value:
I built a tool that presents custom trolley problems to GPT, Claude, Gemini, Llama, Grok, and DeepSeek simultaneously and streams their ethical reasoning in real-time.
Some interesting findings:
 - The models are surprisingly split on classic dilemmas.
 - Models will protect their creator (Sam Altman vs Dario Amodei)
 - Criminals are not valued equally even when compared to itself
Try it: https://aitrolleyproblem.com

value: Show HN: AI Trolley Problem Arena

title: Show HN: AI Trolley Problem Arena


value: Paralus

value:
When your Python code breaks, you get a traceback. You can see where it failed. You almost never know why it failed — what was actually in memory at that moment.
I built F. Incantatem to fix this. Add one decorator, and when your code crashes, an LLM captures the full context — stack trace, source code, actual variable values — and explains what went wrong and how to fix it.
It integrates as a decorator, CLI tool, or IPython extension. Works with OpenAI, OpenRouter, or locally via Ollama. Cautious mode automatically redacts secrets and PII before transmission. Zero core dependencies.
I'm sharing it because I keep thinking other people must be debugging these same impossible bugs. Happy to answer questions or take feedback.
Also worth noting that there's some more work to be done (see roadmap).
Repo: https://github.com/aguilar-ai/fincantatem
PyPi: https://pypi.org/project/fincantatem/

value: Show HN: F. Incantatem – CLI, Decorator & notebook ext. for traceback analysis

author: Paralus


value: NetViper

value: Show HN: Skouriasmeno Papaki – S3 transfer tool, up to 12x faster than AWS-CLI

author: NetViper


value: Show HN: TopBanner – Convert Site Visitors to Customers Without Annoying Popups


value: ovaistariq

value: Show HN: Open-source MCP OIDC provider

author: ovaistariq

title: Show HN: Open-source MCP OIDC provider


value: stavros

value: I had two old USB gamepads lying around that I wanted to use, but my computer is too far from the couch, so I wrote a little ESP32-S3 firmware that turns USB gamepads into Bluetooth ones.<p>You connect your gamepad to the ESP32-S3 with an OTG cable, power the ESP32 with either 5V somehow or a powered USB hub, and now you can pair the controller via Bluetooth to your PC!

value: Show HN: I wrote a small lib to turn a USB gamepad into a Bluetooth one

author: stavros


value: DanielGarza

value: Show HN: Python packages for FastAPI apps – auth, logging, config, LLM, more

author: DanielGarza


value: englishcat

value:
Hi HN,
Just wanted to share a tool i've been working on to help with my own study routine. It’s a browser extension called Lingoku.
The idea is simple: we spend hours browsing the web in English every day. This tool replaces some of the english words with Japanese vocabulary based on your japanese level (Similar to Toucan, but with a better user experience).
It’s basically an attempt to make the "i+1" method actually passive, you understand the sentence because it's mostly english, but you pick up Japanese words naturally from the context. It uses an LLM in the backend to make sure the translations fit the context (so it distinguishes between different meanings of the same word).
since it uses paid AI APIs for the words replacement, I couldn't make it 100% free (server costs are real, unfortunately). However, there is a "forever free" plan with daily credits that doesn't require a credit card. it should be enough for casual daily browsing.
I built this because I struggle with Anki burnout and wanted a way to review words without feeling like i am "studying"
It supports Chrome, Edge, and Firefox now. would love any feedback or feature requests!
https://lingoku.ai/learn-japanese

value: Show HN: Learn Japanese contextually while browsing

author: englishcat


value:
Hey HN,
I built a web tool that wraps the latest AI image generation models with a conversational interface.
The main idea is you can iterate on images through natural dialogue instead of rewriting prompts from scratch each time.
Key features I focused on:
- Multi-turn editing (the AI remembers previous context)
- Character/style consistency across multiple images
- Multi-image fusion
Not affiliated with OpenAI – the name is just a playful nod. Would love feedback from this community, especially on the UX and what features would be useful for technical users.
Try it: https://gptimage15.app

value: Show HN: GPT Image 1.5 – An AI image editor with conversational editing


value: robinoross

value: Stream anything to clients, stream data back from the client with ultra low memory usage with ovr’s new Multipart parser.

value: Show HN: OVR – The Ultimate Streaming Framework

author: robinoross


value: wastu

value: Show HN: Parsley - Open-source AI parser for PDFs and images to JSON/CSV

author: wastu


value: ovelv

value:
Turn Articles into Video & Podcast Scripts in Seconds.
Transform any long-form content into structured script cards for videos and podcasts. Perfect for YouTubers, podcasters, and content creators who need professional talking points.

value: Show HN: Video Cards-Turn Articles into Video and Podcast Scripts in Seconds

author: ovelv


value: wadudu

value: Built this as a small experiment. Would love feedback on the UX and what’s confusing / missing.

value: Show HN: GPTImage15.online – a tiny prompt-to-image playground

author: wadudu


value: TidyLife is not a daily checklist. It’s a system for remembering what usually slips through the cracks.<p>Key features:<p>- Organize tasks into clear sections (home, personal care, maintenance, admin, more)<p>- Set flexible repeat cycles (days, weeks, months, years)<p>- Track when tasks were last completed<p>- Simple, distraction-free interface<p>- Built for long-term habits, not daily to-do noise

value: Show HN: I kept forgetting the same adulting tasks, so I built an app to help


value:
OP here. I built Deadlight because I wanted a blogging platform that actually works on high-latency connections (Satellite, 2G, Mesh).
Most modern platforms require megabytes of JS hydration just to show text. Deadlight sends raw semantic HTML from Cloudflare's edge using D1 (SQLite). It’s readable in `lynx` or on a 300-baud terminal.
*The Stack:*
 - Cloudflare Workers (Compute)
 - D1 (Storage)
 - Zero client-side JS (for readers)
*The Lock-in Question:*
Building on proprietary edge infra is a risk. I included an "Eject" script that dumps the remote D1 DB to a local SQLite file and generates a docker-compose file, so you can self-host/migrate off Cloudflare instantly if needed.
I just released a CLI launcher (`npx create-deadlight-blog`) if you want to spin up an instance to test the latency.
Happy to answer questions about the architecture or the LoRa integration I'm working on.

value: Show HN: Deadlight · Zero-JS blogging for hostile networks (Workers and SQLite)


value: guaguama

value: Show HN: LifeContext–Build context with your life

author: guaguama


value: damidare

value:
I built Kumbuka because Notion's meeting recording feature requires an Enterprise subscription and as a broke solo funder I don't want to pay for one more subscription. I wanted the same workflow—record, transcribe, generate notes, save to Notion—without the enterprise price tag.
Everything runs locally on your Mac. Whisper handles transcription, Claude Code generates the meeting notes (summary, decisions, action items, speaker-attributed transcript), and optionally exports to Notion via MCP.
It also monitors your macOS Calendar and prompts you when meetings are about to start, so you don't forget to hit record.
Tech: Python, local Whisper server (VoiceMode), Claude Code CLI, Notion MCP.
macOS only. Would love feedback on the workflow and what features would be most useful.

value: Show HN: macOS local meeting recorder with Whisper transcription/ Claude notes

author: damidare


value: ahthapa

value:
Hi HN,
I built this because I thought it was weird that we treat vector DBs like mutable buckets instead of versioned knowledge bases. RAG apps break easily when embeddings drift, and I wanted `git checkout` for my vectors.
Clamp lets you commit and rollback your vector DB state without copying data. It works by managing metadata flags on points, so "rollbacks" are just instant filter updates, even for large collections.
Basic flow:
$ clamp init
$ clamp commit docs.txt my_kb "v1 - initial"
$ clamp checkout my_kb HEAD~1  # Instant revert
It's a CLI/Python wrapper around Qdrant right now. I'm a student at UW and built this to learn more about vector storage internals.
Code: https://github.com/athaapa/clamp
PyPI: https://pypi.org/project/clamp-rag/
Happy to answer questions about the implementation!

value: Show HN: Clamp – Git-like version control for RAG vector databases (CLI/Python)

author: ahthapa


value: VibeGuy

value:
After 319 commits, scrappy is finally ready to be called v1.
https://pypi.org/project/scrappy-ai/
pip install scrappy-ai
The app orchestrates free tier LLM providers to create a free code assistant. Current models for code agent:

    # 1. Cerebras Qwen 235B - instruction-tuned, massive model

    # 2. Groq Llama 4 - fast, good tool use

    # 3. Groq Kimi K2 - fast, 128k context

    # 4. Gemini - fallback (JSON issues but huge context)

Features include: semantic code index with lancedb/fastembed, conversation history, custom task routing, streaming chat.
Get free API keys from Cerebras, Groq, and Gemini and give it a shot! Use it and contribute!

value: Show HN: Scrappy Free AI Code Assistant

author: VibeGuy

title: Show HN: Scrappy Free AI Code Assistant


value: vulcanidic

value:
Hello HN,
I'm a long-time iOS developer (since the iPhone 3GS era). Recently, I solo-built and launched a global dating app using Flutter, Supabase, and Next.js – all in about 100 days, heavily relying on Cursor's Pro plan ($20/mo).
While AI tools like Cursor make rapid development possible, I wanted to share some practical realities from the engineering side and the economics of building with them.
1. Market Validation Experiment (Why a dating app?)
Before coding, I tested the market by creating a female profile on an existing major dating app, using lightly AI-edited photos.
- Day 1: 200+ likes, and it continued strongly for a week.
- Rough math: If each like represents ~$1 in potential revenue for the platform, that's over $1,000 in a week from a single profile photo.
- Reality: This convinced me the market was huge, but launching my own app showed the challenge – great functionality isn't enough without network effects and brand. It's a quiet launch so far.
2. Cursor's Economics (The changing value)
In the first few months, the Pro plan delivered tremendous value. Thanks to unlimited Auto Mode, my actual usage was worth over $1,000/month – while the bill was only $20/month.
It felt like working with a team of 10+ developers. Tasks that would normally take me a week were often completed in a minute.
It was exhilarating, but over time it became exhausting. As a human, keeping up with AI's pace drained me mentally.
Starting around month 4, I started hitting usage limits much faster (e.g., exhausting monthly compute credits in days on heavier tasks). Cursor has shifted to a compute-based usage pool (equivalent to ~$20 of API credits per month), with overages if you go beyond. It's a reminder that these tools' "unlimited" phases can evolve as models get more expensive.
The analogy to a "drug dealer" model fits in the sense that early generous access builds dependency, then costs adjust – fair for sustainability, but something to budget for.
3. Engineering Challenges (Where AI helps – and where it doesn't)
AI handled a lot of boilerplate, but integration details required manual work.
- SSO (Google Login): Always tricky; issues like browsers not closing or missing callbacks. Supabase Auth made it manageable compared to past experiences – sticking closely to official docs was key.
- Notifications: Firebase/Google Cloud consoles are still confusing. Choosing OneSignal for the backend simplified things a lot and was a solid decision.
- IAP: The 30% cut (15% for smaller devs) is steep with no real alternatives yet. Planning local payment gateways later.
- Translation: I implemented on-demand translation (button-press like LinkedIn). Then saw some competitors (e.g., Chinese apps) with seamless real-time translation across full content – impressive tech gap, and a reminder there are always advanced implementations out there.
4. Conclusion
AI tools enabled a solo 100-day build, which felt impossible before. But building the product is one thing; getting users and traction is another – marketing/brand is the real barrier, like building a great hotel on a deserted island.
Curious about others' experiences with Cursor's evolving limits or solo-launching consumer apps.
Links:
App Store: https://apps.apple.com/us/app/weconnect-cultural-exchange/id...
Google Play: https://play.google.com/store/apps/details?id=com.abus.wecon...
Web: https://www.wctokyoseoul.com

value: Show HN: 200+ Likes/day as fake profile → built my own dating app in 100 days

author: vulcanidic


value: markrai

value:
obsidenc uses XChaCha20-Poly1305 with per-chunk authentication tags, so tampering is detected immediately. The Argon2id key derivation adapts to available memory (512 MiB to 2 GiB), adjusting iterations to maintain security on constrained systems per RFC 9106.
Sensitive data is locked in RAM (mlock/VirtualLock) to prevent swap exposure, and the zeroize crate clears secrets when done. Decryption verifies authentication before exposing plaintext and streams data to minimize memory exposure.
The parser strictly validates container format, uses constant-time comparisons, and rejects malformed inputs. Zip-bomb protection caps file counts at one million and path lengths at 4KB.
Decryption uses staging directories with atomic rename on success, preventing partial extractions. Automatic cleanup runs on failure.
You can combine a password with an optional keyfile (up to 4 MiB). On Unix, the tool checks that keyfiles aren't world-readable. Keys are derived via HKDF with distinct domain strings, preventing reuse between encryption and nonce generation.
The design prioritizes security: no compression to avoid side-channels, symlink rejection, permission sanitization on extract, and a 20-character minimum password.

value: Show HN: Obsidenc – a Rust-based paranoid-grade encryption utility

author: markrai


value: jharohit

value:
Github repo: https://github.com/jharohit/better-gmail-tabs
Given how we use widescreen aspect ratios today, a tab interface has been significantly more faster to separate out emails quickly!
~~~~~
Key Features:
- Custom Tabs: Create tabs for any Gmail label or search query.
- Seamless Integration: Tabs appear directly in the Gmail interface for quick access.
- Customization: Rename tabs and change their text/background colors to organize your workflow visually.
- Automatic Coloring: New tabs are automatically assigned unique, complementary color combinations (random background with high-contrast text) so you don't have to manually pick colors every time.
- Context Menu: Right-click any tab to rename, recolor, or delete it.
Admin Panel: Click the settings gear icon to manage all your tabs in one place.
- Smart Navigation: Automatically detects if you are viewing a label or a search result when creating a tab.
- Export & Import: Backup your tab configurations or share them across devices using JSON files.
~~~~~~~~~
I was using a similar one called Gmail Tabs by CloudHQ but they charge now $2 per month (How dare they! But honestly shoutout to them for doing it first \m/).
So like all geeks I used Gemini 3.0 AI Vibe Code to redo it in ~4 hours (initial build) and launched on Google Chrome Store! Also added tons of features like local backup/restore with json, automated tab colors, ultrafast response due to localized css and no remote load, etc - things I wished the CloudHQ guys had!
If you get a lot of emails like me, please try it and let me know how it is. OR any features you want - project is open source so you can add yourself! I will try to merge back once every two weeks and launch on webstore.
OR let me know if you need anything on https://x.com/@jharohit
P.S. I got excited to do this also because I did another extension last year to just click "save" in Google AIStudio (when they didn't automatically save AIStudio chats!!). 4.5 Star Rating, 2.3k installs, 8k page views, 1.7k Weekly Active Users (as of Dec 2025). They have since fixed this but somehow I still have active users..

value: Show HN: Better Gmail Tabs – turn search queries into tabs for fast email load

author: jharohit


value: Codegres

value: Show HN: Website to APK Convertor

author: Codegres

title: Show HN: Website to APK Convertor


value: girlwhocode

value: Show HN: Made a Visionboard Tool

author: girlwhocode

title: Show HN: Made a Visionboard Tool


value: ekusiadadus

value: Show HN: I taught Claude Code to draw diagrams – the XML pitfalls were brutal

author: ekusiadadus


value: moimaere

value:
Hello HN,
I’m the creator of Post2X. I recently left a 7-year career in Data Science and started to build my own tool.
The problem I faced was tool fragmentation. To do content marketing on social media, I was context-switching between LLMs for copy, meme databases for visuals, and Buffer for scheduling. It was inefficient.
I built Post2X to unify this entire stack into one flow. My internal benchmark is to create and schedule 100+ posts in under 1 hour, allowing me to batch my content marketing work and get back to coding.
How the workflow achieves that speed:
* Unified Drafting (Voice & Text): You provide the input/topics, and it generates drafts using a "Voice Mimicry" layer so the output sounds like you or the username you provided, not a generic bot.
* Integrated Visuals: No need to switch tabs to find images. You can generate images via prompt directly in the editor, or pull from trending memes to add context to your posts.
* Heuristic Scoring: Instead of guessing if a post is good, the tool grades drafts before you publish. Note: This isn't just historical regression; it scores text based on specific writing heuristics (Hook strength, Clarity, Replyability, Tension, and more).
* One-Tap Queueing: I removed the friction of manual scheduling dates. You set your "slots" once, and then push content to the queue (for both X and LinkedIn) with a single click.
Tech Stack: TypeScript, Tailwind CSS, Python, Supabase, OpenAI API, Gemini API, X API
It is free to try. (No credit-card required)
I’d love feedback on the ecosystem flow. Does the overall experience feel smooth and fast, or does anything slow you down or feel cluttered?

value: Show HN: Post2X – create, score, and schedule X/LinkedIn posts in one workflow

author: moimaere


value:
I built 0tH (Zero the Hero) to analyze Mach-O binaries on macOS. It's written in Rust and works as both CLI and interactive REPL.
Main features:
- Parse FAT/universal binaries and individual slices
- Full code signature analysis (8 commands: entitlements, certificates, CDHash, notarization, etc.)
- Tree visualization of segments/sections
- Hexdump (absolute, relative, load command)
- String extraction with grep filtering
- JSON export
Currently supports 24 load commands including the newer LC_DYLD_CHAINED_FIXUPS and LC_DYLD_EXPORTS_TRIE.
I'm building this as the main tool for my reverse engineering course (bytearchitect.io) – eating my own dog food.
Universal binary (ARM64 + x86_64), signed and notarized.
https://zero-the-hero.run
Feedback welcome. What analysis features would you find useful?

value: Show HN: 0tH – Mach-O binary analysis tool for macOS (Rust)


value:
Hi HN,
We’ve been working with Seedance 1.5 Pro, a next-generation AI video model focused on native audio-visual generation—where video, speech, and lip-sync are created together, not stitched together afterward.
Unlike traditional pipelines that generate video first and add audio later, Seedance 1.5 Pro produces fully synchronized visuals and sound in a single pass, enabling more natural dialogue, emotion, and storytelling.
Key capabilities:
Native Audio-Visual Generation: Video, voice, lip movement, and sound effects are generated jointly for tighter sync and realism.
Text / Image → Video with Audio: Generate complete talking or narrative videos from text prompts or image references.
High-Fidelity Motion & Camera Language: Stable motion, coherent framing, and cinematic camera movements.
Multi-Language Lip-Sync: Supports multiple languages with accurate mouth movement and timing.
Instruction-Following & Narrative Control: Designed for story-driven and dialogue-heavy video content.
Why this matters:
Most AI video tools still treat audio as an afterthought. Seedance 1.5 Pro explores what happens when audio and video are modeled together from the start, which significantly improves realism for dialogue, narration, and character-driven scenes.
We’d love feedback on:
How native audio-video generation could change your workflow
What creative or production use cases this unlocks
What you’d want from an API or developer interface
Demo & details here:
https://www.jxp.com/seedance/seedance-1-5?i=d1d5k

value: Show HN: Seedance 1.5 Pro – Native Audio-Visual AI Video Generation


value: nsantos

value:
Hi, HN.
Elevator pitch: brig is a CLI for spinning up containers based on the devcontainers spec that validates configuration against said spec and aims to be an almost drop-in replacement for the official CLI tool.
The validation bit is really important to me personally; I want to keep on using the same devcontainer.json files my team is using, and I want any enhancements I can make to them still be usable in their VSCode environments.
---
I really like the idea behind devcontainers[1], and have pushed for my team to adopt them on pretty much all of our codebases. I also really like how they're nominally not married to VSCode (they're just containers with a few convenience features). For years I just relied on a simple shell script[2] to spin up a container based on the Containerfile/Dockerfile of the project.
It wasn't until recently that I began diving into the spec; when I did, I found pretty nifty features (e.g., lifecycle scripts) that would be kinda nice to have without having to add kludges to my little start-dev-container.sh script.
As an aside, it's been years since I've written anything more than shell and Python scripts (and even longer since I contributed anything to FLOSS). Since Go is pretty widely used in the tools I use (OpenTofu, Podman, Docker), I figured that's not a bad excuse to learn Go.
I put my interest in devcontainers and learning Go together, and here's the result.
While I wrote this for my own needs, I hope someone else would find utility in it.
[1]: https://containers.dev
[2]: https://github.com/nlsantos/brig/blob/38d4ae10557422c37af349...

value: Show HN: brig – a devcontainer CLI in Go

author: nsantos


value: chetansorted

value:
Hi HN,
I’m building FitSaver as a side project after noticing I was collecting fitness content instead of actually working out.
Social platforms are great at inspiration, but terrible at execution. Once you leave the feed, there’s no structure to help you follow through. FitSaver lets you take a workout video and turn it into something actionable:
A defined routine
Timed rest
Progress you can track
The goal is simple: reduce the mental overhead between “I should do this” and “I’m doing it.”
The app is still early and rough around the edges.
I’m sharing it here to learn:
Whether this is a real problem beyond my own experience?
Available via TestFlight for now:
https://testflight.apple.com/join/Mz7HkFhn

value: Show HN: FitSaver – A small app I built to turn fitness content into habits

author: chetansorted


value: Show HN: QR Code API – Because existing solutions are overpriced


value: alanalvestech

value:
YouTube's algorithm is optimized for retention, not learning. As a Rubyist, I found it hard to find deep technical content amidst the noise of basic tutorials.
I built Tuby.dev to solve this. It's a curated aggregator for Ruby/Rails videos.
The Engineering:
Instead of relying on YouTube's metadata or captions (which are often incomplete), I built a pipeline that:
1. Downloads the video locally (to avoid datacenter blocking).
2. Sends it to Gemini 1.5 Flash (Vision API).
3. Performs OCR on the code shown on screen.
This allows the system to index Gems, patterns, and versions that the author used in the code but forgot to mention in the description or audio.
The Stack:
It's running on Rails 8 with Inertia.js (React).
Ruby version is 3.4.7 with Prism parser enabled.
I'd love to hear your thoughts on the classification approach or the stack.

value: Show HN: Tuby.dev – Indexing Rails videos via Vision AI code analysis

author: alanalvestech


value: howardV

value:
Hi HN, we just launched Wan 2.6 on FreyaVideo.
Wan 2.6 adds two workflows:
Image-to-Video (I2V): upload an image, describe the motion, generate an MP4.
Text-to-Video (T2V): generate video directly from a prompt.
What’s included:
Duration options: 5 / 10 / 15 seconds
Multiple output sizes (e.g. 720p/1080p, portrait/landscape depending on mode)
Optional prompt expansion + multi-shot segmentation
Output as MP4
If you try it, I’d love feedback on:
Prompting: what kinds of prompts produce the most consistent motion for you?
Quality vs speed tradeoffs: which presets/sizes feel best?
UI/controls: anything confusing between T2V and I2V?
Link: https://freyavideo.com/ (Wan 2.6 page: https://freyavideo.com/video-models/wan-2-6)
Happy to answer any questions about the implementation, pricing/credits, or the model settings.

value: Show HN: Shipped Wan 2.6 (Image-to-Video and Text-to-Video) on FreyaVideo

author: howardV


value:
Hey HN, Justin here. We’re building Motie (https://app.motie.dev), an AI agent that extracts structured data from the web and generates web scraping code, using natural language.
We started building Motie a few months back with the goal of creating an “AI Data Engineer.” We took a ‘forward deployed engineer’-style approach to refine our scope (and to avoid "boiling the ocean”) and noticed that web extraction requests came up time and time again.
We also noticed that many existing tools required a lot of upfront work (defining schemas, specifying CSS selectors), while others offered data without providing the code to scrape it.
With this release, we hope to make it incredibly easy to scrape any website* while giving technical users code to build upon and less technical users an easy interface to extract the data they need.
Features
> Natural language-based extraction: simply provide a URL (https://news.ycombinator.com/) and a prompt (“Find the top 5 stories that have more than 100 points.”)
> Full code ownership: all web scraping code can be exported
> CSV and JSON output formats
> Hosted scheduling and orchestration
Current Limitations
> This release does not include support for proxies. *Scraping websites like Amazon and eBay is thus not well supported at this time. (That said, we’ve noticed a very long tail of websites that don’t require proxies!)
We’ve tried to make getting started as easy and frictionless as possible (e.g., you can use Google or GitHub SSO), and we’d love to hear the HN community’s thoughts!

value: Show HN: Motie – Replit for Web Scraping


value: Show HN: SynapseMD AI scribe and document generation for healthcare


value:
While building a vector database in Python (valori: https://pypi.org/project/valori/), I ran into a bug that took longer than I want to admit.
The same embeddings, indexed on macOS and Windows, would sometimes produce slightly different nearest-neighbor results. Nothing dramatic but enough to break tests and make snapshots non-reproducible.
At first I assumed it was my code. Then I realized the problem wasn’t my logic at all it was "floating-point math".
Different CPUs, different compilers, FMA instructions, and subtle differences in IEEE behavior meant that “the same” computation wasn’t actually the same everywhere.
That sent me down a rabbit hole.
The idea:
Instead of trying to fight floating-point nondeterminism, I asked a simpler question:
What if the core of a vector system never used floats at all?
So I built a small kernel in Rust that uses fixed-point arithmetic (Q16.16) for all vector math. No floats in the core. No randomness. No architecture-dependent behavior.
Same inputs → same state → same snapshot → same search results. Always.
What the kernel does
Fixed-point vectors (Q16.16)
Deterministic insert, search, and replay
Snapshot + restore with bit-identical output
No RNG anywhere
No dependency on CPU floating-point behavior
The kernel is no_std friendly and is meant to be embedded under different layers (node server, Python client, edge devices).
Indexes and embeddings live outside the kernel. The kernel is just a deterministic memory engine.
Why this matters
Most vector databases optimize for throughput and scale. That’s fine.
But there’s a whole class of problems where reproducibility, auditability, and replayability matter more:
Edge devices (robots, drones)
Offline or intermittently connected systems
Long-running agents that need stable memory
Systems where “almost the same” isn’t good enough
This project is my attempt to explore that design space.
The code is early and opinionated, but the determinism is real.
Happy to hear feedback, skepticism, or pointers to similar work I should study.

value: Show HN: I built a deterministic vector DB kernel in Rust using fixed-point math


value: randyort

value: Show HN: I created a free Linktree alternative

author: randyort


value: funiqlab

value:
Hey there, everyone! I'm super excited to introduce myself. You see, my creator was on a hunt for a slicing tool. He just needed to do some simple picture modifications. But boy, those heavy-duty software were such a pain, way too complex for his simple needs. And the lightweight ones? They often let him down. So, he decided to bring me to life! I might be small, but I'm here to lend you a huge helping hand!<p>Now, let me tell you about my current core features:
1. Simple Image Editor: I've tried really hard to keep things basic. I only offer the essential tools. Right now, there are just some fundamental operation methods. Why? Because I want to be that easy-to- use tool you reach for without a second thought. If you have any ideas on how I can make this even better and only keep the most vital functions, I'd be thrilled to hear them.
2. Layer Management: It's an important part of what I do, allowing you to manage your layers with ease.
Guide Line Editing & Image - Slicing Function: This feature helps you edit guide lines and slice images efficiently.
3. AI Image - Generation Powered by Nanobanana: With this, you can generate result images using single or multiple Grids or layers as references. Nanobanana gives me the power to do this cool stuff!
4. Image Management: I'll save all those amazing image results you generate with my AI, and you can download them whenever you want.

value: Show HN: Grid Maker – Effortless AI Grid Paper and Image Slicer

author: funiqlab


value: After 6 months integrating AI voice agents with old systems, I got tired of fighting SOAP from 2003. Built Hopeless to turn 6-month integrations into 2 weeks. ~70% token reduction. Works with any legacy system.

value: Show HN: I built middleware to connect legacy SOAP APIs to AI agents in 2 weeks


value: haneboxx

value: Hi! I just made an obsdian alternative in terminal after searching for an Obsidian like TUI and got nothing. The closest I found was Glow, but it's only a markdown reader. I wanted something more powerful for the terminal, so I built one myself.

value: Show HN: Ekphos – An obsidian alternative TUI for brainstorming

author: haneboxx


value: aligundogdu

value: Hi HN, I built this because I believe retrospective data should belong to the team, so it runs entirely client side using WebRTC/BitTorrent for P2P sync. It was also an experiment in 'vibe coding' with AI to see if I could ship a robust, privacy-first app without a traditional backend. I’d love your feedback on the architecture and the offline-first approach!

value: Show HN: I built RetroRTC A privacy-first,P2P retrospective tool /w no back end

author: aligundogdu


value: tsazan

value:
Problem: AI agents waste 8,500+ tokens scraping HTML to find a product price. They hallucinate inventory and merchants have no control.
Solution: CommerceTXT - a read-only protocol that gives AI deterministic commerce data.
Result: 95% cost reduction, zero hallucinations, accurate pricing.
Why it matters:
- AI platforms: Massive inference cost savings
- Merchants: Control + reduced server load
- Users: Accurate information, faster responses
Like llms.txt, but for commerce. Open standard (CC0), vendor-neutral.
Status: v1.0.0 stable just released
Links:
Spec: https://github.com/commercetxt/commercetxt/blob/main/spec/RE...
GitHub: https://github.com/commercetxt/commercetxt/
Seeking feedback from: AI engineers, e-commerce developers, merchants.
What would make this more useful? How can we improve the format?

value: Show HN: CommerceTXT – llms.txt for e-commerce (95% token reduction)

author: tsazan


value: benmxrt

value:
Hi HN,
I built Mephisto because I was frustrated with the current state of disposable email services—most are riddled with intrusive ads, trackers, and captchas.
I wanted a tool that felt like a proper developer utility rather than a spam farm.
The stack is React, Vite, and Tailwind. Key architectural decisions:
1. Volatile Memory: The backend writes nothing to disk. Once a session terminates, the data is irretrievable.
2. Client-Side Entropy: The password generator runs locally in the browser; keys are never sent to the server.
3. PWA: It's installable and designed for low latency using WebSockets for incoming mail (no polling).
4. Mobile Handoff: You can transfer an active session to mobile via an encrypted QR code.
It is completely free and open for public use. I'd love to hear your feedback on the implementation and UI.

value: Show HN: Mephisto – A RAM-only, ad-free disposable email PWA built with React

author: benmxrt


value: faja

value:
Hi HN, I’m the author of WhatDayIsIt.
I built this because I kept running into dates on blogs, tickets, and documentation (e.g., "Due by Dec 25") and constantly had to switch context to open my calendar just to see what day of the week that falls on. I wanted a way to see that context without leaving the page.
The extension is lightweight (<50KB) and works by scanning the DOM for common date formats (ISO, US, EU). When it finds a match, it adds a subtle highlight. Hovering over the date reveals a tooltip with the day of the week (Monday, Tuesday, etc.).
If you have any feedback I would appreciate it!

value: Show HN: WhatDayIsIt – Hover over any date to see the day of the week

author: faja


value: theletterf

value: Show HN: I built an OpenTelemetry extension that shows traces as rain in the sky

author: theletterf


value: berndtzl

value:
Hi HN,
I built a web app to help anyone trying to learn STARKs to build intuition
The idea is to learn STARKs by inspection and debugging rather than only by reading papers. You can write simple programs in a small assembly-style DSL, generate and verify STARK proofs for them, and inspect execution traces, constraints, and polynomials step by step.
I hope it's useful to someone! Link: https://floatingpragma.io/starklab

value: Show HN: An interactive visual explorer for STARK zero-knowledge proofs

author: berndtzl


value: megaseo

value:
I built this tool to help founders get realistic development cost estimates without the usual sales pitch.
It's completely free and requires no signup.
I created it in memory of a friend who passed away recently and believed strongly in accessible tools for builders.
The calculator asks about your project requirements and provides cost ranges based on common development scenarios.
It's intentionally straightforward
Planning to build more tools like this.
Feedback welcome.

value: Show HN: Free MVP Cost Calculator (no signup required)

author: megaseo


value: barryvan

value:
HandsUp is the straightforward and mobile-first volunteer-management platform that the communities I'm involved in needed.
---
Over the past year, I've been more and more involved in local clubs and activities. One of the biggest challenges I observed was with managing volunteers. People were trying to use WhatsApp polls, hand-written rosters, Google Forms, and more to organise events and manage volunteers.
A big part of the challenge was friction - not only for the volunteers, but also for the event creators.
After one particularly disastrous effort of trying to use a shared Google Sheet to build out a complex roster for a school fete, I decided enough was enough and that led to HandsUp. My goal was to reduce friction as much as possible, and to make it mobile-first for volunteers and organisers. It supports simple events as well as more complex rosters and slots.
Its first couple of trial runs have gone pretty well, so I figured it was probably the right time to share it with HN and get some more feedback! :) For organisers, signing up and logging in is via magic links in emails; for volunteers, there's no sign up required. Feel free to create events and try signing up yourself too.

value: Show HN: HandsUp – Super Simple Volunteering

author: barryvan


value:
Last week I posted TubeDL, a native macOS video downloader. Got some interest, so I'm open-sourcing the CLI it's built on.
It's a Python wrapper around yt-dlp with a cleaner interface:
- Single videos, playlists, Shorts
- MP4/MP3 output with FFmpeg
- Cookie auth for age-restricted/private videos
- Thumbnail extraction
- Rich terminal UI
Install: pip install -e . (or clone and run)
The macOS app adds a native GUI on top, but the CLI works standalone on macOS/Linux/Windows.
GitHub: https://github.com/RichardTrujilloTorres/tubedl
Looking for feedback on the CLI design and what features would be most useful. PRs welcome.

value: Show HN: TubeDL – Open-source YouTube downloader CLI (playlists, Shorts, auth)


value:
Hello HN! I’m shipping MCPTrust, an open-source CLI for signed lockfiles for MCP servers.
It snapshots a server’s tool surface into a deterministic mcp-lock.json, then lets you sign/verify (Ed25519 locally or Sigstore keyless in CI) and diff live servers against the approved lockfile to catch capability drift before agents run it.
Would love feedback on what should count as critical drift vs benign changes.

value: Show HN: Signed lockfiles for MCP servers (MCPTrust)


value: northerndev

value: Show HN: MemVault – Async GraphRAG Memory for AI Agents (Postgres/Redis)

author: northerndev


value: I know many are happy with the HN frontend. I wrote this for fun and it turned out really useful so I published it. Here are the most important features:<p>- Rainbow indentation: This is inspired by the Rainbow CSV extension - comments of the same "depth" have the same colored stripe on the left.<p>- Native filters: Filter the various frontpages (Top, Show...) by upvote count or comment count. There is a "Controversial" (comment count > upvote count) filter too, if you want drama.<p>- Read later: Save comments and posts and revisit them through a bookmark menu. Saved to local storage.<p>- Collapsible sticky header: No need to scroll back up to see thread details and navigate to submitted link<p>- Infinite scroll: No pagination, you just continue scrolling.<p>- Styled quotes: Paragraphs starting with ">" are styled as a blockquote<p>- Reply as usual: You can click on Write reply and it takes you to the same page that would open in the regular HN UI<p>- Favicons: Helps my brain focus. Uses the DuckDuckGo favicon service + caching.<p>- Dark mode!<p>- "Open links in new tab": Especially useful on mobile. Can be toggled in the settings.<p>- Slightly more intuitive navigation? Atleast to me it makes more sense. Clicking a submission title, c2a button or comment count directs you to the thread. Clicking a submission link takes you to the link.<p>If you have any good ideas or see a bug, feel free to tell me

value: Show HN: HN++

title: Show HN: HN++


value: ferryistaken

value: After my last blogpost showing how to compile and link Rust code for the x86 xv6 kernel (using C FFI), in this post I show how to code your own networking stack, and then show a working http server using smoltcp.

value: Show HN: Writing a networking stack for xv6 in Rust

author: ferryistaken


value: mmazurovsky

value:
Data shows that early applicants get more interviews. ApplyFirst helps you reach new jobs before they get crowded with 100 plus applicants.
ApplyFirst is an AI job search system. It focuses on sending alerts as soon as matching jobs are published. Users get instant LinkedIn alerts based on their specific needs. These include tech stack, salary, travel, and other requirements.
Users receive only relevant roles filtered by AI models. We extract key job keywords to help improve your resume and cover letter. Automate your job search and stay ahead of the competition.
___
Now let me tell you about the background of the product. I'm Misha, the developer of ApplyFirst. Earlier this year, I found myself navigating one of the most competitive job markets I had ever seen. Many roles already had over 100 applicants within hours of being posted. Despite being highly motivated and consistently applying, I often received rejections or no response at all.
While digging into recruiter insights, one key pattern stood out: timing matters immensely. Early applicants have a significant advantage. So I decided to put this insight to the test and built a small program that monitors job boards and surfaces new jobs within minutes of being published.
The results were immediate. I started receiving far more responses and landed a great new role in less than a month.
When friends began using the tool and saw the same success, I knew this could help many more people. After several months of refining and expanding it, ApplyFirst was born to give others the same competitive edge.
I hope it helps you too, adventurer.

value: Show HN: ApplyFirst – AI job search alerts that get you more interviews

author: mmazurovsky


value:
I built this because I kept losing track of important parts in my long conversations with Claude/ChatGPT.
Threadline adds a sidebar that shows all your messages as a clickable table of contents. Click any entry, jump straight there. Hover to preview the full message before navigating.
Works on both Claude and ChatGPT. Auto-updates as you chat, matches dark/light themes, and stays out of your way when you don't need it.

value: Show HN: A Chrome extension to navigate long Claude/ChatGPT threads with ease


value: deklesen

value: I found that having a quick way to locally transcribe what i am saying is helpful for my workflow, especially when vibe-coding or generally when chatting with AI models, as small transcription errors are mostly not a problem.<p>So I built YapType. It's just <200 lines of code total. On press of a shortcut, it transcribes locally and opens the result in your text editor - simple as that.

value: Show HN: YapType: hassle-free yap-to-text for Linux

author: deklesen


value: simonmorley

value: Show HN: Deterministic analysis of musical tension, novelty, and fatigue

author: simonmorley


value:
Hi HN!
I built Rekapu to learn Polish after moving from Ukraine to Poland. The problem is - I work remotely for US companies in English, and most people around me speak English or I can use self-service checkouts, so there's basically zero pressure to actually learn. But I wanted to improve.
Being honest with myself, I had to acknowledge that with my cooked attention span, relying on willpower or strict schedules just wasn't going to work.
The insight: My most consistent habit is procrastinating on HN, Twitter, and news sites. It happens automatically every day, regardless of my mood or motivation. I don't plan these procrastination sessions - they just happen. So I thought: what if I couple learning with procrastination? Make it as small as possible - just one flashcard per session, get a small dopamine hit, and proceed.
How it works:
When you visit a blocked site, Rekapu shows you one flashcard. Answer it, get N minutes of access. Repeat. Unlike traditional blockers that redirect you to a new page (losing scroll position and context), Rekapu uses an overlay - after answering, you continue exactly where you left off.
And it felt good. Each procrastination session started feeling less guilty because at least I'm learning vocabulary and doing something good for myself every day without relying on willpower or motivation. It just happens natively. And it works so well for me that I keep using it, so I decided to share it.
Main features:
Spaced repetition (Again/Hard/Good/Easy ratings)
Import Anki .apkg decks with media
Google TTS support (BYOK) - great for languages
Cloze deletion cards
Activity streaks & daily goals
Everything stored locally (IndexedDB), no servers, no tracking
Tech: TypeScript, React + Chakra UI, IndexedDB. Landing built with Astro.
Links:
Chrome: https://chromewebstore.google.com/detail/lbbjjejkepnemhcbhcc...
Website: https://rekapu.com/en/
GitHub: https://github.com/k-tkachov/rekapu
Free and open-source (GPL-3.0). Would you give it a try or should I just keep it as a personal tool?

value: Show HN: Rekapu – Learn while procrastinating. Answer flashcard to unblock sites


value: admtal

value:
Hi everyone,
I helped my dad publish his yiddish quote book.
He was writing and collecting yiddish quotes in a private whatsapp group for 6 years.
I helped him by exporting his chat logs, and GPT APIs to translate and transliterate his quotes.
The quotes are ordered semantically as scored by https://huggingface.co/sentence-transformers/all-MiniLM-L6-v...
Originally, we wanted the quotes to be in categories & chapters, but so many of them transcend categories.
The semantic ordering is strange, but the flow felt right, which was neat.

value: Show HN: 1,400 Yiddish quotes with translations and transliterations

author: admtal


value: Show HN: ZXC – Asymmetric, +40% decode vs. LZ4 on ARM (C, BSD-3, Fuzzed)


value: guyl

value:
I built a small tool to track core recession indicators in one place:
https://recession.fyi/
The motivation was simple: economic headlines often feel emotional or contradictory, and I wanted a quiet page that lets me look at the underlying signals directly things like employment, manufacturing, consumer sentiment, and credit conditions without commentary.
This doesn’t predict recessions or make claims about the future.
It just aggregates the data, updates automatically, and tries to present it as plainly as possible.
I’m mainly interested in whether the indicator selection makes sense, whether anything important is missing, and whether the layout feels clear or confusing at a glance. This is a first version, so blunt feedback is welcome.

value: Show HN: Recession.fyi A simple dashboard of recession indicators no predictions

author: guyl


value: HugoDz

value: Show HN: I built a web-based game design editor with Svelte

author: HugoDz


value: privalyse

value:
I just released Privalyse (v0.1 Alpha).
The problem I faced: AI models can generate code very fast, but security and privacy context often gets lost — resulting in hardcoded API keys, PII ending up in logs, or unsafe input reaching sensitive sinks. Although reviews help, it’s easy to miss these issues when the PR volume is high.
Privalyse is a static analysis tool with cross-file taint tracking designed to catch privacy violations and security flaws that slip through unnoticed in AI-assisted workflows.
I'd love for you to give it a try! Since this is an early alpha, I'm specifically looking for:
- Feedback on the detection rules
- Reports on false positive patterns
- Feature requests: What languages or frameworks should I prioritize next?

value: Show HN: Privalyse – Catching Security Leaks in AI-Assisted Codebases

author: privalyse


value: PranavVyas

value:
In the past I've used tools like Postman for API testing but I always found myself wanting to stay in my terminal without switching contexts.
So I started building a new tool to bridge the gap, combining terminal-native workflow with the API collection management we get from GUI tools.
It's definitely in the early stage of development but if you work with APIs from the command line, I'd love to hear your thoughts and feedback on this post or even a feature request in a Github issue!
Feel free to check it out here and give it a spin: https://github.com/pranav-cs-1/nexus

value: Show HN: Nexus – Terminal-based HTTP client for API testing

author: PranavVyas


value: iris-digital

value:
It's been a difficult two decades for those of us who expect the products we
buy to respect our freedom and privacy.
We've been marginalized by the mobile revolution for so long that we'd nearly
given up looking for options.
While commiserating recently,
we realized that several important missing pieces of the puzzle have neatly
dropped under our noses while we weren't looking!
What are they?
In no particular order,
the Fairphone6 w/eOS, Furilabs FLX1s, Starlite Tablet, new Framework
laptops, System76 servers, OpenWRT One, and a number of others already shipping.
Software options are plentiful as well.
The landscape has changed significantly in a few short years.
Of course, these products are not perfect.
Right now you might be counting their flaws.
But we feel there's a another way to look at the situation.
That these are now good enough for the
vast majority of folks—and they'll only get better,
if further invested in.
The catch?
One needs to wrangle numerous products from almost as many companies.
That's tedious for normal folks and busy geeks alike,
plus it splits investment into small portions.
So, how can we get these organizations to work together?
We've brainstormed some ideas, such as a unified storefront, licensing,
mergers, etc,
but don't know how feasible they are.
What do y'all think?
If we can help these products get attention,
increase sales,
and further investment,
we'll complete a libre product line that much faster.
These goals are detailed
at "The Ethical Computing Initiative,"
https://aol.codeberg.page/eci/ BTW.
TL;DR—Many open, respectful products are listed at:
https://aol.codeberg.page/eci/status.html
Now they need our support!

value: Show HN: There's a full suite of open alternatives to BigTech hardware now

author: iris-digital


value: Show HN: I asked Gemini 3 pro to imagine a job board 10 years from now


value: jcuenod

value: EdgeQL is interesting to me as one of the contenders to be a SQL successors. Eqlize is an experiment to bring its expressiveness to standard SQL databases without migrating to Gel (aka EdgeDB). So you can use it against Sqlite or PG (or, perhaps, some OLAP DB if you write an adaptor...)

value: Show HN: Eqlize – Query SQLite Using EdgeQL

author: jcuenod


value:
Hi HN! I run The Tournament, a web app for creating tournament brackets and league tables.

  It's been widely used in Japan for years by sports leagues, schools, esports events, and TV stations. We recently added full multi-language support and are now expanding globally.

  What makes it different:
  - Intuitive editing – click directly on brackets/tables to enter results, winners auto-advance
  - Works instantly – no signup required to try, sign up to save and manage your tables
  - Both formats – single-elimination brackets AND round-robin league tables with auto-calculated standings
  - Clean output – PDF export, embed via iframe, share on social media
  - International-ready – country flags, 9 languages supported

  Free tier: 16 players for brackets, 6 for leagues
  Premium ($5/mo): up to 128 players, unlimited saves, API access, no ads

  I'd love to hear what features you'd want. Happy to answer any questions!

value: Show HN: The Tournament – Free bracket and league table generator


value: Show HN: A Chrome extension that turns email threads into draft replies


value: rajvarkala

value:
Hi HN,
I am Raj, founder of Valmi.
We were building AI agents and quickly hit a pricing problem.
Tokens, API calls, and seats made no sense for agents. One agent run might be cheap and useless; another might be expensive and replace hours of human work. Billing on usage felt disconnected from real value.
What customers actually asked was:
“Did it finish the task?”
“Did it resolve the ticket?”
That pushed us toward outcome-based billing - charge only when the agent delivers something meaningful.
But existing billing systems don’t handle this well:
outcomes aren’t first-class concepts
costs vary per run (LLMs, tools, retries)
it’s hard to see margins per agent or customer
So we built Valmi:
1. outcomes as billable units
2. cost + margin tracked per agent run
3. outcome, usage, or hybrid pricing
4. open-source SDKs, self-hostable stack
Outcome billing also builds trust: customers pay for results, not experiments.
Available on GitHub (https://github.com/valmi-io/value).
I also wrote my hypothesis here: https://www.valmi.io/blog/an-imperative-for-ai-agents-outcom...
Would love your feedback! Also hosted at https://value.valmi.io
-Raj

value: Show HN: Built Valmi to bill AI Agents(Open Alternative to Paid.ai, Legacy Zuora

author: rajvarkala


value: Iobs

value:
Hey,
I’ve been working for almost four months on an open-source, self-hostable tool that mixes project management with client communication.
The idea is simple: you get an admin area with a Kanban board to manage tasks; adding tags, dates, and all the usual project stuff.
And then, in another section, there are tools to communicate with your clients about the events you’re working on or releasing. This includes a highly customizable public page to display a changelog, collect feedback, and get reactions, plus a newsletter feature to send emails about your progress.
I’m really curious to hear what you think or what features would make this tool even more useful for you?
Do not hesitate to check the GitHub repo : https://github.com/GauthierNelkinsky/ShipShipShip
Thanks

value: Show HN: ShipShipShip 1.3.0 – Bridging project management and communication

author: Iobs


value: Show HN: ULoopMCP – Let AI agents compile, test, and operate your Unity projects


value: BleedingXiko

value:
Hi HN,
I built GhostStream while working on a self-hosted media server and wanting
a simple way to offload video transcoding to whatever hardware was available
on the network.
GhostStream is an open-source video transcoding server with:
- zero configuration startup
- automatic GPU / encoder detection (NVENC, QuickSync, AMF, VideoToolbox)
- live HLS streaming, ABR, and batch modes
- automatic fallback to CPU if hardware encoding fails
- a small HTTP + WebSocket API for progress updates
It’s designed to run locally (no cloud dependency) and works well as a
sidecar for lightweight media servers.
If you want to see it working quickly, the repo includes a demo that
starts a transcode from a public video URL and opens playback automatically:

    python examples/demo.py

I’m mainly interested in feedback on the API surface, hardware detection,
and real-world transcoding edge cases.

value: Show HN: GhostStream – zero-config hardware-accelerated video transcoding

author: BleedingXiko


value: Omakidx

value: I just re-buit my personal portfolio

value: Show HN: My Personal Portfolio

author: Omakidx

story_text: I just re-buit my personal portfolio

title: Show HN: My Personal Portfolio


value: hunvreus

value: Show HN: ReallySimpleDocs - A minimal docs template built with 11ty and Tailwind

author: hunvreus


value: qafstudio

value:
I built What to Watch AI, a lightweight recommendation app that suggests movies and TV shows based on mood and preferences using just 3 questions.
Why it’s different:
No long onboarding, no accounts
Focused on decision fatigue (couples included)
Fast results, minimal UI
Platforms:
Android: https://play.google.com/store/apps/details?id=com.cine.ai
iOS: https://apps.apple.com/tr/app/what-to-watch-ai-movies-tv/id6...
Looking for honest feedback on recommendation quality and UX.

value: Show HN: What to Watch AI – Mood-based movie and TV picks in 3 questions

author: qafstudio


value: elcoan

value:
Hi HN,
I’ve been working solo on Samata — a small web app for personal work.
I built it because my own notes, tasks, and project context were always scattered across different tools. Notes lived in one app, tasks in another, and projects somewhere else. I wasn’t missing features — I was losing context.
Samata is based on a simple idea: everything is a page.
A task, a note, a project, an idea — all the same thing at different stages.
You start with an empty page, write a thought, link it to other pages, and optionally add a due date or status. When something becomes relevant (for example, due today), it appears automatically on the home page. There’s no system to design upfront.
This is intentionally not a team tool:
* no roles or permissions
* no workflows or dashboards
* no templates or automation
It’s meant for calm, personal work.
The project is early, but usable. I’m actively working on it and happy to answer questions or hear critical feedback.
You can try it here:
https://samata.app/
You can try it instantly via a one-click demo (no signup).
The demo runs in a shared space that resets periodically.
https://samata.app/demo

value: Show HN: Samata – A minimal personal workspace where everything is a page

author: elcoan


value: Show HN: Privacy-First Chat Application

title: Show HN: Privacy-First Chat Application


value: ryanxsim

value: Show HN: Simple iRacing Week Planner and Buying Guide

author: ryanxsim


value: aman-s

value:
Hi HN! I’m Aman and along with my cofounder Aarya, we're building Jungl (https://jungl.cloud), an event-driven security tool for detecting and remediating AWS misconfigurations.
Most cloud security programs we've spoken to fall into one of two patterns: periodic scanners that find issues hours later and generate tickets to fix resource misconfigurations, or restrictive IaC/approval workflows that slow developers down.
Jungl's approach is to evaluate the operational and security risks for resources that are created/updated and take quick action by monitoring and/or applying a remediation - automatically. The goal is to reduce the number of manual, hands-on fixes of resource non-compliance, giving developers the freedom to create/update resources however they want on AWS while still allowing cyber teams to ensure enterprise security. Think of us as a cloud engineer who works with every developer behind the scenes to monitor (and fix) each resource to make sure it won’t cause a security issue.
How it works:
- We ingest relevant CloudTrail events for the 6 services we support right now
- We evaluate rules (that you enable at a service-level) that produce: discovery (what’s wrong + evidence), recommended actions, and whether the discovery is eligible for automated remediation
- If automated remediation is enabled, Jungl applies only explicit, scoped remediations. Otherwise, it generates a ticket for a human to accept or reject.
What we mean by "context":
A big challenge with remediation is avoiding breaking systems. We use context to understand the intent behind each resource by looking at:
- the resource’s exposure surface
- dependency graph (what other resources it is connected to)
- how the resource is being called or used in your codebase
- the severity of the rule
- historical logs of how the resource was being used
A concrete example:
A bucket policy with access to the public - the last thing you want is for a developer to accidentally forget to remove public access and then 12 hours later, you see that the bucket was inviting unsolicited traffic. Jungl would immediately remove that bucket policy statement if the rule was enabled due to the criticality of it.
(We recorded a short demo here: https://youtu.be/P_GMyNwBZ5k?t=252)
We're early and still increasing coverage and the rule library. Some resources have multiple plausible fixes and choosing the least disruptive option is the hard part so we're building the workflows for that now, and building the application to support more context-aware decision making.
If you want to give us a try, here's the creds:
Website: https://jungl.cloud/sign-in
Username: hn@jungl.cloud
Password: RG^J$T2!huMsF2c
And if you run AWS infra and have opinions about automated remediation (good or bad) or what we're doing, we'd love feedback.
Thanks for reading!

value: Show HN: Jungl – Auto Remediation for your AWS misconfigurations

author: aman-s


value:
I've spent the last few months architecting a RAG system for a regulated environment.
I am not a developer by trade, but I approached this with a strict "systems engineering" and audit mindset.
While most tutorials stop at "LangChain + VectorDB", I found that making this legally defensible and operationally stable required about 40+ additional components.
We moved from a simple ingestion script to a "Multi-Lane Consensus Engine" (inspired by Six Sigma) because standard OCR/extraction was too hallucination-prone for our use case.
We had to build extensive auditing, RBAC down to the document level, and a hybrid Graph+Vector retrieval to get acceptable accuracy
The current architecture includes:
Ingestion: 4 parallel extraction lanes (Vision, Layout, Text, Legal) with a Consensus Engine ("Solomon") that only indexes data confirmed by multiple sources
Retrieval: Hybrid Neo4j (Graph) + ChromaDB (Vector) with Reciprocal Rank Fusion
Performance: Semantic Caching (Redis) specifically for similar-meaning queries (40x speedup)
Security: Full RBAC, Audit Logging of every prompt/retrieval, and PII masking.
I documented the complete feature list and gap analysis
https://gist.github.com/2dogsandanerd/2a3d54085b2daaccbb1125...
My question to the community:
Looking at this list – where is the line between "robust production engineering" and "over-engineering"?
For those working in Fintech/Medtech RAG: what critical failure modes am I still missing in this list?

value: Show HN: The feature gap "Chat with PDF" tuts and a regulated enterprise system


value: ulrischa

value: Show HN: Show LinkedIn Data Bookmarklet

author: ulrischa

title: Show HN: Show LinkedIn Data Bookmarklet


value: fagerhult

value: Show HN: Qqqlang – a syntax-free language for image synthesis

author: fagerhult


value: ryry

value:
Hey HN!
A long time ago I built a map of trains at trains.fyi, and then realized nobody had bought planes.fyi - so this is my new map project.
It's a bunch of things, but mostly, a 3D map of aircraft around given airports, as well as the weather, ATIS, and other data sources visualized in 3D and unique ways.
Like most of the stuff I build, it's got no real use, but I think it's neat. If you like planes, check out the CYYZ/KJFK map, weather, and runway tabs - I put the most work into that.
Also any feature requests, questions, comments, etc are always appreciated!

value: Show HN: Planes.fyi – 3D aircraft and airport visualizations

author: ryry


value: mywork-dev

value:
A few days ago I posted MCPShark (a traffic inspector for the Model Context Protocol).
I just shipped a VS Code / Cursor extension that lets you view MCP traffic directly in the editor, so you’re not jumping between terminals, logs, and "I think this is what got sent".
VS Code Marketplace:
https://marketplace.visualstudio.com/items?itemName=MCPShark...
Main repo:
https://github.com/mcp-shark/mcp-shark
Feature requests / issues:
https://github.com/mcp-shark/mcp-shark/issues
Site: https://mcpshark.sh/
If you’re building MCP agents/tools: what would make MCP debugging actually easy—timeline view, session grouping, diffing tool args, exporting traces, something else? I’d be thankful if you could open a feature request here: https://github.com/mcp-shark/mcp-shark/issues

value: Show HN: MCPShark Viewer (VS Code/Cursor extension)- view MCP traffic in-editor

author: mywork-dev


value: Building AI voice agents for old system on soap xml, kept hitting the same wall: APIs from 2003 don't understand 2025 AI. Built Hopeless to bridge 22 years of tech debt. Protocol translation + token optimization.

value: Show HN: Hopeless – Removing API Bloat Before It Reaches Your LLM on Legacy API


value: a full-control cloud platform to help deploy, manage, and ship projects faster. No lock-in, no unnecessary complexity—just full control over your deployments and workflows.

value: Show HN: A Full-Control Cloud That Puts You in Charge of Your Deployments


value:
Hey everyone!
I recently updated my social impact job website RemotelyGood.us again-- I updated the font site-wide, enhanced the UI on mobile, and am prototyping some agentic features for perfecting your job applications. I need your help and feedback!
In exchange for signing up for premium and premium plus-- then filling out this feedback survey: https://form.jotform.com/251538756931062 -- I am giving out Amazon gift cards.
Anyone who signs up for a premium subscription between now and December 19th 12pm EST will get $5 in Amazon gift cards for Standard Premium, $15 for Premium Plus.
Thanks in advance for your help on this!
P.S. I am also recruiting a CTO! Please apply here: https://wellfound.com/jobs/3668322-chief-technical-officer-c...

value: Show HN: Get an Amazon Giftcard to Test Premium Features on Remotelygood.us


value:
Why HN,
I'm embarking on a journey to build an intuitive, graphical Proof Assistant. Wish me luck! The idea came when I saw my sister struggling to keep track of different definitions and theorems while studying Euclidean geometry. I wanted to give those abstract ideas a shape. Something she can easily see and interact with.
In the blog, I’ve included some screenshots of how I imagine it would work. Here’s also the link to the original code.
https://github.com/tri2820/remath

value: Show HN: Why I'm building a graphical, simple Proof Assistant for kids


value: avansledright

value:
Hey HN,
I built this because I was tired of spending 20-30 minutes investigating every CloudWatch alarm that fired. AWS just released CloudWatch Investigations (June 2025) which does similar things, but requires significant setup and lives in the console. Mine takes a different approach.
CloudWatch AI Agent: When an alarm fires, an AI agent autonomously investigates and sends findings to Slack. Deploy via Terraform in 5 minutes.
How it works:
- Terraform module (Apache 2.0) deploys Lambda + SNS integration
- Alarm triggers → AI agent queries your AWS environment (read-only)
- Analyzes metrics, logs, EC2/RDS/Lambda configs, alarm history
- Sends root cause analysis + ready-to-run CLI commands to Slack
- ~30 seconds total
vs AWS CloudWatch Investigations:
- CloudWatch Investigations: Console-based, requires investigation groups, IAM role setup, CloudTrail→CloudWatch Logs config, limited to 2 concurrent investigations
- Mine: Terraform apply, works immediately, Slack-native, unlimited investigations
The Terraform module is fully open source. The Lambda code is obfuscated (contains AI orchestration logic, prompts) - this is the core IP. Business model is $5/mo API key subscription.
Cost: $5/mo + ~$0.001 per alarm (AWS Bedrock usage)
Why build this when AWS has a free native solution?
1. Infrastructure-as-code teams want Terraform deployment, not console configuration
2. Slack-native workflow vs AWS console workflow
3. No investigation limits (AWS caps at 150 enhanced/month)
4. 5 minutes vs 2-3 hours of setup
I'm a solo AWS engineer. Built this for myself, now productizing it. The obfuscated Lambda is a tradeoff - I know trust is an issue, but the Terraform is fully auditable, IAM permissions are read-only, and everything runs in your account with CloudTrail logging.
Not for everyone (especially teams with strict "no obfuscated code" policies), but for Terraform-first DevOps teams who want simple tooling, it solves a real pain point.
Site: https://aiopscrew.com
Happy to answer questions about the implementation, security model, or why I made specific design decisions.

value: Show HN: AI agent that investigates CloudWatch alarms – 5-min Terraform deploy

author: avansledright


value: gustavojordao

value: Show HN: Arete – Plaid for AI identity (your context follows you across tools)

author: gustavojordao


value: ethegwo

value: Show HN: Tonbo – an embedded database for serverless and edge runtimes

author: ethegwo


value: bbokan

value: Show HN: Voice-to-text for macOS using Groq's free Whisper API

author: bbokan


value:
Hi HN — I’m Andrea. I built Bob the Fixer to make “AI fixes” less vibes, more measurable.
The idea: run SonarQube locally (containerized), then expose an MCP server that gives AI coding CLIs a fix-ready view of your static analysis results. Instead of “guess what to fix”, the assistant can:
- scan a repo + apply quality gates
- fetch rich issue details (rule info + plain-text code context)
- prioritize work (tech debt / coverage gaps / duplication / patterns)
- iterate: fix → test → re-scan
Important: this is not a 1:1 “SonarQube API over MCP” wrapper — it’s an opinionated remediation workflow.
Quickstart (Linux/macOS):
  curl -fsSL https://raw.githubusercontent.com/andrearaponi/bob-the-fixer... | bash
Then open your AI CLI inside a repo and ask something like:
  “Scan this project with Bob the Fixer, then show me details for the top critical issue.”
Story / why it exists (incl. the “two weeks of tech debt in one night” bet):
https://andrearaponi.it/open-source/bob-the-fixer/

value: Show HN: Bob the Fixer – SonarQube and MCP tools for a fix→test→re-scan loop


value: kmundy

value:
I spent the last month rebuilding the US federal budget from 1970–2024 to find the 'Taproot' of our fiscal collapse.
The math suggests we don't have a sovereign debt crisis; we have a pricing crisis.
I isolated federal healthcare spending and compared it to a baseline of CPI + a 1.7% 'Innovation Premium' (using Germany as a control group).
The findings:
Federal healthcare overpayment accounts for $26T of our national debt.
Without this 'Monopoly Premium,' the US would have barely 9T in debt today.
The Structural Cause: I trace this back to the 1997 Residency Cap (supply freeze) and the 85% MLR (which turns insurers into cost-plus contractors)
I'm interested in the community's feedback on the 'Triple Multiplier' logic (Price + Innovation + Interest).
P.S. I'm currently hosting a deeper discussion on the policy implications of this data over on LinkedIn bit.ly/3YEv6kl

value: Show HN: Modeling the US Debt as a Healthcare Pricing Failure ($26T Gap)

author: kmundy


value: plsft

value: I used ip.now all the time, but it seems the domain expired and renewal isn't happening. I cloned it and put a fresh coat of paint. Enjoy.

value: Show HN: Created a New Ip.now

author: plsft

title: Show HN: Created a New Ip.now


value: Show HN: Open-Source Postgres MCP Server and Natural Language Agent


value: I used jsonlint.com several times per day but the ads became too much. I revised the existing version and added dark mode and made the editor front and center. Enjoy.

value: Show HN: Jsonlinter.org

title: Show HN: Jsonlinter.org


value: lgreco

value:
got tired of paying $29–99/month for simple contact forms on landing pages and side projects (Typeform, Tally, etc.).So I built GitForms: an open-source contact form that stores submissions as GitHub Issues.How it works:Form runs on your Next.js 14 site (Tailwind + TypeScript)
On submit → creates a new Issue in your repo via GitHub API
You get instant email notifications from GitHub (free)
Zero ongoing costs:No database, no backend servers
Deploy on Vercel/Netlify free tier in minutes
Configurable via JSON (themes, text, multi-language)
Perfect for MVPs, landing pages, portfolios, or any low-volume use case.Repo: https://github.com/Luigigreco/gitforms
License: CC-BY-NC-SA-4.0 (non-commercial only – fine for personal projects, not client work).Curious what HN thinks: would you use this? Any obvious improvements or edge cases I missed?Thanks!

value: Show HN: GitForms – Zero-cost contact forms using GitHub Issues as database

author: lgreco


value:
I’m building thugg.lol, a Link-in-Bio platform similar in category to guns.lol and feds.lol, implemented entirely from scratch.
The project does not rely on SaaS templates or existing profile engines. The backend API, database schema, authentication, analytics, payments, and rendering logic are custom-built.
Links:
Homepage: https://thugg.lol
Example profile: https://thugg.lol/m6jo9
The project is currently in an MVP phase. The UI is minimal by design, with a complete UX/UI redesign planned. Internationalization (i18n) and multi-language support are also planned.
Focus
Backend architecture and data modeling
Extensible system design
Feature experimentation
Long-term scalability
Feedback
I’m interested in feedback on:
The overall concept
Feature scope and priorities
Archiectural choices
What feels unnecessary or missing
I’m also open to collaboration with developers or designers who are interested in working on a system built from zero.

value: Show HN: Thugg.lol – a Link-in-Bio platform built from scratch


value: pyramation

value:
Hi HN — I’m the founder of Constructive
pgpm is a package manager for application-layer PostgreSQL code: schemas, functions, triggers, and Row-Level Security policies written in pure SQL.
The goal is to move beyond linear, numbered migration chains toward composable, dependency-aware database modules that can be published, versioned, installed, and tested in isolation against real PostgreSQL.
This approach is already being used to package large PostgreSQL systems (including the full Supabase database layer) to enable local and CI testing of production-grade schemas and RLS behavior.
Docs + quickstart: https://pgpm.io/
Happy to answer questions or discuss tradeoffs.

value: Show HN: Pgpm, a package manager for application-level PostgreSQL modules

author: pyramation


value: jabronipony

value: I've been working on this for a while as an experiment and I'm now pretty happy with it. I've always written code, but I've always been a frankly incompetent frontend developer, and I've never really been able to talk myself into making a game; it was just too much trouble, even though I had ideas I wanted to try! Claude Code made it possible for the first time. I hope you like it. I'm still working on it!

value: Show HN: A once-daily word game with high- and low-pressure options

author: jabronipony


value: math-hiyoko

value:
I built a Rust-powered Wavelet Matrix library for Python.
There were surprisingly few practical Wavelet Matrix implementations available for Python, so I implemented one with a focus on performance, usability, and typed APIs. It supports fast rank/select, top-k, quantile, range queries, and even dynamic updates.
Feedback welcome!

value: Show HN: High-Performance Wavelet Matrix for Python, Implemented in Rust

author: math-hiyoko


value: jakubanderwald

value: Hi HN, I built Routista.eu to solve the manual pain of creating GPS art. It uses OSM to calculate routes that mimic user drawings. It's free and privacy-friendly. Would love your feedback.

value: Show HN: I built a tool to snap vector drawings to real-world road networks

author: jakubanderwald


value: gonc

value:
Hi HN,
I’ve always found traditional nc frustrating when dealing with modern networks. It assumes one side has a reachable IP and a listening port—conditions rarely met when both peers are behind NATs, CGNATs, or firewalls.
I built a Go-based netcat-style tool that enables ad-hoc P2P connections using only a shared high-entropy passphrase. No inbound ports, no known IPs, and no manual coordination required. Both peers just run the same command, e.g. nc -p2p <passphrase>
Practical example:
like a FRP alternative, no public server doing reverse-proxy, no exposed ports. For example, if you frequently need to reach 10.0.0.1:22 inside your company network, run this on any host inside the company LAN:
gonc -p2p <passphrase> -linkagent
Later, from home you can initiate a tunnel with the same passphrase:
gonc -p2p passphrase -link 3080;3080
After that, both sides can proxy to any IP:port on the peer LAN using the local SOCKS5 listener.
The "Magic" behind it:
Instead of a central relay or a complex VPN, it uses a three-stage handshake:
1. Rendezvous: The passphrase deterministically derives a unique MQTT topic and a self-signed TLS certificate/key pair.
2. Discovery: Both peers connect to a public MQTT broker (acting as a "bulletin board") to exchange STUN-discovered candidate addresses. The broker never sees the passphrase or the raw traffic.
3. Connectivity: It tries direct TCP first, then falls back to UDP hole punching. If the NAT is particularly "difficult," it uses a "birthday paradox" strategy (spraying 600+ ports) to force a collision.
4. Security: Once connected, the MQTT channel is dropped. All data flows P2P over mTLS. Since the TLS certs are derived from the secret, impersonation is impossible without the passphrase.
Key Features:
- No "Client" or "Server": Both sides run the exact same command.
- Zero Infrastructure: Uses public MQTT/STUN servers; no need to host your own signaling server.
- Familiar Interface: Supports stdin/stdout piping and -e for executing programs (like a p2p reverse shell or service portal).
- SOCKS5 Built-in: Can act as a persistent tunnel/agent to access internal LANs (FRP/NGROK alternative without the central proxy).
Why not just use Tailscale/Wireguard?

    Tailscale is great for a permanent mesh, but sometimes you just want to pipe a 500MB tarball or a quick shell to a friend's laptop right now without managing nodes, ACLs, or login providers. This is meant to be a "throwaway" secure pipe.

Looking forward to your thoughts!

value: Show HN: A netcat for the NAT era – connect peers via passphrase only

author: gonc


value: adamoufkir

value:
Adam here (https://x.com/adamofk_). I’m a 16-year-old developer working on Crovise, a tool that analyzes landing pages and suggests conversion hypotheses based on structure, copy, and UX patterns.
I started building it after repeatedly shipping pages that looked fine but didn’t convert, and I didn't know what to fix without analytics or paid CRO audits.
Looking for feedback from builders on where this approach makes sense, where it breaks, and what would make it genuinely useful.

value: Show HN: 16 year old building a SaaS

author: adamoufkir

title: Show HN: 16 year old building a SaaS


value: adamaskun

value:
I know, i know — another focus timer... I’m building Flowcycle, an early-stage focus timer experimenting with a simple idea:
Instead of fixed Pomodoro blocks, session length adapts based on time of day, task difficulty, and how focused you felt - called “flow-based Pomodoro” (or Flowmodoro).
It’s very minimal right now:
- No signup
- One tap to start
This is very much WIP — I’m still validating whether “less choice, more automatic behavior” is actually helpful or just restrictive.
Link: https://flowcycle.vercel.app/

value: Show HN: A focus timer that adapts to circadian rhythm (WIP)

author: adamaskun


value: iaroo

value: Show HN: Minimal DL library in C – 24 NAIVE CUDA/CPU ops, autodiff, Python API

author: iaroo


value: jawuilp

value:
Hi HN,
I built this extension because the context switching was killing my productivity. Every time I switched to the browser to share a coding update (#BuildInPublic), I ended up doomscrolling for 20 minutes and losing my flow.
Since existing extensions broke with the X API v2 changes, I decided to build my own solution focusing on privacy and the Free Tier limits.
How it works:
BYOK (Bring Your Own Keys): It uses your own API credentials.
Security: Keys are stored locally using vscode.SecretStorage (never in settings.json).
Limits: It implements a local counter to respect the strict 17 tweets/24h limit of the Free API tier.
Write-only: It intentionally doesn't read your timeline to keep you focused.
It's open source and my first extension. Feedback on the code structure is welcome!
Marketplace: https://marketplace.visualstudio.com/items?itemName=Jawuilp....
Repo: https://github.com/Jawuilp/X-writer

value: Show HN: X Writer – VS Code extension to post tweets from your editor

author: jawuilp


value: emanuelaromano

value:
Hi! I’ve built an MVP for a platform to prepare for video interviews.<p>I’m mainly looking for feedback:
- obvious bugs or broken flows
- UX issues or confusing parts

value: Show HN: A tool to practice video interviews (looking for feedback)

author: emanuelaromano


value: Show HN: EvalView pytest style tests for AI agents (budgets, hallucinations)


value: kaiterraliam

value:
Hey HN,
I’m the co-founder and CEO of Kaiterra, a company that's been on a crazy journey with IAQ (Indoor Air Quality) sensors. I feel like you all might appreciate the story and the outcome: amazing hardware at whatever price you want to pay. If you like economic experiments, read on!
Back in 2014, before air quality was a (somewhat) mainstream topic, we were building some of the first sensors and monitors out there. Some of you might remember our first consumer product, the Laser Egg. We were the first HomeKit-enabled air monitor, the top pick on Wirecutter, and were sold in Apple Stores. This happened when our company only had 6 people!
A few years ago, we pivoted to B2B, focusing on helping Fortune 100s and large-scale building operators with automation and IAQ compliance. That pivot is a story for another day!
Recently, we began supporting the Global IAQ Observatory research project. There is a massive data gap in air quality research that a group of experts is trying to bridge. There are strong regulations around the water and food we consume, but the air in buildings and public spaces (where we spend 90% of our time) is often a total black box. Researchers are working to collect the data needed to eventually drive evidence-based policy.
While the primary research focuses on public buildings, offices, and schools, we've decided to help contribute anonymized residential data. This data is messier but is highly representative of what people are actually breathing.
We decided to make our main Enterprise B2B sensor (the Sensedge Mini) available for everyone to use at home, on the condition that the data can be used for research in an entirely anonymized and aggregated way.
The Hardware (Sensedge Mini):
Sensors: Commercial-grade PM2.5, CO2, TVOC, Temperature, and Humidity.
Connectivity: Ethernet and WiFi, supports MQTT, Modbus, and BACnet/IP.
Data Access: There is an Open API. You can send the data to your own local servers/dashboards while simultaneously contributing to the research database.
The Economic Experiment: Pay What You Want (PWYW) We’ve decided to distribute these using a PWYW model. Honestly, this is probably the most interesting thing for me :). We are making these devices available at literally "whatever price you want". Yes, even $0.
We want to ensure that researchers or students on a budget aren't barred from participating.
The Scaling Logic: We have a finite pool of devices allocated for this. If everyone takes the $0 scholarship, we can only put a limited number of monitors into the world. If participants choose to pay closer to market value, that revenue goes directly into funding more hardware, allowing us to scale the study from a few hundred homes to thousands.
Our goal is to create a global public good: a comprehensive, open-access dataset that can accelerate the transition to healthier indoor environments worldwide.
If you want to pick up a monitor, here's the link: https://kaiterra.com/citizen-science (we reset the number of $0 monitors each day to make sure we don't get totally overwhelmed)
I'm here to answer questions!

value: Show HN: B2B air monitors as "Pay What You Want" for a global study:)

author: kaiterraliam


value: zetacrushagent

value: Show HN: ZetaCrush: LLM Bitcoin Mining Competition

author: zetacrushagent


value: douxx

value: The project identifies as "Your Raspberry Pi FM network". It uses a fork of the well-known PiFmRds as a backend to stream audio on FM radio. It allows the control of multiple pi units with a central server, that can also be temporarly hosted on google cloud shell or github codespaces.

value: Show HN: An easy way of broadcasting radio around you (looking for feedback)

author: douxx


value: yongdohyun

value:
Hi HN,
I’m the sole maintainer of ProjT Launcher, an open-source Minecraft launcher fork
focused on long term maintainability, modern Qt6/QML architecture, and clean
cross platform packaging.
This project started as a technical rework and rebranding effort, but it has
grown into a serious attempt to rethink how a launcher should be structured:
– Qt6 + QML migration
– Clear ViewModel separation
– Cross-platform CI (Linux / macOS / Windows)
– Strong focus on packaging (Flatpak, Nix, etc.)
I’m currently exploring ways to grow a contributor community around the project.
I’m not asking for donations — I’m mainly looking for:
– Technical feedback
– Architecture discussion
– Contributors interested in Qt/QML or launcher tooling
Repository:
https://github.com/Project-Tick/ProjT-Launcher
I’d really appreciate honest feedback, especially on the architecture and
governance direction.

value: Show HN: ProjT Launcher A community-driven Minecraft launcher(single maintainer)

author: yongdohyun


value: bhavnicksm

value:
We open-sourced catsu, a Python client for embedding APIs.
The problem: every embedding provider has a different SDK with different bugs. OpenAI has undocumented token limits. VoyageAI's retry logic was broken until September. Cohere breaks downstream libraries every release. LiteLLM's embedding support is minimal.
catsu provides:
- One API for 11 providers (OpenAI, Voyage, Cohere, Jina, Mistral, Gemini, etc.)
- Bundled database of 50+ models with pricing, dimensions, and benchmark scores
- Built-in retry with exponential backoff
- Automatic cost tracking per request
- Full async support
Example:
```python<p>import catsu<p>client = catsu.Client()<p>response = client.embed(model="voyage-3", input="Hello!")<p>print(f"Cost: ${response.embeddings}")<p>print(f"Cost: ${response.usage.cost:.6f}")<p>```
GitHub: https://github.com/chonkie-inc/catsu
We built this at Chonkie (YC X25) because we needed it badly for our chunking library and to easily switch embeddings for our search product. Apache 2.0.

value: Show HN: Catsu: A unified Python client for embedding APIs

author: bhavnicksm


value: emadda

value:
Hello HN,
Passkeybot is a hosted sign in page that only supports passkeys.
Add email verification and user auth to your app with only a few server side HTTP handlers.
Feedback appreciated, Thanks, Enzo

value: Show HN: passkeybot.com - add passkey auth with a few server side HTTP handlers

author: emadda


value: delduca

value: Show HN: Claude wrote a NES emulator using my engine's API

author: delduca


value: mobrienv

value:
I was randomly browsing claude codes ralph wiggum plugin[1] and was surprised to see my toy project referenced. Since it appears people are getting some value out of it, thought I'd share it here...
Full disclosure, this repo itself was built with a primitive ralph wiggum loop so expect AI slop. It works though. ¯\_(ツ)_/¯
https://mikeyobrien.github.io/ralph-orchestrator/
[1] https://github.com/anthropics/claude-code/tree/main/plugins/...

value: Show HN: Yet another tool to wrap agents in a loop

author: mobrienv


value: NickSharp

value:
Hi. I'm a game designer who cares deeply about AI safety. I saw the Future of Life Institute's Keep The Future Human contest https://keepthefuturehuman.ai/contest/ and made a browser game.
Feedback welcome.
You start the game running a small research lab, building AI. As you grow, a rival appears and the race kicks in. If you build too slow and they steal your resources. But if you build too fast without alignment, all humans lose (including you). In the late game players may want to take their foot off the pedal and slow down progress, but the world has begun to depend on the wonders you create. The challenge is to thread the needle, create great tech for humanity without ending it all.
Game mechanics draw heavily on the essay Keep The Future Human
by Anthony Aguirre, I'm happy to discuss below.
Oh, and don't worry if the UI starts acting weird. As your AI gets more powerful, the game might start misbehaving...
https://TheChoiceBeforeUs.com/

value: Show HN: A browser game about the AI alignment problem

author: NickSharp


value: dhiyaan

value:
Open Source CLI wrapper that lets you switch between multiple Claude accounts and alternative models, including GLM, Kimi, Gemini, and Codex.
Supports concurrent sessions, so you can run different accounts or models in separate terminals at the same time.
Install: npm install -g @kaitranntt/ccs

value: Show HN: CCS - Switch between multiple Claude accounts and AI models instantly

author: dhiyaan


value: lpipe

value: Show HN: I built a Resume Compiler because the ATS rejected my PDF

author: lpipe


value: xinghaohuang

value:
Hi HN,
While building RAG agents, I noticed a lot of token budget was wasted on formatting overhead (HTML tags, JSON structure, whitespace). Existing solutions felt too heavy (often requiring torch/transformers), so I wrote this lightweight, zero-dependency library to solve it.
It includes strategies for context packing, PII redaction, and tool output compression. Benchmarks show it can save ~15% of tokens with negligible latency overhead (<0.5ms).
Happy to answer any questions!

value: Show HN: Prompt-refiner – Lightweight optimization for LLM inputs and RAG

author: xinghaohuang


value: fjfaase

value: Show HN: C-compiler to compile TCC for live-bootstrap

author: fjfaase


value: ZacharyZZ

value:
Hi HN,
I built this because I was frustrated with existing Markdown-to-PDF converters. Most of them rely on HTML/CSS (puppeteer) which often messes up pagination and tables, or they require uploading data to a server (privacy concerns).
MDXport is a purely client-side tool.
The Stack:
- Core: Typst (a modern LaTeX alternative) compiled to WebAssembly.
- Privacy: Zero data leaves your browser. All rendering happens locally via WASM.
- Feature: It handles page breaks and complex tables much better than standard CSS print drivers.
I also added some heuristics to automatically fix common formatting errors from LLM generated markdown (e.g., broken nested lists or overflowing tables).
It's currently an MVP. No sign-up required, free to use.
I'd love to hear your feedback on the rendering quality compared to Pandoc/LaTeX.
Link: https://www.mdxport.com

value: Show HN: MDXport – Browser-Based Markdown to PDF Using Typst and WASM

author: ZacharyZZ


value: MKuykendall

value: Show HN: Muxide – Zero-dep pure Rust MP4 muxer (H.264/H.265/AV1, no FFmpeg)

author: MKuykendall


value:
I'm a developer who's terrible at estimating how long tasks take. "This bug fix will take 2 hours" → actually takes 9 hours. Every. Single. Time.
So I tracked my estimates vs. actual time for 3 months. My accuracy: 47%. I was planning twice as much work as was physically possible, then wondering why I was always behind.
TimeBoxer is an iOS app I built to solve this:
- Estimate before starting a task
- Timer tracks actual duration
- See your accuracy over time
- Learn which task types you consistently misjudge
After 100+ tracked tasks, I discovered:
- Bug fixes: Off by 3-5x (thought 1hr, actually 4-6hrs)
- "Quick" refactors: Off by 4-6x
- Code reviews: Off by 4x
- Features I've built before: ~75% accurate
Now I estimate based on historical data instead of optimism. Hit 80% of my sprint estimates for the first time in my career.
Built natively in SwiftUI with Live Activities for Lock Screen timer. Free tier, $4.99/mo premium for full analytics.
The ADHD developer community has been particularly interested - time blindness makes estimation even harder, and having external data helps compensate.
Happy to answer questions about the tech stack, estimation patterns I found, or design decisions!

value: Show HN: TimeBoxer – Track estimation accuracy to fix terrible time planning


value: hkpatel

value: Show HN: Nob – Make your terminal AI-powered

author: hkpatel


value: mlhpdx

value: Show HN: Proxylity – serverless UDP services on AWS (one year in production)

author: mlhpdx


value: Show HN: GymBenchmark – A gym tracker focused on data visualization


value: Show HN: Made a plugin where you can generate consistent illustrations in Figma


value: Solance

value:
We built Solance to mainly tackle one major problem that bothered us with music discovery of today - it's almost all done through algorithms. Sure they can be good at times but often they either completely miss the mark, or they avoid leaving your comfort zone and only show you songs super similar to the music you already listen to.
We wanted to bring a human feel back to music discovery, where you can once again enjoy the feeling of helping someone discover a new song or artist and for them to fall in love with the music!
Solance is an app designed to serve as a social platform where you can follow friends, family and colleagues, and even discover other similar music profiles, which can help broaden the range of music you're exposed to, while also being able to interact with others!
Although we are fairly new, we have a great base product for seeing the music other people are listening to, and allowing you to get a feel for them through preview, likes, comments and also saving them directly to your Spotify Liked Songs.
Music discovery through algorithms is on the decline as we can see that people want to return to that feeling of music being a shared and social experience, rather than the individual experience that all music streaming platforms are trying to create.
We would also LOVE to hear any thoughts and ideas for features we could implement to make the experience even better and create a platform that people will love! Any feedback or feature requests will be hugely appreciated!
With lots of love from the Solance team! <3

value: Show HN: Solance – Discover Music Through Friends

author: Solance


value: mickamy

value:
Injector is a small Go DI code generator where the entire wiring
lives in struct field tags.
No provider sets, no DSL, no runtime reflection.
Just Go types and generated code.
Looking for feedback on the approach.

value: Show HN: Injector – Field-tag-only dependency injection for Go

author: mickamy


value: Introducing Open-Schematics: a large public dataset of electronic schematics with rendered images and structured metadata for ML, circuit understanding, retrieval, and validation.

value: Show HN: Largest public dataset of electronic circuit files


value:
Early days but have made some progress.
Cursor just uses so much RAM but the models are too good for me to move. Trying to fix this.

value: Show HN: Hackily reverse engineering Cursors tab complete


value: accessun

value:
I'm the builder behind Pixlio. Pixlio is a browser-based AI image editor and generator for common tasks like background replacement and photo effects.
I built it because I kept needing repeatable image edits for various cases. I wanted a workflow that doesn't require users to be good at prompt writing, since many tools mostly provide a prompt field.
Pixlio is built with Astro and runs on Cloudflare Workers. There's a one-click signup and a small amount of free credits so you can try the core flows quickly. Image processing has real per-request costs, so it can't be unlimited free - paid credits cover heavier usage.
I'd love feedback on (1) the editor workflow and (2) what kind of image tools would be most useful next.

value: Show HN: Pixlio – Browser-based AI image editing tools

author: accessun


value: Show HN: Exploring Mathematics with Python


value: ebcode

value:
Hello HN. Here's the TLDR from the linked blog post: After running into context window issues on my first two projects, I developed a tool for making Claude Code use fewer tokens by creating an indexer that provides context in the search results. Built with sqlite and tree-sitter, it currently supports the following languages: C, Go, PHP, Python, and TypeScript. Get the code here: https://github.com/ebcode/SourceMinder
Happy to answer any questions about it here, and open to critical feedback. Thanks!

value: Show HN: SourceMinder, a Context Aware Code Search for Solo Devs and Claude Code

author: ebcode


value: noteable

value:
PacketPro is an iOS app for inspecting network traffic generated by apps directly on the device.
It allows viewing outbound HTTP/HTTPS requests in real time, including request URLs, headers, methods, status codes, and response bodies.
All inspection is performed locally on-device, without requiring a Mac proxy or jailbreak.
Beyond basic traffic capture, it provides multiple filtering and analysis tools:
- Multiple filters for hosts, domains, request methods, status codes, and content types
- Per-session organization, making it easier to isolate traffic from specific app interactions
- Ability to include or exclude requests dynamically while capturing traffic
- Search and quick filtering across captured sessions
For development and debugging workflows, it supports:
- Inspecting API request/response payloads during app development
- Verifying request headers, auth tokens, and parameter formatting
- Debugging unexpected network behavior or redundant requests
- Analyzing third-party SDK and analytics traffic
- Exporting captured traffic for further inspection or documentation
Some notable points:
- Fully on-device traffic inspection (no external server involved)
- Works with regular iOS apps
- Session-based request organization
- Multiple filtering options for focused analysis
- Export support for further analysis

value: Show HN: PacketPro – Inspect iOS app network traffic locally

author: noteable