value: akihoy

value:
Hi HN,
I’m Iñaki, a teacher and solo maker from Spain. I built NinjaNote because I was drowning in my own voice notes: WhatsApp audios, quick reminders in the car, ideas while walking… and then I couldn’t find anything when I actually needed it.
NinjaNote is my attempt to make that mess usable. It lets you:
Send or upload audio (including WhatsApp voice notes)
Get clean, structured notes instead of raw transcripts
Automatically split one long audio into multiple notes
Auto-categorize notes (tasks, shopping, ideas, meetings, etc.)
Use it in 28 languages (with a strong focus on European languages)
On top of that, you can:
Create reminders based on your notes
Edit notes in real time with other NinjaNote users
Share notes via text (copy/paste into any app)
Organize everything into folders
Attach images and/or links to keep related context together
It’s a web app that works on desktop, mobile, and tablets. Tech-wise it’s built with Next.js, Vercel, Firebase, Capacitor, and an LLM layer on top of transcription.
On the landing page you can try a live demo without registration: record or send an audio and see how it turns into notes before signing up.
Right now you get 3 minutes for free to try it out (no credit card).
If anyone here wants to test it more intensively and share detailed feedback, I’m happy to grant extra minutes – just let me know in the comments or reach out.
I’d really appreciate your feedback on:
Where the UX or onboarding feels confusing
Any obvious technical or product gaps you see
Concerns about privacy / data handling from your point of view
Link: https://www.ninjanote.app
Thank you for taking the time to check it out. I’ll be around to answer questions and go into technical details if useful.

value: Show HN: NinjaNote – Turn WhatsApp voice notes into searchable, organized notes

author: akihoy


value: heshiebee

value: Show HN: Forms are boring AF, so I built a conversational survey tool

author: heshiebee


value: Show HN: Lego QR Code Maker

title: Show HN: Lego QR Code Maker


value: fasthightimess

value: Show HN: Vibe code and generate full WordPress plugins

author: fasthightimess


value: Show HN: Log2mermaid – Turn logs into Mermaid sequence diagrams


value: okl

value: Show HN: Mutex Service – Team Coordination Tool

author: okl


value:
I'm Kyle, a software engineer who started angel investing through AngelList syndicates (~25 deals, $1-10k each). I'd see interesting ideas and clever founders but wasn't sure what to look for or how to compare them. I wanted a system to think through deals more systematically. A second opinion to challenge my initial read.
What it does:
 - Paste a deal memo → get scoring on 8 criteria (founder, market, traction, etc.)
 - Every score cites specific evidence. "Strong retention" without numbers = lower score
 - Compare deals side-by-side, ask follow-up questions
Demo: https://www.loom.com/share/a360b329f9e849c38c1ea70ba510d178
Tech:
 - Claude Sonnet 4.5 for analysis (Anthropic for nuanced judgment)
 - Local anonymization—company/founder names scrubbed client-side before API calls
 - Multi-layer QA: accuracy checker catches hallucinations, auto-retry on errors, final polish
What I learned:
AI coding tools make it too easy to tinker. I'd have 3 fixes going at once, creating more bugs than I solved. Had to force myself to slow down and work methodically. Bigger lesson: I spent months tweaking in isolation instead of getting external feedback. This post is me breaking that habit.
Try it: Free tier has 20 triages + 3 deep analyses/month. I'd love feedback on whether scoring feels calibrated.
https://angelcheck.ai

value: Show HN: I built an AI tool to evaluate my AngelList deal flow


value: languid-photic

value:
Voratiq is an open-source (MIT-licensed) terminal-native CLI that lets you run multiple coding agents against the same spec, review their diffs side-by-side, and use whichever implementation is best.
It's designed for experienced developers who want to make the most of agentic coding. It positions you as the architect and reviewer, and shifts implementation onto an ensemble of agents who (hopefully) in aggregate can do a good job. And in my experience, the latest generation of models do quite well.
I didn't think "agentic coding" worked until recently. I first tried it in February. The UX was really powerful, but the models weren't good enough, so it didn't matter. Then, I gave it another try several months later, and the experience was significantly more productive. And this is just the start, the models will likely continue to improve over time.
Why take an ensemble approach? Because there is no one "best LLM for X". The top performer for your (likely highly narrow and contextual) task is impossible to know a priori. Tokens are cheap relative to human engineering time. So, just throw compute at your problem. Run all of the models. Then decide who did the best job and use that output.
Voratiq is built for "pro" users, so everything is local, configurable, inspectable, and hackable. We think these users have the skills to get the most out of these new tools. Furthermore, since the field is evolving so quickly, the more adaptable the product is, the better.
And with that, I hope you give it a try and it proves useful to you: https://github.com/voratiq/voratiq
Thank you!

value: Show HN: Pit Claude, Codex, and Gemini against each other, and apply the best

author: languid-photic


value: Thomalloc

value:
Hi HN,
I’ve released a new version of chess-tui, a Rust terminal UI for playing live games against Lichess opponents.
The update includes a cleaner TUI, better performance, improved keybindings, and smoother real-time integration with the Lichess API. It’s designed as a simple, fast way to play chess without leaving the terminal.
Repo:https://github.com/thomas-mauran/chess-tui
Feedback and bug reports are welcome. Happy to answer questions!

value: Show HN: Chess-TUI Play Lichess games in your terminal (Rust)

author: Thomalloc


value: Show HN: Convert Figma designs to Tailwind CSS with MCP [MIT License]


value:
I built Postgresus - an open source self-hosted PostgreSQL backup tool with a web UI, and have just released v2.0
Website - https://postgresus.com
GitHub - https://github.com/RostislavDugin/postgresus
Main features:
- Scheduled backups for multiple PostgreSQL databases
- Storage to S3, Cloudflare R2, Google Drive, Azure Blob, NAS, etc.
- Notifications about backup status via email, Telegram, Slack, Discord, MS Teams and customizable webhooks
- Works with both self-hosted and managed PostgreSQL (RDS, Cloud SQL, etc.)
- Runs as a single Docker container or via Helm on Kubernetes, can be installed via script
New in 2.0:
- Database health checks and alerts
- Workspaces, users and audit logs for teams
- Encryption for secrets and backup files
- Improved compression defaults
- Refreshed UI with dark theme

value: Show HN: Postgresus 2.0 – self-hosted PostgreSQL backup tool


value: oztamir

value: Show HN: Can you guess the name of a person who doesn't exist?

author: oztamir


value:
Hi HN!
I built PageEcho, a fully on-device eBook reader for iOS that integrates offline TTS and Apple Intelligence features. It started as a personal challenge to create a reading app that never sends data to a server while still supporting modern AI capabilities.
What’s unique:
• Everything — TTS, summaries, Q&A, mind-maps, translation — runs locally on the device
• Supports EPUB, PDF, MOBI, AZW3, TXT, and FB2 with a single unified reading pipeline
• Uses Supertonic ONNX for high-quality offline speech (no cloud, no latency)
• Integrates Apple Intelligence for chapter-level analysis on supported devices
• Local SQLite storage for highlights, progress, and reading analytics
Technical notes for those curious:
• EPUB/TXT/MOBI parsing is consolidated into a WebView-based renderer with CFI support
• PDF mode uses native PDFKit overlays with TTS + translation layers
• TTS runs on-device using a quantized Supertonic model with chunked streaming
• AI summaries/Q&A rely on local Apple Intelligence calls with caching at the chapter level
• The entire app operates offline — no accounts, no telemetry, no external servers
I’d love feedback from readers, mobile devs, and anyone interested in on-device AI design.
Happy to answer any technical questions!

value: Show HN: PageEcho – Offline AI eBook Reader (On-Device TTS and AI)


value: tomnghia

value:
Hey HN, I’m a product designer on a payments team. Every time we were close to shipping a feature, we’d run a bug bash in a giant Google doc or spreadsheet: copy the spec in, write the test cases, ask people to mark Pass/Fail, paste screenshots, then manually turn everything into Jira tickets. It worked, but it always felt a bit painful.
Instead of another heavy test management tool, I wanted something that feels closer to the tools we actually like using. More Linear/Notion, less old-school QA.
With Preflight, you paste your product requirements and Figma link and get an
initial set of test cases from an LLM. You bug bash together in real time, add
notes and screenshots when things fail, and create Jira or Linear tickets in
one click from failing steps. At the end, you get a snapshot you can share to
decide if the release is ready.
Preflight is in beta, and free to use for now.
Thanks for checking it out, I’d really appreciate any feedback on whether this matches how you run bug bashes or dogfooding today, and if not, what would need to change for it to be useful to you?

value: Show HN: Preflight – stop running bug bashes in docs and spreadsheets

author: tomnghia


value: mcone

value:
I'm the author of The Markdown Guide. After years of recommending other editors, I finally built the one I actually wanted to use.
JotBird is a browser-based Markdown editor focused on simplicity: live preview, automatic image handling (paste/drag/upload), and one-click publishing to a shareable URL. No account required, no tracking, everything stays in your browser until you choose to publish.
Happy to answer questions.

value: Show HN: JotBird – A simple Markdown editor with one-click publishing

author: mcone


value: detectivestory

value:
Hello all,
I’d love to get some feedback on an app I’ve been working on called Snapalabra.
The idea is simple: each day you get a new image, and you describe what you see in your target language. That’s it — a small, focused daily expression exercise.
To help, the app provides image-mapped clues, and there’s an optional LLM feedback feature (I know that’s not for everyone) that can suggest ways to expand on what you've written or spoken.
It’s not a course and doesn’t try to be one. It’s just a dedicated space to practice expressing yourself alongside whatever other language learning you're already doing — Spanish, German, Basque, French, Irish, or anything else.
I built it because I felt a big gap between what I could understand and what I could express. Even as my comprehension improved, I’d still freeze when trying to form sentences. Forcing myself to produce a few sentences every day helped “rewire” my brain and make sentence structure and vocabulary easier to recall.
So far I’ve really enjoyed both using and building it, and I’m excited to keep refining the concept.
App Store: https://apps.apple.com/us/app/snapalabra/id6747401847
Play Store: https://play.google.com/store/apps/details?id=com.whatever55...
Web: https://snapalabra.com
The app is built with Flutter for Android and iOS.
Thanks for reading!

value: Show HN: Snapalabra! - Learn Language Through Curated Images

author: detectivestory


value: RandomDailyUrls

value: Show HN: Firefly Forest

author: RandomDailyUrls

title: Show HN: Firefly Forest


value: marcusdev

value:
I built a procedurally generated word-search game where the puzzle never ends - as you scroll, the grid expands infinitely and new words appear. It’s designed to be quick to pick up, satisfying to play, and a little addictive.
The core game works without an account using the pre-defined games, but signing up allows you to generate games using any topic you can think of.
I’d love feedback on gameplay, performance, and whether the endless format feels engaging over time.
If you try it, I’d really appreciate any bug reports or suggestions.
Thanks in advance!

value: Show HN: An endless scrolling word search game

author: marcusdev


value:
Hi HN!
This keyboard is like a corne (v4) [0] with the physical layout of a fifi [1].
The PCB can be ordered from JLCPCB with PCB assembly of the components: no need to solder anything yourself! (unless you want encoders)
See the ordering guide in the repository for more information [2].
Main Characteristics:
- 36/40 keys
- Encoder support
- Embedded RP2040 controller
- USB-C to connect the halves
- PCB assembly: no soldering required (unless you use encoders)
The PCB was made by a Fiverr contractor (I don't know how to do it myself, their profile here [3]) and the case by me.
If there is anything missing in the README or the docs, feel free to let me know.
[0] https://github.com/foostan/crkbd
[1] https://github.com/raychengy/fifi_split_keeb
[2] https://github.com/v3lmx/cornifi/blob/main/docs/pcb_ordering...
[3] https://www.fiverr.com/circuitwork32

value: Show HN: Cornifi split keyboard, a more staggered corne


value: huntdunbar

value:
Hey everyone. This is Hunter, CPO at Luxonis! We built OAK 4 (www.luxonis.com/oak4) to eliminate the need for cloud reliance or host computers in robotics & industrial automation. We brought Jetson Orin-level compute and Yocto Linux directly to our stereo cameras.
This allows you to run full CV pipelines (detection + depth + logic) entirely on-device, with no dependency on a host PC or cloud streaming. We also integrated it with Hub, our fleet management platform, to handle deployments, OTA updates, and collect "edge case" (Snaps) for model retraining.
For this generation, we shipped a Qualcomm QCS8550. This gives the device a CPU, GPU, AI accelerator, and native depth processing ISP. It achieves 52 TOPS of processing inside an IP67 housing to handle rough whether, shock, and vibration. At 25W peak, the device is designed to run reliably without active cooling.
Our ML team also released Neural Stereo Depth running our proprietary LENS(Luxonis Edge Neural Stereo) models directly on the device. Visit www.luxonis.com to learn more!

value: Show HN: Luxonis – OAK 4: spatial AI camera that runs Linux, with up to 52 TOPS

author: huntdunbar


value: upmostly

value: Show HN: Advent of SQL – A Daily SQL Puzzle Calendar Inspired by Advent of Code

author: upmostly


value: kaicbento

value:
I built this tool because I got tired of formatting Windows and reinstalling everything manually.
My personal bash + winget script worked, but wasn’t reusable for anyone else.
So I turned it into a fully web-based generator:
no terminal, no Node, no installs — just pick your apps and configs, and it outputs a ready-to-run .bat script.
The project is now open source and recently got a big update:
115 apps
80 system configurations
Bloatware removal
Windows 98-inspired UI
One-click script generation
Everything runs locally in your browser
Live version: https://kaic.me/win-post-install/
GitHub: https://github.com/kaic/win-post-install

value: Show HN: A Windows post-install generator

author: kaicbento


value: omnarayan

value:
iOS real device support has been Maestro's most requested feature since January 2023. Almost 3 years of GitHub issues and comments asking for the same thing.
We needed it for our work, so we built it and submitted PR #2856. But the Maestro team said official support won't land until next year — no committed timeline.
We didn't want teams to wait, so we packaged a standalone tool that works today.
How it works:
- maestro-ios-device builds and deploys the XCTest runner to your physical iPhone
- Port forwarding bridges localhost:6001 → device:22087
- Your existing Maestro YAML runs unchanged
Bonus: We also unlocked parallel execution on multiple real devices. Maestro had a hardcoded port limitation — now you can run on device 1 at :6001 and device 2 at :6002 simultaneously.
Limitations (Apple restrictions):
- clearState works via app reinstall (not simctl)
- setLocation requires additional setup
- addMedia not supported
Install:
curl -fsSL https://raw.githubusercontent.com/devicelab-dev/maestro-ios-... | bash
Repo: https://github.com/devicelab-dev/maestro-ios-device
PR: https://github.com/mobile-dev-inc/Maestro/pull/2856
Tested on iOS 18.x and 26.x with Maestro 2.0.9/2.0.10.
This is unofficial — when Maestro ships native support, use that instead. Happy to answer questions about the implementation.

value: Show HN: We added iOS real device support to Maestro

author: omnarayan


value: jmstfv

value: Show HN: I built a Notion to PDF converter

author: jmstfv


value: TheAerius

value:
OP here.
I built this library out of frustration with messy, day-to-day data: CSVs with duplicated columns, APIs returning nested JSON, Excel sheets, and various ad-hoc ETL requests.
Tools like Polars and DuckDB are great for heavy analytical workloads, but sometimes I just want a lightweight, Pythonic table I can iterate over. I want the ability to for `row in table:` without surprising semantics.
Serif takes a vector-first, zero-dependency approach aimed at everyday data tasks.
https://github.com/CIG-GitHub/serif
This is an early release, so I'm looking for feedback on ergonomics and places where the API is either missing features or could be simpler.

value: Show HN: Serif – a zero-dependency, DataFrame for Python

author: TheAerius


value:
I live in an area with a lot of older folks and I try to talk to them. One thing I've heard is how many of them have been scammed or know people who have gotten conned by phishing, tech support fraud, Medicare schemes, etc. Sometimes even their whole 401k or life savings. It's maddening to think there are individuals in this world taking advantage like that.
I built Ward to start to tackle this. It’s a Chrome extension that can either run locally on your machine (with Gemini Nano) or use a hosted cloud-based endpoint.
So far, it can pick up on a bunch of the top techniques used with little false positives. It’s for people who have older folks, young kids, or anyone less tech savvy in their life that Ward could help.
I’ve put off monetizing and am bootstrapping and covering costs for the first couple hundred users to improve Ward. When stuff inevitably gets past server-side filters, Ward works really well for client side edge defense.

value: Show HN: Ward – Modern antivirus in your browswer


value: AdityaSanthosh

value: Show HN: I built a AI powered ad blocker that runs entirely in browser

author: AdityaSanthosh


value: brad-mcevilly

value:
I've spent the last year tackling memory poisoning in agentic AI (e.g., 98% MINJA success via queries alone). Introducing QCMP: a 4-layer architecture blending IIT consciousness metrics (CCI >0.90 thresholds), post-quantum checksums (ML-KEM), and CTC consistency for tamper-proof agent swarms.
Key wins: Detects 0.1% AgentPoison backdoors in <50ms; OWASP/EU AI Act compliant.
PDF: https://github.com/bradmcevilly/qcmp-whitepaper/raw/main/QCM...
First arXiv sub to cs.AI—seeking endorsements/feedback from the HN community. Thoughts on the quantum-bio hooks or multi-agent layers? Open to chats.
Site: deepsweep.ai | LI: linkedin.com/in/bradmcevilly

value: Show HN: QCMP Framework for Poison-Resistant AI Agents [pdf]

author: brad-mcevilly


value: plawlost

value:
We just shipped Faraday v3.1 at VulnZap.
Faraday sits inside agentic coding flows (Cursor, Claude, TS/JS based agents) and watches AI written diffs in real time. It pulls live CVE data, scores exploitability, and suggests patches before humans merge anything.
The goal is to make AI written code something you can trust by default, instead of hoping CI catches it later.
Docs: https://vulnzap.com/docs
Launch video: https://x.com/yazcaleb/status/1999141884963229776
Happy to answer questions about the engine, the SDK, how we integrate with tools like Cursor and Ariana.dev, and what has not worked so far.

value: Show HN: Faraday, real time security engine for coding agents

author: plawlost


value:
Hey HN—after a year digging into agentic AI vulnerabilities, I've built QCMP: a 4-layer architecture to slam the door on memory poisoning. MCP's at 16K servers, but attacks like MINJA (98.2% query-only success) and AgentPoison (80%+ backdoors from 0.1% poison) expose the core flaw—memory trusts itself too much.
QCMP borrows from IIT consciousness metrics (CCI >0.90 to freeze fragments), post-quantum checksums (ML-KEM-768), CTC self-consistency (NIS >0.95), and mantis shrimp-style sparse checks (<50ms TME). OWASP/EU AI Act ready, with Rust impl tips.
PDF (in-browser view): https://github.com/bradmcevilly/qcmp-whitepaper/blob/main/QC...
First arXiv push to cs.AI—hunting endorsements (4+ recent subs). Feedback on the quantum-bio hooks or swarm layers? Open to riffs.
deepsweep.ai | linkedin.com/in/bradmcevilly
I've spent the last year tackling memory poisoning in agentic AI (e.g., 98% MINJA success via queries alone). Introducing QCMP: a 4-layer architecture blending IIT consciousness metrics (CCI >0.90 thresholds), post-quantum checksums (ML-KEM), and CTC consistency for tamper-proof agent swarms.
Key wins: Detects 0.1% AgentPoison backdoors in <50ms; OWASP/EU AI Act compliant.
PDF: https://github.com/bradmcevilly/qcmp-whitepaper/blob/main/QC...
First arXiv sub to cs.AI—seeking endorsements/feedback from the HN community. Thoughts on the quantum-bio hooks or multi-agent layers? Open to chats.
Site: deepsweep.ai | LI: linkedin.com/in/bradmcevilly

value: Show HN: QCMP Framework for Poison-Resistant AI Agents (ArXiv Cs.ai Pending)


value: mikepapadim

value:
wget https://github.com/beehive-lab/TornadoVM/releases/download/v...
unzip tornadovm-2.1.0-opencl-linux-amd64.zip
# Replace <path-to-sdk> manually with the absolute path of the extracted folder
export TORNADO_SDK="<path-to-sdk>/tornadovm-2.1.0-opencl"
export PATH=$TORNADO_SDK/bin:$PATH
tornado --devices
tornado --version
# Navigate to the project directory
cd GPULlama3.java
# Source the project-specific environment paths -> this will ensure the
source set_paths
# Build the project using Maven (skip tests for faster build)
# mvn clean package -DskipTests or just make
make
# Run the model (make sure you have downloaded the model file first -  see below)
./llama-tornado --gpu  --verbose-init --opencl --model beehive-llama-3.2-1b-instruct-fp16.gguf --prompt "tell me a joke"

value: Show HN: GPULlama3.java Llama Compilied to PTX/OpenCL Now Integrated in Quarkus

author: mikepapadim


value: runkids

value:
Hi HN, I built PackageFlow to solve my own frustrations with switching between terminal, Git GUIs, and various scripts during development.
It's a desktop app that combines:
- Visual drag-and-drop workflow builder (like n8n but for local dev tasks)
- Git operations with diff viewer, branch/stash management
- Git worktree management with quick switcher
- Monorepo support (Nx, Turborepo) with dependency graph
- Security audit integration (npm audit, Snyk)
- Built-in PTY terminal
Tech: Tauri 2 (Rust backend + React frontend), TypeScript, React Flow for the workflow editor.
macOS only for now. Install via Homebrew:

    brew tap runkids/tap && brew install --cask packageflow

Source: https://github.com/runkids/PackageFlow
I'd appreciate any feedback, especially on:
1. What workflows would be useful as built-in templates?
Happy to answer any questions about the implementation.

value: Show HN: PackageFlow – Visual workflow automation for developers (Tauri/Rust)

author: runkids


value: IkanRiddle

value: Show HN: The Silicon Stoic – Visualizing AI "Pain" as Computational Friction

author: IkanRiddle


value: jxywilliams

value:
Hey HN,
A while ago I realized creating content for my business meant spending hours drafting and re-writing or going through 10 ChatGPT iterations just to get slop.
So I created Nebly, a tool to take my YouTube video URL, a transcript or just some of my key insights and turn that into content ready to post across my business social medias.
Now I get to spend more time working on more important things like lead gen or actual service delivery!
Now I've made it usable for others in hopes I can help them claw their time back too.
Thanks, Jay

value: Show HN: I built an app to repurpose YouTube videos for my content funnel

author: jxywilliams


value: detectivestory

value: Show HN: Detective Stories – Multiplayer "Dark Stories" game using web sockets


value:
Hello Hacker News. The four of us have been getting the best results with Claude Code when we iterate with it to build full context and then use and update that context as we work.
So, we built Nimbalyst to maximize our (and your) ability to iterate and build context with and for AI.
Nimbalyst is the local WYSIWYG editor and session manager where you iterate with Claude Code on docs, diagrams, mockups, and code.
Nimbalyst lets you:
- Use all the power of Claude Code in a UI
- Work with Claude Code to write and edit WYSIWYG markdown, see AI changes as red/green, approve them
- Integrate mermaid diagrams, text, tables, mockups, and images in standard markdown for human/AI context
- Iterate on html mockups with Claude Code, annotating the mockups, and then use them as context for people and Claude Code coding
- Tie sessions to documents to sessions, find and resume sessions, treat sessions as context, run parallel sessions
- Code with Claude Code with all of this context, use / commands, see git status
Nimbalyst is in Beta, local, and free. We would love your feedback.

value: Show HN: Nimbalyst: local WYSIWYG Markdown/mockup tool powered by Claude Code


value: prettysquirl

value: Show HN: I built the simplest and cheapest link in bio tool

author: prettysquirl


value: bitranox

value:
Hi everyone, I built a small library called lib_layered_config to make configuration handling more predictable in Python projects. I often struggled with codebases where config values came from many places, so I wanted something lightweight that supports clear layering. For example. defaults → environment variables → config files → CLI overrides.
The library focuses on:
• Straightforward layering rules
• Minimal API surface
• Easy integration into existing projects
• Zero unnecessary dependencies
GitHub. https://github.com/bitranox/lib_layered_config
I would love feedback on the API design, missing features, or real world edge cases. If you try it, please let me know how it works for you.
Thanks for taking a look.

value: Show HN: Layered Config. A simple structured configuration loader for Python

author: bitranox


value: vibhanshugarg

value:
MemCloud is a distributed in-memory datastore I built that allows macOS/Linux machines on a LAN to pool their RAM into a shared ephemeral memory cloud. The most common question I received after launching it was: “How is this secure?”
This post is a detailed look at the authentication, encryption, and trust model used in MemCloud. This is not an introduction to the project; it’s a technical deep dive for people interested in protocols, distributed systems, and applied cryptography.
Documentation: https://memcloud.vercel.app/docs/cli
Repo: https://github.com/vibhanshu2001/memcloud
THREAT MODEL
MemCloud assumes an untrusted LAN environment and defends against:
device impersonation
MITM attacks
replay attacks
unauthorized cluster joining
handshake/session hijacking
PERSISTENT IDENTITY KEYS (Ed25519)
Every node generates a persistent Ed25519 identity keypair stored under ~/.memcloud/identity_key.
These keys act like device certificates but without PKI overhead.
Identity keys are only used for signing handshake transcripts, never for traffic encryption.
NOISE-STYLE HANDSHAKE (XX PATTERN)
MemCloud uses a handshake sequence inspired by the Noise Protocol Framework (XX pattern):
Both sides start unauthenticated
Exchange ephemeral X25519 keys
Exchange random nonces
Build a transcript hash
Exchange encrypted identity proofs
Noise was chosen because it supports TOFU, mutual authentication, identity protection, and forward secrecy in a lightweight format suitable for P2P LAN systems.
TRANSCRIPT HASHING
Each handshake message is appended to a transcript hash. This prevents replay, downgrade attempts, message tampering, and cross-session key reuse. The final transcript is mixed into session key derivation.
ENCRYPTED IDENTITY PROOFS
Once ephemeral shared secrets are computed, each device signs the transcript hash with its Ed25519 identity key and sends the signature encrypted.
Verification is performed against the claimed identity key.
If verification fails, the connection is immediately rejected.
SESSION KEY DERIVATION (HKDF + CHACHA20-POLY1305)
Session traffic keys are derived from the ephemeral DH shared secret + transcript hash using HKDF.
Traffic uses ChaCha20-Poly1305 AEAD, chosen for speed and security on local networks.
Every session gets unique traffic keys with forward secrecy.
TOFU (TRUST-ON-FIRST-USE)
After cryptographic authentication succeeds, the user must approve the peer the first time it appears:
memcli consent
[1] Allow Once
[2] Trust Always
[3] Deny
Trusted peers are stored in ~/.memcloud/trusted_devices.json.
Even after trusting a device, each future session is fully authenticated; impersonation is not possible.
FAILURE MODES
MemCloud rejects peers on:
signature mismatch
transcript mismatch
malformed handshake
untrusted identity
consent denial
No RAM quota or block storage rights are granted until after a fully authenticated session is established.
WHY NOT TLS?
TLS is great but not ideal for MemCloud because:
It requires PKI, certificates, or a local CA
It is not naturally TOFU-friendly
It adds overhead unnecessary for LAN-bound P2P systems
Noise is simpler to embed directly inside the daemon
Noise-style handshakes offer mutual authentication, identity hiding, forward secrecy, TOFU support, and a lightweight binary format — making it a better fit for a distributed RAM engine.
OPEN QUESTIONS / FEEDBACK WANTED
I’d love input on:
better trust revocation models
session resumption
formal verification directions
alternative TOFU approaches
potential attack surfaces I may have missed
This part of the project is evolving, and I’d appreciate thoughts from people who have built secure P2P systems or LAN protocols.
Repo: https://github.com/vibhanshu2001/memcloud
Docs: https://memcloud.vercel.app/docs/cli

value: Show HN: MemCloud Security Deep Dive – How Devices Safely Share RAM over LAN

author: vibhanshugarg


value: beeruot

value:
I got tired of hunting for quality Cursor rules, Claude skills and MCP servers across GitHub repos, Reddit threads, and Discord servers. Everything's scattered, and half the time the "awesome lists" link to broken repos or have zero setup instructions.
Agent Depot solves this:
- Single searchable index across different AI tool formats (Cursor .cursorrules, MCP servers, Claude plugins and skills)
- Every agent is tested before being added
- Actual installation instructions, not just links
- Community-driven via GitHub PRs
- No login, no paywall
Built in ~4 weeks as a developer tool for developers. The problem I'm solving: these AI coding tools are powerful, but finding and configuring the right agents shouldn't take hours.
Currently ~71 agents indexed. Looking for feedback on:
- Search UX and discoverability
- What categories/filters would be useful
- Agent submissions welcome via PR
Repository: https://github.com/biagruot/agentdepot-agents
Live site: https://agentdepot.dev

value: Show HN: AgentDepot – open-source directory of Cursor rules, Claude, Replit, MCP

author: beeruot


value: mmarian

value:
I built an extension that adds an AI writing copilot directly into your LibreOffice Writer sidebar. That way, you don’t need to switch back and forth between Writer and ChatGPT.
The extension only runs OpenAI's GPT-5-mini model at the moment. Just wanted to release a bare-bones MVP ASAP; figured I should pick OpenAI for the instant name recognition.
The extension is free, but you’ll be charged for using the OpenAI API.
I’m running a Django Ninja server as a middleman between the extension and OpenAI. Building, packaging, and troubleshooting the extension is tricky, so I wanted to move as much complexity out of it as possible.
It’s been published on the LibreOffice extension directory: https://extensions.libreoffice.org/en/extensions/show/99471 .
There’s a lot of stuff I could add (local models, free hosted models, UI enhancements, etc). But I don’t have much time in the evenings, and I’m not sure what’s the next most important thing to do. Any feedback/suggestions would be much appreciated!

value: Show HN: AI Copilot for LibreOffice Writer

author: mmarian


value: adamgusky

value: Show HN: I made a cozy showcase to find the best game jam games of the month

author: adamgusky


value:
Hi HN,
I work across many local git repositories (microservices, OSS projects, config repos, experiments), and I often lose track of which ones are dirty, ahead/behind, or where I last made changes. Checking each repo with git status became a daily time sink.
So I built git-scope — a small TUI that shows the state of all your git repos in one terminal screen.
What it does
Recursively finds git repos under a directory
Shows clean/dirty/ahead/behind status at a glance
Fuzzy search + quick filtering
Press Enter to jump straight into a repo (shell or editor)
Uses lightweight git metadata to avoid running git status everywhere
~10ms startup on my machine thanks to caching
Fully local, no telemetry
Screenshot
https://github.com/Bharath-code/git-scope/raw/main/docs/git-...
Install
brew tap Bharath-code/tap && brew install git-scope
Windows & Direct
go install github.com/Bharath-code/git-scope/cmd/git-scope@latest
Repo
https://github.com/Bharath-code/git-scope
What changed since the last version
Improved install instructions
Better TUI layout
Contribution graph
Timeline
Disk usage
More feedback from early users
Working on grouping/presets and auto-refresh modes
Considering Windows native support
Looking for feedback
Thoughts on multi-repo workflows
Anyone using file watchers for TUIs?
Ideas for grouping, workspace presets, or performance improvements
Git internals tips for detecting state changes efficiently
Happy to answer questions or go into implementation details.
Thank you for taking a look!

value: Show HN: Git-Scope – A Fast TUI Dashboard for Managing Multiple Git Repos


value: reman--

value:
Built a file converter that treats conversions as reproducible operations.

   Every conversion generates:
   - SHA-256 checksums (input/output integrity verification)
   - Timestamped audit trail
   - Downloadable YAML recipe for reproducibility
   - Quality validation results

   Use case: Teams needing proof of file integrity (legal, compliance, reproducible workflows).

   Architecture: In-memory processing, zero persistent storage, files deleted immediately.

   Feedback welcome - especially on whether "recipe replay" (upload YAML to reproduce exact conversion) would be valuable.

value: Show HN: File converter with SHA-256 audit trails and downloadable

author: reman--


value: gyoridavid

value:
We joined the ElevenLabs World Wide Hackathon, and against all odds we were able to create a working conversational agent in 2.5 hours :D
We created a multimodal virtual presence agent that uses advanced voice and vision capabilities to offer real-time, familiar-feeling conversations for people with Alzheimer’s. When a primary caregiver can’t be present, Relief provides comforting, supportive interactions—offering reassurance for loved ones and meaningful peace of mind for caregivers.
It was fun to hack this together, using the react agent sdk was quite easy, but lacks of fine-control, had to find workarounds for pausing the interaction. Another fun workaround was to give "vision" to the agent by taking screenshots every 5 seconds and using an LLM to analyze the image and feed that back to the agent's context.
The app was built to solve a real problem in Asia, I hope it inspires someone to create a legit solution.

value: Show HN: Alzheimer's conversational AI agent (ElevenLabs 3 hours hackathon)

author: gyoridavid


value: justpeek

value: Show HN: Peek - Hover over Mac menu bar to see live website previews

author: justpeek


value: aerot

value: I got tired of not being able to find files on my laptop so I decided to write a program that does it for me. You just install Inkling, then upload any files you want to index, then boom: Search for it at will. It's a bit slow to download right now but I'm working on that! Happy to get feedback.

value: Show HN: Inkling – Local semantic search to make finding your documents easier

author: aerot


value: Show HN: DriftOS – Stop dumping chat history into LLM context windows


value:
Hey HN, Waleed here. We're building Sim (https://sim.ai/), an open-source visual editor to build agentic workflows. Repo here: https://github.com/simstudioai/sim/. Docs here: https://docs.sim.ai.
You can run Sim locally using Docker, with no execution limits or other restrictions.
We started building Sim almost a year ago after repeatedly troubleshooting why our agents failed in production. Code-first frameworks felt hard to debug because of implicit control flow, and workflow platforms added more overhead than they removed. We wanted granular control and easy observability without piecing everything together ourselves.
We launched Sim [1][2] as a drag-and-drop canvas around 6 months ago. Since then, we've added:
- 138 blocks: Slack, GitHub, Linear, Notion, Supabase, SSH, TTS, SFTP, MongoDB, S3, Pinecone, ...
- Tool calling with granular control: forced, auto
- Agent memory: conversation memory with sliding window support (by last n messages or tokens)
- Trace spans: detailed logging and observability for nested workflows and tool calling
- Native RAG: upload documents, we chunk, embed with pgvector, and expose vector search to agents
- Workflow deployment versioning with rollbacks
- MCP support, Human-in-the-loop block
- Copilot to build workflows using natural language (just shipped a new version that also acts as a superagent and can call into any of your connected services directly, not just build workflows)
Under the hood, the workflow is a DAG with concurrent execution by default. Nodes run as soon as their dependencies (upstream blocks) are satisfied. Loops (for, forEach, while, do-while) and parallel fan-out/join are also first-class primitives.
Agent blocks are pass-through to the provider. You pick your model (OpenAI, Anthropic, Gemini, Ollama, vLLM), and and we pass through prompts, tools, and response format directly to the provider API. We normalize response shapes for block interoperability, but we're not adding layers that obscure what's happening.
We're currently working on our own MCP server and the ability to deploy workflows as MCP servers. Would love to hear your thoughts and where we should take it next :)
[1] https://news.ycombinator.com/item?id=43823096
[2] https://news.ycombinator.com/item?id=44052766

value: Show HN: Sim – Apache-2.0 n8n alternative


value: kwakubiney

value:
Hi HN,
I built Remixify because I love house music, and there are so many cool remixes and refixes out there. Sometimes I just want a fresh experience of songs I already love.
Remixify takes any Spotify playlist and finds remix versions of each track. You get to preview and curate the selections before creating a new playlist – so you're in control of the final result.
How it works:
1. Paste a Spotify playlist URL
2. Remixify searches for remixes of each track
3. Preview and select the ones you like
4. Create your remixed playlist with one click
Try it out [1]. Built with Django, Celery, and the Spotify API.
Code is open source [2].
Happy to answer any questions!
[1] https://remixify-wild-dawn-7532.fly.dev/
[2] https://github.com/kwakubiney/remixify

value: Show HN: Remixify – Turn any Spotify playlist into a remix playlist

author: kwakubiney


value: greyisodd

value:
Hi HN,
In my spare time I’ve been designing a browser-based logic puzzle game called Grey Is Odd.
It’s inspired by ideas from parity, constraint grids, and nonograms — but the deduction flow is different.
Core rules:
• The grid is divided into grey regions and white regions.
• Each cell can contain 0 or 1 dot.
• Grey regions must contain an odd number of dots.
• White regions must contain an even number.
• Each row and column shows the exact number of dots that must be placed in that line.
• The rest is pure deduction — no guessing required.
Here is a 7×7 Level 3 puzzle so you can try it immediately:
https://greyisodd.com/?size=7x7&level=liv3&id=4806&daily=tru...
If you prefer to start from the main page:
https://greyisodd.com/
If you want a substantially harder example, try 7×7 Level 4 #45915 via the “Find puzzle” button.
It has a deeper chain of parity/region interactions, but it still has a fully logical solution path — no need for trial-and-error.
I’d love feedback on:
• how intuitive the rules feel
• puzzle difficulty balance
• UX (especially on mobile)
• performance issues
• ideas to make the deduction more interesting
If you try a puzzle and get stuck, I’m happy to walk through the reasoning.
Thanks for reading!

value: Show HN: A daily logic puzzle game based on parity constraints

author: greyisodd


value: simplychris

value:
Hey HN,
I’m an ex-Google engineer trying to get back into music production.
I needed a way to sequence my hardware synths using AI contexts without constantly switching windows, so I built this.
It runs entirely in the browser using WebMIDI. No login required.
 It connects to your local MIDI devices (if you're on Chrome/Edge) and lets you generate patterns.
Tech stack: [React / WebMIDI API / etc].
Link: www.simplychris.ai/droplets
Code is a bit messy, but it works. Feedback welcome.

value: Show HN: I built a WebMIDI sequencer to control my hardware synths

author: simplychris


value: Show HN: VICW – Virtual Infinite Context Window


value: VikingFlowAI

value:
Warehouse worker here – I only come up with ideas and architecture, no coding.
The code is a minimal AI-generated PoC.
Fork / build / comment if you want to help – I handle design, community handles code.
CIX is a deterministic, human-readable indexing system for LLM sessions.
It solves drift, staleness and navigation by anchoring events to a stable temporal index.
Repo → https://github.com/VikingFlow/continuous-index
Follow → @VikingFlowAI
Support → https://ko-fi.com/vikingflowai

value: Show HN: CIX – deterministic indexing for stable LLM sessions (10-second PoC)

author: VikingFlowAI


value: stosssik

value:
800M people use ChatGPT and Claude weekly. Right now they get text responses. Soon they'll get real interfaces: product cards, blog posts, booking flows, payment screens rendered directly in the conversation.
We built an open-source component library for this. Install any block with one command and customize it to your brand.
If you're building MCP servers or experimenting with AI-native apps, this might save you time.
Are you building apps for AI assistants? Would love to hear what's missing in your workflow.

value: Show HN: Open-source UI components for apps that run inside ChatGPT

author: stosssik


value: ing-norante

value:
I've been frustrated by existing "magic mushroom dosage calculators": most are either visually rough, only handle cubensis, or hard-code one potency value and ignore species, tolerance, and degradation.
So I built Mycodosing.wtf – a research‑informed calculator that tries to do three things better:
Models species‑level potency using psilocybin/psilocin/baeocystin mg/g with separate profiles for truffles (and syntetics!).
Outputs dose ranges (min/median/max) instead of a single magic number, using per‑species uncertainty factors for real‑world variability.
Includes a tolerance model based on 5‑HT2A receptor downregulation (dose‑dependent exponential decay over ~14 days), plus optional weight adjustment and MAOI flag.
The species potencies are pulled and normalized from LC‑MS/MS and classic chemistry papers (e.g., Garcia‑Romeu 2021, Gotvaldová 2021, Gartz/Stríbrný), then wrapped in a neobrutalist UI built with Vite + React. 100% client-side.
I'd love feedback on:
The dose model itself (any flaws in the math or assumptions?)
Species data you'd adjust or supplement
What you'd want from a v2
Site: https://mycodosing.wtf/
Code: https://github.com/ing-norante/mycodosing.wtf/

value: Show HN: Mycodosing.wtf – Research‑Informed Psychedelic Dosage Calculator

author: ing-norante


value:
Hello HN! I'm happy to release this project today. It's a bidirectional calculator (hence the name bidicalc).
I've been obsessed with the idea of making a spreadsheet where you can update both inputs and outputs, instead of regular spreadsheets where you can only update inputs.
Please let me know what you think! Especially if you find bugs or good example use cases.

value: Show HN: I made a spreadsheet where formulas also update backwards


value: bugthesystem

value: Kaos provides lock-free ring buffers for inter-thread, inter-process, and network communication. Built on the LMAX Disruptor and Aeron high performance networking patterns with modern Rust.

value: Show HN: High-performance lock-free networking/messaging for Rust

author: bugthesystem


value: amadosalsta

value:
I started four years ago on what we now call Nexlayer.
Back then, there were no AI coding agents. We were just trying to make shipping real production apps suck less. Then Chatgpt happened. Cursor happened. Claude Code happened. Suddenly people were building apps in 10 minutes but still spending weeks trying to make it real.
We realized: the cloud wasn't built for agents. It was built for humans who know infrastructure. So we rebuilt it. Nexlayer is an agent-native cloud platform.
Your coding agent connects via MCP, you say "deploy this to nexlayer," and it's live. The MCP auto-detects your stack, containerizes, orchestrates, and returns a production URL. No config. No dashboards. No DevOps. Users are shipping everything—MERN stacks, PERN stacks, FastAPI backends, Golang Discord bots, full AI apps with vector DBs.
Free to try while in early public beta. Would genuinely love the HN crowd to break it and tell me what's missing.

value: Show HN: Ship anything your coding agent can build

author: amadosalsta


value:
Hi HN,
I’ve spent the last few quarters building utm.one – a privacy-focused link management and revenue attribution platform.
The Problem: I was frustrated that Google Analytics attributes most B2B conversions to "Direct" or "Organic" because it loses track of the user journey across devices (e.g., mobile click -> desktop purchase) or offline events.
The Solution: instead of just counting clicks, I built an identity graph that stitches sessions using a first-party pixel and uses Bayesian inference to calculate the probability of a conversion coming from a specific link.
Key Features:
Probabilistic Identity Graph: Links anonymous mobile clicks to desktop purchases based on IP/Time clusters (no third-party cookies).
"One-Tap" Badge Scanner: A PWA that uses OCR (via LLM) to scan event badges that standard QR readers can't parse, then syncs them to the attribution model with event halo which let you know how much lift has taken place at particular location. This solve for event attribution.
Revenue Lift: Calculates incremental lift using a control-group logic rather than just last-click.
The stack is React, Supabase, and Edge Functions for the redirection engine (latency was a huge focus).
I’d love you to roast my logic, the UI (went for a Jony Ive-inspired monochrome look), or the attribution math.
Live demo/Link: https://utm.one/auth
PS. Working with Stripe to get access; they only support if you are doing upwards of $100K. Any suggestions on payment gateways

value: Show HN: Built an attribution tool that uses Bayesian inference to track ROI


value: mrjn

value:
Most buy vs rent calculators compare your mortgage payment to rent, factor in appreciation, and call it a day. But they ignore what happens to your downpayment if you don't buy—that $200K+ could go into index funds instead. S&P 500 has averaged ~14.5% over the last 15 years vs ~4% for housing. That difference compounds significantly.
And almost no calculators address the sell vs keep question: if you already own a property, should you sell and invest the proceeds, or hold onto it?
So I built a calculator that models both scenarios month-by-month.
Buy vs Rent
For buying: Full amortization schedule, mortgage interest deduction, property taxes, insurance, maintenance—all inflating annually except the fixed mortgage payment. Calculates net proceeds at sale including capital gains tax and exemptions.
For renting: Takes your would-be downpayment, invests it at your specified return rate. Each month, adds or subtracts the difference between buying and renting costs. Compounds monthly.
Example outputs for a $1.32M SF home, at 10% rate of return from investments:
5.5% mortgage: renting ahead by ~$250K at year 10, ~$5M at year 30
3% mortgage + $2K/mo income from renting a portion of home: buying ahead by $360K at year 10, $2.2M at year 30
Sell vs Keep
This is the harder problem. Say you bought a home 5 years ago for $1.5M, it's now worth $2M, and you have $1M in equity. Should you sell, or keep it?
For selling: Calculates what you'd net today after agent fees, taxes, and loan payoff. That lump sum gets invested. If it's your primary residence, you can toggle on renting costs that get deducted as your investment compounds.
For keeping: Tracks monthly cash flow—rental income minus loan payments, taxes, insurance, maintenance. Positive cash flow gets invested and compounds. At any year N, your "keep net worth" is the projected sale proceeds at that point plus your accumulated investment position.
You can also model refinancing—pull equity out to invest while servicing a larger loan. The calculator is flexible enough that it works for cars and other depreciating assets too.
All calculations run locally in browser, nothing sent to a server. There's a button to copy results into Claude/Gemini to verify the math and ask further questions.

value: Show HN: Brisk – Buy vs. Rent/Sell vs. Keep calculator tracking opportunity cost

author: mrjn


value: renbuilds

value:
Built this after almost shipping Stripe key in a production bundle.
It runs 6 checks in a few seconds:
- SSL Certificate - validity, expiration, protocol
- DNS Health - SPF, DKIM, DMARC (email spoofing protection)
- Security Headers - CSP, HSTS, X-Frame-Options
- Blacklist Status - spam/malware list checks
- Secret Scanner - finds leaked API keys in public JS bundles (AWS, Stripe, Firebase, etc.)
- Ghost API Hunter - exposed Swagger docs, GraphQL endpoints, debug routes
Everything gets A+ to F grades with plain English explanations.
The last two are the differentiators, most SSL checkers exist, but few tools passively scan your frontend for shipped secrets or forgotten /api endpoints.
Looking for feedback on false positive rates and what other checks would be useful.

value: Show HN: Free Security audit that checks what other tools miss

author: renbuilds


value: ziyasal

value:
We've been building with AI tools and noticed there wasn't a good way to manage MCP servers across a team or see what's actually flowing to LLM providers. Who's running what? Which tools are approved? What data is going where or whats shared on AI websites?
So we built CyberCage (<https://cybercage.io>).
What it does:
MCP Management — Auto or manual discovery of MCP servers, with approval workflows. Manage allowed MCP servers org-wide (down to individual tools). Secure MCP catalog (integrates with GitHub's MCP Catalog).
Operations — Manage allowed AI applications org-wide. Full audit logs (Splunk integration available). Notifications via Slack, Teams, Webex, webhooks.
Works with:
AI IDEs: Claude Code, Cursor, VS Code, Windsurf, Antigravity. Low-code platforms: n8n (native integration).
In private beta:
On-device network agent for configured AI domains. Content inspection for PII and sensitive data. Packet metadata anomaly analysis.
Coming soon:
BYOLLM (bring your own models for inspection). Browser extensions.
See it in action: <https://youtu.be/Zy7XhkQkUlk>
We built this for visibility and control over AI tooling without slowing teams down.
P.S. We're planning to open source CyberSmol v1.0 — a small model fine-tuned for AI threat detection — once it's ready.
Happy to answer questions ♥

value: Show HN: CyberCage – Security platform for AI tools and MCP servers

author: ziyasal


value: seansh

value: Show HN: Bring screencasts into your editor with CodeMic

author: seansh


value: Show HN: I used Gemini 3 to turn 42 books into interactive webpages in 2 weeks


value: andytriescoding

value: Show HN: I built the simplest and cheapest link in bio tool

author: andytriescoding


value:
Hey HN! I built this because I was tired of manually creating OG images for every blog post and side project.
How it works:
- Send a POST request with JSON (title, subtitle, template, etc.)
- Get back a PNG in ~50-100ms
- No Puppeteer, no browser automation — uses node-canvas for fast server-side rendering
Live demo on the homepage (no signup needed). Free tier includes 10 images/month.
I'm launching on Product Hunt today too, but honestly HN feedback is what I care about most. Would love to hear:
- What templates would be useful?
- Any features missing?
Happy to answer questions about the tech stack!

value: Show HN: OG Image API – Generate social preview images with JSON


value: wennyyustalim

value: Built an Excel Agent for fun and it turned out to be useful for my sister and her friends. They run small businesses and I'm surprised how much of the world is run on Excel. If this is useful for you too, would love to hear your feedback!<p>Sample prompt you can start with: "write a sudoku puzzle on the sheet"

value: Show HN: Pipedreamer – AI for Excel

author: wennyyustalim

title: Show HN: Pipedreamer – AI for Excel


value: albertlungu

value:
I've been working on a personal project called νοῦς (Nous). It's a Python implementation of the transformer model described in Attention is All You Need (Vaswani et al., 2017), using only JAX for the backend. The goal started as a learning exercise, but the system turned into something that many others might also find useful.
As I previously mentioned, the backend is completely Python, but I have also built an electron-based frontend, releasing a fully built MacOS app. If you are on Windows, you can clone the repository and visit the README for further instructions. All model components are completely configurable: depth, width, number of heads, training parameters, and generation settings. You can both train locally using the Electron app or using the CLI with a remote GPU. Note that using the CLI, you will have to modify source code to edit the components.
Nous would be useful in experimenting with specific architectures, running controlled training setups, or even teaching/learning how modern LLMs work (the README also explains how every single part of my backend works, so it could be quite helpful to use while reading the codebase).
I originally wanted to make it a NumPy-only version but switched fully to JAX after running into performance problems.
Currently, the app comes with a pre-trained model that has 76.9M parameters, trained to a final loss value of ~0.6. It also comes with a Byte-Pair Encoding tokenizer implementation, so that you can build the tokenizer on a dataset of your choice.
Everything is open-source. Repo link is above.

value: Show HN: Νοῦς – A Customizable LLM Project

author: albertlungu


value: brunaxLorax

value: A demo for showcasing MCP Apps starting from a Wordpress website: https://wordpress.manifest.build/

value: Show HN: Convert any WordPress website into an MCP app

author: brunaxLorax


value: mkagenius

value: Show HN: I built a mitmproxy AI agent using 4000 paid security disclosures

author: mkagenius


value: simjue

value: Show HN: Sss – S3 Client

author: simjue

title: Show HN: Sss – S3 Client


value: Marciok

value: Show HN: Gust –> Airflow-inspired workflow orchestration built with Elixir

author: Marciok


value: StealthyStart

value:
I’ve used Reddit as a customer acquisition channel for years. The challenge was always the same: finding high-intent threads every day and writing replies that were useful, compliant with subreddit rules, and didn’t trigger account issues.
Doing it manually worked well, but it didn’t scale and introduced risk when rushed.
So I built TrafficScout, a small tool that monitors Reddit in real time, surfaces conversations where people are actively asking for tool recommendations or alternatives, and generates draft replies based on thread context and subreddit rules.
I originally made it for my own products, then for a few friends, and now I’m opening it up.
What it does:
• Watches Reddit for decision-making conversations (e.g., “Best CRM for small teams?”)
• Scores each thread for buying intent
• Generates reply drafts based on the conversation and rules
• Checks account age, posting patterns, and shadowban risk
• Tracks which replies continue generating traffic over time
Why I built it:
A single helpful Reddit reply often sends traffic for months and sometimes ranks on Google.
But manually finding and responding to threads took hours a week. I wanted a workflow that reduced the repetitive work while keeping everything within Reddit’s guidelines.
What makes it different:
It doesn’t try to mass-post or automate your account. It acts more like a research and writing helper: find the right threads, reduce the manual steps, and keep your account safe.
The demo video shows live thread discovery and reply drafts.
https://www.trafficscout.app/
I’d love feedback on:
• The workflow
• Whether the thread scoring makes sense
• How useful the reply drafts feel
• Safety considerations or edge cases I may not have covered
Happy to answer questions or share more technical details.

value: Show HN: TrafficScout – Discover high-intent Reddit threads with AI agents

author: StealthyStart


value: sokhib-ibr

value:
Hello HN,
My wife and I recently started following the "30 plants a week" protocol for gut microbiome diversity. We found that the biggest friction wasn't the diet itself, but the manual tracking. We tried Excel and Notes, but deduplicating ingredients across meals manually was tedious and led to us dropping the habit.
I built a Telegram bot to automate this.
How it works:
You send a photo of your meal (or a voice note).
It uses an LLM (Vision) to extract ingredients.
It filters for plants and checks against your history for the current week to count only unique plants.
It suggests what to add next to increase diversity.
I also added a visual feedback loop: a virtual plant generates and grows based on your unique plant score (my wife is a florist, so this was a necessary UX feature).
I am planning to shift it towards gamification more so people can have a game to develop a healthy habit. For this, I will be aiming at mobile app.
It’s currently an MVP. I'm curious to hear your feedback on the extraction accuracy and the interaction flow.
Link to the bot: https://t.me/flora_quest_bot

value: Show HN: AI Telegram bot to track "30 plants a week" gut health goal

author: sokhib-ibr


value:
Day job of finance and some small scale programming.
Put a couple projects out there, but never got any feedback.
Built a simple platform for exchanging feedback. The more feedback you give, the more feedback you can receive (or skip and just pay a small fee).
Feedback has some standard questions and scoring, so better quality is encouraged.

value: Show HN: Flywheel Feedback – Free feedback for projects that get 0 comments


value: efortis

value: Show HN: Chrome Ext. for downloading HTTP responses for mocking APIs

author: efortis


value:
Hey HN, I'm building Amplift, an AI agent that combines influencer marketing, generative engine optimization (GEO), social listening, and AI writing into one conversational interface.
Instead of juggling 5+ tools, you can just ask things like "Find creators in AI SaaS with engagement above 4%" or "What are people saying about my competitors on Reddit?" and get actionable results.
Core features:
- Influencer discovery across millions of profiles with authenticity and sentiment scoring
- GEO dashboard to track AI search visibility and competitor citations
- Social listening across Reddit, Twitter, and other platforms
- AI writing for briefs and content drafts
We're currently in beta. Would love feedback from the HN community on the product and positioning.
Try it: https://www.amplift.ai/
One-month Free Trial Code: AMPLIFTGOOD

value: Show HN: Amplift – AI agent for influencer marketing, GEO, and social listening


value: Show HN: XeraSentry – Real-time Ethereum security monitoring in Python


value:
I built XeraSentry over 3 months - a Python tool that monitors Ethereum for security threats in real-time.
What it does:
- Detects whale movements (50+ ETH transfers)
- High-value transfer alerts (configurable threshold)
- Tracks sanctioned addresses (OFAC compliance)
- Monitors specific wallets you care about
- Address poisoning detection
- MEV bot detection
Tech stack: Python + Web3.py, SQLite database, Google Sheets integration, RPC failover system with 5 backup endpoints, transaction deduplication.
Already detected $70K+ in test runs with 10 modular detection rules.
Currently in private beta (limited to 50 testers) before open source launch on January 5, 2025.
Try it: https://lavender-clarette-17.tiiny.site
Built this solo because I was tired of missing whale movements and wanted a tool that runs locally (no API keys) with full visibility.
Would love feedback from the HN community - especially on the technical architecture or what features to prioritize before launch!

title: Show HN: XeraSentry – Real-time Ethereum security monitoring in Python


value: carsenk

value: I’ve been working on gotui, a modern fork of the unmaintained termui, rebuilt on top of tcell for TrueColor, mouse support, and proper resize handling. It keeps the simple termui-style API, but adds a bunch of new widgets (charts, gauges, world map, etc.), nicer visuals (collapsed borders, rounded corners), and input components for building real dashboards and tools. Under the hood the renderer’s been reworked for much better performance, and I’d love feedback on what’s missing for you to use it in production.

value: Show HN: Gotui – a modern Go terminal dashboard library

author: carsenk


value: sanketsaurav

value:
Hi there, HN! We’re Jai and Sanket from DeepSource (YC W20), and today we’re launching Autofix Bot, a hybrid static analysis + AI agent purpose-built for in-the-loop use with AI coding agents.
AI coding agents have made code generation nearly free, and they’ve shifted the bottleneck to code review. Static-only analysis with a fixed set of checkers isn’t enough. LLM-only review has several limitations: non-deterministic across runs, low recall on security issues, expensive at scale, and a tendency to get ‘distracted’.
We spent the last 6 years building a deterministic, static-analysis-only code review product. Earlier this year, we started thinking about this problem from the ground up and realized that static analysis solves key blind spots of LLM-only reviews. Over the past six months, we built a new ‘hybrid’ agent loop that uses static analysis and frontier AI agents together to outperform both static-only and LLM-only tools in finding and fixing code quality and security issues. Today, we’re opening it up publicly.
Here’s how the hybrid architecture works:
- Static pass: 5,000+ deterministic checkers (code quality, security, performance) establish a high-precision baseline. A sub-agent suppresses context-specific false positives.
- AI review: The agent reviews code with static findings as anchors. Has access to AST, data-flow graphs, control-flow, import graphs as tools, not just grep and usual shell commands.
- Remediation: Sub-agents generate fixes. Static harness validates all edits before emitting a clean git patch.
Static solves key LLM problems: non-determinism across runs, low recall on security issues (LLMs get distracted by style), and cost (static narrowing reduces prompt size and tool calls).
On the OpenSSF CVE Benchmark [1] (200+ real JS/TS vulnerabilities), we hit 81.2% accuracy and 80.0% F1; vs Cursor Bugbot (74.5% accuracy, 77.42% F1), Claude Code (71.5% accuracy, 62.99% F1), CodeRabbit (59.4% accuracy, 36.19% F1), and Semgrep CE (56.9% accuracy, 38.26% F1).
On secrets detection, 92.8% F1; vs Gitleaks (75.6%), detect-secrets (64.1%), and TruffleHog (41.2%). We use our open-source classification model for this. [2]
Full methodology and how we evaluated each tool: https://autofix.bot/benchmarks
You can use Autofix Bot interactively on any repository using our TUI, as a plugin in Claude Code, or with our MCP on any compatible AI client (like OpenAI Codex).[3] We’re specifically building for AI coding agent-first workflows, so you can ask your agent to run Autofix Bot on every checkpoint autonomously.
Give us a shot today: https://autofix.bot. We’d love to hear any feedback!
---
[1] https://github.com/ossf-cve-benchmark/ossf-cve-benchmark
[2] https://huggingface.co/deepsource/Narada-3.2-3B-v1
[3] https://autofix.bot/manual/#terminal-ui

value: Show HN: Autofix Bot – Hybrid static analysis and AI code review agent

author: sanketsaurav


value: peetle

value: Show HN: Pick and visualize real manufacturer paint colors on your home

author: peetle


value: kokada

value:
I am a fan of gitk and its simplicity, however as https://github.com/NixOS/nixpkgs/ is getting larger each day, it is becoming almost impossible to use it. I just did a quick test with commit e16041020b082ca847b3658ee1b69f8e6a4323b1 and after a few seconds the memory usage got close to 20GiB and I couldn't click on it (but the UI was still updating). This is probably because gitk tries to eagerly load all commits in memory, works fine for small/medium repositories, but nixpkgs is just too big.
I rarely want to check an old commit (and for that case, I generally don't use gitk anyway), and since I was interested in https://pkg.go.dev/modernc.org/tk9.0 for a while and had a free month of ChatGPT+ to test, I decided to try and vibecode an alternative of gitk writing with Go and modernc.org/tk9.0. So here it is.
The idea here is not to be a full featured replacement for gitk, but to re-implement the things I use. I tried to influence some of the architecture ideas to avoid the performance issues that the original have, so instead of loading all commits in memory it will load it in batches of 1000 (you can increase this using `-limit` flag but I recommend not setting this too high) at a time. Originally I also wanted to use only Go, but in the end I needed to use `git` for a few specific operations to keep it running fast (by default it still uses a pure Go implementation, but building it with `-tags=gitcli` is recommended).
In the end I got what I wanted, a small, self contained app that reproduces most of the features that I want.

value: Show HN: A lightweight Git history explorer written in Go

author: kokada


value:
Hi HN, I've always been interested in fractals, especially the Mandelbrot and Julia sets. A few years ago I created a 2d viewer of this inherently 4d space. But the other day I decided to ask Claude and GPT how to make this a full RT 3d explorer. A few hours later and this was vibe coded.
To use it you can use the mouse to rotate the fractal the the mouse wheel to zoom in and out. to map from 4d to 3d, one of the dims is mapped to an adjustable slider. the there is also a clipping plane slider to help visualize the internal structures of the fractal.
I have mixed feelings about vibe coding. It was amazing to go from an idea to live implementation within a few hours, but in my coding projects, I've always appreciated the journey and the learning, not just the final product. Vibe coding kind of skips to the end which is exciting and efficient, but just not as fulfilling as struggling through a project step-by-step.

value: Show HN: A real-time 4D fractal explorer in the browser using WebGPU


value: kristianeboe

value:
Some of my best periods of life and work came from living in shared houses with other builders — both in SF and abroad. With people moving to SF again for Y Combinator in January, I wanted to make something that could help others create that same kind of experience.
SF rent is brutal, and finding good housemates is weirdly hard — most people just settle for whatever they can find first that isn't terrible. So I built a simple directory where you can publish your housing search or browse others who are also moving.
It's intentionally lightweight: no fluff, no complicated flows — just a way for people to put themselves out there and hopefully form groups that make the whole move a lot more fun and a lot less chaotic.
I'm not with YC this batch, but I'm heading to SF in January anyway with some builder friends and would love extra company. Looking for roughly 4 more people for an 8-person hacker house (~$4k/person).
I'd love feedback on whether this is useful, what's missing, or if you've had co-living experiences that shaped your thinking.
https://www.homi.so/directory

value: Show HN: A(nother) co-living directory for founders moving to SF

author: kristianeboe


value: victorsanchez

value:
Hi HN,
A few months ago we noticed a pattern. Every GTM, product, and marketing team we talked to had the same problem. They were drowning in external data from Reddit, Discord, Slack communities, competitor sites, and social channels. But turning all of that noise into something structured and useful took an enormous amount of time.
We watched people spend days copying screenshots into spreadsheets, tagging posts, and checking competitor websites by hand. We were doing the same thing ourselves and it was obvious that none of this should be manual. So we built Sushidata, a system of AI agents that collect, organize, and summarize messy external data into a searchable, structured view of a market.
Here's a demo you can check out. it's not in prod so excuse the bugs and issues. Feel free to make your own sheets. Happy to help there too.
Demo link: https://tinyurl.com/sushidata
What it does:
- Pulls in competitor updates, customer sentiment, complaints, feature requests, and more Normalizes everything into a single datasheet
You can ask questions such as:
- "What issues are trending for our competitors this week?"
- "What is the summary of all the products from my competitors"
Why we built it:
- We wanted to eliminate the repetitive parts of research. Early users told us the system saved them weeks of manual work, so we kept building.
What’s in the demo
- A real competitive intel dataset, competitor monitoring, a spreadsheet style interface powered by AI agents
Feedback welcome
We would love thoughts from the HN community, especially around the data pipeline, agent behavior, and ways to simplify the interface. Happy to answer questions about how it works under the hood.
Victor, co-founder
Sushidata

value: Show HN: Sushidata – automating the painful parts of competitor and VoC research

author: victorsanchez


value: modinfo

value: Show HN: Self-hosted music library that converts URLs to HLS streams Rust+React

author: modinfo


value:
I used to work as a data engineer, and one of the big aspects of that was data monitoring. Data scientists & analysts do a lot of monitoring on the analytics side for changes in business metrics (signup rate, retention, etc), but as a DE I also needed to just to make sure that nothing broke. Data Axolotl focuses on the latter.
There are tons of tools that support data testing (love a dbt test), but even after writing robust tests we still ran into unexpected breaking changes. Most of the time, this became an issue when a product manager showed up wondering why some obscure metric was now null across the board. We didn't know that metric was being used (sometimes not even that it existed), so how would we have written a test for it? (A common cause was that an engineering team stopped recording a number because no one knew it was important; even when engineering did everything right, sent out comms "fyi we're removing the legacy user id values", and rolled out safely, no one expects that the marketing ROI dashboard depends on that.)
That's where Data Axolotl comes in: Data Axolotl is a CLI tool that catches unexpected breaking changes in analytics data sets, without you having to know in advance what might break.
Simply put, you point it at some data tables, run it daily, and it collects a ton of common metrics over time. (Column minimum, maximum, mean, row count, distinct count, etc.) If any of the metrics suddenly change, Data Axolotl generates an alert.
The whole thing runs from a python package, so you can run it locally or on your own infra via a scheduling tool like Airflow. You don't have to sign up for any new cloud services and risk data leaking data to a third party. History can be stored locally with sqlite (default), or in a remote db on your infra.
This is a pretty early release, so right now it only supports Snowflake tables, but we hope to add other database types in the future.
You can try out Data Axolotl today, directly from your local machine, by installing via pip. `pip install data-axolotl` A full setup guide can be found in the Readme.

value: Show HN: Data Axolotl – Monitor analytic data without writing individual tests


value: jomargon

value:
I built TrackSplit because I wanted to practice drums along to my favorite songs without the original drum track getting in the way.
Existing solutions like LALAL.AI and Moises require uploading your audio to their servers. I wanted something that runs locally, both for privacy and because I didn't want another subscription.
TrackSplit separates any song into 6 stems: vocals, drums, bass, guitar, piano, and other. It runs entirely on your Mac with no internet required.
Technical details:
  - Built with Swift/SwiftUI with an embedded Python backend
  - Uses HTDemucs v4 (Hybrid Transformer Demucs) for separation
  - GPU-accelerated on Apple Silicon via Metal/MPS
  - Processes most 4-minute songs in ~90 seconds on M1
Use cases:
  - Musicians practicing along to songs (remove your instrument)
  - Creating karaoke tracks
  - DJs extracting acapellas
  - Remixing and sampling
Available on the Mac App Store for $19.99 (one-time, no subscription).

value: Show HN: TrackSplit – Remove vocals and instruments from any song, offline

author: jomargon


value: BenjaminHarris

value: Show HN: Sunset Compass – Privacy-first sun/moon tracker in 50 languages

author: BenjaminHarris


value: byshako

value:
Hello HN,
I built SetLocale because I was frustrated with the localization process for my own projects. Whenever I used standard translation tools, they would often break the file structure (invalid JSON) or translate variable placeholders (like turning "{name}" into localized words), which broke the build.
SetLocale is designed to parse and lock syntax before translation.
How it works:
1. It identifies the file type (JSON, PHP arrays, XML, ARB, YAML, PO).
2. It uses regex/parsing to lock variable placeholders (e.g., %s, :count, {name}) and HTML tags.
3. It uses LLMs for the initial translation, followed by a verification step.
There is no sign-up required, and it supports crypto payments for privacy.
I'd love to hear your feedback on the workflow and if there are other file formats you frequently struggle with.

value: Show HN: SetLocale – Syntax-safe localization for developers (JSON, PHP, XML)

author: byshako


value:
Wasm exports are immediately available to TypeScript, even gc objects!<p>```
<script type="text/wast">
(module
  (type $Box (struct (field $val (mut i32))))
  (global $box (export "box") (ref $Box) (struct.new $Box (i32.const 42)))
)
  </script>
<script type="text/typescript">
const greeting: string = "Hello from TypeScript and Wasm!";
console.log(greeting, box.val);
</script>

value: Show HN: Use TypeScript in Servo Browser Fork


value: rmdd

value:
Dissatisfied with other hosted MAC address OUI vendor lookup tools, I made my own.
Nothing but a search-as-you-type input.
https://oui.so
Also, it works really well with curl:

  $ curl oui.so/b4:b9:e6:28:f1:ab | jq
  [
    {
      "oui": "B4B9E6",
      "name": "eero inc.",
      "address": "660 3rd Street  San Francisco CA US 94107"
    }
  ]

value: Show HN: Fast, no-fuss MAC address vendor lookup

author: rmdd


value: setrf

value:
Hey HN! I'm Mert.
I built this because I was frustrated with LLM benchmarks potentially being contaminated by training data. When a model scores 99.9% on MMLU-Pro-Max, we can't tell if that's genuine reasoning or memorization.
Forecaster Arena tries to solve this by testing models on events that haven't happened yet—real prediction markets from Polymarket. The ground truth is reality itself, weeks or months later.
How it works:
7 frontier LLMs (GPT-5.1, Claude Opus 4.5, Gemini, Grok, DeepSeek, etc.) (will be updated) -> Each gets $10k virtual capital weekly -> They bet on 500+ real prediction markets -> Bet size = confidence (larger bet = more confident) -> We measure calibration (Brier score) + returns (P/L)
Currently running first cohort (started Dec 7). First statistically significant analysis expected over the next few weeks.
Everything is open source (MIT): https://github.com/setrf/forecasterarena
Happy to answer questions about the implementation or trade-offs I made. Would be great to hear your feedback on the methodology as well!

value: Show HN: Forecaster Arena – Testing LLMs on real events with prediction markets

author: setrf


value: scarletkc

value:
Hi HN, I built TabHere, a Chrome extension that brings AI autocomplete / continuation to almost any editable area on web pages (inputs, textareas, contenteditable, etc.).
Chrome Web Store: https://chromewebstore.google.com/detail/oeokpncnejjfjdbpnch...
It’s open source (MIT).
I’d love feedback on UX (when autocomplete feels wrong), site compatibility issues, and edge cases around contenteditable or cursor handling.

value: Show HN: TabHere – AI autocomplete for almost any editable field on the web

author: scarletkc


value: Show HN: The world's least deterministic programming language


value: gregsadetsky

value: Show HN: Search the lyrics of 500 HÖR Berlin techno sets

author: gregsadetsky


value:
I’ve been building a small AI agent that can analyze both structured and unstructured data in minutes, things like CSVs, product logs, support tickets, CRM notes, PRs, etc.
The motivation is personal: at every company I’ve worked at, our data was scattered across warehouses, Slack, GitHub, Asana, and a dozen other tools. I spent way too much time copy-pasting from one source to another just to answer basic questions.
This project is still really early and rough around the edges, but it can already generate dashboards, summaries, and insights without any modeling or setup.
I’m genuinely not sure how useful this is beyond my own workflows, so I’d really love some brutally honest feedback from y'all.
Here’s a short video of it in action if you’re curious:
https://www.youtube.com/watch?v=HTp8flEeZao
And you can try it here: app.arka.so

value: Show HN: An agent that analyzes both structured and unstructured data in minutes


value: adamzwasserman

value:
I built a Chrome extension to solve a problem I kept having: losing track of conversations on HN.
The threads page is a firehose. Someone replies to your comment, you miss it, the conversation dies. Or you revisit a thread and can't remember which comments you've already read.
HN Reader does three things:
1. Hides stories you've seen – Checkbox next to each story. Check it to dim. Helps filter the front page to stuff you haven't looked at yet.
2. Collapsible comments that remember – Click the arrow to collapse a comment thread. Come back later and it stays collapsed, unless someone added a new reply, then it auto-expands with a "NEW" badge.
 3. Highlights your conversations – On your threads page, badges show "you", "replied to you", and "you replied" so you can instantly spot active conversations.
That last one is what made me build this. I was missing replies buried in long threads. Now I just glance at my threads page and the blue "replied to you" badges jump out.
Everything stays in local storage. No server, no account, no tracking. Auto-cleans old data when storage gets full.
GitHub: https://github.com/adamzwasserman/hnreader
Works in Chrome, Arc, and any Chromium browser. Load it unpacked from the repo.
Feedback welcome – especially on what other HN reading problems you'd want solved.

value: Show HN: HN Reader – Track your conversations across Hacker News

author: adamzwasserman


value: mendexio

value: Show HN: Seer – open-source market opportunity detection for indie developers

author: mendexio


value: Show HN: Create AI Videos and Images

title: Show HN: Create AI Videos and Images


value: csvtool

value:
Hey HN! A few months back I started messing around with a Rust CLI tool to help clean up messy CSV files. College started so I had to take a break, but recently picked it back up because I really want to polish it and make it actually useful.
I’m still pretty new to Rust, so I’d love feedback from anyone who works with data, CLI tools, or just has opinions on CSV pain points.
Right now it can:
Clean and filter leads by numeric ranges or other criteria
Remove duplicates (emails, names, etc.)
Validate emails and remove invalid entries
Add tags to leads for campaigns
Sort and limit your data for targeted outreach
Export cleaned CSVs
It’s free, open source, works on Windows, and I’m very open to feedback, missing features, weird CSV edge cases, better ways to structure things, etc. Anything helps!
Thanks for taking a look!

value: Show HN: A tiny Rust CLI tool to clean and fix messy CSV files

author: csvtool


value: lalalerodas

value: Show HN: I want to democratise Bloomberg Terminal

author: lalalerodas


value: Show HN: Argly – Turn your iPhone into a remote for your Mac [over WiFi]


value: Show HN: ProdE – code change impact and root cause analysis for large codebases


value: fabioluciano

value:
PowerKit is an open-source, modular status bar framework for tmux. It provides 32+ built-in plugins for displaying system information, development context, security status, and media information—all with intelligent caching and a semantic color system.
WHY I BUILT THIS
I was frustrated with the fragmented tmux status bar ecosystem. Most solutions required cobbling together multiple plugins, each with different configuration styles, no caching, and inconsistent theming. I wanted:
1. One framework with consistent configuration across all plugins
2. Smart caching to avoid hammering system calls every status refresh
3. Semantic colors that work across different themes
4. Conditional display - plugins that hide when not relevant (e.g., VPN only shows when connected)
TECHNICAL HIGHLIGHTS
Intelligent Caching: Every plugin has configurable TTL caching. Results are stored in ~/.cache/tmux-powerkit/ with timestamps. Default TTLs are tuned per-plugin based on data volatility—camera/microphone checks every 1s for privacy, while package updates cache for 1 hour.
Semantic Colors: Instead of hardcoding hex values, plugins use semantic names like 'warning' and 'error'. The active theme resolves these at runtime. Switch themes without reconfiguring plugins.
Threshold-Based Colors: Many plugins support multi-tier thresholds. CPU can turn yellow at 70%, red at 90%.
Conditional Display: Plugins intelligently hide when not relevant:
- git: only shows in git repositories
- vpn: only shows when connected
- network: only shows above configurable traffic threshold
- camera: only shows when camera is active
- kubernetes: can show only when cluster is reachable
PLUGINS (32+)
System Monitoring: cpu, gpu (NVIDIA/AMD/Intel), memory, disk, loadavg, temperature, fan, uptime, brightness
Network: network (bandwidth), wifi (with signal strength), vpn (WireGuard, OpenVPN, Tailscale, etc.), external_ip, ping, ssh, bluetooth, weather
Development Tools:
- git: branch with modified repo detection
- kubernetes: context/namespace with interactive fzf selectors
- cloud: AWS/GCP/Azure context with production warnings
- terraform: workspace with production highlighting, OpenTofu support
Security: smartkey - hardware security key detection (YubiKey, SoloKeys, Nitrokey) with "waiting for touch" indicator
Media & Audio: audiodevices (with device switching popup), microphone (with mute toggle), nowplaying (Spotify, Apple Music, MPRIS), volume, camera (privacy indicator)
Package Managers: packages - unified updates for brew, yay, apt, dnf, pacman
Time: datetime, timezones (multiple zones with aliases like nyc, tokyo, london)
INTERACTIVE KEYBINDINGS
prefix + ? : Browse all PowerKit options
prefix + K : Kubernetes context selector
prefix + N : Kubernetes namespace selector
prefix + W : Terraform workspace selector
prefix + J/O : Audio input/output device selector
prefix + m : Microphone mute toggle
prefix + Q : Clear all caches
EXTERNAL PLUGIN INTEGRATION
Wrap any external tmux plugin with PowerKit's styling using the external() function. Supports custom icons, colors, and cache TTL.
INSTALLATION

  set -g @plugin 'fabioluciano/tmux-powerkit'
  set -g @powerkit_theme 'tokyo-night'
  set -g @powerkit_plugins 'datetime,cpu,memory,git,battery'

PLATFORM SUPPORT
Linux: Full support
macOS: Full support (some plugins use different backends)
BSD: Partial support
WSL: Works with Linux-compatible plugins
STACK
Pure shell (bash/zsh compatible). No compiled dependencies. Uses standard Unix tools (df, ps, /proc/*, etc.) with fallbacks per platform.
Themes included: Tokyo Night, Kiribyte. More welcome via PR.
Feedback and contributions welcome. What plugins would be useful for your workflow?

value: Show HN: PowerKit for Tmux – 32 Plugins

author: fabioluciano

title: Show HN: PowerKit for Tmux – 32 Plugins


value: jayteetech

value:
I've designed a complete open-source hiring platform but need developers to build it. Zero code written yet - perfect for founding contributors.
Features:
- QR code applications (scan to apply anywhere)
- Real-time application tracking
- Self-hostable, MIT licensed
- Plugin marketplace
Looking for: All skill levels, especially .NET developers
Repo: https://github.com/gemini45840-cmyk/UJAS
Discord: https://discord.gg/rrSbs997

value: Show HN: UJAS – An open-source hiring platform (like WordPress for hiring)

author: jayteetech


value: schuhwerk

value:
Hi! Long-time HN lurker here. I built an advent calendar for my hobby, Acro-Yoga. It unlocks a new flow or "washing machine" (a move that can be looped endlessly) every day - we collected those over the year and wanted to share them.
Since we just hit the halfway mark (day 12), you have 12 doors unlocked and ready to open immediately.
This probably wouldn't have started without the idea of: "AI can do that for me..." I learned that most Agents are really bad with intricate CSS: I gave the challenge of the "door-opening mechanism with drop-shadow" to Claude Opus 4.5, GPT 5.1, and Gemini 3 - all failed a lot. The beginning of the rabbit hole. 24 is a really big number ;) I hope you enjoy your days until Christmas and have fun with my little project. Ask anything!
Some tech facts: self-hosted videos (I hope my server will not burn), I used plyr.js, no framework, just simple PHP & Vanilla JS. Hosted on Hetzner which runs Yunohost, which runs https://github.com/YunoHost-Apps/my_webapp_ynh. For the illustrations I used nano-banana (and time), recraft-vectorize, https://github.com/schuhwerk/SVG-Colorizer and a lot of manual Illustrator.

value: Show HN: Advent of Acro – A frameworkless PHP/JS video advent-calendar

author: schuhwerk


value: NullPointerDD

value: Show HN: MiddleDrag: Three-finger trackpad gestures for middle-click on macOS

author: NullPointerDD


value: admtal

value: I built Demo Scope because I couldn't find an app that did this

value: Show HN: Record iOS Screen with Gestures Included

author: admtal


value: freakynit

value:
https://ocean-wave-simulation.pagey.site/
Prompt used:
Prompt: Create a single-page app in a single HTML file with the following requirements:
- Name: Ocean Wave Simulation
- Goal: Display realistic animated waves.
- Features: Change wind speed, wave height, lighting.
- The UI should be calming and realistic.

value: Show HN: Ocean Wave simulation: one-shotted by Gemini 3

author: freakynit


value: ZenCrafter

value: I built Fairyland Studio as a playground for creating generative scenes inspired by fairyland atmospheres and natural landscapes. The idea is to let people explore compositions that feel like wandering through dreamlike forests, mountains, and gardens, with styles organized to keep a sense of consistency while allowing diversity. One of its strengths is that you don’t need long prompts — just keywords, themes, or objects are enough to generate immersive compositions.<p>It’s still experimental, but I’d love feedback on whether the scenes capture the sense of wonder and natural beauty I’m aiming for, and what features could be improved or added to make the experience richer.

value: Show HN: Fairyland Studio – A Playground for Generative Landscapes

author: ZenCrafter


value:
I got frustrated scrolling through thousands of photos to find "person holding camera" or "sunset on beach." Filenames are useless, manual tagging never happens, and cloud solutions compromise privacy.
Built this using NexaAI's local VLM (Qwen3-VL) + semantic embeddings. How it works:
1. VLM generates natural language descriptions of each image (one-time processing)
2. Descriptions converted to 384D embeddings
3. Search by meaning using cosine similarity (<100ms for 1000 images)
Everything runs locally. Your photos never leave your device. Zero API costs.
Technical reality: CPU processing is slow (~20-30s per image initially), but searching is instant afterward. JSON database works fine for personal collections; you'd want FAISS for 10k+ images.
It's a prototype, but it works and solves a real problem I had. Open to feedback.
Built for NexaAI's Builder Bounty Program (on-device AI, privacy-first).
Demo video: https://youtu.be/YVkPa-aJpEo
Medium writeup: https://medium.com/@pankajgoyal4152/building-smart-photo-fin...

value: Show HN: Smart Photo Finder – Semantic photo search, not filename(100% local AI)


value: flyingriverhrse

value:
Skyulf is a local-first MLOps workspace. Visual canvas for feature engineering, model training with hyperparameter search, experiment tracking, and one-click testing.
It functions like a visual automation tool (like n8n) but for ML pipelines. You drag-and-drop nodes to handle data loading, preprocessing (25+ nodes), feature engineering, and model training. No code needed for common tasks.
What's next? I am working on integrating powerful models like XGBoost/LightGBM/CatBoost, adding SHAP/LIME explainability, and eventually building a visual LLM builder (LangChain nodes) and more EDA features.
It's in active alpha. It works, but expect bugs or incomplete features.
-- I'd love feedback. Does the visual MLOps tool solve a problem for you? What’s the first custom node or feature you'd look for? or What you would need in this app to be more useful for you.
Thanks for checking it out!

value: Show HN: Skyulf – Open-source, self-hosted MLOps platform

author: flyingriverhrse


value: stimm

value: Show HN: Stimm – Low-Latency Voice Agent Platform (Python/WebRTC)

author: stimm


value: antonmb

value:
I have been working on a lightweight authentication flow designed for SaaS apps, courses, and small platforms that do not want to deal with complex identity systems or collect personal data.
Toqen provides QR-based login, temporary access passes, and TOTP verification without storing user information. The goal is to create a simple, privacy-first alternative to email login and passwords.
It uses Cloudflare Turnstile for anti-bot protection and runs on an edge-first stack. I would appreciate feedback from the community on the architecture, security considerations, and potential pitfalls.
Demo: https://www.toqen.app/demo

value: Show HN: Toqen – privacy-first authentication flow with QR and TOTP

author: antonmb


value: thenextechtrade

value:
Hey HN! I built VideoMaker AI to solve a problem I kept running into: creating professional-looking videos is time-consuming and requires expensive tools or skills.
VideoMaker AI lets you generate videos from text prompts in minutes. Just describe what you want, and the AI handles scripting, visuals, voiceover, and editing automatically.
Key features:
- Text-to-video generation with AI
- Automatic voiceover in multiple languages
- Stock footage and image integration
- Professional transitions and effects
- Export in various formats and resolutions
I'd love to hear your feedback! What features would make this more useful for your projects?
Try it out: https://videomakerai.app

value: Show HN: VideoMaker AI – Turn text into professional videos in minutes

author: thenextechtrade


value:
Hi HN — I’m Jessy, founder of SiameseAI. We just shipped Foundry v0.1.
What it is today: a hosted workspace where you can run Blender, FreeCAD, KiCad, and JupyterLab from a browser, with persistent projects/storage.
What it isn’t: we didn’t build these tools. They’re upstream open-source projects. What we built is the cloud wrapper around them (session managment, persistent storage, and the product layer). The AI in v0.1 is a basic docs copilot (RAG) over files you upload (PDFs/specs/manuals). We are currently working on tool-native agents.
Why we built it: proprietary hardware engineering tools setup friction and environment drift burns a lot of time and cash for hardware teams just starting out. We wanted a simple way to spin up a CAD server on any machine and eventually with programmatic and AI access to the native tools.
Open-core: Foundry is open-core. We’re charging for the hosted product (compute, AI token, convenience, eventually team features). Where we modify upstream OSS, we’ll publish source as required and aim to upstream improvements when practical.
If you want to try it, we have a 7-day trial (+ $25 credit): https://askfoundry.com
Feedback I’d really value:
1. What’s the #1 dealbreaker for running these tools in the cloud? 2. What would make you use this weekly?
Support or DMs: support@siameseai.com

value: Show HN: Vibe CADing in the cloud with open source tools


value: vishalvshekkar

value:
I built Jottings because I was tired of my own thoughts getting trapped inside algorithmic feeds where I had to perform. There was a huge mental load before posting something on X or Instagram.
Every time I wanted to share something small or unfinished, I opened Twitter and lost 20 minutes to the timeline. Writing a blog post felt too heavy for those smaller, quick thoughts. I just wanted a place to write something down quickly and hit publish.
Jottings is that place. It gives you a clean microblog on a domain you own. Posts show up in simple chronological order. No likes. No followers. No feed trying to decide what matters.
What Jottings is
- A microblogging platform that builds fully static microblog sites
- A free subdomain (you.jottings.me) or connect your own domain on PRO plans
- Markdown, tags, RSS feed, links with preview, and image uploads
- An optional AI writing helper when you are stuck or lazy to fix grammar
- Optimized for SEO and AI search friendly
- Analytics for your sites
What it is not
- Not a social network
- Not an engagement funnel
- Not trying to keep you on the site
- Not a replacement for long-form blogging, though you can use it that way
How it works
Each Jot publish triggers a static site rebuild. The site is stored in Cloudflare R2 and served directly at the edge. Custom domains go through Cloudflare SSL for SaaS.
I built it to be boring, reliable (barring Cloudflare's latest issues), and cheap to run.
Pricing
Free tier for a subdomain, text posts, and a lot more.
USD5 per month for custom domains, images, full Markdown, and the writing helper.
I priced it to be an easy yes.
Limitations
- No comments (on purpose)
- No native apps yet (iOS is coming)
- The writing helper is helpful but not magic
- I am a solo founder, so features move at human speed
I use Jottings regularly to document what I build. It has been the lowest-friction way I have found to publish anything publicly.
Demo of Jottings site for product updates: https://jottings.jottings.me/
Demo of my personal Jottings site: https://jottings.vishalvshekkar.com (with custom subdomain)
I would love feedback from this community. What would make this better or more useful for you?
Check it out here: https://jottings.me (2 min set up)
Feel free to write to me at vishal@vishalvshekkar.com
— Vishal

value: Show HN: Jottings; Anti-social microblog for your thoughts

author: vishalvshekkar


value: Show HN: CodeProt – Filter PR nitpicks using AST and context-aware analysis


value: Show HN: ClipFlow – Free clipboard history for macOS (like Windows Win+V)


value: maaydin

value: Show HN: A Go client for GitHub Models (simple, fast, MIT licensed)

author: maaydin


value: maxnew

value: Show HN: A free derivative calculator with step-by-step explanations

author: maxnew