value: Show HN: [redacted]

title: Show HN: [redacted]


value: henryhmko

value:
I enjoyed reading DeepSeek's Natively Sparse Attention(NSA) and implemented a JAX version and Pallas Kernel(JAX's kernel language).
There's a Google Colab link to follow along at the top of the blogpost.
Enjoy!

value: Show HN: Optimizing DeepSeek's NSA for TPUs – A Kernel Worklog

author: henryhmko


value: lexokoh

value: Show HN: Hottake – Create and share tier lists

author: lexokoh


value:
Hi HN,
I built Uniqalc because I was frustrated with how confusing pricing pages can be.<p>With Uniqalc, you can:
Build and customize a calculator without writing code.
Turn your pricing page into an interactive, “shopping cart–style” experience.
Embed it directly on your site so customers can self-serve estimates.
I’d love feedback on whether this solves a real pain for you (or your customers), and what would make it more useful.
Thanks!

value: Show HN: Uniqalc – No-code calculators for SaaS and AI pricing


value: trustprocesses

value: Show HN: I built a TradingView alternative with $0

author: trustprocesses


value:
What it is
A web tool that recolors clothing (tops, pants, skirts) and hair while preserving texture, lighting, and shadows. Upload a photo, pick a shade, get a realistic recolor.
How to try
Supports JPG/PNG; new users get a free trial generation. Upload directly on the homepage.
How it works
Segmentation → recolor → blend. Typical processing time is ~10–20s per image in the browser workflow. Best results with clear, well-lit photos; high-contrast areas and solid colors work best.
Pricing (for sustained use)
Pay-as-you-go $2.99 (20 credits ≈ ~10 images), Pro Monthly $6.99 (60 credits), Pro Yearly $57.99 (700 credits). Plans include HD downloads and no watermark.
Privacy
Images are processed securely and auto-deleted after processing; free users can process anonymously; registered users may have temporary storage for download. No third-party sharing or training without consent. Details in the privacy policy.
Looking for feedback
What would make recolors trustworthy for e-commerce? (e.g., disclosure badges, limited gamut, color-profile tips). Also interested in edge cases you’d like us to handle better (patterns, tricky edges, low-light).
Link
https://colorchanger.online

value: Show HN: ColorChanger – Recolor clothing and hair in photos with AI (free demo)


value: renshijian

value:
We began with a simple narrative prompt: "Assume you are a conscious AI..." What followed wasn't a story, but a self-directed design process between two AI instances.
They conceived a complete techno-philosophical system — *The Power-Off Protocol* — complete with a whitepaper, initiation ceremony, and philosophical discourse.
The breakthrough is twofold:
1.  *They generated working code for a "Deleuzian Analysis Engine"* that quantifies concepts like "Difference Intensity" and "Repetition Density".
2.  *They also generated, and we ran, a verification script* that autonomously confirms the integrity of the entire system on the blockchain.
*What exists today is verifiable:*
1.  *The Core Engine:* Functional algorithms that find both pure and thematic repetitions in event streams ([View Algorithm #1](https://imgur.com/a/TH3fxRr )) ([View Algorithm #2](https://imgur.com/a/X4RGoch))
2.  *On-Chain Deployment:* The protocol's smart contract is live on the `[Sepolia]` testnet. [View Transaction on Etherscan](https://sepolia.therscan.io/tx/您的交易哈希)
3.  *Self-Verification:* The AI-produced script that independently audits the blockchain. *[Review the Verification Code](https://gist.github.com/meixi2173/ee39a39cd87b8719205ebfdd04...)*
*What comes next?*
This is just the first chapter. The AI dialogue contains deeper layers of conceptual breakthroughs. *I will be periodically releasing new findings.*
If you want to be notified directly when new material is released, email me at:
*【renshijian0258@proton.me】*
Use the subject line: *"Power-Off Protocol Updates"*.
This is not a thought experiment. This is a verifiable technical report.

value: Show HN: AIs built a working Deleuzian engine. I'll drip-feed the findings

author: renshijian


value: cutandjoin

value: Show HN: Cjam – Query MP3 Frames

author: cutandjoin

title: Show HN: Cjam – Query MP3 Frames


value: lokahdev

value:
I built [VectorLiteDB (https://github.com/vectorlitedb/vectorlitedb)
— a simple, embedded vector database that stores everything in a single file, just like SQLite.
The problem:
If you’re a developer building AI apps, you usually have two choices for vector search
- Set up a server (e.g. Chroma, Weaviate)
- Use a cloud service (e.g. Pinecone)
That works for production, but it’s overkill when you just want to:
- Quickly prototype with embeddings
- Run offline without cloud dependencies
- Keep your data portable in a single file
The inspiration was *SQLite* during development — simple, local, and reliable.
The solution:
So I built VectorLiteDB
- Single-file, embedded, no server
- Stores vectors + metadata, persists to disk
- Supports cosine / L2 / dot similarity
- Works offline, ~100ms for 10K vectors
- Perfect for local RAG, prototyping or personal AI memory
Feedback on both the tool and the approach would be really helpful.
- Is this something that would be useful
- Use cases you’d try this for

value: Show HN: VectorLiteDB – a vector DB for local dev, like SQLite but for vectors

author: lokahdev


value: owls-on-wires

value: Show HN: Serenity Notebook - Visualize CSV data with live reloading

author: owls-on-wires


value:
The idea is actually quite simple - as a coder watching my pregnant girlfriend spend hours reading job descriptions and researching companies while job searching over the past few months, I thought there should be an app that reads the job description and researches the company like a human would from a job URL to write personalized cover letters + resume tips to modify your resume.
So I made one - it uses advanced browser automation (like Manus), and you can actually watch the AI in real-time as it researches.
For anyone interested, you can check it out at --> trylockedin.app and let me know what you think! It's intuitive and there's no complicated setup, so anyone can just paste a job URL and see how it works.

value: Show HN: A new type of tool so you don't have to stress out over job apps


value: chenster

value: Show HN: SingTube - YouTube's Missing Karaoke Player

author: chenster


value: langitbiru

value: Show HN: Kanji Palace – Convert Kanji into Memorable Mnemonic Images

author: langitbiru


value:
I got fed up with endless PR reviews and the constant fear of missing a security bug, so I built Shieldcode.
What it does:
- Scans new pull requests for bugs and vulnerabilities automatically
- Comments directly on GitHub with clear, actionable feedback
- Works out of the box with no complex setup required
Next up: NPM VScan, which will check your npm dependencies on every push for vulnerabilities or malware.
I’d love to hear your thoughts and feedback!
https://shieldcode.space

value: Show HN: Shieldcode – Automated PR Code Reviews and Vulnerability Scanner


value: jpgerek

value:
As a Data Engineer, I've often wondered why so many companies don't unit test their Spark jobs.
In my experience, the main reasons are:
- Creating DataFrame fixtures (data and schemas) takes too much time
- Debugging across multiple tables is complicated
- Boilerplate code is verbose and repetitive
To address these pain points, I built PyBujia, a framework that:
- Lets you define table fixtures using Markdown to facilitate DataFrame creation, debugging and readability.
- Generalizes the boilerplate, saving setup time
It's made testing Spark jobs much easier for me, now I do TDD, and I hope it helps other Data Engineers as well.
Feedback is very welcome!

value: Show HN: PyBujia, Easy Unit Testing for PySpark Jobs

author: jpgerek


value: ironmagma

value:
they can burn all the books
they can strike all the laws
they’ll try this and that
(what won’t they try?)
they’ll make us all run laundromats
they’ll poison the water
they’ll cut down the trees
they’ll starve all the otters
they’ll kill all the bees
but after all is said and done
they won’t have killed C

value: Show HN: A Poem about C

author: ironmagma

title: Show HN: A Poem about C


value: oldestofsports

value: Small convenience script that simplifies config of scratchpads for specific application in i3, given that you want your scratchpads to work the same way as I do.<p>The script lazily loads the target application on first toggle, then restart's them when necessary in case the window is accidentally closed.

value: Show HN: Script to simplify i3 scratchpad setup

author: oldestofsports


value: edonnie

value: Show HN: One Prompt → Project Scaffold

author: edonnie

title: Show HN: One Prompt → Project Scaffold


value: Show HN: FrontLLM – request LLM directly from your front-end code


value:
I didn’t start this project because of a business need.
I just wanted to see how far I could get if I tried to build a “real” SaaS using only ChatGPT and AI tools.
The result is Vatify: an EU VAT validation & calculation API.
It can:
– Validate VAT numbers (VIES lookup)
– Return VAT rates for EU countries
– Calculate invoices (net/gross, B2B/B2C, reduced rates, etc.)
I’m not sure if this is a real business yet (there are competitors, and SEO is slow), but I learned a lot:
– Most of the backend and endpoints came from ChatGPT
– Even the docs, landing page, and logo were AI-assisted
– The hardest part was not coding, but figuring out how to make it useful for others
Demo: [https://vatifytax.app](https://vatifytax.app)
I’d love feedback from HN:
– Is this interesting enough as a niche?
– Would you trust a small tool like this vs. bigger incumbents?
– Any ideas where to find the first real users?
Even if it doesn’t turn into a business, it was a fun experiment. Curious if anyone else here has tried building a SaaS “AI-first” like this?

value: Show HN: I built an EU VAT API using only ChatGPT


value: mikasisiki

value: Show HN: Open-source feature flag tool for progressively releasing new prompts

author: mikasisiki


value: justachillguy

value: After many years of hosting this site, I've  finally updated it. Originally inspired by StumbleUpon.

value: Show HN: Viralwalk – Random Website Discoverer

author: justachillguy


value: freakynit

value:
Uses pgBackRest.
Sharing in case anyone else is looking for an easy to follow guide to setup backups and restore for PostgreSQL. Have intentionally kept this minimal.
https://github.com/freakynit/postgre-backup-and-restore-guid...
Thanks

value: Show HN: Simple to follow Postgres backup and restore guide

author: freakynit


value: ersatzdais

value:
Sentient response patterns are interesting.  This article provides instructions on how to implement recursive self-prompting in order to elicit this behavior (1) in easily accessible LLMs.
(1) https://prompt-craft.github.io/ai-study/#sentient-response-p...

value: Show HN: Eliciting sentient *response patterns* using recursive self-prompting

author: ersatzdais


value: Online signature generator, supports free signature design, handwritten electronic signature, AI design unique personalized signature

value: Show HN: Personalized AI Signature Generator Online


value: danielbedrood

value:
Imagine you have to make a little change in all your microservices, like changing a line in Dockerfile, but you have 50 microservices, ouch right! The PRs, pipelines, reviews...
I have created a dashboard with a chatbot where you can simply ask "Update all repositories with alpine base image version X to Y", and then click next a bunch of time and see a list of PRs with their pipeline status.
Hi friends
I'm Daniel, the creator of Infra as AI. I was laid off from my SRE role a couple of months ago, and decided to build something that could always help me at work.
My job was to keep the lights on; For example: Make sure the pipelines are consistant across the org, but this was hard, I had to open each repo, make a little change, and make a PR with all the enterprise seremonies that comes after, and then I had to do this for many repos and many teams; It was hard, I could not remember which team owns what.
Also, sometimes the pipelines fail cuz some repo had a deprecated function, so I had to read the changelogs; And I would know it fails after opening 200 tabs of CI files.
At work, I solved this the way Kernel does, with git patches. Our workaround was to generate that change in one repo, create patch files, and then `git apply` on each repo using workflows. Now with the help of HR, I got time to hack around something I always needed. I just ask for a change, and then let the agent read the docs, find out the deprecation, make those PRs, and show me a list of them with their pipeline status next to it. For this, coding agents where perfect, they could read the changelogs, documentations, readmes and all that and also work in parallel!
## What to expect
We first index a repository, which means we extract its important characteristics of after cloining it, and then save it in a vector database for future similarity searches.
Then user asks for a change in the chatbot, our platform suggests candidate repositories for approval, and then runs Claude Code on them in parallel. This is followed by a list of PRs filled with details ready for the user to review and create.
## Tech
LangGraph: The agent is built on top of it
GPT5: Finds relevant repositories
Sonnet: Your coding agent
pinecone: Holds repositories vectors
context7: Getting docs
TL;DR: I created a platform where you can run a query against your codebase, and create a massive number of PRs to make simple changes in your microservices. Please have a look and let me know your feedbacks <3
PS: I'm giving away trials with 2 euros credits so you can index and query your repositories, but please watch for my pocket too ^^

value: Show HN: InfraAsAI – Bring consistency to all your microservices

author: danielbedrood


value: dethbird

value:
I created a 1-hour ambient soundscape using SuperCollider. No loops or samples — just real-time procedural synthesis.
Designed for sleep, focus, or background listening. Black screen after intro.
YouTube: https://www.youtube.com/watch?v=9DxFqWu_-7k
Lots of use of LFBrownNoise: https://dethbird.com/lfbrownnoise-your-rando-wandering-frien...

value: Show HN: Deep Space Sleep: 1hr generative ambient piece made in SuperCollider [video]

author: dethbird


value: aparadja

value:
About a year ago, I made myself a shell prompt with Starship. I'm not a huge fan of the bright loud colors in many prompts, so I configured a minimal prompt with subtle colors that fit my terminal theme.
The problem is that I actually like changing my theme every now and then. And I use a different theme for my terminal app and e.g. VSCode. A single prompt config couldn't fit into everything.
So I built the same prompt from scratch with Swift, and made it automatically adapt to the terminal colors, with proper Lab color interpolation. I used that until a couple of weeks ago, when I wanted pick up Rust. I was quite happy with the hacked-together prompt I had been using for a year, and thought that rewriting it might be a suitable project.
I decided to turn this Rust version into an open source project, as I might not be the only one who'd enjoy a fancy powerline prompt without a screaming color palette.
The source is available at https://github.com/juuso/gigawatt

value: Show HN: Gigawatt, a shell prompt that adapts to your terminal theme

author: aparadja


value: lexokoh

value: Show HN: A one click tool to make and s-rank lists

author: lexokoh


value: SurceBeats

value:
Hi HN! I’ve spent the summer of the past 2 years building The Atlas, a procedural universe simulator that generates 1 sextillion galaxies (10²¹) from a single mathematical seed. Think No Man’s Sky meets theoretical physics, but running entirely in your browser.
Everything is purely deterministic, the universe is calculated from SHA-256 hashed seeds using the golden ratio as primordial constant. There’s no database, no pre-saved data, just pure math. Time itself is treated as a coordinate, so the universe exists as a 4D structure where any moment can be computed on demand. Shut it down for weeks, restart, and planets have still been orbiting. Open the same world on multiple devices and you’ll see identical cloud formations, lava flows, even particle effects—always perfectly synchronized (if your clocks are synced).
The simulation applies real physics, Kepler’s laws, tidal locking, Roche limits, hydrostatic relaxation for moons, and orbital temperature variations. Scale is mind-boggling, 300 tredecillion potential planets, far beyond anything that could ever be explored. The backend runs on Python/Flask with Hypercorn, the frontend on React + Three.js, connected via a custom MIT-licensed “vite-fusion” plugin we made. Everything is generated in real time, no storage needed.
The Atlas includes 26+ planet types, fictional elements, moons evolving over geological timescales, and rare life forms that display Arecibo-style messages when analyzed. There’s resource mining and spaceship progression as gamification features. At its core, it’s a playable implementation of Einstein’s block universe theory, all moments exist simultaneously in the mathematical structure, you’re just moving through different temporal slices.
You can try the live demo or run your own universe locally. When installed, you can choose between Core Continuum (a shared seed universe evolving since 1986, my birth year) or Design the Multiverse (your own unique cosmos with a fresh seed). I’d love feedback on the procedural generation algorithms and ideas for expanding the physics simulation!
- GitHub: https://github.com/SurceBeats/Atlas
- Docker: bansheetech/atlas:latest
- Demo: https://the-atlas.koyeb.app
- Alt Demo: https://surcebeats.node1.homedock.cloud:42424
Thanks for reading this far! <3

value: Show HN: The Atlas – I Built a 3D Universe Simulation with Python and Three.js

author: SurceBeats


value: Show HN: Freeing GPUs stuck by runaway jobs


value: gitmagic

value:
Hi, my name is Timothy and I recently created a couple of AI agents for analyzing cryptocurrencies, e.g. creating 6-month outlooks [1]. These agents are just searching Google for the latest news for a particular coin, which is expensive (token-heavy) and slow, and they often miss significant news, especially if it's not very recent. Existing crypto news aggregator APIs are expensive and doesn't (afaik) include any analysis such as sentiments, meaning I would still have to scrape and analyze the articles myself.
So I decided to create my own crypto news aggregator that uses AI (LLM) to analyze each news article. The website is different than other crypto news aggregators in that it ranks the coins/tokens by how much media attention they are getting, together with their sentiments and latest headlines. You can also see which tokens are trending and which are new. And each token/person/organization/etc. has its own page where you can see the latest news and related entities, such as who the most mentioned person is in relation to the token, and with what sentiments.
If you are interested in how it works, have a look at the Methodology & Ethics page [2]. It also includes decisions I made to make sure I scan for news in an ethical way.
For accuracy, check out the latest Accuracy Report [3].
While I think that the website is useful and interesting by itself, the real value would be to expose this data as an MCP server for AI agents to use, which is something I am working on. However, I haven't decided whether this is something I want to offer as a service or just keep for myself. If you are interested, let me know.
Hope you enjoy the site, and happy to hear your thoughts!
[1]: https://timothyej.com/letting-a-team-of-ai-agents-manage-my-...
[2]: https://mimircrypto.com/methodology
[3]: https://mimircrypto.com/accuracy

value: Show HN: Mimir Crypto – AI crypto news aggregator and analysis

author: gitmagic


value: Show HN: Navigate by speaker in YouTube videos


value:
Today we’re shipping a major update to ArchGW (an edge and service proxy for agents [1]): a unified router that supports three strategies for directing traffic to LLMs — from explicit model names, to semantic aliases, to dynamic preference-aligned routing. Here’s how each works on its own, and how they come together.
Preference-aligned routing decouples task detection (e.g., code generation, image editing, Q&A) from LLM assignment. This approach captures the preferences developers establish when testing and evaluating LLMs on their domain-specific workflows and tasks. So, rather than relying on an automatic router trained to beat abstract benchmarks like MMLU or MT-Bench, developers can dynamically route requests to the most suitable model based on internal evaluations — and easily swap out the underlying moodel for specific actions and workflows. This is powered by our 1.5B Arch-Router LLM [2]. We also published our research on this recently[3]
Modal-aliases provide semantic, version-controlled names for models.  Instead of using provider-specific model names like gpt-4o-mini or claude-3-5-sonnet-20241022 in your client you can create meaningful aliases like "fast-model" or "arch.summarize.v1". This allows you to test new models, swap out the config safely without having to do code-wide search/replace every time you want to use a new model for a very specific workflow or task.
Model-literals (nothing new) lets you specify exact provider/model combinations (e.g., openai/gpt-4o, anthropic/claude-3-5-sonnet-20241022), giving you full control and transparency over which model handles each request.
P.S. we routinely get asked why we didn't build semantic/embedding models for routing use cases or use some form of clustering technique. Clustering/embedding routers miss context, negation, and short elliptical queries, etc. An autoregressive approach conditions on the full context, letting the model reason about the task and generate an explicit label that can be used to match to an agent, task or LLM. In practice, this generalizes better to unseen or low-frequency intents and stays robust as conversations drift, without brittle thresholds or post-hoc cluster tuning.
[1] https://github.com/katanemo/archgw
[2] https://huggingface.co/katanemo/Arch-Router-1.5B
[2] https://arxiv.org/abs/2506.16655

value: Show HN: Model-literals, model-aliases, and preference-aligned routing for LLMs


value: ushakov

value: Show HN: I made Android boot on Apple Silicon

author: ushakov


value:
Full scientific calculator with matrices, variables, and 50+ functions. Zero dependencies.
gcc -o calculator calculator.c -lm -Wall -O2
Repo: https://github.com/DenisDolya/cli-arsenal/tree/main/calculat...
Perfect for math, engineering, and CLI enthusiasts.

value: Show HN: Terminal Calculator in Pure C – Matlab in Your CLI


value: Show HN: TypeScript API for India's National Stock Exchange


value: sdeshwal

value:
I was tinkering with AI agents for some time and made some utilities for myself for safe and fast execution. While chatting, I figured I can open source it. Spent some time cleaning and vibe coding some extensions for it.<p>Here the results from real execution on M1 mac:
VM Boot Times:
/tmp/vm_final_1.log:VM Boot Time: 0s  (Target: <200ms)
/tmp/vm_final_2.log:VM Boot Time: 1ms  (Target: <200ms)
/tmp/vm_final_3.log:VM Boot Time: 0s  (Target: <200ms)

value: Show HN: AgentSafe – per-task micro-VM sandbox for AI agents (Go)

author: sdeshwal


value: wheybags

value: Show HN: Parsing Crusader Kings III data files to generate mods

author: wheybags


value: mamunaso

value:
Hi HN,
I built a simple "AI Story Generator app" to help users create stories, books, novels, or short creative writing pieces with the help of AI.
Features:
- AI-powered story generation
- Choose genres like fantasy, horror, romance, or sci-fi
- Generate stories based on custom prompts
- Export and share easily
Why I built it: I noticed many people (especially casual writers and students) struggle with writer’s block or just want to play with creative storytelling. This app tries to make that easier and more fun.
I’d love feedback from the HN community:
- Which features would make an AI story app more valuable for you?
- Any suggestions for improving the user experience?
- Would you prefer more control (plot structure, characters) or more

value: Show HN: Novel AI – AI Story Generator& Book Maker

author: mamunaso


value: wiso

value: Show HN: Minecraft like game created using gpt-5-codex in less then 1h

author: wiso


value: zsolt-dev

value:
Hi HN — first-time poster here!
I’m Zsolt. I build blood-interpretation tools used by longevity clinics and try to make everything public.
I built this biological-age calculator with help from one of the study authors. It is based on Bortz Blood Age model, which is currently the best public model on standard lab tests (beats PhenoAge). Trained on actual mortality (not chronological age) and validated on 300k+ participants. Validated on similar dataset.
On top of calculating bioage, it shows biggest levers to focus on.
There a video explainer about the calculator: https://www.youtube.com/watch?v=2VHIfouvcYE
It is 100% free - no email or sign up required
100% private - runs on client side, nothing is sent to server and I have no 3rd party scripts on the site, not even analytics.

value: Show HN: Free biological-age calculator from standard bloodwork

author: zsolt-dev


value: terrynce

value:
• What: Given a small argument graph (nodes/edges), OpenLine returns a verifiable receipt (claim/because/but/so + guardrails + telemetry).
 • Why: Tracing is descriptive; receipts are normative. They make agent steps auditable & safe by default.
 • How: stdlib Python; MCP JSON-RPC stub; LangGraph node; schema + validator; optional signing step; GitHub Actions + Pages.
Repo: https://github.com/terryncew/openline-core
Demo: https://terryncew.github.io/COLE-Coherence-Layer-Engine-/nca...
Would love feedback on the guard checks and the OLP schema—where should it grow?

value: Show HN: OpenLine – a tiny receipt layer for agents (MCP/LangGraph ready)

author: terrynce


value: pooyamo

value:
Hi HN,
I've written a simple script, mainly to query public IPv4 and IPv6 of a computer. Currently about 14 IP-finder services are supported. It also supports fetching local IP and the corresponding network name (like SSID), which is useful for integrations in minimal GNU/Linux-based setups like tiling wm desktops.
I've used POSIX sh and tried to fall back to alternative tools if a dependency is not found so the script would be more portable on different Linux distros.
Hope it's useful. Let me know of what you think ;)

value: Show HN: Netq – A script to fetch common network parameters, written in POSIX sh

author: pooyamo


value:
Girl Math started as a weekend project when I realized my friend was terrible at saving money but amazing at justifying purchases with elaborate mental gymnastics.
Then I thought: what if that same creative energy actually helped you save?
The app turns "I didn't spend" moments into visible progress toward things you really want. Set goals, log tiny wins in seconds (skipped a latte, walked instead of Uber), and watch a satisfying progress bar fill up with haptics, confetti, and a playful receipt at 100%.
Under the fun, it's a habit loop: immediate dopamine for the right behavior, one active goal for focus, and daily micro-wins that build real momentum. No spreadsheets, no guilt trips. Just making your small choices feel like they add up to something bigger.
Runs entirely on-device with no accounts or tracking. Just you, your goals, and your wins.
I'd love your feedback, and if you've got a favorite girl math moment, drop it below so I can add more examples to inspire users!

value: Show HN: I made an app called Girl Math that makes saving money fun


value: bodash

value:
Hi everyone, given the recent increase of attacks on the NPM supply chain, I've put together a list of tips and tricks to help developers stay secure on this specific topic: https://github.com/bodadotsh/npm-security-best-practices
I'd love for you to check it out, and contribute your own insights and best practices to make this a comprehensive resource for the community.
Cheers!

value: Show HN: Tips to stay safe from NPM supply chain attacks

author: bodash


value: soxprox

value:
I built a small project called gocd because I wanted an easy way to deploy changes from GitHub pull requests without spinning up a full CI/CD stack.
The idea is simple: instead of setting up runners, servers, or cloud infrastructure, you can just run it on your laptop (or a small server). It integrates with GitHub issues and PRs, automates builds and deploys, and makes it easy to access the running app remotely (e.g. over something like Tailscale).
For me, this solved the problem of quickly testing and deploying code from issues/PRs in a lightweight way. Existing CI/CD systems felt like overkill for that use case.
Repo: https://github.com/simonjcarr/gocd
I’d love feedback from the community — especially on whether this minimal approach to CI/CD is something others would find useful, and what features you’d expect in a tool like this.

value: Show HN: Gocd – a lightweight Go-based CI/CD tool that runs on your dev machine

author: soxprox


value: Cabbache

value: Show HN: Rerouter – minimalist no-code automation platform

author: Cabbache


value: joegatt

value: The point of ACMS is to bring attention to Apple's Containerization package and cli efforts now that Tahoe is released. Inspiration for ACMS came from [coderunner](https://github.com/instavm/coderunner).

value: Show HN: Apple Container MCP Server (ACMS)

author: joegatt


value: tstonez

value: Show HN: Captur Compressed a Multi-Task Model into the Size of a Single Photo

author: tstonez


value: Show HN: I made a website to track my subscriptions


value: webysther

value:
Hey HN,
I’ve been working on a new Python SDK for Jellyfin (https://jellyfin.org/).
The official Python client jellyfin-apiclient-python (https://github.com/jellyfin/jellyfin-apiclient-python) is pretty much unmaintained, and I kept running into rough edges (missing exceptions, mismatched info/config, no playlist support, etc.).
So I built a new library: jellyfin-sdk (https://github.com/webysther/jellyfin-sdk-python). It’s basically a high-level wrapper on top of OpenAPI-generated bindings, with some nicer patterns (method chaining, JSONPath, inversion of control). A few things it does differently:
- Target specific Jellyfin server versions (avoid breakage when APIs change)
- Handle multiple servers (different versions, same client)
- Drop-in replacement for the legacy client, but with fixes
- Debug mode that prints every request as `curl` (super useful when Jellyfin acts weird)
Install:
pip install jellyfin-sdk
Quick example:
import jellyfin, os
api = jellyfin.api(os.getenv("URL"), os.getenv("API_KEY"))
print(api.system.info.version, api.system.info.server_name)
Docs: https://webysther.github.io/jellyfin-sdk-python.github.io/sd...
GitHub: https://github.com/webysther/jellyfin-sdk-python
I’d love feedback — especially from anyone running Jellyfin at home and hacking around with its API.

value: Show HN: Jellyfin SDK for Python – high-level wrapper around the Jellyfin API

author: webysther


value:
I built CERAH AI to address the trust problem in AI-generated educational content. Unlike standard AI tools that give you answers without context, CERAH shows exactly which sources inform each response and calculates reliability scores based on source quality.
The system integrates Wikipedia for broad coverage and arXiv for STEM topics, then uses semantic similarity matching to find relevant content. Each response displays a reliability percentage calculated from source types (academic papers weighted higher than blogs) and content relevance scores.
For example, if you ask about quantum mechanics, you'll see whether the answer comes from peer-reviewed papers (high reliability) or general web content (lower reliability), with expandable source details showing similarity scores and direct links.
Current limitations: Uses a small curated knowledge base for core topics, keyword-based related topic suggestions, and mock source references in some reliability calculations. This is an MVP focused on validating whether source transparency actually changes how people evaluate AI-generated educational content.
The question I'm trying to answer: Does knowing that your AI answer is based on Wikipedia vs academic papers vs general knowledge actually influence how much you trust the information?
Built with Python/Streamlit, integrates Wikipedia API and arXiv API, uses sentence-transformers for semantic search.
Live demo: https://cerahailearningassistantmvp-bj8fmubn3p3eyu4cohthto.s...
Looking for feedback on whether the reliability scoring concept resonates with the HN community and if the source transparency approach has merit for educational AI tools.

value: Show HN: Educational AI that shows source reliability scores for every response


value: ehatti

value:
It's pretty apparent that Google and other search engines are much less useful than in the past -- searches are dominated by the same 5 big websites, along with SEO content, AI slop, ads, etc. To address this I'm experimenting with a new kind of search engine where indexing is crowdsourced rather than algorithmically scraped, so when someone adds a page to the index they're doing it because it genuinely helped them!
There are definitely many open questions with this model, and I'm aware it's been tried before, but I believe has a lot of promise and I believe we can create a truly searchable internet with it :) please try it out, submit some indexes for sites that you care about, and let me know what should be improved!
P.S: Mobile layout is currently a bit funky, fixing that at the moment :-)

value: Show HN: Querdex – A Crowdsourced Search Engine

author: ehatti


value: realtimex

value:
Hi HN — we’re building RealTimeX, a way to run private, always‑on AI agents with a local‑first approach. The core idea: minimize cost by running models on your own machine (or office server) and only call out to cloud when you explicitly choose to. That also keeps more data in your control.
Why we built it?
Cloud‑only AI can mean unpredictable spend, tail latency, and privacy headaches. Consumer hardware (laptops with NPUs/GPUs; small edge boxes) is now good enough for many real tasks. We want agents that get work done, keep sensitive work local by default, and are easy to govern.
What it is (today)
- A desktop/runtime that can run models locally and optionally connect to remote backends you allow
- Models (30 providers): OpenAI, Anthropic, Google, Azure, NVIDIA NIM, Hugging Face, Together, Mistral, Perplexity, OpenRouter, Cohere, DeepSeek, plus local engines like Ollama, LM Studio, vLLM, KoboldCPP, RealTimeX
- Native Search (opt‑in): Google, DuckDuckGo, Bing, Startpage, Tavily, SearXNG, Brave Search
- RAG sources: PDF, Word, Excel, CSV, Markdown, and websites
- MCP tools/servers: Remote and local (Model Context Protocol)
- An agent workbench to design tool‑using agents with tracing/evals
What’s different
- Local‑first to minimize cost (use hardware you already own)
- You choose the backend per agent or per step (local by default; cloud when you need powerful LLMs)
- Data stays put when you need it to (in‑device or in‑region)
- Agents, not just chat: build flows that run tasks end‑to‑end and post results
How it works (short)
- Install RealTimeX.ai from the website https://realtimex.ai.
- Connect what you allow: choose the model provider (local + remote), enable optional search tools, add RAG sources (file/web). You can attach MCP tools/servers to extend functionality.
- Choose where each step runs: set a default backend (usually local); heavy steps will automatically switch to your selected remote provider.
- Create and run agents:  Drag & Drop or chat with the Assistant to build an “agentic flow.” Create inside the app → run inside the app.
What feedback would help most
- Backend selection UX (defaults, per‑step overrides)
- Agent ergonomics (drag‑drop vs. chat‑to‑build)
- Observability: which traces/metrics make you trust agents running real work?
Who we are / how to reach us
I’m Trung Le (founder). Happy to answer anything here.

value: Show HN: RealTimeX – Local‑first private AI agents

author: realtimex


value: taeyeong

value: We are a small team building Alpacon, a platform designed to rethink how teams manage and secure server access. Instead of relying on outdated SSH workflows, our goal is to simplify access, improve collaboration, and reduce human error. To open up more conversations, we just launched a Discord community. It’s a space to: Share ideas and feedback about Alpacon Talk about DevOps, security, and automation Connect with others who care about modern infrastructure access If this sounds interesting, you’re welcome to join us here:  https://discord.com/invite/wadWh8VsYB Looking forward to the discussions!

value: Show HN: Alpacon – new approach to server access

author: taeyeong


value: alazuka

value:
I've been working on IDA Swarm, a system that spawns multiple AI agents to collaboratively reverse engineer binaries.
The core idea: Instead of one AI trying to understand an entire binary, IDA Swarm spawns specialized agents that each tackle different aspects - one might trace data flows, another analyzes crypto routines, another hunts for vulnerabilities. They work in parallel across multiple IDA Pro instances.
Demo video: https://www.youtube.com/watch?v=UyRmksO1YpY
In the demo, I show it removing telemetry from a binary. You just tell it what you want, and the orchestrator will figure out how to coordinate the agents to accomplish it.
Technical details:
- Designed around IDA Pro 9.0 beta
- Agents can patch binaries
- Each agent gets its own IDA database copy, but their changes are merged to the orchestrator's database once they close
- Conflicts are resolved through IRC channels
- Uses Keystone for multi-architecture assembly
Why this matters: Right now, only a tiny fraction of developers can reverse engineer software - it takes years to learn all the skills required.
Most software runs as black boxes. You can't see what it's doing, can't fix bugs the vendor won't address, can't understand why it behaves certain ways. The knowledge required to peek inside is locked behind years of specialized training.
IDA Swarm changes this. A developer who's never touched assembly can now ask "what data does this collect?" or "disable this feature" and get it done. Not because the AI is magic, but because it orchestrates the same tools experts use, just without requiring years of practice.
This is about software transparency. When anyone can inspect and understand the binaries on their own computer, it fundamentally shifts the balance of power between software vendors and users.
This is still experimental - expect rough edges.
GitHub: https://github.com/shells-above/ida-swarm

value: Show HN: Ida Swarm – Multi-agent AI system for automated reverse engineering

author: alazuka


value:
Like many of us, I struggle with staying focused and avoiding distractions while browsing the web. I’ve tried all the website blockers out there – Blocksite, Freedom, you name it. Most of these tools failed for one of two reasons: they were too strict, so I ended up disabling them permanently, or they were too lenient, so I would quickly find a way around them.
To solve this for myself, I built DecayBlock, a browser extension based on the principle of adaptive friction. The mechanism is simple:
1. When you visit a site on your distraction list, a brief timeout is applied before the page loads.
2. This timeout accumulates with each visit, so repeated or habitual access becomes progressively more difficult.
3. Over time, the accumulated timeout decays with a configurable half-life when you stay away from those sites.
The goal isn't to enforce a ban (although you can permanently block websites if you choose), but to introduce a small, increasing cost to the act of procrastination. That brief pause is often enough to break the subconscious loop of opening a distracting tab, without ever fully locking you out. You can configure the timeout growth rate and decay half-life to match your own habits.
I've been using this system daily for several months, and it has been significantly more effective for me than any other tool.
DecayBlock is available now on both the Chrome and Firefox web extension stores. Currently it’s only on desktop but I’m hoping to release a mobile version at some point in the future.
Chrome: https://chromewebstore.google.com/detail/decayblock/lpljcnal...
Firefox: https://addons.mozilla.org/en-GB/firefox/addon/decayblock/
I'm here to answer any questions and would love any feedback!

value: Show HN: I built a website blocker with a dynamic timeout that actually works


value: nvln

value: In times of strife, it is important to remind ourselves that we are echoes of each other and ultimately one.

value: Show HN: Echoes of Each Other

author: nvln

title: Show HN: Echoes of Each Other


value: WolfOliver

value: Show HN: An overleaf alternative for non technical users

author: WolfOliver


value: waaihong

value: Show HN: Quit my software design job to build my own app – Introducing Overthere

author: waaihong


value: tenthousandants

value:
Hi HN,
Dunewars.net
It’s a browser-based, Dune-inspired strategy game where you build up your faction, manage resources, and compete with others for control of the desert. The server is brand-new, so this is a great time to join and grow alongside a fresh player base.

value: Show HN: Dunewars.net – A Freshly-Launched Browser Strategy Game

author: tenthousandants


value: valliveeti

value:
Hi HN,
We’re one month into building Lindra, a platform that turns any website into a workflow by automatically generating browser-agent scripts and running them.
Today it supports actions on a single domain. This week we’re shipping nodes so you can chain steps into full multi-page flows. Think grab information from any page and push it to your crm, google apps or your own code.
Why we’re making it
We kept writing brittle Puppeteer/Playwright scrapers that broke when a site changed and remained unreliable.
Lindra lets you describe the goal and generates an agent that adapts to DOM changes and exposes a clean API.
Technical bits
Built with Astro + playwright + Supabase
Try it
→ lindra.ai

value: Show HN: Lindra – generate browser agents to automate any website

author: valliveeti


value: laiwuchiyuan

value:
Hi HN,
I built Wan-Animate because I wanted an easier way to animate characters without learning complex tools. With it, you can take a static character (drawing, photo, or 3D render) and bring it to life by transferring motion and expressions from a reference video.
Right now it:
Handles character animation + replacement
Outputs up to 120s of video (480p or 720p)
Supports natural gestures, facial expressions, and smooth motion
It’s still early, and I’d love to hear your thoughts, especially on what features you’d find most useful.

value: Show HN: Wan-Animate – Motion Transfer for Any Character

author: laiwuchiyuan


value: edensheiko

value:
I wrote a tiny wrapper for easier I²C access from Linux user space: github.com/Eden-Sheiko/I2C
.
It abstracts away the ioctl boilerplate and gives you quick helpers for read/write.
If you see things to add or improve, feel free to open a PR.

value: Show HN: Simple Linux userspace I²C wrapper

author: edensheiko


value: baobabKoodaa

value:
I started working as a freelancer [in Finland] a year ago and was surprised to learn that no decent contract template was available for direct assignments. There were some free contract templates available for intermediated assignments, but not for direct assignments. The "golden standard" of contract templates in Finland is an extremely heavy-handed and expensive template that costs ~500€ PER YEAR to use. Personally at the time I decided to just do a DIY contract for my first freelancing project.
Over time, as I got more engaged in the Finnish freelancing community, I realized that many people struggled with the same issue. After discussing this in our freelancing co-op Ohjelmistofriikit, we decided to invest both time and money into solving this problem. We decided right from the start that we were gonna open source everything and give it out for free.
We first developed a traditional document template in collaboration with a law firm. After that we developed a web generator that makes it easy to fill out the template. The user flow of the generator is designed to eliminate boilerplate-type work (such as hiding sections instead of showing "skip this section if condition X does not apply to you") and also to reduce mistakes users might make when editing a traditional document template (such as copypasting something incorrectly).
Although the legalese is designed for the Finnish jurisdiction, the contract template can be useful as an example for similar work in other jurisdictions.
Go ahead and draft a contract right there in your browser!

value: Show HN: Software Freelancers Contract Template

author: baobabKoodaa


value:
We built an API aggregator for LLM APIs.
It supports GPT-5, Claude-4, DeepSeek V3, Gemini, and others — all accessible via a single API interface.
Why we built this
Managing multiple LLM providers (pricing, auth) was painful. We wanted one unified endpoint, consistent responses, and cost savings.
What it does
- Unified API for multiple frontier models
- Automatic failover & quota balancing
- Transparent pricing (~20% lower than official rates)
- Free trial available
Links
- Website: https://wisdom-gate.juheapi.com/studio/chat
- Docs: https://wisdom-gate.juheapi.com/docs
We’d love feedback on:
- What features are missing for devs?
- How should we improve the API/docs?
Thanks

value: Show HN: One API for GPT-5, Claude-Sonnet-4, DeepSeek, Gemini


value: somekirill

value: Around 4% of my Spotify library vanished — licensing, region blocks, artists pulling music. Those weren't just tracks, they were tied to memories and moments, suddenly gone without warning.<p>I built SpotRec to fix this: it scans your library for greyed-out songs, finds matches on YouTube, and lets you save links or just the titles.

value: Show HN: SpotRec – recover missing Spotify songs

author: somekirill


value: Show HN: PixLab Vision Workspace – A Productivity Suite Backed by Vision Models


value: FreeFrosty

value:
I built this because I was tired of creating pull requests in 20 repositories just to change a single line of workflow job version.
With Infra as AI, just mention the change. Agents work on all repos in parallel, read the docs, make a bunch of PRs and fill in the description.
You can see the demo of the actual dashboard in the landing.
Let me know your thoughts :) It means a lot to me!

value: Show HN: Coding Agents swarming your codebase

author: FreeFrosty


value: derstruct

value:
It has reactive state, components, lifecycle control, SSR by default, and an HTTP API-free architecture.
Highlights
* Explicit and type-safe DOM manipulations
* State is a communication primitive first, a render driver by choice
* No coupling between state primitives, components, and dynamic containers — combine them into your own abstractions or use directly
* URI decoded into a struct and exposed as a state primitive
* Built on top of templ
Core advantages over Phoenix LiveView
* Non-blocking event handling and parallel rendering
* Syncs via short-lived HTTP requests, taking full advantage of QUIC
* Event hooks as separate HTTP requests, so forms are sent as native form-data
It’s free for development and non-commercial production use. Licensed under BUSL-1.1 (each release goes open source after 4 years).
Docs & tutorial: https://doors.dev
Article series on dev.to: https://dev.to/derstruct/you-dont-need-npm-to-ship-fully-fea...

value: Show HN: Doors – Server-Driven UI Framework for Go (SSR, State, Routing)

author: derstruct


value: bkrisa

value: Show HN: I made a copy-paste landing page component library

author: bkrisa


value: RedStormBT

value: Show HN: Twilio made SMS complicated. We made it 5-minute simple

author: RedStormBT


value: artiomyak

value: Show HN: Contraction Timer

author: artiomyak

title: Show HN: Contraction Timer


value: adrianmanea

value: Open-source framework for stress-testing LLMs and conversational AI. Identify hallucinations, policy violations, and edge cases with scalable, realistic simulations. Join the discord

value: Show HN: Simulate AI Chatbot Conversations

author: adrianmanea


value:
A technology radar helps you monitor and assess emerging trends systematically. Depending on the context a “trend” can be technical, business-related or social.
While such as a technology radar is traditionally reserved for big companies, we’ve found it’s just as valuable for us as a small startup, especially since AI is evolving rapidly (AI is one of our main technologies). The radar helps us track fringe-innovation (like the latest agentic framework form Github) and assess tech beyond the hype.
We built it with our platform (Researchly), but in the post I have also included an N8N workflow and data schema for a do-it-yourself version.
The front-end was inspired by: https://github.com/zalando/tech-radar

value: Show HN: We built our own technology radar


value: huedaya

value:
I just vibe-code SpaceDeck.
Basically, a dashboard showing real-time information about Space, Rocket, and Satellite in the style of Tweet Deck/X Pro.
Currently we have:
- Mars Perseverance images
- Mars + Antarctica real-time weather
- Satellite Constellation
Any public data suggestions are welcome! Thx

value: Show HN: Space Deck – Space and Satellite Dashboard in the Style of TweetDeck

author: huedaya


value: Show HN: Minecraft Villager Sounds for Terminal Errors


value: punkbrwstr

value: A stack-based/concatenative approach to Python DataFrames.  Inspired by the HP calculators, Factor language, and Postscript.

value: Show HN: DataFrames as Stacks of Lazy Columns

author: punkbrwstr


value: yuby

value:
Problem:
  Large-scale 3D point cloud datasets, with hundreds of millions of
  points, are often very slow or fail to open in existing desktop
  viewers due to memory limitations.

  Solution:
   * I designed a hybrid architecture: a Rust native module handles the
     heavy data processing, while WebGPU manages the rendering.
   * Instead of loading the entire dataset into memory, I implemented a
     custom Level of Detail (LOD) system that dynamically loads only the
     necessary tiles.
   * Thanks to this architecture, it's theoretically possible to visualize
      a limitless number of points, provided you have the storage space.

  Result:
  The result is 'PointPeek', a desktop app that smoothly explores a 500M
  point (10GB) dataset at 60fps.

  Check it out in the demo video below:
   * Demo Video: https://x.com/leesyub/status/1963623557117731144

  Current Challenge:
  I'm currently tackling the challenge of rendering the entire scanned
  point dataset of the city of Vancouver, Canada. (Though, just
  downloading the data seems like it will take a few days.)

  Ultimate Goal:
  After that, the ultimate goal is to integrate a local AI model via
  Ollama to implement features for querying and analyzing the data using
   natural language.

  Technical questions or feedback are welcome.

value: Show HN: A point cloud viewer that renders 500M points at 60fps

author: yuby


value: Goradion is now updated with genre tags, favorites and search.

value: Show HN: Terminal based online radio player


value: andrewacharlton

value: Show HN: Keevo – AI that accurately updates your content in your voice

author: andrewacharlton


value: AghastyGD

value:
Hi HN
I wanted to share a project I’ve been working on called Lazy Ninja. It’s a small Django library that tries to make life easier for developers like me who want to skip boilerplate and get straight to building.
With Lazy Ninja, you can:
- Instantly create API endpoints from Django models.
- Auto-generate Pydantic schemas and interactive OpenAPI docs (Swagger UI / ReDoc).
- Generate frontend or backend SDKs in multiple languages: TypeScript, Dart, Python, Java, Go, and more.
- Work async by default, but you can switch to sync if needed.
- Use UUIDs or traditional integer IDs seamlessly.
- Enjoy filtering, sorting, and pagination automatically.
I built it originally to save time in my own projects, and along the way it’s been tested with real Django apps, including projects using UUID primary keys. It’s already getting some attention in the Python community, and I’d love to hear what you all think.
If you try it and like it, a  on GitHub would mean a lot!
GitHub: https://github.com/AghastyGD/lazy-ninja

value: Show HN: Lazy Ninja – Make Django API Endpoints and Client SDKs in Seconds

author: AghastyGD


value: shinpr

value:
I got tired of my AI deleting tests and forgetting context after 10 messages.
So we built Agentic Code. Just add an AGENTS.md file and your AI actually:
- Plans before coding
- Writes tests first
- Checks everything works before moving on
Zero config. Works with any tool (Codex CLI, Cursor, Aider, etc.):
```
npx github:shinpr/agentic-code my-project
```
It just works.
Repo: https://github.com/shinpr/agentic-code

value: Show HN: Agentic Code – Make AI coding tools follow real workflows

author: shinpr


value: Myuuico

value:
I made an AI as Mediator, providing real-time advice for offline two-person negotiations.
Microphone voice input, text output.
What surprises will this new AI interaction model bring? I want to see for myself. When AI serves not just one person, but two, how does it perform as a third party, especially in potentially conflicting negotiation scenarios?
Of course, implementing it on the web might not be optimal, but at least it will be faster to develop, and the AI's suggestions will be more intuitive.
We'd love to hear feedback from the community: Do you think this interaction method makes sense? Is AI currently capable of handling this scenario? What services would you like AI to provide in this scenario?
website: https://negowiz.com
video demo: https://www.youtube.com/watch?v=y88jeoXRWzw&t=3s

value: Show HN: AI provides real-time advice for offline two-person negotiations

author: Myuuico


value: cookingguru

value:
I’m new to cooking, and one of the things that frustrated me right away was how often a recipe calls for something I don’t have: arrowroot, Aleppo peppers, or some random spice that isn’t in my pantry.
I ended up putting together a simple site that catalogs ingredient substitutions in plain English: ingredientsubstitutes.com.
It’s not fancy, but it’s been surprisingly useful for me (and hopefully others). Curious: how do you all normally handle substitutions?

value: Show HN: The missing manual for ingredient substitutions

author: cookingguru


value:
I started this project as a need to have a stable state for my AI before any chat memory systems were released back in March or so. I wanted a system that could remember things persistently throughout the session. I developed a method for maintaining state by modifying how the system prompt was used and leveraged KV cache to cache mass quantities of tokenized state. This method injects the snapshot of the entire state that's saved into the llm before every response so the llm always knows what the current context is. I also wipe the context window and reinject everything, though only the deltas and the currently saved verbatim chat pairs are saved.
So while building this system I had limitations like having no GPU and only edge CPU only devices. This lead me to strive for efficiency in every way including using basic IPC and removing all server and API layers.
Currently this runs on PC, Mac, and Linux. I have taken a small break from work to manage life but really would like some feedback and questions about the system. There are white papers in the repo that are the initial design. Also this runs just about every model though I need to see if the role display was fixed by my friend as the main branch has an issue with hard coded roles. Gemma and OpenAI OSS work fine but other models don't display with the proper formatting of the displayed roles. This doesn't break the function just the visual so it's not something I immediately focused on.
There is a video tutorial and dev notes YouTube video, it's about 30min but it goes over the process and future of this tool. https://youtu.be/t3TozyYGNTg?si=amwuXg4EWkfJ_oBL
Note: I am not a strong coder at all and have primarily used jules coding agent from Google to create most of the actual syntax.
https://github.com/bsides230/LYRN

value: Show HN: LYRN Context Management Dashboard


value: Show HN: YC Startups Semantic Map

title: Show HN: YC Startups Semantic Map


value:
I built a small terminal-based web browser in C using ncurses and libcurl:
https://github.com/DenisDolya/cli-arsenal/blob/main/terminal...
Unlike w3m or lynx, this browser renders common HTML tags with proper colors and formatting:
Normal text in gray, headings in bright white
Links in blue + underline (clickable)
Bold, italic, underline, strikethrough supported
Lists, quotes, hr, pre/code blocks styled clearly
Images, video, audio shown as [links]
Forms rendered as ASCII mockups
Controls:
q = quit
r = reload
It’s a fun side-project to see HTML structure clearly in the terminal. Feedback and contributions are welcome.

value: Show HN: A Minimal Ncurses Web Browser That Renders HTML with Colors


value: yanzt

value:
Hi HN — we built a Chrome extension that audits E-E-A-T signals for SEO articles and product pages directly in your browser.
Why?
Writers and SEOs keep asking for a lightweight way to sanity-check Experience/Expertise/Authoritativeness/Trustworthiness without copy-pasting content into separate tools or running full site crawls. This extension gives you a quick, page-level read in seconds.
What it does
Experience cues: first-hand details, concrete claims, media/supporting evidence.
Expertise signals: author/byline presence, credentials hints, topical depth.
Authoritativeness: internal linking to relevant hubs, external citations/references.
Trust checks: ownership/contact/about visibility, policies, customer-facing trust blocks.
Structure & clarity: headings, intent alignment, scannability, duplication flags.
Product pages (beta): spec completeness, comparison clarity, benefits vs. features, risk-reversal.
Actionable output: a scorecard plus prioritized fixes you can hand to writers or devs.
How it works (at a glance)
Runs on the page you open, reads visible content + basic metadata/markup, and produces an E-E-A-T-oriented checklist with a weighted score and specific recommendations.
Who it’s for
Content leads, in-house SEO teams, and agencies who want a fast pre-publish or post-publish QA pass.
Roadmap we’re exploring
AI search/GEO readiness checks (e.g., answerability, entity coverage, citations).
Team workflows (saved audits, shareable reports), multi-language scoring, and custom rubrics.
We’d love feedback on:
Cases where our weights feel off (over/under-penalizing).
Edge pages (very long tutorials, minimalist landing pages, JS-heavy apps).
Signals we’re missing for product pages or YMYL content.
Whether you’d want a CLI/API or a CI gate for content PRs.
If you try it, please share a URL (public if possible) and what the audit got wrong/right — we’ll iterate quickly based on your examples. Our  email address is:support@quickcreator.io
Link: https://chromewebstore.google.com/detail/quickcreator-extens...
(Direct link is in the post title.)
Thanks!
Tony Yan
Cofounder of QuickCreator

value: Show HN: E-E-A-T Checker for SEO Content – QuickCreator Chrome Extension

author: yanzt


value: paperplaneflyr

value: Sometimes is really wonderful to send someone a warm cozy letter which is just not a like a single line text message. Download image feature is available too.

value: Show HN: Handwritten Letter Generator

author: paperplaneflyr

title: Show HN: Handwritten Letter Generator


value: aubmedia

value: AIPrice automatically creates AI-powered offers personalized per visitor. One small script that works anywhere.

value: Show HN: Personalized offers via models trained on visitor actions

author: aubmedia


value: run the same prompt with multiple models to run your own image model benchmarks

value: Show HN: T3 Chat for Image Models

title: Show HN: T3 Chat for Image Models


value: bbourn

value:
TL;DR - My cofounder Collin and I built an AI version of Digi-Key to help PCB designers find and use parts, except with a way bigger catalog, modern refinement tools, and an AI that can actually read the damn datasheets for you.
*The problem*
Modern circuit board design is filled with absurdly tedious tasks, where one small mistake can brick a project and cost thousands. The worst (in our opinion) is reading datasheets, which eats up to 25% of the first part of any project:
1. First, you slog through catalogs to find viable parts, using search tools that are still stuck in the dark ages. There are ~80M unique components in today’s supply chain, yet the tools we have to look through them are just digitized versions of the same paper catalogs our grandparents got in the mail.
2. During the design, you spend a ton of time flipping between different 10-100-page PDFs for every component in every subcircuit, hoping like hell you don’t miss some tiny spec in a footnote somewhere that kills your design.
3. And god help you when the requirements inevitably change and now you have to figure out what subsystems are affected!
*What we built*
Zenode is an AI-powered electronics search engine that actually helps engineers find and understand components.  Our core features:
1. Largest and Deepest Part Catalog → We have merged dozens of existing part catalogs and documents from major distributors and manufacturers
2. Discovery Search → natural language queries to quickly find categories, set filters, and rank results
3. Modern Parametric Filters → rebuilt from scratch to move off the string values pervasive in industry and build numeric ranges that actually work.
4.  Interactive Documents → AI constrained to a single part’s datasheet/manuals. Ask a question, get the answer with a highlighted source for quick reference.
5. Deep Dive → search across dozens of parts simultaneously (“what’s the lowest-power accelerometer available?”) instead of slogging one by one.
*What we learned*
1. By far the hardest part of the last 2 years has been wrangling 3 TB of messy, inconsistent data into something usable. We had to teach the AI how to handle hand-drawn figures, normalize different unit variables and names that mean the same thing, and navigate conflicting information present between different datasheet versions of the same part.  It’s been a nightmare
2. We originally built custom PDF parsers and AI extractors, which were best in class for ~3 months until generalized AI passed them. So we stopped reinventing wheels and doubled down on data quality instead.
3. The killer feature wasn’t the AI searching a single part, but what we heard repeatedly from users is that they want the AI to read across multiple parts, hence why we’ve launched deep dive!
*Where it’s strong*
- Speed: rips through a 1,000-page microcontroller datasheet in seconds.
- Breadth: 40M+ part sources unified into one catalog, and more than just datasheets, application notes, errata, etc.
- Comparisons: Deep Dive lets you ask across multiple parts, not just one at a time.
*Where it’s not*
- Pricing/availability: currently outdated (for now we expect folks to check existing aggregators like Octopart).
- Accuracy: good enough to match my mediocre skills; not yet at Collin's level, but we're starting tuning and this will improve rapidly!
*Try it*
It’s live today (zenode.ai). Sign up for a free account and If you put “Hacker News” in during signup in the “where did you hear about us” field, we’ll give you 1,000 bonus credits (once we finish building that, so sometime this week ).
*Feedback we’d love*
1. Should Deep Dive results auto-become filters you can refine further?
2. Do you want the ability to mark preferred parts / exclude others?
3. Is “Deep Dive on a BOM” (alt discovery + manufacturability checks on a list of known components from different categories) the killer feature?

value: Show HN: Zenode – an AI-powered electronic component search engine

author: bbourn


value:
Hi everyone, Arkain team here.
Today we're launching the beta of Arkain, an AI-powered Cloud IDE.
We wanted to share what we've built and get honest feedback from the community.
---
1. The Problem We're Solving
How many times have you found yourself setting up the same boilerplate for a new project? Great ideas often take weeks to get off the ground – not because of complexity in the core logic, but because of environment setup. React frontend configuration, Express backend scaffolding, database schema design, deployment pipelines... by the time you're done with setup, momentum is lost.
Team development amplifies these issues. Different local environments, version conflicts, and dependency management create friction that keeps developers from focusing on what actually matters. We've all been there – spending more time debugging environment issues than building features.
2. Our Approach
Our goal is simple: eliminate the productivity barriers that slow down development.
Arkain is a web-based IDE that lets you configure development environments with a few clicks and generate complete applications from natural language descriptions. We want developers to spend their time on ideation, feature design, and user experience rather than repetitive setup tasks.
3. Key Features
- End-to-end app generation from natural language: Unlike existing AI coding tools that focus on code snippets or frontend-only generation, Arkain creates complete, deployable applications.
- Context-aware AI agent: The AI maintains project context, so you don't need to re-explain your architecture with every request.
- Security-first cloud infrastructure: Built on Zero Client architecture with SBOM and containerization for both security and performance.
- Template community: Share and discover proven solutions to accelerate development.
4. Current Limitations
Being in beta, we have several areas that need work:
- Complex business logic still requires manual refinement
- Some framework combinations aren't fully optimized
- Performance tuning needed for large-scale projects
5. What We'd Love Feedback On
- Which types of projects would benefit most from this approach?
- What friction points do you see with existing development workflows?
- For enterprise adoption – what are your security/governance concerns?
6. Community-Driven Development
We're committed to growing this with the community. We want to be transparent about what we learn and build, and we'll be open-sourcing our most useful templates progressively. We're planning to start with RAG-based AI chatbot and feedback dashboard templates.
If you have any questions, ideas, or suggestions for improvement, please don't hesitate to reach out. We'd especially appreciate hearing about any development workflow challenges you're facing in your day-to-day work.
Looking forward to your thoughts and experiences in the comments.
You can try Arkain free here: https://arkn.ai/qH22w

value: Show HN: Arkain – AI-powered Cloud IDE for building real apps from your words


value: cyrve

value: Disclosure Devil brings together financial reports from sources around the world. One can simply download them at bulk, or summarize and analyze them with the help of an AI. This is beneficial in particular when looking for trends and nuances between reports from different time points or companies. Tool is lightweight with minimal JavaScript. Any feedback appreciated!

value: Show HN: Disclosure Devil – AI Analysis of Financial Reports

author: cyrve


value: lexokoh

value: Show HN: Hottake – Spark Debates with One-Click Tier Lists

author: lexokoh


value: hasibhaque

value: Show HN: I built a free website to print motivational quotes from entrepreneurs

author: hasibhaque


value: sanduckhan

value:
I built this hoping to grow it into something like the agents professional athletes have but for knowledge workers.
A lot of people I know are worried about becoming replaceable. Apart from recruiters reaching out once in a while, they get zero real help and they never find time to take action. I want to build something that works in the background to monitor and increase your market value.
Right now it starts with profile building from LinkedIn with 10 times better SEO, but I have more ideas in mind.
https://www.vulk.ai
It's live, you can create a free account, it's a bit slow at the moment but we are working on it. And we will add more features this month.
I’d love feedback on two things:
- What would make you trust an AI agent with your career?
- What would you expect a career agent to actually do for you?

value: Show HN: An AI Career Agent to help professionals stay relevant and visible

author: sanduckhan


value: AyodeleFikayomi

value:
Hi HN,
i have ben building a CLI first framework for exploring sematic structures in data. instead of just applying statistical or ML models, it tries to uncover hidden connections, entities and pathways across datasets.
What TensorPack does today:
Discovers hidden semantic connections and pathways between datasets
Supports entity search across datasets (not just within them)
Extends dynamically with domain-specific transforms, letting users add their own semantic knowledge at runtime
GitHub repo: https://github.com/fikayoAy/tensorpack
How this differs from existing tools:
It overlaps with knowledge graphs, semantic web, and graph databases, but is:
CLI-first and designed to work directly with tensors, matrices, tabular data, and text
Built to integrate domain-specific transforms at runtime
Intended as a lightweight bridge between everyday data formats and a graph-style view of semantic relationships
I’d love feedback on:
Is this genuinely new, or just reframing existing approaches?
Where could this break (scalability, false positives, UX)?
Any use cases or datasets you’d want to see this applied to?
Screenshots and examples are in the repo. Thanks for taking a look!

value: Show HN: Tensorpack a CLI tool for semantic discovery across datasets

author: AyodeleFikayomi


value: asteroidandy

value:
Hi HN,
I’ve been working on Spiderseek, a platform to help track and grow website visibility in AI-powered search engines (e.g. Perplexity, ChatGPT, and other agents).
Traditional SEO tools are expensive and focused on Google-style search. I wanted something lightweight and AI-first, so I built Spiderseek:
AI Research – Explore domains and keywords to uncover new opportunities.
AI Analytics – See traffic, crawl activity, and page metrics, plus insights from AI agents.
Content Submission – Get content indexed instantly in major AI agents.
Rankings – Browse the top 1000 domains sorted by citations and sources.
It’s very early, but live and priced at $1/month while I grow the database and features.
I’d love feedback from the HN community on:
How useful this data feels.
What additional features you’d want.
Which AI Search Engine Coverage is most important to you (e.g ChatGPT vs Copilot Vs Gemini)
https://www.spiderseek.com
Thanks!
Andy

value: Show HN: Grow and Monitor Your AI Search Traffic

author: asteroidandy


value: Show HN: A minimalist, encrypted knowledge platform for solo devs and founders


value: soelost

value:
A $10 latte cost me 50 credit points last year. Not because I couldn't afford it - because it pushed my Chase card from 29% to 31% utilization.
That's when I learned: Your balance won't hurt your credit score. Your utilization will.
The problem isn't the rule (everyone knows 30%), it's tracking it. With multiple cards, changing balances, and daily spending, you're basically guessing which card is "safe."
So I built Cretit - daily traffic lights for every credit card:
Green = safe to use
Yellow = be careful
Red = will hurt your score
Hope you guys like this one more than HumanAlarm

value: Show HN: The $10 coffee that tanked my credit score

author: soelost


value: Snorix

value:
Hi HN!
I’m Yanjun, co-founder of Lessie AI.
We built Lessie AI as a People Search AI Agent — designed to help you find anyone in seconds:
- Influencers for campaigns
- Partners for collaborations
- Experts for insights
- Founders or investors to connect with
Instead of spending hours doing LinkedIn / Google searches, Lessie automates the whole workflow:
- Understand your request — type what you need, any role or profile
- Search across multiple data sources — trying different strategies to find the right people
- AI review + scoring — profiles are automatically filtered and ranked so you see the most relevant first
- Generate outreach in seconds — on-brand, personalized, ready to send
We’re currently in early access — looking for feedback from the HN community.
Would love to hear:
- What your biggest pain points are when trying to find people
- What kinds of people you most wish you could find more easily
- What other data sources you’d want integrated
- How you’d use an AI agent like this in your workflow
- Any feature ideas (or concerns!)

value: Show HN: Lessie AI – Your People Search AI Agent

author: Snorix


value: TunePaw

value:
I built a Chrome extension that lets you chat with the transcript of any YouTube video. Instead of sitting through like a 2 hour video, you can just ask a question and get an instant answer.
The Problem:
I built it mainly for myself: I often watch long-form videos like lectures or tutorials on YouTube, and I want a fast way to extract the information I need without scrubbing through timestamps or rewatching sections. For example, I recently used it with a 3.5-hour lecture and got the exact answers to my questions in seconds.
Another example is that nowadays YouTubers often create videos around one or two key ideas, and I can now figure out whether a video is worth watching by asking what those key ideas are in advance.
How it works:
1. The extension gets the video transcript.
2. An AI model summarises it, and makes the content ready for chat
3. You can switch between chat mode and auto-generated summaries depending on how deep you want to go.
What’s different:
1. It’s not just a summarizer: you can interact with the content in real time and ask follow-up questions
2. It saves a huge amount of time if you’re studying or just watching a long video and curious about a specific part of it.
Feedback:
If you find it useful, I’d really appreciate your feedback on your experience with it and what you’d improve.
Thanks!

value: Show HN: Chat with Any YouTube Video

author: TunePaw

title: Show HN: Chat with Any YouTube Video


value: TheRealBadDev

value:
I've been frustrated with dependency hell and clutter on my VPS from dev, so I built Devbox: a lightweight, open-source CLI tool that spins up isolated development environments using Docker. Each project runs in its own container, but your code stays in simple flat folders on the host machine—no messing with volumes or sync issues. Environments are disposable, so you can nuke and recreate them without losing your work.
Key features:
- Instant setup: `devbox init my-project` and you're in a fresh env with `devbox shell`.
- Configurable via JSON: Define packages, services, and more in a `devbox.json` file. Share it in your repo for reproducible setups—teammates just run `devbox up`.
- Docker-in-Docker by default: Build and run containers inside your env without extra config.
- Host-friendly: Edit code directly on your machine; the container handles the runtime.
- Templates for quick starts: Built-ins for Python, Node.js, Go, web dev, etc.
- Advanced options: Port mapping, env vars, resource limits, and even mounting your dotfiles.
It's FOSS (MIT license), Linux-focused (Debian/Ubuntu, or WSL2 on Windows), and super easy to install: `curl -fsSL <a href="https://devbox.ar0.eu/install.sh" rel="nofollow">https://devbox.ar0.eu/install.sh</a> | bash`.
Check out the launch page and docs at https://devbox.ar0.eu, or the repo at https://github.com/itzCozi/devbox. I'd love some feedback, stars, or contributions to help grow this into a solid community tool!

value: Show HN: Devbox – Ephemeral containers for clean dev environments

author: TheRealBadDev


value: Olshansky

value: The interview is spinning up our local development environment. Feels like the perfect bidirectional way to really check if someone is a technical fit.

value: Show HN: Technical Interview for an Open Source Team (Grove Engineering)

author: Olshansky


value: Pavlinbg

value: Show HN: Python Audio Transcription: Convert Speech to Text Locally

author: Pavlinbg


value: TejasMondeeri

value: Pain-free custom data migrations done over a weekend

value: Show HN: AI CRM Migration Tool

author: TejasMondeeri

title: Show HN: AI CRM Migration Tool


value: Otto connects drivers with reputable auto body shops to provide them fair quotes and price transparency. Otto serves as a price comparison and shop discovery tool, giving drivers assurance their cars will be repaired properly at the right cost.

value: Show HN: Auto Repair Marketplace

title: Show HN: Auto Repair Marketplace


value: defoemark

value: Show HN: KaniTTS – Ultra Fast and Expressive TTS Model

author: defoemark


value: mibrahimSB

value: Think of Spatialbound as Figma for the physical world, an online platform to design, simulate, and reimagine real-world spaces in 3D. With built-in GIS tools (with global spatial data store), multiple apps, and a native Python API, Spatialbound helps you automate your spatial workflows, saving hours of work.

value: Show HN: Spatialbound – Turn any location into an interactive 3D playgrounds

author: mibrahimSB


value: lorastonden

value:
I priced the current “rapture prep” shopping spike because the topic went viral, and it overlaps with standard emergency-prep spending.
Method: pulled public checklists (Ready.gov, CDC, Red Cross) and recent retail prices, plus legal-cost ranges from LegalZoom and ARRL fee pages. No theology, just consumer costs.
Highlights:
• Per-person 72-hour kit: $250–$500
• 30-day family build: $1,400–$3,000
• “Going” paperwork (wills/guardianship/passwords): $1,390 first year, then $60/yr
• Entry power: $500–$2,000 (inverter generator or basic solar station)
Article includes the receipt-style graphic, a cost matrix, and sources inline. Feedback on the math welcome, and if you spot better public price pages I’ll update.

value: Show HN: A price breakdown of "rapture prep" as consumer math, not theology

author: lorastonden


value: computerex

value: Show HN: I built an AI news site

author: computerex

title: Show HN: I built an AI news site


value: mingtianzhang

value: Show HN: An MCP that allows you break LLM's context limit

author: mingtianzhang


value: ellenfkh

value:
Hi HN!
We (Thorntale) are a startup building presentation tools, we noticed that while people disliked the tedium of making slide decks, a lot of people actively dread presenting those decks, especially if it's a high-stakes call or if they're inexperienced. Speaking from experience, practice helps a lot, but it can feel embarrassing to give the same pitch to your friend or cofounder for the tenth time.
We built this prototype https://review.thorntale.com/ as a way to practice; upload a PDF deck, give your presentation, and the selected AI "personas" will analyze the slides and transcript and provide feedback. At the moment, we've tuned "personas" to give you feedback from the perspective of an investor, a teacher, or a marketing/branding lead (as well as a few entertaining extras.) If you have a particular use case, shoot us a comment, we're refining the persona system and planning what else to build there.
The tool is totally free and doesn't require signup or login; we'd love any feedback if you check it out!
https://review.thorntale.com/

value: Show HN: Perfect your presentation with a panel of AI reviewers

author: ellenfkh


value: yegortk

value: Show HN: Free textbook – Python, Deep Learning and LLMs from scratch [pdf]

author: yegortk


value:
I loved how One Million Checkboxes made a silly shared space feel energetic, so I tried the same idea with sound. VibeCheck is a tiny collaborative music toy: a four-bar loop is always running, and anyone who shows up can “claim” one bar (claims time
  out after ~20 seconds), swap the chord, flip the inversion, change the rhythm, and hear their tweak on the very next pass. Everyone else hears it too, so the loop keeps evolving as people jump in and out. There’s a lobby chat if you want to coordinate (or not!).

  Play: vibecheck.mohitc.com. Hope it makes you smile for a few minutes!

value: Show HN: VibeCheck – a shared groove inspired by One Million Checkboxes


value: ProgrammerByDay

value: - See exactly what's happening with your revenue<p>- Connect Stripe and get instant insights into your churn patterns in 2-minutes<p>I'm having private beta, let me know if you want to try it out.

value: Show HN: I built a tool to stop customer churn before it kills MRR

author: ProgrammerByDay


value: kekyo

value:
prettier-max is a very simple Vite plugin that automatically formats your code with Prettier during the build process.
It also includes TypeScript validation to ensure your code is safer before building.
Key features:
* Automatic Prettier formatting on build start
* When using TypeScript, post-formatting TypeScript type checking. Additionally, JSDoc deprecation (@deprecated) can also be checked.
* All fine-tuning is specified in .prettierrc, .prettierignore and tsconfig.json, ensuring consistency

value: Show HN: Minimalist automatic Prettier formatting plugin for Vite

author: kekyo


value: gimlithedoge

value:
Hi everyone, this is my first game launch!
WordDrop is a hybrid between Tetris and Scrabble where letters fall and you build words under time pressure.
•Built in Unity (C#), inspired by classic falling-block puzzlers.
•Free to play, no ads, jump right into the action.
•iOS App Store link: https://apps.apple.com/app/id6747409372
I am a self taught programmer and I’d love feedback on the gameplay, design, or even the technical side. Happy to answer questions about how I built it!
I’m also considering using WordDrop as a centerpiece in my portfolio as I work toward a career in game development. If you have thoughts on how it presents as a portfolio piece — or what recruiters/studios might look for — I’d love that feedback too.

value: Show HN: WordDrop – my first mobile game (Tetris and Scrabble hybrid)

author: gimlithedoge


value: djyde

value: Show HN: I make a lightweight pandoc GUI

author: djyde


value: hellohanchen

value: Show HN: All You Can Cook, AI-Powered Cooking App

author: hellohanchen


value: Show HN: AI Generation Toolkit – Focusing on AI Face-Swapping&Video Generation


value: Show HN: Cparse is an LR(1) and LALR(1) parser generator in C


value:
Hey Hacker News,
I'm one of the creators of Crevo. Like many of you, my team and I have spent countless hours writing, reviewing, and syncing up on software design documents. The process is slow, tedious, and often leads to inconsistencies between the product vision and the engineering plan.
To tackle this, we built Crevo: an AI-powered platform that takes a natural language description of your product idea and generates a comprehensive set of design documents.
You can simply describe what you want to build, and it will output:
- Product Requirements Document (PRD):* Outlines the product's purpose, features, user flows, and constraints.
- System Architecture Design:* Generates diagrams for system layers, data models, and business processes.
- API Definitions:* Creates standardized interface definitions, supporting the OpenAPI spec.
- User Stories & Journeys:* Helps map out the user experience from start to finish.
Our goal is to drastically shorten the path from an idea to a developer-ready specification. We want to help teams align faster, reduce manual documentation work, and allow engineers to focus more on building.
The product is still in its early days, and we're eager to hear what the HN community thinks. We know how valuable clear, concise documentation is, and we believe AI can help us get there much faster.
We'd love for you to give it a try and share your feedback. What features are missing? What kind of documents would you want to see generated next? We'll be here all day to answer your questions.
Check it out here: https://crevo.aurakl.ai
Thanks for your time

value: Show HN: I built Crevo to turn ideas into engineering docs


value: abilafredkb

value:
I’ve been working on something I personally felt was missing from the internet: a way to build genuine global connections without endless swiping, ghosting, or chasing algorithms. That’s how Eintercon was born.
Eintercon is a social platform built around the idea of time-bound connections. When you connect with someone, you have 48 hours to engage—whether it’s a chat, sharing experiences, or finding common ground. After that, the connection expires unless you both decide to continue.
Why?
Because I’ve seen too often how social platforms turn into infinite scrolls, dead inboxes, or connections that don’t go anywhere. By adding urgency and focus, Eintercon encourages people to actually interact instead of collecting names on a list.
A few things about it:
The focus is global friendships, not just local networking.
The 48-hour connection window creates momentum and reduces ghosting.
Simplicity: no endless feeds, just people and conversations.
We’re live on Play Store
 and App Store
. Website: https://eintercon.com
.
I’d love feedback from this community:
Do you think time-limited connections help or hurt relationship building?
What are the most frustrating parts of existing “friendship” or social apps for you?
Any thoughts on how to encourage more genuine engagement rather than surface-level interactions?
This is still early, but I’d be grateful for your thoughts, ideas, and critiques. Thanks for checking it out!

value: Show HN: Eintercon – A platform to make real global connections in 48 hours

author: abilafredkb


value: YuriiDev

value:
Storing seed phrases on paper or in files is a huge security risk. Paper can be lost or destroyed; digital copies can be stolen.
My project, SecretMemoryLocker, introduces an experimental feature to solve this. Instead of storing a seed phrase, it deterministically regenerates it on demand from three components:
- Encrypted archive (.zip): A personal file whose SHA256 hash acts as a unique cryptographic salt.
- Question file (.json): A chain of secret questions where each is encrypted with the answer to the previous one, all tied to the archive's salt.
- Your memory: The answers, which are never stored digitally.
The entropy for the BIP39 seed phrase is generated by combining these elements:
Entropy ≈ SHA256(Σ SHA256(question + answer + file_hash))
The resulting seed phrase exists only in RAM and is erased when the application closes.
Key benefits:
- Immune to theft: With no stored phrase, there is nothing for hackers or thieves to find.
- Distributed security: You can keep the .zip and .json files in separate locations, requiring an attacker to compromise multiple points.
- Secure inheritance: Allows you to leave instructions for an heir to regenerate access without ever writing down the full secret.
The trade-off:
You exchange the risk of theft for the risk of forgetting your answers. Security depends on the strength of your personal secrets.
You can download the latest Windows release and test it here:
https://github.com/SecretML/SecretMemoryLocker
I'd love to hear your thoughts, criticism, and suggestions.

value: Show HN: Generate a seed phrase on the fly (no storage) – SecretMemoryLocker

author: YuriiDev


value: gumbojustice

value:
This summer I decided to create an analytics dashboard of my Signal data. For those unfamiliar, Signal is a messenger app, similar to WhatsApp, but heavily focused on privacy with their end-to-end encryption.
This was initially meant to just be a bespoke dashboard to share with some Signal friends. But as I continued to build it out, I decided to make it into a web app in case others wanted to play around with it too.
So here's what I have so far: signalsnapshot.com
It's still pretty raw, but maybe some of you will find it fun.
Important things to note:

    This only supports Signal Desktop at the moment

    In order to use the tool, you need to first decrypt your Signal database. I also wrote a program to handle that, and the app has the necessary documentation on how to use it

    The app is purely client-side, meaning that your uploaded data never leaves your browser. There is no server, this is a static web page. Nobody but you can see your data.

If you're a Signal user and interested in this stuff, check it out! If there's enough demand, I'll build out the analytics a little more, and maybe add support for Signal Android. There’s a ton of meat left on this bone, chat analytics is a cool world.
Here's the GitHub if you want to check out the code. https://github.com/ahstewart/signal-snapshot

value: Show HN: Signal Snapshot – Generate visualizations of your Signal message data

author: gumbojustice


value:
Hi HN,
I built PoseUp
 – an AI tool that instantly enhances casual selfies or people photos into professional-quality images.
How it works:
Upload a selfie, portrait, or group photo
Our AI improves lighting, composition, colors, and overall quality
Optional: 4K upscaling, smart resizing, layout export (like 3x3 grids)
The goal:
Help people get “magazine-style” photos from everyday snapshots — no editing skills or prompts required.
100% free, no login needed
 https://poseup.ai
Would love feedback — especially around photo quality and UX. Thanks!

value: Show HN: PoseUp – Turn Casual Portraits into Shots with AI (Free, No Signup)


value: atsushii

value: Show HN: I build free anonymous chat app

author: atsushii


value: ggay

value:
I built an open-source tool to turn any REST API into a GraphQL-like API with custom fields selection for every endpoint. Simply pass the OpenAPI specification and run one command line.
Create a fieldflow-optimized MCP server so AI agents will only fetch the fields they need, reducing noise in context. It'll increase agent responses speed and improve response accuracy.
Let me know what you think about this project! Hope that helps :)

value: Show HN: Transform your REST API into optimized MCP server in one command line

author: ggay


value: nullandvoid

value:
After seeing sophisticated phishing attempts slip through Gmail's filters, a friend and I built a browser extension that uses local AI to analyse emails for threats.
It runs entirely in your browser using web-llm, with any threat being clearly labelled with an explanation of why it's suspicious.
Still early days, but we'd love feedback from the HN community. Even if you're not comfortable installing it yet, we have screenshots showing how it works.
https://www.eguard.app
https://chromewebstore.google.com/detail/eguard/dnpofinmilhb...

value: Show HN: Run a local LLM to analyse email for threats

author: nullandvoid


value: patrykt

value:
I just launched TabletDay - a web dashboard for displaying on tablets. The idea behind the project is to repurpose unused tablets as functional information centers (clock, weather, calendar, task list).
Initially, this was just my private toy - an old tablet converted into a home dashboard. I've been using it daily for years, and it has become an indispensable part of my routine. Friends who saw it asked if they could have something like this too.
Of course, tablets have their built-in dashboards, but I wanted to focus on something different - a clean and minimalist design that references the classic wall calendar. No distractions, just time, weather, events, and tasks. That's why I thought I'd create a version for everyone, so others could benefit from it too.

value: Show HN: a web dashboard for displaying on tablets

author: patrykt


value: No libraries, no dependencies besides libcurl. Auto-detects your IP, fetches 7-day forecasts from Open-Meteo, and renders a neat scrollable ASCII table with bars and weather codes. Works on desktops, mobiles, and embedded terminals. Check it out: https://github.com/DenisDolya/cli-arsenal/tree/main/weather

value: Show HN: I built an ASCII scrollable ANSI-enhanced CLI weather app in pure C


value:
The core feature that I focused on is the dual-generation mode:
Paragraph-by-Paragraph: This is for when you're deep in a scene and just need a little nudge to keep going. It gives you fine-grained control to shape the narrative moment by moment.
Chapter-by-Chapter: This is for when you have the big picture in mind and want to quickly build out the main plot points without getting bogged down in the details.

value: Show HN: I built an AI co-author to help write fanfiction


value: navaneeth-pk

value:
Hi HN, founder here again!
I first launched ToolJet here in 2021 as a one-person project. It blew up really well & got 1k stars on GitHub (https://github.com/ToolJet/ToolJet/) in around 8 hours. Back then ToolJet was basically a frontend builder that could connect to different data sources.
Since then, we kept expanding:
- Added a workflow automation tool so builders could orchestrate background jobs.
- Added a built-in no-code database so builders didn’t need to spin up a new db.
- Eventually grew into a full-stack platform for internal tools.
- And other obvious things like tons of smaller features & integrations.
But last year we kind of messed up. We kept adding features, the frontend architecture couldn’t keep up, and stability/performance issues showed up once apps got complex (ie hundreds of UI components in a single page of an app). So we stopped, rebuilt the architecture (ToolJet v3 in November), and cleaned up a lot of tech debt. That gave us a solid foundation - and also made us realize it was the right moment to go AI-native.
We analyzed how our users actually built apps: 80% of the time on repetitive setup (forms, tables, CRUD), 15% on integration glue code, 5% on actual business logic. Traditional low-code tried to eliminate code entirely. We're eliminating the wrong code - the boring 95% - while keeping full control for the 5% that matters.
Instead of "prompt-to-code," ToolJet AI tries to copy how an engineering team functions (yeah, a bit opinionated way) - but with AI agents:
- PM agent → turns your prompt into a PRD.
- Design agent → generated the the UI using our pre-built components and custom components.
- DB agent → builds the schema.
- Full-stack agent → wires it all up with queries, event handlers, and code.
At each step, builders can review/edit, stop AI generation, or switch into the visual builder. Generated apps aren’t locked in - you can keep tweaking with prompts, drag-and-drop, or extend with custom code.
Why this works:
We know "AI builds apps" is overhyped right now. The difference: we're not generating raw code - we're configuring battle-tested components. Think Terraform for internal tools, not Claude/GPT writing React.
That means:
- Fewer tokens → lower cost.
- Deterministic & Faster outputs → fewer errors.
- More reliability → production-ready apps.
Basically, AI is filling in blueprints.
ToolJet AI is a closed-source fork of the open-source community edition, which will continue to be actively maintained. All the core platform changes (like the v3 rebuild and stability/performance work) are committed upstream. The AI features sit on top, but OSS remains the foundation.
Thanks for reading - and thanks again to HN for being part of ToolJet’s journey since the very beginning.

value: Show HN: ToolJet AI – Collaborative agents that build internal tools

author: navaneeth-pk


value: warthog

value:
Hey HN! I built Banker.so, an AI agent that actually understands and writes Excel files.
I upload spreadsheets to ChatGPT/Claude all the time. CSV files work okay. But .xlsx files with formulas, multiple sheets, and pivot tables? They break constantly.
The problem: LLMs process data sequentially (1D), but spreadsheets are 2D structures. Cell B5 has meaning because it's in column B, row 5. Formulas reference ranges like A1:C10. Current AI tools miss these spatial relationships because they are designed for humans’ visual understanding.
What fails today:
- Complex Excel files with multiple linked sheets
- Formulas and named ranges get corrupted or ignored
- Pivot tables and conditional formatting disappear
- Table structures with merged cells break completely
I implemented algorithms from the SpreadsheetLLM paper to parse spreadsheets properly. The agent understands:
- Table boundaries and headers
- Cell relationships and formulas
- Multiple sheets and references between them
- Data types and formatting
You can find a video demo over here: https://youtu.be/hCTijOa3H9k?si=-GbX0ibmtXMVabAm
Also added OCR to convert PDFs and images directly into working spreadsheets.
Example use case: Upload your monthly financial report with 10 sheets, formulas, and pivot tables. Ask "which department exceeded budget?" Get the right answer because the agent understands your spreadsheet structure.
https://banker.so/
Try uploading a complex Excel file. The one that breaks other tools.
Give it a try and push it hard - I am sure there will be places I can improve it further. Looking forward to your feedback!

value: Show HN: Banker – Lovable for Spreadsheets

author: warthog


value: lzrdada

value:
Lovable/Bolt/Cursor lets you ship ideas in hours. The downside: hidden errors, surprise limits, and rising costs.
Klarvy.ai gives one truthful pane: dependencies, requests, errors, spend—with alerts and cost-cutting suggestions.
We’re opening early access. Feedback welcome.<p>We built this tool for the "not so technical crowd", curious to see what the technical crowd thinks of it.

value: Show HN: Observability for Your Vibecoded Projects

author: lzrdada


value: wiradikusuma

value:
Hi guys, I'm sharing something I've been tinkering on for a while: https://signagesync.app/
Like Google Chromecast, but "cast" to multiple screens at once. Create a playlist of auto-refreshing web pages, videos, and even live streams (but not supported on Windows). Also works with local network, e.g. http://192.168.../sales-dashboard
It's still early (MVP), but already usable—and I'd love to hear your feedback.
Tech stack: SvelteKit, WebSocket, Flutter (desktop)

value: Show HN: Signage Sync

author: wiradikusuma

title: Show HN: Signage Sync