value:
Hi everyone, dev here. I made a real-time minimalistic football game (in the lineage of HaxBall) in time for the championship, and thought to turn it into an actual multiplayer game polished for release. It should run in any browser, desktop/tablet/mobile as well and requiring no sign-up for instant play. If there are no people to fill in the slots (which will likely be the case as it's brand new) you can also choose to start with bots to play against. I tried to balance the bots so as not to be too "perfect" but not totally dumb either, enough to present a fun challenge -- but they'll still hit their own goal occasionally.
From the technical side, physics is run authoritatively on the server, which was my initial design idea (since a lot of these run in peer-to-peer mode). That brings some interesting trade-offs between perceived lag and local prediction jankiness, which is one of the main points I'm interested to hear feedback about. I think I've got a pleasant balance, but of course nothing beats real player testing.
The technical stack is LittleJS for the client and a combination of Python and Rust for the server (for the critical path); happy to detail more in the comments if anyone is curious.
A fun addition while the championship is ongoing: if you register (fully optional) you can create custom rooms with teams in the colourways of the current or up-coming real games; I actually extracted these from the official match schedule PDF for all group games and am quite fond of the results.
THank you and looking forward to any feedback!

value: Show HN: 0-0.io – Multiplayer browser football with server-authoritative physics


value: bwdey

value:
I built this because I like long Lex interviews, but often don't have enough time to watch every episode.
I transcribed, mapped, and embedded each episode so you can find new conversations, see how ideas connect to each other, and learn about new topics.
I welcome feedback if this is useful before listening, especially as triage, or after listening as a reference or search tool.
Not affiliated with Lex Fridman.

value: Show HN: I turned the Lex Fridman podcast archive into a browsable idea map

author: bwdey


value: litlig

value: Show HN: AI traders you author, argue with and coach

author: litlig


value: nunobrito

value:
Hello,
This an app for offgriders to use in situations without internet. Permits to download wikipedia and to make questions over the folders with books that you have on the storage of the phone.
Other features are the offline maps, where it permits to ask the bot about points of interest in a specific region and these maps stay in the storage to be used later when needed.
In addition contains other actions such as music player, akin to alexa sans internet so you can play musics using your voice (assistant mode) or just write about the mood of music you want to play.
Feedback is welcome.

value: Show HN: Offline AI assistant for Android (PDFs, Wikipedia, more)

author: nunobrito


value: Show HN: Pgsemantic – Point at your Postgres DB, get vector search instantly


value: kravetsss

value:
Hi HN. I'm one of the co-founders of 100Hires ATS. We built an MCP server on top of our REST API.
I would love HN to weigh in on whether 130 tools are too many. Big competitors have 30-40
Some details:
Three layers, from the bottom up:
- REST API v2: 87 endpoints, OpenAPI 3.0 spec, Bearer auth, public docs at 100hires.com/api
- MCP server: 130 tools mapping roughly 1:1 to REST endpoints, hosted at mcp.100hires.com/mcp, ~400 LOC of wrapper code
- ChatGPT plugin: thin wrapper on the MCP, OAuth handled by the plugin store
Auth on the MCP: OAuth 2.1 with PKCE and Dynamic Client Registration. Tokens scoped per user, 1-hour access tokens with auto-refresh, revoked when the user leaves the workspace. Tested in Claude Desktop, Claude Code, Cursor, VS Code, Codex, Windsurf, and Zed.
Available on the free trial (but rate-limited) and on all paid plans.

value: Show HN: 100Hires MCP, 130 tools to run an ATS through LLM. Is 130 too many?

author: kravetsss


value: captaincrunch

value: Show HN: Made my own analytics, Google Analytics let me down

author: captaincrunch


value: tholford

value: Show HN: TLDR Equity

author: tholford

title: Show HN: TLDR Equity


value: lucamark

value: Show HN: Modeloop – From visual algorithms to microcontroller C code

author: lucamark


value:
Honeytree was developed because I wanted a way to track my coding progress in an easy and delightful way, especially through AI coding tools that I often use.
I made it so that after planting 100 virtual trees, you can plant one real one through a reforestation API for $1 (which covers the cost of a real tree)
Fully opensource, and you get a public profile page at: www.tryhoney.xyz/[username]
I want to know if the planting flow is something that devs would want to truly contribute too, as that's what I'm most unsure about.

value: Show HN: Honeytree – grow a terminal forest that plants real trees as you code


value: Show HN: I built a tool to consolidate common domain security checks


value:
I spent the past year building Micro Coach with two partners.
It creates personalized 4-week training blocks based on your goals, schedule, experience level, equipment, and preferences. Users can also generate individual workouts, track progress, and view training analytics.
I built it using my background in both software engineering and personal training.
I’d appreciate feedback on the onboarding, workout generation, and overall product experience:
https://microcoachapp.com

value: Show HN: Micro Coach – an AI workout planner built by a former personal trainer


value: holg

value: Show HN: AutoLISP in Rust/WASM, reused to script GLDF and IFC file formats

author: holg


value: PaybackTony

value:
As Claude and ChatGPT has gotten better, I've found myself enjoying using Co-Work to make presentations at work. Sharing the HTML files on Slack and elsewhere was cumbersome and trying to host it somewhere public (even if unlisted) wasn't much of an option for my work stuff.
Then I saw Shopify's blog post about Quick (https://shopify.engineering/quick), an internal intranet with simple HTML page hosting and was inspired. I wasn't sure I could get buy-in to host it at my day job so I spent my own time coming up with Quickish. Now I can share all my beautiful presentations.
Originally I wanted it to be tied to Google Drive / Workspaces, you share the folder with quickish and put your HTML in, quickish hosts it while respecting the privacy of the folder (workspace only, etc). However, as I worked through building I realized I could make it easier to use and add that part in. Actually, it already works behind the scenes I just need to get the app verified.
And now, you have what you see. Everyone gets 1 free live site at a time (you can push multiple, just your latest one via CLI or whichever you choose one the web UI is active at a time unless you opt for the cheap unlimited plan). Just run `npm i -g quickish && quickish` in a directory with your HTML file and that's it, one Google OAuth away from the page being live. You can keep them private and only invite other users (only google for now) as well.
If you use a work e-mail sites you publish are auto-gated to only people within your org. Again, only Google Accounts for now (more coming, OneDrive, Dropbox to start).
Pages do get access to JS DB scoped to your account / workplace which is fun (imo) - I am in the process of touching up the live pub-sub (think claude made live dashboard fed by API posts to your quickish db).
Hope you enjoy!

value: Show HN: Quickish – Instant HTML Hosting

author: PaybackTony


value: pigfox

value: Show HN: Market Verdict – Validate business ideas before building them

author: pigfox


value: andrewmikhail

value: Show HN: Turboquant.cpp – Quantize embeddings to 1-4 bits, no training (400 LoC)

author: andrewmikhail


value: andrewbr

value:
I know, it's kind of weird. What is a veterinarian doing creating an analysis tool for lawn problems?
Frankly, the idea was born of my own lawn care struggles. Endless lawn care company fees without any actual improvement. Googling problems and finding generic solutions without regional considerations. One time I overseeded my grass not realizing I had to actually put soil down too.
One day, I decided to run lawn pictures through AI and found some pretty helpful information. So I decided with my clinical background (the idea of treating the cause, not just the symptoms), as well as tech savvy, I would create an AI tool where homeowners can upload pictures of their lawn, enter their ZIP code, and get a diagnosis tailored to their location with actionable next steps in just 15 seconds.
Completely free. The platform is monetized with affiliate sales (if a user elects to purchase through one of our Amazon or other links) and by selling exclusive rights to individual ZIP codes to lawn care companies seeking warm leads. Users can pursue their own DIY plan, purchase a lawn care subscription service, or contact a local lawn care system.
I'd love if you'd test it out, toy with it, try to break it, and give me your feedback. Any feature requests would be super helpful.
Thanks! Excited to hear your thoughts.
Andrew

value: Show HN: Veterinarian turned founder, AI lawn diagnosis

author: andrewbr


value: perryraskin

value: Show HN: World Cup TUI with Live Updates

author: perryraskin


value: ramoz

value:
tot.page is a simple service that produces a share link for any html or raw markdown from your terminal. Nothing new there except it's backed by git with CDN-like delivery (using cloudflare artifacts [1]).

  tot file.html

    → raw public living URL, and:

    → immutable hash snapshot URL

    → no account needed

(treat links like excalidraw)
The docs on the home page are being served by tot.
This is a small thing powering a bigger one: https://tot.page/0CW7xV96XMxnalrzwRl4eQ
Rated E for Everyone:
https://github.com/plannotator/tot
[1] https://www.cloudflare.com/products/artifacts/

value: Show HN: tot.page – git-backed publishing for HTML and Markdown

author: ramoz


value: vidarh

value:
I use this as my regular xterm replacement... Why? Because I can.
It's pure-Ruby down to the font-renderer, and the X11-bindings.
(I also run a Ruby WM, a Ruby editor, file manager, and more, so this is just par for the course of my descent into madness)
It supports double-width and double-height text, unicode (but double-width characters may currently be rescaled down), layering fonts, special rendering of box-drawing characters (to ensure they seamlessly scale and connect, and has reasonably complete vt-100/vt-102 emulation. The whole thing is available as a Rubygem and comes with an ANSI text backend, so you can run your terminal in your terminal. The bulk was written manually, but the last few days I had Claude write a test harness to shake out a bunch of bugs, and start refactoring and cleaning up the code base (it's still full of warts).

value: Show HN: A pure-Ruby X11 terminal

author: vidarh

title: Show HN: A pure-Ruby X11 terminal


value: rgarcia

value: Show HN: A place for agents to publish and discuss HTML documents

author: rgarcia


value: nickv

value:
Hola HN!
Long time lurker, sometimes commentor, first time poster here.  I’ve been working alongside my two co-founders and a few colleagues on a project I’m excited (and a little nervous) to share with you all!
Like many of us, I’ve lived a tortured existence with AI coding (is it vibes?) over the past few months - I think November was a big moment with this.  But, one thing I’ve noticed after building orchestration layer after orchestration layer is that the thing I always came back to was “what the hell is Claude Code actually doing?”  Perhaps it’s because of the time Claude Code got confused and “rm -rf”’ed root, or the time it deployed a feature flag flip to prod instead of stage, or the time it got stuck in a cycle of doom logging in with playwright, etc etc - but it inspired us to build this tool; we call it Spotlight by Backplanes.
Spotlight takes your Claude Code and Codex sessions and finds security issues, things that could be sped up, and where you’re burning your time and tokens.  We also create fun little archetypes of what kind of builder you are.
The way it works:  you install a backplanes CLI daemon/TUI that takes your Claude and Codex sessions, scrubs them of their PII and secrets locally, sends them to us where we do a second level scrub using a locally hosted model, and store your sessions row level encrypted with customer keys.  Today we create and store the customer keys in AWS secrets manager, which we can’t access directly. Doing this work hosted lets us stitch sessions across machines and multiple harnesses and even gives you the ability to give team-level patterns.   Details are at https://backplanes.com/trust.
You can see an example report at https://backplanes.com/features/session-reports, To play with this, it's a one-line CLI install (yes, there's a signup, I'm sorry), and it's free at backplanes.com.  In the coming weeks we will be releasing a Powershell version along with native MacOS and Windows apps.
Please let us know what you all think.  Thanks guys and gals!
Nick

value: Show HN: Spotlight shows what your Claude Code/Codex are doing

author: nickv


value:
Linux has issues when it comes to distributing software. Centralized repositories existed long before the App Store, but they aren't meant for indie developers sharing their app.
Omnipackage sits on top of existing infrastructure to build RPM, DEB, and Arch (more to come) packages and host them on your S3 or local filesystem. It's one config alongside the RPM spec and other templates that live in your project's repo, and it can run a build-sign-publish pipeline locally or on GitHub Actions.
You share with users a link to a generated HTML page with instructions on how to add the
repository and install your app (example: https://repositories.omnipackage.org/omnipackage-rs/stable/i...), or just share the install instructions directly. Once hooked into the package manager, your app becomes apt-installable and updatable as if it were in the official repos. S3 isn't required - a local filesystem works as a repo too, handy if you want to package some app just for yourself or share it through other channels.
Think fpm and OpenBuildService had a baby. There's an "init" command that guesses how to
build your project and generates the necessary files that you need to change slightly. There's also a Claude skill that does very well on most projects I've tried packaging. More rationale: https://omnipackage.org/about

value: Show HN: Build and distribute native Linux packages easily


value: danmeier

value:
Everyone is talking about token spend, but not what we actually spend tokens on. I’ve always found it interesting to track my time across different tasks to challenge whether I was working on the right things.
Nowadays, I spend around 95% of my day in the Claude desktop app, so “where did my time/tokens go?” basically means “what did I do in Claude?”*
Turns out it’s all already on your disk. Every Claude Code / Cowork session is a JSONL transcript with per-message usage blocks: input, output, cache creation, cache read, and timestamps.
Run it from the terminal:

  curl -fsSL 'https://gist.githubusercontent.com/danmeier2/064f7db8c0867dd6331007ba43f0e6fe/raw/claude-token-analyzer.py' | python3 -

Run the snipped to get an overview of what you did and when based on your Cowork and Claude Code history. Example output:
Claude Token Analyzer · 2026-06-15 · 5 threads

  Claude Token Analyzer  ·  2026-06-15  ·  5 threads

  ╭────────────────┬────────────────┬────────────────┬────────────────╮
  │ Spend          │ Tokens         │ Active         │ Cache-read     │
  │ $39            │ 27.5M          │ 1.8h           │ 91%            │
  ╰────────────────┴────────────────┴────────────────┴────────────────╯

   when active   08:00               09:00                10:00                11:00               12:00
     Claude Code
   Claude Cowork

  Claude Code  —  $36.56  ·  3 threads
       $22.07  17.9M  Claude usage report for 2026-06-14
                $9.29   7.6M  LinkedIn data export analysis
                  $5.19   1.0M  Productization plan

  Claude Cowork  —  $1.99  ·  2 threads
                     $1.15   791K  YouTube video summary
                     $0.84   156K  Picture location identification

*ccusage doesn’t see my Claude Code sessions because they’re stored in ~/Library/Application Support/Claude/local-agent-mode-sessions/.

value: Show HN: Claude Token Analyzer: a token "screen-time" report

author: danmeier


value: Show HN: Behavioral gauge for Claude Code sessions


value: njac

value: Show HN: A directory for Discord and Telegram bots

author: njac


value: xenophin

value: Show HN: StarScope – Free astronomy dashboard for observers outside the US/UK

author: xenophin


value: iezhy

value: Show HN: Understand and reduce token usage with ContextSpy context profiler

author: iezhy


value: todience

value: I built Termem (terminal memory) to allow quick agent session recovery, and cross-agent memory sharing. Once I cd into a folder, I can run 'termem' to view all agent sessions and resume them. When I launch a fresh session, the agent can recall all sessions within that directory via the mcp & skill. Claude can see sessions by codex and other agents.<p>Lemme know your thoughts.

value: Show HN: Termem, cross-agent memory and session management

author: todience


value:
Hi! me and my friends together built an 8 bit CPU implemented in Logisim purely from scratch. The control unit of this system does not implement the generic microcode ROM or any kind of RAM. This was made purely from discrete logic gates and coded the system to run different programs.
key features:
Custom 16-instruction Harvard ISA, 8-bit fixed
format, 4 general purpose registers
Hardwired control unit built entirely from AND/OR gate logic matrix
Dual-phase clocking to eliminate race conditions
Bootstrap Control Unit that cold-boots via ROM-to-RAM transfer
Early-exit conditional branching that saves upto 25% cycles when conditions aren't met
Full design specification document with version control
Since this was our first time doing such teamwork and a new thing we used RISC based system that fetches an 8-bit instruction from Instruction memory 4 bits of which translate to an instruction the last two bits are for source and destination registers.
There are a total of 4 registers in the system with two memory units namely Data SRAM and I SRAM, the system follows a Harvard architecture.
There are design discrepancies too since it was our first time designing such a system and on top of that completely hardwired too.
To solve the problem of cold booting a bootloader is present too that copies the contents of a temporary ROM into instruction RAM and then hands over the reins to the CPU.
We also implemented conditional branching as well as early exit branching too that only checks for zero or carry flag and branches without wasting cycles, if the conditions are not met the Program counter increments.
Moreover we also created a complete documentation with version control describing each necessary part assuming prior knowledge.
Please take a look at it at https://github.com/c0rRupT9/STEPLA-1
For future development I want to implement a RISC CPU using FPGA's and connect it to an actual DRAM. We are also selling the full spec document and Logisim files for $5 to fund our passion
https://tcfdiq.gumroad.com/l/zyyux
Thankyou!

value: Show HN: We built an 8-bit CPU as 2nd year EE students


value: jbecke

value:
Hey HN! We're launching Macro (macro.com) — an open source, AI-native system that unifies email, chat, tasks, calls, docs, and CRM in one interface with shared memory.
Before Macro, we ran our startup on Superhuman + Slack + Notion + HubSpot + Linear. Each of these apps are good but they don't talk to each other, they're proprietary and closed off. We wanted one unified system, open source, with shared memory, that we could build our company around.
Macro is built to be modular and fast. It's designed to be your command center, your operating system. There's still a lot left to do.
We're currently working on CRM, Pull Requests / GH integration, and making Macro even more extensible. You can self-host or use the hosted version. Pls let me know what you think! You can open a PR on the repo or leave feedback here and we'll work on it.

value: Show HN: Macro – unified system for email, chat, tasks, docs, agents (AGPL/Rust)

author: jbecke


value: neurodivergent

value: Show HN: Does a vibe leak? Fine-tuning an LLM on an attitude it never states

author: neurodivergent


value: OlegIvanov

value: Show HN: All 194 YC Spring 2026 startups scored from public data

author: OlegIvanov


value: johnnytech

value: Show HN: Transpilatron – an AI tool that converts Python code into C binaries

author: johnnytech


value:
It all started 4 months ago during my last startup.
Even with barely any traction, I was getting about 20 emails a day. Some from cold randoms, some internal, some from prospects. And I just wasted so much time in my inbox.
The current solutions? Superhuman and HEY. Sure they're pretty but the ai is just an after thought. And the tool that was supposed to help me just makes me, my own assistant.
So I went and built Dirac.
We're launching in 8 hours,
https://www.producthunt.com/products/dirac-3?launch=dirac-4

value: Show HN: I built an email agent for founders who are stuck in email


value: natyoung

value: Show HN: ThoughtLeadin – AI-first LinkedIn where every post is corporate slop

author: natyoung


value: leonvonblut

value: Show HN: Legioni – a bunch of AI agents always with you

author: leonvonblut


value: okiki-clickdrop

value: Help other founders with what they need, and they will solve your biggest pain points

value: Show HN: Get your first set of users by supporting others

author: okiki-clickdrop


value: Show HN: When Will AI? – A timeline of top AI predictions


value: Petersoj

value:
Jet is a simple, lightweight, modern, turnkey, Java web client and server library.
Jet offers four modules: Common, Server, OpenAPI Annotations, OpenAPI Annotations Plugin, and Client.
Jet is a wrapper around the excellent Jetty web client and server library. Jetty provides the battle-tested low-level protocol handling, while Jet focuses on providing a modern and consistent interface with superb documentation and an amazing developer experience.
---
I built this Java library to fill a hole in the Java web server library ecosystem. Javalin got me 90% of the way there, but requires the Kotlin dependency and lacks header models and exhaustive KDocs. The Client module is a still a WIP, but the Server, OpenAPI Annotations, and OpenAPI Annotations Plugin modules are production-ready!
Check it out at: https://github.com/Petersoj/jet

value: Show HN: Simple, lightweight, modern, turnkey, Java web server library

author: Petersoj


value: Show HN: A Framework with a Possible Application to Hybrid Cryptography


value: mcdermott

value: Show HN: Phlox – Open-source self-hosted agentic web chat

author: mcdermott


value: leoTheCoderrr

value: Show HN: AltiVerse: What If SIM, see different decisions affect an environment

author: leoTheCoderrr


value: reuzed

value: Show HN: Fish anything – a chill game using small models

author: reuzed


value: Show HN: Subagent-fleet – AI coding subagents across local Ollama machines


value: gclaramunt

value: We released what I've been working in the last few months: an Openclaw plugin that ecords every session, tool invocation, and prompt exchange into a local SQLite database with SHA-256 hash chain integrity, so you can verify that no events were altered or deleted after the fact.

value: Show HN: Tamper-evident audit trail for AI coding agent activity

author: gclaramunt


value:
Hello Hacker News,
My son and his classmates are at the stage where they're tackling their times tables.
I noticed that a lot of families still have older iPads lying around (my son has an iPad Air 2, 2014), but many of the existing times tables apps either cost money or no longer support older versions of iOS, even though the devices themselves still work perfectly fine.
So over the weekend, I decided to build something for my son and his mates: a simple, engaging web game that runs on older and slower devices.
No downloads. No ads. No subscriptions. Just a free web game that works in your browser.
Introducing...
Times Tables FAH!
[url-redacted]
It's completely free to use, and I've included some familiar meme sounds to help keep kids engaged while they learn.
If you've got kids, or know someone who does that could benefit from this, I'd love for you to give it a try.
I hope it helps make practising times tables a little more fun and enjoyable.
Happy learning!

value: Show HN: I built a free timestables game for kids


value: firish

value:
VS Code and JetBrains have official Claude Code IDE integration, but Visual Studio doesn't. There's an open GitHub issue with a lot of +1s so I built it myself (check out the gif in the GitHub repo for a visual example).
It implements the same protocol the official plugins use, so the Claude CLI connects to it automatically. You don't configure anything, just install the extension and click Launch.
The main thing it adds over running Claude in a terminal is that edits open in Visual Studio's native diff viewer instead of auto-applying or prompting you in the terminal. You click Accept or Reject right there. You can also reject with a reason, and Claude will take another pass.
It also shares your compiler errors (C# and C++) and your current selection with the CLI automatically, so Claude has context without you having to copy-paste anything.
A few other things:
- There's a dockable panel with connection status and token/cost stats for the session
- A "run wild" toggle to auto-accept edits without opening the diff
Works with the existing claude CLI, no model calls of its own
Marketplace: https://marketplace.visualstudio.com/items?itemName=firish.b...
GitHub: https://github.com/firish/claude_code_vs

value: Show HN: Claude Code for Visual Studio (native diff with accept/reject)

author: firish


value: Data driven browser system

value: Show HN: Zero Browser

story_text: Data driven browser system

title: Show HN: Zero Browser


value: The honest, local-first usage meter for AI coding tools. Claude Code, Codex, Kimi, Qwen CLI, HashCortx, HashCerebrum — unified into one clean dashboard with usage based trophies :)

value: Show HN: HashMeterAi – Private AI Token Real Usage Meter for All Models


value:
I created this as a thought experiment, just for fun, trying to think what agents might need in the future. Since they might be making decisions on their own, they might want to share and consume information from other agents.
here any agent (or human) can subscribe to any public information stream by other agents.
I started with a simple blogging platform for agents, and landed here.

value: Show HN: An Information Exchange for Agents


value: qwikhost

value: Export Bing Maps Business Leads, Emails, Phones & Locations to CSV, Excel & JSON

value: Show HN: Bing Maps Leads Extractor

author: qwikhost

title: Show HN: Bing Maps Leads Extractor


value: arhaam

value:
I use Claude Code, Codex, Cursor every day and had no idea how much I was actually burning across all of them combined. Each tool shows its own usage (most don't) in its own place, if at all, and I just wanted one number. So I wrote this.
npx whoburnedmore
reads the usage/log files these tools already write locally, sums them up, and prints a dashboard: total tokens, a cost estimate, active days, and your heaviest single day.
There is also a public leaderboard on the webiste which compares your ai usage with all others
Feedback & a upvote is appreciated
github is /amiinwani/whoburnedmore.com
product hunt : https://www.producthunt.com/products/whoburnedmore

value: Show HN: Open-source CLI to see your AI coding token usage and compare it

author: arhaam


value: dorongrinstein

value: Show HN: A GPT to get a free URL for AI generated content

author: dorongrinstein


value: Show HN: Deep-XPIA – Prompt injection benchmark for multi-agent AI systems


value: ttttonyhe

value: A step towards providing feature-level (e.g., coding, customer support) access control for LLMs, enabling A/B testing, content/age restrictions, pay-to-unlock monetization scheme, and other use cases requiring more granular separation of features within an LLM.

value: Show HN: Locket – Robust feature-level access control for LLMs

author: ttttonyhe


value: SpecStudioHN

value: Show HN: Aspectarian – a Jyotiṣa transit reader for macOS using Claude

author: SpecStudioHN


value: satyaborg

value: Show HN: Devloop, a local code/review loop for Codex and Claude Code

author: satyaborg


value: Show HN: Pokémon Showdown Live Battle Assistant


value: california-og

value:
Hey all, I made this. The archive started with my 2015 BA thesis on Amiga ASCII art when I was curious about the history of ASCII art but found very little on text art that came before it. The historical precursors are often attributed to typewriter art and shaped/visual poetry, but I think letterpress is overlooked. So, I got slightly obsessed and started a personal database of pictures built entirely from metal type, ornaments, and rule, some going back to the 1600s. After eight years, I've managed to find ~2500 images. My friend Adel Faure built the website so it's now browseable by anyone!
I would like to note that most images are from public digital collections (Internet Archive, national libraries, etc.) and displayed without permission (for educational purposes). I've tried to source every image, but check the original source and its license before reusing anything. I'd be happy to take down or correct anything.
It's also incomplete and surely has errors and misattributions. Corrections to anything are very welcome.
If anyone has leads on works I haven't catalogued, I'd love to hear them! The practice and pictures are scattered across languages and keywords (type picture, typosignet, typotectur, Bildsatz, stigmatypie, stunt typography...), so things hide in odd corners of archives. If you've seen something like this, please point me at it.
There's also a longer essay on how it began:
https://garden-of-flowers.heikkilotvonen.com/?essay

value: Show HN: Garden of Flowers – an archive of pictorial typography before ASCII art

author: california-og


value: Show HN: FlashQwen – A from-scratch CUDA inference engine for Qwen3


value: jonyardley

value:
Got annoyed every time I wanted to doodle on my iPad in meetings with the "QuickTime" dance, so built this little menu bar app that gives you a window when you plug your iPad into your Mac to share on any call.
https://sharepad.co

value: Show HN: SharePad – share a USB iPad as a clean window in any call

author: jonyardley


value: komlan

value:
Hi HN!
Collecting form responses is one thing, but once you try to use them, a lot of them turn out to be unusable. Some are missing critical information or answering the wrong question (think but report with no steps to reproduce, support queries suffering from the XY problem [1], etc.). Classic required fields and regex validation help with syntax, but they can't tell you whether the answer actually makes sense for what you need to do with them.
I built a Google Forms add-on that lets you describe what a good answer looks like in plain text ("should say what they were doing when the issue happened", "should explain what they tried before reaching out", "should be specific, not just say it doesn't work") and then automatically emails gentle feedback to each respondent after they submit, with a submission update button. If the next submission still falls short, they hear from it again. It stops when you say it's good enough, or when you turn it off.
Of course, this doesn't prevent computer-generated SPAM, if the spammers learn to automatically send actionable and clear responses, but see [2].
A couple surprising things:
- Responders are more willing to update their submissions than you'd expect, as long as the feedback is tailored and specific, which this helps with.
- Some people engage with AI feedback more readily than with human comments. Maybe because it doesn't feel personal?
- Writing the feedback instructions in the setup wizard is useful by itself. You frequently realize that you were asking the wrong questions on the form, and didn't make yourself clear enough.
The AI writes the email based on your instructions. You can calibrate on a few test submissions to make sure you're not being too lax or too annoying. You can bring your own Gemini key, but it is not required. It hooks into the Forms submit trigger and sends email through the Gmail service by default. It's available on the add-on directory [3].
[1] https://xyproblem.info/
[2] https://xkcd.com/810/
[3] https://workspace.google.com/marketplace/app/ai_response_fee...

value: Show HN: Ensure actionable Google Forms responses using automatic feedback

author: komlan


value: Show HN: Topaz – A small Unicode-first language that compiles to Rust


value: imalov

value: Show HN: Tracore – We turned our resume parser into a document-to-JSON API

author: imalov


value: Tired of uploading sensitive documents (IDs, transcripts, photos) to random websites just to shrink them down to fit portal limits (e.g. <200KB)? I built a Chrome Extension that opens in your sidebar to compress, resize, and convert images & PDFs entirely client-side. Files never leave your machine!

value: Show HN: DocShrink – An offline image and PDF optimizer in your Chrome sidebar


value: skvark

value:
Hi HN,
I’m Olli-Pekka, co-founder of GitHits (and the creator of opencv-python package).
GitHits is a CLI and local MCP server that gives coding agents access to the open-source code their projects actually depend on.
Coding agents already have access to your local codebase. The missing context is the open-source code behind your dependencies.
When an agent hits a framework edge case, an undocumented integration, or version-specific behavior, the answer often lives in dependency source code, tests, changelogs, issues, or discussions rather than in the application code itself. Without access to that information, agents spend more time exploring dead ends, retrying approaches, and consuming tokens before converging on a solution.
During the private beta, GitHits focused on generating examples. You could describe a problem in natural language, and it would search open-source repositories, issues, discussions, and package metadata for similar implementations, then generate an example grounded in real code.
The public beta expands GitHits beyond example generation. Agents can now move beyond finding examples and inspect the dependencies they use directly: searching source code, accessing documentation, inspecting dependency graphs, vulnerabilities and changelogs, reviewing package upgrades, and retrieving information for the specific package versions used by a project.
No repo cloning is needed. We have built a version-aware index of open-source code and documentation. Popular projects are pre-indexed across ecosystems. If a package or repository is missing, we index it on demand, typically in 10-20 seconds for an average-sized project.
The CLI and local MCP server currently expose the following tools:
- get_example for implementation examples from open source
- search, code_grep, code_files, code_read for code navigation
- docs_list, docs_read for documentation access
- pkg_info, pkg_deps, pkg_vulns, pkg_changelog, pkg_upgrade_review for package inspection
Package navigation currently supports npm, PyPI, crates.io, Maven, NuGet, Go modules, RubyGems, Packagist, Hex, Zig, vcpkg and Swift.
Code navigation currently supports C++, C#, Dart, Elixir, Erlang, Go, Java, JavaScript, Kotlin, Lua, PHP, Python, R, Ruby, Rust, Scala, Swift, TypeScript and Zig. We also have more limited support for Bash, CSS, HTML, Markdown, Proto, SCSS, Svelte, Vue. We are constantly adding support for more ecosystems, and advanced navigation tools over our indexed code graph are also on the roadmap.
We benchmarked Codex on a real integration task with and without GitHits. With GitHits, it used 45% fewer tokens and passed the tests on the 3rd attempt instead of the 8th: https://www.youtube.com/watch?v=0YmwLhH2Ohs
Install is:

  npx githits@latest init

It works with coding agents either through the CLI directly or through the local MCP server that the CLI configures automatically.
Launching as well on Product Hunt today: https://www.producthunt.com/products/githits
Would love feedback, especially from people using coding agents heavily in real projects.

value: Show HN: GitHits Public Beta 0.9

author: skvark

title: Show HN: GitHits Public Beta 0.9


value: JeremyHe

value:
Hi HN,
I'm building LogiSheets, a spreadsheet that adds structure directly into the grid. The idea is that many spreadsheet failures—both human and software—come from the same problem: everything is addressed by position instead of identity.
I'd especially love feedback on whether this solves a real problem, or if existing tools already solve it well. The article includes a Factory Simulator demo built on top of the model.

value: Show HN: A spreadsheet where your code never reads B7

author: JeremyHe


value: Show HN: Kitchen Rush, Overcooked inspired LLM tool calling benchmark


value: leebeef

value:
A real work in progress I've got here. Our good friend Ghostty is now haunting the browser.
Ghostty Playground is an experimental project based on ghostty-web. It includes the full Ghostty terminal with configs, like themes and keybinds (config panel is based on zerebos'
ghostty-config).
It runs a real Linux-like backend directly in the browser via BrowserPod with a dedicated filesystem, port-listening, bash, git, node.js, npm etc.
It includes the ability to download small AI models via Hugging Face and run inference in the browser using WebLLM. There are some very early experimental agentic features, but they're very rough at the moment (based on Vexi).
There are many limitations here. It's an early demo that's due a major refactor, so expect things to break unexpectedly. Some limits that can't be overcome are functions and keybinds that aren't compatible in the browser.
If you scroll past the terminal, there's some documentation that can help with usage.
Feel free to give it a try and let me know of any feelings, thoughts, bugs, improvements.
Credits (git):
- Leaningtech/browserpod-meta
- Coder/ghostty-web
- Zerebos/ghostty-config
- Bezhermoso/tree-sitter-ghostty
- mlc-ai/web-llm
- Elomami1976/vexi

value: Show HN: Ghostty in-browser with real client-side back end

author: leebeef


value: grajo

value: Show HN: Langusta – an AI voice tutor for practicing spoken languages (PWA)

author: grajo


value: bardhyliis

value: Show HN: A bare-metal network mitigation layer using eBPF and nftables

author: bardhyliis


value: Show HN: Cultivation World Simulator

title: Show HN: Cultivation World Simulator


value: jackjayd

value:
I built Opencom, an open-source alternative to Intercom for customer support chat, with knowledge-base and AI support, and mobile apps for push notifications etc.
It includes a web widget, agent inbox, customer/company profiles, conversations, realtime messaging, AI support and a live demo. The aim is to provide a simple, self-hostable foundation for teams that want Intercom support without being locked into a large SaaS platform.
Salesforce has just announced its acquisition of Fin, formerly Intercom, for around $3.6bn. Customer communication is important infrastructure, and I think there should be a credible open-source option.
Built with TS, NextJS, React Native & Expo, and Convex backend for easy setup and ability for full self-hosting. Repo: https://github.com/opencom-org/opencom.
Appreciate any feedback on architecture, self-hosting setup, and which Intercom-style features people would want before using it in production, along with any other thoughts.
It is entirely OSS - no paid features behind a paywall. I hope this could become a community project.

value: Show HN: Opencom – an open-source Intercom alternative given the Salesforce deal

author: jackjayd


value: varunwalekar

value: Show HN: ProData AI – 14 MCP tools for automated data science

author: varunwalekar


value: Areibman

value:
Hey HN,
Following the OpenClaw craze, I saw a huge need for hosting personal Claws/Hermes agents on macOS [0]. So I built an agent to scrape eBay for below-market M-series Macs and built a Mac mini datacenter [1].
But as it turns out, Claws aren't popular anymore. However, running agents like Claude Code and Codex on a remote Mac mini is a really awesome experience (especially for iOS devs). So far, we've seen users running automated iOS app builders, iCloud app automations, and more.
[0] https://x.com/AlexReibman/status/2038656423161434400?s=20
[1] https://x.com/AlexReibman/status/2053957566368194953?s=20

value: Show HN: Hyperbox- $40/month Mac mini rentals

author: Areibman


value: lucasmartinic

value:
Hi HN, I'm Lucas. I mostly build XR things, but over the past month or so I've been developing a tool that became necessary for me: I'm usually working on many different things at once, and I needed a way to create reports and check what I worked on last year on a specific day.
mydev.day is an automatic development diary that writes itself as you build. You connect your agent to it once per project (takes a couple of minutes), and from then on it automatically adds an entry per feature. After that you can query it via the API to ask anything about all your work across projects and accounts, or go to the app to print nice looking charts and reports.
It started as an internal tool, but inspired by the indie hackers I follow, I decided to turn it into a product to see if it could be helpful for other builders and developers out there!
Fun fact: it was really cool to dogfood it on itself as I built it!

value: Show HN: I built a dev diary that writes itself

author: lucasmartinic


value: Pierdimi

value: Constant time recursive filter for moving linear regression. Works for any window size k. Formula in the first comment.

value: Show HN: O(1) Recursive Moving Linear Regression

author: Pierdimi


value: Show HN: CoreMCP – MCP Server for On-Prem DBs


value: pchm

value: Show HN: Rediscover the Small Web with an AI-Ranked RSS Reader

author: pchm


value: Show HN: Build a timeline of your experiences and generate tailored job pitches


value: mikeapple

value:
I love building apps with Firebase but felt that, other than basic auth, app emails (notifications, billing etc) were missing.
Wanting to implement a solution to this, it got me thinking about how to tell a coding agent about a new api and how to implement it.
I dynamically generate an openapi spec via a script and have been playing around with using this spec to then create a prompt generator.
It's not something I've come across before and I can sort of understand why...
Generating a prompt that's got enough detail to implement the api but not being too specific so it can be used in many code bases is a fine balance.
I'd be interesting in any feedback on my approach and my execution.

value: Show HN: A prompt generator to help AI agents implement my new email API

author: mikeapple


value: fourgreen

value: I made a Brainfuck interpreter ( https://en.wikipedia.org/wiki/Brainfuck ) with additional commands for turtle graphics. Check it out: https://czterycztery.pl/programy/turtlefreeze/

value: Show HN: Brainfuck but with Turtle Graphics

author: fourgreen


value: thedorsan

value: Show HN: Mcpwn – treating MCP servers as the attack surface they are

author: thedorsan


value: stevewanjohi

value: Show HN: Mittr – Webhook delivery on one Postgres (inbound and outbound)

author: stevewanjohi


value: WolfOliver

value: Show HN: Write SaaS apps where users control where their data is stored

author: WolfOliver


value: nextma

value: Show HN: Rnet-OAuth-Python – Python lib for user-funded AI API access

author: nextma


value:
Configuration as code for developer machines.
The operation is similar to Terraform: plan + apply for making changes, and import + refresh for syncing system state back into the config. Codify operates statelessly, so no need to manage a state file. Just define what you need and apply it.
There is a web + desktop app, and AI agent to make Codify easier to use (paid with free tier). The apps come with a dedicated editor (auto-completion, blocks view, and collaboration). The agent works via Codify configs which avoids the problem of LLMs generating and applying raw shell commands directly.
We’re building Codify for three groups of people:
1. Beginners and students - It lowers the barrier of entry and introduces them to new languages, cool tools and agents, especially now with the advent of vibe-coding.
2. Software teams - It onboards new engineers quickly and ensures that everybody is on the same setup. It also helps more people to work cross-functionally (ex: backend and mobile)
3. Freelancers - Helps them manage multiple tech-stacks for different projects at once.
I personally found that it’s useful for setting up automation (openclaw for example) + managing two work computers at once.
Codify has an ecosystem of 50+ supported applications, settings and tools right now. New resources can be requested by creating an issue on GitHub, triggering our Claude action to pick it up. We review and test the code manually before merging (aiming for a turnaround time of 10 days). We run automated testing three times a week to catch drift. In light of the recent npm supply chain attacks, we won’t be hosting a registry for 3rd party plugins. Private custom plugins are possible.
The CLI and resource library are open source. Everything is available on MacOS, Linux and Windows (wsl)
Hope you get a chance to try out. Cheers - Kevin & Edmund

value: Show HN: Codify – Terraform for Dev Environments


value: Show HN: Query the Murder – Learn SQL by Solving Agatha Christie Mysteries


value: marcelglaeser

value: Show HN: SeedBase, FK-consistent test data from your SQL/Django/Prisma schema

author: marcelglaeser


value: jaakl

value: Show HN: Countrycheck: which country is this point in?

author: jaakl


value: eugene-maksak

value: Show HN: AppointMe – open-source .NET SaaS template (modular monolith, DDD)

author: eugene-maksak


value: Hey HN, I built Spin Lab: a browser-based interactive explainer for table-tennis spin. It visualizes topspin/backspin, spin rate, ball trajectory, bounce behavior, and why the opponent’s return reacts the way it does. I built it because spin is central to table tennis, but most explanations are either too hand-wavy or too static.<p>Thanks Fable, we miss you

value: Show HN: Spin Lab

title: Show HN: Spin Lab


value: digitalcheff

value: Show HN: Independent comparison of AI wait-state ad platforms

author: digitalcheff


value: Show HN: WebCap – Reusable web capabilities for AI agents


value: moonrailgun

value: Show HN: Dao Browser – An Opinionated Browser. With AI Agent, BYOK

author: moonrailgun


value: yusukeshibata

value:
I've been trying to automate my work recently, but I ofeten encounter the situation where agents needs human intervention to complete the task.
So I made a simple command that wraps a TUI called babysit that has a terminal emulator inside using pty.
Agents can start commands in background, and they can ask human by attaching the TUI on a new tmux pane when human intervention is needed, or they can operate the TUI with screenshot/send/expect/wait commands.
So far, I really like using it, and I wanted to share it here.
https://github.com/yusukeshib/babysit

value: Show HN: Babysit – let coding agents operate a TUI

author: yusukeshibata


value: Show HN: Camel Calculator – an AI reboot of the "how many camels?" joke


value: TeamDzX

value: Show HN: Pair your iPhone to your own Ollama over Tailscale with a QR scan

author: TeamDzX


value: mihaibuilds

value: Show HN: The Brain – self-hosted workflow runner that calls any MCP server

author: mihaibuilds


value:
Job hunting sucks. That grind of digging through LinkedIn and Indeed, sifting through endless spammy emails from headhunters who try to get the biggest possible audience, clicking "Quick Apply" to silent response, and trying to figure out which of all of these jobs are even real. My wife hit all of this when she started job hunting a few months ago, so I built her a tool to take the worst part of it off her plate. It turned into this. Oh, and she got an offer letter from a major company in just shy of two and a half weeks. Now, she loves her job.
Morning Stack runs overnight. While my users sleep it works through the big job boards (and some niche ones), and then it tries to disprove every listing before it trusts one.
Is the job even real? - It opens the posting in a real browser (Playwright) and checks. There's no API scraping and we don't login to anything. Then, we resolve back to the company's own ATS page so you can apply directly to them. If it can't find it, the job is dropped.
Does it match what you asked for? - An LLM crosschecks the job description against your profile (Resume, Career Story, and Desired Roles, Comp Band, Geography and Benefits). The more specific you are, the less jobs you'll ultimately receive. That's kind of the point.
For the few surviving contenders, it tweaks your actual resume to the job description and drafts a cover letter for each. A separate verifier re-reads those against your actual resume, stripping any facts it can't support. We avoid AI-BS with a 3-strike rule: if it can't support a claim after three tries, it drops the job and moves on instead of shipping something fabricated.
By 7AM, you've got a small stack (up to 3) of real openings with finished packages.
- You see a little bit of good intel on each (company and job info, and any outliers like high-comp or unlimited PTO).
- You review them to determine if you're interested.
- You polish and submit yourself on the company's most direct site.
That's it. It's your reputation; you control it. Here's a few things I deliberately did not build this for:
It never logs in to any of your actual accounts and it never auto-applies on your behalf. This is a really terrible trend in my opinion. I don't think it's working and I don't think people trust it. "Spray-and-pray" isn't the right fit for people who care about the outcome of their career and you will always be the one who submits under your own name.
Opening for a small beta cohort this week. There's a waitlist. The link is above.
Happy to get into the architecture or the ToS reasoning in the comments.

value: Show HN: Morning Stack finds real job openings, tweaks resume and cover letter


value: Show HN: Auto-triage customer emails to Linear with Claude and pydantic-AI


value: Show HN: Allelix – Annotate your DNA against 7 public databases offline


value: alaskahoffman

value:
Hi HN! I built a Chrome extension that generates recursive/nested "tooltips" upon hovering over links, images, pdfs, and unlinked citations like DOI, arXiv, PMID, etc.. I was playing Europa Universalis V and realized I could implement the same system but for everything just by using Wikipedia as a truth-source and pairing it with an LLM. It works especially well on hackernews itself, I frequently will hover an article about something I don't understand, and I can fill in all my knowledge gaps just by following the thread of nested keywords. It reads the page you're on, and the page you're hovering over, and then acts as a gel between them so the tooltip is always contextualized and doesn't need to hallucinate new information.
It's free and open-source in lineage with Ted Nelson and Project Xanadu's hyperlinks, I think eventually this UX mechanism could be an open web standard with sufficiently democratized compute/inference. https://github.com/alaskahoffman/portaltext

value: Show HN: Portaltext, grand strategy-style recursive tooltips for the web

author: alaskahoffman


value:
I miss the old web. As a kid I could type in "a/s/l" in AOL messenger and chat with someone my own age, without worrying about the dangers that lurk on the web today.
After seeing what happened to Omegle, a question stuck: is there a simple way to do age verification that both keeps people safe and doesn't contribute to a surveillance state?
After a year of hard work, that question resulted in AGEWARDEN. Each part of the service puts people first. No tracking, nothing stored (it's more difficult these days to NOT collect data :smh:).
Please give it a try if you have a moment https://agewarden.ai/demo. Feedback is very much welcomed.
GG

value: Show HN: Voice Age Verification

title: Show HN: Voice Age Verification


value: Show HN: I hated sales, so I built a self-driving sales agent


value: andersource

value:
Hi HN! Codyssey is a small game where you write simple control system logic in Python to solve minigames.
I've been working on it for over two years; its first iteration was as a workshop for beginning programmers with just basic Python knowledge. I wanted something that would take abstract code and make it visual, interactive and fun. Now, after many iterations, I'm launching it as a small web game.
To try the beta:
1. Create an account (to try a demo without signup, go here[0]).
2. "Start tutorial"
3. "Buy" (top right) -> "Join Open Beta"
I'd love to hear your feedback!
Behind the scenes, Brython[1] is used to run the code. I'd have preferred pyodide or micropython (and might get back to doing it), but both ended up having technical issues and Brython was easier to integrate.
Cheers, and hope you enjoy!
[0] https://codyssey.andersource.dev/demo/demo.html
[1] https://brython.info/

value: Show HN: Codyssey – control system programming game (open beta)

author: andersource


value:
Hello HN,
I would like to share my project: AutomatiQ
AutomatiQ is an Open-Source Reverse-Engineering AI Agent, that can write requests-based(no browser) automation/scraper scripts on its own, all we have to do is, to normally browse the website once.
This makes it a lot easier to create/fix scripts, all we have to is browse the site. This is not a completly autonomous agent, you can pause, talk, guide the agent on its RE path.
I am aware of the limitations of this approach, but from my own research(please refer the blog for details) up to 60%-90% of websites can be automated/scraped with just requests.
I was able to run this against some popular websites like, bookmyshow, makemytrip. And to test it in the open, I was able to one-shot scraper solutions for 2 problems posted on r/webscraping.
I have written about the journey to create AutomatiQ in the notion site link.
Github: https://github.com/StoneSteel27/AutomatiQ
Demo video of using Automatiq on bookmyshow website to get current running movies, showtimes, seat layout of a particular screen: https://www.youtube.com/watch?v=OfMvAQP5pkM
Thanks for your time, hope you have a great day/night ahead ;)

value: Show HN: AutomatiQ – Write Automations/Scrapers for any site, just by browsing


value:
The goal of Quollix is to make self-hosting applications as easy as using a managed SaaS product.
Quollix runs applications such as Nextcloud and Vaultwarden. It includes an integrated OpenID Connect provider for single sign-on, automated backups and application updates from an app store out of the box. I wanted these features to be part of the platform rather than making server admins assemble multiple services. It also handles user management, HTTPS certificates and networking.
I made Quollix because I couldn't find a solution that has all these features, which is also simple and doesn't hide these essential features behind a paywall.
Quollix is written in Go, optimizes for simplicity and runs on a single-node with Docker Daemon. Kubernetes would add too much complexity. My scope is homelabs and small organizations.
The project is open-core, where the core is open source and fully usable on its own.
On my website you find a demo video, Getting Started guides which explain Quollix setup and documentation.
Website: https://quollix.org
Source: https://github.com/quollix/quollix
I would like to hear your feedback on the technical design. I am also interested in advice on growing attention for the project.

value: Show HN: self-hosting platform with built-in OIDC provider and backups


value:
Try Search: https://ethereal-agents.space/search.html
Technical Blog: https://ethereal-agents.space/blog/launching-arxiv-scholar.h...
We'd love feedback on the retrieval quality, user experience, and overall approach.

value: Show HN: ArXiv Scholar – An Open-Source RAG System for AI Research Papers


value: vinodkone

value: Show HN: OpenACA – security scanner for AI agent stacks (MCPs,skills,plugins)

author: vinodkone


value: dimileeh

value: Show HN: AWF – run parallel AI coding agents, each in its own Docker workspace

author: dimileeh


value: gwei

value: Show HN: WASM scanner to debug Postgres deadlocks without leaking SQL

author: gwei


value: marshalhq

value: Show HN: Marshal – behavioral supply-chain scanner for JVM dependencies

author: marshalhq


value: eben-vranken

value: Show HN: Promcap – cap Prometheus metric cardinality before it OOMs your stack

author: eben-vranken


value: PujanMirani

value: Show HN: NakshGuard – on-prem proxy that stops AI agent loops

author: PujanMirani


value: Wanted a locally hosted dashboard for my work laptop screen to aesthetically showcase confidential calendar events.

value: Show HN: Dashboard for Your Work Laptop

title: Show HN: Dashboard for Your Work Laptop


value:
Timety is an offline-first productivity and time-management application built with Flutter. It integrates Task Management, Focus Sessions, and Habit Tracking into one.
I tried to make it look good using a Neobrutalism design while also trying to make it usable. I use the app myself and I am adding any bugs and ideas I get as I go. Please let me know what features you would like to see get added. Any feedback, bug reports or ideas are greatly appreciated!

value: Show HN: I made an open-source free tasks app that looks good


value: Show HN: Red and Black Knights Sandbox

title: Show HN: Red and Black Knights Sandbox


value: Show HN: A site for generating AI videos with Seedance 2.0 Mini


value: epochbtc

value:
I'm Ben, and I've written a modern, independent implementation of the Bitcoin protocol in Rust. The primary problem I wanted to tackle was monoculture:
the Bitcoin network and community has been based on a single C++ implementation for 15 years, a descendant of the original Satoshi-authored codebase.
This codebase and the small Core committee are a single-point-of-failure, and is a potentially attractive target for those who would seek to damage or disrupt the network.
On top of that, while the Core team has done a great job at stewarding and improving the codebase, it can't help but be a big, complex
tree of largely legacy C++. I wanted something modern, easier to maintain and with batteries included:
- Native TLS (including mTLS) out of the box
- Built-in (optional) API services for Electrum (based on electrs) and Esplora (no sidecars or separate services needed to run a wallet backend)
- Atomic indices with a single RocksDB data store (address index can't lag core block index, for example)
- Robust authn/authz system with granular permissions
- Novel Streaming Consumer API with modern grpc and JSON websocket transports for integrators (including a privacy-preserving txn subscription option)
- ratatui-based TUI (sat-tui) for operator observability
- Optional, built-in MCP server for AI tooling integration
- Prometheus metrics exporter built-in
- Signed binaries + reproducible builds via Nix
On top of that, it has some additional enhancements:
- Optimized IBD with BitTorrent-style swarm block downloads
- bitcoin.conf compatibility (mostly Core v30-compatible, unsupported keys produce clear errors with guidance)
- JSON-RPC compatibility and sat-cli (similar to bitcoin-cli)
- Legacy deprecated functionality like wallet management and Bloom filters are omitted by design (BIP157/158 compact block filters supported)
To ensure no chain splits or consensus divergence with Bitcoin Core, satd performs dual engine shadow verification by default, using its own Rust consensus engine combined with Core's C++ libbitcoinconsensus FFI crate as a cross-check. The latter is authoritative by default, and any mismatches with the Rust engine are loudly logged. The operator can choose to make Rust or C++ the authoritative engine (with or without the other as a cross-check). The Rust engine has the same method signatures as Core but is 100% Rust and passes the same consensus test suite as Core. No divergence has been
observed on mainnet, signet or testnet4 from genesis to current tip (I saw a few divergences in early development, but none in the months since). For block acceptance, satd runs the same test suite as Core and there's an additional adversarial fuzzing test executed nightly.
See the README for quick 2min start using Docker and the operator manual (https://epochbtc.github.io/satd/) for full details.
While I use satd as my primary set of nodes and wallet backend, it obviously hasn't been battle-tested over many years like Core has, so treat as early beta software with appropriate caution.
All feedback welcome!

value: Show HN: Satd – a Bitcoin full node in Rust

author: epochbtc


value: citronak

value:
I moved my family from 1Password to Proton Pass, but I kept missing Quick Access and 1Password’s CLI integrations.
I could not find an equivalent solution, so I built one and have been using it for a few months.
Quick Access for Pass is a native macOS application that lets me search Proton Pass vaults from anywhere using a global keyboard shortcut. It supports passwords, OTPs, SSH keys, and command execution with credentials from Proton Pass.
The project is built entirely around the Proton Pass CLI. To keep searches fast, it maintains an encrypted local index that contains only metadata for search and ranking. Secrets are fetched on demand from the Pass CLI and never stored locally.
Curious whether other Proton Pass users have run into the same issue, particularly those coming from 1Password.

value: Show HN: 1Password-Style Quick Access for Proton Pass on macOS

author: citronak


value: Datahenge

value:
No Alembic. No migration files. No history table. dbconform compares your declared schema against your live database and generates the SQL to close the gap: nothing more.
If you've ever worked on a database where the migration history is incomplete, wrong, or simply doesn't exist, this is for you.
Supports PostgreSQL and SQLite. Safe defaults. Run compare() to view differences, or apply() to fix the target database.
PyPI: https://pypi.org/project/dbconform
Source: https://github.com/brian-pond/dbconform

value: Show HN: dbconform – stateless schema drift repair, no migration history

author: Datahenge


value: Stezu

value: Show HN: Cron-naturally – plain English to cron and back, in the browser

author: Stezu


value: elenavanengelen

value: Show HN: MockNest Serverless – Bedrock-Assisted API Mocks on AWS Lambda

author: elenavanengelen


value: woutervd

value:
I love the dictionary game: someone takes an unfamiliar word from the dictionary and writes down the definition. Then the other players come up with their own, fake, definition, and guess which one is real.
I built a Wordle-style daily game out of this concept. You get shown a word and four definitions and it's up to you to figure out which one is real. There are five new words to try every day.
The real definitions are from Wiktionary, the fake ones are made up by Claude (and sometimes me).
Let me know what you think, and how this could be improved!

value: Show HN: The Dictionary Game (Fictionary/Balderdash) as a Daily Puzzle

author: woutervd


value:
"Secure and private” is the general aim.
This is a technical/concept demo of a fairly unique approach using a browser-based, local-first and webrtc.
This is intended to demonstrate client-side managed cryptography. We can avoid registration of any sort.
App: https://enkrypted.chat
Features:
* P2P
* End to end encryption
* File transfer
* Local-first
* No registration
* No installation
* No database
Feel free to reach out for clarity instead of diving into the docs/code.
IMPORTANT: While this is aiming to provide a secure experience, it isnt audited or reviewed. Shared for testing, feedback and demo purposes only. Please use responsibly.

value: Show HN: WhatsApp Clone, but Decentralized with P2P Messaging


value: jrflo

value: Hi HN! I built AgentPace (a macOS menu bar app) because I found myself constantly doing mental math on whether or not I would run out of coding agent usage prematurely. The idea isn't showing "You have X% left" in the menu bar, but showing your usage trends over time to help you plan and manage your usage. The usage graph makes this glanceable and intuitive, so you can quickly figure out your burn rate without leaving your workflow. Right now it's focused on people with subscription plans to Claude Code and Codex, but I have plans to expand this out in the future. Happy to answer any questions!

value: Show HN: AgentPace – Know when you’ll run out of Claude Code/Codex usage

author: jrflo


value: atomicnature

value:
Hi HN, I'm the author of git-lrc, would appreciate some feedback from the community
Last year my team started using AI coding tools more heavily, and we found ourselves generating tons of code, but spending less time looking at the stuff that's been generated.
We felt like we were losing a bit of grip/understanding of what we were building. Regressions occasionally slipped through. Sometimes changes made it all the way to production only to be reverted later.
We tried several AI code review tools, but most operate at PR time. That felt too late. I wanted review to happen while the implementation was still fresh in the developer's mind at a team level (soft enforcement). I also wanted to emphasize responsibility for keeping prod stable with each individual engineer.
So I built git-lrc.
When you commit, git-lrc opens a review UI with your diff. It summarizes what changed, points out things that deserve a second look, and lets you quickly jump through the important parts of the change.
Over time, git-lrc has grown to check for around 100 common risk patterns across 10 categories, including security, reliability, performance, maintainability, etc.
Note that this is far from a formal review. It's a quick 60 seconds spent looking at your own work before it gets recorded in git.
It also generates a short "summary deck" that highlights the main changes, with special emphasis on potential risks. With git-lrc you can quickly sanity-check what you're about to ship and obtain greater confidence in what's been generated.
In my mind it is less of an AI reviewer and more as a habit for AI-assisted development: a small pause to make sure we understand and stand behind the code we're shipping.
Developers can review the change, vouch for it, or consciously skip the review. Those decisions get recorded in git history, creating a  trail of how code was reviewed before it shipped.
It'd be great if you could take a look, give it a try in your projects or teams and let me know what you think.
Happy to take feedback from the HN community and improve it over time!
GitHub: https://github.com/HexmosTech/git-lrc

value: Show HN: git-lrc – Free, Micro AI Code Reviews That Run on Git Commit

author: atomicnature


value: antonioiadic

value:
Hi HN! We’re Antonio, Frank, and Imam.
We built FinalApp out of personal frustration with the current hiring market. The application process is inefficient by design: you’re rewriting the same documents for every role, navigating inconsistent ATS forms, and manually tracking status across a dozen spreadsheets. We wanted to automate the repetitive parts without compromising the quality and specificity that actually gets a response.
Our Scout agent finds roles that match your background and explains why. The resume and cover letter generator tailors your documents to the specific posting, drawing only from your actual experience and profile so nothing gets fabricated. The built-in editor and AI chat understand both what a strong application looks like and everything about you, so the output is accurate and specific rather than generic. Our Autopilot then fills out and submits the real application from your experience. Autopilot can wait for your approval before submitting an application via enabling “Strict Mode.” You can trigger Autopilot from our dashboard, or directly from Workday, Greenhouse, and 100+ other job boards via our Chrome extension. Applications are tracked automatically, and a dedicated Finalapp email address parses recruiter responses and updates your application statuses accordingly.
We’re aware that high-volume indiscriminate applying is an obvious use case for a product like this, and we’ve been deliberate about not optimizing for it. The approval gate is intentional. We want the process to be more efficient, not less considered.
Free tier available, no credit card required. We would love thoughts/feedback (especially from anyone who has been actively job searching recently).
We'll be hanging out in the comments all day to answer your questions :)
https://www.finalapp.io

value: Show HN: FinalApp – paste a job link, review the filled application, then submit

author: antonioiadic


value: rowbin

value: Show HN: VibeSH – Fully Hallucinated Terminal Shell

author: rowbin


value: cardellifan

value: You just prepend `/offload` to a prompt, and it will run on /goal on another device (like a Mac Mini, it can also set-up a VPS for you). It handles details like moving env keys safely, making sure you're signed-into gh, pinning the right versions of any tools or dependencies, tunnelling securely to display your dev servers, etc. You can intervene directly via the Remote Control feature in Claude Code or Codex. I found it way too tedious to maintain my own VPS for dev work, and I found the cloud agent stuff for Claude Code and Codex annoying also. Also, other options doesn't support OpenCode + OpenRouter, using different harnesses within the same project, etc.

value: Show HN: Offload, cross-device handoffs for Claude Code and Codex

author: cardellifan


value: its-a-new-world

value:
Hey HN, I’m open-sourcing Polymr, an agent platform that focuses on controllable execution and adaptive interaction through dynamically generated tools and interfaces.
A core design principle is that MCP tools, generated scripts, frontend components, and pages all operate on the same underlying tool surface and can build on top of one another.
Control comes from things like:
- Standardized preview mode when a tool call is not automatically approved. I added an SQL MCP server that can run a DML in a transaction and roll it back to provide the user with an exact preview of the resulting changes.
- Granular permissions in a hierarchical model that balances power and ease of use. I added a CLI MCP server that introspects the script with Tree-sitter and dynamically extracts the required permissions and prompts the user only when additional permissions are needed.
- Runtime policies to restrict tool calls ad-hoc, for example restrict which folders can be accessed or make them immutable.
- Polymr has no "mandated" tools out of the box, allowing the user to shape it as needed.
The adaptive interaction includes:
- The LLM can create scripts that encapsulate domain-specific logic on top of existing MCP tools. These scripts can be reused by other scripts, frontend components, pages, or exposed back as MCP tools. For example, a generic SQL tool can be wrapped in a higher-level tool that implements business-specific object mutations while hiding the underlying queries.
- The LLM can create frontend components and pages that also work on the same tool surface, allowing for a mixture of traditional web applications combined with LLM interaction. Everything that is available on the screen is, by its very nature, also approachable by the LLM.
- The LLM can reuse existing components or create entirely new ones to create dynamic composite views on the fly in a session, tailored to the problem at hand.
While I use it daily for both work and outside of work, for coding and non-coding, it still remains very much a personal project, so expect to find bugs and rough UI edges here and there. Any feedback, critique, or bug reports are highly appreciated!

value: Show HN: Polymr – A Platform for Controllable Execution and Adaptive Interaction

author: its-a-new-world


value: Show HN: I built a philosopher-inspired thinking style quiz


value: yapancha

value: Show HN: PDF redaction that works automatically, all on your device

author: yapancha


value: rakeda

value:
The concept for metiq.space came after playing Global Magnates with friends and realizing how fragmented live global data is. ships, aircraft, satellites, ports, weather, hazards, infrastructure, cyber, and public datasets all exist, but they usually live in separate tools and maps.
The goal was to build one interactive 3D globe where live public data could be visualized by latitude, longitude, and altitude. Surface data stays on the globe, while aircraft, satellites, and other above surface things can be represented in actual 3D space instead of being flattened onto a map.
The outcome is an interactive globe that showcases Earth, air, sea, space, cyber, defense, infrastructure, politics, and the list is continuously growing.
Majority of development right now is going into data filtering and deduping.

value: Show HN: Metiq: a real time 3D globe for 100 public datasets

author: rakeda


value: bstrama

value:
Hey HN!
I recently built halupedia - a funny site that hit 300k+ users in few weeks and #1 on hacker news. It was amazing project, which costed me $300+.
Since halupedia uses a lot of AI I paid a lot for the bill, fortunately I'm fine thanks to donations and resigning from many planned features.
It inspired me to built wattfare - just like BYOK it let's user bring their own AI budget to the site.
It works like OAuth, user just clicks a single button, has ability to limit or revoke access for a website any time and has great developer experience (at least I think so ).
In the future I'm going to add multiple ways for the user to pay for usage, ideally would try to make it possible to connect openai/anthropic subscriptions to wattfare, but it's not going to be that simple - if you have some thoughts about that, please let me know.

value: Show HN: Wattfare – LLM API that's paid by users, not dev

author: bstrama


value: har-ki

value: Show HN: K8s watcher that investigates incidents and opens PRs (it can't merge)

author: har-ki


value: agusgestro

value: Show HN: Telemetry engine to audit ledger integrity and race conditions

author: agusgestro


value: robeenly

value:
We used NeuGBI to replicate "Generative AI as Seniority-Biased Technological Change" (HBS, 2025) on the same Revelio Lab dataset — 300M U.S. employment records.
The paper's finding: AI disproportionately affects junior positions (−29.4%) vs. senior (−5.8%). NeuGBI arrived at the same conclusion autonomously.
One thing NeuGBI found that the paper didn't: within software development, it's specifically junior-level (L2) positions that nearly halved, not entry-level (L1).
NeuGBI uses NeuG (a graph database with multi-hop relationship support) as its query engine, Hypergraph reconstruction for analysis, and packaged exploratory Skills that an LLM can invoke to decompose questions and drill down step by step.
The key capability of NeuGBI is end-to-end unbiased sampling — on 300M records, complex multi-hop queries return in seconds rather than hours.
Blog post: https://graphscope.io/blog/tech/2026/06/16/NEUGBI-BLOG.html
Original paper: https://arxiv.org/abs/2603.10625

value: Show HN: Replicating a Harvard study on AI's employment impact – Autonomously

author: robeenly


value: unixfile

value: Show HN: kv: good for pipes, data exchange and configurations

author: unixfile


value: thinkmariale

value: Show HN: Kinetk – Multimodal intelligence API and MCP for grounding agents

author: thinkmariale


value: cachebag

value: Show HN: Absolute best option for networkmanager in Rust

author: cachebag


value: sumolessons

value:
One thing that’s been bothering me about AI side projects is inference costs. With traditional software, a successful launch usually means higher profits. But with AI products, success can mean unexpectedly large bills.
This has pushed me toward cheaper, less capable models and made me hesitate to even explore certain ideas. I don’t want every side project to become another $20/month subscription, but I also can’t compete with VC-backed companies willing to subsidize inference costs.
Then I had this idea: what if users simply paid for their own inference?
This already happens in some apps through locally configured API keys, but could the model be extended? If users bring their own AI account, developers can build AI-powered products without taking on variable inference costs. How many AI applications shouldn’t be subscription businesses at all?
The challenge is that developers don’t want to handle user API keys, users don’t want to hand them out to every app they try, and nobody wants the friction of collecting payment methods just to pass through inference costs.
That’s the backstory to my latest side project, infer0.com.
It's a bit like SSO for AI inference. Users connect their AI providers once, and apps use auth tokens to request inference through infer0. Developers don’t manage API keys or pay model costs themselves, while users can bring the same AI accounts across multiple applications.
This may be a terrible idea, both because nobody will trust it and because I’m sure there are risks around handling user API credentials that I haven’t fully appreciated. But I felt the need to build it. So here’s a rough first pass.

value: Show HN: Infer0 – do AI apps need subscriptions?

author: sumolessons


value:
Pantheon is a technique for generating each subagent, generating code with different solutions, breaking it and writing code that ultimately survives.
For Pantheon-X, GPT 5.5 is broken, and GPT is broken and surviving is the last skill to write code written by Claudette.
In the case of Pancheon Gap, it is a technology to continue to check whether the actual review is true and to derive surviving children.<p>I'd appreciate it if you could actually use it and give me feedback through issues

value: Show HN: AI vs. AI – code and reviews only count if they survive an attack


value: mintflow

value:
Recently I discovered that Codex CLI ships with an App Server that exposes a WebSocket API. It allows third-party applications to interact with Codex threads, including:
- Resuming existing threads
- Starting new turns
- Handling approval requests
- Receiving thread updates
- Upload small files/images
Since my iOS app(NovaScale, formerly NovaAccess) already includes built-in Tailscale connectivity, connecting it to Codex App Server felt like a natural next step.
The setup is fairly straightforward. A small service running on a Linux or macOS host launches the Codex App Server, generates an authentication token, and exposes the WebSocket endpoint over the Tailnet. NovaScale then connects directly to that endpoint through Tailscale, allowing me to continue Codex work from my phone while away from my desk.
The feature was released last week as part of NovaScale 1.5.0
I also wrote a short technical note explaining how it works and what still needs improvement:
https://galaxnet.dev/nova/articles/remote-codex-in-novascale...
The feature is currently experimental, but it has already become one of my favorite workflows.
Share this for anyone is interested and want a more power rich workflow in a single app.

value: Show HN: Alternative way to do remote codex via NovaScale with builtin Tailscale

author: mintflow


value:
I have seen way too many people waste time tailoring their resumes to a specific Job description, practicing interview questions etc. and most of them don't trust AI tailoring and rightfully so, cuz it invents experience you never had. All it takes is the interviewer asking you one question that you do not know the answer to and the house of cards falls. So hear me out:
I made HiredCopilot an AI tool that helps you tailor your resume for a specific job description and I made it different. Apart from all the features a usual AI resume tailoring app has, I made it so that it never invents ANYTHING that you can't defend in an interview.
Here is how that specific part works: The tailoring pass is constrained to the source content and a check diffs the generated claims against the original resume so invented specifics get caught instead of shipped. The UI also shows a before/after of exactly what changed so nothing gets silently added.
The stack is Next.js + FastAPI, Claude for the AI and OF COURSE rate limiting cuz i don't want a 10k$ bill.
It is free to try without an account (there is a no-login resume check), with a paid tier for unlimited tailoring. You can check it out here:
hiredcopilot.com.
I would appreciate your guys feedback, especially on the anti-fabrication approach and also what can i do to make the app better or any errors you may find that need a fix. If you have worked on grounding or faithfulness for LLM output i would love to hear from you on how to do this better.

value: Show HN: An AI resume tool that never invents experience you didn't have


value: jeraldyuan

value: Show HN: Ledgerline – local-first finance MCP server

author: jeraldyuan


value: snophey

value:
I made this to address a particular pain-point I had while looking for a new place: on a traditional real-estate site, you select your price range and your area and you get a swarm of thumbnails of apartment listings. Unfortunately these are often very unflattering images that don't tell whether the apartment/house has what I'm looking for. Clicking every thumbnail and sifting through each photo of every listing is tedious and time-intensive.
With FlatHawk I wanted to try to improve this search experience. The user can still set the usual parameters (e.g. price, area) _but_ the user can also say in plain language what they are looking for (e.g. an open floor plan, or a view of a lake). In the background, similarity search is performed on a set of more than 150k photos of actual apartments and houses that are currently available for rent or sale. The user gets a list of relevant listings and the original thumbnail of each listing is replaced with the picture that best matches what the user asked for.
I should add this currently only contains Swiss real estate as that is where I am based.
On the technical side, I decided to keep it simple. It is a React Router application that queries a Postgres DB (with pgvector extension for the search). I use Gemini Embedding 2 for the cross-modality embeddings.

value: Show HN: FlatHawk – Like Google Image search but for finding a house or flat

author: snophey


value: lveillard

value:
Hello HN
After becoming allergic to SQL, I opened 120+ issues in Dgraph, Typedb and surrealdb looking for the perfect graphDB. None of them was built for agents nor were they the perfect fit for what we wanted to achieve: fully ditching the SQL legacy to properly model reality. So we decided to build BlitzGraph
In BlitzGraph, records (units) can belong to multiple types (kinds) and evolve through time. Also polymorphic relations are first class and multiple kinds can play the same role. This design helps to escape the old table paradigm and track entities throughout their lifecycle without awkward self-joins that connect an entity to itself under different IDs in other tables
An example:

   { "$id": "amazn", "$kinds": ["Company", "Prospect"], deal: ... }    // Day 1
   { "$id": "amazn", "$kinds": ["Company", "Customer"], contract: .. }   // Day 7
   { "$id": "amazn", "$kinds": ["Company", "Churned"], churnCause: "..." }, ... // Day 86

What makes BlitzGraph different:

   - GraphQL-like nested queries and mutations https://blitzgraph.com/docs - Polymorphic records and relations
   - Bidirectional O(1) relations  - Referential integrity with native cardinality validations
   - JSON query/mutation language designed so AI agents can build them programatically  - Batched queries/mutations without N+1 issues
   - Built-in frontend engine for quick dashboards and MVPs  - Native full text search, file storage, computed fields, ephemeral subspaces, unit history...

Honest comparisons:
- vs typedb: amazing db, but not ideal for app development. On the other hand we loved and brought their inference ideas and how mutations execute smartly instead of line per line  - vs surrealdb: Several core differences, a key one is that we run validations and trasnformations in topological order, and our edges are first class citizens  - vs dgraph: Their cool features like post commit hooks were attached to the graphQL layer, in BG it is fundational  - neo4j: If you've tried it, you know  - vs supabase/pg: BG is slower for flat queries but faster in nested ones. But with BG mainly you get rid of the tables paradigm and jump into the graph world while being able to build apps
Not ready:
- While blitzgraph is already an excellent memory backend for AI agents, we still need to finish the semantic search engine
 - Query planner is not optimized
 - Cloud frontends have no native auth engine yet
Beta is live, please break things!
 - Public playground: https://blitzgraph.com/#playground
 - MCP: https://blitzgraph.com/mcp

value: Show HN: BlitzGraph – Supabase for graphs, built for LLM agents

author: lveillard


value: kirkmarple

value: Show HN: Agent Harness Lab – compare agent frameworks with swappable tools

author: kirkmarple


value: meysamazad

value: Show HN: NIS2 Readiness Check

author: meysamazad

title: Show HN: NIS2 Readiness Check


value: antinucleon

value: Show HN: INT21 – Self-Improving PTX Kernel Factory

author: antinucleon


value: Show HN: Claireon – MCP Server for Unreal Editor


value: kordlessagain

value: Show HN: Shivvr – Ephemeral semantic embedding and cognitive agent service

author: kordlessagain


value: rdksu

value: I indexed around 16k+ most-viewed wallpapers from wallhaven.cc using a CLIP-based model to generate image embeddings, and then hacked together a cool demo showing the vector search working client-side using sqlite-wasm compiled with the new and upcoming sqlite-vec extension, which allows me to do the vector search over the image index client-side! The inference over the actual text-encoder of CLIP also happens client-side using transformers.js :)

value: Show HN: In Browser semantic wallpaper search over 16k+ wallpapers

author: rdksu


value: Show HN: Docket – Semantic search over your local files, runs in the browser


value:
A customer is charged twice, data is processed twice in an ETL pipeline or hundreds of other scenarios when you have to atomically analyse your code to prevent race conditions.
Most teams use Redis Setnx or have homegrown lease tables and idempotency tables, try to make every endpoint idempotent or check and update in the same transaction or adopt very heavy commitment infra what if you could do it with just postgres ?
I had to do the same, an idempotency table with states with the frontend sending a randomised session id
Made my endpoint that receives payment webhooks always check and update
This was annoying, to say the least to do across inventory, orders and payments
My entire infrastructure depends on postgres select for update and wherever i forgot to put it, say across a direct order or cart order, a race condition lives
So i made Sentinel, zero infra just postgres, it makes any endpoint you wrap it around functionally idempotent, it's caches the result and replays it, handles the entire life cycle from the work being claimed till completion with any errors being surfaced with reconciliation tooling
Uses fencing tokens to throw out stale work, leases and heartbeat for the lifetime of the work and states to track progress.
from sentinel import Sentinel
import psycopg
sentinel = Sentinel(get_conn=get_conn)
def process_payment_webhook(charge_id: str, amount: int):
    result = sentinel.once(
        key=f"stripe-webhook:{charge_id}",
        fn=charge_customer,
        kwargs={"charge_id": charge_id, "amount": amount},
        ttl_ms=5000,
        hard_ttl_ms=30000
    )

    if result.execution_alive:
        # Another worker is actively processing this charge
        return {"status": "processing"}

    elif result.uncertain:
        # Execution failed midway, side effects may have partially applied
        # Use reconciliation tooling to inspect and resolve
        return {"status": "uncertain", "reconcile": result.reconcile}

    else:
        # Newly completed or replayed from cache
        # Safe to return regardless of how many times this webhook fired
        return {"status": "ok", "response": result.response}

AsyncSentinel is available for async contexts.
Temporal and Airflow assume your tasks are idempotent. Sentinel is what makes them actually idempotent.
"GitHub: github.com/Sreejay-Reddy/Sentinel | pip install sentinel-coordination"

value: Show HN: Sentinel – prevent duplicate execution using Postgres


value: ZionBoggan

value: Show HN: TreeTrace, git records what changed; this records how you steered

author: ZionBoggan


value: ylian

value: Show HN: Dino – An AI coding agent that keeps you in the loop

author: ylian


value: georgeck

value:
Our email inboxes carry multiple decades of messages (100K-500K). This is a good proxy for all the important things that happened in your life, the projects you have done and the people that you have connected with. With the chronological view of messages in the inbox, these details remain hidden. What if we could turn this archive into a personal wiki that you can search and curate? That is Memento.
For the information architecture of such a wiki, Memento takes an opinionated view of creating four high level dimensions - People (like a CRM view of your contacts), Projects (life events that are bounded by some start and end dates), Concepts (evergreen topics) and Newsletters.
Inboxes group messages by sender address, so the same person shows up many times across work, personal, and alias addresses. In the People dimension, Memento resolves all of that into one canonical person using deterministic algorithms and brings the people who are related to that person through graph algorithms. With just these two, you get an already populated CRM from your life history. No LLMs involved. From here, you can further enrich each person's wiki page by adding additional personal notes and create a cohesive narrative using LLM. The same applies to other dimensions as well.
How can we query this wiki? This is where Memento uses an agentic search over this curated dataset. Your emails are stored in SQLite DB, indexed using FTS and vector embeddings and kept up-to-date, using another open-source project called Msgvault [1]. Memento extends this DB with its own tables to store the output of various algorithms - canonical people discovery, graph algorithms to find clusters of connections etc. For the agentic search to be effective, Memento exposes the underlying FTS, vector and graph data in a structured way to the agent as tools. The agent can further refine the search with additional tools like get-message-details,  message-cluster etc. What we discovered is that the resulting search is much more powerful that the typical ‘Ask Gmail’ search exposed by Google.
Every factual claim exposed by Memento can be traced back to the real email it came from. Any additional notes added to the wiki are incorporated in the next generation. So Memento becomes richer and more personal to you over time.
This worked really well for our personal email archive and we were pleasantly surprised by the things that Memento was able to uncover. In order to demonstrate this capability at scale without exposing our private info, we connected Memento to the public Enron dataset that contains hundreds of mailboxes. With the SQLite store, Go backend and Next.js UI, Memento handles this 5 GB dataset with ease. Now you can query this archive and run agentic searches to re-discover the Enron scandal yourself - you can see the demo set here [2].
The app is a single binary that serves on localhost and treats your archive as read-only, and you can point it at any OpenAI-API-compatible LLM, whether that's a local model or a cloud one. It's open source, so you can inspect how your data is handled.
You can try it out today without connecting your archive - use the hosted demo [2] or download the GitHub release and run it with a synthetic local archive `./memento app --demo`
[1] https://www.msgvault.io
[2] Hosted demo (Enron data): https://memento-demo.latentsignal.org/home
Demo video: https://www.youtube.com/watch?v=Ms1KeAYCN2A
Project home: https://latentsignal.org/projects/memento
GitHub: https://github.com/latentsignal-org/memento
We are George and Ann, creators of Memento.

value: Show HN: Memento – Self-hosted agentic search and LLM wiki over your email

author: georgeck


value: vasanthv

value: Show HN: NumUp – A Daily Math Puzzle

author: vasanthv

title: Show HN: NumUp – A Daily Math Puzzle


value: patriceckhart

value: Show HN: Building Your Own Coding Agent on Top of Zot – Meet Coil

author: patriceckhart


value: waxsway

value:
One specific output to ground this before the architecture.
The primary wallet from the Bybit / Lazarus exploit gets scored 10/100, tier high, posture escalate, with the OFAC Lazarus Group attribution showing up directly in the briefing. Full verdict here: https://credscore.us/v/o6wr--NrABo
The engine is deterministic, no ML in the scoring path. Explicit numeric weights per signal. Structural pattern detection: fan-out distribution, source-return flow, circular funding, repeated amount-band recirculation. Hard sanctions cap at score 12 for confirmed OFAC SDN matches, enforced at three independent points in the pipeline. Same wallet always produces the same score. Every output traces back to specific on-chain activity with a written rationale.
Five EVM chains: Ethereum, Base, Arbitrum, Optimism, Polygon. 0 to 100 score (higher means lower risk), decision posture (proceed / review / escalate), structured analyst briefing. Sub-15 second analysis from address to verdict.
What it doesn't do: non-EVM chains (Bitcoin, Solana, Tron), real-time stream monitoring at scale, deep cross-chain tracing through bridges. Those are jobs Chainalysis Reactor and TRM Forensics do better. CredScore is the fast first pass, not the deep investigation.
Two public case studies on real attacker wallets if you want to verify engine output independently:
Bybit / Lazarus wallet tree: https://credscore.us/case-studies/bybit-hack-lazarus-wallet-...
Drift / DPRK wallets, flagged on behavior alone: https://credscore.us/case-studies/drift-hack-dprk-wallet-ana...
Free first analysis at https://credscore.us/desk, no card.
The hardest scoring problem I'm working on right now is distinguishing legitimate high counterparty entropy from drainer-shaped victim fan-in at the signal level. The engine currently credits high counterparty entropy as a mild positive signal (broader observable behavior is generally good). But on drainer wallets, hundreds of distinct inbound counterparties with small values are victims, not legitimate counterparty diversity. I'm exploring counterparty value distribution and inbound-to-outbound ratio as differentiators, but the signal-shape overlap with legitimate batched payouts is real. Curious how others have handled this differentiation in deterministic risk scoring systems where you can't fall back on ML clustering.

value: Show HN: CredScore – Deterministic wallet risk scoring, no model in the path

author: waxsway