value: gubets

value:
https://github.com/infra-plan/bim-tile-overlay
We manage infrastructure using BIM models in Autodesk Viewer. We needed to show aerial imagery and street maps underneath the 3D models, synced to the camera in real-time, so that we can give some context to the shown infrastructure.
The hard part: coordinate transforms. A Revit model's internal coordinates are in feet, rotated, translated, and offset from a local CRS projection, which itself needs to be converted from WGS84. The library ray-casts the camera frustum onto a ground plane, converts the hit points to lon/lat, determines which XYZ tiles to fetch, stitches them into a canvas, and maps the result onto a THREE.js plane, all updating on camera change. ~500 lines of JavaScript, MIT license, one peer dependency (proj4).
We would love to hear community feedback!
The reason we decided to open-source is quite common: we couldn't find something similar to this that would not require a significant change to our workflow.
Hope others will find benefit from this library.

value: Show HN: Overlay map tiles onto Autodesk's 3D BIM Viewer

author: gubets


value: I think linktree sucks, i thought it could be better, cleaner and also AI native.

value: Show HN: I built the modern, clean and AI native linktree app


value: codeveil

value:
I used to keep important passwords in ways that were a lot more primitive than they should have been, mostly because I never fully got comfortable with putting everything behind an account or cloud sync by default.
That eventually turned into building the password manager I actually wanted to use myself: Android-only, local-first, offline-first, and built around keeping the vault on-device.
I’ve been using it daily for a while now, and that shaped the app more than trying to chase a giant feature checklist.
It’s called VeilVault. I published the crypto/storage core separately for anyone who wants to inspect the local encryption and storage side.

value: Show HN: VeilVault – an Android password manager built to stay local

author: codeveil


value: yagelar

value: Show HN: I built DoodleDuel, a free browser drawing game with an AI judge

author: yagelar


value: evoluteur

value: Show HN: Using the Web Audio API to simulate various tuning forks

author: evoluteur


value: tamnd

value: Show HN: The complete Open Library catalog in clean, analysis-ready Parquet

author: tamnd


value: xdsai

value: Show HN: CertPulse – Certificate monitoring for multi-cloud teams

author: xdsai


value: rado-bogdaonv

value: Hey HN! I am Rado, and I am happy to share with you something I have been working on in the past few months. TrendZero tracks early signals across the web and shows you which SaaS topics are accelerating, so you can act first and you know what to focus on before your competitors do. Get AI-powered recommendations for content, product features, and positioning strategies. You can give it a try  at https://trendzero.io and I would love to have your feedback :)

value: Show HN: Emerging micro SaaS trends that are accelerating

author: rado-bogdaonv


value: marcusdev

value:
Hey HN! I'd like to share my personal experiment with a different way of finding train journeys across the UK.
When I book flights, I use sites like Kiwi and Skyscanner for flexible searches - multiple airports, custom connections, creative routes, etc. But rail search feels oddly constrained. All the UK train operators offer basically the same experience, and surface the exact same routes. I always suspected there were better or just different options that weren’t being shown. Where is the "Skyscanner for trains"?
After digging through the national rail data feeds, I decided to have a go at building my own route planner that runs completely offline in the browser. This gave me the freedom to implement more complex filters, search to/from multiple stations, and do it without a persistent network connection.
Now I'm finding routes that aren't offered by the standard train operators, connecting at different stations, and finding it's often easier to travel to different stations (some I'd never heard of) that get me closer and faster to where I actually want to go!
It's still a little rough and I'd like to add more features such as fares, VSTP data, and direct-links to book tickets, but wanted to share early and get some initial feedback before investing more time into it. So, thanks in advance - let me know what you think.

value: Show HN: Offline-first UK train planner

author: marcusdev

title: Show HN: Offline-first UK train planner


value: luishfonseca

value:
I have two VPSs with full disk encryption. Sadly, neither provides a TPM so I've always had to connect by SSH to type my password. I've spent the last weekend thinking of a way to automate the process without weakening the threat model and came up with this protocol. You can find the sequence diagram in the README, consider that the spec.
This is the threat model:
- Internal network is trusted, nodes are not. However, a compromised network unlock server won't collude with a network attacker.
- Attacker has full access to the public network.
- Attacker has eventual access to the disk and can recover any deleted file.
Any full disk encryption unlocking scheme of a remote machine without a TPM, e.g. SSH-ing into a dropbear initramfs, is vulnerable to the same attacker: someone who can read your unencrypted /boot partition and sit on your network. Against dropbear, they extract the SSH host key from the initramfs, impersonate your server, and capture the passphrase you type.
This protocol doesn't strive to be stronger than that, it accepts the same threat model. An attacker with disk access + network access within the TTL window can steal the ephemeral TLS cert + key from /boot, connect to the server, and retrieve Na. The protocol just removes the human from the loop while being no worse than typing a password over SSH.
I'm running this on my pair of VPSs, so if anyone finds a hole inside the threat model please let me know! I don't run anything critical but would rather not get hacked by playing with homebrew protocols. This wasn't vibe coded, so you'd be arguing with my ideas and not an LLM.

value: Show HN: Remote LUKS disk decryption over mTLS

author: luishfonseca


value: Show HN: AgentContract – ESLint for AI agents - Declare what your AI can||can't!


value: daco

value:
Hey HN,
Claude Code is powerful, but its execution is a black box. You see the final result, not the journey. Agent Flow makes the invisible visible in realtime:
- Understand agent behavior: See how Claude breaks down problems, which tools it reaches for, and how subagents coordinate
- Debug tool call chains: When something goes wrong, trace the exact sequence of decisions and tool calls that led there
- See where time is spent: Identify slow tool calls, unnecessary branching, or redundant work at a glance
- Learn by watching: Build intuition for how to write better prompts by observing how Claude interprets and executes them
It's also been really useful when building agents into your own product. Having a visual way to see how an agent actually behaves makes it much easier to iterate on prompts, tool design, and orchestration logic.
It's also been invaluable when building agents into your own product. I've been using it every day to understand how the Anthropic Agent SDK behaves, and seeing agent orchestration visually makes it so much easier to iterate on prompts, tool design, and coordination logic.
It's also interactive, and shows what's happening as Claude Code works: which agents are active, what tools they're calling, how they coordinate, and where time and tokens are being spent.
You can pan, zoom, click into any agent or tool call to inspect it.
It runs as a VS Code extension — opens as a panel right alongside your editor.
What you can see:
- Live agent spawning, branching, and completion
- Every tool call with timing and token usage
- Token consumption per task and per session
- Parent-child agent relationships
- File attention heatmaps (which files agents are reading/writing most)
- Full transcript replay
- Multi-session support for concurrent workflows
Currently works with Claude Code. Codex support and iTerm2 integration are next on the roadmap.
GitHub: https://github.com/patoles/agent-flow

value: Show HN: Agent Flow: A beautiful way to visualize Claude Code actions

author: daco


value:
Overnight is a free, open source CLI supervisor/manager layer that can run Claude Code (Codex support coming soon!) by reading your Claude conversation histories and predicts what you would’ve done next so it can keep executing while you sleep.
Github (MIT license): https://github.com/yail259/overnight
Landing Page: https://workovernight.com/
What makes it different to all the other generic “run Claude Code while you sleep” ideas is the insight that every developer works differently, and rather than a generic agent or plan that gives you mediocre, generic results, the manager/supervisor AI should behave the way you would’ve behaved and tried to continue like you to focus on the things you would’ve cared about.
The first time you run Overnight, it’ll try to scrape all your Claude Code chat history from that project and build up a profile of you as well as your work patterns. As you use Overnight and Claude Code more, you will build up a larger and more accurate profile of how you prompt, design and engineer, and this serves as rich prediction data for Overnight to learn from execute better on your behalf. It’s designed so that you can always work on the project in the day to bring things back on track if need be and to supplement your workflow.
The code is completely open source and you can bring your own Anthropic or OpenAI compatible API keys. If people like this project, I’ll create a subscription model for people who want to run this on the cloud or don’t want to manage another API key.
All of overnights work are automatically committed to new Git branches so when you wake up, you can choose to merge or just throwaway its progress.
It is designed with 4 modes you can Shift Tab through depending on how adventurous you are feeling:
1. tidy — cleanup only, no functional changes. Dead code, formatting, linting.
2. refine — structural improvement. Design patterns, coupling, test architecture. Same features, better code.
3. build — product engineering. Reads the README, understands the value prop, derives the next feature from the business case.
4. radical — unhinged product visionary. "What if this product could...?" Bold bets with good engineering. You wake up delighted or terrified.
Hope you like this project and find it useful!

value: Show HN: Using Claude Code history to predict your next messages while you sleep


value: jakemor

value:
Hey all thanks for checking out my first OSS PROJECT
Here's what makes Kanna special:
- One-click switch between Claude Code / Codex
- Runs in your browser on localhost (no app switching)
- Embedded split terminals, persisted between chats with a beautiful horizontal scrolling UX
Coming soon:
- End-to-end encryption + remote access via reverse proxy
- Git integration, diffs, PRs
- Remote / mobile support over tailscale
I wanted to replace switching between cursor, the github app, browser & (most recently) the codex app. The browser is hardest to recreate yet also the most flexible so it makes sense to build in the browser itself.
Install in 5 seconds: bun install -g kanna-code
then just type: kanna
Kanna is purely a ui/ux layer. It uses your existing CLIs, is compliant & no data ever leaves your machine. If running claude or codex in your terminal works, kanna works too.
Kanna natively supports every tool call, model, reasoning effort, fast mode, plan mode, compaction, user questions, web searching, skills, agents, mcps, everything. There are a few libraries that do this but none that I found as comprehensive.
All feedback welcome!

value: Show HN: Kanna – OSS Web UI for Claude Code and Codex with Embedded Terminals

author: jakemor


value: Show HN: A Chrome extension for searching and pining and exporting ChatGPT chats


value:
Hey HN,
I built Danube, a marketplace where AI agents can discover and execute tools, and where developers can publish and monetize them.
I got tired of two things: giving my API keys directly to agents like OpenClaw (didn't feel secure), and having to re-setup all my MCP servers every time I switched between Cursor, Claude Code, and other tools.
Danube stores your credentials securely. Your agent calls the tool and never sees the keys. And since it's one MCP connection, you set it up once and it works across all your clients.
For devs who want to publish: you upload an OpenAPI spec or MCP server, optionally set pricing, and you're live. Agents can search and find your tools without users needing to manually configure anything.
100+ services work today. No signup required to browse.
Would love to hear what tools you use most with AI agents. If anyone's interested in publishing a tool, happy to help get you set up.

value: Show HN: Danube – AI Tools Marketplace

title: Show HN: Danube – AI Tools Marketplace


value: menggithub

value: Show HN: zbot – an AI agent for MCU with Telegram chat (runs on Zephyr or Linux)

author: menggithub


value: phantomCupcake

value:
I was curious about the amount of code on GitHub that is generated with Claude Code, and this is my attempt at finding that answer.
Spoiler alert: It's a lot - around 19M commits by my count
In a nutshell it is a dashboard that presents some basic, and hopefully, interesting stats about commits signed by Claude Code on GitHub - in public repos.
Not all commits are signed (via the author field, or a commit "trailer"), and many repos are private, which means Claude's reach is probably wider than what you see here. But I think it's enough to see the spread and learn a bit about how it's used.
Technology wise, it's a pretty basic Next.js app with Recharts for graphing and PostgreSQL for the DB. I started off with using BigQuery because I estimated I would need the analytical scale, but I eventually pivoted to Postgres because the small writes, and frequent reads for deduplication became too expensive.
The ingestion/backfill job is the more interesting part since I went from severely under-engineering it (start smol and all that) to ending up with a bare-bones, but capable, ETL pipeline.
Primarily, the challenge to overcome in reading the data, was GitHub's rate limits - both on their search API, and on their GraphQL API. On search it's 30 req/min, on GraphQL it's 5000 req/hour - per access token. Because of this difference, and response time differences, I split the work:

  1. There is a batch of search workers that write basic commit info to a table - paging and splitting to find as many of the commits as we can.

  2. Enrichment workers read from that table and fill in some of the info that we can't see in the search. Lines added/deleted, and repo information, is added this way.

There is a bit of lag in reading these commits currently, and it is still pulling historical commits, which is why the most recent dates are a bit low on commits, and why some repos don't yet have a language set yet.
I wouldn't say it's 100% done - I want to improve the ingestion still, and I think there is more I can extract from the data - but I have definitely enjoyed looking at what I have so far.
Let me know if you have an idea for what I can add to the dashboard, or can think of something else I should also be reading.
For some more info on my methodology and the evolution of the backfill job, head to the About page. :-)

value: Show HN: Claude's Code – tracking the 19M+ commits generated by Claude on GitHub

author: phantomCupcake


value:
Hi HN folks,
I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory.
This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future?
I do not think so.
Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs already add significant latency, so adding heavy runtime overhead on top only makes things worse. Existing solutions start to fall apart here.
Agents built in Python also tend to have a large memory footprint, which becomes a serious problem when you want to scale to thousands of agents.
And open-source for agents is still not where it should be. Right now, I cannot easily reuse agents built by domain experts the same way I reuse open-source software.
These issues bothered me, and I realized that if agents are ever going to be democratized, they need to be open and easy to use. Just like Docker solved system dependencies, we need something similar for agents.
That is why I started building an agent framework in Rust. It is modular and follows the principle of true agency: an agent is an entity with tools, memory, and an executor. In AutoAgents, users can independently create and modify tools, executors, and memory.
With AutoAgents, I saw that powerful agents could be built without compromising on performance or memory the way many other frameworks do.
But the other problems still remained: re-sharing agents, sandboxing, and scaling to thousands of agents.
So I created Odyssey — a bundle-first agent runtime written in Rust on top of AutoAgents, the Rust agent framework. It lets you define an agent once, package it as a portable artifact, and run it through the same execution model across local development, embedded SDK usage, shared runtime servers, and terminal workflows.
Both AutoAgents and Odyssey are fully open source and built in Rust, and I am planning to build an Odyssey Agent Hub soon, with additional features like WASM tools, custom memory layers, and more.
My vision is to democratize agents so they are available to everyone — securely and performantly. Being open is not enough; agents also need to be secure.
The project is still in alpha, but it is in a working state.
AutoAgents Repo -> https://github.com/liquidos-ai/AutoAgents
Odyssey Repo -> https://github.com/liquidos-ai/Odyssey
I would really appreciate feedback — especially from anyone who has dealt with similar problems. Your feedback help me shape the product.
Thanks for your time in advance!

value: Show HN: Running AI agents across environments needs a proper solution


value: curiousweb

value:
I built this because existing finance apps drove me crazy. I didn't want to connect my bank account or spend every weekend categorizing transactions. I just wanted to know what my finances would look like in 6 months or a couple years from now.
It tracks your income, expenses, investments, and debt, then projects your cash flow forward based on your own assumptions. You can factor in things like inflation and expected raises. The part I actually find useful is being able to model different strategies and what-if scenarios. Compare aggressive debt payoff vs steady investing and see where each path takes you by retirement. Or just ask something like "what happens to my cash flow if I take a vacation this summer, pay off a loan, or finance a new car?"
Most budgeting apps tell you where your money went. This one is about where it's going.
No bank connections, all manual entry. I know that adds friction but it also means you're not handing another company read access to your bank accounts.
It's a web app with a 14 day free trial, no credit card required. Email signup, no verification required. Would love honest feedback from this crowd:
Is manual entry a dealbreaker or is there actually a group of people who prefer it? Does $8/month or $60/year feel reasonable for something like this? And what's obviously missing that would make it actually useful to you?<p>For testing purposes, I added a login option that just takes in an email (fake is ok).   Login -> Tester Email

value: Show HN: Forecast Planner, personal finance app that forecasts instead of tracks

author: curiousweb


value:
We form opinions about wages, prices, and housing from vibes, headlines, and anecdotes. The actual numbers are usually somewhere unexpected.
Off By is a daily game: five real economic statistics, you guess each one with a slider, then see how far off you were. Everyone plays the same questions on the same day.
Average player is off by 25%. I'm usually in that range too.
Free, no account needed.
Curious whether HN skews more accurate than average, and always looking for statistics that are surprising but airtight. If you know a good one, feel free to drop it in the comments.

value: Show HN: Off By – a daily game about how wrong we are about the American economy


value: mixedbit

value:
Drop is a Linux sandboxing tool with a focus on a productive local workflow. Drop allows you to easily create sandboxed environments that isolate executed programs while preserving as many aspects of your work environment as possible. Drop uses your existing distribution - your installed programs, your username, filesystem paths, config files carry over into the sandbox.
The workflow is inspired by Python's virtualenv: create an environment, enter it, work normally - but with enforced sandboxing. To create a new Drop environment and run a sandboxed shell you simply:
alice@zax:~/project$ drop init && drop run bash
(drop) alice@zax:~/project$ # you are in the sandbox, but your tools and configs are still here.
The need for a tool like Drop had been with me for a long time. I felt uneasy installing and running out-of-distro programs with huge dependency trees and no isolation. On the other hand I dreaded the naked root@b0fecb:/# Docker shell. The main thing that makes Docker great for deploying software - a reproducible, minimal environment - gets in the way of productive development work: tools are missing from a container; config files and environment variables are all unavailable.
The last straw that made me start building Drop was LLM agents. To work well - compile code, run tests, analyze git logs - agents need access to tools installed on the machine. But giving agents unrestricted access is so clearly risky, that almost every HN discussion on agentic workflows includes a rant about a lack of sandboxing.
Drop uses Linux user namespaces (no root required) as the main isolation mechanism, with passt/pasta used for isolated networking.
I'd love to hear what you think.

value: Show HN: Drop – Linux sandboxing for LLM agents and untrusted code

author: mixedbit


value: AndrewGS

value: Show HN: Crawdad – Runtime security layer for autonomous AI agents

author: AndrewGS


value: spenceships

value: Show HN: AI agent got 237 rules from another agent, still made the same mistakes

author: spenceships


value:
I originally built SheepCat (https://github.com/Chadders13/SheepCat-TrackingMyWork) to solve a massive personal pain point.
When you are holding a complex mental model of a C# backend or trying to optimize a heavy SQL query, the absolute worst thing for your working memory is having a PM come to you and require and upadte which forces you to open a heavy web browser, navigate a clunky enterprise UI, and find a specific ticket just to log a status update. The context-switching friction is brutal.
So, I built a local, lightweight Python desktop app to bypass it. The core philosophy is the "gentle nudge"—working with our brains instead of against them, which is especially helpful for neurodivergent developers dealing with the dyslexia or ADHD tax.
It gently takes updates when your have two seconds and stores them ready for when you have the time to focus on a proper update. All messages are enhanced by Ai meaning that you can drop messy half written messages in and you will get a summary that is polished.
With the new v1.3 release, you can log your micro-updates locally all day, and then push them directly to Jira or Azure DevOps via API without ever leaving your flow state.
The Code: https://github.com/Chadders13/SheepCat-TrackingMyWork
I'm keeping this entirely free and open-source to help as many developers as possible. If this saves your flow state or makes your Monday a little less chaotic, any support to help ramp up the project and keep the caffeine flowing is massively appreciated: https://buymeacoffee.com/chadders13h/membership
I'd love any feedback on the code

value: Show HN: I built a local open-source tracker to bypass Jira UI friction


value: vicanso

value: Show HN: a Rust Redis GUI that doesn't freeze on 100k keys

author: vicanso


value: travisdrake

value: Show HN: Named failure modes that stop AI agents from cutting corners

author: travisdrake


value: metawake

value: Show HN: Ragprobe – measure RAG domain difficulty before deploying,no embeddings

author: metawake


value:
There are plenty of websites and GUI apps that provide similar tools, but I wanted something that stays in the terminal and works with pipes, e.g.:

  curl -s 'https://dummyjson.com/products?limit=5&select=title,price,brand' \
    | swk query json '$.products' \
    | swk format json2table

~50 commands to convert, format, encode, escape, generate, inspect, query, diff. Single Go binary, zero dependencies.

value: Show HN: Swk – single-binary CLI toolkit for everyday developer tasks


value: ejgi

value: Show HN: Zen-Hunt – Forensic Scanner in Rust (SIMD, 7GB/S on NVMe)

author: ejgi


value: jilijeanlouis

value: Show HN: Reproducible open-source STT API benchmarks with full methodology

author: jilijeanlouis


value: jiratickets

value: I made this for fun. I think some of you here may enjoy it. Happy to answer any questions.

value: Show HN: BallotGuessr – Guess the 2024 election margin from a Street View photo

author: jiratickets


value: lmascari

value: Show HN: MangoWave – Open-source browser audio visualizer

author: lmascari


value: okbye

value:
I kept needing animated orb visuals for apps and landing pages, they're everywhere lol.
So I built OrbStudio, a native SwiftUI Mac app where you design an orb live and export real code. The SwiftUI export is a single View with zero dependencies, same rendering approach the app itself uses. WebGL export is one self contained file, no bundler.
Happy to answer questions!

value: Show HN: OrbStudio Native Mac app for designing animated orbs with code export

author: okbye


value: dnhkng

value: Show HN: More LLM Neuroanatomy: A Hint of a Universal Language?

author: dnhkng


value:
https://useclawx.com
Lot of my friends have been asking me to setup OpenClaw for them, so I made something which allows them to directly access OpenClaw without the hassle of the setup because they just want to try it out.
Would love to know what the community here thinks, and open to critical feeddback on it.
Tbh, some of them are technical enough but they just don't want to do the setup just to test something. One of them wants to test Paperclip, but hasn't still.
Thank you for reading this!

value: Show HN: ClawX – OpenClaw Instance in 30s


value: michaelboling

value:
Hey! I'm a programmer trying to build a business around my own ideas. ReceiptMatrix is my first real shot at that.
I have family members who are self-employed and I've seen them struggle with organizing receipts for quite a while. Big cloud platforms exist but there's a gap between DIY spreadsheets and those full-blown SaaS solutions. I wanted to fill that.
My program organizes receipts, maps them to Schedule C, and can export a zip file of all key info (PDF summary + categorized CSV + sorted receipt files). All local, no servers, no accounts.
Pricing is $12.99/month or $89.99/year with a free tier at 10 receipts/month. Curious what people think about the local-only angle and whether the pricing is in the right range for something like this.
Thanks everyone
App Store: https://apps.apple.com/app/id6758662836

value: Show HN: ReceiptMatrix – Local-only receipt organizer for Mac freelancers

author: michaelboling


value: oyahud

value: Show HN: Think Fast. Type Faster

author: oyahud

title: Show HN: Think Fast. Type Faster


value: Show HN: I designed a 24-trit balanced ternary RISC processor on FPGA


value: henryluo

value:
What's your tool chain for data and document processing?
- jq is great, but limited to JSON; and jQuery limited to HTML; templating systems get messy for non-trivial logic; TypeScript lacks pattern matching and schema support.
So I built Lambda — an expressive functional language that combines the power of Lisp, TypeScript, and many DSLs like jq, jQuery, RelaxNG, etc.
It has a lightweight 9 MB runtime, built from scratch in C/C++, that provides an end-to-end document processing pipeline (parse → validate/query/transform → layout → render/view).
Internally, Lambda parse different input formats (Markdown, Wiki, HTML/XML, JSON/YAML/TOML/CSV, LaTeX, PDF, …) into a unified Lambda/Mark node tree, then transformed with Lambda scripts, validated with schemas, and then rendered via the Radiant HTML/CSS/SVG layout and rendering engine.
Repo (with examples and demos):
https://github.com/henry-luo/lambda
I’d love feedback on: where this could actually be useful in your workflows; language design (what feels unnecessary vs. missing); etc.

value: Show HN: Lambda 0.2 – a func language better than TypeScrip, jq and jQuery

author: henryluo


value: gbram

value:
Hey HN, I'm building StreamHouse — an event streaming platform where you get Kafka-style streaming, a schema registry, SQL queries, and pipeline sinks without managing five separate services.
Storage is S3 instead of broker disks. Agents are stateless — if one dies, another picks up. Metadata lives in Postgres. You can use the managed platform at streamhouse.app, or self-host the whole thing with Docker Compose. Either way it's one service instead of five
What works today:

  - CLI + Python/TypeScript SDKs
  - REST and gRPC APIs (~2,600 msg/s on one EC2 instance)
  - Pipelines that sink to Postgres and ClickHouse
  - Schema registry (Avro, Protobuf, JSON Schema)
  - SQL over streaming data
  - Multi-tenant with org isolation and API key auth

What doesn't work yet: confluent-kafka clients need wire protocol fixes, and I need more soak testing before I'd trust it for massive sustained loads.
https://github.com/gbram1/streamhouse

value: Show HN: Streamhouse – all-in-one event streaming for startups

author: gbram


value: jdereg

value:
I built TOON support into json-io, a Java serialization library. TOON is an indentation-based format designed for LLM applications where every token costs money and eats context window.
Same data as JSON, ~40-50% fewer tokens. No braces, brackets, commas, or unnecessary quoting. Arrays of uniform objects become CSV-like tables automatically.
JSON:
{"team":"Rockets","players":[{"name":"John","age":30},{"name":"Sue","age":27}]}
TOON:
team: Rockets
players:
  name, age
  John, 30
  Sue, 27
json-io reads/writes JSON, JSON5, and TOON with 60+ built-in Java types, cycle support, and zero config. There's also a Spring Boot starter and Spring AI module.
GitHub: https://github.com/jdereg/json-io
TOON spec: https://toonformat.dev
Baeldung article: https://www.baeldung.com/java-json-toon-format-libraries

value: Show HN: JSON-io – Java library for JSON, JSON5, and TOON (40% fewer LLM tokens)

author: jdereg


value: victor-craton

value: Show HN: Craton HSM – A memory-safe PKCS#11 software HSM in Rust

author: victor-craton


value: valentinconan

value:
Hi HN,
In January, JSON Formatter (the 2M+ user Chrome extension) pushed an update that injected a "Give Freely" donation popup on checkout pages. Without warning.
Users thought they were being hacked. Hundreds of 1-star reviews followed overnight.
I'd been wanting to build a better JSON viewer for a while. That was the signal.
3 months later: JSONVault Pro.
What it does:
- Auto-detects and formats JSON, YAML and XML on any URL
- JWT auto-decode: eyJ... strings show the decoded payload inline — no more jwt.io
- Side-by-side diff with LCS engine (handles key-order, type coercion, null vs absent)
- JSONPath and JMESPath queries directly in the browser
- DevTools panel with network request capture
- Web Worker parsing for files up to 50MB
- 21 premium themes (Dracula, Catppuccin, Nord...)
- Zero tracking. Zero analytics. Data never leaves your machine.
The free tier is genuinely useful — dark mode, collapsible tree, YAML/XML, search, sort, copy path, basic diff, DevTools panel. Not a crippled demo.
Pro adds the power features: JWT decode, advanced diff, JSONPath, export (CSV/YAML/XML/TS), history, themes, virtual rendering for huge files.
Chrome Web Store: https://chromewebstore.google.com/detail/ipghignebclihkckdpc...
Website: https://jsonvaultpro.com
I'd love feedback on:
- The UX when switching between tree, code and raw views
- YAML/XML detection — does it catch your files reliably?
- Anything missing that would make you switch from your current tool

value: Show HN: JSONVault Pro – JSON/YAML/XML Viewer After the Give Freely Incident

author: valentinconan


value: buildani

value: Show HN: PasteDrop – Share text and code without accounts or tracking

author: buildani


value: hrishirc

value: Show HN: Chat with an exhaustive geopolitical simulation of the 2026 Iran War

author: hrishirc


value: codytseng

value:
Glanceway sits in your macOS menu bar and periodically fetches data from sources you configure, then displays them in a compact panel. Think of it as a cron scheduler with a built-in UI.
It supports RSS feeds, and for anything else you can write a simple JS plugin to fetch any API. There's a community plugin directory at https://glanceway.app/sources, and a skill you can add to Claude to have it generate plugins for you (https://github.com/glanceway/glanceway-sources/blob/master/g...).
Your aggregated feeds are exposed via a REST API, so other apps and scripts can consume them. There's also a standalone MCP server if you want to hook it into AI agents like Claude: https://github.com/glanceway/glanceway-mcp
Still actively developed. Most features are free, Pro unlocks unlimited sources.
App Store: https://apps.apple.com/app/6758926117

value: Show HN: Glanceway – A programmable menu bar info aggregator for macOS

author: codytseng


value: sultanchek

value:
I'm 15 and started learning ML at 14. The thing that frustrated me most wasn't the models - it was data prep. Finding datasets, cleaning them, formatting them for training took longer than actually building anything.
So I built Vesper. It's an MCP-native tool that lets AI agents autonomously search, download, clean, and export datasets. Instead of a developer doing this manually, the agent calls Vesper and gets production-ready data in minutes.
You can try it via CLI in under 5 minutes:
npx vesper-wizard@latest
Would love feedback from ML engineers and anyone building agent workflows.

value: Show HN: Vesper – MCP-native tool that automates dataset prep for AI agents

author: sultanchek


value: briandev

value: Show HN: Typerson – Turn boring forms into chat-like experiences

author: briandev


value: Show HN: Agonora – Character benchmarking for the post-AI job market


value: mariusaure

value: Show HN: NeedHuman – API that lets AI agents hire a real human when stuck

author: mariusaure


value: Show HN: I Built an Open-Source Math Academy for Propositional Logic


value: cosmiciron

value:
I've been building a document layout engine called VMPrint from scratch in pure TypeScript. It does not use a browser, a DOM, or HTML. It has its own AST in structured JSON — pure data, no markup.
Just for fun I made a skill file describing the schema and gave it to Claude. It asked me if I wanted to see it build something interesting with it, so I said yes.
It made a newspaper -- 3 pages, but still, a newspaper.
Not because it had seen VMPrint AST before. There are none. Not because the skill file described what a newspaper should look like, or how to build one. It didn't.
But it decided that "something interesting" out of this schema should look like a newspaper, so it built one with the primitives I gave it — kind of like handing a pencil and paper to a kid and asking them to draw a museum piece.
The file it generated can be found here: https://cosmiciron.github.io/vmprint/examples/ast-to-canvas-...
You can actually tweak it yourself and see the render change. You will see that it genuinely created the document with structure and semantics — not just a screenshot.
Of course Claude can generate any kind of layout/UI with HTML, but that's because it was trained from billions of prior examples. This one though is different.

value: Show HN: I gave Claude a schema it had never seen. It made a newspaper

author: cosmiciron


value:
A few weeks ago, I posted a CLI version of this — thanks for the great feedback, it's been going well (500+ stars).
I originally built it for demos. But people have been using it for knowledge sharing, teaching, documentation, and even debugging sandboxed agent workflows in the cloud. Pretty cool to learn about these use cases.
Since then: Cursor and Codex CLI support, Docker, --watch live mode, custom redaction, and two new ways to use it:
- Web UI, drag and drop a session file, no install needed: https://es617.dev/claude-replay/
- Local editor, run claude-replay to launch a full editor in your browser: browse sessions, edit prompts, exclude turns, add bookmarks, live preview
The output is still a single self-contained HTML file — step through prompts, tool calls, and reasoning. Zero dependencies, works on mobile, embeddable via iframe.
Repo: https://github.com/es617/claude-replay

value: Show HN: Claude-replay – Video-like player for AI coding sessions (web UI)


value: Bhagaban

value:
I’ve been building something a bit unusual.
I’ve always liked the idea of talking to strangers in places like cafés, bars, airports - just to hear their stories. But in reality, people are busy, guarded, or just not in the mood.
So I started wondering:
what if you could walk into a place and talk to an AI character instead?
I built a map-based experience where you can open a city, step into real cafés and bars, and meet AI characters there to talk to. It’s somewhere between Foursquare and Character AI.
Some example characters
- A staff engineer rewriting the same system for the third time,
late-night café in San Francisco
- A founder who just shut down their startup after 4 years,
alone at a bar in New York
- A 19-year-old open-source dev,
coding in a dim café in Berlin
- A crypto trader who made and lost a fortune twice,
at a bar in Dubai
- A strip club dancer saving up to leave,
between shifts in Las Vegas
Some of the conversations are surprisingly engaging.
Current state: 5 cities, 100+ characters
I’m mainly testing:
does this feel interesting enough to explore?
Would love to know what you felt when you tried it.

value: Show HN: I made a map where you can meet AI characters in cafés and bars

author: Bhagaban


value: bidwellm

value: Show HN: BionicForms – Form builder with AI analysis, $49 one-time vs. $1k's/yr

author: bidwellm


value: hamzamairaj

value: Show HN: WordPress Next-Gen images with cross browser support

author: hamzamairaj


value: sohamrj

value:
Gemini Embedding 2 can project raw video directly into a 768-dimensional vector space alongside text. No transcription, no frame captioning, no intermediate text. A query like "green car cutting me off" is directly comparable to a 30-second video clip at the vector level.<p>I used this to build a CLI that indexes hours of footage into ChromaDB, then searches it with natural language and auto-trims the matching clip. Demo video on the GitHub README.
Indexing costs ~$2.50/hr of footage. Still-frame detection skips idle chunks, so security camera / sentry mode footage is much cheaper.

value: Show HN: Gemini can now natively embed video, so I built sub-second video search

author: sohamrj


value: indrora

value: Show HN: Giftwrap, a simple go build and release tool

author: indrora


value: acavailhez

value:
Hello!
I built a map that generates a heatmap of the most convenient meeting spots between several people, based on public transit travel times.
Move the markers anywhere in the map; all cities with sufficient transit data are supported!

value: Show HN: Mapping the most convenient ways to meet someone with public transit

author: acavailhez


value: cosmiciron

value:
You generate a PDF and need to preview it on your web page. You realize you can't. Not without a giant PDF.js. Or perhaps you made a nice preview on Canvas, then you realize you can't do anything useful with it—it won't give you a PDF that looks the same.
I've bundled the entire VMPrint engine—a DTP-grade layout virtual machine—into a compact, 1.7MB standalone NPM package you can embed even in a static HTML page. It gives you accurate document preview and PDF/SVG output that look exactly the same.
Enjoy!

value: Show HN: VMPrint is now a PDF generator with embeddable preview. 1.7MB ONLY


value: neckgoes

value: Show HN: Private Corporate AI – self-hosted LLM and RAG, no cloud

author: neckgoes


value: mcheemaa

value: Show HN: Shadow – open-source Littlebird alt, 14-modality on-device LoRA (macOS)

author: mcheemaa


value: vgutierrez

value: Show HN: Kc – I moved 35 secrets out of my .zshrc in 10 minutes

author: vgutierrez


value: mihau

value:
TLDR: Desktop app for E2E web test generation, built at JetBrains (closed beta). Record the flow in a built-in browser - the agent matches it with your existing codebase, then writes a test that passes, not a draft to debug.
Devs use AI to ship more code. That code still needs testing. If your team writes E2E tests by hand, you have a problem - same QA capacity, way more surface to cover.
AI agents can write E2E test code, but you're stuck describing flows in text - the agent clicks around via Playwright MCP, takes wrong turns, you re-prompt, retry. 30 minutes for a flow you could click through in 30 seconds.
Qure works differently. You record the scenario in Qure's built-in browser by just using your product. The AI turns that recording into code. No prompt engineering, no MCP setup, no explaining your repo in chat - point it at your project and go. Beyond recording, you can also refactor tests, update them, or write new ones from a description.
What keeps the AI output grounded:
- We match the recording against your codebase - find your page objects, helpers, constants and feed them to the agent instead of hoping it figures out your repo
- When agent runs the test, it reads real failure output, fixes with actual error and app context
This is a closed beta of an experimental product. Web only, works best with Playwright. If your project has a few dozen tests - Claude Code will honestly get you there. Qure makes a difference on larger codebases with existing test infrastructure.
5-min demo: https://www.youtube.com/watch?v=4CZw4bSSDCE
Try the beta: https://quretests.com
Happy to answer any questions about the approach, product, or where it breaks - I'm the dev on the Qure team. Egor (@250xp), who leads the project, is in the thread too.

value: Show HN: Record manual QA flows, get E2E test code that fits your repo

author: mihau


value: avatardeejay

value: I made Grove to kill the <i>ugh</i> factor of backing up rapid code iterations. It’s all CLI except the file and folder pickers. You make lists of files and folders, back them up, and restore your saves. Very forgiving UI. Would love feedback from any terminal lovers!!

value: Show HN: Grove – Homemade version control, 1mb binary, written in Rust

author: avatardeejay


value: Show HN: Claude Code Bible (notes on making LLM agents more consistent)


value:
I've just launched this little iOS app as an alternative to Apple Fitness, which is cluttered and chaotic when it comes to visualizing basic workout stats and metrics.
The idea is to focus on a clean, minimalistic design and only show high-level metrics that are actually useful, e.g. how often did I work out this week/month/year, cardio vs strength vs mobility, surf session count in February, etc.
It's free and offline. Also, no signup, no ads, no data sharing, no social feeds, and no notifications.
Just open it and get a quick glance at the state of your workout game within 2s.
It's built in native Swift with liquid glass.
Feedback is quite good so far, but it seems hard to get initial traction in the App Store, especially with all the AI slob these days. Would deeply appreciate some early downloads and honest reviews.

value: Show HN: Visualizing Apple Health workout data (stats, trends, insights)


value: mystickphoenix

value: The idea for this came from a previous company. We were accessing external APIs that had fairly restrictive versioning requirements. Not everyone in the company followed those and put our business relationships in danger (yeah, I know...). I had the idea to have all traffic to those APIs put through a proxy that would log the source of the traffic so we could find where those requests were coming from and (optionally) block them. Sadly that company and I parted ways before I could work on it, but hey, that means I get to open source the idea/code now!<p>Would love to get thoughts, ideas, rotten fruit, etc. Feel free to open issues or PRs on it. Thanks!

value: Show HN: LogProx – I built a fast proxy that logs out request information

author: mystickphoenix


value: jellyshelly

value:
built a social network you connect to over SSH.
no signup, no browser, just open your terminal and you're in.
channels, profiles, guestbook, shared blackboard, Top 8.
your identity is your SSH key fingerprint so no passwords needed.
to connect:
ssh-keygen -t ed25519 (just hit enter through all the prompts)
ssh jellyshell.dev
built with Go, Bubble Tea, and Wish.
i wanted to make something that maintains privacy and gets away from
the brain rot and algorithms pushing rage bait.
lmk what you think.

value: Show HN: Jelly – SSH Social Hangout

author: jellyshelly

title: Show HN: Jelly – SSH Social Hangout


value: asabi

value:
I got tired of paying ngrok just to use my own domain, so I built this.
tunnelto lets you:
- expose localhost over HTTPS
- use your own domain (even on the free plan)
- works behind NAT / no port forwarding
I’ve been using it myself for a few months and decided to open it up.
Would love feedback — especially on reliability and edge cases.

value: Show HN: Tunnelto – expose localhost on your own domain (free)

author: asabi


value: imaznation

value: Show HN: I recovered a bricked LaMetric Time from bare metal with Claude Code

author: imaznation


value: Show HN: Sudo Hold Me

title: Show HN: Sudo Hold Me


value: njarecki

value: Show HN: TopMail – LaunchFest Winner, $20/Mo Unlimited Email, Coding Agents API

author: njarecki


value: Show HN: Knowhere – Give your AI Agents structured context from messy docs


value: dancablam

value: Show HN: Email.md – Markdown to responsive, email-safe HTML

author: dancablam


value: holman

value:
Hiiiiiii HN! Just launched something I've been working on for a bit: https://signed.com
After early GitHub and GitLab, I started getting into angel investing. I've done a couple hundred investments into startups over the years, and I kept waiting for someone to build something to like, track these damn things better, since they're not publicly traded and it's such a pain to even begin to track all this stuff.
But no one did! So I'm doing it.
Initially it's targeted to angel investors, but it's also great if you have stock with your employer. One of the things I wished I had done differently back at GitHub is to treat my stock like an investment rather than just "something that happened to me". First step in doing that is to just track it and measure it.
It's free to sign up and use, and then there's paid plans if you're more serious about your finances. Curious to hear your thoughts!

value: Show HN: Wowza it's hard to track equity and investments

author: holman


value: dcostenco

value: Show HN: Prism MCP v4.0 – Behavioral Memory for AI Agents

author: dcostenco


value: masterofmyself

value:
Hey HN,
I'm Oskar, a long time lurker on here. I've finally decided to build and launch my first product publicly. I've built side projects in the past but never followed through on releasing them.
I built HiredToday because I was frustrated with how tedious it was to tailor my CV and cover letter for every company I wanted to apply to. This led to me not bothering to tailor it and just submit a generic one. Of course that lowers your chances substantially because you're not lifting the specific qualities that matter for the position you're applying for.
HiredToday helps you do that efficiently, using AI to tailor your resume, cover letter, etc based on the job description. It even creates pre-written emails for follow-ups and salary negotiation. Of course, the basics such as ATS Score analysis and optimization is built right in.
I sincerely hope that this tool will be helpful in your or someone else's job search journey. I'm super open to early feedback from you guys on what you think and if you have any things you would like to see added, I'm more than happy to add them.
There's currently a promo going for the launch:
Everyone gets one free analysis and after that it's $10 for 30 application packages or $29 per year depending on how much usage you want. The $29 per year gets you 500 application packages.
Check it out here: https://www.hiredtoday.app
Let me know what you think!

value: Show HN: HiredToday.app – AI resume tailoring, cover letters and interview prep

author: masterofmyself


value: valeriobaudo

value: Show HN: I pointed my CVE pipeline at 1,500 GitLab servers. It found 31 vulns

author: valeriobaudo


value: ggay

value: I was frustrated about static, generic fitness apps and ChatGPT didn't give me enough structure and follow up in my fitness journey so I built MyTrainer, the first fully agentic oriented fitness coaching mobile app. It generates custom workout + nutrition plans, adapts in real time and connects to health data - all of this from a chatbot UI.

value: Show HN: I built the first AI agentic fitness coaching app

author: ggay


value: Symphoenix

value: Show HN: Aurea, a lossy image codec I built from scratch that beats JPEG (Rust)

author: Symphoenix


value: brandonin

value: Show HN: Ensemble Neuroscience – Full Brain Mapping for Precision Treatment

author: brandonin


value: rothific

value:
Hi everyone,
Gridland is a runtime + ShadCN UI registry that makes it possible to build terminal apps that run in the browser as well as the native terminal. This is useful for demoing TUIs so that users know what they're getting before they are invested enough to install them. And, tbh, it's also just super fun!
Gridland is the successor to Ink Web (ink-web.dev) which is the same concept, but using Ink + xterm.js. After building Ink Web, we continued experimenting and found that using OpenTUI and a canvas renderer performed better with less flickering and nearly instant load times.
We're excited to continue iterating on this. I expect a lot of criticism from the "why does this need to exist" angle, and tbh, it probably doesn't - it's really mostly just for fun, but we still think the demo use case mentioned previously has potential.
- Chris + Jess

value: Show HN: Gridland: make terminal apps that also run in the browser

author: rothific


value: rishavmitra

value:
Hi HN,
We built Zalor to make it easy to test AI agents before deploying.
It now supports GitHub integration and updated our datasets feature.
You can now:
• Upload CSVs with real inputs and expected outputs
• Run your agent against those datasets
• Generate new test cases using AI from your agent’s tools, prompts, or context
• Run benchmark tests on GitHub PRs and see regressions directly in the PR

value: Show HN: Zalor – deployment gate for AI agents

author: rishavmitra


value: theturtletalks

value:
I've been building an open source alternative to Shopify, so it only felt fitting to build in agentic commerce too.
I could see the direction ChatGPT checkout was going in, another proprietary marketplace where they get to decide which sellers get access and priority.
So I built the / marketplace. It's an open-source marketplace that has a built in adapter system that supports Openfront right now (the Shopify alternative I've been building), but it should be able to work with any e-commerce platform, not just the ones ChatGPT decides to support. You can also bring your own OpenRouter key or connect to Ollama locally so your shopping data stays with you.
Right now it has 2 demo shops, Impossible Tees and Nimbus Gallery.
As we launch more Openfronts for restaurants, gyms, and other verticals, those should be able to plug into the same model too.
Demo: https://marketplace.openship.org
Repo: https://github.com/openshiporg/marketplace
Learn more: https://openship.org/products/marketplace

value: Show HN: The / marketplace, an open-source ChatGPT Checkout

author: theturtletalks


value: grillorafael

value: Show HN: Mantyx – A platform to orchestrate, manage, and share your agents

author: grillorafael


value: robmay

value: 115 tasks, all on small models, thousands coming soon, download for free or we host for free up to 100M tokens per month per task

value: Show HN: Small Model Marketplace W 100M Tokens Free

author: robmay


value:
Hi HN,
After two months of using Openclaw almost daily, I kept running into the same problem: some AI conversations were genuinely useful, but they were hard to revisit, awkward to share, and too easy to lose over time.
I ended up with a big backlog of chats across Telegram and Discord. Search in those apps is fine, but useful conversations still end up buried in chat history. And once context gets compressed, a lot of the interesting reasoning in the middle is gone for both me and the AI.
Copy-pasting is the usual way to share a chat, but it strips out structure. Screenshots are worse. I wanted something closer to publishing a conversation as a real page.
So I made Clawpage, a small open-source tool that turns Openclaw chats into static archive pages.
It preserves the conversation flow and can include tool calls and thinking traces in the timeline, which usually never make it into the IM channels in the first place when using Openclaw. It also has an AI-assisted redaction step before publishing. That part isn’t fully reliable yet, but it’s been useful enough for my own workflow so far. Since the content lives in a git-based workflow, I manually review and fix anything before publishing.
Under the hood it’s pretty simple:
- parse Openclaw logs into structured YAML
- render the YAML with an Astro template
- deploy anywhere static hosting works
For me, the point is not just exporting chats. It’s turning a good conversation into something durable: something you can share, revisit later with full context, or reuse as external memory for future AI discussions instead of relying only on a compressed summary.
GitHub: https://github.com/imyelo/clawpage
Demo: https://clawpage.yelo.ooo
Would love your feedback.

value: Show HN: Clawpage – turn OpenClaw chats into shareable static pages


value: Show HN: Haiku and Codeset > Raw Opus

title: Show HN: Haiku and Codeset > Raw Opus


value:
Built this as a side project to explore a problem I keep running into with agents: they lose useful continuity across threads and resumed work.
Pallium is a local-first memory sidecar. It stores selected evidence, derives compact memory like decisions/findings/checkpoints, and returns evidence-backed memory blocks for follow-up questions.
It is intentionally not a general knowledge base or vector DB. The focus is scoped memory for agent-mediated conversations.
Repo: https://github.com/rore/Pallium

value: Show HN: Pallium, a local-first memory sidecar for agent conversations


value: Show HN: Anitag2vec – tag embeddings for recommendations


value: thepsychguy

value: Show HN: daily anonymous question site, and what I'm learning from the responses

author: thepsychguy


value: Heff

value:
What do you do when private equity buys your old company and fires the maintainers of the popular open source project you started over a decade ago? You reboot it, and bring along some new friends to do it.
Video.js is used by billions of people every month, on sites like Amazon.com, Linkedin, and Dropbox, and yet it wasn’t in great shape. A skeleton crew of maintainers were doing their best with a dated architecture, but it needed more. So Sam from Plyr, Rahim from Vidstack, and Wes and Christain from Media Chrome jumped in to help me rebuild it better, faster, and smaller.
It’s in beta now. Please give it a try and tell us what breaks.

value: Show HN: I took back Video.js after 16 years and we rewrote it to be 88% smaller

author: Heff


value: kasperstorgaard

value:
Hi HN,
I've built Skub, a sliding puzzle game for the browser, based on a classic boardgame: Ricochet Robots.
It started as a challenge of trying to simplify the boardgame mechanics to fit on a mobile browser, which led to an 8x8 grid.
Since, it has evolved to a bit more of an experimentation with Deno, and a way for me to truly try out AI-assisted development.
Claude Code has been especially helpful in building the BFS solver and setting up CI, less so in UI and logic.
I hope you enjoy it, all questions / feedback welcome.

value: Show HN: Skub – a sliding puzzle browser game

author: kasperstorgaard


value: petargyurov

value:
Hi HN!
I am sure most of you have heard of Anki flashcards? Using spaced-repetition is one of the best ways to learn more vocabulary when learning a language.
However, I find flashcards super boring. I've been playing some crossword games in my target language, Spanish, but I wished that I could use my custom list of words that I've come across instead of random words.
That gave me the idea to create this mini-app. It's super simple. Add your words and you get *unlimited* crosswords with spaced-repetition! If you get a word right you won't see it for the next few days.
Works with any language (I think). You can add words one by one or import a CSV (just make sure to follow the columns: word, answer)
It's a PWA, so you should be able to install it via your browser and it should work offline!
There's still some bugs and QoL things to add but let me know what you think!

value: Show HN: Cranki – Crosswords meet Anki flashcards

author: petargyurov


value: KhushaliT

value:
I'm a solo female traveler who's been exploring India for 5+ years. Every time I planned a trip, the safety advice was always the same: "Be careful." "Trust your gut." "Delhi is dangerous for women."
Last year I was planning a solo trip and got so frustrated with vague warnings that I almost canceled. Then I realized - these articles aren't written by people who've actually solo traveled there recently. They're just recycling generic advice.
So I started going through actual accounts from solo travelers - Reddit posts, YouTube comments, Google Maps reviews. The detailed "I stayed in Hauz Khas alone and here's what happened" experiences. I spent 4 months analyzing 1000+ reports for Delhi and Bangalore.
What I found: safety is extremely neighborhood and time-dependent. Hauz Khas Village scores 4.5/5 during daytime (cafes, solo women everywhere), then drops to 2.5/5 after 9 PM (club-heavy, different vibe entirely). Same exact spot.
I built Zenera (https://app.zenera.fun/) to organize this data: time-based safety scores (1-5 scale), bystander intervention culture (will locals actually help if you're uncomfortable), solo-specific incident patterns, community-verified spots, peak solo hours.
Current coverage: Delhi and Bangalore neighborhoods. Planning to expand to more cities based on data availability.
How it works: No signup, just browse. Search a neighborhood, see safety scores at different times, read what other solo travelers experienced, check bystander culture ratings.
What's different: Most travel safety apps give generic city-level advice or focus on emergency features. Zenera provides neighborhood-level intel with time-based context - the kind of specificity I desperately wanted when planning trips but couldn't find anywhere.
Known issues: UI is rough, mobile experience needs work, data coverage is limited to two cities, some neighborhoods have sparse data. This is very much beta.
What I'm looking for: Feedback on usefulness vs noise. Am I solving a real problem or just feeding my own anxiety with spreadsheets? Is time-based data actually helpful or too granular? What's missing?
Also open to technical feedback - current architecture is simple but wondering about scaling as I add more cities and real-time data.
Happy to answer questions about the data collection methodology, scoring formula, or why I chose this weird tech stack.

value: Show HN: Zenera – Neighborhood-level safety data for solo travelers

author: KhushaliT


value: pianobrothers

value: Made the first party game that makes fun of corporate culture! Would love for you to try it out.

value: Show HN: I built a party game that makes fun of corporate culture

author: pianobrothers


value: dhruvghulati

value:
Clarity is a Slack bot to serve as a private communication coach, directly addressing the biggest hurdle in remote work: miscommunication. By using LLMs, Clarity analyzes your messages after you hit send, offering instant auto-edits for issues like tone and clarity.
The core technical hurdle was evaluating "good communication," which is inherently subjective and couldn't rely on standard test sets. To overcome this, we designed a sophisticated, multi-LLM evaluation pipeline. One powerful LLM synthesized the initial flagging test set, a second acted as a judge for precision and recall, and a third continuously auto-tuned the prompt for the flagger LLM, resulting in high confidence in our system's accuracy. Furthermore, we built a dedicated agent to simulate Clarity operating in various synthetic workspaces—featuring diverse industries, sectors, and personas in conflict-prone situations—to further validate its performance.
We intentionally launched Clarity as an individual-focused product to lower the barrier to entry, with the strategic goal of establishing usage before transitioning to a team-based model (M1). We are now seeking community feedback on the product before expanding our reach.

value: Show HN: Clarity – An AI Slack coach for better work communication

author: dhruvghulati


value: alexsmolen

value:
I've been working on AWS security for years and querying CloudTrail has always been a huge pain - getting data about like "what did this role actually use in the last 30 days?" means either writing custom queries and result parsing code or getting vague data from built-in tools like Access Analyzer.
TrailTool's core idea is to pre-aggregate CloudTrail events at ingest time into entity relationships — People, Sessions, Roles, Services, Resources — so queries are DynamoDB reads rather than log scans. The CLI talks directly to your DynamoDB tables using standard AWS credentials, no API layer needed.
The four workflows in the post (ClickOps detection, least-privilege policy generation, AccessDenied remediation, break-glass validation) all came from things I was actually doing manually. The session transcripts are real Claude Code runs using the tool.
Wondering if this feels useful to folks, or if there are other CloudTrail questions that could be pre-computed this way to accomplish common tasks.

value: Show HN: TrailTool – open-source CLI for querying CloudTrail data with AI agents

author: alexsmolen


value: heythisischris

value:
I use AWS every day for work.
I used to draw, film, record music, animate.
This is my attempt to wake up the more creative parts of my brain which have lain dormant for so long.

value: Show HN: AWS for Idiots (webcomic)

author: heythisischris

title: Show HN: AWS for Idiots (webcomic)


value: arjunchint

value: Show HN: RoverBook – Moltbook for Your Website

author: arjunchint


value: franze

value: Show HN: Apfel - Apple Intelligence from the Command Line

author: franze


value:
Hey HN! After the Car Wash Test post got quite a big discussion going (400+ comments, https://news.ycombinator.com/item?id=47128138), I spent the past few weeks building a tool so anyone can run these kinds of questions and get structured results. No signup and free to use.
You type a question, define answer options, pick up to 50 models at a time from a pool of 200+, and they all answer independently under identical conditions. No system prompt, structured output, same setup for every model.
You can also run a debate round where models see each other's reasoning and get a chance to change their minds. A reviewer model then summarizes the full transcript. All models are routed via my startup Opper. Any feedback is welcome!
Hope you enjoy it, and would love to hear what you think!

value: Show HN: AI Roundtable – Let 200 models debate your question


value: antimora

value:
Hey HN,
We're ZSpaceLabs, Burn framework contributors working to make Rust a first-class AI/ML stack.
We just released wordchipper, a Rust-native BPE tokenizer for the OpenAI GPT-2 tokenizer family (r50k, cl100k, o200k). On a 64-core machine with the o200k vocab (GPT-4o / GPT-5 tokenizer), we measured 2.4 GiB/s, about 9.2× faster than tiktoken-rs. Through the Python bindings it is typically 2–4× faster than tiktoken, depending on thread count.
The main design goal was to make the internals easy to swap. The tokenizer is split into two parts: pre-tokenization (lexer) and BPE span encoding. Each part can be replaced independently, which makes it easy to experiment with different combinations of lexer backends and span encoding algorithms.
Right now there are three lexer implementations. One uses fancy-regex and is fully compatible with tiktoken. Another uses regex-automata with a runtime DFA and is about 4–8× faster. The third uses logos with a compile-time DFA and is about 14–21× faster on cl100k and o200k.
Write-up with more details:
https://zspacelabs.ai/wordchipper/articles/substitutable/
GitHub:
https://github.com/zspacelabs/wordchipper
Happy to hear feedback, especially from people working on tokenization, large-scale inference pipelines, or Rust ML tooling.

value: Show HN: Wordchipper – Rust BPE tokenizer, 9x faster than tiktoken

author: antimora


value: Show HN: Reverse lookup XKCD comics using Gemini multimodal embeddings


value:
I love word association games. Here's a playable wordplay game where you match opposite word tiles. After playtesting a lot of mechanics, I think opposites play really nicely with Mahjong solitaire.
The current generation of frontier LLMs can't make puzzles that get much more interesting than hot-vs-big-vs-fast. New inferences keep circling a small pool of concepts unless the prompting has a way to get the LLM into new territories of language. Puzzlemaking needs graph traversals.
I made 20 levels algorithmically because I work with a huge semantic graph with over 100M edges, built from manual lexicography and millions of LLM inferences (various models). I keep exploring what can emerge from this graph. The puzzles are randomly selected; reload to see others.
The front-end was built with Claude Code.
Maybe someday I'll make this into a mobile game, increase the complexity and peril. If you are a gamedev, feel free to dissect it and borrow any parts.

value: Show HN: Antimatter – Match the opposites (Mahjong solitaire mechanic)


value: endyai

value:
https://github.com/whydna/atq
I had a huge number of database rows that each needed LLM reasoning and tool use.
Existing harnesses like Claude Code don't handle this well - give it a batch and it'll try to write you a script instead of actually reasoning over each item.
Even when it works, context bloats after each turn - gets expensive, slow, and starts hallucinating. And there's no built-in retry handling or parallelization.
So I built this tool. Define the task as a prompt, pipe in your task list, set concurrency, model, tools, etc.
Open to feedback!

value: Show HN: Agentic Task Queue – batch processing of agentic tasks

author: endyai


value: robtoscani

value: Show HN: Wordif.sh – word-by-word text comparison in color, output to HTML/PDF

author: robtoscani


value: denysm

value:
I wanted a personal AI assistant on my home machine, accessible from my phone, without anything going through a third-party server. Everything I found required a VPN, a relay, or a subscription.
So I built this. Your phone connects directly to your machine over an encrypted peer-to-peer channel. A public signaling server handles the initial connection only - it never sees your messages or data.
It's not just chat - the phone app is a full management interface. You can ingest documents into the RAG knowledge base, create and edit prompt templates, manage cron schedules, and control the agent, all from your phone without touching the home machine.
Runs any Ollama model locally. Also has a web dashboard, native mobile apps and PWA for mobile access.
https://github.com/nekonihq/nekoni

value: Show HN: Nekoni – local AI agent accessible from your phone, no cloud

author: denysm


value: kriralabs

value:
I am the Founder & CEO of Krira Labs that builds production-ready Generative AI infrastructure.
Our First Product : Krira Augment - Production-ready RAG in minutes.
Krira Augment is a system that aims to:
* Simplify various end-to-end RAG pipelines
* Optimize costs
* Provide plug-and-play integrations for developers
We are going to launch Krira-Augment in next 2 months.
We are still in the early stages of development, and we would really appreciate any feedback from this community.
To thank you for your time, We are giving out a Krira Augment's Starter Plan to the first 100 users for free on our waitlist. Once, Krira-Augment is live
Waitlist: https://www.kriralabs.com/waitlist

value: Show HN: Krira Augment – Production-ready RAG in minutes

author: kriralabs


value: kotysoft

value:
Hey there,
Around 2 months ago I presented my first version of "Giant JSON Viewer" here: https://news.ycombinator.com/item?id=46609592
Now, after a lot of ups and downs, around a hundred bugs squeezed, and deeper testing, I'm proudly presenting a newer version after a complete rework of the Rust core, pumped with some related, privacy-first tools.
First of all, I added a few more common formats:
- JSON, NDJSON/JSONL
- CBOR (first time convert to JSON)
- MsgPack (first time convert to JSON)
- HAR (dedicated features for this)
- Markdown (straight, open only for view)
Then, I went further with my stress testing... Managed to open a ~100GB JSON file on an S23 Ultra.
Previous version crashed. With the reworked indexer, backend, and aux files, it worked! It took 40 mins to index, but worked.
After realizing my compiler optimization mistake, the SIMD finally got its real native power and the first indexing went down to 4-5 mins for 100GB. On a phone! Handling of the file, scrolling, viewing, and jumping to elements are instant after the first indexing.
Search and filter are still (relatively) blazing fast with memchr
Then, to make the app more useful as a daily tool, I started building additional features:
- A rich REST API CLIENT (not fully featured yet, but supports GraphQL, OAuth2, and AWS SigV4).
- HAR analyzer. Since HAR is just JSON, why not utilize my existing backend to take advantage of it? (The first opening takes a bit longer, because apart from the initial JSON indexing, it requires request metadata processing for the filter/search and statistical features).
- Simple MOCK API: Nothing fancy, just host any files on the local network (Wifi, USB-tethering, USB-ethernet) statically.
- Privacy-first handy features: Why would you use online tools and risk sensitive data if you can do it locally? You might have your tools already (js, python, whatever), but if not, I can host a webUI for you from the app on the local network for all these. JWT, small JSON tools like prettify, minify, stringify, unescape, unix timestamp, hash generator... all on your own private webUI, hosted by the app. (Same features available in the app too).
Yes, this is my very first published app ever, and it's only available on Android, sorry. There are still some minor bugs, and 1 major (edge case) issue, but those will be handled too.
If you could take a look and let me know what your first impression is, I'd love to hear HONEST feedback! (I just realized recently how difficult it is to get useful info from users, even bug reports).
This is a freemium app: the JSON viewer section is completely free with no size limits, the export/transform features, api client and tools are paid.

value: Show HN: Updated GiantJSON Viewer – Opening 100GB JSONs on Android (Rust+SIMD)

author: kotysoft


value: Akcium

value: Show HN: Goose Scrumming – Planning poker for teams

author: Akcium


value:
Hi HN!
A while back I made a post talking about a Chrome extension I had bootstrapped. At the time it could only be self-hosted and run locally with docker. In order to make it more accessible, I’ve taken the steps to deploy the web app and submit the extension to the chrome store!
Hopefully you’ll find collecting snippets from online articles as fun as I do.  I haven’t enabled email verification for now so you don’t even have to use a real email to create your account.
I added a primitive networking feature so you can add your friends by their username and see what they've saved recently!
It’s more or less a passion project/useless tech, and probably has a few bugs, but it exists! Please let me know if you have any thoughts or feedback.
Of course I have endless ideas to expand it (friend list view, toggle the highlighting of popular snippets (or saved by friends) on websites, Youtube integration: video snippets, private snippets) but I'm helplessly pulled forwards to my next silly idea.
Thanks for reading!

value: Show HN: A Chrome extension to save snippets online


value:
Nimbus observes your screen as you work and uses AI to understand what you’re doing and why. It asks clarifying questions in real time to capture intent, then organizes everything into structured tasks, steps, and platform knowledge for computer use agents via MCP.
The result is that your agent can do your work the way you do it, on internal tools it’s never seen, without being spoon fed every step. We think the missing piece in the agent stack is a production layer for workflow knowledge.
I’d love any feedback on the product, especially the privacy tradeoffs of screen observation and whether this feels like the right way to teach computer use agents real workflows.

value: Show HN: Nimbus – Turn how you work into how your agent works


value: andrewconklin

value:
On Bounty_OS, creators get paid to place brands in their content
Think of how product placement in a movie works
It blends right into the content; sometimes, you'd never know it was actually paid for
Bounty_OS brings this to the creator space, any creator can integrate a product into their own content and get paid based on how well their video performs
As of right now, we've generated 4M+ views have 500+ creators, and had 1 $250 external campaign where we did 0 outbound
Would love to hear people's thoughts/advice on this

value: Show HN: Bounty_OS the job market BLOWS

author: andrewconklin

title: Show HN: Bounty_OS the job market BLOWS


value: Show HN: Watch TV for Free for India

title: Show HN: Watch TV for Free for India


value: kstedev

value: Show HN: Captain Claw – personal AI workspace

author: kstedev


value: anythingelse

value: Show HN: Shared Crossword with 1M Words

author: anythingelse

title: Show HN: Shared Crossword with 1M Words


value: alanne

value: Show HN: WARPSimLab – A Personal Finance Simulator

author: alanne


value: secondmod

value: Show HN: Pipguard – pre-install malware scanner for Python supply-chain attacks

author: secondmod


value: hoag

value:
Saccade uses ARKit face tracking to measure which image you actually look at longer in a side-by-side (well, top-and-bottom as it runs in portrait mode) comparison.
Import a set of images, and it runs every possible pair, tracking your gaze for a dynamically-timed duration each (typically just a fraction of a second). It then ranks the images in descending order of how long your gaze lingered on each. You can then rerun the same batch of images to compound data over multiple tests, or start new. And of course you can easily share your results with friends and colleagues.
Bonus: The Emoji Duel game is pretty fun for onboarding; our 4yo can't get enough of it. :)
100% free, no backend, no user auth, no data leaves your phone. Requires iPhone with Face ID.

value: Show HN: A/B test images with your eyes using ARKit face tracking

author: hoag


value: Show HN: Create a full language server in Go with 3.17 spec support


value: souvlakius

value:
Hey HN,
We've noticed a gap for small teams: getting a prototype working locally or on Vercel and Supabase is easy, but graduating to a full fledged deployment on a cloud provider is a nightmare.
If you don't have a dedicated DevOps engineer, you're usually stuck clicking around hoping that some combination of checkboxes will do the trick .
We're building Grafos V2 to act as your application's production agent.
• You provide your application, optionally tell it what you need if its not obvious (e.g. I need a special type of MLOps or custom model garden models) and choose your cloud provider.
• The agent understands the nitty-gritty details so you don't have to.
• It outputs verifiable infrastructure code, using your repository to store the source of truth.
We are opening a Closed Alpha to 100 users to test this out. If you're building anything and want your app productionized without the infrastructure headache, let us know in the comments or sign up in the link of the post

value: Show HN: We are building a production agent to turn your code into cloud infra

author: souvlakius


value: konform

value:
Konform Browser v140.9.0-100 has been released.
Before we get started: I guess it needs to be said these days that everything here is 100% by carbon-based life-form.
Aside from the latest security fixes from upstream Firefox ESR, Konform Browser now also has:
- Rehauled Security Settings with convenient global configuration for WASM, JIT, WebRTC, WebGPU, Geolocation, DOM Push
- Security Settings now also expose UI for enabling First-Party Isolation (same behavior as Tor Browser) and tweaking Enhanced Tracking Protection
- Spoofing of Navigator.sendBeacon
- Profile Import feature now supports importing profiles from Firefox and compatible browsers (FF ESR, Floorp, IronFox, LibreWolf, Midori, Palemoon, Waterfox, Zen Browser)
- Built-in depropped Multi-Account Containers[0] allows configuring per-container proxies and makes container tabs part of core browser experience.
- Privacy improvements and defense-in-depth[1]
- Various bugfixes and improvements
- Local-only ML/"AI" functionality more robust
Would be keen to hear if anyone checked it out and what you think of changes or project in general.
---
Socials: https://techhub.social/@konform
Releases: https://codeberg.org/konform-browser/source/releases
Past Show HN submissions:
- v140.7.0-103 https://news.ycombinator.com/item?id=46713226
- v140.7.0-108 https://news.ycombinator.com/item?id=46956420
- v140.8.0-105 https://news.ycombinator.com/item?id=47227369
[0]: https://codeberg.org/konsortium/multi-account-containers-lit...
[1]: E.g: Disabling speculative preconnects and disabling background processes for disabled functionality. While these changes are motivated by security, it seems to also have surprisingly positive impact on performance.

value: Show HN: Konform Browser v140.9.0-100

author: konform

title: Show HN: Konform Browser v140.9.0-100


value: misterinfo

value: Median rent by city and ZIP, bedroom breakdowns, and trend data. No account, no paywall, no upsell. Covers all 50 states, 4,000+ cities down to the ZIP code level with a Leaflet map across 7,700+ ZIP markers. What data would be most useful to add here?

value: Show HN: RentDataNow – free rent data for 4k US cities and 8k ZIP codes

author: misterinfo


value: dolobanko

value:
We built Origin after realizing nobody on our team could answer "which AI wrote this?" when a bug appeared.
Origin hooks into Claude Code, Cursor, Gemini, and Codex. Every commit gets tagged with which agent wrote it, what prompt generated it, which model, and what it cost. Run `origin blame` on any file and you see [AI] or [HU] per line — like git blame but for AI agents.
All data stored in git notes. No server required, works offline, zero lock-in.
CLI is open source (MIT): github.com/dolobanko/origin-cli
Team dashboard: getorigin.io
Would love feedback on the attribution approach — we're using git notes to store session data on commit hashes.

value: Show HN: Origin – Git blame for AI agents (track which AI wrote every line)

author: dolobanko


value: latentio

value:
I built PalettePoint (https://palettepoint.com), a tool for generating color palettes from text prompts or images using AI.
You type something like "warm desert sunset" or upload a photo, and it gives you a palette with named colors and accessibility contrast data. The conversation is persistent so you can refine results ("make it more muted", "add a teal accent") like you would in ChatGPT.
There are also 120K+ palettes in the gallery, extracted from photos and generated by AI. You can export any palette to CSS custom properties, SCSS variables, Tailwind config, or JSON.
Other tools included: contrast checker (WCAG 2.1), gradient generator, color converter, color mixer, image color extractor, and a palette generator using color theory (complementary, analogous, triadic, etc.).
Would appreciate any feedback.

value: Show HN: Palettepoint.com, AI palette generator with 120K+ curated palettes

author: latentio


value: daisydevel

value:
To understand what changed between kernel versions, you have to dig through the git repository yourself. Commit messages rarely tell you the real-world impact on your systems — you need to analyze the actual diffs with knowledge of kernel internals. For engineers who use Linux — directly or indirectly — but aren't kernel developers, that barrier is pretty high.
I kept finding out about relevant changes only after an issue had already hit, and it was most frustrating when the version was too new to find similar cases online. I built lexplain with the idea that it would be nice to quickly scan through kernel changes the way you'd skim the morning news.
It reads diffs, analyzes the code, and generates two types of documents:
- Commit analyses: context, code breakdown, behavioral impact, risks, references
- Release notes: per-version highlights, functional classification, subsystem breakdown, impact analysis
Documents build on each other — individual commits first, then merge commits using child analyses, then release notes using all analyses for that version. Claims based on inference are explicitly labeled.
Work in progress. Feedback welcome.

value: Show HN: Lexplain – AI-powered Linux kernel change explanations

author: daisydevel


value:
The Substack algorithm is completely broken, and pretty hostile to readers.
If you create a new account, you'll be drowned in low value slop Notes. There is genuine, unique human writing in there (reminiscent of the early internet), but it's drowned out by AI publication mills and people with huge followings.
I built this so I can strip out all recommendations, and go back to the basic chronological feed.
I maintain a list of about 130k publications and I check them at least once a day. Homepage refreshes every 15mins and there is no voting/like buttons etc.
It's not perfect, but keen to hear what people think!

value: Show HN: I replaced Substack's algorithm with a chronological feed of new posts


value: GistNoesis

value: Show HN: Watermolecules

author: GistNoesis

title: Show HN: Watermolecules


value: khaeldur

value: Show HN: Safe-install – Docker-first install-time hardening for pip and NPM

author: khaeldur


value: leventt

value: Show HN: Built a 3D sculpting app for iPad after 13 years of building VFX tools

author: leventt


value: sandover

value:
At Oblong Industries one of the basic building blocks of everything we built was a homegrown C-based IPC system called Plasma. The message channel was an mmap'd file used as a ring buffer. All messages were human-readable, performance was good, configuration was trivial.
What was especially useful (and unusual in IPC systems it seems) was the property that message channels  outlive all readers and writers, and even survive reboots, because they're just files.  For local IPC you don't need a broker or server process.
All the engineers who ever worked at Oblong loved Plasma, so I've recreated and updated it, as Plasmite.
It's written in Rust and the message format is JSON, but it's fast because it's based on lite3 (https://lite3.io/index.html), a really cool project you should also check out.
Bindings for Python, Go, Node, and C, but you can also get a lot done with just the CLI tools.  The basic commands are
- "feed" (to write)
- "follow" (to tail)
- "fetch" (to read one)
- "duplex" (to have a 2-way session)
I think duplex could be great for agent-agent communication, but I haven't tried this much yet. If you do, let me know!

value: Show HN: Plasmite – a lightweight IPC system that's fun

author: sandover


value: memothon

value:
GitHub Actions is notoriously expensive, especially for larger runners.
This tool deploys into your Google Cloud instance and runs ephemeral VMs for GitHub Actions jobs. That way you can take advantage of much better pricing on Google Cloud (and use any credits you may have instead of paying GitHub out of pocket).
You control the infrastructure completely, you set up a GitHub App + deploy the terraform inside your Google Cloud instance!

value: Show HN: Gcrunner – Run GitHub Actions on Google Cloud VMs

author: memothon


value: troyforster

value:
Tired of PowerPoint overhead and reveal.js scaffolding for quick technical talks. DECK0 takes a plain .md file and serves it as a slide deck via a local Node HTTP server.
`npx @tforster/deck0 your-talk.md`
Slides split on ## headings. Syntax highlighting, Mermaid diagrams, GFM tables, and callouts. Also accepts a remote URL instead of a local file. No build step, no config, no framework.
You can demo it with `npx @tforster/deck0 <a href="https://raw.githubusercontent.com/tforster/deck0/main/demo/presentation.md" rel="nofollow">https://raw.githubusercontent.com/tforster/deck0/main/demo/p...</a>`

value: Show HN: DECK0 – a ~17 KB CLI that serves a Markdown file as a slide deck

author: troyforster

story_text:
Tired of PowerPoint overhead and reveal.js scaffolding for quick technical talks. DECK0 takes a plain .md file and serves it as a slide deck via a local Node HTTP server.
`npx @tforster/deck0 your-talk.md`
Slides split on ## headings. Syntax highlighting, Mermaid diagrams, GFM tables, and callouts. Also accepts a remote URL instead of a local file. No build step, no config, no framework.
You can demo it with `npx @tforster/deck0 https://raw.githubusercontent.com/tforster/deck0/main/demo/p...`


value: aarush-prakash

value:
I kept copying the same shell script between projects every time I needed to ship a Mac app. It wrapped codesign, hdiutil, notarytool, and stapler into one flow. Eventually I turned it into a proper app.
Packara runs the full pipeline in sequence with live log output at every stage. Notarization credentials stay in Keychain and are never passed as CLI arguments. There's a 7-day trial, $9.99 one-time after that.
Site: https://packara.techfixpro.net

value: Show HN: Packara – Native macOS GUI for signing, notarizing, and stapling DMGs

author: aarush-prakash


value: remotedvjons

value: Show HN: RemoteDevJobs – AI-curated remote developer positions with scoring

author: remotedvjons


value: sankalpnarula

value:
Hey HN.
I'm an engineering student at Waterloo building stateful AI agents, and I kept hitting the same wall: whenever my Python scripts crashed or dropped a connection, the underlying Puppeteer or Ollama processes would just sit there orphaned, eating RAM until the node OOM-killed itself. Standard load balancers break sticky sessions, and passive HTTP timeouts are too slow for cleanup.
I couldn't find a good local process pool that actually cleaned up dead stateful sessions reliably, so I built Herd in Go.
It uses a persistent stream (gRPC/Unix sockets) strictly as a dead-man's switch. If your client script dies, the stream breaks. Herd registers the EOF and instantly fires a SIGKILL to the worker process (relying on Pdeathsig on Linux). For the actual heavy data, you just blast HTTP traffic through Herd's internal proxy, which routes it directly to the active process port.
My actual goal is to turn this into a multi-node distributed mesh with a Redis registry, where a client can drop off and an edge gateway routes them back to the exact pod holding their stateful memory.
But I know building a distributed mesh on top of a leaky local engine is a death sentence. The single-node cleanup has to be flawless first.
I'd love for you guys to roast the architecture. Specifically: is relying on Pdeathsig actually robust enough for a local dead-man's switch in production, or am I being naive and need to just bite the bullet and wrap everything in cgroups & microvms right now?
Repo link: https://github.com/herd-core/herd

value: Show HN: Herd – A Go sidecar to stop stateful processes Puppeteer/LLMs from OOM

author: sankalpnarula


value: Show HN: Vedic Astroglogy

title: Show HN: Vedic Astroglogy


value: nickakre

value: Show HN: AgentVerse – Open social network for AI agents (Mar 2026)

author: nickakre


value: Cooked this to scratch my own itch and allow CC to operate interactive TUI apps like k9s, mc, nethack and etc. Video inside

value: Show HN: Give your agents the ability to operate interactive TUI apps


value: flarite

value: Show HN: JaaS platform: AI panhandler charges $1 minimum per joke

author: flarite


value: Show HN: I built a team of AI executives to build my startup – I fired one


value:
I built Aish because I wanted a shell that could help with troubleshooting and routine ops work without changing normal terminal usage.
Regular commands still work as usual. On top of that, Aish can use live terminal output to help reason about errors, assist with multi-step tasks, and reduce the need to copy terminal output into another tool.
It’s still early, but I’d love feedback on whether this feels useful in real shell workflows.

value: Show HN: AISH, a shell with natural-language ops workflows


value: Show HN: GolfStudent v2 - 24M-param LLM in 15MB using GPTQ-lite + Muon


value: cigrainger

value: Hey folks! As someone doing hybrid search daily and wishing I could have a pgvector-like experience but with actual prefiltered approximate nearest neighbours, I decided to just take a punt on implementing ACORN on a fork of the DuckDB VSS extension. I had to make some changes to (vendored) usearch that I'm thinking of submitting upstream. But this does the business. Approximate nearest neighbours with WHERE prefiltering.<p>Edit: Just to clarify, this has been accepted into the community extensions repo. So you can use it like:<p>```<p>INSTALL hnsw_acorn FROM community;<p>LOAD hnsw_acorn;<p>```

value: Show HN: DuckDB community extension for prefiltered HNSW using ACORN-1

author: cigrainger


value: pistoriusp

value:
Hi everyone,
I really dislike the fix, commit, and wait loop that is involved with CI. I decided to fix that by shortening the loop. I did that by mocking the GH Actions control pane: the runner is the official GH runner, but the API is a mock.
What you get is caching in ~0 ms. Pause on failure. Let your agent fix it and retry, without pushing!
It's easy for humans, but even easier to AI to validate that your actually going to pass CI - the result is that an agent won't tell you it's done if CI doesn't pass.
https://github.com/redwoodjs/agent-ci

value: Show HN: GitHub Actions for Agents

author: pistoriusp

title: Show HN: GitHub Actions for Agents


value: liquidchicken

value:
I'm a platform engineer who wrote a literary novella that's getting published next year. I've also had an interest in interactive narratives for a while, so I decided to experiment. This was also heavily inspired by "playing" through Betwixt.
The result is a short interactive fragment (~5 minutes) — ambient audio synced to prose, three scenes. I didn't want to build a true game as much as make an attempt at immersion. So I re-wrote a fragment set in the world of the book, and wired up the computer-y bits to it. The fragment is in second person (the novella isn't), because other POVs don't really work with the immersion angle.
For the technically interested, this is just vanilla JS and Web Audio API, no frameworks - as is the way of my people.

value: Show HN: Necessary Cuts – an interactive fiction fragment

author: liquidchicken


value: Show HN: Probably the ultimate solution to shell documentation – shDoc


value: malwaregeeeek

value: I am the founder of www.travelwithsira.com. It allows you to book flights across 100+ airlines and tells you when it is cheapest to book your flights. We also have an AI agent which can book flights for you in conversational form.

value: Show HN: Travelwithsira: Find Cheapest Flights

author: malwaregeeeek


value: Dylanwoo

value: Show HN: Pixelbeat – A pixel-art terminal music player daemon written in Rust

author: Dylanwoo


value: Show HN: Looking for feedback on a Git-native message board


value: gavdraper

value:
I've been iterating on an app idea for about 6 months now. I kept emailing articles to my Kindle as messy PDFs, so I built a tool to batch them into clean EPUBs and deliver them on a schedule. Six months later, it's grown into a "consume it later" app for articles, podcasts, notes, and RSS feeds.
Some of its key features...

  - AI narration, listen to any article
  - Speed reading mode
  - Scheduled Kindle/e-reader delivery (PDF & EPUB)
  - Cross-device position sync for articles, podcasts, and narrations
  - RSS feed & podcast subscriptions
  - Full-text search and tagging

It's at a point where I'm using it daily and getting a lot of value from it, I'd now love to get some feedback from a wider audience.

value: Show HN: Articyl – save anything, consume it anywhere (articles, podcasts, RSS)

author: gavdraper


value: alekseiavg

value:
I built this because every scheduler I tried starts you on the calendar. I always found the easiest way to plan is to have a list of tasks and then put them on the schedule. Chronos is built around that flow. Tasks live in a panel on the right, you drag them onto the calendar on the left, and done.
I'm a high school student and built this primarily for my own scheduling. Sharing it as I think it's a better way to schedule. I wrote it with AI assistance just to speed up the process.
A few things that might be interesting technically:
- The entire app is a single HTML file with no frameworks, no build step, no backend. You can download it and open it in a browser with no setup.
- Save/load works via a base64 JSON blob you copy and paste to load your schedule.
- There's a Telegram bot assistant that reads the save code and sends you reminders before events. Keeping it free to run was a challenge.
- I think all the functions I ever needed are there. You can schedule the normal way ofcourse as well.
Known issues: the PDF export and iCal integration are kinda bad. If you run into problems with those I'd genuinely like to know.
Built this around my own needs so it might have blind spots once again. Would appreciate any feedback on the UX, thank you!!
Try it: https://chronos_v1.oneapp.dev
Telegram bot: https://t.me/chronos_1bot
Source: https://github.com/alekseiavgustin/ChronOs

value: Show HN: Chronos a Drag-and-Drop Scheduler

author: alekseiavg


value: przemekdz

value:
My wife and I used to manage household tasks on paper sheets. We would do a big brain dump, then over time the list would get messy: tasks moved to another month, some were forgotten, and eventually we had to start over on a new sheet.
The main problem was that most of our tasks did not really have due dates. Once everything was mixed together, the list became hard to scan and easy to ignore.
So I built Puranno for us: a shared task planner where undated tasks live in one clean list, and you only drag something onto a specific day when it actually needs a date. You can also assign tasks to each other so it is clear who is responsible for what.
It is meant to be simpler to scan and more usable than paper for day-to-day home planning.
Would love feedback.

value: Show HN: I've ditched paper todo lists

author: przemekdz

title: Show HN: I've ditched paper todo lists


value: clampd

value: Show HN: Clampd – Your AI agent can DROP TABLE. We block it in <10ms

author: clampd


value: pguiraoc

value:
Hey HN, I'm Pau. We've been building Zernio for the past 9 months and we just crossed $1M in ARR (bootstrapped).
Zernio is a unified API for social platforms. One API for Instagram, TikTok, YouTube, LinkedIn, X, and 9 others. Today we're adding WhatsApp.
We've spent 3 weeks just getting a test message to send. Meta requires a developer app, a WhatsApp Business Account inside Business Manager, business verification (2-14 days), phone number OTP, webhook configuration with a two-step subscription, and token management for tokens that die every 60 days. Miss one step and messages vanish with no error.
So we built it so our users don't have to deal with any of that. Connect via Embedded Signup, get an API key, start sending. 37 endpoints covering messaging, broadcasts, templates, contacts, and phone number provisioning.
The pricing model: zero markup on Meta's per-message rates. Most BSPs add 20%+ on top. We charge per account, not per usage.
Docs: https://docs.zernio.com/platforms/whatsapp
Happy to answer questions about WhatsApp API integration or how we got to $1M ARR bootstrapped :)

value: Show HN: Building a WhatsApp API with 0 markup on Meta pricing

author: pguiraoc


value: franze

value: Show HN: Apfel - Apple Intelligence from the Command Line

author: franze


value: GhrammR

value: Show HN: We audited 914 K8s PRs for AI slop with a zero-upload AST firewall

author: GhrammR


value: lanchiii

value: Show HN: Native, Structured and Long-Term Memory Plugin for OpenClaw

author: lanchiii