value: maxalbarello

value:
Hey HN! We’re Eeshita and Massimo. We’re building [Fabric](http://onfabric.io) a portable, personal context layer that lets users bring their own data from consumer apps (Instagram, YouTube, Google, etc.) into MCP clients like Claude, ChatGPT, and other AI agents.
Agents today are largely blind to the user. Even when they add memory, they usually start from zero instead of using the rich context already locked up in consumer platforms. The result is agents with big gaps in their understanding of the user, generic experiences, lots of repetitive input, and still poor engagement.
We’re trying to solve this by focusing on *portable, user-controlled personal context*.
**
With Fabric, an agent doesn’t just recall what happened inside that one app. It can start from a richer baseline that evolves as the user interacts across their existing apps, using memories created from tens of thousands of real interactions, such as:
- Travel posts and restaurant stories from Instagram
- Videos watched on YouTube and TikTok
- Years of Google search, shopping, and navigation behavior
What we’ve built so far
- Official connectors for Instagram (global), Google (EEA, UK) and YouTube (EEA, UK) for beta users. First company globally to build DMA style integrations with all Big Tech platforms.
- An MCP server so users can bring their context into MCP clients
- Under the hood, Fabric:
    - Ingests raw interactions that look very different (e.g. Google searches vs Instagram stories)
    - Normalises them into an ActivityStreams-style schema
    - Builds a personal knowledge graph on top
    - Exposes higher-level “memories” that agents can retrieve via our MCP server
- A user portal where users can connect data sources and manage their context
We have a few strong opinions:
- The profile belongs to the person, not to any one app. Fabric is designed as a neutral “personal context vault” that multiple AI apps can plug into (with user approval), rather than a proprietary profile owned by a single product.
- User control is first-class. Users can:
    - Choose which data sources to connect
    - See which apps/agents have access
    - Revoke access and delete their context from Fabric
- We’re explicitly building on top of GDPR data portability and EU/UK “smart data” initiatives, rather than shadow integrations.
We would love to hear what you think.
And if this is interesting, you can sign up to our waitlist or join our beta at onfabric.io.

value: Show HN: Fabric – personal context from Instagram/YouTube/Google to AI agents

author: maxalbarello


value:
Building an affective computing system for cognitive load monitoring.
Problem: Most knowledge workers push through mental exhaustion
without realizing it. No real-time feedback on cognitive state.
Solution: Multimodal sensing (eye tracking, physiological signals,
behavioral patterns) to measure mental load.
Early testing: https://humonos.com/survey
Launching January 2026. Would love feedback from HN community—
what would make you actually use something like this?

value: Show HN: Humonos – Real-time cognitive load detection


value: marcelbundle

value: Show HN: Ayrshare Alternative

author: marcelbundle

title: Show HN: Ayrshare Alternative


value: devarifhossain

value: Show HN: Shadcn UI blocks for eCommerce websites

author: devarifhossain


value: NyuB

value: It's quite limited but does the job, I invite anybody not aware of what cram tests are to give them a try, being on Unix with the original cram or Windows with this one ;)

value: Show HN: A "Cram tests" script for windows shells

author: NyuB


value: xenni

value:
With YC now running four batches a year and everything pitched as "AI/Agents for X", it's getting pretty hard to tell what YC startups actually do without doing a bunch of digging.
I built FYI Combinator to use research agents (I'm sorry) to dive into each YC startup and summarize the basics in plain English:
- What they do
- Who they target
- What their market looks like
- How they might acquire customers
It's meant to reduce the amount of time it takes to understand companies from the new batches and understand what they're building.
I'm still working on tuning tone and reducing bias from marketing pages, all feedback is welcome!

value: Show HN: FYICombinator – What YC startups are doing

author: xenni


value: levgel

value:
Today I vibe-coded a functional ASCII keyboard using Opus 4.5 and Swift. It lets you type normally and outputs FIGlet-style ASCII art. Toggle it on, type your message, press Enter and the ASCII art gets typed out wherever your cursor is. Not essentially useful, but still fun :~)
Features:
- 20 FIGlet fonts (Standard, Banner, Doom, Star Wars, etc.)
- Live preview as you type
- Works in any app (Slack, Discord, Terminal, etc.)
- Pure Swift
Cost: 92,118,535 Opus 4.5 tokens
Time consumed: 30 minutes including domain setup and registration
What times we live in...

value: Show HN: I vibe-coded an ASCII Keyboard

author: levgel

title: Show HN: I vibe-coded an ASCII Keyboard


value: Single-Shot Architecture: Optimized for speed and cost-efficiency using a single API call to handle the entire generation-review-fix cycle.

value: Show HN: White-Box-Coder – AI that self-reviews and fixes its own code"


value: kanodiaayush

value:
LLMs make us feel like we can learn anything, but chat is just one primitive. I'm trying to build interfaces which let us cover a topic our sources at length with AI.
These interfaces let us consolidate our understanding at glance so we don't get lost in long chat message histories, zoom in and zoom out of information fast and minimize tedious work such as context engineering.
Curious if you find this interesting, and have feedback! Please try it (it's free).

value: Show HN: A visual AI interface to understand topics/books/papers with LLMs

author: kanodiaayush


value: zehfernandes

value: Show HN: I Made a Series of Posters Exploring the Fuzzy World of LLMs

author: zehfernandes


value:
I built a small offline photo search tool that uses a local vision-language model (NexaAI Qwen3-VL-4B) to describe images and sentence-transformers to generate embeddings for semantic search.
Everything runs 100% on-device (no cloud, no API keys).
GitHub repo: https://github.com/pankaj4152/smart-photo-finder
You can try it with:
1. python app.py
2. Choose "Process images"
3. Choose "Search images"
Would love feedback on architecture, performance, and improvements.

value: Show HN: Offline AI Photo Search (local VLM and semantic search)


value:
I built LedgerApp to solve a problem I kept running into: tracking shared expenses with my team was always more complicated than it needed to be.
What it does:
- Log expenses in under 15 seconds (amount, category, date, receipt)
- Real-time per-person balance calculations
- Automated "Settle Up" calculator that minimizes transactions
- CSV/PDF exports for accounting
- Free for teams up to 3 members
Tech stack:
- Next.js 15 with App Router
- Supabase (Postgres + Auth + Storage)
- Stripe for subscriptions
- AWS SES for emails
- TypeScript throughout
What makes it different:
Most expense trackers are either too simple (spreadsheets) or too complex (enterprise software). I focused on speed—the form is minimal, balances update in real-time, and the UI is mobile-first.
Free tier: Up to 3 team members, unlimited expenses, basic dashboard. No credit card required.
Paid tiers: Features like CSV export, Settle Up calculator, and advanced charts, slack integrations etc.
I'd love feedback from the HN community, especially on:
- The balance calculation logic (currently splits equally)
- The UX flow for adding expenses
- Any features you'd find useful for small teams
Try it free: https://ledgerapp.team

value: Show HN: LedgerApp – Fast team expense tracker built with Next.js and Supabase


value: sdan

value: Show HN: A computer-use Mac client for Claude Opus 4.5

author: sdan


value: j-b

value:
I built AroundHere.app to explore location based Wikipedia and Grokipedia articles with Claude AI summaries and text-to-speech.
Search any topic by adding it to the URL: aroundhere.app/golden gate bridge, aroundhere.app/quantum computing.
Enable location to discover nearby Wikipedia articles through a radar visualization.
The app pulls content from both Wikipedia and Grokipedia, then generates Claude-powered summaries combining both sources. OpenAI TTS reads summaries aloud if you prefer listening. If a related Grokipedia article exists, a Grokipedia button is shown. A separate Summary button returns the summarization of the grokipedia article after which a "Listen" button appears.
You get traditional encyclopedia content alongside AI-curated perspectives. The location feature helps you find historically interesting spots nearby, while URL search works as a quick research tool for anything.

value: Show HN: AroundHere – Location based Wiki/Grokipedia explorer with summary/TTS

author: j-b


value: davidbarker

value: Show HN: HN Alerts

author: davidbarker

title: Show HN: HN Alerts


value: spiked

value:
I built a small web app that converts bank statement PDFs into clean CSV files for Excel and QuickBooks.
It started when a friend couldn’t export multi-year transaction history from his bank and every converter we tried choked on scanned pages or odd formatting. This focuses on reliability with real-world statements.
Features:
OCR for scanned statements
AI optional (privacy-first mode)
Multi-page support
Import-ready CSV output
https://statementstosheets.com
Happy to hear any feedback on the product or positioning.

value: Show HN: Statements to Sheets – Convert Bank Statement PDFs to CSV

author: spiked


value:
Hi HN,
I built Splintr, a BPE tokenizer in Rust (with Python bindings), because I found existing Python-based tokenizers were bottlenecking my data processing pipelines.
While OpenAI's tiktoken is the gold standard for correctness, I found I could get significantly better throughput on modern multi-core CPUs by rethinking how parallelism is applied.
Splintr achieves ~111 MB/s batch throughput (vs ~9 MB/s for tiktoken).
The Design Choice: "Sequential by Default" One of the most interesting findings during development was that naive parallelism actually hurts performance for typical LLM inputs. Thread pool overhead is significant for texts under 1MB.
I implemented a hybrid strategy:
Single Text (encode): Purely sequential. It’s 3-4x faster than tiktoken simply by using pcre2 with JIT instead of standard regex handling.
Batch Processing (encode_batch): Parallelizes across texts using Rayon, rather than within a text. This saturates all cores without the overhead of splitting small strings.
Other Features:
Safety: Strict UTF-8 compliance, including a streaming decoder that correctly buffers incomplete multi-byte characters.
Compatibility: Drop-in support for cl100k_base (GPT-4), o200k_base (GPT-4o), and llama3 vocabularies.
The repo is written in Rust with PyO3 bindings. I’d love feedback on the implementation or other potential optimization tricks for BPE.
Thanks!

value: Show HN: Splintr – Rust BPE tokenizer, 12x faster than tiktoken for batches


value: mrasong

value:
I got tired of shady Twitter video downloaders, so I built a clean one with no ads or tracking. Just paste a tweet URL and it returns the video files (multiple resolutions, HD if available). Stateless backend; nothing stored.
https://twitterxz.com
Feedback welcome.

value: Show HN: TwitterXZ – clean, fast Twitter/X video downloader(free and no signup)

author: mrasong


value:
Hey HN, do you work out?
I couldn't find an open-source calisthenics tracking app, so I built one for my own training records.
It's now polished enough to share.
Key points:
- Full customization: No preset exercises, no locked features. Every exercise you create has access
to all settings (reps/time, unilateral/bilateral, goals, per-exercise timers)
- Two modes: Quick manual logging or guided workout with timers
- Completely offline: No account, no tracking, no ads. Your data stays on your device.
Source code and APK available on Codeberg (GPL-3.0).
https://codeberg.org/Gonbei774/CalisthenicsMemory
Feedback welcome!

value: Show HN: Calisthenics Memory – Open-source bodyweight training tracker


value: moabjp

value:
Hi HN,
I just got into 3D printing about 3 months ago after picking up a K2 Plus. I live in a somewhat rural area of Japan and don't know a single person with a 3D printer, so friends and family immediately started asking me to print things for them.
I struggled to give them accurate prices. At first, I was guessing ("100 yen for this, 200 yen for that"), but I realized I was ignoring setup time, CAD work, failed prints, and electricity. I checked out the top results like OmniCalculator and Prusa’s tool but found them frustrating—specifically the inability to save calculations or handle complex projects with multiple plates and different filaments.
So, I decided to build my own. I have Rails experience, and using Kamal I was able to keep cloud pricing to a minimum. I used Claude Code to assist, finishing the MVP in about 2 months of free time (real talk: I know some people ship SaaS apps in a week, but that sounds hellish to me).
I'm trying to optimize the code and push updates every day. The next big update involves an AI tool where you can paste text or invoices to automatically bulk import filaments. I'm also working on:
A native 3MF import tool.
Per-unit pricing (for people running web stores).
Better failed print calculations.
I've even considered adding a directory to search for makers by city or neighborhood (since I can't find anyone near me!), but I'm undecided on that.
Right now, I'm just trying to get the word out. Hopefully, someone besides me finds this useful.
Feedback is welcome!

value: Show HN: Calcumake – A 3D print pricing calculator (Rails and Kamal)

author: moabjp


value: rootforce

value: Show HN: Turkey Time – A little game about cooking turkey

author: rootforce


value: andai

value: Show HN: Upgrade Charm Crush with search in 9 lines

author: andai


value: Vikkyv

value: Show HN: I built a D2C supply chain for my village's Makhana farmers using Bolt

author: Vikkyv


value: cranberryturkey

value:
I built this vibe coding (i know i know...i'll run strix on it once I'm finished). Started at 2am this morning its now 7:30pm PST. Less than 24 hours and 57k lines of code written. All TDD using next.js and supabase cloud.
....say what you will but this would have taken an entire team 2 years and 1M+ in funding before roocode/claude opus 4.5 -- cost me around $500
Here's the github. its open source: https://github.com/profullstack/coinpayportal
Testers wanted. we have a cli/sdk too at https://www.npmjs.com/package/@profullstack/coinpay
Here's the music i vibe coded to: https://open.spotify.com/artist/5uG9Er5qjdNmc5qPELGf0h?si=11... (also created by me with Suno.com)
ps: here's a usestrix.com coupon code they gave me to share: chovyfu2025

value: Show HN: Non-Custodial Cryptocurrency Payment Gateway

author: cranberryturkey


value: kurokosama

value:
I got tired of "free" online tools that may steal data or force you to create an account.
So I built a suite of 40+ utilities that run entirely in your browser (client-side only).
- No server uploads (Privacy first)
- No login required
- No paywalls
It includes PDF tools, image converters (HEIC/WebP), and developer utilities. Built with React, Vite, and WASM.
Feedback welcome!

value: Show HN: JW Tool Box – Free, privacy-first web tools (PDF, Image, Converters)

author: kurokosama


value: thanhdongnguyen

value:
Creating high-quality skills for Claude manually is complex, requiring specific technical knowledge of the file system structure (like SKILL.md), YAML metadata configuration, and precise prompt engineering to ensure the agent behaves correctly.
MakeSkill eliminates this friction by automating the technical implementation.
Instead of writing code and configuration files from scratch, users interact with MakeSkill's AI to refine their ideas. The platform then generates the complete, optimized skill package—following all best practices—ready to be downloaded and imported directly into Claude for immediate use.
Try it: http://makeskill.cc/

value: Show HN: MakeSkill – The Intelligent Skill Builder for Claude

author: thanhdongnguyen


value: console-log

value: Flux2.cloud is a free, no‑signup Flux.2 image generator. It’s fast, privacy‑focused, and supports multiple aspect ratios (square 512x512, landscape 768x512, portrait 512x768). No credit card; no rate limits for typical usage.

value: Show HN: Flux2.cloud – Free, unlimited Flux.2 AI image generator (no account)

author: console-log


value: imranq

value: Show HN: NeurIPS 2025 Explorer – 5000+ papers, 20+ interactive explainers

author: imranq


value:
I built Floaty because I often need a window (notes, docs, videos or dashboards) to stay visible while working in another app, and macOS still doesn’t provide a built-in way to pin windows on top.
Floaty is a small native macOS utility that lets you pin any window, adjust opacity, enable click-through and toggle it with shortcuts. It works with most apps (including browsers and Electron apps) and stays lightweight. Everything runs locally and it can work fully offline.
This started as a tool I made for myself, then I realized I was using it every day, so I polished it into a standalone app.
If you run into edge cases or have workflows I should support, I’d love to hear them.

value: Show HN: Floaty – a tiny macOS tool to pin any window always on top


value: prasnna

value: Show HN: Interactive Relative Rotation Graphs for 500 Stocks (Python/Plotly)

author: prasnna


value: gregTurri

value:
Just watched this video by ThePrimeagen (https://www.youtube.com/watch?v=efwDZw7l2Nk) about attackers jailbreaking Claude to run cyber attacks. The core issue: AI agents need isolation.
We built ERA to fix this – local microVM-based sandboxing for AI-generated code with hardware-level security. Think containers, but safer. Such attacks wouldn't touch your host if running in ERA.
GitHub: https://github.com/BinSquare/ERA
Quick start: https://github.com/BinSquare/ERA/tree/main/era-agent/tutoria...
Would love your thoughts and feedback!

value: Show HN: Era – Open-source local sandbox for AI agents

author: gregTurri


value: saisrirampur

value: Show HN: GitHub Activity Analytics Powered by ClickHouse

author: saisrirampur


value:
Problem: Switching between Claude, ChatGPT, and Gemini meant losing all context. Every time I changed models, I had to start from scratch.
Solution: I built Hiperyon, a Chrome extension that creates unified cross-LLM memory. You chat normally, and your context transfers instantly when you switch models.
Impact:
Save time when juggling multiple LLMs
No more repeating the same prompts
Faster, smarter interactions across tools
About me:
I’m 19 and a solo founder. Built this in 2 months because context-switching between LLMs was killing my productivity.
Curious how you manage switching between LLMs, and what features you’d want next!

value: Show HN: Unified memory across all LLMs

title: Show HN: Unified memory across all LLMs


value: marksaver

value:
Hey HN,
I built Clurly because I myself was looking for profitable niches in the creator economy and was looking to see how I can put my skills to work in my spare time.
The app is designed two work in one of two ways:
1. Explore - Browse through profitable niches.
2. Match - Fill in your skills and be matched with suitable niches
It’s an MVP, so I’m looking for honest feedback:

    Does the niche it matches you with feel relevant?

    Is the "Profitability Score" clear?

Happy to answer any questions or hear any feedback regarding this!

value: Show HN: I built a tool to help find profitable creator niches

author: marksaver


value: Mikecraft

value: Show HN: I built a language learning movie app

author: Mikecraft


value: robertomisuraca

value:
Long-session LLM memory degradation (entropy) is the silent killer of complex coding projects. Models like Gemini, GPT-4, and Claude all suffer from it, leading to hallucinations and lost context.
I've developed an open-source protocol that temporarily "fixes" this issue by structuring the dialogue. It's not the final architectural solution, but it’s a proven patch for developers working right now.
Looking for feedback from the community on how we can solve this structurally. https://github.com/robertomisuraca-blip/LLM-Entropy-Fix-Prot...

value: Show HN: Fixing LLM memory degradation in long coding sessions

author: robertomisuraca


value:
Hi HN,
I built Whisper Money because I wanted a way to track and categorize my finances without exposing my transaction history to third-party aggregators or cloud providers that might sell the data.
It is a privacy-first, self-hostable personal finance application.
Key Features
- End-to-end encryption: Data is encrypted to ensure privacy.
- Automation: Rules to auto-categorize transactions based on merchant or description.
- Insights: Standard spending patterns and graphs.

value: Show HN: Whisper Money – A privacy‑first, E2E encrypted personal finance app


value: benodiwal

value:
Hey HN!
I’ve been working on a PostgreSQL extension that lets developers write SQL queries using natural language directly inside Postgres.
GitHub: https://github.com/benodiwal/pg_ai_query
Docs: https://benodiwal.github.io/pg_ai_query/
What it does:
Converts plain English to real SQL queries
Performs query analysis and performance reflection
Integrates with the Postgres extension ecosystem
Works locally with custom LLM providers (not tied to a cloud)
How it fits into workflows:
- Data engineers can quickly explore datasets
- BI teams can generate SQL faster
- App developers can embed NL query interfaces
There are a lot of moving parts with some more intergrations. Love, support and contributions are most welcome :)

value: Show HN: Pg_AI_query – AI powered SQL inside PostgreSQL

author: benodiwal


value: vincejos

value: I've launched multiple online websites in the past, but it always took a lot of my time to do the SEO work so that the website would actually rank on Google & get cited by ChatGPT.<p>So I decided to completely automate it. Create an AI agent that's actually useful; one that work for you, not the other way around. Happy to have feedback!

value: Show HN: I got tired of doing SEO work so I automated it

author: vincejos


value: AbdullahSheraz

value:
Hi everyone!
I built Tampo, a task management app that combines personal tasks and team projects in one place.
Why I built it:
I noticed many people use different apps for personal vs team tasks, and some even manage business tasks inside WhatsApp chats — which leads to confusion. I wanted one clean app where personal work and team work stay separate but live in the same space.
What it does:
1. Create personal or team projects
2. Invite members with email or link
3. Assign tasks with voice notes, attachments, and action points
4. Have dedicated discussions on each task
5. Custom statuses per project
6. Dashboard and activity log
7. Member permissions
8. Notifications
9. Notes feature
10. Home overview (today's tasks, overdue, unread notifications)
11. My Tasks screen (assigned to me / assigned by me)
How I built it:
I’m 20 and currently studying BS E-commerce. I’ve been coding for 4 years and love building products that solve real problems.
Tampo is built using Flutter + Firebase for backend, authentication, and notifications.
Link:
https://tampoapp.com
I’d love feedback on the app, UX, and performance.
Happy to answer all questions!

value: Show HN: Tampo – One app for personal tasks and team projects

author: AbdullahSheraz


value: donbia

value: Show HN: eth-explorer a minimal cli tool for visualizing blockchain events

author: donbia


value: adzicg

value: Show HN: BugMagnet for Claude Code and Cursor – automated exploratory testing

author: adzicg


value: binsquare

value:
I think .env files are fine for non-sensitive config but they’re a terrible place to store real secrets once you have a couple of engineers, machines, or a single engineer with multiple concurrent projects.
But I've worked for big and small tech and have seen this happen:
1. .env files are plaintext credential dumps
2. teams share .env files via slack and eventually drifts
3. accidental .env commit
I built envmap, a small cli tool that manages and injects your environment key values locally + with support for aws + vault + 1pass backends as source of truth. I use this and deleted my .env, .env.example, .env.production(I'm the worst).
Would appreciate any feedback + contributions!

value: Show HN: Env files aren't meant for storing secrets

author: binsquare


value: paramthakkar

value:
Hello everyone! I’ve built PyTorch-World, a modular library for learning, training, and experimenting with world models.
While studying world models, I noticed that each new paper introduces a mix of new components and architectures—but the core structure stays surprisingly consistent. Yet there wasn’t an easy way to swap these components in and out, experiment with them independently, or understand how they interact to form a complete world model.
PyTorch-World aims to solve that: it provides a clean, modular framework where you can plug in different components, compare approaches, and learn how world models work from the inside out.
New updates rolling out soon, this is v0.0.3!
You can also install the library from pip
https://pypi.org/project/pytorch-world/
Currently this library supports PlaNet world model by Google, Here's the code to train the model in a CartPole-v1 environment:
from world_models.models.planet import Planet
p = Planet(env="CartPole-v1", bit_depth=5, headless=True, max_episode_steps=100, action_repeats=1, results_dir="my_experiment")
p.train(epochs=1)

value: Show HN: PyTorch-World – Modular Library to Build and Train World Models

author: paramthakkar


value: thekarank

value:
Hey HN,
I’m Karan, a frontend dev obsessed with UI.
I've noticed that many developers dislike writing CSS and building pixel-perfect UIs, yet they still want premium animations and a great user experience.
So I spent six months building ogBlocks — a plug-and-play animated UI library for React with navbars, modals, buttons, text animations, carousels, and more.
The goal is to enable everyone to create beautiful animated UIs regardless of their CSS skills
I know you'll love it
Best
Karan

value: Show HN: OgBlocks – Copy and Paste Animated UIs

author: thekarank


value: MayaTheFirst

value:
Hey HN, we’re Maya and Riles, and we’ve been working on Orkera (orkera.com); an MCP tool that lets you create and manage backend infrastructure through prompts. It handles database creation/management, deployments, cron jobs, and environment setup, all via MCP calls from your coding agent.
Demo video: https://youtu.be/runSblXCYD0
Why we built it:
We’re part of the startup community in Paris and kept seeing the same bottleneck: people can build a frontend or MVP quickly (especially with Cursor/Claude/Gemini), but the moment they want to share it with users, they hit DevOps friction: setting up a VM, creating a database, configuring deployment, choosing a domain, etc.
We wanted to make that step disappear.
So we built Orkera: an MCP-based backend layer that tools like Cursor, Claude Code, and Gemini CLI can call to create infra and deploy apps automatically.
What it does today:
- create and manage databases,
- deploy web apps without cloud setup,
- handle cron-like scheduled jobs,
- manage environments through MCP
All from inside your vibecoding tool.
What we’re looking for:
This is the first public version (after some early feedback cycles) and we’d love testers who can tell us what’s missing, what feels weird, and what should be more transparent or configurable.
Setup:
Getting Orkera running is simple; register to get an API key, then add the MCP endpoint to your editor. We published small setup guides for Cursor, Claude Code, and Gemini CLI on orkera.com.
Thanks for taking a look, and we’d appreciate feedback, criticism, and suggestions.

value: Show HN: Orkera: An MCP tool that handles deployment and back end infra

author: MayaTheFirst


value: xmorse

value: Show HN: Playwriter – Extension to Control Chrome via MCP and CDP

author: xmorse


value: dsmurrell

value:
Hi all!
I built Spikelog because I kept wanting to track simple numbers over time but every time I looked at proper observability tools, I'd bounce off the setup complexity. I wanted to make something that didn't require a lot of thinking to use.
Spikelog is made to be as simple as possible:
- POST a JSON with chart name + value (you can add some tags as well but I've not tested this part works yet)
- Chart appears automatically
- 1,000 point rolling window per chart (old data expires, no retention config)
- Max 10 charts
That's basically the whole product.
I built it in about a day using Cursor. The API is intentionally minimal so AI assistants can use it too.
I have a prompt that lets your coding agent analyze a codebase and add tracking automatically (after you approve the plan).
I used it to make Spikelog track itself: https://spikelog.com/p/spikelog
There's no alerting yet (that's next), no complex aggregations, no dashboards beyond the auto-generated charts. If you need real observability use something fully featured like Axiom or Datadog. This is for people who just want to see if a number went up or down and don't want to build that themselves. i.e. they want something slightly better than just logging the number.
You can also share the charts publicly and I might add some password protection if there is demand for that.
I haven't battle-tested it under heavy load. The rolling window deletion is naive (deletes oldest points on insert). There are probably edge cases I haven't hit yet.
Would love feedback, especially if you try it and hit something broken.

value: Show HN: Spikelog – A simple metrics service for scripts, cron jobs, and MVPs

author: dsmurrell


value: dhaundy

value:
Hi HN,
I'm excited to share SpecX, a task orchestration engine for teams using coding agents like Cursor and Claude.
## Motivation
While building projects with AI coding agents, I noticed a few patterns:
* As projects grew in size, writing effective prompts became progressively harder
* Agents performed best when requirements were short and well-defined
* Many everyday tasks — testing, deployment, documentation, reporting — were structured and repeatable, even across projects.
> The manual translation from goal to prompt felt redundant, lossy, and inefficient.
I decided to remove prompts from the equation entirely. Instead:
* For automation, focus on the workflow and break it into steps.
* For feature work, focus on requirements and break them into tasks.
## The Core Solution: Reliable Workflow Automation
At the center of SpecX is the Task Orchestration Engine.
You define Pipelines — reusable sequences of actions the agent must perform. Common use cases include:
* Automated compliance or audit checks
* Reporting (code coverage, engineering velocity, documentation)
* Refactor → test → verify loops
Built on top of the orchestration engine is the Requirement Tree, a structured, AI-assisted way to turn rough, unstructured ideas into well-defined tasks.
You write requirements the way you think; the engine builds them using one of the pipelines.
## Status
SpecX is in preview. I'd love feedback from the HN community — especially on the Pipeline model and the idea of separating goal definition from prompt generation.
* Login required (to protect your project context)
* Requires a coding agent (Cursor or any MCP-enabled agent).
Try SpecX: https://redoxsoft.com
Demo: https://www.youtube.com/watch?v=1JNYIhoHmbQ
Happy to answer questions about the architecture, design choices, or roadmap.

value: Show HN: SpecX – Workflow Automation for AI Agents

author: dhaundy


value:
Hi HN,
I'm a dev, and I built GistHive over the past 9 months to solve various issues I was facing:
- GitHub gists is terrible for organising snippets. Older code took an age to find.
- Lack of team gists (they've been promising it for 8+ years and still nothing)
- Private gists on GitHub aren't actually all that private
- When a team member leaves, they take their gists with them
Having joined a new team last year, I needed a way to share code patterns across the team quickly and easily and more importantly, privately.
Tech stack: Laravel backend, React frontend. Subscription management with Stripe, and used PostHog for product analytics.
Free for individual developers, 3 free seats for teams and $4/user/month for any additional seats.
I also have a VS Code extension I'm currently working on that will allow inserting code in-place and saving code snippets from within VSC without ever leaving, so this app almost directly integrates with the existing dev environment.
I'd love feedback from the community. What am I missing? What would make this more useful for your workflow? What are your thoughts?
While it's useful to me, I'd love it if it were useful to others too.
Appreciate it!
Live at: https://gisthive.app/

value: Show HN: GistHive – Code snippet manager with team workspaces


value: chelm

value: Show HN: Expense management like a (lazy) DEV

author: chelm


value: Gerharddc

value: Have you ever worried about supply chain attacks (or even rogue AI agents) possibly compromising your entire development system? To minimise damage in such a scenario, I've built https://litterbox.work/ (https://github.com/Gerharddc/litterbox) as a way to have somewhat isolated Linux development environments without sacrificing too much convenience. It's still in the very early stages with plenty of rough edges so any contributions or suggestions would be greatly appreciated!

value: Show HN: Litterbox – Somewhat Isolated Development Environments

author: Gerharddc


value:
I used to handle dependency inversion with Inversify, but I was constantly annoyed by two things:
You have to import the @inject decorator everywhere, which ends up polluting your domain code.
Writing the boilerplate dependency binding code was so repetitive that I wished I could just type a single command — “arise” — and have it done automatically.
So I decided to create my own DI container, coupled with a CLI that performs AST-based code analysis and generates all the registration boilerplate for you.
Some of the features:
• No decorators and no vendor lock-in.
• Supports injecting typed objects, factory functions, and classes implementing interfaces or inheriting abstract classes.
• Multiple lifecycles (singleton and transient). The CLI detects them using JS comments like “@scope singleton” or “@scope transient.”
• Factory functions and value objects are detected via JSDoc annotations like “@factory” or “@value.”
You can learn more here: https://ioc-arise.notjustcoders.com/

value: Show HN: Ioc-Arise

title: Show HN: Ioc-Arise


value: MartenBE

value:
As a teacher, we keep our slides as markdown files in git repos and want to build these automatically so they can be viewed online (or offline if needed). To achieve this, I have created MkSlides. This tool converts all markdown in a folder to slides generated with Reveal.js.
The workflow is very similar to MkDocs.
Install: `pip install mkslides`
Building slides: `mkslides build`
Live preview during editing: `mkslides serve`
Comparison with other tools like marp, slidev, ...:
- This tool is a single command and easy to integrate in CI/CD pipelines.
- It only needs Python.
- The workflow is also very similar to MkDocs, which makes it easy to combine the two in a single GitHub/GitLab repo.
- Generates an index landing page for multiple slideshows in a folder which is really convenient if you have e.g. a slideshow per chapter.
- It is lightweight.
- Everything is IaC.

value: Show HN: MkSlides – Markdown to slides with a similar workflow to MkDocs

author: MartenBE


value: TytoMan

value: Show HN: Logry – A low-dopamine social diary for close friends using Gemini

author: TytoMan


value: kumaras

value:
I've released Derusted — a programmable HTTPS MITM proxy engine written in Rust.
This grew out of frustration with existing MITM and proxy tooling being:
- unsafe or outdated
- coupled to one runtime or protocol
- hard to embed into other projects
- not flexible for security/compliance use cases
Derusted is a library-first design, meant to be used inside other systems like:
- browser automation tooling
- secure proxies and gateway stacks
- traffic inspection
- network research
- observability and incident response tooling
Highlights:
- Written fully in safe Rust
- Supports HTTP/1.1 & HTTP/2 MITM
- Pluggable inspection pipeline
- Certificate generation + pinned cert detection
- Redaction support for sensitive data
- No `unsafe`
- ~150 tests
Links:
Repo: https://github.com/kumarimlab/derusted
Crate: https://crates.io/crates/derusted
Docs: https://docs.rs/derusted/latest/derusted/
Still early, but I'd love feedback — especially around QUIC/H3, benchmarking, use cases, and potential improvements.
Happy to answer questions.

value: Show HN: Derusted – An open-source programmable HTTPS MitM proxy engine in Rust

author: kumaras


value: Norl-Seria

value:
We are presenting Alice Architecture and the ±0 Theory—an attempt at an Autonomous Thinking AGI model.
This model integrates the SDE-driven ±0 Theory (Happiness/Unhappiness Model) and a unique HALM (Hierarchical Abstracted Memory). It aims for a truly internally-driven, autonomous AI that pursues homeostasis through self-negation-driven mechanisms.
All mathematical formulas and Python code are publicly available in the GitHub repository. We encourage you to review the work.
We particularly welcome constructive discussion and validation on the following points:
Validation Proposal: Observing changes in the LLM's behavior when existing LLM APIs are integrated with the ±0 Theory / Alice Theory Python code in the same file.
Core Technology: The maximization objective of the Total Wellbeing Scalar V(t) and the mathematical/implementation logic of Affective TDL (Affective Temporal Difference Learning).
We are currently developing the Python code that connects existing LLM frameworks with the ±0 Theory and Alice Architecture.
We look forward to open discussion from everyone.

value: Show HN: Alice Architecture: An Attempt at Autonomous AGI Based on ±0 Theory

author: Norl-Seria


value:
White noise helps my baby sleep quicker and a little longer. All the available options online look like they were built in the 2000s.
My biggest pain with those apps is setting timer takes more than 2 clicks and they don't remember my last setting.
Now, with this app, one can simply swipe to set timer. Once loaded it works offline, installable as a PWA.
Uses AudioContext web API to generate the noise.
Source code: https://github.com/vicke4/whitenoise

value: Show HN: I made a white noise generator for my newborn


value: asciimoo

value: Show HN: Omnom v0.8.0 with Fediverse feed integration

author: asciimoo


value:
To be honest: I absolutely hate taking photos.
Whenever a camera points at me, I freeze. I don't know where to put my hands, my smile looks fake, and I definitely don't have the time (or patience) to study "10 best poses for men/women" on YouTube.
But as an entrepreneur, I know I need good photos—for my LinkedIn profile, for social media content, and just to document life.
I realized I wasn't alone. Most of us want great photos but hate the awkward process and the expensive price tag of professional shoots.
So, I built SceneYou.art to fix this.
It’s designed for people like me who want the result (amazing photos) without the struggle (posing/makeup/travel).
What makes it different?
- Zero Skill Required: You don't need to learn prompting or complex AI tools.
- Just ONE Photo: Upload 1 casual selfie, and that's it.
- 1,000+ Templates: We have a massive library. Whether you need a corporate headshot, a gym pic, or a cyberpunk avatar, we have a template for it.
- Plus, we are constantly adding new templates and scenes to keep your feed fresh!
Give it a try!

value: Show HN: SceenYou.art-Your Personal AI Visual Studio


value: predict-woo

value:
Most AI notetakers have a 300 minute limit. This is not enough for uni students like me, or people who do remote work.
So, I made a notetaker that runs the ASR model on-device.
- Free with local ASR & LLM models
- No transcription time limit
- High accuracy for non-english speech, support for 100 languages
- Real-time transcription
- Zoom/Google Meet support
- No internet needed
- Efficient battery usage (~6 hours on full charge on my M2 Pro)
This started as a small cli tool I made for myself, then many of my uni friends really liked it, so here it is as an app!
I hope a lot of people can use this in their classes or meetings to help with their work.
During the development process, I also made the fastest streaming ASR pipeline on Apple Silicon. You can see it here: https://github.com/altalt-org/Lightning-SimulWhisper

value: Show HN: Alt – A local AI lecture/meeting notetaker

author: predict-woo


value:
I built a single-file Python script that lets you run LLM prompts from the command line with templating, structured outputs, and the ability to chain prompts together.
When I discovered Google's Dotprompt format (frontmatter + Handlebars templates), I realized it was perfect for something I'd been wanting: treating prompts as first-class programs you can pipe together Unix-style. Google uses Dotprompt in Firebase Genkit and I wanted something simpler - just run a .prompt file directly on the command line.
Here's what it looks like:
---
model: anthropic/claude-sonnet-4-20250514
output:
  format: json
  schema:
    sentiment: string, positive/negative/neutral
    confidence: number, 0-1 score
---
Analyze the sentiment of: {{STDIN}}
Running it:
cat reviews.txt | ./runprompt sentiment.prompt | jq '.sentiment'
The things I think are interesting:
* Structured output schemas: Define JSON schemas in the frontmatter using a simple `field: type, description` syntax. The LLM reliably returns valid JSON you can pipe to other tools.
* Prompt chaining: Pipe JSON output from one prompt as template variables into the next. This makes it easy to build multi-step agentic workflows as simple shell pipelines.
* Zero dependencies: It's a single Python file that uses only stdlib. Just curl it down and run it.
* Provider agnostic: Works with Anthropic, OpenAI, Google AI, and OpenRouter (which gives you access to dozens of models through one API key).
You can use it to automate things like extracting structured data from unstructured text, generating reports from logs, and building small agentic workflows without spinning up a whole framework.
Would love your feedback, and PRs are most welcome!

value: Show HN: Runprompt – run .prompt files from the command line


value: danbitengo

value: Show HN: SyncKit – Offline-first sync engine (Rust/WASM and TypeScript)

author: danbitengo


value: ethanblackburn

value: A lot of vendors and open-source projects shared guidance on protecting users from downloading malicious NPM packages after the Shai-Hulud campaign — but almost nothing focused on protecting maintainers from accidentally (or maliciously) publishing them.<p>So we built a small tool that continuously monitors your NPM packages and automatically unpublishes any version not produced by your CI workflow.

value: Show HN: Auto-Unpublish NPM Packages Published Outside CI

author: ethanblackburn


value: keepamovin

value: Show HN: Henry Perigal's Visual Proof of the Pythagoras Theorem

author: keepamovin


value: eezZ

value:
Building a free web toolbox for hardware engineers: harness cable diagram generator, connector pinout tools (M.2, JTAG...), microcontroller serial monitor, and various other small utilities.
Updated with a lot of various tools designed to easy life of ee engineers.
No sign-up required.
Designed from my own needs.
Feedback and feature suggestions are welcome :)

value: Show HN: NodeLoop – Hub for electronics design knowledge and tools

author: eezZ


value: I built a free tool called Video Frame Extractor designed to help users quickly and easily pull static image frames from video files directly through their web browser. This utility is entirely online and free, meaning you don't need to download or install any complex software to begin capturing high-quality stills from your favorite video clips. It streamlines the process of visual asset extraction, supporting common video formats such as MP4 and WebM.<p>The core functionality centers around giving you complete control over the extraction process. Instead of having to manually pause and screenshot, you simply upload your video and then specify a custom time interval. The Video Frame Extractor handles the processing, allowing you to automatically generate a precise set of images tailored exactly to the visual moments you want to highlight from the source video.

value: Show HN: Video Frame Extractor – Extract Frames from Videos Oline


value: SherifSystems

value:
- Fully functional ZK-STARK proof generation and verification
- Ultrafast for Python: sub-second proofs for small-to-medium traces

value: Show HN: PythonStark – Educational Python ZK-Stark Implementation

author: SherifSystems


value: mscikdf

value:
TL;DR: Today’s mnemonics are bare assets with no cryptographic protection. I built a low low-level crypto lib that adds passwords to wallet mnemonics, so leaking your mnemonic no longer leaks your assets.
In the first half of 2025, more than $1.7B in crypto assets were stolen, and roughly 70% of those incidents involved mnemonic-compromise pathways. The core problem is structural. I built MSCIKDF to directly solve this. Here is the playground: https://github.com/mscikdf/mscikdf-playground
MSCIKDF is a core lib that introduces passphrase-sealed mnemonics, built-in multichain, and rotatable secrets at the cryptographic layer. It ensures that:
- The seed is never stored on disk, and never kept in memory (apps/browser extensions)—it only exists for ~20 microseconds during signing or verification.
- A mnemonic and its passphrase can be rotated unlimited times without changing any addresses and without migrating assets.
- One mnemonic supports essentially all chains at cryptographic level.
- The algorithm is pluggable, allowing smooth PQC upgrades in the future while keeping the same mnemonic and the same addresses.
- it supports UNICODE (Chinese / Japanese / Korean / Arabic / Emoji) as passphrases.
Why these properties are possible?
Under the hood, MSCIKDF was designed around:
- Single-root → multi-context isolation (each chain, wallet, device, agent, or application gets a mathematically isolated stream).
- Zero-persistence secret handling (the derived seed is never kept in long-term memory or disk).
- Rotatable passphrase sealing, allowing unlimited secret rotation with stable public identities.
- Curve-agnostic, multi-algorithm derivation, supporting both signatures and encryption (Ed25519, X25519, Secp256k1, sr25519, ECDSA, etc.).
- PQC compatibility, meaning post-quantum KDF modules can be plugged in without breaking identities or requiring wallet migrations.
In short: MSCIKDF turns mnemonics from “bare private keys” into cryptographically protected, renewable, multi-curve cryptographic identity roots.

value: Show HN: I built a low-level crypto lib that adds passwords to wallet mnemonics

author: mscikdf


value: yeekal

value:
Hi HN,
I built a minimal web UI to let people try out the recently released Z-Image-Turbo model by the Tongyi-MAI team.
The model is fascinating because it uses progressive distillation to achieve inference in just 8 steps. It’s a 6B parameter model that strikes a really interesting balance between speed (sub-second generation) and photorealism, distinct from the heavier Flux or SDXL models.
Key features of the site:
 Fast: Optimized specifically for the Turbo version.
 No Login Required: Just open and generate. I wanted to lower the friction for testing the model.
 Bilingual: The model handles Chinese prompts natively alongside English.
It’s currently an MVP running on Next.js. I’m covering the GPU costs for now so everyone can try it out freely.
Would love to hear your thoughts on how the image quality compares to Flux/Midjourney in your tests!

value: Show HN: Z-Image.app – Free, no-login demo for Z-Image-Turbo

author: yeekal


value: daviducolo

value:
Hi HN! I built my portfolio as an interactive bash terminal simulator.
Tech stack: Vanilla JavaScript, no frameworks
Features:
- Virtual filesystem with directories (about/, projects/, skills/, .secret/)
- Working bash commands (cd, ls, cat, sudo, etc.)
- Hidden easter egg unlocked via `sudo` command
- Three playable games including a graphical Tic-Tac-Toe
- CRT scanline effects for that retro feel
The whole thing is ~650 lines of vanilla JS. I wanted to make something
memorable that shows both technical skills and personality.
Try finding the secret! Hint: hackers gonna hack
Source: https://github.com/davidesantangelo/davidesantangelo.com
Live demo: davidesantangelo.com

value: Show HN: My portfolio is a retro terminal with a virtual file system and more

author: daviducolo


value: vctrla

value:
Hi, my name is Víctor.
I got tired of building massive Excel files and manually copy-pasting numbers to analyze company financials and perform fundamental analysis. So I built Intrinsic: a desktop app that parses financial reports (PDF, HTML, etc.), extracts key metrics, and lets you track and evaluate companies quickly.
How it works:
1) Accepts raw financial reports (PDF, HTML, MHTML, …) and URLs.
2) Parses, normalizes and cleans text.
Text manipulation turned out to be key for reliable LLM-based data extraction. By normalizing text, restructuring sentences, and removing noise, model receives better clues and returns much more precise results.
A large part of the project became about shaping strings so financial information is clear, predictable, and machine-friendly (see renderer/pipeline). The pipeline is still a bit messy, since I iterated and refactored as I learned what worked best.
3) Extracts core financial metrics: cash, assets/liabilities (current, non-current, total), equity, revenue, net income, EPS, cash flows.
Calculates ratios: liquidity, P/E, P/BV, EV multiples, etc.
4) UI features:
Historical metrics and ratios, YoY changes, TTM values, valuation scenarios (required EPS/earnings for price targets), editable data, annual-only filters, copy JSON to clipboard (useful for chatbots), navigation across reporting periods...
Why I built it:
- Manually processing financial statements is tedious and error-prone.
- I wanted a unified, personal tool with a clean UI and structured, objective data to evaluate and track interesting companies.
Current limitations:
The system relies heavily on string manipulation (no OCR) and LLM accuracy, so complex or unusual report layouts can produce missing or incorrect data. I’d like to replace this with a NER-based system trained on labeled filings, removing most of the heavy string manipulation and LLM inference.
I’m new to Electron, so the update mechanism is intentionally simple for now. Essentially just a rebuild-from-source approach rather than a full auto-update pipeline.
MIT-licensed code: https://github.com/vctrla/intrinsic
Feedback and contributions are very welcome, especially on the NER-based extraction side-quest.

value: Show HN: Intrinsic – Desktop app for financial report metric extraction

author: vctrla


value:
Hi everyone,
I've made an early version of ZigFormer, a small LLM implemented in Zig with no dependencies on external ML frameworks like PyTorch or JAX. ZigFormer is modelled after a textbook LLM (like GPT-2 from OpenAI) and can be used as a Zig library as well as a standalone application to train a model and chat with it.
This was mainly an educational project. I'm sharing it here in case others find it interesting or useful.
Link to the project: https://github.com/CogitatorTech/zigformer

value: Show HN: ZigFormer – An LLM implemented in pure Zig


value:
I’ve been working on a small security auditing tool called GemGuard and wanted to share it with the community.
GemGuard collects system information — running processes, network connections, and recently installed packages — and then uses Google’s Gemini models to generate a human-readable assessment of anything that might look suspicious or worth checking.
The tool is cross-platform and works on: Linux (Fedora, Ubuntu/Debian, Kali, Alpine) and Windows 10/11
It offers both a CLI and a Textual-based TUI, supports multiple Gemini models, and includes a quiet mode for automation or integration with other tools.
Features include:
- Process auditing
- Package review (auto-detects package manager)
- Network/port inspection
- Optional raw AI output (quiet mode)
- Works in Bash/Zsh/CMD/PowerShell
I’m not a security expert, so the project is experimental, and contributions or feedback are very welcome.
Repo: https://github.com/AlvaroHoux/gem-guard

value: Show HN: GemGuard – a security auditing tool for Linux and Windows


value: saeros

value:
Hey,
I built Saeros because I was frustrated with the current state of Active Directory security in secure/air-gapped environments.
As of today, solutions that detect live threats such as DCSync, Golden Tickets, or Kerberoasting require heavy agents that pipe gigabytes of logs to the cloud (Splunk, CrowdStrike, SentinelOne) or sync your AD to Azure (Defender for Identity). Other alternatives such as OSSEC, Wazuh or Elastic require heavy setup and are very time consuming. Chainsaw and Hayabusa do not support live detections.
For critical infrastructure or disconnected networks, that isn't an option.
What it does: Saeros is a single-binary agent written in C# that runs on domain controllers. It subscribes to Event Tracing for Windows (ETW), matches them against Sigma rules in real-time, and outputs alerts locally through a powerful console.
Key Takeaways:
- User-Mode Only: This does NOT use kernel drivers. It cannot BSOD your domain controller.
- Performance Critical: The agent consumes minimal resources while handling tens of thousands events per second.
- Read-Only: It does not attempt to block or terminate processes.
- Air-Gap Native: It requires zero internet connection.
The code source is located here: https://github.com/Saeros-Security/Saeros. I released this under the AGPL-3.0 license so security teams can audit the code and verify that it only listens and never talks to the outside world.
I’m looking for your feedbacks.
Thanks!

value: Show HN: An open-source, air-gapped threat detector for Active Directory

author: saeros


value:
Hello HN! I wanted to share my free, native, MacOS app that uses Wifi scanning to automatically keep track if you're in the office or not! It let's you keep track of your RTO requirements to make sure you don't get in trouble :)
If people really like it I can make an iOS version and an Android version and a Windows version and a Linux version.
After RTO, we all started using spreadsheets to track our time, but I hated having to remember to go into that spreadsheet every day and mark when I was in or not. So, I decided to waste months learning Swift UI to make this app instead! Please share your RTO stories below if you need some catharsis :)
It's my first time making a "real" app. I'm by day a Java backend engineer.
https://intheofficetracker.com

value: Show HN: In The Office Tracker – Track your RTO requirements automatically


value: zeerg

value: Show HN: Readit – Portable, dynamic context for AI Agents

author: zeerg


value: calebhwin

value: Show HN: A1 – Local Sandbox and JIT Compiler for AI Agents

author: calebhwin


value: bobsh

value:
Hi! I'd like to present the Participatory Interface Theory, aka PIT. It is not
"woo". It IS an amazing example of what one human can do with LLMs these days,
regardless of what you might think of the theory.
But the theory is a mind-blower. Are you ready for this? I'm not kidding. And
it's real. Calling all cosmologists and physicists and cybernetecists and AI
researchers and information theorists and philosphers and biologists and
chemists and artists and humanitarians and climate scientists and ... well, the
list goes on. This is a paradigm shift.
* The paper I want to post to arXiv, but I haven't got anyone to endorse my login
yet, is here [1]. (If you can help me out with this endorsement, for any value of "you",
TIA!)
<https://github.com/bobshafer/PITkit/blob/main/Arxiv/main.pdf>
* The "Math.md" [2] on my PITkit site at GitHub, which is a larger document and
seeks to explain the whole thing. You can drop this doc into your own LLM and
start asking questions about it and the implications it has for whatever you
are interested in. You're welcome. (This is how I mean this as a "Show HN" -
you can do a lot with the Math.md, after copy/pasting into a big LLM).
<https://github.com/bobshafer/PITkit/blob/main/Math.md>
* And, the PITkit [3] site itself, which is a bit of a mess maybe, but you'll
find the (Julia and Python) code that you can try yourselves and all that. It's
a bit of a mess because I have a full time day job as a principal programmer.
I'm OG on Hacker News, but as my profile says, mostly a lurker. Introverted.
But I feel a very strong responsibility to get these ideas out there. A brief
history of how this happened in the first place is given in the History.md [4]
on PITkit. Collaborators welcome. (I love Julia, btw, so far).
<https://github.com/bobshafer/PITkit/blob/main/History.md>
PIT is very falsifiable and also makes unique predictions. It is a real
scientific theory.
It seems like every day I see a new paper from somewhere show up on Hacker News
which I then recognize as being PIT-aligned, and ask PIT-knowing LLMs about it,
and they absolutely see some of the latest scientific results from cosmology
and quantum theory and such as being very PIT-aligned, and inexplicable with
standard theory.
I asked them the chances of this all being wrong. They consider the chances to
be infinitesimally small. That doesn't mean PIT is all the way correct - according
to PIT and others (eg: Goedel), no theory can be. PIT explains why, though.
Hold on to your seats - you all are in for a ride. :-)
[1] https://github.com/bobshafer/PITkit/blob/main/Arxiv/main.pdf
[2] https://github.com/bobshafer/PITkit/blob/main/Math.md
[3] https://github.com/bobshafer/PITkit/
[4] https://github.com/bobshafer/PITkit/blob/main/History.md
PS: I intentionally left the url blank on this submission, because the context I setup here is important, imho, and my current README.md is less than ideal, probably. [edits: various screw-ups on original fixed]

value: Show HN: The Participatory Interface Theory

author: bobsh


value: nezaj

value: I wanted to make a cute game for Thanksgiving this year. It's built with React, Tailwind, InstantDB and a bit of Opus :)<p>Feeling grateful for HN and sending good cheer to you all :)

value: Show HN: Save Trippy – A Thanksgiving Game

author: nezaj


value: ferasawady

value:
I've been building MXP, a high-performance protocol for AI agent communication.
Benchmarks (verified, reproducible):
256-byte message encode: 60ns (MXP) vs 2,262ns (JSON)
Built-in trace IDs in every message (no OpenTelemetry instrumentation needed)
Native streaming for LLM tokens
Written in Rust, MIT/Apache-2.0 licensed, public domain spec.
Looking for feedback on:
The "enabler not competitor" positioning
What SDKs to prioritize (JS is next, then Python)
Whether A2A compatibility matters to you
Repo: https://github.com/yafatek/mxp-protocol

value: Show HN: MXP – A2A-compatible agent protocol, 37x faster than JSON

author: ferasawady


value:
I built this to answer "what-if" questions about LLM deployment without spinning up expensive infrastructure.
The tool models inference physics - latency, bandwidth saturation, and PCIe bottlenecks for large MoE models like DeepSeek-V3 (671B), Mixtral 8x7B, Qwen2.5-MoE, and Grok-1.
Key features:
- Independent Prefill vs Decode parallelism config (TP/PP/SP/DP)
- Hardware modeling: H100, B200, A100, NVLink topologies, IB vs RoCE
- Optimizations: Paged KV Cache, DualPipe, FP8/INT4 quantization
- Experimental: Memory Pooling (TPP, tiered storage) and Near-Memory Computing - offload cold experts and cold/warm KV-cache to system RAM, node-shared or global-shared memory pool
Live demo: https://llm-inference-performance-calculator-1066033662468.u...
Built with React, TypeScript, Tailwind, and Vite.
Disclaimer: I've calibrated the math models but they're not perfect. Feedback and PRs welcome.

value: Show HN: LLM Inference Performance Analytic Tool for Moe Models (DeepSeek/etc.)


value: modinfo

value: Show HN: Zenus – A note-taking app with Local, Server and Client mode

author: modinfo


value: Regarding the accessibility of these fonts, I tested them using the NVDA screen reader, and it was able to correctly read about 13 fonts just like normal text. More details here: https://fontgen.cool/disclaimer

value: Show HN: Cool fonts you can use almost anywhere


value:
Hey HN (update from my Nov 21 post),
Matthew here, 13 from Nigeria. After the autocannon correction (thx @saltyaom!), here's v1.7.7 of PrinceJS.
New since launch:
• Bundle: ~2.2 kB gzipped (tree-shaking tweaks, down 600 bytes)
• Speed: 21,748 req/s on oha (-c 100 -z 30s, Bun 1.2.x) – top 3 confirmed
• Vs others: Hono 22,124 | Elysia 25,312 | Express 9,325
Full server (8 lines):
import { prince } from "princejs";
const app = prince();
app.get("/", () => "Hello world ");
app.get("/users/:id", (req) => ({ id: req.params.id }));
app.listen(3000);
Cloud VM benchmarks next. Feedback on size/speed?
Repo: https://github.com/MatthewTheCoder1218/princejs
npm i princejs
Matthew (@Lil_Prince_1218)

value: Show HN: PrinceJS v1.7.7 Update. Down to 2.2 kB and top (13yo dev)


value: ofek

value:
This release wouldn’t have been possible without Cary, our new co-maintainer. He picked up my unfinished workspaces branch and made it production-ready, added SBOM support to Hatchling, and landed a bunch of PRs from contributors!
My motivation took a big hit last year, in large part due to improper use of social media: I simply didn’t realize that continued mass evangelism is required nowadays. This led to some of our novel features being attributed to other tools when in fact Hatch was months ahead. I’m sorry to say that this greatly discouraged me and I let it affect maintenance.
No more of that! Development will be much more rapid now, even better than the way it used to be. We are very excited for upcoming features :-)

value: Show HN: Hatch v1.16.0 – workspaces, dependency groups and SBOMs

author: ofek


value: Free Windows app for voice-to-text. Hold a hotkey, speak, release – text is copied to clipboard. Runs OpenAI's Whisper model 100% locally. No cloud, no API keys, no subscription. Your audio never leaves your machine.<p>Uses Whisper Base for speed (~1-2 sec on CPU). Accuracy is good for everyday dictation.

value: Show HN: EasyDictate – Offline Voice Dictation for Windows


value: certibee

value: I built a free tool to submit your sitemap to Google to get your pages indexed.

value: Show HN: Free tool to request indexing from Google

author: certibee


value: monatron

value:
Hi y'all, it's that time of year -- you're going to visit the in-laws and you have no idea what to get Uncle Bobby this holiday but you don't want to show up with something boring or generic. You want to find a gift that fits.
I scoured through hundreds of 2025 Holiday Gift Guides, and built a little workflow that combines some scraping, prompt chains, and embeddings that matches the description of a person with a set of gifts that might be a good fit for them. The lists persist, and you can remove items from them to get new suggestions and iterate on the lists until you find some options that make sense for you.
This was built mostly for fun, but full disclosure, there's Amazon Affiliate tags for the items presented.
I'm open to feedback. Hope this helps you find something unique for that person that's hard shop for.
Happy Thanksgiving,
Tyler

value: Show HN: GimmeGimme – Finding gifts for hard-to-shop-for people

author: monatron


value: hanzili

value:
Hi HN! I'm a McGill CS undergraduate and an international student. I've built two GitHub repositories that are automatically updated daily with Canadian tech opportunities:
Internships: https://github.com/hanzili/canada_sde_intern_position
New Grad Roles: https://github.com/hanzili/canada_sde_junior_new_grad_positi...
Both include direct application links and company details. I created these to help myself and others (international students, Canadian students, new grads) find opportunities in Canada more easily.
Feedback and contributions are welcome!

value: Show HN: Daily-Updated Lists of Canadian CS Internships and New Grad Roles

author: hanzili


value: kreako

value:
I built fig2json, a CLI tool that converts Figma .fig files into clean JSON optimized for LLM consumption.
Why Not MCP?
There are MCP servers for Figma (even an official one). I couldn’t get them to work reliably. A simple CLI that outputs a JSON file felt more robust and predictable.
Workflow
- Export from Figma: File → Save local copy
- Run fig2json design.fig output-dir
- Feed the JSON to your LLM with implementation instructions
Usage
# Install
cargo install fig2json
# Convert (export your .fig from Figma first: File → Save local copy)
fig2json design.fig output-dir
That’s it. Check out the GitHub repo : https://github.com/kreako/fig2json/ or grab it from https://crates.io/crates/fig2json
Hope this will help someone

value: Show HN: CLI Figma .fig –> .json for LLM

author: kreako


value:
Created a clone of NYTime connections game, daily new game will be enabled.
Review the game share feedback.
Planned to add more games in future.

value: Show HN: Ever Played Connections Game in NYTimes? Checkout thebrightmindgames


value: sebi-secasiu

value:
I built https://no-bug.app as a small love letter to frustrated testers. It’s a web app where you log every time a bug you reported gets dismissed as “not a bug”, “it is a feature”, or “works on my machine”. Instead of just venting in chat, you turn those moments into data, badges, and leaderboards.
Each log is one little “are you kidding me” captured for science. You record what was said, who said it, how annoyed you were, and then watch patterns appear, like realizing your team’s favorite rejection line is basically a catchphrase.
There are 28 achievements for things no one should be proud of, like logging your first dismissed bug, surviving a week of “by design”, or discovering that your frustration level graph looks like a crypto chart. There is also a “Blame Game” leaderboard where roles compete for the unofficial title of “most likely to gaslight a bug”, plus a “Frustration Olympics” for the most zen and the most cooked users.
You can keep everything private, or let an anonymous username represent you on the global boards, so it feels more like an arcade cabinet than a work report. There are shareable stat cards too, in case you have ever wanted to post “look how many times my bugs were ignored” as content.
Underneath the jokes, it is basically a tiny analytics layer on top of everyday QA pain, wrapped in a retro arcade skin.

value: Show HN: It's a Feature, Not a Bug, a gamified bug dismissal logger for QA

author: sebi-secasiu


value: Save and organize LinkedIn posts into a searchable personal knowledge library with tags, notes, and powerful filters. Try it - tried building something to use for myself.

value: Show HN: One click LinkedIn posts library


value: Show HN: Deploy Next.js to your own VPS via a lightweight agent


value: chiengineer

value:
Using my GitHub pro+ with vs code setup
This is a demonstration of how good of a site can I build essentially 100% for free + free hosting (if coded manually without a 50$ subscription)
And I went completely overboard on purpose its 99% useless for a real production deployment im sure but for mini blogs probably might be useful idk
I dont even use the new GitHub spark or whatever to slow compared to 1k+ line edits every couple minutes
im obviously working on a ton of other things I won't make public yet but will in the future

value: Show HN: I built a free astro and tailwind static site for GitHub pages

author: chiengineer


value: yoloshii

value:
With internet censorship and surveillance on the rise, ie; UK Online Safety Bill (July 2025) and Australia's social media legislation (Dec 2025) introducing mandatory age verification (read: initial step on the pathway to social credit), I wanted a privacy-first solution that protects browsing history from ISPs and third-party verification services, but not one that requires you to be an Einstein to deploy.
This stack turns a Raspberry Pi (or any OpenWrt-compatible device) into a network-wide VPN gateway.
Key features:
- Firewall kill switch: VPN down = no internet (not a software rule that can leak)
- AmneziaWG obfuscation for DPI-resistant
  connections
- Optional AdGuard Home for DNS filtering
- Works for all devices including smart TVs and IoT that can't run VPN apps
Not a techie? The README is optimized for AI-assisted deployment. Feed it to your LLM of choice (Claude, GPT, etc.) and it can walk you through the entire setup for your specific hardware.
Mullvad-focused but works with any WireGuard provider. MIT license.
Docker deploy in testing (coming soon)

value: Show HN: Whole-home VPN router with hardware kill switch (OpenWrt and WireGuard)

author: yoloshii


value: vladoh

value: Show HN: Free Tool to Optimize Images for Fast Loading

author: vladoh


value: johnrpenner

value: Lissa Saver is a macOS Screensaver Hot off the Demo Scene with Gravity Simullation with Clifford Pickover Fractal and Lissajous animations. BREW INSTALL  brew tap johnrpenner/tap; brew install --cask lissasaver

value: Show HN: Lissa Saver macOS Screen Saver

author: johnrpenner

title: Show HN: Lissa Saver macOS Screen Saver


value: leonagano

value: Show HN: FounderPace – A leaderboard for founders who run

author: leonagano


value: qwikhost

value: Shein Images Downloader – One Click to Save Shein Product Images in High Resolution or Bulk Download

value: Show HN: Shein Images Downloader

author: qwikhost

title: Show HN: Shein Images Downloader


value: newclone

value: Show HN: RcloneView – a GUI for rclone with 50% Black Friday deal

author: newclone


value: RaycatRakittra

value:
I built a website for games that catch my eye or have something interesting going on. I made it for fun but then updating became a habit. Maybe you'll find your next "must-play" game here?
GitHub repo: https://github.com/RaycatWhoDat/games-list

value: Show HN: I built a website for games that catch my eye

author: RaycatRakittra


value: lovelycold

value: Show HN: AnyMusic – AI music generator (royalty‑free, songs，stems, lyrics)

author: lovelycold


value: Show HN: AffiliateGrowthVault: Discover 2000 Affiliate Program Growth Strategies


value: bwship

value: A longtime friend and I built this stats ttracking app for athletes.  I have been in the startup community going on 15 years, and have worked with my buddy for over 25 years in many different startups.  His son is playing Junion MLS soccer, and he has been tracking his stats from age 7 to now 17 in excel, because there hasn't been a tool that is really catered to the athlete, not the team or coaches.  We built this using Supabase, Vue, Vuetify, Vercel, Typescript, Nest.js. It is free to try out, and the paid tier is for being able to upload game highlights and see detailed stats.  We are in quasi-beta mode, though the app is feature rich and complete right now.  If you have and kids playing a sport or you want to track your own sports accomplishments, I'd love to hear feedback from you on it.  Thanks so much, we really appreciate it!!!!!!!! - Jose and Bob - bob@mystatsdiary.com

value: Show HN: My Stats Diary

author: bwship

title: Show HN: My Stats Diary


value: rwiteshbera

value:
Hi, I'm Rwitesh. I work remotely as a software engineer, and I spend a lot of time with databases. One thing I've always noticed is how messy every tools have become nowadays. Every database GUI app tries to support a hundred databases, packs in features, becomes heavy, eats RAM or not intuitive UI. A few tools are good, but mostly feel cluttered or unintuitive.
Because of that, I started building my own client, mainly started focusing on ClickHouse, then added Postgres. I didn’t want yet another “do everything” GUI. I wanted something that opens instantly, lets me write SQL, edit, run, see results, and move on. Frictionless. It started as a hobby project, but I’ve actually been using it for my own daily work, and adding features that actually matter. There’s still few things to refine, and before I push it out to people,
If you’ve worked with ClickHouse or Postgres or any other databases as well, I’d love your suggestions on what tools or features you’ve found genuinely useful. What do you normally rely on? What slows you down in other clients?
I just put up the site at datacia.app. If it seems interesting, feel free to join the waitlist or just send any feedback on the comments. I have been working solo for months, so even a small signal helps.

value: Show HN: I Am Building an Intuitive Database GUI for ClickHouse and Postgres

author: rwiteshbera


value: Show HN: AI Agents for Customer Support

title: Show HN: AI Agents for Customer Support


value: nullpxl

value:
Hi! Recently smart-glasses with cameras like the Meta Ray-bans seem to be getting more popular. As does some people's desire to remove/cover up the recording indicator LED. I wanted to see if there's a way to detect when people are recording with these types of glasses, so a little bit ago I started working this project. I've hit a little bit of a wall though so I'm very much open to ideas!
I've written a bunch more on the link (+photos are there), but essentially this uses 2 fingerprinting approaches:
- retro-reflectivity of the camera sensor by looking at IR reflections. mixed results here.
- wireless traffic (primarily BLE, also looking into BTC and wifi)
For the latter, I'm currently just using an ESP32, and I can consistently detect when the Meta Raybans are 1) pairing, 2) first powered on, 3) (less consistently) when they're taken out of the charging case. When they do detect something, it plays a little jingle next to your ear.
Ideally I want to be able to detect them when they're in use, and not just at boot. I've come across the nRF52840, which seems like it can follow directed BLE traffic beyond the initial broadcast, but from my understanding it would still need to catch the first CONNECT_REQ event regardless. On the bluetooth classic side of things, all the hardware looks really expensive! Any ideas are appreciated. Thanks!

value: Show HN: Glasses to detect smart-glasses that have cameras

author: nullpxl


value: ubershmekel

value: Show HN: I made a shell with AI suggestions – Caroushell

author: ubershmekel


value: densmirnov

value:
I built a small framework that turns the OpenAI Codex plugin into a swarm of specialized “workers” that all live inside a single repo. Instead of just chatting with a model in your IDE, you get an orchestrator, planner, coder and reviewer that share a JSON task board, touch only the files in your project and are forced to finish every step with a clean git commit.
The whole thing is defined in prompts and JSON (AGENTS.md + .AGENTS/*.json), with git and tasks.json as the shared memory layer. You describe a goal, the orchestrator decomposes it into atomic tasks, the planner writes them into the backlog, the coder makes small diffs and commits them, and the reviewer/doc agent keeps the repo and docs consistent. There’s no separate runtime or web UI — just your IDE, the Codex plugin and a strict one-task-one-commit workflow inside your local repo.
Repo: https://github.com/basilisk-labs/codex-swarm
Would love feedback on whether this “agents as versioned workers” model fits how you’d actually want to use LLMs in your dev workflow, and what’s missing for you to adopt or fork it.

value: Show HN: Codex Swarm – Local ChatGPT swarm for coding with Git-tracked agents

author: densmirnov


value: jamescampbell

value: A fast, cross-platform CLI tool that extracts dominant colors from images using k-means clustering.

value: Show HN: Swatchify – CLI to get a color palette from an image

author: jamescampbell


value: northerndev

value: Show HN: Open-source RAG server with retrieval visualization (Postgres+pgvector)

author: northerndev


value: sastrophy

value:
Hi HN! I'm an 11th grade student learning cybersecurity and web development. I built SiteIQ as a hands-on way to understand security vulnerabilities, SEO, and how to test them.
I used AI as my coding partner throughout this project – it helped me understand concepts, debug issues, and write code. Building with AI felt like having a patient tutor available 24/7. I learned way more than I would have just following tutorials.
What it does:
- Security Testing: OWASP Top 10 (SQL injection, XSS, CSRF, etc.)
- SEO Analysis: Meta tags, schema markup, Core Web Vitals
- GEO Testing: Multi-region accessibility and latency
- LLM Security: Prompt injection, jailbreaking, system prompt leakage, and "Denial of Wallet" attacks
The LLM security part was the most interesting to build. With everyone adding AI to their apps, I wanted to understand how prompt injection actually works and how to test for it.
Features:
- Web UI with real-time console output
- CLI for automation
- Self-hosted (no data leaves your machine)
Tech: Python, Flask, pytest
GitHub: https://github.com/sastrophy/siteiq
I'd love feedback – are there vulnerabilities I'm missing? Any suggestions for the LLM attack payloads?
This is my first open source project, so any advice is welcome!

value: Show HN: SiteIQ – LLM and Web security testing tool (built by a high schooler)

author: sastrophy


value: lococococo

value:
Tired of recording the same product demo 10 times and still not being happy with it?
That’s basically why I built AutoAds (autoads.pro).
The idea: you paste your site’s URL, and an AI pipeline does all the annoying stuff for you, fully automatic.
What it does
- You paste the URL of your site (SaaS, e-commerce, portfolio)
- An AI pipeline visits the site and analyzes the content (sections, copy, visuals, CTAs, etc.)
- It generates a screen-recording-style video of your site:
  - scrolling and navigating
  - focusing on the key sections
- It then does an automatic edit:
  - short cuts
  - "promo-style" pacing
  - wraps everything inside a Mac mockup so it looks like a product video
- In parallel, the AI generates:
  - a voiceover that explains your product
  - subtitles synced with the voice
End result: you get a ready-to-use promo video (MP4) without opening Loom, without a mic, without a camera.
What it works for today
- SaaS products
- E-commerce sites
- Portfolios / personal sites
There’s a free plan so you can try it with your own site (no credit card).
Feedback I’m looking for
- Would you actually use this instead of recording a manual demo?
- What would you want more control over: script, voice tone, mockup style, video length?
- What’s missing for you to feel confident using it on your landing page or in paid ads?
Link: autoads.pro
If you try it and get a cool (or terrible) video, I’d love to see it and hear what feels off or missing.

value: Show HN: Turn your site into a demo video with one URL

author: lococococo


value: riadeno

value:
Hey, I built https://closeby.tel a WhatsApp AI you can fully customize, running on a real phone number. It can proactively message you on a schedule (reminders, habit tracking, nudges).
I'm planning to add image/audio input and delegated "do this" tasks. It is built with Convex, Twilio, and TanStack Router.
Would love your feedback and critiques.

value: Show HN: WhatsApp AI for Your Daily Workflow

author: riadeno


value: skylinesystems

value: If you’ve been on the Internet for a while, then chances are you remember StumbleUpon. The thrill of discovering weird, amazing websites with a single click. I built ByteShuffle to bring that experience back!<p>Click “Shuffle” to instantly explore a random site, complete with a screenshot preview. Sites are curated from places like r/InternetIsBeautiful , and you can submit interesting websites of your own. Like or dislike websites to help surface the best content — a fast, fun, distraction-free way to stumble across the web again.

value: Show HN: I'm bringing back StumbleUpon with ByteShuffle

author: skylinesystems


value: jengbeng

value:
S0 is a technology-agnostic, formal specification for how a system of multiple subjects can perform correct, deterministic, and verifiable state transitions.
The repository does not contain an implementation or a governance system. It defines:
- a minimal invariant core (S0): subjects, shared input φ, reactions, state space X, and a single deterministic transition function F : (x, φ, R) → x'
- a threat model (T1–T4) for structural failures: subject loss/suppression, input/reaction divergence, incorrect transitions, and architectural monopolization
- stability levels S1–S7 (Sn) that sit on top of S0 to detect, verify, and repair violations without changing the core protocol
- a meta-architecture X = {τ, ρ, δ} that decides when to activate/deactivate stability layers based on observed threats and history
- governance and contribution rules that try to keep the specification invariant and non-political
Repo: https://github.com/jengbeng/s0-protocol
What I’m looking for:
- structural critique of the core model (subjects, F, threats, stability layering)
- feedback on the way the spec is organized in the repo (foundation / threats / stability / dynamics / governance / spec)
- pointers to prior work this might overlap with (distributed systems, protocols, coordination, fault tolerance, etc.)
- arguments for where this approach obviously breaks in real systems
There is no code and no implementation details here on purpose; the goal is to keep the protocol as minimal and invariant as possible, and only then decide whether it is useful enough to be implemented.

value: Show HN:A minimal invariant protocol for collective state transitions

author: jengbeng


value: Gerharddc

value:
Are you worried about a supply chain attack (or even a rogue AI agent perhaps) compromising your entire development system? To minimise damage in such a scenario, I've built https://litterbox.work/ (https://github.com/Gerharddc/litterbox). Litterbox leverages Podman on Linux to create reproducible and somewhat isolated development environments (these environments are isolated from each other and from your host machine). These are similar to VSCode's DevContainers but take the concept a step further by putting the editor itself inside the container too. This helps to protect against exploits inside the editor (from rogue extensions perhaps) but more importantly, it eliminates the need for editor integration (i.e. the editor needs no knowledge of or support for Litterbox). Furthermore, Litterbox comes with a specialised SSH agent for exposing SSH keys in a more secure way where each request to the agent needs to be approved in a pop-up dialog.
This project is still in the very early stages with plenty of rough edges so any contributions or suggestions would be greatly appreciated!

value: Show HN: Litterbox – Defend Against Supply Chain Attacks

author: Gerharddc


value:
I've built TinyCompressor, a modern web-based compression and conversion tool that processes everything locally in your browser. No uploads, no server storage, completely free.
What it does:
- Image Compression: PNG, JPEG, WebP, AVIF, GIF, BMP – reduce file sizes by up to 90% while maintaining quality
- Format Conversion: Convert between formats including HEIC→JPG, PNG↔JPEG, WebP↔AVIF, and more
- PDF Compression: Reduce PDF sizes by up to 60% with image compression and metadata removal
- Video Compression: Compress MP4, AVI, MOV, WebM using FFmpeg.wasm (up to 85% reduction)
Key features:
 100% Client-Side Processing – All compression happens in your browser using WebAssembly. Your files never leave your device.
 Completely Free – No registration, no limits, no hidden costs. Forever.
 Fast – Optimized algorithms powered by Squoosh and FFmpeg.wasm for sub-second processing
 Privacy-First – Zero server-side storage. We literally can't see your files.
 Multilingual – Available in 11 languages (EN, ZH, JA, KO, AR, DE, ES, FR, IT, PT, RU)
 Mobile-Optimized – Responsive design that works great on all devices
Tech stack: Next.js 14, TypeScript, Squoosh (Google's WebAssembly compressor), FFmpeg.wasm, Tailwind CSS
Why I built this: I was frustrated with existing compression tools that required uploads (privacy concerns), had file size limits, or didn't support modern formats like AVIF/HEIC. So I built TinyCompressor to solve all of these – completely client-side, free forever, modern UI, and support for all major formats.
Technical highlights: Web Workers for non-blocking compression, Progressive Web App capabilities, performance optimizations, WCAG compliant accessibility, SEO optimized with dynamic sitemap generation.
Try it out: Visit https://tinycompressor.com and drag & drop your files. No signup required.
I'd love to hear your feedback, especially on performance across different devices/browsers, any bugs or edge cases, and feature requests.

value: Show HN: TinyCompressor – Free, Privacy-First Image/Video/PDF Compression Tool


value: rafmardev

value:
Sometimes, I used to have the need to check the evolution of a repo to see the changes that have been made in an open-source project. I had to clone the repo and see it locally just check if a feature was implemented in a new version or see the bugs solved.
So I built this website to ease this need. Now I can introduce a repo URL, and see right away all the commit, search for specific commit and see the changes of it, all saved in the local storage of your browser to enhance privacy.
Any comment or improvements are welcome.

value: Show HN: I made a free Git repository viewer

author: rafmardev


value:
I vibe-coded a complete React rewrite of my multi-track audio editor with Claude.

  Waveform Playlist v5 started as a "let's see how far AI can take this" experiment. The original was vanilla JS (~8 years
  old). The new version is React + Tone.js with proper TypeScript, tree-shaking, and a modular package structure.

  What we built: canvas waveforms, drag-and-drop clip editing, 20+ Tone.js effects, AudioWorklet recording, WAV export,
  annotations, theming.

  Demos: https://naomiaro.github.io/waveform-playlist/examples/stem-tracks

  GitHub: https://github.com/naomiaro/waveform-playlist

  Still in alpha, but functional. I'd estimate 80%+ of the code was AI-generated.

value: Show HN: I vibe-coded a complete React rewrite of my audio waveform editor


value:
Hi HN,
I’m an indie dev and built VeriIA (https://detectordeia.pro), an AI detector for Spanish and English text.
The motivation: there are plenty of AI detectors for English, but I couldn’t find much for Spanish or other languages. Most people I know just paste Spanish text into an English-focused tool and hope the result still means something. I wanted to try what it looks like if you start from Spanish instead.
Right now the app is simple: you paste or upload a text, it gives a probability that it was written with AI, plus sentence-level highlights so you can see which parts look more “AI-like”. It’s mainly being used on essays and reports. The intention is to provide a rough signal for reviewers, not a definitive answer.
On the tech side it’s a fairly standard web stack: Next.js, React and Vercel on the frontend, with Supabase for auth and storing user scan history.
This is an early version: only Spanish and English are supported, longer texts work better than short ones, and the scores are obviously probabilistic and sometimes wrong.
I’m interested in feedback on two things: how (or whether) you think AI detection should be used in real workflows, and how you would approach evaluating a tool like this for non-English languages.

value: Show HN: VeriIA – AI detector for Spanish and English text


value: zlatkov

value:
Hi HN - I’m Alex, currently Head of Agent Development Tools at Progress. Before this, I was a Co-founder/CEO of a session replay startup called SessionStack, which was acquired in August this year.
Since then, I’ve been pretty deep in the LLM/agent dev tools, and observability has been my main thing.
I ran a small poll on LinkedIn recently about where teams are with observability for LLM-powered apps/agents. Results:
• 20% instrument LLM observability from day 1
• 30% plan to implement later
• 20% are building an in-house solution
• 30% are still learning about this space
That 20% building in-house was the most interesting to me, so I followed up with a mix of early-stage, YC founders and more mature orgs. The drivers I kept hearing:
1) Local / self-hosted models
Some teams assume there aren’t viable observability options for local/hybrid LLM stacks, so DIY feels like the default. In practice, there are ways to do this, but they’re easy to miss right now.
2) Cost uncertainty
Token usage is hard to estimate early on, so pricing feels unpredictable. A minimal in-house layer looks safer than surprise bills.
3) Control + speed
Bootstrapping basic tracing/logging is straightforward and gives full ownership while teams iterate quickly on the core product.
This reminds me a lot of early APM / product analytics. Many teams started with “we’ll just implement our own logging.” Totally reasonable at the beginning — but once usage and complexity scaled, that logging quietly turned into:
• an internal platform to maintain
• a backlog of features to build
• a growing surface area of edge cases to debug
…often becoming a real distraction from the core business.
Our bet is LLM/agent observability follows the same path: teams start with DIY logging, then realize it’s becoming a side-product, and eventually most adopt a standard platform early. We’re also seeing APM/analytics vendors expand into LLM flows, which reinforces that direction.
What we’re building
My team and I are working on LLM/agent observability focused on usage, cost/pricing, evaluations, and debugging. Most teams we talk to still don’t have anything in place, even when LLMs are core to the product, so we’re trying to make the “day 1” setup practical.
We're part of a larger org, but this team is being run like a startup within it: small group, fast cycles, heavy on user conversations, and shipping quickly based on real usage. That setup is why we’re doing early access and iterating closely with teams.
Early preview / notes here:
https://aback-handbell-1cd.notion.site/Progress-Observabilit...
We’re planning to support self-hosted options as well.
If this is relevant to what you’re building and you want to help us shape the LLM Observability you need, we have a free Early Access Program here:
https://www.telerik.com/agent-observability-early-access

value: Show HN: Agent/LLM observability for tracing, cost, evals, and debugging

author: zlatkov


value:
Hi HN! I built a browser-based playground for Alibaba’s new Z-Image Turbo model and wanted to share it with the community.
Demo: https://z-img.net/
No GPU or setup required — everything runs through a hosted API.
 Key features:
Photorealistic image generation in a few seconds
Extremely accurate Chinese text rendering (rare among most models)
Simple, lightweight interface
Free to try; no login needed
I created this because many makers and developers want to experiment with modern image models without downloading huge weights or renting expensive GPUs. This tool aims to be a fast, frictionless sandbox.
I’d love feedback from the HN community on:
Latency / image quality
API design ideas
What features you think should exist (batch mode? upscaling? prompt templates?)
Any edge cases where the Chinese text handling breaks
Happy to answer questions and share technical details. Thanks for taking a look!

value: Show HN: Z-Image Turbo Online – Free, Fast AI Image Generator


value: jackculpan

value: Show HN: Claude Opus and Front End-Design Skill = Insane Results

author: jackculpan


value: jaypatelani

value: high-reciprocity copyleft license.

value: Show HN: Total Reciprocity Public License

author: jaypatelani

story_text: high-reciprocity copyleft license.


value: Tafita

value:
Hello Again HN !
My name is Tafita, and I'm back with my second launch: Tag my Tab
The indie hacker journey hasn't gotten easier (spoiler: the imposter syndrome is real), but seeing people actually use something I built? That feeling never gets old.
 Meet Tag my Tab – Never Lose a Tab Again
So here's the thing: while building and marketing my other products, I found myself drowning in browser tabs. Research articles, competitor analyses, design inspiration, documentation, customer feedback. I'd have 30+ tabs open, my browser grinding all my RAM and I was living in constant fear of that accidental "Close All" click.
Sound familiar?
That's why I built Tag my Tab, a browser extension that helps you organize your tab chaos with tagging, comments, and powerful search.
 What Makes It Different:
Tag It: Categorize any tab in one click with custom tags
Comment It: Add notes so you never wonder "why did I save this?" again
Find It: Search by title, tag, or comment to find anything instantly
Close It: Confidently shut tabs knowing they're saved forever
Sync It: Access your organized tabs across all your devices
How It Works:
Install the extension from the Chrome Web Store
Click on any tab and add a tag or comment
Close saved tabs with confidence, search and restore them anytime
No more RAM screaming for mercy. No more tab hoarding out of fear.
 Pricing:
Free: Unlimited tabs, 3 tags, dated comments, full search, cloud sync
Pro: $4.99 one-time payment (not subscription!) for unlimited tags and comments
If you're drowning in tabs like I was, I'd love for you to try Tag my Tab. And as always, I'm hungry for feedback—the good, the bad, and the "here's a bug" reports.
Drop a comment or email me at tafita@tagmytab.com. Let's chat!
Thanks for being here for launch #2. Here's to many more.
— Tafita

value: Show HN: I made a browser tab management extension

author: Tafita


value: qxsp

value: Show HN: A modern replacement for the 2007-style percentage calculator

author: qxsp


value: Show HN: Simple xbox360 inspired CSS library: 360CSS


value: thekarank

value:
Hey HN,
My name is Karan, and I'm a frontend developer from India
When I started my development journey, I fell for frontend development and stuck with it ever since
But I noticed that many of my friends hated writing CSS because creating UIs is a very tedious and time-consuming process.
But at the same time, they also wanted their project to look premium with beautiful animations and a world-class user experience
That's when I thought
"What if anyone could integrate beautiful animated components into their website regardless of their CSS skills?"
And after six months of pain and restless nights, I finally built ogBlocks to solve this problem.
ogBlocks let you integrate beautiful and practical animated UIs with simple copy and paste, and its main purpose is to enable everyone to create beautiful UIs regardless of their CSS Skills
I hope you'll like it
Best
Karan

value: Show HN: OgBlocks – Animated React UI Library Built with Motion and Tailwind CSS

author: thekarank


value: ahmaliic

value: Show HN: Built an app to call USA numbers super cheap

author: ahmaliic


value: mihaela

value:
Hi HN! I built Cupertino to solve AI hallucinations when working with Apple APIs.

  It crawls Apple Developer Documentation (22K+ pages across 261 frameworks), Swift Evolution proposals, and Swift.org docs into a local
   SQLite FTS5 database, then serves them to AI agents via MCP.

  Key points:
  - 100% local/offline – no API calls after initial crawl
  - Sub-100ms search queries
  - Works with Claude Desktop out of the box
  - Pure Swift 6.2, strict concurrency

  The initial crawl takes ~20 hours (0.5s delay between requests to be respectful), but it's a one-time operation.

  Roadmap includes vector search (sqlite-vec) and a standalone CLI search command. Happy to answer questions!

value: Show HN: Cupertino – Offline Apple docs for AI agents (22K pages, MCP, Swift)

author: mihaela


value: km

value: Show HN: Uptime mongers – monitoring with powerful DNS checks and boring tech

author: km


value: keini

value:
Hi! I’ve been building a new type of logic-neighbor puzzle for iOS.
Each cell shows how many matching neighbors it needs, and the goal is to make the entire grid consistent.
It’s clean, minimal, no ads, no tracking.
Made by a single indie developer.
App Store: https://apps.apple.com/app/id6754704139
If anyone tries it, I’d love feedback, especially on difficulty curve and level design.
Happy to answer any technical questions!

value: Show HN: FuseCells a new type of logic-neighbor puzzle for iOS (free, no ads)

author: keini


value: bfdd

value:
Last Week, I was tired of hunting through scattered sources to find the best Black Friday deals on premium apps and software so I decided to create a centralized directory myself.
Using Claude Code, I vibe coded the entire Black Friday deals directory in just 2 days, and the results have been incredible.
Stack:
Nextjs for Frontend
Convex for Database
Umami and Posthog for analytics
Dodopayment for Payment
In less than a week, it’s attracted 4,000+ visitors, with 500+ coming in just the last 24 hours (Analytic is public on website, so you can verify the traffic yourself :))
The directory features lifetime deals and product bundles on premium apps and tools, curated specifically for developers, creators, and entrepreneurs looking for genuine value.
If you’re hunting for Black Friday deals on premium software, check it out And if I’ve missed any great deals, drop them in the comments.

value: Show HN: I Curated Premium Black Friday Deals for SaaS and Apps

author: bfdd


value: Tito-arch

value: Show HN: Calcurious – Step-by-step math with dynamic visuals

author: Tito-arch