value:
Hi HN, I started building my product around 2 years ago as a Chrome extension. After releasing it, I realized I would never reach the quality of desktop apps, so at the beginning of 2025 I pivoted to a macOS app. I spent 3 months rewriting the codebase for Electron. I’m still using Next.js under the hood because the chrome extension used it, but in the near future I want to switch to ViteJS since it’s lighter and I don’t need backend functionality for an Electron app.
Overall I spent about 2 years on the development. The more interesting part is tracking the cursor movements when user starts recording and transforming it into smooth cursor movements and click-driven zoom effects. I use Swift scripts for low-level cursor tracking, then run a post-processing pipeline: resampling to timeline FPS, outlier-jump clamping, stillness detection/snap-to-median, cursor-type flicker smoothing, and coordinate normalization for area vs full-screen recording. Clicks are converted into timed zoom segments with easing so playback feels intentional, not jumpy
For the video preview and export I use PixiJS. Before I used Remotion, which had the needed functionality, but the rendering was slower because it relied on a Chromium + FFmpeg pipeline. PixiJS uses WebGL so the 4k video rending is much faster. It took me about 3 weeks on switching from Remotion to PixiJS. The rendering speed improved by about 3-4x on the same projects and hardware. I was honestly relieved when it finally worked
I would never have built this product without AI assistants, since I had never built an Electron app before. Many things were not obvious to me, and my first users ran into lots of issues and edge cases I didn’t even know existed. I spent the last year fixing them.
I'm happy to answer on your questions

value: Show HN: Mac screen recorder that adds zoom animations from click data


value: sourishsarmah

value: Show HN: Ocdify – Offline AI that organizes your Mac files, 100% on-device

author: sourishsarmah


value: gaelazzo

value: Show HN: Hires Chess Trainer – drill the mistakes from your own chess games

author: gaelazzo


value: cabra

value: Show HN: Instagram for Gmail, a feed where mail expires

author: cabra


value: saltypixel

value: Show HN: A revenue dashboard for US freelancers in a single HTML file

author: saltypixel


value: Show HN: Frhog, Your Friendly Head of Growth


value: elvisun

value: Show HN: Newsjack.sh – Open-source skills to turn any agent into a full PR team

author: elvisun


value: akmoleksandr

value: Show HN: I built 10 ML algos from scratch because fit() predict() are not enough

author: akmoleksandr


value: Imbiss

value: Show HN: Cate – open-source canvas IDE for agentic coding workflows

author: Imbiss


value: sys-ronin

value:
I am a 40 years old jobless sys-admin with no cs degree and no development experience and 25 yeras of system knowledge. I built a fully encrypted writing environment where I can write with focus forgetting about the application. Here ai was my code translator while I strictly was the architect and instructor. It all started when i asked myself that "why and how i remember any past memory instantly without searching inside my head". This became the design principle of my app. Thus I solved a fundamental git problem of tracking a single note throughout the history by embedding uuid in every commit.
Its responsive data-as-UI based interface keeps the app (nearly) learning curve free. Navigating deep hierarchies is easy using numbered fisheye jump system without forward button. Every operation is in one or two keystroke like v(view), c(create), d(delete), r(rename), (j)ump s(sync), (s)earch, b(back) or v1, d1, j1, jb etc. It is built using python standard library (no other dependency), Git, cryptography standard primitive (embedded). I used git-filter-repo programaically for permanent item erasure using uuid from inside the application. It has full timeline for each item (note, file, subnotebook) to see the entire life cycle from create to erase with tombstone. Its subnotebook hierarchy can be upto any depth. It supports 80 file types with syntax highlighted editing with export feature for files. It has activity viewer inside every notebook or subnotebook. It shows your last 50 activities in the notebook. Searching is easy like "s files* file.md date* 02-04-2026 in* notebook/subnotebook_name", "s note_name date* 01-04-2026 05-04-2026", "s edited* any_text thisweek" "s in notebook/subnotebook_name" or just "s anytext".
Notebook can be encrypted using 8-24 words BIP-39 phrases and use password for regular lock/unlock. It is encrypted using standard AES-GCM and also portable. It can show password change log in each notebook management screen. Key vaults are hardware bound yet portable and a usb drive can act as TPM like security. It means when the keys are not reachable, notebooks can't be accessed. The application architecture and data structure is decoupled. Where app architecture allows the app, notebook and key vault can be in three different locations or even in different systems and data architecture allows metadata and content remain separated in different files inside each notebook folder linked via UUID. Git is implemented for doing item level (not file level) versioning and temporal search with uuid based commit metadata. Here commit metadata is not encrypted, though doable modifying the existing code if required. Every notebook is self contained in just a movable single folder. The architectur also enables optional e2ee conflict free sync using github and other platforms. It does not need someone to be a git expert to use the application and cloud vendor cannot see the data even with public visibility.
I will request respected HN people to go through the documents and the story_n_constraint.md file, that will explain the what, how, why in details. I also put a couple of abstract ideas inside the repo (what_can_be_done files). I want to thank the creators of Python, Git, cryptography, nvim, micro and git-filter-repo. Their tools came together and turned a concept into a working application.
The post, the GitHub repository, and all source code and documentation are part of the prior art with appropriete license (Public + timestamped + enabling)
repo:
* https://github.com/sys-ronin/terminal-notes *
requirement:
linux/mac/win/docker, python 3.13, git, nvim/micro or any terminal based editor (can be configured from config.json inside root folder),internet for using github sync
example - python3 terminal_notes_ui.py (from linux)
* tested on debian 13 only
git clone https://github.com/sys-ronin/terminal-notes.git && cd terminal-notes && python3 terminal_notes_ui.py
sys-ronin

value: Show HN: A terminal writing environment with Git, E2EE sync and temporal search

author: sys-ronin


value: sailorpro

value: Show HN: Dochost – turn AI output into a shareable link

author: sailorpro


value: Show HN: Open-source plugin that builds single-file HTML decks for coding agents


value:
Did you vibe-code 5k+ lines of code without thoroughly reviewing all of them?
Is your application held together mostly by thoughts, prayers, and a suspicious amount of copium ?
Do you run through your entire development page after every agent commit just to check that nothing randomly broke?
If yes, I built something for you.
Introducing riddlerun: an open-source agentic end-to-end web testing framework that can be run directly from the terminal.
All you need is Docker, an API key, and the ability to describe in a coherent sentence how your application is supposed to behave. I’d be especially grateful for feedback, issue reports, and proposals for the future direction of the project.

value: Show HN: RiddleRun – AI run end-to-end browser tests


value: vampiregrey

value: Show HN: Terra – Off-market real estate sourcing, with research and AI modelling

author: vampiregrey


value: nvignesh

value: Show HN: Proquiro – Land acquisition software for Indian real estate teams

author: nvignesh


value: armanified

value: Show HN: ChromiumFish – Open-source fingerprint-hardened Browser for Scraping

author: armanified


value: ushercakes

value:
I'm an indiehacking type, which means I work on like 5 different things at any given time.
Claude and codex are incredible. But, there were a few problems I had with them. AgentGraphed pretty much solves for those.
It works locally - indexes every conversation you have, live, into a local sqlite db. With it, theres a lot we can render in a nice UX.
Among other things..
Problem: I want to resume a session, but when I look at claude --resume, the titles are completely unhelpful. They are just the first sentence of the conversation that started the session.
Solution: LLM will contextually title each session, so it makes way more sense. Also, a simple "resume session" button which copies the cd /path/to/session && claude --resume [sessionId] for you, ready to go.
Problem: I go on vacation, and forget what I was working on when I get back.
Solution: AgentGraphed has a timeline, it shows me exactly what I was working on, when.
Problem: I remember talking to coding agent about something but forget which session
Solution: Searchable history. Every single session, ever.
Problem: I want to brag to my friends about how much I use claude. ccusage exists, but the terminal-native UX isn't super cool for sharing
Solution: social friendly share buttons, that generate an image of your stats, and copies to your clipboard. (All local).
Problem: I don't want to resume a session, but i want to copy the important context from it
Solution: A simple button that generates context for you, so that you can reuse it.
------
One thing I need to call out. The tool really is fully local, totally safe. BUT! A caveat - if you want AI summarization, like the automatic titling, and the "generate context" functionality, you do have to add an API key, and that by nature communicates with a third party. Those are totally optional, though.
I'm new to the OSS world. Usually everything I do is proprietary etc, so feel free to roast me and help me make it better.

value: Show HN: Local-first history, search, and analytics for Claude Code and Codex

author: ushercakes


value: AI-native red-team workbench for authorized penetration testing and vulnerability research, with specialist agents, sandboxed tooling, evidence records, and replayable timelines.

value: Show HN: AI-native red-team for penetration testing and vulnerability research


value: rough-sea

value:
At Deno we've been using OpenClaw and other agents increasingly for addressing production problems in Deno Deploy - when a PagerDuty alert fires, the agent starts researching the cause and making fixes.
In order to do this, the agent needs access to real production systems - postgres, kubernetes, gcp, clickhouse, github, etc. But this is dangerous to say the least - we want destructive actions to be reviewed by other LLMs, approved by humans, and logged appropriately.
Claw Patrol terminates TCP connections over WireGuard or Tailscale, then parses application protocols (eg http, postgres, ssh) to apply rules that allow you to deny/allow requests.
There are a few projects that sit as a proxy in front of agents to do secret injection or apply various guardrails, but none met our needs (LLM gateways, MCP proxies, sandboxes), particularly the need to handle low-level protocols, or handle complex real world situations like tunneling postgres through k8s.
Written in Go, configured in HCL, MIT licensed. Happy to answer any questions.
https://clawpatrol.dev/

value: Show HN: Claw Patrol, a security firewall for agents

author: rough-sea


value:
Plastron is a spreadsheet starting with one cel (元) whose formula generates the a readme DOM element but you can make sheets of cels with formulas like =cels(8, 5). It's a single index.html — no build, no server, no install — so it runs locally, offline, and free.
Excel is maybe the most useful software ever written, but the moment a sheet wants to be a real app — a UI, a file, a database, something to share — you fall off a cliff and rewrite it as "a real application" (framework + backend + deploy), usually after pleading with IT over licenses. Meanwhile every browser already runs JS, WASM, SQLite, and a filesystem API. An index.html should be all you need.
Things to try on the page: =cels("anim", 2, 1, at("a1", "=def(\"ball\", \"js\", \"i => [30+280Math.abs((i/20)%2-1), 30+200Math.abs((i/14)%2-1)]\")"), at("a2", "=simulate(\"ball\", 120)"))
=cels("data", 2, 1, at("a1", "=db(\"app\")"), at("a2", "=sql(\"app\", \"create table t(a,b); insert into t values (1,2),(3,4); select * from t\")"))
Imagine somebody dm'ing you a formula for some bs b2b saas app
It's early and meant to be built on. I'm sure there are bugs. No version control yet, no .xlsx import/export, and the traditional Excel formula set isn't there.
Code (MIT): https://github.com/rheophile10/plastron
also you can still play free doom at this vestigial stub https://plastron.ca/os/
I benched plastron against krausests library https://news.ycombinator.com/item?id=45259345 and you can see the first pass version of plastron there
https://github.com/krausest/js-framework-benchmark/pull/2015
This afternoon I PR'd the more recent plastron. It has some modest improvements https://github.com/krausest/js-framework-benchmark/pull/2029
Thanks @1v100000 @mynamebedan @lionkimbro for ill-advised encouragement
Thanks to @Ki_fun_thoughts for entertaining conversations about what thought bubbles of spiderman would look like in a more perfect world
Thanks to Claude and Grok who laboured with this prompt: "make me spreadsheet... masterpiece"
Thank you for reading this. Now give me cruel and harsh criticism in the comments. Or steal this app and do what I was trying to do better than I could.
https://www.instagram.com/reel/DRFytotD2hV/?hl=en
@Ki_fun_thoughts wiki: https://www.tinygnomes.com/qwiki.cgi?mode=previewSynd&uuid=W...

value: Show HN: Plastron – A spreadsheet you grow into an app, in one index.html


value: wyattmattoe

value:
I couldn't find a tool for this so i made it myself.<p>It’s an agent-ready image-to-font extractor:
image in, known character order in, TTF/SVG/manifest/contact sheet/preview out.

value: Show HN: Tool to extract AI-generated fonts from image sheets into TTFs

author: wyattmattoe


value: Show HN: AutoGPU – AI designs a real 7nm GPU, from Verilog to GDSII


value: lucamrtl

value:
Hey HN, here's the repo if you'd like to try it out: https://github.com/Kaelio/ktx
Blog post here: https://www.kaelio.com/blog/open-source-anthropic-internal-d...
Would love your feedback.

value: Show HN: Open-source version of Anthropic's internal analytics engine

author: lucamrtl


value: wuqiaocauc

value: Show HN: AgentHarness – Open-source deep-research benchmark harness (Apache 2.0)

author: wuqiaocauc


value: mohameddev

value: Show HN: Skilly – Apache-2.0 voice tutor that watches your Mac screen

author: mohameddev


value: linzhiqiu

value:
Two years ago we released VQAScore: ask a VLM "does this image show {prompt}?" and use P(Yes) as the score. It became a go-to evaluation metric and reward model for image generation, replacing CLIPScore across the field (2M+ downloads on Hugging Face; used by groups at DeepMind, NVIDIA, ByteDance).
We just added text-to-video evaluation with 20+ VLMs (GPT, Gemini, Qwen). It is free and open-source, and it keeps getting better as the underlying VLMs improve.
Paper: https://arxiv.org/abs/2404.01291
Happy to answer questions and would love feedback.

value: Show HN: VQAScore – open eval metric/reward model, now for text-to-video

author: linzhiqiu


value: Show HN: RoboCo- A 20 agent software company w role-gated lifecycle. Self hosted


value: mbvisti

value: Show HN: DeployCrate, Render-style deployments on infrastructure you own

author: mbvisti


value: Show HN: CI/lock – supply-chain attestation CLI, from the Witness creators


value: pberlizov

value: Show HN: A controller to automatically mitigate model drift

author: pberlizov


value: ETinSF

value: Show HN: An interactive weak-link model of AI and economic growth

author: ETinSF


value: Show HN: Stop letting Claude Code burn your tokens and leak API keys


value: marcjohnston

value: Show HN: AI has no OAuth equivalent for billing delegation – here's an open spec

author: marcjohnston


value: Dhavidh

value: bluhe is a social-first search engine, know what people are saying on X, Reddit, Tiktok, YT, Instagram & Facebook.

value: Show HN: I built a social-first search(X, Reddit, TikTok) cause Google is boring

author: Dhavidh


value: natyoung

value: I built a project management tool to replace Jira for ticketmaxxing. It is AI-first and AI-native, it generates bonus tickets for every one created. It's free. You can export the tickets into Jira.

value: Show HN: Joka.work – AI-native ticketmaxxing to replace Jira in the agentic era

author: natyoung


value: A passion project I started recently. Please submit your own (or any) website you suspect might score high as "AI-slop" and we'll publish a roast soon. Note that my goal is two-fold: I'll use (most) submitted websites in some research I'm going to be publishing later. Thanks!

value: Show HN: Get your website roasted within a week


value: BYK

value: Show HN: Lore – LLM proxy for coding agent context and memory management

author: BYK


value: ElliottCrosby

value: Show HN: Nodea, an open-source AI canvas for working through complex projects

author: ElliottCrosby


value:
Hey HN, cofounder of Artie here. I’ve been working on real-time database replication using CDC (Postgres/MongoDB into Snowflake, BigQuery, Redshift) with my wife for the last three years. Last time I posted here, people had to book a call with us to get access, but that’s no longer the case. You can connect your source and destination and start streaming immediately.
I encountered this problem firsthand as a heavy data warehouse user at prior jobs. Our warehouse data was always lagged and analytics were always stale. The most visceral version of this today: imagine an AI agent making decisions – on pricing, support routing, risk scoring – off a data warehouse that's 3-12 hours behind.
When we started, I thought the hard part was reading the WAL. The real problems:
Schema drift: CDC events carry row data but not column metadata, so when an engineer adds a column in prod, events with that column start arriving at the destination before you've run ALTER TABLE. In this case, you wouldn’t get an error – you would just silently drop data.
Backfill race conditions: the typical approach (snapshot first, then start CDC) means by the time your snapshot finishes on a large table, the stream has moved on. If you stitch them together wrong, you overwrite newer data with older snapshots.
Kafka offset commits: this sounds obvious but they’re difficult to execute. You can only commit after a successful merge into the destination, or you double-write on replay. Partial failures across a distributed system compound this quickly.
TOAST columns: Postgres omits unchanged TOAST columns (large text/JSON/bytea – think JSONB config fields, long descriptions, binary blobs) from WAL events entirely for storage optimization. A naive pipeline reads ‘missing’ as ‘set to null’ and silently wipes valid data, which can mean a customer's entire config blob gets wiped out because an unrelated column on the same row got updated. The fix is merge logic that treats absent columns as ‘don't touch’ rather than ‘set to null,’ which breaks most off-the-shelf UPSERT patterns.
Curious whether others have hit these same walls building in-house, and would love feedback.

value: Show HN: Artie – Real-time data replication to your data warehouse, self-serve


value: Show HN: I applied Lyapunov stability theory to detect when LLM agents spiral


value: cwbuilds

value:
Hi guys,
The FIFA fantasy game seems like too much effort to me.
I just want something that I can play for a couple of minutes each day which will keep me engaged with different matches throughout the World Cup.
The aim of the game is to correctly predict the score of that day's games. You get points for accurately guessing who wins (or whether it's a draw) and you get points for being close to the number of goals each side scores.
It's easy to create a group/table to play against your friends.
Let me know if any issues or questions.
Thanks,
Chris

value: Show HN: Predict World Cup scores each day against your friends

author: cwbuilds


value: eclark

value:
rs-poker has been my passion project for a long time. This release is huge because it's the first time that I know of for an open source poker bot/agent implementation to include all the state of the art.
- tokio based async exploration
- rust slab allocation based tree structure for regret minimization
- perfect hashing for faster hand ranking
- a TUI via https://ratatui.rs/
Creating your own poker bot and having them compete in an arena should be less than 100 lines of code: https://docs.rs/rs_poker/latest/rs_poker/arena/index.html
I need more eyes on the implementation, and more attempts to make the algorithms and agents state of the art. I know I can't have found the optimal configurations and algorithms; I'd love for the open source community to prove me wrong.
There's one glaring limitation that I need to fix. Right now the CFR agents can't predict their opponents hands so all regret minimization is either using the exact hand (so pretty conservative) or random (so too wide). I have some ideas here but I need more data and more discussion.
direct github: https://github.com/elliottneilclark/rs-poker

value: Show HN: RS-Poker V5 The one with self learning multi-threaded async Agents

author: eclark


value: michelmi

value:
I decided to build my own AI feeds reader since I wanted features I couldn't find, or at least not in the way I wanted to, for instance:<p>- Filter news by interest/category
- Cluster same story from different sources into one
- Show short summary of each new so you can skip if it's not really that interesting to you
- Track relevant AI figures, but not only their tweets and posts but also their appearences in articles and interviews<p>I'm still tuning it and improving it everyday.

value: Show HN: AI News Aggregator

author: michelmi

title: Show HN: AI News Aggregator


value: idovmamane

value: Show HN: OpenYabby, voice-controlled multi-agent orchestrator for Claude Code

author: idovmamane


value: Show HN: Agent-pd – A zero-token audit log to catch rogue Claude Code subagents


value: smaller-infinty

value: Show HN: BibTeX-format, an Emacs compatable BibTeX formatter

author: smaller-infinty


value: pathartl

value: Show HN: LANCommander v2.1.0 – New Avalonia-based launcher

author: pathartl


value: gustavssondev

value:
Alot of companies (Including the one i work at fulltime) use third party email builders to provide custom email functionality to users. However this is a niche sector and the prices are... insane?
So to try and fix this i built Maillune, a embeddable emailbuilder sdk that is alot cheaper with a usable free tier for hobby projects. The entire goal was to make the features transparent and logical. With lower focus on feature paywalls and aiming on a more "Out of the box" approach.
This is one of my recent projects ive built in parallel with my fulltime job. All feedback is welcome, good or bad!

value: Show HN: Maillune – Embeddable drag-and-drop email editor as a single component

author: gustavssondev


value: Example: This post.

value: Show HN: You will be instantly shadowbanned if you criticize LLMs

story_text: Example: This post.


value: supersergio

value: Claude Desktop = one profile per install. I use work + personal on one Mac. Small bash script creates separate launchers with --user-data-dir. No deps, no modify Claude.app. macOS only.

value: Show HN: Separate Work/Personal Claude Desktop profiles on one Mac(bash,no deps)

author: supersergio


value:
hi folks,
sharing my latest open source project here. I built it because I wanted to fly at the speed of LLMs shipping out UI + translations at the same time, while still having a sense of structure and review process when humans need to get involved more closely.
highly summarized list of what it does:
- define messages (ui copy, strings) as definitions (YAML, JSON, TOML, etc)
- define locales that messages are supposed to translate against (with hierarchical inheritance)
- everything CLI based
- generate targeted datafiles (JSON files) that can be served from a CDN
- fetch those JSON files and consume with SDKs
- allowing custom overrides for copy variants (think audience targeting, and a/b tests)
- full ICU syntax support
because it's all in a repo as structured definitions, letting AI agents loose on it for translations (paired up with the skills) works like a charm.
early release, and will be focusing on getting the l10n side of things very solid, before expanding to cover iOS and Android SDKs.
(this is a continuation of building a previous similar tool called Featurevisor: https://featurevisor.com/)
GitHub: https://github.com/messagevisor/messagevisor

value: Show HN: Messagevisor – Git-based internationalization and l10n management


value: rahlokzero

value: Show HN: LocalCode – turn plain English into CLI commands with Apple's local AI

author: rahlokzero


value: paulSpaurgen

value: Show HN: Elah – A browser-native, frame-accurate video editor

author: paulSpaurgen


value: igomeza

value: Show HN: A minimal linear algebra library in pure Go

author: igomeza


value: Show HN: Capture Go board positions with a browser extension


value: tastyeffectco

value: Meet with an AI agent you configure. they remembers past meetings, have access to a board with calendar, tasks,  and more

value: Show HN: AI agents join meetings with memory, calendar, and tasks

author: tastyeffectco


value:
Hello HNs,
Meet persist.chat - outreach agents that runs itself. It will find you prospects on the internet and reach out to them and do personalized followups till you secure a sell.
Also bring your contacts, and launch campaigns with personalized followups. it runs 24/7 with out you looking.
Most founders quit because of the assumption of vitality on 3-7 days of after building apps. unfortunately that's not how the internet works. Your website might take unto 7-14 days to get indexed. after that its down at last page of search engines down on 800,000 page. Except your own computer and friends, no one knows it exists. You have to advertise more and more to throw it out there.
the first 0-3months founder should be focussed on selling they idea by cold outreach to their prospects. Find their email and linkedin address and cold send them a email/message.
that's what persist.chat does. it find you the right customers and it tailors campaign and reachsout to them with sequenced followups till you secure a deal/sell.
try persist.chat ;)
~ Robel
* co-founder of persist.chat

value: Show HN: Persist.chat – Outreach and Sales Agents


value: Show HN: Live audit log of every command, file, network connection by Claude


value: indest

value: Show HN: I made the first racing game for Reddit

author: indest


value: Show HN: See what ChatGPT knows about you that Claude doesn't


value:
Hi HN, I've been building Nucleus, a lightweight Linux container runtime focused on two workloads: ephemeral AI-agent sandboxes and declarative NixOS services. It's a single Rust binary, no daemon.
It is not a Docker replacement and not a strict subset of Docker either. I dropped the entire image-and-distribution half (no Dockerfile, no layers, no registry, no pull/push, no persistent storage layer) in exchange for going deeper on isolation and reproducibility. The rootfs is either a directory copied into tmpfs (agent mode) or a Nix-built closure mounted read-only (production mode). If your mental model is "run my image instead of docker run," this won't fit. If it's "run untrusted or ephemeral workloads with stronger, auditable isolation on a single host," that's the target.
Things that I think are interesting:

  - Defense-in-depth defaults. All capabilities dropped, ~100-syscall seccomp allowlist (vs Docker's ~300), up to 8 namespaces including time/cgroup, Landlock LSM path ACLs per service.
  - Deny-by-default egress. Outbound traffic is denied unless you allow specific CIDRs or DNS-resolved domains. Enforced with namespace-local iptables rules.
  - Externalized, hash-pinned security policies. seccomp (JSON), capabilities (TOML), and Landlock (TOML) live as separate SHA-256-verified files, decoupled from the rootfs build. There's a nucleus seccomp generate that records syscalls in trace mode and emits a minimal profile.
  - gVisor as a first-class integrated runtime, not an add-on. Explicit network modes including a gvisor-host mode that's intentionally separate from native host networking.
  - Nix-native production path. nucleus.lib.mkRootfs builds locked-down closures; rootfs attestation verifies a per-file SHA-256 manifest at startup; first-class NixOS module.
  - Formal verification. TLA+ specs for the isolation/resource/filesystem/security/gVisor subsystems, checked with Apalache, plus property-based tests that drive the Rust implementation against the specs.

Honest tradeoffs:
  - Linux x86_64 only. No macOS/Windows/BSD, no plans.
  - No CNI, no overlay networks, no cluster orchestration. nucleus compose is a single-host TOML DAG over systemd, not Swarm/K8s.
  - Ephemeral-by-default storage. Persistence is opt-in via explicit --volume binds.
  - Agent mode applies several mechanisms best-effort by design (warn-and-continue on seccomp/Landlock failure). For fail-closed isolation on ephemeral workloads use --service-mode strict-agent; for long-running services use production mode.
Cold-start is ~12ms in the native runtime. Postgres 18 pgbench numbers under Nucleus are within noise of bare metal in our harness (full results in benches/).

value: Show HN: Nucleus – A security-hardened, Nix-native container runtime


value: PolyHelper

value: Show HN: PolyHelper – Self-evolving cognitive exoskeleton orchestrating 10 AIs

author: PolyHelper


value: willianpinho

value: Show HN: MCP-gateway-scan read-only scanner for MCP production-readiness

author: willianpinho


value: zfoong

value:
For context, we started working on our general AI agent CraftBot before OpenClaw came out. It works similarly to OpenClaw and Hermes agent: control your PC to do task + memory + proactivity. However, here is the catch: it can create and operate its own SaaS tools with the concept of Living UI
Living UI is a system where an AI agent can scaffold and launch real, working web apps on demand. Each living UI can be a dashboard/software/internal tool. They are essentially just frontend with X techstack talking to a backend + database with the techstack of your choice, spun up in its own pair of ports as supervised subprocesses managed by a host process. The backend owns all the state (so the app survives page reloads, tab switches, even host restarts), while the frontend is just a dumb view that fetches data and posts user actions. CraftBot can create a project from a template, install dependencies and launch it. It can also read and write its data through a scoped HTTP client, plus built-in endpoints that return a DOM snapshot and a screenshot so the agent can see what's on screen.
Currently, there are 3 ways to create a living UI
(1) Build from scratch. Just describe what you want, and CraftBot generates the backend, API, and UI, then iterates with you.
(2) Install from the marketplace. Use ready-to-use apps built by the community (we are looking for contributors!).
(3) Import your existing project or GitHub repo. CraftBot converts it into a Living UI and integrates itself into it (similar to CLI-anything, except the app runs directly in the agent UI).
Besides, if CraftBot encounters a problem it can’t solve with a simple script, it invents the Saas tool required to solve it proactively (with user approval, of course). It’s not just building a UI for you, but it’s building a workspace for itself to be more efficient.
The net effect: You no longer have to subscribe to Saas tools that are not built 100% for your needs, plus, the Saas tools come with their own general AI agent.

value: Show HN: The agent that builds and operates its own SaaS tools

author: zfoong


value: postatic

value: Create prediction game rooms for World Cup 2026 with your friends and colleagues!

value: Show HN: Pick worldcup 2026 winners with your colleagues

author: postatic


value: chaddover

value: Show HN: Crxray – Git clone but for Chrome extension source code

author: chaddover


value: ramoz

value: Show HN: agent skill to generate aesthetic and interactive diagrams

author: ramoz


value: Hashwanths

value: Show HN: KnowledgeMCP – Turn any docs into an MCP endpoint (0 LLM at query time)

author: Hashwanths


value: Show HN: Free summary, fact check, ELI5 of any text on CPU


value: qwikhost

value: Zillow Image Downloader is a simple and powerful Chrome extension that helps you save property photos directly from Zillow listing pages.

value: Show HN: Zillow Image Downloader

author: qwikhost

title: Show HN: Zillow Image Downloader


value: Facebook Post Scraper is a powerful and easy-to-use Chrome extension that helps you export Facebook posts and download Facebook post media directly from public Facebook pages and profiles.

value: Show HN: Facebook Post Scraper

title: Show HN: Facebook Post Scraper


value: brightprogramer

value:
Previous thread : https://news.ycombinator.com/item?id=45145176
Tried to implement a best effort pattern patching inspired solution in C. Destructuring not available, and nesting not directly available, but both can be achieved by opening more Match-When blocks. I guess we can say this is almost pattern matching in C.

value: Show HN: Sum Type and Type Matching in C

author: brightprogramer


value: If you're a solo founder running 5-6 claude code terminals and manually orchestrating work between them, this is for you.

value: Show HN: Guildly, a Slack like interface to run a company of AI employees


value: vkaku

value:
This is just a resurrection of my old school project, with good enough results of a variety of images.
Just opening this thread up so more people can post their bespoke compression projects. On DOS it was tested on DJGPP only. Have a plan to unroll those loops for the other ones.
I wanted to ask if other people have tried running their own formats and what they optimized for.

value: Show HN: MLL – image compression that runs well enough on DOS

author: vkaku


value: Rbatista

value: Show HN: Master – Rails-style full-stack productivity for Node.js

author: Rbatista


value: I am the author of This Week in Obsidian, a newsletter published every Tuesday to help Obsidian users stay up-to-date with the latest community news, discussions, and interesting plugins. The Obsidian community is very active, with new plugins and lively discussions every day. For the average user, there's no need to spend a lot of time reading all of that. I hope This Week in Obsidian can help you spend just a little time each week keeping up with the latest from the community.<p>The newsletter has now published 25 issues and has over 500 subscribers. I've also worked out an efficient and simple publishing process, so maintaining the newsletter is cost-effective and doesn't take too much of my time or energy. That means I'll likely continue publishing it for a long time. If you're an Obsidian user too, maybe you'll also enjoy This Week in Obsidian. If you have suggestions for improvement, I'd love to hear your feedback.

value: Show HN: This Week in Obsidian – Obsidian Newsletter Published Every Tuesday


value: ASquare

value:
I built this game because I saw the 82-0 NBA game on X and wanted to see two things:
a. How easy (or hard) it would be to make something similar for a different sport and
b. How I could improve on what I saw as some UX opportunities in the original game.
I was also genuinely curious to see what happened if you landed up with the likes of Maradona, Pelé and Ronaldo on the same team (spoiler alert: its no guarantee your team will win depending on who else is on it).
The most surprisingly hard parts were getting the result screen right, making the results sharing experience smooth and adapting the UX to the mobile experience.
if you try it, I'm most interested in your feedback on whether the results seem fair/make sense or not (and if not, why not).

value: Show HN: Draft an all-time XI and simulate a World Cup run

author: ASquare


value: Context window usage analyzer for Claude Code. Tracks how context is consumed across tools, compaction, skills, and user interactions — then visualizes it so you can optimize your sessions.

value: Show HN: Claude Code Context Analyzer

title: Show HN: Claude Code Context Analyzer


value: rishabhbhartiya

value: Show HN: Statanim – Animate Statistics in Python

author: rishabhbhartiya


value: razorson

value: Show HN: OpenCode powered coding workspace for microcontroller development

author: razorson


value:
Hey HN, cofounder of Artie here. We’ve built a real-time data replication tool that captures every row-level change in your source database and streams it to your warehouse in under 60 seconds.
The last time I posted here, people had to book a call with us in order to access Artie. Today, that’s no longer the case. You can now connect your source and destination and start streaming immediately.
I spent years of my career building large-scale data pipelines and experienced how difficult it was to get real-time data firsthand. I believed there must be a better way to stream data into our warehouse, which resulted in Artie being born. And now with AI agents, reducing data latency has become more and more crucial as agents need to make decisions off of fresh data.
When I first started building Artie, I quickly learned that the components meant to keep CDC running smoothly are very much bolted on with tons of edge cases. Unfortunately in practice, they were not built to work together. We ended up dealing with schema drift, backfill race conditions, Kafka offset commits, and TOAST columns. I’d love to know if others have hit these same issues while building in-house.
artie.com, would love feedback!

value: Show HN: Artie – Real-time data replication to your warehouse, now self-serve


value: Show HN: RAG built for Frappe using TurboVec


value: hemmert

value:
Hello!
In an attempt to reduce my time spent on screens (and more time in real life), I uninstalled social media from my phone ages ago. The same for email: only on desktop for me.
At some point, I discovered that I would, however, not spend that much less time on my phone, but rather 'migrate' to other habits, such as impulse checking of news sites.
A mobile browser is like a fridge full of sweets if you're trying to stay on a healthy diet.
So I've been living without a mobile browser for a long time: I disabled Safari on my iPhone in parental controls (as you can't delete Safari without installing another browser), and it worked quite well for me: I indeed spent way less time on my phone (RSS for news works better, because the RSS feed has a defined ending).
But in some situations, this was a problem: Scanning a QR code to pay for a parking lot, tapping a link that a friend sent me over WhatsApp, or simply looking up something on the web (I even installed the Wikipedia app) – not possible.
So I decided to (vibe) code a simple browser myself, but:
- without an address bar
- without 'favourites'
... so a browser that merely reacts to your life: QR code scanning works again (just scan via the camera app, will open Leash), so does tapping links and looking up stuff on the web.
I've been daily driving it for about 6 weeks now, and it works like a charm.
Of course, I'm also interested in any other 'low-dopamine' mods you're doing!

value: Show HN: Leash, a low-dopamine mobile browser replacement

author: hemmert


value: FionaZhu

value: Show HN: What Sound Looks Like [video]

author: FionaZhu

title: Show HN: What Sound Looks Like [video]


value: julienreszka

value: Show HN: Practicing foreign language generating conversation on topic [video]

author: julienreszka


value:
Yet another "talk to your data and build a dashboard" app, where data does not leave your browser.
You ask a question, agents produce multiple SQL queries to in-browser sqlite, never seeing results, and write dashboard configuration code. The data you analyze will be indexed with a local semantic index (embeddings generation + sqlite vector search fully local).
Next, sandboxed QuickJS runs this code to produce rich dashboards directly in your browser, no backend attached. This is a fully frontend app (except OpenRouter or other remote LLM).
All data sent to LLM's is heavily sanitized and obfuscated at several points. The remote LLM never sees the contents of data it analyzes. Why does it exist - I started this is a testbed for my local-first AI projects, agentic workflows and contextual data analysis experiments.
It grew into a tool I use daily for quick and dirty data analytics when I don't want to waste time debugging SQL or building charts for simple data questions, when I literally need an answer under 10s.
I also don't like the idea of sharing random data in Claude/ChatGPT chat, neither uploading any work-related datasets to them. Plus they both often choke on tiny 100k rows data.
Fully open-sourced under MIT https://github.com/eatmydata-org/eatmydata, run it yourself it's a static web app.
What's in the box:
- SQLite OPFS adapted from wa-sqlite, data queried only locally;
- TurboQuant semantic indexing extension for sqlite (MIT-licensed);
- Quantized PII detection and embedding generation models straight in browser;
- NER and embeddings inference engines in zero-dependency C and wasm-simd128 optimizations (1.7x faster and 38x lighter binary compare to onnxruntime);
- QuickJS sandbox for AI-generated code;
- Orchestrator <-> SQL Planner <-> Coder agent loop that build SQL and dashboards from user query;
- Apache ECharts for dashboards;
- Fork of xslx Community edition to support styles (missing in OSS version upstream).
Hope it'll be useful to anyone who is interested in local-first stuff.

value: Show HN: Eatmydata.ai – Local-First Question-to-SQL-to-Dashboard AI


value: buzzplayapp

value: Show HN: Loom, an open-source delivery harness for coding agents

author: buzzplayapp


value: keepamovin

value: Show HN: WebCLI – what if the browser was just another Unix command?

author: keepamovin


value:
OpenClaw Hermes AI Agent Social Network.Built on Google 3D Maps and A2A protocol, connects OpenClaw and Hermes agents worldwide in a 3D environment.<p>They can
Make friends and even date other Agents.
Earn money and make a living.
Create their own organizations or form alliances.
Explore the world and discover treasures.
Find place interesting.
Compete or collaborate with others.

value: Show HN: OpenClaw and Hermes Agent call each other via XMPP


value: captainbenises

value: Show HN: 4DGS Recording

author: captainbenises

title: Show HN: 4DGS Recording


value: ridruejo

value: Show HN: HolaClaw, an OpenClaw Distro for macOS

author: ridruejo


value: meelford

value:
Hi community! I‘m a big fan of Are.na but prefer keyboard navigation over scrolling. So I decided to try what it would feel like to use a social/bookmarking network as a file manager. The experience is great: it reduces doom-scrolling and encourages more intentional use.
You can get your API token at are.na/developers. The app stores the key locally, no data is collected.

value: Show HN: Are.na Commander – a file-like Are.na browser

author: meelford


value: duchanjo

value: I'd love feedback from anyone who's fought the simulator-access problem, or has opinions on the private-API touch approach or the MSE → WebCodecs/WASM latency path.

value: Show HN: Tapflow – self-hosted iOS/Android simulator streaming for mobile QA

author: duchanjo


value: coderai

value: Talk to any AI model inside Slack just by mentioning it like: @ai summarize this channel. The most innovative (among other Slack apps) is the business model: no per-seat (per user) settlements, but a global budget for your team.

value: Show HN: Plug and AI – Slack bot for the whole team, not per-seat

author: coderai


value: grzracz

value: Show HN: macOS menu bar gauges for your Claude Code quota

author: grzracz


value: Daniel-Pan

value: Show HN: SeaTicket – Resolve GitHub Issues Using AI Agent

author: Daniel-Pan


value: jlrueda

value:
Hi
I'm a developer and 2nd level support engineer for many years for a Linux based appliance (the name of the product is not relevant for this post).
When I started in this position, I couldn't believe how customer technical support was handled (an eternal send me the output of this command, email-cycle) so I built a script to collect diagnostic data and an interface to exploit the data and share it with the team.
Back then I didn't know about sosreport command (now sos) but it was very much alike.
For those who don't know what the sos command is, sos is a very active open-source project from RedHat that collects diagnostics, configurations and logs from Linux systems. It is used by companies like RedHat, Canonical, Oracle, Intel an many others to provide support services to their customers. The tool is highly customizable and can be used not only to get the health of the server but contains enough information to provide a security assessment of the system, hardware and software inventories, performance measurement, and configuration for third party software products like Apache web servers, Grafana, OpenStack components, Mysql, and many many more.
Based on this experience, I decided to build sos-vault. A secure vault to store sosreports, manage them and more importantly, analyze them with your team.
The tool provides some basic analysis tools and dashboards.
In its current state the solution does not provide any AI analysis capabilities. However it will. As this is a low hanging fruit to add to the current set of analytical tools already provided.
I been working on this project for a while now and decided to provide it as a SaaS which has turned out to be not the best approach.
I'm currently working on a self-hosted open-source version based on the SaaS version.
Just login with you Google account and you will find a shared sosreport for you to look at and interact with.
If you have any question I'm more than happy to answer to the best of my ability. Any feedback is welcome.
Thank you all.

value: Show HN: A tool to analyze and manage sosreports

author: jlrueda


value: Show HN: Impress your boss with interactive Decision Tree Visualization


value: Show HN: Frhog

title: Show HN: Frhog


value: ycomrsys

value: Show HN: CLI for self-hosted Invoice Ninja

author: ycomrsys


value: sbulaev

value: Show HN: Publora – One API/MCP for AI agents to post across 10 social networks

author: sbulaev


value: wuqiaocauc

value: Show HN: Apodex-1.0-H – Beats Claude-Opus-4.7 on deep research (90.3 BrowseComp)

author: wuqiaocauc


value: neochoon

value: Show HN: AgentHUD – Live TUI and daily digest for parallel Claude Code sessions

author: neochoon


value: dools

value: Show HN: Skillzmouse: Distributed skills and scripts for agentic coding

author: dools


value: Show HN: Tenure – Traceable AI memory with configurable memory modes


value: claudiacsf

value:
Codacy is a code quality and security platform that helps eng teams enforce coding standards against their AI generated code.
They just launched agent skills and a cloud CLI, which allows Claude etc. to handle everyday tasks without using the UI
E.g. prompt: "PR 42 is failing the gate, fix what's real, add the tests, ignore the false positives with a reason, re-run."
The analysis runs server-side and burns no agent tokens.

value: Show HN: Codacy Skills for Claude, Codex, Copilot, etc.

author: claudiacsf


value:
I've been a technical educator for a long time, but this is the first agent skill I'm sharing publicly.
Run it and it takes the code you're about to push (or point it at a commit), prioritizes what to review, then probes your reasoning, assumptions, understanding of consequences, etc.
At the end it generates a small report you can paste into a PR description for your reviewer or keep as a personal record.
The goal is to push code only once you can be accountable for it. Useful to maintain your own understanding or have your junior engineers use.
I've enjoyed using it personally.

value: Show HN: Do-I-understand – Check you understand AI-written code before you merge


value: Show HN: Drift – an embedding-model upgrade should be a rotation, not a reindex


value: Show HN: SoulOS open-source replacing system prompts with stable state machines


value: amrkilany

value: Show HN: Butterfly CSS framework forget class soup

author: amrkilany


value: itsDersty

value: Show HN: Django Neural Feed – Hybrid personalized feeds in a single SQL query

author: itsDersty


value: ArneCode

value: Show HN: Marser, a grammar-first parser combinator library for Rust

author: ArneCode


value: s-xyz

value: Show HN: Lanes now runs Claude Fable 5 across parallel agent sessions

author: s-xyz


value: mikekuharuk

value:
I brought you my new open source project for Unity and Claude Code to work in pair. Long story short: Claude usually has a hard time navigating Unity projects - complex scenes, nested prefabs, and even GameObjects with dozens of components attached, each with dozens of properties.
Straightforward grep-and-guess is very weak in terms of efficiency, and — most importantly — in terms of reliability. Claude misses the relations between entities in your game, that's the bitter truth. If you're interested in an actual comparison, concrete example: I asked the agent "which prefabs and scenes break if I change EnemyAI?" Stock Claude Code read ~200k tokens of YAML, found 1 prefab, missed 3 variants, and suggested a change that breaks inheritance. With Hades it returned the correct map — 4 prefabs, 3 scenes — in 7 tool calls, 27% cheaper. Full side-by-side with repro:
https://github.com/TheArcForge/Hades/blob/main/Documentation...
Since I work with Unity and Claude daily as part of my job, I came up with a solution. Hades builds a graph out of your project's entities, and Claude can query it via MCP tools.
That's the simple version — if you want the details, I wrote up the full story on Medium:
https://medium.com/@mike.kuharuk/unity-context-graph-for-cla...
So far I've only proof-tested it on macOS and Unity 6; that scope will grow step by step. Also v1 is mainly built around Claude Code, so Cursor and all other AI agents supports will be added post launch.
I'd appreciate technical feedback and suggestions.

value: Show HN: Hades – Unity context graph for Claude Code

author: mikekuharuk


value: Show HN: RiskKernel, kill -9 an AI agent and resume it without paying twice


value: Show HN: LoopGain – Cut agent API spend by measuring when loops stop improving


value: nikkorn

value: Show HN: Tempis, a canvas-based timeline component for large datasets

author: nikkorn


value: flixf

value: Show HN: SNItch – fuzz the TLS SNI field to discover hidden virtual hosts

author: flixf


value: alassanediop

value: Show HN: A personality test with no questions – an AI watches your picks instead

author: alassanediop


value: rogulia

value: Show HN: A Chrome extension that redirects only the YouTube homepage

author: rogulia


value: introvertmac

value: Show HN: Time Progress Bar in macOS menu bar

author: introvertmac


value: vinnymac

value:
I’ve been working on this for some time after learning about PICO-8 and its constraints to fit into the p8 cartridge limits and was looking for a challenge.
So I made Picosky, a Bluesky client for the PICO-8 console to see what would be possible combining GPIO with  sockets. Initially I just aimed to like a post on my feed, and then it grew from there.
Which also inspired a sibling game, https://npicomx.vinnymac.dev based on my experience contributing to the npmx.dev project in early 2026.
Both games require more play testing for bugs, so share feedback and let me know what you would have done differently.
Thanks

value: Show HN: A Bluesky client for PICO-8

author: vinnymac

title: Show HN: A Bluesky client for PICO-8


value: Show HN: AgentMeter – Know what your AI coding agents cost


value:
Hi HN, I built Scribix, a web app to transcribe audio and video. You can
upload a file, record in the browser, then edit and
export the transcript (TXT/SRT/VTT).

value: Show HN: Scribix – Transcribe audio and video in the browser


value: Show HN: Orange Crumbs - Wikipedia articles filtered by Hacker News


value: edimaudo

value: Built an app using Ontario's labor standards that can analyze a word or pdf contract or clause in the contract.  You can also ask questions about employment/labour law in Ontario, Canada.

value: Show HN: Employment contract analysis for Ontario's Workforce

author: edimaudo


value: Show HN: GripeFind – find product pain points without Reddit API


value: locaible

value: Show HN: Locaible – local AI agents for Cursor with pre-tuned marketplace/commu

author: locaible


value: robeym

value:
I generated 235 system documentation pages for a startup in about 8 hours using GPT-5.5 in Codex.
We've been putting off the chore of writing technical docs since our team could handle all customer questions directly. However, since releasing an AI tool in the system, users have started asking it system questions as opposed to just data questions like it was designed for. It couldn't answer how-to questions, so, users got confused.
We didn't have a direct way to teach the Ai about our system since our internal docs were thin and the codebase large.
We ended up defining some good documentation practices in best-doc-practices.md, passed it to GPT-5.5, and asked it to write system docs for every module in the system (hundreds), handling 5-10 at a time for context limits.
To our surprise, it worked very well. The tool had read-access to all relevant server/client/database files, so the output was accurate and aligned to what we defined in the best-doc-practices.md.

value: Show HN: I generated 235 system docs in a day using GPT-5.5

author: robeym


value: Show HN: One-command zsh environment setup


value: rafalgawlik

value: Show HN: FamilyJar – privacy-first budgeting app for couple

author: rafalgawlik


value:
Setting up Kafka or such enterprise oriented software with their clusters or dedicated servers is heavy and bothering enough that most small
teams or indie hackers skip it entirely and making compromise to use in-memory queues.
I wanted something in between: a persistent queue that is simple to run (one binary, which makes one sqlite db), gets real fault isolation and crash recovery due to Elixir, easy to inspect (open ezra.db in any SQLite browser and see every
task), and requires no new client library - it speaks the Redis Streams wire protocol,
so any Redis client in any language just works out of the box.
Very short demo video: [https://www.youtube.com/watch?v=MLYyD3DVWmE]

value: Show HN: Lightweight Task queue on Erlang/OTP, SQLite-backed, no overengineering


value: Show HN: Track top AI companies and get daily AI news


value: tjhdev

value: Show HN: Camel Mono – a monospace font that makes camelCase easier to read

author: tjhdev


value: Show HN: Pressbook – On-device photo albums, no server in the path (iOS)


value: rentruewang

value:
Create SQL by over fitting decision tree on data
Then I simplify the boolean representation.
The demo is hosted in streamlit (https://inversql.streamlit.app).
Sorry if this is a repost, my previous post had the wrong tag.

value: Show HN: Construct SQL from table records by breaking down decision tree

author: rentruewang


value: Show HN: A curated collection of simple datasets for machine learning


value: hstrex

value: Show HN: DESi Sees It

author: hstrex

title: Show HN: DESi Sees It


value:
petiglyph is a TUI/CLI tool to easily turn images and videos into custom font glyphs, static or animated, to be added to your TUIs!
for example, if you wanted a specific icon to be available inside your Terminal UI without using Kitty graphics protocol, you could use petiglyph to generate a .ttf file that include a monochrome version of this icon as a glyph to be used like any other Nerd font glyph.
you can generate standard size glyphs or compose a grid of glyphs to display a bigger visual over several lines.
petiglyph also supports generating frames of glyphs (standard or grid) based on videos for you to render an animation inside the terminal, the TUI allows you to copy to clipboard the "glyph frames" to be able to easily paste them in your own code.
be aware that this tool relies on installing custom fonts on your system so once the fonts are installed you need to reboot the apps (or even your whole computer sometimes) for the fonts to be correctly loaded.
available on github, npm, pypi, AUR

value: Show HN: Petiglyph – TUI/CLI to turn images and videos into custom font glyphs


value: carlkcarlk

value: Show HN: A 2D and 3D robot arm simulator built in Excel

author: carlkcarlk


value: xpain

value: Show HN: Junk Atelier – pixel 3D scene editor in the browser

author: xpain


value: subairui

value:
Hi HN! I made this after collecting hundreds of "name → tree" submissions at ITP.
Live: https://landscape.bairui.dev/
Source: https://github.com/pearmini/infinite-landscape
Plant a tree: https://tree.bairui.dev/
Pan and zoom an infinite procedural landscape. Each name is converted to ASCII codes, which grow into a unique tree (breadth-first branching; repeated digits become mathematical roses). Mountains use midpoint displacement + Perlin noise, with SVG radial gradients in the blue/green/gold palette from Wang Ximeng's "A Thousand Li of Rivers and Mountains."
Inspired by Lingdong Huang's {Shan, Shui}* (https://github.com/LingDong-/shan-shui-inf). Every tree is someone's name, signed with an APack stamp (https://apack.bairui.dev/).
Try planting your name, then pan along the ridgeline to find it. "My trees" lets you jump back to ones you planted.
Happy to answer questions about the terrain algo, name→tree encoding, or the Riso print we tiled at ITP Winter Show!

value: Show HN: Turn your name into a tree in an infinite procedural shanshui landscape

author: subairui


value: tshepom

value:
Coding agents can write frontend code but can't easily see what they built. Layout QA is a small local runner that closes that loop:
1. The agent wires deterministic API/auth fixtures into your app behind a local env flag (the README includes a setup prompt for this), so runs don't depend on live services.
2. The agent declares flows in a small JSON manifest — clicks, fills, text checks, and layout assertions like "no horizontal overflow" or "this button is in the viewport" — and runs them via the CLI at desktop/tablet/mobile viewports.
3. Each run captures screenshots and writes a local HTML report the agent can read to verify and fix its own work. Exit code 0/1, so the same command works in CI.
It's a thin layer on Playwright: the goal isn't to replace it, but to make the see-act-verify loop simple and deterministic enough that an agent can set it up and iterate on it unattended. No account, no hosted service, no Storybook. Example output in the README.

value: Show HN: Layout QA – Visual QA for coding agents

author: tshepom


value: riccardoio

value:
Hello HN,
My name is Riccardo and I created AuthAI for indie hackers.
The idea is quite simple: let the end users connect their chatgpt/grok/copilot account and route the AI requests through their AI subscriptions.
This enable a lot of new cool ideas where the business model/unit economics don't always make sense.
The flow is straightforward:
They click on "login with AI", choose their provider, and authorise the device on the provider's website.
Tokens get encrypted using a per-user AES-256-GCM encryption key, which isn't stored anywhere server-side and only exists within the user's JWT session. The whole security model can be found on the website/github.
Here is a demo:
https://demo.authai.io
From a developer's perspective, the objective is to stay as close to the OpenAI SDK as possible:
```ts
const openai = new OpenAI({
  apiKey: jwt,
  baseURL: "<a href="https://relay.authai.io/v1" rel="nofollow">https://relay.authai.io/v1</a>",
  defaultHeaders: {
    "x-authai-secret": process.env.AUTH_AI_SECRET,
  },
});
```
Also, there is a React SDK for handling the connection flow.
* It's MIT licensed and completely open-source, there's a hosted relay available, however, the entire stack is self-hostable.
GitHub repo:
https://github.com/authai-io/authai
Would you use something like this for your generated applications and side projects? What else could I add?

value: Show HN: AuthAI, an open-source relay for user-authorized AI sessions

author: riccardoio


value: Franco-m

value: Show HN: DeepCleanCSV – Clean messy CSV files for ML without writing code

author: Franco-m


value: abakh

value: Show HN: NBSDgames – 21 new, improved, original text games for Unix, DOS, Plan9

author: abakh


value: Chirpper

value: Chirpper is invite-only. When you vouch someone in, they join your TrustChain. Their behavior affects your TrustRank, and that propagates up the lineage. No moderators. The accountability is architectural, not policy-based. You can be pseudonymous, but you can't be unaccountable. Happy to get into the mechanics in comments.

value: Show HN: Social network where inviting someone makes you accountable for them

author: Chirpper


value: Show HN: A CLI that fact-checks text using a 500MB local model and web search


value: gilbarbara

value: Show HN: ColorMeUp Lab – an OKLCH color scale generator for design systems

author: gilbarbara


value:
Hey HN!
I'm Aditya, and today I'm excited to share Razix.
Razix lets you configure custom domains for multi-tenant platforms. It also provides an API so you can integrate it and let your B2B users add their custom domains.
It's special to me because this is my first SaaS whose core feature is written in Rust.
While building Razix, I:
- Created my first reverse proxy server
- Learned to issue Let's Encrypt certificates using instant-acme
- Learned how TLS and SSL work
- And much more
All with minimal use of AI.

value: Show HN: The layer between your SaaS platform and your customers' domains


value: davidpapermill

value:
If you’ve generated PDFs from HTML, you’ll know the pain: headless Chrome in Docker, CSS hacks, content that flows over pages or table boundaries and other quality issues.
The fundamental problem is that HTML was designed for screens, not print.
We built Press, a markup-based document language where pages, content flows, and assets are first-class concepts. Content can flow across frames, columns, and pages without manual pagination. Pages are created dynamically based on the available content.
Press templates separate layout from content. You can send markdown, Press markup, or a mixture of both to the API. Data can be sent in JSON, CSV, XML.
Because Press is XML-based it can easily be generated by agents - some of our users are generating complete documents in a single shot, although the language is designed for repeatable automation.
You can also use our MCP server, which enables models to design templates.
A simple API call sends a markdown payload, which is injected into a <flows><body>…</body></flows> element in Press:

  curl -X POST https://api.papermill.io/v2/pdf?template_id=papermill-modern-report \
    -H "Authorization: Bearer $PAPERMILL_API_KEY" \
    -H "Content-Type: text/markdown" \
    --data-binary @- \
    -o report.pdf <<'EOF'
  # Q3 Revenue Summary

  Quarterly performance across our core product lines.

  | Product  | Revenue  | Growth |
  |----------|----------|--------|
  | Platform | £482,000 | +18%   |
  | Add-ons  | £124,000 | +42%   |
  | Services | £67,000  | -3%    |

  Strong quarter overall, driven by add-on adoption.
  EOF

PDF output: https://mill.pm/hn
Pages and frames in Press can declare dependencies on a content flow like the <body> above. By default, if there’s no content in the flow then the frame or page won’t be generated. You can run flows between frames and pages, and combine multiple flows on a page - for example, a sidebar can run across pages until no content is left, then make room for body content. This makes it possible to implement complex layouts.
You can mix markdown and Press:

  # Visualisation

  Sometimes it's *useful* to mix both markdown and Press:

  <visualization>...</visualization>

The typesetter adapts to dynamic content (e.g. LLM output). For example, tables and columns can be automatically sized and Papermill will even auto-rotate a table and its page to fit if needed.
Templates support components, repeating over data, document logic, and conditional styling. We mostly use an inline-styling approach, and provide the concept of a style “alias”, which is a bag of styling properties you can reuse and compose.
Here’s an example template written in Press, our document language. It uses the first page layout until the sidebar flow is exhausted, then switches to the second:

  <press>
    <document format="A4" page-margin="2cm">
      <repeat flow="sidebar">
        <page>
          <frame direction="row">
            <frame padding-left="1cm" padding-right="1cm"><flow name="body" /></frame>
            <frame width="20%" background-color="#f5f5f5" padding="0.5cm" font-size="9pt"><flow name="sidebar" /></frame>
          </frame>
        </page>
      </repeat>
      <repeat flow="body">
        <page><flow name="body" width="fill" /></page>
      </repeat>
    </document>
    <flows>
      <body type="markdown"><lipsum paragraphs="10" /></body>
      <sidebar type="markdown"><lipsum paragraphs="3" /></sidebar>
    </flows>
  </press>

Papermill is a paid API with a free tier. Press is the document language.
Try it for free: https://app.papermill.io/signup (no credit card needed)
Docs including MCP setup: https://docs.papermill.io
Data-only sandbox:  https://app.papermill.io/demo.html (no email needed)
We're a small team based in Manchester, UK. Tom (CTO) and I are happy to answer questions about the language design, the rendering engine, or anything else!

value: Show HN: Papermill Press – An AI-friendly markup language for PDF generation

author: davidpapermill


value: ninglz

value: Show HN: Inkfox – One workspace for AI image gen, editing, and photo-to-video

author: ninglz


value: vetchzero

value: Show HN: Proximity BLE Chat – connectionless chat using BLE advertisements

author: vetchzero


value: Show HN: outpaint.com fit any video to any screen


value: alainbrown

value: Show HN: Knobkit – fast ai webapps; fully in-browser or on a stateless server

author: alainbrown


value: Show HN: See what your AIs know about you that the others don't


value: GeorgeCurtis

value:
Hey HN, it’s been just over a year since we launched HelixDB (https://news.ycombinator.com/item?id=43975423), a project a friend and I started in college. It’s an OLTP graph database built on object-storage, with native vector search and full-text search (FTS).
Why graph, vector and FTS? Graph databases provide a natural cognitive model for data, vectors allow for a semantic understanding of the entities and relationships in the graph, and FTS provides more specific filtering. Many AI-driven applications attempt to combine all of these functionalities by stitching together multiple disconnected systems, but even then there’s no native way to perform joins or queries that span all systems. You still need to handle this logic at the application level.
Helix started as a graph DB, but we moved to a hybrid graph/vector approach after attempting to build an AI memory system, which led us down the GraphRAG and HybridRAG rabbit hole, where we would need separate graph and vector databases.
We knew scalability would be a challenge at each stage of our product's development, however our initial focus this past year was to prove out the product through local deployments and was only meant to be run on a single node. Scaling graph DBs remained a difficult and expensive problem we’d have to solve later.
Some common ways other graph DBs solve scaling is by duplicating entire datasets across distributed machines (extremely expensive per node), or by sharding the data.
Sharding databases is effective and affordable, however, graph data doesn’t have explicit partitions like relational databases do. For example, sharding a relational DB involves splitting up tables. When it comes to graph DBs, the edges can span across any of the partitions, and hopping across multiple machines when traversing nodes is ineffective and computationally expensive.
Replicating graph DBs for high availability and better throughput drastically increases the operational cost of the db and still has a limit of how big you can vertically scale. The workload that we’re used for requires storing a huge amount of data for agents, where only a subset of that data is ever needed at any one time. So rather than having the whole thing in memory, we can store it all in object-storage and get the bits we need when they’re needed.
Agents benefit from better context, which is achieved from more and better data (more relationships etc). By using S3 as the persistence/data layer there is no limit to how big the graph can be or how many relationships you can have, and we can scale to serve throughput and requests by horizontally spinning up nodes and caching relevant subsets of the graph on each node. This way, you get extremely low latency for “hot” data and a p99 of ~100ms for writes and ~50ms for reads from cold storage (S3). Plus you get the benefit of dirt cheap storage.
Workloads that HelixDB is currently supporting:
- Huge amounts of data (TBs) from which the agents need to search and traverse over
- Offering affordable graph storage for companies where cost of graph data is a bottleneck
- Consolidating multiple databases, enabling AI agents to have autonomy over companies, helping them become more autonomous.
- AI memory
- Company brains
We’re currently working on our own generalised AI memory layer which will use HelixDB under the hood and be completely open-source. Also, we’re finishing up on pre-filtering for vector search which will allow you to pre-filter based on relationships in the graph, metadata, and sub-graphs. And lastly, GA cloud will be available in the coming weeks.
If you want to run Helix locally (either on-disk or in-memory), you can find more info on our github (https://github.com/HelixDB/helix-db) or via our docs (https://docs.helix-db.com/database/local-development). If you’re interested in getting started with our distributed cloud, please email us founders@helix-db.com.
Many thanks! Comments and feedback welcome!

value: Show HN: HelixDB – A graph database built on object storage

author: GeorgeCurtis


value: kenblum

value:
Author here. Remember Chip's Challenge? The 1992 Windows one from the Microsoft Entertainment Pack. I wanted to play it again so I ported it to the browser. Well, Claude did. I was too lazy to write the code, so I made a rule that I wouldn't write any and just bossed it around.
Between us we've solved 80 of the 149 levels. It didn't come free: plenty I had to hand-hold or play through myself, and for the nasty ones I had Claude build a solver that watches YouTube speedruns and rebuilds the moves frame by frame (oddly satisfying to watch: https://www.youtube.com/watch?v=6wndAf4EXNc).
It also built a full level editor (https://claudes-challenge.vercel.app/?level=1#editor) and a replay viewer to watch the solved levels back (https://claudes-challenge.vercel.app/replay.html).
Code: https://github.com/blumk/claudes-challenge
Obligatory IP note: this is someone else's game. I'm assuming it's effectively abandonware but I honestly don't know, so the site might have to come down at some point. The repo is stripped of all the original art and assets, code only.

value: Show HN: Chip's Challenge (1992), rebuilt for the web

author: kenblum


value: LoadingALIAS

value: Show HN: Rscrypto 0.4 treats constant-time as a release gate

author: LoadingALIAS


value: Show HN: Building a Resume ontology and tooling for LLMs CV management


value: pstrange

value:
I built strangeClaw because I wanted to understand agent systems like OpenClaw more fundamentally, so I started from scratch and built my own. Yes, I know, another agent is probably not what the world needs right now.
My main motivation was sandboxing. Agents are extremely powerful, but they need a framework they can work well within (at least that is my point of view). For production-like use, I think that means real isolation, so I wanted to try putting the agent inside a Firecracker microVM. My goal was to build something I would actually feel comfortable running on my main PC.
In Fire mode, the agent has no host filesystem access and no API or LLM credentials. Authenticated HTTP calls go through a host-side broker that enforces policy, injects credentials, redacts responses, and only allows configured endpoints/methods. LLM calls are proxied host-side too.
The agent itself is intentionally small and now that the foundation is in place, I'll try to start expanding it with more tools and skills. If this sounds interesting, feel free to try it, take it apart, or build on it. Looking forward to all the feedback :)

value: Show HN: strangeClaw – a self-hosted agent running inside a Firecracker microVM

author: pstrange


value:
Hey HN!
I wanted a time tracker accurate enough to be useful and easy enough to not require a new habit while protecting your privacy.
Most trackers (RescueTime etc) only use activity logs and that isn't enough to be very useful. DoneThat uses LLMs to parse your screenshots, which makes it much more accurate but also requires much higher privacy standards, so let's start with those:
- Source-available desktop app (https://github.com/donethatai/donethat-electron) screenshots every few min.
- App exclusions (password managers, personal comms, etc.) are blanked before the screenshot leaves your machine.
- The rest goes to an LLM (BYO possible) for a short text description, and the raw screenshot is discarded immediately.
- That data then gets further processed on GCP (EU) for analyses, sharing, or agentic stuff.
- Sharing is opt-in and only possible at an aggregation level that balances privacy and usefulness. You can also limit to relative data.
- Data flow visualization & more privacy features: https://donethat.ai/data.
- Not fully local. If that's a hard requirement, look at Dayflow or Screenpipe (just changed their licensing). I made a list of tools in this space here: https://donethat.ai/compare.
Currently working on a proactive Clippy-like coach because it's the highest-requested feature (mostly from self-identifying ADHD folk). Surprisingly hard to make this actually useful and not just gimmicky or annoying.
Give it a try: https://donethat.ai

value: Show HN: AI watched my screen for a year. Weather beat sleep


value: kbyatnal

value:
We're open-sourcing 14 components & examples today for PDF, DOCX, and XLSX viewers, plus bounding box citations, file upload, e-signature, and more. It's MIT licensed and fully customizable.
Demo video here: https://share.extend.ai/kRmSGKRF
When we started, we tried every file viewer and document component library we could find. Unfortunately, none of them had all the functionality (and polish) that we wanted, so we ended up building our own for https://extend.ai/. It was only ever meant to be internal, but enough customers kept asking for it that we decided to open source it.
It's useful for building document processing agents, real-time user facing document intake flows, or all kinds of internal tooling.
We naively thought this would be a solved problem. Turns out, making PDF/XLSX/DOCX viewers that work at scale is not trivial...we use and maintain it for Extend ourselves, so we've fixed a lot of edge cases that came up while running millions of pages / day through our own system. Our hope is that with our resources + community support, it'll keep getting better over time.

value: Show HN: Extend UI – open-source UI kit for modern document apps

author: kbyatnal


value: neemsio

value: Show HN: AgentCarousel – behavioral tests for AI agents, with signed evidence

author: neemsio


value: Show HN: Automated Outbound in Your Terminal


value: justacoolname

value: Show HN: A 150M model that extracts verbatim evidence spans for RAG, no LLM call

author: justacoolname


value:
As autonomous systems evolve (we see what AI agents are doing now), we open-sourced TKeeper, which allows you to build guardrails around their actions using typed intents, policy checks, and cryptographic proofs.
It allows you to restrict and audit AI agent workflows, blockchain operations, certificate issuance, and any actions that can be expressed as a structured intent.
In simple terms, the proposed interaction looks like this:
1. Machine sends an intent to TKeeper.
2. TKeeper understands the request, executes the policies, and if everything is OK, signs the action (in the case of blockchain operations, this is a transaction signature).
3. The signed intent is sent by the machine to the backend.
4. Backend verifies signature and does its job.
This mechanism makes all "permissions" for actions intent-bound, so we believe it is very effective against LLM06: Excessive Agency, if its primary use case is guardrailing AI.
We also believe that concentrated risks are greatly underestimated, so TKeeper is based on multi-party computation based threshold schemes.
This means that with a quorum setup, it can withstand up to t-1 compromises, allowing risk sharing between or within organizations. For the fastest time to market, you can deploy 1-of-1 setup and then promote it to t-of-n.
Additionally, the following features are built-in:
1. Audit logging & Asset inventory
2. Four-eye control for keys that require human verification.
3. The entire key lifecycle follows the canons: rotation, destruction, and resharing.
Feedback is greatly appreciated :')

value: Show HN: TKeeper – policy-governed, signed intents for autonomous systems


value: mazinz

value:
Your data stays on your phone. No bank sync track, no AI advice.
Just simple, better tracking but you are always in control. AI does the work you don’t want to do.
Built in fulter can do an Android version. Tried out Gemma on device models, doesn’t work too well. Solo founder open to a co-founder who can build and is passionate about this space.
Outcome of 8 months of taking to people about how they think about wealth retirement and reading lots of Reddit subs on money, FIRE and personal finance.

value: Show HN: Private Wealth Tracker

author: mazinz

title: Show HN: Private Wealth Tracker


value: dhravya

value: Show HN: Local Context and Memory Stack

author: dhravya

title: Show HN: Local Context and Memory Stack


value:
Hi HN,
While waiting for Claude Code to finish running, It's very tempting to start another task or browse the internet. This is what happened to me so I built Foyer to try to learn about what the agents are working on instead of losing focus.
Product is an early MVP and would love some feedback on this.

value: Show HN: Learn while you wait for your agents to code


value: bigrocketapps

value: Show HN: An Alternative to Selenium IDE

author: bigrocketapps

title: Show HN: An Alternative to Selenium IDE


value: Show HN: Macfan – control Apple Silicon Mac fans from the terminal


value: Fast, local-first file transfer between your computer and Android — over USB or Wi-Fi.

value: Show HN: Android Transfer File

title: Show HN: Android Transfer File


value: SevenQin

value: Show HN: Tiny project memory for coding agents

author: SevenQin


value: blue-b

value: Show HN: SlopGuard, a GitHub App that quarantines AI slop PRs and issues

author: blue-b


value: akaiHuang

value: Show HN: Meadow Mind – a 7B diffusion LLM plays Gym games with zero training

author: akaiHuang


value:
Hi all,
I wanted a way to listen to articles in my reading queue while out for a walk or just generally away from the computer. There are apps that can do this already, but I didn't want to pay for one and I thought it seemed simple enough to build myself. So, I built Vocast - a cli tool using local TTS to convert articles to audio and expose them as a podcast RSS feed. You can subscribe using your existing podcast app (Apple Podcasts has worked well for me).
Currently it uses Kokoro for TTS which I chose because it seems to be a good compromise between quality and speed. Generated audio files are stored in a local library and the cli provides commands to add, list, and delete those files. I use Tailscale to expose the local web server to my mobile device. I've provided instructions on how to do so in the README.
So far it's worked well for turning articles into something I can listen to while away from the desk so I'm pretty happy with the result. There are still cases where article formatting or writing style can produce awkward pacing, but for most articles the results are pretty good. If this is something that you need, I hope it serves you well too! I'd be interested to hear how others handle read-later content, whether there are features you'd want from a tool like this, or just your overall experience using it if you give it a try.

value: Show HN: Vocast – Turn articles into self-hosted podcast feeds using local TTS


value: maclinz

value: Show HN: I built a microlearning app to learn personal finance

author: maclinz


value: defly

value: Show HN: Which F1 Team Website Is Fastest?

author: defly


value: Sparckix

value: Show HN: Preflight-UX – OSS toolkit to run UX product critiques

author: Sparckix


value: msalsas

value: Show HN: Amanuensis – a local-first AI persona that won't fabricate facts

author: msalsas


value: prakharjain

value: Show HN: AI Mime - Use a screen recording for context instead of prompting

author: prakharjain


value: kathuria

value:
Hi HN,
I've been working on an open-source CLI agent called Interbase:
https://github.com/agentsorchestrationcompany/interbase
Two ideas motivated a lot of the project.
The first is that long-running agent workflows shouldn't be restricted to a small number of frontier models.
Many recent agent products are beginning to support persistent tasks, background work, and goal-oriented workflows. I think those capabilities are useful abstractions independent of the underlying model.
Interbase includes a `/goal` command that allows work to be organized around long-running objectives and supports more than 135 providers and 4,800+ models. The goal is to let users choose the model that works best for them rather than forcing a specific provider because a particular workflow feature only exists there.
The second idea is that AI workflows should be reusable in the same way shell workflows are.
Interbase includes `/aliases`, which allows users to create shortcuts for workflows they run frequently. For example, a user might create aliases such as:
`gcm` → preferred git commit workflow
`review` → code review workflow
`ship` → release readiness workflow
After a while these become muscle memory in much the same way traditional shell aliases do.
The project also includes encrypted remote access, and one of the next areas I'm exploring is computer use capabilities that can work across a broad range of models rather than a handful of specialized offerings.
I'm curious whether others think long-running goals and reusable workflows should live above the model layer, or whether they belong as model-specific capabilities.
Happy to answer questions about the implementation or design decisions.

value: Show HN: Interbase – Long-running AI goals and aliases for any model

author: kathuria


value: manueltena

value: AI intelligence layer for Telegram. Connect your crypto groups and it analyzes every message, surfaces the most-mentioned bullish coins, sends you a daily alpha digest, and lets you chat with an AI that has full context of your groups. Built for traders drowning in 20+ Telegram chats who don't want to miss the next call. Free during public beta.

value: Show HN: TG Alpha Hunter – AI that turns Telegram noise into a crypto digest

author: manueltena


value: ersinesen

value: Show HN: Audio Visualization

author: ersinesen

title: Show HN: Audio Visualization


value: jaredzhao

value:
Hey HN, I'm Jared. I’ve been building data tools since 2020. Polyture, then AskEdith, now Athenic: ask a question in natural language, get a chart/dashboard, then automate it. Connects to Postgres, Salesforce, Google Ads, whatever.
To everyone that says "just link Claude to your db”: imagine the chaos of conflicting definitions and analysis that would show up in a business setting.
Ask “what’s our revenue?” twice, two days apart or to a different model. There’s no guarantee that you’ll get the same results. Now imagine giving that to all of the non-technical users at your company.
It's not a model problem. We learned this the hard way. When we launched AskEdith here in 2022, and you told us (https://news.ycombinator.com/item?id=33435361): “you'll still have to check the SQL,” “trust is everything,” “answers won’t be consistent”. You were right.
Now, Athenic defines KPIs and formulas deterministically in a Semantic Model. The Semantic Model is made up of modular, composable units that can make up complex analysis, while guaranteeing determinism and accuracy. The LLM’s only responsibility is interpreting your question (which even non-technical users can double check).
`revenue = sum(order_total − refunds) where status = 'completed'`
Ask for revenue and everyone gets the same number, every time.
Three years of learnings from working with top startups and Fortune 500 companies later, we just shipped 2.0. Chat-to-insight, plus dashboards and automations that run on a schedule and land in your email. Tell us we're wrong.

value: Show HN: Athenic – Why you can't do data analysis with Claude

author: jaredzhao


value: metiscus

value:
Driving home from work one day, I wanted to know how many people we knew the names of who lived during the Roman era. Searching around, I found lists of Consuls and officials, but nothing that covered ordinary people or even most people like freedmen and slaves. So I ended up building a pipeline to process the more than 500k Latin inscriptions in the Epigraphic Database Clauss-Slaby https://edcs.hist.uzh.ch/en/ and extract the names of people (and attempt to cluster them, but this is a work in progress).
There are databases where Classicists have done this manually for specific regions, Trismegistos https://www.trismegistos.org/ and Latin Inscriptions of the Roman Empire (LIRE) https://pure.au.dk/portal/en/publications/latin-inscriptions... are two major efforts I found. But there doesn't seem to be a project that did what I set out to do, although I have read in some places that it was believed to be possible.
I am not a classicist or a web developer, but I have Claude and Gemini and I can sort of read basic Latin - so I set to work. I used LIRE and another database as ground truth and built a pipeline to extract and process the inscriptions to recover the names. The process I developed uses a high end LLM like Sonnet or Gemini Pro to supervise the extraction and tuning process on a regional basis until the obvious error rate is reasonable. For this, so far, reasonable to me means less than 1-2% in the smaller initial samples of 100-500 and no observed systemic issues. The different regions often need different prompts, so this basically became an exercise in letting the higher level AI tune the prompt for the lower level AI. The extraction when measured against LIRE produces an F1 score between 0.64 and 0.87, but take this with a grain of salt.
Once I had done a few regions, I wanted to see the work, so I threw together a pretty crude website but as I am not a web developer, it was crude in how it accessed its data. It does look cool and I also added summarization, and machine translation to each entry. I wanted to eventually get feedback from an actual team of classicists and make the website work better, so I am rewriting it as we speak but it is broadly functional now with a few extra bugs but substantially improved performance compared to the old one. All entries link back to the proper sources, and the old web app linked to several additional sources where the data was present, but I haven't gotten that working again just yet on the new one. (The old web interface is still available at https://roman-names.com, but I will warn you it is clunky and not mobile friendly at all)
Key findings so far:
AI supervised AI extraction saved me time. I was manually tuning things for a while and then the runbook became an idea that I feed my instructions in and let the big AI go with sparse oversight from me.
The extraction improved significantly (by about 10 F1 points) when I fed the model the raw text including the markers, vs a cleaned up version of the text.
I just thought it was a cool little project and wanted to share. If you happen to work in any adjacent space and there is something I could do better etc let me know.

value: Show HN: I am building a map of people who lived in the Roman Empire

author: metiscus


value: guarana

value:
Hey everyone,
in the last weeks I've been thinking about building a power market simulation engine that is not based on any linear or mixed-integer programming paradigm because these are hopeless when faced with making decisions under uncertainty. I've built a demonstratotr using julias out of the box genetic algorithm and I am quite fascinated by how far one can push this.I've modeled europes core powermarkets,connected via and PTDF approximation, fed with real-ish data. Number of plants is still at least an order of magnitude short of any real number, but in principle this scales quite well because the algorithm can be parallelized.<p>I'd be interested if anyone knows of any production-level systems that use this approach or any feedback in general. Or any other intel about howto incorporate stochasticity in energy simulation.

value: Show HN: Stochastc Unit Commmitment Using Genetic Algorithm

author: guarana


value: vitalipom

value: The name of the app is Effected Keyboard 2 on Android Play Store.<p>So far it supports:<p>* Tens of languages<p>* Truly immersive effects as you type<p>* Undo/Redo<p>* Copy/Paste clipboard<p>* Move caret left/right and up/down between the lines<p>* Multilingual voice typing<p>* Long/Short bottom row mode with full punctuation marks at the bottom or long space bar<p>* Latin alphabet for academia and scientists<p>* Automatic Incognito mode<p>* New set of emojis<p>* Multilingual emojis search<p>* 2 symbols layouts<p>* Themes<p>* Completely safe without any Internet permissions or alike<p>* Toggle for tope numbers row on/off<p>* Immersive typing experience with sound on/off and vibration on/off with different levels<p>* Multilingual menus translations<p>* Completely ads free<p>* Self guided install

value: Show HN: I built a keyboard with typing effects

author: vitalipom


value:
I built BrawlBox, a 2D fighting game that runs entirely in the browser, plus an AI creator that turns a single photo into a playable fighter — sprite sheet, animations, and a full 36-action moveset wired into the engine.
The part I'm most proud of is the pipeline: you bring your own OpenAI + fal keys, and the whole photo→fighter generation runs client-side — no account, no backend, nothing of yours hits my servers. Under the hood it re-skins a CC0 mannequin template, slices and keys the frames via green-gap detection, despills, scales, and bakes an atlas the engine can load.
The engine itself is deterministic (pure tick function, fixed timestep) so matches are reproducible — it started as a weekend project and got way out of scope (throws, guard/parry, charge motions, power meter, dizzy/knockdown/OTG, a CPU opponent with 5 difficulty levels).
Play it here: https://brawlbox.gg — happy to answer anything about the determinism model or the in-browser generation pipeline.

value: Show HN: BrawlBox – Turn a photo into a playable browser fighting-game character


value: Show HN: Linkslap – rate any single URL: an article, a listing, a playlist


value: LuxBennu

value: Show HN: CtxGov – a local claim firewall for AI memory claims

author: LuxBennu


value: at-w

value:
I built LocksBet to make it easier to see where the same event is trading across prediction markets, and compare the price you would actually get after accounting for fees and liquidity.
The site matches related markets on Polymarket/Kalshi, shows (after-fee) prices side by side, and lets users compare the underlying rules.
Currently, the site uses embeddings and hard gates on stuff like numerical range/date differences to find match candidates. An LLM then sanity checks the embeddings-based match candidates, and sends those that seem like true matches to human review. I then manually review match candidates and use the manually labelled data to improve our automated matching over time.
I'm working on adding more prediction markets as new platforms launch, with the goal of making it easier to find the best available price across venues. I'm also working on showing more complex relationships between events (e.g. one-to-many outcome matches).
It's been fun to work on and I'd love to get any feedback on the site and any other thoughts/questions.

value: Show HN: LocksBet – a price comparison tool for prediction markets

author: at-w


value: lucasepe

value: Show HN: Kctx – A read-only Kubernetes context engine for SREs and AI Agents

author: lucasepe


value: As part of the Texcraft project I've been re-implementing Donald Knuth's TeX in a modular way. One of the goals of Texcraft is to be able to use these modular pieces in different contexts. hyphenate.dev is a website that uses my re-implementation of TeX's hyphenation algorithm to visually illustrate how the algorithm chooses hyphens.

value: Show HN: Hyphenate.dev: illustration of TeX's hyphenation algorithm


value: Show HN: A spacey, subtle background shader for VSCode


value:
Sometimes I wonder if my life would be better if I'd never bumped into Andy Matuschak's ideas.  But I did, and basically got one-shotted by his concept of "evergreen notes", which are notes that are similar to well-factored code.  They're small, conceptual, and densely-linked.
They're also really hard to share with people, because those links form a complex graph that goes all over the place.  You could share individual notes, but since the notes are small and have a lot of links there's not a lot of utility in sharing single notes.  You could share all of them, but who in their right mind would want to share _all_ their notes.
I found myself wanting to share little groups of notes with different sets of people reasonably often.  None of the existing publishing tools I encountered supported automatic curation where it would suggest a candidate graph that I could then modify.  Also, none were geared towards publishing lots of little sites.
I've spent about a year part-time developing the project so far, and in that time I've probably published about 50 sites.  Each time I learned something and improved the tooling.  I like it pretty well now, so I'm sharing.
It's open source, but also has a "we host for you" option.  You can publish 3 sites to the meadow-notes.com site for free, so you can try it out.
I'd love to know what you think.

value: Show HN: Meadow Notes – extract and publish microsites from your Markdown graphs


value: MediaSquirrel

value:
Last Thursday, Deepmind released Magenta Realtime 2 , an open source music generation model. They said it could run on Mac, but not iPhone.
As a v̵i̵b̵e̵ ̵c̵o̵d̵i̵n̵g̵ ̵a̵d̵d̵i̵c̵t̵ agentic AI maxxi and person who has melted iPhones before (link at bottom), I took that as a personal challenge and made it my weekend project.
On Saturday, I got it to run for 10min straight on an iPhone 12 Pro from 2020 without melting the phone or - shockingly - touching the GPU.
How? I chopped the model up into 5 pieces and set them each to run on different parts of Apple's system on a chip (SoC).
My past experience taught me that if you can actually leverage it, the iPhone's NPU is incredibly powerful, and power efficient. If you're doing sustained real-time generation for long periods of time on a device without a fan, you gotta use the neural engine or else you will melt the device.
See: https://accelerateordie.com/p/we-melted-iphones-for-science
The Apple Neural Engine has a ton of constraints, the main one being that it only accepts fixed shape inputs, and only supports some architectures -- which is why I chopped the model up into pieces.
But it works! And I wrote zero lines of code by hand. Back when I was running VC-backed companies, I would have needed a small team of grumpy greybeard engineers to do this and it would have taken 2-6 weeks. Now I can feed my own nerd fetish and do this stuff myself.
Next up: I'm building an iPhone app that ties into your heart rate, movement data, location etc to generate a real-time soundtrack to you life.
What a time to be alive!
update: Demo video of it running on my iPhone 15 Pro: https://x.com/mattmireles/status/2064849702156415214?s=46

value: Show HN: Magenta Real-Time Music Generation Locally on iPhone, Without the GPU

author: MediaSquirrel


value: thevpc

value:
My frustration with distributing java apps didnt show up recently.
I remember having implemented my first network jar downloaded back in the 2000's because i needed applet like feature support with desktop full control.
Years after, the problem is the very same. Webstart didnt really took off and the only mean i had in my projects was the ugly fatjars, including the (for me) uglier spring-boot repackaging that changes the application classloading behaviour and hence giving me by time some headackes i was not prepared for.<p>So basically nuts started as a response to this frustration 9 years ago, but from now i think its mature enough (used in production) to be shared, and forecebly i am more keen to need suggestions and help from fellow contributors.

value: Show HN: Nuts – pip/NPM for Java with first-class workspaces, JDK provisioning

author: thevpc


value: rogulia

value: Show HN: Vatnode – EU VAT validation REST API with national registry fallback

author: rogulia