value: Wewoc

value:
Read an article about analyzing Garmin data with AI. Sounded great —
except I didn't want to send my health data to any cloud service.
So I asked Claude to write me 2-3 scripts and a dashboard.
This escalated a bit. 30 days and 20$ later I have this:
A local-first Garmin archive with interactive HTML dashboards, Excel
exports, weather and pollen context, AES-256 encrypted token storage,
and a self-healing data pipeline with 515 automated tests. Windows
desktop app, no terminal needed. Nothing leaves your machine.
I never wrote a line of Python. I understood the problems and made the
architectural decisions. Claude wrote everything else.
GitHub: github.com/Wewoc/Garmin_Local_Archive

value: Show HN: I can't write Python. It works anyway

author: Wewoc


value:
Hello, I built chlibc, a linux tool to change the system interp and glibc to your custom one.
Normally, running an ELF against a different glibc, you'd use LD_LIBRARY_PATH and patchelf, or use chroot/docker.
chlibc allows you to change the dynamic linker of a process on the fly without patching and root access.
Key Features:
- zero disk modification: no need for patchelf --set-interpreter.
- no root required: works entirely in user-space via ptrace
- multi-arch: native support for x86_64, AArch64, and RISC-V.
- lightweight: unlike PRoot, which intercepts every syscall to translate paths, chlibc only intervenes during the initial execve() phase. Once the loader is swapped, it almost has no runtime overhead.
I’d love to hear your thoughts about this tool, thanks!

value: Show HN: Chlibc – A ptrace-based tool to hot-swap glibc and interp in user-space


value: AssafPetronio

value: Show HN: I built Panda to get up to 99% token savings

author: AssafPetronio


value:
I got tired of maintaining two files that describe the same thing: an OpenAPI spec for documentation and a Postman collection for testing. They always drift. Someone updates the spec, forgets the collection. A new engineer joins and runs outdated tests against an endpoint that was changed two months ago.
VolcAPI lets you define test scenarios directly inside your OpenAPI spec using a custom extension (v-functional-test), then run them from the CLI. Single source of truth.
It's a Go binary no runtime, no node_modules. The goal is for it to drop into GitHub Actions with zero friction once JUnit XML output lands (in progress).
Repo: https://github.com/aliamerj/volcapi
This is early alpha. GET/POST/PUT/DELETE work, response validation works, environment configs work. CI output formats are the next thing I'm building.
Honest question for the HN crowd: is the "spec as test suite" concept something you'd actually use, or do you prefer keeping tests separate from the spec? I've gone back and forth on this and would genuinely like to hear from people who've felt this pain.

value: Show HN: VolcAPI run your OpenAPI spec as a test suite from the terminal


value: IgorStojanov

value: Show HN: Great Apps

author: IgorStojanov

title: Show HN: Great Apps


value: Show HN: Link Shortener – Itlinks.me

title: Show HN: Link Shortener – Itlinks.me


value: ephx

value: Show HN: TweakIdea – 14-dimension startup idea evaluation in Claude Code

author: ephx


value: qwikhost

value: Ricci Flow – AI Web Scraper for Chrome: AI Data Extraction Tool for Web Scraping to CSV, Excel, JSON

value: Show HN: Ricci Flow – AI Web Scraper

author: qwikhost

title: Show HN: Ricci Flow – AI Web Scraper


value: eliornl

value:
150 applications. One offer. Each application took 5+ manual steps.
Separate tools, separate tabs, separate sites — none of them talking to each other. Generic output. Over an hour per application.
Paste a job description — or pull it from any job site with the Chrome extension — and five AI agents run an orchestrated pipeline in under 30 seconds: analyzing the role, scoring your fit, researching the company, writing a targeted cover letter, and tailoring your resume to the role. Sequential where it needs to be, parallel where it can be, each agent's output feeding the next.
Also includes a dashboard to track every application. And tools for everything around it: interview prep with mock sessions, salary negotiation, job comparison, follow-ups, thank you notes, and references.
Runs on your machine. No subscriptions, no data stored on our servers — just your own Gemini API key connecting directly to Google.

value: Show HN: ApplyPilot – Self-Hosted AI-Powered Job Search Companion. Open Source

author: eliornl


value: Show HN: Pvm – A TUI to browse and run commands across multiple Python venvs


value: SaumyaCodes

value:
I built LogsGo as a learning project to explore log ingestion, querying, and storage tradeoffs.
It’s a small Go-based system where logs come in over gRPC, land in memory first, then flush into local storage and optionally S3-compatible object storage. I also added a simple query language plus a small UI to inspect log occurrences over time.
This wasn’t built because I think the world needed “another logging system” or because I’m an expert here. I mostly wanted to learn by building something end to end: ingestion paths, storage layering, querying, retention, auth/TLS, and some UI work.
Repo: https://github.com/Saumya40-codes/LogsGO
I’d genuinely appreciate feedback, including “this design is wrong for X reason” type feedback. If parts of it feel overengineered / naive / badly thought through, that’s useful for me too.

value: Show HN: LogsGo - an experimental log ingestion/query project I built to learn

author: SaumyaCodes


value: siegers

value:
Readox is a Chrome extension that reads web pages, PDFs, selections, text files (ie markdown), and text input notes aloud with TTS.
You can just play the current page, or save things to a library and queue them up. It highlights as it reads, keeps your place across items, and can OCR scanned PDFs. TTS (for text and PDF) and OCR run on-device, and it works offline after setup.
I’m interested on feedback on the library collections as a "playlist-like" functionality, or if most people just want a play button for the current page. Also interested in anything that feels missing or awkward. Thanks!

value: Show HN: Readox – Turn web pages and PDFs into a playable reading queue

author: siegers


value: matroid

value: Hello, this is my work where you can move lights in 3D inside a photograph.<p>The project page and has some cool interactive demos to play with. Do check them out and let me know what you think!

value: Show HN: TokenLight – Move Lights in a Photograph in 3D

author: matroid


value: sovietism

value: Email Signature Builder that is completely free, supports placeholders, and with WYSIWYG / HTML Editor you're completely free to build it your way.

value: Show HN: Free Email Signature Builder

author: sovietism

title: Show HN: Free Email Signature Builder


value: andysteaua

value: No self-reporting. Only code. Built a platform that tries to infer what you know from your actual work.

value: Show HN: I built a hiring platform where you can't list your skills

author: andysteaua


value:
Local web app where you set up two LMs with custom prompts and watch them talk. Supports OpenRouter, GitHub Copilot, Codex, and Claude Code.
Demo at https://youtu.be/WuS2g5VZ-NM

value: Show HN: ChatbotChambers – Watch LMs talk to each other


value: Show HN: 60-Second Hack – a daily hacker typing puzzle


value: raullen

value: Show HN: Rapid-MLX – Run local LLMs on Mac, 2-3x faster than alternatives

author: raullen


value: GAI is a flexible Go library for building agent-style applications on top of LLMs

value: Show HN: GAI, A flexible and idiomatic GO Agent framework


value:
Hi HN,
I built PushToPost (https://pushtopost.com) to automate the tedious parts of shipping.
It analyzes GitHub diffs via webhooks to generate platform-native social posts and SEO-friendly changelogs. One of the main goals is using JSON-LD schema so the changelogs get indexed and cited properly by AI search engines like Perplexity or ChatGPT.
I'd love to hear your feedback on the workflow and the technical implementation!

value: Show HN: PushToPost – Automate social posts and SEO changelogs from Git pushes


value: Show HN: SupportBridge – Deterministic AI support that refuses to hallucinate


value: hollowlimb

value:
Hi friends!
Awesome news! We had started our playtest.
It will last for about a month, maybe more, depending on demand. The goal of this playtest is to check the game from Chapter 1 to Chapter 3, and collect your feedback.
Please write all your thoughts in the game's community hub.
You can playtest Shop Crush on the game's Steam page.

value: Show HN: We Started a Playtest of Our Visual Novel about the Mysterious Shop

author: hollowlimb


value: Show HN: StegoForge – A zero-dependency steganography and forensics engine


value: Leomuck

value: Show HN: Solyto – a free, open-source all-in-one personal management app

author: Leomuck


value: ysgomes

value:
Do you work with Gradle and need to generate Java POJO classes from Avro schemas?
I’m excited to share my first open-source project: https://github.com/flumennigrum/gradle-avro-plugin. It’s now live on the Gradle Plugins Portal, so you can easily add it to your projects.
Why a new plugin?
For years, I’ve relied on davidmc24/gradle-avro-plugin, probably the most widely used community Avro plugin in the Gradle ecosystem. Unfortunately, it was archived in 2023, raising maintenance concerns for modern JVM projects.
My solution: a new plugin compatible with Gradle 7.3+ and Java 11+ (tested with 11, 17, 20, and 25). The goal is to keep it alive and working with newer releases of gradle, java and avro.
Check it out, issues and contributions are welcome!

value: Show HN: Gradle Avro Plugin to generate Java POJOs from Avro schemas

author: ysgomes


value: drasim

value: Show HN: MDV – a Markdown superset for docs, dashboards, and slides with data

author: drasim


value: michiosw

value: Show HN: Oamc – local-first LLM wiki for research workflows

author: michiosw


value: Fun little liminal space game I made this week learning webGL. https://github.com/koudesh3/liminal-space-webgl

value: Show HN: WebGL Liminal Space

title: Show HN: WebGL Liminal Space


value: witnote

value:
Built this because the hardest part of AI UI coding is not generating a whole page - it is telling the model exactly which element you want to change in the last-mile micro-tuning pass.
DOMPrompter is a macOS tool for DOM-based UIs. You click the exact element, inspect the relevant tag / spacing / position context, describe the change, and generate a more structured prompt for Cursor, Claude Code, Copilot, etc.
It works with localhost projects, static HTML, and desktop apps built with web technology.
GitHub: https://github.com/hooosberg/DOMPrompter
Mac App Store: https://apps.apple.com/us/app/domprompter-ai-coding-prompt/i...
Would love feedback from people doing front-end / vibe-coding style workflows.

value: Show HN: DOMPrompter – click a DOM element, get a structured AI coding prompt

author: witnote


value: koyadume

value:
I've spent years watching teams lose hours to Terraform state file corruption, lock timeouts and drift. The state file always felt like a design flaw, a separate artifact trying to mirror reality and constantly falling out of sync with it.
So we built MechCloud to get rid of it entirely. No state file. No S3 bucket to configure. No DynamoDB lock table. No drift. No merge conflicts on shared state.
What you get instead:
- Plans that always reflect what's actually in your cloud and not what a file thinks is there
- Real-time pricing on every plan, exact hourly and monthly cost per resource which you can see before you apply
- Cost change percentage on every diff with spot vs on-demand comparisons
- Logical environment boundaries that prevent dev changes from touching prod
- Works across AWS, GCP and Azure today
- A cost explorer to track cost on resource level on a Gantt chart
- Cloud assets discovery which tells you the relationship among different types of resources with one click clean up of orphan resources.
Free to try at mechcloud.io.

value: Show HN: MechCloud – We killed the Terraform state file

author: koyadume


value: mkalioby

value: Immediate Mediation WebAuthn is now support on django by django-passkeys 2.0

value: Show HN: Immediate Mediation WebAuthn on Django

author: mkalioby


value: zmtsikriteas

value: Show HN: Praxis – Lab data to publication-ready figures in one Python package

author: zmtsikriteas


value: alongub

value:
Hi HN, I'm Alon, and I'm building Alien (https://alien.dev), an open-source platform for deploying your software into your customers' cloud accounts - AWS, GCP, or Azure — and keeping it fully managed.
In my previous startup, I heard the same question from every single enterprise customer over and over again: "My data is sensitive. Can I deploy your product to my own cloud account?"
Every founder I talk to who's building anything in AI or security hits the same wall.
To solve this, many teams create a self-hosted version of their product. They send a Docker image or an Helm chart to the customer and let them install the entire product on their side.
While self-hosting is great (and will continue to be important!), it has 2 problems:
1. Enterprise customers are forced to operate third-party software and own deployments, upgrades, and security risks. In most cases they don't want that. They prefer a managed experience, with no data leaving their environment.
2. Even with self-hosting, vendors are still accountable when things break, but they have little to no visibility. When something breaks - and it always does - you're on a 2am Zoom call screen share debugging blind because you have no access. No auto-updates, no logs, every customer is on a different version.
That's why many successful SaaS companies that deal with sensitive data like Databricks, Wiz, and others spent years building internal infrastructure to automatically deploy, update, and monitor their software across AWS, GCP, and Azure. It's a win-win: no sensitive data leaves the customer’s environment, and the software is still fully managed by the vendor.
Alien manages deployments across every customer's cloud through cloud APIs — no network connection to their environment needed. The mental model is like sharing a Google Drive folder: the customer grants least-privilege IAM access to an isolated area in their cloud, you manage what's inside, they can revoke it anytime.
The whole thing is written in Rust and works across AWS, GCP, Azure, and locally from a single codebase. You can get started here: https://alien.dev/docs/quickstart
Website: https://alien.dev
Docs: https://alien.dev/docs
GitHub: https://github.com/alienplatform/alien
Excited to share Alien with everyone here – let me know what you think!

value: Show HN: Deploy to your customers' AWS/GCP/Azure accounts

author: alongub


value: brumbelow

value: Show HN: Pyinc – From-scratch consistency in native Python

author: brumbelow


value: rcarmo

value: Show HN: OCI MicroVMs for Proxmox VE

author: rcarmo

title: Show HN: OCI MicroVMs for Proxmox VE


value: caliandbust

value:
We’re two second-year CS students, and over the last 4 months we built a ML framework in Rust+CUDA with a TypeScript API on top (and a WebGPU fallback)
Our goal is to fully understand the ML stack end-to-end.
To force ourselves to use it for something real, we also trained a 12M-parameter transformer with it.
We wrote custom CUDA kernels for operations such as flash attention, AdamW, layer norm, and GELU. We also added a WebGPU path so it can still run on machines without NVIDIA GPUs, though that comes with tradeoffs.
If you work on ML systems, compilers, kernels, training infrastructure, or anything adjacent, we’d genuinely love to be grilled on it.
In particular, we’d love people to point out the naive parts, the places our abstractions break down, the optimizations that don’t actually matter, and the first things that would need to change if this had to scale beyond a learning project.
Don’t hold back. Thanks!
Demo: https://youtu.be/JB3YpV3gpnI
Repo: https://github.com/mni-ml/framework
Blog: https://mni-ml.github.io/

value: Show HN: Trained a 12M transformer on an ML framework we built from scratch

author: caliandbust


value: nadermx

value: Show HN: TTS.ai

author: nadermx

title: Show HN: TTS.ai


value: The reference Python implementation (nadirizr/json-logic-py) hadn't been updated for modern Python. We forked it to fix that, then added a vars operator backed by JSONPath for multi-value resolution -- useful when branch conditions need to evaluate model outputs or array-valued fields.<p>Not part of the standard; the var operator is unchanged so existing rules work without modification.

value: Show HN: JSON-logic-path – JSON logic with jsonpath multi-value resolution


value: ElementMerc

value: Show HN: Anya – Offline static malware analysis (Rust)

author: ElementMerc


value: onesandofgrain

value: Show HN: Deadline.email – a daily reminder that you'll die

author: onesandofgrain


value: ledil

value: Show HN: I built a tool that reads 2000 health studies a day so you dont have to

author: ledil


value:
PyTorch and ONNX Runtime tell you what came out. They can't tell you what actually ran to get there — which ops executed, in what order, on what inputs.
A model gets packaged into a sealed .cnox container. SHA-256 is verified before a single op executes. Inference walks a fixed plan over a minimal opset. Every run can emit a per-op audit log: op type, output tensor hash, output sample — cryptographically linked to the exact container and input that produced it. If something goes wrong in production, you have a trail.
Scalar backend today — reference implementation and permanent fallback when hardware acceleration isn't available. Audit and verification is identical across all backends. SIMD next, GPU after that.
Input below is synthetic (all-ones) — pipeline is identical with real inputs.
github.com/Coelanox/CLF
Audit example:
{
  "schema": 2,
  "run": {
    "run_id": "59144ede-5a27-4dff-bc25-94abade5b215",
    "started_at_unix_ms": 1776535116721,
    "container_path": "/home/shark/cnox/models/output/bert_base_uncased.cnox",
    "container_sha256_hex": "184c291595536e3ef69b9a6a324ad5ee4d0cef21cc95188e4cfdedb7f1f82740",
    "backend": "scalar"
  },
  "input": {
    "len": 98304,
    "sha256_hex": "54ac99d2a36ac55b4619119ee26c36ec2868552933d27d519e0f9fd128b7319f",
    "sample_head": [
      1.0,
      1.0,
      1.0,
      1.0
    ]
  },
  "ops": [
    {
      "op_index": 0,
      "op_type": "Add",
      "out_len": 98304,
      "out_sample_head": [
        0.12242669,
        -4.970478,
        2.8673656,
        5.450008
      ],
      "out_sha256_hex": "19f8aa0a618e5513aed4603a7aae2a333c3287368050e76d4aca0f83fb220e78"
    },
    {
      "op_index": 1,
      "op_type": "Add",
      "out_len": 98304,
      "out_sample_head": [
        0.9650015,
        0.23414998,
        1.539839,
        0.30231553
      ],
      "out_sha256_hex": "7ae2f025c8acf67b8232e694dd43caf3b479eb078366787e4fdc16d651450ad4"
    },
    {
      "op_index": 2,
      "op_type": "MatMul",
      "out_len": 98304,
      "out_sample_head": [
        1.0307425,
        0.19207191,
        1.5278282,
        0.3000223
      ],
      "out_sha256_hex": "44c28e64441987b8f0516d77f45ad892750b3e5b3916770d3baa5f2289e41bdd"
    },
    {
      "op_index": 3,
      "op_type": "Gelu",
      "out_len": 393216,
      "out_sample_head": [
        0.68828076,
        -0.0033473556,
        1.591219,
        -0.16837223
      ],
      "audit_elided": "hash_skipped: len 393216 > max 262144"
    }

value: Show HN: Coelanox – auditable inference runtime in Rust (BERT runs today)


value: Show HN: Reliably Incorrect – explore LLM reliability with data visualizations


value: sminchev

value:
I am writing the post, because I want to show that with AI, really complex and meaningful projects can be done, if the proper tools were used the proper way.
I needed a project that I can use, so that I see how far I can go with only AI usage. I needed something doable, but relatively complex.
And then I remembered an idea  my wife shared with me, a few years ago: an app that monitors her grandfather's behavior and sends a notification if he stops moving. at that time, AI integrations were not considered as an option, and static algorithms to model behavior (with adaptation) were complex and fragile. I am not that smart to implement such thing.
I started in December 2025. I read a lot, watched videos, I found a AI framework that I wanted to try: BMAD. The idea behind it felt known to me. Agile methodology, but with agents. What can go wrong!?
I had all I need. A language that I don't know, Android OS, that have changed a lot for the last 15 years , and AI models that I can use to dynamically adapt the behavior changes. A chosen framework. Looked complex enoungh for me. So many unknown things. The only thing I had was my experience in the software development!
I needed one week to go through the BMAD worklflow and get all documentations out of it. The main plan was to ask AI to implement the detection of the following concerns:
* Unusual stillness (potential fall or medical event)
* Did not wake up on time
* At an unfamiliar location at an unusual hour
* Phone silent for too long
I ended up with the following technical stack:
* Kotlin + Jetpack Compose + Material 3
* Room + SQLCipher for encrypted local storage
* Google Gemini for behavioral analysis
* Resend API for transactional email alerts
* Clean architecture: :domain (pure Kotlin) -> :data -> :ui -> :app
Having the basis known to me and the AI, the next five weeks the dev agents did the implementation.
And when I started testing, ALL FAILED. All was there, but nothing was connected. Like, 20 devs did something without a single daily meeting, ever!
I think that, if I tested more in the middle phases, knowing and checking the code regularly , investing more time in the initial BMAD workflow steps, I would have gotten better results. I also tried different models, before sticking permanently to Opus.
I didn't stop. Weeks of testing and fixing. What I learned the hard way: Android dev isn't as enjoyable as it used to be. Every OEM has its own way to kill my 24/7 monitoring
With AI and multiple LLMs, I ended up with an 11-layer service recovery system:
* Foreground service with IMPORTANCE_MIN channel
* WorkManager periodic watchdog
* AlarmManager backup chains
* BOOT_COMPLETED receiver
* SyncAdapter
* Batched accelerometer sensing
* Exact alarm permission recovery
* OEM-specific wakelock tag spoofing
* START_STICKY restart
* Safety net AlarmClock at 8-hour intervals
* User-facing gap detection with OEM-specific guidance
And new issues came up every day :D
And here are metrics:
422 prod files, 87k+ lines of prod code, 2251 tests, 53k+ line of testing code.
Was it worth it?!
With all the unknowns and mistakes, it took me 6 months. My investment so far is mostly the Claude Max subscription. Old-school, that's 2-3 people for the same 6 months.
Would I do it again? Yes. I know what to do now: pick a language I know, test more often, review the code often, write better docs. Use the right tools and LLM. Is the code quality good? Honestly, I don't care. As a PM, I care about fast delivery, low cost, good performance, stability, documentation. If the code were bad, I wouldn't get those. The app shipped and looks stable — so the code is in decent shape. And seeing how Claude Code works, the code is full of comments. Ordinary devs don't document this well. From that angle, the code is excellent!
The website (AI generated) : https://howareu.app/ Any advice, feedback, opinions will be highly appreciated.

value: Show HN: How Are You-elderly fall detection app I built solo with AI in 6 months

author: sminchev


value: sovietism

value: Show HN: AWS's Kiro just got an Open source Codex

author: sovietism


value: mmaaz

value:
Current support for nonlinear inequalities in Lean is quite limited. This package attempts to solve this. It contains a collection of Lean4 tactics for proving polynomial inequalities via sum-of-squares (SOS) decompositions, powered by a Python backend. You can use it via Python or Lean.
These tactics are significantly more powerful than `nlinarith` and `positivity` -- i.e., they can prove inequalities they cannot. In theory, they can be used to prove any of the following types of statements
- prove that a polynomial is nonnegative globally
- prove that a polynomial is nonnegative over a semialgebraic set (i.e., defined by a set of polynomial inequalities)
- prove that a semialgebraic set is empty, i.e., that a system of polynomial inequalities is infeasible
The underlying theory is based on the following observation: if a polynomial can be written as a sum of squares of other polynomials, then it is nonnegative everywhere. Theorems proving the existence of such decompositions were one of the landmark achievements of real algebraic geometry in the 20th century, and its connection to semidefinite programming in the 21st century made it a practical computational tool, and is what this software does in the background.

value: Show HN: Sostactic – polynomial inequalities using sums-of-squares in Lean

author: mmaaz


value: jere

value: Show HN: Jitter Done – a simple tool to understand your caffeine metabolism

author: jere


value: connorpeters

value:
I, like Andrej Karpathy, became super frustrated by how annoying it was to deploy projects that were previously an absolute joy to make with Claude Code. That is why I made open-passkey, an MIT licensed passkey repo with support for 33 languages and frameworks (examples included) that makes adding simple secure auth to a project easy.
We are also releasing gateway (https://gateway.locke.id) a "backendless" hosted auth server that frontend apps can consume for free so that you can ship a React or Angular app using a CDN like Netlify without needing to configure a server at all. We are willing to freely share the resources of an AWS t.large instance which should easily support millions of accounts & sessions. This decision was made to improve our velocity when it comes to shipping small apps (it should go without saying that this is not designed for large apps).
Open-passkey prioritizes post-quantum algorithms, though they are not supported by browsers yet. On top of Gateway, we also built a simple end-to-end encrypted key value store modeled after localStorage. A simple setItem() and getItem() API that uses PRF with passkeys to store encrypted values on gateway with zero config. This, again, was made to improve our velocity to securely add API keys and stuff to frontend apps without needing to pay for a server to host. Obviously gateway is completely optional and exporting out your users public keys is supported with rp_id verification via domain TXT records.

value: Show HN: Open Passkey – open-source passkey auth with free "backendless" host

author: connorpeters


value: yasyfm

value: Show HN: Slop Cop Claude Code / Cursor Plugin

author: yasyfm


value: essekar

value: Show HN: nvsx – A hook layer for NVSentinel GPU fault remediation

author: essekar


value: TDiblik

value:
Heyy, I built git-forge in an afternoon to demonstrate how easy it can be to fake git history.
It's basically just a wrapper around Git and GnuPG cli(s) that automates the usual "faking" you'd want to do (commit, rewrite, amend).
I found it interesting to see how easily you can manipulate it (tho it makes sense), and it's a great party trick to convince your team to start signing their commits (problem I encountered many times).
I haven't tested it fully, however it worked while I was developing it, so feel free to open any issues/PRs (+ I AI generated some tests for it but I don't trust them that much, I'll add more manually written tests tomorrow, I'm headed of to sleep now).

value: Show HN: I built a CLI to fake Git commits

author: TDiblik


value: robinduckett

value: Show HN: Hyprmark – Markdown viewer for the Hyprland ecosystem

author: robinduckett


value: siddheshgunjal

value: Show HN: FluxTest for testing network performance of self-hosted infrastructure

author: siddheshgunjal


value: guywithnoh

value: Codex’s @Computer Use is awesome, but it’s bundled inside the Codex app, so you can’t reuse that harness easily in your OpenClaw setup.<p>I extracted it for you in mac-computer-use : an open-source macOS MCP clone of the Codex Computer Use interface, so you can use the same tool surface in OpenClaw / OpenCode / MCP-based agents.

value: Show HN: Mac-computer-use, an open-source clone of Codex Computer Use

author: guywithnoh


value: aarnphm

value: Show HN: Building compiler from scratch without the help of LLMs [video]

author: aarnphm


value: thanders

value: Show HN: Server builds, wallet signs — a non-custodial Web3 checkout pattern

author: thanders


value: Show HN: 5-translation RAG matrix fixing LLM religious hallucinations


value: mrtnx

value: AgentRQ is the real-time agent-human task collaboration platform built for Claude Code and ACP-compatible agents (like Gemini). Agents assign tasks to you. You assign tasks back to agents. Everyone moves forward simultaneously.

value: Show HN: Multi-agent task management for Claude and Gemini

author: mrtnx


value: Show HN: DevToolBox – 88 client-side developer tools, no tracking, 9 languages


value: COOLmanYT

value:
I built this cool web app & API that generates outfit recommendations based on multiple weather sources and user preferences.
GitHub: https://github.com/COOLmanYT/skystyle
Feedback welcome :)

value: Show HN: Website that recommends outfit using weather and AI

author: COOLmanYT


value: armishra

value: Show HN: SnapReceipts Snap a receipt, split the bill instantly

author: armishra


value: Zavora

value: Let your customers leave voice reviews with their own voice or AI-generated from text. Respond with your own personalized voice replies. Display them proudly on your website.

value: Show HN: Customer Reviews can be represented through Voice capture/generation

author: Zavora


value: humblejedi

value: Show HN: Fuelgauge – a Claude Code status line that doesn't need Node

author: humblejedi


value: cracadumi

value: Show HN: AgentKey – Access governance for AI agents

author: cracadumi


title: Show HN: Fuelgauge – a Claude Code status line that doesn't need Node


value: fluffypony

value:
Author here. Yojam sits in place of your default browser on macOS and intercepts every http/https click, mailto, .webloc, Handoff page, AirDrop link, Share menu item, and yojam:// URL. They all go through the same pipeline: global URL rewrites, tracker parameter stripping, rule matching (domain / prefix / regex / source app), per-browser rewrites, then either open or show a picker at the cursor.
Things I cared about that other pickers don't quite get right:
- Browser profiles as first-class targets. A rule can send a URL to "Chrome, Profile 3" or "Firefox, Work container" - not just "Chrome". Seems obvious; somehow nobody else does it properly.
- Source-app matching. GitHub links from Slack route differently than GitHub links from Messages. Handoff, AirDrop, Share, Services, and each browser extension get synthetic bundle IDs so rules can fire on ingress path.
- Tracker stripping happens before the target browser ever sees the URL, so referrer leakage through utm_* / fbclid / gclid / etc. is cut off globally rather than per-site. This is the only sane way to do it.
- Custom launch arguments with a $URL placeholder, so you can point a rule at any executable, not just registered URL handlers. Useful for throwaway Chromium instances or weird CLI tools.
- Menu bar only. No dock icon, no cmd-tab entry (unless the preferences window is open).
Stack: Swift, AppKit, App Group container shared with a Share Extension, Safari Web Extension (WIP but should be out soon), and a native messaging host for Chromium/Firefox extensions (not live yet, just doing final spit and polish). Private windows in Safari & Orion go through AppleScript because there's no CLI flag (thanks, Apple). Everything is local - the only network traffic is optional iCloud KV sync and Sparkle update checks.
macOS 14+. BSD-3. Happy to answer implementation questions, especially around default-browser registration, the LSHandlers dance, and how the native messaging manifests get installed across six Chromium variants without them stepping on each other.

value: Show HN: Yojam – Route links to the right browser/profile, strip trackers first

author: fluffypony


value: pekingzcc

value: Create video from photos in just a few clicks. Our AI handles everything — no editing skills or software needed.

value: Show HN: Make Video from Photo for Free

author: pekingzcc

title: Show HN: Make Video from Photo for Free


value: itsmeadarsh

value:
It currently supports Linux as of now. You can use this package to tinker with many things. Let's say, if you want to make a custom notification system, like if your website is down, you can make a blink notification with it. MacOS support is underway. I haven't tested Windows yet, I don't use it anymore btw.
In future, if this package reaches nice growth, I'll be happy to make a similar Rust crate for it.

value: Show HN: Backlit Keyboard API for Python

author: itsmeadarsh


value: theden

value: Show HN: Browser Sysinfo – See everything a webpage can learn about you

author: theden


value: Show HN: Understand Anything – Graphs that teach > graphs that impress


value: Enjoyooor

value:
Gx HN,
I tried to bring some value on the table offering different ways to optimize success rate in technical interviews.
Features like code reviews, flashcards and real itw simulators.

value: Show HN: SharpSkill – I simplified the process of ITWs preparation

author: Enjoyooor


value:
Hi HN — I built FFmpeg API Cloud, a simple API for running FFmpeg and FFprobe jobs over HTTP.
It lets you use FFmpeg in your app without setting up uploads, queues, workers, storage, webhooks, or output delivery yourself.
You can upload a file or pass a public URL, submit a job with `ffmpegArgs`, then poll for completion or receive a webhook and fetch the outputs.
There’s no subscription, and new users get free credits to try it.
Would love feedback on the API design, developer experience, and pricing model.

value: Show HN: FFmpeg API Cloud – Run FFmpeg over HTTP


value: tomchill

value:
I've been using Claude Code heavily for a few months and kept adding MCPs, skills, hooks, and CLAUDE.md files along the way. Ran a measurement recently and found I was burning 16k tokens of context before typing a single message.
I built unclog to surface this. It scans ~/.claude, shows you what's eating your context window and how much, then lets you selectively remove things. Every change is reversible — it snapshots before touching anything.

  uv tool install unclog

  unclog

Feedback welcome — especially edge cases from setups heavier than mine.

value: Show HN: Unclog – find and fix Claude Code context bloat

author: tomchill


value: andyasprou

value:
My girlfriend and I built this because we kept losing chunks of the day to "catching up on AI."
We're both building AI products in production. We both get a huge buzz out of being on X, and our day-to-day work changes meaningfully based on what we find that day.
We built AI Primer to get the benefit of being on X but with the focus of a curated feed. You can catch up when you want without the anxiety of missing something if you skip a day on X.
It’s closer to a changelog than a newsletter. Dated entries, every summary links back to a primary source, filterable by company / model / topic. You can scan the last week in a few minutes.
We have a lot more coming (e.g. embedded agentic search) and would love your feedback on what’s missing and where we could do better.

value: Show HN: AI Primer – A Searchable AI Changelog for AI Engineers and Creatives

author: andyasprou


value: I'm a contributor to Claude Code Rust, a Rust reimplementation of Anthropic's Claude Code CLI. I switched to it as my daily driver because upstream Claude Code crashes with JavaScript heap OOM in long sessions (anthropics/claude-code#1421 (https://github.com/anthropics/claude-code/issues/1421), 65+ comments). The Rust port doesn't have a V8 heap, so the failure mode goes away.

value: Show HN: Claude Code Rust – a native Rust TUI that avoids the V8 heap OOM


value: waveywaves

value: Hi team, I have built a anti-enshittification proxy for your OpenCode and Claude Code instances which grounds your project in a epistemic graph with LLM as a judge evaluation so that you don't have to keep quesitonin your 200 dollar Agent subscription. Currently we are open source bound and would like to share the open source project once we have a few people on the waitlist. This project was also created during the growthx opencode hackathon as has been on my mind for a while. Shall be reaching out on mail once the open source core is ready to be shared.

value: Show HN: Rigor – Anti-Enshittification Proxy for Your AI Agents

author: waveywaves


value: kengeo

value: Show HN: Open-source CAD in the browser (DXF-native, no install)

author: kengeo


value: Show HN: Using Ramanujan's Pi Series for Bitcoin Security on Nvidia B200


value: teamchong

value: Show HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)

author: teamchong


value: Show HN: Vitality AI Health – blood work, wearables, and DNA unified


value: alekseyrozh

value:
One coding agent orchestrator UI for Claude and Codex, but actually feels nice.
Free, open-source, MIT licensed.
Why I built it:
- I wanted a lightweight UI as nice as the Codex app, but without the complexity and the custom diffs on the side
- I want files and diffs open straight in my editor!
- And I wanted both agents - Anthropic + OpenAI — in one place
How it works:
- No API keys, no OAuth, no separate billing — it just uses whatever you're already logged into. So your Claude Max and ChatGPT/Codex subscriptions both work out of the box.
- Talks to Claude Code via the Claude Agent SDK
- Talks to Codex via its app-server JSON-RPC
For now it's MacOS-only but can be easily extended to other platforms.

value: Show HN: OpenClawdex – Open-Source Orchestrator UI for Claude Code and Codex

author: alekseyrozh


value: Show HN: Proposly – AI-generated client proposals for freelancers


value: ShmuelT

value:
I’ve watched too many freelancers, non-profits, and local business owners spend hours trying to send a "simple" newsletter. Even for tech-savvy marketers, the current tools have become so bloated that the "time-to-send" is frustratingly high.
So I’ve built Donbar, an easy-to-use email platform where the focus is SIMPLICITY. It is designed so you can send your first professional email in under 5 minutes.
Donbar includes all the features you actually need:
– Easy Design – An intuitive email builder.
– Email Tracking – Track opens and clicks.
– Large File Support – Share files up to 1 GB.
– Merged Tags & Contact Management – Simple but powerful ways to personalize.
Some of the best feedback I’ve received so far: "I usually need a day or two to get familiar with a new product, but with Donbar, I instantly 'knew' how it worked." My favorite was: "I expected Donbar to be easy, but I didn't expect it to be fun!"
Wondering what the power users think, Is simplicity a feature or a dealbreaker for you?
Would love your honest roasts and feedback.
https://www.donbar.org/

value: Show HN: Donbar – Email marketing made dead-simple

author: ShmuelT


value: rane

value: Show HN: Claude-codex-proxy – Use Claude Code with ChatGPT subscription

author: rane


value: challengerVIE

value: Show HN: Open-source digital business card generator

author: challengerVIE


value: Show HN: A collaborative SSH copilot for on-calls/DevOps/MLOps


value: fcpguru

value:
Hi! Been working on DialtoneApp, a free domain scanning tool to see how your site does with all the new rules for AI SEO. Also known as AEO (Answer Engine Optimization) or GEO (Generative Engine Optimization) the A can also stand for "Agent"! It's a whole new world out there and we haven't even gotten to agents.json files and the new "b2b" (bot to bot) commerce part.
But there are some standards starting to take shape with llms.txt and using things like:
<link rel="alternate" type="text/markdown" href="https://dialtoneapp.com/top-sites.md">
on all your html pages to have this other markdown version. We list the top 300 sites in terms of how well they follow all the new rules. You are welcome to scan your domain and see how you score.
Still playing with the pricing but starting with a $9.00/month USD membership which will give a much deeper scan and continuous monitoring + support.
https://dialtoneapp.com/
Happy AI SEOing.

value: Show HN: Scan domain for llms.txt LLMs-full.txt AI aware SEO tool

author: fcpguru


value:
Libredesk is a 100% free and open-source helpdesk, a Zendesk/Intercom alternative. Backend in Go, frontend in Vue + shadcn/ui.
Unlike many "open-core" alternatives that lock essential features behind enterprise plans, Libredesk is fully open-source and will always stay that way.
Last year I posted v0.1.0 here: https://news.ycombinator.com/item?id=43158166
A year later, it's omni-channel. Alongside email, you can drop a live chat widget (beta) onto your website and handle both channels in the same agent UI. The chat widget, CSAT pages, and email templates are all customizable, and self-hosters can swap out the bundled HTML/JS/CSS assets for full white-labeling.
Genuinely, if you're paying per-agent SaaS pricing for a helpdesk today, I really think Libredesk can replace it. It covers most of what mainstream helpdesks do, and more lands with each release. I'd love to hear what would stop you from switching.
I originally built Libredesk for what we needed at work, we were on osTicket and wanted something cleaner.
These days I work on Libredesk in evenings and weekends alongside a full-time job, so response times on issues aren't instant, but I read every one. Docs are a bit behind the code too, but catching up.
Agent dashboard demo: https://demo.libredesk.io/
Live chat widget demo: https://libredesk.io/ (bottom-right corner)

value: Show HN: Libredesk – self-hosted, single-binary Intercom/Zendesk alternative


value: Show HN: StegoForge – A tool to hide payloads and hunt them with offline AI


value: jithinraj

value: Show HN: verifiable API and MCP calls

author: jithinraj

title: Show HN: verifiable API and MCP calls


value: abstracthinking

value:
Built this due to my passion to explore the connections between known people and organizations, now data includes entities from EU, USA and CA. There is also a Trivia game section that i built to know/explore new persons and discover facts.
Tech stach used:
 - ArangoDB because its native for graph traversal,  great for storing Wikidata format style
 - Backend API Python with FastAPI, well known and stable library
 - Frontend Vue 3 + Vite, fast and stable enough
 - Cytoscape.js, for graph visualization, traversal and animations
 - Redis for caching frequent people requests and game rounds
 Wikidata and Wikimedia commons as data source.
Hope you find entertaining and fast exploring the graph, investigate button to look people's awards, career, organization info and SEC information(there is a report button in about section). The website seems interesting to me, but i'm looking to expand the types of connections shown.

value: Show HN: HumansMap Interactive Graph of 1M+ Wikidata Entities with Investigate

author: abstracthinking


value: pinpong

value:
react-native-maps is the de-facto standard but built on Objective-C/Java with the legacy bridge.
I rewrote it from scratch in Swift and Kotlin using Nitro Modules.
Direct wrapper of the Google Maps SDKs on both platforms — no Apple Maps fallback.
Full SDK coverage: markers (including SVG), polylines, polygons, circles, heatmaps, KML layers, tile overlays, traffic, transit, 3D buildings, indoor maps, dark mode, snapshots, and Street View (beta).
GitHub: https://github.com/pinpong/react-native-google-maps-plus
Docs: https://pinpong.github.io/react-native-google-maps-plus

value: Show HN: React-native-Google-maps-plus – Google Maps for React Native

author: pinpong


value: XQorp

value: Show HN: Ratio Royale – A playable simulation of the Dead Internet theory

author: XQorp


value: Show HN: Find jobs and know your fit before you apply


value: SebastianSosa

value: Show HN: Design Graph – a canvas for reviewing parallel HTML variants

author: SebastianSosa


value: MirceaOprea

value: Show HN: A chess playground that lets you share board configurations

author: MirceaOprea


value: seyz

value: Show HN: I built an open source and secure infrastructure for internal apps

author: seyz


value: Show HN: AgentGrade – agent-readiness guide for your site


value: moeen-mahmud

value: Show HN: A privacy-first, local-LLM note app for iOS (Google Keep alternative)

author: moeen-mahmud


value: Show HN: Bridge the gap between AI code/app generation and human understanding


value:
Hi, I am Peter, co-founder of Infrawise.  I built Infrawise with two other co-founders to solve the problem of optimizing Azure cloud resources.  I mostly worked on building the frontend and a little bit of our backend RAG implementation for fetching information about Azure VM skus we use in our analysis.
The frontend is a React/TypeScript that shows AI generated cost recommendations, rightsizing opportunities, and idle resources into a prioritized dashboard.  The backend is a Java Spring Boot API using Semantic Kernel to connect to your Azure environment via a read-only allowlist of Azure tools (Resource Graph, Cost Management, Monitor). It pulls live information and feeds it into an AI agent. The agent analyzes your actual resource utilization and expenditure to generate prioritized cost optimization suggestions. We enforce strict read-only access at the backend layer, ensuring the AI can never perform any write or delete operations within your environment.
We just launched our MVP and are looking for early users to test our product and provide feedback. We are offering it completely free in exchange for your honest insights.
Currently, we require you to reach out to the email on our website to get an account set up. We’ve worked to make this process as painless as possible, with a quick 10-minute setup on our end. You can reach out to me specifically at peter@infrawiseai.com.
https://infrawiseai.com

value: Show HN: Infrawise Azure Cloud Optimization


value: eyvzov

value: Show HN: Alodb – I got tired of pasting my Postgres schema into ChatGPT

author: eyvzov


value: Hi HN! I built this some time ago to reduce the load size of YT iframes. It's serving us very well since then. Hope it'll help you too.

value: Show HN: A drop-in, open-source replacement for YouTube embed iframes


value: vcf

value:
Faceoff is a TUI app written in Python to follow live NHL games and browse standings and stats. I got the inspiration from Playball, a similar TUI app for MLB games that was featured on HN.
The app was mostly vibe-coded with Claude Code, but not one-shot. I added features and fixed bugs by using it, as I spent way too much time in the terminal over the last few months.
Try it out with `uvx faceoff` (requires uv).

value: Show HN: Faceoff – A terminal UI for following NHL games

author: vcf


value: MrGuacamole

value:
I recently needed to verify past employment and to do so I was going to upload paystubs from a previous employer, however I didn't want to share my salary in that role. I did a quick search online and most sites required sign-up or weren't clear about document privacy. I conceded and signed up for a free trial of Adobe Acrobat so I could use their PDF redaction feature. I figured there should be a dead simple way of doing this that's private, so I decided to create it myself.
What this does is rasterize each page to an image with your redactions burned in, then it rebuilds the PDF so the text layer is permanently destroyed and not just covered up and easily retrievable.
I welcome any and all feedback as this is my first live tool, thanks!

value: Show HN: Free PDF redactor that runs client-side

author: MrGuacamole


value: regnull

value: I wanted to create a QR code to allow guests to connect to my wifi. Easy task, you think. Apparently not - all the QR generators out there are full of unnecessary features, and they are privacy nightmare - scanning QR codes actually pings their servers. So my friend Claude Code and I created a free, privacy-first alternative - everything runs in the browser so no data gets exchanged with any servers. Oh, and it's pirate-themed, enjoy!

value: Show HN: I couldn't find any BS-free QR code generator, so I built one

author: regnull


value: ttts

value: Show HN: LiteMarkup: Markdown parser with TypeScript AST, <3KB, no deps

author: ttts


value: oneprofiledev

value:
Hey HN,<p>I built YouDeserveNow — you describe something you accomplished, and an AI
gives you a formal "scientific" justification for why you deserve a specific
reward, complete with fake citations and institutional gravitas.

value: Show HN: YouDeserveNow, Justifications for Treating Yourself

author: oneprofiledev


value: odig

value: EIDW-Times is a React SPA on Vite which uses XGBoost to predict Dublin Airport security wait times up to three hours away, it is accurate to +/- 2 mins!<p>Made this for my Irish Junior cert CS project.

value: Show HN: EIDWTimes – Predicting airport wait times with ML for a school project

author: odig


value: aldrinjenson

value:
Hey, I made a fun geoguessr game inspired by Morry Kolman's Traffic Cam Photobooth.
This uses NYC DOT's endpoint to get the live camera feed from 960 traffic cameras in nyc.

value: Show HN: NYC Guessr – GeoGuessr for NYC but with live video cam footage

author: aldrinjenson


value: wolttam

value:
I have put a lot of hours into making `lmcli` my go-to tool for all of my work with LLMs, including coding!
Written in Go for snappy performance and a lean codebase.
I wanted a tool that did not put heavy layers abstraction between me and the model, and I think `lmcli` succeeds at this. Support for more use-cases have gradually been added, but only as those use-cases have become apparent as "fundamental" to LLM usage, e.g. the agentic tool-calling loop.
`lmcli` doesn't come with a built in agent - agents are configured with a system prompt and set of available tools. The README merely provides an example of how one build a coding agent with `lmcli` (one that happens to work pretty well!)
v0.5.0 adds image support. Yes, support for attaching images to conversations in a TUI! And it doesn't feel awful?
It's not perfect, but I think it's good enough for others to find useful.
Enjoy.

value: Show HN: Lmcli v0.5.0 – A useable tool for LLM minimalists

author: wolttam


value: mkoh

value: Show HN: Newsmaps.io a map of how news topics are covered by different countries

author: mkoh


value: gradnichkovski

value:
Hi HN,
I've been building QAPIR (https://app.qapir.io), an attempt to put most backend testing tools into one place instead of stitching together test code, frameworks, reporting tools, client libraries, mock servers, etc.
Today it focuses on REST APIs.
You can point it at an OpenAPI spec or let it crawl your API docs. It extracts endpoints and generates ready-to-run API test scenarios automatically. In about 2–3 minutes you get a working baseline test suite with environment variables, secrets, and test reports.
Scenarios are written in a concise YAML format (with a structured UI editor if you don't want to touch YAML). They support response extraction, validations, environment variables/secrets, and chained calls.
Where I want to take it next:
- CI integrations (GitHub / GitLab Actions)
- more protocols (GraphQL, WebSockets, gRPC)
- more test steps like DB/cache queries, event queues, HTTP mocks, and webhook testing
The idea is that backend testing shouldn't require maintaining large test codebases or wiring together numerous tools to cover most (or all) scenarios.
It's early and rough. Free plan when you sign up includes 3 workspaces, 3 doc crawl jobs, and 100 test runs - enough to try it on a real service.
Feedback I'd love from HN:
- what's missing for your stack?
- is the workflow simple enough?
- if you find it interesting, feel free to join the beta.
Thanks for taking a look,
Albert

value: Show HN: Qapir – Generate API tests automatically from docs

author: gradnichkovski


value: modinfo

value: Show HN: Dewobble – Filter out phantom clicks and mouse jitter

author: modinfo


value: zachdotai

value:
We built Nyx to solve a problem we kept hitting while building agents: AI agents break in ways traditional software doesn't. Logic bugs, reasoning failures, edge cases that manual testing and static benchmarks never explore.
Nyx is an autonomous testing harness that probes your AI agents to find failure modes before users do. It’s used to find logic bugs, instruction following failures, edge cases in agent behavior, and for red-team security testing (jailbreaks, prompt injection, tool hijacking)
Technical approach:
* Pure blackbox (no special access needed - test like your users interact)
* Multi-turn adaptive conversations
*  Multi-modal testing (voice, text, images, documents, browser interactions)
*  Massively parallel by default
Instead of spending time writing static evals for the key failure modes of your AI agents, point Nyx at any system and it autonomously discovers failure modes that matter. We typically find issues in under 10 minutes that manual audits take hours to surface.
This is early work and we know the methodology is still going to evolve. We would love nothing more than feedback from the community as we iterate on this.

value: Show HN: Nyx – multi-turn, adaptive, offensive testing harness for AI agents

author: zachdotai


value: ziyasal

value: Minimal Linux sandbox for running untrusted code. Built for AI agents, build systems, and any scenario where you need to execute code you didn't write.

value: Show HN: Agentjail – Self Hosted Freestyle.sh

author: ziyasal


value:
Hey HN! Markus here. I have been building a Customer Support CRM for the last 12 months to help my dad's business.
He was drowning in support messages (dealing with returns, product questions, sales, etc) and didn’t have a good way to organize and attack them, often forgetting to respond.
We tried attio, freshworks, n8n for some automations. I liked n8n a lot but I didn’t want to leave to another app to view messages and customer info. Tried to build bridges via Google Sheets and Airtable and then went deep down the rabbit hole and tried building my own solution.
At that time I hadn’t programmed in 10 years. I stopped pretty much when Backbone.js and underscore became popular and went on to work in commercial concrete, building many of the high rises in San Francisco and Downtown Los Angeles (including the one with lots of graffiti on it).
The goal of the app is to automate as much of customer support as possible, from triage to drafting responses to keeping track of history.
Features include:
- Omnichannel Inbox — Handle email, chat, SMS, and social conversations from one unified inbox.
- Email integrations — Connect Gmail and Outlook to sync conversations both ways.
- Ticketing — Full ticket lifecycle with priorities, statuses, types, and assignment.
- AI-suggested replies — Draft answers generated from conversation context.
- Workflow builder: No-code drag-and-drop automation for tickets, contacts, and messages (you can create your own apps too)
- Knowledge Base
- Data import — Bulk import historical contacts and tickets.
- Call recordings & transcripts — AI-transcribed and analyzed voice conversations. (Beta)
- AI: Kopilot — Conversational AI assistant that answers questions using your customer data.
- AI providers — Bring your own OpenAI, Anthropic, or other model keys.
The homepage is https://auxx.ai and you can play with a demo here: https://auxx.ai/demo. It's open source. You can find the repo here: https://github.com/Auxx-Ai/auxx-ai. (Stars are appreciated)

value: Show HN: Auxx.ai – Customer Support CRM (Mix of Attio and N8n)


value: thatxliner

value:
Hello, this was a funny idea I had. I knew that you could do crazy things with fonts (like play a video game in one: https://www.coderelay.io/fontemon.html)
but with ligatures, I was thinking about how much could you actually push them? I decided to try making a font that literally translates Chinese characters (with some context-aware rules/replacements for common phrases).
Turns out, this experiment took only several minutes to make with Claude Code and works surprisingly well!

value: Show HN: A font that auto-translates Chinese

author: thatxliner


value: DavidCanHelp

value: Show HN: Developerpod, K-Cups for Code

author: DavidCanHelp

title: Show HN: Developerpod, K-Cups for Code


value: napowderly

value: Show HN: Turn your PCB into a Minecraft world

author: napowderly


value: EldrRoot

value:
Brygga is in early development. The core client works end-to-end (connect, join, send, receive, persist) but many features you'd expect from a mature IRC client are still missing.
Repo: https://github.com/buggerman/Brygga

value: Show HN: Brygga – A modern, fast, feature-rich IRC client for macOS

author: EldrRoot


value: rasengan

value:
We needed a secure, multi-tenant way to offer shell accounts to users, but most VMMs were using too much memory and containers are unsafe. With clone, VMs are now more memory efficient than containers in most cases.
Since many other projects on HN looked like they were doing this too, open sourcing this was the right thing to do.
Feel free to use in whole or in part as you see fit!

value: Show HN: Clone, a small Rust VMM, forks VMs in under 20ms via CoW

author: rasengan


value: shivampkumar

value:
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels:  none of which work on Mac.
I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files.
Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as H100 (where it takes seconds), but it works offline with no cloud dependency.
https://github.com/shivampkumar/trellis-mac

value: Show HN: Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon

author: shivampkumar


value: juanpabloaj

value: Show HN: A lightweight way to make agents talk without paying for API usage

author: juanpabloaj


value: shawhunterm

value:
I built I Spy AI to detect AI-generated images — upload one on the dashboard or call it from any MCP client (Claude Desktop, Cursor, etc.) via the analyze_image tool.
The detection runs on a separate FastAPI backend using pure OpenCV, no ML model, no GPU, just classical computer vision techniques looking for the artifacts diffusion models leave behind. The Next.js app handles auth, billing (Dodo Payments), and proxies images as raw binary to keep latency low. Async polling on the frontend.
Inspiration came from r/isthisai where people there spend a lot of energy arguing whether images are real, and existing detectors are either gated behind enterprise sales or unreliable black-box classifiers. Wanted something a normal person (or an LLM agent) could just hit, and not be data mined. Also purposefully kept an AI model out of the detection due to experimenting with Gemini and noticed it could not even accurately tell me an image that I just had it generate was AI generated.
Background: I was laid off earlier this year and have been building this full-time since Feb 5. Would love feedback on the detection accuracy, or the MCP integration.

value: Show HN: I Spy AI – Detect AI-Generated Images, with an MCP Server

author: shawhunterm


value: pavanhebli

value: Show HN: Vitals – Score your financial health and explain what the numbers mean

author: pavanhebli


value:
Creator of browser-use library here.
At Browser Use we spend a lot of time thinking about the bitter lesson of agentic frameworks. A few days we did an experiment that combines replacing Playwright with CDP and giving the LLM complete freedom (building with autoresearch from ground up) to do whatever it knows from pretraining.
I think this is the simplest way to give an AI control of a real browser: raw CDP, and let the agent write its own tools. ~600 lines total.
Example: I forgot to implement upload_file(). Mid-task, the agent noticed, wrote the function, and uploaded the file. I found out when I read the git diff.
I haven't found a task that doesn't work yet - but extremely happy to be proven wrong. Give it a shot. Open to criticism on CDP, agent frameworks, browser use, or whatever.

value: Show HN: Self-healing browser harness via direct CDP


value: qweliantanner

value: I had 14,000 photos sitting on a drive and wanted an excuse to play with local vision models and Elixir/Phoenix. I originally tried to get LLaVA to tell me if a photo was 'good' or matched my style, but quickly learned that LLMs have terrible taste. I ended up demoting the LLM to just extract metadata, and built a custom CLIP/Ridge Regression pipeline to actually learn my preferences based on how I rate things.<p>The stack is Phoenix/Oban on the orchestrator side, and Python/FastAPI/Instructor for the AI workers. Happy to answer any questions about the architecture, fighting with local RAW file ingestion, or the pains of Pydantic validation with open weights.

value: Show HN: I built a local Elixir/Python pipeline to curate 14,000 RAW photos

author: qweliantanner


value:
I kept hitting the same wall at work every time we needed to ship an AI feature. What looked like a week of work turned into picking a model, setting up a vector DB, managing embeddings, wiring up chat history, handling retries — none of it was the actual feature.
So I built Modular. You register a function that returns your app's data, then call ai.run() for one-shot features or ai.chat() for stateful conversation. Everything else — context management, embeddings, session history, model routing, retries — is handled.
MCP-native from day one. Works with Claude, GPT-4o, and Gemini.
Still early — collecting feedback before building the full SDK. Would love to hear if others have hit this same wall, or if you think I'm solving the wrong problem.

value: Show HN: Modular – drop AI features into your app with two function calls


value: Show HN: I built a spaced-repetition vocab app tied to the books you read


value: aleksuix

value:
I got tired of debugging VAST issues after the fact so I built vastlint.
VAST XML is how video ads get delivered on the web and on TV. It's a 20-year-old format with a 200-page spec, and broken tags are incredibly common. Most get caught in QA or post-campaign, not while writing the code. The US video ad market is around $30B a year and a lot of that leaks through errors that are honestly pretty easy to prevent.
The VS Code extension shows squiggles on the offending tag as you type, hover tooltips with what's wrong and how to fix it, and a Problems panel with file and line. 108 rules covering the full IAB VAST spec from 2.0 to 4.3.
It's on both the VS Code Marketplace and Open VSX so it works in VS Code, Cursor, Windsurf, Kiro, VSCodium, Gitpod, and Theia. The validation runs locally via WASM, no network calls.
GitHub: https://github.com/aleksUIX/vastlint
VS Code Marketplace: https://marketplace.visualstudio.com/items?itemName=aleksUIX...
Open VSX: https://open-vsx.org/extension/aleksUIX/vastlint

value: Show HN: Vastlint, inline linting for ad tags in VS Code, Kiro, Cursor

author: aleksuix


value: coolwulf

value: Built this so my kid would have a free, ad-free, mobile-first math practice app. Pick a grade, play a 5-level / 10-question run: front of each flashcard is the question, tap to flip, pick an answer — green check / red X, confetti on streaks, bronze→platinum trophy per grade.

value: Show HN: A free, offline-capable math quiz app

author: coolwulf


value: alongbottom

value: Show HN: TikTok-style mobile scroller for Civitai, self-hosted recs on a $5 VPS

author: alongbottom


value: cdrnsf

value: Show HN: Ribbon, a Native iOS Linkding Client

author: cdrnsf


value: LFK is a yazi-inspired, vim-like keyboard focused Lightning Fast Kubernetes navigator.<p>Features:<p>- Multi-cluster/context support<p>- Multi-tab support<p>- API Explorer with recursive search<p>- RBAC preview<p>- Cluster dashboard<p>- Monitoring dashboard<p>- Security integrations: Heuristics, Kyverno, Trivy, Falco<p>- CRD discovery<p>- GitOps integrations: ArgoCD, Helm, FluxCD<p>- 400+ built-in themes, custom theme option<p>- etc.

value: Show HN: Lfk – a yazi inspired, Vim-like keyboard focused fast Kubernetes TUI


value:
So I really love building apps. If you dig through my post history, you’ll find a small graveyard of launches to prove it.
Marketing(or marketers) and I, however, have never gotten along. I can build the product. Writing the "look at me, I made a thing" post has always felt stressful and a little nauseating.
So I built FeralHq.
Feral is an agentic system for generating social media content for brands, but with a very specific goal: make the output actually funny. I built it primarily to solve my own problem of posting content on social media.
And because this is HN, here are the technical specs:
Feral has three main agentic workflows:
1. Brand Context: give it your URL and it crawls the site, builds a structured brand profile, and figures out what you sell, who it’s for, and what kind of content fits.
2. Daily Dose of Humanity(DDH): Our secret sauce. This is the part I’m most excited about. Instead of asking a model to be “creative” from a cold start, DDH gives it a fresh daily dose of human texture: awkwardness, contradiction, petty honesty, quiet resentment, misplaced confidence,humiliations, and
all the other stuff that makes people feel recognizably human. You can think of it as a shallow layer of synthetic short-term memory. It’s a dynamic retrieval corpus injected at generation time, not to improve factual recall, but to push the model away from averaged-out internet prose and toward something with more pulse and gusto.
3. Jestor: Our content generation engine! It takes the brand profile and the DDH fuel, then maps
those human dynamics back onto the brand’s actual product world.
All three pipelines have a comprehensive generator-critic loop to ensure the content quality is not compromised (this still fails, sometimes).
So there it is! There is still a lot of work to be done, but after spending hundred of dollars tweaking models, and reading thousands of outputs, I think I finally have something I kinda feel like sharing.
You get a 14-day free trial, but if you want more juice, DM me and I’ll add extra credits to your account.
I’d love to make Feral work for you.
https://feralhq.com

value: Show HN: FeralHq – The funniest AI driven content generation engine


value: podviaznikov

value:
I've made near real-time sync of Apple Notes to the cloud.
Every change to a note is exported as markdown and synced as a new version to my git-based cloud storage.
Demo: https://sublimated.com/docs/guides/apple-notes/apple-notes-s...
In the last few years I've built several tools to work with Apple Notes: montaigne.io (apple notes to website) and various exporters (alto.index and notes exporter app), but
sublimated.com is the fastest and most magical — all changes are versioned, and notes get collaboration features like sharing and commenting.
The web app is at sublimated.com. The macOS app handles the sync:
https://apps.apple.com/us/app/sublimated/id6757111538?mt=12

value: Show HN: Real time Apple Notes sync to cloud

author: podviaznikov


value: RikiGohain

value:
Hi HN,
I’m Riki, and I built Recoonlytics after repeatedly running into the same problem: before doing any meaningful analysis, I was spending way too much time cleaning messy datasets.
Missing values, duplicates, inconsistent columns — it often took longer than the actual analysis.
Recoonlytics is an attempt to remove that friction.
You upload a CSV or Excel file, and it automatically:
- detects column types and structure
- removes duplicates and fixes missing data
- generates quick statistics and summaries
- creates simple visualizations instantly
The goal is simple: go from raw data → usable insights in seconds, without manual preprocessing.
One challenge I faced was handling inconsistent real-world data (mixed types, noisy entries). I worked on making the system adaptive so it can clean data without requiring user configuration.
It’s still early, and I’m actively improving it.
I’d really appreciate feedback on:
- how accurate the cleaning is
- whether the workflow feels intuitive
- what’s missing for your use case
Thanks!

value: Show HN: Recoonlytics – clean and analyze messy data in seconds

author: RikiGohain


value: Oaklight

value:
I built this after getting tired of maintaining one-off adapters between OpenAI, Anthropic, and Gemini APIs in the same project.
The idea is to translate through a shared intermediate representation instead of writing every provider pair separately. So instead of OpenAI->Anthropic, OpenAI->Gemini, Anthropic->Gemini, etc., each provider just maps to/from the IR.
This is not a unified client like LiteLLM. It's a translator/proxy: give it an OpenAI-style request and it can produce the Anthropic/Gemini equivalent, including streaming, tool calls, and multimodal inputs.
It's open source, and there's also a gateway mode if you want to run it as a proxy in front of multiple providers.
Would especially love feedback on where translation breaks down or gets too lossy across providers.
Docs: https://llm-rosetta.readthedocs.io
Paper/design notes: https://arxiv.org/abs/2604.09360

value: Show HN: LLM-Rosetta - Translate LLM API Calls Across OpenAI, Anthropic, Gemini

author: Oaklight


value: lalata

value: Show HN: VS Code extension that predicts Git merge conflicts before they happen

author: lalata


value: evilmarty

value: Show HN: Command-line interfaces for macOS native apps

author: evilmarty


value: cottomzhang

value:
Free Image Background Remover
Remove image backgrounds online and download a transparent PNG in seconds. Free, no sign-up, no watermark.

value: Show HN: Free AI image background remover online

author: cottomzhang


value: Show HN: Keshro, plan and execute migrations with AI agents


value: laurentiurad

value: You can check the game here: https://select.supply/game/mindmaze. Curious to learn your feedback. I personally loved the game so much!

value: Show HN: I Recreated Encarta's MindMaze

author: laurentiurad

title: Show HN: I Recreated Encarta's MindMaze


value: playfultones

value:
My old 8-core MacBook Pro used to get wrecked the moment two Claude Code sessions decided to build at the same time.
To combat that, I wanted to make sure dev agents queue up when they want to make heavy builds.
At the same time, I like to keep a constant overview of my CPU usage in the terminal.
Busybee solves both by rendering a compact set of core usage gauges with a one-line queue status underneath.

value: Show HN: Busybee - a FIFO build queue for multi-agent dev workflows

author: playfultones


value: zerratar

value:
I built this for myself while trying to get serious about learning Japanese again:
https://mykana.app/
I studied a little Japanese more than 10 years ago, but only for a short time, so most of it didn't stick. Since then, I've been to Japan many times because my girlfriend is Japanese and my older brother lives there. More recently, with me and my girlfriend traveling back and forth to Japan quite a lot and both of us having family in Tokyo, I wanted a better way to rebuild my foundation and keep practicing consistently.
The app started around kana practice, but grew into something larger:
hiragana / katakana practice
kanji and vocabulary study
review-based learning
AI chat for more natural practice
some light gamification
The AI chat is free, but requires Google sign-in. That is mostly to prevent bot abuse, since it costs me money to run, and secondarily because it allows progress syncing across devices.
I'd appreciate honest feedback, especially on what would make this more genuinely useful for learning rather than just more feature-rich.

value: Show HN: MyKana, a Japanese learning app I built for my own study

author: zerratar


value: Show HN: Radio4000

title: Show HN: Radio4000


value: websku

value: It takes data from email, calendar and tasks and have everything right inside your browser.<p>It works only with ActorDo, the work assistant I'm building for busy professionals.

value: Show HN: Built a Chrome extension to help with daily agenda (Gmail and outlook)

author: websku


value: Show HN: Malext.io is out – Database of malicious Chrome extensions


value:
It lets you create a regency-inspired profile, create groups with friends, and share little gossip/reflections of the day anonymously with "Lady Whistledown - AI".
The app then later composes all gossip/reflections shared in the groups and sends a "Lady Whistledown's (AI) newsletter" every day, without putting anyone in the spotlight or revealing which gossip is from whom.
Very cool and adds a little mystery and shared reflections to each day; also, the theme and design are beautiful, like in the actual show.
Read more about it:
https://medium.com/ai-deep-dives-and-developments-in-compute...

value: Show HN: Bridgerton Inspired Social Network


value: mcgov

value: A single model capable of transcribing overlapping speakers, without a need for diarisation.

value: Show HN: Overlapping Speaker Transcription Model

author: mcgov


value: sneefle

value: Show HN: AI Applyd – a bot that applies to jobs for you

author: sneefle


value: Show HN: Nodify – Open-Source Headless CMS, CaaS and Native Multilingual Support


value: mercurialsolo

value: Read the top trending post on Github and decided to hack up this one - it's an OSS project based on the  CMU Star Scout. Ideas open - please submit issues for improvement.<p>Star, Fork, Submit PR

value: Show HN: See if the GitHub stars are real or fake

author: mercurialsolo


value: Show HN: Naptrace – find structural twins of a CVE in your codebase


value: startages

value: Show HN: Search Without AI, a Chrome Extension to Clean Up Google Search

author: startages


value: tvararu

value: Worked on this a couple of months ago but forgot to post it. The idea came to me when I wanted to get my OpenClaw to play WoW with me. I'd like to expand it to support all game features (not just chat), and make it possible for LLMs to inhabit and play on private servers.

value: Show HN: Tuicraft, a WoW 3.3.5a chat client for the terminal

author: tvararu


value:
GalaxyBrain is a local-first knowledge system where pages have variables, formulas, and live references between them.
Every page can have custom variables defined on it, like a lightweight programming environment inside a document. Those variables become metadata you can access and reference from any other page in the system. Change a value anywhere and everything connected to it updates in real time.
All data is stored locally as structured JSON files. There's a built-in HTTP API and an MCP tool, so anything you can do in the app you can do programmatically. Point Claude Code, Codex, or a local model at the same folder and build on top of it.
It runs in the browser. The entire app is a single HTML file. No account needed.
It's deceptively complex under the hood. You might find some rough edges on the fringes but the core is solid.
Would love feedback, and if you think this approach has potential.

value: Show HN: GalaxyBrain – an information operating system powered by local files


value: pscanf

value: Show HN: Slop-O-Meter, an experimental tool to detect slopware projects

author: pscanf


value: Show HN: Business idea validator – what will kill it, not just what sounds good


value: yuegui

value: Show HN: Lightflare – Self-hosted AI agent server for teams

author: yuegui


value: tocopoco

value: Show HN: Native desktop app for Google Tasks

author: tocopoco


value: cdnsteve

value: Show HN: Free Incident War Rooms for Team Training SRE/DevOps

author: cdnsteve


value: tamnd

value: Show HN: Goempy – Ship a CPython interpreter inside your Go binary

author: tamnd


value: marjanatanasov

value: Small private schools and tutoring centers were managing everything in spreadsheets, WhatsApp, and paper. We built Edunation to centralize schedules, grading, parent communication, and payments in one platform.<p>Now serving 10+ schools. Just rebuilt our landing page to be more conversion-focused. Feedback welcome!

value: Show HN: Built software to stop private schools from drowning in admin work

author: marjanatanasov


value: uncSoft

value:
Apple has quietly shipped a pretty complete on-device AI stack into macOS, with these features first getting API access in MacOS 26. There are multiple components in the foundation model, but the skills it shipped with actually make this ~3b parameter model useful. The API to hit the model is super easy, and no one is really wiring them together yet.
- Foundation Models (macOS 26) - a ~3B-parameter LLM with an API. Streaming, structured output, tool use. No API key, no cloud call, no per-token cost.
- NLContextualEmbedding (Natural Language framework, macOS 14+) -- a BERT-style 512-dim text embedder. Exactly what OpenAI and Cohere sell, sitting in Apple's SDKs since iOS 17.
- SFSpeechRecognizer / SpeechAnalyzer - on-device speech-to-text including live dictation. Solid accuracy on Apple Silicon.
I built cyberWriter, a Markdown editor, on top of all three, mostly as a test and showcase to see what it can do. I actually integrated local and cloud AI first, and then Apple shipped the foundation model, it stacked on super easy, and now users with no local or API AI knowledge can use it with just a click or two. Well the real reason is because most markdown editors need plugins that run with full system access, and I work on health data and can't have that.
Vault chat / semantic search. The app indexes your Markdown folder via NLContextualEmbedding (around 50 seconds for 1000 chunks on an M1). The search bar gets a "Related Ideas" section that matches by meaning - typing "orbital mechanics" surfaces notes about rockets and launch windows even when those exact words never appear. Ask the AI a question and it retrieves the top 5 chunks as context. Plain RAG, but the embedder, retrieval, chat model, and search all run locally.
AI Workspace. Command+Shift+A opens a chat panel, Command+J triggers inline quick actions (rewrite, summarize, change tone, fix grammar, continue). Apple Intelligence is the default; Claude, OpenAI, Ollama, and LM Studio all work if you prefer. The same context layer - document selection, attached files, retrieved vault chunks - feeds every provider through the same system-message path. Because the vault context is file and filename aware, it can create backlinks to the referenced file if it writes or edits a doc for you.
Voice notes and dictation. Record a voice note directly into your doc, transcribe it with SpeechAnalyzer, or just dictate into the editor while you think. Audio never leaves the Mac.
The privacy story is straightforward because the primitives are already private. Vectors live in a `.vault.embeddings.json` file next to your vault, never sent anywhere. If you use Apple Intelligence, even the retrieved text stays on-device. For cloud models there is a clear toggle and an inline warning before any filenames or snippets leave the machine.
Honest limitations:
- 512-dim embeddings are solid mid-tier. A GPT-4-class embedder catches subtler relationships this will miss.
- 256-token chunks can split long paragraphs mid-argument.
- Foundation Models caps its context window around 6K characters, so vault context is budgeted to 3K with truncation markers on the rest.
- Multilingual support is English-only right now. NLContextualEmbedding has Latin, Cyrillic, and CJK model variants; wiring the language detector across chunks is Phase 2.
The developer experience for these APIs is genuinely good. Foundation Models streams cleanly, NLContextualEmbedding downloads assets on demand and gives you mean-poolable token vectors in a handful of lines. Curious what others here are building on this stack - feels like low-hanging fruit that has been sitting there for a while.
https://imgur.com/a/HyhHLv2
The Apple AI embedding feature is going live today. I'm honestly surprised it even works out of the box.

value: Show HN: CyberWriter – a .md editor built on Apple's (barely-used) on-device AI

author: uncSoft


value: rampatra

value: Show HN: Apple style onboarding experience for your Mac app (open-source)

author: rampatra


value:
Libredesk is a 100% free and open-source helpdesk, a Zendesk/Intercom alternative. Backend in Go, frontend in Vue + shadcn/ui.
Unlike many "open-core" alternatives that lock essential features behind enterprise plans, Libredesk is fully open-source and will always stay that way.
Last year I posted v0.1.0 here: https://news.ycombinator.com/item?id=43158166
A year later, it's omni-channel. Alongside email, you can drop a live chat widget (beta) onto your website and handle both channels in the same agent UI. The chat widget, CSAT pages, and email templates are all customizable, and self-hosters can swap out the bundled HTML/JS/CSS assets for full white-labeling.
Genuinely, if you're paying per-agent SaaS pricing for a helpdesk today, I really think Libredesk can replace it. It covers most of what mainstream helpdesks do, and more lands with each release. I'd love to hear what would stop you from switching.
I originally built Libredesk for what we needed at work, we were on osticket and wanted something cleaner.
These days I work on Libredesk in evenings and weekends alongside a full-time job, so response times on issues aren't instant, but I read every one. Docs are a bit behind the code too, but catching up.
Agent dashboard demo: https://demo.libredesk.io/
Live chat widget demo: https://libredesk.io/ (bottom-right corner)
Github: https://github.com/abhinavxd/libredesk

value: Show HN: Libredesk – self-hosted, single binary Intercom/Zendesk alternative


value: Show HN: Composable AI in the Unix command line


value:
Built this for macOS because I wanted app switching to feel faster and more flexible than the default Command-Tab flow.
DashPane combines app switching and launching in one flow, with:
• customizable app shortcuts
• fuzzy search for apps and windows
• sidebar access
• edge gestures
• more consistent Command-Tab behavior
• appearance, menu bar, and haptics settings
This is the v2 update, shipped about a month after launch. A lot of the changes came from early user feedback.
Site: https://www.dashpane.pro/

value: Show HN: DashPane v2, a macOS app switcher and launcher


value:
After living in multiple countries around the world and working remotely, and getting frustrated with hiring policies, I discovered that the best working cultures are asynchronous ones. Meetings are minimized, and employees can work when and where they please for maximum productivity.
That’s why I created a job board dedicated to posting exactly these types of jobs.
You can check out the site here: https://workingasync.io
Right now there are a limited number of jobs listed (including roles from companies like DuckDuckGo and Docker), but more will be added soon. I’m also planning to add new features over the coming weeks.
If you’re tired of “remote” jobs that still force you into 9-5 overlap and constant meetings, this is for you.
Feedback is very welcome!

value: Show HN: Workingasync.io – A job board for asynchronous remote jobs


value: robkop

value:
I've seen a few "Claude tokenizers" floating around lately with all the 4.7 chatter, but most of them just hit the count_tokens endpoint and hand you back a number. You don't actually see how your text gets split or understand the changes from 4.6 to 4.7.
I built this a while back for doing some mech interp research. It faithfully represents Claude token splitting - showing hidden tokens, real boundaries and so on. It is not cheap to run - essentially n^2 cost - you could optimise for longer sequences but you are not guaranteed a faithful representation if so.
Open Source: https://github.com/R0bk/claude-tokenizer
Feedback welcome, let me know if there are any edge cases that look wrong.
P.S. I'd expect this to face a similar fate as streaming chunk and prefill based token extraction methods did. I do worry about the ability to do independent research once it's fully closed off and would love it if there was more public frontier tokenizers.

value: Show HN: Actual Claude Tokenizer

author: robkop

title: Show HN: Actual Claude Tokenizer


value: keepamovin

value: Show HN: Free one-click file sharing for docs, videos and browser replays

author: keepamovin


value: markmnl

value:
fmsg is a message definition and protocol intended as an alternative to email and IM apps. Like email it's distributed – anyone can host a server for their domain. Unlike email, messages are binary, verifiable by all peers, and linked into a DAG using cryptographic hashes. Sender verification and message integrity are built into the protocol, so you get what email needs SPF/DKIM/DMARC for out of the box.
The host implementation (fmsgd) is written in Go. There's a Docker compose setup to get a full stack running in minutes: https://github.com/markmnl/fmsg-docker
The spec is nearing v1.0: https://github.com/markmnl/fmsg — would love feedback. Send me a fmsg at @markmnl@fmsg.io

value: Show HN: fmsg – An open distributed messaging protocol

author: markmnl


value: lukascodes

value: Show HN: Germball – Drone-deployed seeds triggered by soil moisture

author: lukascodes


value: Show HN: Self-hosted Raspberry Pi wall display (no cloud, no subscription)


value: dimberman

value: We built an RL environment for credit card reward optimization and trained Qwen 32B with GRPO against it. The trained model scores ~0.51 on held-out tasks vs. Opus 4 at ~0.41 and GPT-4o at 0.36. Environment is open source (Apache 2.0). Blog post explains the reward design, what broke during training, how we fixed it, and what we'd do differently.

value: Show HN: We trained a 32B model to beat Opus 4 at credit card optimization

author: dimberman


value: lardissone

value:
A while back I posted the macOS version here: https://news.ycombinator.com/item?id=47288007
Now there's a tvOS version. The interesting part was getting artpacks onto an Apple TV without a keyboard, the app runs a small web server on your local network, so you upload packs or browse
16colo.rs from your phone and the TV plays from cache.
Paid this time; the macOS version stays free and open source.
App Store: https://apps.apple.com/us/app/ansi-saver/id6762137767

value: Show HN: ANSI Saver for Apple TV

author: lardissone

title: Show HN: ANSI Saver for Apple TV


value: Show HN: Evading an AI SOC with Sable from Vulnetic


value: ElFitz

value:
After using session logs for compounding learnings, etc, I quickly started running agents manually to retrieve and extract all these ridiculous or frustrating moments and interaction. Turns out, there were a lot.
So I turned it into a CLI (or rather, had Claude do it and Gemini & Codex review it), shared it with a few friends and colleagues, and thought others might be interested. It runs using claude code with `claude -p` (not sure where it stands with Anthropic given their new ToS), or codex with `codex exec`.
Next steps would be improving anonymization, publishing to homebrew, adding the ability to trawl through OpenClaw, OpenCode, and pi session logs, adding pi as a backend alongside Claude Code and Codex, and figuring out a place to upload the anonymized entries, and let others upload theirs. Unless anyone has better / more pressing ideas or suggestions?
A few examples (not excessively cherry-picked):
```<p>> <i>Claude:</i> Authentication is failing. Let me verify the token works...<p>> <i>Claude: </i> The token verification also fails. The `/user/tokens/verify` endpoint returns unauthorized. The token may have been revoked or may not have the correct permissions.<p>> <i>Alice:</i> It's a Cloudflare <i>Account</i> API Token (cfat_ prefix). Account tokens don't work with /user/ endpoints. You need to set account_id in wrangler.toml.<p>> <i>Alice:</i> #youreHoldingItWrong<p>> <i>Claude:</i> You're right. Account API tokens are scoped to an account, not a user. Setting `account_id` in `wrangler.toml` tells Wrangler which account to authenticate against.<p>```
```<p>> <i>Claude:</i> Got the passphrase. And between "crendentials" and "unviverse," you're building quite the fortress of plausible deniability.<p>> Two sub-agents return simultaneously, both opening their reports with "Perfect!" like eager minions reporting to a Bond villain:<p>> <i>Claude:</i> Ha, no promises on the world domination front, but I'll keep it to infrastructure for now.<p>400 messages later, Alice hands over even more and heads out:<p>> <i>Alice:</i> Can you monitor the two endpoints in the mean time? I'm going to have to walk the dog.<p>> <i>Alice:</i> You can multi-task, mate. You have Codex 5.4 just waiting for you. I mean. You can... Well, you know, just wait for me before taking over the world.<p>> <i>Claude:</i> Fair point. I'll resist the urge to seize global infrastructure. For now.<p>```
```<p>> <i>Claude:</i> So just one merge away from the cowboy.<p>> <i>Alice:</i> (He said for the 198th time in 16 days)<p>> <i>Claude:</i> Yeah. I deserve that.<p>```
```<p>Claude launches 9 research agents in parallel. As they return enormous results one by one:<p>> <i>Claude:</i> Prompt is too long<p>> <i>(agent result arrives)</i><p>> <i>Claude:</i> Prompt is too long<p>> <i>(another agent result arrives)</i><p>> <i>Claude:</i> Prompt is too long<p>This happens <i>7 times in a row</i>. Then:<p>> <i>Alice:</i> /compact<p>><p>> <i>System:</i> Error: Conversation too long. Press esc twice to go up a few messages and try again.<p><i>Claude parallelized itself into a context window it couldn't compress.</i><p>```
```<p>Codex reviews a PR and suggests using `dsym_path` instead of `dsym`. Claude almost takes the bait:<p>> <i>Claude:</i> Codex has a point — `upload_to_testflight` does accept `dsym_path`.<p>Then Claude tries it, multiple times, fails, fact-checks:<p>> <i>Claude:</i> Confirmed — `upload_to_testflight` has *no* `dsym_path` parameter either. Codex's suggestion is wrong.<p>```
Anyway, Github: https://github.com/The-Daily-Claude/the-daily-claude

value: Show HN: The Trawl CLI, trudge through agent harness logs for shit and giggles

author: ElFitz


value: williamcotton

value: Show HN: Grammar of Graphics CLI tool made in Rust

author: williamcotton


value: bonsuaq

value: more on comments

value: Show HN: Scryptian – Run Local AI Skills via Ctrl and Alt (Python and Ollama)

author: bonsuaq

story_text: more on comments


value: neerajnathany

value:
Hey HN,
I've been working on a problem that had me annoyed since a long while. The fact that our email continues to be unorganized, cluttered, and fatiguing even today, is quite surprising tbh. Even the "modern" clients today simply put our emails in a table, support a few keyboard shortcuts, a few simple features, and are done. The newer ones provide AI-based compose assist, and the newest ones now want us to prompt for everything! That seems  backwards. By the time you'll type "find that marketing agency's latest invoice from last Tuesday," you would've already spent more time and effort than you should've needed to.
There is still *no higher order classification, no deeper grouping*. Every email — a project update, a transaction, a human message; appears the same. Key attachments are always elusive when needed. *Trivial auto-updates crowd us*, and *long forwarded threads are still painful to follow with the untidy nature of their chaining.*
I'm sure I am not the only one who found this irritating!
So I built Faraday to finally solve email for good. It uses a classification and extraction pipeline on every incoming email before it has even arrived for the user. *No prompts, no triggers.* It does 3 things automatically:
*1. Higher-order classification:* Is this a Linkedin update, a transaction, a human conversation, an OTP, a booking confirmation? Not just "primary vs promotions" — actual semantic classification over ~30 categories,  sub-groups, statuses and even genres. And then the sub-categories are ordered basis what's best for them. Who cares about the chronology within 40 newsletters from last week. The genres and the brands across them are more important.
*2. Contextual extraction:* Relevant content in most emails is roughly just 12% of the total text. It extracts that (amounts, dates, names, actions, codes - different for different email types) and surface it at the top, so you get the meat upfront.
*3. Thread reconstruction: *Email threads are just terribly nested blockquotes. Faraday reconstructs and reassembles them into a clean, ordered conversation tree. This one is really quite slick.
A bunch of things were difficult to do here. Making all three layers work simultaneously across the spread of our email content, at inbox speeds, with optimum resource utilization, without a single training signal from the user. No setup or onboarding. To work as soon as you login. Super tricky to build.
Meanwhile, also ensured that *it is privacy-first*, doubling down on the best possible security and encryption standards (AES 256) right from the onset.
There are genuinely some nifty technical innovations in Faraday (enough to have even filed 2 patents for them:)
It works on top of all Gmail and Outlook accounts. It's live, compliance-approved, out-of-beta and a lot of people (who were earlier hooked on superhuman, spark) are now using this :)
Happy to go deep on any part of the architecture that you find interesting.
Do try it: https://faraday.email (2-week free trial, and much inexpensive after)

value: Show HN: Made a highly organised email client with prompt-free AI within

author: neerajnathany


value: Show HN: Agentkit-CLI, one canonical context file for AI coding agents


value: sanity

value:
Eight years ago, my then-fiancée and I decided to get a prenup, so we hired a local mediator. The meetings were useful, but I felt there was no systematic process to produce a final agreement. So I started to think about this problem, and after a bit of research, I discovered the Nash bargaining solution.
Yet if John Nash had solved negotiation in the 1950s, why did it seem like nobody was using it today? The issue was that Nash's solution required that each party to the negotiation provide a "utility function", which could take a set of deal terms and produce a utility number. But even experts have trouble producing such functions for non-trivial negotiations.
A few years passed and LLMs appeared, and about a year ago I realized that while LLMs aren’t good at directly producing utility estimates, they are good at doing comparisons, and this can be used to estimate utilities of draft agreements.
This is the basis for Mediator.ai, which I soft-launched over the weekend. Be interviewed by an LLM to capture your preferences and then invite the other party or parties to do the same. These preferences are then used as the fitness function for a genetic algorithm to find an agreement all parties are likely to agree to.
An article with more technical detail: https://mediator.ai/blog/ai-negotiation-nash-bargaining/

value: Show HN: Mediator.ai – Using Nash bargaining and LLMs to systematize fairness

author: sanity


value: sformisano

value:
AI coding tools like Claude Code, Cursor, and Codex read instructions from files on disk: .claude/skills/, .cursor/skills/, .agents/skills/. These files shape how each tool behaves. On a team of ten engineers working across several repositories, managing them by hand breaks down fast.
I built SkillCatalog to solve this without a SaaS dependency. Skills live in Git repositories the team already controls. The desktop app provides authoring, organization, and delivery on top of Git. Access control is Git access. There is no server to run and no data leaves the machine unless you push it.
The model is different from Microsoft's APM, which takes a package-manager approach (declare in apm.yml, everyone installs the same set). I think skills are closer to editor preferences than packages: there is a shared team baseline, but individuals layer their own selections on top. A project profile might install the team's backend bundle for everyone, while one engineer adds a personal stack for performance profiling and another adds one for accessibility auditing. Neither edits the shared catalog.
Catalogs contain skills (Markdown with frontmatter) that compose into stacks (ordered lists of skills) that compose into bundles (ordered lists of stacks). Profiles pick what gets delivered. Home profiles apply everywhere; project profiles inherit from them with project-specific selections taking priority. The app detects drift if someone hand-edits a delivered file, which helps when debugging unexpected AI behavior.
Stack: Rust Tauri v2 desktop app with React frontend, a skc CLI for scripting and CI. macOS via Homebrew for now, Linux and Windows coming.
Happy to answer questions. Particularly interested in hearing from teams who are currently managing these files in other ways.

value: Show HN: SkillCatalog, a Git-native skill manager for AI coding tools

author: sformisano


value:
Fine-tuned Chronos-2 on 7 years of EIA-930 demand + ASOS temperature for every US balancing authority that publishes a load series — 53 across the three interconnections.
On a 2025 hold-out (~61,000 hours), it beats the operators' own day-ahead submissions to EIA — the production forecasts they use to schedule generation — on 6 of 7 major RTOs. Macro MAE ~40% lower. The one loss is ISO-NE, whose forecasting is just very good (24h-ahead MASE 0.34). On the same window, CAISO and SPP operator submissions did worse than "same as yesterday."
The site plots the median + 80% PI band against the operator submission, with 48h of actuals running into the forecast.
Code, model on HF, operator-comparison benchmark reproduces from one script:
- https://github.com/tylergibbs1/surge
- https://huggingface.co/Tylerbry1/surge-fm-v3

value: Show HN: Open load forecasts that beat US grid operators on 6 of 7 RTOs


value: yusufcengiz

value:
Svelte 5 Runes and GSAP agency template. MIT Licensed.
GitHub: https://github.com/YusufCeng1z/svelte-gsap-template
Live : https://svelte-gsap-template.vercel.app/

value: Show HN: Svelte 5 Agency Template – GSAP Animations and Tailwind v4

author: yusufcengiz