value: K-dash

value:
Finding open source issues is easy.
Deciding which ones are worth your time is not.
I built Contrib.FYI as a simple web app to reduce that decision cost.
Instead of relying on static, curated lists, it uses live GitHub API data
and shows issues in chronological order, so discovery stays fresh.
On top of that, it surfaces a few early signals (language, stars,
no comments, no linked PRs) to help you avoid opening issues that are
already being worked on.
The goal is not to find more issues,
but to find better candidates to spend your time on.
Source code is available here:
https://github.com/K-dash/contrib-fyi
Feedback is welcome.

value: Show HN: A simple way to find open source issues to contribute to

author: K-dash


value:
I spend some days to develop a content-aware AI named AiRenamify app to name your files.
It's smartest way to organize files. AiRenamify uses content-aware AI to give every file its true name. Drag, drop, and let AI name your files instantly.
Features:
1) Intelligent Recognition
Uses advanced AI (LLMs and Vision) to "read" and "see"your files. It understands the content of images and documents to generate descriptive, human-readable names automatically.
2) Privacy & Security
We prioritize your privacy. Your files are transmitted securely via encrypted connections solely for analysis and are immediately discarded. We never store your personal files.
3) Smart Customization
Full control over your filenames. Configure case styles, set target languages, or add custom instructions like "Always include the primary color" to guide the AI.
4) Bulk Processing
Rename thousands of files in seconds. AiRenamify handles large queues efficiently, saving you hours of manual work while maintaining high accuracy.
You can access to the app by: https://airenamify.com.

value: Show HN: AiRenamify – Smartly Rename Your Files with AI


value:
My note-taking habit has always been to write things down the moment a thought occurs.
For a long time, that meant sending messages to myself in chat apps, or quickly jotting things down in text editors like Emacs with org-mode — scattered, unstructured, and immediate.
Over time, I realized that this unstructured way of writing was simply how I think.
I wasn’t looking for structure upfront; I just wanted a fast, free-form way to write things down as they happen.
So I built a note-taking app that could naturally embrace this style.
Strflow lets you capture unstructured thoughts quickly in a chat-style, chronological timeline, and organize them later if needed.
Features like tagging, todos, and reminders are integrated directly into the timeline-based UI. They’re there when you need them, but they don’t interrupt the core flow of just writing something down and pushing it forward.
I put a lot of effort into making sure the system never blocks writing things down: the textfield is always focused, global shortcuts are available, and the app prioritizes native, coherent UI as a first-class citizen in the Apple ecosystem.
As a heavy user myself, I have 20,000 notes in Strflow, and it still runs smoothly.
I shared an early version two years ago, and I’ve been iterating on it based on feedback and day-to-day use.
Please give it a try and share your feedback — I’m happy to answer any questions.

value: Show HN: A texting-yourself-style note app for capturing unstructured thoughts


value:
I built a small web tool that lets people create Arabic calligraphy without needing design software. Most existing tools are either too complex or very limited, so I wanted something simple and accessible.
Features:
• Write Arabic directly or translate from English
• 11 classic calligraphy styles (Thuluth, Naskh, Kufi, Diwani, etc.)
• Adjust layout, colors, line height, stroke, and rotation
• Export as PNG, JPG, or SVG
• No signup required
I’d appreciate any feedback on performance, UI, or calligraphy accuracy. This is a solo side project and still evolving.
Site: https://arabiccalligraphygenerator.online

value: Show HN: Arabic Calligraphy Generator – 11 styles, free, no signup


value: realdexter

value:
OP here. I built RepoReaper to solve code context fragmentation in RAG.
Unlike standard chat-with-repo tools, it simulates a senior engineer's workflow: it parses Python AST for logic-aware chunking, uses a ReAct loop to JIT-fetch missing file dependencies from GitHub, and employs hybrid search (BM25+Vector). It also generates Mermaid diagrams for architecture visualization. The backend is fully async and persists state via ChromaDB.
Link: https://github.com/tzzp1224/RepoReaper

value: Show HN: RepoReaper – AST-aware, JIT-loading code audit agent (Python/AsyncIO)

author: realdexter


value: tskulbru

value:
I built something embarrasingly simple. It changed my workday. When COVID began and I started working from home, I as many others, bought a standing desk. $500, height memory, digital display, the works.
How much did I actually stand? Maybe 15-20 minutes a day. I just kept forgetting.
- Tried phone alarms, annoying, started dismissing them without thinking.
- Tried sticky notes, lasted 2 days.
- Tried "I'll just remember", never worked.
So I built Stao. It's a timer that reminds you to switch positions. That's basically it.
I use the 20-8-2 pattern: 20 min sitting, 8 min standing, 2 min moving around (grab water, stretch, whatever). Short cycles so you never get stiff. There's also a 45-15 mode if you want longer focus blocks.
After a month my back feels noticeably better and the 3pm energy crash is gone.
What it does:
- Timer with customizable sit/stand/move intervals
- Gentle notifications when it's time to switch
- Tracks your daily/weekly standing time
- Lives in system tray on desktop (if not using the mobile apps), doesn't get in the way
What it doesn't do:
- No account required
- No cloud sync (all data stays on device), however I might add this in the not too distant future
- No social features, gamification, or AI nonsense
Tech stack: Flutter for cross-platform (yes, even desktop). Hive for local storage. Native integrations for system tray and notifications. No backend.
Available on Mac/Windows/Linux/iOS. Android is in beta testing.
Website: https://www.stao.app
Simple demo video: https://www.youtube.com/shorts/-skrtpOcOh0
AppStore: https://apps.apple.com/app/stao-standing-desk-reminder/id675...
The name is Norwegian — "stao no pao" roughly means "keep going" in one of the many Norwegian dialects.
Happy to answer questions about the implementation, the standing desk research rabbit hole I fell into, or anything else.

value: Show HN: Stao – A simple standing desk reminder I built for myself

author: tskulbru


value: ebhn

value: Abstract Port Graphs is a framework for building Domain Specific Languages (DSLs), where programs are represented as graphs. This project was created in pursuit of the ARC AGI challenge (more details on that at https://arcprize.org). By building a DSL on top of it for ARC puzzles, I was able to create compact solutions to the puzzles, which can be generated automatically via program synthesis techniques. The goal here was to explore program synthesis and symbolic AI approaches. The rationale for using graphs was the ability to detect isomorphisms between equivalent programs, leading to reusable components. The page includes additional links to the GitHub repo and a brief paper on the project. Would love feedback on the work, and to connect with anyone interested in ARC!

value: Show HN: Abstract Port Graphs

author: ebhn

title: Show HN: Abstract Port Graphs


value:
I built a small cross-platform CLI tool to apply deterministic, backup-style retention rules to arbitrary file sets.
The goal was predictable and auditable deletion behavior for backups, archives, or generated artifacts, beyond ad-hoc cleanup scripts or logrotate-style approaches.
It supports multiple time-based retention modes (hours to years), cumulative rules (e.g. daily + weekly + monthly), post-filters like max-age, max-size, and max-files, and a mandatory dry-run mode with detailed decision logs.
Documentation is provided via README and a man page.

value: Show HN: Deterministic file retention CLI for backups and archives


value: reallyaaryan

value:
I built Squair because my phone was giving me anxiety - and it was also the only thing in my pocket when anxiety hit.
The problem: 4 cups of coffee just to focus. Then the anxiety kicks in. Chest tight. Racing thoughts. Scrolling reels to "calm down" made it worse.
I Googled "anxiety relief" at 2am and found box breathing — same technique Navy SEALs use in combat. First time I tried it, I felt the difference within 2 minutes. No crash. No pills. Just breathing.
So I built an app around it.
6 months. Solo dev with a full-time job. 112 builds. 2 App Store rejections. Finally live.
What it does:
- Zen Button — One tap, instant guided breathing when panic hits
- Box Breathing — 4-4-4-4 pattern (inhale, hold, exhale, hold)
- 4-7-8 Breathing — For falling asleep faster
- Works offline, no account required
Try it: https://squair.app
iOS: https://apps.apple.com/us/app/squair-breathe-focus/id6753581...
Would love feedback. What do you do when you need to calm down fast?

value: Show HN: Squair – Breathing app I built after Googling "anxiety relief" at 2am

author: reallyaaryan


value: larsky

value:
I built Orwdle.com, a daily anagram word game (the domain itself is an anagram of "Wordle.")
Tech stack is intentionally minimal: vanilla JS, no frameworks, hosted on Cloudflare Pages with a Workers backend for the daily word API. Total JS is under 15KB.
I wanted to make something where the domain name was part of the joke and it really was just for me and my kids. It was just too addictive and nice not to share with the wider community.
Open to full criticism and feedback.

value: Show HN: Orwdle – A Daily Anagram Puzzle

author: larsky


value: tsazan

value:
OP here.
I took the unofficial IKEA US dataset (originally scraped by jeffreyszhou) and converted all 30,511 products into a flat, markdown-like protocol called CommerceTXT.
The goal: See if a flatter structure is more efficient for LLM context windows.
The results:
- Size: 30k products across 632 categories.
- Efficiency: The text version uses ~24% fewer tokens (3.6M saved total) compared to the equivalent minified JSON.
- Structure: Files are organized in folders (e.g. /products/category/), which helps with testing hierarchical retrieval routers.
The link goes to the dataset on Hugging Face which has the full benchmarks.
Parser code is here: https://github.com/commercetxt/commercetxt
Happy to answer questions about the conversion logic!

value: Show HN: 30k IKEA items in flat text

author: tsazan

title: Show HN: 30k IKEA items in flat text


value: joshuafkon

value:
America's TFR is 1.67. I wanted to understand what it would actually take to get back to replacement (2.1), so I built a simulator where you can stack policies and see the projected effects.
Every policy has cited effect sizes (Cohen, Milligan, Raute, etc.) with confidence levels. You can click any policy title to see the methodology and sources. The model includes:
Fiscal tracking (policy costs, deficit impact, GDP effects)
Diminishing returns when stacking similar interventions
Immigration with selection mechanisms and generational convergence
Tax increases and entitlement reform as funding options (with growth drag)
A few "illiberal" policies for analytical completeness
The honest answer seems to be: it's really hard. Most realistic packages get you to ~1.9-2.0 at enormous cost, and that's assuming the effect estimates transfer to the US context (they might not).
Built with vanilla JS. Feedback welcome - especially on the methodology or effect estimates I got wrong.

value: Show HN: Can you hit replacement? A fertility SIM with cited sources

author: joshuafkon


value:
Hi HN,
I’m building ElixirFeed, a small web app that helps people follow health and longevity research without having to read full scientific papers.
The app:
- pulls in new articles from PubMed
- filters for topics like longevity, lifestyle, diet, exercise, and metabolic health
- uses an LLM to generate short, plain-language summaries, actionable takeaways, translations
- labels study types (RCT, review, preclinical)
- adds simple, editorial-style visual explanations
- lets users vote and discuss papers
The goal is not to give medical advice, but to make it easier to understand what the research actually says.
I’m especially interested in feedback on:
- whether AI summaries are useful or risky in this context
- how to signal uncertainty and evidence quality better
- whether the product is valuable enough to justify a small subscription
Link: https://elixirfeed.co
Happy to answer questions about the technical pipeline, data sources, or design choices.

value: Show HN: ElixirFeed – AI summaries of new PubMed research on longevity


value: Olibier

value:
Hi HN, I’m the creator of YoloForge. I built this because I hit a wall with a hobby computer vision project: I needed a custom dataset, and zero-shot tools like Grounding DINO just weren't accurate enough for my specific classes. I decided I’d rather write code for a couple of weeks than draw another box by hand.
I previously experimented with Grounding DINO and SAM3. While they are amazing for generic objects, I found they struggle with specific semantic requests (e.g. specific manufacturing parts, game characters or distinguishing "a worker" from "a worker without a helmet").
I discovered that Gemini 3 Pro is surprisingly underrated for bounding box tasks if you prompt it with detailed visual descriptions. It handles semantic understanding significantly better than standard zero-shot detectors.
url: yoloforge.com
The Workflow:
Upload a zip of raw images (stored in Cloudflare R2).
Describe class/classes in plain English.
The system generates a .jsonl batch file and sends it to the Gemini Batch API. This allows us to process thousands of images in parallel at 50% of the standard cost.
You review/correct boxes in the UI and export the YOLO train/val/test dataset.
Technical Challenges:
One hard part was getting valid JSON out of the LLM consistently. I ended up writing a robust parser that uses regex fallback strategies to literally "salvage" valid bounding boxes from malformed responses.
The Stack:
- Frontend: Next.js
- Backend: FastAPI, Celery (for async zip processing and polling the batch API), Redis.
- Storage: Supabase (Auth/DB), Cloudflare R2 (Image Storage).
- Model: Google Gemini 3 Pro via Batch API.
There is a live demo on the landing page (no sign-up required) where you can upload a single image to test the detection logic. But of course the tool really shines with datasets that have thousands of images with multiple classes.
If you have any technical questions please ask!

value: Show HN: YoloForge – Create object detection datasets using Gemini 3 Pro

author: Olibier


value: vunderba

value:
“Lend Me Your Ears” is an interactive web-based game inspired by the classic Simon toy (originally by Milton Bradley). It presents players with a sequence of musical notes and challenges them to reproduce the sequence using either an on-screen piano or a connected MIDI keyboard. Each correct response leads to a progressively more difficult sequence, testing the player’s concentration, memory, and pitch recognition.
The game features two modes:
- Simon Mode (focuses on auditory recall)
- Practice Mode (displays standard sheet music notation to support learning)
The game also includes options for key signature selection, dyads, and a metronome to enhance the training experience.

value: Show HN: Lend Me Your Ears – A Simon-style ear training game with MIDI support

author: vunderba


value: Show HN

title: Show HN


value: Show HN: KektorDB – Lightweight, Embeddable Vector+Graph Database Written in Go


value: Show HN: Create personalized offline PWAs


value: dterm

value: Show HN: Claude generated code for a 80s vector game engine in HTML+JS

author: dterm


value:
I am a Developer turned Solution Architect. One pattern I saw in my career path is that a lot of experienced developers (including myself) find our career plateau out even though we are technically quite strong.
We are good at learning and delivery but career decisions are mostly reacting to opportunities. Lot of advice out there but what I needed was a hands-on system to help make routine decisions to pro-actively control my career path.
I built a lightweight "control system". Really a few markdown files in a GitHub repo. The idea is to work through to come up with a self guidance system that is unique for ourselves and is very tactical.
Core Idea:
- 5 worksheets (baseline, leverage, direction, positioning, execution)
- Everything feeds into one roadmap.md file
- Read it weekly, update monthly, review quarterly
The system forces you to answer:
1. What am I optimizing for right now?
2. What are the things that actually get results?
3. What do I not want to do?
4. What is my current direction and bet?
5. What signals should I watch for to indicate a need for change?
I've templated it here: https://github.com/balaji-techascend/career-roadmap
Would genuinely appreciate feedback from other senior folks - what am
I missing? What would make this more useful?

value: Show HN: Career Control System – Roadmap in a repo for experienced developers


value:
Hi HN,
We’ve been working on usage-based billing infrastructure, and one recurring pain point we saw across teams was event ingestion.
Most teams already have usage data in Kafka or internal pipelines, but getting that data into a billing system reliably ends up meaning:
- Writing and maintaining Kafka consumers,
- Handling batching and backpressure,
- Retry logic for partial failures,
and scaling ingestion separately from the billing logic itself.
We built The Flexprice Collector to solve just that.
It’s a service built on top of Bento that reads events from sources like Kafka, transforms them into a billing-friendly format and reliably delivers them to Flexprice with batching and retries.
The goal was to remove the ingestion plumbing that teams repeatedly rebuild when doing usage-based billing.
We have the docs here if you want to look at config, batching, and deployment details:
https://docs.flexprice.io/docs/collectors
Would love feedback from folks who’ve built similar pipelines or thoughts on what we’re missing.
If you would like to jump in-depth, we also have a open-source community and would love to see you there: http://bit.ly/4huvkDm
Happy to answer questions.

value: Show HN: A Bento-based service for reliably streaming usage events into billing


value: raj-shekhar

value:
Hey HN,
I'm always curious about what's happening: news, tech, markets, science.
So I built one stream that shows everything. Real-time, just flowing by. (Yes, HN is one of the sources!)
Tech stack:
- Backend: Python (FastAPI), SSE for real-time
- Frontend: React + Vite
- Hosting: Cloudflare Pages + Digital Ocean
I'm building Vestro (fashion tech startup), this is a side project I made for myself.
What sources would you add?

value: Show HN: Worldstream – Real-time stream of headlines from everywhere

author: raj-shekhar


value: democat

value:
Hi HN, author here.
I built Tylax because I wanted to migrate my old LaTeX papers to Typst but found existing regex-based scripts too fragile for nested environments.
Tylax parses LaTeX into an AST (using mitex-parser) and converts it to Typst code. It supports:
- Full document structure (not just math snippets)
- Complex math (matrices, integrals)
- Experimental TikZ -> CeTZ graphics conversion
- Runs in browser via WASM
Repo: https://github.com/scipenai/tylax
Web Demo: https://convert.silkyai.cn/
Happy to answer any questions!

value: Show HN: Tylax – A bidirectional LaTeX to Typst converter in Rust

author: democat


value: Hey, in the vast majority of push-based systems (think slack, shopify, or even custom entreprise ones) there are quite a lot of things you end up building (bufferization, errors, replays, ratelimiting, concurrency ...) before having a production-ready system.<p>Basehook is designed to solve these problems for you, you have a UI where you can configure and inspect/replay webhook payloads, and a single API you can consume in a pull-based way so you don't have to worry about being overwhelmed in case there is a spike.

value: Show HN: Pull, Replay or Inspect any webhook event


value: jamesjezusek

value:
Hi HN — I built Feedstack, a small tool for product teams who keep hearing “listen to your users”, but struggle with two problems:
- the loudest users dominate inboxes / DMs
- real sentiment is scattered across places like Reddit, forums, comments, reviews, etc.
Feedstack tries to create a “zero point of truth” for prioritization by combining:
Discovery: automatically find relevant conversations about your product across the web
Distillation: use AI to turn messy threads into clean, review-ready feedback items (title, summary, evidence, suggested feature)
Validation: publish the best items to a public voting board so you can see what has real demand (not just what one person asked for)
How it works (quick)
You enter your product name + site
Feedstack uses SerpAPI to find discussions (e.g. Reddit + forums etc.)
OpenAI extracts the actual requests, groups duplicates, and produces a prioritised list
You can approve items and push them to a public board for voting
What’s different vs “classic” feedback tools
Most tools start with “collect feedback from your users”. Feedstack starts earlier: what are people already saying when they’re not in your app, not on your forms, and not trying to be polite? Then it adds a second layer (voting) to validate.
Current state
Working MVP, used on my own projects + a few early testers
Still rough in parts — especially clustering/duplication and source quality filtering
Link
feedstack.app (happy to share a demo board if anyone wants)
What I’d love feedback on
Does “discovery → distil → vote” match how you’d actually prioritise?
Which sources would you want first (Reddit, GitHub issues, app reviews, Twitter/X, etc.)?
Any obvious privacy/ethics footguns I should address up front?
Thanks — I’ll be around in the comments.

value: Show HN: Feedstack – SerpAPI/OpenAI extract feature requests from Reddit/forums

author: jamesjezusek


value:
Built an MCP server for data work with memory and rules.
Use cases:
- Engineers: query your data from Claude/Cursor, debug issues, build with analytics in dev flow (like [1] but with memory and observability built in)
- Data teams: chat with your DB, define rules for how AI should query, share dashboards and analysis
Works with Postgres, Snowflake, BigQuery, Redshift, and more. Any LLM. Swap or mix instantly
What's different:
- Memory – stores context, preferences, usage down to table/column level. Learns over time.
- Rules – instructions, terms, guardrails with versioning. Git sync with dbt, markdown, code.
- Observability – traces, plans, evals, feedback. See exactly what happened.
Would love to receive feedback!
[1] https://x.com/bcherny/status/2007179856266789204

value: Show HN: The Data MCP – chat with any database, with memory and rules


value: slogansand

value:
Last month I shared Loki Mode here. Since then, benchmarks came back.
SWE-Bench: 99.67% (299/300 problems)
HumanEval: 98.78% Pass@1 (162/164)
For context, most single-agent systems hit 30-50%. Best proprietary ones hover around 70-80%.
The difference is architecture. 37 specialized agent types across 6 swarms (engineering, ops, business, data, product, growth). Parallel 3-reviewer code review. Feedback loops that actually learn.
To stress test it, I pointed it at a blank folder and said "build a ServiceNow replacement." It ran for 19 hours and built FireLater - complete ticket management, workflows, CMDB, knowledge base, self-service portal. I wrote zero lines of code.
New in this version:
- Kanban board to visualize agent actions in real-time
- Perpetual improvement via self-healing feedback loops
- Smarter swarm coordination
Still open source. MIT license. Still not selling anything.
Loki Mode: https://github.com/asklokesh/claudeskill-loki-mode
FireLater (built by Loki Mode): https://github.com/asklokesh/FireLater
Happy to answer questions about the architecture or benchmarks.

value: Show HN: Loki Mode hit 99.67% SWE-Bench – MAF built a SaaS overnight

author: slogansand


value: Notefy turns meetings into summaries, presentation-ready slides, and AI chatbots trained on your conversations.

value: Show HN: Turn meetings into presentation-ready slides


value: cazala

value:
Hey HN,
I made Party, a particle system and physics engine. It uses WebGPU compute shaders when available, falling back to CPU + Canvas2D otherwise.
This was mostly an opportunity for me to experiment with WebGPU. Having built particle systems several times before, I wanted something modular and performant that I could actually reuse and extend over time, and that would let others create their own modules too.
On the homepage https://caza.la/party a few different configurations run automatically, so you can get a sense of what it can do just by watching for a bit.
If you go into the playground, you can interact with everything in realtime: spawn/remove particles, apply forces, draw joints, tweak parameters live, undo/redo, save/load sessions, etc.
Repo: https://github.com/cazala/party
It includes several force modules (boids-like behaviors, ellastic collisions, smoothed particle hydrodynamics, physarum-like networks, and more) and a few basic render modules.
Would appreciate any thoughts, especially if you end up writing custom force/render modules.

value: Show HN: Party – WebGPU particle system and physics engine and playground

author: cazala


value: Show HN: Arbor – A graph-native code indexer in Rust for LLM context (MCP)


value: qwrwenm

value: It also features "Country Quest," a websocket-based multiplayer game where players race to identify a country from clues. The backend uses Node.js/Socket.io and the frontend is React.<p>It's free and open to play.

value: Show HN: I built a 3D World Map and multiplayer geography game using Three.js

author: qwrwenm


value: Show HN: Zilnic – a simple daily rhythm tracker


value: seongjaeryu

value:
Hi HN,
I’m a product designer by background, not a traditional software engineer.
Over the last year, tools like Claude and ChatGPT completely changed how I work. I started with small internal tools, and now I’m “vibe coding” multiple highly personalized apps.
As I iterated between Claude Code (implementation) and ChatGPT (ideation / thinking), I kept running into the same problem.
To think clearly, I had to repeatedly re-explain:
- what the code currently does,
- what changed recently,
- and why certain decisions were made.
That re-summarization step became a real bottleneck.
So I built Bindly (bind.ly).
Bindly is a persistent knowledge layer that sits outside any single AI tool.
The key idea is simple:
AI doesn’t remember. It re-reads shared context from the same place every time.
The concrete workflow I use today:
1) Claude Code → Bindly: After coding or refactoring, I ask Claude Code to summarize what changed and why, and store that context in Bindly.
2) ChatGPT → Ideation using Bindly: I then switch to ChatGPT and ideate based on that stored context — architecture, tradeoffs, next steps. Those ideation results are saved back into Bindly.
3) Claude Code → Reuse the ideation: Finally, I bring those ideation results back into Claude Code to continue implementation.
Bindly becomes the shared memory that closes this loop,
without constantly restating everything.
To reduce both cognitive load and token usage,
Bindly applies lightweight diffs (inspired by Git) and progressive search, so AIs only re-read what actually changed or what’s relevant right now.
In short:
- Bindly doesn’t try to replace AI thinking. It stores what exists, what was decided, and why - so any AI can continue from the same point.
Personally, this workflow already saves me a lot of cognitive overhead. But I’m unsure whether this is just a personal productivity hack or something others would actually pay for. I’m curious whether others who bounce between multiple AI tools run into the same problem.
Infrastructure uncertainty (and my move toward Cloudflare)
I initially built the MVP on Fly.io. It worked, and I don’t think Fly.io is a bad platform. But as Bindly grew, I became uncomfortable with how opaque things felt — volumes, persistence, failure modes.
Bindly is meant to be a knowledge layer. If data is lost, silently corrupted, or hard to reason about, that completely breaks the trust model.
For a tool whose purpose is “don’t lose context,” operational uncertainty felt like a product risk, not just a technical one.
I realized I was spending more time worrying about infrastructure behavior than thinking about the product itself.
So I started moving parts of the system onto Cloudflare,
aiming to reduce operational uncertainty and keep the infrastructure as boring and predictable as possible.
So far, it feels simpler and more transparent. I’m still unsure whether going fully Cloudflare-first is the right long-term decision, but reducing cognitive overhead at the infrastructure level has already made a noticeable difference.
What I’d really appreciate feedback on:
1) Does a shared “knowledge / memory layer” like this feel useful beyond one person?
2) As a non-engineer, does moving toward a Cloudflare-first setup seem like a reasonable long-term direction?
Some extra context (optional):
I joined HN back in 2018 to apply to YC and interviewed in Nov 2018 — didn’t make it in the final round.
This is my first time posting here in years.
Current ways I personally access Bindly:
- via Claude (Web)
- via ChatGPT APP (Web)
- via MCP (IDE)
If you have time to share thoughts - even critical ones — I’d be very grateful.
Happy New Year, and thanks for reading!

value: Show HN: Bind.ly – Persistent memory for AI across tools

author: seongjaeryu


value: calebkaiser

value: Show HN: Opik Optimizer – open-source agents for self-improving LLM applications

author: calebkaiser


value: aedrax

value: I learned about ElevenLabs from HN and I've been a big fan. An extension called ElevenReader exists, but you have to import content to a new page. I wanted to just read the content on the original page

value: Show HN: ElevenPage Reader – Chrome Extension to read your page with ElevenLabs

author: aedrax


value: seridarivus

value: I’ve always found the initial research phase for any new topic frustrating. I usually end up with 5 Wikipedia tabs for the "depth" and a YouTube tab to actually see the "how-to" or a visual summary.<p>I built PLI 7.9 to automate this specific workflow. It’s a minimal search engine that forces a "3+1" layout: 3 Wikipedia articles and 1 YouTube video per query. No infinite scroll, no SEO-bloated blogs, just the core sources.

value: Show HN: A unified search engine that delivers 3 Wiki articles and 1YT per query

author: seridarivus


value: iamsamwood

value:
I’m one of the developers behind Luther. We make it easy to operate “mega-workflows” – the long, cross-team, multi-system processes (insurance claims, mortgages, etc.) that today are usually held together with bespoke glue code and mismatched workflow tools, which are always about to be upgraded “any day now”.
Our platform replaces that glue code with a Common Operating Script, that runs reliably and consistently across teams and systems. Systems are connected to the platform rather than each other, so it’s easy to add and remove systems as your tools change, and changes must be approved by all teams before they go live, so everyone’s operating the same process, all the time.
To show how this works in production, we open-sourced a full Claims Settlement case study (75k loc in production, 12 systems and 5 teams), and you can check it out here:
Repo: https://github.com/luthersystems/cross-department-claims-set...
Video walkthrough: https://vimeo.com/1141432607?fl=pl&fe=cmt
How it works
• Logic: Write process-operations logic in a transactional Common Operating Script. Nodes execute this logic to process events across the various teams, while maintaining consistency.
• Infra: We provide a drop-in Kubernetes cluster with Prometheus/Grafana pre-wired. Use our managed version, or deploy to your cloud via Terraform.
• DevEx: Our connector hub provides 100s of ready-to-go connectors (S3, Postgres, etc.) and a prebuilt GitHub Actions pipeline. We provide a production-ready repo for you.
We’d love feedback from the HN crowd, especially on the pain points you’ve seen when stitching together complex workflows in enterprise environments.

value: Show HN: Luther Enterprise: Dev platform for operating end-to-end mega workflows

author: iamsamwood


value: xiaoshumiao

value:
I built One Card Tarot (https://onecardtarot.org
), a simple web app that gives instant, one-card tarot readings with AI-generated interpretations.
The idea came from noticing that most tarot apps are either:
Overwhelming (full spreads, long rituals), or
Too generic (static meanings with no context)
I wanted something closer to how many people actually use tarot day to day:
ask one clear question, draw one card, and reflect.
What it does
Ask a question (or draw without one)
Draw one random tarot card (upright or reversed)
Get an AI-powered interpretation tailored to:
Love & relationships
Yes / No questions
Daily guidance & reflection
Free users get limited daily readings, and there are optional paid plans for heavier use.
Why one card?
From my own experience, one-card tarot is:
Faster
Easier to reflect on
Less likely to turn into over-analysis
The constraint actually makes the interpretation more interesting.
Tech stack (high level)
Next.js
Serverless APIs
AI-generated interpretations (prompted to stay concise and grounded)
Stripe for payments
No accounts required to try it.
What I’m looking for
Feedback on the UX and interpretation quality
Thoughts on whether AI adds real value here or not
Suggestions for features that don’t make it more complicated
Happy to answer questions or share implementation details if useful.
Thanks!

value: Show HN: One Card Tarot – AI-powered one-card tarot for quick daily insight

author: xiaoshumiao


value: ndrwnaguib

value:
I wanted to share a small research / engineering project I’ve been working on: zk-auctions, an experiment in building sealed-bid auctions using zero-knowledge proofs.
The basic idea is to let participants submit bids privately, then prove (in zero knowledge) that the auction was evaluated correctly without revealing losing bids. The current implementation uses RISC Zero to generate proofs and explores different auction mechanics and constraints. It’s very much an exploration of the design space rather than a production-ready system.
This project was supported by a grant from the Ethereum Foundation (Privacy, Scaling, and Explorations team), which helped make the experimentation possible — grateful for that support.
Code and documentation are here:
https://github.com/ndrwnaguib/zk-auctions
Happy to answer questions or hear thoughts, especially from folks who’ve worked on zk systems, auctions, or verifiable computation.

value: Show HN: ZK-auctions – experimenting with zero-knowledge sealed-bid auctions

author: ndrwnaguib


value: Sheeplover

value: Show HN: FailWatch – A fail-closed circuit breaker for AI agents

author: Sheeplover


value: AISovereignDev

value:
I am a builder, not a researcher. I don’t care about theoretical AGI that only exists in white papers—I care about what runs in a kernel. I got tired of seeing "AI" projects that are just wrappers for LLM APIs, so I built a Causal Decision Engine that functions as a Sovereign Life OS.
Project: Velocity_Nova_Prime (v0.8.0) is a system designed to ingest 134 synchronous data streams (from biometrics to network entropy) to create a unified world-model. It doesn't just predict the next token; it predicts the next consequence.
Source Code: https://github.com/AI-Sovereign/Multimodal-AGI-Architecture-...
Technical Highlights:
1. TCS-25 Plasticity: A custom learning rule where weight updates are driven by Surprisal (S) and Causal Risk (C). It’s optimized for real-time implementation, not just backprop simulation.
2. HTSP (Hierarchical Temporal Synthetic Plasticity): A dual-stream architecture using a Fast Stream (GRU) for reflexes and a Slow Stream (LSTM) for episodic memory fusion.
3. 134 Modalities: The system maps a 134-dimensional vector in real-time. This includes everything from biometric data (HRV, GSR) to environmental noise and network packet entropy via Scapy.
The Stack: Python 3.10+, PyTorch, Polars (for high-entropy data handling), Scapy (for network ingestion), and FastAPI for the WebSocket interface.
The complexity depth is currently theoretical at 1.1 quintillion operations, and I'm pushing for local-only inference to maintain sovereignty. I'm looking for feedback from builders who have actually scaled multimodal ingestion or worked with non-Euclidean data in spiking neural networks.
The code is live. Let me know where the implementation breaks.

value: Show HN: A 134-Modality AGI Core Built in Python (Velocity_Nova_Prime)

author: AISovereignDev


value:
I built Audit8n (https://audit8n.com) to solve a problem I kept running into: n8n workflows that looked fine on the surface but had hidden security vulnerabilities, performance bottlenecks, and reliability issues.
The tool runs 100+ checks across three categories:
• Security: hardcoded secrets, public webhooks without auth, RCE via Execute Command nodes
• Performance: aggressive polling intervals, AI token bloat, N+1 SQL patterns
• Reliability: fragile loops without error handling, blocking merge nodes, infinite loops
All local analysis happens in the browser—zero data transfer. There's also an optional AI-powered deep scan using AI for semantic analysis of complex workflows.
Built with Next.js 16, React 19, and TypeScript.
Would appreciate any feedback on the detection rules or UI/UX.

value: Show HN: Audit8n – Free, privacy-first n8n workflow analyzer


value: whatbackup

value:
Aukpad is a simple online notepad for collaboration in your browser.
I've created aukpad for the following reasons:
- to have one synced notepad on various devices
- sharing and working on notes and configs live with colleagues
- notes can be created and downloaded via HTTP post request in any CLI (curl, etc)
There is no persistent storage and unused pads/notes are getting deleted after n hours.
It is a simple FastAPI application that uses Websockets for the sync and optional valkey/redis for caching. That is all.
An installation guide with Docker/Podman in the README and it is licensed under Apache License Version 2.0, January 2004.
https://git.uphillsecurity.com/cf7/aukpad
Feedback is welcome.
Cheers.

value: Show HN: Aukpad – open-source real-time collaboration notepad

author: whatbackup


value: Show HN: I spent 12 months building a conversational agent for social media


value: arturss

value:
Hi HN
I built PhotoMeh, a small web app that analyzes car photos and gives direct, sometimes blunt feedback on what works and what doesn’t.
The idea came from posting car photos online and noticing how inconsistent and subjective feedback usually is. I wanted something that:
focuses on composition, lighting, angle, and background
doesn’t sugarcoat
is fast and simple (upload → feedback)
It’s aimed mostly at car enthusiasts, sellers, and anyone who wants better photos without learning photography theory.
Tech stack (if relevant):
Next.js
OpenAI vision models
Stripe for payments
Site: https://photomeh.com
Would love feedback on:
whether this solves a real problem
pricing expectations
other use cases you see beyond cars
Happy to answer questions and share implementation details.

value: Show HN: PhotoMeh – Upload a car damage photo. Get an instant repair estimate

author: arturss


value: amstrdm

value:
Hi HN,
When researching stocks, I found myself spending a lot of time reading Reddit and Seeking Alpha posts to understand the main bullish and bearish theses around a company. The problem was less about lack of information and more about filtering and connecting opposing viewpoints.
I built Episteme to scratch that itch.
Given a ticker, it scrapes Reddit and Seeking Alpha discussions and attempts to:
- extract investor theses
- assign sentiment
- surface common bullish and bearish arguments
- link criticisms directly to specific theses so counterpoints are easier to evaluate in context
The project is fully open source:
https://github.com/amstrdm/episteme
There’s also a small hosted demo here:
https://episteme.cloud
This is very much an experiment and still rough around the edges. It started as a personal research tool, not a trading system. NLP quality varies by ticker, sources are limited, and there are plenty of edge cases. I’m mainly interested in feedback on:
- whether this framing of “thesis ↔ criticism” is useful
- obvious methodological flaws or bias
- sources or analysis approaches you’d add or remove
- If Episteme (or a slightly altered version of it) would be useful to you
Happy to answer questions or explain how anything works.

value: Show HN: Episteme – Aggregating and critiquing retail investor theses with NLP

author: amstrdm


value:
Hey all,
During Christmas and new year I was building this app day and night, I basically built it for my own use, I've got so much headache switching between tabs of different LLMs and chatbots, and other ai chat apps were missing some features that I really really wanted:
- be able to chat with multiple models at once
- be able to do debate with multiple models
so thats why I built it, I launched it 2 days ago, 0 users so far, I really appreciate any feedbacks.
I offer free plan with 20 message / day in some cheap models (I'm indie dev with no budget) and 2 plans that gives you higher limits.
for those who want to upgrade, they can use code WELCOME20 to get %20 discount.
any feedback is appreciated!
Thanks

value: Show HN: I made a small AI chat app that lets you chat with 4 models at once


value: aradzhabov

value: In addition to the DIY edition, AbleMouse has gained a new open-source AI module for face-controlled mouse navigation. Now you just need to point your nose at the desired spot on the screen—regardless of the screen's width—and the cursor will move there. Unlike eye trackers, it feels natural and doesn't strain your eyes. AbleMouse now provides specialized sub-products for various physical needs. View Product Compatibility with User Physical Abilities on the project website.

value: Show HN: AbleMouse AI. Nose-point cursor. Screen-size independent

author: aradzhabov


value: rokontech

value: Show HN: Sumoffy (macOS) – Offline Document Intelligence You Can Trust

author: rokontech


value: Show HN: KyubiSweep – Fast, local secret scanner written in Go (visual reports)


value: myztika

value: Show HN: Clean normalised US equity fundamentals via API (free tier available)

author: myztika


value: todaycompanies

value:
Hey HN,
 HOAs are essentially small governments that often operate without checks and balances. They rely on the fact that most homeowners won't read 50 pages of CC&Rs (Covenants, Conditions, and Restrictions) to fight a $50 trash can fine.
 I built FightHOAFines.com to balance the scales.
 How it works:
It scans your violation notice and cross-references it with your specific HOA bylaws and state statutes. It then generates a response letter that is technically "polite" but legally "pedantic"—pointing out procedural errors, vague language, or conflicting state laws.
 The Goal:
To make it more administratively painful for the HOA to pursue the fine than to just drop it.
 I’m currently tweaking the prompt engineering to dial in the "tone"—I want it to sound firm but not trigger an immediate lawsuit. Feedback on the generated letters is welcome!

value: Show HN: FightHOAFines – An AI agent that reads bylaws to dispute HOA violations

author: todaycompanies


value: jjoe

value:
I'm not a researcher. I'm just someone who gets unreasonably excited when a new paper drops in a field I have no business reading about. Protein folding? Fascinating. Quantum error correction? Sign me up. The problem is, jumping into a paper on topological qubits when your physics education stopped at "electrons go around the nucleus" is... humbling.
So I built PaperSplain for myself. It implements the Three-Pass Method – an actual academic reading technique where you read a paper three times at increasing depth. First pass: what's the paper about and why should I care? Second pass: what did they actually do, with attention to the figures that usually make or break my understanding. Third pass: what are the assumptions, limitations, and implications?
The idea isn't to replace reading the paper. It's to give me enough scaffolding that when I do read it, I'm not just pattern-matching words I've seen before. There's also a foundation survey that identifies prerequisite concepts I'm missing – turns out knowing what you don't know is half the battle.
The library already has a few hundred papers across different fields, and I keep adding more as I find interesting ones. You can borrow already analyzed papers or upload your own.

value: Show HN: I built a tool to stop pretending I understood research papers

author: jjoe


value: perpetuus

value: Show HN: GTKX – React for native GTK4 Linux apps, no Electron

author: perpetuus


value: Show HN: Waveform Playlist v5 – Multi-track Web Audio editor (React and Tone.js)


value: freemanjiang

value:
Each moving arrow represents one real bike ride out of 291 million, and if you've ever taken a Citi Bike before, you are included in this massive visualization!
You can search for your ride using Cmd + K and your Citi Bike receipt, which should give you the time of your ride and start/end station.
Everything is open source: https://github.com/freemanjiang/bikemap
Some technical details:
- No backend! Processed data is stored in parquet files on a Cloudflare CDN, and queried directly by DuckDB WASM
- deck.gl w/ Mapbox for GPU-accelerated rendering of thousands of concurrent animated bikes
- Web Workers decode polyline routes and do as much precomputation as possible off the main thread
- Since only (start, end) station pairs are provided, routes are generated by querying OSRM for the shortest path between all 2,400+ station pairs

value: Show HN: I visualized the entire history of Citi Bike in the browser

author: freemanjiang


value: jsonviewertool

value:
Hi HN
I built a small browser-based tool to convert YAML to JSON.
Why:
- I often work with Kubernetes manifests and APIs that require JSON
- Most online tools upload data to servers, which isn’t ideal for configs
What it does:
- Converts YAML → JSON entirely client-side
- Handles nested objects, arrays, and common YAML edge cases
- No sign-up, no uploads
Link:
https://jsonviewertool.com/yaml-to-json
Would love feedback from folks working with DevOps, APIs, or infra tooling.

value: Show HN: YAML to JSON converter focused on Kubernetes configs

author: jsonviewertool


value: luthiraabeykoon

value:
I built vid2ascii - a browser app that turns video into ASCII art in real time using WebGPU.
This started as a super rough v0 I hacked together on my commute, literally on my phone, just from a simple prompt. It worked, but it was janky and CPU-bound like most video-to-ASCII demos. I kept thinking about it, came back later, and polished it properly.
The big change was moving the core work onto the GPU. Instead of slow canvas pixel loops or WebGL hacks, vid2ascii uses WebGPU compute to process each frame at full resolution. That’s what makes smooth playback and high-fidelity ASCII actually possible in the browser.
Live demo: https://wspr-zeta.vercel.app/
Code: https://github.com/Luthiraa/vid2ascii
I’m mostly curious what people would build with this (creative tools, retro livestreams, OBS effects), and if anyone has ideas for better exporting or performance tuning
Source is open and easy to hack on

value: Show HN: Vid2ascii – real-time video to ASCII with WebGPU

author: luthiraabeykoon


value: jackhulbert

value:
Long time lurker here. My family had a pen-and-paper game we'd play on long drives to visit my great-grandmother. After she passed, I spent the holidays recreating it:
https://a26z.fun
How it works:
Find 15 words from a category (like "Stone Fruits," "US States," or "Dog Breeds") as fast as you can. Once you meet the 15 word minimum, you can play for as long as you want.
Each letter shows how many target words start with it (A¹ = one word starts with A, N² = two words start with N)
That small ² in the bottom-right? Multi-word answers allowed. For "US States" with N², both "NEW YORK" and "NORTH DAKOTA" count
Unlimited guesses, 2 hints, and a shuffle button to reorder by frequency.
Example:
Category: US States | Letters: A¹ M¹ N² S²
Answers: ALABAMA, MONTANA, NEW MEXICO, SOUTH DAKOTA
If you're into Connections or Strands, this scratches a similar itch but with a deduction twist.

value: Show HN: A to Z – A word game I built from a childhood road trip memory

author: jackhulbert


value: sneurgaonkar

value:
I built this to solve a problem we kept hitting: deals would go quiet, and by the time we noticed, the context for a good follow-up was scattered across HubSpot, Slack conversations, and old call transcripts.
The agent runs daily and:
* Pulls stale deals from HubSpot (configurable pipeline stages)
* Researches each prospect by aggregating context from multiple sources: HubSpot contact/deal/account data, internal Slack discussions about the prospect, Fireflies call transcripts, and recent company news via web search
* Uses Claude to generate personalized follow-up emails that reference specific pain points or initiatives mentioned in past conversations
* Delivers a daily HTML digest for review before sending
It’s essentially giving your sales team a research assistant that remembers every interaction and can craft contextual outreach at scale.
Tech stack: Python, HubSpot API, Slack MCP, Fireflies API, Claude API. Setup takes about 10-15 minutes if you have the accounts already connected.
GitHub: https://github.com/sneurgaonkar/sales-followup-agent
I’m actively working on this and would love to hear what features would make it more useful for your workflow. What other data sources would be valuable? Different notification methods? Integration with other CRMs?
Happy to answer questions about the implementation or help anyone get it running.

value: Show HN: Basic AI agent that auto-generates B2B sales follow-ups

author: sneurgaonkar


value: elmascato

value:
Hi HN,
I am a CTO/Founder usually stuck in "development hell" on long-term projects. To fix my perfectionism, I started a challenge on Jan 1st: Ship 12 startups in 12 months.
This is Project #01: TierWise.
The Problem: Most SaaS founders price for the US/EU market. Charging a flat $49/mo excludes huge segments of users in LATAM, India, or Southeast Asia where purchasing power is lower. Building a custom GeoIP/PPP logic for every side project is a distraction.
The Solution: I built a drop-in JS widget that detects the visitor's country and calculates a discount based on their local Purchasing Power Parity (PPP).
The Stack (The "Speed Run" setup):
Backend: Laravel 11 (API).
Frontend: Nuxt 3 (SSR).
Logic: Custom Middleware + Redis (for request throttling) + MaxMind GeoIP.
Design: Soft Brutalism (trying to avoid the generic "AI dark mode" look).
Business Model: It’s an open SaaS.
Free: 500 adjustments/month (no credit card).
Paid ($9/mo): Unlimited + White-label.
I’d love your feedback on the implementation and the "Soft Brutalism" UI approach.
Link: https://tierwise.dev/

value: Show HN: TierWise – PPP pricing widget for SaaS (Built in 7 days)

author: elmascato


value: joelsol

value: Show HN: LLM-First Personal Knowledge Management

author: joelsol


value:
Hi HN,
I built a platform to identify government procurement signals before RFPs are published. Instead of just scraping tender sites, I use FOIPOP (Freedom of Information) requests to aggregate contract expirations and untendered spending history.
Would love your feedback.

value: Show HN: ScotiaSignal: Public sector intent data for Nova Scotia


value: ramoz

value:
I'm well aware of the 1000 other tools that do this. In fact, I have a VSCode extension that does the same. I ended up building the web experience so I could at least better visualize context sizes of GitHub repos. It's also convenient to pull in PR diffs and issues.
This is not a product. 9times/10 I rely on Agentic Discovery. And I don't see that changing. 9times/10 that agent is Claude Code. If you're looking for a product that does your own context building or provides a different context building experience I recommend RepoPrompt. The founder's quite bearish on Agent Discovery & agentic context building.
The prompt tower experience is completely offline other than a proxy, so I can download zips from GitHub. I didn't feel like rolling out any formally authenticated experience. If you upload your own folder, all code stays in the browser.

value: Show HN: Prompt Tower – build and visualize your context

author: ramoz


value: baristaGeek

value: Show HN: Startup Simulator – AI Choose Your Own Adventure

author: baristaGeek


value: williamcotton

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

author: williamcotton


value: philistine

value: I spent way too long making a visual list of all Game Boy releases to help with my writing. The whole thing is all HTML and CSS, no scripting anywhere.

value: Show HN: Game Boy Release Timelines

author: philistine

title: Show HN: Game Boy Release Timelines


value: OakNinja

value:
Hi HN,
A few years ago, I wrote a tool called MakeMeFish. MakeMeFish is a wrapper around fzf to list Makefile targets and show what they contain. I use MakeMeFish myself every day, it's a pretty simple tool but it has been immensely useful to me and many others.
MakeMeFish is great, but only works in Fish shell. I've been planning a rewrite to support other shells for years, but hadn't gotten around to do it. We all know that AI tools are great at writing boilerplate python, js and java code, but struggle with novel and edge case code. When Gemini 2.5 was released, I used MakeMeFish as my foundation for trying out an AI assisted rewrite of a specialized tool. Short story, it went really well.
I've also missed writing blog posts. We should do more of those. Especially ones handwritten ones. I took the opportunity to write about the conversion, link below.
If you like and utilize fzf (Which you should), I truly think MakeMe can be a great starting point for writing fzf wrapper tools in general.
I'd be delighted to hear if it's useful to you, and if you have any feedback :)
Blog post: https://blog.oak.ninja/development/2026/01/02/makeme-a-cross...
MakeMe: https://github.com/OakNinja/MakeMe
MakeMeFish (Legacy version): https://github.com/OakNinja/MakeMeFish

value: Show HN: MakeMe – A Makefile tool rewritten from Fish to Go

author: OakNinja


value: freemanjiang

value: -- Please refer to the main url: https://news.ycombinator.com/item?id=46530832

value: Show HN: bikemap.nyc – visualization of the entire history of Citi Bike


value: opiniateddev

value: Show HN: Tool for Testing MCP Servers

author: opiniateddev

title: Show HN: Tool for Testing MCP Servers


value: raymondtana

value:
Raymond here from Butter.dev, an LLM response cache built as a chat-completions proxy. Today we're launching a key feature for the platform: the ability to generalize on dynamic, templated inputs.
Caching at the HTTP request level has the obvious problem of generalizability. Nearly no request is identical, due to templated variables (like names) and metadata (like timestamps), so exact-match cache lookups rarely hit. We solve this at Butter by using LLMs to detect dynamic content in requests and derive their inter-relationships, allowing the cache entry to be stored as a template + variables + deterministic code. This allows future requests to contain different variable data, yet still serve from cache.
We've found this approach greatly improves cache hit rate, and believe it could be useful for agents performing repetitive back-office tasks, computer use, or data transformations where input data is frequently of the same shape.
- You can see a demo of learning patterns here: https://www.youtube.com/watch?v=ORDfPnk9rCA
- We wrote more about the technical approach here: https://blog.butter.dev/on-automatic-template-induction-for-...
- It's free to try out here: https://butter.dev/auth

value: Show HN: An LLM response cache that's aware of dynamic data

author: raymondtana


value: igorzij

value:
Hi HN - we’d love your feedback on anyware.run - a Remote Control for Claude Code that you can use from your phone or browser while it runs on your machine.
- Approve requests without being at your desk
- Works with any model  - Opus, Sonnet, 5.2 codex, GLM 4.7 -  1M free tokens to try!
- $25/mo flat rate, after, during early access, we’re super hungry for feedback (with fair usage limits to prevent abuse)
We originally built this for managed cloud VMs, but quickly realized - as Karpathy put it - “while agent swarms running in the cloud feels like the "AGI endgame", we live in an intermediate and slow enough takeoff world of jagged capabilities that it makes more sense to run the agents directly on the developer's computer”, So we shipped anyware that attaches to a CC session running on your laptop (or any other place you start it)
Demo below: https://www.loom.com/share/11fe3459d7aa401888b22d44f102d281

value: Show HN: Anyware – Remote Control for Claude Code

author: igorzij


value: fanel

value:
Hi HN,
I built PostureGuard after my back pain from remote work got unbearable.
It uses your webcam + TensorFlow.js to detect bad posture in real-time and alert you (tab flashing, sounds, overlays).
Everything runs locally in your browser - no data sent anywhere.
Try it: https://posture-guard-theta.vercel.app/
Tech stack: Next.js, TensorFlow.js, MoveNet pose detection
Would love feedback from the community!

value: Show HN: PostureGuard – Free posture monitoring using webcam

author: fanel


value:
I built NewsMap, a mobile app that aggregates SF/Bay Area news and displays stories on an interactive map at precise locations.
I always wanted this to exist and finally used AI to build it.
I'm using an LLM to extract locations from article text, then verify and geocode them with Google Places. The app currently processes over a thousand articles/day from 40+ sources and is focused on the Bay Area for testing, although expanding to other metro areas would be relatively straightforward now.
A few screenshots are on the site: https://newsmap.me/
Short demo video here: https://www.reddit.com/r/SideProject/comments/1ndlc5g/i_got_...
Have you seen any similar examples of using LLMs for geospatial extraction and visualization? I've only seen extremely broad mapping like to specific cities before, which never felt specific/relevant enough to me. I would hope to see more applications like this in the near future, though.

value: Show HN: NewsMap – local news on a map (like Zillow but for news)


value: mzitelli

value:
After getting frustrated with existing workout planning and tracking apps, I decided to build my own with the following requirements:
- allow tracking weight, reps, difficulty per series
- being flexible, so I have my plan, but can adapt without having to change my original plan
- it surfaces the relevant context when needed
- it adapts to my trains
- i can access it anywhere
- don't let me get distracted
- low maintenance
I decided to solve that with an app with the following user flow:
- you plan workout days with exercises in order, no weights, supersets, or anything, that will be detected and defined as you train
- when working out, you select an exercise, put the desired weight, reps, rest time, and start one set
- when done with the set, mark its difficulty
- a sound will ring when your rest time ends
- as you go, and have a history of exercises, the app automatically suggests the next exercise, weights, reps, and resting time based on a few heuristics
- you can always select another exercise from the day, from another day, or even search for a completely different exercise
The app includes the entire state in the URL, so no server is required. We use the amazing https://github.com/yuhonas/free-exercise-db. Big kudos to the creators! In the end, the entire site and DB are hosted by GitHub.
The URL is from my current usage. You can view my daily plans and previous sessions there. In the plans, you can click on exercises to see suggestions based on previous sessions.
All edits you make will alter the URL and be saved in local storage. When you return to https://mateuszitelli.github.io/trainlink/, the page restores its previous state.
It has been very pleasant to use it in the last few days. Please, try it and let me know what you think. You can find the source code here: https://github.com/MateusZitelli/trainlink

value: Show HN: An offline first, with state-in-URL, workout planning and tracking app

author: mzitelli


value: offbynull

value:
PAlignPrims, short for Pairwise Alignment Primitives, is a C++23 header-only library that provides the building blocks for assembling pairwise sequence aligners. I created PAlignPrims because I fell in love with the idea of sequence alignments after learning about them from a bioinformatics textbook. PAlignPrims is my attempt at generalizing alignments beyond bioinformatics, providing a framework for experimenting with and building aligners for other domains.
I've done my best to make PAlignPrims flexible and configurable. Sequences can hold arbitrary element types, scoring is defined by user logic instead of fixed substitution matrices, and alignment behavior is controlled by swappable graphs and backtracking algorithms. The library exposes low-level control over algorithm choice, memory allocation, data layout, and safety checks, letting users trade safety for performance or vice versa.
If anyone seeing this is playing around with alignments or using them in some non-traditional way, I'd be curious to hear about what you're working on. There are features I wanted to add to PAlignPrims but haven't been able to because of life events. Extending to support multithreaded and GPU-assisted alignments were two big features. Fingers crossed I'll be able to look at these in the next few years.
Full disclosure: I'm neither a bioinformatician, nor an expert in sequence alignment, nor an expert in C++23. 0% vibe coded.

value: Show HN: PAlignPrims – C++ library for sequence alignment beyond bioinformatics

author: offbynull


value: AlekseN

value: Show HN: The kissing number theorem predicts particle masses from sphere packing

author: AlekseN


value: zipqt

value: Show HN: Corli – Major update focused on stability and new features

author: zipqt


value:
This is a declarative orchestration framework for stateless short-lived LLM agents.
Agents are orchestrated by hierarchical state machines (HSMs).
The HSMs and agents are defined in YAML, with hooks and
webhooks for additional functionality.
* FlatAgent: A single LLM call: model + prompts + output schema.
* FlatMachine: A state machine that orchestrates multiple agents, actions, and state machines.
Examples:
- https://github.com/memgrafter/flatagents/tree/main/sdk/pytho...
- https://github.com/memgrafter/research-crawler-flatagents
- https://github.com/memgrafter/claude-skills-flatagents

value: Show HN: Flatagents: State machine orchestration with stateless LLM agents


value:
Hi all,
I just open-sourced AgentOS, a tool I built for myself when I got tired of juggling a bunch of terminal windows while working with Claude Code. You can run it yourself and it's completely free.
It's a web UI that lets you:
- Run multiple Claude sessions side-by-side (up to 4 panes) and each pane can have multiple tabs for those power users out there
- Fork conversations when you want to try different approaches
- Organize sessions into folders
- Have an embedded terminal right there
- Sessions persist via tmux so you can close your browser and come back
Stack is Next.js 16 + React 19, SQLite for local persistence, xterm.js for the terminal.
GitHub: https://github.com/saadnvd1/agent-os
Would love to hear any feedback or feature ideas. I'm still planning on adding more to it!

value: Show HN: AgentOS – Self-hosted web UI for managing multiple Claude Code sessions


value: jsmithoner

value:
Hello everyone and happy new year.
SpaceXYZ is a 3D sound spatialization app. Currently it supports 3 algorithms: VBAP, Improved DBAP and HRTF.
No need to sign in or pay you can test the product in your browser.
Any feedback is greatly appreciated.
Don't hesitate reaching out for concerns about app or payments
contact@angular-audio.com

value: Show HN: SpaceXYZ

author: jsmithoner

title: Show HN: SpaceXYZ


value: plurb-unus

value:
Website: https://ai-swarm.dev
GitHub: https://github.com/ai-swarm-dev/ai-swarm
Hey everyone, I just released v3 of AI Swarm.
It is basically a way to host your own Claude Code or Gemini agents on your own infrastructure. I built it because I wanted to be able to build and deploy features for my apps from my phone or IDE, without having to watch it create the code and being unable to use it while it's deploying and fixing bugs for 30 minutes. I took inspiration from Kilo Code's Cloud Agents and decided to build it myself. It runs in Docker and uses Temporal for workflow orchestration.
Main Features:
- Self-Hosted: Deploy to your own Linux box running your apps or dev environment. It can deploy to remote servers via SSH.
- Claude Code and Gemini CLI: Built-in support for both, including Z.ai API keys for Claude. Looking for feedback on other tools and subscriptions.
- Pro/Max Support: You can use your Claude Pro/Max or Gemini AI subscription with it (just have to sign in manually to each worker after deployment).
- IDE or Web Chat: Pass tasks from your IDE or chat directly in the portal to have agents code, test, and deploy.
- Sovereign Auth: Uses Passkeys and CLI magic links instead of external providers.
- Safety and Verification: Separate dev container for build tests before deployment, and a Playwright sidecar for screenshot verification after deployment, with support for web apps gated behind authentication (Basic Auth support).
- Multi-Project Workspace Support: Just select which project you want to chat about on the portal from the dropdown menu.
It supports Caddy, Nginx, and Traefik for setup, and has a local-only mode for web access.
I am really looking for some feedback from the community. If you are interested in self-hosting your AI development workflows, please check it out and let me know what you think.
Thanks.
-plurb-unus

value: Show HN: AI Swarm v3 – Self-host your own headless AI agents

author: plurb-unus


value:
https://getreplay.app/
Hi builders!
I was falling asleep when the idea for this project came to me. So, I've decided to vibecode it via Lovable to the best of my ability. This is a personal/inspirational project, but I've tried to make it as good as possible given the constraints.
Tech Stack:
- Frontend: React 18 + Vite + Tailwind CSS
- AI: Gemini 2.5 Flash (via Lovable AI Gateway) for low-latency, empathetic coaching (to keep costs low and ship fast)
- Infrastructure: Lovable Cloud / Supabase
TL;DR: An LLM-powered "What If" view of your potential. Built for fun, inspiration, and research.
I would appreciate your feedback, guys. Thank you in advance!
P.S. Initial post got bugged, so I had to recreate it without a hyperlink.

value: Show HN: LLM-powered What If text gen for fun


value: kanodiaayush

value:
Most research tools help you collect links. Kerns is built for ongoing research.
You define topics and sources once. Kerns continuously tracks them over time, surfaces what changes, and structures the material so understanding compounds instead of resetting each session.
The key difference is the interface layer. Beyond feeds and summaries, Kerns organizes research into reasoning-ready views—maps, structured summaries, and synthesized perspectives—so you can actually think through complex areas rather than just store information.
We built this for people doing deep, long-running research (researchers, analysts, investors,  founders, autodidacts) where the hard part isn’t finding sources, but keeping a coherent mental model as the space evolves.
Would love feedback, especially from people who’ve tried to maintain research across weeks or months.

value: Show HN: Kerns – A Continuous Research Workspace

author: kanodiaayush


value: siegers

value: Add to Sheets Chrome Extension - Build automations that pull data from any page into Google Sheets when you use on a website without code (unless you're using CSS selectors). Start with a template, map columns, and run.

value: Show HN: Workflow automation builder for saving website data

author: siegers


value: notmarkeloff

value: Show HN: I made Python library for high-order combinators (retry, timeout, etc.)

author: notmarkeloff


value: Show HN: I made Python library for composition-first AI programming


value: Xlab

value: 'uvx upd-skill' to install skills from GitHub and other Git servers to Claude Code, Codex, Open Code, Amp and ClawdBot. All skills united! (kinda)

value: Show HN: Install agent skills from many sources using one command

author: Xlab


value: arthurstarlake

value:
Every content, digital or not, ever created can be more generally thought of as an answer to the question “how to be happy?”. We all want to be happy. Why are we not using anything we consume (e.g. social media) to be happy? Because sometimes we consume it with no goal. If we put the goal first and derive everything from there, we will be happier.
I started from that thought and built True Path Finder from it, a tool to facilitate collaboratively finding the truth about what will take a person from one state to another, using a very generic method:
1. Anyone can post any goal they have, or try publicly posted goals.
2. Anyone can try publicly posted methods to achieve that goal, or post their own.
3. Users try the methods and share their experiences as reviews. Repeated reviews are enabled and encouraged, to understand how their experience changes.
4. Users learn about each other’s experiences and may find a better method to achieve their goals.
There's a mode called "My Cave" in which you can do all that but privately. Following the cave analogy prompted me to make subsequent page's background color darker, and also making it so that you can only exit the cave through the page you entered. It was fun :) I hope it helps make it extra clear when you are interacting publicly or privately with the app, since everything else looks the same.
There are also "events" which anyone can create on any method, which are scheduled in advance, people RSVP to, and have 3 phases: Arrival, Practice and Close. A text chat is enabled only in the Arrival and Close phases. The text chat is accesible only in batches of 7 to 21 people, to ensure focused community discussion and allow the group to self-moderate. If all batches are full and a group would have 6 or less people, they would be allowed in full batches.
These events allow people to ask focused questions, reflect, foster community and discipline. They also avoid long asynchronous discussion which may distract from achieving goals. There's a 21 min daily limit for using the app, to drive the point further :)
There's a guest mode available so that you can do everything except post public reviews and chat in events unless you create an account.

value: Show HN: app to find the best methods to achieve any goal by reviewing publicly

author: arthurstarlake


value: johnboygiz

value: Decided to try a PWYW pricing model on a new site to see if it can genuinely work.Feels like it goes entirely against the grain of all common sense and logic, knowing how human nature will usually resort to suspicion over something that seems odd, or to how thi scan be exploited to ones gain for the littlest amount possible (aka zero).Its early days but I am interested to know what others think about such a crazy pricing model.I have read it has worked before for some (though not sure about for sofware).

value: Show HN: New Pricing Model] – Pay What You Want for Software Experiment

author: johnboygiz


value: Show HN: Bad Apple on an iPod nano (7th generation) [video]


value:
Hi HN! I'm Lane, and I've been building SonicJS for 7+ years (3,900+ commits).
SonicJS is a headless CMS built specifically for Cloudflare Workers. It runs on the edge in 300+ data centers with sub-100ms response times globally.
Why I built this:
- Traditional CMS platforms are slow and centralized
- Edge computing is the future but there wasn't a good CMS for it
- Wanted TypeScript-first with real type safety
Technical highlights:
- 6x faster than Node/Express in benchmarks
- Zero cold starts (V8 isolates)
- SQLite at the edge via Cloudflare D1
- Built on Hono.js framework
- Advanced plugin system
- Full admin UI included
Get started: `npx create-sonicjs my-app`
It's MIT licensed and 100% open source. Would love feedback from the HN community - especially on performance, architecture decisions, or features you'd want to see.
GitHub: https://github.com/lane711/sonicjs
Docs: https://sonicjs.com
Discord: https://discord.gg/8bMy6bv3sZ

value: Show HN: SonicJS – Open-Source Headless CMS for Cloudflare Workers


value: davepoon

value:
So I made a thing
Got tired of that Gemini watermark in the corner of Nano Banana images, so I built a little Chrome extension to remove it.
It's called "Peel Banana"  and I just put it up on the Chrome Web Store.
Completely free.
- The whole thing runs locally in your browser using a reverse-blending algorithm(based on allenk's GeminiWatermarkTool).
- No uploads, no servers, no external API.
- Just drag in your images and it spits out clean versions in about a second. Keeps the original quality too.
- You can do a whole batch at once if you've got a bunch of images to process.
Anyway, figured I'd share in case anyone else finds it useful. Let me know what you think!
If this helped you out, would love it if you dropped a rating. Five stars would make my day
Big thanks to anyone who leaves a review

value: Show HN: I built a simple "Gemini" watermark remover extension, "Peel Banana"

author: davepoon


value: cjlooi

value: Show HN: LaTeX → structured ArXiv data for scientific RAG

author: cjlooi


value: Show HN: Prediction Market Aggregator (Prediktion)


value: gigamick

value:
In 2021 after getting thoroughly sick of Privnote, i built "Burner Note". Even though i didn't promote it in anyway other than a post here and a post on reddit, it ended up getting about 7000(ish) unique users per month.
I kinda let it die about 18 months ago but i've resurrected it with some big upgrades. Most botable the app is now truly end to end encrypted (previously was just server side), and I've added dark mode as well as a few other bells and whistles.
Feedback welcomed.

value: Show HN: I upgraded my open source, zero knowledge, self destructing note app

author: gigamick


value: exAspArk

value:
Hi HN – founder here.
We built Telio to power AI agents for 24/7 call and text support.
Voice calls and texts need fast access to context. Our approach: aggregate data from multiple sources into a unified data lakehouse using APIs, webhooks, and logical replication.
Key advantages:
- Sandboxed read-only environments for AI agents, so they can't disrupt other systems
- Storing and processing large data volumes on S3 at a fraction of typical costs
- Retrieving all context at once, reducing tool calls and LLM token usage
- Building and querying vector embeddings for semantic search
- Granular column-level permissions to manage PII access
We query the lakehouse as if it were PostgreSQL, but with much better performance on complex queries using our open-source project https://github.com/BemiHQ/BemiDB.
We’d love feedback from the HN community!

value: Show HN: Telio – AI agents for call/text support, built on sandboxed lakehouses

author: exAspArk


value: aykuty

value: Hi HN, I built Visionary to let anyone create professional videos from text prompts without complex editing software. It supports cinematic, anime, and 3D styles. It's free to use and I'd love your feedback on the generation quality.

value: Show HN: I built a free AI text-to-video generator in browser

author: aykuty


value: telui

value:
Be sure to read the README.md!
GUI is in the works, the GUI will not exist yet
BEFORE USING, V.AI is in beta
V.AI is an easy to use AI platform where you can add your own AI, chat with other AI, and what makes it different is that V.AI is community-based and free to use, without the newer age limits most AI character platforms have, no hype, just easy to use AI.
Community can contribute to the new features, new packages, etc, for a new official V.AI feature

value: Show HN: V.ai: a open source character platform

author: telui


value: kyoungsookim

value:
Hi HN,
I built this to solve my own task paralysis problem.
The app only shows one next task instead of a full list.
It’s open source and still very early.
Feedback welcome.

value: Show HN: DailyWave – a one-task-at-a-time productivity app (open source)

author: kyoungsookim


value: PanicSellGuru

value:
With today’s tech world dominated by rapid shifts—from AI infrastructure races to major platform updates—investors are paying closer attention to how innovation-focused funds adjust their positions. This tool provides a clear, visual breakdown of how Cathie Wood’s ARK Invest reshaped its top holdings across 2025’s first three quarters, helping users see how market narratives translate into real portfolio moves.
Explore the latest ARK portfolio data here:
https://www.13radar.com/guru/catherine-wood

value: Show HN: Ark Portfolio Tracker – Visualizing Cathie Wood's Holdings

author: PanicSellGuru


value: stets

value: I made SludgeReport to update itself daily with AI and Startup news. I hope you like it.

value: Show HN: SludgeReport.io – like that other site but AI, Tech, Startup News

author: stets


value: AntiRush

value:
Over Christmas, I read a blog post here about attempting to port Graph::Easy (a very cool Perl module) to TypeScript using LLMs:
https://news.ycombinator.com/item?id=46318080
The author didn’t get the results they wanted, but I was nerd-sniped into giving it a try with some different tools.
The link is a web page where you can enter Graph::Easy graph descriptions to generate graphs in the various supported formats. It runs both the TypeScript and the Perl versions and compares the results.
The code is available: https://github.com/fairfieldt/graph-easy-ts/
I also wrote a longer post about the process: https://tomisin.space/projects/graph-easy-ts/

value: Show HN: Graph:Easy ported to TypeScript with GPT-5.2

author: AntiRush


value: trissim

value:
We prove that identifying decision-relevant coordinates in a decision problem is coNP-complete. Finding the minimum sufficient coordinate set is also coNP-complete.
Formally: given state space S = X_1 × ... × X_n and utility U : A × S → Q, a coordinate set I is sufficient if s_I = s'_I implies Opt(s) = Opt(s'). Checking whether I is sufficient reduces to TAUTOLOGY. Finding minimum I reduces to the same.
Main results:
SUFFICIENCY-CHECK is coNP-complete
MINIMUM-SUFFICIENT-SET is coNP-complete (Sigma_2^P structure collapses)
ANCHOR-SUFFICIENCY (fixed coordinates) is Sigma_2^P-complete
Dichotomy: polynomial when |minimal set| = O(log |S|), exponential when Omega(n)
Tractable cases: bounded |A|, separable U(a,s) = f(a) + g(s), tree-structured coordinates
Engineering consequence: over-modeling is not laziness. Determining which configuration parameters matter requires solving coNP-complete problems. Including everything costs O(n). Minimizing costs Omega(2^n). For large n, over-specification is optimal.
This explains: config files that grow forever, heuristic feature selection (AIC/BIC/CV), absence of "find minimal config" tools. These are not tooling failures. They are optimal responses to intractability.
2760 lines of Lean 4 proofs. 106 theorems. Zero sorry.

value: Show HN: Knowing What Matters is coNP-complete (Lean 4 formalized)

author: trissim


value: Show HN: Cymatica.app

title: Show HN: Cymatica.app


value: chynnahe

value:
Hi HN! I built titanaiexplore.com — a free, community-curated discovery hub for AI tools, tutorials, projects, and resources.
It categorizes emerging AI platforms and educational content to help both beginners and experienced builders find high-quality tools and learning paths in one place. Features include:
• Curated collection of AI tools across categories
• Tutorials, guides, and explainers for practical usage
• Featured projects and community contributions
• Search and tagging for quick discovery
Would love to hear what the HN community thinks — feedback, ideas, or areas to improve are all welcome!

value: Show HN: Titan AI Explore – A curated hub for AI tools, tutorials, and projects

author: chynnahe


value: mjshashank

value:
I've been reading HN for years, but I've noticed a pattern: I'd open the front page to "stay current on tech etc." then emerge an hour later having read hundreds of comments on some political thread or startup drama. I built this extension to keep myself honest.
What it does:
The extension uses an LLM to analyze HN stories and adds metadata badges directly to the front page:
- Topic classification (AI/ML, Web Dev, Systems, Startups, Policy, etc.)
- Content type (Show HN, Tutorial, Paper, News, Discussion…)
- Technical depth score (0-1 scale)
- Tags (specific technologies, descriptors like "deep-dive" or "beginner-friendly")
You can then filter stories by topic/type, or set a technical depth threshold. My favorite feature is hiding just the comments link on non-technical stories so I'm aware of them without falling down the rabbit hole.
How it works:
- A Cloudflare Worker runs hourly, fetching top stories and queuing them for enrichment
- Gemini analyzes the title, URL, and top comments to generate structured metadata
- Results are cached in Cloudflare KV
- The extension fetches enrichments and injects badges into the page
Privacy-wise, it only sends public story IDs to the API, no personal data.
Chrome: https://chromewebstore.google.com/detail/enriched-hn/mmcdcoa...
Firefox: https://addons.mozilla.org/en-US/firefox/addon/enriched-hn/
GitHub: https://github.com/mjshashank/enriched-hn
Would love feedback on the categorization accuracy or feature ideas!

value: Show HN: Enriched HN, LLM-powered topic filtering for Hacker News

author: mjshashank


value: chcardoz

value: When you think of AI videos, you think of something like Sora or Veo 3 (diffusion). What if the AI could write the code for a video like a website? This thought experiment led me to create Mike. It writes React code which can be rendered into a video. You can ask the AI to use any Node library to render graphs, animations, simulations.

value: Show HN: I built Mike – AI motion graphics

author: chcardoz


value: Show HN: A skill that finds expert methodologies before generating AI skills


value: todaycompanies

value: A platform for aggregating and verifying user reports on border sectors.

value: Show HN: ReportBorderCrossings – Crowdsourced mapping of border activity

author: todaycompanies


value: I built a dashboard to organize the unsealed indictments, court schedules, and filings regarding the United States v. Nicolás Maduro case in the Southern District of New York.

value: Show HN: MaduroTrials – Tracking the SDNY indictments and court documents


value:
Hi HN
I built a small CLI to safely clean up stale GitLab branches.
It’s designed to be:
- Safe by default (dry-run enabled)
- CI-friendly (proper exit codes)
- Automation-ready (JSON output)
- Boring and predictable (by design)
I built it because repos tend to accumulate abandoned branches, and cleanup often happens too late or too manually.
Would love feedback, edge cases, or ideas for repo hygiene automation.

value: Show HN: A safe CLI to clean up stale GitLab branches (dry-run, CI-ready)


value: Show HN: How I generate animated pixel art with AI and Python


value: xakpc

value:
I use Plausible as a main analytics platform, but apparently I don’t have the discipline to check it regularly for all my side-projects. So it just sits there and I'm actually paying money for that just to exists.
To fix it for myself I decided that I needed a quick peek at current stats - something that’s always one gesture away and extremely easy so I could do this without any friction.
Unfortunately I use Windows, and Windows has this thing called Widgets. Apparently, that’s exactly the surface I needed. Their docs are terrible and 100% of existing widgets are useless, but the panel is fast enough for my task.
So I crafted Wizible - a Windows 11 widget for Plausible Analytics.
It’s intentionally super simple: open widgets → glance at stats → if something changes, then I open the full dashboard.
It's free, use like 20MB of RAM, and should be relatively secure and pretty fast because I put some real software engineer work there and not vibe-code slop.
It's available in Microsoft Store, I'm also might open-source it if people are interested.
Curious: what’s the ONE metric you’d want on the widget by default? (active visitors / top pages / referrers / conversions?) I choose metrics based on my understanding, but maybe my understanding is wrong.
And: would you prefer "real-time only" or "today vs yesterday" trend at a glance? Thinking how a currently missing "small size" might look.

value: Show HN: I Created Wizible – A Windows Widget for Plausible Analytics

author: xakpc


value: brynlai

value:
I'm a final-year student from Malaysia.
Been spending some time reading up on Fraud in Fintech.
Would love feedback on what I've learned, and what I should be focusing more on.

value: Show HN: Modern Fraud Systems Cheatsheet for Intern's

author: brynlai


value:
Runs local browser instances of Meta's SAM Audio playground so you can isolate vocals/drums from audio of any length without running SAM locally or hosting inference.
- Audio >29s is chunked with ffmpeg
- Audio chunks and prompts are submitted in parallel to the playground via Playwright
- Web UI for storing tracks and re-editing previous outputs
Demo video: https://github.com/user-attachments/assets/d5d3b53d-6ac9-40f...

value: Show HN: Audio Isolation with SAM Audio for free


value: trissim

value:
I formalized the Single Source of Truth (SSOT) principle in Lean 4 (~2.1k LOC, zero sorry) and proved two core results:
Structural SSOT is achievable only when a language provides definition-time hooks and runtime introspection. Macros/codegen (before definition) and reflection (after definition) are insufficient.
These requirements are derived, not chosen: because structural facts are fixed at definition, derivation must occur at definition time and be introspectable to verify DOF = 1.
Would appreciate review, critique, or independent checking of the Lean scripts.

value: Show HN: Lean4 proof that SSOT requires definition-time hooks and introspection


value: I'm a PhD Engineer in Aviation. I built this because I was tired of technicians "passively" watching training videos without focus. Trainivio forces a replay of video segments if a question is answered incorrectly. Looking for feedback from the HN community

value: Show HN: Trainivio – Interactive video training that ensures 100% mastery


value: aselitto

value: Show HN: Claudit – A deterministic contract auditor for identifying weasel words

author: aselitto


value: zonghao

value:
Two years ago, someone on HN shared an interesting ADHD hack: a tiny LED that blinks at 120 bpm and gradually slows to 60 bpm, supposedly helping your brain sync and calm down into focus mode.
I found Qiaogun's implementation (ADHD_Blink) for M5StickC Plus, and adapted it for the newer M5StickC Plus2 with some tweaks - simpler 50% duty cycle flash, configurable ramp-down, auto sleep, etc.
Honestly, I'm not sure if it actually works. I'll be trying it out myself to see. But the building process itself was quite fascinating.
I used Claude Code for the entire implementation - from reading the original codebase, to modifying the firmware, to flashing the device. There's something surreal about an AI having full control over a physical piece of hardware.
It made me wonder: in the future, could AI-connected devices dynamically rewrite their own firmware based on user needs? Imagine telling your device "make this button do X instead" and it just... does.
Original HN comment: https://news.ycombinator.com/item?id=38274782 Based on: https://github.com/Qiaogun/ADHD_Blink Hardware: M5StickC Plus2 (~$20)
Happy to hear thoughts, or if anyone has actually tried this LED trick.

value: Show HN: ADHD Focus Light

author: zonghao

title: Show HN: ADHD Focus Light


value:
SquareFaceIcon is a super–simple “avatar protocol” plus a free web tool for generating consistent square face icons:
https://squarefaceicon.top/
Instead of every product/team reinventing avatar styles, SquareFaceIcon treats a face icon as a tiny, structured “packet”:
- fixed square canvas
- a small set of discrete layers (skin, hair, eyes, mouth, extras, background)
- finite, enumerable options per layer (IDs instead of pixels)
That gives you:
- *Deterministic avatars from a tiny config* – you can describe an avatar with a short JSON (or even a few bytes), then render it anywhere the “protocol” is implemented. No binary image assets needed.
- *Consistent visual language* – all combinations obey the same square-face “grammar”, so a community, product, or game can have hundreds of unique avatars that still look like one family.
- *Cheap to store and send* – configs can live in your DB/user table, be generated from a hash, or passed in URLs, then rendered on the client.
The hosted generator at squarefaceicon.top is one reference implementation:
- 200+ pre-defined parts across multiple categories.
- Pure HTML5/JS, runs fully in the browser, no sign-in and no server-side image processing.
- Exports transparent PNGs in multiple sizes (64–512px) for Discord, forums, games, etc.
Why this might be useful:
- Small apps or side projects get a drop-in avatar system without dealing with uploads, moderation, or external CDNs.
- Communities can standardize on a simple avatar spec and still let users fully customize their look.
- Devs can re-implement the renderer in any stack (Canvas/WebGL/SVG/native) as long as they respect the same part IDs and layout rules.
If folks are interested, happy to:
- document the “protocol” more formally (schema for the avatar config)
- publish a minimal open-source renderer
- add an API for server-side generation
Feedback welcome on:
- what you’d want from such a tiny avatar “protocol” (versioning? theming? accessories?)
- whether you’d use this in a product, and what integrations you’d need (React/Vue components, game engines, etc.)

value: Show HN: SquareFaceIcon – a tiny "protocol" for consistent square avatars


value: patrickbuahgiar

value: I've create a small daily word puzzle based on real UK headlines from the day before. The letters are scrambled and you rebuild the original headline.

value: Show HN: Shuffle Times – a daily puzzle to unscramble real headlines

author: patrickbuahgiar


value: Modern applications depend on reliable databases, but setting them up often requires significant infrastructure work. Bult.ai simplifies this process by allowing teams to deploy databases in just a few clicks using the Canvas UI.

value: Show HN: Build a Database Services in Minutes with Bult.ai


value: mohamedchs

value: Show HN: A Python tool to turn your ChatGPT history to Word clouds and md files

author: mohamedchs


value: covibes

value: We're releasing the next-gen Ralph Wiggum architecture - agent clusters with independent validator agents with clear rejection mandates. As long as the code is not feature-complete and production-grade, it will not be approved. The result is AI without any need for babysittng.

value: Show HN: Open-source autonomous dev teams for Claude Code

author: covibes


value: idrissmbellil

value:
Github mirror: https://github.com/idrissbellil/gossiper
Deployed project: https://app.v3m.pw/
Video Demo: https://odysee.com/@jostiniane:a/gossip_dispatch:3?r=ApaoeHU...
Any feedback would be appreciated

value: Show HN: FOSS Email-to-Webhook Bridge (Turn Monitoring Alerts into API Calls)

author: idrissmbellil


value:
Hi HN,
I’ve been working on Flowscape, a 2D canvas engine focused on building interactive editors and visual tools.
It provides low-level control over scenes, nodes, and interactions without enforcing a specific UI or workflow.
The goal is to give developers a flexible foundation for custom editors, diagrams, and canvas-based interfaces.
I’d love feedback on the API design, architecture, and real-world use cases.
Demo: https://flowscape-ui.github.io/core-sdk/?path=/story/interac...
GitHub: https://github.com/Flowscape-UI/core-sdk

value: Show HN: Flowscape – A developer-first 2D canvas engine with full scene control


value: brobles

value: Show HN: PDF Hubs for Airbnb hosts with international guests

author: brobles


value: egretfx

value: Mytosis is a short experimental incremental game about evolving life forms from simple cells to complex procedural organisms. Collect DNA from single cells, use mutation upgrades to accelerate DNA harvesting, and evolve into procedural multicellular organisms.

value: Show HN: I am making an incremental game about DNA evolution

author: egretfx


value: akacj

value:
Hi HN,
I built an open-source tool called production-readiness:
https://github.com/chuanjin/production-readiness
It analyzes a repository and generates a production readiness report that surfaces operational design risks the way senior engineers often do in reviews. It does not lint code or enforce policy. The focus is on design-level risks such as weak rollback strategies, fragile observability, unsafe migrations, and missing operational safeguards.
The goal is to turn tacit senior judgment into deterministic, repeatable signals that teams can run on every project.
I’d really appreciate feedback from people who’ve run production systems. In particular, I’m interested in gaps you think are critical, and whether this kind of tool would fit real workflows.
Thanks for reading.

value: Show HN: Production-readiness – Turn Sr engineer intuition into automated checks

author: akacj


value: marcalsantos

value: Getting a SOC 2 quote usually involves signing NDAs and jumping on multiple sales calls just to get a ballpark figure. I thought that was broken, so I built a calculator based on real market data to make pricing transparent.

value: Show HN: Open SoC 2 Pricing Data – Estimate audit costs without talking to sales

author: marcalsantos


value: Show HN: Sticky Notes TUI – Keyboard-Driven Notes Built with Python and Textual


value:
AI memory systems often become a black box. When an LLM produces a wrong answer, it’s unclear whether the issue comes from storage, retrieval, or the memory itself.
Most systems rely on RAG and vector storage, which makes memory opaque and hard to inspect, especially for temporal or multi-step reasoning.
An alternative is to make memory readable and structured: store it as files, preserve raw inputs, and allow the LLM to read memory directly instead of relying only on vector search.

value: Show HN: File-base agent memory framework, like Claude's skills


value: bustylasercanon

value:
I built this after seeing people on subreddits like /r/30_plus_gamers sharing images of their Top 25 games and arguing in the comments. I wanted a more playful way to do that than static lists.
Versus lets you rank games by pitting them head-to-head. Under the hood it uses a simple ELO-style system, but the goal isn’t mathematical purity, it’s to make ranking games feel like a game itself. Over time it builds your personal Top 25, which you can turn into a clean shareable image. There are also global rankings and public profiles so you can see how wildly wrong everyone else is.
It’s completely free. No ads, no paywalls, no accounts required to get started.
Mostly just sharing because I think it’s fun, and maybe a few other people here will too.

value: Show HN: Versus – A free app that ranks video games using head-to-head matchups

author: bustylasercanon


value: jatinlalit

value: Show HN: Voice Control for Any Website Hands-Free Browsing

author: jatinlalit


value: Show HN: Free AI Blog Outline Generator

title: Show HN: Free AI Blog Outline Generator


value: prashantl

value:
Hi HN,
We just shipped an updated version of Flexy, a productized way to outsource small-to-mid C#/.NET and Azure work with fixed budgets instead of hourly billing.
It’s aimed at solo devs and small teams that need reliable help for bug fixes, features, modules/libraries, or Azure tasks, without hiring or managing a long retainer.
Link: https://www.flexytasks.dev/

value: Show HN: Flexy – fixed-budget bug fixes and features for .NET teams

author: prashantl


value: appdevfun

value:
I built Zone, an iOS app blocker using Apple's Family Controls API. The differentiator is simple: it counts how many times you try to open blocked apps.
Most app blockers just block. But I found the attempt count more revealing than the block itself. Seeing "you tried to open Instagram 47 times today" was a wake-up call I didn't get from blocking alone.
Technical notes: Family Controls API is poorly documented but provides system-level blocking that actually works (unlike overlay approaches). Had to handle some edge cases around authorization persistence and count tracking across app restarts. The API requires Screen Time permissions which adds friction to onboarding but ensures reliable blocking.
One interesting discovery: users seem to prefer seeing raw attempt counts over gamified metrics (streaks, badges, etc). Less is more for this use case.
Built in SwiftUI, local storage only, no subscriptions. Took about 3 months part-time.
Curious if others have worked with Family Controls API and what challenges you faced. Also interested in thoughts on digital wellness apps in general - does tracking behavior change it, or just make you more aware without actual change?

value: Show HN: App blocker that tracks your failed attempts to open blocked apps

author: appdevfun