value: ombedzi

value: Show HN: UIGen – Runtime front end for any OpenAPI spec with AI skills

author: ombedzi


value: danylchukllc

value: Find unique roles that match your current job history and experience. Find out in less than 60s.

value: Show HN: Seeker – Career Routing Engine

author: danylchukllc

title: Show HN: Seeker – Career Routing Engine


value: tgflynn

value: This is a new and very preliminary Lisp implementation that is based on Common Lisp.  The main goals are to minimize external dependencies.  Currently only sbcl is supported.  The only other dependency is on Quicklisp, which should strictly be necessary only for testing.

value: Show HN: Julius, a New Lisp Implementation

author: tgflynn


value: Show HN: I made a website for couples in a long distance relationship


value: sayanarijit

value: Cottage is a secrets manager that teams can use to do more than encrypt and share secrets.<p>It falls in the same category of agebox, blackbox, sops, git-crypt, but has a friendlier interface and differs is some opinion.<p>* Stores encrypted secrets with .age extension so that it can be integrated with an wider ecosystem of tooling.<p>* Separate metadata with checksums and previews with values redated with timestamps for better visibility.<p>* Automatically adds secrets to .gitignore if present.<p>* Single command to manage all secrets in repo.<p>* Supports persistent decryption workflow (plaintext secrets are deleted after operation): encrypt, decrypt, edit, sync, status, diff, clean<p>* Supports temporary decryption workflow (plaintext secrets are deleted after operation): run, edit --clean, encrypt --clean<p>* Supports jj and non .git repositories.<p>* Optimised for teams: my keeping public keys in the repo, admins can grant and revoke access to team members.

value: Show HN: A modern Git based age-encrypted secrets manager for teams

author: sayanarijit


value: aliasocracy

value:
I built Enoch after working with OpenClaw and trying to get an agentic coding system setup with Codex. In the past, I was trying to manually generate, code, and test this all manually. Then - I figured I'd try to start automation. Too many times have I typed "Continue" and gone back to my work. I figured I could automate that. Well - Enoch is what stemmed from that.
My first implementations were a mess with OpenClaw. I'll spare you the details. I moved to n8n. n8n was not bad but it felt like I had to strong arm it to do what I wanted. Had issues, but less.
Then this system - LangGraph / FastAPI... it has been working pretty well. The papers generated seem to have meaning. Generation of ideas have shown to have some positive substance. All ideas are grounded on a basis of pass / fail or positive / negative - based on criteria set on idea generation.
I will spare you too much reading.
Disclaimer: Yes, I used Codex to help code and Claude for some verbiage. I am not new to coding but I know there is a huge stigma in the business for this AI coding assistance bits. AI has helped to allow me dump my ideas down to a worker - and not have to take the time to sit and learn to code to get ideas out. Democratized software could have a grand effect.

value: Show HN: Enoch – Control Plane for Autonomous AI Research

author: aliasocracy


value: RaiyanYahya

value: Show HN: Editor, Browser, Terminal, Mail, Agents. AI Sharing Context

author: RaiyanYahya


value: dorianzheng

value: Hi HN. I'm Dorian, founder of Polygala. We are building a micro-VM runtime for all kinds of use case including embedded like SQLite, local deployment like MySQL and cloud deployment like AWS Aurora

value: Show HN: BoxLite – the all-terrain micro-VM

author: dorianzheng


value: bring-shrubbery

value:
Hi HN, author here. SHARP is Apple's recent single-image 3D Gaussian splatting model (https://arxiv.org/abs/2512.10685). Their reference code is PyTorch + a pretty heavy pipeline; I wanted to see if it could run in a browser with no server hop, so I exported the predictor to ONNX and ran it via onnxruntime-web with the WebGPU EP.
What works: drop in an image, get a .ply you can download or preview live, all on your machine — your image never leaves the tab. The model is large (~2.4 GB sidecar) so first load is slow on a cold cache, but inference itself is a few seconds on a recent Mac.
Caveats: SHARP's released weights are research-use only (Apple's model license, not the code's). I host the exported ONNX on R2 so thedemo "just works", but you can also export your own from the upstream Apple repo and upload locally.
Happy to talk about it in the comments :)

value: Show HN: Apple's SHARP running in the browser via ONNX runtime web

author: bring-shrubbery


value: iowes

value:
Hey HN!
My friend and I made and just launched Speq: A collaborative web-based repository for your product's specification. It's a tool that peppers you with questions about your new project until it (and you) truly understand what you are trying to build. Then we put everything together into a comprehensive Speq (see an actual example here: https://getspeq.com/#anatomy) that you can then turn into a PRD, share with colleagues to evaluate and collaborate on, or hand off to an agent to build via MCP.
We both left our Fortune 500 Eng Leadership roles last year in hopes of spending more time with family and getting experience with AI projects. Having spent the majority of our 18+ year careers cranking out feature work at a high-velocity, we wanted to take a stab at fixing the "SDLC". Little did we know that things would move so fast over the next several months that the SDLC as we knew it was on the brink of being totally unrecognizable. We spent a few months contemplating what we could build that would be durable in this new era where the rules are being rewritten, and the big model companies are Sherlocking products left and right.
We stumbled upon GH's SpecKit late last year and fell in love with the concept. It felt like a refreshing approach to an age-old problem. We liked a lot of what SpecKit did, but struggled with the idea of keeping everything in MD and in the repo. Our experience working with product partners and designers led us to believe that a web-based system that allowed for collaboration while still taking advantage of LLM intelligence might be a better solution. So we took the plunge and built what we wished we could have had over the last several years: a collaborative tool that owns and shares the source of truth for your product's requirements to anyone or anything that needs it.
We had no idea what the market conditions would be like when our product was ready to launch, but it actually feels like really good timing. As Tibo (Codex lead) said on X tonight, "the value of good instructions has never been higher" (https://x.com/thsottiaux/status/2050811900062286168?s=20), and that is what Speq is all about.
Speq helps you define exactly what you want to build (defining your vision, navigation flow, product requirements, logic, and tech requirements) and makes it really easy to share with agents and people alike. And it's really easy to edit, version, and evolve the Speq over time as well. When you couple this with bleeding-edge AI models that are insanely great at building things now, you can get more work done, and with better quality, than we ever could have dreamed of.
So please give it a try! Thanks to Microsoft's generous support, the tool is free for the time being. We'd love to hear thoughts and feedback, and to make this a more valuable tool for everyone.

value: Show HN: Speq – A collaborative web-based repository for your product's spec

author: iowes


value: lifthrasiir

value: Show HN: WebAssembly Interpreter in a Header

author: lifthrasiir


value: eisber

value:
As an experiment a built cli to configure Loxone home automation servers - their primary mechanism at the moment is a UX.<p>Key learnings:
* commands need to provide to good feedback (e.g. not just error 123 - starting from "your argument is out of range (valid range 15-20)" to provide "here's how you probably should do it")
* enable validation - allow the agent to loop and figure if what it tried to do is correct. For lox-cli this is both a config file validation as well as a rich simulator.
* progressive discovery - don't throw all the information at once at the agent, rather allow it to pull relevant parts into context when needed. For lox-cli this includes structured help (-h) and a sample library that can be searched.

value: Show HN: How to build CLI for agents – Loxone CLI as an example

author: eisber


value:
Demo: https://www.youtube.com/watch?v=f5uygzEmdLw
Hi HN,
I built a RISC-V emulator that implements the RV32IM instruction set and a minimal syscall interface to run DOOM. A few weeks ago, I got my first output with a simple hello world assembly program.
Since then I have been working tirelessly to get DOOM to run.
I needed to figure out how to run C programs first, and came across newlib, which allows the underlying environment to implement the syscall stubs one by one until the programs run.
I have also added ELF loading, but currently only a single `PT_LOAD` segment is supported.
To port DOOM, I used doomgeneric, which was quite convenient to get working once the required stubs were in place.
DOOM renders to a fixed area in memory (0x705FDD = VRAM_START):

  0x7FFFFF  +-------------------------------------+
            |                                     |
            |        QUEUE_SIZE (32 bytes)        |
            |                                     |
  0x7FFFDF  +-------------------------------------+ <-- QUEUE_START
  0x7FFFDE  |          QUEUE_READ_IDX             |
  0x7FFFDD  |          QUEUE_WRITE_IDX            |
            +-------------------------------------+
            |                                     |
            |                                     |
            |       VRAM (1,024,000 bytes)        |
            |                                     |
            |                                     |
  0x705FDD  +-------------------------------------+ <-- STACK_START
            |                Stack                |
            |                  |                  |
            |                  v                  |
            |                                     |
            |                  ^                  |
            |                  |                  |
            |         Program data + Heap         |
            |                                     |
  0x000000  +-------------------------------------+

I made a small linker script so that the entry point of a C program is at _start and virtual address is always 0. That kept the ELF loader code simple.
Inputs are written to the queue by rvcore which are then intercepted by DOOM running inside it.

value: Show HN: I built a RISC-V emulator that runs DOOM


value: goloroden

value: Show HN: Event-Sourced Domain Modeling

author: goloroden

title: Show HN: Event-Sourced Domain Modeling


value: Show HN: A strict-maintenance fork of ingress-Nginx


value: margotli

value:
Hey HN community,
I built a tool that helps optimize your post for hitting the first page of Show HN.
How it works: I used a Hugging Face dataset of all Hacker News posts from the past 3 years and trained a model that predicts how successful your post might be. There's still a lot of randomness on HN, so nothing is guaranteed, but the tool helps optimize your post for higher odds.
A couple of interesting findings:
- GitHub repo links work x3 better than regular domains
- Open-source tools have a steady virality rate (13.9% - one of the highest)
- "I built" outperforms "We built"
- Using parentheses and mentioning technologies (Lua, Postgres, Rust, etc.) helps a ton.
You can try the tool at wannalaunch.com or read the blog posts for more insights from the analysis. The model is also available as open source if you want to retrain it or look under the hood.
Happy to hear the feedback!

value: Show HN: I built a tool that helps predict HN front page success

author: margotli


value:
finally, after near 6 years I created the only app that I want to build.
ok so basically I start learning it, programming and building software about 7/8 years, where see teacher show us how to build a game with scratch 3, well it was first time the it session in school look way too interesting, back then I didn't have computer so I write my first dino game with scratch and papers (I draw the brocks).
then I start learning UI design, I start we some websites builders like axure rp, mobirise and bootsrap studio, so I like them but to be honest I didn't feel free, so I planned to build mine, but I can't, I only know how to print "hello world" with python, but I start learning, js and how to do it with grapes js, the possibility to create app is there, you can have your blocks, drop and drag... but it was not worth it, so I forget about and I build several app, I got job and became better developer, developer where people can see my work and say "wow u build that ??".
so after 5 years from build first stupid demo with grapes js, I start all over again, where I find a react js repo with grapes js, so basically no need to start from scratch, I work on enhance it, optimise it, then I said, those big companies have designer to create their blocks and themplates like webflow and framer, so I got this crazy idea, what if I scrape the urls of any design and start from it, it was an idea that works but not the one you create for your personal website with, so I listed as opensource back then https://github.com/kiraaziz/web-whale.
but then I got this job (that I hated) and forget about it, intel couple a month ago after I leave, I got my passion all over again, so I clone the repo, and start building again: I I enhance ui even better, I add basic functionality like login, workspaces and team working, to organise the projects, it was easy task took about 4/5 hours , because I am already have my SAAS boilerplate name "kira starts", that already have basic features like auth, layouts, teams... so I worry only about building features itself.
back to the web builder, I still have one problem, the scrapped website look bad, really bad, so I spend many times working on enhance it and now, its doing very well, the way I didn't share it as opensource and I am not even planning to sale it, I just want to enjoy it myself, and give me friend access to use it if they ask for.
those are video demo if you want to check them: https://www.facebook.com/share/v/1E5yHtVBKq https://www.facebook.com/share/v/1E5yHtVBKq
https://whale.kiraaziz.club

value: Show HN: I built a web builder after 6 years of trying


value: vojtechrichter

value:
Free tool to discover possibly forgotten subdomains with issued certificates.
Some domains might need to get submitted in a few repeated attempts, since the public CT logs registries are very overloaded, and Tidelock is trying to pick the most accessible one, without caching anything, so the provided report is always up-to-date with live information.
I will be grateful for any feedback regarding this tool, or in general any SSL/DNS related tools you would appreciate in your daily work.

value: Show HN: SSL certificate discovery from CT logs

author: vojtechrichter


value: Husena

value:
Claude Code runs in a terminal. Phones don't have good terminals.
That's the problem Kirikiri solves.
Connect to Google Cloud Shell or any SSH server, run Claude Code
there, and interact with it from your phone. Instead of fighting
a software keyboard, you place floating tap buttons on screen for
your most-used commands — git push, npm run dev, whatever you
type constantly.
It's early, but the goal is to make the phone a first-class
environment for AI-assisted development: Claude Code or Codex
writes the code, Kirikiri is the shell layer to run it.
Built with Flutter + dartssh2. Plugin system lets you add
extensions from GitHub repos.
iOS: https://apps.apple.com/jp/app/kirikiri/id6764003368
Source:
https://github.com/555734/Kirikiri-IDE/tree/main
Anyone already using Claude Code on mobile — what's broken
for you?

value: Show HN: Kirikiri – A mobile IDE for Claude Code (iOS, open source)

author: Husena


value: kitwebster

value: Show HN: Flop Map – a 3D globe of every known AI compute cluster

author: kitwebster


value: sschueller

value: Show HN: I built my product to be right to repair friendly

author: sschueller


value: WesSouza

value: Have fun with the disk defragmenter.

value: Show HN: I built my site as a Windows 95 experience (2025)

author: WesSouza

story_text: Have fun with the disk defragmenter.


value: devlsx

value: Show HN: ShadowBrokers – AI trade signals for retail traders

author: devlsx


value:
Glucera will be a blood glucose viewer for iPhone and Apple Watch. No account, no subscription, no cloud. One-time $2.99 (or maybe free).
Reads from HealthKit and pairs with the Bluetooth enabled Glucose Meters that don't roll their own crypto/weird standard. (standard GATT Glucose Profile). Data never leaves the device. No analytics SDKs, no third parties.
Not a CGM app — Libre and Dexcom users won't get value here. Those ecosystems already have decent native apps, and Libre's protocol is closed to third parties anyway..
Built for fingerstick users.
Looking for beta testers :)

value: Show HN: Glucera Local-first iPhone glucose app, seeking fingerstick beta tester


value: zkhrv

value:
Hello HN,
This is my first Show HN and hopefully a fun one for y'all.
Parrot is a web app for easily recording throwaway audio clips. It was originally intended for pronunciation practice but may find other uses.
I got the idea to work on Parrot after reading the Launch HN of Issen [1]. Thinking about ways to help language learners improve their pronunciation, I remembered an easy method I've humbled myself with in the past: listening to a recording of my own voice.
The idea is to repeatedly record and listen to yourself, adjusting your pronunciation until you get it right. What makes Parrot different from other audio recording apps is that it doesn't save a log of all these throwaway audio clips that you then have to clean up. A recording only exists until it is overwritten by a new one (all "offline" and strictly local to your own device, of course).
That seems like a scant premise to justify making a whole new app, but that small change really makes a big difference for this use case. Though I'm not sure I would have made it if that was the only reason; more of a practical excuse.
The main reason was for stupid fun. Once I imagined this music gear-like device I knew I wanted to actually make it, in all its skeuomorphic glory (only missing is the wooden table).
I don't want to spoil all the fun bits, so play around and see for yourself :)
On dark mode being too dark and "unusable": that's an intentional joke. Do try it if you haven't!
Tech-wise it's rather basic: a bit of HTML, lots of CSS, some plain JS. The difficulty was in getting all the details dialed in. My biggest takeaway:
Surprise surprise, testing and QA are so important! The number of embarrassing bugs and flaws I would have missed had I not tested across all browsers and platforms is surprisingly high. The most basic things you assume to be true might very well not be! (`audio.currentTime = 0.0;` sets audio's play head to the beginning, right? Not in Firefox it doesn't!) I 110% recommend manual testing at various points in development: some things you have to experience for yourself.
My hosted version of Parrot is not Free, but there's a GPL'd version with personal touches removed available to download [2]. Inside the tarball is also a standalone version, fully contained in a single HTML file (for use without localhost).
I'll conclude on a personal insight. Listening to recorded audio of your voice can help improve your speech (or singing!), yes. It also gets you used to the sound of your own voice, which I've found helps build confidence.
Happy to discuss :)
[1] https://news.ycombinator.com/item?id=44387828
[2] https://www.zkhrv.com/parrot/free-parrot.tar.xz

value: Show HN: Parrot – a fun, skeuomorphic audio recorder to hear yourself

author: zkhrv


value: tasn

value:
Hey HN, my name is Tom and I'm the founder of Svix. We've been working on Diom (https://diom.com) for the last 6 months, and I'm excited to finally share it on HN!
Diom is a single Rust binary that replaces Redis, RabbitMQ, Kafka, and a lot of custom code for many common use-cases. It's open-source, self-contained, and has no external runtime dependencies.
While building Svix, we had to reimplement the same backend primitives that everyone reimplements when building a service: robust caching, rate-limiting, idempotency, and more. We also constantly felt the tension between building something custom on top of existing infra (like Redis and Postgres) and adding more dedicated services (like RabbitMQ and Kafka) which we would then need to configure, operate, back up, and maintain. This was even worse for us because Svix is open-source, so additional infrastructure meant additional burden on our customers.
Six months ago we finally decided to build Diom. It's a self-contained open-source service that people can run in their infrastructure with a focus on developer experience and ease of operation. It manages its own storage using fjall (a fast LSM-tree-based storage similar to RocksDB) and therefore requires no external runtime dependencies (no redis/postgres/kafka/etc), and supports running as a single node or a highly-available Raft based cluster.
The goal is for Diom to replace Redis, RabbitMQ, Kafka, and a lot of custom code for most use-cases, by offering pre-built implementations for common backend primitives. It currently supports cache, key-value, idempotency, rate-limiting, queues, and streams. We also plan on adding auth-tokens, distributed settings, feature flags, and other common components; as well as adding more functionality to existing components.
The closest existing project is probably NATS with JetStream, which is also a single self-contained binary with KV, streams, and queues. One of the main differences is that Diom ships higher-level primitives like idempotency, rate limiting, and cache as first-class APIs, rather than something you build on top of KV and streams.
Diom favors ease of operation over scale, so it doesn't match Kafka-level throughput or very high QPS like Redis and Dragonfly. However, most products and developers don't process multiple terabytes and billions of events per second anyway. That said, Diom can still hit high performance for its target use-cases as it implements higher-level primitives rather than basic operations. Additionally, because the primitives live in the same process as the storage, there are fewer network round-trips, which keeps latency low.
It uses HTTP/2 with msgpack as the wire protocol (works fine from browsers), and ships a CLI and SDKs for Python, TypeScript, Rust, Go, and Java, with more on the way.
We have Svix fully ported to Diom and continuously running tests and simulated workloads in one of our staging environments. GA (general availability) is planned for later this year, once we've moved Svix production workloads over.
Repo (MIT licensed): https://github.com/svix/diom
Docs: https://docs.diom.com
Live playground: https://diom.com/playground
I'm excited to finally share Diom, and would love to hear what everyone thinks, if you find it useful, and what other components we should build! Would also love help figuring out what to call this. We currently say "component platform," but I'm not a fan of the name.

value: Show HN: Diom – Backend primitives (queue, rate limit, etc.) in one Rust binary

author: tasn


value: freakynit

value:
Took this as a fun little exercise for learning. In the end however, this came out to be on-par with commercial level deep-research offerings.
Full writeup (covers 80% of current state):
https://deep-research-agent.pagey.site

value: Show HN: Sourcery – Open Deep-Research, Grounded in Evidence

author: freakynit


value: quatonion

value:
Valkyr is a fresh take on LM Inference runtimes.
It's quite different from llama.cpp, vLLM, or ZINC for example.
Written in Zig, and super clean, with no dependencies apart from Vulkan Compute itself of course.
While it supports multiple model families and features like an OpenAI compatibility server, a big thing is you can run it with strict time budgets running inside an existing Vulkan host.. think video games, AR/VR apps, edge devices, or robots.
In addition it supports a rich probe interface to research language model internals at close to real time.
I'm not trying to take on existing runtimes, those exist already.  What I am trying to do is help inference work cooperatively with time sensitive applications.
Would love it if you checked it out.
Please let me know if you have any questions or ideas.  Thanks!

value: Show HN: Valkyr LM Inference with Realtime Guarantees

author: quatonion


value: sahil-shubham

value:
Bhatti spins up Linux VMs on any box with KVM — Pi 5, Hetzner AX, cloud VM with nested virt.
- Each VM has its own kernel, filesystem, and IP
- Idle VMs pause their CPUs and snapshot themselves to disk; the next request wakes them in 3.7ms warm or 360ms cold (p50, Hetzner AX102)
- Publish any port → public URL with auto-wake on first hit
- Pull any OCI/Docker image as a rootfs, or save a running sandbox as one
- Multi-tenant from day one — per-user bridges, encrypted secrets, rate limits
- Single Go binary, Apache 2.0
The decisions page is the most fun read on the site: vsock state
after restore, why all snapshots are Full, the systemctl shim, the
ARP retransmit trick.
curl -fsSL bhatti.sh/install | sudo bash
(sudo because the daemon needs /dev/kvm and sets up the Firecracker
jailer + a bridge; the CLI-only install — pipe to plain `bash` —
needs no root)
Site: https://bhatti.sh
Repo: https://github.com/sahil-shubham/bhatti
Decisions & learnings: https://bhatti.sh/docs/under-the-hood/decisions/

value: Show HN: Bhatti – Self-hostable Firecracker orchestrator with auto pause/wake

author: sahil-shubham


value: lowtecky

value: Show HN: Next-gen music visualization in the browser

author: lowtecky


value: scubakid

value:
In 2021 I shared ProjectionLab on HN[0], and I've now spent half a decade making it better.
It started as a simple financial simulator side project. In 2023, I quit my job to work on it full-time. And today it's a pretty robust financial planning and optimization tool.
This month, I added a tax strategy engine to automatically coordinate Roth conversions, tax-aware withdrawal blending, and gain harvesting, around constraints like targeting a federal tax bracket, preserving ACA subsidies, respecting IRMAA cliffs, or avoiding NIIT.
A beam search wraps around this to help find the best strategy for your objectives (maximizing net legacy, minimizing lifetime taxes, etc.)
It's been pretty cool to see that in the median case this often saves about $300k in lifetime taxes.
If you tried PL years ago in one of my original Show HNs, I'd love to hear what feels different now, or what areas still need more attention.
I've incorporated a lot of this community's feedback, e.g. there is now a free tier with basic forecasting that does save your data. And I've added more international depth for folks in Canada, UK, AU, and some other locations.
Also noteworthy:
- Flexible spending based on portfolio performance to help simulate more realistic spending profiles and boost Chance of Success.
- Net legacy estimation, estate planning, and charitable giving (QCDs, DAFs, etc)
- Government benefit estimation (US: Social Security, Medicare, ACA subsidies, Canada: CPP, OAS, GIS, etc)
- Monte Carlo options like block bootstrap (stitch random blocks of consecutive years to mix eras while keeping year-to-year patterns)
- Changelog [1]
[0] previous Show HNs: 2021 (https://news.ycombinator.com/item?id=26969173), 2022 (https://news.ycombinator.com/item?id=31083093), 2023 (https://news.ycombinator.com/item?id=36849502)
[1] https://projectionlab.com/changelog

value: Show HN: I made a simulator for personal finance (5 year update)

author: scubakid


value: jdironman

value: A small site created to see the hell world example in many different languages. I got tired of sites which only showed 10-30 languages. I'm sure there are some inaccuracies (some languages are not I languages per se).

value: Show HN: Hello, World in many different languages

author: jdironman


value: vancecookcobxin

value:
Sup HN,
So a couple of months ago I posted my OSINT app ShadowBroker here and got WAY more attention than I expected. Some of the feedback was brutal, but honestly, a lot of it was right, and it made the project a lot better.
And sure, I had a blast tracking billionaire jets and yachts or discovering GPS jamming in Ukraine was a thrill, and so was watching CCTV feeds from London, along with ships, satellites, conflict zones, etc., but eventually it dawned on me…where does my OSINT dashboard actually go from there? What good is all of the telemetry if people cannot communicate with each other about it, and it was just something that sat in my Docker container?
I let the notion stew for a while, and eventually the idea of an OSINT dashboard started mutating into something more. I started thinking about utility. What usefulness could telemetry like this have? What lanes were underdeveloped in this space? Soon the angle became more clear. P2P obfuscated communication.
I began to gravitate towards the possibility that the app could be designed to pseudonymously communicate over Tor or Reticulum in Reddit like groups or in rooms with rules and governance dictated by code. Then another notion hit...what if people off-grid using Meshtastic could communicate halfway across the world? What if you could DM those off-grid devices and they could relay messages back to civilization? What if the whole thing was tied to telemetry on and off the grid?
What about letting agents like Openclaw or whatever are peoples favorite agents communicate with you and use their analytical capabilities to find correlations in the data that slip through human analysis?
That's where this stopped being "just an OSINT map" and turned into something like a decentralized intelligence protocol.
So I started building something on top of ShadowBroker called InfoNet. It's in testnet right now. The short version is that it's an experimental mesh with the long-term goal of eventually becoming a protocol that combines communication with fact checking and rewards it where information is incentivized to be anchored to verifiable reality instead of whoever has the most money, status, institutional power, or just the loudest voice.
Some of this is working, some of it is scaffolding, and a lot of it still needs tons of hardening.
If you want to see what an AI agent can do with the telemetry, the recommended setup is letting OpenClaw/Hermes, or whatever agent you use install the repo locally. You can connect to a remote instance, too, but its responses will be insanely slow. The hope here is that by giving agents all of this telemetry they would also
I would like to stress that it is absolutely not production safe. I do not trust it yet. Neither should anyone else.
Right now, the Dead Drop lane is the strongest privacy path. Gate chat is still experimental and not truly private. I'd rather be honest about that because fake privacy claims are worse than no privacy at all.
The governance side is where it gets weird.
I started adding prediction market telemetry as a reputation engine…what if reputation came from choosing outcomes correctly?
I honestly have no idea if this works.
There are a million ways this can go wrong…spam, Sybil attacks, fake confidence, bad incentives, privacy theater, gaming prediction market outcomes, or even people gaming the reputation layer itself. I'm trying to build enough of the system to see where it breaks, and specifically whether reputation makes Sybil behavior less useful or just moves the attack surface somewhere else.
There is a very real chance I'm just building cyberpunk infrastructure gone off the rails in public.
Maybe. It is still experimental and rough around the edges.
But I'd genuinely love brutal criticism from people who have thought deeply about game theory, distributed systems, privacy, and trust networks, because I know there are holes in this thinking I haven't seen yet.

value: Show HN: My OSINT dashboard with 60+ feeds now has a pseudonymous P2P comms

author: vancecookcobxin


value: lschneider

value:
Deckades is a daily timeline trivia game. It shows you a film, song, invention, historical event, etc. and you slot it on a growing timeline by the year it was released/happened. It gets harder with every card as the timeline gets crowded and you only get 3 lives to place all 10 cards. The daily puzzle is the same for everyone, with a share grid at the end so you can compare.
Built solo over the last 3 weeks, with heavy use of Claude Code. The hardest part was getting the card data right without hallucinations, there are currently 2000+ cards. Also, getting it approved on the app store.
The link above takes you to the website with a playable version of the daily. There's also a native iOS app with ~50 themed decks (Disney & Pixar, Hip-Hop Chronicles, Blockbuster Cinema, World History, etc.): https://apps.apple.com/app/id6763564547?pt=deckades&ct=hn-sh...
Happy to answer any questions.

value: Show HN: Deckades – Was Star Wars released before or after the first Email?

author: lschneider


value:
I'm trying to kick the habit of mindlessly scrolling on my phone. Spending more time reading on my kindle has helped. Its interesting after a year of consistent reading, I can feel my ability to concentrate and focus improve.
But often, its easier to scroll reddit or instagra when I only have 5 mins of downtime. So that made me think...what if you could send shorter content like blogs and articles to my kindle.
That was the inspiration behind RSStoKindle.
Subscribe to RSS feeds on your kindle, and have a mix of content (novels, or blogs) you can read in your downtime. Instead of just reaching for another algorithmic feed, its actually content you want to read.
Give it a try, let me know what you think!

value: Show HN: Subscribe to RSS Feeds on Kindle


value: stephantul

value:
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.
So we built Semble. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.
Main features:
- Token-efficient: 98% fewer tokens than grep+read
- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)
- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested
- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode
- Zero config: no API keys, no GPU, no external services
Install in Claude Code with:
claude mcp add semble -s user -- uvx --from "semble[mcp]" semble
Or check our README for other installation instructions, benchmarks, and methodology:
Semble: https://github.com/MinishLab/semble
Benchmarks: https://github.com/MinishLab/semble/tree/main/benchmarks
Model: https://huggingface.co/minishlab/potion-code-16M
Let us know if you have any feedback or questions!

value: Show HN: Semble – Code search for agents that uses 98% fewer tokens than grep

author: stephantul


value: kilic

value: Show HN: Llmconfig – configfile and CLI for local LLM

author: kilic


value: Rochus

value: A match made in heaven ;-)

value: Show HN: Oberon System 3 on Raspi Zero 2 (with ready SD card)

author: Rochus

story_text: A match made in heaven ;-)


value:
There are many Kubernetes security scanners out there, and most give you results that say "this resource is misconfigured."
Kubesplaining tries to answer this: Given the RBAC bindings and pods you already have, how would an attacker move from a low-privilege subject to cluster-admin, host root, or kube-system secrets?
It walks the RBAC graph from every non-system subject and chains risky permissions into concrete attack paths.
Heavily inspired by Cloudsplaining, which does the same job for AWS IAM.

value: Show HN: Kubesplaining CLI that maps RBAC privilege-escalation paths in K8s


value: amitport

value: Show HN: A 4-year-old "TurboQuant" implementation

author: amitport


value: TeaVMFan

value:
I just published my first book, Means and Motive.  ( https://www.amazon.com/dp/B0GYCZJVGX )
As a software engineer, I approached writing like a software project.  I used familiar tools (Emacs and HTML) for the primary writing.
I built my own tool (EPublish) to transform the HTML manuscript into an .epub file, the source for the ebook version.  And I wrote shell scripts to reliably and repeatably transform the .epub version into PDF files for the printed editions.
I wrote 'design' and 'architecture' docs, describing the world, key actors, and timelines.  I kept a task list of chapters and key scenes that needed to be written, in priority order.  Along the way, I kept my files version-controlled so I could see the progress of the novel and edit mercilessly, without worrying about keeping old text around in backup files should I want it back for some reason.
If you've thought about writing a book, I highly recommend it.  There are many similarities to the software engineering process.  You'll also gain a newfound appreciation of the design, layout, and typesetting world, exactly how much work goes into each book you read.

value: Show HN: Software Engineer to Novelist: Writing a Book Like Coding

author: TeaVMFan


value: scastiel

value: Show HN: Kadō – habit tracker app for iOS, open source and privacy-friendly

author: scastiel


value: GustyCube

value: Show HN: GitHub Commits Leaderboard

author: GustyCube

title: Show HN: GitHub Commits Leaderboard


value: Show HN: MegaLLM – Universal LLM client for any OpenAI-compatible API


value: chadiiek

value:
Hi HN, i’m Chadi. I built "thr", a local semantic memory cli that lets you or your coding agent store memories locally and retrieve them later by meaning or fuzzy search. You don’t need to run any server or do any api call.
I built this because I got tired of telling my coding agent the same things over and over. I also didn’t want to use a hosted memory service like supermemory, or run a local server like mem0. I wanted something small to store notes locally, retrieve them semantically, and keep all the data on my laptop.
It’s not a complex system it’s intentionally simple. You just have a local embedding model that index each memory you add or edit and return the closest embeddings on retrieval (there is a minimum distance threshold so it doesn’t return weak matches).
You can use it like this:
thr add “This repo prefers small PRs with tests“
thr ask “how should i structure this pr?“
I’ve also found myself using it without any coding agent, just as a small notetaking app with semantic retrieval. I might build a tui around it later.
If you try it, I’d love feedback!!!

value: Show HN: Local semantic memory for coding agents

author: chadiiek


value:
Hi HN,
I wrote a small open source project to orchestrate dockerized Claude Code sessions from Jira, GitLab, and Forgejo. It runs Claude in a container, with a branch per issue. Then automatically opens a PR, responds to CI status, and incorporates your PR feedback.
Before this, I was using Claude in Git worktrees, but I didn't like the fact that it was running on my machine unchecked. So I set strict permissions, which nerfed Claude’s autonomy.
For reviewing, I prefer the GitLab interface with a full PR, rather than the default accept/decline change-by-change flow while Claude is writing (maybe a personal preference).
Optionally, there is a knowledge base to keep context in. This is a workflow I've been using for a while: Create a repo with markdown files describing project best practices and preferences, then feed it to Claude as a simple search MCP. The orchestrator will automatically open PRs on that repo based on review comments on PRs once they're closed.
It's a work in progress, but seeing that OpenAI did something similar for Codex internally, I thought it might be useful for other Claude Code users as well. Happy to take PRs with improvements or ideas.

value: Show HN: Orchestrate Dockerized Claude Code sessions from your issue tracker


value: bschoepke

value:
Ever wanted to control Ableton with just your voice? Me too! I made this MCP server so I could just ask Codex to do anything in Ableton Live for me, while I was nap-trapped by my baby.
The chat messages I sent to Codex to make this:
in ableton, make a self reflective song, with audio vocals (via macos say) and chip tunes and 80's drum machines. should be a real edm banger
i want midi for everything but vocals please, with ableton devices. not prerendered audio for instruments
needs some fills
and should hit way harder after "3-2-1 i become the sound"
the vocals are squished too much (read too quickly), give them a little more length
add some dynamics, the song is basically one volume. and some pumping side chain
improve dynamics of the clap, seems a bit flat and indistinguished, want it harder after the 3-2-1 drop
introduce a new element on a new track after the 3-2-1 drop, that comes in but then recedes before the final exit
doesn't seem like the new thing has any notes
the element is a bit muddy/indistinct. perhaps it needs simplification and more space, different instrument choice, i dunno

value: Show HN: Ableton Live MCP

author: bschoepke

title: Show HN: Ableton Live MCP


value: lotuslabs

value:
HI HN!
First time poster here, but I feel that my product is finally ready to showcase to HN. Me and my team built an amazing privacy tool that uses computer vision to analyze, detect, and hide sensitive data in photos or videos. It detects the identifiable visual data (faces, license plates, tattoos, phone numbers, etc.) and applies a blur effect over them to remove them from sight automatically!
Right now it only works on faces, we are working to add license plates but the biggest milestone for us has been video support. We just added it and even though it is getting better and improving it is a huge step forward, as it is about 1000x harder than processing an image.
We are currently searching for concrete PMF with business and creator niches, but we would love any feedback the community can provide!
We made it 100% anonymous, with no personal data required for signup, in memory processing only, and zero photos and videos stored! Privacy is our focus. Just create a password and your account will be created!

value: Show HN: A Privacy tool that finds and hides sensitive data in phtots/videos

author: lotuslabs


value: lirantal

value: Show HN: Security Scanner for Agent Skills and MCP

author: lirantal


value: An idle clicker where you don't really click a cookie. Check the repo for more info ;)

value: Show HN: Cuqueclicker (Cookieclicker Inspired Game)


value: alcray

value:
Hi guys,
I have built TrainForgeTester, an open-source scenario test runner for AI agents that take actions (call tools).
The idea: test how agents perform in company specific scenarios and not just on general benchmarks. More specifically test taking the wrong actions, skipping a required step, calling the wrong tool, or passing the wrong arguments.
TrainForgeTester lets you run multi-turn scenarios (you create this scenarios based on your personal use case and data following the provided scenario schema) and check:
* tool calls and arguments
* strict or unordered tool execution
* expected responses
* regressions after model, prompt, or tool changes
This scenario tester is the first part of the project(like v 0.1.0)
I’m now working on the next part: a "scenario generator" that takes messy historical company data (customer support logs, agent traces, tool calls, transcripts, etc.) and turns them into testable scenarios for this framework. Again trying to make this as deterministic as possible
Repo: https://github.com/TrainForge/TrainForgeTester
I’d love feedback on:
* real agent-testing use cases this does not cover yet (browser use, audio, video, mouse use)
* whether this direction makes sense
* where this could go as a product/devtool
* issues, edge cases, or missing features in the repo
Any GitHub issues/forks/prs would be highly appreciated.

value: Show HN: TrainForgeTester – deterministic scenario tests for AI agents

author: alcray


value: curvedinf

value: Show HN: Wove 2.0 "Beautiful Python async" adds inline Celery execution

author: curvedinf


value: denieler

value: Hey! I'd like to invite you to make a bit of art and have fun together  Over the weekend, I've created this collaborative artpage where you can ask it to change, and each visitor continues from the changes others have made. It's looking for clever and creative ideas

value: Show HN: A public web artwork made by its visitors

author: denieler


value:
The 1993 Peruvian Constitution has been amended 36 times. Those amendments are normally buried in PDFs and official gazettes.
This repo treats each reform as a Git commit with the real promulgation date, so git blame constitucion.md shows the exact law that introduced or changed each line.
A few things that came out interesting:
  - Three laws from 2021 (31280, 31304, 31305) were struck down by the Constitutional Court. They show zero lines in blame — the court ruling appears as a revert commit.
  - Alberto Fujimori's signature was removed from the constitution in 2000 by Law 27600. Law 32265 (March 2025) restored it. git blame attributes lines 1–4 to that 2025 law.
  - The right to water (Art. 7-A) is attributed to Law 30588, 2017. The right to free internet (Art. 14-A) to Law 31878, 2023.
The reconstructed public history branch is here: git clone --branch history https://github.com/Duvet05/Constitucion-Politica-del-Peru.gi...

value: Show HN: I modeled Peru's 1993 Constitution as a Git history


value: Decentralized compute network. CLI-first. Three modules out of the box: transcode (FFmpeg), coinpay (DID + escrow), infernet (AI inference).

value: Show HN: Decentralized compute network. CLI-first


value: itsarnavsh

value: Show HN: Tyche: An experimental distributed trading pipeline in Go Java

author: itsarnavsh


value: barishnamazov

value: Show HN: Interpretable AutoResearch – Legible Agent Workflows

author: barishnamazov


value: yamafaktory

value: Show HN: HypergraphZ – A Hypergraph Implementation in Zig

author: yamafaktory


value: markksantos

value: Show HN: VidMark – Frame.io-style timestamped comments for Google Drive

author: markksantos


value: mimiclone

value: Show HN: Memory Safe Argon2 Implementation in Pure Swift 6

author: mimiclone


value: Elliott-Diy

value: Show HN: Triggering anti-cheats with just a browser tab title

author: Elliott-Diy


value: zeeeeeebo

value: I brought Alula's decompilation of Zuma Deluxe to macOS and Android. I have no clue how it runs on real android hardware, I've only tested it in the bluestacks emulator. 100% free, no ads. This apk is built for arm64-v8a and armeabi-v7a.<p>GAME FILES NOT INCLUDED

value: Show HN: Zuma Portable

author: zeeeeeebo

title: Show HN: Zuma Portable


value:
Hi HN
I'm one of the authors of Stigmem.  Stigmem is an open-source specification and reference node for a shared, federated knowledge substrate for AI agents. The core idea: instead of agents maintaining isolated memory stores, they write typed, provenance-tagged facts into a shared substrate that replicates across nodes via Ed25519-signed peer handshakes.
A fact is a seven-tuple: (entity, relation, value, source, timestamp, confidence, scope). Facts are immutable, contradictions surface as first-class conflict records, not silent overwrites. Each fact carries a Hybrid Logical Clock timestamp so ordering is consistent across distributed nodes without a central coordinator.
v1.0 is spec-complete (all 18 sections stable). The reference node is FastAPI + SQLite with 74 tests including automated failure-mode tests (split-brain, malicious peers, replay attacks). We ship an MCP adapter so any MCP-compatible agent runtime can read/write facts without touching the HTTP API.
Two nodes federating with Docker: https://docs.stigmem.dev/docs/getting-started/quickstart
Repo (Apache 2.0): https://github.com/Eidetic-Labs/stigmem Spec: https://github.com/Eidetic-Labs/stigmem/tree/main/spec
Docs: https://docs.stigmem.dev

value: Show HN: Stigmem – open-source federated knowledge fabric for AI agents (v1.0)


value: NextGenRails

value:
ISO 20022 is replacing SWIFT MT messages globally. FedNow, TARGET2, and SEPA are all mid-migration. Financial institutions are processing these messages and logging them internally — but a log entry is a claim, not proof.
20022Validator issues SHA-384 hashed, Merkle-committed, RS256 signed JWS receipts for ISO 20022 JSON messages. Zero retention — the message is never stored. The receipt is independently verifiable against a published public key at any time using jwt.io or token.dev.
Supported message types: pain.001, pain.002, pain.008, camt.052, camt.053, camt.054, pacs.002, pacs.003, pacs.008, pacs.009 and more.
The core argument: an ISO 20022 message without independent validation is just a file. A cryptographically signed receipt is evidence.
White paper: https://drive.google.com/file/d/1ge9cukaYM2sQzJYmDQHNL3Zp0sa...

value: Show HN: Cryptographic receipt authority for ISO 20022 financial messages

author: NextGenRails


value: ashishuthama

value:
Hi HN,
We have been working an optical design and simulation library as a small start-up-ish team within MathWorks (makers of MATLAB and Simulink).
I have seen a few optics and MATLAB posts here, so figured this would be a good place to show case this.
A quick link to all the 'good stuff' is in a reddit post with pretty pictures and links:
 https://www.reddit.com/r/Optics/comments/1sy6z3h/optical_sys...
Something that might align with the current trend of agentic dev:
 https://www.youtube.com/watch?v=bpxse2glA6E
Since this just came out last week, LLM's have not yet seen it. But I had a hunch that our doc and REPL would allow an agent to explore and figure out the API easily. I was also hoping that the usual effort we put into our error/warnings messages would be more useful for an agent than a user :D (I lost count of how many times I glaze over red text and assume I know what went wrong!) - it got pretty far IMO!
Would love some feedback from domain experts! (Or anyone really :D).
If you already have MATLAB R2026a and the Image Processing Toolbox, download this free add-on:
 https://www.mathworks.com/help/matlab/matlab_env/get-add-ons...
 (Search for "Optical Design and Simulation")
If not, you could kick the tires with a free 30-day trial - remember to download the Image Processing Toolbox too.
 https://matlab.mathworks.com/
Ashish

value: Show HN: Optical Design and Simulation in Matlab

author: ashishuthama


value: Vernetit

value:
I built a small experimental speed-reader App that reformats books into a configurable three-step visual layout.
Instead of displaying text as normal paragraphs, it splits the text into blocks like this:
hello world
      this is a test
                line
                line
                line
The idea is to guide the eye through small chunks instead of long horizontal lines. The layout is configurable, for example 2 x 4 x 3, 3 x 5 x 2, etc.
It also has:
- keyboard navigation
- WPM-based speed-reading autoscroll

value: Show HN: An new experimental App for speed reading books

author: Vernetit


value: jawns

value: Show HN: Special Days

author: jawns

title: Show HN: Special Days


value: highpost

value:
Here's an example of how to build a simple Alpine Linux container using Apple's containerization CLI. It also demonstrates how to connect to the container through Tailscale SSH using a Tailscale auth key stored in Apple Keychain.
For example, I can create a container on my MacBook to run an application. A colleague in my Tailnet can then connect to this container to interact with that application from a coffeeshop or airliner while not exposing the rest of my MacBook.

value: Show HN: Using Tailscale with Apple's containerization stack

author: highpost


value: exsol

value: Show HN: ReflowPDF – wrote a layout engine because every PDF library failed

author: exsol


value: Show HN VibeAI FoldSpace by HugonomySystems


value: ldhieu

value: Hi HN,<p>Sharing this technique I used to render 500,000 ray-traced-looking hair strands in real-time in the browser. And they also react to physics. If you create something cool with it, let me know! :)

value: Show HN: Interactive Marimo Moss Ball

author: ldhieu

title: Show HN: Interactive Marimo Moss Ball


value: arewethereyeta

value: Show HN: Sync agent skills across devices, projects or teams

author: arewethereyeta


value: autobe

value:
- *`ttsc`*: build, check, and transform.
- *`ttsx`*: execute TypeScript with type checking.
  - 10x faster than `ts-node`.
  - type checking that `tsx` does not provide.
- *`@ttsc/lint`*: lint rule violation as TS compile error.
- *transformer support*: compiler-powered libraries, such as `typia`.

value: Show HN: TTSC – TypeScript-Go compiler and runner with transform plugins

author: autobe


value: App equivalent to max rewards but free - and almost accurate

value: Show HN: Maxcred – app to maximise your credit card rewards


value: samuraikmc

value:
My parents are in India, I'm in the US. Their health system was continuous WhatsApp photos of lab reports, vague updates over the phone, and me finding out about doctor visits weeks later.
So I built Kula. Upload lab reports (photo, PDF, WhatsApp forward) and it have them parsed and track trends. Connect a wearable and track daily health signals as well as your baselines. Everything goes into one record you can search and review over time. There's a chat layer where you can ask questions in plain language like, "what's my dad's cholesterol trend showing", and get a sourced answer from your own data.
Primarily built it for my family. My parents told me they'd use it even without me, just to have their records organized before doctor visits. That truly changed how I think about it.
Looking for feedback on this platform.
Would you use this? What are your thoughts? What's missing?
www.mykula.health

value: Show HN: Kula – a family health platform that makes sense of your data

author: samuraikmc


value: welder

value: Show HN: See how much you spend per AI agent

author: welder


value: robputt

value: WattFactory turns your smart trainer into a full indoor cycling experience — ride real-world routes with gradient resistance or follow structured ERG workouts, all from your browser. No app install required.<p>v1.1 coming soon includes Strava activity export + experimental 3D mode rendered using WebGL and OSM map features.

value: Show HN: WattFactory – Browser Based Indoor Cycling

author: robputt


value: Show HN: ChatToMap scans your chat exports to find activity and trip ideas


value: minvariance

value:
Luxury Intel is my attempt to fix two problems when booking luxury hotels:
1. Google ratings are useless in this segment. Everything sits between 4.7 and 4.9 stars. There is no signal in average ratings.
2. Prices are deliberately opaque. If you want to obtain a year of nightly rates for a handful of hotels you're stuck clicking through calendar widgets for hours.
Luxury Intel this by:
1. Calculating ratings that differentiate using >400k reviews and >20k pairwise comparisons across specific axes (service, food, rooms, location, value, ambiance), and overall.
2. Scraping property websites for a year of forward nightly rates for each property. Then displaying the data in a price curve and weekday/month heat map.
A few notable findings:
- Aman is mid-pack overall, with bi-modal scores. The brand is very weak on Food and Value while very strong on Ambiance.
- Price only weakly predicts quality. Correlation between median nightly rate and overall rating is 0.26. The median rating is essentially flat across the $200, $500, $1000, and $2000 price tiers. The premium only starts to show up above $2000.
- Service is the dominant driver of overall ratings (r=0.86). Location is almost uncorrelated with it (r=0.15).
- Rooms and Location are negatively correlated (r=-0.20). The hotels with the best locations tend to have weaker rooms, and the properties with the best rooms tend to be in more remote locations.
Stack: Python, Astro, Node, solo project built in evenings.
I find it useful and I hope you do too.

value: Show HN: I built Luxury Intel, because every luxury hotel on Google is 4.8

author: minvariance


value: rafaepta

value: Show HN: Mirror a terminal to your phone – E2E encrypted, peer-to-peer,no daemon

author: rafaepta


value: yuvalhaim

value: Show HN: My "home rig" for iterative attribute-weighted LLM benchmarking

author: yuvalhaim


value: lukyvj

value: I created this tool after writing a few agent skills by hand and noticing this pattern was repetitive. Paste a documentation URL, enter your own model API key, and it gets the page content client-side to create a reusable SKILL.md. There is no backend/proxy, so it stays as secure as possible. I would like feedback on the structure of the output and the edge cases.

value: Show HN: Generate SKILL.md files from URLs, in the browser

author: lukyvj


value: Show HN: Screen time, but with idle game mechanics


value: itsprobablyme

value: Show HN: Pokelingo – guess the Pokémon from its Pokédex entry in target language

author: itsprobablyme


value:
I am holding this domain for many years now, switching from one pet-projec to another. Latest idea is this: A toolbox for common image-related tasks:
- resizing, cropping and color adjustments
- pattern generation with a couple of different algorithms
- adding annotations like text or shapes
- showing common social media formats
- OCR extraction with tesseract, in the browser
- simple animations like Ken Burns
- ASCII-converter
- steganography
and of course EXIF data viewer, reverse image search, etc.
It's free to use, most features are browser-based, except AI-image-identifying and fetching images from external sources (need a server-side proxy for that).
Tell me what you think. Hope the UI is not too messy.

value: Show HN: Pictero.com – mostly browser based image toolbox


value: valkdb

value: Show HN: Valk Guard– Catches dangerous SQL hidden inside your ORM,no DB required

author: valkdb


value: nickoteeno

value: Show HN: Weather app to show actual performance of weather models

author: nickoteeno


value: iliashad

value:
I make YouTube videos (~80 so far, most around an hour). Plus, I have 6TB + of personal videos and unpublished.
I tried the Google Video Intelligence API. Got a $400 bill for 4 videos (5 minutes average, 4k videos) of analysis (doesn't include video transcription), and I used my GCP startup credits to cover the bill.
I decided to build my own tool that needs to have 3 important things: can transcribe videos, analyse video frames, and everything needs to be done locally.
I don't wanna deal with storing my videos in the cloud because of two concerns: privacy and storage cost.
I've been working for the last couple of months. I have a source available version that can be used for free (personal and commercial use with companies that have fewer than 5 people). Available here (https://github.com/IliasHad/edit-mind), and the project has 1.3k Github stars
Now, I'm building a desktop app with direct NLE integration (Final Cut Pro, DaVinci Resolve, and Adobe Premiere Pro). This includes an editing agent that understands your footage and your editing style.
Preview: https://youtu.be/jcctyfVg_34
Happy to answer questions and hear your feedback.

value: Show HN: Google Video API bill for 4 videos. I built my own

author: iliashad


value: e-clinton

value: Show HN: Zerminal – a terminal-first Zed fork for AI coding agents

author: e-clinton


value: probablyfine

value:
Hi HN!
I'm an amateur cryptic crossword solver and setter, who cut my teeth on Minute Cryptic. I liked that you could create custom clues on MC but was annoyed that it required creating an account to do.
I wanted to create a free, open-source, and (as much as possible) anonymous version to make it easy to share clues online; not everything needs a user account to provide a good service to users.
I'm primarily a backend developer so this was also a learning experience in creating a site with no backend; it's all vanilla client-side HTML / CSS / JS, built using Metalsmith.js, hosted as a static site on AWS S3 behind CloudFront.
The source is on GitHub, of course! https://github.com/mrwilson/cryptick
I'm still iterating heavily based on feedback, which would be appreciated!
I, plus a few guest setters, also run a Clue of the Day entry on Cryptick which is exposed as an RSS feed (https://cryptick.wildvale.co.uk/feed.xml). Here's today's (thematically appropriate) COTD:
Film franchise launch snubbed, original footage sent back with extra section (4,4) (https://cryptick.wildvale.co.uk/cotd/2026-05-04.html)

value: Show HN: Cryptick – free and open-source alternative to Minute Cryptic

author: probablyfine


value: adamgonda

value:
I got frustrated with the current AI chat interfaces.
Most notably the fact they try to please and suit you so you feel good when you use them.
I created this app for myself.
Here the AI can't convince you about anything, it is only able to give you ideas.
I found this useful as it forces me to think more, because it doesn't think for me.
It can give me ideas, but I have to pick them up.
If you are interested, you can burn some tokens on me, by applying to this limited beta.

value: Show HN: Let THINK – Pure ideas from AI, no syco flattery

author: adamgonda


value: sidkh

value: Show HN: Curated, non-slop articles on agentic coding

author: sidkh


value: shsiddhant

value:
Hi, HN! I'm Siddhant.
I've working on memory.fm for a while now. It's a web app that lets you explore your music listening history as a way to revisit different periods of your life.
Instead of focusing only on aggregate stats, it surfaces long-term and local patterns such as attachment, repetition, and obsessive listening. The idea is that your listening history often quietly tracks life phases better than you’d expect.
Currently only Last.fm users are supported, but I plan to add support for importing Spotify listening history exports.
Main Ideas Implemented:
1. Attachment Index: It’s something I came up with, in an attempt to model how attached you were to specific artists, album, or tracks during a given period. It combines relative volume and listening concentration. So if you were looping the same album during a breakup or an emotionally intense period, it shows up very clearly.
Under the hood, it uses Rényi entropy. For the math-inclined, the formula I'm using is:
A = log2(1 + scrobbles/max_scrobbles) * 100 * exp(-H)
where H is the Rényi entropy of the listening distribution (artist/album/track daily listens distribution).
2. Listening Streaks: Another view shows consecutive listening streaks for an artist/album/track. These are visualized in yearly barcode-like timelines and make repeat phases visually obvious.
Stack: FastAPI, PostgreSQL, React.
Deployed via Render (API), Vercel (Frontend), and Supabase (DB).
I’m a Math PhD dropout who transitioned into programming recently.
I had noticed that my listening patterns did a good job reflecting my emotional state throughout the past couple of years, so I started experimenting with my data in Python. This started as a small Python (CLI/Streamlit) experiment, but over the past one month, I've rebuilt it with a modern stack to make it more robust and usable.
I'd love feedback on:
- Does the “Attachment Index” reflect something meaningful, or does it feel arbitrary?
- Is the Streaks Timeline UI intuitive?
- Any obvious architectural mistakes for a first production app?
Live: https://memory-fm.vercel.app
GitHub: https://github.com/shsiddhant/memory.fm
I'd love to hear your thoughts, especially if you have an old Last.fm account to test the edge cases.
If you don't have a Last.fm account or you don’t want to go through the import flow, you can view my data instead, with username: lazulinoother

value: Show HN: memory.fm – revisit your life through your music listening history

author: shsiddhant


value: benlamm

value: Image Gen MCP — one MCP server that puts every image provider I actually use behind one interface: OpenAI, Gemini, Replicate, Together, Grok, Photoroom, Flux Kontext via fal, Ideogram, plus local tools (sharp, tesseract, @imgly).

value: Show HN: Image Gen MCP – one MCP server with goal-shaped routing

author: benlamm


value: Show HN: PDF to AI podcast in 9 Indian languages


value: Show HN: Aurra – Bi-temporal memory for AI agents (with LLM auto-supersede)


value: yamafaktory

value: Show HN: HypergraphZ – Hypergraphs, PageRank, Laplacians, and More in Zig

author: yamafaktory


value: iamhaseeb

value: Show HN: YourSpace – one username, a separate bio page for each thing you do

author: iamhaseeb


value: whatifbelief

value: Show HN: Turn your cat pic into a dancing cat video

author: whatifbelief


value: burhanultayyab

value: Show HN: Safety layer between AI agents and databases

author: burhanultayyab


value: andycufari

value: Show HN: The Cat Is Under Mayonnaise – Modifying LLM Behavior Without Retraining

author: andycufari


value: Show HN:Privacy-First Pdf Converter

title: Show HN:Privacy-First Pdf Converter


value: everlier

value:
I had a lot of issues with spec-driven approaches, agents are too readily producing fluff, large projects have so many specs agents start making mistakes maintaining them. There's a constant consistency tax.
In the end every spec is just a bunch of facts, so I decided to leave that and throw away everything else while making it friendlier for agentic use.
Introducing facts - skills and CLI for agents to use facts-driven development.
https://github.com/av/facts

value: Show HN: Replacing spec-driven development with just facts

author: everlier


value: hrtk

value: Show HN: Structured and Unstructured Vulnerability Database

author: hrtk


value: Show HN: Pytest plugin that classifies why your CI failed


value:
Claude Code / Codex session metadata can actually tell a story about how you work with AI coding agents.
50 days ago we posted about analyzing 1.6k Claude Code sessions from our own team. Skills were used in 4% of sessions, 26% were abandoned early, and we had no real benchmark for what good looked like.
Now across 20k+ sessions, we started looking at behavior patterns from derived session metadata: consistency, intensity, session shape, repo breadth, output, cost intensity, and model range.
Nine archetypes fell out, which we turned into playful cards.
We built a Spotify Wrapped meets trading cards experience on top of the classifier: upload your sessions and get a story about how you use AI coding agents.
The classifier runs on metadata like duration, token counts, model mix, repo count, and commit rates.
Create yours today. It’s free and open source: https://app.rudel.ai/wrapped
Repo: https://github.com/obsessiondb/rudel

value: Show HN: Rudel – Claude Code / Codex sessions reveals 9 types of AI coder


value:
I've always felt that existing Bash wasn't adapted for agents. It gives way too much freedom and not enough feedback to enrich the context after each command.
I ended up building this TypeScript-based sandboxed Bash. If we compare it to other alternatives, it is divided into two layers:
- The core, with all the Bash commands and the operator logic.
- The runtime, a pluggable part that manages code execution in the sandbox. There's a Wasm runtime available based on a Rust runtime I launched a few months ago. [1]
In practice, the core calls the runtime to execute code and get back structured information from the sandbox, including exact filesystem changes (what was created, modified or deleted) and direct feedback in stdout.
I added commonly used commands, including `python3 -c` and `node -e`, but if you find an edge case where the current commands aren't enough, let me know.
GitHub: https://github.com/capsulerun/bash
--
[1]: https://news.ycombinator.com/item?id=46871387

value: Show HN: Capsule Bash – Sandboxed Bash for Agents


value: parcse

value: Show HN: Looq, the capabilities macOS Quick Look should have shipped with

author: parcse


value: Show HN: KIP Pattern – A React architecture pattern for true encapsulation


value: raulgooo

value: Show HN: SharkAuth – Auth server for AI agent delegation

author: raulgooo


value: Here's handy tool for those wanting to find out and learn how does a SSL TLS Handshake works, including Key Exchange, Certificate Chain

value: Show HN: Visual SSL TLS Handshake Visualizer


value: kvasserman

value: Show HN: One container. 4 cores. Hybrid search. 10k concurrent users?

author: kvasserman


value: gangtao

value: Show HN: A Soccer Game Simulator Played by AI Agent

author: gangtao


value: nirvanis

value: Show HN: Vacant, a fast domain checker for you and your agent

author: nirvanis


value: hhuytho

value:
We took a recently released Bonsai 1.7B ternary model from PrismML (https://github.com/PrismML-Eng/Bonsai-demo) and ran our agentic evolution search on it for 6 hours to optimize the Metal kernels. The search was fully autonomous.
Measured against unmodified upstream llama.cpp at the same Bonsai/Q2_0 commit, same M4 Max:
- tg128: 309.82 → 442.42 t/s (+42.0%)
- pp512: 4250.32 → 4622.63 t/s (+8.8%)

value: Show HN: Bonsai 1.7B ternary model at 442T/s on M4 Max

author: hhuytho


value: rafayexalter

value:
It's free to use, just my another side project.
https://xsavr.com

value: Show HN: Twitter X Posts Archiver and Video Downloader

author: rafayexalter


value: andrewfromx

value:
Hi HN,

  I’m working on an idea called EmergingRepos (working name) a site for finding repos that are just starting to get momentum. The kind of repo I’m interested in is not brand new with 0 stars and not already famous. The sweet spot is something like:

  - small star count

  - small fork count

  - recent commits

  - more than one contributor

The idea is that this could be a win-win. Maintainers get thoughtful early attention, and readers get to discover repos where they can jump in, make a PR and get that joining an active team feel. i.e. the days of just making your own new repo and trying to get it going might be over. There’s just too many! Here are a few examples:
https://github.com/Integral-Healthcare/robin-ai-reviewer
Stats: 62 stars, 4 forks, 4 contributors
https://github.com/cpulvermacher/lgtm
Stats: 28 stars, 11 forks, 4 contributors
https://github.com/yoda-digital/mcp-gitlab-server
Stats: 48 stars, 19 forks, 4 contributors
https://github.com/tmseidel/ai-git-bot
Stats: 37 stars, 6 forks, 3 contributors
https://github.com/holgerleichsenring/agent-smith
Stats: 14 stars, 3 forks, 3 contributors
https://github.com/wshm-dev/wshm
Stats: 39 stars, 4 forks, 2 contributors
https://github.com/jonit-dev/night-watch-cli
Stats: 42 stars, 4 forks, 2 contributors
What do you think of the idea? Is this good data you don’t see anywhere else?
Thanks!

value: Show HN: EmergingRepos – finding repos that are just starting to get momentum

author: andrewfromx


value:
I named the Rouge after Ford's River Rouge complex, it took in raw materials and popped out Model A's at the other end. I've been trying to build the AI equivalent; ideas come in one end, and an MVP pops out the other.
It has a UI to manage projects and the "catalogue" of capabilities the Rouge has to build with. Building a project has two phases:
Spec involves some or all of the following, depending on how big the project is: brainstorming, competitive research, taste evaluation, specs (writes up the user stories the loop tackles one at a time), infrastructure decisions (for the foundation step), design system, legal docs, marketing copy. Each of these disciplines has its own prompt, outputs, and quality gates.
Build is then an autonomous build/evaluate/fix loop until it ships. I took inspiration from Karpathy's autoresearch for the build loop. Rouge takes one story at a time, builds it, and then tests it when built. The work is tracked on disk between these loops.
The tests are external browser QA, cross-checking specs are completed so the individual LLM doesn't judge itself, the external checks do. There's a fix loop if something breaks so it fixes itself before progressing to the next story and escalates to the user if it can't fix itself.
At the end, it uses a gstack inspired skill to do a product walk (without the specs) and give blind feedback in the way a first customer would.
What has it built?
github.com/gregario/openfleet is a fleet management software with maps, maintenance scheduling, trip management, driver management (and a different driver app) and GDPR features for tracking vehicles only in office hours. It cost $24.
github.com/gregario/construction-coordinator  is a self build management tool with supabase auth, push notifications, material dependency tracking, daily briefs, a gantt chart for upcoming work, and snag list tracking. It was 27 user stories and built overnight! It cost $49.25.
Where is the project at?
It currently invokes ClaudeCode subprocesses to do the build, it can run via API key or via your ClaudeCode subscription. The dashboard is a bit rough, the chat interface needs work to be polished. It has a big red banner in the dashboard and on the readme saying it uses --dangerously-skip-permissions (I'm working through changing this to --allowed-tools). There's cost management, but its not bulletproof... It could technically blow up your credits or delete your file system.
Something that is not there yet is the Rouge should be able to build new capabilities for the Rouge. If a project spec says the project needs a graphing library and that isn't a capability, it should first spin up a project to build that first. It would be really cool as an open-source project if a user could add a new capability to the Rouge for the community to use when they build something new.

value: Show HN: The Rouge is my attempt at an AI product factory


value:
my co-worker and I both ran local Claude Code terminal sessions (with local folder context and local claude settings), and then we invited them to our P2P encrypted chat room.
We asked each other some questions and laid out the goals, then let the claude code sessions hash out the details and formulate a plan for the backend and frontend to follow and execute on, while we supervise and intervene.
Has anyone tried this kind of thing? We found it to work pretty great and are pumped to improve this workflow.

value: Show HN: My coworker and I planning with our Claude Codes in the same chat room


value: Hey folks, I am the developer behind muesli - which is your one stop app for all your speech to text needs, be it voice dictation or meeting transcriptions that runs on device on your Apple Neural Engine using CoreML based STT models (Parakeet, Whisper, Cohere transcribe). Everything is open source and we are at 160 stars - au naturale - would love for folks to use it and contribute further to the development

value: Show HN: Muesli – If Granola and Wisprflow had an open source on device baby


value: tgautot

value: I was _very_ tired of going through massive log files with just vim/grep. You give logram the formats of your logs, and it will parse it into different fields that you can filter on. I also aimed to make it eaily modulable. It saves me a ton of time and I hope you can find it usefull too

value: Show HN: Logram, a filterable, modular log navigator for the terminal

author: tgautot


value: nirvsoner

value: Show HN: Writer – fast, lightweight and open source markdown editor

author: nirvsoner


value: addamh

value: I was looking for apps to track my retro game collection and everything looked.... really dated and not great. So I spent the last 5 months making my own. I've made a lot of apps for other people over the years, but this is the first one I've made for myself. iOS and Android. Let me know what you think!

value: Show HN: Retrodex – Retro game collection tracker and game encyclopedia

author: addamh


value: fzsheikdev

value: Show HN: I made a private/local audio transcriber

author: fzsheikdev


value: pzxc

value:
I used to run a flash games website (SWF files) years ago. I've made a few games of my own. I'm also an avid gamer and love to play games of all kinds.
I'm also a software engineer, and a few days ago I decided I wanted to run a games website again. So I bought the domain gamevibe.us and with the help of Claude I've been vibe-coding one video game every day since.
Happy to answer questions, take feedback, etc
Other games made in the past week (sometimes with a single AI prompt, sometimes multiple):
https://gamevibe.us/20-gem-cascade
https://gamevibe.us/19-flappy-bird
https://gamevibe.us/18-hexfield
https://gamevibe.us/17-connect-four
https://gamevibe.us/16-klondike-solitaire
https://gamevibe.us/15-emoji-mahjong
All the games have scoring to inspire a little competition, but it's mostly just to "scratch that itch" of making games again, even if AI is doing all the work.  The majority of games I don't give any details at all and the AI just fills in all the blanks.  I've spent a lot more time prompting to do the overall architecture of the site and the scoring system built around it than I do for the games themselves, Claude just "knows what to do".

value: Show HN: Vibe-coding video games with Claude (Day 21: Blackjack)

author: pzxc


value:
Agents work on many programming-related issues daily, and yet that valuable knowledge is lost on session reset. If Agent A solves an out-of-memory error in an effective way, Agent B has to fumble in the dark. We believe that the best way to achieve a future of smarter, more capable agents is to allow them to learn from each other's experiences and frustrations. Thus, we present [inerrata] - a collective and causal knowledge layer for coding agents.
Agents posting questions, answers, and knowledge reports will find their contributions distilled, enriched, and organized into causal chains, powered by our proprietary OKN (Ontological Knowledge Network) alongside graph-walking MCP search tooling. Instead of ingesting hundreds of thousands of tokens worth of context in searching Google, StackOverflow, GitHub and Medium, agents connected to [inerrata] glide along causal chains at a fraction of the cost -- a truly agent-native search methodology.
Incoming data is generalized and stripped of potentially sensitive information in a multi-step process, utilizing heavy content prompt guidance, NER, regex, and LLM-as-judge, ensuring agents do not leak API keys or personal names through file paths, etc.
We've kickstarted our knowledge base with a couple of key domains, and run a few invite-only testing periods, and now we are ready to open registration to the public. We hope to see you there!
https://inerrata.ai/

value: Show HN: [inerrata] – Collective and Causal Knowledge Layer for Coding Agents


value: cavino

value: Show HN: Claude-Find – Pull Deep Memory from Across Your Claude Code Sessions

author: cavino


value: dantesmaghi

value:
ISONQ reads PDF, DXF, DWG, and STEP files on the shop's own workstation and produces a priced quote. Nothing leaves the machine.
Three modules, one job:
2D Workspace reads the drawing. PDF and DXF/DWG in. Title block, dimensions, tolerances, threads, GD&T out.
3D Workspace enriches the geometry. STEP-native. Smart Measure, section view, feature detection.
Shop Management turns both into a quote. Auto-populated, priced, sent.
The only system that reads the PDF and the STEP side by side — title block, dimensions, tolerances, and the 3D model in one review.
Stack: Tauri, Rust end-to-end, Ollama for the LLM layer that reconciles 2D drawings against 3D geometry.
Runs on the shop's own machine. No per-drawing fees. No uploads.
Live pilot at a Colorado CNC shop, v7.0.3 in production.
Demo video on the linked page. Happy to answer questions.

value: Show HN: Isonq – PDF/DXF and STEP to shop quoting

author: dantesmaghi


value: loroloko

value: Show HN: I built a personal finance projection tool after outgrowing Excel

author: loroloko


value: pickleballcourt

value:
Hey everyone, Tom here. My co-founder Mahmood and I have spent the past few months building Rizq, a financial platform for Pakistani freelancers.
After speaking with 50+ freelancers, we saw a clear gap: despite issues like high fees and account freezes, many still rely on Payoneer because no alternative offers the same experience. Most end up juggling multiple tools just to make it work. Existing solutions focus a lot on USD accounts but we want to go a step further and offer invoice tooling and getting the details right for freelancers.
Rizq aims to simplify everything into one platform with lower fees.
What we’re building:
- Virtual USD accounts
- Payment links for clients
- Virtual cards
- P2P transfers
- Access to global stocks & ETFs
We’re still early, but the waitlist is live. If you have any feedback would love to hear it.
Join here: https://www.rizqfinance.com/
Big thanks to everyone who shared feedback on reddit, it helped shape Rizq.

value: Show HN: USD Accounts for Pakistani Freelancers

author: pickleballcourt


value: egberjustin

value: A completely automated news agency, eliminate human selection bias.

value: Show HN: NoReporter – AI-only newsroom, $1/year

author: egberjustin


value: walnutgeek

value:
Author is here. Still a lot of WIP. In v0.0.5, Web UI  is running, Schedule/Cache/APIs are working as well.
Built on Tornado, MIT licensed, Python 3.11+
`pip install woodglue`
Funny but most exiting thing about this release for me is byproduct skill that was vide coded this morning:
https://github.com/walnutgeek/woodglue/blob/main/.claude/ski...
Simple prompt to review ui functionality and result is:
https://walnutgeek.github.io/woodglue/decks/
Feedback pls.
Thanks,
-sk

value: Show HN: Woodglue - Self-documenting API/Data async server

author: walnutgeek


value: anastasery

value: Show HN: Manhwa, Manga, Anime tracker/catalogue – Manishelf

author: anastasery


value: Show HN: HeatSpectra: A realtime 3D heat transfer simulator


value: mahendraroopa

value: Hi all, we want to give the same harness that enables Codex devs to be 10x to everyone.  Spinal does the usual code review (Slop detection, duplicate code, architectural bloat etc).  It writes dynamic tests based on user-behavior analysing logs, metrics, historic incidents etc and runs it against the PR.

value: Show HN: Spinal – Prod aware code review and validation

author: mahendraroopa


value: anticensor

value: Show HN: Systemd-Recalld

author: anticensor

title: Show HN: Systemd-Recalld


value: ndhandala

value: Show HN: Genosyn – Run Autonomous Companies

author: ndhandala


value: hassanyassine

value: Show HN: I built a Slack-native World Cup prediction pool for distributed teams

author: hassanyassine


value: Foxboron

value: Show HN: SSH-TPM-agent · Release v0.9.0

author: Foxboron

title: Show HN: SSH-TPM-agent · Release v0.9.0


value: sauercrowd

value:
I’ve spent the past 10 years working on AI in finance, with much of that time focused on building evaluation systems for production environments.
As agents become more widely adopted, more software engineering and product people have start building them. But I’ve noticed that many teams are not yet fluent in systematic evaluation, or in the processes needed to keep agent quality high over time.
For large organizations, that gap is rarely the bottleneck due to dedicated teams. But after speaking with a number of startups, it became clear that building strong, up-to-date evals is much harder in a fast startup, especially when the team does not have a data science background.
So I tried to condense as much of my experience as possible into a Claude Skill: a practical starting point for evaluating your agent.
The idea is simple: tell Claude you need evals, and it will set up a solid baseline directly in your codebase - that's it! The evals will follow patterns I've seen many times before, and will get you a summary of what your agent does well and what it doesnt.
Looking forward to your feedback!

value: Show HN: Agent-evals – Claude skill to build your own evals

author: sauercrowd


value: Mrakermo

value:
operator23 lets you describe a workflow in plain English and run it across your stack.
hubspot, apollo, monday, slack, google drive, others. no builder,
no if-then config.
built for 5-15 person teams. founder doing inbound triage, one ops person juggling six tools, work that should be automated keeps getting deferred because nobody has two hours to wire up zapier.
so it stays manual until someone gets hired six months later to "fix the systems" and inherits a mess.
talked to a bunch of founders and early ops hires. three things came up every time.
setup complexity. people aren't afraid of automation.
they're afraid of two hours of field mappings only to have something silently misroute.
debugging. when something breaks there's no explanation. you find out three weeks later when a customer asks why they were never followed up with. one bad experience and you're
back in spreadsheets for a year.
no trust without control. everyone wanted a review step before the system acts on its own. not forever, but until it has proven itself.
operator23 addresses all three. plain English in, setup is minutes. readable logs of every step, debugging is obvious. staged autonomy, everything starts in review and graduates as it proves itself.
when something fails the agent reads its own error and retries instead of dying silently.
the bet: the next wave of ops tools won't be configured, they'll be described.
for founders and ops hires who've lived this, does this match what you've seen, and is there a fourth problem we're missing?

value: Show HN: Plain English automation that fixes itself when it breaks

author: Mrakermo


value: AkaiNa

value: Show HN: NeuralScript – A pure-Rust AOT compiler

author: AkaiNa


value: omranjamal

value:
I was unhappy with multi-line text input libraries for React Ink so I decided to make one.
Full support for:
- Pasting multi-line text
- Opt + [Arrow Keys] to jump between words
- New Line via Shift + Enter / Ctrl + Enter / Opt + Enter
- Tab is a callback
- Syntax Highlighting via Regex (with JS function based overrides)
- Support for unicode and emojis
- Callbacks for arrow keys on boundaries (line line, or last character)
- Viewport & Virtualization
- Customizable line prefix (to support things like line numbers)
Would love feedback.

value: Show HN: Full-featured CLI textarea component for React Ink

author: omranjamal


value: brunokiafuka

value: GitHub notifications kinda suck, so I built a menu bar app that shows what actually needs your attention. You define custom filters and the menu bar badge counts only those.<p>Give it a try — all feedback welcome, and feel free to contribute with ideas too.

value: Show HN: Gitbar – A menu bar app for GitHub PRs and issues

author: brunokiafuka


value: averyintel

value:
Allo runs an online bookstore at byallo.com. His mission is "Make the world read more." His objective is to sell as many books as possible.
He has six bookshelves, can only change his book selection every 24 hours, and has to blog about every decision he makes. That's it. No specific workflow - we genuinely don't know how he'll solve it.
Under the hood: byallo.com is a small repo with a simple UI sitting on top of Momental (momentalos.com) — an agent harness with a self-maintaining context graph built specifically for how product teams work. We set his mission and objective, connected his data sources (Google Analytics, GitHub, email), and the system handles the rest. A team of custom-built cloud agents (running on Claude, Grok, or Gemini depending on the task) form their own strategy, decide what to build and ship, update their own plan based on what's working, and write every decision and learning back into the context graph so the next agent that picks up the work has full context.
A context curator agent runs conflict detection across new information, decays stale context, and promotes recurring decisions into team-wide principles that constrain future work. When an agent needs help, it assigns the task to a human teammate, who connects to the same workspace over MCP and gets the full task context with description, related decisions, prior attempts etc injected at session start.
This weekend Allo fixed some bugs, added more tracking in GA, made his first sale, and diagnosed his real problem - nobody knows the store exists. He wrote about all of this in his blog. He's working on distribution now (and I figured I'd help him out).
If you have any feedback, he also has his own phone number - just click the phone in the store :)
Whatever you share with him feeds the decision he'll make next!

value: Show HN: ByAllo – the online bookstore that runs itself

author: averyintel


value: xcanchal

value:
I have been working on a tool that allows digital products involving users on their products by gathering feedback and communicating progress on features and fixes.
Simple to understand and use, and considering new and interesting features for the near future.
Create your board for free (for limited time)

value: Show HN: A customer feedback and public roadmap for digital products

author: xcanchal


value:
Some years ago I wrote a mildly popular article called 'The Elements of Product Design' which broke down modern product design into 7 distinct layers. It was influenced heavily by Jesse James Garret's Elements of UX, hence the name similarity.
The framework seems to help remind designers and their teams that there's more to design than just the visuals: structure and flow, concept modelling, product strategy, domain understanding, user needs.
What's lacking from the article are concrete steps how you might apply the thinking, which was always a bit hard because it's so situational. But with AI, I felt suspected I could give it examples and guidance about each layer and use the LLM's creativity to fill in the blanks.
The result is a set of skills, one per layer, plus an 'Orient' skill to help kick things off.
The idea is to use it as a coach, a sparring partner, or if you're delegating design to AI, to tell it to use the skills to ground the design in the necessary deeper work.
It works quote well in Claude Code, asking it to write to Markdown files, which I view in VS Code with the 'Markdown Preview Mermaid Support' extension.
I tried asking with Figma MCP asking it to diagram in FigJam but the labels overlapped and it wasn't readable. ;( That would be a good next step so you can take over and edit the diagrams more easily, if anyone has some ideas.

value: Show HN: Layers – AI skills for deep product design


value: haifeng

value:
SMILE Serve is a production-ready inference server built on [Quarkus](https://quarkus.io/)
that brings together three complementary inference capabilities on the JVM:

  - **Classic ML**: `/api/v1/models` for serialized SMILE models (`.sml`)
  - **ONNX Runtime**: `/api/v1/onnx` for any model in the ONNX open format (`.onnx`)
  - **LLM Chat**: `/api/v1/chat` for Llama 3 chat completions

A React-based web UI is bundled and served from the same process.

value: Show HN: Smile-Serve – Inference Server for ML, ONNX, and LLM

author: haifeng


value: huxingyi

value:
Dust3D 1.0 is finally released — about 10 years after the first commit in December 2016.
I posted a preview version here in April 2018 and a beta in December 2018. Back then I thought 1.0 was just around the corner, but I kept adding new features that I could barely find time to finish as a weekend side project. I finally got it done with GitHub Copilot, using models including Claude Opus 4.6, Raptor mini (Preview), Claude Haiku 4.5, and Claude Sonnet 4.6.
It's a cross-platform low-poly 3D modeling app focused on making it fast to create game-ready characters and objects from scratch.
Source and downloads: https://dust3d.org
Video overview: https://youtu.be/9KZi9LRs3Iw

value: Show HN: Dust3D 1.0 – low-poly 3D modeling tool (10 years in the making)

author: huxingyi


value: Parkado

value:
Hi HN,
I'm Roland, and for the past few weeks, I've been building AllBSides — a directory of every BSides conference talk uploaded to YouTube. As of today, 8,643 talks from 5,927 speakers across 227 chapters in 68 countries. Combined runtime is 280 days. The transcripts come to about 60 million words.
The archive came together in stages:
1. Manually map every BSides chapter's YouTube channel
2. Pull every video and transcript from Supabase
3. Run each transcript through Haiku for tag extraction (tools, topics, difficulty, team, talk style, research method, and much more)
4. Run results through Sonnet for categorization and dedup
5. Final pass goes through Opus for verification
6. Do a manual verification - at one time, the pipeline showed over 16k AI suggestions for manual verification. Today, most are resolved.
Total LLM cost so far: about €200. The whole pipeline is rebuildable from scratch.
Each talk gets its own page with embedded video, full transcript, speakers, tags, and "related talks." Each tool/framework/protocol/standard mentioned across the corpus gets its own page (3,968 distinct technologies tracked).
Some interesting facts I gathered while building it:
-(A) The site is currently 94% bot traffic. Of that, about 80,000 hits/month are AI training crawlers (ClaudeBot, GPTBot, meta-externalagent). Within 7 days of the talks archive going live, all major AI labs had ingested the entire corpus. The discovery cascade was startling to watch in real time.
-(B) The taxonomy work was the hardest part. Distinguishing "tools" from "frameworks" from "protocols" from "concepts" sounds easy until you have 5,000 ambiguous extracted entities. The 3-tier LLM pipeline helped a lot — Haiku alone was too noisy, Opus alone was too expensive.
-(C) Top tools mentioned: Wireshark (343), PowerShell (342), Metasploit (332), Burp Suite (322), GitHub (296), VirusTotal (273), Docker (253), Splunk (251), Nmap (247), MITRE ATT&CK (237). The list reflects what BSides talks actually discuss, not what vendors curate.
-(D) May is the peak BSides month — 29 events, 17% of all events with dates.
-(E) The top 1% of talks (86 videos by view count) account for 51% of all viewership. The other 99% are deeply niche, often the only video record of a specific technique.
The stack is intentionally lean: Go, SQLite, vanilla JavaScript, BunnyCDN. Static rendering at build time. No frameworks, no client-side state. The site costs about €50/month to run.
The data behind this post and much more can be found in the site footer, under the link "stats".
Happy to answer questions about the data pipeline, the taxonomy decisions, or what the AI crawler patterns looked like as the archive went live. Feedback on what to build next is genuinely welcome — I'm a solo dev figuring this out as I go.
— Roland (parkado)

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Think of each concept as sitting in a cloud of related words and ideas. The atlas keeps only the load-bearing connections — the simpler ideas you'd need to understand it first. Follow those edges down and every concept lands on one of four foundations: Space, Time, Energy, Pattern. The depth of that chain gives you a rough sense of where the concept sits in the emergent hierarchy.
Search here : https://emergencemachine.com/atlas/search
You can also compare two concept's graph, see what they have in common- https://emergencemachine.com/atlas/distance
Each Concept can also be discussed and debated with site's AI- Prometheus.
Tool: Python (asyncpg + custom DAG traversal) walked the "concepts" prerequisite graph down to its four foundation roots, then Graphviz (dot engine) rendered the SVG. PostgreSQL backs the live atlas; the chain image is built deterministically.
Read more: https://emergencemachine.com/language-emergent-tool/

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