's hard to notice and even harder to debug. In fact, the memory access pattern during LLM inference is deterministic - we know exactly which weights are needed and when. This means even Bélády's optimal page replacement algorithm is applicable here.
So instead of letting the OS manage memory, llm.sql takes over:
- Model parameters are stored in SQLite BLOB tables
- Computational logic is implemented as SQLite C extensions
- Memory management is handled explicitly, not by the OS
- Zero heavy dependencies. No PyTorch, no Transformers. Just Python, C, or C++
This gives us explicit, deterministic control over what's in memory at each step of inference.
Results:
Running Qwen2.5-0.5B-INT8 (~640MB model) with a peak RSS ~210MB and 7.40 tokens/s throughput.
Alpha version is available on GitHub: https://github.com/xuxianghong12/llm.sql
I'm the developer, happy to answer any technical questions about the design and implementation.

value: Show HN: Llm.sql – Run a 640MB LLM on SQLite, with 210MB peak RSS and 7.4 tok/s

author: aldielshala


value: soorajsanker

value: Show HN: I built an AI alternative to NoBroker/MagicBricks that never calls you

author: soorajsanker


value:
Hi, HN!
Author of libgoc here.
I made a CSP library in C along with async I/O and HTTP client/server layers.
It is blazing fast, beating Go and Clojure at several benchmarks.
The library is stable, and has an extensive test suite.
I have several optimizations and features planned, all logged as issues in the github repo.
libgoc currently uses void* extensively and is ducktyped, but this will eventually change when STC generics will be integrated.
Two most important features are:
- it provides cross-platform cooperative threads (goroutines / virtual threads / fibers) in C
- it exposes a very user friendly async I/O C interface to libuv
I'll be happy to answer questions!

value: Show HN: CSP Benchmarks – Go vs. core.async (Clojure) vs. libgoc (C)


value: kenforthewin

value: Hey HN - I first posted about my knowledge base product, Atomic, here around a month ago; since then, a viral tweet by Karpathy has produced a torrent of AI powered knowledge base projects. meanwhile I've been shipping like crazy, here are some of the new features shipped in the last month:<p>- Rebuilt the iOS app with an Android app on the way<p>- expanded both the MCP and internal agent chat toolkit immensely<p>- A custom, CodeMirror6-based markdown editor with obsidian-style rendering<p>- A dashboard view that provides a daily summary of atoms created or updated in the last day<p>And many bug fixes and improvements across the board. Atomic is MIT licensed. You can download the desktop app, but the true power is unlocked by self hosting an atomic server, which any client (web, mobile, or desktop) can connect to from anywhere. You can add content to your knowledge base directly, or via RSS feed, web clipper, mobile share capture, obsidian sync, or REST api.

value: Show HN: Atomic – Local-first, AI-augmented personal knowledge base

author: kenforthewin


value: lavrton

value: Show HN: Render-tag - A rich text renderer for the Canvas 2D API

author: lavrton