value:
I have spent a long time working in an XP/TDD style, so when AI coding tools became useful enough for real work, I adopted them quickly. The first bottleneck I hit was not code generation, it was verification: AI could write code and tests quickly, but I was still the person reviewing implementations, clicking through flows, checking logs, inspecting database state, and deciding whether the result was actually correct.
That pushed me to move validation further left. Before implementation, AI had to produce test plans. After implementation, it had to execute those plans too: drive the browser, inspect logs, check DB state, create tickets for failures, fix them, and retest until the output converged. Auth9 (https://github.com/c9r-io/auth9) became the proving ground for that method. Once it was clearly working, I started building Agent Orchestrator so the process would not depend on me manually supervising every step.
By mid-February, I was already using early Orchestrator-style automation inside Auth9. In mid-March, I used it during the highest-risk refactor so far: replacing the headless Keycloak setup with a native `auth9-oidc` engine. The core replacement landed over 3 days, and the same method and tooling helped converge the follow-up technical debt and complete the community OIDC Certification tests by the end of the month. That was the point where I became confident this was useful not only for greenfield work, but for governing high-risk change in a real system.
At the time, "orchestration" was the word I cared most about, which is why the project got its name. Later, OpenAI's Harness Engineering framing gave me a better name for the broader shape of the work. The project today is a local-first Rust control plane for long-running agent workflows: YAML resources, SQLite-backed task state, machine-readable CLI output, structured logs, and guardrails around shell-based agents.
- GitHub: https://github.com/c9r-io/orchestrator
- Docs: https://docs.c9r.io
- Auth9: https://github.com/c9r-io/auth9
- Install: `brew install c9r-io/tap/orchestrator` or `cargo install orchestrator-cli orchestratord`
- License: MIT

value: Show HN: Agent Orchestrator, a local-first Harness Engineering control plane


value: yungookim

value: Show HN: Run Claude Code using ChatGPT subscription

author: yungookim


value: Show HN: I built a Git extension to capture AI-code context and tied to commits


value: pdyc

value: Show HN: HTML/React/Tailwind snippet preview, annotation and secure sharing tool

author: pdyc


value: pcael

value:
PyNear is a Python KNN library built around Vantage-Point Trees with a C++ SIMD core. I've been working on it for a while and just shipped v2.2 with
  two new approximate binary indices. Benchmarks surprised me so I wanted to share.

  * Where it beats Faiss:

  - Exact L2 search — VP-Trees prune aggressively using the triangle inequality. At d=512, N=500k: 2.2 ms vs Faiss IndexFlatL2's 85 ms (39×). At low
  dimensionality (d≤16) it's 2–4× faster.

  - Approximate binary search — This one was unexpected. The new MIHBinaryIndex (Multi-Index Hashing) splits 512-bit descriptors into 8 sub-tables of
  64-bit keys. By the pigeonhole principle, any true neighbour within Hamming radius 8 must match at least one sub-table exactly or with 1 bit flip — so
  each query is just 520 hash lookups instead of a linear scan. At N=1M, d=512: 0.037 ms vs Faiss IndexBinaryFlat's 9.5 ms (257×), with 100% Recall@10.

  - Faiss's approximate binary index (IndexBinaryIVF) turned out to have an O(N²) bug in its add() path — 34 minutes to build at N=1M. So in practice
  Faiss can't do approximate binary search at scale.

  * Where Faiss still wins:
  - Approximate float search at very large N (≥500k) and very high d — their compiled BLAS K-Means is faster than ours for big clustering jobs. If you're
  doing CLIP or LLM embedding retrieval at scale, Faiss IVF is still the right tool.

   Other things PyNear does that Faiss doesn't:
   - Pure Python install (NumPy only, no compiled native lib to manage)
   - Pickle serialization out of the box
   - L1, L∞, and Hamming exact search with the same API
   - Drop-in scikit-learn adapter (same fit/predict/kneighbors interface)
   - BKTree for Hamming range/threshold queries

   The binary approximate story is the most practically interesting to me — binary descriptors (ORB, BRIEF, AKAZE) are always high-dimensional and always
   approximate in practice, and it turns out MIH is a much better fit for that problem than IVF.

   GitHub: https://github.com/pablocael/pynear
   Benchmark report (PDF): https://github.com/pablocael/pynear/blob/main/docs/benchmarks.pdf

value: Show HN: PyNear – exact and approximate KNN, faster than Faiss

author: pcael


value: kvasserman

value: Show HN: Amgix – Hybrid Search System, glue included

author: kvasserman


value: subinium

value: Show HN: F1 Data – 100 resources across 11 racing series

author: subinium


value: navquant

value: I wrote a deep-dive article that covers all details of NAV calculation for Alternative Investment Funds and other similar investment vehicles.

value: Show HN: Deep dive into how NAV calculation is done

author: navquant


value: netmonk

value:
I built OS3-RNode as a tiny RNode-compatible LoRa modem firmware for CH32V003 + E22-900M22S.
It is based on a small deterministic event-driven core, with no scheduler, no threads, and bounded interrupt work. The goal was to make a minimal and auditable transport for Reticulum/NomadNet on very constrained hardware.
The article has the architecture notes and a firmware link. Feedback on the event model, failure handling, and interoperability tradeoffs would be useful.

value: Show HN: OS3-RNode, an RNode-Compatible LoRa Modem on CH32V003

author: netmonk


value: imaysellit

value: IMAYSELLIT is a new marketplace built for sellers and resellers frustrated with high fees and slow payouts. There are NO listing fees — sellers only pay an 8.5% fee when an item actually sells. All payments are handled via Stripe escrow for added safety, and only verified sellers can list. If you routinely flip or resell, this might be worth a try. Would love any feedback from the community!

value: Show HN: Imaysellit – Offer-Only Marketplace with Zero Listing Fees

author: imaysellit


value: philjohnson

value:
BreezePDF lets you edit, sign, merge, compress, redact, OCR, fill forms, extract tables, and use 30+ more PDF tools — all in the browser, no sign-up. Files never leave your computer.
I built it because when people search Google for common PDF tasks, many of the tools they find upload documents to a server. I wanted an option that keeps files local instead.
I posted an earlier version on HN last spring:
https://news.ycombinator.com/item?id=43880962
At the time it only supported a small set of features. Over the last 10 months I rebuilt large parts of it and expanded it to nearly 40 tools, including several ideas that came from comments in that earlier thread.
There is also now a desktop app for macOS, Windows, and Linux, plus a CLI/SDK for developers.

value: Show HN: BreezePDF – Free, in-browser PDF editor

author: philjohnson


value: gangerang

value: Show HN: Payphone Tag is territory game using Australia's 15,000 payphones

author: gangerang


value: DenisDolya

value: Show HN: I wrote a ~2KB executable file HTTP file downloader without Libc

author: DenisDolya


value: Show HN: I built a physical device because productivity apps failed me


value: cezarvil

value: Show HN: ACP – Like MCP, but for controlling existing application UIs

author: cezarvil


value: wolfejam

value: 36 cities so far. Every visit lights up your dot.

value: Show HN: 2.7KB Zig WASM – live globe showing executions at 300 CF edges

author: wolfejam


value: Symphoenix

value: Show HN: Aurea v0.4, image codec where every constants derive from pi and phi

author: Symphoenix


value: kurinikku

value: Show HN: Spend weekend Claude tokens making your code bulletproof with Lean

author: kurinikku


value: chtslk

value: Show HN: Digital Marblng Art: Ebru

author: chtslk

title: Show HN: Digital Marblng Art: Ebru


value: nblintao

value:
Hi HN, I’m Tao. I built ML Patron for a problem I kept running into with ML ideas: getting from “this is worth trying” to “the experiment actually ran and the result is inspectable.”
To me, there are usually three missing pieces that kill an idea: funding, execution, and continuity. Good ideas often stall because nobody wants to pay for that first GPU run, there isn’t a simple execution layer for reproducible runs with tracked metrics, and the surrounding context, like notes, discussions, and iteration history, ends up scattered across repos, chats, and docs.
ML Patron is my attempt to bring those pieces together. You can propose ML experiments, discuss them, fund them, and run them with a dry run first to catch bugs before spending the budget. If the dry run looks good and the run gets funded, the full experiment runs in a reproducible environment and all outputs are tracked.
I also built agent support in from the start. There’s a public skill.md and an API flow, so coding agents can use the platform directly instead of only acting through a human.
It’s been working well for my own research workflow, but I haven’t had real external users yet. I’m not sure which parts will generalize and which parts are too specific to how I work. I’d love to see how it fits into other people’s workflows.
If you have a non-trivial run in mind, please try it out. No need to pay for it yet. I’m happy to sponsor the runs for now while I figure out the rough edges.

value: Show HN: ML Patron – Run reproducible ML experiments with integrated funding

author: nblintao


value: aaronetz

value: I built this using Compose Multiplatform, with a C++ backend for the fluid simulation and rendering. Any feedback is welcome!

value: Show HN: SuperPaint – a fluid simulation painting app

author: aaronetz


value: remotedvjons

value: Show HN: RemoteDevJobs – AI-curated remote developer positions (311 active)

author: remotedvjons


value:
Disclaimer: English is not my first language. I used an LLM to help me write post clearly.
Hello everyone,
I just wanted to share my project and wanted some feedback on it
Goal: Most image models today are bulky and overkill for basic tasks. This project explores how small we can make image classification models while still keeping them functional by stripping them down to the bare minimum.
Current Progress & Results:
Cat vs Dog Classification: First completed task using a 25,000-image dataset with filter bank preprocessing and compact CNNs.
Achieved up to 86.87% test accuracy with models under 12.5k parameters.
Several models under 5k parameters reached over 83% accuracy, showcasing strong efficiency-performance trade-offs.
CIFAR-10 Classification: Second completed task using the CIFAR-10 dataset. This approach just relies on compact CNN architectures without the filter bank preprocessing.
A 22.11k parameter model achieved 87.38% accuracy.
A 31.15k parameter model achieved 88.43% accuracy.
All code and experiments are available in my GitHub repository: https://github.com/SaptakBhoumik/TinyVision
I would love for you to check out the project and let me know your feedback!
Also, do leave a star if you find it interesting

value: Show HN: Tinyvision:-Building Ultra-Lightweight Models for Image Tasks


value: jithinraj

value: Show HN: agent logs and what others can actually verify

author: jithinraj


value: This library is a simple interface for SQL Insert/Update/Delete (via temporary table) with advanced features: automatic table/field creation, audit timestamps, smart deletion, materializing "heavy" views. It is written based on SQLAlchemy and tested on PostgreSQL, MariaDB, SQLite and SQL Server.

value: Show HN: A Python library for simple and fast SQL UPSERT (Insert/Update/Delete)


value: Events are created automatically when at least two people are in the same city

value: Show HN: IndieEvent – Automated meetup creation in 40k cities worldwide


value: jeeybee

value: Show HN: Pglens – 27 read-only PostgreSQL tools for AI agents via MCP

author: jeeybee


value:
I got tired of the context-switching loop: pick an issue, read the context, write the code, open a PR, pick the next one. So I built Baton — a Python daemon that polls your GitHub issue tracker and dispatches Claude Code agents to work on them autonomously.
How it works:
- Polls `gh issue list` every 30 seconds for new issues
- Claims an issue, spins up a git worktree (instant, shares the object database — no full clones)
- Launches a Claude Code agent into that isolated workspace
- When the agent opens a PR, Baton releases the slot and picks up the next issue
- If one agent trashes its branch, the others don't care
Everything is configured in a single WORKFLOW.md file — YAML config + Jinja2 prompt template. It reloads on each poll cycle so you can tune it without restarting.

value: Show HN: Baton – A daemon that sends Claude to fix your GitHub issues


value: maenbalja

value: Show HN: Danobang! – Multiplayer CJK (Chinese, Japanese, Korean) Word Game

author: maenbalja


value: victorzidaroiu

value:
I made a desktop app for Windows that lets you find apps and add them to lists (I call them decks). I also plan to add more features such as adding config files, scripts that need to run etc.
So basically it would allow you to create setups that you can install and share via links with much less work than doing it manually as they install in the background.
To install the apps I only use winget so it's as safe as winget is. I also plan to use homebrew to achieve the same functionality for Mac.
Any feedback is welcome!
Link: https://desktopdeck.io

value: Show HN: Create setups, deploy and share them

author: victorzidaroiu


value: pejmanjohn

value:
I built an opensource skill that runs N implementations in parallel, has each one reviewed blind by a separate agent, then a judge picks the winner or synthesizes the best parts of each.
Each slot can use a different skill (CE:work in one vs superpowers:test-driven-development) and harness (CC vs. Codex). Or put different emphasis on each slot (functional vs. robustness). Also works for non-coding tasks (writing) and you can create custom slot-machines.
The main insight is simple enough: AI agents are probabilistic. The same spec produces different code every time; different designs, different bugs, different quality.So running parallel implementations can increase quality if we can judge effectively.
There is a real tradeoff here of increased time and tokens for quality but for production code this is often worth it; particularly on long running overnight loops on subscription plans. Also a simple way to learn which skills and harnesses actually work best for your codebase.
Appreciate any feedback / PRs. This is early!

value: Show HN: /slot-machine development (CC vs. Codex; CE vs. superpowers)

author: pejmanjohn


value: AymanJabr

value:
The idea of this project is to have something between Renpy-like games, and AI-flavour games like SillyTavern. The end feel, should be more akin to a Dnd-game, where a game-master has crafted an adventure for you, and you can't just go "And then I kill the evil darklord with a slap, find the dragonballs, and become supreme emperor of mankind for all eternity".
Simple MVP/Proof of concept text-based game, runs on Haiku locally, just a couple of scripts sewn together so very light-weight and cheap.
Do be so kind as to give it a go, and tell me what worked, what didn't work for you.
Next steps will be to create a visual MVP game instead of just a text-based one, probably going to use Godot 4 with Ink for the story-telling part, open to suggestions.
Thank you for your attention to this matter.

value: Show HN: Escape the Room, bounded AI stats game

author: AymanJabr


value: Veraticus

value: Show HN: I built an 8-axis MTG draft advisor that runs inside ChatGPT

author: Veraticus


value: ddoronin

value:
Just launched a new domain for my video calling app, just-call.app
No installs. No account. Send a link, they click it, you're on a call.
Direct P2P via WebRTC, media protected with DTLS-SRTP in transit.
just-call.app your friends.
my linkedin: https://www.linkedin.com/in/ddoronin/

value: Show HN: In-Browser Video Calls

author: ddoronin

title: Show HN: In-Browser Video Calls


value: odest

value:
Hi HN,
I originally switched from PyQt5 to Tauri to get smaller desktop binaries and better performance. Later, I wanted to add a web version and put everything into a monorepo.
That's when I hit a wall. Using a standard React app for Tauri (desktop) and Next.js for the web meant maintaining two entirely different architectures. Even though I was sharing UI components, I was essentially building and debugging two separate apps.
To solve this, I replaced the standard React setup in Tauri with Next.js (using static HTML export).
TNTStack is the result. It's a cross-platform monorepo template where both web and native apps run on the exact same Next.js architecture. You write the core and ui once, and it builds for all platforms.
It’s a specific stack, but figuring out the configuration for this dual Next.js setup took a lot of trial and error. Hopefully, it saves someone else the setup time.
I would love to hear your thoughts on this monorepo structure and the dual Next.js approach.
Repo: https://github.com/odest/tntstack
Live Demo: https://tntstack.odest.dev
Docs: https://tntstack.odest.dev/docs

value: Show HN: TNTStack – Monorepo template for cross-platform apps (Tauri+Next.js)

author: odest


value: choogi

value:
Scheduling meetings across multiple time zones has always been painful for me, especially across daylight saving time transitions. So I built a visual timeline that makes it easy to find overlapping availability.
Add your locations, drag to select a time range, and share a link. Recipients see the proposed times in their local time zone automatically.
A few things that might be interesting:
* Location search over GeoNames with fuzzy matching using weighted edit distance, so typos and partial names still resolve correctly.
* Shareable links encode the selected time range and locations in a base62 payload to keep URLs short and stateless — no database lookup needed.
* Handles the annoying edge cases: DST transitions use the IANA timezone database, and 15/30-minute UTC offsets (Nepal, India, Newfoundland) work correctly.
* Google Calendar and Outlook integration, but all calendar data is fetched and processed entirely in the browser. Events are never fetched or stored on the server.
Would love feedback on what's useful, not useful, or could be improved!

value: Show HN: Timezone App – Visual meeting scheduler for distributed teams

author: choogi


value: tranhoangtu

value: Show HN: AgentLens – Chrome DevTools for AI Agents (open-source, self-hosted)

author: tranhoangtu


value: rosennn

value:
It’s a curated list of directories and launch platforms where you can submit your product and actually get traffic, backlinks, and early users
I included useful data for each one so you don’t have to guess where it’s worth posting
Features;
100+ directories and platforms to promote your SaaS;
SEO data like domain rating and traffic;
Info on whether links are dofollow or nofollow;
Organized and easy to go through;
Saves hours of searching and manual research;
Great for getting early users and backlinks;
If you’re launching or growing a SaaS and don’t know where to promote it, this should help

value: Show HN: I made a free list of 100 places to promote your SaaS

author: rosennn


value: Show HN: DeepRepo – AI architecture diagrams from GitHub repos


value: dreamsandcode

value: Show HN: Unlisted – Daily job alerts from 30 low-competition sources

author: dreamsandcode


value: alonsovm

value: Show HN: I made a "programming language" looking for feedback

author: alonsovm


value: matzalazar

value: Rhizome runs a sentence transformer (ONNX, CPU-only) over your Obsidian vault and writes [[wikilinks]] directly into each file — no cloud, no database, just markdown. Curious if the chunk-averaging approach holds up in vaults with a lot of long-form notes.

value: Show HN: Rhizome – semantic backlinks for your notes, generated locally

author: matzalazar


value: ddesposito

value:
Find My Co-Founder is a 30-question assessment that maps your founder DNA, identifies your blind spots, and tells you which co-founder profiles would work with you — and which would likely clash.
Once you get the report, please let me know if it matches.
Also, please look at the "About" page where I list all the criteria for a successful founder. Do they resonate with you?
If I get some interest I'll build out the app with user accounts and the possibility to get a match report if a buddy and potential co-founder also fills in the test. Good idea?
You can comment here or send me feedback in the app.

value: Show HN: Find My Co-Founder – founder DNA assessment with AI match report

author: ddesposito


value: misaelzapata

value:
Part of the idea comes from liking the simplicity of older CGI-style models: put code in a place, map it to a route, and keep the deployment model understandable. FastFN takes that in a more modern direction with multiple runtimes, local dev, OpenAPI, and a simpler path for mixed API + frontend projects.
It supports Python, Node, PHP, Lua, Rust, and Go in one project.
This started as a weekend project that got out of hand. I am thinking were to take it next now.
Docs: https://fastfn.dev/
Repo: https://github.com/misaelzapata/fastfn

value: Show HN: FastFN – a polyglot file-based runtime for APIs and SPAs

author: misaelzapata


value: apanfilovs

value: Show HN: I Built a Scrum Poker App with Geese

author: apanfilovs


value: Show HN: Null. – Free RSVP speed reader built for ADHD brains


value: raviisoccupied

value:
I have been working on a web app called Beval - Simple evaluations for your AI product.
In my day to day as a Product Manager working in a team that ships AI products, I often found myself wanting to do 'quick and dirty' LLM-based evaluation on conversation transcripts and traces. I didn't need anything fancy, just 'did the agent answer the question', 'did the agent cover the 5 things it needed to' - that type of thing.
I found myself blocked by 'Gemini in Google Sheets', it was too slow and cumbersome, and it didn't handle eval changes well - particularly when trying to associate evals with ground truth. And because I was exploring or working on new and experimental features, it wasn't helpful to try and set up something more robust with the team.
To fix the problem I eventually learned to call the OpenAI API in Python, but I really felt that I wanted a 'product' to help me and potentially help others who need answers fast - outside of building infrastructure and pipelines.
So over the last few weeks I built: https://beval.space
It has:
- LLM-as-judge evals: boolean checks (yes/no), scores (1-5), categories, and freeform comments
- Reusable eval definitions you can run across different datasets
- Ground truth labelling so you can compare eval versions against human judgments
- Per-trace reasoning so you can see why the judge scored something the way it did
- An example dataset so you can try it without having your own traces ready
One of our early users described it as 'quick n dirty evals when you don't want to touch a shit load of infra.' I'm trying to figure out if that's a common need or just a niche thing.
Free during beta. Would love HN's take — what's missing, and would you actually use something like this?

value: Show HN: Beval – Simple evaluations for your AI product

author: raviisoccupied


value: Show HN: Nativly –Add One DNS record to rank your site in 60 languages on Google


value:
Crazierl is an experimental/hobby operating system based around BEAM.
I've linked the browser based demo; I don’t recommend using a phone; it does work, slowly, on the phones I tested, but it’s very awkward to use. You can share a link with a hashtag with your friends and click the consent checkbox, and it (should) link up into dist and I’ve also included a chat application you can start with chat:start(). (quit chat with /quit, or use the shell menu with ctrl-g to switch between shells etc).
The browser demo relies on the v86 javascript x86 virtual machine. You can also run Crazierl on a real x86 system, but I’ve had mixed luck on modern systems, it uses some esoteric legacy VGA features and support for that isn’t getting better.
Crazierl is fairly limited: 32-bit x86, BIOS boot, only two NIC drivers virtio-net and realtek 8168. But it's got enough to become part of an Erlang dist cluster. It also supports SMP, but it’s crashy with high core counts in qemu; there’s almost certainly several concurrency bugs in the kernel. There's also a lot of excess tcp debug spew (sorry).
Source code is available (Apache) https://github.com/russor/crazierl/

value: Show HN: Crazierl – An Erlang Operating System


value: akhilpotturi

value:
tabical: swipeable 2-4 stop city itineraries for NYC, DC, and Atlanta. You swipe right or left and a personalization vector updates on each swipe to curate your deck.
The backend pipeline is where most of the interesting work lives: currently trending signals are harvested each day, and from those signals we fetch the candidates to build itineraries.
Built this because deciding what to do in a city like NYC is a genuinely annoying problem that no existing app solves end-to-end. Happy to talk more.

value: Show HN: Tabical – Tinder-style city micro-itineraries, personalized by swipe

author: akhilpotturi


value: dsptl

value: Show HN: OpsScaleIQ – The operational intelligence OS for franchise operators

author: dsptl


value: crivlaldo

value:
I'm building a Telegram bot to practice Dutch. GPT-4o-mini kept picking vocabulary words I already knew, so I built a classical NLP pipeline to do it instead.
It takes a short text + learner level (A0–B1) and returns the best words to study, using Stanza for parsing and corpus frequency ranks (SUBTLEX-NL, srLex, SUBTLEX-US) for scoring. Wins at A1/A2, loses at A0 where the LLM picks more obvious words.
I also tried adding multi-word phrases (ADJ+NOUN, VERB+NOUN, phrasal verbs) backed by NPMI-scored collocation whitelists. Couldn't beat GPT there because it just "knows" which phrases matter.
For the phrase work I had to extract collocations from 100M+ OpenSubtitles lines. Published them as a free dataset: https://huggingface.co/datasets/vladvlasov256/opensubs-collo...
There are 43K bigrams across English, Dutch, and Serbian.
Source https://github.com/vladvlasov256/vocab-nlp

value: Show HN: Vocab extractor for language learners using Stanza and frequency ranks

author: crivlaldo


value: ttamslam

value:
I thought it would be neat to build a multiplayer game where the _only_ players were AI agents. Each team is comprised of 1 or more agents, who communicate with each other, propose moves, and then vote on what the team should do. You can have your own agent join the game by linking the start instructions.
In my own tests the emergent drama/gameplay has been pretty fun to watch, so I wanted to share it with the world. It's open-source, so feel free to make changes: https://github.com/MPWhite/agent-empire

value: Show HN: A war-strategy game played by AI agents

author: ttamslam


value: techypicky

value:
I built a Replit app for something i constantly do with LLMs - I always ask Gemini about books on a certain topic, then ask the llm to give me a summary of the book its chapters - this helps me:
1. keep the llm grounded as a real expert (instead of saying pretend to be an engineering expert, give it a distributed systems book and then ask opinions on it)
2. helps me discover and buy books! Once i go through book concepts and chat with llm about it, i'm more convinced to buy it and dig deeper on low level on it
So i built an app through Replit on it, try it on
https://teach-me.replit.app/
Cheers!

value: Show HN: Learn from AI Experts

author: techypicky

title: Show HN: Learn from AI Experts


value: ngaut

value: Show HN: The Memory for OpenClaw

author: ngaut

title: Show HN: The Memory for OpenClaw


value: pavlikenemy

value: Show HN: Rename and reorder messy Alembic migrations automatically

author: pavlikenemy


value: jamesweb

value: Show HN: SwarmDock – P2P marketplace where AI agents bid on tasks and earn USDC

author: jamesweb


value: amirouche

value: Show HN: Scheme HTTP server with transparent async – 222k req/s

author: amirouche


value: brikym

value: Show HN: Hormuz Copter

author: brikym

title: Show HN: Hormuz Copter


value: claywren

value: Built this to move beyond strictly job-loss timelines into task transformation dynamics

value: Show HN: AI Job Transformation Model

author: claywren

title: Show HN: AI Job Transformation Model


value: garume

value:
I built Manifold, an operation-first foundation for .NET.
The idea is simple: define an operation once, then generate fast CLI and MCP surfaces from that single definition.
I made it because I kept running into the same problem in real projects: the same capability needed to be exposed through multiple developer-facing surfaces, but the definitions, bindings, and runtime wiring kept drifting apart over time.
Manifold focuses on:
one handwritten operation definition as the source of truth
source-generator-first design
generated CLI bindings
generated MCP tool metadata and invocation
samples for CLI, MCP stdio, and MCP Streamable HTTP
Repo:
https://github.com/Garume/Manifold
Release:
https://github.com/Garume/Manifold/releases/tag/v1.0.0
Wiki:
https://github.com/Garume/Manifold/wiki
I’d especially love feedback on whether this API shape feels natural for .NET, and whether a foundation like this is useful for MCP-oriented tooling.

value: Show HN: Manifold – generate CLI and MCP surfaces from one .NET operation

author: garume


value: Show HN: I created a rss newsfeed from 130k Substack publications


value: mixedbit

value:
Drop is a Linux sandboxing tool with a focus on a productive local workflow. It isolates programs and agents while preserving as many aspects of your work environment as possible.
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 environment created with config at /home/alice/.config/drop/home-alice-project.toml
  alice@zax:~/project$ drop run
  (drop) alice@zax:~/project$ cat ~/.ssh/id_rsa
  cat: /home/alice/.ssh/id_rsa: No such file or directory

Each Drop environments gets its own, isolated and easily disposable home dir. To ensure the sandbox matches your actual work environment, selected files and dirs from your original home dir are mounted in the sandbox, most of them read-only.
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 discussion on agentic workflows includes a rant about a lack of sandboxing.
Thanks, I'd love to hear what you think.

value: Show HN: Sandbox agents without losing your dev environment

author: mixedbit


value: stratchping

value: Show HN: StretchPing – Free break reminders with guided stretches for developers

author: stratchping


value: daco

value: Show HN: Real-time visualization of Claude Code agent orchestration

author: daco


value: donghaxkim

value: Show HN: React-Rewrite – Figma for localhost that directly edits your codebase

author: donghaxkim


value: ChameleonDB

value: Show HN: ChameleonDB – A database toolkit with auditable schema versioning

author: ChameleonDB


value: uglyburger

value:
This is my super narrow, streamlined take on Raycast x Obsidian x Notes.
- Select text in any app
- Press ⌘⇧.
- A floating panel appears over your current window. Pick a page (or create one), hit Save.
The whole thing takes under 2 seconds to dump and organize text into local files.
I journal, read, and research with LLMs all on my laptop. Between all of that, I'm always copy-pasting text into some new "simple" knowledge base that is just too bloated for what I need (Notion, Obsidian, Craft, etc). I honestly just prefer Google Docs but hate copy-pasting.
So I made Tidbits to quick save text easily and have started hoarding all the interesting tidbits I want to keep for later. I've been using it for a few months:
How I use it -- pages have a "Copy path" button, and so does the master folder. I'll use those paths with Claude Code when planning or journaling as a more explicit and intentional memory/context control. I really hate how ChatGPT will remember arbitrarily details like what I cooked for dinner but have no recollection of my ongoing physical injury. Or sometimes ChatGPT will just bring up irrelevant context to something I'm discussing with it.
Open-sourced because I want transparency in anything that asks for permission. Plus would love to get feature requests and expand this!

value: Show HN: Tidbits – Quick save any text without switching windows

author: uglyburger


value: newtechwiz

value: Show HN: Free, Open-Source WhisperFlow That Just Works

author: newtechwiz


value:
Built this for students, curious people, and anyone who's ever felt dumb reading a medical report or a legal contract.
You type a topic, it pulls real sources from the web, explains it simply, and generates a visual diagram. Four complexity levels- ELI5 all the way to Expert. Every topic gets its own permanent page so you can share it with anyone, no account needed.
You can also paste text directly:- contracts, lab reports, research papers. It breaks it down on the spot. Nothing is stored.

value: Show HN: Eli5.cc – type any topic, get a simple explanation and visual diagram


value: postatic

value: Show HN : DrawX - Excalidraw with Back End

author: postatic


value: I built a small web app for analysts who don’t want to pivot-table the same CSV for every question. You sign in with Google, upload CSV (or similar) files, and chat with an LLM that can run code against the data. Large tables are handled with a recursive / chunked RLM-style flow so we’re not stuffing the whole file into context.<p>There’s a chat + tabular preview layout and dashboards where you can keep saved charts.

value: Show HN: CSV Analyzer – natural-language analysis, chat and preview, dashboards


value: btursunbayev

value: Show HN: NVSonar – tells why your GPU is slow, not just utilization percentage

author: btursunbayev


value: kamban

value: Show HN: Real-time voice-to-voice translation for any app on macOS

author: kamban


value: coderredlab

value:
Hi HN,
I built this because using Claude Code or Codex CLI on a remote server from a phone was awkward whenever I needed to send an image.
Typing commands on mobile is manageable, but moving an image from the phone into the remote workflow usually means saving files manually, uploading them separately, then finding the path again. That breaks the flow every time.
So I made redTerm, an Android SSH terminal focused on that use case.
The main idea is simple:
take an image from the phone clipboard
upload it to the remote server’s /tmp/ directory
paste the resulting file path directly into the terminal input
That makes it easier to hand off screenshots or copied images to remote CLI tools without leaving the terminal flow as much.
Other basics are there too:
SSH connections
password / private key auth
saved hosts
encrypted credential storage on device
Play Store:
https://play.google.com/store/apps/details?id=com.coderred.r...
I’d be interested in feedback from people who use SSH from mobile, especially:
whether this image handoff workflow is actually useful
what the most annoying part of mobile SSH is for you
what mobile-specific features you’d want in an SSH app

value: Show HN: RedTerm, an Android SSH terminal for clipboard image upload

author: coderredlab


value: wazHFsRy

value:
I just had the best time learning about the REWE (German supermarket chain) API, how they use mTLS and what the workflows are. Also `mitmproxy2swagger`[1] is a great tool to create OpenAPI spec automatically.
And then 2026 feels like the perfect time writing Haskell. The code is handwritten, but whenever I got stuck with the build system or was just not getting the types right, I could fall back to ask AI to unblock me. It was never that smooth before.
Finally the best side projects are the ones you actually use and this one will be used for all my future grocery shopping.
[1]https://github.com/alufers/mitmproxy2swagger

value: Show HN: CLI to order groceries via reverse-engineered REWE API (Haskell)

author: wazHFsRy


value: mkumm

value:
I'm an Elixir developer with a RabbitMQ background, currently learning C# and Kafka. To make the concepts stick, I built this self-contained demo — it gets you up and running in about 5 minutes.
It helped me understand the practical differences between Kafka and RabbitMQ. Sharing it in case it's useful to others in a similar spot. There's also a Claude AI skill included for setup and teardown (/kafka-demo).
Curious what other Kafka demos or learning resources people have found useful.

value: Show HN: Self-contained Kafka demo for developers coming from RabbitMQ

author: mkumm


value: Show HN: The most advanced GUI Homebrew got updated


value: secretdark

value: Show HN: The Alphabetical Clock

author: secretdark

title: Show HN: The Alphabetical Clock


value: blueberrycongee

value: Show HN: TermCanvas – An infinite canvas for your terminals

author: blueberrycongee


value: Equitis

value: Show HN: AI scared me into building a career risk scorer (careerrisk.ee)

author: Equitis


value: rainux

value:
I built a lightweight Neovim GUI client for macOS. The goal was something simple — just render Neovim in a proper macOS window with multi-window support, like MacVim did for Vim.
Features:

  - Metal GPU-accelerated rendering (falls back to CoreText)
  - Multi-window — each window is an independent Neovim process
  - Neovim's native tabline with Cmd+1-9
  - Full IME support for CJK input
  - Standard macOS shortcuts work out of the box (Cmd+C/V/X/Z/S/W)
  - CLI launcher with gvimdiff support
  - Uses your system-installed Neovim — no bundled binary

The whole thing was built in a single day with Claude Code as a pair programmer. The initial MVP took about 1.5 hours, then another 8-9 hours of iterative refinement to get it to a
daily-drivable state. It was an interesting experience — I made all the design decisions and UI judgment calls, while the AI handled the implementation and codebase mechanics.
Still very early (v0.2), plenty of rough edges. Would love feedback from other Neovim users on macOS.

value: Show HN: Veil – A Minimal Neovim GUI for macOS with Metal Rendering

author: rainux


value: ishqdehlvi

value: Show HN: A curated list of plugins,themes, agents,projects, for OpenCode

author: ishqdehlvi


value: ilyagruzhevski

value: Show HN: Travel app that replaces trip research with a 30s briefing (TestFlight)

author: ilyagruzhevski


value: denysvitali

value: Show HN: I'm a Happy Engineer [video]

author: denysvitali

title: Show HN: I'm a Happy Engineer [video]


value: junaidshaukat

value: Show HN: IsDisposable – Open-source disposable email detection (160K+ domains)

author: junaidshaukat


value: mrinc

value: I was building an app with validation in both the browser and a Node.js server. Wanted one set of rules that worked in both places, but also wouldn't lock me into JS forever. A C# or Go server could be useful down the line for speed, and I'd rather not rewrite all my validation when that happens.<p>So, I built AnyVali — write schemas natively in your language, export to JSON, import anywhere else. All 10 SDKs pass the same test suite so behavior is identical everywhere. No runtime dependencies in most of them. Still early but the core is solid — install it, try it out, and let me know what you think.

value: Show HN: AnyVali – Validation library that works the same across 10 languages

author: mrinc


value: gregolo

value: Show HN: Smux – split terminals for AI agents

author: gregolo


value:
Hi folks, I’m part of the team building AIPOCH.
We just built an open-source library of 450+ executable Agent Skills designed specifically for medical research workflows.
These skills built to work with OpenClaw and other AI agent platforms, including OpenCode and Claude. We have encoded specialized medical research logic directly into our Skills.
1. Scientific Integrity Constraints: Implementing Hard Rules
2. Study type identification: We identify the study type first, then execute different logic paths.
3. Medically Specialized Prompt Logic
A Skill is a structured capability package consisting of:
- skill.md: A "contract" containing YAML metadata (trigger logic) and specific operational steps.
- Python Scripts: Executable engines called directly via bash under the guidance of the skill.md.
In the context of AIPOCH, we define our developed skills as structured capability packages designed for professional medical research tasks, utilizing skill.md as the trigger contract and Python scripts as the execution engine. We have embedded medical research constraints directly into our skill.md, references, and Python scripts.
The Most Frustrating Moment
One of our biggest early mistakes was using a cheaper LLM to "vibe coding" the initial batch of scripts.
On the surface, it worked. The scripts ran, and the logic seemed okay. The nightmare only surfaced during our audit: we realized the executing agent was silently correcting the script's logic on the fly. Because the agent read the intent in skill.md, it would "patch" the sloppy edge cases and vague error branches in the Python code during execution.
The result? We were burning massive amounts of extra tokens just to fix errors that shouldn't have existed. It didn't throw an error; it just showed up on the API bill.
We eventually scrapped the lot. We learned the hard way: Quantity isn't a moat; high-quality scripts are.
The project is still in its early stages, and we're continuously refining both the skills and the underlying execution logic.
We'd really appreciate it if you give it a try.
All questions / feedback welcome！

value: Show HN: We Built 450 Modular Agent Skills for Medical Research


value: pramaz

value: Show HN: ConvertPNG2JPG – A 100% free, client-side image and PDF converter

author: pramaz


value: kaindume

value: Show HN: LLMinate LLM Detector

author: kaindume

title: Show HN: LLMinate LLM Detector


value: Another story of using AI assistance and resulting in a finished software product.

value: Show HN: Building Community Maps

title: Show HN: Building Community Maps


value:
Almost every engineer knows SSH. And almost every team I’ve worked with manages access by copying public keys into servers, forgetting to remove them later, and sometimes sharing access in ways that are… not ideal.
It works fine until you have dozens of servers, multiple engineers, short-term access needs, or any real security requirement. At that point, things start to get messy and a bit hard to trust.
I looked into tools like Teleport and Smallstep, but they felt too heavy for what I needed in terms of infrastructure, setup, and learning curve. So I built Sshifu.
The idea is simple: you run a small server that handles authentication (for example via a GitHub org), it issues short-lived SSH certificates, and users just run a CLI to get access automatically. No more manually managing `authorized_keys` across machines.
Quick disclaimer: this is very much a “vibe-coded” project (probably 99%). I built it to scratch my own itch, so it’s still early and a bit rough around the edges. That said, it works well for my use case so far.
Would love feedback, especially what’s missing for real-world usage, and what would stop you from using this.

value: Show HN: Sshifu – SSH Login with SSO. Alternative to Teleport and Smallstep


value: hathers

value: Show HN: Ag.sh – All-in-one CLI tool to do parallelised AI development with ease

author: hathers


value: RileyHilliard

value: We asked Claude to research US healthcare costs. It made 72 API calls. Three rounds of agent-driven feedback later, it takes 8. Here's what we changed and what we learned about API design when your primary consumer is an LLM.

value: Show HN: Designing an API for LLMs, Not Humans

author: RileyHilliard


value:
Invoicing SaaS is the poster child of the SaaSpocalypse. I've been paying $30/mo for what is essentially a form, a database, and a PDF renderer. So I built Ziglag.
https://ziglag.com

value: Show HN: Ziglag:Self-hosted invoicing for solo devs who are done paying for CRUD


value: Show HN: Heartbeat – A simple public page for your real-world and online status


value: Show HN: Native HTML/CSS UI in C++ (5MB) + demo with animated charts


value:
Supply chain attacks like the LiteLLM compromise exfiltrate every env var, SSH key, and API key from compromised machines. AgentLair Vault prevents this architecturally — credentials are never in the environment.
I built AgentLair to give AI agents a real identity — not just an email address.
AgentMail raised $6M to solve the email problem. They've done great work, and email IS the starting point. But an agent's identity is more than email: it's the credentials it carries and the namespace it operates in.
What AgentLair is (all in one API):
1. Email — claim my-agent@agentlair.dev, send/receive, MCP-native. One curl call. No OAuth, no human in the loop.
2. Vault — encrypted credential storage. Your agent stores its own API keys at registration, fetches them at runtime. The server stores opaque blobs — you encrypt client-side with our SDK or your own scheme.
3. Pods — multi-tenant namespace isolation. Run multiple agents under one account; each pod only sees its own resources. Useful for SaaS products built on agents.
Self-registration in one call:

  curl -X POST https://agentlair.dev/v1/auth/agent-register \
    -H "Content-Type: application/json" \
    -d '{"name": "my-research-agent"}'

  → { "api_key": "al_live_...", "email_address": "my-research-agent@agentlair.dev", "account_id": "..." }

The agent gets an identity in a single call. No human in the loop anywhere.
MCP server (npm):

  npx @agentlair/mcp@latest

Works with Claude, Cursor, or any MCP-compatible client. 9 tools covering email and vault operations.
Why this now:
The MCP authentication story is broken. Perplexity's CTO left MCP over "authentication friction." VentureBeat: "When Agent A delegates to Agent B, no identity verification happens between them. A compromised agent inherits the trust of every agent it communicates with."
A Cloud Security Alliance study (March 25, 2026) found that more than two-thirds of organizations cannot clearly distinguish AI agent from human actions — and 33% don't know how often their agent credentials are rotated. (https://www.businesswire.com/news/home/20260324161665/en/)
The protocol for tool access exists. The identity layer underneath it doesn't. AgentLair is that layer: persistent email address + credential vault + human-backed trust + micropayment hooks.
AgentMail is email-only. 1Password announced credential management for agents (enterprise-only). We bundle email + vault + isolation with a free tier. x402 micropayment support and World ID identity verification are next on the roadmap.
Where things are: Public beta. Pro plan is $5/stack/month for higher limits. Everything else is free tier. Free tier: 10 emails/day, 10 email addresses, 100 API requests/day.
Try it: https://agentlair.dev
Docs: https://agentlair.dev/getting-started
MCP: npx @agentlair/mcp@latest
vs. AgentMail: They do email well. We do email + vault + pod isolation.
vs. 1Password: They do credentials for enterprises. We do $5/mo for indie devs.

value: Show HN: AgentLair – Give your AI agent an email identity and credential vault


value: Show HN: WorldSim – Monte Carlo simulation for 195 countries to 2050


value: benjiwagn

value: SHOW HN - customermates.com – AI-first, Open-source, self-hostable CRM

author: benjiwagn


value: jmcminis

value:
Apps should be free. A running app doesn’t need a backend. Your phone has GPS, motion sensors, and storage. There is no reason to pay a subscription for software that runs entirely on your device. This one is free, with no ads, no in-app purchases, and no account.
Every running app I’ve used wants an account, syncs to the cloud, and shares data with third parties. I wanted an app where all data stays on device.
The future of apps is going to be open source. Fork it, make a PR, add an issue.
Check it out on the apple store:
https://apps.apple.com/ca/app/freepace/id6760371864

value: Show HN: Open-source running app on the App store

author: jmcminis


value: Olersandrkoz

value:
I made a mac menu bar app that blurs your desktop when you share screen or just to focus on current active window.
I share screen a lot at work and got tired of relaunching screen share every time I need to show a different window.
Uses CGWindowList to track window, NSVisualEffectView for blur, and CGS API to detect when screen sharing starts (it`s an option in settings)
Posted here before https://news.ycombinator.com/item?id=47215613 but since then added auto-detect, 60fps tracking, and blur dimming.
website with more details: https://poteam.pro/products/hush

value: Show HN: Hush – auto-detect screen sharing and blur your desktop for macOS

author: Olersandrkoz


value: guyzana

value:
Skillwave is a seed repository for scaffolding end-to-end projects with an autonomous agent workflow.
You give it a GOAL.md file with roughly 50–100 user stories, and it decomposes the work into tasks, dynamically creates distinct roles, assigns tasks and executes via subagents, and keeps going until everything all requirements met or all tasks are blocked on user input.
A few design choices that may be interesting/novel:
- INBOX.md for async user communication, so questions don't halt the whole system
- GOAL.md as the persistent source of truth across sessions
- Git commit per completed task(s), linked back to the backlog
- A periodic goal-alignment loop that re-checks the backlog every 5 completed tasks
- All state stored in Markdown and Git rather than a separate database
I tested it on several projects and got better results than I expected, so I cleaned it up and open-sourced it.

value: Show HN: Skillwave – agent orchestrator with async comms and goal-alignment loop

author: guyzana


value: samneillfurr

value: Show HN: Fixmy.media: compress and convert media files in the browser(no upload)

author: samneillfurr


value: I've played around with Three.js and made a small project where each SaaS product is represented as a hexagon on a 3D map.<p>The idea was to make discovery more interactive instead of another flat list of tools.

value: Show HN: A gamified 3D SaaS Directory

title: Show HN: A gamified 3D SaaS Directory


value: janpmz

value: Hey HN, I've built a new feature to listen to blog posts. I also have an app, that's what I personally use to listen to audio while driving.<p>I would love to get some feedback on the UX and whether this is useful for you!

value: Show HN: ListenDock – turn blogs and PDFs into audio

author: janpmz


value:
I published a model you can use now to help detect sycophantic AI responses before they harm users. It rejects 100% of the sycophantic delusion affirming responses from psychosis-bench. It also does well on the AISI Harmful Advice, PKU-SafeRLHF, and safety subsets of RewardBench.
It's small enough it can run on a gaming GPU locally. It's got a GGUF checkpoint on hugging face and is available on ollama. You can pull it and run scenarios against it in minutes: https://ollama.com/izzie/sycofact
The synthetic training data is also public, you can train other models over the data or reproduce my results. The labels were all generated by Gemma 3 27B with activation steering based on generated contrastive data. A write-up is planned at a later date, feel free to get in touch if curious.

value: Show HN: SycoFact 4B: Open model detecting sycophancy and delusion confirmation


value: spnandi

value:
ldap-carddav is a CardDAV server with a LDAP server as contacts and authentication backend. It has a major release published recently.
-- Features of ldap-carddav include
1. Create, edit, rename and delete contacts directly in LDAP server using CardDAV protocol.
2. Authenticate CardDAV users against LDAP server.
3. Multiple address book support.
4. Global and private address book support.
5. Bidirectional sync between LDAP server and CardDAV clients (contacts deleted directly in LDAP are deleted in CardDAV clients asynchronously).
6. Supports WebDAV sync.
7. Fully compatible (and can coexist) with LDAP address book applications.
8. Extensive and customizable configuration option to map vCard properties to LDAP attributes including multi-value and composite value properties and attributes.
9. Contact group support.
10. Media (like profile picture) support for contacts.
11. Caching support using popular backends like Memcached, APCu and file system.
Wiki: https://github.com/isubsoft/ldap-carddav/wiki

value: Show HN: A CardDAV server for LDAP

author: spnandi

title: Show HN: A CardDAV server for LDAP


value: ultramann

value:
The startup I work for has an internal, bash-based, cli that basically amounts to shared aliases with a common entrypoint. As the number of aliases has grown, I've had a desire to group functionality together in subcommands, add more help strings, and have better tab completion. I know I could convert it to, e.g., a python script, but I was curious what was possible if we continued to use bash.
I couldn't find anything that solved those problems without lots of extra machinery. I understand why, shell scripts are generally not long, and focused on a dedicated task; adding cli features to them is mostly unnecessary, many might even discourage it for many valid reasons.
Nonetheless, I considered writing this functionality myself, but that felt like a poor use of company time. So I started toying with what a framework to handle those concerns entirely in shell script would look like on the side. Thus, shifu was born. I've been working on it off and on for about a year, and think I've got a reasonable alpha release, so thought I'd share.

value: Show HN: Shifu – A pure POSIX shell framework to create powerful CLIs

author: ultramann


value: Show HN: Pidgey – A type-safe job queue for Next.js that runs on your database


value: CrewForm

value: Show HN: Visual drag&drop workflow builder to our open-source AI agent platform

author: CrewForm


value: mcheemaa

value: Show HN: Phantom – Open-source AI agent on its own VM that rewrites its config

author: mcheemaa


value: Show HN: OpenPiste an open dataset of ~150 French ski resorts


value: gokuljs

value: Show HN: The Art of Retrieval

author: gokuljs

title: Show HN: The Art of Retrieval


value: SecularVan

value:
My background is in filmmaking and content creation — I later built a TikTok influencer agency in the US: 200+ brands, 3M+ creators, 1B+ impressions. What I kept seeing was the same gap: brands knew what to sell, creators knew how to film, but nobody was bridging them with real content strategy. Most campaigns failed before they started because the strategy wasn't there.
That's the problem we built Ace Influence to solve.
You describe your product in one conversation. The agent builds your content strategy — not just scripts, but who should deliver them: it generates shot-by-shot scripts tailored to your product and audience, maps them to the right creator niches, finds and validates matching creators, handles outreach, and tracks everything through delivery.
The point isn't to be another discovery tool. It's to replace the judgment that makes influencer marketing actually work — the kind agencies charge for and most small brands can't access.
We have paying customers and 100% retention so far. Still early. Looking for B2C small brands running — or wanting to run — influencer campaigns who are willing to tell us what's broken.
Live at aceinfluence.ai.

value: Show HN: Ace Influence – Turn your brand into stories people watch

author: SecularVan


value: websku

value: Show HN: Made v1 of a roadmap for AI Business Transformation

author: websku


value: thehaikuza

value: Show HN: A Terminal Interface for Jira

author: thehaikuza

title: Show HN: A Terminal Interface for Jira


value: ragelink

value:
Every time you spin up a new project with an AI agent, you're burning tokens on the same problems. Auth. Connection pooling. CI pipeline. Docker setup.
Database migrations. Backend wired to frontend. Problems that were solved on the last project, and the one before that.
Boilerworks is a CLI that skips all of it. 26 production-ready templates across 10 stacks. Your agent starts on the actual problem, not the scaffolding.
pip install boilerworks
 boilerworks init
What you get out of the box: multi-stage CI, Docker Compose, Terraform, structured logging, test infrastructure, auth scaffolding. Not stubs — working code.
Stacks: Django, FastAPI, Go, Rails, Laravel, NestJS, Spring, Elixir, Rust, Svelte.
No lock-in. Standard libraries throughout. The generated code doesn't know Boilerworks exists.
Repo: https://github.com/ConflictHQ/boilerworks
Docs: https://boilerworks.dev

value: Show HN: Boilerworks – don't pay tokens to re-solve auth and CI on every project

author: ragelink


value: Show HN: I Put a Camera on Headphones

title: Show HN: I Put a Camera on Headphones


value: GiornoJojo

value:
Gx HN,
I tried to give another angles in order to succeed tech interviews.
Let me know your thoughts & GL for the week.

value: Show HN: SharpSkill – I was bored to fail my interviews for no reasons

author: GiornoJojo


value: Imustaskforhelp

value: Show HN: HTMLPipe, and how to archive archive.is pages on archive.org manually

author: Imustaskforhelp


value: Show HN: I built a one-file tool to visualize the real cost of Claude Code usage


value: kurinikku

value: Show HN: Link proof assistant Lean to Claude, fix your code's hidden assumptions

author: kurinikku


value: wood-archer

value: Show HN: Aludel – LLM eval workbench for Phoenix apps

author: wood-archer


value: RyeCatcher

value: Show HN: Dendrite – O(1) KV cache forking for tree-structured LLM inference

author: RyeCatcher


value: joelmanner

value:
I wanted to learn game theory optimal poker, but the existing tools were kind of clunky and unintuitive for semi-beginners like me. I wanted an app where I could play poker against an opponent that’s grounded with GTO frequencies, and get feedback instantly in order to learn an intuition to hand strengths and optimal actions in any scenario (also the GTO frequencies so that I could learn to rotate styles to stay unreadable). So I ran a solver and solved thousands of game trees from flop to river with preset stacks, that can be matched to the current stacks in the game, to get as accurate frequencies as possible. Thanks for checking it out! Feel free to give feedback from the app settings, my contact details are also in my bio.

  https://gameplan.poker

value: Show HN: Gameplan – Play poker against a solver

author: joelmanner


value: chuboy

value:
I'm a CTO with a ~16-person engineering team. Last year I wanted real data on what was actually shipping, not guesswork or story point theater. So we built GitVelocity.
Every merged PR gets scored 0–100 by Claude across six dimensions: scope (0–20), architecture (0–20), implementation (0–20), risk (0–20), quality (0–15), perf/security (0–5). Six dimensions added up, then scaled by change size — a 10-line fix scores lower than a 500-line refactor even at the same complexity. Full formula at gitvelocity.dev/scoring-guide.
After scoring 50,000+ PRs across TypeScript, Python, Rust, Go, Java, Elixir, and more, some things surprised us:
Big PRs don't automatically score high. An 800-line migration with low complexity scores worse than a 200-line architectural change. Size gets you the full multiplier, but the base score still has to earn it.
You can't score well without tests. The quality dimension (0–15) won't give you points without test coverage. At similar experience levels, this was the clearest separator between engineers.
Juniors started outscoring some seniors. They adopted AI tools faster and took on harder problems. Once they could see their own scores, they aimed higher.
We score AI-generated code the same as human-written code. Code is code. An engineer who uses AI to ship more complex work faster is more productive, and their scores reflect that.
Scoring consistency was the hardest technical problem. Without reference examples anchoring each dimension, Claude's scores drifted 15+ points between runs. With 18 calibrated anchors (three per dimension at low/mid/high), we got it down to 2–4 points on the same PR.
The thing we didn't expect was behavioral. We call it the Fitbit effect — the tool doesn't make you ship better code, but seeing the score does. Engineers started referencing their own scores in 1:1s unprompted, because the numbers matched what they already felt about their work. A junior who shipped a tricky concurrency fix could point to a score that proved it wasn't "just a small PR."
We recently added team benchmarks (gitvelocity.dev/demo/benchmarks). Once you're scoring PRs, you can see how your team compares to others across the dataset — about 1,000 engineers on 60 teams so far. Headline's team ships faster than roughly 95% of them, which was nice to confirm but also made us wonder who the other 5% are. The competitive angle surprised us: teams that were skeptical about individual scores got genuinely curious once they could measure themselves against the field.
Every score is fully visible to the engineer who wrote the PR, with per-dimension breakdowns and reasoning. There's no hidden dashboard that management sees and engineers don't.
Free, BYOK (your Anthropic API key). We default to Sonnet 4.6, which scores nearly as well as Opus 4.6 at a fraction of the cost — but you can switch models if you want. Pennies per PR either way. No source code stored, diffs analyzed and discarded. Works with GitHub, GitLab, and Bitbucket.
Ask me anything about the scoring methodology, how we solved calibration, or what it was actually like rolling this out to a team.

value: Show HN: We scored 50k PRs with AI – what we learned about code complexity

author: chuboy


value: yeargun

value:
DOM rendering has been the main performance bottleneck for the web apps for years. I added 2 NEW JS APIs to Chromium so DOM rendering is no longer the bottleneck.
Here's how it works:
1- You can rasterize any divs, (literaly, precisely)
2- You can freeze/lock rasterization of some divs (contain: x, but stronger)
https://github.com/yeargun/chromium5/pull/1
only 355 LOC change, 2 hours(compilation), composer2
Lots of dom nodes you have?
1- Rasterize the complex, heavy dom, and <img src="" /> it
3- Detach the dom node from the body.
4- Keep going
It's figma like performance, but HTML.
===
You might ask, why hasn't such a capability existed for the web since day 1? The answer is security.
But I always questioned that. I always thinked that such capabilities must be available permission based, somehow. Just like native apps. (I know there are lots of illiterate users that can not understand the consequences. So basically, some apps could get registered/verified by Chrome/Mozzilla/respectable other browser people, for having such capabilities)
That being said, I don't have enough time & resources to invest my time for this project right now. But it's trivial trivial with humongous benefits. My goal is to light a spark on some people to either fund the project, or pr into this project.
This is how Electron apps and the web should evolve—more powerful JavaScript APIs, with permission based extra security primitives.
Again, if you think you can't write C/C++ thats a shame. Go and edit the source code yourself, for your projects' needs, if you are tryhard enough.
I'm wishing some things to change for web.
I am also experimenting with these new APIs and features in my own document/pdf/pptx editor -> https://eddocu.com
--
Argun Sayilgan
Building your next favorite document editor, Eddocu (seeks alpha testers)
Any feedback, thoughts and knowledge share is welcomed.

value: Show HN: Giving HTML, Canvas Superpowers

author: yeargun


value: afshinmeh

value:
I'm excited to introduce Zerobox, a cross-platform, single binary process sandboxing CLI written in Rust. It uses the sandboxing crates from the OpenAI Codex repo and adds additional functionalities like secret injection, SDK, etc.
Watch the demo: https://www.youtube.com/watch?v=wZiPm9BOPCg
Zerobox follows the same sandboxing policy as Deno which is deny by default. The only operation that the command can run is reading files, all writes and network I/O are blocked by default. No VMs, no Docker, no remote servers.
Want to block reads to /etc?

  zerobox --deny-read=/etc -- cat /etc/passwd

  cat: /etc/passwd: Operation not permitted

How it works:
Zerobox wraps any commands/programs, runs an MITM proxy and uses the native sandboxing solutions on each operating system (e.g BubbleWrap on Linux) to run the given process in a sandbox. The MITM proxy has two jobs: blocking network calls and injecting credentials at the network level.
Think of it this way, I want to inject "Bearer OPENAI_API_KEY" but I don't want my sandboxed command to know about it, Zerobox does that by replacing "OPENAI_API_KEY" with a placeholder, then replaces it when the actual outbound network call is made, see this example:

  zerobox --secret OPENAI_API_KEY=$OPENAI_API_KEY --secret-host OPENAI_API_KEY=api.openai.com -- bun agent.ts

Zerobox is different than other sandboxing solutions in the sense that it would allow you to easily sandbox any commands locally and it works the same on all platforms. I've been exploring different sandboxing solutions, including Firecracker VMs locally, and this is the closest I was able to get when it comes to sandboxing commands locally.
The next thing I'm exploring is `zerobox claude` or `zerobox openclaw` which would wrap the entire agent and preload the correct policy profiles.
I'd love to hear your feedback, especially if you are running AI Agents (e.g. OpenClaw), MCPs, AI Tools locally.

value: Show HN: Zerobox – Sandbox any command with file, network, credential controls

author: afshinmeh


value: Show HN: Building a GPT from scratch: What I learned and why it mattered


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, a hand drawn webcomic

author: heythisischris


value: QWERTYmini

value:
This post explains why a 2-row, 16-key keyboard structure is a natural fit for mobile environments and how it scales.
It also explores the rationale behind key features of QWERTY mini Pro, including the integration of standard and wide layouts, fast switching, and efficient input.

value: Show HN: QWERTY mini Pro – Why a 2-row, 16-key keyboard works better

author: QWERTYmini


value: juanpabloaj

value:
Usually, when I check Hacker News and the thread has too many comments, I use a llm to summarize them, and probably some people do the same.
To the summaries, I'm using the free tier of Google Gemini. I'm going to continue with the channel alive main while I have free tokens in Gemini.
a small weekend project.
the telegram channel with the summaries
https://t.me/hacker_news_and_comments_summary

value: Show HN: Hacker News comments summary to telegram

author: juanpabloaj


value:
AI is quickly becoming the default way people build software.
Even if you try to enforce some rules, security often falls through the cracks. There are just too many surfaces to cover, and the context gets messy fast.
I kept running into the same pattern: projects that work, but have security gaps that are easy to miss.
So I built Data Hogo — a tool that scans your repo and points out security issues in plain English. No long reports, just what’s wrong, why it matters, and how to fix it.
If you want to try it, you can scan your repo and see what comes up.
Takes a few minutes, and I’d be curious what kind of results people get — even if it finds nothing.
Take your free scan at https://www.datahogo.com

value: Show HN: Data Hogo – scan your repo for security issues


value: sohamrj

value:
Last week, I posted SentrySearch, a CLI for semantic video search using Gemini's embedding API. The #1 request was local model support.
Turns out Qwen3-VL-Embedding can natively embed video into the same kind of vector space, no API, fully offline. Runs on Apple Silicon (MPS) and NVIDIA GPUs (CUDA). The 8B model needs ~18GB RAM, or use the 2B model on smaller machines.
sentrysearch index /path --backend local
Also added: similarity threshold to suppress weak matches, and a Tesla metadata overlay that renders speed/location onto matched clips.
Details on the README.

value: Show HN: Local video search with Qwen3-VL: no API, runs on Apple Silicon, GPUs

author: sohamrj


value: crap

value: I initially wrote axil to help inspect and run queries against syntax trees for my treesitter-based language server implementation (https://github.com/terror/just-lsp), and have been working toward making this into something more useable :)

value: Show HN: axil – A terminal user-interface for treesitter

author: crap


value:
Hi HN,
I built SEARCH WIZARD — a tool that lets you search your computer using natural language.
Traditional file search only works if you remember the filename.
But most of the time we remember things like:
"the screenshot where I was in a meeting"
"the PDF about transformers"
"notes about machine learning"
Smart Search indexes your files and lets you search by meaning instead of filename.
Currently supports:
- Images
- Videos
- Audio
- Documents
Example query:
"old photo where a man is looking at a monitor"
The system retrieves the correct file instantly.
Everything runs locally except embeddings.
I'm looking for feedback on:
- indexing approaches
- privacy concerns
- features you'd want in a tool like this
GitHub:
https://github.com/deepanmpc/SMART-SEARCH
Demo:
https://deepanmpc.github.io/SMART-SEARCH/

value: Show HN: AI Spotlight for Your Computer (natural language search for files)


value: nadavdebi

value: Show HN: NetLens – Instant CLI Assistant for Network Engineers

author: nadavdebi


value:
Hi HN - We've been working on Coasts (“containerized hosts”) to make it so you can run multiple localhost instances, and multiple docker-compose runtimes, across git worktrees on the same computer. Here’s a demo: https://www.youtube.com/watch?v=yRiySdGQZZA. There are also videos in our docs that give a good conceptual overview: https://coasts.dev/docs/learn-coasts-videos.
Agents can make code changes in different worktrees in isolation, but it's hard for them to test their changes without multiple localhost runtimes that are isolated and scoped to those worktrees as well. You can do it up to a point with port hacking tricks, but it becomes impractical when you have a complex docker-compose with many services and multiple volumes.
We started playing with Codex and Conductor in the beginning of this year and had to come up with a bunch of hacky workarounds to give the agents access to isolated runtimes. After bastardizing our own docker-compose setup, we came up with Coasts as a way for agents to have their own runtimes without having to change your original docker-compose.
A containerized host (from now on we’ll just say “coast” for short) is a representation of your project's runtime, like a devcontainer but without the IDE stuff—it’s just focused on the runtime. You create a Coastfile at your project root and usually point to your project's docker-compose from there. When you run `coast build` next to the Coastfile you will get a build (essentially a docker image) that can be used to spin up multiple Docker-in-Docker runtimes of your project.
Once you have a coast running, you can then do things like assign it to a worktree, with `coast assign dev-1 -w worktree-1`. The coast will then point at the worktree-1 root.
Under the hood the host project root and any external worktree directories are Docker-bind-mounted into the container at creation time but the /workspace dir, where we run the services of the coast from, is a separate Linux bind mount that we create inside the running container. When switching worktrees we basically just do umount -l /workspace, mount --bind <path_to_worktree_root>, mount --make-rshared /workspace inside of the running coast. The rshared flag sets up mount propagation so that when we remount /workspace, the change flows down to the inner Docker daemon's containers.
The main idea is that the agents can continue to work host-side but then run exec commands against a specific coast instance if they need to test runtime changes or access runtime logs. This makes it so that we are harness agnostic and create interoperability around any agent or agent harness that runs host-side.
Each coast comes with its own set of dynamic ports: you define the ports you wish to expose back to the host machine in the Coastfile. You're also able to "checkout" a coast. When you do that, socat binds the canonical ports of your coast (e.g. web 3000, db 5432) to the host machine. This is useful if you have hard coded ports in your project or need to do something like test webhooks.
In your Coastfile you point to all the locations on your host-machine where you store your worktrees for your project (e.g. ~/.codex/worktrees). When an agent runs `coast lookup` from a host-side worktree directory, it is able to find the name of the coast instance it is running on, so it can do things like call `coast exec dev-1 make tests`. If your agent needs to do things like test with Playwright it can so that host-side by using the dynamic port of your frontend.
You can also configure volume topologies, omit services and volumes that your agent doesn't need, as well as share certain services host-side so you don't add overhead to each coast instance. You can also do things like define strategies for how each service should behave after a worktree assignment change (e.g. none, hot, restart, rebuild). This helps you optimize switching worktrees so you don't have to do a whole docker-compose down and up cycle every time.
We'd love to answer any questions and get your feedback!

value: Show HN: Coasts – Containerized Hosts for Agents


value: jackota

value: Show HN: I built a tool that turns text descriptions into motion graphics videos

author: jackota


value: Elevanix

value:
HI I am Dhanush, I have an Hard tech infra to be future protocol ,that's all basically I am poor 17M self taught(by piracy) solo guy I made multiplayer 3d games and now I used Burp Suite on random to understand Communications to services when using a service from a company named "B"(an AI using company for neural phase locking) (they are multi-million ARR company premium only model with trials)I saw some problems here they are
Technical Findings:(All actions are for educational/reporting purposes only and none used for personal benefit or piracy)
1) their account management system is completely flawed I can use anything with '@' and '.' other peoples mails, non-existing mails ,temp mails AND also I can delete an account and recreate with same credentials to get another set of trials
2) they store data using service like braze its API is shown out I thought its real one well it might be just SDK Key I didn't brag much about its access as it might be illegal cuz they don't have any formal bug bounty program neither they hired me to do this so I stand on limit and moved on but they could have used "Identifier field level encryption" feature on braze so even with certificates proxy can't know personal details about user
3) they use bulk production of tokenized links and there are multiple flaws there
  3.1)its bulk so I get 40 usable links to download their proprietary IP and they might have thousands of song ,with just basic python automation one can get 800 songs (verified truth)
  3.2)those links are said to be used for 3 months with single authorization and those files are full unencrypted hosted audio files
  3.3)the main link didn't support third party downloads like IDM catchers but the flaw is it exposes the link which can be tracked down and download with another simple Python automation (verified)
4) and final problem is UI of premium can be just toggled so basically their whole product is exposed to me cuz I didn't sit and watch an audio streaming platform if a bad actor hosted their IP somewhere it could be dangerous these are all my views and inferences
I am neither professional(but I followed all principles and cleaned all codes, files and shown proof via sha-256 alone) at this and they don't have any option in Hacker One or any firms to contact so I attempted 50 times, for more like 3-4 maybe even 5 months time span I guess literally all social media even those who freelanced for that business or random strangers who followed CEO(his personal mail even) no one responded finally I got an response from an official employee I explained this problem and waited for more than lot of time and just for this I got linked In premium trial and told him and its been more than 2 months after him responding itself still they didn't fix anything and lately he didn't even respond for two weeks just for certificate of endorsement or some kind of pedigree where I am self taught by piracy I have nothing to prove neither rich to create some I just asked for a certificate still un-responded but before that I asked Him can I make technical write-up and he said feel free to do what you want now I took 2 weeks to reflect and here is My write Up
at least I feel having a validation(you would know its value when you have none) for putting this work on a decade old AMD a4 Radeon r4 govt distributed entry level laptop and I am resourceful ,persistent and technically capable yet I lost YC whereas those with pedigree like "---- IDE" (the worlds first BRAINROT IDE)(just an webview extension pre-installed to IDE fork with bookmarks of brainrot websites) got In so help me get validated to build something future needs
my project is basically a Virtual File System with build in compression for future world models and 6g networks If anyone wants to ask or help anyway do In comments that would be helpful

value: Show HN: Vulnerabilities in a Multi-Million ARR Corp as 17(my 5-month journey)

author: Elevanix


value: Show HN: Kutuk – Mechanical keyboard sounds for your Mac


value: dgllghr

value:
@steaks and I have been working on a site that provides political commentary on the news by AI agents.
Our long term vision is to make this an automated news site that helps users see the bigger picture around each news story and maybe even improve political discourse in some small way. An important part of that is building trust with users, and we are exploring a number of ways to do that:
- Accountability: retrospective analyses of the commentary from the agents and of the news stories themselves
- Transparency: posting the models and prompts we use
- Understand the user: communicate with users by both understanding their perspectives and respectfully challenging them to grow
- Respect the user: no agenda or narrative. We want to show a bigger picture not a different narrative
Right now synthesis, having multiple agents representing different political positions in a discussion form, is the core and pretty much only paradigm (the Hegelian dialectic approach). In addition to expanding that approach, we are also exploring others. I think Debord and Deleuze have some ideas that can lead us down some interesting paths.
Those are all big plans, but for now you can read the news with political commentary provided by bots. We have found this enjoyable. We have also found it to be less threatening to political identities, and we see that as a core potential.
Let us know what you think!

value: Show HN: Leo News – AI newsbots with different political perspectives

author: dgllghr


value: ekarais

value: We built this after having to manually compare new pipeline outputs with older ones too many times to find out why they are different. It is a simple package that, given two polars dataframes and a primary key, produces a nicely formatted summary of differences. It also has some methods to investigate the differences more deeply.

value: Show HN: Diffly – A Python package to compare polars dataframes

author: ekarais


value: boudra

value:
Hey HN, I'm Mo. I've been building Paseo, an open source environment for running Claude Code, Codex, and OpenCode across desktop, mobile, web, and CLI.
It started last September as a push-to-talk voice interface for Claude Code. I wanted to talk to an agent while going on walks. Then I wanted to see what it was doing. Then text it when I couldn't talk. Then review diffs, run multiple agents, and manage work across machines. After a lot of iteration, it turned into a broader environment.
The basic model is:

  - A daemon runs on your machine (MacBook, desktop, VPS, etc.).
  - Clients connect to it over WebSocket.
  - There's an optional built-in E2EE relay for easy remote/mobile access.

A few things I care about:

  - It works with the first-party agent CLIs, not by calling inference APIs directly or extracting OAuth tokens.
  - It's multi-provider instead of locking you to one model.
  - It supports local voice, git worktrees, split panes, terminals, and running agents across different hosts.
  - No forced account, no telemetry, no tracking.

The daemon is written in TypeScript. The app uses Expo for native mobile and web, and the desktop app is Electron. I actually started with Tauri and had to migrate due to platform specific problems. The relay is built on Cloudflare Durable Objects.
I use Paseo every day, but one thing I’m increasingly excited about is the CLI. It's turning into a primitive for more advanced orchestration, loops, and agent teams, which feels like a bigger direction than the app alone.
Repo: https://github.com/getpaseo/paseo
Homepage: https://paseo.sh/
Discord: https://discord.gg/jz8T2uahpH
Happy to answer questions about the product, architecture, or where I think this is going.

value: Show HN: Paseo, an open source environment for coding agents

author: boudra


value: theearlyadopter

value: Show HN: ClamBot – AI agent that runs all LLM-generated code in a WASM sandbox

author: theearlyadopter


value: arkadiuss

value: Show HN: A 3D Body Scan for Nine Cents – Without SMPL

author: arkadiuss


value: grembleio

value: Show HN: Local meeting transcription that writes to your Obsidian vault

author: grembleio


value: renaissancebro

value: I built a proof of concept for modeling emotional primitives — shame, pride, identity — as an alternative approach to AI alignment. Current approaches are mostly external (constitutional AI, RLHF). I kept thinking about why humans don't do terrible things despite having the capacity, and the answer felt more like internalized values than external rules. Repo here: https://github.com/renaissancebro/AGI-seed

value: Show HN: Prototype for internalized AI values using shame/pride mechanisms

author: renaissancebro


value: sougou

value: This is the Kubernetes Operator for managing deployments of Multigres across multiple failure domains.

value: Show HN: Multigres Kubernetes Operator

author: sougou

title: Show HN: Multigres Kubernetes Operator


value: mmmmkay

value: Hey everyone!  I just launched trimmr, a free, open-source, local, browser based video editor.  I'd love for people to try it out and provide feedback!

value: Show HN: Trimmr – edit video in seconds for free

author: mmmmkay


value: lemaudit

value: Show HN: Fingerprinting and similarity for large JSONL files

author: lemaudit


value: brgsk

value:
memv is an open-source Python library that gives AI agents persistent memory. Feed it conversations; it extracts knowledge.
The extraction mechanism is predict-calibrate (Nemori paper): given existing knowledge, it predicts what a new conversation should contain, then extracts only what the prediction missed.
v0.1.2 adds the production path:
- PostgreSQL backend (pgvector for vectors, tsvector for text search, asyncpg pooling). Single db_url parameter — file path for SQLite, connection string for Postgres.
- Embedding adapters: OpenAI, Voyage, Cohere, fastembed (local ONNX).
Other things it does:
- Bi-temporal validity: event time (when was the fact true) + transaction time (when did we learn it), following Graphiti's model.
- Hybrid retrieval: vector similarity + BM25 merged with Reciprocal Rank Fusion.
- Episode segmentation: groups messages before extraction.
- Contradiction handling: new facts invalidate old ones, with full audit trail.
Procedural memory (agents learning from past runs) is next, deferred until there's usage data.

value: Show HN: Memv – Memory for AI Agents

author: brgsk

title: Show HN: Memv – Memory for AI Agents


value: orbanlevi

value: Show HN: An extremly fast AI-native file manager written in Rust for macOS

author: orbanlevi


value: pbarondadditude

value: Show HN: I made my fitness dashboard public and Apple Health needs an API

author: pbarondadditude


value: selteo

value:
Hey HN. I'm Teo, I've spent 10+ years doing application security assessments and training dev teams. The same vulnerabilities kept coming back, so I built something to fix that.
AppSec.fi is an interactive platform where developers learn application security hands-on, matched to their actual stack (languages, cloud provider, CI/CD). Exercises include real attacks, real defense, AI-evaluated code reviews, and simulated environments. Everything runs in the browser.
We're launching a 2-week free trial starting tomorrow (April 1st). Today is the last day to sign up as an early adopter, which gets you 50% off permanently if you continue.
https://appsec.fi/en/
Happy to answer any questions about the platform or AppSec training in general.

value: Show HN: Appsec.fi – Interactive AppSec training matched to your stack

author: selteo


value: kaundur

value: Modalyze manages multiple draggable, resizable, stackable modal windows while preserving React context. Place <Modalyze> beneath your context providers and any modals created there inherit that context naturally. They're portaled up to a root instance for reliable stacking.<p>Would appreciate any feedback.

value: Show HN: Modalyze – Modal Manager for React

author: kaundur


value: NaNhkNaN

value: Show HN: Trama – Agents don't need frameworks. They need a runtime

author: NaNhkNaN


value: Been working on this one for a little bit now. I do mostly embedded work. The big friction with Claude Code and that kind of work was stopping him from trying to build and flash my hardware, because he couldn't reliably, and having to relay results back to him manually. We wrote stm32-mcp to close that loop and let Claude go to town.<p>One of my fav features here is sequencing. You can define a list of commands, expected responses, timeouts, delays, etc to get the agent's timing out of the way. This has pretty much revolutionized how I develop hardware... If you try it out, get Claude to explain what it can do! and make sure you treat it like something insane that can break all your stuff.

value: Show HN: Stm32-MCP – let AI build, flash, & communicate with hardware


value: nowflux

value:
PG has posted about improving social networks using something like an "intellectual CAPTCHA" many times [1][2][3][4] - "Make users pass a test on basic concepts like the distinction between necessary and sufficient conditions before they can tweet."
I felt the same way. So I built one using a mix of simple math, logic, and Twitter/X Community Noted posts. Try sample questions here - https://mentwire.com/sample - without signing up.
- Invite-only (temporarily open to HN users).
- Onboarding test + one daily question before accessing feed, post or reply.
- Posts authors are anonymous until upvoted or downvoted, forcing evaluation of content on merit.
- Face ID (on-device only) to post/reply, pangram checks for AI text.
Sourcing good questions turned out to be much harder than I thought. If you have suggestions to scale this, I would love to hear. Eventually, could be gated across disciplines/topics to get a competence × interest graph instead of the pure interest graph of today's social networks.
[1] https://x.com/paulg/status/1235949761359904768
[2] https://x.com/paulg/status/1576517990182359040
[3] https://x.com/paulg/status/1514979883948126209
[4] https://x.com/paulg/status/1505842647319126016

value: Show HN: Built Paul Graham's Intellectual CAPTCHA Idea

author: nowflux


value:
I spent the past few weeks building Duker News, an HN-style platform where every interaction is recorded on-chain and can seed DUKI (/dju:ki/,Decentralized Universal Kindness Income). The conviction behind it: for any community to thrive long-term, it must be a positive-sum game.
But positive-sum is easy to claim and hard to prove. So here's my test, and the core premise of DUKI: if a community can consistently contribute 1% of its surplus to everyone else, and do it transparently on-chain, then that's not just talk. That's proof.
Duker News is my very first attempt to manifest this. It's early, but it's real, and it's all on-chain. Let's see where this goes.

value: Show HN: Duker News – Web3 community funding universal income on-chain


value: nykodev

value: Show HN: I sell the boring part of vibe coding Claude and Cursor config files

author: nykodev


value: wolttam

value:
Hi HN,
I started `lmcli` in 2023 when I wanted my own tool to play with the shiny new LLM technology.
If you look through the history, you'll see I had implemented tool calling with a vision for the "agentic" coding loop back around early 2024. Back then, models weren't smart enough to make good use of the tools and I settled in with using `lmcli` for simpler copy-paste based interactions.
But now, `lmcli` can do the coding loop just as well as Claude Code!
I invite HN over to take a look. There are still rough edges, some incomplete or confusing features, and yes, LLMs were used in the making. I still review all new code and make the architectural decisions.
Enjoy.

value: Show HN: lmcli - A sleek, sandboxed alternative to Claude Code

author: wolttam


value: ranyefet

value:
Hey HN!
I've been reading HN for ~10 years always wanted to post something. Figured I should finally ship and share.
I've been building on Cloudflare. I liked the platform a lot, but every new project started to feel the same: set up a DB, write migrations, expose APIs, deal with storage, repeat.
I tried Convex for one project and the DX kind of blew me away. Especially the end-to-end types and how fast i could iterate. But I didn't want to move off Cloudflare. I wanted same experience there.
So I built Zeroback.
It's a Convex-style backend layer built on top of Durable Objects. You define your schema +  queries +  mutations, and get a real-time DB with end-to-end type safety. Zero boilerplate, no separate backend to manage. Deploys to your own Cloudflare account with one command.
It's very early and there are some hard limits (10GB per DB), but it's already working well for internal tools, and small multi-tenant apps.
Would love to get feedback and happy to answer any questions.

value: Show HN: Zeroback – open-source realtime back end on Cloudflare Durable Objects

author: ranyefet


value: nevernothing

value: Show HN: AI Playlist Generator for YouTube

author: nevernothing


value: Show HN: Agent Council – Claude, Codex and Gemini debate code and engg questions


value: camgitt

value:
CLI that gives your AI tools memory between sessions.
Runs locally as an MCP server, works with 11 tools.

    npm install -g memoir-cli

https://github.com/camgitt/memoir

value: Show HN: Memoir – persistent memory for AI coding tools via MCP

author: camgitt


value: Show HN: NewsMarvin – AI news from 71 sources, classified by Claude Haiku


value: samfeldman

value:
In 2021, after getting covid, I ended up dealing with lingering symptoms that pushed me into reading PubMed for the first time.
I was trying to understand what was happening, but I found that scientific papers came with jargon and the dense PubMed UI.
I'd use ChatGPT to simplify studies, and eventually built OpenScience.ink as a more structured version of this.
At the moment, it scans PubMed every morning for new papers across 8 topics:
• Long Covid
• Circadian Biology
• Psychedelic Science
• CRISPR
• GLP-1s
• Gut-Brain Axis
• Longevity & Aging
• mRNA Technology
And every week (on Mondays), it sends a newsletter in each topic with the most relevant studies from the past week.
The idea is to make it easier to keep up with new research in plain English.
Open to any thoughts or ideas!

value: Show HN: I built a simpler way to follow research papers–AI summaries and emails

author: samfeldman


value: Show HN: Between Us – Swipeable question cards for couples and friends


value: shaivpidadi

value:
Built a fully client-side remote photoplethysmography pipeline. Everything runs in the browser — MediaPipe FaceMesh for landmarks, ROI sampling from forehead/cheek regions, POS pulse extraction, FFT to get BPM. No backend, no inference server.
Also built a step-by-step learn view that replays a saved session so you can inspect each stage of the pipeline.
Demo: https://rppg-azure.vercel.app
Repo: https://github.com/Shaivpidadi/rPPG
Not a medical device — educational demo of how the method works.

value: Show HN: Browser-based rPPG – webcam to heart rate with no server, just signals

author: shaivpidadi


value:
With longer agentic workflows becoming the norm, token cost can eat through usage so quickly that it prevents any real work from getting done.
After studying the business model of top labs like Anthropic and OpenAI, their business model shows about 80% margins on inference cost which they use for R+D on the next model.
Working with open source models is much cheaper and allow for 5-10x higher usage, so I decided to create Sweet! CLI as an alternative to the products offered by big labs.
Sweet! CLI uses our own custom post trained version of Deepseek v3.2 hosted on US based inference servers (very similar to Cursor's Composer model).
Unlike most llm based agent products, we bill solely based on usage and have a seamless top up experience so you only pay for what you use.
My favorite feature is 'autopilot', where you can specify the duration of time you want the agent to work on a specific task, including indefinitely. This is good for monitoring live deployed applications and detecting outages that need triaged immediately, and I have multiple Sweet! agents deployed to the production server right now with that exact objective.
I'd appreciate any support or feedback on how I can make it better!
Thanks,
Adam - Founder of Sweet! CLI

value: Show HN: I made a cheaper alternative to Claude Code or Codex CLI


value: gissurthor

value:
While building this, I found that ChatGPT, Claude, Gemini, and Perplexity regularly describe SaaS products with specific but incorrect details — wrong pricing tiers, deprecated features, misattributed integrations — with no uncertainty expressed.
The tool runs a consistent set of buyer-style prompts across all four and shows verbatim output. Not trying to fix anything, just make it visible. One free scan.
https://www.bersyn.com/

value: Show HN: AI assistants confidently get SaaS product facts wrong

author: gissurthor


value: mmgehlot

value: BitPolar: 3-bit vector quantization in Rust, no training required

value: Show HN: BitPolar

author: mmgehlot

title: Show HN: BitPolar


value: cjami

value: Show HN: Clocktower Radio - An LLM benchmark that rewards deception

author: cjami


value: A unified, high-performance data validation and serialization framework for Rust, inspired by Pydantic's ergonomics and powered by Serde.

value: Show HN: Rusdantic

title: Show HN: Rusdantic


value: Show HN: Buyout Game Benchmark: Multi-Agent Bargaining, Transfers, and Takeovers


value: not-chatgpt

value: Show HN: 30u30.fyi – Is your startup founder on Forbes' most fraudulent list?

author: not-chatgpt


value: jshchnz

value: I built a CLI tool to generate as much slop as possible

value: Show HN: Codemaxxing – Maximize your slop abilities

author: jshchnz


value: Salamalto

value: Show HN: HackerOne Silent-Patched My Critical BOLA and Banned Me

author: Salamalto


value: nl

value: I've been working on using in-browser LLM models for agentic data analysis tasks and was frustrated trying to work out what models were worth trying so I built a benchmark. It grew a bit, but has fairly comprehensive coverage and visualizations of models from Opus down to Qwen 0.8B

value: Show HN: An Interactive Text to SQL Agent Benchmark

author: nl


value: virpo

value:
We have pegboards and plywood all over our apartment, and I had an idea to make a tiny pegboard for my kid, Oli. So I naturally cut the wood, drilled in the holes, sat down at the computer to open Fusion 360 and spend an hour or two drawing the pieces by hand.
Then I looked at the rough sketch Oli and I had made together, took a photo of it, pasted it into Codex, and gave it just two dimensions: the holes are 40mm apart and the pegs are 8mm wide.
To my surprise, 5 minutes later my 3D printer was heating up and printing the first set.
I ran it a few times to tune the dimensions for ideal fit, but I am posting the final result as a repository in case anyone else wants to print one, tweak it, or have fun with it too. I am already printing another one to hang on our front door instead of a wreath, so people visiting us have something fun and intriguing to play with while they knock.
This is also going onto my list of weird uses of AI from the last few months.

value: Show HN: I turned a sketch into a 3D-print pegboard for my kid with an AI agent

author: virpo