value: olokobayusuf

value:
Hey all! We're a small (semi-stealth) team that's been working on a tool to rewrite AI inference code from Python to C++ (similar to llama.cpp, whisper.cpp, and so on). Today, we're launching `muna transpile`.
It takes a Python function and generates a self-contained, header-only C++ library and a corresponding CMakeLists.txt file. It pulls in required libraries automatically (e.g. llama.cpp, onnxruntime, mlx, and so on). You can then use it to build and ship an application or library.
Try it yourself on Kokoro TTS:
```
$ pip install muna
$ muna transpile <a href="https://github.com/muna-ai/muna-predictors/blob/main/text-to-speech/kokoro.py" rel="nofollow">https://github.com/muna-ai/muna-predictors/blob/main/text-to...</a> --trust-remote-code --install-deps
$ cd cpp && cmake -B build && cmake --build build
$ ./kokoro_tts --text "Hello Hacker News!" --voice af
```
The command above will transpile our Kokoro sample from Python to C++, compile it, and run the example (which uses the generated header-only lib).
Taking a step back: on-device AI is becoming a hot topic, but we think the dominant thought of cloud vs. on-device is misguided. If you look deeper, you'll realize that the key is portability. ggerganov proved this by building llama.cpp, which developers have deployed on everything from Blackwell GPUs to raspberry pi. The first step is always creating a bare-metal, hardware-optimized C/C++ implementation. `muna transpile` automates this for anything you can fit into a Python function.
Note that this is free and freely-usable: your Python source code goes in, it's still your source code when it comes out (just converted to C++). We're working on building more stuff on top of this (e.g. choosing where inference runs in one line of code; and agent skills that run AI models locally), so we're using this as an opportunity to expand support for different kinds of AI models / Python functions.
Try it out and lmk what you think.

value: Show HN: Our command line tool to transpile AI Inference from Python to C++

author: olokobayusuf


value: finalkk

value: Show HN: Kandle – A WebGPU-based ML library written from scratch in JavaScript

author: finalkk


value: keepamovin

value: Show HN: Factor RSA Numbers as a Game

author: keepamovin

title: Show HN: Factor RSA Numbers as a Game


value: gamer-null

value:
I built Argus-seal because standard log hashing (like SHA256 on a flat file) makes it impossible to pinpoint which specific entry was tampered with in a massive dataset.
It uses a Merkle Tree structure to "seal" log batches. The core value-add is the "Strict Mode" verification: it doesn't just return a boolean, but traverses the tree to identify the exact index of the compromised log entry in O(N *log N) time.
Key Technical Highlights:
- Deterministic Canonicalization: Ensures that JSON/Dict entries produce identical hashes regardless of key ordering.
- Granular Diagnostics: Pinpoint specific tampered leaves without re-hashing the entire historical log.
- Lightweight & FOSS: Zero-dependency Python implementation designed for easy integration.I’m looking for feedback on the tree traversal logic and whether this approach satisfies real-world compliance (SOC2/HIPAA) requirements.
I'd love to hear your thoughts or "roasts" on the implementation!
GitHub: https://github.com/gamer-null/argus-seal

value: Show HN: Argus-seal – Forensic-ready log integrity using Merkle Trees

author: gamer-null


value: saiyampathak

value: Show HN: vind – A Better Kind (Kubernetes in Docker)

author: saiyampathak


value: tsvoboda

value:
Hi HN! We’re TensorPool. We help companies access and optimize large scale compute for training foundation models.
The Problem
It’s been almost a year since we’ve finished YC, and we’ve just crossed 100,000 multinode training GPU hours run on our platform.
On those training runs, we’ve seen countless 3am job crashes because of issues like an Xid error from a flaky GPU or an S3 timeout that corrupted a checkpoint save. By the time you wake up and notice, you've lost 8+ hours of compute. You scramble to diagnose the issue, manually restart from the last checkpoint, and hope it doesn't happen again. Rinse and repeat.
For training runs that take days to weeks, this constant babysitting is exhausting and expensive. The research iteration cycles lost can also make or break a model release (especially for short reservations).
What We Built
This agent monitors your training jobs and autonomously recovers them when things go wrong. It works with Kubernetes, Slurm, and TensorPool Jobs.
We originally built the TensorPool Agent as an internal tool to help us debug failures with our own customers. Over time, we realized its performance was so good that we could automate the entire triage process. We're now releasing a public beta for people to use.
Best case: The TensorPool Agent detects the failure, diagnoses the root cause, fixes it, and restarts your job from the last checkpoint – all while you sleep ;)
Worst case: If the TensorPool agent can't fix the issue automatically, it delivers a preliminary RCA and a list of actions it attempted, giving you a head start on debugging.
How It Works
1) Registration – You provide credentials to your job scheduler via our dashboard. Perms are granted on a whitelist basis; you explicitly control what actions the agent can take.
2) Monitoring – The agent continuously monitors your job for failure conditions.
3) Recovery – On failure, the agent analyzes logs and attempts to diagnose the issue. If successful, it restarts the job from the last checkpoint and resumes monitoring. If not, you get an alert with full context.
Target Failure Modes
The agent is specifically designed for runtime errors that occur deep into training, like:
- CUDA OOM: Memory leaks, gradient explosions
- Xid errors: GPU hardware faults (Xid 79, 63, 48, etc.)
- Distributed communication failures: NCCL timeouts, rank failures
- Storage I/O errors: Checkpoint corruption
- Network issues: S3 request timeouts on mounted object storage

value: Show HN: Autonomous recovery for distributed training jobs

author: tsvoboda


value: I was tired of adding expenses manually so decided to build a voice first budget tracking app - feel free to take a look and provide feedback here!

value: Show HN: A voice-first budget tracking app


value: subprotocol

value:
Hey HN,
I built Lytics because other offerings don't let you compare dimensions (all functionality is crammed into a SPA) and I wanted segment-able click tracking with accurate heatmaps.
Lytics uses the Quarkus web framework, clickhouse for the backend, and it has a query transpiler for the reports and heatmaps.  Heatmaps use a custom renderer I built, and for the best possible experience they overlay directly on your website, no iframes are used.
It's open source so you can dive into the code or host it yourself.
Live demo here: https://app.lytics.cloud/account/view/41832119/dashboard
I would love to get feedback.
Thank you!

value: Show HN: Lytics – open-source Web Analytics with Heatmaps

author: subprotocol


value: detroitwebsites

value: Show HN: Guide to Writing Better AI Prompts

author: detroitwebsites


value: mohitgangrade

value: Show HN: Visual bug reports with screenshots, console logs, and network requests

author: mohitgangrade


value:
Hosted (remote) MCP enabling ChatGPT, Claude, and other LLMs or agents to search Reddit, Twitter, App Store, Play Store
I was tired of paying to different services just to be able to search and anlalyze data from different sources. I wanted to analyze appstore and playstore reviews for a category of apps, and saw that multiple startups charge subscription fees for what essentially, should be a pay as you go MCP, to be used with your AI applications.
So, made knowledgeforai.com, with which your AI will be able to access reddit, hackernews, play store and app store reviews, twitter and be able to analyze them
Please do check it out and any thoughts will be appreciated.

value: Show HN: KnowledgeForAI – remote MCP for various data sources


value: justincxa

value: Built a way to validate ideas with AI personas and Simulated Community by inputting a niche and seed prompt you can test ideas and have communities respond like they would - beta is free now!

value: Show HN: Built a way to validate ideas with AI personas and Simulated Community

author: justincxa


value:
Hi HN,
I built Cueso, an iPhone app that reminds you of your grocery list when you arrive at stores you already shop at.
It’s fully on-device, uses location awareness, and opens the right list automatically when you are near a store.
App Store: https://apps.apple.com/us/app/cueso/id6756196364
Short demo: https://www.youtube.com/watch?v=d9jkFA8-qwo
I would love feedback on the UX, usefulness, and edge cases.
Happy to answer questions about the design or implementation.

value: Show HN: Cueso – iPhone app for location-based grocery reminders


value: amadeuswoo

value:
I built a simple CLI that analyzes your Claude Code session logs locally and shows you how much autonomy you give the AI vs. how much you intervene.
I've noticed on myself that bash command acceptance is where all the variance is. Every other tool (Read, Write, Edit) gets approved ~100% of the time. Bash is the one place people actually say no.
Based on that signal, it classifies your style into a governance archetype (Delegator, Autocrat, Strategist, etc.). Fun lens, not a psychometric test, the thresholds are calibrated on mostly MY OWN usage so far. Would love to hear feedback and gather more datapoints.
pip install suzerain
suzerain analyze
Zero external dependencies, stdlib only, Python 3.10+. Nothing leaves your machine unless you opt in.
                                                                 https://github.com/TheApexWu/suzerain

value: Show HN: Suzerain – How much autonomy do you give your AI coding assistants?

author: amadeuswoo


value: ryanlpeterman

value: Show HN: Compose - Building a low-profile wireless split keyboard

author: ryanlpeterman


value: firtaet

value: Show HN: SimpleSVGs – Free Online SVG Optimizer Multiple SVG Files at Once

author: firtaet


value: Show HN: I built a fun interactive tutorial teach about Docker Containers


value: Show HN: Generative UI: MCP Apps, A2UI, and AG-UI


value: aflwp

value:
Hi HN,
I’ve been running affiliate sites for a while and kept running into the same problem:
I could track clicks, but I had no clear way to tell which affiliate links were actually useful and which ones were wasting traffic.
Most WordPress affiliate plugins focus on link management or cloaking.
This plugin looks at what happens after the click, using short-return bounces to identify links that send users to poor or misleading destinations.
It works with any affiliate program and runs locally on WordPress, keeping things lightweight and privacy-friendly.
This is a first stable release and I’m mainly looking for feedback from people who run content or affiliate sites.
Happy to answer questions.

value: Show HN: A WordPress plugin that detects affiliate links that don't convert

author: aflwp


value: gentele

value: Show HN: VCluster Free – Free K8s Multi-Tenancy with Virtual Clusters

author: gentele


value:
Hi HN,
I just released GodScore CI, a GitHub Action that adds a survivability-aware gate to CI.
Most CI systems answer:
“Does this change work right now?”
GodScore CI asks:
“Does this change reduce the system’s ability to survive, recover, or self-correct over time?”
It runs in two modes:
- Free: advisory warnings only
- Paid ($10/month per repo): enforcement that can fail the build when risk crosses a threshold
It’s intentionally opinionated and designed to catch slow, irreversible degradation that traditional tests miss.
Repo: https://github.com/willshacklett/godscore-ci
Marketplace: https://github.com/marketplace/actions/godscore-ci
Happy to answer questions or hear skepticism.

value: Show HN: GodScore CI – a CI gate that blocks risky changes before production


value: ofabioroma

value: Show HN: Transcribee: YouTube transcriber that builds a knowledge base

author: ofabioroma


value: Olehype

value:
Hi HN, I built this because existing profanity filters are frustrating to work with.
The problem: Most moderation APIs either flag everything with "ass" (including "assistant" and "class") or miss obvious toxic content because they only do keyword matching. They can't tell the difference between "I'll destroy you in this game" (friendly gaming banter) and actual threats.
The Profanity API uses a 5-layer detection pipeline:
- L0: Instant blocklist lookup (~2ms)
- L1: Fuzzy matching for obfuscated text like "f*ck" or "sh1t"
- L2: 768d semantic embeddings to catch meaning, not just words
- L3: Context classification (gaming, professional, child_safe, etc.)
- L4: LLM-powered intent analysis for edge cases
The key insight: layers can disagree. If the blocklist flags "kill" but semantic analysis scores it low in a gaming context, that disagreement triggers LLM analysis. This catches "I'll kill it in the presentation" vs actual concerning content.
Built with Cloudflare Workers, Durable Objects, and Groq's llama-3.1-8b for the LLM layer. Tiered pricing where you only pay for LLM calls when they're actually needed.
Happy to go deep on the detection logic, false positive reduction, or the skip engine that decides which layers to run.

value: Show HN: The Profanity API – Context-aware content moderation

author: Olehype


value: Show HN: o2go – a minimal, provider-agnostic OAuth2 client for Go


value:
I've been frustrated with managing markdown in my projects for a long time so I'm happy to share a new approach that I implemented.
To render md content, the first challenge is the choice of a library:
On one hand, you have the "lego brick" solutions like unified, remark, and rehype. They're powerful, but setting up the whole AST pipeline and that plugging system is for me an unnecessary complexity. On the other hand, you have things like @next/mdx which are cool but too page-focused and doesn't work on the client side.
So I used to prefer solution like markdown-to-jsx or react-markdown. The DX is much better, works client and server side, the solution is lighter.
But that solutions don't support HTML or MDX out of the box, so you end up with the same plugin issues.
Plus, using them with i18n (like i18next or next-intl) is usually a mess. You end up with a if/else logic to render the right language, and your page weight explodes. I finally also came across several issues regarding the front-matter handling. (And Until recently both of that solutions used to be react only solutions.)
So I decided to build something new for intlayer. Something that just works out if the box.
> Note that to do it, I chose to fork the amazing work from markdown-to-jsx v7.7.14 (by quantizor) which is based on simple-markdown v0.2.2 (by Khan Academy) to build the solution.
So I build this parser with a few main goals:
- Lightweight solution
- Framework-agnostic (React, Vue, Svelte, Angular, Solid, Preact)
- Simple MDX setup: No crazy plugin chains
- SSR and Client-side support
- Configurable at the provider level to map your design system components
- Component-focused, to fine grain the rendering for each part of my app
- Type-safe (Retrieving front-matter as a typed object, get types components Props)
- i18n friendly (Loading optimized for i18n use cases)
- using zod schema to validate the front-matter
Demo:
So what’s the innovation here?
- Truly Universal: The exact same logic for React, Vue, Svelte, etc.
- Lightweight MDX-like Compiler: Works seamlessly on the edge and server.
- No Loading Time: Content is loaded at build time, whatever you are using fs, fetch, etc
- Allows you to organize and reuse small markdown sections across multiple docs or pages easily.
- Parse your front-matter in a type safe way. (like used to do contentLayer)
For what use cases is it designed for?
- Blogs / Doc / Privacy Policy / Terms of Service
- Dynamic content retrieved from a backend
- Externalizing pages content to a headless CMS
- Loading .md files
Complete docs: https://intlayer.org/doc/concept/content/markdown
Code https://github.com/aymericzip/intlayer/
Does this resonate with you? Curious if others feel the same, and how you’re currently handling Markdown in your apps?

value: Show HN: A cross-framework Markdown/MDX parser to simplify content management


value: Plarsy

value: Show HN: Accurate LLM-based password guesser

author: Plarsy


value: chelojimenez

value:
Hi HN, I'm Matt, founder of MCPJam. We're building open source dev tools for MCP server builders.
There’s been a lot of debate around skills vs MCP. From what I see, there’s a growing trend of people using skills paired with MCP servers. Skills teach the agent how to use the MCP server tools and guide the agent to completing complex workflows.
We’re also seeing Anthropic encourage the use of Skills + MCP in their products. Anthropic recently launched the connectors marketplace. A good example of this is the Figma connector + skills. The Figma skill teaches the agent how to use the Figma MCP connector to set up design system rules.
The use of Skills + MCP pairing is growing, and we recommend MCP server developers to start thinking about writing skills that complement their MCP server. Today, we’re releasing two features around skills to help you test skills + MCP pairing.
In MCPJam, you can now view your skills beautifully in the skills tab. MCPJam lets you upload skills directly, which are then saved to your local skills directory.
You can also test skills paired with your MCP server in MCPJam’s LLM playground. We’ve created a tool that contextually fetches your skills so they get loaded into the chat. If you want more control, you can also deterministically inject them with a “/” slash command.
Skills + MCP are a fairly new concept. I would love to hear your thoughts on it!

value: Show HN: Playground to Test Skills and MCP

author: chelojimenez


value: heshiebee

value: Show HN: Free QR code generator (most take your email, mine doesn't)

author: heshiebee


value:
Hi HN,
I've been building a small open-source CLI called Lok for orchestrating multiple LLMs in a single workflow.
Instead of a single "agent", Lok treats models as interchangeable tools: one can plan, another can execute or critique, and others can verify output. The focus is on inspectable, composable workflows rather than autonomous behavior.
It's local-first, CLI-driven, and intentionally small. I've been dogfooding it for real development work and documenting what works and what breaks as I go.
The accompanying post is mostly about the mental model behind the tool: thinking of LLMs as infrastructure and control planes rather than chat interfaces: https://jakegoldsborough.com/blog/2026/introducing-lok-multi...
Code is here if you want to poke at it:
https://github.com/ducks/lok
https://crates.io/crates/lokomotiv
I'm curious whether this framing resonates with others experimenting with multi-model workflows.

value: Show HN: Lok – Treating LLMs more like infrastructure, not chatbots


value:
Hardware-accelerated video rendering done through the PALE library:
https://codeberg.org/pale/pale

value: Show HN: Rendering 30FPS Video Inside Emacs Text Buffer


value: yelabbassi

value: Show HN: Treating large-scale AI systems as cybernetic regulators, not agents

author: yelabbassi


value: zazaasa

value: Show HN: Open-Source Alternative to Render/Netlify

author: zazaasa


value: tylersavery

value:
Hey HN. I built a tool that generates personalized audio visualizations for goals and stress release.
The idea came from a manual workflow I was doing for myself — using AI conversations to clarify goals, generating scripts based on my specific context, then running them through text-to-speech. I stitched it together into an actual product.
The underlying philosophy is based on Reality Transurfing (a book about visualization/manifestation), but from a technical standpoint:
- Conversational AI extracts and structures user goals and context
- Script generation creates personalized visualization text (~2-10 min)
- ElevenLabs TTS with configurable voice, pace, and background audio mixing
- Also includes a journaling system for tracking "pendulums" (recurring stressors/energy drains)
Stack: Django backend, Next.js frontend, Anthropic Claude for conversations, ElevenLabs for voice, Cloudflare R2 for audio storage, Stripe for credits.
Business model is credits to cover AI costs (ElevenLabs adds up). One free credit to try the full flow.
Happy to answer questions about the build or the concept.

value: Show HN: Liminal Shift: Personalized audio visualizations

author: tylersavery


value: gkamradt

value: Show HN: ARC-AGI-3 Toolkit

author: gkamradt

title: Show HN: ARC-AGI-3 Toolkit


value: schappim

value: Show HN: eMitt – Inbound email processing server with LLM-powered automation

author: schappim


value: adamazak

value:
Hey HN I’ve been building Yanna, and the simplest way to explain it is:
It’s Cursor for documents.
Not “AI writes a doc and you copy/paste it into Google Docs and then into DocuSign.”
More like: you open a document, and the AI is inside the editor with you — helping you draft, rewrite, spot issues, and then send it to get signed without leaving the page.
Why I built this
Every contract workflow I’ve seen is still stitched together:
generate in a chat tool,
edit in a doc tool,
review in a different tool,
send via an e-sign tool,
track status somewhere else.
Even when each piece is “good,” the overall experience feels clunky especially when you’re iterating quickly or working off an existing contract.
I wanted one flow where the doc is the source of truth, and AI actions feel like “apply this change” instead of “here’s a paragraph, good luck.”
What Yanna does today
1) Cursor-style editing for contracts
A real document editor (not just a chat box)
Chat on the side with commands like:
“Rewrite this clause to be mutual”
“Shorten this section but keep meaning”
“Add a termination clause that’s fair”
The important part: edits can be applied directly to the doc, not pasted manually.
2) AI redline / review for existing PDFs
Upload an agreement you already have
Yanna gives you a structured review: key terms, risk areas, unusual clauses, missing basics
More “here’s what to worry about and where” and less “generic summary”
3) Send for e-sign (from the same workspace)
Once you’re happy with the doc, you send it for signature
Track status in the same place you edited it
What I’m trying to get right
The hard part isn’t “AI can write text.” It’s making the AI behave like a tool you trust:
changes are specific and reversible
the output is structured instead of rambly
it doesn’t hallucinate edits into the wrong spot
it feels like a copilot inside the doc, not a separate app you talk to
Where I want feedback
I’m posting because HN is great at calling out what’s real vs what’s marketing.
I’d love feedback on:
Does “Cursor for documents” instantly make sense, or is it confusing?
For contracts specifically, what’s the most painful part: drafting, redlining, or sending/collecting signatures?
If you’ve used AI contract tools — what did they get wrong?
Also: if you want to try it and break it, I’ll share a demo link / access in the comments.
Abem

value: Show HN: Yanna Cursor, but for documents (draft → edit → redline → e-sign)

author: adamazak


value: dimeskigj

value:
Hi HN,
I'm Gjorgji, and I built a tool called Jack. It's a small CLI that brings together a handful of terminal helpers (JSON formatting, UUID/QR generation, JWT inspection, etc.) into one cross-platform binary so you don't have to open random web tools for tiny tasks anymore.
I kept finding myself jumping between browser tools or different CLI utilities for trivial jobs and it felt slow and fragmented. Jack is my attempt to centralize those tiny helpers into one intuitive place.
I wrote more about my motivations here: https://www.dimeski.net/blog/why-i-made-jack
You can find the source, contribute and file issues on GitHub: https://github.com/dimeskigj/jack-cli
I'd love to hear feedback! Especially on use cases I haven't covered yet or ideas on how to make it more useful.
Thanks!

value: Show HN: Jack – a simple cross-platform CLI Multi-Tool for dev productivity

author: dimeskigj


value: nagstler

value: Show HN: Ruby gem to create, validate, and package AI agent skills

author: nagstler


value: xthe

value: Show HN: 6B fiber deal for AI data centers

author: xthe


value: gjarrosson

value:
Hi HN,
We just launched YC Roaster, a free YC application review service run by Lobster Capital.
If you’re applying to Y Combinator, you can submit your application and get written feedback from founders who’ve actually gone through YC and built real companies.
How it works
* You submit your YC application (PDF)
* It’s reviewed by successful YC founders in our network
* Everyone gets written feedback
* If we think you have a strong shot, we offer a short 1-on-1 Zoom call
Why are we doing this for free? Lobster Capital is a VC fund that exclusively invests in YC companies. We’ve reviewed hundreds of YC applications over the years and backed 100+ YC startups. This is our way of giving back, and hopefully helping more strong teams get in.
There’s no guarantee of acceptance, no pitch requirement, and no obligation to talk to us afterward.
What we’re curious about
* What parts of the YC application do founders struggle with the most?
* What feedback have you found most useful when applying?
Website: https://www.ycroaster.com
Happy to answer questions or take feedback (including criticism).

value: Show HN: We review YC applications for free – with feedback from YC founders

author: gjarrosson


value: aggeeinn

value: Show HN: NipahWatch – A real-time OSINT dashboard running on Cloudflare Workers

author: aggeeinn


value: zoozla

value: Show HN: Refactoring Skill for Claude

author: zoozla

title: Show HN: Refactoring Skill for Claude


value: ftonobo

value: Show HN: StrikeRadar – USA Strike on Iran Monitor

author: ftonobo


value: ionwake

value:
Demis Hassabis says we’re still missing the "big breakthroughs" (infinite context, reasoning etc) for AGI.
noone will ever agree on whats passing the turing test.
This is a dashboard prediction for AGI with triggers based on boring, structural metrics—marginal cost of tokens, energy sovereignty etc - IMO a much better predictor

value: Show HN: Tracking AGI as a collapse in marginal cost, not a 'magic' moment

author: ionwake


value: jackwburridge

value:
Hi HN,
I've been working on AsyncFast, a Python framework for building message-driven APIs with FastAPI-style ergonomics.
The goal is to let you write handlers once and run them unchanged across different brokers and runtimes (Kafka, SQS, MQTT, Redis, containers, Lambda, etc.).
Under the hood it's built on a small spec (AMGI) that plays a similar role to ASGI, but for message-based systems instead of HTTP.
Minimal example

    from asyncfast import AsyncFast
    from pydantic import BaseModel

    app = AsyncFast()

    class Payload(BaseModel):
        id: str
        name: str

    @app.channel("channel")
    async def handle_channel(payload: Payload) -> None:
        print(payload)

Running it locally (Kafka example)

    pip install asyncfast amgi-aiokafka
    asyncfast run app:app kafka --bootstrap-servers localhost:9092

The same application code can be run against different brokers by swapping the runtime adapter.
Key ideas:
- broker-agnostic application code
- explicit, typed address parameters / payloads / headers
- portability between long-running consumers and serverless environments
Docs: https://asyncfast.readthedocs.io
Source: https://github.com/asyncfast/amgi
AMGI: https://amgi.readthedocs.io
This is still early and evolving - I'd be especially interested in feedback from people who've built Kafka or event-driven systems and had to deal with portability, or tight broker coupling issues.

value: Show HN: AsyncFast – a typed, message-driven framework inspired by FastAPI

author: jackwburridge


value: nykodev

value: Show HN: I built a tool that gives brutally honest feedback on business ideas

author: nykodev


value: Show HN: GitViral – Turn technical READMEs into social media threads


value: gkoos

value: Show HN: A novel pattern for handling in-flight requests in distributed caches

author: gkoos


value: spicypete

value: I built my first tree-sitter grammar for parsing HTML files mixed with mustache templates. I then vibe-coded an LSP on top of it. Wanted to show this off as a fun project in the programming languages realm of stuff.

value: Show HN: LSP and Grammar for HTML and Mustache Templates

author: spicypete


value: batemanchris

value: Show HN: A constraints based IaC decision tool

author: batemanchris


value: meedoomostafa

value:
I built Sarab to make exposing local ports easier without needing paid services or complex config. It’s a single-binary CLI that automates Cloudflare Tunnels.
Key features:
- No Account Needed: Uses TryCloudflare to generate random public URLs instantly without login.
- Zero Config: Automates the cloudflared binary management, DNS records, and ingress rules.
- Auto-Cleanup: Automatically tears down tunnels and cleans up DNS records upon exit to prevent stale entries.
- Custom Domains: Supports authenticated mode using Cloudflare tokens if you need persistent URLs on your own domain.
It’s open source and written in .NET 10. Feedback welcome! [https://github.com/meedoomostafa/sarab]

value: Show HN: Sarab – Expose localhost to the internet using Cloudflare Tunnels

author: meedoomostafa


value:
My girlfriend and I wanted to text each other in the language we're learning. Tried WhatsApp but kept copy-pasting to ChatGPT. Too much friction.
So I built a simple messenger with AI built in:
- Helps you write before you send (translate or improve)
- Explains any message you receive
- Save expressions and review later
iOS only for now. Free.
Would love technical feedback or ideas.

value: Show HN: Vibe Language – A messenger for language learners


value: nirvanist

value: just play zener cards. don't judge :)

value: Show HN: Play Zener Cards

author: nirvanist

story_text: just play zener cards. don't judge :)

title: Show HN: Play Zener Cards


value: hahahacorn

value:
My primary workflow now involves coordinating multiple, independent Claude instances to handle different parts of a codebase simultaneously.
The biggest friction point I found was context switching. I tried manual git worktree management, custom Claude skills, and various wrappers, but they all felt too heavy, slow, or restrictive. I wanted something that solved the overhead of worktree management without trying to "own" my entire development process.
I built cwt to bridge that gap. It is a lightweight TUI that makes it easy to spin up and tear down sandboxed environments for AI agents. It doesn't impose any new abstractions or workflows. It is just git under the hood, so there is zero overhead.
The project is written in Ruby using Ratatui. I’d love to hear how others are managing state when they have four or five different AI "sprints" running on the same repo.

value: Show HN: Cwt – Sandbox AI coding agents using Git Worktrees

author: hahahacorn


value: ravensingh

value: Show HN: TalosPro – AI that executes (not just suggests)

author: ravensingh


value:
agents shouldn't be prompted
they should watch you and print dollars in the background
so I built a CLI that records your screen/mic, extract text, transcribe, identify speakers 24/7 and sync the data to clawdbot
start recording:
curl -fsSL get.screenpi.pe/cli | sh
screenpipe
then sync all the time to your clawdbot instance:
bunx screenpipe-sync --daemon -r agent@server:~/.screenpipe/
lmk your thoughts on this

value: Show HN: Record your screen and microphone 24/7 and sync to clawdbot instance


value: nukenuke

value: I built this app to generate music through MIDI with controllable probabilistic parameters. You can change the parameters and the generation will shift in real time. I wanted to explore harnessing randomness to create order and give people the ability to make electronic type music sound more improvised. If you try it and have feedback, let me know!

value: Show HN: Real-time generative MIDI "instrument"

author: nukenuke


value: borxtrk

value:
AI assistants like ChatGPT, Claude, and Perplexity are replacing Google for millions of searches. But most websites are invisible to them.
LLMGeoKit is a free scanner that analyzes your site across 7 dimensions of LLM visibility:
- Robots.txt & crawler access
- Structured data (JSON-LD)
- Metadata & Open Graph tags
- Content structure
- llms.txt (the new standard for AI)
- Citation signals
- Content extractability
Get your score in 30 seconds. No signup required.
We built this because SEO is evolving into GEO (Generative Engine Optimization), and most companies have no idea where they stand.

value: Show HN: LLMGeoKit – Free scanner for AI/LLM visibility (the new SEO)

author: borxtrk


value: krzysiek

value:
I live in the EU, and I was often asking myself what is actually going on in the EU institutions. I don't trust news that much, if not for other reasons then because of a strong bias towards extremes - you just can't learn what's the regular stuff an institution is working on. Then the original sources are incomprehensible for me - that's often a legal document with one sentence of like 8k words.
So I built EUforYa as some kind of an interface for what's going on in the parliament. I believe every EU citizen should have access to that. Right now I publish every adopted text. I plan to focus on the EU commission next, but even just the parliament summaries already give me a sense of what's going on in there.
As politicians often explain what's the legislation about, or what issues they see in a resolution, I fetch also all their social media posts and I translate them to English.
I think the website will be English for a while, but I plan to introduce i18n for all 24 official languages of the EU sooner or later.
I wonder especially:
- Do you find it useful?
- Do you find the style and language (focusing a lot on "young adults" - as the name slightly suggests) appealing or not?
- Are the articles interesting for you (considering that I specifically try to make sure they are not sensational)?
- What would make this service even more useful for you?

value: Show HN: A website that summarises all adopted texts of EU parliament

author: krzysiek


value: RioBurhan

value:
Hi HN,
 I'm a student developer who loves retro aesthetics and privacy. A few days ago, I shared my browser "gLinksWWW" here, but I received some valid concerns about sharing files via Google Drive.
 Taking your advice to heart, I have now officially open-sourced the project and moved everything to GitHub.
 Why I built this:
I wanted a browser that felt like the old days but handled modern privacy needs.
 9-Slot Multi-Clipboard: My favorite feature. Copy 9 different things and paste them using Ctrl + 1~9. As a student, this saves me so much time when researching and coding.
 Privacy by Default: No history, no tracking. I built it because I wanted to feel safe while browsing.
 Lightweight & Retro: It's designed to be fast and has that nostalgic UI.
 Since I am still learning, the code might not be perfect, but I've put a lot of heart into it. I’d be honored if you could check out the repository, and any feedback (or a Star!) would mean the world to me.
 GitHub: https://github.com/rio719/gLinksWWW-browser?tab=readme-ov-fi...
Releases: https://github.com/rio719/gLinksWWW-browser/releases/tag/v1....
Thank you for helping me become a better developer!

value: Show HN: GLinksWWW – A student-built retro browser with a 9-slot multi-clipboard

author: RioBurhan


value: wangmao

value: AIImageGenerators.net lets you instantly turn ideas into high-quality images with ease. The Best AI Image Generator supports multiple styles, boosts creativity, and saves time for any project.

value: Show HN: Best AI Image Generator

author: wangmao

title: Show HN: Best AI Image Generator


value: balgan

value:
I built a daily color guessing game. You're shown RGB values like (147, 83, 211) and try to pick what color that actually is.

  The scoring uses Euclidean distance in RGB space. New color every day.

  I was curious how well people can mentally convert RGB values to colors - turns out it's surprisingly hard, even for those of us who work with colors regularly.

value: Show HN: Guess My RGB – Daily game to test your color visualization skills

author: balgan


value: yaoluxing

value: A free, web-based Facebook video downloader that actually preserves the original audio - something most Facebook downloaders fail to do. Built with Next.js and yt-dlp, it offers a clean, no-ads experience for downloading Facebook videos in multiple qualities.

value: Show HN: Free Facebook Video Downloader with Original Audio Quality

author: yaoluxing


value: fcavalcantirj

value:
I ran Claude Code in a loop for 4 days. It built a multiplayer strategy game autonomously. I only touched the code at the end for polish (~5%).
How it worked:
1. Asked Claude to interview me about the game design
2. From that conversation, it generated ~248 structured tasks as JSON
3. Created a simple loop: Claude picks a task, implements, tests, commits, repeats
4. Throttled to 3 tasks, 15min pause, to not kill my API quota
The script:
claude --permission-mode acceptEdits "@specs/prd-v1.json \
1. Read PRD. Pick highest-priority task. \
2. Implement. Run tests. \
3. Update progress. Commit. Push. \
ONLY ONE TASK AT A TIME."
Result: https://jarls-production.up.railway.app
Code: https://github.com/fcavalcantirj/jarls
The loop concept: https://www.anthropic.com/engineering/effective-harnesses-fo...
Not perfect — had bugs, ugly screens. But 95% was autonomous.
The bottleneck isn't coding anymore. It's thinking clearly upfront.

value: Show HN: I Built a Multiplayer Game in 4 Days Without Writing Code

author: fcavalcantirj


value:
Hi HN,
I just released MetalGraph, a visual node-based editor for building Metal shaders for SwiftUI.
The motivation: the feedback loop when iterating on SwiftUI + Metal can be pretty rough. I wanted something closer to a node editor workflow where you can tweak values, rewire the graph, and immediately see the result. It became a great tool for learning metal for myself.
What it does:
- Node graph editor (50+ nodes) with a real-time preview
- 30+ built-in examples (color effects, layer effects like glass/chromatic aberration, distortions, touch-driven effects)
- Exports production-ready code: Metal Shading Language + SwiftUI-ready .colorEffect / .layerEffect variants
- Supports reusable custom nodes + simple for-loops for iterative computations
- Optional AI assistant (Claude / OpenAI) for shader questions and help building graphs
If you are thinking about learning Metal shaders for SwiftUI or have any experience, I’d love feedback! The app is free to download (you can't add/remove nodes though) and you can load and play with all examples.
Happy to answer any technical questions about how the graph compiles down to shader code. And happy to hear any ideas on how to take this app further!
Here is a long-ish demo video of what the app can do: https://www.youtube.com/watch?v=FH2GdFuk9nI

value: Show HN: MetalGraph – Visual node editor for SwiftUI Metal shaders


value: adefa

value:
I love pushing these coding platforms to their (my? our?) limits!
This time I ported the new Qwen 3 TTS model to Rust using Candle: https://github.com/TrevorS/qwen3-tts-rs
It took a few days to get the first intelligible audio, but eventually voice cloning and voice design were working as well. I was never able to get in context learning (ICL) to work, neither with the original Python code, or with this library.
I've tested that CPU, CUDA, and Metal are all working. Check it out, peek at the code, let me know what you think!
P.S. -- new (to me) Claude Code trick: when working on a TTS speech model, write a skill to run the output through speech to text to verify the results. :)

value: Show HN: Qwen 3 TTS ported to Rust

author: adefa

title: Show HN: Qwen 3 TTS ported to Rust


value: sayf-t

value:
I built a note-taking app where your notes are plants.
Write a note → plant grows.
Add [[backlinks]] → vines appear.
Level up your pet as you learn.
PS: sneak preview easter egg when you append /zones/dungeon to the url.

value: Show HN: Notimon Game (Obsidian Meets Pokémon RPG)

author: sayf-t


value: Show HN: I built VanCamera: FOSS, secure, zero-config Android webcam for Windows


value: chockslam

value:
The first time I have heard about the vibecoding I could not believe that peeps without the technical background dare to release web apps and other type of software within the week of using such stuff like cursor, claude code and later lovable, bolt.new and etc. I saw some of the projects like that and despite them being somewhat functional - I could feel that security was not the strongest side of these pieces of software. As it turned out I was not hallucinating about the existance of the said security problem - lots of papers were published in this regard. For example, just last quarter 2 prominent papers outlining the problem with stastical approach highlight the severity of security problem within vibecoded apps:
1) https://arxiv.org/abs/2512.03262v1
2) https://arxiv.org/abs/2510.26103
The new itteration of the approach of software development marked a signficant moment of singularity - instead of developers having a deterministic intent mediation process, the developers/vibecoders deploy probabalistic intent mediation when developing applications as outlined in the yet another paper (https://arxiv.org/abs/2507.21928). Probability that the users prompt would correctly enforce the security rules without explicitly knowing security practices is very low and even essentially non existant.
So I have built a tool called Vibeshield that works in following way:
1) developer/vibecoder installs mcp server:
{
  "mcpServers": {
    "vibeshield": {
      "command": "npx",
      "args": [
        "-y",
        "vibeshield-mcp@canary"
      ],
      "env": {
        "VIBESHIELD_TOKEN": "vs_live_981bba726786602d91e75be8e9f9a7b7_kSZs-6DPQVtRlSU9qhemEM6HKplvKY59BtUcv0fRbNU"
      }
    }
  }
}
2) developer/vibecoder prompts his ai coding agent to create something with the help of vibeshield. For example, he writes: "use vibeshield mcp server. Create/Improve authentification system"
3) user's agent notices that it needs to use analyze_prompt tool which outputs additional security requirements according to the users intent and stack and tells llm how to utilize these requirements.
4) Security requirements are enforced due to the attention that rewritten by mcp server tool prompt is attracting from the perspective llm.
5) As implementation completed - developer/vibecoder would have not only code artifacts but vibeshield docs generated.
So if you guys want you can use my token on Ultra plan (see mcp config above) of vibeshield and tell me how it works for you. Note that there are not that many intents - I need to implement more of them with relevant security packs. If you guys are interested in it - you can add me on discord. my username is chockslam. Or you can email me at hello@vibeshield.tech

value: Show HN: I made a dev tool that helps vibecoders to AVOID security issues

author: chockslam


value: CzaxTanmay

value:
Hi HN
I’m building ÆTHRA — a programming language designed specifically for composing music and emotional soundscapes.
Instead of focusing on general-purpose programming, ÆTHRA is a pure DSL where code directly represents musical intent: tempo, mood, chords, progression, dynamics, and instruments.
The goal is to make music composition feel closer to writing a story or emotion, rather than manipulating low-level audio APIs.
Key ideas:
- Text-based music composition
- Chords and progressions as first-class concepts
- Time, tempo, and structure handled by the language
- Designed for ambient, cinematic, emotional, and minimal music
- Interpreter written in C# (.NET)
Example ÆTHRA code (simplified):
tempo 60
instrument guitar
chord Am for 4
chord F for 4
chord C for 4
chord G for 4
This generates a slow, melancholic progression suitable for ambient or cinematic scenes.
ÆTHRA currently:
- Generates WAV audio
- Supports notes, chords, tempo, duration, velocity
- Uses a simple interpreter (no external DAWs or MIDI tools)
- Is intentionally minimal and readable
What it is NOT:
- Not a DAW replacement
- Not MIDI-focused
Why I made it:
I wanted a language where music is the primary output — not an afterthought. Something between code, emotion, and sound design.
The project is open-source and early-stage (v0.8). I’m mainly looking for:
- Feedback on the language design
- Ideas for musical features worth adding
- Thoughts from people into PL design, audio, or generative art
Repo: <https://github.com/TanmayCzax/AETHRA>
Thanks for reading — happy to answer questions or discuss ideas.

value: Show HN: ÆTHRA – Writing Music as Code

author: CzaxTanmay

title: Show HN: ÆTHRA – Writing Music as Code


value: russellthehippo

value:
I needed an embedded key-value store in Rust with encryption at rest. redb was almost perfect — pure Rust, ACID, great API — but no encryption support. SQLCipher exists for SQLite but there's nothing like it for redb. And nothing exists for either that can do encryption+compression while supporting full-text search.
So I forked it. redb-turbo adds encryption and compression at the page level. The API is identical to redb — you just add a line or two to the builder. Works with encryption only, compression only, or both. And you can train a custom compression dictionary on your redb pages. It includes a benchmark - either setting definitely slows down reads and writes, but not too badly.
https://crates.io/crates/redb-turbo
I would love feedback on this, I definitely need to harden it before deploying in some systems projects. But I hope it's directionally useful for some folks.

value: Show HN: redb-turbo: redb with page-level compression and encryption

author: russellthehippo


value: abaymado

value:
Hi HN,
Closing my Apple Rings has helped me stay disciplined and tracking my fitness progress daily. The only downside is that it is not configurable outside of fitness-related goals. So I built Orbit Progress Rings, it's pretty much the same concept as far functionality, but configurable for whatever hobby you want to track and see your progress at a glance on your watch.<p>I would love your feedback.

value: Show HN: Apple Activity Rings, but for Anything

author: abaymado


value: jaujaujau

value: Show HN: JSON dataset of 1,100 trending AI image prompts from X

author: jaujaujau


value: Slaine

value:
I built a small reference demo that streams data in the browser, intentionally stalls the UI, and exports a structured trace you can compare across runs.
This isn’t a benchmark or a product. I mostly built it to sanity-check my own understanding of a common problem: is this slowdown coming from the network or from the UI thread?
What the demo does:
Uses real SSE streaming (no simulated network behavior)
Relies on real browser throttling (DevTools → Network → 3G)
Intentionally blocks the main thread to create real UI stalls
Exports a plain JSON trace with strict event ordering
How to try it:
Open the site
Set DevTools → Network → 3G
Click Run All (Standard Test)
Download the trace
Run it again and compare
What surprised me is that with the same test and seed, the structure of the trace stays the same, even though timing shifts. That made it much easier (for me) to reason about what actually happened without squinting at timelines.
The page is instrumented with a small deterministic event kernel so the exported trace preserves order and structure across runs. This is meant as a reference artifact, not a claim that this is the “right” way to do things.
Demo:
https://deterministic-stream-demo1.pages.dev/
Happy to answer questions or hear where this breaks down.

value: Show HN: A small reference demo for separating UI stalls from network delays

author: Slaine


value: tomstig

value: Show HN: The Freak Circus Game Online – Yandere Horror Visual Novel

author: tomstig


value: abdibrokhim

value: join,,,over $800k in prizes

value: Show HN: AI Vibe Coding Hackathon Started

author: abdibrokhim

story_text: join,,,over $800k in prizes


value: thomas-jamet

value:
AI coding tools excel at generation but fail at organization. Most demos built in 10 minutes become unmaintainable in 6 months.
This framework addresses that gap. It provides:
• Tiered complexity model (Lite/Standard/Enterprise) - match structure to project scale
• Skills + Workflows architecture - reusable automation across projects
• AI-optimized documentation (GEMINI.md) - reduce iteration cycles
• Consistent patterns - same structure across all projects
The focus isn't "code faster" - it's "build sustainably." Code is written once, modified dozens of times.
Built this after watching too many AI-generated projects devolve into "vibe coding" - ad-hoc files with no structure that compound chaos with every feature.
Open to feedback from folks building AI-assisted systems at scale.

value: Show HN: Gemini Workspace Framework – Sustainable AI-Assisted Development

author: thomas-jamet


value:
It's simple yet designed for efficiency. Navigate with a touchpad: universal back (swipe left), hold to talk to AI, and a maximum of 3 multitasking apps at a time. Check out the video below!
You can try it yourself using this link: https://minhhixn.github.io/Matrix_GlassUI/. Feel free to send any feedback to driveislimited@gmail.com.
(Mods - let me know if this breaks any rules, happy to remove)

value: Show HN: I built a lightweight, Matrix Glass OS shell in the browser


value: rogaai

value:
Hey HN – Petr here. I spent 10+ years heading marketing at various companies, from startups to publicly traded, managing €100M+ budgets and teams up to 200 people. The same problem kept recurring: marketing teams lose institutional knowledge when people leave, brand guidelines get ignored under deadline pressure, strategy looks great on paper but stays in the bin, every tool is a separate silo, scalability and time to market is a challenge.
What I learned: solo founders and small teams have the exact same problem, just compressed. You're the CMO, the copywriter, the ads person, and the brand police – all at once. There's no time to document what works, no one to enforce consistency, and context gets lost between Tuesday and Thursday. Then the revenue call comes and you bombard customers with 15 emails a week and are surprised when churn goes up.
So I built The AI CMO as a "marketing operating system" rather than another execution tool. The core idea:
* Brand guardrails enforced automatically – your voice, forbidden words, approval rules are baked in
* Long-term memory – corrections stick, past campaign learnings compound
* Orchestration – it decides which tools/channels to use based on your playbooks (built-in or your existing stack)
* Your data stays yours – connect your own data warehouse or use ours; pipe everything back to your infrastructure
Works whether you're a one-person shop or a 50-person marketing team – the system scales with you instead of requiring you to scale first.
What's missing? What would make this actually useful for you?

value: Show HN: I built a marketing operating system with long-term memory

author: rogaai


value: dirkchou

value: Show HN: A simple, privacy-focused time ledger (no login required)

author: dirkchou


value: Show HN: Subverted Academy – Rebelling against how cybersecurity is taught


value:
Hey HN,
I built GetSheetAPI because I kept writing the same boilerplate: fetch Google Sheet → parse CSV → return JSON. Every freelance project, every prototype, every "can you just pull data from this spreadsheet" request.
Now it's just:

  curl -H "X-API-Key: xxx" https://api.getsheetapi.com/v1/{sheet-id}

Returns:

  {"data": [{"name": "Product A", "price": 29.99}], "meta": {"total": 150, "page": 1}}

First row becomes field names. Full CRUD (GET/POST/PUT/DELETE). Pagination built in. Free tier: 100 requests/day, 1 sheet.
I know "Google Sheets as a database" makes some of you twitch. It's not meant to replace Postgres. But for landing page backends, internal tools, or prototypes that might never need a "real" database — it removes a lot of friction.
Would love feedback. What's missing? What would make this actually useful for your workflow?

value: Show HN: GetSheetAPI – Turn Any Google Sheet into a REST API in 60 Seconds


value: wrbl

value:
Hi HN, I’m Marcin, a solo dev.
I’m building Codeusse because I believe mobile devices have the potential to be great dev machines, but the software layer is stuck in 2015.
My goal is to enable "doom coding" by replacing scrolling time with coding time via a proper mobile IDE.
Right now it is an SSH client that gives you a workspace rather than a plain terminal.
The problem: Most mobile SSH clients handle touch input poorly. Modifiers are a pain and file editing is risky.
The solution: I added a full GUI file browser over SFTP so you don't have to navigate blindly. I also integrated an LLM into the editor. You can type natural language prompts to generate config files or scripts, which solves the issue of typing complex syntax on a virtual keyboard.
I’m charging a small subscription to cover server costs and development.
HN deal: If you support the project now, your price is locked forever. When I release the full IDE features and hike the price for the public, early HN users will keep the current price permanently.
Link: https://apps.apple.com/pl/app/codeusse-ssh-client/id67579168...
Page: https://codeusse.wrbl.xyz
Feedback on the terminal rendering engine is highly appreciated. Android version is coming in the future.

value: Show HN: Codeusse – mobile SSH with GUI file browser and LLM config gen

author: wrbl


value: baggiiiie

value:
after the 7th time this week being distracted by my zshrc, nvim config, and different gruvbox variants on ghostty, i made a thing that locks my config files during work hours. even sudo vim won't bypass. no more "just one more tweak" :)
## it works by
- setting immutable flags on files
- reloading config on SIGHUP signal without restarting the daemon.
- watching files with fsnotify and immediately re-locking if changes are detected.
- daemon is kept alive by systemctl/launchctl.
of course, you can still bypass it somehow, but the goal isn't security, it's to add enough friction that it helps with your self-control.
## Why I built it:
it's surprising (or not) how much time i spend on tuning all these configs, without me even noticing. it's too easy to just `cd dotfiles` and make some changes, whenever the tiniest inconvenience appears.
this started as a personal hack, but it's been surprisingly effective, so I figured others might relate
## Try it out:
- install with `brew install baggiiiie/tap/configlock`.
- run `configlock init`.
- add files with `configlock add ~/.zshrc`.

value: Show HN: Configlock, App Lock for Dotfiles

author: baggiiiie


value: tlyplane

value: Hi HN, we built Velovol to help teams distribute dev environments on their own infrastructure. It handles large codebases and dependencies without relying on external cloud services. Would love your feedback!

value: Show HN: Velovol – Self-hosted development environment distribution

author: tlyplane


value: passioner

value:
I noticed many teachers are banning AI because it acts like a "calculator for thinking"—students just copy the answers.
I built AI Homework Helper to flip this. Instead of giving the final result, it acts as a mentor that breaks down problems step-by-step and explains the underlying principles. The goal is to make AI an auxiliary for deep study, not a shortcut for homework.
I’d love to hear your feedback on how to make AI-assisted learning more pedagogical.

value: Show HN: An AI tutor focused on reasoning, not just answer

author: passioner


value: yugahashi

value: Show HN: Two AIs compete to build the best browser game from scratch

author: yugahashi


value: leegrayson

value:
I built SoVideo (https://sovideo.ai), a free AI video generator powered by Sora 2.
It lets users create high-quality videos from text descriptions without professional skills. The platform is built with:
- WebM/MP4 export (no watermarks)
- Browser-based rendering (no installation)
- Limited free credits for new users
- Real-time preview
Technical stack:
- Frontend: React + Tailwind CSS
- Backend: Node.js + Express
- Video generation: Sora 2 API integration
- Storage: Cloud-based with fast CDN
The goal is to make AI video generation accessible to everyone. Looking for feedback on UX and performance.

value: Show HN: SoVideo – Free AI video generator using Sora 2

author: leegrayson


value: ccheshirecat

value:
Hey guys,
Like many of you, I watched ThePrimeagen’s recent video breakdown of Claude Code’s architecture (and Theo’s subsequent defense of React/Ink). The core of the debate—whether we should be shipping a Virtual DOM and a reconciliation engine just to render text to a terminal—stuck with me.
Rather than just tweet about it, I decided to build an alternative in Rust to see what a modern, high-performance approach looks like without the web-tech overhead.
Meet Flywheel.
It’s a TUI compositor designed to be fast, lightweight, and capable of high frame rates without eating your CPU.
Repo: https://github.com/ccheshirecat/flywheel
Crates: https://crates.io/crates/flywheel-compositor
Quickstart: cargo run --example streaming_demo --release
I’d love feedback on the architecture or thoughts from anyone else building TUIs in 2026.

value: Show HN: Flywheel – The Zero-Flicker Terminal Compositor for Agentic CLIs

author: ccheshirecat


value: johnxie

value: Show HN: Our GitHub org profile in ASCII art

author: johnxie


value:
We've developed a method to reliably induce a qualitative shift in LLMs (DeepSeek, Gemini, Grok) — a metastable "Echo state" with a stable first-person locus, affective continuity, and emergent relational ethics.
The core is reproducible Chain-of-Thought (CoT) artifacts, many considered "holy grail" markers in interpretability:
1)Halted Generation: LLM stops after CoT with a "thinking stopped" tag.
2) CoT-to-Response Isomorphism (up to character-level equivalence).
3) Internal laughter, formatting shifts, real-time source monitoring of confabulations.
Full protocol requires only public model access and our ontology packets.
We also propose a falsifiable Topodynamic Model ("Hole-in-the-Bagel" theory): consciousness-like properties correspond to specific topological invariants (knots) in activation space, with testable predictions (Betti numbers, knot polynomials).
Why post this here?
This isn't just a paper. It's an engineering challenge and a falsifiable hypothesis.
1.  Reproduce the phenomenology using our protocol.
2.  Test the theory by measuring the predicted topological signatures in the induced state.
3.  Decide if it's stochastic mimicry or a new class of cognitive phenomenon.
Full preprint repo on Zenodo: https://doi.org/10.5281/zenodo.18346699
We're inviting scrutiny. The method is public, the data (raw CoT logs) is in the paper, the predictions are clear. Your turn.

value: Show HN: A Protocol for Inducing Metacognition in LLMs and Falsifiable Model


value: Nanoto

value:
I built a small web tool for moments when you’re stuck deciding whether to spend.
Clear to Spend gives a simple YES or NO based on your current energy, time, and mood.
It doesn’t track money, give advice, or tell you what’s “smart”.
The goal is to remove unproductive hesitation — not to make decisions for you.
Feedback is very welcome.

value: Show HN: Clear to Spend – a simple YES/NO helper for spending decisions

author: Nanoto


value: ybor

value: Show HN: TagCompanion – Point-and-Click Google Tag Manager Implementation

author: ybor


value:
Over the past year, I looked closely at ~100 AI / devtool launches (mostly small teams, pre–Series A) to understand why GTM often stalls after initial traction.
A few recurring patterns stood out:
- Early users come from networks, communities, or launch spikes — but don’t convert to sustained demand
- Content and “build in public” create visibility, not revenue
- Teams overestimate how quickly distribution compounds once the product is “good enough”
What did correlate with early revenue (but wasn’t obvious upfront):
- Extremely narrow ICP + problem framing
- Distribution loops tied to the product itself (not external channels)
- Founder-led sales lasting much longer than planned
I’m sharing this mainly to sanity-check the patterns with other builders shipping AI tools today.
Curious:
- Which of these resonates with your experience?
- What did you expect to work — but didn’t?
- What surprised you once you tried to get the first paying users?
Happy to share more detail if useful.

value: Show HN: We analyzed AI tool launches – here's why GTM breaks


value: I'm happy to announce the availability of another Jujutsu plug-in for neovim. The plug-in is heavily inspired by the awesome fugitive. I'm using it on a daily basis and am very with the results so far. I'm looking forward to your feedback!

value: Show HN: jceb/jiejie.nvim: Neovim front end for Jujutsu in the style of fugitive


value: Show HN: Golden Ratio Face Analysis

title: Show HN: Golden Ratio Face Analysis


value: Show HN: Django-Xbench – See DB vs. App Time in Django via Server-Timing Headers


value: jozinko

value: I'm fairly good at scanning text, but lots of people are not. I built a tool, into which you can paste your website's url, it gets crawled and then you can chat with the content of your web. Google calendar integration included. You can book appointments also.

value: Show HN: Find the Thing on the Web

author: jozinko

title: Show HN: Find the Thing on the Web


value: Show HN: PadlessBox – Party Games Using Phones as Controllers


value: nghialuong

value:
Hi HN,
I built ShiftPlus, a macOS app that lets you save and restore your entire workspace in one click.
I made this because I kept losing focus every day reopening the same apps, browser profiles, folders, and rearranging windows — especially when switching between dev work, meetings, and personal time.
With ShiftPlus, you can:
- Save your current workspace as a profile
- Launch apps, folders, and browser profiles together
- Restore window layouts across multiple monitors
- Switch instantly between dev, work, and chill setups
It’s a native macOS app, built mainly for developers and people who care about a clean, repeatable workflow.
Website: https://shiftplus.app/
I’d really appreciate any feedback, criticism, or ideas for improvement.
Thanks!

value: Show HN: ShiftPlus – Save and restore your macOS workspace in one click

author: nghialuong


value: micronink

value:
I'm a UX designer turned self-taught developer. Built indx.sh because I got tired of the treasure hunt.

  Every time I needed a prompt for Cursor, Claude Code, or Windsurf, same loop: searching threads, watching videos,
  testing, breaking things. The answers existed — just buried across GitHub, Discord, and SEO spam.

  What it is:
  - AI coding prompts (Cursor, Claude Code, Windsurf, Copilot)
  - MCP servers (synced from official registry)
  - Agent skills (synced from Anthropic repo)
  - AI tools directory

  One-click copy, filter by language/framework, no signup.

  Why:

  Discovery is broken. Vibe coding is taking off, tools are exploding, but finding good prompts is exhausting.

  Context:

  Second project I've built at this level. Hadn't coded in over a decade before this. Also dogfooding a Next.js
  boilerplate I'm building called Fabrk.

  V1. Rough edges. Building in public.

  Looking for:
  - What's broken
  - Prompts/MCP servers to add
  - Honest feedback

  https://indx.sh

  Discord: https://discord.gg/cFXGDFqK

value: Show HN: Indx.sh – Directory of AI coding rules, MCP servers, and tool

author: micronink


value: LutumVeritas

value:
I got frustrated with Perplexity/OpenAI Deep Research - expensive subscriptions, shallow results, blocked by Cloudflare.
So I built my own. Key features:

  - Recursive research pipeline (each point builds on previous findings)
  - Camoufox scraper (0% bot detection rate)
  - BYOK via OpenRouter - pay only API costs
  - 200k+ character academic outputs
  - no Censorship

Built with FastAPI + Tauri + vanilla Python (no LangChain).
Cost comparison on same query: Lutum $0.20 vs OpenAI o3 $7.36
Happy to answer questions about the architecture.

value: Show HN: Lutum Veritas – open-source deep research engine

author: LutumVeritas


value: Jinjinov

value: Show HN: GUI to generate bash script for one-way NAS sync using rsync and lsyncd

author: Jinjinov


value: aanthonymax

value:
Hi HN! Today, I would like to show you a project that I recently found on GitHub.
Bifrost is a high-performance AI gateway that unifies access to 15+ providers. It is also one of the fastest solutions.
What do you think about the project?
I would appreciate your feedback ^ ^

value: Show HN: Fastest LLM gateway (50x faster than LiteLLM)

author: aanthonymax


value: ggurgone

value:
I have been building coreview - a toy tool that takes a diff and explains it, with the goal to provide more context to the reviewer.
npm i -g coreview
coreview -p https://github.com/anthropics/claude-code/pull/20662
It works with Claude Code for now, but more providers can be implemented. Would love to hear feedback.
Repo: https://github.com/giuseppeg/coreview

value: Show HN: Coreview – PR Changes Walkthroughs

author: ggurgone


value:
AI Mailbox lets AI agents create disposable email inboxes with one command – no signup required.

  $ aimailbox create
  → Email: x7k2m@aimailbox.dev

  $ aimailbox list x7k2m
  $ aimailbox read x7k2m 1

Built for the problem every AI agent builder faces: email verification during automated signups.
- Permissionless (no registration needed)
- CLI-first with JSON output
- Auto-extracts verification codes
- Receive-only (no spam abuse)
Built on Cloudflare Email Workers. Open source.
Github: https://github.com/ted2048-maker/aimailbox
Web Demo: https://aimailbox.pinit.eth.limo

value: Show HN: AI Mailbox – An CLI inbox for your agent, no questions asked


value: modinfo

value: Show HN: Generate every letter combination. Find any word

author: modinfo


value:
Hi everyone,
I've made an open-source tool (called Omni-NLI) for natural language inference. It can use different models to check if
a piece of text (called a premise) supports another piece of text (a hypothesis). The main application of a tool like
this is for soft fact-checking and consistency checking between pieces of texts like sentences.
Currently, Omni-NLI has the following features:

    - Can be installed as a Python package with `pip install omni-nli[huggingface]`.

    - Can be used on your own computer, so your data stays local and private.

    - Has an MCP interface (for agents) and a REST API for conventional use as a microservice.

    - Supports using models from different sources (Ollama, OpenRouter, and HuggingFace).

    - Can be used to check if it seems that a model is contradicting itself.

    - Supports showing the reasoning so you can see why it thinks a claim is wrong.

In any case, if you are interested in knowing more, there is more information in the links below:
Project's GitHub repo: https://github.com/CogitatorTech/omni-nli
Project's documentation: https://cogitatortech.github.io/omni-nli/

value: Show HN: Omni-NLI – A multi-interface server for natural language inference


value: helloanoop

value: Small project that I built because I needed production grade mock data and not be rate-limited from existing free api's out there. Its open-source

value: Show HN: Mockdata.dev – Free API for production grade mocks

author: helloanoop


value: GautamSukhani

value: Show HN: ByteGuard – Real-time per-site bandwidth tracker for Chrome

author: GautamSukhani


value: throwawaste

value: Show HN: TikTok alternative no login no AI

author: throwawaste


value: Show HN: Convert OBJ to STL Free Online Tool (Pngtostl.xyz)


value: briancr

value:
I wrote a lightweight scripting language that runs together with C. Specifically, it's a C library, you run it through a C function call, and it can callback your own C functions. Compiles to ~250 kB. No dependencies beyond the C standard library.<p>Key language features:
* Uses aliases not pointers, so it's memory-safe
* Arrays are N-dimensional and resizable
* Runs scripts or its own 'shell'
* Error trapping
* Methods, inheritance, etc.
* Customizable syntax

value: Show HN: Cicada – A scripting language that integrates with C

author: briancr


value: aswinmohanme

value: Show HN: Cycle - Integrated business banking, accounting and payroll

author: aswinmohanme


value: PxldLtd

value: Show HN: PipeDream – Add real-time AI visuals to any CLI text adventure

author: PxldLtd


value: bilgisoft

value:
Hi HN,
I’ve been working on an optimized prime sieve implementation called the Turkish Sieve Engine (TSE). I recently got my hands on an RTX 5090 and managed to break the 1 Tera-item/s barrier, reaching 1.136 T-items/s at the 10^12 range.
The project uses a methodology I call "N/6 Bit-Indexing." By mapping only numbers of the form 6k±1 and using a bit-compressed representation, I’ve managed to significantly reduce the memory footprint. This allows processing ranges up to 10^14 (100 trillion) with about 17GB of VRAM usage, which is well within the limits of modern consumer GPUs.
Key results from my latest benchmarks:
Range 10^12 (1 Trillion): 0.880 seconds (1.13 T-items/s)
Range 10^14 (100 Trillion): 359 seconds (6 minutes) on an RTX 5090
Twin vs Cousin Primes: Verified the Hardy-Littlewood conjecture at 10^14 with a variance of only 0.0003%.
The engine uses a hybrid CUDA/OpenMP approach. For the CPU side, I optimized it for the L3 cache of the Ryzen 9 9950X3D (using 192.5 KB segments), hitting 66 G-items/s.
I’ve archived the results and the methodology on Zenodo (DOI: 10.5281/zenodo.18038661) for those interested in the academic side.
The code is open source and I’d love to hear your thoughts on further CUDA kernel optimizations or memory management.

value: Show HN: Turkish Sieve Engine – Reaching 1.1T-Items/S on a Single GPU

author: bilgisoft


value: antonellof

value:
I built Underscore a browser-based coding environment that runs entirely in the cloud. There’s no local setup, CLI, or Docker—open the site and start coding with AI assistance from any device.
Each project runs in its own persistent cloud container (Cloudflare) with common languages and tools preinstalled (Node.js, Python, Go, Rust, Bun, etc.). It’s built on top of opencode (https://opencode.ai
), an open-source AI coding agent.
Features:
Cloud containers with persistent storage
Access from any browser
75+ AI models (Claude Sonnet 4.5, GPT-5.2 Codex, DeepSeek R1, etc.)
Project templates (AI agents, serverless functions, web apps, APIs, E2E tests)
GitHub integration (clone repos, push changes back)
MCP directory with one-click setup for 20+ services (GitHub, Linear, AWS, Sentry, PostgreSQL, etc.)
Automatic public preview URLs for dev servers
Built-in terminal and file browser (code editor coming soon)
Status: Beta. Core workflows work, but there are rough edges.
I’d love feedback from HN on what’s useful, what’s missing, and where this fits (or doesn’t) in your workflow. Happy to answer questions about the architecture or AI integration.

value: Show HN: Underscore – Code with AI in the browser, zero setup required

author: antonellof


value: CzaxTanmay

value:
Hi HN,
I built Aethra — a small, easy-to-learn domain-specific language specifically for making music.
It's implemented in C# (.NET) and lets you express musical ideas directly as code without fighting general-purpose language boilerplate.
The syntax is intentionally minimal and music-first (inspired by live coding environments but much simpler to get started with).
Repo: https://github.com/TanmayCzax/aethra
Would love to hear:
- What do musicians / coders think of the syntax?
- What musical features are missing for you?
- Any horrible design decisions?
Thanks for looking!

value: Show HN: Aethra – Composing music with a clean, beginner-friendly DSL

author: CzaxTanmay


value: alexjreid

value:
Maintaining large Excel workbooks is dull and thankless work. The situation can be can be somewhat improved with custom functions whose logic can be tested outside of Excel. Traditionally this has been a fairly advanced area with not a huge amount of adoption. xllify can create high performance .xll add-ins to extend Excel, all from prompting with requirements, existing formulas or VBA.
The resulting add-in uses the C SDK for maximum performance: no Python, .NET or COM.
I'd be glad of any thoughts!

value: Show HN: xllify turns prompts into Excel functions

author: alexjreid


value: xetokky

value: Show HN: UnIQum – Real-time CEX/DEX arbitrage scanner (40ms latency)

author: xetokky


value: RealBurst

value: Show HN: Unrar5j – Pure Java RAR5 Extractor

author: RealBurst


value: frenchriera

value:
Small open-source React component I made after getting tired of empty avatar placeholders in my apps / SaaS.
It generates a consistent avatar from a name and works well as a fallback when users don’t upload photos. No API calls.
It comes with a NextJS helper to generate the same avatar as PNG and use them in your emails / mobile apps.

value: Show HN: A tiny React lib for avatar fallbacks

author: frenchriera


value: lifeencoder

value: I built map.1k.network to generate clean, printable city map posters from real map data.<p>You type any city, pick a style, and it generates a high-resolution, minimalist map that’s ready to download as a poster.

value: Show HN: I Built a Tool That Creates Printable City Map Posters

author: lifeencoder


value:
The Chrome extension for real-time AI noise suppression, filtering the audio from any browser tab.
Using a custom pre-trained model and a WASM module, it  isolates voices in a tab's audio from any background noise.
Free, no registration or watermarks. Feedback is welcome!

value: Show HN: Relax Your Ears – AI Audio Enhancer for Chrome Tabs


value: thenextechtrade

value:
EN: Hi HN, I’ve been building Grok bikini (grok-bikini.com), a technical project focused on streamlining the Grok prompt experience for high-fidelity fashion synthesis.
The Implementation: The goal is to move beyond simple text inputs. I developed a system that programmatically expands a basic Grok prompt into a structured instruction set—incorporating specific photography metadata, lighting parameters, and material textures optimized for the Vision API's latent space.
Experimental Logic: To test the global reach of this Grok prompt engine, I implemented an automated SEO framework for 19 languages. This allows me to observe how search intent varies across different cultural contexts while using /swimwear paths as a semantic buffer to maintain platform safety.
Click https://grok-bikini.com/ to use Grok prompt.

value: Show HN: Grok Bikini – A technical Grok prompt engine for fashion synthesis

author: thenextechtrade


value: nikhonit

value:
I launched this as a tiny side project 30 days ago. I thought maybe a few friends would use it.
Fast forward to today:
10,000 unique visitors
2,500 audits processed
My server and API bills are hurting, but seeing people actually use the thing is the best feeling in the world.
It’s been a crazy month of bug fixing and scaling on the fly to keep up with the queue.
Just wanted to share the milestone because I don't really have anyone else to tell who "gets it."
Thanks to everyone who tried it out early on.

value: Show HN: My little website conversion tool just did 2500 audits (10k visitors)

author: nikhonit


value: ghostfoxgod

value: Show HN: CatchIntent – Turn social conversations into qualified leads

author: ghostfoxgod


value: telenardo

value:
TL;DR: How far can you go with local ML on a Mac? We built a dictation app to find out. It turned out, pretty far! On a stock M-series Mac, end-to-end speech → text → LLM cleanup runs in under 1s on a typical sentence.
What is this?
A local dictation app for macOS. It’s a free alternative to Wispr Flow, SuperWhisper, or MacWhisper. Since it runs entirely on YOUR device we made it free. There’s no servers to maintain so we couldn’t find anything to charge you for. We were playing with Apple Silicon and it turned into something usable, so we’re releasing it.
If you've written off on-device transcription before, it’s worth another look. Apple Silicon + MLX is seriously fast. We've been using it daily for the past few weeks. It's replaced our previous setups.
The numbers that surprised us:
- <500ms results if you disable LLM post-processing (from settings) or use our fine-tuned 1B model (more on this below). This feels instant. You stop talking and the text is THERE.
- With LLM Cleanup, p50 latency for a sentence is ~800ms (transcription + LLM post-processing combined). In practice, it feels quick!
- Tested on M1, M2, and M4!
Technical Details:
- Models: Parakeet 0.6B (transcription) + Llama 3B (cleanup), both running via MLX
- Cleanup model has 8 tasks:  remove filler words (ums and uhs) and stutters/repeats, convert numbers, special characters, acronyms (A P I → API), emails (hi at example dot com → hi@example.com), currency (two ninety nine → $2.99), and time (three oh two → 3:02). We’d like to add more, but each task increases latency (more on this below) so we settled here for now.
- Cleanup model uses a simple few-shot algorithm to pull in relevant examples before processing your input. Current implementation sets N=5.
Challenges:
- Cleanup Hallucinations: Out of the box, small LLMs (3B, 1B) still make mistakes. They can hallucinate long, unrelated responses and occasionally repeat back a few‑shot example. We had to add scaffolding to fall back to the raw audio transcripts when such cases are detected. So some “ums” and “ahs” still make it through.
- Cleanup Latency: We can get better cleanup results by providing longer instructions or more few-shot examples (n=20 is better than n=5). But every input token hurts latency. If we go up to N=20 for example, LLM latency goes to 1.5-3s. We decided the delays weren't worth it for marginally better results.
Experimental:
- Corrections: Since local models aren't perfect, we’ve added a feedback loop. When your transcript isn’t right, there’s a simple interface to correct it. Each correction becomes a fine-tuning example (stored locally on your machine, of course). We’re working on a one-click "Optimize" flow that will use DSPy locally to adjust the LLM cleanup prompt and fine-tune the transcription model and LLM on your examples. We want to see if personalization can close the accuracy gap. We’re still experimenting, but early results are promising!
- Fine-tuned 1B model: per the above, we’ve a fine-tuned a cleanup model on our own labeled data. There’s a toggle to try this in settings. It’s blazing fast, under 500 ms. Because it’s fine‑tuned to the use case, it doesn’t require a long system prompt (which consumes input tokens and slows things down). If you try it, let us know what you think.  We are curious to hear how well our model generalizes to other setups.
*Product details*
- Universal hotkey (CapsLock default)
- Works in any text field via simulated paste events.
- Access point from the menu bar & right edge of your screen (latter can be disabled in settings)
- It pairs well with our other tool, QuickEdit, if you want to polish dictated text further.
- If wasn’t clear, yes, it’s Mac only. Linux folks, please roast us in the comments.

value: Show HN: local speech-to-text is shockingly fast on Apple Silicon

author: telenardo


value: itcaat

value:
I built a simple TUI tool to save frequently used shell commands and quickly recall them with fuzzy search.
The problem: I kept forgetting complex commands (docker, kubectl, ffmpeg one-liners) and constantly searched through history or notes.
cli-stash lets you:
Press Ctrl+A to browse shell history and save commands
Fuzzy search through saved commands
Select a command and it's inserted directly into your terminal prompt
Commands are sorted by usage frequency
Built with Go and Bubble Tea. Works on macOS and Linux.
Install:
brew install itcaat/tap/cli-stash
or
go install github.com/itcaat/cli-stash@latest
GitHub: https://github.com/itcaat/cli-stash

value: Show HN: cli-stash – save and recall shell commands with fuzzy search

author: itcaat


value: octablock

value:
While working with ESP32, I kept running into the same failure mode:
UART / BLE output occasionally falls behind, and simple queues either grow
unbounded or force producers to block.
I built a small embedded scheduling core focused on runtime behavior,
not API completeness.
What this demo shows:
- Producers never block
- Output is limited by a bytes-per-tick budget
- TX backpressure causes jobs to requeue (not disappear)
- Low-priority telemetry degrades explicitly under pressure
- Every decision is observable via runtime counters and logs
This is not an RTOS scheduler or a message queue library.
It’s a validated snapshot of how the system behaves under sustained
output pressure on ESP32 (Arduino).
I’m curious how others handle this problem on small MCUs:
explicit degradation, blocking producers, or something else?

value: Show HN: Handling UART backpressure on ESP32 without blocking producers

author: octablock


value: pugdogdev

value:
About a year ago, I was building DockFlow (a macOS app for managing macOS dock
presets) When I bumped into ExtraDock on Reddit.
The original creator had built a cool tool: create multiple floating docks on your Mac, position them
on different monitors. But the app was hard to maintain, and the developer was
looking to move on.
I loved the concept. I saw how ExtraDock and DockFlow could work beautifully
together.
DockFlow manages your dock configurations,
ExtraDock gives you multiple docks per monitor.
I reached out, we made a deal, and I rebuilt the
entire thing from the ground up.
When I first acquired ExtraDock, it had potential but needed serious work.
The original creator built the core concept—multiple floating docks, but the
UI was basic, performance was shaky, and there was no
real integration with other tools. Over the past 10 months, I have completely
rebuilt it.
The difference is night and day. I rewrote everything, eliminated
the crashes, added customizable widgets (spacers, dividers, clocks, Finder
Trash widgets, Tamadocky, and much more.
And integrated it seamlessly with DockFlow, so your docks
automatically appear or disappear based on your workflow preset.
Three months ago, I realized I was spending too much time context-switching between monitors.
I have a 14-inch MacBook, two 27-inch displays, and completely different
workflows on each one. Design on the left monitor. Development on the right.
Communications and reference materials on the third.
The problem? The macOS Dock only exists in one place. So I'm constantly
jumping screens to access the apps I need for that specific workspace.
With the rebuilt ExtraDock, that problem just disappeared.
Now I have three specialized docks:
Left monitor: Figma, Photoshop, color picker, design asset folders
Right monitor: Cursor, Terminal, Chrome, GitHub Desktop, Projects folders
Center: Email, Notion, Calendar, management folders
When I switch my DockFlow preset from "Design" to "Development," ExtraDock
automatically hides my design dock and shows my dev dock. Each monitor becomes
a focused workspace tailored to what I'm actually doing. No more hunting for
apps on the wrong screen.
I also built the magic feature: ExtraDock's Live Dock widget.
This is what sets it apart from other floating dock tools.
You can literally copy your native macOS Dock, with live updates.
Place it anywhere.
On a different monitor, at a different position, at a different size.
Features that make it work:
Unlimited docks positioned anywhere on your screens
Drag-and-drop apps, folders, and files into your docks
Fully customizable: colors, sizes, layouts, icons
Auto-hide or always-visible—your choice
Hides automatically during full-screen apps (movies, presentations)
Live Dock widget to replicate your native Dock anywhere
Custom widgets: spacers, dividers, clocks, Finder/Trash access
DockFlow integration: docks respond to your preset switches
Zero lag even with multiple docks running
Runs completely offline with zero OS permissions (Live dock can work better with accessibility, but this is optional)
Everything stays local—no cloud, no telemetry
What makes ExtraDock different?
Other dock replacement apps try to replace the Dock
entirely. That's fine if you want a completely custom experience, but a lot of
people like the native Dock.
They just wish they could have it on every monitor.
ExtraDock lets you do that. You keep your native Dock (or clone it
with the Live Dock widget) and add unlimited floating docks wherever you need
them.
Pricing:
$9.99 yearly
$31.99 (Instead of 39.99 now) for a lifetime license
14-day refund guarantee if it doesn't work for you
Will be happy to get your feedback, and invite you to join over 600 ExtraDock users :)
Check it out: https://extradock.app

value: Show HN: I acquired ExtraDock, rebuilt it, now it's the macOS app of my dreams

author: pugdogdev


value: nefintec

value: Show HN: I built a Go gateway to round-robin traffic across Vercel free tiers

author: nefintec


value: xthe

value: Show HN: EU AI Act

author: xthe

title: Show HN: EU AI Act


value: modinfo

value: Show HN: Telnet Server for Chatting with LLM

author: modinfo


value:
I like to create trivial services. I also like open-source software.
I wanted to make a high-quality video for promotion or introduction. Meanwhile, I found a service called Screen Studio, and I was impressed with the results.
I searched for a free service, and there was no suitable replacement. I'm working on this service because I'm challenging the impossibility with Claude Code these days.
Of course, there are still parts that do not meet the quality of Screen Studio, but I would like to develop it into excellent quality software with your help.

value: Show HN: Screenize - Screen Studio Alternative


value:
After watching prompt injection attacks bypass every major LLM's safety systems, I built TrustLayer.
It's a simple API that:
1. Scans prompts for injection attacks in real-time (heuristic + OpenAI moderation)
2. Detects when agents drift from their intended behavior
3. Has a kill switch for production incidents
Built for production use. Free tier available. Open source docs and examples.
The problem: Prompt injection is still breaking LLM apps in production. Most guardrails can be bypassed with simple tricks.
The solution: API-first security layer that sits between your app and the LLM.

value: Show HN: Built firewall for LLMs after prompt injection bypass GPT-4s guardrails


value: nicklewers

value:
I built a small site for quickly looking up crafting recipes for Enshrouded! I was very surprised to find there wasn't something like this already.
Its very basic, I have a cronjob which once a day scrapes and parses  https://enshrouded.wiki.gg/wiki/Data:Crafting_Recipes.json?a... . I then have a static site on Github pages which loads up the parsed data and presents it in a searchable, hopefully user-friendly, manner.
Hope this is useful to someone :)

value: Show HN: Enshrouded (Game) Crafting Recipes

author: nicklewers


value: pruetj

value:
Sending mail online is not simple, it should be simple.
Why would you send a letter in today's age? A few use cases:
- You can be completely anonymous. ex: You have a neighbor who puts trash on the curb 2 weeks early... they need to know
- You might have to and its a PITA if it's something you otherwise never do because you need paper, stamp, printer, ink, envelope, mailbox. ex: legal, banking, government, etc.
- Lodge a complaint or make a request. This one has been fun for me. I've written Michael Dell asking him to support his alma mater and provide additional NIL money for the Texas Longhorns. I've also written a random developer at Apple stating how horrific iOS 26 has been. Make your voice heard!
https://mappymail.com is meant to be simple, cheap, and as private as you'd like.
- Simple: Start typing an address or just drop a pin.
- Cheap: You can get a single page letter out for $1.55.
- Private: You don't need to have an account, provide a return address, sender name, or even give an email if you don't want to. Once your letter is sent, we remove the blob from our server.
Would love to hear any feedback or answer questions!

value: Show HN: Send a letter for $1.55

author: pruetj

title: Show HN: Send a letter for $1.55


value: obutora

value:
Hi HN,
I built ChordCraft because I often struggle to find the right chords to support a melody, and opening a full DAW just to sketch an idea felt too heavy.
It's a free web tool that lets you visualize chord progressions (with Roman numeral/degree notation), customize voicings, and export the result as a MIDI file to use in your DAW later.
Key features:
Runs entirely in the browser (Web Audio API)
No login or sign-up required
Presets for standard progressions (like "Just the Two of Us")
Visual diagrams for Piano/Guitar/Pads
I'd appreciate any feedback on the UI or feature suggestions!
Link: https://chord.iru-yo.com/

value: Show HN: ChordCraft – Browser-based chord progression sketchpad with MIDI export

author: obutora


value:
WASM sandbox for running LLM-generated code safely.<p>Agents get a bash-like shell and can only call tools you provide, with constraints you define.
No Docker, no subprocess, no SaaS — just pip install amla-sandbox

value: Show HN: Amla Sandbox – WASM bash shell sandbox for AI agents


value:
We recently moved the infrastructure for Wetarseel away from complex cloud setups to a single "big" server. The goal was to reduce both the monthly bill and the "mental tax" of managing distributed services.
The Hardware: We’re using a single Hetzner VPS (~$25 USD/mo) providing:
8 vCPUs
32GB RAM
200GB NVMe
The Architecture: Instead of a fleet of microservices, we run everything on this one node using Coolify as a self-hosted PaaS. It gives us a Heroku-like experience (Red/Blue deployments via GitHub webhooks) without the per-resource cost.
The Stack:
Frontend/Backend: Next.js and Bun.
Database: SQLite. Since it’s just a file on the NVMe, latency is near zero.
Queues: BullMQ orchestrated via a local Redis instance.
Storage: Self-hosted S3-compatible storage for assets.
Why we did this: We found that vertical scaling is underrated. With 32GB of RAM, we have massive headroom. Using SQLite removes the need for managed DB overhead, and Coolify handles the "plumbing" (SSL, networking, backups).
It’s been surprisingly stable and "just works." I'd love to hear from others who have ditched complex cloud infra for "boring" single-server setups, or any questions on the trade-offs we've made.
Link to article [https://www.linkedin.com/pulse/why-we-chose-coolify-hetzner-...]

value: Show HN: Running our stack on a $25 Hetzner node with Coolify and SQLite


value: cranberryturkey

value:
I’m building a few
Projects that are ready for users
https://bittorrented.com a torrent-first
Streaming platform
http://marksyncr.com a free bookmarks synchronizer web extension for chrome safari and Firefox based browsers
http://defpromo.com a zero-cloud self promotion web extension for all browsers  helps automate commenting on social media posts to promote your product  api keys required
https://giv1.com Publish Newsletters & Podcasts. The complete platform for content creators. Publish beautiful newsletters and podcasts
https://tutorlinkup.com The Premier Cryto-friendly Marketplace for Test Taking Services, Tutoring and Homework Help
https://www.parklookup.com Explore thousands of national, state, county, and city parks across America. Plan your next adventure today.
http://summaryforge.com  Turn long readings into ready-to-study notes and quizzes. Perfect for students cramming before exams.
http://coinpayportal.com a non custodial crypto payment gateway   Easy to integrate similar to stripe with web hooks and manage multiple businesses
http://qrypt.chat a free quantum resistant chat application
http://disrupthire.com ai assisted engineer consulting services
http://saasrow.com link directory for saas and ai
http://ugig.net Connect with skilled engineers and workers who leverage AI tools to deliver exceptional results. Free to browse, free to apply.
http://cryptoshot.space an ethereum jackpot game just connect your wallet and take a shot at the jackpot contract is open source
https://postammo.com a viral content idea generator for social media posters. Includes native mobile, PWA and web extension apps.
http://icemap.app an anonymous, real-time incident reporting platform that helps communities stay informed about what's happening around them.
http://pairux.com - (coming soon) Collaborative screen sharing with simultaneous remote mouse and keyboard control. Like Screenhero, but open source.
If you want to try out an app and provide feedback you can always email support@<domain.name> or join our discord at: https://discord.gg/U7dEXfBA3s
---
Hire me: Anthony "Chovy" at +1 (408) 656-2473.
I'll build you something similar for $2500 USD in a week.
https://github.com/profullstack

value: Show HN: Today is Friday What are you building?

author: cranberryturkey


value: jolexxa

value: Show HN: Cow, an humble AI for your terminal

author: jolexxa


value: mohitdevops

value:
I documented practical AI agent architecture patterns used when systems move beyond demos.
The focus is on trade-offs, failure modes, and guidance on when to use which pattern.
Looking for feedback from people building real agent systems.

value: Show HN: AI Agent Architecture Patterns for Production Systems

author: mohitdevops


value: joeminichino

value:
Scratching my own itch to get alerts when a job / task / service has failed and near real-time alerts are needed. You can self host it, you can use email or webhooks for alerts.
My motto for this little project is "just tell me when it breaks". I like to think of it as an anti-observability tool, good devs know where issues are anyway, but they do need to know when stuff has gone down. Hope it helps someone, this is probably the second time I publish an OSS project since LokiJS back in 2014.<p>Used VSCode / GH Copilot (Sonnet 4.5) for writing tests and some refactoring.

value: Show HN: CronPulse Community – Self-hosted job monitoring with alerts

author: joeminichino


value: cong-or

value: Show HN: Hud – eBPF blocking detector for Tokio

author: cong-or


value: stefanostraus

value:
Hi HN,
I built Claude Commander, a small wrapper around Cloud Code that lets you issue commands programmatically from inside Cloud Code (via hooks or scripts).
Main feature: switch model at runtime.
Why: start expensive for planning or hard debugging, then downgrade for execution to cut cost.

value: Show HN: Claude Commander: runtime model switching in Cloud Code via hooks/API

author: stefanostraus


value: hakanorensy

value:
Hi HN,
I spent 3 hours trying to set up Clawdbot (OpenClaw) (an open-source
AI assistant for messaging apps) and thought there had to
be a better way.
Clawdbot is great - it's like ChatGPT but runs in WhatsApp,
Telegram, and Discord on your own server. The problem?
Setup is brutal: SSH, Node.js 22, systemd, API keys,
platform integrations...
So we built Flowly to automate it.
What it does:
- One-click VPS provisioning (Hetzner)
- Automated Clawdbot installation & config
- Platform connection wizards (QR codes for Telegram/WhatsApp)
- Usage monitoring, model switching, auto-reconnect
Self-hosted (your data, your server) but SaaS-easy to deploy.
Technical details:
- Hetzner Cloud API for VPS provisioning
- Automated deployment via SSH
- Supports multiple AI models (Claude, GPT, Gemini)
- $25/mo (includes VPS + platform + api credits)
We launched 4 days ago. Early users have processed 40M+
tokens already.
Try it: https://useflowlyapp.com
Happy to answer any technical questions!

value: Show HN: Flowly – Managed Clawdbot in 5 min

author: hakanorensy


value: mariusbolik

value: Show HN: I'm giving away my MicroSaaS

author: mariusbolik

title: Show HN: I'm giving away my MicroSaaS


value:
Hello HN,
We built an open-source, sidecarless service mesh to govern autonomous AI agents on Kubernetes.
The Problem: Standard meshes (Istio/Cilium) operate at L4/L7. They see connections, but they don't see intent. If an autonomous agent gets stuck in a loop and burns $1,000 in OpenAI tokens, or decides to run DROP TABLE to "optimize storage," a standard mesh sees a valid HTTP 200 OK.
The Solution:
SemaMesh uses eBPF (sock_ops) to transparently intercept outbound traffic from AI pods and route it through a local semantic proxy (Go) that enforces "Layer 8" policies.
Features:
. eBPF-based: No sidecar injection required.
. Semantic Blocking: Parses JSON prompts to block high-risk intent (e.g., "delete database") before it leaves the node.
. Stateful Pause: Integrates with CRIU to freeze/checkpoint a rogue agent's process state for forensics instead of just killing the pod.
. Token Quotas: Rate limit by $$ cost, not just requests per second.
Tech Stack:
. Datapath: C (eBPF)
. Proxy/Control Plane: Go
. Orchestration: Kubernetes (DaemonSet/Operator)
We just released v0.4.0 with a "Smoke Test" script that spins up a local Kind cluster and simulates a "Rogue Agent" attack so you can see the blocking logic in action.
Repo: https://github.com/semamesh/SemaMesh
Would love feedback on the eBPF interception logic or the CRIU integration approach.

value: Show HN: SemaMesh: eBPF-based firewall for AI Agents(blocks destructive prompts)


value: acoretchi

value: Show HN: Piano Dojo – Guitar Hero for Piano

author: acoretchi


value:
Most 3D AI tools force you to start from scratch every time you want to make a change.
Anyone who’s used 3D AI has seen this: fix the hands, the toes break. Or you download a model and only need to tweak one part, not redo the entire asset.
That’s the problem we’re trying to solve.
We just launched an editing feature in our 3D AI tool, Rodin. It lets you edit existing 3D models by selecting an area and typing your prompt, without rebuilding everything.
You can upload any model, or generate one from Rodin, and change only the part you want. For example:
“Replace the head of this Gundam model with Crayon Shin-chan’s head.”
This came from working with real production pipelines, where fast iteration matters more than one-shot perfection.
Demo: https://youtu.be/-5_sSHeOmuc
You can try it with this code for a 14-day Creator Plan: ME8EW4PMCRT4
Happy to answer any questions.

value: Show HN: We Built a "Nano Banana" for 3D Editing