value: ViktorKuz

value: Show HN: MCA-Entity Coverage as a Memory Retrieval Gate

author: ViktorKuz


value: Xlab

value:
Hacked together a service in Go https://github.com/xlab/cdn-s3-go that routes content request to any arbitrary S3-compatible bucket.
Some infra providers are offering free egress, but buckets are only private (hello Railway), and not geo-distributed. An L4 balancer will cost a fortune (hello Cloudflare).
cdn-s3-go solves this by looking up objects, generating a pre-signed URLs and redirecting the clients directly. Free egress, cheap public geo-distributed CDN!

value: Show HN: Free egress public buckets on Railway and others

author: Xlab


value: StealthyStart

value:
Hi HN, I built this because I kept manually copying blog posts from WordPress into Medium and kept worrying about duplicate content and broken formatting.
PublishRelay.com connects to a WordPress site and a Medium account, then syncs new posts automatically using Medium’s official API. It sets the canonical URL back to the original WordPress post and preserves basic formatting like headings, images, lists, and code blocks.
You can try it by:
Connecting a WordPress site (plugin)
Authorizing a Medium account
Creating a test post in WordPress and watching it sync
There is a free tier to try the app, no credit card needed.
Happy to answer:
• How the canonical handling works in practice
• API limits and failure modes
• What breaks with complex WordPress themes
• Whether this actually helps or hurts SEO in real-world use
This is a small side project that grew out of our own content workflow.

value: Show HN: PublishRelay – Sync WordPress posts to Medium with canonical links

author: StealthyStart


value: DmitriiBaturoIC

value:
TL;DR:
ICT (Information–Consciousness–Time) treats matter as fixed information, consciousness as the rate of informational change, and time as the structuring of this change. The model unexpectedly drew interest from researchers in information physics (feedback below) and includes three concrete falsifiable experiments.
1. Core Idea
Three base relations:
1. Matter = fixed information
M = I_fixed
2. Consciousness = rate of informational change
C ∝ dI/dT
3. Reality = interaction of stable and flowing information
R = f(I_fixed, dI/dT)
This connects to Landauer’s limit, free-energy principle, Bekenstein bounds, and integrated-information concepts, but treats information itself as the fundamental physical substrate.
2. Time as informational process
In ICT, time is:
> “The transition of potential information into structured experience.”
This links phenomenological time, physical time, and informational/computational time.
Consciousness “shapes” this transition → generating an effective local arrow of time.
3. Experimental roadmap (falsifiable)
1. C ∝ dI/dT — Neuroenergetic test of consciousness speed
Multilevel oddball / sequence-learning tasks with controlled entropy and update rate.
EEG/MEG + metabolic markers test whether higher dI/dT increases energetic cost and large-scale neural integration.
2. R = f(I) — “Structure without energy”
Equal energy input, but different informational structure (compressible vs pseudorandom) in sensory/light patterns.
If informational form matters, outcomes (neural/behavioral/physico-chemical) should differ despite equal power.
3. M = I_fixed — Energetic price of information fixation
Write/erase cycles across substrates (DRAM/Flash, memristors/PCM, spintronic media, possibly neural cultures) to estimate energy-per-bit thresholds at which information becomes “stable,” probing matter-as-stabilized-information.
4. Independent feedback
A researcher specializing in information physics & the nature of time (background: MSU’s “Institute for Time Nature Explorations,” EE, systemic research) reviewed ICT on Academia.edu.
Key excerpts:
“The author proposes a highly interesting approach to the relationship between matter, consciousness and information, incorporating the complex concept of time.”
“‘Matter as fixed information’ opens a path toward an information physics of consciousness.”
“The experimental framework is clear and promising.”
— Irina L. Zerchaninova, researcher in information physics & time studies
5. Why I’m posting on Hacker News
ICT sits at the intersection of physics, computation, philosophy of mind, information theory, and AGI.
I’m looking for technical critique.
Preprint (equations, criteria):
https://www.academia.edu/s/8924eff666
Main publication (open access):
DOI: 10.5281/zenodo.17584783
PDF:
https://www.academia.edu/144946662/The_Conceptual_Model_of_t...
Happy to answer any questions.

value: Show HN: ICT – New Information Model for Matter Time Consciousness (Experiment)

author: DmitriiBaturoIC


value: keooodev

value: In my area I have 8 ANPR car parks within a 10 min radius that are free to park in, but you need to remember to enter your registration plate if not you are hit with a £70+ fine. This is easy to forget for older people. People who are with the kids. ect ectt. so I have made an app that sends a push notification after you enter one.Its free. I will add pro features in the future to keep it alive and server costs. Im in the process of populating the car parks but users can still add there own in the local area if they want

value: Show HN: My pushback against ANPR carparks in the UK

author: keooodev


value: thoughtlesslabs

value: Simple vibe coded app with vercel that gives you a quick task to help you market your app. starts super small and builds as you go.

value: Show HN: Advent of Marketing – Daily marketing task helping founders market

author: thoughtlesslabs


value: drankou

value:
Locu is a desktop app for engineers who want to do focused work consistently. We're 3 engineers who built it for ourselves - started as a Pomodoro replacement, grew into the system we use daily for all work-related stuff.
How it works:
Import tasks from Linear/Jira. Break them down, add private notes. Each morning, pick your priorities for the day. Then execute: pick a task, enter focus mode, distractions blocked (apps + websites), work on one thing.
Sessions are based on ultradian cycles (90 min focus blocks) - Andrew Huberman covered this on his podcast. Works way better than Pomodoro for actual coding.
Time tracking happens automatically as you work. Useful if you're contractor or just want to see where your hours went.
Other stuff:
- Google Calendar integration: see all your meetings for the day, capture meeting notes
- Slack integration: turn messages into tasks and sync your DnD status
- Brag book: log achievements for perf reviews
- Rich Text Editor for work-related knowledge base: code snippets, small docs for yourself.
- Local-first (powered by Replicache) - especially relevant for Jira users since we almost entirely eliminate the need for visiting Jira website :) for Linear users it's just a native persona extension ;)
- Available on both macOs and Windows, Linux and Mobile are coming as well
Curious how other engineers here structure their day. Do you do any kind of planning ritual? Time-block? Or just working as it goes? We landed on this session-based approach after trying a bunch of things, but always interested in what works for others.
Also happy to hear what's missing.

value: Show HN: Locu – Deep work sessions for engineers (Linear/Jira, local-first)

author: drankou


value: lcmchris

value: Starting an art and writing advent challenge!

value: Show HN: Advent of Art and Writing

author: lcmchris

title: Show HN: Advent of Art and Writing


value: chwmath

value: Show HN: ER Intern Simulator Built in 3 Hours via Natural Language Rules

author: chwmath


value: admtal

value:
Hi everyone,
I made this app
Demo Scope lets you record or stream any mobile website with your face on screen and touch indicators showing every tap.
I built it because I needed to demo my own side projects and hated the OBS/desktop setup.
Load your site in the app, hit record, done. Or stream straight to Twitch/YouTube.
Free to try: https://apps.apple.com/us/app/demo-scope/id6755395174
Would love feedback if anyone tries it.
I made a landing site centered around mobile demos for professional projects, but my main use case honestly has been Twitch streaming browser based games from my phone.

value: Show HN: Demo Scope – Show your work

author: admtal

title: Show HN: Demo Scope – Show your work


value: Show HN: MCP Traffic Analyze with NPM

title: Show HN: MCP Traffic Analyze with NPM


value: yunier-rojas

value: Show HN: Simweb is a discrete-event simulation for web processes

author: yunier-rojas


value: roampal

value:
Matthew McConaughey was on Joe Rogan two months ago describing the exact AI he wanted: a private model trained only on his own writings and experiences. I built it — and added outcome-based learning.
On 130 adversarial scenarios designed so the query semantically matches bad advice better than good advice:
→ plain vector search: 0–3% correct
→ Roampal: 100% correct
Efficiency: 63% fewer tokens — retrieves 1 outcome-verified result vs RAG's top-3 semantic matches.
Core mechanism
• AI marks outcome → success +0.2, failure −0.3 (explicit or auto-detected from conversation)
• New memories: 70 % embedding / 30 % outcome score

  Proven memories (5+ uses): 40 % embedding / 60 % outcome score

• Over time, “sounds right” gets demoted, “actually worked” gets promoted
Key difference from Mem0/Zep
They update on relevance/consistency. Roampal updates on real outcomes.
Reproducible results (JSON in repo):

                Plain Vector   Roampal

Finance (100)      0 %         100 %
Coding (30)       3.3 %        100 %   ← p=0.001, Cohen’s d=7.49
Learning curve: 58 % → 93 % accuracy as memories accumulate (p=0.005, d=13.4)
I’m not a programmer — psychology degree, MBA, day job managing $6.5 M contracts. Nine months of nights & weekends with only Cursor, Claude, and copy-paste.
100 % local · runs offline with Ollama, LM Studio, or Claude Desktop · MIT license · no telemetry · no signup
GitHub (full benchmarks + all 130 adversarial scenarios):
https://github.com/roampal-ai/roampal
Website + demo video:
https://roampal.ai
Happy to answer technical questions or take brutal feedback in the comments.

value: Show HN: Roampal – a local memory layer that learns from outcomes

author: roampal


value: Show HN: PVAC FHE over hypergraphs with LPN security


value: ngninja

value: Show HN: Built a LinkedIn highlighter to screen candidates 5x faster

author: ngninja


value: BigBigMiao

value:
Finally, after landing component preview support and moving the codebase under the RazorConsole org, we think it’s the right time to introduce
RazorConsole to Hacker News.
# RazorConsole
RazorConsole is a library for building interactive terminal applications using Razor components, rendered through Spectre.Console. If you’ve used React Ink, the idea will feel familiar: a declarative component model that stays cleanly separated from your application logic. If you like how Blazor/Razor expresses UI but want to target the terminal, RazorConsole might be a good fit.
# Highlights
- Author terminal UI using familiar Razor/Component syntax
- Render Razor components directly into Spectre.Console renderables
- Keep your UI declarative and composable, similar to Blazor and React Ink
# Links
- GitHub: https://github.com/RazorConsole/RazorConsole
- Website: https://razorconsole.github.io/RazorConsole
A special shout-out to Nick Chapsas, who created an excellent introduction video:https://www.youtube.com/watch?v=1C1gTRm7BB4. His coverage brought a huge boost during RazorConsole’s cold-start phase, and we sincerely appreciate it. If you want a quick, clear overview of what the project does, his video is the perfect starting point.
# What’s next
- More interaction: mouse and scroll-wheel events
- More layouts & styling: additional layout primitives (e.g., flex-like patterns), potential CSS-style syntax
- More components: a component registry experience similar to shadcn

value: Show HN: Net RazorConsole – Build Interactive TUI with Razor and Spectre.Console

author: BigBigMiao


value: launchaddict

value: Have had a number of projects that I'm working on in the past years which all has the same problem. Users have issues, and its a guesing game. So I developed this really simple app which provides all the information that is generally required, with a nifty little feature of also taking and attaching a screenshot..<p>Obvi it's not perfect, but's it's suitable for my needs. Might be of help to others too ¯\_(ツ)_/¯

value: Show HN: SupportInfo.. A scratch my own itch project

author: launchaddict


value: logicflux

value:
Hey HN,
I’ve been working on WizWhisp, a Windows desktop app for running OpenAI’s Whisper locally. I wanted something native, simple, and privacy-friendly. Existing tools are great but didn’t quite fit what I needed, so I built my own.
What it does:
Drag in audio/video and get transcripts (TXT, SRT, VTT).
Runs Whisper 100% offline.
Uses CUDA if you have an Nvidia GPU; otherwise falls back to CPU.
Handles long recordings.
Tech:
C# + WinUI3 for UI, whisper.cpp for inference.
Business model:
Free for standard transcription.
A one-time Pro upgrade (no subscription) unlocks batch processing and removes the length limit when using the Large model.
Happy to hear any feedback or feature ideas.

value: Show HN: WizWhisp – Offline, Whisper Transcription GUI for Windows

author: logicflux


value: martincsweiss

value: I woke up Sunday morning ready to schedule my week at NeurIPS. To my immediate horror, the NeurIPS.cc poster sessions have 1k+ posters in a stupid little dropdown. So I built a little app to help navigate them by research area/keywords/etc. Built it in a few hours with codex, gemini-cli, and Claude code. Same stack that produced 50% of the papers at NeurIPS ;)<p>Free to use, no signup.

value: Show HN: NeurIPS 2025 Poster Navigator

author: martincsweiss

title: Show HN: NeurIPS 2025 Poster Navigator


value: jsxyzb

value: Xpptx: Your One-Click Professional PowerPoint Solution. Making PowerPoint & Slides Creation Easier

value: Show HN: Xpptx – Revolutionize the way you create PowerPoint presentations

author: jsxyzb


value: openjny

value:
Hi HN,
I built dotgh, a CLI tool for managing AI coding assistant configuration files as reusable templates.
If you use GitHub Copilot, Cursor, or similar AI assistants, you've probably created files like `copilot-instructions.md`, `AGENTS.md`, or `.github/prompts/<i>.prompt.md` many times across different projects.
dotgh lets you save these configs as templates and apply them to new projects with a single command:

  dotgh push my-golang-template   # Save current project's configs
  dotgh pull my-golang-template   # Apply to a new project

What it manages by default:
- AGENTS.md
- .github/copilot-instructions.md
- .github/agents/.agent.md
- .github/copilot-chat-modes/.chatmode.md
- .github/instructions/.instructions.md
- .github/prompts/*.prompt.md
- .vscode/mcp.json
- (customizable via config)
Written in Go, cross-platform (Linux/macOS/Windows), single binary with no dependencies.
GitHub: https://github.com/openjny/dotgh
I'd love to hear your feedback!

value: Show HN: Dotgh – CLI to manage AI-assistant config templates

author: openjny


value:
No signup required—just upload or record a video to verify its truthfulness. You can test it on anyone: internet clips, your significant other, or even yourself.
I know there are tons of scammy 'lie detector' apps out there, but I built this using SOTA multimodal models in hopes of creating a genuine breakthrough in the space. It analyzes micro-expressions, voice patterns, and context. In my own testing (over 50 trials), it reached about 85% accuracy, which honestly felt a bit scary.
It’s also fun to test on famous YouTube clips (like Obama talking about UFOs). I’d love to hear what you think and will be improving Watsn.ai every day based on your feedback!

value: Show HN: Watsn.ai – Scarily accurate lie detector


value:
Explicode is a VS Code extension that lets you write Markdown directly inside your code comments. It provides a live preview of both code and documentation, making it easy to create clean, structured docs—almost like Overleaf, but for your code editor. Great for open source projects and academia.
Key features:
- Write Markdown in multiline comments
- Live preview renders Markdown and code side by side
- Supports most popular programming languages
- Export documentation to Markdown or HTML
- Docs live in the code and update automatically with Git
Demo GIF:
https://raw.githubusercontent.com/benatfroemming/explicode-e...
Download on the VS Code Marketplace:
https://marketplace.visualstudio.com/items?itemName=Explicod...
I’m looking for feedback on usability, bugs, and new feature ideas. If you’re a developer and want to contribute to making Explicode even better, please reach out!

value: Show HN: Explicode – Write Markdown in code comments


value:
I love having multiple git repos in a directory, all of which relate to one project (microservices, etc.). I dislike monorepos since I usually code in different languages and they seem like too much overhead. I know there are multi-repo tools in the market, but most of these seemed not-so-user-friendly to me.
One of the challenges I've struggled with in this setup is keeping track of what's been committed/deployed when (yes I use semver), so I built a quick tool to keep track of last commit times, be able to quickly check git status/log for each repo, and get a R/A/G indicator if a particular repo has been committed or not, so I can then see what has changed or needs to be committed, and update deployments accordingly.<p>Built for my personal use but wanted to get feedback here in case anyone else found it useful. I recognize this may be due to bad CICD workflows but as a solo dev I don't have the bandwidth to set up a full process for it.

value: Show HN: Multi-Repo Tool

title: Show HN: Multi-Repo Tool


value: parkkichoel

value: Show HN: PKC Mark – open-source local benchmark for LLMs and Diffusers

author: parkkichoel


value: Show HN: Personal AI Assistant

title: Show HN: Personal AI Assistant


value: jadesee

value:
I needed a lightweight way to archive documentation from a website. wget and similar tools failed to clone the site reliably (missing assets, broken links, etc.), so I ended up building a full website-cloning tool using Node.js + Puppeteer.
Repo:
https://github.com/jademsee/webclone
Feedback, issues, and PRs are very welcome.

value: Show HN: Webclone.js – A simple tool to clone websites

author: jadesee


value: tomstig

value: Show HN: Imposter Game Oneline

author: tomstig

title: Show HN: Imposter Game Oneline


value: xsonerx

value:
I built a mobile app that scans barcodes or photos of food in your pantry and uses GPT-4 to suggest recipes based on what you actually have.
Tech stack: React Native (Expo), Supabase for backend/auth,
react-native-vision-camera for scanning, OpenAI API for recipe generation.
Built it in about a week.It's free to use with a premium tier. iOS only for now.
App Store: https://apps.apple.com/us/app/eatelligence/id6755645485
Would love feedback or questions!

value: Show HN: Eatelligence – Scan pantry items, get AI recipe suggestions

author: xsonerx


value: PStarH

value:
OP here. I built VisiLens because I love VisiData's speed but struggle to remember its CLI shortcuts. I wanted a mouse-friendly interface that doesn't sacrifice performance. How it works: It runs a local FastAPI server that wraps VisiData's engine. The frontend (React) uses a virtualized grid, streaming only the visible data slices via WebSocket. Key features:
Speed: Opens 1M rows in ~1.7s (benchmarked on M2 Air).
Privacy: 100% local. No data leaves your machine.
Stack: Python 3.10+, FastAPI, React.
Repo:https://github.com/PStarH/VisiLens
I'd love to hear your feedback

value: Show HN: VisiLens – Local-First and Fast Web GUI for VisiData

author: PStarH


value: levmiseri

value:
Hello HN!
I'm excited to share the 'v1' of a new web-based markdown editor!
There is plenty of markdown editors on the market, so one is spoiled for choice, but Kraa's approach is a little different. It's not trying to be the next Notion or Evernote – Kraa is focusing on a minimal, distraction-free writing (and reading!) experience while having rich customization options and some pretty unique features (namely a real-real-time chat, see an example link below)
It's good for notes, collaborative editing, blog, but even chat or entire communities.
Some example use cases:
Chat: https://kraa.io/hackernews
Blog article:
https://kraa.io/kraa/examples/echolibrary
Long story: https://kraa.io/kraa/examples/insidekick
You don't need an account to use Kraa – we would love to hear your feedback!

value: Show HN: Kraa – web-based markdown editor

author: levmiseri


value:
I built dataframe-expectations to solve a problem I kept running into: how to validate pandas and PySpark DataFrames without rewriting validation logic every time or wrestling with complex setup.
Key features:
- Single API that works for both pandas and PySpark DataFrames
- Minimal dependencies (won't bloat Docker images or slow builds)
- Decorator-based validation for automatic function output checks
- Tag-based filtering to run specific validations by environment or priority or any other criteria
- Reusable expectation definitions across your codebase
The library is lightweight and integrates easily into existing CI/CD pipelines, helping you catch data quality issues before production.
Links:
• PyPI:
https://pypi.org/project/dataframe-expectations/
• GitHub:
https://github.com/getyourguide/dataframe-expectations
• Docs:
https://code.getyourguide.com/dataframe-expectations/
The project is still in its early stages and I'd love to hear your feedback and answer any questions!

value: Show HN: Dataframe-Expectations – Validation for Pandas and PySpark DataFrames


value:
Hi HN,
I built a minimal AI image editor on top of Nano Banana Pro.
Highlights:
- 2K → 4K generation
- Better spatial reasoning than most models I’ve tried
- Reference-image conditioning (up to 10 images)
- Text rendering that doesn’t look like an eldritch summoning ritual
Stack: React + Node/TS (antigravity did save me a lot of time!).
Use cases: product shots, posters, concept art, “my cat as a medieval knight in 4K”, etc.
Feedback on latency/security/pricing is really appreciated.
Credits only charge on successful renders.
And no, I still don’t know why it’s called Nano Banana Pro.

value: Show HN: An AI image editor using Nano Banana Pro (finally renders text correct)


value: Show HN: Multi langugage parser for local code comprenhension


value:
Hi HN,
We’ve been building Kling O1, a unified multimodal video model that handles nearly every major video-generation task—reference-to-video, text-to-video, start/end frames, editing, transformations, restyling, and camera extension—all inside one system.
Key Features of Kling O1
• Input Anything – One Model for All Tasks
Images, clips, characters, layouts, and text instructions all flow into a single Kling O1 pipeline. No switching tools.
• Multimodal Understanding
Everything you upload becomes part of the “prompt.” Kling O1 interprets subjects from multiple viewpoints to generate accurate motion and fill missing frames with precision.
• All-in-One Reference for Consistency
Feed Kling O1 reference images or video, and it preserves characters, props, and scenes across shots—solving the long-standing video-consistency problem.
• Stack Multiple Edits at Once
Add subjects, change backgrounds, restyle, and apply element-based controls in a single generation.
• 3–10 Second Shots for Storytelling
Control pacing with flexible clip lengths. Start/End Frame control is coming soon.
Where Kling O1 Is Useful
Advertising – Upload product, model, and background images + a short prompt to generate polished product shots quickly.
Fashion – Build endless virtual runway videos from models + clothing refs.
Film Post-production – “Remove the bystanders” or “make the sky blue”—Kling O1 handles pixel-level corrections via language.
Filmmaking – Lock in characters, props, and scenes for consistent multi-shot sequences.
We’d love feedback on:
• Which parts of the unified workflow help you most
• What multimodal controls you’d want next
• Scenarios where 3–10 second shots or start/end frames would be most useful
Thanks for reading!
Try Kling O1 here: http://klingo1ai.net/?i=d1d5k

value: Show HN: Kling O1–Unified Multimodal Video Model for Consistent Video Generation


value: Show HN: Quantica v0.1.1 for Classical and Quantum Programming


value: Show HN: Spin the Yes or No Wheel – Get Your Free and Instant Decision


value: touge

value:
Hello HN,
I built a CLI tool in Go to recover corrupted SQLite databases, implementing the "Corrupt Recovery" strategy inspired by Tencent's WCDB.
The Problem:
Standard tools like sqlite3 .recover often fail if the file header or the sqlite_master table (usually Page 1) is corrupted. They rely on the file's internal structure to locate data.
This project is also an experiment in AI-assisted coding. The core logic (binary parsing, page scanning) and the CLI structure were primarily written by Gemini 3 Pro (Preview).
Repo: https://github.com/tougee/sqlite-repair-go
Feedback is welcome!

value: Show HN: SQLite-repair-go – Recover corrupt SQLite DBs when standard tools fail

author: touge


value: niklauslee

value: Show HN: Nakso – An Excalidraw Alternative for Desktop

author: niklauslee


value: Show HN: I was reintroduced to computers: Raspberry Pi


value: Show HN: I want food – Simple swipe based restaurant discovery app


value:
Whenever I start a new iOS app, I am wondering which versions to support.
To fix this once and for all, I built a small tool that shows:
• iOS version market share
• a quick way to pick a minimum deployment target
It’s fast, simple, and lives in the browser.
Already thinking about adding API support, if the app is useful for people
• which APIs or frameworks each version supports (like SwiftData, CloudKit, HealthKit, etc)
• maybe add modifiers?
Link: https://ioscompatibility.com/
Happy to answer questions. Feedback welcome. Do you find this useful?

value: Show HN: I made a free tool to check iOS version market share


value:
I've spent the past two years building an app for quick, ad-hoc data manipulation because I was dissatisfied with the existing landscape of tools. I thought others might find it useful too, so I'm throwing it out into the world.
Currently, if you want to grab some CSV or JSON data and do a sequence of operations on it (filter, sort, aggregate, etc.), the path of least resistance is to open an IDE or notebook and write code. This is fine for simple tasks, but can get messy quickly and doesn't offer the same immediate visual feedback loop that something like a spreadsheet does.
I thought there ought to exist a tool that offers a similar UX to a spreadsheet but with the power of a dataframe library, so I built one.
There's no signup and it runs entirely within the browser, using Rust compiled to WebAssembly for the data processing and Solid.JS for the UI. Projects are persisted to IndexedDB and files are read directly from disk using the file system API, so nothing sensitive ever leaves your computer.
I know documentation is currently scarce, but if there's enough interest, I'm happy to work on this.
Any questions or feedback are of course welcome. I'm just curious whether anyone would actually find this tool useful.

value: Show HN: Visual, local-first data tool

title: Show HN: Visual, local-first data tool


value: qinggeng

value:
Hello HN,
I built this tool after seeing a Reddit thread where a historical documentary creator described their painful workflow. They produce 30-minute videos requiring over 240 unique images. Currently, they have to manually write prompts, generate, and download images one by one for every scene.
To solve this bottleneck, I built AI Bulk Image Generator.
The Tool: https://aibulkimagegenerator.com
How it works:
I designed the workflow around two core features to maximize efficiency:
1、Prompt × N (Batch Variations):
If you need to explore styles or get the perfect shot, you can input a single prompt, set a specific quantity (e.g., 10 images), and the tool will generate all variations in one batch. No more clicking "generate" repeatedly.
2、Prompts via CSV :
This addresses the Reddit user's problem. You can upload a CSV file containing a list of pre-written prompts (one per line). The system automatically parses the file and creates images for every single prompt in the list. This allows you to generate assets for a full video script in one go.
Models Supported:
Currently, I support a mix of models including Nano Banana / Pro, GPT-4o, and SeaDream v4. I plan to add more models based on user demand.
This is an MVP aimed at content creators who need volume. I’d appreciate any feedback on the UI or the batch processing flow!
Thanks!

value: Show HN: I built a bulk image generator after seeing a YouTuber's struggle

author: qinggeng


value: control-h

value: Show HN: SemVer for Books

author: control-h

title: Show HN: SemVer for Books


value: lingonland

value: Show HN: I made a yule themed secret Santa system for my family

author: lingonland


value: cak

value:
I built a CLI tool called elf to streamline Advent of Code workflows. It removes a lot of the repetitive steps around fetching inputs, submitting answers safely, and checking private leaderboards.
The tool focuses on:
- Input fetching with caching (no repeated downloads, works offline)
- Safe answer submissions with guardrails to prevent duplicate or invalid guesses
- Private leaderboard viewer (table or JSON)
- Status calendar and guess history viewer
- Optional Python API for scripting or automation
It’s built with Typer, httpx, Pydantic, and Rich, and aims to be clean, predictable, and easy to extend.
Repo: https://github.com/cak/elf
PyPI: https://pypi.org/project/elf/
Feedback and questions are welcome.

value: Show HN: Elf – A CLI Helper for Advent of Code

author: cak


value:
Hey HN,
I’ve been working on Quash, a mobile-first QA tool where you can write test flows in plain English and have an agent execute them on real Android devices.
We’ve finally shipped a downloadable desktop app (macOS, Intel + Apple Silicon), and you can try it now. No managed “demo” environment – it runs tests against your own apps and devices. (Can connect any device locally as well)

value: Show HN: Quash – A mobile-use agent for Android QA


value: davailan

value:
Hi HN,
Would love your thoughts on Open Paper Digest. It’s a mobile feed that let’s you “doomscroll” through summaries of popular papers that were published recently.
Backstory
There’s a combination of factors lead me to build this:
1. Quality of content social media apps has decreased, but I still notice that it is harder than ever for me to stay away from these apps.
2. I’ve been saying for a while now that I should start reading papers to keep up with what’s going on in AI-world.
Initially, I set out to build something solely for point 2. This version was more search-focussed, and focussed on simplifying the whole text of a paper, not summarizing. Still, I wasn’t using it. After yet another 30 min doomscroll on a bus last month, point 1 came into the picture and I changed how Open Paper Digest worked. That’s what you can see today!
How it works
It is checking Huggingface Trending Papers and the large research labs daily to find papers to add to the index. The PDFs gets converted to markdown using Mistral OCR, this is then given to Gemini 2.5 to create a 5 minute summary.
I notice that I am now going to the site daily, so that’s a good sign. I’m curious what you all think, and what feedback you might have.
Cheers,
Arthur

value: Show HN: Doomscrolling Research Papers

author: davailan

title: Show HN: Doomscrolling Research Papers


value: andresrl

value:
Hi HN,
I run a small web engineering boutique and I was struggling with a high volume of unqualified leads (mostly looking for cheap WordPress maintenance).
Instead of adding a CAPTCHA or a long form, I decided to rewrite my site from Elementor to Nuxt 4 and turn the Hero section into an interactive CLI (TechTerminal.vue).
My hypothesis: If a user can't handle a simple terminal prompt or doesn't find it "cool", they probably aren't the CTO/Founder target I'm looking for.
I also implemented:
Real-time Latency Calculator: Pure frontend logic (Vue 3 Composition API) to estimate revenue loss based on load times.
Viability Scanner: A state-driven wizard to qualify projects pre-call.
Stack: Vue 3, Nuxt 4 (SSR), Tailwind, and deployed on a dedicated server (scoring 99/100 on Lighthouse).
It's been a fun experiment in "Engineering as Marketing". Would love to hear your thoughts on the code or the UX approach.
Code snippets or implementation details are available if anyone is curious about the terminal parser logic

value: Show HN: I replaced my portfolio's hero with a Terminal to filter non-tech leads

author: andresrl


value: SilentCoderHere

value:
I built this app for my own use, but I see that it can help others, so I uploaded it to GitHub.
You can switch between multiple AIs in this app and use them at the same time.
If you encounter any bugs or errors, feel free to create an issue.

value: Show HN: AI Hub – One app for all AIs

author: SilentCoderHere

title: Show HN: AI Hub – One app for all AIs


value: ndhandala

value: Show HN: OneUptime – open-source Observability Platform

author: ndhandala


value: halfdaft

value: Show HN: Townlands of Ireland – customised map posters

author: halfdaft


value: teemingdev

value:
Hi HN,
I built smollaunch.com, a lightweight launch platform for developers who want to share projects without the noise, voting games, or marketing theatrics of bigger launch sites.
I’ve shipped a lot of small tools and noticed that most launch platforms are built around growth-hacking rather than genuine discovery. Many devs just want a clean place to publish something they built and get feedback from peers who care about the craft. So I made a simpler version of that.
What it does:
- Post your project with a short description and link
- Get a clean, minimal launch page and profile
- Other builders can follow, comment and send feedback
- No algorithms, no gamified voting, no “launch timing optimization”
- Built to feel more like an engineering feed than a leaderboard
Why I built it:
I wanted a calm space where developers can share ideas and prototypes without having to run a Product Hunt style campaign. A lot of great projects never see daylight because the launch process is intimidating or feels overly optimized. Smollaunch is meant to be the opposite: simple, fast and low-pressure.
Stack / tech details:
- Rails 8
- Hotwire for real-time interactions without JS complexity
- Postgres
- Tailwind CSS
- Deployed as a small, fast monolith
What I’m looking for:
Feedback from builders who regularly ship small tools or experiments. I’d love to hear:
- What feels missing for a “dev-first” launch platform
- Whether you want integrations (GitHub, tags, RSS feeds etc.)
- If the philosophy of “no hype, no ranking pressure” resonates
You can try it here: https://smollaunch.com
Thanks for reading and happy to answer anything.

value: Show HN: Launchpad for developers to ship and showcase their projects

author: teemingdev


value: tdi

value: I created this tiny app because I liked OrbStack menubar feature that reminded me to switch off container runtimes when on laptop.

value: Show HN: Colimabar – A Colima macOS Menubar

author: tdi


value: pattle

value:
I've spent about a year building this game which is inspired by the BBC show "Race across the World".
For those that don't know you have to race from point A to point B only using public transport and without flying.  You have starting money but if you need to top up your funds you can work jobs.
Any feedback is greatly recieved.

value: Show HN: Race from one side of the world to the other

author: pattle


value: adriencloud

value:
For the last 6+ months I’ve been building Nexroo, an automation platform focused on solving what I call the “last mile problem” of automation.
Most automation tools make it easy to build workflows (Zapier, n8n, etc.), but much harder to deliver them as something your users or clients can actually use. In real life, that “last kilometer” often turns into custom UIs, micro-SaaS wrappers, deployments, versioning, and lots of glue code.
Nexroo tries to fix that.
Build, Version, Ship anywhere with clean end-user experience

value: Show HN: Nexroo – From automation workflows to real products

author: adriencloud


value: AwkwardPanda

value: Show HN: OnlyRecipe 2.0 – I added the features HN requested – 4 years later

author: AwkwardPanda


value:
Hi everyone, I built a new actor framework based on `std::execution`.
This framework turns your C++ class into a stateful async service by one line of code. All method calls to it will be queued and executed sequentially, you don't need any locks in your class. It offers a simple way to build highly concurrent programs - just write plain classes.
Compared to other C++ actor frameworks, it has cleaner non-intrusive API, pluggable scheduler, and can compose with everthing in the `std::execution` ecosystem.
It only requires C++20, the core functionality is tested in our company's production, feel free to have a try.
I'll appreciate it if you could have a look. Any question/feedback/criticism is appreciated. Leave a star if you find it's interesting, it'll encourage me a lot :)

value: Show HN: ex_actor – New C++ Actor Framework Based on `std:execution`


value:
Howdy HN! I spend a lot of time roaming around the Pyrenees and wanted an interesting way to learn the names of mountain peaks, so I built Quest.
It's a geography challenge where you are given the name and elevation of a peak and have to determine where it is on the map.
It's built using OSM data, you get 10 rounds of 2 minutes max to pinpoint the location of a random peak, either globally or in a specific region.
It's free and does not require an account to start playing, but you can create one if leaderboards are your thing.
On the technical side, I used DuckDB-WASM to offload spatial queries to the client, so that I can (hopefully) keep this running on low-end hardware. The server is running a Sveltekit app on Hetzner. Major props to Better Auth for making auth enjoyable.
Happy to hear feedback and answer questions.

value: Show HN: Quest - A GeoGuessr-inspired game for finding mountain peaks


value:
A simple tool to compare how different PDF parsers handle your documents.
Shows naive parsing (pypdf) vs layout-aware parsing (Docling) side-by-side.
Helps spot issues with scans, tables, and multi-column layouts before theycause problems in your RAG system.
Parsers are easy to swap if you want to try alternatives.

value: Show HN: Side-by-side PDF parser comparison for RAG pipelines


value: rishsriv

value:
Hi HN! We're FactIQ - an AI driven data explorer for the US economy.
Live Demo: https://factiq.com/
Datasets: BLS, EIA, BTS, Census EITS, and more
What FactIQ is
- data analysis tool for 7M+ economic data series
- meant for folks who need quick access to reliable economic statistics and contextual analysis
- emphasis on data traceability and citations
For reference, here is a quick FactIQ analysis that compares how sources of electricity in the US and China changed over time: https://www.factiq.com/share/7c0bf9a6d8304c01849af763f39362e...
Why we built it
- we worked on Defog.ai for 3 years, selling fine-tuned AI data analysts to large enterprises based on our open-source text to SQL models
- saw non-trivial usage and adoption, but it became more of a consulting company instead of a scalable product, because we needed to spend a lot of time gathering context and understanding DB schemas for each customer
- decided it was a better bet to build something which was widely useful and where we could design and control the database schema to make it more AI friendly
- saw how broken the current state of economic data discovery was, and decided to fix it
Technical Details
- we get large data dumps from government agencies, standardize them into a common internal schema, use LLMs to extract metadata and enrich the data, and create embeddings to make it searchable
- when users ask questions, we do an embeddings-based search (to discover relevant data series) along with an agentic analysis pipeline for actually answering questions
What's next
- More comprehensive data about the US economy
- Adding data for China, India, and the EU
What we're looking for
- Ask complex questions, and give us feedback on what we can improve
- If you work regularly with economic data, tell us what your workflow needs are

value: Show HN: FactIQ – A Data Explorer for the US Economy

author: rishsriv


value: Show HN: Ahai – Find your ideas scattered across files - local, private


value: tacoooooooo

value: Show HN: Generate a 1M-document RAG eval dataset from a single prompt

author: tacoooooooo


value: judahmeek

value:
First, I'd like to thank METR.org[0] for recently agreeing to fund my work on this project.
That little bit of validation goes a long way.
Second, I have questions for patent lawyers:
* When do I know if I have enough specification detail to start working on filing a patent?
* Does this public repository qualify as prior art?
Third, if my post text seems overly sparse with the details, I promise to answer any questions you have as fast as Hacker News allows me.
0: https://metr.org/

author: judahmeek

title: Show HN: Significance-Hypothesis-Based-ARC-AGI-2-puzzle-solver


value: gdcbe

value: We sit down with Rust developer Martin Algesten for a deep dive into WebRTC and the Sans IO approach to protocol design. Martin traces the surprising origins of WebRTC, explains why real time media over UDP is both powerful and painfully complex, and walks through how peer to peer connections work under the hood. The conversation then shifts to Sans IO design, why it matters for clean protocol implementations in Rust, and how Martin applies it in his own WebRTC stack, str0m.

value: Show HN: Netstack.fm Podcast Ep16: WebRTC and Sans IO with Martin Algesten

author: gdcbe


value: vinxu

value: Show HN: CastReader – Visual AI reader with relationship maps for novels

author: vinxu


value: yuzong

value: Show HN: A lightweight issue tracker for managing issues in your Git repository

author: yuzong


value:
Hi HN, we built "deck" to solve our frustration with slide creation workflow.
Key features:
- Write slides in Markdown, design in Google Slides (separation of concerns)
- Iterative updates with `deck apply --watch`
- Use existing Google Slides templates/themes
- Supports images and code blocks (with external commands)
We wanted to write slides in our favorite text editor and
version control presentation content, while leveraging
Google Slides' design capabilities.
Feedback welcome!

value: Show HN: deck – Create Google Slides Presentations from Markdown


value: Show HN: Feedvote – Deep 2-way sync for Linear and Jira feedback ($149 LTD)


value:
Hey HN, I’m the author.
I built Steer because I was tired of the 'Confident Idiot' problem—where agents output broken JSON or hallucinate facts, and I only find out when the app crashes.
Existing tools just log the error. I wanted to fix it.
Steer creates a local feedback loop:
1. Catch: It blocks the bad output (e.g., Markdown in a JSON field).
2. Teach: You click 'Teach' in the local dashboard to define the fix.
3. Fix: It injects that rule into the agent's context for future runs.
It’s Python-native, works with any LLM (OpenAI/LangChain/etc), and stores data locally.
pip install steer-sdk
Repo: https://github.com/imtt-dev/steer
I’d love feedback on the API design—specifically the @capture decorator pattern.

value: Show HN: Steer – Stop debugging agents, start teaching them (Open Source)


value: sirinnes

value:
Hi HN,
Piperead is a free web tool that uses AI to act as a personalized librarian. Instead of just genre tags, we use specific "personas" to generate recommendations.
The goal was to make something simple, fast, and free. Because we achieved to keep minimum cost.
The site is live at https://piperead.com
We are trying to fine-tune the recommendation quality. Feedback on the UX and the recommendation relevance is very welcome.

value: Show HN: Piperead – An AI librarian to find your next book

author: sirinnes


value: clubers

value: A simple interactive visualization of chromaticity and display gamuts using animated point clouds. It compares sRGB, P3, Rec.2020, and a computed “max gamut,” shows how the visible spectrum maps onto chromaticities, and highlights out-of-gamut regions through desaturation.

value: Show HN: Interactive Chromaticity and 3D Color Gamut Visualization

author: clubers


value: thibaultmthh

value:
I built this to bring virtual try-on to Shopify stores. The backend wraps Google's new VTO model, so the "AI" part was solved.
The real challenge was the UX: building a zero-friction experience for shoppers to maximize conversion. I’ve run a some of A/B tests on the widget design and copy, and learned not to trust my intuition, multiple times, the "best" variant wasn't the one I expected.
It's hard to gather strict numbers without large-scale data yet, but early estimates show that customers who engage with the try-on are ~25% more likely to buy.
Live demo here: https://demo.genlook.app

value: Show HN: Genlook – Virtual try-on for Shopify stores

author: thibaultmthh


value:
We built Diode Collab because even "secure" messaging apps still route everything through someone else's servers.
The problem with "secure" apps
Most messaging and file sharing apps pass your data through centralized corporate servers. Even encrypted apps like Signal and WhatsApp:
Route traffic through their servers (central point of failure)
Keep metadata about who you talk to and when
Tie your identity to phone numbers or personal identifiers
How Diode works
Diode connects devices directly in a secure perimeter. No servers in the middle.
Peer-to-peer: Data moves directly between your devices
End-to-end encrypted: Unlike other apps that break encryption at the server, Diode keeps everything encrypted end-to-end. Not even Diode has access to your data.
Zero leakage: No central point that can be logged, breached, or subpoenaed
Solving the P2P availability problem
Traditional P2P requires both devices online simultaneously. We solved this with Zone canisters on the Internet Computer Protocol (ICP) - a decentralized relay that ensures 24/7 availability without becoming a server that can be compromised. Your data stays encrypted and under your control.
Anonymous by design
Diode only requires a username - no phone number, email, or personal details. Access is managed through decentralized credentials that you control.
Who this is for
Teams and families who need their messages, photos, and documents to never touch corporate infrastructure.
https://diode.io/products/collab-family/
Happy to answer questions about Diode and digital sovereignty.

value: Show HN: Messaging and file sharing that stores nothing on servers


value: Show HN: Hacker News Visualized

title: Show HN: Hacker News Visualized


value: networkcat

value: OpenProxyDB automatically fetches IP addresses that have been blocked by Wikipedia for proxy-related reasons (VPN, Tor, webhost, etc.) and exports them to a CSV file. The crawler runs daily via GitHub Actions and publishes the results to GitHub Releases.

value: Show HN: Turning Wikipedia's blocklists into a proxy detection database

author: networkcat


value: mvijaykarthik

value:
Frequent memory allocations can be costly in Go. Go's sync.Pool helps with object reuse but isn't type-safe and can introduce bugs. You need type assertions, and it's easy to accidentally return objects twice or forget to return them at all.
SafePool provides type safety with Go generics, eliminating the need for type assertions and making pool usage safer and clearer.
For objects that need to live across function boundaries, we added PoolManager. It tracks all objects obtained from pools and ensures they're all returned when the manager is cleaned up, preventing leaks across function calls.
We built this after hitting sync.pool issues at Oodle AI, where efficient memory management is critical for processing high-volume telemetry data.

value: Show HN: SafePool – Type-safe object pooling for Go

author: mvijaykarthik


value: charlie-haley

value: Show HN: Marmot – Single-binary data catalog (no Kafka, no Elasticsearch)

author: charlie-haley


value: Show HN: TinyTune – fine-tune open-source AI on your own data with no code


value: nocodebcn

value: Show HN: Max MRR Calculator – Find Your Startup's Growth Ceiling

author: nocodebcn


value: nallana

value:
Hi, I’m Nabeel. In August I released RunMat as an open-source runtime for MATLAB code that was already much faster than GNU Octave on the workloads I tried. https://news.ycombinator.com/item?id=44972919
Since then, I’ve taken it further with RunMat Accelerate: the runtime now automatically fuses operations and routes work between CPU and GPU. You write MATLAB-style code, and RunMat runs your computation across CPUs and GPUs for speed. No CUDA, no kernel code.
Under the hood, it builds a graph of your array math, fuses long chains into a few kernels, keeps data on the GPU when that helps, and falls back to CPU JIT / BLAS for small cases.
On an Apple M2 Max (32 GB), here are some current benchmarks (median of several runs):
* 5M-path Monte Carlo
    * RunMat ≈ 0.61 s
    * PyTorch ≈ 1.70 s
    * NumPy ≈ 79.9 s
         → ~2.8× faster than PyTorch and ~130× faster than NumPy on this test.
* 64 × 4K image preprocessing pipeline
     (mean/std, normalize, gain/bias, gamma, MSE)
    * RunMat ≈ 0.68 s
    * PyTorch ≈ 1.20 s
    * NumPy ≈ 7.0 s
         → ~1.8× faster than PyTorch and ~10× faster than NumPy.
* 1B-point elementwise chain (sin / exp / cos / tanh mix)
    * RunMat ≈ 0.14 s
    * PyTorch ≈ 20.8 s
    * NumPy ≈ 11.9 s
         → ~140× faster than PyTorch and ~80× faster than NumPy.
If you want more detail on how the fusion and CPU/GPU routing work, I wrote up a longer post here:
https://runmat.org/blog/runmat-accel-intro-blog
You can run the same benchmarks yourself from the GitHub repo in the main HN link. Feedback, bug reports, and “here’s where it breaks or is slow” examples are very welcome.

value: Show HN: RunMat – runtime with auto CPU/GPU routing for dense math

author: nallana


value:
I'm happy to announce that Jester News is finally available as a companion app to https://jesterengine.com.
Jester is a free (with a premium tier) web-based RSS/Atom reader that sprinkles some AI features on top to help with topic discovery and content consumption, for example:
* Jester groups similar articles via similarity search into "Stories" to make finding articles on a specific topic/event more natural.
* You can leverage your followed feeds/folders of feeds to automatically generate podcasts or videos along with a list of sources.
* You can even build your own story generation pipeline from your whitelisted sources (though that requires usage of the website).
Of course, the website also offers a ton of the features you would expect from a modern news feed platform, such as subscriptions/filtering/actions/scrape tools/etc.
The new mobile app makes heavy use of stories and generated digests as a more light-weight experience.  It still needs some design work, but any early feedback is greatly appreciated.  Everything is available at the free tier, so please give it a try and let me know if you have any questions/issues.

value: Show HN: Jester News - An RSS/Atom Companion App


value: MartyD

value:
I built CoThou after seeing search and AI answer engines give completely incorrect information about my company. Turns out, they prioritize structured, citable content, so I reverse-engineered how they choose sources and built CoThou to become the source of truth.
How it works
For businesses:
Create a company profile. When search and AI answer engines are asked about your company, they’ll cite your company profile and its content, not Wikipedia or outdated info.
For publishers and knowledge workers:
Publish at your personal profile with proper citations (300M+ academic papers indexed). When someone asks search and AI answer engines about your topic, it will cite your work linking to your profile and allowing citation tracking.
Try it now (unlimited during beta):
→ https://cothou.com
It’s v0.01 and rough around the edges. Try it and let me know what breaks.
What’s next:
Currently training a custom 32B MoE (Mixture of Experts) LLM with 3B active parameters scheduled to go live in Q1/2026. The key difference: it breaks down complex queries into parallel subtasks that execute live on an infinite canvas. You’ll see agents plan and build in real time, instead of waiting for a progress bar.
Examples:
“Write a 300-page book on the history of computing”
“Create a 60-second TikTok ad for my SaaS”
It handles research, outlines, storyboarding, asset generation, voice-overs, and music simultaneously.
Since only ~3B parameters are active per token, it runs 8–10× cheaper and faster than dense 32B models, while still matching or outperforming premium models on reasoning, coding, and long-context tasks.
Building through partnerships with NVIDIA Inception and Microsoft for Startups.
Would love HN feedback on:
- Improving citation accuracy
- Building trust with AI parsers
- What sources to add next (currently 100M companies + 300M academic papers)
- Anything else
Marty (Founder)

value: Show HN: CoThou – Control what AI search engines say about your business

author: MartyD


value: mfolaron

value:
Hi HN,
We're launching CoChat, which extends OpenWebUI with group chat, model switching, and side-by-side comparison.
What makes it different: CoChat is designed for teams working with AI.
- Group chat with AI facilitation. Multiple users collaborate in the same thread. The AI detects group discussions, tracks participants, and facilitates rather than dictates.
- Switch and compare models. Run GPT, Claude, Mistral, Llama, and others side-by-side or switch mid-conversation.
- Intelligent web search. Context-aware search activates only for real-time information.
- Artifacts and tool calls. Generate documents and code inline. MCP tool integration coming soon.
- No subscription fee. Pay for usage/tokens only at exact list price.
Non-obvious things we learned building this.
Through building CoChat, we've learned some surprising things about LLM behavior. I'll share two (happy to discuss more in comments).
First, models don't understand they're not the only AI in the room. When you tag a new model into a conversation and ask "what do you think of Claude's response above?", the model assumes it wrote that previous response. It will defend it, build on it, or awkwardly try to reconcile the question with its false memory of writing it. We solved this by injecting model attribution into the conversation context - explicitly marking which model generated each response. Once models understand they're looking at another model's output, they engage critically rather than defensively. The quality of cross-model analysis improved dramatically.
Second, LLMs have a compulsive need to "solve" group conversations. In a multi-user thread, the AI wants to answer every question and resolve every disagreement, even when humans are working something out themselves. System prompts telling it to "facilitate, don't dictate" weren't enough. We had to restructure how we frame the AI's role in group context: it's a participant who speaks when addressed, not an omniscient moderator. Getting this balance right is still ongoing - we're curious how others have approached this.
We also ran into interesting challenges around memory and tool execution in multi-user contexts (whose preferences apply? whose tools get executed?) but that's probably a separate post.
Why this matters: Different models excel at different tasks. Current tools lock you into a single vendor. CoChat lets you choose the best model for each task while enabling real team collaboration.
We're planning to submit all updates back to the core project or maintain an active open-source fork.
Try it at: https://cochat.ai
Would love feedback from teams already using AI collaboratively, or anyone interested in model comparison workflows.

value: Show HN: CoChat – Group chats with multi-model AI, built on OpenWebUI

author: mfolaron


value: sr-white

value:
Hi HN! I built gopin to solve a gap in my dependency management workflow.
Renovate and Dependabot keep my CI workflows updated automatically, but they don't touch go install commands in them. I was stuck manually checking and updating versions for tools like golangci-lint, goimports, etc.
gopin fixes this by:
- Pinning @latest to actual versions (e.g., @latest → @v2.6.2)
- Updating outdated pinned versions to the latest (e.g., @v2.5.0 → @v2.6.2)
It queries proxy.golang.org and rewrites your files in-place:

  gopin run --dry-run  # Preview changes
  gopin run            # Apply

GitHub: https://github.com/nnnkkk7/gopin
Would love feedback – especially if there are edge cases I missed!

value: Show HN: Gopin – Automatically pin latest to specific versions in Go install

author: sr-white


value: drdmitry

value:
Hi HN,
Every developer knows this pain:
"I just need to convert this XML to JSON..."
-> Write a script, test on sample data
-> Need custom output? Calculated fields?
-> ChatGPT back-and-forth, 20 iterations
-> "How do I run this Python with all those dependencies?"
I've been building file converters since 2012 (conversiontools.io). Standard formats are solved, but I constantly get requests for custom edge cases: supplier feeds, proprietary logs, weird XMLs from legacy systems.
So I built AI Converter Studio.
Upload your file. Describe the output format. Get a hosted converter with a web interface and API.
The system analyzes your file structure locally – your actual data isn't sent to the LLM.
Need to change a column name later? Prompt in the chat. AI updates the converter. Done.
10 minutes from "here's my file" to a working automated workflow with API.
Currently in Beta – feedback welcome!
Free to try (100 AI credits/month): https://conversiontools.io/ai-converter-studio
Happy to answer questions!
Thanks!

value: Show HN: I Built an Agentic AI That Creates Hosted File Converters

author: drdmitry


value: ten-fold

value:
Hi HN,
I recently left Uber after an intense decade as Senior and then Staff Engineer.
Coming from a small startup, it took me years to learn how to be successful in tech. When I left, I decided to write down the raw, unfiltered advice that you rarely hear from managers.
It’s a fun, quick read through 7 playbooks.
Enjoy the free PDF for the next 48 hours.
Ask me anything! :)

value: Show HN: I wrote a book for software engineers, based on 11 years at Uber

author: ten-fold


value: CoregentisAI

value:
Hi HN,
The entire Multi-Agent ecosystem is built on sand: no lifecycle, no state model, no governance, no auditability, no reproducibility.
Every framework — LangGraph, AutoGen, MCP, OpenAI Swarm, Google Antigravity, CoreAgent — fails for the same structural reasons.
Today we’re releasing MPLP — Multi-Agent Lifecycle Protocol,
the first open, vendor-neutral protocol that defines how agent systems must work: Context, Plan, Confirm, Trace, Role, Dialog, Collab, Extension, Core, Network — plus PSG, Drift Detection, Delta-Intent, AEL/VSL, and execution profiles.
This is not a framework.
This is the TCP/IP moment for agentic AI.
Full spec, schemas, tests, and SDKs (Apache-2.0):
https://github.com/Coregentis/MPLP-Protocol
Full rationale in first comment.

value: Show HN: MPLP – Why Prompt Engineering Is over (Protocol Engineering Begins)

author: CoregentisAI


value:
Ainisa is the agentic AI platform that actually works: Train agents on your data (products/FAQs/orders), deploy to WhatsApp, Telegram, or website in minutes, and let them take real actions—book meetings, trigger n8n/Zapier, fetch orders/products, fill forms, close deals, show buttons etc.
Bring your own OpenAI/Claude key for zero hidden costs. No markups, no hallucinations.
Launch special: First 100 sign-ups get 20% off for 3 months. 4 ready templates: General, Customer Support, E-commerce, Lead Gen.
Perfect for e-com stores, agencies, and solo founders. Start free (200 messages/50 chats/mo).

value: Show HN: Ainisa – No-Code AI Agents for WhatsApp/Telegram (BYOK)


value: peterwoodman

value: A lightweight note-taking app built with Go + HTMX, featuring nested pages, templates, shared spaces, and OCR to make scanned PDFs searchable. The web app uses responsive design to make it scale to desktop or mobile.

value: Show HN: A simple Markdown note app with templates and PDF support

author: peterwoodman


value: durron

value: Show HN: Ship of Theseus CLI

author: durron

title: Show HN: Ship of Theseus CLI


value:
I got tired of waiting 3 months for security teams to approve Kubecost/CastAI agents. So I built this.
It's a bash script that wraps kubectl top. It calculates waste locally. It generates a report in your terminal.
No Helm charts.
No DaemonSets.
No data upload (unless you want to share it).
Open source (MIT). Roast my bash skills: https://github.com/WozzHQ/wozz

value: Show HN: I built a client-side K8s auditor because I hate installing agents


value: ivandalmet

value: Show HN: Open-source full-stack starter built on TanStack Start

author: ivandalmet


value: dannylmathews

value: Show HN: Can we build WeChat Mini Apps using open web standards?

author: dannylmathews


value:
I just shipped the first fully working open alternative to closed corporate AI watermarking systems.
Repo: https://github.com/TamTunnel/PoG
Invisible watermark + on-chain receipt in 5 lines of code
Dual hashes (exact + perceptual) → survives compression & edits
Full creator anonymity — only a random wallet address is shown
Tiered verification: Strong/Medium/Weak/None
OpenAPI spec → TypeScript client in one command
Live contract, Python client, verifier, tests, docs — everything is already there
C2PA is mostly future, commercial tools are closed and expensive.
PoG is Apache 2.0, deployed today, costs ~$0.001, and you can verify any image with a single drag-and-drop.
Gasless relayer coming Q1 2026.
Looking for contributors on the relayer, browser extension, and getting ComfyUI/A1111/InvokeAI to ship it by default.
PS. Stars are real momentum — every star helps us become the default open standard, so appreciate every star.
Repo: https://github.com/TamTunnel/PoG

value: Show HN: PoG – the only open-source, live, privacy-first AI provenance system


value:
Link: https://docs.trysoma.ai/
For the past ~9 months I’ve been building Soma, an open-source AI agent & workflow runtime written in Rust, with a TypeScript SDK (Python coming soon). It’s not a framework; it’s meant to sit underneath whatever agent/tooling code you already write (Vercel AI SDK, LangChain, custom code, etc.). It provides features around your framework + a better DX for building agents. I’ve tried to take a Next.JS model: open-source, good DX, self-deployable.
I originally set out to build a vertical back-office/operations product for SMEs. I needed a way to coordinate multiple AI agents, workflows, and SaaS integrations (Xero, Gmail, Slack, etc.) behind a single chat interface — something that behaves like “one employee”, even though it’s made of many agents under the hood.
It’s very early. The core runtime, MCP server, and TypeScript SDK are usable, but the multi-agent routing layer is still experimental. I’d really appreciate technical feedback, critiques, or use cases the runtime does or doesn’t fit.
Soma aims to make it easy to run long-lived agents and workflows with the plan being to eventually offer a unified UX to route chats to agents and a governance / observability plane. Features:
* Fault-tolerant runtime
* Built-in chat, MCP server debugger
* Generates Google A2A-compliant endpoints to serve agents & chats
* MCP proxy server that handles credentials + encryption
* Strongly-typed generated clients for your MCP tools
* Multi-platform TS SDK
Coming soon (in active development): Python SDK, multi-agent coordination layer, OIDC/API-key auth middleware, and a VM-based compute sandbox.
I hate how a lot of core features in products, like MCP servers with SaaS integrations are paywalled or not being able to self-host code that models your business processes (which is your IP!!) or needing to buy in to very custom DSL's that don't scale with your business... Plain 'ol code tends to be better if pre-AI orchestrators like Temporal (Conductor) & Restate have anything to say about it under free market conditions and developers will most likely fight tooth and nail to avoid custom DSLs (I've got scars from debugging complex AWS step functions). So what's started out as an experiment to create something to unlock a better developer experience and work towards a more unified AI agent UX & observability has led me here.
I've been working on my own on this for a helluva long time and I'm keen to start getting some feedback and understanding people's use case for the project. I have a few alpha testing clients and engagements with potential enterprises but I'm struggling to understand the right customer to go after. If you have that sort of feedback please reach out to me directly but otherwise, I'm interested in hearing the community's thoughts on this and how to shape it moving forward. (Maybe all my opinions are wrong ¯\_(ツ)_/¯ but I'm keen to engage in the dialogue )
Credit to Restate ( https://restate.dev/ ) and Turso / Libsql / Sqlite fork ( https://turso.tech/ ) for doing a lot of the heavy lifting.

value: Show HN: I built an open-source Rust/TS AI agent runtime with a Next.js-style DX


value:
Hi HN – For the last year, I’ve been building Unpatentable.org, an AI “innovation lab” that does one thing:
generate new inventions and immediately publish them so they can’t be patented later.
The engine runs continuously; the public side is now a library of hundreds (900 so far) of novel AI-generated inventions across domains like energy, life sciences, robotics, space tech, and more.
Each innovation is documented in three separate reports totaling 120+ pages:
a main report (problem, mechanism, constraints)
an implementation guide - Step by step how-to
an societal/industry impact overview
We publish them on the site, anchor PDFs on the Arweave blockchain for immutable timestamps, assign metadata for discoverability, and place them into the USPTO prior art archive. The research papers are structured to meet or exceed international criteria for defensive disclosures (public, enabling, novel, time-stamped, non-confidential, specific, reproducible by a skilled practitioner). They are intended to be treated as prior art blueprints that enable others to build from them.
The engine itself isn’t a product; it's more of a publisher. That said, we do grant engine access to thinkers and inventors who want to solve and open-source solutions to particular challenges. We’re exploring sponsorships where organizations fund tracks like “wildfire resilience” or “decentralized compute,” and the output is a stream of open, unpatentable innovations in that domain. Monetizing the project has been an afterthought. I built this because I believe information wants to be free, and that in the coming age of ai, humanity is at risk of losing shared knowledge to well funded corporate patent trolls. Published innovations, are free forever, for everyone on earth.
Side note: for human inventors, there’s also an Unpatent tool (/unpatent) that lets you upload your own write-up and have it published as prior art (USPTO-linked prior art archive + Arweave + search indexing) for a flat fee. That’s separate from the AI engine, but built on the same plumbing.
You can browse the public innovation library here:
https://unpatentable.org/innovation
I'll add some detail in the comments below and try to address a few anticipated questions. Feedback, critique, and suggestions are very welcome.

value: Show HN: I built an automated AI lab that generates and publishes inventions


value: alibad

value:
Hi HN,
I built systemdesigner.net, a free platform for learning and practicing system design, ML systems, and GenAI architectures.
I originally built it for myself while preparing for system design and ML/AI interviews. I realized my workflow was a mess: reading a blog here, watching a video there, taking notes in Google Docs, and drawing diagrams in entirely different tools. When mentoring engineers I saw the same problem. I wanted a single platform where the material, the practice problems, and the diagramming were part of one continuous loop rather than scattered across the internet.
Here’s what it does today:
- Guided learning plans you can generate based on what you want to study (distributed systems, ML, LLMs, etc).
- Every page has built-in quizzes so you can check understanding immediately.
- In-page tools: highlight text, take notes, ask questions, get explanations, or have AI discussions on any concept.
- Interactive whiteboards for designing architectures and practicing interview problems.
- Projects: you can create full system-design or ML/GenAI problems, including requirements, constraints, back-of-the-envelope calculations, data modeling, etc — or you can have AI auto-generate a complete project from a description.
I'd love feedback from this community:
1. What feels missing or confusing?
2. Which system-design topics or architectures should I add next?
3. What do you think of the Projects feature for practical system design use cases on the job, not just interviews?
Thanks for taking a look.
Happy to answer questions as they come.

value: Show HN: Systemdesigner.net – a free interactive system-design learning platform

author: alibad


value: hackingmonkey

value: Show HN: Success Motivation for Builders (Daily Success Quotes)

author: hackingmonkey


value: leftnode

value:
I built the HTTP request catcher that I wanted to use. Every other one I've dealt with is slow, bloated with ads, or doesn't let me easily see a listing of past requests. Thus, httpthing was born.
It's dead simple to use: send a request to `<a href="https://httpthing.com/{endpoint}" rel="nofollow">https://httpthing.com/{endpoint}</a>` and the request will be logged for you to inspect. A unique URL will be returned so you can easily see the request details in a browser.
If you want to see all requests sent to `{endpoint}`, just go to `<a href="https://httpthing.com/_/{endpoint}" rel="nofollow">https://httpthing.com/_/{endpoint}</a>`. Requests are public, so it's best to use a randomly generated `{endpoint}`.
Simple, I know, but I've started using it for all of my webhook testing. Let me know if you find it simple and useful as well.

value: Show HN: I built a simple HTTP request catcher

author: leftnode


value:
RIMC (Recursive Intelligence Market Cycle Hypothesis, pronounced “RIM-see”) is a hypothetical theoretical framework, not a finished asset-pricing model.
The goal is to treat markets not as static equilibria, but as learning systems with finite-speed information processing.
Very roughly, RIMC tries to do three things:
1. Model the market as a learning process that observes an underlying value process V(t),
   generated by technological recursion or factor models, with delay and noise.
2. Define the coupled dynamics between technological recursion R(t) and economic value V(t)
   (the “RV equations”) as a general system of differential equations.
   This is not limited to “tech-driven” stories — Fama–French 5 and other factor structures
   can be embedded as special cases of the RV system.
3. Reinterpret CAPM α not as unexplained regression residue, but as a structural drift term
   arising from observation delay and finite-time learning dynamics (“α-drift”).
Conceptually, the framework has three layers:
- Generative layer
  A value-generation engine where recursion / factors drive V(t) via an RV-type system.
- Observational layer
  A continuous-time CAPM-like structure where the market only sees a delayed,
  noisy projection of that value.
- Alpha-drift layer
  A structural α term
  α_drift(t) built as an exponentially weighted memory of the gap
  ε_R(t) = r_real(t) − r_market(t) over a finite window T with forgetting rate λ.
This is a working hypothesis about how structural α can emerge purely from
finite-speed learning and observation delay, rather than a claim of a new “better CAPM”.
Repo (manuscript + notes, English; some Japanese commentary as well):
<GitHub repository URL>
https://github.com/rimc-lab/RIMC
I would really appreciate any kind of feedback:
- Pointers to prior work I’m implicitly rediscovering
- Objections to the way α is treated as a structural drift
- Thoughts on whether this is a useful lens for practical quant research
  (factor models, RL-based strategies, macro regimes, etc.)
Even “this is obviously wrong because X” is very helpful.
Thanks for reading.

value: Show HN: RIMC – An Alpha-Drift Framework for Recursive Market Dynamics


value: ToonDN

value:
Hi HN,
I'm a creator of Floww, a self-hostable workflow automation tool designed for developers who got frustrated by the limitations of visual builders like n8n.
It's still a very early prototype, but the goal is to provide these same features in a code-first approach which allows for building and maintaining more complex workflows more easily.
Example:

  import { Discord, GitHub } from "floww";

  const discord = new Discord()
  const github = new GitHub()

  github.triggers.onPush({
    branch: "main",
    owner: "usefloww",
    repository: "floww-dashboard",
    handler: async (ctx, event) => {
      const user = event.body.sender.login;
      const repository = event.body.repository.name;

      await discord.actions.sendMessage({
        channelId: "1436671493651169290",
        content: New push to ${repository} by ${user}
      })
    },
  });

Would love to hear what you think, and what functionality we need to add for you to consider this for your next automation

value: Show HN: Floww – A code-first alternative to n8n

author: ToonDN


value:
Hey! We’re the founders of ZeroEntropy (www.zeroentropy.dev), we train models for information retrieval, and also provide an end-to-end hybrid search API.
Last night, we decided to release a free tool that lets you ask natural language questions over the 2025 NeurIPS papers.
We hope it will be useful to this community, whether you are at the conference or just curious to learn more about the papers that made the cut this year.
No account required. Just type a question and get a sourced answer from relevant papers.
Try it: https://neurips.zeroentropy.dev
Let us know if something doesn’t work we’ll fix it!

value: Show HN: Ask Anything over NeurIPS 2025 Papers (Free, No Signup)


value: ViktorKuz

value: Show HN: VAC Memory – 80.1% LoCoMo accuracy vs. Mem0's 68%

author: ViktorKuz


value: daviducolo

value: Show HN: Numla – The notepad that thinks in numbers

author: daviducolo


value: theturtletalks

value:
I'm building an open source Amazon.
In other words, an open source decentralized marketplace. But like Carl Sagan said, to make an apple pie, you must first invent the universe.
So first I had to make open source management systems for every vertical. I'm launching the first one today, Openfront e-commerce, an open source Shopify alternative. Next will be Openfront restaurant, Openfront grocery, and Openfront gym.
And all of these Openfronts will connect to our decentralized marketplace, "the/marketplace", seamlessly. Once we launch the other Openfronts, you'll be able to do everything from booking hotels to ordering groceries right from one place with no middle men.
The marketplace simply connects to the Openfront just like its built-in storefront does. Together, we can use open source to disrupt marketplaces and make sure sellers, in every vertical, are never beholden to them.
Marketplace: https://marketplace.openship.org
Openfront platforms: https://openship.org/openfront-ecommerce
Source code: https://github.com/openshiporg/openfront
Demo - Openfront: https://youtu.be/jz0ZZmtBHgo
Demo - Marketplace: https://youtu.be/LM6hRjZIDcs
Part 1 - https://news.ycombinator.com/item?id=32690410

value: Show HN: I'm building an open-source Amazon (Part 2)

author: theturtletalks


value: ebod

value: Show HN: Pure-bash Git analyzer – No Python, No node, Just shell

author: ebod


value: DHDEV

value: Show HN: DynamicHorizon – Dynamic Island for macOS

author: DHDEV


value: advisador

value: Show HN: CodeBake – so that PM tasks aren't extra work

author: advisador


value: andrewgm

value:
Hey HN, I'm a founder at Ovlo a supply chain company.I had a problem. After every batch of customer interviews/research/feedback sessions, I'd run ideas through an LLM to help me decide what we should build next.
Except it was obvious to my cofounder I wasn't really validating anything. LLMs are incredibly good at agreeing with you in subtle ways, especially when you feed them context that already reflects your thoughts. I'd ask "Does this make sense?" and get a beautifully worded essay about why yes, obviously, this is the best thing ever.
I was using AI as an echo chamber without noticing. It was like a game, you bias the model a little, it biases you a little back.
So my cofounder hacked together a tiny internal tool over the weekend to break that loop. Instead of one model validating my thinking, it puts multiple personas with different expertise in the same conversation. They naturally push back on each other.
It's basically a rubber duck that argues with itself.
We gave it some UI and called it Roundtable: https://roundtable.ovlo.ai
Would love to hear what you'all think!

value: Show HN: Roundtable – A rubber duck that argues with itself

author: andrewgm


value: CuriouslyC

value:
Hi y'all,
In my work to reduce the amount of time I spend in the agentic development loop, I observed that code structure was one of the biggest determinants in agent task success. Ironically, agents aren't good at structuring code for their own consumption, so left to their own devices purely vibe-coded projects will tend towards dumpster fire status.
Agents aren't great at refactoring out of the box either, so rather than resign myself to babysitting refactors to maintain agent performance, I wrote a tool to put agents on rails while refactoring.
Another big problem I encountered trying to remove myself from the loop was knowing where to spend my time efficiently when I did dive into the codebase. To combat this I implemented a html report that simplifies identifying high level problem. In many cases you can click from an issue in the report directly to the code via VS Code links.
I hope you find this tool as useful as I have, I'm working on it actively so I'm happy to field feature requests.

value: Show HN: Valknut – static analysis to tame agent tech debt

author: CuriouslyC


value: ericgtaylor

value:
I built this because my dad was losing sleep from high-anxiety news cycles.
I wanted a calmer alternative that didn’t manipulate attention.
Steady News publishes a single finite edition every day at 6 AM PT.
No infinite scroll, no engagement traps, no editorial spin.
How it works:
• Fetches top US stories from AP, Reuters, BBC, NPR, WSJ
• Summaries run through GPT-4.1-mini to remove sensational language
• Produces calm, neutral "Steady Voice" summaries
• React/Vite frontend, Node/Express backend, PostgreSQL (Neon)
• Hardened image proxy (SSRF-safe, strict allowlist)
• Slug immutability + 301 alias system for SEO continuity
• Hourly incremental updates for new articles
• Optional audio via OpenAI TTS
• Privacy: anonymous Plausible analytics + optional Meta Pixel for paid acquisition testing (no ads, no personalization)
Footprint:
• No tracking-dependent UX
• No personalization
• No ads
• No infinite scroll
• No algorithmic feed
Would love feedback on the philosophy, the UX, or the architecture.

value: Show HN: A calm, finite daily news briefing (no infinite scroll, no ads)

author: ericgtaylor


value: vsind

value:
Hi HN,
I'm a security engineer and I find most security awareness boring and easily forgettable, so I tried turning it into a practical, positive, habit-building experience that effectively helps people.
Safe Habits focuses on 12 practical security habits based on CISA, ENISA, and NIST guidance, but written in plain language for non-security experts (think MFA, phishing, updates etc.).
This app is free, without ads, and privacy first (no tracking, no analytics, no data collection). It employs very light gamification (progress ring, friendly mascot, small celebrations).
Happy to answer questions, any feedback is very welcome.

value: Show HN: Safe Habits – A tiny iOS app for security awareness

author: vsind


value: rawkode

value: Show HN: I built an alternative to the Cloud Native Landscape

author: rawkode


value: pancakeguy

value: Show HN: 1T row challenge in 76s using 10,000 CPUs

author: pancakeguy


value: yashesmaistry

value: Show HN: WhatDoYouDo – A subscription-free rolodex/Relationship management tool

author: yashesmaistry


value: EmilStenstrom

value:
I got frustrated with HTML parsing in Python.
I wanted a Python HTML parser that was both correct and easy to install. The C-based ones (lxml, selectolax) are fast but not HTML5 compliant. The pure Python ones (html.parser, BeautifulSoup's default) are easy to install but choke on real-world HTML. html5lib is 80% correct but painfully slow.
So I wrote JustHTML. It's:
• 100% HTML5 compliant – passes all 8,500+ html5lib tests. If a browser can parse it, JustHTML can.
• Pure Python, zero dependencies – pip install and go. Works on PyPy, Pyodide, anywhere.
• Fast enough – ~0.1s to parse Wikipedia's homepage. Not C-fast, but 50% faster than html5lib.
• Simple API – doc.query("div.foo > p") with CSS selectors. One method to learn.
Example:

  from justhtml import JustHTML
  doc = JustHTML("<div><p class='intro'>Hello!</p></div>")
  print(doc.query(".intro")[0].to_html())

I've fuzz-tested it with 3 million malformed documents.
Would love feedback, especially on the API design.

value: Show HN: JustHTML – A pure Python HTML5 parser that just works

author: EmilStenstrom


value: buttersmoothAI

value:
We've built a 3-step validation system for AI code outputs that's currently running in production.
Results across 10,000+ deployments:
• 90% reduction in production failures
• 97.8% epistemic certainty (Guardian-validated)
• Zero false positives
• 100% endpoint success rate
• 12-29ms response times
One deployment caught 47 bugs before users saw them.
The system validates at three layers:
1. Pattern validation (epistemic certainty framework)
2. Adapter validation (integration safety checks)
3. Convergence validation (system coherence)
Architecture:
• 8 Guardians (pattern validators)
• 6 Guard Services (integration validators)
• TypeScript/Python/JavaScript implementations
• Framework templates: React, Vue, Next.js, FastAPI, Express
We're hosting a free 60-minute technical deep-dive on Tuesday (Dec 2, 2 PM EST) where we'll cover:
• Complete architecture
• Production code examples
• Performance optimization
• Integration patterns
All code examples and documentation will be shared ($597+ value).
[Registration Link]
Happy to discuss technical details, answer questions, and share learnings.
What validation challenges are you facing with AI code?

value: Show HN: Validation system eliminates 90% of AI code failures (97.8% accuracy)

author: buttersmoothAI


value: dsiddharth

value:
We just deployed Qwen3-Omni to production. As far as we know, this is the only place you can hit an open-source speech-to-speech model via playground with zero setup.
The S2S landscape right now: OpenAI (GPT-Realtime), Hume (EVI), and now this. The first two are closed-source. Qwen3-Omni is open.
What we built: real-time inference stack optimized for voice, deployed across multiple regions. You can test latency directly at the link.
Honest take: we've seen faster results chaining ASR/LLM/TTS compared to native S2S. But the progress on end-to-end models in the last few months has been impressive, and we wanted to make it easy for people to experiment.
Would love feedback from anyone who tries it, particularly on latency, voice quality, and where it breaks.

value: Show HN: Live Qwen3-Omni API (open-source speech-to-speech)

author: dsiddharth


value: flashgordon

value:
I used to play WeeWar (browser-based hex strategy game, RIP 2015) way too much, and wanted a way to extract map data from screenshots.   So I built a little tool that extracts tile data from hex-based strategy game screenshots (WeeWar, Civilization,  etc.) to scratch my own itch. Runs entirely in the browser using TensorFlow.js.
Previously I tried this using p-hashes but those didnt work quite well when units stood on tiles.   So this time I tried it with MobileNet for embeddings + KNN for classification.  So now doesnt need training and can learn from few examples and runs entirely in the browser.
It's a bit rough around the edges but works well enough that it generalizes to other hex games (tested on some Civilization screenshots too). Lots more that could be done - better UI, model persistence, maybe regression for tile yields - but it was a fun learning project.   I also found the best way for me to learn more of the advanced ML concepts was just to build it for something I found fun.
Demo: https://buildmage.com/demos/weemap-scanner/
Source: https://github.com/panyam/weemaps/tree/main/part2-scanner

value: Show HN: WeeMap – Map Extractor – With KNNs and Tensorflow.js

author: flashgordon


value: laCour

value: Show HN: SMART report viewer – Simple tool to analyze smartctl outputs

author: laCour


value: xtafnuihc

value:
My motivation was pretty simple: every “spinning ring” I tried felt a bit fake.
They don’t actually spin — not like a real bearing.
So I wanted to see if I could make one that truly does.
At the end of the day it’s still just a ring. A small, minimal piece of jewelry.
But it happens to hide a tiny bearing that can hit around 800 RPM with a single flick.
That contrast is what makes it fun.
The way I think about it is this:
it’s like a normal-looking car that quietly turns out to fly.
Nothing loud or showy — until you decide to reveal what it can actually do.
If you’ve used a good fidget spinner for focus or stress, the feeling is similar.
This is basically that experience compressed into a silent, smooth, 2mm-thick ring you can wear without thinking about it.
The D20 idea grew out of that.
It’s not the main point — just a side effect of the spin being clean and consistent.
The reactions have been funny.
As a ring, some people say it’s “too plain.”
As a fidget spinner, it’s “not extreme enough.”
As a D20, it’s “not official enough.”
That’s what happens when something lands between categories.
To me it’s simply a pared-down version of all three.
If I wanted something louder, flashier, or more decorative, that’s easy to explore later.
This first version is about getting the core idea right:
a real, precise spin inside a clean 2mm ring.
Once that foundation works, the rest — styling, variants, whatever comes next — is straightforward.
Hope that gives a clearer sense of what I was trying to do.
Demo video (just the mechanism in motion):
https://vimeo.com/1139679503
More Info: https://spinity.co/

value: Show HN: A 2mm micro-bearing ring that spins like a real fidget spinner

author: xtafnuihc


value: teugent

value:
Open standard for attractor-based cognition - replaces agent loops and prompt chains with a recursive control layer.
Any model (GPT, Claude, Grok, Mistral) can plug into it via _generate().

value: Show HN: Sigma Runtime ERI – 800-line open cognitive runtime for LLM continuity

author: teugent


value: MLoffshore

value:
Hi HN — I built a bit-for-bit deterministic analytics engine.
It runs classical ML pipelines (normalization → canonical transform → deterministic K-Means) with zero nondeterminism:
• no floating-point divergence
• no randomness
• no environment drift
• no timestamp or locale sensitivity
• Docker-pinned numeric behavior
• reproducible across machines, OSes, and hardware
The OSS drop includes:
• deterministic ingest + normalization
• deterministic K-Means (Iris + Wine)
• golden-reference hashes
• cross-machine reproducibility tests
• 3-machine ingest demo video
(direct download: https://github.com/bryanziehl/prima-veritas/releases/downloa...
)
• MIT license + full docs + architecture diagrams
If you work in ML, science, infra, or compliance, you already know how painful nondeterministic pipelines are.
This project is a first “Hello World” toward a broader deterministic verification kernel.
Feedback, critique, or reproducibility tests welcome — especially on different machine architectures.
Happy to answer anything live.

value: Show HN: Prima Veritas – Deterministic Analytics Engine for Reproducible ML

author: MLoffshore


value: irqlevel

value: Show HN: Golang Client Library for Gradium.ai TTS/STT API

author: irqlevel


value: acgao

value:
Many years ago my dad used Excel and put together a multiplication table memorization program that I and each of my siblings had to sit down in front of every day. I think most of us made it through 12x12. I'm not sure I adequately appreciated the value of this until well into my adult years.
Fast forward a few decades and I want my kids to do the same. I am not going to try to conjure this up in Excel though. Instead, I made a Mac and iPad app. I spent quite a bit of time letting my kids play with early versions, tweaking things as they uncovered issues that only a kid would think of.
With this version finally out (the app review process is always sooo much fun), MeteorMath will be worked into their daily schooling routine -- hopefully without lessening the enjoyment they've had with it so far.
I'm presently struggling my way through the 13's. There's something about 52, 65, 78, 91 that I'm not "internalizing" quite as quickly as I'd like. The app goes up to 20x20, and I'm going to keep working on getting there.
Check it out and let me know what you (or your kids) think of it.
One last thought: this is the first from-scratch app I've done in quite a while, so I went "all the way": Swift 6, SwiftUI and SwiftData. I've got so many mixed feelings about it all, but it worked, so I can't complain too much.

value: Show HN: Memorizing Multiplication Tables (revisiting my childhood)

author: acgao


value: tonyystef

value: Show HN: Persistent memory for Claude Code sessions

author: tonyystef


value: Ayobamiu

value:
I built a small open-source meeting detection engine for macOS.
The goal is to provide a simple and accurate way for apps to know when a user is in a Zoom/Meet/Teams/Webex meeting.
A lot of meeting recorders, productivity tools, and focus apps try to detect meetings, but the results are often unreliable. Some apps pop up “You’re in a meeting” suggestions even when nothing is happening. I wanted something that works consistently and is easy for developers to integrate.
The engine is written in Rust and exposed to Node/Electron via napi-rs.
It runs a lightweight background loop and uses two tiers:
 1. Native app detection (Zoom, Teams, Webex)
 • process detection
 • meeting-related network activity
 2. Browser meeting detection (Google Meet, Teams Web, Zoom Web, Webex Web)
 • reads browser tabs via AppleScript
 • validates meeting URL patterns
 • supports Chrome, Safari, and Edge
It exposes a very simple JS API:
init();
onMeetingStart((_, d) => console.log("Meeting started:", d.appName));
onMeetingEnd(() => console.log("Meeting ended"));
console.log(isMeetingActive());
Would love feedback, especially from anyone building recorders, focus apps, calendar tools, etc.
Windows + Linux support coming next.

value: Show HN: Meeting Detection – a small Rust engine that detects meetings on macOS

author: Ayobamiu


value: leado

value: Show HN: Leado – AI agent for Reddit that drafts contextual replies using RAG

author: leado


value: marcindulak

value:
Interactive, browser-only timeline and heatmap views of jury ratings.
Designed to support any multi-stage, juried competition, but currently includes only the 2025 Chopin Competition.
Runs client-side with no external API calls.
GitHub repository: https://github.com/marcindulak/competition-rating-visualizat...

value: Show HN: Visualization of Chopin 2025 competition ratings

author: marcindulak


value: ruben-davia

value:
Hi HN,
We’re Ruben, Afnan, and Theo. A few days ago we shared the first early version of Davia, and the feedback was far more active than we expected. For context, you can see the previous discussion and reactions here: https://news.ycombinator.com/item?id=45880716
Based on that response, we decided to push things further and release Davia as a fully open-source package you can run locally.
Davia helps coding agents generate a structured, editable internal wiki for your project. The focus is on high-level documentation: onboarding context, architectural overviews, key design decisions. Traditional tooling often lacks visual context, omits diagrams, and doesn’t integrate well with local workflows or AI-assisted editing.
Davia outputs a workspace where:
– Text is editable in a Notion-like editor
– Diagrams are placed on editable whiteboards
– Everything runs locally and can be modified either in your IDE or within the workspace
One feature people particularly liked is that your IDE’s AI agent can write the documentation while Davia turns it into clean pages, diagrams, and visual structures automatically.
The project is still early, so we’d love your feedback, use cases, criticisms, and thoughts on how you currently document internal knowledge.

value: Show HN: Davia – A Visual Wiki, Generated by Your Coding Agent (Open-Source)

author: ruben-davia


value:
Most companies start with simple pricing. But the moment you land real enterprise customers, everything fragments: every big customer negotiates different discounts, thresholds, exceptions etc. And the real problem isn’t even complexity — it’s inconsistency. Once the numbers stop matching, trust drops, usage lags, renewals stall, and expansion stops before it starts.
Big players like Azure, AWS etc. already know this. They invest millions into their pricing calculators because pre-transaction price estimation directly improves acquisition, qualification, and trust. But for everyone else, building something even close to that in-house is unrealistic.
That’s exactly why I built UniQalc.
Uniqalc lets you create a pricing calculator in less than a minute — no engineering, no custom UI work, no maintenance debt. Your customers get the same kind of interactive, real-time pricing experience that enterprises use to boost conversions, and it’s totally FREE to get started.
Our Site: https://www.uniqalc.com
The OpenAI Calculator: https://www.uniqalc.com/calculators/openai

value: Show HN: Give your customers pricing clarity, especially the enterprise ones


value: enigmazi

value:
Hi HN, I posted docmd here a few months ago when it was just a simple markdown parser.
I'm back with v0.3 because we just hit a major milestone: fully offline, client-side full-text search with zero configuration.
I built this because I wanted a Node.js-native alternative to MkDocs Material. I didn't want to install Python in my CI/CD just for docs, and Docusaurus felt too heavy (React hydration) for simple static text.
Docmd generates raw HTML/CSS, is under 15kb gzipped, and now handles search, versioning, and diagrams natively. Would love to hear what you think of the search implementation.

value: Show HN: Docmd v0.3 – Static documentation generator (built-in search, no React)

author: enigmazi


value: reveriedev

value:
Hi HN,
We’ve been building The Almanac, a tool that creates Wikipedia-style articles for any person using their full public online footprint. Unlike Linkedin where people have to maintain their own profile, we automatically crawl the internet for their roles, art, projects, collaborations, publications, media mentions, everything that’s actually documented. You can make a profile for yourself or anyone else by generating it.
A few things we focused on:
Identity disambiguation: reliably separating people with similar names - this is very tricky and something that GPT/Perplexity gets wrong very often
Full-stack research agent that crawls, filters, and writes structured articles
Detailed profiles that include timelines, infoboxes, sources, and citations
Public search plus the ability to generate your own or someone else’s profile
You can try it here: https://thealmanac.ai
Example profile: https://www.thealmanac.ai/article/davidlieb
Generate a profile (requires login) : https://thealmanac.ai/generate
We’d love feedback from the community on accuracy, product direction, and edge cases where it breaks. Happy to answer anything.

value: Show HN: The Almanac – Generate Wikipedia-style biographies of anyone

author: reveriedev


value: cadence-

value:
Hi everybody,
I was trying to find a very simple trust portal for my own company. All I wanted was a clean page where I could share security documents like SOC 2 and pen test reports with prospects. Nothing fancy.
Everything I found was either a huge trust center platform or something that looked free at first but then had some surprise limits hidden in the fine print. After you hit some random download cap you suddenly have to pay thousands of dollars to continue using it. It felt like every product was built for big companies with big budgets.
So I ended up writing my own tiny trust portal. I was my first user. It did what I needed, so I cleaned it up a bit and decided to share it for other small companies who just want something simple and cheap instead of emailing PDFs or sending a Google Drive link.
This is still very early and I would love feedback from the HN community. I'm especially curious what features you think a small trust portal should (or should not) have.
Thanks for taking a look.

value: Show HN: I built a simple trust portal because the existing ones were too much

author: cadence-


value: shane-jacobeen

value: Show HN: Schema3D – Interactive SQL schema visualization

author: shane-jacobeen


value: rothific

value:
I built Ink Web as a way to bring Ink CLI apps into the browser. This way you could take an app like Claude Code and make it run in both the terminal and the browser. (After you polyfill or replace Node.js-specific APIs.)
This started as a side project to build an open-source CLI-based AI agent that could run in cross platform environments like Tauri.
Currently it uses Xterm.js, but there’s a PR demoing that it also works with https://github.com/coder/ghostty-web.
There's also a library of UI components installable via shadcn.
This builds on top of Ink and bundles it. I chose to bundle Ink instead of rewriting it since Ink is already a mature library with considerable momentum behind it.

value: Show HN: Build CLI apps with Ink that run in the browser

author: rothific