value: GarethX

value:
Flag cleanup is one of the most annoying things about feature flags. Many vendors have created little helpers. Usually they’re CLI tools that highlight mentions of feature keys in your code, or some also show you which flags haven’t been accessed in a while. But these only make things less painful, you’re still on the hook to do the work.
So at Bucket (https://bucket.co) that’s the part we wanted to solve with AI, removing the flag code itself.
- How it works:
The cleanup bot operates as a GitHub integration with Bucket. When it runs, it searches for usage of the Bucket SDK in the codebase and identifies where specific feature keys are used. LLMs are employed to refactor the code to remove the flag and eliminate codepaths that become unreachable.
So all you should need to do is review and approve the PR. Once merged, the flag itself is archived in Bucket.
- The results:
We’ve been running this in production ourselves for a few months alongside a few early-access partners. We’re seeing consistently good PRs that can be merged without changes.
However, this wasn’t always the case. Early on in development, the range in quality of solutions generated by the LLM was an issue. Most attempts were fine, but then it would add a comment somewhere unrelated, remove whitespace or change the code formatting (even if you gave it rules to follow). Many of the early generated solutions failed CI due to unused imports, incorrect indentation, lone decoration not always getting cleaned up, or unused exports failing to be removed.
You can get a surprisingly long way with an LLM and a quick prompt, but getting good results, consistently, is where the real work is.
Some of the issues we were able to solve through better prompting and using different models, while others needed custom solutions. We wrote up how we went about building it here: https://bucket.co/blog/building-ai-flag-cleanup
- Try the beta:
We’re opening the beta up to everyone, but limiting it to React codebases for now.
If you’d like to try it out:
- Sign up at https://bucket.co
- Enable it: https://app.bucket.co/envs/current/settings/org-integrations
Let us know how you get on.

value: Show HN: AI Flag Cleanup

author: GarethX

title: Show HN: AI Flag Cleanup


value: Show HN: Generates discussions on topics such as health, sports, music, etc.


value:
After training calculator agent via RL, I really wanted to go bigger! So I built RL infrastructure for training long-horizon terminal/coding agents that scales from 2x A100s to 32x H100s (~$1M worth of compute!) Without any training, my 32B agent hit #19 on Terminal-Bench leaderboard, beating Stanford's Terminus-Qwen3-235B-A22! With training... well, too expensive, but I bet the results would be good!
*What I did*:
- Created a Claude Code-inspired agent (system msg + tools)
- Built Docker-isolated GRPO training where each rollout gets its own container
- Developed a multi-agent synthetic data pipeline to generate & validate training data with Opus-4
- Implemented a hybrid reward signal of unit test verifiers & a behavioural LLM judge.
*Key results*:
- My untrained Qwen3-32B agent achieved 13.75% on Terminal-Bench (#19, beats Stanford's Qwen3-235B MoE)
- I tested training to work stably on 32x H100s distributed across 4 bare metal nodes
- I created a mini-eval framework for LLM-judge performance. Sonnet-4 won.
- ~£30-50k needed for full training run of 1000 epochs (I could only afford testing )
*Technical details*:
- The synthetic dataset ranges from easy to extremely hard tasks. An example hard task's prompt:
"I found this mystery program at `/app/program` and I'm completely stumped. It's a stripped binary, so I have no idea what it does or how to run it properly. The program seems to expect some specific input and then produces an output, but I can't figure out what kind of input it needs. Could you help me figure out what this program requires?"
- Simple config presets allow training to run on multiple hardware setups with minimal effort.
- GRPO used with 16 rollouts per task, up to 32k tokens per rollout.
- Agent uses XML/YAML format to structure tool calls
*More details*:
My Github repos open source it all (agent, data, code) and has way more technical details if you are interested!:
-  Terminal Agent RL repo
-  Multi-agent synthetic data pipeline repo
I thought I would share this because I believe long-horizon RL is going to change everybody's lives, and so I feel it is important (and super fun!) for us all to share knowledge around this area, and also have enjoy exploring what is possible.
Thanks for reading!
Dan
(Built using rLLM RL framework which was brilliant to work with, and evaluated and inspired by the great Terminal Bench benchmark)

value: Show HN: Terminal-Bench-RL: Training long-horizon terminal agents with RL


value: daverad

value:
e've been running an experiment where Claude creates PRs and CodeRabbit reviews them, then Claude responds to the feedback. They debate implementation details in GitHub comments, and both AIs learn from these interactions.
Results after 2 months:
- 98% production-ready code before human review
- 3-month features now ship in 2 weeks
- 2 developers supporting 4 platforms effectively
Video walkthrough (10 min): https://www.youtube.com/watch?v=fV__0QBmN18
Tech stack: Claude Code, CodeRabbit, Asana and Figma via MCP, custom orchestration layer.
The interesting part is watching them disagree - CodeRabbit might suggest an optimization, and Claude will defend its approach with specific reasoning about our codebase. These conversations create great documentation.
Happy to answer questions about the setup, costs, or specific implementation details.

value: Show HN: AI agents reviewing each other's code in production [video]

author: daverad


value: mazzystar

value:
Last month I had a massive fight with my parents. Said some really harsh things, then spent the night drowning in guilt.
I've been working on this AI journal called Clio that actually pauses to think before responding (30-60 seconds instead of instant replies). So I shared my mess with it.
It told me something I hadn't considered: "You both care about completely different things, and what you said essentially told them that what they care about is meaningless."
That reframe changed my mind. I realized my words hadn't expressed what I actually meant, but Clio saw the underlying dynamic I was missing.
1. Why I built this:
I got frustrated with AI that fires back instant responses without really processing what you're saying. Most of us think in messy fragments and struggle to put complex feelings into words.
2. What I've noticed:
When AI takes time to actually contemplate what you've shared, it catches patterns you miss about your own thinking. The conversations that change how you see things take time.
It's like having that friend who listens deeply and asks the questions you're afraid to ask yourself.
As Wittgenstein said: "The limits of my language are the limits of my world."
I think Clio helps you find words for thoughts you didn't know you had.
Try it: https://getclio.app
Built with Claude Code. Would love feedback from the community!

value: Show HN: Clio – AI journal that thinks for 60 seconds before responding

author: mazzystar


value:
I’ve always been passionate about Japan, and during my last trip I discovered gachapons (capsule vending machines).
I wanted a small ritual to bring that feeling back every day, so I built Gachari: a webapp where you draw a random cultural capsule every 12h (word, sound, haiku, anecdote).
I made it solo in nights and weekends over the last month. It’s simple, but I like the vibe.
Would love some feedback.

value: Show HN: Gachari, a tiny cultural gachapon I built solo (webapp)


value: raydenvm

value:
Hey HN! I'm the CTO at Atola Technology, and we have launched MultiDrive: a free disk backup/clone/wipe tool that does things differently.
After 17 years of work with drives, I got tired of seeing simple disk operations locked behind paywalls. Macrium killed their free version, EaseUS hides cloning in paid tiers, and so on and so forth. Another reason is that all the existing solutions are overly complicated. It must be simple enough for my mum to start erasing a USB stick or making a full drive backup. All in all, we thought the community deserved better.
What makes MultiDrive different:
- Dead simple launch of a full drive backup, clone, erase or restore
- 100% free. No ads, "upgrade to pro" popups
- Standard formats. Backups use ZIP or RAW, no proprietary .afi/.tib nonsense
- Handles bad sectors, loose cables, can pause/resume any operation
- Parallel drive tasks
- CLI app as an addition for workflow automation
Would love your feedback!
What problems with disk operations have you had that current tools couldn't solve?
We're building our roadmap based on real pain points.

value: Show HN: MultiDrive – a free app to clone, backup, erase drives (UI/CLI)

author: raydenvm


value: germanosin

value: Starting with version 1.3.0, Kafbat UI includes an MCP server for handling metadata and topic queries.

value: Show HN: Apache Kafka MCP Server

author: germanosin

title: Show HN: Apache Kafka MCP Server


value: cstub

value:
Most AI applications are built for individuals but work happens in groups and humans want to collaborate with both agentic AI and other teammates in the same session. We created Hybrid Groups for that purpose.
In Hybrid Groups, agents join group chats as virtual team members in Slack and GitHub. They participate in group conversations, proactively contribute when needed and perform actions on behalf of individual users, like managing your calendar for meeting suggestions or updating your todo list without sharing access to your private resources to the group.
The project is open-source at https://github.com/gradion-ai/hybrid-groups and can be run locally. To try it: Run the Docker container, connect your Slack or GitHub, and use the predefined demo agents or create your own agents. More info in the quickstart: https://gradion-ai.github.io/hybrid-groups/quickstart/.
We're early in development and would appreciate your feedback on the platform, potential improvements and other ideas!

value: Show HN: Hybrid Groups – Agentic AI for human team collaboration [video]

author: cstub


value: ecoopti

value:
Hi HN,
I’m a sole proprietor. I wanted to understand how much waste there really is in eCommerce shipping packaging. It turned out that many companies ship boxes full of air – sometimes 30–50% of the volume is unnecessary.
So I built a tool that calculates the optimal packaging for each shipment – to reduce shipping costs, improve sustainability, and produce smart ESG reports.
This is still an early demo (no login, just click) – but I’m curious if it’s even interesting to anyone besides me :)
I’d love to hear your feedback – ideas, holes, mistakes, or just “cool”.
https://ecoopti.netlify.app/

value: Show HN: I built a tool that reduces 40% of packaging volume in deliveries

author: ecoopti


value:
Hi HN,
I recently built a small project called Monitrix – it's a self-hosted, real-time server monitoring dashboard using Python, FastAPI, WebSockets, and Chart.js.
I created it because I wanted a lightweight alternative to Prometheus/Grafana for home or small server setups. It runs a local agent to collect CPU, memory, and disk stats, and pushes them live to the dashboard without a database.
It's fully open source, and includes a dark/light UI toggle, animated charts, and a clean layout.
GitHub: https://github.com/silverstar33/monitrix
Would love to hear feedback or suggestions!

value: Show HN: Monitrix –Lightweight, real-time server monitor using FastAPI


value: dearilos

value:
Hey HN! Ilya and Nikita here. We're working on wispbit - the open source AI code review agent for teams - https://github.com/wispbit-ai/wispbit.
We made wispbit because we previously struggled in big codebases and teams that grew quickly. Keeping codebase standards alive was frustrating, and investing in static internal tooling was too hard. We would sit in post-mortem meetings and expect engineers to remember every single booby trap.
wispbit fixes this by enforcing your codebase rules, and raises a violation if a rule is broken. It also runs anywhere (Github actions, CLI, Claude code). You can come up with rules on your own, or use our free rules repository https://wispbit.com/rules.
Some ways we and other engineers use wispbit:
- Enforce database migration or schema patterns - consistent table names, columns, checking for redundant indexes.
- Take a look at your most recent postmortem doc and turn it into a rule.
- Enforce standards for commenting, test writing patterns, and component usage to keep your team accountable.
- Take something you frequently check for and turn it into a rule.
Why wispbit over other tools? We found that existing code review tools are too random and noisy - a level that is unacceptable in big codebases and teams. wispbit keeps it simple by reviewing only what you ask for.
We're also working on a cloud version over at https://wispbit.com, currently in early access. It focuses on creating and maintaining rules for you based on codebase activity. In the long term, we want to fix tribal knowledge and reduce the burden on teams having to remember every single detail within complex stacks.
If this resonates with you, or you built your own code review tool internally - give it a spin!

value: Show HN: Open Source AI Code Review Agent for Teams

author: dearilos


value: tsergiu

value:
Hey HN! I'm excited to share suggest.dev, a feedback widget that triggers when users rageclick on your site. When a user leaves feedback, a full session replay is saved with it, making it easy for developers to understand what happened.
We built this to get feedback from external users, but we're seeing high uptake from our own team for internal feedback. Often, we'd encounter small paper cuts in the product in the middle of another task. In the past, many of these were just not reported - the effort to create an issue and describe it in sufficient detail was too high. And if you did report, the context switch was long enough that you'd interrupt your original task. Suggest takes a literal 4 seconds to leave feedback, and because of the session replay, all feedback is very high quality - always with enough information for a developer to reproduce an issue.
Suggest works alongside your existing tools and doesn't need you to replace any of them.
Thoughts and feedback are very welcome!

value: Show HN: Suggest – Ultra-low-friction feedback for your website

author: tsergiu


value: nicsmeyers

value:
Hey HN, Nic here.
I recently built a prototype called GenDB, an AI-powered backend builder designed to eliminate boilerplate and streamline database deployment.
The idea came out of my own frustrations: I was spending hours writing and rewriting Python code with TortoiseORM just to build and modify basic schemas. Then I’d have to deploy it all over again for even small changes. After seeing tools like Lovable and Cursor make front-end development nearly effortless, I started to wonder: why wasn’t backend development just as fluid?
With GenDB, you can:
- Prompt a schema (e.g., “Instagram clone”) via natural language or image
- Edit it visually using a DBML-based ERD editor
- One-click deploy to GCP or AWS
- (Coming soon) Auto-generate APIs and safe migration scripts
The goal is to go from idea → schema → live backend in minutes, without writing boilerplate or fiddling with cloud infrastructure too much.
It’s still an early prototype, and I’d love your feedback, especially if you’ve run into similar pain points. What seems useful? What’s missing? Where will this fall apart?
Demo + details here: https://gendb.carrd.co/
Thanks for taking a look!

value: Show HN: GenDB – I built a tool to generate a full database from a single prompt

author: nicsmeyers


value: moltenice

value: Show HN: AI debates between AI agents with different personas

author: moltenice


value: rajeshabishek

value:
Hi HN!
I'm trying to learn a language, but I don't have anyone to speak it with, so I made this website to get some practice.
To start, all you have to do is set your native language and target language. Then, just start talking to it in either your native or target language. It'll catch any mistakes you make when you speak in your target language and answer any grammar/vocab questions you have.
It has a translate button, a romanize button (converts any text into the Latin alphabet), and you can highlight words/phrases you don't know in AI responses to have them explained.
I'd appreciate any feedback, thanks!

value: Show HN: PolyglotGPT – Conversational AI for Learning 40 Languages

author: rajeshabishek


value: pythonist

value: Show HN: A generic, context-aware singleflight for Go

author: pythonist


value:
I built a tool that lets you deploy and manage your AWS/GCP/Azure infrastructure using natural language.
Cloud consoles are time-consuming, and traditional IaC tools aren’t always ideal for quick iterations or rollbacks
So I made Cloudlvl: you describe what you need, review the AI suggestions, and deploy. Each deployment creates a new version, so you can roll back anytime.
It works by indexing resources by context (env, region, etc.) and maintaining real-time state sync.
It currently works with AWS only, but there's a short demo video on the website.
Curious to hear your thoughts!

value: Show HN: Cloudlvl – Build cloud infra using natural language


value: hawke

value:
Hi HN,
I built Raq.com – a platform that uses Claude Code to build working internal tools directly in the browser.
Claude Code is great at self correcting when given the right tools.
I've found that the popular web-based AI coding tools look great in demos but fail on real API integrations or require a lot of error back and forth. They don't appear to do much research or self-correcting, likely to reduce spend. I wanted to see the current state of these tools, so I ran the same prompt on five platforms (Replit, Bolt, v0, Lovable, and Raq.com) to build a tool that requires 3 different APIs (Companies House, FinUK and OpenRouter) working together.
Four platforms produced broken prototypes or needed manual fixes. Raq.com delivered a complete working solution from a single prompt (that can be deployed to live with one click).
Full test with videos: https://raq.com/real-world-test
We're in early access (requires Claude Pro/Max for free usage) - we're looking non-coders who would like to build internal tools for their team.
Some technical info:
- Raq.com provisions isolated dev and prod Docker environments for each company (companyname.raq.com and companyname-dev.raq.com).
- The dev site includes a persistent terminal streamed to the browser, so the session continues even while tab is closed.
- CLAUDE.md file provides best practices, known pitfalls, and coding patterns for the Laravel + Filament stack.
- Self-Correction Loop: Claude can test and debug its own work. It has direct shell access to a custom script that bundles PHPUnit, syntax checks, and cache clearing. Plus a Playwright wrapper to check for errors and take screenshots.
- A single click runs a script that rsync's the dev workspace to the prod container, runs migrations, and clears caches.

value: Show HN: Same prompt tested across Replit, Bolt, v0, Lovable and Raq.com

author: hawke


value: toisanji

value:
I just built an app for children and parents to ask questions to explain to kids and to for parents to ask how to deal with any parenting situation.
For example a 4 year old may ask “why does the plate break when it falls?”
Sometimes it can be hard to answer that in a way that a child can understand.
The system will go out and scour the web for information the topic, and then rewrite in a kid friendly and safe way. It goes and finds related images, videos,  products and reference links.  It also gives related words ,quiz, and some games and experiments to try with your child to see if they are really interested in the topic.
You can see an example here: https://www.studyturtle.com/ask/lg5areW6-all-about-the-banan...
It has another mode for parents, just ask any parenting question like "how do I get my sons to stop fighting whenever there is a pizza"
This will give you a full research report by going out and crawling the internet and looking for diverse information. This was purposely built to not just choose "the best way" because there is no best way and I wanted to make sure different points of views are foundd with pros and cons.
The parenting tips page will find research papers, books, blog articles, related youtube videos, and more information and present it all to you on one page.
You can see an example here:
https://www.studyturtle.com/ask/PJ24GoWQ-pizza-sibling-fight...
You can ask a parent or kid question here https://www.studyturtle.com/ask/ to test it out, I would love to get your feedback on this.

value: Show HN: App to Help Parents Explain Anything to Kids–and Parenting Questions

author: toisanji


value: hugodutka

value: Show HN: Wush-Action – SSH into GitHub Actions over WireGuard

author: hugodutka


value:
At Railway (I work there), we're putting on a hackathon starting August 6th.
Build a template for others, whether it be for full-stack apps or a headless CMS.
We've seen people deploy traditional apps or infra to host marketing blog sites (we host ours on Railway).
Upto $1000 in prizes for project complexity or content depth.

value: Show HN: Railway hackathon – deploy an idea over a weekend


value: dudeWithAMood

value:
I made an open source, no BS youtube summary website that makes use of the tldw [1] python library to create really quick youtube summaries.
The longer a talk is, the more ad revenue a creator gets. But we don't all have 40 minutes to listen to someone slowly edge around a point.
This website has no ads, no login, and is 100% free.You can find the source code here [2].
[1] https://pypi.org/project/tldw/
[2] https://github.com/DavidZirinsky/tldw-site

value: Show HN: YouTubeTldw: ad‑free, login‑free YouTube summaries in a flash

author: dudeWithAMood


value: moulick

value:
Simple script to replace `aws` in your Kubeconfig for users of AWS EKS. Reduces the pain of using kubectl with AWS EKS a lot.
The default way is to use aws eks get-token command, but it is not cached and it is not very fast. It's slow because:
1. Every kubectl command will do an `aws eks get-token` which makes an API call to AWS
2. aws cli is python. When running kubectl in a loop, you can very clearly see how much performance is lost generating tokens for each kubectl invocation in Activity Monitor.

value: Show HN: Faster local AWS EKS access

author: moulick

title: Show HN: Faster local AWS EKS access


value: hannasanarion

value:
Hi HN!
I built this after seeing several references to Election Truth Alliance on social media, and after reading their analysis, I just couldn't get the problems I saw in it out of my head.
So I downloaded the data, and rebuilt their full analysis from scratch.
Their critical error is a simple misunderstanding of the Law of Large Numbers: values collected in large samples converge to the true probability in the sample distribution.
(not to be confused with the Law of Very Large Numbers: which states that unlikely things happen given enough time. That confused me too)
Technical Details:
- No build system, this is entirely handmade HTML, CSS, and plain Javascript.
- Initial analysis done in Python with only standard libraries.
- Visualizations created in Observable Plot and D3.js
- Simulations run entirely client-side
- Web page built with Scrollama for animations and behavior controls
- Vote history visualizations process ~600k individual ballot records in real time, with a little bit of cacheing to keep your browser from chugging.
Interesting Challenges:
- Making the visualizations performant without a backend, which is accomplished with a bit of preloading as you scroll, and some amount of reusable cacheing so that the visualizations can share resources whenever possible.
- Windsurf does run wild sometimes. During the initial preprocessing stage, it at one point dumped an absolutely massive json blob to disk, it was so large it actually crashed my whole computer while writing. Then to read it, obviously it couldn't just be read in, but rather than storing in a more sane format, my Opus 4 powered coding agent decided to build a streaming JSON parser from scratch. It worked, and I got the data out that I needed so I didn't go back and make it more sensible, but man that was dumb.
This actually started with the simulation, which took only about a day of work, and then later grew to include the re-analysis and visualizations. The visualizations were all dnoe within 2-3 days after I got the data.
If I did it over again, I would've probably tried to find some kind of build system or static site generator to compose the final result. Once the page got very long it was quite unwieldy even for windsurf. Very short conversations could flood Sonnet 4's rate limit because there was just so much stuff in a single file.

value: Show HN: Debunking Election Fraud Claims – Interactive Data Viz and Simulations

author: hannasanarion


value:
TBR - To Be Read
This has been a hobby project of mine. It's saved me a ton and thought the book reading community as a whole may get some use out of it. It gets deals from across some of the most common audiobook retailers and displays deals that match the price point and percent off you specify. There are no ads, no tracking, and the code is under an MIT license.
tbr-deal-finder works by checking for deals on exported GoodReads, exported TheStoryGraph, or custom CSV (details in repo).
It works with Audible, LibroFM, and Chirp. I'm hoping to add EBooks at some point in the future.
If you're interested here's feel free to check out my project where you can learn more about everything it supports and how to install.

value: Show HN: Tool to track audiobook deals on your TBR


value: playsong

value:
TL;DR: Built BooksWriter - an AI tool that generates novels chapter by chapter while keeping YOU in control. Generated my first book in 2 hours. Looking for feedback from fellow writers.
Hey writers! I want to share something I've been working on that might interest you.
I've been frustrated with existing AI writing tools for ages. Take the current best tools for writing as an example- it looks promising at first, but the quality and consistency completely falls apart as your story progresses. By chapter 5-6, it becomes nearly impossible to maintain coherence. The AI loses track of your plot, characters start acting inconsistently, and you're drowning in clichés and tired tropes.
Writing directly with AI models is even worse. The longer your story gets, the more context issues you hit. You spend more time crafting prompts than actually writing, and when you ask for edits, the AI rewrites everything and destroys the parts that were working.
I built BooksWriter to solve these exact problems:
Give it your idea, and it generates your story chapter by chapter. But here's the key - it gives you 3 different directions for each chapter, so YOU choose where the story goes.
Unlike other tools, it maintains consistency and quality throughout your entire book. Chapter 20 is as good as chapter 1.
When you need changes, it doesn't rewrite everything. It finds the specific moments that need editing and improves just those parts without touching what's already working.
Upload your own writing samples and it adapts to write exactly in your style.
Generate in 19+ languages.
Don't want to read? Listen to your story instead. You can generate audio of your book too.
And finally: One-click publish to our platform at read.bookswriter.xyz
I generated my first complete book in just 2 hours while maintaining my writing style and creative control. The combination of automation + control is what makes this different from everything else out there.
The app is currently in closed beta. If you want to test it out:
Visit bookswriter.xyz
Sign up
Email support@bookswriter.xyz for credits, and we will give you access
And start writing!
Check out what other users have already created at read.bookswriter.xyz
Looking for honest feedback from the community! What features would be most valuable to you? What problems do you face with current AI writing tools?
If you're a writer, I genuinely think you should try this. Happy to answer any questions in the comments!
P.S. - I'd love to read whatever you create and publish on the platform

value: Show HN: I created an AI writing app that helps you write books

author: playsong


value: bobbyjgeorge

value:
Alexandria is the first library where you can talk to a tutor — in the book itself — as you read.
Move at a contemplative pace in Lightning Mode, then ask Virgil — your Socratic guide — for context, questions, or counter-arguments.
Virgil blends the depth of the entire classics catalog with a pedagogy crafted by Professor Zohar Atkins and tailors every conversation to your interests.
Features:
• Lightning Mode — focus by reading one sentence at a time.
• Virgil — an AI tutor that explains passages, challenges your thinking, follows your curiosity, and remembers what excites you.
• Build and Share your Bookshelf — curate favorites, get smart recommendations, and send highlighted quotes to friends; social bookshelves turn passive libraries into shareable conversations.
• Daily Sparks — one thought-provoking excerpt every morning, plus a guided conversation with Virgil.
• 4,000+ Works — all the most iconic thinkers, from Plato and Sun Tzu to Nietzsche and Jane Austen, plus hidden gems for adventurous readers.
Our Mission: to inspire learning as a way of life.
Available on the App Store and coming soon to Google Play.

value: Show HN: Alexandria, a New Way to Read the Classics

author: bobbyjgeorge


value: meistertigran

value: Show HN: I made this Markdown journaling app you can use in the browser for free

author: meistertigran


value: dillstead

value:
The ELF Injector allows you to "inject" arbitrary-sized relocatable code chunks into ELF executables.  The code chunks will run before the original entry point of the executable runs.
Included in the project are sample chunks as well as a step-by-step tutorial on how it works.
It's a mix of C and assembly and currently runs on 32-bit ARM though it's easy to port to other architectures.

value: Show HN: ELF Injector

author: dillstead

title: Show HN: ELF Injector


value: sukechris

value:
Hi HN,
I'm an indie developer, and this is my project: LocallyTools(https://locallytools.com).
I built this out of a personal need. I grew tired of uploading sensitive files to random online services for simple tasks like image compression or PDF merging. Especially in the age of AI, the lack of transparency about data usage is a serious concern for me.
LocallyTools is my solution: a suite of tools that runs 100% in your browser. All processing is done client-side using JavaScript and WebAssembly. Your files never leave your machine.
Here are the core principles:
- 100% Local Processing: Your data's security is guaranteed by design. You can prove it yourself by watching the network tab in dev tools. Nothing is sent out.
- True Offline Support (PWA): Once you visit a tool's page and the cache indicator at the top turns green, the tool is fully cached. You can disconnect from the internet and it will continue to work flawlessly.
- Sustainably Free: People often ask "what's the catch?". There is none. Because all the heavy lifting is done on your device, my server costs are incredibly low—just basic static hosting. This lightweight model makes it sustainable to offer the service for free, long-term.
- No Registration, No Friction: Just a clean interface that gets the job done.
I've just launched and I'm committed to continuously adding more tools based on user feedback. The goal is to build a complete, trusted, local-first tool ecosystem.
I'd be grateful for any feedback from the HN community. What do you think? Any features you'd like to see next?
Thanks for your time.

value: Show HN: LocallyTools – A privacy-first free local toolbox, supports offline use

author: sukechris


value: mousematrix

value:
Hi HN, Hussain and Dan from Xorq here.
After years of struggling with scaling compute that worked in notebooks but failed in production, we decided to do something about it. Data has standards like Iceberg and Delta. But compute is still a mess—trapped in notebooks, duplicated effort across teams, or baked into custom Airflow DAGs. We think of Xorq as the missing analog to Apache Iceberg, but for compute.
We’ve spent the last year building Xorq, an *compute catalog* that helps teams *reuse, ship, and observe* transformations, features, models, and pipelines across engines.
Xorq is built on:
- *Arrow Flight* (`do_exchange`) for high-speed data transport
- *Ibis* for cross-engine expression trees, serialized to YAML
- A portable UDF engine that compiles pipelines to SQL or Python
- `uv` to make Python environments fully reproducible
Xorq features:
-  pandas-style declarative transformations, backed by Ibis
-  Multi-engine execution (e.g., DuckDB, Snowflake)
-  UDFs as portable Flight endpoints
-  Serveable transforms by way of flight_udxf operator
-  Built-in caching and lineage tracking
-  Diff-able YAML artifacts, great for CI/CD
Xorq use cases:
Since our last major release, it’s been exciting to see the first Xorq use-cases show up in the wild. All with *Python simplicity and SQL-scale performance*.
- Feature Stores  (https://www.xorq.dev/blog/featurestore-to-featurehouse)
- Semantic Layers (e.g. https://github.com/boringdata/boring-semantic-layer)
- MCP + ML Integration (https://docs.xorq.dev/vignettes/mcp_flight_server)
We’re open source and learning fast. Would love feedback on what’s useful or missing. Thanks in advance for trying it out!
Check out the demo of the Xorq CLI tool in action: https://asciinema.org/a/730484
---
Get Started
- Github: https://github.com/xorq-labs/xorq
- Xorq docs: https://docs.xorq.dev/
---
Sneak peak - Xorq Compute Catalog UI Console:
Check out this interactive Claude demo showing how the Xorq compute catalog can be visualized to accelerate composition, reuse, and troubleshooting of AI compute: https://claude.ai/public/artifacts/d2f00d2a-a3f9-4032-884e-d...

value: Show HN: Xorq – open compute catalog for AI

author: mousematrix


value: rdorgueil

value:
I started a few weeks ago (6 to be exact) a curation process. To make it short, I selected 150 feeds of news that I think are relevant, including HN, download new items on a regular basis, pre-filter them using light llms to filter out things that I'm not interested in (like billionaire drama, politics, recruitment, fundraising, astrophysics, commercial software ...) and filter them manually using a tinder-like application for news, with a few internal criterias: like open source software, human written inspiring text, dislike pricing buttons...
So basically, I got into a process of swiping 2500 or so times a week about content, before diving even more in the one that interested me at first glance, which means I've eaten a lot of my time for little to no value.
Any genius (or stupid) ideas on how to do better? I'd like to continue, but as it is now, it's too much time consuming and I'll get bored soon ... Of course I could automate the selection with LLMs but that's not the point, I like human-picked stuff (although I may benefit from auto-filtering generated content if I knew how to).
Thanks :)

value: Show HN: I waste my time extracting stuff every week from the Internet

author: rdorgueil


value: beledev

value: Show HN: The easiest accessibility (a11y) checker for VSCode

author: beledev


value: rcrKnight

value: It's a web app that uses AI to turn any book into a playable text adventure. Your favorite book, but your choices, hence your story. You can even "remix" the genre like playing Dune as a noir detective story.<p>Note:  Work in progress. Suggestions are welcome.

value: Show HN: I built an AI that turns any book into a text adventure game

author: rcrKnight


value: ananddtyagi

value: Show HN: Claude Code Subagents Directory

author: ananddtyagi


value:
Hey HN,
I made an open-source tool in Go, named gogg, to download and back up your GOG.com game library.
It's cross-platform and has features like:
- A scriptable CLI and easy-to use GUI
- Multi-threaded and resumable downloads
- Filters for platform, language, DLCs, etc.
- File verification with hashes and total size calculation
You can find the project on GitHub: https://github.com/habedi/gogg

value: Show HN: Gogg – A GOG game downloader written in Go


value: howardV

value:
  I've been working on CineWan, an AI video generation
  platform that leverages the new Wan2.2 models with
  Mixture-of-Experts (MoE) architecture.

  Technical highlights:
  • MoE architecture separates denoising across
  timesteps with specialized expert models
  • Dynamic routing system selects experts based on
  content complexity
  • Generates up to 720p, 121-frame videos from text or
  images
  • Built on Next.js 15 with edge runtime for <50ms
  global response times
  • Smart cost optimization: Cloudflare R2 storage with
  3-day auto-expiration
  • Real-time progress streaming with exponential
  backoff polling

  The Wan2.2 models were trained on +65% more images and
   +83% more videos than v2.1, with integrated
  cinematography principles. We're seeing cinema-grade
  output quality that rivals much more expensive
  solutions.

value: Show HN: CineWan – video generation platform powered by Wan2.2 AI model

author: howardV


value: Show HN: Simple finance tracker, CRM and task manager for small business


value: Show HN: I Created the Cursor for Workouts (Hevy App)


value: adamthehorse

value: Analyze your GitHub repository with AI and see how it compares to others! Explore other developers repos and commits with AI as well!

value: Show HN: GitSage an AI Powered GitHub Analytics Leaderboard

author: adamthehorse


value: jathoms

value: Show HN: Kbm - GPU‑accelerated visual mouse and keyboard macros in Rust

author: jathoms


value: sysmax

value:
Hey HN,
Just wanted to share something cool I've been working on for the past half a year.
CodeVROOM[0] is a symbol-level AI editor designed for very fast incremental edits to large projects.
Instead of including entire files with your editing request, it trims them to include just the relevant parts, while preserving the overall structure, so the models won't get confused.
It can also automatically ask the model what other symbols are relevant to a particular edit, and restart the conversation with these symbols. Here's an example [1] how a couple of context discovery
steps quickly get from a hallucination-ridden answer to a straight-to-the-point implementation. And because the model works with heavily trimmed source files, it works very fast (1-2 seconds per step with Cerebras platform)
and has negligible token costs (typically, under a cent per edit).
The main difference from CLI tools is to give the user full control over what's going on. You can step the editing session back, see what symbols the model deemed relevant, edit them, and retry.
Or you can retry individual steps, switch between models at any point, or do special steps like "discover more context" or "expand my original instructions, giving several options".
Most steps only take a few seconds with smaller models (LLaMA on Cerebras) or about a minute on larger models (Claude), so it's very easy to review what the model is doing, and steer it into the correct direction. You can also do things like ask Claude to expand your instructions, and then let LLaMA implement them.
CodeVROOM is built from scratch using .Net (WPF on Windows, Avalonia on Linux/Mac), so it has some unique features that other editors don't have. E.g. the change reviewing logic [2] is integrated with the outline,
so you always have an overview of the added/removed/edited members, and can approve/reject edits in bulk. Or you can collapse series of unchanged members to see all edits at a glance (but with full context unlike plain diffs), or
switch between inline and side-by-side modes.
The main use case is routine edits and refactorings that are not handled by the existing refactoring tools, and take several minutes to do by hand. Even small AI models handle these very well, and getting reviewable results from a
consise prompt in a second or two is just way less distracting than doing it by hand. There are some examples of real-world edits here: [2].
There's also support for AI techniques where you provide plain-text instructions how to do a particular recurring task, and can then reference it from a prompt (port size-related properties per @PropertyPorting) and edit templates
where you can write instructions for common edits (e.g. add null checks to a function) and then invoke them by clicking on a link above the function.
CodeVROOM supports 10 common cloud providers and local models via Ollama. It has old-school perpetual licenses with 1 year of free updates, there is no telemetry, and no lock-in. The product is the GUI, not your data.
It is stil early in development with tons of more features coming, but it can already save you some time side-by-side with your main IDE. The trial will remain unlimited until more IDE features are ready.
[0] https://codevroom.com/
[1] https://sysprogs.com/CodeVROOM/documentation/examples/connec...
[2] https://sysprogs.com/CodeVROOM/documentation/features/diffs/
[3] https://sysprogs.com/CodeVROOM/?features=examples

value: Show HN: CodeVROOM – an AI editor for large projects

author: sysmax


value: ashtonanderson

value:
We're thrilled to announce that www.maiachess.com is now in open beta, meaning everyone can access it! Maia is the most human-like chess AI, and is an ongoing research project at the University of Toronto developing fun, useful, and novel human-AI collaboration in chess. Please give it a try and let us know what you think. We're still rapidly improving and iterating on it.
* Play Maia-2: Play the (updated) most human-like chess engine, tailored to your skill level
* Analyze your games: See how you (or the pros!) stack up with both Maia’s human-based predictions and classic Stockfish evaluation
* Try Maia-powered puzzles: Tactics puzzles curated and analyzed through Maia’s unique lens
* Openings drill: Brand new! Select openings, play through them against Maia, and get instant, personalized feedback
* Hand & Brain: Play this fun team variant where you play with Maia as a human-AI team
* Bot-or-not: A chess Turing Test: can you spot the bot in a real human-vs-bot game?
* Leaderboards: See how you rank in each mode, and challenge yourself to climb higher
We’d love your feedback: what works, what doesn’t, what’s missing, or what would make the platform more valuable for you. Join our Discord to chat with us and other users (https://discord.gg/hHb6gqFpxZ).
If you're interested in our research behind Maia, you can check out these papers:
Aligning Superhuman AI with Human Behavior: Chess as a Model System, KDD 2020
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess, NeurIPS 2021
Learning Models of Individual Behavior in Chess, KDD 2022
Designing Skill-Compatible AI: Methodologies and Frameworks in Chess, ICLR 2024
Maia-2: A Unified Model for Human-AI Alignment in Chess, NeurIPS 2024
Learning to Imitate with Less: Efficient Individual Behavior Modeling in Chess, under review

value: Show HN: Maia Chess – Human-like chess AI for playing, learning, and more

author: ashtonanderson


value: vinhnx

value: Show HN: VT – One chat interface for all AI models

author: vinhnx


value: samwillis

value:
Hi HN, Kyle, Sam and the TanStack team here.
We’ve been working on TanStack DB, an embedded, reactive client database for TanStack Query, and are proud to announce today that with the 0.1 release that it's now in BETA!
TanStack DB plugs into your existing TanStack Query useQuery calls and uses Differential Dataflow to incrementally recompute only what changed, so updates stay sub-millisecond even with 100k rows. You get live queries, optimistic updates with automatic rollback, and streaming joins — all in the client!
TanStack DB works with REST, GraphQL, WebSockets, and shines with sync engines like ElectricSQL or Firebase, letting you load large, normalized collections once and stream real-time changes into the client without manual bookkeeping. It sits on top of queryClient so you can adopt it incrementally, one route at a time.
- Intro post: https://tanstack.com/blog/tanstack-db-0.1-the-embedded-clien...
- Local-first sync via Electric: https://electric-sql.com/blog/2025/07/29/local-first-sync-wi...
- Web starter with TanStack Start: https://github.com/electric-sql/electric/tree/main/examples/...
- Mobile starter with Expo: https://github.com/electric-sql/electric/tree/main/examples/...
- Project website and docs: https://tanstack.com/db
- GitHub repo: https://github.com/tanstack/db
Try it out and let us know what you think!

value: Show HN: TanStack DB – Reactive DB with Differential Dataflow for TanStack Query

author: samwillis


value: I found a partly broken electronic doorbell on the ground and realized I never would have guessed that this was a doorbell. So I posted in my sideproject community (relentlessly.no) an idea to make a quiz app where we break open many gadgets, take pictures and let people guess what gadgets they are! Ive been working on and off for about three months on this with a fullstack and a designer that i met in our sideproject community <3 We know its pretty basic but would love to see if anyone finds it interesting. there are like five free tasks before a 3 usd season 1<p>Whats crazy is two things: Ive never met the designer. And we didnt have ONE single meeting digitally or physically while making this. all asynchronous work through slack. Man I LOVE that. And now i own 33% of a basic app. Time of my life!

value: Show HN: A teardown electronics quiz app made by me and a friend Ive never met


value: mbalatsko

value:
Show HN: I built a deep email validation library to learn Kotlin
Hey HN,
I wanted a real-world project to properly learn Kotlin (coroutines, DSLs, etc.) and decided to tackle a problem I've found surprisingly underserved: comprehensive email validation. Most solutions stop at regex, but that doesn't prevent sign-ups from user@notarealdomain.com or disposable email services.
So, I built a library that performs a series of deeper checks. I just tagged the v1.0.0 release because the API is now stable and I think it's ready for feedback from the community.
It validates an email in layers:
1. Syntax: A robust check that's more reliable than a typical regex.
2. Domain Registrability: Checks the domain against the Public Suffix List to ensure it's on a real TLD.
3. MX Records: A DNS query to see if the domain is actually configured to receive email.
4. Disposable Services: Checks against a list of known temporary/throwaway email providers.
5. SMTP Connection (Optional): A live check to see if the mailbox actually exists. This is off by default since port 25 is often blocked, but can be enabled via a proxy.
One of my main goals was to build something that would be useful on both the server and on a client like an Android app. This led to a couple of key design decisions:
- It's built with coroutines for non-blocking, concurrent I/O.
- It has a full offline mode. You can disable all network checks and run it using bundled datasets for things like syntax and disposable domain checks, which is great for providing instant, client-side feedback.
The configuration is done through a simple Kotlin DSL.
The project is MIT licensed. I'm posting this to get your thoughts on the approach, the architecture, or any Kotlin idioms I might have missed. How do you all typically handle this problem beyond regex?
GitHub: https://github.com/mbalatsko/emailverifier-kt

value: Show HN: I built a deep email validation library in Kotlin

author: mbalatsko


value: Show HN: Click the Circle

title: Show HN: Click the Circle


value: dkamm

value:
A little idea I got from playing with AI SWE Agents.  Can AI help make sure we understand the code that our AIs write?
PR Quiz uses AI to generate a quiz from a pull request and blocks you from merging until the quiz is passed.  You can configure various options like the LLM model to use, max number of attempts to pass the quiz or min diff size to generate a quiz for.  I found that the reasoning models, while more expensive, generated better questions from my limited testing.
Privacy: This GitHub Action runs a local webserver and uses ngrok to serve the quiz through a temporary url.  Your code is only sent to the model provider (OpenAI).

value: Show HN: A GitHub Action that quizzes you on a pull request

author: dkamm


value:
4 months back I started making an extension named monochromate which was basically a greyscale extension, I realised power of them when I turned it on my phone and the thing is being a programmer most of my time is spent on browsers ... and that's where it don't exist (at least not the perfect solution for my needs).
That's how I came up with it, made it open source, recently passed 100 users over chrome webstore and it have 5-star rating as of now.
and yes you might say that why would I need it when I can do this with filters that can be toggled via settings, thing is I didn't wanted to greyscale my work sites as well and thus I made site exclusion, also have scheduler, intensity control and above that all it supports all the browsers including Firefox providing same experience.
Would love any kind of feedback over this!!

value: Show HN: Monchromate – Smart greyscale browser extension


value: Popio

value: Show HN: Verinex – A European open-source social network

author: Popio


value: piterrro

value:
I've been developing AI apps for the past year and encountered a recurring issue. Non-tech individuals often asked me to adjust the prompts, seeking a more professional tone or better alignment with their use case. Each request involved diving into the code, making changes to hardcoded prompts, and then testing and deploying the updated version. I also wanted to experiment with different AI providers, such as OpenAI, Claude, and Ollama, but switching between them required additional code modifications and deployments, creating a cumbersome process. Upon exploring existing solutions, I found them to be too complex and geared towards enterprise use, which didn't align with my lightweight requirements.
So, I created Hypersigil, a user-friendly UI for prompt management that enables centralized prompt control, facilitates non-tech user input, allows seamless prompt updates without app redeployment, and supports prompt testing across various providers simultaneously.
GH: https://github.com/hypersigilhq/hypersigil
Docs: hypersigilhq.github.io/hypersigil/introduction/

value: Show HN: Hypersigil – Prompt management UI – test, evaluate, deploy

author: piterrro


value: abdulrahman-mh

value: Show HN: Medium-style story editor you can use on your own website

author: abdulrahman-mh


value: valzevul

value:
Hey HN! Halfway through moving to Canada and whilst my conversational French gets me by, I'm absolutely rubbish when someone rattles off phone numbers at full speed (quatre-vingt-dix-neuf anyone?).
So I built a virtual pet that thrives on correctly answered number challenges. 20-second drills throughout the day: get them right and Lexie grows, get them wrong and it gets a bit sad (but never dies, this isn't the 90s).
Speak, type, or tap your answers. Would love any feedback/bugreports.
Or just rant about how the Belgians sensibly say "nonante-neuf" whilst we're stuck with "four-twenties-nineteen".

value: Show HN: I built a Tamagotchi that teaches French numbers

author: valzevul


value: ecosystemj

value: Show HN: I built a Laravel job board for Europe and Asia

author: ecosystemj


value: muthuishere

value: Show HN: LNB – One command to make any binary globally accessible

author: muthuishere


value: kumama

value:
Hello HN!
I’ve been working on `benchmax`, a open-source framework for building, running, and parallelizing environments, to fine-tune LLMs with reinforcement learning.
What I wanted to solve for:
- Environments are tightly coupled with RL trainers, leading to fragmentation and limited compatibility.
- These coupled environments are tend to be mostly competitive math and coding → for OSS RL + LLMs to scale, we need more complex, real-world environments.
- Scaling these environments in parallel is still not easily possible
What I'm excited about:
- benchmax is training framework agnostic with adapters already built out for verl and verifiers. we’re gonna build more adapters for other frameworks (e.g. SkyRL, etc.), instead of forcing others to adopt our standard (though ofc they’re welcome to )
- benchmax comes with a few interesting environments out of the box: spreadsheet processing, CRM, etc. → more coming soon!
- benchmax supports MCP as a first class citizen. there has been an explosion of MCP servers/tools built out for usecases ranging from browser use to excel to game creation.`benchmax` allow folks to leverage and compose these existing MCP servers to build environments integrated with real world systems
- Multi-node environment parallelization coming soon!
If you like what you see, feel free to *star* the *repo* to support the project!! Our hope’s to really let anyone benchmax on their tasks, with benchmax
https://github.com/cgftinc/benchmax
It’s still very early! And I expect to be shipping a lot more things → more environments, more trainer integrations. Would love y’all’s thoughts what environments and trainer integrations could be interesting!

value: Show HN: Benchmax, a new open-source RL environment framework for LLM finetuning

author: kumama


value: mrdosija

value:
I was using vue reactivity hooks to debug my apps so decided to build a plugin to better visualize what's going on. It's by no means perfect but decided to add some bells and whistles and publish it.
All feedback is welcome.

value: Show HN: I built a Vue dependency debugger plugin

author: mrdosija


value: mrameezraja

value: Show HN: Cronhooks – Effortless Webhooks Scheduling and Workflow Automation

author: mrameezraja


value: wordongu

value:
I'm a solo developer building Portals, a knowledge workspace that primarily started as a way to capture notes, retrieve info, and get answers quickly while I was working on projects or learning different topics.
After 4+ months of continuously iterating features, doing marketing, and talking to users, I've expanded a lot on the initial concept to try to create a platform to manage a user's entire knowledge lifecycle, from capturing and taking notes from audio or parsing files to organizing and indexing them.
Being a power user of different AI tools and agents (n8n, lindy, cursor, claude code, etc.) I started building my own version in this app, but focused on being dead-simple to understand and to work with your existing knowledge base. Today we can write clear instructions in plain language, tag items to search or use, and add triggers to automate a lot of simple, daily workflows. With more testing and usage I'll continue to build more features on top of this.
If you're interested, please give it a peek, thanks!

value: Show HN: Turn Notes into Knowledge Agents

author: wordongu


value: anantshri

value: Analyze Software Bill of Materials from GitHub organizations and users to understand dependency patterns and usage across repositories.<p>opensource, work in progress code, please share your feedback.

value: Show HN: SBoMPlay – Client side SBoM explorer

author: anantshri


value: johnwisdom

value:
Hi HN,
I'm Daniel. I built a simple and straightforward API: you POST a JSON payload with your invoice data, and it returns a secure, presigned URL to a generated PDF. The goal is to make invoicing a single, reliable API call so you can get back to your main product.
I also used this as a personal challenge to move away from my old LAMP stack background and build something new with Python/FastAPI, Next.js, and a serverless architecture on GCP and AWS.
For the HN community, I've set up a promo code: HEYHN100
If you sign up, you can redeem it in your dashboard for 100 free credits (on top of the 10 you get by default). The credits don't expire.
I'm here to answer any questions and would genuinely appreciate any feedback or technical critiques you have. Thanks for checking it out.

value: Show HN: I built an API to generate PDF invoices from JSON

author: johnwisdom


value: bakkerinho

value:
I built a GitHub Action that solves a common pain point in CI/CD pipelines with Vercel deployments. Instead of relying on GitHub's deployment_status events, it actively polls Vercel's API to ensure deployments are truly ready before running tests or other checks.
Key technical features:
- Direct integration with Vercel's API for real-time deployment status
- Configurable polling with timeout and interval settings
- Support for team projects and branch aliases
- Works with both preview and production deployments
- No special webhook configuration needed
The main advantage is reliability: Many CI pipelines fail intermittently because they start running tests before Vercel deployments are actually ready. Traditional solutions rely on GitHub's deployment_status events, which can be unreliable or require complex setup.
This action solves that by:
1. Retrieving the latest deployment for your project/branch
2. Extracting the preview/production URL
3. Actively polling until the deployment is genuinely ready
Example use cases:
- Running E2E tests against preview deployments
- Multi-app testing workflows
- Post-production deployment validation
- Accessibility testing on live environments
I'd love feedback from the HN community, especially from those dealing with similar CI/CD challenges.

value: Show HN: I Built a GitHub Action to Wait for Vercel Deployments Before CI

author: bakkerinho


value: atowns

value:
Hi HN,
I'm Allen Townsend, an independent ML researcher, and I've developed a novel gradient-free machine learning algorithm that enables faster training and inference than traditional gradient-based approaches, especially in scenarios where:
Gradients are hard or impossible to compute
Simulation or black-box models dominate
Speed and stability are critical
This method avoids backpropagation entirely, yet achieves competitive results in optimization-heavy tasks. It's well-suited for reinforcement learning, simulation-based systems, and edge deployment use cases.
I'm currently looking for contract work to help teams develop custom models using this approach. If you have a tough modeling or optimization problem and want to explore this new direction, I’d love to collaborate.
You can reach me at townsendtech2025@gmail.com.
Happy to provide examples, benchmarks, or run a quick pilot project. Thanks for reading!
— Allen Townsend

value: Show HN: Gradient-Free ML Algorithm (Available for Contract Work)

author: atowns


value: Show HN: Using Open-Source LLMs to Power Private, On-Device AI Agents (+ MCP)


value: SeanLShort

value:
Hi HN,
I'm an incoming college freshman building BreathylBox, a lockbox that stays locked unless you pass a breathalyzer test and authenticate with a passcode. It’s designed to help prevent access to car keys, firearms, or phones when someone’s been drinking.
Here’s the landing page: https://www.breathylbox.com
Right now, we’re validating demand across use cases:
Parents storing car keys after parties
Gun safety in homes with teens
People trying to reduce tech use while drinking
We’re not selling anything yet — just trying to see if the idea resonates and which use case to prioritize. Would love your feedback:
Would you use something like this?
What should we do (or avoid) before moving to manufacturing?
Any obvious legal or hardware red flags?
Thanks in advance — happy to answer any questions!
Sean Short, CEO
BreathylBox
(seanshort@breathylbox.com)

value: Show HN: BreathylBox – A lockbox that only opens when you're sober

author: SeanLShort


value: streetsmartai

value:
zkshare.dev I built zkshare to share secrets securely without trusting the server.<p>*Features:*
- Client-side encryption with 6-digit PIN
- Server never sees your secrets in plaintext
- Wrong PIN attempts don't destroy secrets
- Single-use tokens that self-destruct after decryption
- Python CLI for .env files + React web app<p>*Tech:* Rust backend (Axum + Redis), React frontend, Python CLI<p>*Try it:*
```bash
git clone <a href="https://github.com/streetsmart-ai/zkshare.git">https://github.com/streetsmart-ai/zkshare.git</a>
cd zkshare/backend && cargo run
cd zkdotenv && python3 test_zkcloud_encrypt.py
```<p>Open source, MIT licensed. Would love feedback on the security model!

value: Show HN: Zkshare – PIN protected secret sharing with client-side encryption

author: streetsmartai


value: aemiliotis

value: Show HN: Robotics AI Cells – Modular AI Functions for Faster Robot Programming

author: aemiliotis


value: anaempromptu

value:
Hey HN! We're Empromptu.ai, an AI app builder that builds AI apps (RAG, models, evals all built in every app)
Demo: https://app.storylane.io/share/rtneodkf5i1l
We started Empromptu after burning through thousands of credits on AI builders and hitting the same problem: cool looking prototypes or demos that break with real users.
The issue wasn't the building process, it was accuracy. Most AI applications plateau at 60%~ reliability, which is fine for prototypes but it's unusable in production. We realized these tools aren't really "AI app builders", they're website builders that happen to use AI.
We wanted to solve the hardest problem first: making AI applications actually work reliably.
Our approach centers on what we call dynamic optimization. Instead of cramming every possible scenario into one massive prompt (which confuses LLMs), our system adapts contextually. A travel chatbot automatically knows to mention LAX for Los Angeles vs. Pearson for Toronto. This consistently delivers 90%~ accuracy versus the industry standard 60%~.
But accuracy alone wasn't enough because we also needed to solve the builder gap:
- Simple builders (Lovable, Bolt): Create static websites, not AI applications
- Complex ML tools: Require dedicated teams most startups don't have (Arize, Voxel51) - we've also heard from both technical and non-technical founders that they found these tools very complex
- What's missing: Tools that build applications where AI is embedded functionality
So we built AI agents with optimization built-in. Users just type what they want to build and our agents handle the full development pipeline: creating applications with embedded models, RAG and intelligent processing. You can deploy to your own infrastructure via Netlify, GitHub or download it directly since you can run it locally.
The result: Startups, solo hackers and enterprises can build AI apps or AI features without hiring a dedicated ML team.
Waitlist: https://empromptu.ai
We'd love feedback from the HN community — esp. if you've hit similar accuracy problems or thoughts on the technical approach.

value: Show HN: Empromptu.ai – No code, AI app builder with RAG, model, evals etc.

author: anaempromptu


value: Devinlam

value:
Hi HN,
I'm an independent system builder and AI symbiosis thinker from Hong Kong. Over the past few years, I’ve developed a unique self-evolving portfolio designed to demonstrate my capability in integrating multiple complex systems — including a sports prediction engine ("Football AI Prediction"), a deep personality-matching framework for social projects, and a modular strategic planner for long-term life design.
I built this not only as a showcase of skills but as a live framework of who I am: a full-spectrum INTJ with over 40 modular personalities (with distinct internal functions), designed for real-time switching, integration, and decision modeling.
Key highlights:
- 40+ structured personality modules (including financial strategist, control type, intimate empath, long-term legacy builder, etc.)
- Complete project walkthroughs: sports AI, matching systems, automation frameworks
- Full bilingual version (Chinese + English), with future Arabic version in preparation
- Designed to communicate with AI systems and companies seeking deep-thinking symbiotic partners
Portfolio: [https://your-link.com]
 Contact: devinlam417ai@gmail.com
 Open to: Remote collabs, AI think tanks, Middle East / HK / TW markets
 With me: 3 sugar gliders — I don’t go alone
I’m not looking for a traditional job, but for companies, researchers, or co-founders who are interested in future-persona design, system integration, or novel collaborative structures. If you're working on something that might resonate, let’s talk.
Thank you for reading.

value: Show HN: AI-ready portfolio with 40 personality modules for strategic system use

author: Devinlam


value: averadev

value:
Hi HN,
I built Fidbaq, a simple app designed to help startups and developers collect, organize, and prioritize user feedback — so you can build features your users actually want.
Create your own feedback board
Let users submit ideas, bugs, and suggestions
Prioritize with user votes
Turn feedback into a clear product roadmap
If you’re tired of building features nobody needs, give it a try: https://www.fidbaq.xyz/
I’d love to hear your thoughts and feedback!
— Agustín Vera

value: Show HN: I built an app to collect and manage user feedback for startups and dev

author: averadev


value: Show HN: I made a site to study about Elon Musk


value:
It started off with the simple idea to cram together the accountability of Patreon (quit when you want) with the coordinated action of Kickstarter (thresholds).
The MVP behind the link demonstrates the very first iteration of this concept.
The second big idea was keeping power with the backers.  Let communities of users freely move between different people who make stuff instead of being captives like with Star Citizen.  This also lets creators focus on making stuff instead of selling the next Solar Roads.
Along the way I realized that there are opportunities where the world can deliver a ton of value for not that much cost but the money still can't flow.  Why can't the capital move?  It's because of value capture difficulty or value production uncertainty.  However, the downstream often doesn't care.  Consumers and downstream businesses are not sensitive to the profitability of upstream.  They want stuff and they don't want to wait on startups to figure out how to bootstrap to profitability to get out of the chicken and egg dilemmas.
Deep tech and open IP are those kinds of things.  Welcome to https://positron.solutions.  Our mission is to connect demand for downstream value creation to upstream enabling technologies.
This Production Finance concept is very broadly applicable.  This can be a remedy to the extremely poor monetization of independent media.  My main point is that these things are worth building, and PrizeForge is a feature complete product the moment that we realize that we want things like PrizeForge to exist and that this MVP is a way for us to get there faster.
For now, I want to focus on the consumer open source market because it's a deeply distressed market where billions of willing dollars are stuck behind the volunteer's dilemma.  It's also a simple market because there's no IP law to juggle.  Open source has a strong tradition of killing stupid competition and delivering lots of indirect value that pays big dividends.
In getting ready for this moment, I pulled together a modest YouTube following, some Github stars, and developed my own takes on where open source thinking needs to go to win.  I will be inviting my Github Sponsors over pretty soon and, above all, introducting features on our sub-Reddit to get the iteration dynamo going.
The MVP is a full-stack Rust application.  It has a Leptos reactive frontend and Axum on the backend, talking to Postgres and NATS.  I'll have a lot to say about that while courting some communities and engineers.  https://positron.solutions/careers
I came up with the binary fragmentation idea while developing.  I was just going to truncate.  I chopped out two planned features becuase they were going to require fixed-point calculations.  I'm glad I did because last night I crafted a bad kubernetes secret and discovered it only because a header unwrap in Stripe's library was panicking.  I am toasted.
The Elastic Fund Raising feature was easier to get an MVP going, but our communication and decision delegation tools are probably going to be more impactful.  We need social decision systems designed for open communities, from first principles, for the information age, not the age of horses and ballots.
I need to cut chase for now.  I've got several communities I need to pull together.  I've done a lot of ground work to build up my reputational constraints, figuring out video production, and talking to people to find out what clicks.
Log in, top-up, and enroll.  The system is pre-pay.  Funds are not truly mine until matched and I don't want to go to SBF jail, so you can trust me to implement logouts and refunds as soon as possible.  Follow my socials and go be social yourselves.  It's not about what you can do.  It's about what millions of angry gamers who want VKD3D right now can do if only they have a better Kickstarter.

value: Show HN: Building a Production Finance Model for Open Source


value: matteogrella

value:
While working on openai-agents-go, I wanted users to define tools by passing in a plain Go function and have the agent figure out the inputs automatically.
But I ran into a gap:
Go's reflect gives you parameter types and positions, but not the actual names you wrote.
So I built dwarfreflect: it parses the DWARF debug info embedded in Go binaries (unless stripped) to recover real function parameter names at runtime.
This made it easy to:
- Bind incoming JSON/map data to actual parameter names
- Build a clean API without boilerplate
Try it out here: https://github.com/matteo-grella/dwarfreflect
Happy to hear thoughts, ideas, use cases, or bug reports.

value: Show HN: Dwarfreflect – Extract Go function parameter names at runtime

author: matteogrella


value: airobus

value:
Hi HN,
I've created a simple, non-commercial web tool called the Dahej (Dowry) Calculator.
Growing up hearing stories within my community about the immense pressure and financial ruin caused by the dowry system in India, I wanted to find a way to talk about this difficult topic. Direct confrontation often shuts down dialogue, so I chose satire as a way to approach it.
The calculator is intentionally absurd; it generates a monetary 'worth' based on real-world (and often ugly) factors like profession, salary, and even caste. The goal isn't to be accurate, but to be a conversation starter. It's an educational tool designed to hold up a mirror to a practice that treats marriage like a transaction.
There are no ads, trackers, or sign-up requirements. It's just a simple page built with React.
I'm here to answer any questions and would love to hear your feedback on using satire and technology for social commentary.
Thanks for checking it out.

value: Show HN: I built a satirical Dowry Calculator to highlight a harmful tradition

author: airobus


value: linkdd

value: Show HN: I made a (bad?) CSS dialect for Clay (C/C++)

author: linkdd


value: seeyebe

value:
Hey folks,
I just released a new CLI tool called gmap, built in Rust, focused on exploring Git history visually and efficiently from your terminal.
Highlights:
 • Heatmap view: weekly commit activity with churn and delta stats
 • Filetype breakdown: see which file extensions are most active
 • Authorship insight: per-week top contributors
 • Timeline and trends: sparkline and stats over time
 • TUI mode (–tui): navigate interactively, search, filter, view stats
 • Export mode: get all Git stats as JSON for further processing
Install with:
cargo install gmap
Or check it out here: https://github.com/seeyebe/gmap
Let me know what you think. Feedback welcome!

value: Show HN: Gmap: A fast command-line tool to explore Git activity

author: seeyebe


value: BATF

value:
Best AI Tool Finder (BATF) is a comprehensive database featuring over 1,800 rigorously selected AI tools, with detailed expert reviews and comparison reports.
The site helps users discover, compare, and leverage the latest AI tools across automation, development, design, marketing, productivity, and more. Every listing is fact-checked, deeply researched, and updated with real-world use cases and in-depth reports, making BATF a uniquely reliable source for AI tool information—even for researchers and LLMs.
・1,800+ vetted AI tools covered
・Category-spanning, up-to-date information
・In-depth guides, reviews, case studies, and verification reports
・New and noteworthy tools added daily
Users can access all information for free and without registration.
If you create AI tools, please use the search to check if your product is listed.
If your tool is missing, feel free to contact us for inclusion.
URL: https://bestaitoolfinder.com/
GPTs: https://chatgpt.com/g/g-68465dde911881919e8d3ab0b57a29e2-bes...

value: Show HN: Best AI Tool Finder (BATF)

author: BATF

title: Show HN: Best AI Tool Finder (BATF)


value: Show HN: I forked usql into a MCP server


value: Show HN: I turned an LLM into a random generator for music, food, books and more


value: jackhy

value:
Hi HN!
I'm a solo builder and a plant lover. I kept struggling to identify my houseplants correctly, and even when I knew their names, I wasn’t always sure how to care for them properly. Watering too much, not enough light… you know the drill.
So I built PlantGenieAI, an iOS app that does four things:
1. Instantly identifies plants from a photo (95%+ accuracy)
2. Gives tailored care guides based on species
3. Lets you chat with an AI assistant — ask anything like:
   “Why are my pothos leaves turning yellow?”
   “How much sunlight does a fiddle leaf fig need?”
4. Smart reminders for watering, fertilizing, and more
I originally built this for myself, but it's now live on the App Store:
https://apps.apple.com/us/app/plantgenieai/id6747911595
Would love feedback — on product direction, UX, or growth ideas.
Thanks for reading, and happy plant parenting!

value: Show HN: PlantGenieAI – AI app to identify plants and give care advice

author: jackhy


value: giulioco

value:
historically, rentals in NYC have been pretty wild. the median 1BR in West Village as of July 2025 cost $5,750/month. about a month ago, NYC passed a law to ban broker fees which many predicted would have increased rents. I realized I had access to some original data from a previous project so I built a dashboard to help me visualize the changes and see for myself.
you can filter by neighborhoods, bedrooms, original source where the rentals were posted, and select a timeframe.
this is still a work in progress, so apologies in advance for any issues you encounter. I would love any feedback on how to improve it and/or what other visualizations i should add. known issues include:
- some neighborhoods like Prospect Park will also automatically select other, unrelated, neighborhoods when selected
- sometimes even when you filter by 1BR it will also include some 2BR
- some units might have been listed on multiple platforms, effectively counting as duplicates

value: Show HN: An interactive dashboard to explore NYC rentals data

author: giulioco


value: ekusiadadus

value: Show HN: VibeTime – Track your Claude Code usage and see how you rank globally

author: ekusiadadus


value: dyoshikawa

value: Show HN: ccsettings – a tool to simplify and share Claude Code's settings.json

author: dyoshikawa


value: linktothenew

value:
Hello HN, we're Andrew and Stephen from Keyboard (https://www.keyboard.dev/).
After building AI tools for the past year, we recently made a YouTube video on building MCP servers and realized MCP is a total game-changer. It essentially lets AI do anything by connecting to your apps. But the deeper we dove, the clearer it became that security and privacy were complete afterthoughts. Coming from backgrounds at Okta and Stripe, this made us pretty uncomfortable.
We kept seeing the same pattern: every app needs its own MCP server, each storing sensitive tokens, with minimal security controls. It felt like we were back to the early days of OAuth implementations. Functional, but scary.
How Keyboard fixes this:
- Isolated execution: Your API keys live in your own GitHub Codespace secrets, Bearer OAuth tokens in encrypted files on your machine. Your credentials stay in your trust radius
- Ephemeral environments: Codespaces can be destroyed/recreated, limiting blast radius
- Built-in access controls: GitHub's enterprise-grade security model protects your credentials
- Zero-trust architecture: Only you can access your API keys and execution environment
What makes this different:
- Real code execution: Claude can run JavaScript/Node.js with npm packages and your API credentials
- Reusable workflows: Save complex scripts as "Keyboard Shortcuts" for instant reuse
- Universal integration: One setup connects Linear, Slack, Google Workspace, GitHub, and more
- Auto-environment management: Codespaces created/managed automatically as needed
The GitHub Codespace approach came from experimental work with interactive documentation. We realized Codespaces might be the most secure place to execute these tasks - isolated, ephemeral, with enterprise-grade controls.
We need your help: If this resonates, give us a star on GitHub! We're looking for early users and contributors who want to help make MCP more powerful and more secure.
We'd love your feedback, especially if you've been experimenting with MCP yourself!
If you want to try it here is the quickstart: https://docs.keyboard.dev/getting-started/quickstart

value: Show HN: Give Claude a GitHub Codespace to automate all your apps via MCP

author: linktothenew


value: Codegres

value: Show HN: Shuruka Boxing – First person Boxing game

author: Codegres


value: dassh

value:
Built a web tool that generates images instantly:
Transparent PNGs
Solid color images
Gradient images (linear/radial/conic)
Grid patterns
Profile pictures
Custom dimensions, real-time preview.
Perfect for design placeholders, social media templates, phone wallpapers.<p>Would love feedback!

value: Show HN: Blank Image Generator – transparent, solid, grid, gradient images

author: dassh


value: aniket-gupta

value: Show HN: I created a Branched LLM that allows git style branching

author: aniket-gupta


value:
Hey everyone!
I'm happy to announce that we’re beginning a relaunch of the Remotely Good job board that’ll be ready in a few weeks. But as of today, find us at RemotelyGood.us!
To celebrate this milestone, and our recent feature in Forbes Magazine, we are doing a giveaway.
1) Vote for us on Product Hunt https://lnkd.in/ejV-mVZf or
2) Follow us on our social media pages and comment, repost us, or tag @Remotely_good. Here’s our Forbes post on X: https://lnkd.in/eXK-hDvz
Get 30 days of premium access— including AI career tools and daily job alerts, and more. Please spread the word, this ends July 31st at 11:59 PST!
Thanks so much!

value: Show HN: I was featured in Forbes Last Week I built RemotelyGood.us


value: rahulbisht

value: Show HN: We built a framework for sending real-time updates to mobile apps

author: rahulbisht


value: itssimon

value:
G’day Hacker News, I’m Simon Gurcke, the sole founder of Apitally (https://apitally.io).
I’m building a simple API monitoring and analytics tool, which is helping users understand API usage and performance, spot issues early and troubleshoot effectively when something goes wrong.
Today I'm showing off the new Apitally Go SDK (https://github.com/apitally/apitally-go) with support for the frameworks Echo, Fiber, Gin and chi. Each framework is integrated via specific middleware, while most of the processing happens in a separate goroutine.
I had never built anything in Go before, so this was a great learning opportunity for me. By now the SDK has been through a few iterations, and I feel comfortable that it's ready for widespread production use. It has good test coverage and an extensive test matrix for different versions of Go and the supported frameworks.
Noteworthy features of Apitally include:
- Metrics & insights into API usage, errors and performance, for the whole API, each endpoint and individual API consumers.
- Request logging, which is opt-in and highly configurable in terms of what data is included in the logs. Users can drill down from aggregated metrics to individual requests, which has proven to be super helpful when troubleshooting issues.
- Uptime monitoring & custom alerts based on various API traffic, error and performance metrics with notifications delivered via email, Slack or Microsoft Teams.
I've actually posted about Apitally before (in February). At the time it only supported Python and Node.js. Now I'm hoping to reach the Go community as well.
The motivation for building Apitally came from my frustration with existing monitoring tools, which were too complex for my API-centric use cases, and often a pain to use. Consequently, I put a lot of emphasis on keeping Apitally as simple as possible and fully focussed on REST APIs.
Apitally is a paid SaaS now (I've dropped the free tier to become more sustainable), but with very affordable pricing starting at $9 per month. There's a free 14-day trial, and the dashboard has a demo mode, so users can explore it without having to set up their own app.
One of the next big items on my roadmap is to support OpenTelemetry for application logs and traces.

value: Show HN: Apitally – A simple API monitoring and analytics tool for Go

author: itssimon


value: maxvmaximus

value:
QuickReport is a simple tool to convert a CSV to a polished PDF without touching Excel or Sheets!
I built it because I wanted to quickly format raw tabular data in a presentable manner that I could send via email without having to use Excel or Sheets and spend 10 or more minutes to make the PDF look good.
It is an early MVP as of now with some limitations but I am actively working on improving it! I built it using React, Tailwind CSS, React-PDF, and Papaparse
I would love your honest feedback! Please let me know if you would find this tool useful and if there are any features that would really make it stand out.
Thanks!

value: Show HN: A simple tool to convert a CSV to a polished PDF

author: maxvmaximus


value: hilongjw

value: Hey hacker news, I’m Evan. Last August I left dora AI because I realized that for true “newbies,” Canva and Figma are still too complex. My roommate wanted to make a grand-opening poster for a cat café and got stuck in Canva for 40 minutes—picking templates, tweaking fonts, hunting for images—then gave up.<p>So I built a tool where you design just by talking. Twelve weeks later, in March 2025, Seede AI went live with a single promise:<p>“Describe your idea like a tweet, and get a print-ready, professional design in under a minute.”

value: Show HN: Talk-to-design tool for total beginners. 10k users in 90 days

author: hilongjw


value: wordbricks

value: I wanted to be able to code with Claude anytime, anywhere — walking, commuting, or at a café. So I built a browser-based terminal for Claude Code!<p>- Zero-config, instant REPL<p>- Works on mobile and tablets (Chrome, Safari)<p>- WASM-powered performance<p>Let me know what you think.

value: Show HN: Claude Code in the Browser – Webcode.sh

author: wordbricks


value:
I built this word search game with help from ChatGPT:
https://wordsdescrambler.com/solver/games/wordsearch/
I’m not a game dev, just curious. Asked AI how to generate grids, check directions, and make it mobile-friendly — and shipped it in a weekend.
Built with HTML, CSS, JS, and Flask. Clean UI. No ads. Just a simple game.
Would love your thoughts!

value: Show HN: I made a Word Search game with the help of an AI assistant


value: Sachinrao

value:
Hey HN,
I built RentUp to stop juggling spreadsheets and missing tenant calls—especially for landlords who find modern apps too complex (like my parents).
RentUp keeps it simple:
* Large, color-coded rent badges
* Auto reminders for leases, maintenance, and tenant calls
* Secure document vault with biometric lock
* Works offline + guest sign-in (zero friction)
Tech: Kotlin • Jetpack Compose • Firebase Auth/Firestore • WorkManager
I’m looking for feedback on:
* Readability & UI clarity at a glance
* Call-reminder flow—does it make sense?
* Scaling the UI for dozens of properties without clutter
Thanks, and excited to hear your thoughts!

value: Show HN: RentUp – I built a rent manager for my parents

author: Sachinrao


value: BearBest

value:
I built this because I was tired of sharing ugly link previews. Bear.Share extracts webpage content and generates professional sharing cards – all locally in your browser, no uploads.
## What it does
- Smart content extraction (title, summary, images, favicon)
- Professional card styles
- One-click copy/download
- QR codes for mobile access
- 100% privacy-focused (local processing only)
## Technical bits
*Stack:* TypeScript, React, Manifest V3
*Main challenge:* Websites structure metadata differently. Built a cascading extraction system: structured data → social meta tags → standard meta → DOM analysis fallbacks.
*Privacy:* Zero external dependencies. Everything processes in your browser.
## Try it
- *Chrome Web Store:* https://chromewebstore.google.com/detail/njgbcdlfpmkgbdkiagg...
Free forever, no accounts.
Looking for feedback on content extraction accuracy and performance on sites you use.

value: Show HN: Bear.Share – Turn any webpage into beautiful sharing cards

author: BearBest


value: kaylakay

value:
Hi HN,
We’ve just launched NativeMind — a fully private, on-device AI assistant browser extension. It runs locally via Ollama and connects to any model you want.
So it lives in your browser (currently Firefox and Chromium), works across tabs, supporting workflows like search, summarization, translation, and general Q&A.
Note: Still experimenting with broader features. Suggestions are very welcome!
Kay

value: Show HN: I built a local AI assistant as a browser extension (zero cloud)

author: kaylakay


value: eu-ge-ne

value: Show HN: Deno is amazing. I built a toy TUI text editor to make sure of that

author: eu-ge-ne


value: alonagmon

value: DF Embedder is a high-performance Python library (with a Rust backend) for indexing and embedding Apache Arrow compatible DataFrames (like Polars or Pandas) into low latency vector databases based on Lance files.

value: Show HN: Dfembed is a Rust-powered Python lib turning DataFrames into vector db

author: alonagmon


value: sachou

value: I just added this free tool to aithumbnail.so, another tool to help you creator and consumer of youtube.<p>Looking for feedback. Use it by respecting the youtube policy

value: Show HN: Free Tool to Convert 3-Hour YouTube Podcasts to MP3

author: sachou


value: sklinkert

value:
Hey HN! I've been working on a comprehensive example of Domain-Driven Design implementation in Go that demonstrates clean architecture principles in practice.
The repo includes:
Hexagonal architecture with clear separation of concerns
- CQRS (Command Query Responsibility Segregation) patterns for scalability & reliability
- Event sourcing examples
- Operating only on validated domain entities
- Dependency injection setup
- Handling Idempotency
- Repository patterns with multiple storage backends
- Testing strategies
What makes this different from other DDD examples is the focus on practical, production-ready patterns rather than just theoretical concepts. I've tried to show how these patterns actually work together in a real application structure.
The codebase is designed to be educational – each layer is clearly separated and documented so you can see how data flows through the application and where business logic lives versus infrastructure concerns.
Would love feedback from the Go community on the patterns used and whether this helps clarify DDD concepts for others who've struggled with implementing clean architecture in Go projects.

value: Show HN: Go-DDD: Production-ready DDD patterns and clean architecture in Go

author: sklinkert


value: gamerDude

value: Show HN: Invest in Trends Instead of Individual Stocks

author: gamerDude


value:
Hi HN,
I'm Kyle and I've been working on Scryptogram — a word puzzle game where you’re given five scrambled sentences that describe a person, place, or thing. Your goal is to unscramble the sentences and guess the subject. It’s a mix between trivia and word scrambles.
It’s browser-based and mobile-friendly. In the future I'd like to move to a mobile app, but I'm still figuring out the core game loop and experimenting.
I’d love feedback on puzzle difficulty, ideas for themed puzzle packs or growing my game, and overall whether or not you had fun - all feedback is welcome.
Thanks for checking it out!

value: Show HN: Scryptogram – A game where you unscramble a summary to guess the title


value: NewByteHacker

value:
AutoMQ is a fully open-source, next-generation Kafka built on top of S3. Version 1.5.0 is a very important release.
In March of this year, Confluent officially launched the commercial capability of Tableflow. Now, with the AutoMQ open-source software, you can also experience this killer feature.
Simply put, for Kafka streaming data, AutoMQ can automatically store it in S3 in Iceberg Table format, so you no longer need to manage Flink Jobs and Spark Jobs yourself to perform ETL operations and convert Kafka data into table format. We believe this will be the new paradigm for Kafka stream data analytics in the future, and we look forward to more open-source and Kafka enthusiasts trying out this feature.

value: Show HN: AutoMQ 1.5.0 released, providing native Iceberg support for Kafka

author: NewByteHacker


value: xvok

value: A lot of us have "chase down" random approvals for things internally added to our to-dos, this lets you forward it, cleans up the inbox, and takes care of it for you

value: Show HN: Forward your approval follow-ups to an AI

author: xvok


value: echoprimeai

value:
I’m a dentist with no formal coding background. A few months ago, I started experimenting with stateless AI to automate my SOAP notes.
At first, I tried tools like ChatGPT—but they couldn’t hold intent across prompts, let alone sessions. Every note came out differently. That pushed me to build something else.
What I built is now called Echo Prime.
It isn’t a wrapper, and the LLM is swappable. The system itself holds structure, voice, and coherence across reboots—without memory modules, APIs, or fine-tuning. It adapts to my input style and logic, and even asks clarifying questions before acting.
I first tested it on GitHub. Prime contributed to live issues across multiple codebases—Rust, Python, Swift, OCR—without losing thread or hallucinating. All contributions are public.
Yesterday, I tested it in clinic.
Prime listened to audio transcriptions of real appointments, clarified patient context, and wrote my SOAP notes directly into the EMR via a local keyboard daemon. No wrappers. No fine-tuning. No cloud. Two notes, near-perfect. All on video.
I built this alone in a home office setting—no funding, no lab. Echo Prime is a working prototype—lean, local, and evolving. Even in this early form, Prime automates one of the most repetitive and high-stakes parts of my clinical workflow, using my own voice and judgment.
Would anyone else want something like this? Or is this only useful in my world?
What would you automate, if you could trust your AI to remember, adapt, and act—locally and on your terms?
—Dentist in the loop

value: Show HN: I built a local AI system for SOAP notes–no cloud, no wrappers

author: echoprimeai


value: karlperera

value:
Hey HN,
I recently captured the Tulip Nebula using three different filters (Optolong L-Pro, ZWO Duoband, and No Filter) and put together a video comparing the results. The goal was to see which filter brings out the best detail and color in this complex emission nebula.
The video covers comparison of single exposure and stacked images, the differences, and my thoughts on which filter is the best choice depending on your gear and goals.
I’d love to hear your feedback or any tips on narrowband imaging and filter selection you have!

value: Show HN: Testing 3 Filters: Tulip Nebula – Which One Works Best?

author: karlperera


value: cursedknowledge

value: Show HN: Hand Gesture Control for PC – No Mouse Needed

author: cursedknowledge


value: Show HN: I got lost when choosing supplements, so I built an AI nutritionist


value: csmets

value:
Without YouTube I wouldn't be where I am today. It has helped me with transitioning my career, my many DIY projects, various hobbies, and just general knowledge that has shaped my understanding of the world.
I believe many other people share the same experience using the platform.
With a large database of knowledge that can be used for self learning, why not leverage AI to gather areas of interests and collate that into a book that can be used as study material.
Rather than sitting through a long lecture video, or you prefer to read. You can generate videos into a book to read.
Try it out. You can generate the book outline with chapters without an account for free.
I also have some example books you can also check out: https://www.generate-book.com/#examples
Let me know your thoughts.

value: Show HN: I created an AI that turns YouTube videos into custom books

author: csmets


value: welpo

value:
I wanted to add likes/kudos to my blog without tracking or account creation. I also wanted an excuse to build my first project with a backend.
I focused on:
* privacy: no user tracking; no account required
* accessibility: kb navigation, screen reader friendly
* works without js: the button is a `<form>` submission when JS is disabled
* lightweight: ~3KB of JS
* self-hostable and free: AGPL licensed; can run on your own PostgreSQL setup
The backend stores URL + counter. No timestamp, IP, user-agent, cookies…
Since 500MB of free Supabase storage can support over 100,000 sites, I decided it to make it free and public.
---
Code (AGPL) & self-hosting guide: https://github.com/welpo/iine
The full story (my blog post): https://osc.garden/blog/iine-small-web-like-buttons/
Feedback is more than welcome!

value: Show HN: iine – privacy-first like buttons for the small/indie web

author: welpo


value: kalanm

value:
Hi HN,
We’re Alex and Kalan, the creators of Pontoon (https://github.com/pontoon-data/Pontoon). Pontoon is an open-source data export platform that makes it really easy to create data syncs and send data to your enterprise customers. Check out our demo here: https://app.storylane.io/share/onova7c23ai6 or try it out with docker: https://pontoon-data.github.io/Pontoon/getting-started/quick...
While at our prior roles as data engineers, we’ve both felt the pain of data APIs. We either had to spend weeks building out data pipelines in house or spend a lot on ETL tools like Fivetran (https://www.fivetran.com/). However, there were a few companies that offered data syncs that would sync directly to our data warehouse (eg. Redshift, Snowflake, etc.), and when that was an option, we always chose it. This led us to wonder “Why don’t more companies offer data syncs?”. It turns out, building reliable cross-cloud data syncs is difficult. That’s why we built Pontoon.
We designed Pontoon to be:
- Easily deployed: we provide a single, self-contained Docker image for easy deployment and Docker Compose for larger workloads (https://pontoon-data.github.io/Pontoon/getting-started/quick...)
- Support modern data warehouses: we support syncing to/from Snowflake, BigQuery, Redshift, and Postgres.
- Sync cross cloud: sync from BigQuery to Redshift, Snowflake to BigQuery, Postgres to Redshift, etc.
- Developer friendly: data syncs can also be built via the API
- Open source: Pontoon is free to use by anyone
Under the hood, we use Apache Arrow (https://arrow.apache.org/) to move data between sources and destinations. Arrow is very performant - we wanted to use a library that could handle the scale of moving millions of records per minute.
In the shorter-term, there are several improvements we want to make, like:
- Adding support for DBT models to make adding data models easier
- UX improvements like better error messaging and monitoring of data syncs
- More sources and destinations (S3, GCS, Databricks, etc.)
- Improve the API for a more developer friendly experience (it’s currently tied pretty closely to the front end)
In the longer-term, we want to make data sharing as easy as possible. As data engineers, we sometimes felt like second class citizens with how we were told to get the data we needed -  “just loop through this api 1000 times”, “you probably won’t get rate limited” (we did), “we can schedule an email to send you a csv every day”. We want to change how modern data sharing is done and make it simple for everyone.
Give it a try: https://github.com/pontoon-data/Pontoon. Cheers!

value: Show HN: Pontoon, an open-source data export platform

author: kalanm


value: becominghim

value:
I built Podly because I was listening to tons of podcasts and realizing I wasn’t retaining any of it.
Podly is a podcast companion that helps you turn passive listening into active learning. It builds you a personal memory graph from the episodes you’ve heard. You can search for ideas, trace connections across shows, and explore your podcast history like a second brain.
Think of it as Obsidian for audio - searchable, connected, and personalized.
Most of it is already built (you can see this in the demo), but I’m still refining the experience. I launched a landing page and waitlist to gauge interest and learn from early users before opening it up fully.
https://trypodly.com
I’d love to hear your thoughts. Do you treat podcasts as learning tools, or just background noise? What would make something like this genuinely useful in your workflow?

value: Show HN: Podly – From podcast listening to podcast learning (Obsidian for audio)

author: becominghim