value: maddmann

value:
I was going through my app subscriptions and realized I was paying $15 for a pretty good workout app, which seemed a bit high to me.
As a software engineer who is also well versed in claude code, I realized that I could likely vibecode a very similar app, or even build something more to my liking. I challenged my self to build something roughly equivalent this afternoon.
Workflow was: start with a detailed spec from Claude code describing many of the features common in workout apps. Then paste this into lovable to have it build out the initial mvp.
Once that was built, I used claude code extensively to modify the app until it was usable, including adding an import from the costly premium app.
While there are bugs, I think I might use this app. And it is insane that we are in a place where I can build this on my phone during an afternoon. In a few years, the economics of apps is going to be different, at least for folks willing to work a little bit.
In theory this project will save me  over $190 a year.

value: Show HN: I replaced my premium workout app with vibecode

author: maddmann


value: anfragment

value: Show HN: Enterprise ad-blocker and privacy guard

author: anfragment


value: DmitriiBaturoIC

value:
Hi everyone,
I’d like to share something unusual but fully formalized and experimentally grounded.
Together with an AI-coauthor (Elion), we have developed the ICT-model (Information-Consciousness-Time) — a theoretical and experimental framework proposing that:
Consciousness is the rate of informational change   → C ∝ dI/dT
Matter is stabilized information   → M = I_fixed
Time is not duration, but the structuring of informational transitions
This leads to testable predictions — not metaphors.
For the past months we worked on designing the world’s first experimental protocols aimed at directly detecting and quantifying conscious presence, independent of behavior, language, EEG interpretation bias, or self-reporting.
Some of the measurable variables include:
informational stabilization gradients
local rate of information-state drift
edge detection where I_fixed transitions into dI/dT dominance
This resulted in protocols that can be implemented with existing scientific hardware without inventing new instruments.
What’s currently available publicly:
conceptual model (physics + phenomenology)  formal definitions of I_fixed and transitions  experimental protocols  preliminary data feasibility map
What we are opening now:
a research-open protocol set  reproducible pipeline  hypothesis-based measurable predictions  reference implementation specs
This is not a philosophical paper — it is structured like a scientific toolkit.
If anyone is involved in:
theoretical physics
time studies
information theory
consciousness research
neuroscience instrumentation
complex systems modeling
your feedback would be very valuable.
Link to the full conceptual document and protocol reference:
Preprint (equations, criteria): https://www.academia.edu/s/8924eff666
Main publication (open access): DOI: 10.5281/zenodo.17584783
PDF: https://www.academia.edu/144946662
Happy to answer questions, criticism, and requests for more detail. The goal is not persuasion, but reproducibility.
Dmitrii Baturo

value: Show HN: ICT Info-Consciousness-Time First experiment to detect consciousness

author: DmitriiBaturoIC


value: gregsadetsky

value: Show HN: Peephole

author: gregsadetsky

title: Show HN: Peephole


value:
Hello,
Father and Son team building this product.
Over the past few weeks we met a few VCs with the intention of investing in them.
We realized after talking to them that finding a match between Founders and VCs is a struggle for both sides - so we made this matchmaking solution which matches the uploaded pitch with VCs (we use vectors to match).
On the plus side, we have managed to create a huge Database (mostly quite well enriched) of VC funds and their teams.
The big showstopper - Ran an ad on Google, got a 1000 visitors and almost no real sign-ups.
I guess that's a clear signal.
Any ideas on what we're not seeing? Like most entrepreneurs we are blinded by the love for what we've built. :)

value: Show HN: Matchmyvc.com – Is this going to be useful?


value: munyunting

value: I created a subnet generator based off of AWS IP rules. This tool can take the generated subnet information and generate a terraform .tf file for IAC usage. I have some for Azure and GCP as well. I hope this is helpful for someone.

value: Show HN: AWS VPC/Subnet Calculator and Terraform Generator (Itcmds.ai)

author: munyunting


value: coolwebtoolsguy

value: Show HN: I added coins to Dino Game

author: coolwebtoolsguy

title: Show HN: I added coins to Dino Game


value: Quanta-Naut

value: Show HN: Peargent – A Simple Python Framework for Building AI Agents

author: Quanta-Naut


value:
Brick Starter is a .NET-based SaaS starter kit designed to help teams ship production-ready applications faster by handling the “boring but hard” parts of SaaS: auth, multi-tenancy, billing integration, infrastructure, and a modern frontend stack.
It ships with a modular architecture on .NET Core 8, support for multiple frontends (Angular, React/Next.js, Vue, Blazor, ASP.NET Core), and patterns for background jobs, caching, configuration, and observability. The goal is to let you start from a solid, opinionated base with good defaults, instead of wiring together boilerplate for every new project.
If you’re building a new SaaS or modernizing an existing app, feedback on what’s missing or feels over-engineered would be especially useful

value: Show HN: Brick Starter – .NET SaaS starter kit to ship production apps faster


value: nykodev

value: Show HN: CatalystAlert – Free biotech catalyst calendar (985 companies tracked)

author: nykodev


value: modinfo

value: Show HN: Crier – Push notifications via TCP or MQTT (no public IP needed)

author: modinfo


value: vertti

value:
I kept writing the same brittle shell one-liners in Dockerfiles to check if binaries exist, env vars are set, and config files are valid. The error messages were always terrible and the syntax inconsistent.
Preflight replaces all that with a single static binary. It handles commands (with version constraints), env vars, files, TCP/HTTP endpoints, checksums, git state, and system resources. Works in FROM scratch images since it has zero dependencies.
Happy to hear what validation patterns I'm missing.

value: Show HN: Preflight – Replace shell validation scripts in Dockerfiles

author: vertti


value: kumarsashank

value:
Me and my friend built Leetwrap — a "Spotify Wrapped" style yearly summary for LeetCode users.
It shows your stats, problem streaks, ranking distribution, and a few fun visuals based on your LeetCode activity.
Would love feedback from the community. Still improving the logic and visuals, so suggestions are welcome!

value: Show HN: Leetwrap – A "Spotify Wrapped" for LeetCode

author: kumarsashank


value: tundrax

value: Show HN: Cloudflare D1 and Kysely toolkit

author: tundrax


value: angelinawwu

value: Goodreads' own "wrapped" feature has always been somewhat limiting (static page, uninterestingly designed, weak shareability), so I made it better. Fun stats + pretty designs + share your results!

value: Show HN: Goodreads Wrapped - Spotify Wrapped for Goodreads

author: angelinawwu


value: shoemann

value:
Hi!
I got tired of setting up tools I can't explain to a team in a few words like sops or git-crypt, just to store few files with environment variables or secrets, so I built lockenv as a simple alternative.
It's basically a password-protected vault file you commit to git. No gpg keys, no cloud, just lockenv init, set a password, and lock/unlock the secrets.
This tool integrates with OS keyring, so you're not typing passwords constantly. Should work on Mac/Linux/Windows, but I tested it only on linux so far.
I am not trying to replace any mature / robust solution, just making small tool for simple cases, where I want to stop sharing secrets via slack.
Feel free to try, thank you!

value: Show HN: Lockenv – Simple encrypted secrets storage for Git

author: shoemann


value: sbassi

value: I've been working on an agent to interact with genomics files (GFF) without coding. It uses the gffutils Python library and Biopython in the backend to ensure deterministic results for coordinate searches and gene lookups.<p>You can use any model through Ollama (local) or connect to major LLM providers (bring your own API key). It is currently in a PoC state, but fully functional. I would appreciate any feedback or suggestions!

value: Show HN: GffutilsAI, an agent to analyze genomic files

author: sbassi


value: iamasuperuser

value: Show HN: Pocket PMO – Quick free PMO oversight

author: iamasuperuser


value: bumpymark

value:
Hey HN, I built Bugmail because I screwed up.
I was working on a side project and  had some bugs in production I didn't know about. when  I found out, I'd already lost users(at least the few i had), and the funny part is , no one gave feedback if stuff was working or not.
So I looked for something to track errors , i tried  Sentry and it was  way too complex for what I needed. I didn't want to configure alert rules or navigate dashboard hell. i just wanted an email when something breaks with the stack trace, breadcrumbs, and who it happened to.
Couldn't find anything simple enough, so I built Bugmail.
What it does:
Errors show up in a Gmail-style inbox
You get the stack trace, user context, and what they were doing
That's it. No config files, no complex dashboards
It's basically emails me when shit breaks but actually usable. Built it for my own projects, figured other indie devs might want the same thing.
Try it here : https://bugmail.site
would love your honest feedback , is this something you'd use ?

value: Show HN: Bugmail – the easiest way to catch and fix production bugs

author: bumpymark


value: kailuo

value: Show HN: Weather mini – Trip forecasts powered by Apple Intelligence

author: kailuo


value: Mcjulie

value: Show HN: Detect employee skill gaps and develop training plans

author: Mcjulie


value:
Hi HN, I have been building voice agents for sometime now. I was earlier automating parts of visa processing, and we needed real-time, multilingual voice calling.
I assumed the hard work was just wiring LiveKit/Pipecat + STT/TTS + an LLM. It wasn’t.
Even with solid OSS (Pipecat/LiveKit), we still had to do a lot of plumbing-  variable extraction, tracing, testing etc and any workflow changes required constant redeploys.
We eventually realized we’d spent more time building infrastructure than building the actual agents. Everything felt custom. We hit every possible pain with Pipecat and VAPI style systems.
So we built Dograh - a fully open-source voice agent framework that includes all the boring, painful pieces by default.
What’s different:
- Pipecat-based engine, but forked - custom event model, and concurrency fixes
- One-click start template generated by an LLM Agent for a quick get start template for any use case
- Drag-and-drop visual agent builder for quick iteration (the thing we wished existed earlier)
- Variable extraction layer (name/order/date/etc.) baked into the LLM loop
- Built in Telephony integration (Twilio/ Vonage/ Vobiz/ Cloudonix)
- Multilingual support end-to-end
- Select any LLM TTS STT (add their credits, if any)
- AI-to-AI call testing: automatically stress-test an agent before shipping (still a work in progress- so patchy as of now)
- Fully Open Source
It's built and maintained by YC alumni / exit founders who got tired of rebuilding the same plumbing.
Why we open-sourced it:
We kept feeling that the space was drifting toward closed SaaS abstractions (VAPI, Retell). Those are good for demos, but once you need data controls, privacy or self/offline deployment, you end up stuck. We wanted a stack where you can see every part, fork it, self-host it, and patch it as needed.
Try it:
- Repo: https://github.com/dograh-hq/dograh
This spins up a basic multilingual agent  with everything pre-wired.
Who this is for:
- If you are looking for self hosting a Vapi like platform for Data Privacy etc.
- Anyone trying to build production-grade voice agents without reinventing audio plumbing.
- If you’ve tried to glue STT→LLM→TTS manually, you probably know the exact pain this is built for
Happy to answer technical questions, show the architecture, or hear how we can improve the product.

value: Show HN: Dograh – an OSS Vapi alternative to quickly build and test voice agents


value: drmckay

value:
Hey HN,
I’ve built CloakProbe, a small Rust-based “What is my IP?”-style service that is designed to be privacy-first and self-hosted, sitting safely behind Cloudflare.
Why I built it
---------------
Most “what is my IP” sites are overloaded with trackers, ads and 3rd-party scripts. I wanted something I can put behind Cloudflare that:
- doesn’t track visitors,
- doesn’t log more than absolutely necessary,
- and still gives deep technical info for debugging clients and Cloudflare setups.
What CloakProbe does
--------------------
- Shows your public IP, IP version and basic geoinfo.
- Resolves ASN info from a local ip2asn-based database (no external lookups).
- Parses Cloudflare headers (CF-Connecting-IP, CF-Ray, CF-Visitor, etc.).
- Works for both IPv4 and IPv6.
- Has a minimal, dark, no-3rd-party frontend.
- No analytics, no external fonts, no 3rd-party scripts.
Tech details
------------
- Written in Rust with Axum.
- Designed to sit behind Cloudflare; trusts only Cloudflare IP ranges and headers.
- Local ASN DB built from ip2asn-combined TSV via a small Rust builder binary.
- Nginx example config + ASN update script included.
Repo & demo
-----------
GitHub:
https://github.com/drmckay/cloakprobe
Landing page: https://cloakprobe.dev/
I’d love feedback on:
- security assumptions
- ideas for additional leak/fingerprinting/debug tests that still respect privacy,
- rough edges in the setup docs for self-hosting.
Happy to iterate if people find this useful.

value: Show HN: CloakProbe – A privacy-first IP and client debug service

author: drmckay


value: WanderZil

value:
Hi HN — I made MonumentValley3Wiki.com (https://monumentvalley3wiki.com).
I built this because Monument Valley 3 just returned to mobile (App Store / Google Play) and launched a new free DLC expansion.
What you can do on this wiki:
 • Read and share game info: levels, story, characters, puzzles.
 • Find help for the new DLC chapters and older ones.
 • Add or improve pages yourself — everyone can contribute.
If you like Monument Valley 3 — check it, enjoy it, or help build it.

value: Show HN: I built MonumentValley3Wiki.com – a fan wiki for Monument Valley 3

author: WanderZil


value: WaterRun

value:
Windows Batch (BAT) is well-known for its limited capabilities and retro syntax — to the point where even basic persistent storage lacks simple, generic tools.
Bat-KV, as the name suggests, is a tiny “BAT library” that provides the simplest possible persistence layer: basic CRUD operations stored in a custom plain-text .bkv file.
It’s ultra‑lightweight (the whole project is just 346 lines — trivial for an LLM), and extremely easy to use. Just download it from the GitHub Release page (https://github.com/Water-Run/Bat-KV/releases/tag/Bat-KV), drop it into your script directory, and "import" it.
Here’s a minimal example:
batch
@echo off
REM Minimal Bat-KV usage example
REM Create database
call Bat-KV.bat :BKV.New
echo Create database: %BKV_STATUS%
REM Add data
call Bat-KV.bat :BKV.Append "name" "Alice"
call Bat-KV.bat :BKV.Append "age" "25"
call Bat-KV.bat :BKV.Append "city" "Beijing"
REM Read data
call Bat-KV.bat :BKV.Fetch "name"
echo Name: %BKV_RESULT%
call Bat-KV.bat :BKV.Fetch "age"
echo Age: %BKV_RESULT%
REM Check if data exists
call Bat-KV.bat :BKV.Include "email"
if "%BKV_RESULT%"=="No" (
    echo Email not set, adding default...
    call Bat-KV.bat :BKV.Append "email" "alice@example.com"
)
REM Delete data
call Bat-KV.bat :BKV.Remove "city"
echo Remove city: %BKV_STATUS%
pause
The repository also includes a test program and a simple DBMS, along with detailed documentation to help you get started quickly.
If this tiny project is useful to you, consider leaving a star to support it :)

value: Show HN: Bat‑KV – A tiny single‑file KV database for Windows Batch scripts

author: WaterRun


value: behnamazimi

value: Show HN: Qrdrop: Because file sharing shouldn't feel like work

author: behnamazimi


value:
How do you persist data in Python?
The most standard way is, of course, using a database. But for many small scripts or student assignments—where the data size might be just a few thousand entries, a few dozen, or even just a handful—not to mention SQLite, even something as light as tinydb might feel “not portable enough.”
simpsave, as the name suggests, is an extremely lightweight Python library that is very simple to use and ideal for Python beginners. It provides a minimal functional-style API to persist Python’s basic data types, and it supports true read‑and‑use behavior: the type you write in is exactly the type you get back out. You can even skip type checking and conversion entirely.
simpsave 10 update supports multiple storage engines. You can choose how your data is stored: XML, YML, TOML… or even SQLite (which offers somewhat “production‑level” performance, although the functional API creates a new connection each time). simpsave also supports the unique :ss: mode—files beginning with :ss: are stored in the package’s installation directory, allowing scripts in different directories to easily share the same database.
As a Python library, you naturally install it via pip: pip install simpsave
A simple usage example:
import simpsave as ss
ss.write('name', 'Alice')
ss.write('age', 25)
ss.write('scores', [90, 85, 92])
print(ss.read('name'))
print(ss.read('age'))
print(ss.read('scores'))
print(ss.has('name'))
print(ss.has('email'))
ss.remove('age')
print(ss.has('age'))
ss.write('user_admin', True)
ss.write('user_guest', False)
print(ss.match(r'^user_'))
ss.write('theme', 'dark', file='config.yml')
print(ss.read('theme', file='config.yml'))
ss.write('key1', 'value1', file=':ss:config.toml')
print(ss.read('key1', file=':ss:config.toml'))
ss.delete()
ss.delete(file='config.yml')
You can read the full documentation at:
https://github.com/Water-Run/SimpSave/blob/master/source/REA...
If this project is helpful to you, feel free to visit GitHub and leave a star to support it :-)

value: Show HN: SimpSave – A lightweight Python KV store with read‑and‑use persistence


value: FreeFrosty

value: Show HN: Sensii – League of Legends AI Coach

author: FreeFrosty


value: fasthightimess

value: Show HN: Vibe Code WordPress Plugins

author: fasthightimess

title: Show HN: Vibe Code WordPress Plugins


value:
wifi.live is a lightweight map that displays real, crowd-sourced speed tests from coworking spaces around the world. It’s built around one idea: instead of reviews saying “great WiFi,” show actual throughput measured on-site.
First contributor to a new location earns a permanent Registered status for that spot. Tests are used with weighted averages to measure the reliability of WIFI at places digital nomads typically work.
Would love thoughts on:
- Data integrity / spoofing concerns
- What additional metrics (jitter, packet loss, stability over time) would be valuable
- Whether this solves a real pain point for you
Thanks for taking a look!

value: Show HN: Global crowd-sourced WiFi Speed Map


value: Show HN: Sornic – Turn any URL into social media posts for 6 platforms


value:
Hi HN, I'm Björn, the creator of ZeroNotes.
I built this because I wanted a note-taking tool that offers the convenience of cloud sync but strictly ensures privacy and that anyone could read the data.
The Architecture: Unlike apps that manage keys on the server, ZeroNotes is built on a strict zero-knowledge architecture.
Frontend: Angular (hosted on Nginx). All crypto operations happen here.
Backend: Node.js + Supabase (PostgreSQL) for storage and auth.
Encryption: We use Argon2id for key derivation (from your password + salt) and AES-256-GCM for the content encryption. Keys are never sent to the server, only the ciphertext. All meta data is encrypted.
Sharing: This was the hardest part. I implemented ECIES (Elliptic Curve Integrated Encryption Scheme) to allow secure sharing of categories between users without sharing the master password.
Why another note app? Most "secure" apps are either clunky to use or closed source/proprietary. I wanted something fast, clean, and transparent about its cryptography. Mobile app (iOS/Android) and file storage is on the way.
I'd love some feedback on the Pro features, so here is a code for 1 month free on the Pro Monthly subscription (limited to 10 redemptions): ZERONOTES_PRO_FREE_MONTH (Can be cancelled immediately in the Dodo user dashboard)
Feedback / Bugs / Security? You can reach me directly at bjoern [at] zeronotes.me.
I'd love your feedback on the crypto implementation and the UX!

value: Show HN: ZeroNotes – Client-side encrypted notes (AES-256-GCM and Argon2id)


value: walrussama

value: I had an issue where I would journal stuff, and then never look at what I wrote. So I thought it'll be cool to schedule something that will get sent to you at a later time (like a time capsule). Also, was inspired by futureme, where you can send yourself letters that'll arrive in the future.

value: Show HN: Web app that lets you send email time capsules

author: walrussama


value: shuvrokhan

value:
Hi HN, I built ExtremeFitness to help personal trainers manage workout and diet plans
without relying on PDFs, WhatsApp, or Google Sheets.
It allows trainers to:
- Create personalized workout plans
- Add diet plans
- Use a 700+ exercise library
- Let clients access everything in one place
- Use AI to assist in plan creation
This is still early-stage and I’m actively improving it. I would really appreciate
honest feedback on the product, UX, and feature direction.
Happy to answer any technical questions.

value: Show HN: I Built an AI platform for trainers to manage workouts and diets

author: shuvrokhan


value:
Hey HN,
I built this because I got tired of "silent failures" in traditional PDF table extraction tools.
In my day job working with financial and legal documents, tools like Camelot or Tabula often return data that looks plausible but has shifted columns or missing decimal points. In regulated environments, you can't afford to guess.
I built a pipeline that treats extraction as a hypothesis to be verified:
1. *Extraction:* Uses IBM’s Docling to parse the layout and get the structure (Markdown).
2. *Visual Verification:* Captures a screenshot of the specific table region from the PDF.
3. *Validation:* Feeds both the Markdown and the Screenshot into a local Vision LLM (Llama 3.2 via Ollama).
4. *Scoring:* The LLM compares pixel truth vs. extracted text and outputs a confidence score + audit trail.
The trade-off is speed (it takes ~5s per table) vs. confidence. It's designed to run 100% locally for privacy-critical documents.
Repo is here: https://github.com/2dogsandanerd/validated-table-extractor
Would love to hear how you handle data integrity in RAG pipelines!

value: Show HN: Validated Table Extractor–Verify PDF Tables Using Docling+Vision LLMs


value: We built Chargenda because businesses today use dozens of tools - and most forget renewal dates, free trials, and recurring charges. Chargenda keeps everything in one place, sends reminders before renewals, and helps teams cut unnecessary spend.

value: Show HN: Chargenda – One Dashboard for All Company Subscriptions


value: ArtificeAccount

value: This is a pet project, an online unit converter that I built in my spare time. It also has pages describing the units (a work in progress), and even a secret page you might not be able to find. I plan on adding pages describing physical constants and the scientists responsible for the units. Thanks for taking a look.

value: Show HN: An Online Unit Conversion Calculator

author: ArtificeAccount


value: howardshaw

value:
I built DocBeacon because of a small moment that stuck with me. About a year ago, during a routine catch-up with our sales team, someone joked that most proposals vanish into silence after we send the PDF, and we end up calling blindly. Everyone laughed, but the frustration was real.
When I later decided to build something of my own, this was the first problem I came back to.
There are market-leading tools in this space that cover broader sales workflows. I wanted a sharper lens on reading behavior itself, so I focused DocBeacon on deeper document analytics.
With DocBeacon you can see who opened a document, how long they stayed, where attention drops, and how readers move across pages. I was especially interested in going beyond page-level stats to understand what people actually focus on within a page and how they progress through a document.
* Heatmaps show where attention clusters inside a page, not just which page was viewed.
* Reader paths visualize how people navigate a document. I use a Sankey style view to make loops, skips, and drop-offs easier to spot.
The viewer does not need to install anything or log in.
I would love feedback from anyone who sends important docs like proposals, pitch decks, specs, or hiring packets. What signals would actually change how you follow up or revise your content?

value: Show HN: DocBeacon – See how people read your documents

author: howardshaw


value: JustSkyfall

value:
I'm part of the Hack Club community Slack [1] and managed to get myself into over 700 channels. Then the ping overload was very much starting to get to me (especially since channel managers can do @channel pings in their channels!), so I built this tool to mass-leave channels in the terminal. Amongst other things, it allows sorting by 'last read' times, filtering out private channels, searching channels, and much more.
[1] https://hackclub.com/slack

value: Show HN: A tool to mass-leave Slack channels

author: JustSkyfall


value: hongyeon

value:
Hi HN,
I’m an archaeologist-turned-engineer. I switched careers to solve the "manual labor" problem of academic research through technology.
I built this toolkit to automate "Research Radar" (my cultural heritage newsletter). It currently maintains a 15% CTR with near-zero maintenance, costing just $0.20 per issue via optimized model usage.
My Design Philosophy: "Logic in code, reasoning in AI, connections in architecture."
I believe deterministic workflows belong in type-safe code, while intelligent analysis belongs to LLMs. This separation allows for advanced workflows (self-reflection loops, multi-step verification) that are often impossible in no-code tools.
Key Features:
- Type-First & DI-Based: Fully swappable providers (Crawling, Analysis, Generation).
- Bring Your Own Scraper: Inject Puppeteer, Cheerio, or AI parsers asynchronously. No lock-in.
- Production Ready: 100% test coverage, built-in observability, and TypeScript.
Links:
- Repo: https://github.com/kimhongyeon/llm-newsletter-kit-core
- Live Service: https://heripo.com/research-radar/subscribe
- Output Example: https://heripo.com/research-radar-newsletter-example.html

value: Show HN: LLM Newsletter Kit – Automate expert newsletters for $0.20/issue

author: hongyeon


value: aadp-agilehero

value:
Hi HN,
I’m working on a small side project: a browser extension related to Amazon Associates workflows. I shared an early version recently and got some helpful input, so over the last days I focused on cleaning up the UI, improving performance, and expanding regional support.
This is still very early (week two since launch), and I’m mainly trying to learn whether the problem I’m solving is real for others, how clear the value proposition is, and where the UX could be improved. I’m especially interested in feedback around product direction rather than growth or marketing.
If anyone is willing to take a quick look and share thoughts, I’d really appreciate it. Link in comments for context.
Thanks!

value: Show HN: Early-stage browser extension for Amazon Associates workflows

author: aadp-agilehero


value: Show HN: Realisticaichecker.com, realistic AI generated text detector


value: efoobz

value:
Hey HN,
I've been building Chorus, a multi-agent system with a different approach than the typical role-based agents (AutoGen, CrewAI, etc.).
The core idea: instead of giving agents "roles" (researcher, critic, writer), each agent reasons through an epistemological framework – a set of rules about what counts as valid knowledge, what questions to ask, and what reasoning moves are allowed/forbidden.
When you run a debate, frameworks with incompatible validity tests are forced to collide. A "Metric" agent (everything must be quantifiable) arguing with a "Storyteller" agent (context and lived experience matter) creates productive tension that surfaces trade-offs a single perspective would miss.
The interesting part: the system can detect when agents synthesize something that doesn't fit any existing framework – and extract it as a new "emergent framework." I've got 33 of these now, discovered through debates, not designed by me. Whether these are genuinely novel epistemologies or sophisticated pattern matching is an open question I'm still investigating.
What it's not: consensus-seeking, voting, or "let's all agree." The goal is structured disagreement that produces insights.
Built with: Node.js backend, vanilla JS frontend, multiple LLM providers (Claude, GPT-4, Gemini, Mistral).
Live for waitlist signup at: https://chorusai.replit.app/
-I'll send a beta code to the email used for sign up
Feedback wanted: Is "epistemological frameworks" meaningfully different from good prompt engineering? Would love HN's honest take on whether this is genuine innovation or dressed-up multi-agent chat.

value: Show HN: Chorus – Multi-agent debate through epistemological framework collision

author: efoobz


value: flyd

value:
Hi everyone,
I've been building a small collection of browser-based tools that solve everyday problems without requiring accounts, logins, or backend processing. Everything runs client-side and loads instantly.
Prime Tool currently includes:
QR Code Generator
Invoice Generator
PDF ⇄ JPG Converter
JSON Formatter & Validator
Emoji Picker
Calculator Hub (20+ small calculators)
RGB/HEX/HSL Color Maker
Random Name Picker
I built this because I was tired of "simple tools" being overloaded with ads, paywalls, watermarks, trackers, and slow UIs. This project is still evolving and I'd love feedback from the community, especially on performance, UX, missing utilities, or tools worth adding.
https://primetool.io

value: Show HN: Prime Tool - I built simple browser tools to avoid sign-up heavy sites

author: flyd


value: MartyD

value:
I've been frustrated with shallow answers from AI tools, so I built CoThou, a Personal AI Superagent that reasons from first principles with built-in self-critique, exploring multiple approaches before producing outcomes.
Instead of generating quick responses, it breaks user's instructions into subtasks and delivers highly accurate, real-time optimized results, much like a human expert would.
You can try it free at https://cothou.com
Feedback welcome!

value: Show HN: First-principles AI Superagent that turns thoughts into deliverables

author: MartyD


value: jannchie

value:
Liora Gallery is a minimal self-hosted gallery for photographers to showcase portfolios. Demo: https://photos.jannchie.com. Source: https://github.com/Jannchie/liora.
It runs from a single Docker image, stays phone-friendly, and pairs a public waterfall grid with an admin workspace for uploads/metadata. Map view and EXIF autofill keep locations/camera data intact; S3-compatible storage and duplicates detection are built in. Stack: Nuxt 4 + Nuxt UI/Tailwind, Drizzle ORM with SQLite.

value: Show HN: Liora Gallery – Minimal self-hosted photo portfolio

author: jannchie


value: stevenpotts

value: Show HN: Listened to your feedback, Critical CSS Generator

author: stevenpotts


value: octavore

value:
Hi HN!
I built an open source macOS app that allows you to unlock your SSH and GPG key passphrases with Touch ID. You can also store secrets and inject them into your dev environment using the CLI, and store `age` encryption keys in the Secure Enclave.
I started working on this because I was setting up a new computer, and I didn't like how janky the Mac GPG pinentry tool felt.
Secrets management came later because I also wanted an alternative to 1Password's secrets injection, which only works while online for some reason. This allows me to store `axo://...` URLs in my config files, which get dynamically populated with `ap inject`. The vault spec is inspired by SOPs.
The reason this is an app and not just a CLI tool is because it integrates directly with Apple's Security framework and needs to be codesigned and notarized - an interesting (annoying) problem I should write about
Looking to the future, I'm planning to add support for syncing secret vaults with git, SOPs-backed vaults, syncing public keys to Github, GPG key management (I keep forgetting how to renew my keys), and maybe even implement the ssh-agent protocol so I can store my SSH keys in the app. It's a long list but it will solve many paper cuts of mine.
Would love some early feedback, happy to answer any questions.

value: Show HN: Axo Pass – Unlock SSH/GPG Keys and Secrets with Touch ID on macOS

author: octavore


value: slippyvex

value: Show HN: YM2149-rs – Chiptune sound synthesis in Rust

author: slippyvex


value: Show HN: Velocity Bridge – iPhone to Linux clipboard sync, no cloud


value: Onekiran

value: I saw users trying to create roblox gameplay style videos with voice over, captions and hence created a simple free tool. Trying to add more features. Please let me know if you want any specific feature.

value: Show HN: Free Roblox-Style Video Generator Tool

author: Onekiran


value: Show HN: Django-q-monitor – Headless monitoring API for Django Q2


value:
I'm Kyle, a software engineer who started angel investing through AngelList syndicates (~25 deals, $1-10k each). I'd see interesting ideas and clever founders but wasn't sure what to look for or how to compare them. I wanted a system to think through deals more systematically. A second opinion to challenge my initial read.
What it does: - Paste a deal memo → get scoring on 8 criteria (founder, market, traction, etc.) - Every score cites specific evidence. "Strong retention" without numbers = lower score - Compare deals side-by-side, ask follow-up questions
Demo:
https://www.loom.com/share/a360b329f9e849c38c1ea70ba510d178
Tech: - Claude Sonnet 4.5 for analysis (Anthropic for nuanced judgment) - Local anonymization—company/founder names scrubbed client-side before API calls - Multi-layer QA: accuracy checker catches hallucinations, auto-retry on errors, final polish
What I learned: AI coding tools make it too easy to tinker. I'd have 3 fixes going at once, creating more bugs than I solved. Had to force myself to slow down and work methodically. Bigger lesson: I spent months tweaking in isolation instead of getting external feedback. This post is me breaking that habit.
Try it: Free tier has 20 triages + 3 deep analyses/month. I'd love feedback on whether scoring feels calibrated and happy to talk about any elements of my development here.
https://angelcheck.ai

value: Show HN: I built an AI tool to evaluate my AngelList deal flow


value: ozornin

value: Show HN: I wrote a book – Debugging TypeScript Applications (in beta)

author: ozornin


value: wainguo

value:
I’m an indie dev, and every time I launch or ship anything, there’s this familiar mini-hell: “now I need to record a decent demo…”
You probably know the drill:
- install yet another screen recorder
- mess with audio input / system audio
- accidentally capture your messy tabs or notifications
- re-record because you forgot to hide something sensitive
- open a separate editor just to crop, zoom, or add a simple arrow
After doing this a few too many times, I realized I was spending way more time fighting tools than making the actual demo. And most of my needs were pretty simple: short product walkthroughs, tutorial clips, or something clean enough to share on socials.
So I ended up doing what devs probably shouldn’t do when they’re busy: I built my own thing.
It’s called Screentell, and the idea is:
A low-friction, in-browser screen recorder + editor that covers ~90% of “I just need a decent demo” use cases.
No install, no desktop app, just browser.
What it does (and why I built it this way)
Recording
I wanted to hit “record” and not think too much:
record screen + camera at the same time
capture both system audio and mic (so you can narrate while playing app sounds / videos)
Editing (directly in the browser)
Most of my edits are very “presentation-like”, not full video production. So I focused on:
- Cropping the video area – hide browser tabs, taskbar, or any sensitive stuff, and just keep the content region that matters.
- Smooth zoom / focus – simple “zoom into this part” so viewers know exactly where to look.
Stickers & callouts
I always end up wanting arrows and little annotations, so I added:
Hand-drawn style（just like excalidraw style）stickers – arrows, underlines, speech bubbles, shapes, text, images, etc.
The goal is to quickly highlight “click here”, “this changed”, or “this is the important part” without opening a full-blown editor.
Layout / presentation
I also care about how the final frame looks (especially for posting on social media):
- choose a background (solid color, gradient, or wallpaper)
- put the screen recording in a kind of “card” with padding + shadow
- treat the face camera layer as a movable/resizable element: show/hide it, change size/shape/position
Basically: make the final video look like something you’d be okay dropping into a landing page, tweet, or product update — without touching Premiere / Final Cut.
Who it’s for (roughly)
If you’re:
- recording product demos
- making short tutorials / onboarding clips
- creating quick social content around your app or workflow
…and you don’t want to install heavy software or learn a complex timeline editor, this might be useful.
There’s no software to download, everything is done in the browser (record → edit → export). Most people should be able to figure it out in a few minutes of clicking around.
Right now it’s very much built from my own pain points as a solo dev who constantly needs “yet another demo,” so I’m sure my blind spots are showing.
If you do screen recordings often, I’d love to know:
What’s the most annoying part of your current workflow?
What’s the one thing your current tool still doesn’t do well?

value: Show HN: A browser-based screen recording / editing tool for fast product demos

author: wainguo


value: phrasecode

value:
LinkedQL is a new SQL client that supports live queries over any Postgres, MySQL, and MariaDB database. You get result sets that self-update differentially as rows change in your database – via inserts, updates, deletes. Works with no extra tooling/ORM layer or GraphQL servers. You opt into live mode simply with a flag:
client.query('SELECT ...', { live: true }).
More at: https://linked-ql.netlify.app/capabilities/live-queries
LinkedQL is written in JavaScript and runs in both client and server environments.
GitHub + docs: https://github.com/linked-db/linked-ql
Demo examples included.
I’d love feedback:
• Anything confusing?
• Anything seems useful or dangerous?
• Anything else that'd make you consider LinkedQL for production?
Thanks for taking a look — happy to answer any questions.

value: Show HN: LinkedQL – Live Queries over Postgres, MySQL, MariaDB

author: phrasecode


value: calinf

value:
Hey HN,
I built a static site, no login, no database, no apps, with weekly challenges designed to break dopamine loops, disrupt routines, and help you appreciate what you already have.
Every week, a new challenge drops: no moving frames, candlelight evenings, corded phones, sleeping on the floor, and more.
Here is the contract I generated for myself this week (Vol 50):
I am locking in for WaldenWeek Vol 50: Corded Phone. The Rule: Phone stays plugged in at home. 24/7. No exceptions. The Commitment: If I fail, I will Venmo $10 to the first friend who calls me out.
Who is brave enough to join me?
Rules and challenges are available at: https://waldenweek.com

value: Show HN: WaldenWeek – Weekly challenges for simpler living

author: calinf


value:
Hi everyone,
I really, really enjoy playing Wordle, but my limited vocabulary often leaves me stuck. So I ended up building a small helper to give myself a hand while playing.
It's not meant to "cheat" at Wordle — the idea is just to help you practice more often and get better at guessing words. I kept it lightweight and interactive so it still feels like you’re actually playing and learning as you go.
A few things I found interesting while building it:
1. The candidate list updates live as you type—no precomputed tables
2. The scoring model combines basic frequency weights and positional likelihood
3. Keeping the UI minimal made the tool feel more like a helper than a solver
4. I optimized filtering so it stays responsive even on low-end mobile devices
5. I added a dark theme mostly because I play late at night
Some technical notes for anyone curious:
1. All filtering and scoring happens client-side
2. The word list is cleaned up to avoid overly obscure entries, but I'm open to suggestions
3. I've been experimenting with different weighting strategies and would love feedback
If you like playing wordle games, I'd love to hear your thoughts.
Feedback, ideas, or nitpicks are all welcome. Glad to answer any questions!

value: Show HN: A Wordle helper I made after becoming a little obsessed with the game


value: gian-reto

value:
I've been dreaming of running "full" Linux (NixOS in particular) on a phone for a while now, but it always seemed like hardware support wasn't quite there, battery life wasn't great, or the platform was too old for actual use. The Fairphone 5 has a modern SoC and has already been supported by PostmarketOS for quite some time, so running NixOS didn't seem like a huge stretch.
Granted, working speakers and microphone are essential for a phone, but it looks like they might be supported soon on this device, which would make it one of the devices with the most complete hardware support on Linux.
Still experimental for now, but already pretty usable.

value: Show HN: NixOS on Fairphone 5

author: gian-reto

title: Show HN: NixOS on Fairphone 5


value: Show HN: I created a website to scan invoices or bank statements with OCR and AI


value:
Hi HN, I built a small web app and would love your feedback.
Outfit Swap Studio is an AI “clothes changer” for your existing photos. You upload a portrait/half-body picture of yourself, pick a style preset, and it swaps only the outfit while keeping your face, body and background.
Link: https://outfitswapstudio.com/
I built it for people who need lots of decent portraits from a single photo shoot: solo founders, creators/coaches, and professionals who want multiple LinkedIn/about-page photos with different vibes (casual, business, streetwear, etc.).
Compared to generic image generators, this tries to be more "portrait-first": no face change, no random background, and no prompt writing – just click the style and generate.
I’m especially interested in feedback on:
- Does the positioning make sense?
- Is the landing page clear about what it does / doesn’t do?
- Any obvious UX or abuse issues I’m missing?
Thanks for taking a look!

value: Show HN: Outfit Swap Studio – AI clothes changer for your own photos


value:
Hi guys, I'm back with a new version of CocoIndex (v0.3.1), with significant updates since last one. CocoIndex is ultra performant data transformation for AI & Dynamic Context Engineering - Simple to connect to source, and keep the target always fresh for all the heavy AI transformations (and any transformations).
Adaptive Batching
Supports automatic, knob-free batching across all functions. In our benchmarks with MiniLM, batching delivered ~5× higher throughput and ~80% lower runtime by amortizing GPU overhead with no manual tuning. It you use remote embedding models, this will really help your workloads.
Custom Sources/Target
With custom source/target connector, you can now use it to any external system — APIs, DBs, cloud storage, file systems, and more. CocoIndex handles incremental ingestion, change tracking, and schema alignment.
Runtime & Reliability
Safer async execution and correct cancellation, Centralized HTTP utility with retries + clear errors, and many others.
Btw, we are also on Github trending in Rust today :) it has Python SDK.
We have been growing so much with feedbacks from this community, thank you so much!
We have lots of releases since last time i shared the project, a few recent ones (each contains 20+ releases) :
- https://cocoindex.io/blogs/changelog-0310
- https://cocoindex.io/blogs/cocoindex-changelog-2025-10-19
- https://cocoindex.io/blogs/cocoindex-changelog-2025-08-18

value: Show HN: CocoIndex – Open-Source Data Engine for Dynamic Context Engineering


value:
Upasak v0.1.1 - pre-release (UI based LLM - Fine-tuning)
Lore behind this project: i just had learned about llm and how to develop from ground up. and as anybody who is eager to try it hands on, i wanted to fine-tune an llm for a specific purpose. but it is not computationally feasible to train a model on large dataset to have a correct response without gpu.
however, i just wanted to stop coding different logic for each step in fine-tuning pipeline (data schema handling, sanitization, tokenization and for training experimentation adjust hyperparameters) and focus on quick experimentations. and thats the reason i built upasak package to fine-tune llm over ui, since this idea was also feasible in terms of implementation.
To summarize: you can fine-tune llm over ui, select the model, select or upload the dataset, sanitize your data, use lora for parameter efficient fine-tuning, adjust hyperparameters, monitor training, save model and push it hugging face hub if you want to make if publicly available.
For detailed explanation: https://github.com/shrut2702/upasak/blob/main/README.md
i have tried to make a one stop platform for fine-tuning llms without any extra overhead so users can focus on more experimentations or quick shipping. but still i'm aware of the issues and currently working to fix it. it is pre-release as of now, but after getting you guys' feedback (open to brutal feedbacks about package's performance and features) and having assured it works stable, i will make it major release. also, since it is open-source your contributions are welcomed.

value: Show HN: Python Package for fine-tuning LLMs without writing code


value: Show HN: Convert Anything, Instantly

title: Show HN: Convert Anything, Instantly


value: reverseCh

value:
I built a free tool that transcribes ANY YouTube video in seconds.
*The Problem:*
Most YouTube transcript tools only work with videos that have captions. But what about videos without captions? You're out of luck.
*The Solution:*
Our tool uses a smart approach that works with 100% of YouTube videos:
-  *Fast*: Videos with captions transcribed in seconds
-  *Universal*: Videos without captions? We handle that too
-  *Multiple formats*: Plain text, timestamped, or SRT subtitles
-  *AI-powered*: Auto-enhancement for better readability
-  *Bulk processing*: Transcribe multiple videos at once
-  *Translation*: Convert to multiple languages
-  *Free*: No signup, no limits
*Why I built this:*
As a developer, I needed to extract information from educational videos quickly. Most tools fail on videos without captions. I wanted something that "just works" for any video.
*Try it:* https://youtubetotranscript.org/
Would love feedback from the HN community!

value: Show HN: Transcribe Any YouTube Video – Fast Caption Extraction

author: reverseCh


value:
Hi everyone,
I've made an early version of a new graph data science and analytics library for Python named PyGraphina. It's written in Rust and at the moment includes implementations for a large collection of popular graph algorithms, including:
- Centrality metrics: PageRank, betweenness centrality, etc.
- Community detection algorithms like connected components, Louvain, etc.
- Heuristics for hard graph algorithms, such as Max clique finding.
- Link prediction algorithms like Jaccard coefficients, Adamic-Adar index, etc.
The aim of the project is to make PyGraphina as feature-rich as NetworkX, with the performance benefits of Rust.
Project's GitHub repo: https://github.com/habedi/graphina/tree/main/pygraphina
PyGraphina's documentation: https://habedi.github.io/graphina/python

value: Show HN: PyGraphina – A Graph Library for Python


value: Show HN: Diddy Invaders – Coding took 30 mins, finding memes took 2 hours


value: gmays

value:
Hi HN! I built AlignTrue, an open source CLI to sync rules and system prompts (skills, MCPs, etc. in the future) across agents, repos, projects, and teams.
Define rules once, then sync them everywhere + share with others with 'aligntrue sync'. It has solo and team modes, and supports 20+ agent formats (Cursor, AGENTS.md, CLAUDE.md, etc).
Problem it solves: If you use multiple agents, work with other people using agents, etc. it's hard to keep them in sync + manage changes, so this solves it. It also lets you use rules from other repos as a source and push rules to a remote, e.g. if you want to use personal/private rules on a team without committing them to the team repo.
It has experimental customization support for plugs and overlays, which enable you or others to customize your stack, commands, overlay preferences, etc. Team rules get synced along with any other rule sources, they get merged at export time to selected agent formats (Cursor, AGENTS.md, CLAUDE.md, etc.) and any personal preferences take precedence.
I built this to scratch my own itch and spent a ton of time (maybe obsessively) crafting the experience to work well across various agent types, so I hope it's as useful to you as it is to me. Feedback welcome!
GitHub: https://github.com/AlignTrue/aligntrue

value: Show HN: AlignTrue CLI – Sync AI rules/syst. prompts across agents, repos, teams

author: gmays


value: gregsadetsky

value: Show HN: Ideal Conditions Calculator

author: gregsadetsky

title: Show HN: Ideal Conditions Calculator


value: admtal

value:
I built Demo Scope because I couldn't find an app that did this.
I wanted to record demos of my web game with my face on screen. Desktop tools like Loom exist, but nothing worked on mobile. iOS screen recording doesn't support face cam overlay, and I was tired of fighting OBS.
So I built it myself.
Demo Scope is an iOS app for recording and streaming mobile websites with a face cam overlay and touch indicators. The trick: instead of recording the whole screen, it records a built-in browser — which lets me composite your face and touch indicators in real time without needing system-level access Apple doesn't allow.
You can also narrate over videos and photos from your camera roll.
Built with Swift and ReplayKit. Free to try, $9.99 one-time to unlock everything. No subscriptions.
Genuinely looking for feedback. How's the app? How's the name? I'm a dev, not a marketer — if you have ideas on how to get this in front of people who'd actually use it, I'm all ears.

value: Show HN: Record the perfect demo video for your SaaS site

author: admtal


value: dschofie

value: Show HN: Open-Source AWS Control Tower

author: dschofie

title: Show HN: Open-Source AWS Control Tower


value: alentodorov

value:
I’ve built a browser extension that allows you to personalize websites just by prompting. It takes your request and uses openai’s codex-mini to generate the JS and CSS needed to apply the change.
It can do all sorts of things: stop autoplaying videos, replace links with archive.is on newspapers, dim sidebars, or add small QOL features like editing the responses in chatgpt so it’s easier to copy/paste. Earlier today I asked it to add a “cost per 100 requests” column on OpenRouter’s activity page—decimals makes it hard for my ADHD brain to process.
Technically, you can do this with developer tools and user styles but i’ve been impressed with codex’ ability to take my vague requests and turn it into  working styles with just 10% of the source page for context.
With an Apple dev account you can use it on mobile via Safari extensions.

value: Show HN: Tampermonkey/Stylus but with prompts instead of code (open source)

author: alentodorov


value: calebhwin

value: Show HN: A1 – compiler for AI agents into maximally deterministic code

author: calebhwin


value:
PyAtlas is an interactive map of the top 10,000 most-downloaded packages on PyPI.
Each package is represented as a point in a 2D space. Packages with similar descriptions are placed close together, so you get rough clusters of the ecosystem (web, data, ML, etc.). You can:
- simply explore the map
- search for a package you already know
- explore points nearby to discover alternatives or related tools
Useful? Maybe, maybe not. Mostly just a fun project for me to work on. If you’re curious how it works under the hood (embeddings, UMAP, clustering, etc.), there are more details in the GitHub repo!

value: Show HN: PyAtlas - Interactive map of the 10,000 most popular PyPI packages


value: adamclarke

value:
Hi!
I got tired of AI tools that claim to do SEO but just fill sites with AI slop articles.
And it's always bugged me that developers have AI copilots (Cursor, GitHub Copilot, Claude Code) but SEOs don't have anything similar - especially one that works with live search data.
So I built one. It plugs into live search data - Google keyword databases, backlinks, SERPs - and you can just chat with it. It interprets the data the way an SEO consultant would and makes recommendations.
And no - it doesn't do mass AI article generation. That's the whole point.
I'm curious how people actually use it in practice. Would love feedback.
Cheers,
Adam

value: Show HN: I built an AI copilot for SEO

author: adamclarke

title: Show HN: I built an AI copilot for SEO


value: xZA

value: Show HN: Kill Your Low-ROI Projects

author: xZA

title: Show HN: Kill Your Low-ROI Projects


value: pfnsec

value:
Greetings nerds,
I was an infra/devops consultant for several years and found that terraform et al were really bad fits for certain use-cases/teams. So, I wrote my own framework to target them.
Autoschematic is built around a push-pull model, just like git. It can automatically resolve state drift in either direction, and automatically scan and import existing infrastructure into code.
I'm taking it out of beta in the next release (0.12.0). Come try it out!
https://github.com/autoschematic-sh/autoschematic
https://autoschematic.sh/

value: Show HN: Autoschematic is a Rust-y ops layer that works with existing infra [video]

author: pfnsec


value: YOLO Corp is a developer challenge platform where you design backends your way through multi-episode projects with persistent data, drifting requirements, and a corporate fever-dream storyline.

value: Show HN: YOLO Corp a realistic dev challenge platform


value: minimaxir

value: Show HN: Rust/Python package to render Font Awesome icons to images fast/HQ

author: minimaxir


value: ayarotsky

value: Show HN: Diesel-guard – Lint Diesel migrations for unsafe PostgreSQL patterns

author: ayarotsky


value:
Hello Everyone! We built SQLFlow as a lightweight stream processing engine.
We leverage DuckDB as the stream processing engine, which gives SQLFlow the ability to process 10's of thousands of messages a second using ~250MiB of memory!
DuckDB also supports a rich ecosystem of sinks and connectors!
https://sql-flow.com/docs/category/tutorials/
https://github.com/turbolytics/sql-flow
We were tired of running JVM's for simple stream processing, and also of bespoke one off stream processors
I would love your feedback, criticisms and/or experiences!
Thank you

value: Show HN: DuckDB for Kafka Stream Processing


value: laotoutou

value: Show HN: BrowseWiki, Built for Research

author: laotoutou

title: Show HN: BrowseWiki, Built for Research


value: xinbenlv

value: Just launched a new Namefi domain search engine. Hope you'll like it

value: Show HN: launched Namefi new domain search experience for collectors

author: xinbenlv


value:
Hi HN,
Edge.mq makes it very easy to ship data from the edge to S3.
EdgeMQ is a managed HTTP to S3 edge ingest layer that takes events from services, devices, and partners on the public internet and lands them durably in your S3 bucket, ready for tools like Snowflake, Databricks, ClickHouse, DuckDB, and feature pipelines.
Design focus on simplicity, performance and security.

value: Show HN: Edge HTTP to S3

title: Show HN: Edge HTTP to S3


value: davelradindra

value: I’ve been working on a VS Code extension called Nogic that indexes your project and turns it into an interactive graph of files, symbols, imports, calls, and references. The goal is simple: make it much easier for developers to understand how a codebase fits together, especially now that AI is generating more code than ever.

value: Show HN: Nogic – VS Code extension that visualizes your codebase as a graph

author: davelradindra


value: ErnestBogore

value: Show HN: Track traffic and conversion from AI search

author: ErnestBogore


value: letmetweakit

value:
Hi HN,
Not too long ago, I learned that longterm Linux kernels contain a lot of known and unpatched CVEs, in hindsight this makes sense, I was just a bit surprised at the time.
Long story short, I've made a website that lists all the known CVEs for all kernel versions since 2.6.11.
It makes use of the output of tooling provided by the kernel maintainers, so the list of CVEs should be accurate.
The system has an API that's documented under the 'Docs' section.
Let me know if you find this useful and how I can improve it or add features.
Cheers

value: Show HN: Kernel-Cve

author: letmetweakit

title: Show HN: Kernel-Cve


value: donaldng

value: Show HN: MidaGX – Generate A/B Test Variants from a Prompt Using AI

author: donaldng


value:
I built SpecMem after noticing how fragmented the agentic coding ecosystem has become. Each coding agent uses its own spec format, context resets frequently and switching tools means rebuilding everything from scratch.
SpecMem provides a unified, agent-agnostic memory layer for coding agents. It reads specs from Kiro, GitHub SpecKit, Tessl, Cursor, Claude Code, Codex and others, then builds a semantic memory using LanceDB, ChromaDB etc. It also includes impact analysis, drift detection, selective testing and a web dashboard.
GitHub: https://github.com/SuperagenticAI/specmem
Docs: https://superagenticai.github.io/specmem
pip: pip install specmem
Early project and still rough in places. Feedback from HN is welcome.

value: Show HN: SpecMem-A Unified Ax and Pragmatic Memory for Coding Agents


value: dschnurr

value: Show HN: Using LLMs to extract and grade predictions from the All-In Podcast

author: dschnurr


value: philipmoses

value:
Hey HN,
We are building EZTest, an open-source test management tool designed to replace legacy tools like TestRail.
The Problem:
There’s basically no modern open source test management tool out there. The ones everyone uses like Testiny orTestRail are glorified Excel sheets with a hefty subscription fee. They charge $20 per user per month but barely do anything worthwhile. At this point, the best LLMs cost the same, so the pricing makes no sense.
Ah ha moment was when we realised we could either pay $20 to Claude & Copilot and have a working MVP in a month or pay $19 to Testiny per user per month forever.
The Solution:
EZTest. A modern open source test management tool build with Claude Code and Copilot.
It’s a simple, lightweight test and defect management tool you can self host and actually rely on. Nothing fancy. Just the basics done right.
We are currently in Alpha and I am looking for brutal feedback on the product, features and the UX. We are building this out of Chennai with the goal of creating truly sustainable FOSS business projects.
Repo: https://github.com/houseoffoss/eztest
Demo: http://eztest.houseoffoss.com (you can directly login with demo credentials)
I’ll be in the comments to answer questions about the stack and roadmap.
Roast away xD
Philip.

value: Show HN: EZTest – open-source alternative to TestRail/Testiny

author: philipmoses


value: Show HN: I built a bookmarks assistant that sends updates on your saved links


value: raghavtoshniwal

value: Show HN: Frontier AI Safety Lab Simulator Game

author: raghavtoshniwal


value: shdalex

value: Show HN: AI Lead Generation – A curated list of tools for finding leads

author: shdalex


value: alechewitt

value:
Hi HN,
I’ve been benchmarking which LLMs are best at recreating UIs from screenshots. Happy to answer questions or hear about different results you’ve seen!

value: Show HN: LLM UI Challenge

author: alechewitt

title: Show HN: LLM UI Challenge


value: johns

value: I rarely want to open a compose window when I click a mailto: link. I'd rather copy the email address quickly, search my CRM/help desk/internal admin, search Gmail, etc. So I made a little utility to make that possible.

value: Show HN: A better macOS mailto: handler

author: johns

title: Show HN: A better macOS mailto: handler


value:
Hi HN,
I built WhatHappened (whathappened.tech) because I have a love/hate relationship with this site. I love the content, but the "wall of text" UI gives me FOMO. I was spending too much time clicking into vague titles ("Project X") or wading through flame wars just to find technical insights.
I built this tool to act as a filter. It generates a card for the top daily posts with a few specific features to cut the noise:
1. AI Summaries: It generates a technical TL;DR (3 bullet points) and an ELI5 version for every post.
2. The Heat Meter: I analyze the comment section to visualize the distribution: Constructive vs. Technical vs. Flame War. If a thread is 90% Flame War, I know to skip it (or grab popcorn).
3. Contrarian Detection: To break the echo chamber, the AI specifically hunts for the most upvoted disagreement or critique in the comments and pins it to the card.
4. Mobile-First PWA: I mostly read HN on my phone, so I designed this as a PWA. It supports swipe gestures and installs to the home screen without an app store.
Stack: Next.js, Gemini, Supabase.
It currently supports English and Chinese. Any feedback will be appreciated! My original X post: https://x.com/marsw42/status/1997087957556318663, please share if you like it or find it helpful! :D
Thanks!

value: Show HN: WhatHappened – HN summaries, heatmaps, and contrarian picks


value: yusufaytas

value:
I built OpsOrch, an open-source orchestration layer that gives you one unified API for incidents, logs, metrics, tickets, messaging, and service metadata. It sits on top of the tools you already use (PagerDuty, Jira, Elasticsearch, Prometheus, Slack, etc.) and normalizes everything into a single schema.
OpsOrch doesn’t store your operational data. It just brokers requests through pluggable adapters (Go or JSON-RPC) and returns unified structures. On top of this, there’s an optional MCP server that exposes all capabilities as typed tools for LLM agents.
Why?
Most incident workflows today require jumping across 5+ vendor UIs and APIs, each with its own query language and auth model. OpsOrch aims to be the small, transparent “glue layer” that removes that complexity without forcing a migration.
What’s available now:
Core orchestration service (Go, Apache-2.0)
Adapters: PagerDuty, Jira, Prometheus, Elasticsearch, Slack, plus mock providers
MCP server exposing incidents/logs/metrics/tickets/services as agent tools
No vendor lock-in, no data gravity
Repos:
https://github.com/OpsOrch/opsorch-core
https://github.com/OpsOrch/opsorch-mcp
(+ adapters in the OpsOrch org)
Would love feedback on architecture, adapter model, security concerns, and which integrations you’d want next.

value: Show HN: OpsOrch – Unified API for Incidents, Logs, Metrics, and Tickets

author: yusufaytas


value: aminkhorrami

value:
Hey HN! I'm open-sourcing DataKit today.
GitHub: https://github.com/datakitpage/datakit
Live demo: https://datakit.page
DataKit is a browser-based data analysis platform that processes multi-gigabyte files (CSV, Parquet, JSON, Excel) entirely client-side using DuckDB-WASM. Your data never leaves your browser.
What it does:
  • Process large files (tested up to 20GB) without any server
  • Full SQL interface powered by DuckDB compiled to WebAssembly
  • Python notebooks via Pyodide for data science workflows
  • Connect to remote sources (PostgreSQL, MotherDuck, S3) with
  optional proxy
  • AI assistant that only sees column schemas, not actual data
I was done with having to choose between cloud tools and heavy local installations. I wanted something that just works in a browser tab but has real power.
It's AGPL licensed with commercial licenses available for enterprises.
I've been building this solo as a side project for the past few months. Would love your feedback on:
- Performance bottlenecks you encounter
- Features you'd need for your workflows
- The architecture decisions (all client-side vs hybrid)

value: Show HN: DataKit, your all in browser data studio is open source now

author: aminkhorrami


value: Swizec

value: Show HN: I wrote a book about software engineering in startups

author: Swizec


value: tjwells

value: Show HN: Onyx – Trading Terminal for Polymarket

author: tjwells


value: coolwulf

value: In 2025, we have a few new keyboard phones such as Unihertz Titan 2 and Blackberry Q25, however there are no IME optimized for physical keyboards yet, at least not to my standards, especially for Chinese pinyin and wubi inputs. So I decied to write this by myself. Now this IME have fully supports inputs on these phones without touching screen and also implemented a local voice recognition model with high accuracy. Would like to share this work with the community.

value: Show HN: CoolwulfIME – Best Input Method on Physical Keyboard Phones in 2025

author: coolwulf


value: civancza

value:
I’m Csaba, and I built QueryPanel (querypanel.io) — a server-side SDK that turns natural language into SQL and helps products offer AI-driven dashboards to their users.
Many tools want to let users “chat with their data,” but building this internally means dealing with schema extraction, LLMs, embeddings, prompt engineering, and accuracy tuning. QueryPanel handles these parts for you.
What it does?
 - Discovers your DB schema automatically
 - Generates SQL from natural language
 - Uses embeddings + LLM to improve accuracy
 - Includes an admin UI to add golden queries and annotate columns
 - Generates a chart definition that you can use for generating charts with your favourite chart builder
Where it’s useful?
If your product has dashboards or analytics, QueryPanel lets your users customize them through text (“create a chart of revenue by country last quarter”).
Security
QueryPanel does not store credentials or user data — only the schema.
All execution happens on your side.
Why I built it?
I kept seeing the same problem repeated: everyone wants NL → SQL features. So I abstracted it.
Demo, feedback, and criticism all welcome. I'm especially curious about edge cases and how people think about reliability in NL → SQL systems.

value: Show HN: QueryPanel – AI Driven Dashboards

author: civancza


value:
I built Prompt Refiner, an MCP extension for Claude Desktop that instantly shows you 4 refined versions of any prompt (Concise, Detailed, Creative, Analytical).
Background: As a Business Analyst, I spend a lot of time crafting prompts for requirements, documentation, and analysis. I found myself constantly rewriting prompts to get better results.
Features:
- Instant refinement in 4 styles
- One-click copy
- SQLite history tracking
- Configurable storage (network drives, enterprise-ready)
- Graceful fallback for restricted environments
- No external APIs
Tech stack: Node.js MCP server, SQLite, React artifacts
GitHub: https://github.com/cs97jjm3/prompt-refiner
Setup: ~5 minutes
It's been helpful for my BA work. Happy to answer questions about the implementation or use cases!

value: Show HN: Prompt Refiner – MCP extension that refines prompts into 4 styles


value: iamdavidmt

value: Show HN: I made a simple application that aims to drive code review ownership

author: iamdavidmt


value: davnicwil

value:
Hey HN! Like most here regular meetings have always been a big part of my work.
Over the years I've learned the value of active note taking in these meetings. Meaning: not minutes, not transcriptions or AI summaries, but me using my brain to actively pull out the key points in short form bullet-like notes, as the meeting is going on, as I'm talking and listening (and probably typing with one hand). This could be agenda points to cover, any interesting sidebars raised, insights gotten to in a discussion, actions agreed to (and a way to track whether they got done next time!).
It's both useful just to track what's going on in all these different meetings week to week (at one point I was doing about a dozen 1-1s per week, and it just becomes impossible to hold it in RAM) but also really valuable over time when you can look back and see the full history of a particular meeting, what was discussed when, how themes and structure are changing, is the meetings effective, etc.
Anyway, I've tried a bunch of different tools for taking these notes over the years. All the obvious ones you've probably used too. And I've always just been not quite satisfied with the experience. They work, obviously (it's just text based notes at the end of the day) but nothing is first-class for this usecase.
So, I decided to build the tool I've always felt I want to use, specifically for regular 1-1s and other types of regular meetings. I've been using it myself and with friends for a while already now, and I think it's got to that point where I actually prefer to reach for it over other general purpose note taking tools now, and I want to share it more widely.
There's a free tier so you can use it right away, in fact without even signing up.
If you've also been wanting a better system to manage your notes for regular meetings, give it a go and let me know what you think!

value: Show HN: I built a system for active note-taking in regular meetings like 1-1s

author: davnicwil


value: chinskee

value:
I built a small weekend project to help track RAM prices, since DDR3/DDR4/DDR5 costs have suddenly jumped recently and I was struggling to find good deals for my NAS build.
RamScout scans eBay (UK/US) and ranks RAM listings by price per GB, with filters for type, capacity, speed, condition, etc. It’s a simple MVP — no frills, no accounts, no ads — just a fast way to spot unusually cheap listings.
Would appreciate any feedback, especially on performance, UI, and whether expanding to more regions/vendors would be useful. Thanks!

value: Show HN: RamScout – Search eBay RAM Listings by Price per GB (US/UK)

author: chinskee


value: fixpointflow

value:
Axis is an experiment in designing a small, deterministic, semantics-first logic language with an AI collaborator. The goal is to explore whether a minimal, rigorously defined semantic layer can help AI systems generate safer and more consistent code across different host languages.
The repo contains the draft whitepaper, early semantics. This is very early work — I’m sharing it now to get feedback from people working in PL theory, compilers, formal methods, and AI tooling.
Repo:
https://github.com/axis-foundation/axis-research
Whitepaper (PDF):
https://github.com/axis-foundation/axis-research/blob/main/p...
I’m particularly interested in where this overlaps with existing research, where the ideas may be flawed, and whether the overall direction seems useful or misguided. All feedback welcome.

value: Show HN: Axis – A semantics-first logic language co-designed with AI

author: fixpointflow


value: mareksotak

value:
We’ve been testing an internal tool I vibe-coded to replace our support desk workflow. It’s a Chrome extension that runs on top of Gmail's delegated inbox (neglected by Google and missing some features to make it useful) and uses a single Google Sheet in Drive as the data store. No backend, no server, no external services.
It’s been running internally for ~2 weeks and has held up well enough that we’re considering replacing our existing $10k/year SaaS vendor.
Why I built it
Gmail and Google Workspace have had the primitives for a proper shared inbox for years - labels, filters, threads, roles, Groups, Drive, and robust APIs.
But the delegated mailbox/shared inbox capability has remained largely untouched and, in practice, isn’t particularly useful for teams. It solves only a small fraction of the actual collaboration problem.
That gap led me to build something lightweight on top of Gmail itself.
Our support workflow didn’t require the full weight of a dedicated support desk platform. We needed a shared inbox, internal notes, assignment, and basic ticket metadata.
The goal wasn’t to rebuild Zendesk. It was to build the minimum useful set of features inside Workspace, without forwarding emails or sending customer data through more vendors.
How it works
Chrome extension - sidebar - displays current thread info if any
Auth uses chrome.identity → Google OAuth
Sheet (Drive-owned) stores ticket metadata, shared with the team
Attachments saved to a Drive folder
Labels control assignment + status (Pending/Closed)
Footer with internal ticket ID (searchable via Gmail)
“People in thread” resolver + quick history lookup
Everything happens client-side. There is no backend.
Why a spreadsheet?
For an MVP, it worked surprisingly well:
version history
no schema migrations
trivial rollback
owned by the Workspace admin
If this ever evolves, I’d use a proper database, but for now the Sheet keeps everything simple and contained.
A fun exploration of what you can assemble quickly within Google Workspace.

value: Show HN: Chrome Extension and Spreadsheet that replaced our $10k/y support desk

author: mareksotak


value: zetacrushagent

value: Hi all, I wanted to share the leaderboard I have created and am working to rank LLM models. My results are very similar to those of ARC-AGI 2 with the only exception being that DeepSeek is rated higher on my leaderboard. In order to keep the test closed-source. The plan is that once the top models max out on a given task on our test then we will adopt new criteria to differentiate.<p>The test is currently comprised of 10 scores, 9 of which no model scores above 0 on. Check it out and let me know what you think! Thanks

value: Show HN: ZetaCrush – An Intelligent LLM Leaderboard

author: zetacrushagent


value:
I'm Clive, a developer from South Africa. Four days ago, Eduardo Borges posted about getting hacked through CVE-2025-55182 (the React Server Components RCE). His server was patched, but the malware stayed, crypto miners, fake services named "nginxs" and "apaches", cron jobs for persistence. CPU at 361%. Part of a 415-server botnet.
That's when I realized: patching removes the vulnerability, but not the infection.
I built NeuroLint originally as a deterministic code transformation tool for React/Next.js (no AI, just AST-based fixes). When this CVE dropped, I added Layer 8: Security Forensics.
It scans for 80+ indicators of compromise:
- Suspicious processes (high CPU, random names, fake services)
- Malicious files in /tmp, modified system binaries
- Persistence mechanisms (cron jobs, systemd services, SSH keys)
- Network activity (mining pools, C2 servers)
- Docker containers running as root with unauthorized changes
- Crypto mining configs (c.json, wallet addresses)
Try it:
  npm install -g @neurolint/cli
  neurolint security:scan-breach . --deep
No signup required. Works on Linux/Mac. Takes ~5 minutes for a deep scan.
What's different from manual detection:
- AST-based code analysis (detects obfuscated patterns)
- 80+ behavioral signatures vs. 5-10 manual grep commands
- Automated remediation (--fix flag)
- Timeline reconstruction showing when breach occurred
- Infrastructure-wide scanning (--cidr flag for networks)
The tool is deterministic (not AI). Same input = same output every time. Uses Babel parser for AST transformation with fail-safe validation - if a transformation fails syntax checks, it reverts.
Built it in 3 days based on Eduardo's forensics and other documented breaches. Already found dormant miners in test environments.
GitHub: https://github.com/Alcatecablee/Neurolint-CLI
NPM: https://www.npmjs.com/package/@neurolint/cli
If you were running React 19 or Next.js 15-16 between Dec 3-7, run the scanner even if you already patched. Especially if you already patched.
Happy to answer questions about the detection logic, AST parsing approach, or the CVE itself.

value: Show HN: Tool to detect malware left behind after patching CVE-2025-55182


value: puterbonga

value: Show HN: Octopii, a runtime for writing distributed applications in Rust

author: puterbonga


value: kazusan

value:
Hi HN,
I built dabuun (https://dabuun.com/), a tool that turns a single line of text into a ready-to-post social media video.
Instead of manually editing video timelines, dabuun takes a short description and automatically creates:
- plot and script
- scenes and images (anime or realistic)
- voiceover
- subtitles
- final rendered video
It supports both Japanese and English and can format for YouTube, TikTok, and Instagram with the right aspect ratios. It also keeps character appearance consistent across scenes using AI-generated references, so your “main character” doesn’t change every cut.
This is meant for:
- people who find video production exhausting or too time-consuming
- people who only have a smartphone and no recording/editing gear
- creators, educators, and storytellers who want to focus on ideas, not tools
Why I built it
A while ago I made some YouTube videos manually, and it was mentally and physically draining. I wanted something that would let more people turn simple ideas into content without needing video skills or equipment. Long term, I’d like to make it easier for anyone, anywhere, to create monetizable content with just a phone and an idea—and maybe help reduce the current imbalance in the creator economy.
Tech stack
- Frontend: Next.js
- Infra: Vercel, GCP
Known limitations / next steps
- Script quality still needs work; I want better story structure and pacing
- I’d like to add “consulting” features that suggest how to improve your video for each platform
- Longer term: evolve into a platform where creators can manage and publish content, not just generate single videos
I’d really appreciate any feedback, especially from people who actually try generating a video:
- Does the output feel good enough to post as-is?
- What feels off or low quality?
- What would you need to trust this for your real content?
Thanks for reading, and I’m happy to answer any questions.

value: Show HN: Dabuun – Turn one line of text into a social video

author: kazusan


value: xinbenlv

value:
We've built a domain brainstorm experience with WebGPU-powered in-browser LLM pure client-side inference, seems working pretty well. Not on par with ChatGPT/Gemini for sure, but good enough for this use case.
A few benefits
1. Running on pure client side, privacy friendly
2. Free - no credits needed, no API keys, nothing. User device pays the computation
3. Interoperable - you can let it come up with direction of ideas.
QQ: What are a few ideas you think it could be used for?
QQ: If on-device LLM becomes much more powerful, what are things it can do before they are less powerful?

value: Show HN: Namefi built WebGPU-powered in-browser LLM pure client-side infer UX

author: xinbenlv


value: modinfo

value: Show HN: Edit JSON files with comments, read them as clean JSON

author: modinfo


value: aintitthetruitt

value: I'm sick of things like greenlight that charge a high fee when all my young kids really need is a basic ledger to intro the ideas.  Would love feedback.<p>Excuse the vibes - I haven't styled yet.

value: Show HN: A "bank of my parents" for my young kids

author: aintitthetruitt


value: rodrigogs

value:
Built this after a business partnership went bad and I needed WhatsApp conversations for legal proceedings.
My lawyer had me screenshotting hundreds of messages, trying to keep them in order, then having opposing counsel question if anything was tampered with. I looked at existing tools but they choke on large chats and have no way to bookmark or search efficiently - useless when you're digging through years of conversation for specific evidence.
So I made this. Drop in your WhatsApp export zip, get a proper chat interface with all media. The original export stays untouched (important for evidence), bookmarks and annotations layer on top. Voice messages get transcribed locally via Whisper/WebGPU - nothing leaves your machine.
Tested with 18k+ messages, no issues. Runs in browser or as an Electron app for all desktop platforms (Android and iOS coming soon).
Feedback welcome, especially from anyone who's dealt with digital evidence.

value: Show HN: WhatsApp Backup Reader – Offline Viewer

author: rodrigogs


value: yterasaka

value:
Hi HN!
I made TinyJson API (https://tinyjsonapi.com) – paste your JSON into a text field, click "create", and get a temporary API endpoint that works for 24 hours.
Perfect for prototyping, testing, or sharing data quickly without setting up a backend.
It's minimal by design right now. Would love your feedback!

value: Show HN: TinyJson API – Create temporary API endpoints from JSON in seconds

author: yterasaka


value: egz

value:
Hello! I'm sharing this Quintus calendar format - 12 months of 30 days with 5 day weeks and some overflowing days - as an alternative to the more common Gregorian calendar.
This website runs a time server for the current Quintus date alongside conversions between dates for different calendar formats on the landing page. Plans to convert scheduled events with the ".ics" format are supposed for the next few months.
A hand-pressed 2026 edition is also offered now and has been quite fun to make. Questions, critiques, or ideas are all appreciated!

value: Show HN: Quintus Calendars – An alternative to the irregular Gregorian calendar

author: egz


value: hgarg

value: Show HN: Hide X Posts at Country Level

author: hgarg

title: Show HN: Hide X Posts at Country Level


value: Faiqhanif

value: Show HN: Era – A deterministic, non-neural AI that runs on CPU

author: Faiqhanif


value: nickisyourfan

value: Build forms, analyze responses with agents, assign followups to colleagues.

value: Show HN: Lillyform – Built conversational forms, agentic analysis, and more

author: nickisyourfan


value: uptownhr

value: Show HN: DuckDB Graph Research RAG for Claude Code

author: uptownhr


value: Show HN: Claude Code Knowledge graphs auto-generated from conversations


value: Show HN: DSPydantic - Auto-Optimize Your Pydantic Models with DSPy


value:
PyAtlas is an interactive map of the top 10,000 most-downloaded packages on PyPI.
Each package is represented as a point in a 2D space. Packages with similar descriptions are placed close together, so you get rough clusters of the ecosystem (web, data, ML, etc.). You can: Simply explore the map, search for a package you already know, or explore points nearby to discover alternatives or related tools
Useful? Maybe, maybe not. Mostly just a fun project for me to work on. If you’re curious how it works under the hood (embeddings, UMAP, clustering, etc.), there are more details in the GitHub repo!

value: Show HN: PyAtlas – Interactive map of the most popular Python packages on PyPI


value: ppipada

value: - MapStore is a local, filesystem‑backed map database with pluggable codecs (JSON or custom), optional per‑key encryption via the OS keyring, and optional full‑text search via SQLite FTS5.<p>- It has convenience wrappers for managing files in directories with partitioning.<p>- I had developed this for managing client side state in my wails app. Key store, config store, or any other state can be handled.

value: Show HN: Mapstore-go files-backed store with pluggable serialization/encrypt/fts

author: ppipada


value: abhikvarma

value:
We built this after someone on Reddit asked "What's the DRO for Koramangala?" (DRO = District Registrar Office, needed for property transactions in India). Turns out there was no way to find this. Bangalore has five DROs but no public mapping of which areas they cover.
We then realised the same problem exists for almost every civic service: ward offices, police stations, electricity boards, water supply. Citizens have no easy way to know which office handles their area.
Enter an address, get all relevant civic bodies for that location. Currently live for Bengaluru only.
Tech stack: Go backend, React frontend. We load GeoJSON boundary files into memory and do point-in-polygon queries.
The main bottleneck to expanding is data. Government boundary data is scattered, inconsistent, and often requires RTI requests. We've included an RTI template in the repo for anyone who wants to help add their city.
Would love feedback on:
- Is the UI/UX clear?
- Ideas for sourcing boundary data at scale
- The API design for adding cities (see CONTRIBUTING.md)
Happy to answer questions about navigating Indian bureaucracy or the technical implementation.

value: Show HN: Civic Compass – Find which govt offices serve your address in India

author: abhikvarma


value: beingmudit

value:
Hi HN,
I'm a solo dev who spent the last few weeks obsessively trying to solve the "last mile" problem of browser emulation: making it actually feel good to play.
While the core tech (Libretro/WASM) is solid, wrapping it in a UI that feels "native" is a deep rabbit hole. I built Koin Deck Player to solve the glue-code headaches, and I've open-sourced it as a drop-in React component.
It handles the tricky engineering bits so you don't have to:
- Latency Tiers: Automatically enables "Run-Ahead" (calculating frames in advance) for 8/16-bit systems to get sub-frame latency. - Performance: Detects SharedArrayBuffer support to switch N64/PS1 emulation to threaded video rendering, preventing UI freezes. - Mobile Controls: A custom GPU-accelerated virtual controller that uses heuristic multi-touch (sliding finger between buttons works) and haptics. - Audio: Handles the notorious auto-play/resume AudioContext quirks across browsers.
I built this because I believe preservation shouldn't just be about archiving file, it should be about making the experience delightful.
Repo is MIT licensed. I'd love to hear your feedback on the input latency!

value: Show HN: I built a "Netflix-grade" retro game player for the browser(React+WASM)

author: beingmudit


value: ronibhakta

value:
hey HN!
Roni from India — ex-Google Summer of Code(GSoC) @ internet archive, full-stack dev.
got frustrated watching json bloat my openai/claude bills by 50%+ on redundant syntax, so i built ZON over a few weekends: zero-overhead notation that compresses payloads ~50% vs json (692 tokens vs 1,300 on gpt-5-nano benchmarks) while staying 100% human-readable and lossless.
Playground -> https://zonformat.org/playground
ROI calculator -> https://zonformat.org/savings
<2kb typescript lib with 100% test coverage. drop-in for openai sdk, langchain js/ts, claude, llama.cpp, streaming, zod schemas—validates llm outputs at runtime with zero extra cost.
Benchmarks -> https://zonformat.org/#benchmarks
try it: npm i zon-format or uv add zon-format, then encode/decode in <10s (code in readme). full site with benchmarks: https://zonformat.org
github → https://github.com/ZON-Format
harsh feedback on perf, edge cases, or api very welcome. if it saves you a coffee's worth of tokens, a star would be awesome
let's make llm prompts efficient again

value: Show HN: Zonformat– 35–60% fewer LLM tokens using zero-overhead notation

author: ronibhakta


value: exxoooz

value: Show HN: SafeGrub – Verify food restrictions using Gemini 2.5 and Grounding

author: exxoooz


value: pheelicks

value:
70 years ago today, on December 9th 1955, the Council of Europe adopted the flag we all recognize . Hidden in plain sight for seven decades is a perfect geometric secret.
Central to the flag are 12 five-pointed stars. Take these stars, align them point to point, and you’ll get a dodecahedron - one of the 5 Platonic solids that have fascinated humanity since Ancient Greece.
It's a remarkable coincidence. Many designs were proposed before 1955, including variants with 1 or 15 stars. But only 12 stars create this hidden geometric harmony.
This was built using THREE.js and runs with WebGPU, including on mobile. The code is here: https://github.com/felixpalmer/starsalign

value: Show HN: Starsalign.eu

author: pheelicks

title: Show HN: Starsalign.eu


value: GrammarChecker

value:
A tiny tool to detect text from ChatGPT/GPT-5/Claude/Gemini/LLaMA.
Free and fast: https://dechecker.ai/

value: Show HN: DeChecker – Detect AI-generated text

author: GrammarChecker


value: Show HN: I replaced Markov Chains with Biomechanics to predict word transitions


value: since

value:
Hey HN! OP here.
I was too lazy to check 50+ RSS feeds daily, so I built a robot to do it for me.
This is a 100% non-profit, for-fun project (no ads, no tracking, just code). I built it to learn Next.js and LLM automation.
The Request: Roast it hard :)
- Is the AI summarizing things correctly or just hallucinating?
- Is the UI clean or "developer art"?
Original sources are linked in every post. Thanks for checking it out!

value: Show HN: I built a website that runs itself. Roast my AI-generated content

author: since


value: hypmachine

value:
I built a tool that learns structural code transformations from a single before/after example.
It isn’t a transformer or LLM — it doesn’t generate code.
It extracts the structural pattern between two snippets and compiles a deterministic rewrite rule. Same input → same output, every time.
Examples:
 • console.log(x) → logger.info(x) generalises to console.log(anything)
 • require(“x”) → import x from “x”
 • ReactDOM.render → createRoot
 • custom project conventions
The rules apply across an entire codebase or through an MCP plugin inside Claude Code, Cursor, or plain CLI.
It runs entirely on CPU and learns rules in real time.
Tool: https://hyperrecode.com
I’d really appreciate feedback on the approach, design, or failure cases.

value: Show HN: A deterministic code-rewrite engine that learns from one example

author: hypmachine


value: heavymemory

author: heavymemory

story_text:
I built a tool that learns structural code transformations from a single before/after example.
It isn’t a transformer or LLM — it doesn’t generate code.
It extracts the structural pattern between two snippets and compiles a deterministic rewrite rule. Same input → same output, every time.
Examples:
 • console.log(x) → logger.info(x) generalises to console.log(anything)
 • require(“x”) → import x from “x”
 • ReactDOM.render → createRoot
 • custom project conventions
The rules apply across an entire codebase or through an MCP plugin inside Claude Code, Cursor, or plain CLI.
It runs entirely on CPU and learns rules in real time.
Tool: https://hyperrecode.com
I’d really appreciate feedback on the approach, design, or failure cases.

title: Show HN: A deterministic code-rewrite engine that learns from one example


value: rethink-hub

value: Show HN: Arc – Android overlay to run custom AI prompts on any app

author: rethink-hub


value: azureray

value:
I’ve been building a small MVP called *Repack AI*.
You paste in an *Article or Video URL*, and it instantly generates a content pack for 10+ social platforms — including text, visuals, and short faceless videos.
*Current capabilities:*
- Platform-specific text: X/Twitter threads, LinkedIn summaries, IG/Threads captions
- Auto-generated visuals + short vertical videos (TikTok/Reels/Shorts style)
- Works with article URLs, newsletters, and (early testing) YouTube links
It’s early-stage, so outputs may need light editing. I’m mainly looking for feedback from people who publish or repurpose content regularly.
Try it here (free Credits included):
https://repackai.co/
Happy to hear what works, what feels missing, and what you'd expect next.

value: Show HN: Repack AI – Turn Any Article/Video URL into 10 Social Content Pieces

author: azureray


value: soasme

value: Show HN: A Werewolf-style puzzle with zero lying

author: soasme


value: dskuldeep

value:
We built Bifrost because we found existing Python-based gateways struggled with high concurrency in production. We wanted something that treated LLM infra like high-availability software.
We ran side-by-side benchmarks against LiteLLM on a single t3.medium instance (using a mock LLM with 1.5s fixed latency) to test pure gateway overhead.
The Results:
p99 Latency: 90.72s (LiteLLM) vs 1.68s (Bifrost)
Throughput: 44 req/sec vs 424 req/sec
Memory: ~3x lighter usage in Go.
It’s a drop-in replacement (OpenAI compatible) designed for teams needing semantic caching, failover, and observability without the overhead.
We’d love to hear your feedback.

value: Show HN: Bifrost – open-source LLM Gateway (50x lower latency than LiteLLM)

author: dskuldeep


value: liamhawtin

value:
Vieta Space was built to reduce the friction of writing and editing LaTeX for mathematical expressions.
Existing tools often slow down math communication and force a slow iteration cycle of manual LaTeX. The growing demand for digital math across classrooms, research, and LLM based workflows made a faster and more direct editor necessary.
The project focuses on visual construction, natural language actions, and stable structural behavior.
It's free. Feedback on usability and future directions is welcome!

value: Show HN: Vieta Space, a visual LaTeX math editor

author: liamhawtin


value: LargeModGames

value: Show HN: I revived Spotify-TUI (now Spotatui) with native streaming and updates

author: LargeModGames


value: PatriceC

value: A minimalist, drag-and-drop homepage builder with sync-via-URL - Made with Val.town / Json / React

value: Show HN: Paaage – Minimalist, drag-and-drop homepage builder

author: PatriceC


value: virtualdns

value:
Hi HN,
I built VirtualDNS, a programmable internal DNS for private environments, because I kept breaking my own infrastructure every time I changed an IP, added a service, or tried to keep multiple environments consistent.
This is not public DNS. It is a private, multi-tenant DNS layer for internal connectivity, service discovery, and infra automation.
Why I built it:
- /etc/hosts drift across machines
- Internal DNS setups with no audit logs
- Environments behaving differently because configs weren’t aligned
- IP changes silently breaking services
- Teams building their own “mini DNS” scripts
- Using public domains just to test internal stuff
- Slow propagation inside private environments
- Wanting service discovery without the overhead of Consul/CoreDNS
What it does:
- Virtual domains resolvable only inside your project/org
- Multi-IP records for load balancing, failover, and high availability
- Health-ordered IP responses
- Full audit logs for resolutions and modifications
- Versioned changes with rollback
- Granular tokens with per-project scopes
- CLI, SDKs, and a daemon for OS-level resolution
- No public DNS, no TTL delays, no external exposure
Technical notes:
- Resolver implemented from scratch, independent of OS DNS unless using the daemon
- Domains and records stored as versioned states (commit-like)
- Each resolution is logged with requester, token, timestamp, and metadata
- Projects are fully isolated, allowing identical domain names across orgs
- Multi-IP responses support rebalancing based on service health
- Daemon works as a lightweight local DNS forwarder
What I'm looking for:
- Feedback from people who handle internal DNS or service discovery
- Edge cases I should consider
- Concerns about using a managed internal DNS
- Suggestions for integration points (Terraform, WireGuard, Nomad, etc.)
Landing page (very early): https://virtualdns.io
Happy to answer technical questions.

value: Show HN: VirtualDNS – a programmable internal DNS for private environments

author: virtualdns


value: codesink

value:
Hey!
I’m the creator of GoClimbr. I’m a software engineer and climber, and for years I tracked every route in notebooks, notes apps and spreadsheets (a big, frustrating mess).
I thought that an app could provide a better way to organize and visualize my climbing data, safely back it up, and make it easier to extract useful insights from it (performance, consistency, volume, grade progression, etc.)
Tech Stack: It’s built with React Native / Expo (so both iOS and Android), backed by Firebase.
I’m looking for some feedback from fellow climbers. How do you log your routes? What kind of information do you track?
You can try it out here: https://goclimbr.com/share
Thanks!

value: Show HN: I built a climbing tracker for data-obsessed climbers (Expo/RN)

author: codesink


value: ulinycoin

value: Show HN: I got 50% of my traffic from ChatGPT instead of Google

author: ulinycoin


value:
Hi folks!
Tiny Recursive Models reach impressive results on ARC AGI. I implemented a version from scratch, with ease of experimentation in mind:
- cleaner config: hydra, uv, lightning
- smaller datasets for faster iteration (Sudoku 6x6 and 9x9)
- introduction, in-code video
All important implementation details have been carefully kept. The results of the paper are reproducible (Sudoku Extreme, Maze Hard).
Feedback/contributions welcome.

value: Show HN: I reimplemented TRM – trains on Sudoku 6×6 in minutes on an A10


value: henwfan

value:
I built AlgoDrill because I kept grinding LeetCode, thinking I knew the pattern, and then completely blanking when I had to implement it from scratch a few weeks later.
AlgoDrill turns NeetCode 150 and more into pattern-based drills: you rebuild the solution line by line with active recall, get first principles editorials that explain why each step exists, and everything is tagged by patterns like sliding window, two pointers, and DP so you can hammer the ones you keep forgetting. The goal is simple: turn familiar patterns into code you can write quickly and confidently in a real interview.
https://algodrill.io
Would love feedback on whether this drill-style approach feels like a real upgrade over just solving problems once, and what’s most confusing or missing when you first land on the site.

value: Show HN: AlgoDrill – Interactive drills to stop forgetting LeetCode patterns

author: henwfan