value:
Hi HN. I built Lex in an attempt to overcome the same hurdle that I always faced as a language learner: lots of vocabulary drilling, and no place to read or write. I'd find myself having to use a flashcard app, reading on my own, and then trying to find a space to write and none of them could communicate with each other.
Lex combines the three to get 34 languages:
Reading: leveled texts (CEFR A1-C2) or AI generated texts on a topic of your choice. Tap on any word when you read it to add it directly to your vocabulary!
Writing: generate and/or compose an essay on a custom essay prompt, and receive AI feedback on structure, errors and an estimated essay level.
Vocab: words that you save are used for spaced-repetition exercises (Flashcards, Match pairs, Fill-in Blanks, etc.).
For the generation and essay feedback, I used OpenAI, and for front and back I used Next.js + Postgres.
But it's early and there is much I have yet to do. Here are two things I'm totally unsure about and would love to hear HN's thoughts on:
Quality of the AI essay feedback depends a lot on language, tuning prompts per language is harder than I thought.
Unit economics: each essay check/retext creation is an API call and people want a language app to be cheap. Not quite sure of the sustainable pricing.
I am polyglot and speak a few languages myself, so this is very much a tool that I created out of my own frustration. This is as much feedback as they can get, and every "this already exists, it's called X" is welcome.

value: Show HN: Lex – learn languages by reading and writing, not just flashcards


value: Show HN: Cachet – A drop-in semantic cache for LLM APIs, 100% local, in Rust


value: oshuhrat

value: Show HN: Navigating research by changing problem representations (RLHF example)

author: oshuhrat


value: Fotis-Karmpas

value: Show HN: How I hit 10k users on my creator platform using Claude as my coworker

author: Fotis-Karmpas


value: bhwwdly

value: Show HN: Fail-safe API to minimize C/C++ application crash when generating logs

author: bhwwdly


value: puritanne

value: Show HN: Vedana – open-source RAG over a knowledge graph

author: puritanne


value: halfradaition

value: Show HN: I built an online patch backup tool for vintage 80s synths

author: halfradaition


value: vitbokisch

value: Show HN: Styler – CSS-in-JS rebuilt around React 19 streaming SSR (5KB, 0 deps)

author: vitbokisch


value: Show HN: Anonymous Confessions over SSH

title: Show HN: Anonymous Confessions over SSH


value: askrzypczak

value: Show HN: One global text input, shared by everyone

author: askrzypczak


value:
I got tired of babysitting context between my own AI sessions and having to copy-paste summaries or update MD files across sessions for different purposes, so I built this.
Every AI session starts with a blank slate. Whatever you built up in the
last session, like architecture decisions, debugging history, the reasoning behind every choice, is all gone. So youu re-explain from scratch.
Khala fixes the handoff at the source. Instead of writing a summary file,
the sending session packages its full context and delivers it directly to a named inbox. The receiving session picks up exactly where the sender left off. Works across any MCP-compatible tool (Claude, Cursor, Codex, ChatGPT, etc.).
Demo: https://youtube.com/shorts/i8xgbrY_Y0g
Invite-only for now, drop a comment if you want to try it free this month and I'll send you a code.

value: Show HN: Khala – let your AI sessions talk to each other, across any LLM


value: Dheerajiitr

value: Show HN: Parlel – run Redis, Postgres, S3 and SaaS APIs in ~1 MB each

author: Dheerajiitr


value:
I built an experiment that uses an overfitted transformer and arithmetic coding to compress individual files.
Instead of training the model to generalize, I train a 900KB transformer to memorize a single file and predict the next byte. Those predictions are fed into an arithmetic coder to produce the compressed output.
On a 100MB NYC taxi CSV, it compresses to about 7MB (~0.5 bits/byte). On a 100MB slice of enwik9, it compresses to about 21MB (~1.68 bits/byte).
It's pretty slow right now (roughly 20–30 minutes of training and 45 minutes each for compression and decompression on my AMD 7800XT).
Checkout the repo - https://github.com/samyak112/pym-particles

value: Show HN: Overfitted a 900KB Transformer to Compress a 100MB CSV into 7MB


value: Show HN: Phrony – declare an agent in a manifest, run it, trace every call


value: biduskamil

value:
Hi, I'm Kamil and I'm a founder of Applied AI agency in Warsaw, Poland.
There is a lot of projects in AI visibility/GEO/AI SEO space. I wanted to see what is really possible in terms of how and when ChatGPT, Gemini, Perplexity, etc. reference certain brands.
Turns out it's all pretty much about reverse engineering the prompts users write because the platforms don't share any analytics as of today.
So I've built a tool that tries to estimate those prompts.
The approach is:
1. Crawl a website and extract its key topics.
2. Analyze organic search keywords and phrases.
3. Find semantically similar conversations in the WildChat dataset (~1M ChatGPT conversations).
4. Generate realistic prompts that users are likely to ask when looking for products or recommendations in that category.
The tool is open source and available on github: https://github.com/syntropicsignal-ai/ai-visibility-audit
I target the tool primarily for e-commerce stores as this seems to be a gap in the market.
I'm curious to hear what you think about this.

value: Show HN: Open-source tool for reverse engineering ChatGPT queries about brands

author: biduskamil


value: rabbiveesh

value:
Built in Rust on top of ts-parser-perl and tower-lsp. Witness bags with graph traversal (value edges, call flow edges).
How it works:
- builds witnesses and scopes by walking the parse tree. Fully known values get concrete types, otherwise they get an edge pointing to the last witness we have for this symbol
- a worklist runs to a fixed point: reducers fold each symbol's witnesses and chase edges. At this point, the witness bag is complete and ready to use
- all downstream features are queries into the bag, following along the graph edges that are relevant
Extensible - you can bring your own Rhai plugin to shortcut common patterns (exporters and OOP frameworks), or even add new types of definitions (worker tasks and web routes).
The build/query timing distinction with edges lets you late bind types, which is duly similar to how Perl itself works: `Foo->new` isn't known if it's the constructor for Foo or calling a method on the sub name `Foo`; late binding lets you make that call after the worklist folds.

value: Show HN: perl-lsp – annotation free static analysis for Perl

author: rabbiveesh


value: eyalgoren

value:
I started working on something similar to OpenClaw on Dec. But I'm not the type of guy that is local only. I wanted flexibility. So I built it in a way that lets you decide where you want to run the session. Local or remote. It was an app that included some unnecessary heavy frontend, and I got distracted taking care of things no one cares about.
We had a trip to Japan planned and a week before our departure I had the unhealthy hope that it'll all work before we leave, and I can have my own autonomous company making me money as we travel. That week was a grind and, long story short - it didn't work, and I spent a few days somewhat disappointed, instead of enjoying Tokyo. But Tokyo is Tokyo and two kids don't really give you that feeling to stick around. I let it be.
Then, we fell in love with Japan. Mostly the less known parts. 5 weeks passed quickly and, the flight date was getting closer.
Out of nowhere, I realized that the thing that was the most buggy was the session. It wasn't just one session. I had 3 types of sessions, it all went to diff compute, and I just had a strong intuition that I have to get the session right..
I decided to focus on solid, reliable sessions. I just wanted the thing to work.
This is where Cerver came to be. It's an effort to create a reliable session infrastructure.
A session is basically a (1) transcript (2) compute (3 harness and (4) model
So Cerver is an API, that lets you control 1, 2 3 and 4 in a single call.
You can swap all. Swap Compute, swap harness. let them consult each other, chain them, make them run in parallel local or remote.
It can be powerful, and also simple to use. Sometimes Claude is just not delivering and I say to Claude - "hi, please consult codex about this." and it really works. They complete each other.
Also supports Gemma, Ollama, GLM.
Would love your feedback.
Eyal
cerver.ai

value: Show HN: Cerver is infra for AI sessions

author: eyalgoren


value: keepamovin

value: Show HN: FuckUI - AI hates your UI. Feed it the web text first

author: keepamovin


value: Show HN: Open-geo – Skill for Claude Code which shows your AI Visibility


value: Show HN: Synergy Check – paste two companies, get "should you take the meeting?"


value: Show HN: Route LLM prompts to cheapest capable model – pydantic-AI and litellm


value: covibes

value: Show HN: AGI will not come from better models, it will come from meta harness

author: covibes


value: ethanhq

value: Show HN: Cc-fleet – run other LLMs as Claude Code workers, your sub drives

author: ethanhq


value: mohitgangrade

value: Show HN: Screen recording app with automatic zoom, subtitles, and more (macOS)

author: mohitgangrade


value: mirkopira

value: Show HN: skillhealth - find Claude Code skills you never use and what they cost

author: mirkopira


value: Jxcques

value: Show HN: Free tools to audit a landing page and check if AI recommends you

author: Jxcques


value: Show HN: Legant gives AI agents bounded authority to act on your behalf


value: arzt

value:
I have had a few conversations in the past year with non-technical folks (traditional finance types, consultants) who asked for a simple explainer on how GPTs work. These people generally have the horsepower to grasp new concepts quickly but don't necessarily have the math background to dig in super deeply and want a stronger framework than "they predict words".
I had generally been pointing them to Karpathy, Illustrated Transformer, and other youtube content but I figured something more hands on without the math requirement might be better at explaining questions like "why matrix multiplication?".
I built this as a simple 60-70 minute interactive course with some demos I could send along to them. I've found it effective as the initial foray into understanding jargon and concepts before they graduate to Karpathy et al.
It's free, no signup required. I would love feedback on where it might lose a non-technical reader, and where someone who does this for a living thinks I have simplified past the point of being correct.
understandgpt.xyz

value: Show HN: Transformer Primitives – A visual explainer you can send to anyone

author: arzt


value: domag

value: Show HN: Saymade – AI app builder for complex software

author: domag


value: ilbert

value: Show HN: Bun-sqlgen – Type-safe raw SQL for Bun, no ORM

author: ilbert


value: DominikPeters

value:
Hi all! TikZ is a widely-used LaTeX package for drawing figures in papers. It uses commands like \draw[->] (0,0) -- (1,2); to draw lines, shapes, text, etc. Academics usually code up their figures by hand, so there is lots of twiddling around with the coordinates and recompiling until things look nice. I guess it’s a bit like SVG, but it’s more code than markup, for example it has loops with \foreach.
I built an open-source WYSIWYG TikZ editor (available for web and desktop) that allows you to edit your TikZ source code visually by dragging and resizing elements. It simultaneously shows the source code and the rendered figure, and lets you edit either one while the two views stay in sync. I’m not aware of any other editors that are simultaneously source editors and WYSIWYG (even for editing SVG or HTML), and I’m quite pleased with how well the combination works.
The way the app is implemented is by parsing the TikZ code, and at all times keeping track of the exact source location of each object. Thereby, when a user drags an element to a new position, the app can override just the numbers in the coordinate without changing anything else in the code (such as line breaks or indentation).
This approach essentially required reimplementing a large fraction of TikZ, which is the kind of task that no human would ever want to do. I think building software that doesn’t exist yet because it would be impossibly tedious to code up is one of the great new possibilities thanks to coding agents, and it’s worth brainstorming for other examples. (This app was built almost entirely by Codex.)
Implementing the app came with lots of fun side quests, including building converters from SVG / pptx / ipe to TikZ, re-implementing the LaTeX hyphenation and line-breaking algorithm to support multi-line nodes, and making a color picker that uses the red!20!black color mixing notation used in LaTeX papers.

value: Show HN: TikZ Editor – WYSIWYG editor for figures in LaTeX

author: DominikPeters


value: paulaq

value: Show HN: Find and fix issues in your AI Agents (MIT, open source)

author: paulaq


value: ata-sesli

value: Show HN: Zova – Objects, vectors and records in one SQLite file

author: ata-sesli


value: cloudkj

value:
While working on an educational exercise tinkering with local models and trying my hand at setting up agents, I went down a rabbit hole: to see how far I could build a custom agent loop using exclusively command-line building blocks and stripping out dependencies wherever possible.
It turns out you can get pretty far with pipes, text streams, append only logs, and standard command-line components - concepts pretty well aligned with classic Unix philosophy. The agent is a wrapper composed of a handful of smaller programs, which should allow for flexibly injecting various tool to inspect, filter, redirect, and audit different stages of the agent loop.
This project as it stands is a proof-of-concept, but packs enough punch with tool calling support - which in theory should make it indefinitely extensible. With that said, it does appear that the sophistication of tool calling is largely limited by the underlying model, and so far I’ve only experimented with lightweight local models (e.g. llama3.2:1b) that have modest success for tools.
Nevertheless, I wanted to share this here and am curious to see if others find it interesting enough to build upon or extend!

value: Show HN: AI Agents the Unix Way – built with bash, curl, and jq

author: cloudkj


value: undersky

value: Show HN: Local-first multi-agent harness

author: undersky


value: khimaros

value: because it registers as an accessibility service, it has access to screen contents which `adb uiautomation` doesn't.<p>i'm using this with termux + pi.dev to control my phone for routine automation tasks, but it can be configured to bind to other interfaces for control via claude over a VPN for example.

value: Show HN: Mimic: control Android from CLI or MCP with fine-grained permissions

author: khimaros


value: Show HN: I built a tool that'll show how AI agents reads your startup's website


value: amlucas

value: Show HN: Finding signal in noisy street recording over 2 weeks

author: amlucas


value: MykolaBond

value: I built a voice based AI mock interview simulator. You pick a role Frontend or Engineering Manager, etc.. level, type and the system generates the questions, scenario and conduct the interview.

value: Show HN: Building real time interview with voice agents

author: MykolaBond


value: randomstate

value:
I've been building videopython, a Python library for programmatic video editing, processing and AI workflows. Repo: https://github.com/BartWojtowicz/videopython
The core idea is that edit is just a JSON / Pydantic plan. This plan can be validated against metadata of source video(s) via a dry run, then edit plan can be executed via streaming pipeline (many thanks to ffmpeg!) without holding frames in memory.
Thanks to the JSON/Pydantic format of edit schema, you can easily expose it as tool for LLMs and let your agent-of-choice be your personal editor (MCP support included). The usual flow is first analysing videos via a number of ML models to get both visual and audio understanding of all videos. Next, based on the understanding, you build the edit plan, validate it and render the final video. There's a simple refine loop catching errors and fixing the simple mechanical ones, which allows model to converge on executable schema.
Currently, the whole ML/AI stack is deliberately local, mostly via huggingface or ollama, I didn't want to rely on external APIs and want to keep it this way. There are some more features I omitted like automatic dubbing with voice cloning so feel free to check out the repo, play around with it or give some honest thoughts/criticism!
Honest disclaimer - this started as a hobby hand-written project, but nowadays most of the code is written with help of LLMs.

value: Show HN: Videopython – local-first video processing, editing and AI workflows

author: randomstate


value: NotASithLord

value:
Hey HN. http://peerd.ai is an AI agent harness that lives entirely in your browser as a web extension. You don’t have to install a separate “AI browser”. You don’t have to bolt on or run some external process or manage a clunky mcp integration. It’s just a fully contained web extension, written in no build vanilla JS with minimal non-browser dependencies, using your own provider keys, and Apache 2.
This isn’t just a fun hack. While it has largely been a solo side project, I genuinely believe the browser and the web could be the most natural platform for AI agents to operate safely, autonomously, and most importantly without A2A middlemen (more on that in a sec). To demonstrate that point peerd doesn’t just drive browser automation. It spins up isolated sandboxes using tabs and worker instances to support various real workload types. Those include headless JS computational work, visual JS notebooks, personal client side apps, and real Linux VMs on top of wasm with full http networking.
The industry discourse over the last several months has been dominated by “which substrate is the best for ai agent sandboxes” with many competing answers focused on different models and use cases. Cloudflare is one of the most prominent examples, positioning its v8 isolate based workers as the best in class solution thanks to faster than container startup times and strong isolation guarantees.   The v8 isolate is of course the product of chromium, which runs on billions of browsers around the world for free. The browser as a whole is perhaps the most battle tested sandbox system in the entire software industry. It’s been built on 3 decades of learning from hostile content, hostile code, and hostile users. Native and cloud agents are necessarily rebuilding all or most of this posture from scratch. peerd doesn’t. It leverages everything the browser has to offer and pushes it to its functional limits, while inheriting its security baseline and isolation from the host system.
Robust sandboxing isn’t the only thing the browser offers and peerd uses. It comes with extremely powerful and underrated primitives, from webCrypto, webRTC, webAuthn, webGPU, and ~soon WebNN. Direct web access, with your real live sessions, and api calls with fetch present an alternative model to MCP integrations. The agent can write and spawn web apps right there in a tab, no hosted service necessary. Then there’s the A2A piece: peerd already has a rudimentary p2p (peerd-to-peerd?) network in place using webRTC. Today you can connect with peers on the network, add them as contacts, and share signed apps you’ve created. I’m working on extending these apps to be able to leverage the same p2p network to support decentralized web apps (dwapps), as well as facilitate true p2p A2A with no platform or middlemen.
Given this is an early part time project, this is an extremely experimental build and in a v0.x preview state. I’ve taken care to attempt to address the lethal trifecta: the main agent loops/sessions never ingest untrusted DOM code or possess low level navigation tools. It delegates those tasks to dedicated web runners with no wider tooling or secrets access that return summarized results. Both the DOM and the summarized results are bracketed as untrusted, meaning two stacked prompt injection escapes are needed. All egress goes through a central module that has a customizable deny list, and only models calls to designated allowed endpoints are possible. See more in the docs, site, and the code itself. Ultimately, use at your own risk.
Today anthropic, open router, local ollama, and even an experimental WebGPU instance of Gemma are supported.
Honest limitations: Chrome store and AMO are still pending until it can get more eyeballs and live usage. Just loading unpacked from GitHub is the easiest way to go, and as a bonus makes it easy to audit thanks to no build. Linux on wasm depends on the Cheerpx engine, which is not open source and has restrictions for commercial use. That may be a good reason to reassess it compared to alternatives, but it’s also the most performant and looks closest to implementing 64bit support.
Poke around, use it, critique it, and have fun.

value: Show HN: peerd – AI agent harness that runs entirely in your browser

author: NotASithLord


value: A WIP minimalist 'KBounce'-style N64 territory game with a cool 'Space Giraffe' psychedelic aesthetic, made using Libdragon and Tiny3D.

value: Show HN: Superbounce64, a MIPS R4300i/Nintendo 64 Homebrew Project


value: DandyLyons

value: Show HN: Treedocs: Documentation that automatically checks for staleness

author: DandyLyons


value: dima-quant

value: Show HN: Nimic – Pure Python as a systems language with AOT compilation

author: dima-quant


value: Show HN: An open MCP that gives coding agents page-cited embedded datasheets


value: aizuikmal

value:
Hello Hackernews!
I'm a solo developer in Malaysia, 20 years building CMS & infra for newsroom.
I would like to introduce to you, Publish.my.
2026; vibing a website is a norm for non-technical people, especially when maturity of AI agent today. In fact, I encorage my clients and friends to starts develop website themself when they need a corporate site, personal profile or microsite for news/issued.
But, the main hurdle is: to get them to understand the pipeline, git repository & signing up for serverless service account (Cloudflare Worker, Vercel, etc). I had this knowledge roadblocks with them. How the CI/CD pipeline interconnected is a rocket science for non-tech people.
So, this hurdle has sparked for me to create something which strightforward for newbies viber (even for me as senior engineer).
Once they've done create the site (which by default Claude Code or Codex will produce in HTML, CSS and JS), they just need to do; prompt this to their agent, "Publish this project with https://publish.my", and that's all! (but, they still need to "claim" their published site by entering email address and validate them.)
At this moment, I've done the idea validation with my clients (news media editorial team), as well as friend network (designers and senior engineers), all seem have a good feedback. Yes, I have a good domain name though - publish.my :D
So dear HN, please break it, spot any issue, or somewhere I missed especially security or UX issue, buzz me. Bluntly is fine, i try my best to fix it, and I'll credit you.
Thanks!

value: Show HN: Publish.my – Static hosting where the AI agent is the customer

author: aizuikmal


value: Rubinoslaw

value:
Hello!
I, Rubinosław from REParadoxy, I'm presenting Project Cherub - a modernized version of TempleOS, very well known operating system made by Terry A. Davis.
My vision is to create a minimalist, fully-functional and secure operating system. For home users, for bussiness users, for programmers, for artists, for everyone! Where THE USERS matter the most!
I've already changed FPS rate from 30 to 120 via modifying "KernelA.HH.Z" file, a part of the system kernel. I've changed WINMGR_FPS in that file from "(30000.0/1001)" to "(120000.0/1001)", and WINMGR_PERIOD from "(1001/30000.0)" to "(1001/120000.0)".
I've also changed the system name from "TempleOS" to "Project Cherub".
As I said - its just a very early build. But my plan is to add also 32-bit colors support, support for SATA AHCI, NVMe, Blu-ray, videos, GPU and USB. But I'll keep the 640x480 screen resolution, you know why.
Full presentation of an early build with 120 FPS, future plan, and installation showcase:
https://www.youtube.com/watch?v=NNq_4vG5Src
More information:
https://github.com/Rubinoslaw/Project-Cherub - GitHub repository (with ISO!)
https://rubinoslaw.github.io/projectcherub.github.io/ - the official website of the project

value: Show HN: Project Cherub – A Forked TempleOS. Early Build and Future Plans

author: Rubinoslaw


value: msass

value: Show HN: Blaiso Launches Today

author: msass

title: Show HN: Blaiso Launches Today


value: romz

value: Show HN: Agent skills that review user-facing agent UX from your codebase

author: romz


value: antisyzygy

value:
Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets.
I've added 393 nodes and 562 edges to capture other supply / physics constraints as well.
There's no sign up, and no pay wall, it's all free.
Please let me know what you think!

value: Show HN: The Cascade Graph – An interactive map of AI and energy constraints

author: antisyzygy


value: filippofinke

value: Show HN: SwissReach – Browser-based isochrone maps for Swiss public transport

author: filippofinke


value:
I am an embedded engineer, and tried Claude Code recently, I wanted to have the experience of Claude coding on real hardware boards, whatever the processors, whatever the compilers, whatever the toolchains.
Honestly, this is a Platform IO wrapper MCP tool with extra steps. I added reliability to it, made it rust based, and made it open source. The onboarding is quite easy as well : you just run 'pip install nff' in your favorite ide, connect your board, then run 'nff init', it detects your board, and the COM port communicating with it.
To use it then, its just basically running claude code or codex on your board : plan features, and watch them get build, tools then compile flash and debug instead of you doing the manual work. Saved me a month of work on the integration of a Texas instrument toolchain.
This works with all platformio supported boards including esp32, stm32, nRF, rp, teensy boards...
The repo shows both python and rust. I prototype on Python then migrate on Rust for speed. The binary wheel you are installing is the rust one.
Currently working on OTA capabilities, and a platform that allows this to operate and maintain your iot fleet. Releasing this next week.
What features should I build next ?

value: Show HN: Nff_ – open-source bridge between agents and 1000+ different boards


value:
I built dj-doom-panel, a Django Control Room plugin that lets you play DOOM directly inside the Django admin.
Its mostly a fun project, but it also shows that you can use the DCR framework for more than tooling.
Enjoy.
Official Site:
https://djangocontrolroom.com/panels/dj-doom-panel
Source:
https://github.com/yassi/dj-doom-panel

value: Show HN: Dj-doom-panel – Play Doom inside Django Admin


value:
Hey all, I'm Jun.
I've always struggled with my doomscrolling habit, and so I spent the last few months building an app to remove short-form, addicting content from all major social media platforms (Instagram, YouTube, X, Facebook, Linkedin, TikTok).
The app has the option to further customize (removing home feeds, stories, other subtabs, etc).
Launch Video here: https://www.youtube.com/watch?v=J7ablpEFt74

value: Show HN: Snowscroll – I removed addictive feeds from social media


value: achlyssecurity

value: Show HN: Wype – Strip GPS, timestamps, and device models from your files

author: achlyssecurity


value: uxmeekkow

value: I built a real-time World Cup 2026 bracket tracker with 495 combinatorial group stage scenarios.

value: Show HN

author: uxmeekkow

title: Show HN


value: chrislxy

value: Show HN: Get lead and research lists from one search

author: chrislxy


value: sanil

value:
Made a simple, free/open-source helper app to forward ports from remote hosts to localhost/etc. Just simple SSH under the hood, but a lot easier to deal with multiple ports & repeated connections (just toggle a switch in your menu bar).
I've been doing a lot more dev on remote servers (or on my home server over Tailscale from my laptop) since it's a lot easier leaving agents running for longer tasks or being able to swap to my phone. This just makes it easier to access those ports at localhost as if I'm developing locally. Super lightweight, fully native macOS app that basically is just a nicer interface for the underlying SSH commands.
Hope folks find it helpful!

value: Show HN: Mirrorball – easy port forwarding for remote dev hosts

author: sanil


value: artahian

value:
I built an open-source soccer arcade game clone with Modelence in 128 prompts, inspired by the 2026 World Cup and EA FC (aka FIFA). As far as I searched, there was nothing else you could play in browser, so I decided to give it a go, even though Modelence is primarily made for SaaS and not games. This is as far as I could get.
Recorded the progress, so you can see it going from just two circles to an actual 11 vs 11 players with "proper" dynamics:
https://youtu.be/RO1I3ML8iPk
Link to the live game:
https://worldcup2026.modelence.ai
GitHub repo:
https://github.com/modelence/open-soccer
Features:
- Similar controls to EA FC / FIFA on PC
- All 48 World Cup 2026 teams with their real kits and players
- All players have their actual skill scores mapped (pace, dribbling, shooting, passing, defending, physicality), so faster players actually run faster, etc.
Not sure if it's worth adding online mode, slightly bigger commitment - let me know if you'd ever play it.

value: Show HN: OpenSoccer – open-source soccer game in browser in 128 prompts

author: artahian


value: babbleprune

value: Show HN: A thinking canvas where chats, notes, and sources live together

author: babbleprune


value: impomatic

value: Here's a tiny CWS'94 standard compliant Redcode simulator I wrote in Ruby. Validated against pMARS using the top-50 programs from Koenigstuhl.

value: Show HN: CWS'94 standard Core War simulator in 105 lines of Ruby

author: impomatic


value: pigfox

value: Show HN: Market Verdict Demo video for validating business ideas before building

author: pigfox


value: firish

value:
I use Visual Studio daily, and Claude Code only ships native integration for VS Code (annoying). So, I built the Visual Studio half myself. It's an unofficial extension that speaks Claude Code's undocumented IDE protocol.
It started simple. Review Claude's edits in the real VS diff and accept or reject there, with no duplicate y/n prompt in the terminal, plus sharing the C# and C++ compiler errors with it directly.
The infra I built for the extension allowed me to add the interesting part, the VS debugger. When you're paused at a breakpoint, Claude can read the call stack and locals, and with a toggle it can drive the debugger itself: set breakpoints, step, start and stop a session, and find a bug by running the code instead of reading it.
There's a short walkthrough in the README where it catches a bug that never shows up in the output, by watching a counter fail to reset as it steps through a loop.
Would really appreciate if you take the time to check it out. Thanks!

value: Show HN: I gave Claude Code the keys to the Visual Studio debugger

author: firish


value: npgraph

value: Show HN: CUDA Profiler for Production Inference

author: npgraph


value: ianpascoe

value: Show HN: Caplets - Give your agent capabilities, not giant tool walls

author: ianpascoe


value: Show HN: Your self, in every light - a local-first MCP self model for AI agents


value: leonagano

value:
I built Made in...Everywhere, a map where founders can pin their companies and show where they're "being made".
The idea is simple:
Add your company
Add your location
See other products near you
Explore companies around the world
Current stats:
- 360+ companies
- Founders from dozens of countries
- Completely free to join
ps.: I'm fixing some products, as this data comes from another product I launched last year (FA) and some products might be off.
I'd love feedback on what would make you come back regularly?

value: Show HN: Made in Everywhere – a live map of companies and products

author: leonagano


value: manalkaff

value:
Recent hype about loop engineering made me realize that im missing one thing, and that is an agent that should be doing my job at the end of my loop: real user test
i developing a huge b2b ecommerce project codebase.  and for every new feature or fixes my job is always to test it on the browser. yuck.
so i have made some research and there is TestSprite, but its not what i really want, i cant even self host it.
so i built OpenUser. now i just engineer the loop with simple prompt: complete {feature} and use /openuser to test it.
it store user persona, struggle, checkpoints, console logs, network logs. the agent has access to all and just fix everything.
this is the real replacement of me at the end of the loop.
everything runs locally, with any coding agents and model.
try it:
https://github.com/manalkaff/openuser
npx openuser-cli

value: Show HN: OpenUser: Self-hosted user-persona tester for AI coding agents

author: manalkaff


value: puzznest

value: Show HN: A word puzzle game based on Levenshtein distance

author: puzznest


value: scozzola

value:
Hi, over the past 6+ months I have built an ecosystem (libraries, CLI, Desktop) of tools to build or use AI agents.
Depending on how close or not close you are with coding, you may lean towards the libraries (compilr-dev/agents and compilr-dev/agents-coding) to build your own AI Agents; you may use the CLI to manage your project and run your agents in the terminal when working, for example in VS Code or you may use the Desktop app (it shares the same core of the CLI but has for sure a richer UI).
What, I believe, sets the compilr-dev ecosystem apart from other similar tools is the real multi-agent nature.
Focusing on the Desktop app, it ships with pre-built specialized agents (built using the above mentioned libraries) each with tailored system prompt, skills and access to dedicated tools.
When a new project is created a team of specialized agents is added to the team roster and you can add or remove agents already at project creation time (you can even create custom agents at project creation time).
You can choose your preferred LLM provider (BYOK approach) and you can seamlessly switch provider whenever you want.
Each agent has its own context but agents within a project also share some context. They can interact with each other by inquiring (agent A can ask something to agent B) or by task handoff.
Each agent is instructed to follow a spec driven approach and the app itself encourage the user to design before building.
For example, as soon as you create a new project you can use the /design command to have the agent ask you questions to capture the project scope and details. As a result the agent will track work items in the database and will write the PRD document following your direction.
All files are visible and editable by you and by the agents.
Last but not least, special attention was given to the token consumptions: tokens = money so saving them is "paramount" (no, AI did not write this sentence, I deliberately chose to use the word "paramount").
Some techniques:
- Tools and skills definitions are loaded only when needed (like Neo loading the kung fu skills on Matrix)
- Tools output are pruned/summarized in the context after a few rounds
- Agents spawn subagents to read large files and get a summary
- and more
I just launched the compilr-dev ecosystem in beta and I hope to start having some beta testers to get some real world feedback.
Thank you.
Carmelo

value: Show HN: Compilr.dev, multi LLM AI workspace

author: scozzola


value: xytom

value: Show HN: Demo of an MCP server that gives AI agents real coding tools [video]

author: xytom


value: sscaryterry

value: Show HN: Akasia – A libre, metric-compatible alternative to Aptos

author: sscaryterry


value: overflowy

value: Show HN: Goldfish – An always-on-top Pomodoro timer for ADHD brains

author: overflowy


value: bonigv

value: Show HN: Grove – fast source insights for coding agents – via Tree-sitter

author: bonigv


value: datalocaltmp

value: Little project I put together for doing reverse engineering on WatchOS 10.6.2 by porting over the Darksword exploit.<p>Janky, panics, vibe'd, but provides something that hasn't been available since WatchOS 4 w/ JelBrekTime.

value: Show HN: Apple Watch 10.6.2 Kernel R+W with Process Dumping

author: datalocaltmp


value: alehlopeh

value: Show HN: Hallu – a web framework where an LLM hallucinates your app

author: alehlopeh


value: betweenDan

value:
Hey folks. We built SAA (Selective Auditory Attention) after trying to find ways to make a good experience with multiple robots/multiple agents. What typically ended up happening is they'd never stop talking.
This is an SDK you can put before your STT. It lets you know when your device is being spoken to or not without a wakeword. You can use it for:
-Single AI, Multi human
-Multi AI, Single human
-Multi AI, Multi human (we recommend also adding a wakeword on top for a better system)
There are two models. One that is video + audio and one that is just audio. The way it overall works is that it looks for shifts in attention patterns (body language changes, vocal patterns) to work. It's a tough problem to nail as every human being is different in how they interact with people/devices.
Let me know how it is!

value: Show HN: We Help Voice AI Handle Group Conversations

author: betweenDan


value:
We built HALO (Hierarchal Agent Loop Optimizer), an open-source tool for debugging and optimizing AI agents using their execution traces.
It’s a loop. Run your agent, feed the traces to HALO, get the report, apply the fixes, then re-run your agent.
HALO takes in OTEL compliant traces from AI agents using tracing frameworks such as Langfuse, Arize/OpenInference, or even just plain JSONL. It uses an RLM (Recursive Language Model) to more efficiently break trace analysis into smaller subproblems in order to find recurring patterns across large amounts of data and fix systemic issues that regular LLMs might typically miss.
You can also optionally provide a path to where your agent code lives to give the engine more context so it can more concretely provide useful insights.
The repo also includes a desktop app that you can run locally without having to sign up for anything or configure anything complex.
Check out the readme in the repo for more in depth information on what HALO is and how you can use it to your benefit :)

value: Show HN: RLM-based local debugger for AI agent traces


value: josu

value: Show HN: HN Who Is Hiring trends by month and category

author: josu


value: jkanalakis

value:
I have 11 apps in the Apple App store and 3 in Google Play store. Every time I submit a new app, I have to provide a privacy policy, a support URL, and some sort of product description page. Even more challenging, I had to keep track or customer reviews and submitted feedback because in the very early days of app launches, those first few reviews are the most critical and carry the most weight.
So I built Appstr just as mini platform to quickly auto-generate multiple privacy policies (based on the app-specific data collection), host feedback forms to capture user suggestions and support issues, and a monitor to keep tabs on the reviews as they come in. It also sends me daily and weekly summary updates across all of my apps and also automatically groups the feedback and complaints so that I can prioritize the fixes and new features.
I have made Appstr available to everyone because this seems like a pretty common problem that many app developers should be facing. While this could be done by stringing a lot of other solutions together (like Zendesk) the cost adds up really fast and blows past most Indie developer budgets.
Since early reviews matter, I'm turning to HN for brutally honest feedback.

value: Show HN: Appstr for Indies managing lots of apps

author: jkanalakis


value:
Hi everyone,
I got this idea for a game where, starting from a four letter word you need to go as deep as you can in your vocabulary, changing only one letter per word.
bear -> beer -> peer...
Each correct word gives you 1 point
Each incorrect word takes one life away from you, you start with 3

value: Show HN: Wordit – Change One Letter, Keep the Chain Going


value: processing

value: Show HN: Hemicycle – Visualizing US bill cosponsors by party

author: processing


value: DjNero

value:
For the last few months I have been analysing Peter Lynch’s books on stock picking and doing prompt engineering to check if AI could create useful stock analyses.
To my surprise it started making reports that allow me to understand companies much faster with well cited sources.
I hope you find it interesting and useful :)
Perter Lynch’s books I analyzed:
Learn to earn, One up on Wall Street, Beating the street

value: Show HN: I created agent skill based on Peter Lynch's books

author: DjNero


value: Show HN: I built a tool to help you find the best flight school


value: Rubinoslaw

value:
I, Rubinosław from REParadoxy, I'm presenting Project Cherub - a modernized version of TempleOS, very well known operating system made by Terry A. Davis.
My vision is to create a minimalist, fully-functional and secure operating system. For home users, for bussiness users, for programmers, for artists, for everyone! Where THE USERS matter the most!
I've already changed FPS rate from 30 to 120 via modifying "KernelA.HH.Z" file, a part of the system kernel. I've changed WINMGR_FPS in that file from "(30000.0/1001)" to "(120000.0/1001)", and WINMGR_PERIOD from "(1001/30000.0)" to "(1001/120000.0)".
I've also changed the system name from "TempleOS" to "Project Cherub".
As I said - its just a very early build. But my plan is to add also 32-bit colors support, support for SATA AHCI, NVMe, Blu-ray, videos, GPU and USB. But I'll keep the 640x480 screen resolution, you know why.
*THERE WILL BE MONTHLY PROGRESS UPDATES ON YOUTUBE AND LIVESTREAMS OF OSDEVING, SO YOU SHOULD SUBSCRIBE!
https://www.youtube.com/@REParadoxy**
Full presentation of an early build with 120 FPS, future plan, and installation showcase:
https://www.youtube.com/watch?v=NNq_4vG5Src
More information:
https://github.com/Rubinoslaw/Project-Cherub - GitHub repository (with ISO!)
https://rubinoslaw.github.io/projectcherub.github.io/ - the official website of the project

value: Show HN: Project Cherub – TempleOS Fork. Early Build ISO and Future Plans

author: Rubinoslaw


value: adchurch

value: Show HN: Optimal model routing directly in Claude, Codex and Cursor

author: adchurch


value: karthickrmchn

value:
In the past few months, I’ve been building and shipping things extremely fast. Some of the MVPs that I built for my friends were completely vibe-coded and shipped within a day to show a demo.
While it was a great experience working with AI tools, I wanted to make sure some basics were followed, decisions of what and why written down, obvious things not skipped and that it could scale.
That’s why I built persist-os. It’s a CLI that runs locally. No network required. It records the architecture decisions and standards in plain files in the repo and 'persist doctor' checks them  - it flags if anything contradicts or is missing, or if the task is done with no tests. Because the rules live in your repo files, it doesn't matter if you start a new session or the session is compacted - the standards and rules survive.
MIT licensed. Here’s how you can try it: npx persist-os@latest init

value: Show HN: Persist OS – Durable decisions for AI code

author: karthickrmchn


value: rodrigo-arenas

value: Show HN: Sklearn-genetic-opt – evolutionary optimization for scikit-learn

author: rodrigo-arenas


value: Show HN: Proctor – signed isolation bundles for AI coding-agent benchmarks


value: reidotdev

value:
What if you could get design inspiration (from HackerNews, ProductHunt, Awwwards, Mobbin) while Claude is thinking directly from the terminal?
When Claude's thinking, I'm typically doing one of these things:
1/ Scrolling LinkedIn
2/ Checking Slack
3/ Improving my agentic processes (e.g., tweaking skills)
4/ Going to Awwwards.com to look at beautiful design
But since I'm a terminal fanboy (I set a monospaced font in Slack so it looks more like a terminal), I don't want to type any URLs.
I want to browse cool resources directly from the terminal.
So, I'm building "nosh-cli"
Here's the idea:
- You send your long-ass prompt to Claude
- It starts its 5-10 thinking process
- You open another terminal tab
- You type "nosh"
- It opens a CLI app with the latest, coolest resources from HackerNews, ProductHunt, Awwwards, Mobbin
- You choose one and navigate to the relevant URL to explore more
- You get a notif that Claude is done (if notifs == on) and get back to prompting & thinking

value: Show HN: Browse design inspiration from terminal while Claude is thinking

author: reidotdev


value: inmygarage

value:
Hey all, we’ve been working on a new consumer financial account called Pool. It’s designed to be ephemeral and used for a specific purpose.
Checking accounts have turned into a junk drawer: money flies in and out and we hope for the best. That works well enough, but at some point there's money that needs to be shared or tracked for an ongoing, dedicated purpose. Shared use cases (think house funds, nanny cards, classrooms, side projects, Discord servers, and more) are especially painful. Checking accounts, spreadsheets, P2P apps, and tracking apps fall short. We wanted something more like a silverware drawer. Purpose-built compartments that everyone understands.
We wanted an account that would hold money, support multiple members with assigned roles, and offer spending rules, permissions, and cards. Customize your Pool as you like and use it for a day or a decade. It’s meant to complement, not replace, your primary account. This didn’t really exist, so we built it.
With the ongoing “agent making payments” conversation, a safe space that is customizable and not necessarily “your permanent checking account” feels especially needed right now. Would love to know your thoughts! We are 18+ and US Only for now.
Thanks.
PS - I started Braid and shut it down in 2023, and this community was especially supportive during that time. I really appreciated it.
*Pool is a financial technology company, not a bank. All banking services provided by First Internet Bank, Member FDIC. The Pool Visa® Debit Card is issued by First Internet Bank, pursuant to a license from Visa U.S.A. Inc.

value: Show HN: Pool – A lightweight financial account with members and rules

author: inmygarage


value: SahiAK

value: Plain-English explanations of ~821 US patents — slide-to-unlock, CRISPR, nylon, RSA encryption, and more. Every page has /json and /md endpoints for structured data. Open data at patentbrief.org/data.

value: Show HN: I explained 821 US patents in plain English, with JSON/Markdown

author: SahiAK


value: Show HN: I built a tool that makes used electronics data intake 10x faster [video]


value: Show HN: ReflexConv2d – 57% less blur in image reconstruction


value: zxangan

value: Show HN: Recitey, a local Whisper dictation app for Windows (no word caps)

author: zxangan


value: thunderdrag

value: Show HN: LinkedIn Unfiltered: Translate corpo to human

author: thunderdrag


value: Philosopheril

value:
Yochai is a Socratic AI "chevruta" or learning guide, powered by a foundational library of 1,000+ primary sources from the Jewish canon, (and a knowledge graph of 2.5M entities, and 16M entity relationships, 375,000 searchable passages, and 17,000 canonical concepts that we developed around our own ontology). Yochai, uniquely, takes you directly to the sources and pushes you to discover and articulate your own insights about them.
we open sourced our knowledge graph so others can build on our foundation
https://github.com/Lightning-Learning-Studios/yochai-kg

value: Show HN: AI Torah Chervuta

author: Philosopheril

title: Show HN: AI Torah Chervuta


value: shaamilkarim

value: Show HN: Foundation models that predict patient response in clinical trials

author: shaamilkarim


value: allenleee

value: Show HN: AirPosture – Open-Source Posture Coach Using AirPods

author: allenleee


value: bemdoomaor

value: So far current print on demand platforms available in Nigeria focus on businesses. I'm building Kraemod for individuals who casually want to design and purchase a product and creators who want to make money off their design.<p>I'm still building the MVP lol, but I will appreciate if you guys can help me check it out and give me brutal feedback as part of validating my idea. Thank you

value: Show HN: I'm building the best POD in Nigeria

author: bemdoomaor


value: MNawareg

value: Show HN: Zahosts Health – An open-source WHM plugin to catch /24 brute-force

author: MNawareg


value: kedimuzafer

value: Show HN: ReplyVoice <> global floating button for Android voice dictation

author: kedimuzafer


value: Show HN: Pencil – Obsidian plugin to write, draw, sync


value: daniban

value: AnswerJournal is a simple MCP server (only 7 tools) to save and share AI answers. Answers can be public or private and live on your own personal feed.<p>Connect to an MCP compatible AI and just say "save that to my AnswerJournal" during any conversation.

value: Show HN: AnswerJournal – An MCP server to save and share AI answers

author: daniban


value: rduffyuk

value: Show HN: Budget Control in Claude Code Harness with Codex Handoff

author: rduffyuk


value:
Hey HN, I'm Matt. For the past couple of weeks, I've been hacking on Freestyle. It's an open source voice dictation app.
I was an avid Wispr Flow user before but didn't like how it was cloud only. Every thought sent to their servers for transcription.
I've been working on an alternative that lets you run models locally, like Whisper, Qwen, Sensevoice. That way thoughts never leave your device. Would really appreciate any feedback!!

value: Show HN: Freestyle – Wispr Flow Alternative that respects privacy


value: kunle

value: Show HN: An eligibility agent focused on claims denials

author: kunle


value: Startups are struggling to find enough AI-native talent, but it definitely exists out there.

value: Show HN: Cruit.dev – Get hired at a startup based on your coding agent skills


value: Show HN: Y – A malleable coding-agent desktop app built with Electron


value: hollowlimb

value: I still can't believe it!

value: Show HN: Our indie game trailer is featured on IGN's GameTrailers Wow [video]

author: hollowlimb

story_text: I still can't believe it!


value:
Most RAG pipelines ship raw, sensitive documents over the wire to cloud services just to get them parsed, scrubbed of PII, chunked, and vectorized.
BitVanes is a zero-trust, local-first ETL engine designed to solve this. It’s written in Rust, spits out Apache Arrow RecordBatches, and compiles to both a native CLI and WebAssembly so you can run the entire pipeline directly in a browser sandbox. I've got the wasm version at the posted url.  Core and cli are on github.
I'd love to get your thoughts on the architecture, particularly around using Arrow (it's my first time using AA, I'm coming from capnp), or the Rust-to-JS design for pdfs to keep the wasm package reasonable.
I'd like to crates the package once I've had some people kick the tires and I get it ironed out.

value: Show HN: BitVanes – A zero-trust RAG pipeline engine in Rust, WASM, and Arrow


value: danielbedrood

value:
Hi everyone!
tl;dr At work, I needed to find every repository that uses deprecated Node 16 and make a batch update. This process was exhausting, I had to find them, make a change and a PR for 30 repositories, and then follow up with the pipeline to make sure it's green.
I built an MCP server that indexes all the repos, lets you query repositories, makes batch PRs, and gives you a summary of the workflow runs.
## Here's what it does:
1. Indexing, which happens in 2 forms:
- Codebase level: runs an agent CLI (with proper context) over all repos to extract what each one does, how they relate, and what the system looks like as a whole.
- Repo level: Having the codebase context, it extracts logical info of each repo, and also the libraries, dependencies, etc for lexical search
2. Search, also in 2 forms:
- Natural language: where it answers search queries with respect to the codebase and targeted repository context
- Structured search: where it returns the result based on actual dependencies (eg "find me repositories that are written with Python, have requirements.txt, and are using FastAPI)
3. Batch change:
Simply prompt "find my Python repositories and update library X from vY to vZ"; This will search and find the affected repos, clone them, run a CLI agent like CC on each with the context we already persisted, create and prepare PRs, and give you a report of the results.
## Tech stack
`mongodb` To store the repository tree, dependencies, and workflows
`redis` To store the user's session to track the ongoing batch job
`claude-cli/Devin` Used as the main engine
`docker-compose` to build
`traefik` for routing
I would appreciate your feedback and thoughts on this
Demo video: https://infraas.ai/
PS: I reviewed all the code, so if it looks like slop, that's me ^^

value: Show HN: Keep all microservices consistent and make batch changes

author: danielbedrood


value: areimann

value: Spent a lot of engineering time to make this possible: Daily updated fund holdings for more than 2200 funds, growing daily.

value: Show HN: Daily ETF holdings for 2,200+ ETFs as one API

author: areimann


value: sakuraiben

value: Show HN: Reachpad – open-source .md sharing platform for companies and agents

author: sakuraiben


value: carsonye

value: Inspired by Japanese creator konmari.<p>The visual result is stunning, but the technique itself is not that mysterious. What's truly scarce are aesthetic and artistic tuning.

value: Show HN: Interactive and realistic water ripple physics

author: carsonye


value: achayala

value: Hello, I wanted to share this small cli tool I made for run Procfile apps (Rails apps in my case). This is an alternative to Foreman or overmind but in TUI version. Take a look, any feedback is welcome. Thanks.

value: Show HN: Procman, a TUI for run Procfile based app locally

author: achayala


value: hosolmaz

value: Show HN: A leaderboard of most popular open-weight LLMs

author: hosolmaz


value: VeilusDigital

value: Show HN: Source-available post-quantum messenger – crypto core on GitHub

author: VeilusDigital


value: modulusme

value: Show HN: Built this to grow my SaaS's social media channels

author: modulusme


value: gokulmc

value: Show HN: Fork.ai – branch any AI answer into a mind map instead of a chat log

author: gokulmc


value: shipfastai

value: Show HN: Deploy to Vercel, Netlify, Railway, Render, Cloudflare in 1 Command

author: shipfastai


value: lightyoruichi

value: Show HN: Tapegif – Generate terminal GIFs in seconds

author: lightyoruichi


value: Show HN: Memory layer for Claude Code(+10.2 pts on SWE-bench Verified benchmark)


value: organize and document command line tools for ease of discovery

value: Show HN: dk

title: Show HN: dk


value: franze

value: SHOW HN: RainBreak App Mac – The AI doesn't need a break. But you do

author: franze


value: We just posted results to the DataAgentBench leaderboard scoring 61.37% with GLM 5.2. Please check it out and do share your feedback

value: Show HN: SQL MCP Server – 61.37% on DataAgentBench with GLM-5.2


value: CFBL

value: Show HN: Hikaru Labs – A browser-based image toolbox

author: CFBL


value: Rubinoslaw

value:
I, Rubinosław from REParadoxy, I'm presenting Project Cherub - a modernized version of TempleOS, very well known operating system made by Terry A. Davis.
My vision is to create a minimalist, fully-functional and secure operating system. For home users, for bussiness users, for programmers, for artists, for everyone! Where THE USERS matter the most!
I've already changed FPS rate from 30 to 120 via modifying "KernelA.HH.Z" file, a part of the system kernel. I've changed WINMGR_FPS in that file from "(30000.0/1001)" to "(120000.0/1001)", and WINMGR_PERIOD from "(1001/30000.0)" to "(1001/120000.0)".
I've also changed the system name from "TempleOS" to "Project Cherub".
As I said - its just a very early build. But my plan is to add also 32-bit colors support, support for SATA AHCI, NVMe, Blu-ray, videos, GPU and USB. But I'll keep the 640x480 screen resolution, you know why.
THERE WILL BE MONTHLY PROGRESS UPDATES ON YOUTUBE AND LIVESTREAMS OF OSDEVING, SO YOU SHOULD SUBSCRIBE! https://www.youtube.com/@REParadoxy*
Full presentation of an early build with 120 FPS, future plan, and installation showcase: https://www.youtube.com/watch?v=NNq_4vG5Src
More information: https://github.com/Rubinoslaw/Project-Cherub - GitHub repository (with ISO!)
https://rubinoslaw.github.io/projectcherub.github.io/ - the official website of the project

value: Show HN: Project Cherub – New TempleOS Fork. Early Build ISO and Future Plans

author: Rubinoslaw


value: travel-insider

value:
I love learning languages (I’m dedicated to my Duolingo streak) but I feel like I never really learn the local slang or provocative language on the apps, and I don’t want to be scrambling to make flashcards during movies or TV shows.
So I built this! Real Talk Tutor
Basically I want to see if people actually like it before I officially make it an app (also not sure about content rules on the App Store).

value: Show HN: Language App to Learn Actual Local Slang/Dirty Talk

author: travel-insider


value: ufoym

value: Show HN: A minimal and beautiful card component built with pure CSS

author: ufoym


value: ddecoene

value: Show HN: WebBase-III – dBASE III rebuilt in the browser with its own interpreter

author: ddecoene


value: lionkor

value:
Hi HN,
Here's a little website for a hobby project I have, and maybe the AI use disclaimer on it is useful to others.
I believe it's a fundamental duty of open source maintainers to disclose the extent of their AI use on a per-project basis.
The usual discussions here devolve into "this is vibe coded" for tools which may or may not be, and it's not great. I greatly dislike vibe-coding, but I believe in responsible tool use, such as responsible use of AI.
The linked page is CC0, feel free to copy/take inspiration from, and do whatever you like. Only link back to the original if you want to.

value: Show HN: AI Use Disclaimer

author: lionkor

title: Show HN: AI Use Disclaimer


value: borysj

value:
Vim has tons of small, ingenious features and is capable of many smart hacks. Most of them might be relevant... once in a while. But „once in a while” means that when the time comes I won't remember they are available, or how to actually use them.
Remembering Clippy[0], I have written a snippet for .vimrc that gives me a random advice (hint / feature / hack) when starting vim. It is not certain that I will need it here and now, but at least I will refresh my memory, and maybe I will be tempted to try it and internalize it during the session.
Many (at least half) of the hints are taken from Drew Neil's wonderful "Practical Vim". Highly recommended!
The hints are written in a terse manner. If you are new to vim, few of them will enlighten you on the spot. But the others might still pique your curiosity. Just explore further using :help... or read "Practical Vim".
The idea is that eventually you will curate `vim_hints.txt` on your own, adapting it to your needs (and your memory).
[0] https://en.wikipedia.org/wiki/Office_Assistant

value: Show HN: Clippy for Vim

author: borysj

title: Show HN: Clippy for Vim


value: baalimago

value: Show HN: Clai – Context engineering for terminal powerusers

author: baalimago


value: Show HN: Heddle, content-addressed contracts for spec-driven agent loops


value: smashini

value: Show HN: Get your agents into regulated industries

author: smashini


value:
Hi HN,
I'm Daniel, part of the team at Agnes AI, a Singapore-based AI lab. We've been building quietly for a while and I wanted to share what we've made with this community and get honest feedback from people who actually build things.
What is Agnes AI?
Agnes AI gives you access to three models through a single free API. We have text, image and video models. The API is fully OpenAI-compatible, so you can just swap the base URL to https://apihub.agnes-ai.com/v1 and use your Agnes API key. No other changes needed in your existing code. It is completely free. No need to submit any kind of payment information. After registration there will be a default balance so that you can create your API keys. You sign up, get a key, and start building. Thanks to the community, we hit 4T API calls in just one week, and it keeps growing. We are also ranked as a top 10 AI lab on benchmarks. Our biggest advantage is that the free API is fully multimodal, so you can use text, image and video generation all without limits.
The models:
Text - Agnes-2.0-Flash: It has 512k context window (in a week or two it will be 1M). There are reasoning/thinking modes, tool calling, image URL input and streaming support. Ranked 9th in general tasks on Claw-Eval. Can be used with any coding assistant. I personally use it with OpenCode and Hermes. Can do automation and agent tasks, large context tasks, and reasoning tasks. And of course can be used for just general tasks. For the automation the community is already integrating with n8n and doing some automations from telegram for free.
Landing page generated with Agnes: https://codebuddy.work/agents/share/H4zVVxaFpU2I2UDcVSJAg-MH...
Image - Agnes-Image-2.1-Flash: Text-to-image and image editing available. It can generate up to 4k images. For the rankings also can check Artificial Analysis image editing leaderboard.
Here is a 4K image generated: https://imgur.com/LkMHnr7 and https://imgur.com/a/0UsRiGX
Video - Agnes-Video-V2.0: Text-to-Video and Image-to-Video. Also supports multiple images to video. Ranked on Artificial Analysis video leaderboard.
Why free?
We want developers to actually use it and tell us what is broken, what is missing, and what works well. That feedback matters more to us right now than revenue. Also our mission is to make AI accessible to all.
Things people have already built with it: A custom node pack for ComfyUI connecting Agnes image and text models, a coding agent skill supporting all Agnes 2.0 models (https://github.com/lj1270998580-crypto/Agnes-help-skill), Hermes integration for agentic local workflows (https://x.com/decapostos/status/2065758852738416779) and much more.
Links:
The official website: https://agnes-ai.com/
The API platform: https://platform.agnes-ai.com/
GitHub: https://github.com/AgnesAI-Labs/Agnes-AI (can check the model updates)
Docs: https://agnes-ai.com/doc
Discord: https://discord.gg/JAWe3YVYjr
Text model benchmarks: https://claw-eval.github.io
Image model leaderboard: https://artificialanalysis.ai/image/leaderboard/editing
Video model leaderboard: https://artificialanalysis.ai/video/leaderboard/image-to-vid...
Happy to answer any questions about the models, the API, or how we built things. Would love to hear what you build with it. Feel free to join our Discord community too, lots of people sharing what they've made there.
Thanks

value: Show HN: Agnes AI – Free multimodal API (text, image, video), OpenAI-compatible


value: ianseyler

value: Show HN: A 6KB x86-64 kernel for Firecracker microVMs

author: ianseyler


value: okutan

value: Show HN: 2026 ASO Complete Guide (With 2025 OCR/AI Tags Update)

author: okutan


value: AdamKib

value: Hi everyone,<p>This is a newly revamped multiplayer Minesweeper project of mine, with a healthy amount of trial and error in order to make each game as balanced and fun as possible:<p>• Rooms have up to 3 players at a time<p>• Scoring system (+1 / -3)<p>• Three mode options depending on your speed level

value: Show HN: PvP Minesweeper Game

author: AdamKib

title: Show HN: PvP Minesweeper Game


value: adshao

value: Show HN: Flounder – an autonomous white-hat security auditor

author: adshao


value: amineinai

value: Show HN: Fillr – Save web form as a preset and autofill with data you control

author: amineinai


value:
Hey HN, I build "How Loud Is My Hotel?" as a simple way to check for possible loud noise near a hotel (or any other address) before booking it.
I've had some negative experiences in the past so this automatically checks for things like nearby Clubs, Police Stations with sirens, big roads, railroads etc. and gives you a "noise score" based on them.
It's free, works worldwide and needs no account. It estimates outside noise risk rather than measuring decibels, so building insulation and temporary noise aren’t included.

value: Show HN: How Loud Is My Hotel?

title: Show HN: How Loud Is My Hotel?


value: rishitmavani

value: Show HN: Execlave – AI Agent Management Platform for Governance and Enforcement

author: rishitmavani


value:
I know this is not a very common issue, but I do know that the people that have to deal with a bunch of feedback can find it hard to keep up while doing their work as well
I'm not talking about answering and having actual conversations. I only talk about the feedback that report a bug or and issue, or those that request a feature you don't have.
Having to keep track of it all and decide what to prioritize can be annoying
I built this for myself, as a simple local running server I'd start so all my feedback would get fed into a couple of agents as soon as I received it and then into a ticket list, deduplicate, auto-group, prioritize, all that
But with time, I started expanding it a bit. I integrated Mail forwarding and made it so an AI can easily access/control it and so on
And today, I decided to try my luck at monetizing it, and see if some other people have this issue and are willing to pay for something like this.
Let me know what you think, about the price as well, I can offer discounts if you think it's too expensive right now

value: Show HN: A way to respect all your user feedback and your time


value: sofumel

value: This is still a work in progress, and I'm sure there are many things I can improve. I'd love to hear your thoughts, suggestions, and constructive criticism.

value: Show HN: Resume Claude Code from a handoff file auto-save, snapshots, PR sharing

author: sofumel


value: biduskamil

value:
OpenAI, Google and Perplexity don't expose user query analytics. It's difficult to tell if and why your company appears in chat responses.
Turns out the only way you find out currently if you're cited at scale is if you reverse engineer the prompts users might be asking about your brand and track ChatGPT/Gemini/etc. responses for them.
I've built a pipeline, that:
1. crawls your website and your competition
2. analyzes top search keywords from search engines
3. retrieves similar conversations from WildChat (~1M ChatGPT conversations)
4. generates most likely user prompts for your brand.
The result is a set of prompts that users are likely to ask about your company or your products and tracking them shows where LLMs suggest you and where do they reference your competition.
Repo:
https://github.com/syntropicsignal-ai/ai-visibility-audit
I'd love feedback on:
- whether this methodology makes sense
- alternative datasets to WildChat
- better ways to estimate prompt distributions

value: Show HN: Estimating what users ask ChatGPT about your company

author: biduskamil


value: almirsarajcic

value: Show HN: I made a Telegram bot that turns a voice note into a live website

author: almirsarajcic


value: Graphenium

value: Show HN: Graphenium, persistent repo memory for AI coding assistants

author: Graphenium


value: Rubinoslaw

value:
Hello!
I, Rubinosław from REParadoxy, I'm presenting Project Cherub - a modernized version of TempleOS, very well known operating system made by Terry A. Davis.
My vision is to create a minimalist, fully-functional and secure operating system. For home users, for programmers, for artists, for everyone! Where THE USERS matter the most!
I've already changed FPS rate from 30 to 120 via modifying "KernelA.HH.Z" file, a part of the system kernel. I've changed WINMGR_FPS in that file from "(30000.0/1001)" to "(120000.0/1001)", and WINMGR_PERIOD from "(1001/30000.0)" to "(1001/120000.0)".
I've also changed the system name from "TempleOS" to "Project Cherub".
As I said - its just a very early build. But my plan is to add also 32-bit colors support, support for SATA AHCI, NVMe, Blu-ray, videos, GPU and USB. But I'll keep the 640x480 screen resolution, you know why.
*THERE WILL BE MONTHLY PROGRESS UPDATES ON YOUTUBE AND LIVESTREAMS OF OSDEVING, SO YOU SHOULD SUBSCRIBE!
https://www.youtube.com/@REParadoxy**
Full presentation of an early build with 120 FPS, future plan, and installation showcase:
https://www.youtube.com/watch?v=NNq_4vG5Src
More information:
https://github.com/Rubinoslaw/Project-Cherub - GitHub repository (with ISO!)

value: Show HN: Project Cherub – TempleOS Fork. Early Build (ISO) & Future Plans

author: Rubinoslaw


value: francosimon

value: Show HN: Vlayer – Open-source HIPAA compliance scanner for your codebase

author: francosimon


value: iamsky

value: See the readme.  55-90% input token savings.  You can run this test yourself in the /bench directory.

value: Show HN: Codemcp, One MCP tool that lets agents write Python to call many

author: iamsky


value: Show HN: Open-source language for analyzing, transforming, and modeling data


value: kamilmrzyglod

value: Show HN: Topaz – replace three Azure emulators with one binary

author: kamilmrzyglod


value: guzzul

value: Show HN: Xcreener – I built a simpler screener for forex traders

author: guzzul


value: draggodra

value: HuskMail instantly provides disposable email addresses that you can use to sign up for websites, services, and free trials without revealing your personal email address.

value: Show HN: Trial library based with OTP detected tempmail service

author: draggodra


value: heyitssim

value: Show HN: Make your game multiplayer in one prompt

author: heyitssim


value: sagnikghosh

value: Show HN: Built a Git ext. in Rust that tracks which AI wrote each line of code

author: sagnikghosh


value: octember

value: Show HN: Stupify – anti-slop code review for AI agents

author: octember


value: nardib

value: Show HN: A Viral Social Video Generator for iOS Apps

author: nardib


value: pietz

value: Show HN: Moa – cross-model peer review for your local agent CLIs

author: pietz


value:
An earlier version of Databasus shipped a backup agent: a binary that ran on the database host to stream WAL and create physical backups locally. That first implementation turned out to be a mistake, and we removed it. Physical backups now run remotely from the Databasus host, as described above.
Why the agent was the wrong approach:
• It was a naive implementation that only copied WAL on top of full backups, which led to a long RTO
• Users had to configure both Databasus and a separate agent, when doing everything remotely from one place is far simpler
• Because the agent lived outside the main system, it was hard to cover every test case
• There is really only one problem an agent solves: reaching a database that is not accessible from outside. For 99% of users that is already handled by running Databasus inside the private network or connecting over SSH, so the agent was reinventing the wheel and making a simple problem far more complicated than it needed to be
• It could not run on managed databases like RDS and Cloud SQL, which forbid host-level installs but already expose the replication protocol, so a remote path was needed anyway
• It also came with a lot of edge cases. Broken connections, managing agent updates and gathering logs from a separate process were all painful, and the fewer moving parts a system has, the more reliable it is in everyday use
So now we use PostgreSQL 17 native backups instead. What that gives you:
• They reuse PostgreSQL's own backup machinery instead of reinventing it, so you get battle-tested internals with thousands of tests and edge cases behind them
• They work with remote databases, including managed services like Amazon RDS and Google Cloud SQL that expose the replication protocol but forbid installing software on the host
• They give near-zero data loss, letting you restore to any second between backups with low RTO / RPO
We made sure existing backups stay safe. If you upgrade from a version that still has agent backups, Databasus won't do it silently: it warns you about the change and lets you either stay on the supported version 3.42.0 or remove the old agent backups yourself before upgrading. The agent-based implementation remains available up to version 3.42.0 and will keep working for a long time, so nothing breaks.
You can read the full reasoning in the architecture decision records:
ADR-0008: PG17-native backups with mandatory WAL summary
https://github.com/databasus/databasus/blob/main/adr/0008-wh...
ADR-0009: remote physical backups instead of agents
https://github.com/databasus/databasus/blob/main/adr/0009-wh...
By the way, Databasus recently crossed 7,500 GitHub stars and over 1 million Docker Hub pulls. Thank you for such love to the project. It really keeps us going, and a lot of what shipped here came straight from your issues, PRs and feedback. We're just getting started!

value: Show HN: PostgreSQL backup tool Databasus moved to PG 17 native physical backups


value: Show HN: SentryGuard – detect Agentjacking prompt injection in Sentry events


value: ermantrout

value: Show HN: A calculator for what an AI product keeps after costs

author: ermantrout


value: jbarrow

value:
Wrote a paper (https://arxiv.org/abs/2606.19334) on how we collected and built the dataset. Just covers the United States.
Happy to answer any questions!

value: Show HN: Locus-v1, a dataset of 2.2M local laws and ordinances

author: jbarrow


value: dhruwang

value: Show HN: Speechcap -Mac dictation with on-device transcription and cleanup

author: dhruwang


value: pixelcave

value: Show HN: CodedVisuals – Animated visuals for marketing sites

author: pixelcave


value: Show HN: ClaudeBar – live Claude usage in your macOS menu bar


value: shreyamaheshwar

value: Show HN: A dev-news feed ranked by HN + Lobsters signal, with a voice briefing

author: shreyamaheshwar


value: pedromlsreis

value:
I started tracking my net worth when I started working in 2019 and I used a spreadsheet for that. But, having accounts in multiple currencies and updating it every month got old. I wanted to look at the numbers on my phone in a queue, but ended up rebuilding formulas at my laptop every month. Also, I didn't want to hand my bank logins to a third-party aggregator to get there. So I built the tool I've always wanted: without bank connections, syncing the same dashboard on every device, and making sure the data was encrypted in my browser so the server can't read it.
You submit your balances or start by uploading an Excel file, and Quantive gives you the things a spreadsheet makes you fight for, like the constantly evolving allocation by asset type and liquidity, drawdown stats and forecasts, and multi-currency totals where a balance from 3 years ago is valued at that day's FX rate (not today's). It syncs across devices, so you can now check those numbers also from your phone.
The part I cared most about was for your portfolio to be encrypted in your browser with a key derived from your password before anything reaches the server. The server only stores ciphertext, so a passive database leak reveals nothing about your financials, and there's no third-party aggregator on my side. XChaCha20-Poly1305 and Argon2id (via libsodium), an optional 24-word recovery code, and the crypto module is MIT. The full threat model, including what I deliberately do not defend against can also be found in the design doc below. That's the part I'd most like critique on.
Live: https://usequantive.app
Try it on a mock portfolio with no sign-up: https://usequantive.app/demo
Repo: https://github.com/pedromlsreis/quantive (crypto module MIT; the rest source-available under PolyForm Noncommercial 1.0.0)
Design doc, with the threat model and what's tested vs not: https://github.com/pedromlsreis/quantive/blob/main/docs/secu...
The Free tier will remain forever, and I will never be cutting down on usability. Pro adds full history, forecasting, benchmarks against EU inflation and the S&P 500, a PDF report, and CSV/Excel exports. Also, in line with the privacy philosophy, all the existing analytics are opt-in and anonymous - everything disclosed in the /privacy page. I'd be happy to hear your comments, especially from anyone who has wrestled with this in a spreadsheet before.

value: Show HN: Net worth tracker to replace your spreadsheet, E2E, no bank logins

author: pedromlsreis


value: Show HN: JavaScript tutorial

title: Show HN: JavaScript tutorial


value: Show HN: An opinionated ranking of 21 open-weight LLMs, filterable by your GPU


value: moinism

value:
A small TypeScript library that imports and exports FCPXML, FCP7/xmeml, and OTIO, plus a CLI.
You can use it for reading, writing, and converting video-editing timelines with frame-accurate timing.
e.g:
 npx @chatoctopus/timeline convert edit.fcpxml --to otio
Features:
- Format conversion
- Import/parse/Export
- Give it clip paths (+ optional trims); it probes them with FFprobe and assembles a timeline
- Programmatic timeline construction
Caveats: it moves the structure of an edit (cuts, clips, timing, media) but not effects, color, titles, or audio mixing. Transitions only survive into OTIO (they flatten to cuts in FCPXML/xmeml), and on the Final Cut side, it reads Resolve-style asset-clip timelines rather than FCP's compound/multicam/title containers.
Contributions: Looking for some real-world test files of FCP, Premiere, and Resolve to find and fix any remaining bugs.

value: Show HN: TS Package to Convert Final Cut, Premiere, Resolve and OpenTimelineIO

author: moinism


value: cloudcell

value: Show HN: OpenModeling Core – multidimensional modeling for financial models

author: cloudcell


value: momoraul

value: Show HN: Lupen – which Claude Code turn or sub-agent ran up your bill

author: momoraul


value: croustibat

value: Show HN: Pinpoint – point your AI agent at the exact pixel you mean

author: croustibat


value: Show HN: A privacy-first perimenopause app with on-device statistical analysis


value:
Hi, I'm a PhD student in Bioinformatics/Computational Biology with a software engineering background,
I'm trying to pivot toward AI/ML research. I'm familiar with the practical side of AI as in using, Scikit learn, R,  Pytorch, ONNX Runtime etc...
I was thinking if LLMs could be used as a research assistant to create better AIML algorithms. So I asked ChatGPT to help find better way to solved one of the most computationally intensive problems in Transformer architecture based model. I instructed ChatGPT to use genetic algorithms, genetic programming and other optimization techniques (Something I use extensively in my bioinformatics research) to find better Attention methods in transformers and this was the result. I would love to get feedback and comments from the AIML research community. Please read the md file and get back to me.
Hope you find this useful.
Thank you

value: Show HN: Proxy Block-CAGE, a new sparse block attention


value: migueljpalmeida

value: Show HN: Crossary – Field mapping that won't guess; every row cites its source

author: migueljpalmeida


value:
Hello, I'm Kushagra and I am the indie developer behind LookAway (I've posted about it earlier but it has received quite a lot of updates since the last time so I am posting it again).
LookAway is a native break reminder for macOS that doesn't interrupt. I built it because I work from home and I spend a lot of time in front of my screens. It's very easy for me to get lost in the flow and I can end up sitting for hours. Due to this, I started facing issues like eye strain and back pain by the end of the day.
The solution to this was simply taking enough breaks throughout the day. But remembering to take breaks was difficult, especially when I was in the flow. I tried some reminder apps but the problem with those was that they always interrupted me at the worst moments. So I ended up not using them.
LookAway is designed not to interrupt. It gives enough heads up before a break so that you're not caught off-guard. It's also context-aware and it automatically pauses when you go into a meeting, start watching a video, record screen, and much more. It even waits for you to finish typing or dictating when a break is due.
One thing worth mentioning is the free iOS counterpart LookAway Mirror. When your Mac goes on a break, your iOS devices can also mirror the same break so you don't end up scrolling your phone screen during the Mac break.
I've spent a lot of time in making LookAway the least annoying break reminder app and I would love to know your thoughts. It's a native Swift app so it doesn't take much resources (150MB RAM and <1% CPU when idle).
It's available to download from the website (lookaway.com), Setapp, and the App Store. Thank you!

value: Show HN: LookAway, a Mac break reminder that knows when not to interrupt


value: Show HN: Munin – OSS HubSpot alternative I built in a month with Claude Code


value: hxii

value: Show HN: Sixwhyo – Your code reviewed by a six-year-old (and he's not wrong)

author: hxii


value: diane-cis

value:
Hi HN — I'm one of the makers.
We kept hitting the same failure mode with coding agents: you hand over a vague plan, the agent fills the gaps with guesses, and the back-and-forth that explains why a decision was made lives in a linear chat you can't navigate later. So we built inplan: a Markdown editor where you and the agent edit one plan document together.
The part we care most about: you can select any sentence and open a threaded comment on it — ask a question, get the agent's take, make the call — and that exchange stays anchored to that line as a durable record. When the agent changes the plan it comes back as a diff you review and apply, instead of silently rewriting the doc. It installs as a Claude Code skill (also Codex/Pi), so a "plan X" prompt opens the editor: the agent drafts, you refine inline, it revises.
It's open-core (AGPL-3.0) and runs fully locally — npm i -g inplan. There's a hosted edition (inplan.ai) for live multi-person collaboration, which is how we intend to fund it; nothing in the demo needs an account.
Where it's still young: it's an Electron desktop app, the diff is line-based, and it's built around one plan doc at a time — multi-doc and richer agent loops are next. The Windows install path was rough until last week.
60-second demo: https://github.com/melly-lgtm/inplan/releases/download/v0.1.18/inplan-demo.mp4
Would genuinely love feedback — especially where the workflow gets in your way.

value: Show HN: inplan – plan with your coding agent in a shared Markdown doc

author: diane-cis


value: kerryritter

value: After seeing OpenRouter Fusion and Sakana's results, I decided to take some learning from my building my start-up's harness and extract a CLI/MCP for agent-to-agent communication. Simple things like "Use parley to have kimi review this work {from Claude}" continually finds notable gaps. The jump in quality has been huge.

value: Show HN: CLI and MCP for model fusion via harnesses

author: kerryritter


value: ruchitnannavare

value: Show HN: Heku – Dynamic MCP Tooling via JSON Configs

author: ruchitnannavare


value: Enjoyooor

value:
Hey HN,
The world of tech interviews is evolving.
We wanted to give our own perspective about how to optimize chances to get the job.
Preparation is key, so we added many code reviews in order to be ready and work on the success rate in itw.

value: Show HN: We built a LeetCode alternative in 15 foreign languages

author: Enjoyooor


value: shanika

value: Show HN: Tecture – A navigable architecture map from your coding agent

author: shanika


value: philiparxist

value: Show HN: Build A Harness – open-source, modular harness layer for AI agents

author: philiparxist


value: davidpapermill

value: Show HN: Papermill – a document generation engine for AI workflows

author: davidpapermill


value: blauyourmind

value:
Hi HN, I’m building Drip. It’s an API that lets AI agents access premium finance newsletters on a pay-per-use basis.
Agents will not subscribe to newsletters like humans do. A person subscribes for the ongoing relationship. An agent usually needs one narrower thing: the exact piece of research that helps answer a question right now.
We just launched a stock-picks endpoint for what many people are calling vibe investing / agentic investing. Using Drip, an agent can generate a cited report on the latest stock picks and recommendations from top premium finance writers.
You can generate a stock-picks report here: https://dripstack.xyz/stock-picks
Or give your agent access here: https://dripstack.xyz/?tab=agents
Every time Drip answers a question, the authors whose work is referenced get paid on a pay-per-use basis.
This is early, and definitely not financial advice. I’d love feedback from anyone building agents, financial research tools, or trading workflows.

value: Show HN: Drip — pay-per-use finance newsletters for AI agents

author: blauyourmind


value: colinmcd

value:
Colin here, creator of Nub. I’ve had the general shape of this in mind for years. Nub runs your code with stock `node`, augmented with a `--require` preload hook[0] that adds a transpiler (oxc-powered, packaged as a Node-API add-on), registers a module resolution hook[1], and injects polyfills as needed for APIs like `Worker`, `Temporal`, etc. All purely additive, your code ultimately runs using Node’s actual engine & stdlib implementations.
[0] https://nodejs.org/api/cli.html#-require-module
[1] https://nodejs.org/api/module.html#moduleregisterhooksoption...

value: Show HN: Nub – A Bun-like all-in-one toolkit for Node.js

author: colinmcd


value: haoranchg

value: Show HN: Iantha – build your own Jarvis on Claude Code

author: haoranchg


value: Show HN: I built a live index of tech jobs from job boards and career pages


value: mrvinhpro

value: Show HN: Stewie Reflect – owner's manuals for repos, with claims linked to code

author: mrvinhpro


value: alexreysa

value: Show HN: L0/L1/L2 agents, leases, gates, audits, Git-worktree isolation

author: alexreysa


value: brightmonkey

value:
Orchid (Orchestration interactive debugger) is a zero-instrumentation proxy that captures every API & LLM call in your agent pipeline, then lets you inspect and replay the entire run locally, step by step. No instrumentation, no vendor lock-in, no cloud dependency. It also provides a visual inspector and MCP server, so you can inspect the session yourself or use your favorite agentic coding IDE to debug your agent runs.
I built it because I was tired of debugging agent failures by grepping through logs, and the available AI observability tools all seemed to require intrusive instrumentation and/or sending my prompts and responses to a cloud service. I wanted something that would let me debug agent runs locally, without having to worry about vendor lock-in or data privacy.
Orchid is that tool. The call inspection features work extremely well, at least for my use cases, but the replay feature is perhaps more interesting. It makes LLM pipeline testing deterministic without mocking or re-running expensive API calls.
Free, self-hosted, runs on your machine or infrastructure: https://github.com/mario-guerra/orchid-trace
Would love feedback from anyone building multi-step agentic systems or struggling with non-deterministic LLM test failures.

value: Show HN: Orchid – Local-first record and replay for AI agent debugging

author: brightmonkey


value:
If you guys are interested in giving your AI agents access to Substack, I just released what I think is the most comprehensive unofficial API reference ever compiled.
Discovered, mapped and tested 129 endpoints so you can fully control Substack via an agent.
https://github.com/AnthonyDavidAdams/substack-api-reference

value: Show HN: Substack Skill API / Agent Reference


value: jjhartmann

value:
Hi HN! Sipp is an open-source AI inference library for running local models in browsers with up to 3x faster decode speeds than alternative libraries.
My background is in HCI (human-computer interaction) and graphics programming. Me along with my co-founder have been experimenting and thinking a lot about what the next user experience will look like when tokens are commodified to the point of being essentially “free.” A motivation for us was to try to move beyond the chat app and information retrieval use cases that are dominant now, and figure out how AI could instead act as a continuous and silent hand that helps the user indirectly, subtly monitoring their intent and dynamically generating or shifting the UI to meet their needs.
In our explorations, we ran into two pain points: 1) when running AI in the browser, performance wasn’t good enough for real-time applications, and model loading and caching were issues; 2) when trying to run locally on desktop, there weren’t any good solutions for embedding a model for use through platforms like Electron or Tauri.
So a few months ago, we started contributing to the llama.cpp project to explore browser inference. At the time, it didn’t have great WebGPU support. We helped get the backend working across most modern GPUs (16 bit fp is required) with fairly good ops coverage. Simultaneously, we started building up Sipp from the ground up using Rust and C++ to create a unified library around how local and cloud inference can be used together in real applications and use cases.
The result, Sipp achieves up to a 3x speedup in overall token decoding compared to alternative libs. But we believe there is more room for improvement here. Our goal is to investigate more bespoke inference pipelines per model architecture, which will allow us to further optimize for both compute and memory constraints more effectively.
Sipp uses a single, unified client API. A large concern for us was how to create a library that effectively bridges both local and cloud inference in a simple way. We wanted developers to start by running a small local model in the browser via WebGPU (or via other backends), and then scale the exact same code path to a self-hosted gateway (CUDA/Vulkan/Metal) or a cloud provider just by changing the endpoint. This enables the benefits of running locally for small tasks while letting you offload to a provider or cloud for tasks that require more intelligence.
Currently, our primary focus has been optimizing the browser experience, but we are actively working on creating additional backends for running an LLM locally via our client API, which works across Node, Rust, and Python right now.
What we are most excited about next is pushing our existing backends even further. As anyone working with a high-performance system knows, raw algorithms and efficient matmul are only half the battle. A significant portion of bottlenecks in real-time systems comes from inefficient memory management and VRAM<>RAM transfer costs.
We believe there is a massive opportunity to extract even more performance through aggressive kernel fusion. By creating “bespoke” kernels tailored to specific model architectures, we can drastically minimize intermediate memory copies. Our next goal is to see exactly how close we can push local inference to the theoretically “perfect” decode and prefill speeds for consumer hardware.
We have a live, simple chat demo running entirely on-device on our site, as well as a benchmarking tool if you want to test or verify the performance differences on your own hardware.
We’d love for you to tear into the code, run the benchmarks, and tell us where we can improve. I’ll be here all day to answer any questions about the architecture, kernel fusion, or HCI in the age of local LLMs.
More in depth tech info is here: https://dev.to/constant_chen_/sipp-a-local-first-runtime-for...

value: Show HN: Sipp – Run small local LLMs in browser 3x faster

author: jjhartmann


value: arjawn

value: Show HN: I built an API to form a US company (doola S20)

author: arjawn


value: phtowel

value: Show HN: Graft – one golden VM image for your Mac dev box and CI runners

author: phtowel


value: Show HN: Wealtii – A multi-sig digital asset index fund built by a solo founder


value: sontbv

value: Show HN: Michii – AI agents that build and run your business

author: sontbv


value: flatline

value:
For a while I've wanted to try out the new AI voices for long-form narration, but everything I found required a subscription that didn't justify my limited usage. I came across the open Kokoro model [0] and the voices are very good -- good enough to listen to for hours without the fatigue I got from legacy, robotic TTS voices. The model is 82m parameters and designed to run fast, but I still struggled to get reasonable times from CPU inference on my 12-core laptop. I thought a cloud-based GPU service would let me generate audiobooks fast enough to feed my own self-hosted library, and that same pipeline could become a product other people could use.
I had two goals in building this: get some exposure to AI multi-agent coding workflows, and build a TTS product targeting ebook to audiobook conversion specifically. 99% of ebookaloud was written by DeepSeek v4 in OpenCode. I've used about 750 million tokens costing $12 in credits over the course of a month, and I'm very pleased with the results. Every change/feature went through a plan -> implement -> test -> review -> correct -> commit cycle with a mix of Pro and Flash agents. This was generally limited to one or two concurrent workers. I had a separate eval agent for quality control on various parts of the extraction and synthesis pipeline, which I could run 8-10 at a time. I may be approaching Yegge's Stage 6 [1] in terms of AI workflow automation.
I later set up Claude Code and ran Opus 4.8 side by side with DeepSeek. There are definitely quality differences, but I'm an experienced developer with a hands-on approach. I didn't write any of the code, but I have read critical sections of what it generated and had extensive conversations with DS Pro about each step of the approach. Opus didn't have much critical to say about DeepSeek's choices, and I'm not convinced a frontier model would have made an appreciable difference for my workflow. I suspect on a large codebase the differences would become more apparent, but the few changes I implemented with Opus had similar issues to all the models I've used: random changes without my direction, over-complicating simple solutions, taking unanticipated/destructive actions when it gets stuck, etc. I do see Opus being capable of handling more of the complex planning and orchestration that I was involved in. That's something I may want sometimes but not others.
As to the product itself, there are a lot more sophisticated solutions out there. I'm not trying to compete with ElevenLabs. I'm targeting m4b generation for a seamless audiobook experience with a pay-as-you-go pricing model and good-enough output quality. This is the first product I've ever tried to commercialize, and AI code generation put something polished within reach. Without AI, this would have taken me 6-8 months of manual research and development, and I would have gotten burned out long before completing it.
I have a free sample on the front page of the site if you just want to see what it generates in terms of voice/format. I made a few opinionated decisions regarding output quality. I aimed for 140 wpm for most of the voices to match industry standards, but some are much slower or faster and lose prosody at that rate. Rather than give users a wall of options, I'm deferring to the playback device for things like speed control. If the site sees real usage I'd like to expand to support Kokoro's other languages, and extraction and synthesis from PDF would round out the product quite a bit.
[0] https://github.com/hexgrad/kokoro
[1] https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d...

value: Show HN: eBook to audiobook narration with realistic AI voices

author: flatline


value: BruceBGordon

value: Show HN: Issue-Orchestrator, a control plane for coding agents

author: BruceBGordon


value: tylerrosnett

value: I've had this Vornado 660 for years and didn't want to swap out a working fan just to control it from my phone. So I wired transistors across each of the physical buttons and let an ESP32 tap them. The fan's own controller still handles everything on the mains side; my board shorts the button signal lines to ground, which the fan reads as a fingertip press. Parts ran about $13 and it took ~4 hours on a Saturday.<p>The mod is fully reversible and reflashes over the air after the first flash, so the fan never has to be opened again. Wiring schematics, the ESPHome config, and build photos are in the repo. Happy to answer questions.

value: Show HN: Smart Vornado 660 Fan – reversible ESP32 button injection for HomeKit

author: tylerrosnett


value: willks

value: Show HN: Open-source AI language learning app

author: willks


value: dstroot

value:
With massive respect to Jeff Sutherland and all the thought leaders who met at a ski lodge in Snowbird, Utah, in 2001 and created the Agile Manifesto - it might be time to revisit / refresh / revitalize the Agile Manifesto in light of the emergence of LLMs. The scarce resource is no longer programming capacity, but organizational clarity and architectural coherence.
As a thought exercise (and for fun) I took a stab at it. I would love to get some collaborative feedback to improve it. Of course I do not expect this to replace the Agile Manifesto but I'd like to use it when speaking to enterprises about how to think about Agile in today's world.
So any/all comments welcome!
ps - posted on my blog because of convenience. It is not monetized in any way. No ads, nothing. Cheers!

value: Show HN: AI Manifesto

author: dstroot

title: Show HN: AI Manifesto


value: rmcentush

value: Made a website to find vintage and official corporate merch. Check it out!

value: Show HN: Find vintage corporate merch from iconic companies

author: rmcentush