value:
Well, I guess better now than never.
Hello, Hacker News readers. My name is Ilya Egorov, and for the past year I have been developing a somewhat esoteric library for Python. The idea behind it is quite simple: primitives (such as locks) that work in different environments at the same time (for example, an asyncio thread and any other threads), one instance for everything (both async-aware and thread-aware/thread-safe), without complicating things for the user.
It all started sometime around 2022-2023, when I was writing a bridge between different messengers (APIs) on AnyIO for personal use. At the time, I wanted to achieve fairness in processing different chats, but part of the work was CPU-bound, and isolating it into separate synchronous threads would have led to noticeable overhead. The most logical thing that came to mind at that moment was to parallelize all asynchronous code, one "asynchronous" thread for each chat (nowadays, such ideas have been popularized with the advent of free-threading), in order to take advantage of preemptive multitasking. But how to synchronize and communicate between different tasks in different threads when none of the available asynchronous primitives are thread-safe? I did not like any of the workarounds known at the time, so I started writing my own.
In 2023, I tried various approaches to creating a universal lock, which I called an "atomic lock" (since I planned to implement other primitives I needed on top of it). The principle of operation is quite simple: each instance uses collections.deque as a token queue (lock owners; something very similar is used in the current implementation of aiologic.lowlevel.ThreadOnceLock) and threading.Lock to synchronize the internal logic. It seemed like everything was working, and I could have stopped there, but the latter bothered me. As long as my lock used threading.Lock, known for blocking event loops and thus causing timeout issues, there could be no question of its fairness. However, all I could achieve at that point was to define the lock recurrently (where the event loop blocking time decreased with increasing nesting but increased overhead for each call).
In the spring of 2024, after digging deep into atomic programming (especially lock-free and wait-free queues), it hit me that something similar could be implemented at the Python level. I did not decide to use effectively atomic operations right away, since almost no one had done so before me (the information available on them is very poor, and I had to research it all myself), but after seeing how reliable they are (they are even used in standard CPython tests, and free-threading fixes are also related to them), I created the first primitive that works without synchronization of internal logic. My worldview changed, and I went on to create other primitives, also without synchronization. A few months later, in August 2024, aiologic was born.
Now, a year later, the library is already quite well developed. It supports a wide variety of concurrency libraries, whether it is gevent or Trio, and everything with as many threads as you want. There are locks, semaphores, queues, and others. A brief description is somewhat humorous: "GIL-powered" but supports free-threading (in essence, this is about effectively atomic operations, which is very interesting on PyPy - see the classic benchmark); "locking" but almost never uses locks under the hood (except in some special cases). The development status is still alpha, but in my opinion, the reliability is quite high, as I constantly recheck the implementation with proactive bug fixing (see the overly detailed logs).
I hope you find my library interesting.

value: Show HN: Aiologic – GIL-powered* locking library for Python


value: Show HN: OmnAI – Sovereign AI infrastructure with multi-vault isolation


value:
Kling O1 (Omni One) is a unified multimodal AI model by Kling AI for creating and editing videos and images from any input. Kling Video O1 enables text-to-video, image-to-video, keyframe-to-video, and reference-based generation. It supports stylization, inpainting, background edits, camera changes, and multi-subject fusion. Kling Image O1 supports precise image detail editing and highly consistent style control. This all-in-one AI workflow is ideal for flawless, professional creative production.
Feedback welcome!

value: Show HN: Kling O1 – Creates AI videos and images from any input


value: adamoufkir

value:
Hey all,
Adam here, developer and founder of Crovise.
The problem I’m focused on is straightforward: understanding why a landing page doesn’t convert is more challenging than it should be. For engineers and founders, CRO analysis usually happens too late (after traffic, analytics, and experiments). For early-stage products, you’re often guessing what to fix or relying on generic advice that isn’t grounded in the actual page.
Crovise approaches this by analyzing a landing page directly. It ingests a URL, parses the DOM, extracts copy, hierarchy, and layout signals, and runs multiple analysis passes to surface concrete conversion hypotheses. The goal isn’t predictions or guarantees, but to generate testable ideas similar to what a CRO engineer might flag during a manual review.
Under the hood, it’s a combination of static analysis, heuristic scoring, and LLM-based reasoning. There’s no session data, no heatmaps, and no traffic signals yet — everything is derived from the page itself. This makes it fast and usable early, but also comes with obvious limitations.
I’ve been building this as a solo project and iterating based on real usage. I’d love feedback from engineers on the approach, edge cases, and whether this kind of analysis is useful without runtime data.
Thanks for taking a look.

value: Show HN: Crovise – Generate CRO hypotheses from landing pages

author: adamoufkir


value: kamilc

value: Show HN: Campers – Remote cloud dev environments that feel like localhost

author: kamilc


value: zittur

value:
I kept messing up Save Data calculations in Chaos Zero Nightmare and losing important rewards at the end of runs, so I built a small mobile-friendly calculator to track everything properly.
It runs well on a phone while the game is open on PC and handles tier caps automatically.
Built with Vite + Tailwind.
Feedback welcome: https://www.cznsavedata.com

value: Show HN: A Save Data Calculator for the Roguelike Gacha Chaos Zero Nightmare

author: zittur


value: dakingffo

value:
Hi HN,
I’m a C++ student and I’ve spent the last few months obsessing over concurrent data structures. I’m sharing daking::MPSC_queue, a header-only, lock-free, and unbounded MPSC queue designed to bridge the gap between linked-list flexibility and array-based throughput.
1. FACING THE CHALLENGE: THE "LINKED-LIST BOTTLENECK"
In traditional MPSC linked-list queues, if multiple producers attempt to enqueue single elements at a high, uniform frequency, the resulting CAS contention causes severe Cache Line Bouncing. In these saturated uniform-load scenarios, throughput hits a physical "floor" that often underperforms compared to pre-allocated ring buffers.
2. ARCHITECTURAL SOLUTIONS FOR REAL-WORLD SCENARIOS
Rather than focusing on average throughput under uniform load, this design targets two specific real-world challenges:
Scenario A: Non-Uniform Contention (Burst Resilience)
In many systems, producers are mostly idle but burst occasionally. By facilitating a high-speed lifecycle where node chunks circulate from Consumer (Recycle) -> Global Stack -> Producer (Allocate) with strictly O(1) complexity, the queue can rapidly establish SPSClike performance during a burst, peaking at ~161M/s.
Scenario B: Bulk Contention Reduction
The enqueue_bulk interface allows producers to pre-link an entire segment in private memory. This reduces the contention from N atomic operations down to a single atomic exchange. The larger the batch, the lower the contention.
3. IMPLICIT CHUNKING & RESOURCE LIFECYCLE
Instead of fragmented allocations, memory is managed via a Page -> Chunk -> Node hierarchy.
Implicit Composition: Unlike chunked-arrays, nodes are not stored in contiguous arrays but are freely combined into logical "chunks." This maintains linked-list flexibility while gaining the management efficiency of blocks.
Zero-Cost Elasticity: The unbounded design eliminates backpressure stalls or data loss during traffic spikes, with heap allocation frequency reduced to log(N).
4. ENGINEERING RIGOR
* Safety: Fully audited with ThreadSanitizer (TSAN) and ASAN.
* Type Safety: Supports non-default-constructible types; noexcept is automatically deduced.
* Lightweight: Zero-dependency, header-only, and compatible with C++17/20.
A NOTE ON BENCHMARKS:
In the interest of full transparency, I’ve benchmarked this against moodycamel::ConcurrentQueue. In highly uniform, small-grain contention scenarios, our implementation is slightly slower. However, daking::MPSC_queue provides a 3x-4x performance leap in non-uniform bursts and bulk-transfer scenarios where "Zero-Cost Elasticity" and contention reduction are the primary goals.
I’d love to hear your thoughts on this repository!
GitHub: https://github.com/dakingffo/MPSC_queue

value: Show HN: An MPSC Queue Optimizing for Non-Uniform Bursts and Bulk Operations

author: dakingffo


value: Sudachidev

value:
After reading this yesterday, https://news.ycombinator.com/item?id=46296863 ,
I wanted a slight alteration, so I made my own.
Basically it scans all the paragraphs of a page, replacing 5 words with Japanese Romaji.  Those words have their colour changed and are bolded.  When you hover over the changed word, a small popup shows the Hiragana and the English word.
At the moment it has a smallish list of words (111), all chosen to be basic level and (hopefully) unconfusing in their context.
I'm planning on pushing the word list out to about 1000, and having it cycle through 200 every week with an overlap.
Edit: a screenshot of it in action - https://freeimage.host/i/fldb3KB
It's also uploading to the firefox addon's store, but that's taking a while.

value: Show HN: I made a browser extension to translate random words to Japanese

author: Sudachidev


value:
After building ML systems in various organizations, I collected some useful kit I wrote into a single library.
What you can do with this:
* Save (and retrieve) model checkpoints (optionally with a content-addressable naming scheme) on blob storage
* Load datasets incrementally from blob storage into Pytorch, using a local disk cache
* store your training metrics into SQLite
Design principles :
* "dumb cloud and smart software" - I prefer commodity services like object storage and container runtimes to framework-like abstractions (e.g. managed MLFlow or similar)
* extend Lightning in the most straightforward way
* let the user assemble a lightweight MLOps process with minimal changes to preexisting model code.
Happy to field any questions and receive feedback !
The library was refined using Sonnet, but thoroughly  checked by eye and hand.

value: Show HN: Lightning-extra, cloud-native plugins for PyTorch Lightning


value: misbahsy

value:
Most products that touch PDFs or images quietly rebuild the same thing: a hacked-together “router” that picks which OCR/vision API to call, normalizes the responses, and prays the bill is sane at the end of the month.
DocsRouter is that layer as a product: one stable API that talks to multiple OCR engines and vision LLMs, lets you route per document based on cost/quality/latency, and gives you normalized outputs (text, tables, fields) so your app doesn’t care which provider was used.
It’s meant for teams doing serious stuff with documents: invoices/receipts, contracts, payroll, medical/admin forms, logistics docs, etc., who are either stuck on “the OCR we picked years ago” or are overwhelmed by the churn of new vision models.
Right now you get a REST API, simple SDKs (coming soon), a few pluggable backends (classic OCR + newer vision models), some basic routing policies, and a playground where you can upload a doc and compare outputs side by side.
I’d love feedback from HN on two things:
1- If you already juggle multiple OCR/vision providers, what does your homegrown router look like, and what would you need to trust an external one?
2 - Would you prefer this or use the LLM/OCR providers directly, with the possibility of changing the provider every so often?
Demo and docs are here: https://docsrouter.com

value: Show HN: DocsRouter – The OpenRouter for OCR and Vision Models

author: misbahsy


value: kroniapp

value:
Hi HN,
I started working on Groceed after Google shut down a shopping list tool I had relied on for years. It wasn't perfect, but it solved a very real problem for me: having a simple, shared list that didn't get in the way.
When it disappeared, I tried replacing it. Most apps I found felt wrong in opposite ways: either they were just plain bullet lists, or they tried to turn grocery shopping into a full-blown system with reminders, recipes, suggestions, and settings I never asked for.
The main frustration was repetition. Every week I was typing the same items again, even though my shopping habits barely change.
Groceed is my attempt to fix that. It's a shared shopping list where past items are first-class citizens. You can search through things you've already bought and quickly bring them back into the current list. Over time, the list starts to reflect how you actually shop, without any explicit setup or planning.
I've tried to keep the scope intentionally narrow. Groceed only does shopping lists, but it does them with the assumption that shopping is a recurring habit, not a one-off task. The app is designed to be quick to open, quick to edit, and easy to share with other people.
You can try it at https://groceed.app
A minimal login is required (magic link or Google), mainly because lists are shared and synced.
Technically, it's built with SvelteKit and runs entirely on Cloudflare Workers, using D1 as the database.
This is my first serious Show HN post. I'm mostly looking for feedback on:
- whether this approach actually makes lists more useful over time
- what would make you keep using (or abandon) a shopping list app
- things that feel overkill or, on the contrary, clearly missing
Happy to answer questions or go deeper into the implementation if that's useful.
Andrea

value: Show HN: Groceed – The shopping list that adapts to your life

author: kroniapp


value: superchargeext

value: Show HN: SuperchargeBrowser – Privacy-first Chrome extension to fix performance

author: superchargeext


value: nicbou

value: Show HN: Map of median rent per m² in Berlin

author: nicbou


value: Clein

value: Show HN: IronWall – 3KB Client-Side Proof-of-Work to Replace reCAPTCHA

author: Clein


value: onecookie

value:
Hi HN,
I built FreeWall after getting an iPhone Air recently.
I wanted to change wallpapers more often, but the process on iOS still feels surprisingly manual:
find an image, download it, go to Settings, set it, repeat.
So I really just wanted a small utility that does one thing well.
Apple doesn’t provide a public API to set wallpapers programmatically.
Instead of trying to work around that, I decided to lean into Shortcuts,
which is one of the most powerful (and underused) extension mechanisms on iOS.
FreeWall works by:
- Limited scope by design
– Curating high-quality images from free sources like Unsplash and Pexels (huge thanks to those communities)
– Letting users trigger a Shortcut to set the wallpaper in one tap
– Keeping the app free, with no ads, and no subscriptions
This won’t be useful for everyone, but it fits how I personally use my phone:
quick changes, minimal friction, and no noise.
I’d really appreciate feedback, especially from people who use Shortcuts or have thoughts on small, opinionated iOS utilities.
App Store link:
https://apps.apple.com/app/id6755912102

value: Show HN: FreeWall – A minimalist iOS wallpaper app using Shortcuts

author: onecookie


value: ogsome

value:
Hello Everyone,
My name is Karan, and I'm a Frontend Developer, but I feel like I'm more of a Design Engineer because of my love for creating UIs
When I started my development journey, I fell for frontend development and have been 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, and you have to be pixel-perfect
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.
It is an Animated UI Library for React that contains all the cool animations that will make it look premium and production-grade
ogBlocks has navbars, modals, buttons, feature sections, text animations, carousels, and much more.
I know you'll love it
Best
Karan

value: Show HN: I built an Animated UI Library with drag and drop components

author: ogsome


value: ColonelParrot

value: Show HN: Syntux – build deterministic, generative UIs

author: ColonelParrot


value: aria-sfl

value:
Hi HN,
I’m the solo founder of Save For Later, a bookmarking app I built after realizing I kept saving links and never revisiting them.
The core idea is simple: instead of just storing links, SFL uses AI to automatically tag, summarize, and help resurface saved content when it’s relevant again.
Current features:
Save articles, tweets, videos, and links
AI-generated tags and summaries
Full-text search across bookmarks
Cross-platform (Android, iOS, web)
This started as a personal project and grew to ~500 users from Reddit. I’m still figuring out positioning and what’s actually valuable vs overkill.
I’d really appreciate feedback on:
Whether this solves a real problem for you
What you currently use instead (Pocket, Raindrop, Notion, etc.)
What would make this 10x better
Happy to answer any questions.

value: Show HN: Save For Later – AI-powered bookmark manager that resurfaces your saves

author: aria-sfl


value: pydubreucq

value:
Email Expiration Manager Thunderbird Add-on helps you automatically manage emails with expiration dates.
Email Expiration Date are not a standard, but some Email Service Provider like Brevo, Sweego or Mindbaz already offer this feature to their customer.
Email Expiration Date (https://datatracker.ietf.org/doc/draft-ietf-mailmaint-expire...)

value: Show HN: Email Expiration Manager Thunderbird Add-On

author: pydubreucq


value: Sajarin

value: Show HN: NPM package size visualizer using React as the unit of measurement

author: Sajarin


value: erikdevriesnl

value:
Hi HN,
shadcn/ui gives you beautifully built React components, but ships with a very neutral theme. Customizing it means dealing with a long list of CSS variables, and most existing “theme generators” just expose those variables in a big form. You still need design knowledge, and it’s easy to break contrast or consistency.
I built uithemes.app to remove that friction.
Instead of tweaking individual tokens, the idea is to start with a base palette. That palette is then mapped across text, backgrounds, borders, muted states, hovers, and sidebars in a way that keeps contrast accessible by default.
When you change the base palette, everything updates together. You don’t have to manually decide which gray to use, whether text should be light or dark, or how borders should adapt. The system handles those relationships for you.
You can use predefined Tailwind palettes, generate a full palette from a single color, or quickly explore themes using a random generator. Each generated theme is usable out of the box, but still fully tweakable.
I’d appreciate any feedback. Most importantly: is the interface immediately clear, or do you find anything confusing about how it works?
Link: https://uithemes.app

value: Show HN: A faster way to theme Shadcn/UI

author: erikdevriesnl


value: ChernovAndrei

value:
We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is:
https://github.com/S-FM/faim-python-client

value: Show HN: Python SDK – forecasting with foundation time-series and tabular models

author: ChernovAndrei


value: stephanebouget

value:
Hi HN,
I manage a lot of GitHub repositories and kept missing security alert notifications.
Between emails, GitHub UI, and multiple orgs, important alerts were easy to overlook.
So I built GitHub Security Alerts: a free, open-source desktop app that centralizes GitHub depndabots alerts from all my repositories in one place.
The goal is simple: one view, no missed alerts.
Tech details:
- Desktop app built with Tauri + JavaScript
- Uses the GitHub API
- Works across multiple repositories and organizations
This is an early version and I’d really appreciate feedback, criticism,
and ideas for improvement.

value: Show HN: Desktop app to never miss GitHub security alerts

author: stephanebouget


value: Mikecraft

value: Show HN: I built an app for vibe-coding games

author: Mikecraft


value:
Inspiration: While building LLM agents, I needed simple web fetch + search (like Claude Code has), but existing tools gave raw HTML, irrelevant markdown, or broke on JS sites.
Evolution: Started as part of another project - pivoted to standalone as it was more feasible and scoped. Trade-off: Prioritized simplicity and LLM-ready outputs (via an LLM layer) over raw speed.
Now: Handles JS-heavy sites, easy integrations (LangChain, Vercel AI SDK, MCP).
Looking for early testers right now - DM me (@liran_yo on X) with your login email and I'll send you credits to try it out! Pricing shown isn't final yet - after gathering stats from users like you, I hope to lower it dramatically based on real usage patterns.
Does this solve your web data pains? Would you use it in production? What’s missing?
Thank you

value: Show HN: Quercle – Web Fetch/Search API for AI Agents


value: nzdevhacker

value: Show HN: Wire code to any cloud in minutes

author: nzdevhacker


value: kekseason

value: Show HN: ArchiKEK – Export clean OSM geometry to Rhino (.3dm) and SketchUp

author: kekseason


value: olekenneth

value: Show HN: I made a script to export Emacs org-agenda to Apple Reminders

author: olekenneth


value: A Chrome extension to help you stay focused. Block access to distracting websites and display a customizable block page.

value: Show HN: Fuuin – A Chrome extension to help you stay focused


value: benbaessler

value:
I built epfiles.ai to make the U.S. House Oversight Epstein document release actually searchable.
The files exist publicly, but they're scattered across nested Google Drive folders in mixed formats: PDFs, images, scanned documents. Manually searching through 20,000+ files is impractical for most people.
This tool lets you query the corpus in natural language. Every answer includes clickable citations to the exact source page, so you can verify against the original. The goal is document discovery, not replacing human verification.
Technical approach:
- OCR'd the entire corpus
- Chunked and embedded for semantic search
- RAG pipeline returns relevant passages with source links
- Citations point directly to the House Oversight Committee's Google Drive
I built this because I think public document releases should be usable, not just technically available. Happy to answer questions about the approach.
Demo: https://youtube.com/watch?v=7sQgRvwK3LE

value: Show HN: RAG-powered search tool for 20k+ Epstein files

author: benbaessler


value: heymattia

value: Show HN: I built the first Threads Wrapped

author: heymattia


value: jobehi

value: My wife wanted to learn more my language, but resources in the internet were scarce and too academical. So we've decided to build a fun engaging and cool video hame together.<p>Demo is ready for everyone to test !

value: Show HN: I made the Duolingo killer – Demo is live

author: jobehi


value:
I had a weird idea: what if getting distracted was so annoying that my brain just... stopped doing it?
Now when I open YouTube instead of working, the FBI OPEN UP meme blasts at full volume until I alt-tab away. I literally cannot hear the video over the sound of my screen being raided.
When I'm focused: lofi beats play.
When I'm distracted: FBI raid.
It sounds dumb but after a few weeks I think this really works for me.
You can configure which sites/apps trigger the raid (regex patterns), and it works on both macOS and Windows.
Native desktop app built with Go + Wails, Svelte + Tailwind for the UI.
No telemetry, your shame stays local and session restricted.

value: Show HN: I built an app to fix my ADHD using the FBI meme


value: tjoskar

value:
Hi HN,
I wanted a dashboard for my home that didn't glow in the dark or look like a computer screen. I built this using a Raspberry Pi Zero 2 W and a Waveshare 7.5" E-Ink display.
Instead of the common approach of running a headless browser and taking screenshots (which is slow and resource-heavy), I render the image directly using Python and Pillow (PIL). This brings the refresh time down significantly and runs smoothly on the Pi Zero.
I wrote a blog post about the build process, the hardware I used, and the code structure.
I also ended up writing a short book/guide for those who want a complete step-by-step walkthrough to build the exact same setup, which is linked in the post.
Happy to answer any questions you might have to build your own display!

value: Show HN: DIY E-Ink Home Dashboard Without Headless Chrome (Python/Pillow)

author: tjoskar


value: Show HN: Open-Source B2B SaaS Starter Kit (Go, Next.js, RBAC, Polar)


value:
Hello HN,
I’m the author of SHM. I built this because I distribute self-hosted software and realized I was "flying blind"—I had no idea how many instances were running or which versions were active.
I didn't want to use Google Analytics (privacy issues) or heavy tools like Countly/PostHog (too complex for simple usage stats).
What makes SHM different:
- Agnostic: You can send any JSON payload (e.g., {"pizzas_eaten": 10}). The dashboard adapts automatically to create columns and KPI cards, group data by application and offer graph view for time-series data.
- Privacy-First: It verifies instance authenticity using Ed25519 signatures (generated on the client) rather than tracking user IP/fingerprints.
- Zero-Config: It's a single Go binary with an embedded UI (AlpineJS + Tailwind).
- Go & NodeJS SDK to easy implement client with authentication, crypt & send metrics.
It’s open source (AGPLv3 / MIT).
I’d love to hear your feedback on the product, architecture...

value: Show HN: SHM – Telemetry for self-hosted apps (no user tracking)


value: georginikolov

value: Show HN: Physically Based Shading Dojo in WebGL2

author: georginikolov


value: Show HN: Generate Schema.org Markup for Right to Repair and DPP Compliance


value: workwithtrp

value:
Hey HN,
I built this because I was tired of setting up the same RAG pipeline for every side project.
The Problem: Most RAG tutorials give you an answer but don't show where it came from. Hallucinations are a dealbreaker for real apps.
* The Solution:* I built a pipeline using Pinecone metadata to map vector chunks back to the original PDF page/text. The UI highlights the source snippet when the AI answers.
Stack:
Next.js 14 (App Router)
Pinecone (Serverless)
LangChain (Streaming)
Supabase (Auth)
Demo: https://www.fastrag.live
Happy to answer questions about the chunking strategy or the citation logic.

value: Show HN: FastRAG Next.js RAG boilerplate with precise PDF citation highlighting

author: workwithtrp


value: pcarroll

value: Show HN: Netrinos – Zero-config mesh VPN

author: pcarroll


value: Show HN: Ada – A governed AI that validates before generating


value: seesea

value:
Here is a minimal reasoning determinism stress test.
It runs the same task multiple times and observes whether reasoning invariants hold under repetition.
No performance claims.
No capability or correctness claims.
v1.0 is frozen.
If the protocol is flawed, I’m interested in where.

value: Show HN: Minimal reasoning determinism stress test (no claims)

author: seesea


value: edmgood

value:
Hi HN,
We've been building out a PDF viewer to help support various tasks in the Architectural, Engineering, and Construction space.
We use it primarily for steel estimation right now, but are exploring how it might be useful across other engineering disciplines!
From a tech perspective, the AI PDF viewers we've ran into use mostly PDF metadata/text, so they dont work well on engineering schematics which are mostly visual. So we put an agent in there that has tool abilities to see both text and image modalities, and its been much better for our tasks.
Hope its interesting to play with and open to ideas where the tech could be useful!

value: Show HN: Agentic PDF Viewer for Schematics

author: edmgood


value: ramoz

value:
Not intended to replace any OTEL solution.
Totally vibe coded desktop app meant to work alongside the CLI.
This is a simple way to peek into all that is happening including with sub agents. Search over previous session logs is nice.
I really built this as I needed a better way to debug an OPA policy engine that integrates with hooks https://github.com/eqtylab/cupcake

value: Show HN: Debug Console for Claude Code

author: ramoz

title: Show HN: Debug Console for Claude Code


value: salesably

value:
We just open-sourced 19 skills that turn Claude into a sales and marketing co-pilot. Built as Claude Code plugins.
This isn't "10 ChatGPT prompts for salespeople"
LinkedIn is drowning in sales trainers posting generic prompts. Copy-paste into ChatGPT, get generic output, repeat.
These are actual skills—structured frameworks with interconnected workflows that build on each other. They remember context, sequence intelligently, and integrate with real research tools. The difference between a prompt and a skill is the difference between a recipe and a chef.
What problem this solves:
Sales prep is broken. Reps spend hours googling prospects, stitching together company research, writing follow-up emails from scratch. Or they skip it entirely and wing it.
These skills automate the grunt work so humans can focus on judgment, creativity, and trust.
What's in the box:
Sales Skills (9): POWERFUL framework for deal qualification, prospect research, company intelligence reports, cold call scripts, call analysis, follow-up emails, multi-stakeholder outreach, and an orchestrator that sequences them.
Marketing Skills (10): Brand voice, positioning, keyword research, landing pages, email sequences, SEO content, newsletters, and content atomization.
How they connect:
Research a company → qualify the account → build prospect profiles → generate call scripts → analyze the call → write follow-ups → coordinate outreach across stakeholders.
Each skill feeds the next. That's the difference.
Optional MCP integrations for Perplexity, Exa, Hunter.io, and Apify for real-time research.
MIT licensed. Feedback welcome.
GitHub: https://github.com/Salesably/salesably-marketplace

value: Show HN: Open-source Claude Code plugins that turn AI into a sales strategist

author: salesably


value: aymenfurter

value: Show HN: Ai3 – An experimental agentic tiling window manager (i3 fork)

author: aymenfurter


value:
Hey HN! I built OmnAI because enterprises keep telling me they can't deploy AI due to compliance barriers.
The problem: Defense contractors need air-gapped LLMs. Hospitals want AI but fear HIPAA violations. Banks need audit trails for every AI decision. Current solutions either lock you into cloud providers or leave you on your own for security.
OmnAI provides:
- 16 isolated vaults using gVisor sandboxing (zero data leakage between tenants)
- Trust-based governance - mandatory human review when confidence drops below 0.90
- Three deployment tiers: SUPERFLY (air-gap/DoD IL6), SOVEREIGN (on-prem/finance), EXCEED (hybrid/R&D)
- Compliance-ready architecture (FedRAMP, HIPAA, SOC, ITAR)
Technical stack: vLLM/Triton for inference, offline PKI with AES-GCM-256, per-vault fine-tuning pipelines.
It's in pilot state - I'm looking for feedback on whether this architecture resonates with production AI challenges you're seeing, especially around data sovereignty and regulatory compliance.
https://github.com/TadTanyaTalaTadenTadhgTaya/OmnAI-v3.5
Happy to answer questions about the design decisions, particularly around the trust system and vault isolation model.

value: Show HN: OmnAI – Sovereign AI infrastructure with multi-vault isolation


value: timatping

value:
Hey HN! We built DNS Directory (https://dnsdirectory.com), a free, searchable database of public DNS servers with live monitoring every 10 minutes.
We needed to find an up-to-date list of DNS servers used by carriers around the world for a proxy fingerprinting / web-scraping project but we were shocked to find that it didn’t exist so we built it ourselves in an internal Hackathon
We’re adding more features but so far we:
Test 77K+ servers every ~10 minutes
Allow filtering by uptime, location, security features (ad blocking, malware protection, DNSSEC)
Show info on IPv6 support, anycast, etc.
Show all historical testing information
We have no plans to monetize the site and it will stay free so it can be used as a public resource.
I’d love to hear ways we can improve the site. It works but certain things like content filtering detection are rough around the edges, and we want to add test nodes in Asia + US for better coverage as right now we just test from Amsterdam.
If you want a DNS server that isn’t already on the website then you can add them via the form and if you’re a large org that has a bunch to add then you can email me at support@dnsdirectory.com and we’ll ingest them.
Cheers!

value: Show HN: Open database tracking 77K public DNS servers every 10 minutes

author: timatping


value: ethanrc

value:
Hi HN; I built DeployFreeze after getting tired of engineers frantically pinging “@here, don’t deploy product-service” during incidents and investigations.
DeployFreeze is a GitHub App that creates deployment freezes using GitHub’s custom deployment protection rules (https://docs.github.com/en/actions/how-tos/deploy/configure-...). It pauses deployments to specific environments without blocking merges or CI, and gives teams a single place to see what’s frozen and why.
This is particularly useful in monorepos, where you might want to freeze `production-api` while keeping `production-web` or staging deployable. The app only uses deployment webhooks and doesn’t require access to source code.
If you don’t use GitHub Actions but still see “Deployments” in your repo sidebar, this may work for you. Many platforms use GitHub’s deployment API behind the scenes, so you may already be set up even if you didn’t explicitly configure it.
I’d love feedback on how your teams handle deployment freezes today or any thoughts on this approach. There’s a 30-day free trial if you want to try it for upcoming end-of-year freezes.

value: Show HN: Deployment freezes for GitHub environments (DeployFreeze)

author: ethanrc


value: Nemorous

value: Show HN: Wordreaper – Scrape targeted wordlists for cracking using CSS selectors

author: Nemorous


value: mrayushsoni

value:
So I've been obsessed with a weird question for the past 6 months, what happens to SEO when everyone starts asking ChatGPT for recommendations instead of Googling?
The answer, apparently, is chaos. And maybe opportunity?
I built FirstClick because I noticed something strange. My previous startup was getting zero traffic from AI assistants, but our competitor (objectively worse product, sorry not sorry) was getting mentioned by Claude and Perplexity constantly.
It drove me crazy. So I reverse engineered why. Turns out there's a whole new game being played and most founders don't even know it exists yet.
FirstClick does three things
1. Writes BOFU comparison content automatically ("X vs Y", "Best alternatives to Z")
2. Optimizes it specifically for AI citation, not just Google
3. Tracks when/if AI models actually recommend you
The uncomfortable part is that I'm essentially helping companies game AI search results. Same way SEO gamed Google for 20 years. Is this... good? Bad? Inevitable?
I go back and forth honestly. Part of me thinks this is just the new reality founders need to adapt to. Part of me wonders if we're all just training AI on increasingly optimized marketing content until nothing means anything anymore.
Anyway, it works pretty well. Happy to answer questions about how AI models decide what to recommend, the technical implementation, or why I probably should have just become a photographer.

value: Show HN: I built a tool to make AI recommend you (and I'm conflicted about it)

author: mrayushsoni


value: shutty

value: Show HN: Fine-tuning Qwen3 at home to respond to any prompt with a dad joke

author: shutty


value: ekzhang

value: Show HN: jax-js, an ML library and compiler for the web

author: ekzhang


value: croolstudio

value: Show HN: Bogami – Android camera for immutable image provenance (C2PA/Solana)

author: croolstudio


value: injung

value:
Everyone's shipping AI tools right now, and here I am with a visual editor. Still, I think many teams are very familiar with the problem of "marketing wants to change the landing page again."
I've run into this for years. Campaign pages come in, engineers get pulled in, and tickets stack up. It's usually the same components, just rearranged.
A few years ago, at a startup I worked at, we built an internal tool to deal with this. You register your existing React components, they show up as drag-and-drop blocks, and the result is a JSX string. No schema to learn, no changes to your component code.
We used it in production, handling real traffic in a messy, legacy-heavy environment. It held up well. Over time, it powered roughly 60% of our traffic. Marketing shipped pages without filing tickets, and product teams ran layout-level A/B tests. That experience eventually led me to clean it up and open-source it.
Composify sits somewhere between a no-code page builder and a headless CMS. Page builders like Wix or Squarespace offer drag-and-drop, but lock you into their components. There are also solid tools like Builder.io, Puck, and Storyblok, but many require you to adapt your components to their model. Composify is intentionally minimal: it lets you use your actual production components as they are.
It's still early. The docs need work, and there are rough edges. But it's running in production and has solved a real problem for us. If you already have a component library and want non-devs to compose pages from it, it might be useful.
Homepage: https://composify.js.org
Happy to answer questions or hear feedback!

value: Show HN: Composify – Open-Source Visual Editor / Server-Driven UI for React

author: injung


value: VincentPresh

value:
I built TimetoTest because writing and maintaining test scripts is tedious. Instead of dealing with selectors, waits, and brittle automation code, you describe what you want to test in plain English.
How it works:
You write: "Verify users can checkout with a promo code"
The AI agent generates a test plan (UI + API steps)
It executes in a real browser with human-like interactions
You get a detailed report with screenshots and logs
It handles UI testing, API testing, E2E, and regression—no code required.
The goal was to make it feel like describing a test to a senior QA engineer who just goes and does it.
Would love feedback from the HN community. Happy to answer questions about the architecture or approach.

value: Show HN: TimetoTest – AI agent runs UI/API tests

author: VincentPresh


value: eigenvalue

value:
Repo:
https://github.com/Dicklesworthstone/beads_viewer
Live Demo:
https://dicklesworthstone.github.io/beads_viewer-pages/

value: Show HN: Beads Viewer (Bv)

author: eigenvalue

title: Show HN: Beads Viewer (Bv)


value:
Hi HN,
I built BustAPI to solve performance issues I kept hitting with Python APIs.
It uses a Rust backend (Actix) with PyO3 so Python devs get speed without
writing Rust.
Key points:
- 5–10x faster than FastAPI in simple benchmarks
- Familiar Flask/FastAPI-style API
- Focused on admin backends and high-throughput APIs
I’d love feedback, especially on API design and real-world use cases.

value: Show HN: BustAPI – Python web framework powered by Rust


value: kantord

value: Show HN: headson - structure‑aware head/tail for JSON/YAML and source code

author: kantord


value: mromanuk

value:
I built an idle/clicker game about the AI hype cycle, because I love following how AI develops and also love idle games. You start circa 2018 with a laptop and a transformer idea, training your first model.
The game follows "real" AI history: Attention Is All You Need → GPT-1 → you get the idea.
Core loop:
- Train models (click or auto) to improve quality
- Serve users to earn money
- Buy hardware: from a used GTX 1060 to Dyson Sphere compute arrays
- Research to unlock bigger models
- Balance energy/compute resources
Built with SolidJS + TypeScript + Vite. ~90KB gzipped. No frameworks, no backend, runs entirely in the browser. Saves to localStorage.
The progression takes ~2-3 hours to reach ASI if you're actively playing, longer if idle. Hardware scales from 1 compute/sec (laptop) to 100M compute/sec (Matrioshka Brain).
I tried to capture the exponential feeling of the AI scaling laws - each era feels faster than the last, which mirrors the real industry, but I didn't want to get into politics, corp, and stuff like that at this moment.
Would love feedback on game balance and pacing. The late game might be too fast or too slow depending on your playstyle.
Hope nobody get too mad at this, remember it's a parody, just a clicker game. And yes, it was made with an agentic LLM (I refuse to say vibe coded).

value: Show HN: GPT Clicker. An idle game about building an AI empire

author: mromanuk


value: mkozak

value:
Hey HN
I built Codeboards, a developer portfolio that updates itself automatically from your GitHub, StackOverflow, LinkedIn, and more.
Problem:
Most dev portfolios are outdated, manual, and painful to maintain. GitHub alone doesn’t show who you are. LinkedIn is noise. Personal websites die after 6 months.
What Codeboards does:
Generates a clean, professional developer portfolio in seconds
Pulls your contributions from multiple sources
Updates itself automatically
Gives you a custom profile URL you can share anywhere
Optional developer verification ($9 one-time)
Free to start, no credit card needed
Why I built it:
As a CTO reviewing hundreds of candidates, relying purely on CVs and GitHub profiles is messy. Devs deserve a single place that shows what they actually do, without maintenance.
Would love feedback.
Link → https://codeboards.io
Happy to answer any questions!

value: Show HN: Codeboards – A Developer Portfolio That Updates Itself

author: mkozak


value:
I’ve been working on MiraTTS, a fine-tune of Spark-TTS designed for high realism and stable text-to-speech. The goal was to create an incredibly fast but high quality model.
Most open TTS models are either computationally heavy or generate 16-24kHz audio. Mira achieves high fidelity and speed by combining two things:
FlashSR: For generating crisp and clearer 48kHz audio outputs.
LMDeploy: Heavily optimized inference allowing for 100x real-time speed and low latency (roughly150ms).
I built this so local users have access to a high quality local text-to-speech model that works for any usecase. It’s currently in its early stages, and I'm currently experimenting with multilingual versions and multi-speaker versions. Streaming is coming soon as well.
Repo: https://github.com/ysharma3501/MiraTTS
Model: https://huggingface.co/YatharthS/MiraTTS
I also wrote a breakdown on how these LLM based TTS models work: https://huggingface.co/blog/YatharthS/llm-tts-models

value: Show HN: MiraTTS, a 48kHz Open-Source TTS at 100x Real-Time Speed


value: s-stude

value:
I created a simple app that lets your kid call Santa AI via video call. Parents can use this webpage to schedule a 5-minute or 10-minute call with Santa, who will speak to their child.
Very, very realistic and could support a dialogue, ask questions, and even tell if there is a gift for a kid.
I use the HeyGen AI avatar, and I DO NOT record any video. Would you show this to your kid? Could you give me some feedback?
https://CallSantaTonight.com

value: Show HN: Call Santa AI with your kids. My new side project

author: s-stude


value:
Hi HN, I’m a student working at a small company. I noticed some of my colleagues were accidentally pasting sensitive data and internal docs into ChatGPT, Gemini, etc.
Enterprise DLPs were a bit too expensive and complex for us, so I built this browser extension. It scans prompts and docs in real-time, alerts the user, and can anonymize data before it’s sent.
I’d love to hear your thoughts on the technical approach (browser-native vs network level) and if you find this useful.

value: Show HN: A lightweight DLP browser extension to prevent data leaks in LLM tools


value:
Hi HN,
We are the OpenDCAI group from Peking University. We built Paper2Any, an open-source tool designed to automate the "Paper to Slides" workflow based on our DataFlow-Agent framework.
The Problem: Writing papers is hard, but creating professional architecture diagrams and slides (PPTs) is often more tedious. Most AI tools just generate static images (PNGs) that are impossible to tweak for final publication.
The Solution: Paper2Any takes a PDF, text, or sketch as input, understands the research logic, and generates fully editable PPTX (PowerPoint) files and SVGs. We prioritize flexibility and fidelity—allowing you to specify page ranges, switch visual styles, and preserve original assets.
How it works:
1. Multimodal Reading: Extracts text and visual elements from the paper. You can now specify page ranges (e.g., Method section only) to focus the context and reduce token usage.
2. Content Understanding: Identifies core contributions and structural logic.
3. PPT Generation: Instead of generating one flat image, it generates independent elements (blocks, arrows, text) with selectable visual styles and organizes them into a slide layout.
Links:
- Demo: http://dcai-paper2any.cpolar.top/
- Code (DataFlow-Agent): https://github.com/OpenDCAI/DataFlow-Agent
We'd love to hear your feedback on the generation quality and the agent workflow!

value: Show HN: Paper2Any – Open tool to generate editable PPTs from research papers


value: willm

value:
Hi HN,
Up to the middle of 2025, I was the CEO/CTO of a startup called Textualize. Somehow I had managed to land seed funding for my Python libraries for fancy terminal output. We wrapped up after three years because the funding ran dry.
I honestly thought I was sick of coding at that point. But it turns out I was sick of the stress and working all hours. A few weeks rest was all I needed.
It was about that time that coding agents exploded, and I could no longer ignore them. I wasn't impressed with the UI these tools offered. Having worked in the terminal for a few years, I knew you could get a better user experience. And so this project was born.
I had planned to create some kind of layer between the agent's SDK and the front-end. Fortunately after I started building this, Zed Industries released Agent Client Protocol (https://agentclientprotocol.com/). Which was precisely what I needed.
I've just released the code (It was a private repo for a while). Toad (a play on Textual Code) can run a large number of AI agents, which a nicer terminal UI.
Think of it as a "bring your own agent" coding CLI. Use whatever agent you want. I'm not trying to sell you tokens.
Ask me anything. I'll be hanging around for a while, if this post takes off.

value: Show HN: Toad. A unified terminal UI for coding agents

author: willm


value: teemingdev

value:
I built a small tool for indie founders who just want a usable logo quickly without dealing with heavy AI generators, editors, or subscriptions.
I also added a lightweight “smart suggestions” feature where you can describe what your app does and it suggests icons and colors based on keywords. This part uses simple rules and mappings, no external AI services or image generation, so it’s fast and essentially free to run.
No accounts, no watermark, no folders of assets, just a single logo file you can download and use.
I built this as part of SmolLaunch, a launchpad for small dev tools and indie projects, but the logo generator itself is completely free.
Would love feedback on whether this is useful compared to existing logo tools!

value: Show HN: I built a free to use logo generator using icons and colors

author: teemingdev


value:
I built a small browser extension to help create reminders from selected text on any website. I kept seeing dates/times in emails and webpages (“next Tuesday 3pm”, “25.12.2025”, “14:30”) and wanted a convenient way to turn them into reminders without switching apps.
How it works:

  - Select text on any webpage → right‑click “Add to Reminders”
  - It detects common date/time formats (English + European/German) and pre-fills a reminder time
  - Review/edit/delete reminders in the popup, or add reminders manually
   - Dates can also be edited/added manually, and the highlighted text need not contain a date.

  Optional integrations (only when you choose them):

  - Google Calendar sync via OAuth (creates an event)
  - macOS Apple Reminders via a small local bridge you run on your machine

  Links:

  - Chrome Web Store: https://chromewebstore.google.com/detail/create-reminders/ecifdofkbodefbieanakcmhnncjhflkh
  - Firefox Add-ons: https://addons.mozilla.org/en-US/firefox/addon/create-reminders/

value: Show HN: A small browser extension to turn selected text into reminders


value: thijser

value:
I  started  an experiment to let a WASM-powered webapp interact with a local git repo and see how well this works. It turns out, it works pretty well!
I made it into a "git wrapped" tool that shows you when you committed the most, and what languages and files you most touched.
Despite the scary prompt when you use the filesystem API everything happens locally and your code stays private. (You can of course also just try it on cloned public github repos).
Let me know what you think!

value: Show HN: Git rewind – your Git year in review

author: thijser


value: superdario

value:
I've been learning Python and I decided to make something useful.
I created a SaaS that tries to protect videos from being downloaded in some common ways, like using browser extensions or direct video links. I think attacker will have to take some time to actually download a video from my website (at least I hope so).
You can always screen record it, but there is no protection against it, unless you use DRM.
Here is example of such video (it needs to be unlocked). https://peachka.net/v/1
I still don't know what to do with that solution, maybe offer it to creators or such.

value: Show HN: Peachka – Protecting Videos from Stealing

author: superdario


value: transkatgirl

value:
Ever since the GPT-2 days, I've always felt like base model LLMs were something special. It felt like an entirely new art form; Every piece was a collage made of all the written works that came before it.
But, the issue is that all of the interfaces for them have sucked. And trust me, I've tried them all: The original OpenAI playground, Loom, Loomsidian, Exoloom, etc...
So, I decided to try to learn from their mistakes and make something better.
My new interface is called Tapestry Loom. It's still a work in progress, and there's a lot more I want to add to it, but I think it has finally reached a point where it's ready for other people to try.
Let me know you think.

value: Show HN: A better interface for base model (completion) LLMs

author: transkatgirl


value: Show HN: Turn your startup logo into a holiday Google doodle


value: taklaxbr

value: Show HN: ZAI Shell – Self-healing CLI agent that fixes command errors

author: taklaxbr


value: iowadev

value:
Built this because I was tired of sketchy PDF sites that upload your files. Everything here runs client-side – your documents never leave your machine.
What it does:
Merge, split, compress, rotate, delete pages, add watermarks, extract text, organize pages, add headers/footers, edit metadata, and more.
The privacy angle:
No uploads. No server processing. Just local JavaScript doing all the work.
MIT licensed.
Try it: https://pdf.makr.io
Source: https://github.com/renedeanda/pdf.makr.io

value: Show HN: Free PDF tools that run in the browser

author: iowadev


value: admtal

value:
I built Demo Scope because I wanted to stream my web game on Twitch and couldn’t find a good way to do it from my phone.
iOS screen recording doesn’t have face cam. Loom is desktop only. OBS works but it’s a whole thing. I just wanted to open my phone, load my game, and go live with my face on screen.
So I made it.
Demo Scope is an iOS app that lets you record or stream any mobile website with a face cam overlay and touch indicators. Load any URL in the built-in browser, drag your face cam where you want it, and every tap shows on screen.
Also works for narrating over videos and photos if you want to do reaction content.
Free to try, $9.99 one-time to unlock everything. No subscription.
https://demoscope.app
Would love feedback, especially from anyone else streaming web games.
Also, feel free to comment a link to your game if you want me to play/stream it

value: Show HN: I made my own streaming app specifically for web games

author: admtal


value: kodefreeze

value:
Hi HN, sharing a SaaS product that I built to automate web UI testing based on natural language test descriptions. The approach we use is a multi-agent system that uses an augmented vision approach for interacting with the UI, rather than relying on the DOM. The goal is to mimic how your users would naturally use the UI, rather than following a predefined path.
I built this because I got tired of doing manual QA for my last SaaS product. I couldn't trust automated UI tests (Selenium/Playwright) because they were flaky, broke whenever the DOM changed, and missed obvious usability bugs.
We are completely bootstrapped, but we want to gather as much feedback as possible in these early days, so we have made the product available for free (for now).
Happy to answer any questions about it here, and would love your feedback.
Thanks!

value: Show HN: Kodefreeze – Automate UI testing with natural language tests

author: kodefreeze


value:
AI agents forget everything between sessions. Vector DBs give you similarity search, but not actual learning.
Shodh is different – it's a cognitive memory system:
  - Hebbian learning – memories recalled together strengthen their connections
  - Activation decay – unused memories fade (like biological memory)
  - Knowledge graphs – entities extracted and connected automatically
  - Three-tier storage – working → session → long-term (based on Cowan's working memory model)
Technical details:
  - Single ~15MB Rust binary, runs 100% offline
  - Sub-millisecond graph operations (entity lookup: 763ns, 3-hop traversal: 30µs)
  - MiniLM-L6 embeddings + TinyBERT NER, bundled
  - MCP server (works with Claude, Cursor), Python SDK, REST API
  - RocksDB for persistence
Edge deployment:
Designed for local-first and air-gapped environments. Runs on Jetson, Raspberry Pi, industrial PCs, drones – anywhere you need on-device memory without cloud round-trips. Zero network dependency.
What it's not:
Not a vector database. Not RAG. It's meant to give agents persistent memory that actually learns and consolidates over time.
GitHub: https://github.com/varun29ankuS/shodh-memory
Website: www.shodh-rag.com/memory
Happy to answer questions about the architecture or cognitive models.

value: Show HN: Shodh – Cognitive memory for AI agents (runs on edge)


value: brennerm

value: Show HN: Explore Prometheus /metrics endpoints from your terminal

author: brennerm


value: cataPhil

value: Some early results and next steps in adapting DeepMind's Genie world model for creating the music player of my dreams.

value: Show HN: Melodjinn v0.1 – Adapting DeepMind's Genie world model for music

author: cataPhil


value: neilgsmith

value: Show HN: Mapping AI narratives by M.I.N.D. structural alignment

author: neilgsmith


value: lulzx

value:
I needed to generate invoices in a Node.js app. jsPDF is 229KB. I only needed text, rectangles, lines, and JPEG images.

  So I wrote tinypdf: <400 lines of TypeScript, zero dependencies, 3.3KB minified+gzipped.

  What it does:
  - Text (Helvetica, colors, alignment)
  - Rectangles and lines
  - JPEG images
  - Multiple pages, custom sizes

  What it doesn't do:
  - Custom fonts, PNG/SVG, forms, encryption, HTML-to-PDF

  That's it. The 95% use case for invoices, receipts, reports, tickets, and labels.

  GitHub: https://github.com/Lulzx/tinypdf
  npm: npm install tinypdf

value: Show HN: TinyPDF – 3kb pdf library (70x smaller than jsPDF)

author: lulzx


value: lukekim

value:
Hi HN, we’re Luke and Phillip, and we’re building Spice.ai OSS - a lightweight, portable data and AI engine and powered by Apache DataFusion & Ballista for SQL query, hybrid-search, and LLM-inference across disaggregated-storage used by enterprises like Barracuda Networks and Twilio.
We first introduced Spice [1] on HN in 2021 and re-launched it on HN [2] in 2024 re-built from the ground up in Rust.
Spice includes the concept of a Data Accelerator [3], which is a way to materialize data from disparate sources, such as other databases, in embedded databases like SQLite and DuckDB.
Today we’re excited to announce a new Ducklake-inspired Data Accelerator built on Vortex [3], a highly performant, extensible columnar data format that claims 100x faster random access, 10-20x faster scans, 5x faster writes with a similar compression ratio vs. Apache Parquet.
In our tests with Spice, Vortex performs faster than DuckDB with a third of the memory usage, and is much more scalable (multi-file). For real-world deployments, we see the DuckDB Data Accelerator often capping out around 1TB, but Spice Cayenne can do Petabyte-scale.
You can read about it at https://spice.ai/blog and in the Spice OSS release notes [4].
This is just the first version, and we’d love to get your feedback!
GitHub: https://github.com/spiceai/spiceai
[1] https://news.ycombinator.com/item?id=28448887
[2] https://news.ycombinator.com/item?id=39854584
[3] https://github.com/vortex-data/vortex
[4] https://spiceai.org/blog/releases/v1.9.0

value: Show HN: Spice Cayenne – SQL acceleration built on Vortex

author: lukekim


value: chiengineer

value:
Made with vs code + github copilot pro + subscription and lots of patience
I found as of 2025 there are almost zero good templates for github pages that have easy setup or configuration
And thats why i made this
Has a section for demo animations which I cant believe I got working on a static site

value: Show HN: Astro 5 and TypeScript production ready GitHub pages template

author: chiengineer


value: Show HN: SCADABreach – a browser-based SCADA/ICS security simulation


value: casmn

value:
Hi HN,
I just shipped v2.3.12 of dotenv-diff
This update fixes some false positives in secret detection and reduces noisy output.
Feedback welcome :)

value: Show HN: Dotenv-diff 2.3.12 – better secret detection, fewer false positives

author: casmn


value:
Hey everyone! After struggling a lot with data enrichment for SMBs, I launched a Google Sheets add-on that gives you direct access to an AI-powered webscraper.
Vurge lets you get structured information from any website right inside your Google Sheet, eliminating the need for learning a new tool or adding a new dependency for data enrichment.
Let me know what you think!

value: Show HN: It's Like Clay but in Google Sheets


value: Show HN: Social Media Post Quality Checker


value: suyashkumar

value: Example actions to Jules (an independent cloud coding agent from Google Labs) from GitHub actions.

value: Show HN: Jules AI GitHub Actions

author: suyashkumar

title: Show HN: Jules AI GitHub Actions


value:
Hi HN, I’m Safoan, founder of Narrativee.
We built Narrativee because we realized how it sucks it to get a clear picture from raw spreadsheet files. I found myself spending hours staring at rows and columns, trying to extract context and manually turn that data into documents I could actually share with my team.
The Problem Spreadsheets are great for calculation, but terrible for communication. To get the "story" behind the data, you usually have to screenshot charts or write long emails explaining what the numbers mean.
This is why we built Narrativee to remove that friction in minutes.
> You upload your spreadsheet (CSV/XLS),
> Get an narrative document that explains the "what" and "why" behind your data
> Edit & Share a readable report instead of raw numbers
We have a free plan, give it a try and let us know what we can improve !

value: Show HN: Narrativee – Make sense of your data in minutes


value: Jeramo

value:
I've been working on a native iOS app called Clouder. The goal is to provide a better experience than the mobile web dashboard for managing your infrastructure on the go.
The app supports a wide range of services. You can manage Compute resources like Workers deployments, Pages projects, Durable Objects, and Queues. It connects to your Storage and Data to let you query D1 databases, browse R2 buckets, and manage KV namespaces. It also handles Media features like Stream and Images, as well as AI products like Vectorize and Workers AI.
For general networking, you can manage DNS records, Tunnels, WAF rules, and view Zone analytics. The app supports multi-account setups, includes home screen widgets for traffic and database stats, and is localized in five languages. You log in directly using your Cloudflare API token.
I'd appreciate any feedback or feature requests.

value: Show HN: A native iOS client for managing Cloudflare

author: Jeramo


value:
Hi HN! We're fellow devs tinkering with agents like everyone.
We believe sometimes its worth spending more time aligning on agent goals & evals before coding for e.g. spec alignment, generating evals, provisioning sandboxes, etc.
Right now our package only works for langchain agents & supports a few MCPs. Roasts are welcome.

value: Show HN: Eval based agent builder (pls roast us)


value: ezekg

value: Show HN: analog.watch – read 3 analog clocks as fast as you can!

author: ezekg


value: ayushmaanbhav

value:
Product-FARM (Functionality, Attribute, and Rule Management) is a domain-agnostic rule engine that lets you configure complex business logic without writing code. Whether you're calculating insurance premiums, taxes, regulatory law and compliance, generating trading signals, or evaluating loan eligibility and other fintech operations, Product-FARM provides:
Configure complex business rules with visual DAG editing, AI assistance, and sub-millisecond execution.
1. Visual Rule Builder - Drag-and-drop JSON Logic blocks, no coding required
2. AI Assistant - Create rules from natural language descriptions
3. Sub-millisecond Execution - 3.5x faster with tiered bytecode compilation
4. DAG Visualization - See rule dependencies and execution flow
5. Real-time Simulation - Test rules instantly with live feedback
6. Finance friendly - No penny loss or conversion issues, can work with currencies and precision with support of custom scale and precision datatypes as well
Built on Rust with 85+% code coverage, benchmarked and performance tested.

value: Show HN: High-Performance Domain-Agnostic Rule Engine with AI-Powered Config

author: ayushmaanbhav


value: Show HN: Agentry: Intelligent orchestration for dynamic AI agent workflows


value: keithwirch

value: Show HN: Webhook.build – Instant, powerful webhook inspection and debugging

author: keithwirch


value: akhilnchauhan

value:
Hey there - I've been looking for interesting ways to stand out when applying for jobs. I'm being selective in where I work next so I'm picking a handful of companies to really go after.
Instead of just applying through the front door, I want to stand out. [1]
I used the Deepgram Voice Agent API to create a Sales Roleplay, where you have to pitch Deepgram to an AI prospect!
Fun little project. Has already drummed up interest from a couple folks.
Wish me luck!
[1] https://quarter--mile.com/Standing-Out

value: Show HN: I built sellmedeepgram.com to help get a job at Deepgram

author: akhilnchauhan


value: reagantriminio

value:
I wanted a place to say things I couldn't tell my friends or family—but I still wanted to be heard. So I made Inkwells.
This is a personal project I built for anonymous writing. You're identified only by a number on your diary. Otherwise there are no profiles or usernames at all. You can browse other people's entries, save inkwells you like, and even comment/react without any ties to your own personal diary.
Any comments, questions, or suggestions are greatly appreciated!
I would also appreciate if you guys could point out any bugs, or features you find unintuitive.
URL: https://www.inkwells.io/

value: Show HN: Anonymous platform for writing and discovering personal diary entries

author: reagantriminio


value:
I’m building Nautilus, a cross-platform desktop manager for Linux servers. While there are many SSH clients out there, I wanted something that combined a high-level dashboard, a full SFTP explorer with an integrated editor, and specialized tools like a Cron job manager and Process monitor—all in a modern, lightweight desktop package.
Key Technical Highlights:
Tauri + Node.JS Sidecar: We use Tauri for the native windowing and Rust security, but the heavy SSH/SFTP lifting is done via a Node.js sidecar (using ssh2). This allowed us to leverage a very mature ecosystem while keeping the frontend performant.
Security: We don't store passwords in plain text. We use the OS-native secure vault (macOS Keychain, Windows Credential Manager, or Secret Service via keytar).
Modern Stack: React 19 and Vite 7.
Features: Multi-tab terminal (xterm.js), real-time CPU/RAM/Network monitoring, visual Cron management, and a reusable snippet library with "secure password" sending.

value: Show HN: Nautilus – Modern, OSS Linux SSM Built with Tauri/Rust and React


value: antiochIst

value:
A few weeks ago I shared Yandori here — a system that tracks how news spreads across ~200k websites in near real time. The main criticism was fair: similarity + RSS timestamps aren’t enough to identify who actually broke a story, and many large sources were missing.
Since then I’ve reworked the core of the system to explicitly model source attribution and article-to-article references, not just textual similarity.
What’s changed:
Clusters still start from full-text embeddings, but are now refined using explicit citations and source mentions inside articles.
External sources (sites we don’t actively crawl) are added as nodes when referenced, so lineage isn’t limited to the monitored set. This captures many large outlets that don’t provide usable RSS feeds.
“First to publish” no longer relies purely on RSS timestamps — publish times are validated against citation order and reference structure.
The flow view now reflects an inferred derivation graph, not just a timeline of similar headlines.
Added search and browsing across historical story archives.
Still English-only for now and not pulling from social platforms yet — trying to get attribution right before expanding.
Happy to answer questions and hear suggestions (they helped a lot already).

value: Show HN: Improved real-time news propagation tracking via source citation graphs

author: antiochIst


value: ernestobellei

value:
While organizing my studio's various projects, I realized our documentation was a mess: different tools, inconsistent versions, and mismatched layouts. I wondered if it really had to be this complex to have a unified system for simple docs.
For the past couple of weeks, in my spare time, I’ve been standardizing everything into Markdown files within a /docs folder in each repo. To render them as static HTML, I built mdgen.
Initially, I planned a simple CLI to render HTML with a default stylesheet. Then, I remembered a Markdown editor I experimented with a while ago using the browser’s FileSystem API. I decided to dust off that code and create an online generator that requires zero effort. You just point the browser to your /docs folder, it processes everything locally, and you download the generated HTML package ready for your server.
However, I quickly realized that doing this manually for dozens of projects is tedious—so I built a CLI version to automate the process.
I wanted something lightweight that doesn't feel like a heavy static site generator but gets the job done for simple, clean documentation.
Side note: I’m actually thinking of adding some "fantasy" themes to use it for publishing D&D adventures.
Side side note: I'm fully aware that this currently works best for very 'standard' documentation and that the sidebar navigation system, in particular, still needs a lot of improvement... but I'm still having fun building it :)

value: Show HN: Mdgen – A browser tool to unify documentation into static HTML

author: ernestobellei


value: reallynattu

value: Show HN: Screenshot2Charts – Turn screenshots or CSV into beautiful charts

author: reallynattu


value: theZilber

value:
I was thinking for a while about making the Linux experience a bit more appealing, so I experimented with the idea of attaching bash commands to a UI. I built a small prototype that turns parameterized command strings into forms. You write a command with placeholders like {param}, the tool generates a form to fill them in, and you can then copy the final command and run it in your shell.
This is only a proof of concept; everything is stored locally. I plan to add essential features to make it fully usable, depending on feedback.
Under the hood, it uses a small application engine I built on top of Svelte.
Feedback is appreciated.

value: Show HN: I Hacked Together a GUI for Building Terminal Commands

author: theZilber


value: aggeeinn

value: Show HN: I built a tool to score your website's LLM readability

author: aggeeinn


value: matthakimi

value: Show HN: New backgammon P2P wagering app

author: matthakimi


value: nucleic-se

value:
Tried to put my homelab infra in Git. Things escalated into a deterministic build engine for structured data. It merges JSON classes and instances using multi-parent inheritance and composable aspects, validates against schemas, and renders template-driven outputs. Generate configs, docs, and code from one canonical model. No vendor lock-in, composable stacks, fail-fast validation. Same inputs always produce the same outputs.
I can now officially call myself a proud maintainer of an open-source project.
Warning this is still alpha software, breaking changes are expected, but if you want to try it:
npm install -g @nucleic-se/struktur@alpha

value: Show HN: Composable, Deterministic Builds from a Single Source of Truth

author: nucleic-se


value: rohitghumare

value:
Visualize any GitHub repository with beautiful interactive graphs and star history charts.<p>Features:
 Interactive Force Graph - Explore repository structure with draggable, zoomable nodes
 Pack View - Circle-packing visualization showing files nested inside folders
 Star History - Beautiful charts showing repository growth over time
 Embeddable Charts - Add live star history SVG to your README (dark & light themes)
 Commit Timeline - Watch commits animate through the codebase
 Multi-Repo Comparison - Compare star histories of multiple repositories
 Chrome Extension - View star history directly on GitHub pages

value: Show HN: Git repo visualization and interactive stars and commits history

author: rohitghumare


value: ddtaylor

value: Show HN: Squache – A self-hosted HTTPS caching proxy for web scraping

author: ddtaylor


value: indigophone

value:
Thought I would share this new library that you probably haven't heard of!
TiliaJS is an open source functional library that manages your application state. It also has React integration, but it's not coupled to React. I've personally used it in the Bun runtime and the browser runtime.
Also it's written in ReScript, so it can be used with JavaScript, TypeScript, and ReScript.

value: Show HN: TiliaJS FRP JavaScript/TypeScript/ReScript State Management

author: indigophone


value:
I've been using Claude Code and OpenAI Codex CLI daily, and found myself constantly
digging through nested directories to find past sessions.
Claude Code stores logs in ~/.claude/projects/<hash>/.jsonl
Codex CLI stores logs in ~/.codex/sessions/YYYY/MM/DD/.jsonl
Browsing these manually is tedious, so I built two simple shell scripts that use
fzf for interactive selection with previews.
*claude-logs*: https://github.com/wondercoms/claude-logs
*codex-logs*: https://github.com/wondercoms/codex-logs
Features:
- Interactive project/session selection with fzf
- Preview conversations before opening
- Real-time monitoring with `tail` command
- Status check (is the CLI running?)
- Formatted output with colors
Requirements: fzf, jq (macOS only for now)
Usage is simple:

value: Show HN: CLI tools to browse Claude Code and Codex CLI logs interactively


value: This tool is inspired by zellij terminal panes. I did not like how buggy zellij was so I switched to using WezTerm terminal panes, but missed the ability of being able to CTRL+C in a pane and restart the command.<p>I created a simple CLI in OCaml that wraps a command and allows you to interrupt and restart the defined command easily. I made the utility with terminal panes in mind for a roll your own IDE solution.

value: Show HN: Infexec – A utility for pinning commands to terminal panes


value: Show HN: Patch PHPUnit to shard your Laravel test suite


value: jackdoe

value:
If you have kids < 15yo chances are they are playing roblox, and most of the games there are quite brainrotty, so I made this game to teach my daughter some OOP, particularly 'self' vs 'other' and how they get compiled, and demystify methods vs functions.
Basically you program yourself and your robots to defend the "core", the robots can check the gundam's targets and shock them: self.shock(G3.target()) or do things like self.teleport(G3.pos)
You can play it here: https://www.roblox.com/games/92507403623309/Python-Tower-Def...
I might need to finetune the numbers but if you want to modify it just clone the repo and run it locally in roblox studio or just publish your version. Now it is not easy to go past wave 4-5 if you are alone.
The game can also be used to teach what compiling does and how to debug and step through the bytecode.
PS: hard to say "I" made it, as it is all written by opus 4.5 for about a week and 200$ in api calls, but I am having fun playing it, and managed to play a game with my daughter which was lots of fun :)

value: Show HN: Roblox Python tower defense game

author: jackdoe


value: claudeomusic

value: I dread a future where every internet interface is just a prompt. I built this library more as inspiration than a formal recommendation; my goal is to show that we can still have wonderful, intuitive, and varied UI/UX without being limited to the ubiquitous chat interface found in every product today.

value: Show HN: LLM-as-a-Form

author: claudeomusic

title: Show HN: LLM-as-a-Form


value: Omakidx

value: Input any long URL into the input field to shorten it and re-use the link anytime AND anywhere you want.

value: Show HN: I create a link shortening platform

author: Omakidx


value: PranavVyas

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

value: Show HN: Postman-style TUI for API testing built with Rust and ratatui

author: PranavVyas


value: enigmazi

value: Show HN: Tree-fs – Turn ASCII directory trees into real files

author: enigmazi


value: deadmooncr

value:
“Hi HN,
I’m a developer from Costa Rica, and I’ve been working on Eon, an experimental AI architecture that moves away from the traditional ‘reactive’ prompt-response model. My goal was to create a system that feels more like a continuous flow of thought rather than a static tool.
What makes it different? Most LLM implementations rely on a limited context window and lack true persistence. Eon uses a multi-layer agent architecture where specialized micro-agents collaborate and validate information before delivering it. It’s designed to build a dynamic knowledge graph that evolves with every interaction.
Why I’m building this: In my local tech scene, there isn't much support for deep AI R&D, but I believe the future of AI isn't just bigger models, but smarter architectures. I’ve focused on:
Heuristic Self-Correction: Reducing hallucinations by auditing logic in real-time.*
Modular Memory: A way for the AI to "remember" and relate concepts across different sessions without bloat.*
Event-Driven Execution: To keep it efficient and scalable.*
The project is still in development, and the code is open on GitHub. I’d love to get feedback from this community on the architectural approach and how to better manage the state between these agents.
Repo: https://github.com/jeremy-sud/Eon-AI-Project
Thanks for taking a look!”

value: Show HN: Eon – A modular AI architecture for continuous cognitive flow

author: deadmooncr


value: anniegracehu

value: Show HN: Daily Set Puzzle – I rebuilt it after setgame.com's SSL cert expired

author: anniegracehu


value: jignb

value:
I think that defensive programming the way it's taught is more like sweeping bugs under the rug. Rather than gently handling programmer errors, I wrote a tiny framework inspired by DbC to track bugs as early and as fast as possible.
While I may be biased, I do think it's made tracking bugs a less unpleasant experience.
The plugin is pretty rough around the edges, it's an internal tool that I figured someone could find useful.

value: Show HN: SkyLifeguard, tiny UE5 plugin for assertive programming

author: jignb


value: A Promise-like primitive which is lazy/cancelable, has typed errors, and emits synchronously instead of on the microtask queue. In a way LazyPromise is what you get if you take an Observable and make it impossible to misuse it for what the Signals were built to do.

value: Show HN: LazyPromise = Observable – Signals


value: I've created a networking library in C++ which works on Linux, Windows, & MacOS and made the core of it based on C++ coroutines and native asyncio from each of these platforms. Check it out: https://github.com/adityarao2005/WebCraft. It has an official vcpkg port. Here is an example of how to use it https://github.com/adityarao2005/http-server-webcraft.

value: Show HN: WebCraft: A C++ 23 async IO library


value: CULPRITCHAOS

value:
Interlock is a drop-in circuit breaker for AI systems (Express, FastAPI, core library) that tracks confidence, refuses low-certainty responses, and generates cryptographically signed certification artifacts and incident logs. It includes CI-driven stress tests, a certification badge, and reproducible benchmarks. Repo + quickstart: https://github.com/CULPRITCHAOS/Interlock
What it does
Tracks AI confidence, hazards, and triggers a reflex (refuse/degrade) rather than silently returning incorrect answers.
Produces tamper-evident audit trails (HMAC-SHA256 signed badges, incident logs, validation artifacts).
Ships middleware for Express and FastAPI; adapters for 6 vector DBs (Pinecone, FAISS, Weaviate, Milvus, LlamaIndex, LangChain).
CI workflows to test, stress, benchmark, and auto-generate certification badges. Evidence artifacts are preserved and linkable.
Why it matters
Many systems log “success” when an LLM confidently hallucinates. Audit trails and refusal policies matter for safety, compliance, and risk reduction.
Interlock aims to make interventions reproducible and certifiable, turning “we think it failed” into “here’s signed evidence it did and what we did.”
Notable validation & metrics (from README)
Total interventions (recorded): 6 (all successful)
Recovery time (mean): 52.3s (σ = 4.8s)
Intervention confidence: 0.96
False negatives: 0
False positive rate: 4.0% (operational friction tradeoff)
Zero data loss and zero cascading failures in tested scenarios
If you care about adoption
Express middleware: drop-in NPM package
FastAPI middleware: remote client pattern
Core library for custom integrations
If you want to try it
5-minute quickstart and local AI support (Ollama) in docs
Pilot offer (shadow mode, free): contact listed in README
Why I'm posting I built this to reduce silent corruption and provide verifiable evidence of interventions; I’m looking for pilot partners and feedback on certification semantics and enterprise fit.
Relevant links
Repo: https://github.com/CULPRITCHAOS/Interlock
Quickstart: ./docs/QUICKSTART.md (in repo)
Case study & live incidents: linked in repo
Suggested top-level OP comment after posting (short) Thanks for reading — happy to answer technical questions. If you want to run a pilot (shadow mode) or want sample artifacts from our stress chamber, DM or open an issue. Repo: https://github.com/CULPRITCHAOS/Interlock (feedback pls) (new coder)

value: Show HN: Interlock – Circuit breaker for AI infrastructure with signed audits

author: CULPRITCHAOS


value: Show HN: Crudler – Production-ready REST, gRPC APIs from your database schema


value: benmxrt

value:
Hi HN,
I’m building MephistoMail, a privacy-focused disposable email service designed to decouple digital identity from online services. After seeing how many "temp mail" providers log IP addresses or sell metadata, I wanted to build something that prioritizes total digital hygiene.
New Key Features:
Volatile Memory Storage: We use a RAM-only infrastructure. No incoming messages or session data are ever written to a physical HDD. Once the session terminates, the data is cryptographically wiped.
Client-Side Password Generator: Integrated entropy-based generator that runs locally in your browser. Create high-security passwords for your registrations without them ever touching our servers.
Secure QR Handoff: Transfer your active "burner" session from desktop to mobile instantly via an encrypted QR link. No need to type complex random addresses on your phone.
Multi-Account Tunneling: Manage multiple anonymous identities simultaneously through a unified interface.
Custom Alias Creation: Define your own usernames on our secure domains for more professional-looking temporary identities.
The Stack:
Frontend: React, Tailwind CSS, Lucide icons.
Backend Logic: Powered by Mail.tm's stable API for zero-latency delivery.
Privacy: Strict No-Logs policy, TLS 1.3 encryption, and no tracking cookies.
I’m looking for technical feedback on the UX and the "Persona" tool integration.
URL: https://www.mephistomail.site/ Source: https://github.com/jokallame350-lang/temp-mailmephisto
Happy to answer any questions about the implementation!

value: Show HN: MephistoMail – RAM-only ephemeral email with client-side privacy tools

author: benmxrt


value: Cabbache

value: Uses color to show the magnitude of the function output.

value: Show HN: Riemann Zeta Visualizer

author: Cabbache

title: Show HN: Riemann Zeta Visualizer


value: matanblay

value:
Hello, I’ve created a new website called UTMKit.
The idea came from a simple need: I wanted to show a popup to users coming from Instagram. But building it manually took too much time. Every new UTM meant more code, more testing, and another production update.<p>So I built UTMKit a faster way to create UTM-based popups, without the hassle.

value: Show HN: I Built UTMKit to Save Time on Popups Specifically UTM Triggered Popup

author: matanblay


value: quietbyte

value:
Hi HN,
I built LogShield as an experiment in deterministic log sanitization.
I wanted to see whether a purely rule-based approach could reliably remove secrets from logs without breaking structure or causing false positives.
LogShield is a small open-source CLI designed to work as a drop-in filter in pipelines:
- Reads from stdin, writes sanitized logs to stdout
- Explicit, inspectable rules (no probabilistic or ML-based masking)
- Same input → same output, every time
- Focused on avoiding false positives that break debugging
Typical use cases I had in mind:
- Sharing logs in issues, Slack, or support tickets
- CI/CD pipelines before uploading artifacts
- Log shipping to third-party services
Example:
cat app.log | logshield scan --strict > safe.log
The rule set is intentionally conservative and fully inspectable.
I’m especially interested in feedback on:
Rule coverage vs. false-positive risk
Edge cases where this approach would fail or be unsafe
Situations where you would not trust a tool like this in your workflow
Repo: https://github.com/afria85/LogShield
Thanks for taking a look.

value: Show HN: LogShield – Deterministic CLI tool to sanitize secrets from logs

author: quietbyte


value: StellaMary

value:
This hobby project was created using stolen code from deno tbh. Also I have submitted my framework to TechEmpower benchmarks and other bench marks. I'm attaching the framework score for your reference.
https://www.techempower.com/benchmarks/#section=test&runid=2...

value: Show HN: My own stolen JavaScript Server powered by Rust

author: StellaMary


value: Show HN: Kiteform – The Simplest Form Builder


value: Track AI coding assistants / Copilot's real impact without spreadsheets. Takes 2 minutes to setup and have everything working.

value: Show HN: GuageAI – Free Beta for GitHub Copilot ROI Analytics


value: deckiedan

value:
Hi fellow code wranglers!
Here's a series of short videos I've been making over advent to help some junior Django & JS developers I know have some tools and awareness of refactoring techniques & tools they can us.
I thought some other people might find it interesting or helpful!

value: Show HN: Advent of Refactoring"

author: deckiedan

title: Show HN: Advent of Refactoring"


value: Show HN: AI Vocabulary Quiz Maker

title: Show HN: AI Vocabulary Quiz Maker


value: epochcore

value: Show HN: QuantumVICK – 106-agent AI swarm for VSCode (free trial)

author: epochcore


value: ashfromsky

value:
Helix is a dynamic mock server that generates data on the fly using LLMs (supports Ollama, DeepSeek, Groq).
Key difference from other AI generators: Schema Enforcement.
One of the biggest issues with LLMs is non-deterministic output. To solve this, Helix allows you to provide a "System Schema" (using TypeScript interfaces, JSON Schema, or plain text rules). The engine ensures the generated JSON strictly adheres to your defined keys and types, while still generating realistic values for the content.
Features:
- Local-First: Works completely offline with Ollama (Llama 3.2 is recommended).
- Schema Safety: You can force strict structure compliance so your frontend doesn't break.
- Chaos Mode: Optionally simulate latency and error rates.
- Stack: Python 3.11, FastAPI, Redis, Docker.
It's open source (AGPLv3). I'd love to hear your feedback, especially regarding the schema validation approach!
Repo: https://github.com/ashfromsky/helix

value: Show HN: Helix – AI-powered API mocking with strict schema enforcement

author: ashfromsky


value:
Hey HN,
I’m a 23 year old solo developer who quit his job and is working on building an AI business over the next year.
I realized that local shop owners (bakers, mechanics, florists) know they need to be on social media but hate managing social media. They often to abandon their socials because being consistent is a chore and hiring an agency isnt in their budget.
I built Peitho to solve this:
It learns the "vibe" of the business so the posts don't sound like generic AI spam.
It generates weeks of ready-to-go text posts for LinkedIn, X, and Facebook instantly.
I posted about this previously but I’ve spent the last week making it self-serve, so thought I’d write an update.
I hope this helps some busy owners get back to actually running their business rather than worrying about what to write. Would love your feedback on the quality of the site and any ideas for how to improve the product!
Nikhil

value: Show HN: Peitho – Automated social media writing for mom-and-pop shops


value: TheCodingDecode

value: Show HN: Orbit a systems level programming language that compiles .sh to LLVM

author: TheCodingDecode


value: dawitworku

value: Show HN: Xsql – SQL schema conversion via an intermediate representation (Rust)

author: dawitworku


value: Show HN: Xsql – SQL schema conversion via an intermediate representation


value: - NanoAI unifies prompt-led generation, guided image transforms, and a precision browser canvas inside one localizedworkspace so teams can jump from concept to polished assets without context switching.<p>- Remix uploaded references through Image to Image, refine every brush stroke directly in the canvas editor, andfinish deliverables with built-in resizer/converter tools before publishing.

value: Show HN: Nano AI - Infinite Canvas AI Image Editor


value: Omakidx

value:
I built this link shortening platform using Next.js + MongoDB + Redis for rate limiting. The main challenge was preventing abuse while keeping redirects fast. I avoided third-party analytics and implemented my own event pipeline..
You can check it out to know more about the app

value: Show HN: I built a self-hosted link shortener with analytics and rate limiting

author: Omakidx


value:
link - https://link6ync.app
The moment you submit, paste something like this (example tailored to you):
Hi HN
I built this as a learning project to explore rate limiting, analytics, and abuse prevention at scale.
Tech stack:
Next.js (App Router)
MongoDB for persistence
Redis for rate limiting and click counters
Edge middleware for fast redirects
Interesting challenges:
Preventing link abuse without slowing redirects
Designing analytics without third-party trackers
Handling high write volume efficiently
It’s early and not production-ready, I’d really appreciate feedback on the architecture and any mistakes you spot.

value: Show HN: I built a self-hosted link shortener with rate limiting


value: krazyjakee

value:
AGENTS.md is a great idea but it stops working once a codebase or agent workflow gets large.
I built AGENTS.db which keeps the spirit of AGENTS.md while scaling it into a layered, append‑only, vectorized flatfile database for LLM agents.
Instead of one mutable markdown file, context lives in layers:
- Base - immutable, human‑verified source of truth
- User - durable human additions
- Delta - proposed / reviewable changes
- Local - ephemeral session notes
Higher layers override lower ones (`local > user > delta > base`), with full provenance and fast local semantic search.
No server. No SaaS. Works offline. Source‑control friendly. Exposes an MCP server so agents can read/write context safely instead of rewriting docs.
This is an early reference implementation targeting a public spec, and I’m trying to pressure‑test whether this is a better long‑term primitive than “just keep adding to AGENTS.md”.
Repo: https://github.com/krazyjakee/AGENTS.db

value: Show HN: Agents.db – an AGENTS.md alternative for LLM agent context

author: krazyjakee


value: JustinXie

value: Show HN: A simple web wrapper for OpenAI's new GPT Image 1.5 model

author: JustinXie


value:
This is an early validation landing page for CodexPad.
The idea: a knowledge-base / editor like Notion, but made for devs who prefer Vim-style text manipulation and command-driven workflows.<p>At this stage I’m validating interest before building out the backend.
Would love feedback, including why this may or may not be useful.

value: Show HN: CodexPad – a Notion-style workspace with Vim-native editing


value: mondra

value: Show HN: CodeGate – An open-source CLI to detect AI-hallucinated packages

author: mondra


value: kevinroleke

value: HN themed daily game: Score points by guessing the publish date, point score, and comment count of historical HN posts.

value: Show HN: Hacker News Guesser—Daily Game

author: kevinroleke

title: Show HN: Hacker News Guesser—Daily Game


value: keepamovin

value: Show HN: Hacker News, but every headline is hysterical clickbait

author: keepamovin


value: dgrcode

value:
I was so annoyed with the bluetooth auto-switch in macOS causing awful audio quality, that I decided to make an app to fix it.
I've been improving it with feedback from other users, and I've just released version 2.
You can get it for free, which it's already the best way out there to manually change audio devices. You can also buy a license for $10 one time payment and the app automatically select your preferred device. There's a 1 week free trial for the license version so you can see if it does what you want.

value: Show HN: Recadio – The best way to handle audio devices on macOS

author: dgrcode


value:
Hi HN, I'm Mohammed, a technical founder who loves shipping and giving back to the community. I'm open-sourcing the full-stack engine that powers my B2B product, apflow.co.
What it is: A production B2B starter with a Go backend and Next.js frontend. Both are fully Dockerized with separate containers. No Vercel. No Supabase. Deploy the whole thing on a $6 VPS, or split frontend and backend across different providers. You own the infrastructure.
The problem I was solving:
Every SaaS starter I evaluated had the same issue: they locked me into someone else's platform. Vercel for hosting. PlanetScale for the database. Serverless functions billing per invocation. Fine for prototypes, but costs become unpredictable at scale and migrating away is painful.
I wanted something I could deploy on any Linux box with docker-compose up. Something where I could host the frontend on Cloudflare Pages and the backend on a Hetzner VPS if I wanted. No vendor-specific APIs buried in my code.
Why Go for the backend:
Go gives me exactly what I need for a SaaS backend:
Tiny footprint. The backend idles at ~50MB RAM. On a cheap VPS, that headroom lets me run more services without upgrading.
Concurrency without complexity. Billing webhooks, file uploads, and AI calls run concurrently without callback hell.
Compile-time type safety. Using SQLC, my SQL compiles to type-safe Go. If the query is wrong, it fails at build time, not in production.
Predictable performance. No garbage collection pauses that surprise you under load.
The architecture (Modular Monolith):
I didn't want microservices complexity for a small team, but I needed clean separation. I built a Modular Monolith: features like Auth, Billing, and AI are isolated Go modules with explicit interfaces, but they deploy as a single binary.
This structure also made AI coding tools (Cursor, Claude Code) dramatically more effective. Because every module has strict boundaries, the AI knows exactly where new code belongs and doesn't break other modules.
Full-stack, not just backend:
Backend: Go 1.25 + Gin + SQLC (type-safe SQL, no ORM) + PostgreSQL with pgvector
Frontend: Next.js 16 + React 19 + Tailwind + shadcn/ui
Communication: The frontend consumes a clean REST API. You can swap Next.js for any framework that speaks HTTP.
Infrastructure: Separate Dockerfiles for frontend and backend. Deploy together or apart.
What's pre-built:
The boring infrastructure is solved so you can focus on your actual product:
Auth + RBAC: Stytch B2B integration with Organizations, Teams, and Roles. Multi-tenant data isolation enforced at the query level.
Billing: Polar.sh as Merchant of Record. Handles subscriptions, invoices, and global tax/VAT. No Stripe webhook edge cases.
AI Pipeline: OpenAI RAG using pgvector. The retrieval service enforces strict context boundaries to minimize hallucinations.
OCR: Mistral integration for document extraction.
File Storage: Cloudflare R2 integration.
Each feature is a separate module. Don't need OCR? Remove it. Want Stripe instead of Polar? The billing interface is abstracted.
Real-world proof:
This isn't a template I made for GitHub stars. It's the exact code running apflow.co in production. When I added document OCR, I built it as a new module without touching Auth or Billing. The architecture held.
How to try it:
Clone the repo, read setup.md to check the prerequisite, run ./setup.sh, and you have a working B2B environment locally in minutes.
Feedback I want:
I'd appreciate feedback from Go developers on the module boundaries and cross-module interfaces. Also curious if anyone has suggestions for the Docker setup in production deployments.
GitHub: https://github.com/moasq/production-saas-starter
Live: https://apflow.co

value: Show HN: I open-sourced my Go and Next B2B SaaS Starter (deploy anywhere, MIT)


value: gokh

value: Show HN: NestScore – A property evaluation tool for London house hunters

author: gokh


value:
Hello HN, author here.
I built ZXC, a lossless compression library designed specifically for "Write Once, Read Many" scenarios (Game assets, Firmware, App bundles).
The Concept: Standard real-time codecs (LZ4, Snappy) are designed for symmetry. ZXC explores an asymmetric approach: it spends significantly more CPU cycles at compression time to generate a bitstream specifically structured for modern, wide-issue execution pipelines (maximizing Instruction Level Parallelism).
Performance (Verified via lzbench integration): ZXC was merged into the official lzbench repository monday.
ARM64 (Apple M2): 6,365 MB/s decoding (~40% faster than LZ4).
x86_64 (AMD EPYC 9554): At Level 5, ZXC achieves compression ratios comparable to zstd --fast  while maintaining nearly 2x the decompression speed (3.8 GB/s vs 2.0 GB/s).
Implementation Details:
Written in C99, zero internal allocation (caller provides buffers).
Designed for easy FFI (Go/Python/Rust bindings).
Validated via continuous fuzzing (ClusterFuzzLite) and static analysis.
PR for OSS-Fuzz integration is ready.
This is a v0.1.1 release. The bitstream format is stable but open to improvements based on feedback from the community.
https://github.com/hellobertrand/zxc

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