value: szdoro

value:
Hi everyone! I built Allein, a Markdown editor powered by Ollama — use your own LLMs locally for AI-assisted writing, completely private, no account required, and works offline.
What it does:
- Context-aware autocompletion (think GitHub Copilot for Markdown)
- Writing improvements (spelling, grammar, readability)
- Flexible model selection—swap between different LLMs based on your needs and your device's capabilities
- Full-featured Markdown editor with live preview
Why I built it: I wanted GitHub Copilot-like autocompletion in a lightweight text editor—something between Apple Notes' basic suggestions and a full IDE. As a non-native English speaker, I also wanted built-in writing improvement tools to help me improve my English.
Tech: Built with Tauri, React, Rust, and Ollama for local LLM inference. Ollama is incredibly powerful, and this felt like a perfect use-case for it.
I'd love to hear your thoughts. Feedback on the concept and any features you'd like to see are very welcome!
GitHub: https://github.com/szilarddoro/allein
Website: https://allein.app

value: Show HN: Allein - Markdown editor with AI autocompletion, completely offline

author: szdoro


value: rbren

value: Show HN: OpenHands Software Agent SDK

author: rbren

title: Show HN: OpenHands Software Agent SDK


value: florianwueest

value: Show HN: I built the world's first AI model that fixes lazy eye in photos

author: florianwueest


value: pegor

value: As part of a little research and also some fun I decided to try my hand at seeing how small of an ESP32 board I can make with functioning WiFi.

value: Show HN: F32 – An Extremely Small ESP32 Board

author: pegor


value: Show HN: Gallery of 4,600 website design patterns indexed by fonts and colors


value: rakeshmenon

value: Search package registries across multiple ecosystems (npm, PyPI, RubyGems, Crates.io, Packagist, Hex) and fetch package context (README, downloads, GitHub info, usage snippets). It features smart ecosystem auto-detection, so your AI can find packages without needing to specify which ecosystem to search.

value: Show HN: PackageLens MCP – search package registries across multiple ecosystems

author: rakeshmenon


value: ivanramos

value:
Indie founders tweet a lot of updates, bug fixes, ideas, and small wins.
But none of that ever makes it into a roadmap or changelog.
I built a small tool that uses those tweets as the source of truth and turns them into a public roadmap you can share.
Nothing fancy.
Just practical.
1. Connect your account
2. Drag tweets into Planning / Building / Done
3. Share your roadmap URL
Link: https://publicroadmap.to/
Would love your thoughts.

value: Show HN: PublicRoadmap – turn your tweets into a public roadmap

author: ivanramos


value: caliweed

value: Show HN: Fishy History

author: caliweed

title: Show HN: Fishy History


value: AzamatKh

value:
Hi HN,
I built a Chrome extension to help with Reddit's karma requirements. It suggests comment ideas based on the post and subreddit you're reading —> you edit and post manually.
The problem: New or low-karma accounts can't post in most subreddits until they hit karma thresholds. Writing good comments takes time and subreddit knowledge. Generic AI comments get downvoted.
What it does:
Reads the post you're viewing
Suggests comments that match the subreddit's tone
You edit before posting (no automation)
Demo: https://chromewebstore.google.com/detail/karma-flow-to-me/ik...
This is early and I want to avoid any spammy territory. Looking for your feedback

value: Show HN: KarmaFlowToMe – your Reddit comment co-pilot

author: AzamatKh


value: Show HN: PgEdge Control Plane, a declarative API for multi-region Postgres mgmt


value: lakshmananm

value: Show HN: Online Coworking Space – WeWork on Cloud

author: lakshmananm


value: gardnr

value:
After the major outages from AWS and Cloudflare, I began wondering how to make my own services more resilient.
Using nameservers from different providers is doable but a bit tricky to manage. OctoDNS helps automate keeping the zones synced so AWS, Cloudflare, etc. are all serving the same information.
In an age of centralized infrastructure, we can exploit the capabilities from the origins of the decentralized internet.

value: Show HN: OctoDNS, Tools for managing DNS across multiple providers

author: gardnr


value: articsputnik

value: Show HN: Presenterm – Create beautiful terminal presentations from Markdown

author: articsputnik


value: matheusdevmp

value:
Hello HN,
I’m the author of this paper. I've been researching cognitive architectures and found that while LLMs are scaling impressively, they are hitting architectural ceilings regarding persistent memory, reasoning depth ("System 2"), and energy efficiency.
I wrote this paper to propose MQ-AGI, a theoretical framework that attempts to solve these bottlenecks through Orchestrated Modularity rather than just parameter scaling.
The core concepts:
Decomposition: Instead of a monolith, the system uses specialized Domain Expert Networks (DENs) coordinated by a Global Integrator Network (GIN) — heavily inspired by Global Workspace Theory.
Quantum-Inspired Routing: The "secret sauce" is treating the selection of experts not as a statistical gating problem (like in MoEs), but as a Combinatorial Optimization problem. I model this using Hamiltonian energy minimization to find the optimal coalition of experts for complex tasks.
Note: I explicitly address the QRAM bottleneck and latency issues in the paper. The proposal focuses on optimizing low-dimensional metadata (routing signals), making it feasible for NISQ devices or classical simulation via Tensor Networks.
DREAM Memory: It integrates a hierarchical memory protocol (Episodic/Semantic) with adaptive retention (TTL) based on user engagement, rather than raw context window stuffing.
The paper includes the full mathematical formalization (Hamiltonians, Free Energy Principle equations) and a critical feasibility analysis.
It’s a preprint and a conceptual blueprint. I’d love to hear your feedback on the routing topology and the feasibility of the hybrid core.
https://zenodo.org/records/17654543

value: Show HN: MQ-AGI A neuro-symbolic architecture for modular AGI

author: matheusdevmp


value: vykthur

value: Show HN: I wrote a book on How to build your own agent framework

author: vykthur


value: dogancan

value:
I built a a specialized RSS reader for researchers. It aggregates papers from 3000+ journals (Nature, Science, arXiv, etc.) into a clean, unified feed. It's in beta.
I found it difficult to keep up with the flood of new papers across different journals and arXiv categories. Existing RSS readers felt clunky for this specific use case, and I wanted something faster and more focused (more than Twitter/Bluesky)
Let me know what you think!

value: Show HN: Uncited

author: dogancan

title: Show HN: Uncited


value:
This is a mac app that supports local media indexing, text-to-audio transcription, and video-to-text generation with VEO 3.1
The app runs offline and lets you index local and external drives without needing to upload your content to the cloud. To use VEO you'll need an internet connection and your own API key.
We're only supporting Silicon Macs right now, but would consider Intel and Windows releases if there's interest. Price is $19.99 for a lifetime license that includes future updates.
If you have any feedback or want to join our discord, drop me a comment.
Demo: https://www.youtube.com/watch?v=fUUOd3TVBdY

value: Show HN: I made a macOS app to index media and transcribe audio locally


value:
Hi HN,
I can overthink dating (probably just like many of you) and thought that I can't be the only person over-thinking texts. So I decided to build Lucen - an AI relationship coach that analyzes your texts to give you advice. Goal to help people with questions like:
- “Is this person actually into me or just bored?”
- “Am I doing too much?”
- “Did I mess things up with that text?”
What Lucen does
You upload transcript, screenshots or screen recoding of messages and Lucen:
- Gives you a report with interest, compatibility, red/green  flags.
- Allow you to ask questions about your conversations.
- View and select your messages to ask about them.
How it works (high level)
Ingestion – You upload screenshots or recording. We OCR and reconstruct message sequences (sender, timestamps, etc.).
Conversation modeling – We build a structured representation: who said what, when, to whom, and in what order.
LLM reasoning – Analyze the texts, generate report with evidence and seed chat featue.
Tech-wise:
React Native/Expo (web + iOS), Firebase (Auth/Firestore), RevenueCat for IAP, and OpenAI on the analysis side, plus analytics via PostHog.
Biggest challenge:
Parsing texts from a phone screen-recording. Users can scroll at different speeds.
What I’m trying to do differently:
Goal is to bridge the gap between the two extremes in dating related to cover the weird state before labels, when you have a lot of confusion and overthinking.
- (Upper funnel of dating) AI wingman apps to automate dating apps e.g. PlugAI, RizzGPT.
- (Apps targeted at couples) Couple apps: Quiz apps, couples therapy, etc.
Status & pricing
iOS app + web are live.
Still iterating on onboarding, the analysis UX, and pricing.
There is a paid subscription, but if the price is a blocker and you’re willing to give me thoughtful feedback, I’m happy to sort out access codes / discounts.
What I’d love feedback on from HN
- UX/product
- Is the upload flow too clunky?
- Accuracy
- Pricing
- What’s missing?
Monetization
If you’re in a situationship, talking to someone new, or just curious, you can try it here:
https://lucen.app
I’ll be in the comments all day responding and would really appreciate any feedback, skepticism, or brutal honesty from this crowd.

value: Show HN: Lucen – AI dating coach for over-thinkers (like me)


value: baqiwaqi

value:
Hi HN,
I built this tool because I found it difficult to access content on NPO Start (the Dutch public broadcaster) as a non-native speaker. While there is a lot of good local content, very little of it has English subtitles.
This is a command-line tool that fetches the Dutch subtitles from the NPO stream and translates them into English (SRT/VTT format) so you can use them with your media player. It helps bridge the gap for expats living in the Netherlands or people trying to learn the language who need English context.
It's open source and runs locally on your machine. I'd love to hear any feedback on the translation quality or suggestions for feature improvements.

value: Show HN: NPO English Subtitles – Watch Dutch Public TV with Translated Subs

author: baqiwaqi


value: Roccan

value: Show HN: The Open Rate Sheet – A Glassdoor for digital art commissions

author: Roccan


value: Show HN: GraphQL Schema Generator for Golang


value:
Hi HN,
We’ve built Sora Watermark Remover, an AI-driven Sora watermark remover designed for pixel-accurate detection, motion tracking, and clean inpainting on Sora / Sora 2 videos. Upload a file or paste a Sora URL — the system removes the watermark while keeping motion, lighting, and audio perfectly intact.
Key Features
• Pixel-accurate detection & tracking
Automatically locks onto static or moving Sora 2 watermarks and tracks them frame-by-frame.
• Frame-consistent removal
No blur filters, no smearing. Motion, shadows, and lighting remain stable across the full sequence.
• Seamless AI inpainting
Rebuilds textures, edges, and fine details so the repaired area blends naturally into the shot.
• One-click workflow
Fully in-browser: Upload → Generate → Done. No installs, no masking, no timeline editing.
Use Cases
• Publish watermark-free Sora 2 clips on YouTube, TikTok, Instagram
• Prep Sora footage for editing, VFX, remixing, or multi-shot compositions
• Maintain consistent branding and visual style across creative projects
• Create clean source material for AI pipelines and post-production workflows
We’d love feedback on detection accuracy, edge cases, and features you’d like to see next.
https://unsorawatermark.com/?i=d1d5k

value: Show HN: Sora Watermark Remover –Pixel-Accurate Cleanup for Sora / Sora 2 Videos


value: mifydev

value:
Hi HN! I’m Ivan, one of the founders of Sourcewizard.
It’s a CLI tool that works with AI coding agents (like Cursor and Claude) to install and set up SDKs correctly including middleware, pages, env vars, everything.
Similar to the PostHog Install AI Wizard: https://posthog.com/docs/ai-engineering/ai-wizard
But for more packages.
Run this to try it:
npx ai-setup clerk
We built it after watching agents fail basic installs.
Wrong packages, deprecated methods, half-finished setups.
Sourcewizard uses package-specific prompts that complete clean installs ~90% of the time on Next.js apps.
Currently we support:
A Few Auth Providers: Clerk and WorkOS
Search APIs, Emails and Notifications: Firecrawl, Resend, Knock
Quickstart: https://docs.sourcewizard.ai/quickstart
More context: https://sourcewizard.ai/blog/why-your-sdk-breaks-in-cursor
Notes:
- The client code is open source: https://github.com/sourcewizard-ai/sourcewizard
- We explicitly ignore existing Env Files: https://github.com/sourcewizard-ai/sourcewizard/blob/main/in...
Would love feedback!
— Lukas & Ivan

value: Show HN: Sourcewizard – AI installs SDKs in your codebase

author: mifydev


value: hweihwang

value: Show HN: CodexUse – A local GUI to manage Codex CLI profiles and rate limits

author: hweihwang


value: claygood

value: Show HN: Proxilion – An Open-Source MCP Security Gateway

author: claygood


value: sabareesh

value: Modern MCP agents often blow past context limits because tools return huge blobs (DOM dumps, network traces, screenshots, etc.). MCP Compact is a small proxy that sits between your agent and the upstream MCP server, forwards every tool call, and uses an LLM to summarize the response according to per-tool rules (token budget, fields to preserve/drop). It supports StreamableHTTP, tracks token usage, and can reconnect sessions, so you can keep agents focused without rewriting agent code or tools.

value: Show HN: MCP Compact – Middleware to trim noisy browser/DOM/tool outputs

author: sabareesh


value: jokera

value: Show HN: Batch process images with AI workflows

author: jokera


value: Hey HN! We built Faraday, an AI scientist that can run complex, multi-step biotech workflows from literature search, molecule design to clinical data analysis, retrosynthesis planning etc. It’s designed to behave like a capable research coworker.<p>We’re keeping access gated for now to avoid unexpected usage spikes, but if you’re interested, sign up ascentbio.xyz/join and I’ll send access within 12 hours!

value: Show HN: Faraday – the most capable AI scientist for biotech


value: hariharprasadd

value:
i built a fun little python package called ardage (ARxiv DAtaset GEnerator) that lets you generate markdown datasets of research papers using natural language queries, blazing fast. it's perfect for generating post-training datasets for llms, rag knowledge bases, and more.
you can install it with 'pip install ardage', and use it in interactive mode in the cli, use it directly in the cli with flags, or import the library into your own code and build with it!
demo: https://x.com/hariharprasadd/status/1991346557459841196?s=20

value: Show HN: Ardage, a tool to build ArXiv Markdown datasets using natural language

author: hariharprasadd


value: poieticdog

value:
Fragment is an AI-native notebook designed for people who think in structures —
researchers, engineers, educators, writers.
It combines:
• Markdown notes
• YAML-based “Prism Protocol” for tone/persona/boundaries
• AI-rendered Scene/Flow diagrams
• Config blocks that define scope, audience, and language
The goal is to make AI collaboration consistent, reusable, and calm — not prompt-chasing.
Demo: https://fragment.place/demo/prism
Docs: https://fragment.place/docs
Happy to hear feedback or questions.

value: Show HN: Fragment – An AI-native notebook with YAML-based Prism Protocol

author: poieticdog


value: CommSearch is a leading AI in Telecom solution for telecommunications search. Accelerate your telecommunications Research & Development with advanced AI technology.

value: Show HN: AI Search Engineer in Telecoms for Research and Development


value: Show HN: Pusher's Maze – a browser-based puzzle game


value: vijayanandg

value:
I built Quantum4J because I couldn’t find a clean, dependency-free way to experiment with multi-qubit unitaries from Java. Most QC libraries target Python, so this is meant to bring quantum circuit construction and state-vector simulation to the JVM.
It includes:
- state-vector simulator (~25 qubits)
- standard gates (X, Y, Z, H, S, T, RX/RY/RZ, CX, CZ, SWAP, ISWAP, CCX)
- measurement + classical registers
- OpenQASM 2.0 exporter
Example:
QuantumCircuit.create(2).h(0).cx(0,1).measureAll();
Would love feedback on the architecture, gate engine, or ideas for what to add next.
Iam considering expanding Quantum4J into a more complete JVM quantum tooling ecosystem.
Interested in feedback from practitioners or researchers using Java in QC or simulation.

value: Show HN: Quantum4J – A Pure Java Quantum Computing SDK

author: vijayanandg


value: qwikhost

value: One click to save entire Facebook albums or selected albums.

value: Show HN: FB Album Downloader

author: qwikhost

title: Show HN: FB Album Downloader


value: ItsKaranKK

value:
Hey all!
I created ogBlocks because, honestly, dealing with complex CSS and figuring out animation libraries was always a massive headache and most of them didn't even allow customization
The goal was to enable everyone—not just animation experts drop in beautiful, interactive UI elements and animations into their React projects without worrying about the messy code underneath.
Think of it as the easy button for awesome user experiences without worrying about styling and animation complexities
I know you'll love the product - just check it out
Karan

value: Show HN: I Built an Animated UI Library for React Lovers

author: ItsKaranKK


value: snasan

value: Show HN: An A2A-compatible, open-source framework for multi-agent networks

author: snasan


value:
Hi Developers,
I want to share the results of a workflow experiment: testing the full speed and efficiency of an advanced AI assistant (Gemini 3) across the entire development lifecycle.
The Premise: Idea to Launch in Under 2 Hours
The goal was to move from a raw concept for a needed utility suite (my frequently used developer tools) to a fully deployed, domain-bound website in under two hours.
The resulting site is a functional, privacy-focused toolkit. We prioritize client-side execution because sensitive operations (like debugging a JWT token or calculating a password hash) should never hit a third-party server.
You can view the live result here: devtool.com
The Code is Open Source github

value: Show HN: Pro Dev Tools (Client-Side) Coded by Gemini 3 in 30 Minutes


value: reducks

value:
Hi HN! We've been building a tool to simplify running ML, data, and simulation workloads in the cloud. The goal is to remove most of the setup (instances, environments, IAM, scaling rules, cleanup, etc.) and let you focus on the actual job.
The basic workflow is simple: take whatever command you already use and prepend "adviser run".
Examples:
adviser run python train.py
adviser run Rscript analysis.R
adviser run ./simulate
Adviser handles provisioning, environment setup, cost/performance optimization, and teardown automatically. No Terraform, Docker, or cluster config required.
We built this after seeing ML and research teams lose a lot of time to infra glue code. We're opening up a self-serve version now and would really appreciate feedback from this community. Whether that's around design, CLI ergonomics, missing features, or reasons this shouldn’t exist.
To make testing easier, the first $100 of compute is free.
Link: https://github.com/adviserlabs/docs
Slack: https://www.adviser.sh/
Demo: https://www.youtube.com/shorts/F9feaOr3TbA
Happy to answer questions or take blunt feedback.

value: Show HN: Adviser – User-friendly CLI tool for running jobs in the cloud

author: reducks


value: momeemt

value:
This is my reproducible dotfiles setup built with Nix flakes.
I'm currently working on integrating Terraform and Kubernetes to manage services and clusters as IaC.
Feedback or suggestions are welcome.

value: Show HN: Reproducible Dotfiles with Nix Flakes

author: momeemt


value: stranger-ss

value:
Hi HN,
I built a small command-line tool inspired by Thanos’s snap.
It selects randomly exactly half the files in a directory and (optionally) deletes them.
Repo: https://github.com/soldatov-ss/thanos
PyPI: https://pypi.org/project/thanos-cli/
It’s intentionally simple — feedback is welcome.

value: Show HN: Thanos-CLI – a Python tool that deletes half your files

author: stranger-ss


value:
I built LeanOS because I was tired of drowning in startup operations while trying to build product.
LeanOS is an open-source AI-native operating system build on Claude skills that builds and runs startup ops autonomously - customer research, sales pipeline management, marketing campaigns, business decisions, engineering - all coordinated through specialized AI agents with minimal human intervention.

value: Show HN: LeanOS–10 AI agents that build and run startup operations autonomously


value:
Hi HN,
I’ve been experimenting with Apple’s new on-device Foundation Models (iOS 18 / macOS Sequoia) and built a small tool around them: AppReview AI, a Mac + iPad app to analyze App Store reviews privately and offline.
The motivation was simple: as an indie developer, reading hundreds of reviews from competitor apps was slow, noisy, and hard to extract signal from. Existing tools rely on cloud processing, API keys, or external servers. I wanted something lightweight and private that used Apple’s new local AI instead.
What it does
- Summarizes reviews using Apple’s on-device models
- Extracts sentiment, recurring issues, bugs, and feature requests
- Shows per-country ratings to detect market differences
- Displays basic estimated downloads and revenue (SensorTower public data)
- Syncs selected apps and analyses through iCloud
All AI processing stays on-device. No external servers, no accounts, no OpenAI key.
Why I built it
I wrote a recent article on Apple Foundation Models and was surprised how far the local models can go with the right prompts. This project was a way to test how practical on-device analysis could be in a real use case for developers.
Free tier
The app has a small free tier (1 app + 3 AI analyses) so anyone can try it without registration.
If you’re curious, here’s the link: https://apps.apple.com/lu/app/appreview-ai-review-analyzer/i...
I’d love feedback, criticism, or ideas for what to analyze next (keywords, rankings, crashes, changelogs, etc.).
Happy to answer technical questions about the on-device AI integration as well.

value: Show HN: AppReviewAI Analyze App Store Reviews Locally with Apple's On-Device AI


value: irshadnilam

value: Show HN: Build A2A Compatible AI Agents with Rust

author: irshadnilam


value: aladybug

value:
As a long time Japanese learner, I've always wanted there to be a simple, free online trainer for learning and grinding Japanese kanji and vocabulary - like Monkeytype in the typing community (fun fact: we actually have 2 Monkeytype devs on our team now!).
I'm doing this because I grew tired of all the subscriptions and paywalls, which are unfortunately very common in the Japanese learning apps market. That's why I want to make the most fun, user-friendly and customizable platform for learning Japanese currently available, and the first one that is (and will be, forever) completely free, ad-free and open-source - driven not by profit, but made by the community, for the community. We already managed to get around 600 stars on GitHub and about 7k monthly users for the app. That being said, I recognize that the app is still in its very early alpha stages and is, objectively speaking, behind paid Japanese learning solutions (at least for now) - which is why I want to change that and make it the first entirely free and open-source platform for learning Japanese to provide a real, high-quality alternative to all the current paid Japanese learning resources, which I believe we can achieve with the open-source community's help. Why am I doing this? Because weebs deserve to have a free, open-source learning platform too (I'm one of them, don't judge...)
どうもありがとうございます!

value: Show HN: Open-source Japanese learning Platform inspired by Monkeytype

author: aladybug


value: yoloshii

value: Author here. Built this to elegantly implement Anthropic's new approach to MCPs. Used existing recent implementations and fused them together for optimal hybrid feature integrations, while staying as close as possible to Anthropic's vision.

value: Show HN: MCP Code Execution Enhanced – 99.6% Token Reduction for Claude Code

author: yoloshii


value: adityaathalye

value:
I figured hey might as well drop a show HN, after I commented at https://news.ycombinator.com/item?id=45925109 "CLI tool to check the Git status of multiple projects"
Because it's kind of nicer to roll your own, if you do it functional-programming style.

  ls_git_projects ~/src/ | take_active | count_repos_by_remote

  ls_git_projects ~/src/bitbucket | take_stale | proc_repos git_fetch # only stale repos

  ls_git_projects ~/src/bitbucket | proc_repos git_branch_current # all repos

Each function is a line or five. Source the whole `bulk-git-ops` file in the shell and get cli TABTAB completions as one does for normal cli tools.
I do this opportunistically, but if I go back to maintaining lots of repos (release manager), then I'll just have it sourced in my shell at startup.

value: Show HN: Bash functions as CLI utilities to bulk-maintain Git repos

author: adityaathalye


value: daviducolo

value: Show HN: CTON: JSON-compatible, token-efficient text format for LLM prompts

author: daviducolo


value: We built an open-source tool that scans your codebase and generates OpenAPI docs in minutes. Right now it supports Rails, Go, Python, and NodeJS frameworks, and we will keep adding more. It picks up routes, params, request bodies, and models directly from the code and outputs a clean OpenAPI spec you can drop into your workflow.

value: Show HN: Open-source tool to generate OpenAPI docs from your code


value: bepitulaz

value: Show HN: I let AI to do sound design with hardware synth

author: bepitulaz


value:
Hi HN, I’m Amir (solo dev) and I built Screenfully because I wanted an easier way to record and showcase my mobile apps without needing a Mac or desktop just to record my mobile phone screen beautifully.
• What it does: record your phone screen, apply templates, export video, share anywhere. So it’s like Screen Studio but on mobile for mobile screen recordings.
• Why: I constantly need to plug my phone to a Mac or desktop just to record my mobile app for demo which doesn’t make sense, recording your mobile phone for a demo but you need another device? What? — so I made everything mobile.
• Get for free: https://apps.apple.com/us/app/screenfully/id6754534534 + https://www.producthunt.com/products/screenfully?launch=scre...
• Would love your feedback on UX, template library, export formats you use.
Thanks for checking it out let me know what you think.
– Amir

value: Show HN: Screenfully – mobile screen-recording and showcase tool for builders


value: soaple

value:
I'm currently creating learning materials that make cloud computing (especially AWS) easy to understand.
AWS official documentation is mostly text-heavy, which makes it quite difficult for beginners to grasp.
Also, video lectures have the downside of taking a lot of time and being hard to follow at your own pace.<p>That's why I made learning materials that include visual aids and hands-on practice examples.
For now, as a trial, I've created materials about EC2, and I'm distributing it for free.
You can check out the materials at the link, so please take a good look and leave me some feedback!

value: Show HN: Cloud Computing Camp – tidy and structured AWS learning materials

author: soaple


value: Show HN: A Conceptual Whitepaper on the Abstractive Thinking Model


value: Show HN: Data-driven insights into patterns in work, wellbeing, and habits


value: andrewdany

value:
Most enterprise work isn’t slow because of bad data.
It’s slow because the interface to that data is scattered.
A single question like “Which deals are stalled?” touches dashboards, spreadsheets, a CRM, BI tools, internal scripts, and a few Slack threads. Acting on the answer requires switching between systems again. The friction is in the middle.
Worqlo is an experiment in removing that friction by using conversation as the interface layer and deterministic workflows as the execution layer.
The idea is simple:
natural language in → validated workflow out.
The LLM handles intent.
A structured workflow engine handles execution: CRM queries, field updates, notifications, permissions, and audit logging.
The model never executes actions directly.
Below is how it works.
Why Conversation?
People think in questions.
Systems think in schemas.
Dashboards sit between them.
Interfaces multiply because every system exposes its own UI. Engineers end up building internal tools, filters, queries, analytics pages, and one-off automations. That’s the UI tax.
Conversation removes the surface area.
Workflows add safety and determinism.
Architecture (simplified)
User → LLM (intent) → Router → Workflow Engine → Connectors → Systems
LLM
Extracts intent and parameters.
No execution privileges.
Intent Router
Maps intent to a known workflow template.
Workflow Engine
Executes steps in order:
schema validation
permission checks
CRM queries
API updates
notifications
audit logs
Connectors
Strict adapters for CRMs, ERPs, internal APIs, and messaging systems.
The workflow engine will refuse to run if:
fields don’t exist
data types mismatch
permissions fail
workflow template doesn’t match user intent
This prevents the usual LLM failure cases: hallucinated fields, incorrect API calls, unsafe actions, etc.
Example Query
User:
"Show me this week's pipeline for DACH"
Internal flow:
intent = llm.parse("pipeline query")
validate(intent)
fetch(data)
aggregate(stats)
return(summary)
Follow-up:
"Reassign the Lufthansa deal to Julia and remind Alex to follow up"
Workflow:
find deal by name
validate ownership change
write CRM update
send Slack notification
write audit log
Everything runs through deterministic steps.
Why Start With Sales
Sales CRMs are structured and predictable.
Workflows repeat (reassign, nudge, follow-up).
Latency matters.
Output is measurable.
It makes the domain a good test environment for conversational workflows.
The long-term idea is not sales-specific.
The same pattern applies to operations, finance, marketing, and HR.
Why Not Just Use “ChatGPT + API”?
Because that breaks fast.
LLMs are not reliable execution engines.
They hallucinate field names, IDs, endpoints, and logic.
Enterprise systems require safe, auditable actions.
Worqlo treats the LLM as a parser, not a worker.
Execution lives in a controlled environment with:
workflow templates
schema contracts
RBAC
logs
repeatable results
This keeps the convenience of natural language and the reliability of a classic automation engine.
What We’re Testing
We want to see whether:
conversation can replace UI for narrow, structured tasks
deterministic execution can coexist with natural language intent
multi-turn workflows can actually reduce operational load
a connector model can scale without creating another integration mess
engineers prefer exposing functionality through workflows instead of UI layers
It’s still early.
But the model seems promising for high-volume, low-level operational work.

value: Show HN: Worqlo – A Conversational Layer for Enterprise Workflows

author: andrewdany


value: I built a swipeable tiktok embed that allows you to quickly paste in any YouTube Short (unlisted or public) and place a CTA (product desc, calendar, form) next to it.<p>We then auto track swipe data. Better way to command the message and ensure your website visitor walks away with the message you want. See live example on users like parkplacefinance.com or pinatagrams.com or gelblasters.com

value: Show HN: Swipeable TikTok Style Video Embed – Pitch Your Visitors Async


value: ZackMomily

value:
Hi HN,
I built Taskai, an AI-powered reminder app that turns natural-language inputs into actionable tasks. Unlike typical to-do apps, it also offers motivational support and emotional nudges as you get things done.
Chat naturally — no forms or commands
Morning summary / evening review
Encourages and celebrates small wins
I’d love to hear your feedback, thoughts, or ideas.
Live today on Product Hunt: https://www.producthunt.com/products/taskai-2?comment=499972...
— Tsahi

value: Show HN: Taskai – AI-powered reminders that reduce mental load

author: ZackMomily


value: 3 shots to build this cam app with Gemini 3.<p>Stay a little away with the screen, open your palm and use index finger to cut the fruit!

value: Show HN: Fruit Ninja Created with Gemini3


value: Mateleo

value: Show HN: Long Courrier – A custom web player for a 1h Barber Beats mix

author: Mateleo


value: vullnetsahiti

value:
Hi HN,
I'm the solo founder of Cllavio, an email marketing + email API platform that I built from scratch. My goal was to create a simpler, faster, and more transparent alternative to typical email tools that hide essential features behind expensive tiers.
Cllavio includes:
- Email campaigns
- SMTP + REST email API
- Deliverability tools (SPF, DKIM, DMARC)
- Bounce tracking
- Analytics (opens, clicks)
- Contact management
- High-deliverability Postfix infrastructure on AWS
If you’re into email infrastructure, Spring Boot, Postfix, or scalable messaging systems, I’d love your feedback or questions.
Link: https://cllavio.com

value: Show HN: Cllavio – Email Marketing and Email API platform built from scratch

author: vullnetsahiti


value: andupotorac

value: Text inputs are too slow for complex prompting if you're vibe coding or generating media. I built a full-stack Voice Mode component (UI + logic + transcription) for React/Next.js. It handles the awkward browser audio stuff so you don't have to.<p>Also used Gemini 3 to generate that entire page in one prompt. :-)

value: Show HN: I made a drop-in Voice Mode for AI startups

author: andupotorac


value: Hi HN! I'm the creator of lazygotest.    I built this because I love lazygit's UX and wanted the same smooth experience for Go testing.   Instead of switching between terminal and editor, or scrolling through verbose test output, you   can now:    Browse packages and tests with vim-style navigation (j/k, gg/G)   Run tests with Enter and watch real-time results in split panes   Track test history and quickly rerun failed tests with 'r'   Filter tests with '/' to focus on what matters    Technical highlights:   Built with tview (https://github.com/rivo/tview) for the TUI framework   Catppuccin Mocha color scheme, WCAG 2.1 AA compliant for accessibility   Auto-detects go.mod by walking up parent directories   Supports Docker for isolated testing environments    The interface has 4 panes: Packages (left), Tests (top-right), History (middle-right), and Log   (bottom). The status bar shows context-sensitive keyboard shortcuts.    Looking for contributors! Here are some areas where I'd love help:   CI/CD integration: GitHub Actions workflow for running tests in lazygotest   Benchmark mode: Support for go test -bench with visual graphs   Coverage visualization: Show code coverage % per package/test   Test generation: Auto-generate table-driven tests from function signatures   Plugin system: Allow custom commands and key bindings    If you're interested in contributing, the codebase follows Clean Architecture with clear   separation between domain/application/infrastructure layers. PRs are welcome – I aim for small,   focused changes (≤20 lines when possible) with tests included.    I'd love to hear your feedback, especially from fellow gophers who spend a lot of time running   tests!    Install: go install -tags tview github.com/YuminosukeSato/lazygotest/cmd/lazygotest@latest

value: Show HN: Lazygotest – Terminal UI for running Go tests interactively


value: NeroVanbierv

value:
Hi HN! I’m Nero from Marple, and we build time series tooling for Aerospace & Automotive. In 2020, we launched our first product: a sort of Grafana on steroids designed for high performance analysis. After integrating with a lot of different TS databases, we kept running into their design limits when trying to deliver the best performance & UX to our users.
That’s why we are launching Marple DB today (https://www.marpledata.com/marple-db).
Marple DB transforms measurements files (CSV, MAT, HDF5, TDMS, …) into a queryable lakehouse. We designed for extreme ingestion performance. A typical customer example: one MDF file can have ~60k channels, going up to 1kHz, for 1 hour. That is a total of 100B datapoints for a single file.
To make this work, we are using a combination of Parquet files on Apache Iceberg + PostgreSQL. Parquet gives us the scalability we need, and Postgres acts as an extremely fast visualisation cache. We provide Python and MATLAB SDKs to talk to these storages in a unified way. Marple DB is available as a paid product only, but we do offer self-managed hosting.
Me and my co-founder MBaert will join the discussion below.
We are happy to hear your questions!

value: Show HN: Marple DB – Querying billions of time series datapoints on Parquet+Pg

author: NeroVanbierv


value: cjlooi

value:
I’ve been frustrated with PDFs and found arXiv HTML lacking, so I built a fully interactive paper reader.
Features:
• Hover references, citations, equations
• Light/dark mode
• Auto-generated dependency graphs for definitions/lemmas/theorems
• Table of contents that syncs with scroll
• Highlighting + annotations
• “Copy raw LaTeX” anywhere
Featured paper: Video models are zero-shot learners and reasoners (Veo 3)
https://www.sciencestack.ai/arxiv/2509.20328v2

value: Show HN: Interactive research papers (a big step up from ArXiv HTML)

author: cjlooi


value: oknoorap

value: Show HN: I built a simple Zustand alternative

author: oknoorap


value: skuldnorniern

value:
Recently, I've been working on Lamina, a compiler infrastructure that generates native assembly for multiple architectures without relying on LLVM or Cranelift. It's designed for building compilers for new languages, educational projects, and any projects that can utilize a custom syntax of code generation.
Instead of depending on external backends, Lamina provides a complete pipeline from a single SSA based IR directly to the supported target's assembly generation. The IR is readable, also provides a IRBuilder API that is easy to use via programmatic construction.
For better management of the code generation process, in the future, it will use a new pipeline IR -> MIR -> native assembly with the optimization passes.
Key features:
- Direct code generation: IR -> assembly/machine code without LLVM/Cranelift
- SSA based IR: single assignment form optimized for analysis and optimization passes
- MIR based codegen(experimental): new intermediate representation with register allocation and advanced optimizations
- IRBuilder API: fluent interface for building modules, functions, blocks, and control flow
- Readable IR: easy to debug and lower than high level languages
- Zero external backend dependencies: simplified builds and transparent pipeline while being faster to build
Optimization passes (experimental MIR flow only):
- Control flow: CFG simplification, jump threading, branch optimization
- Loop optimizations: loop fusion, loop invariant code motion, loop unrolling
- Code motion: copy propagation, common subexpression elimination, constant folding
- Function optimizations: inlining, tail call optimization
- Arithmetic: strength reduction, peephole optimizations
Performance: On a 256×256 matrix multiplication benchmark (300 runs), Lamina's experimental MIR-based codegen (which includes all optimization passes) generates code comparable to C/C++/Rust (within 1.8x) and faster than Java, Go, JavaScript, and Python. The experimental  MIR based flow's result is much faster than the IR-> Assembly based codegen.
Written in Rust (2024 edition), Current Version 0.0.7. Optional nightly features available for SIMD, atomic placeholders, and experimental targets.

value: Show HN: Lamina – A compiler backend that is not LLVM or Cranelift

author: skuldnorniern


value: Show HN: A Modern Open-Source Database Studio Tool


value: RyanDavid

value: Show HN: I built a Chrome Extension to inject custom UTM parameters into URLs

author: RyanDavid


value: Show HN: Agent Smith Is OSS

title: Show HN: Agent Smith Is OSS


value: kinders

value: I built Wasda, a tiny experiment to turn transformer attention into a playable musical instrument. Each token in the sequence becomes a piano note, and you can see the attention arcs between tokens while you listen. Temperature controls the “improvisation” of the model, and you can solo individual attention heads — for example, I’ve found temp ~1.7 with one head selected gives surprisingly relaxing patterns.

value: Show HN: Wasda – Experience transformer attention as music

author: kinders


value: ayushshivani

value:
We built Arka because MCP adoption is slow in real teams.
The same three problems show up everywhere.
1. Context gets too big
Teams add more MCP servers and the context grows fast.
The model sees too many tools and picks the wrong one.
Accuracy drops and workflows break.
This blocks adoption because the setup works in demos but fails in real use.
2. Setup does not scale
Each MCP server needs its own config, port, and auth.
Teams end up running many servers with no shared control.
It is hard to track and easy to break.
Most teams stop here because it takes too much time to maintain.
3. No enterprise security
No SSO. No audit logs. No user rules.
Most teams pass around raw tokens.
Security teams block the rollout.
This stops MCP adoption in companies that care about compliance.
Arka solves these adoption problems with one gateway.
One setup for all MCP servers
• One token per user with SSO
• User rules and tool rules
• Tool filtering to keep context small
• Better accuracy with many tools
• Logs for every call
• Open source or cloud
We are also adding:
• Agent access with safe rules
• Workflows you can convert into your own MCP server
Our goal is simple: make MCP adoption possible at scale.
Repo: https://github.com/KenisLabs/arka-mcp-gateway
Cloud: https://www.kenislabs.com
Would love your feedback!

value: Show HN: Arka – an MCP gateway that makes MCP adoption possible

author: ayushshivani


value: catstor

value: An awesome list about Java platform Micro edition(J2ME). Documentation, academic papers, tutorials, communities, IDEs, SDKs, emulators, apps, video games. J2ME is a Java specification designed for old keypad phones and PDAs. MIDP, which is built upon CLDC, is used to create Midlets, which have `.jad` or `.jar` extension, and run on platforms like old keypad phones, Symbian and PDAs. MIDP is supported till Java ME SDK 3.4.

value: Show HN: Awesome J2ME

author: catstor

title: Show HN: Awesome J2ME


value: rokontech

value: Show HN: Solved hiring by deleting the hiring step; your crew almost ready

author: rokontech


value: vyntral

value: Show HN: God's Eye – AI-powered subdomain recon with local LLM

author: vyntral


value: godlabs

value:
I made a chrome extension game with Gemini where you can destroy any website, literally.
If you don't like a website, use your cursor as a spaceship, but beware the website will try to protect itself by sending drones.
Kill drones, harness xp, upgrade your ship, anhiliate the web!
Game is on github: https://github.com/prabhjotSL/website-destroyer

value: Show HN: Gemini made a game to destroy all websites

author: godlabs


value: wwwpatdelcom

value: Show HN: SocialPredict v2.1.0 – Easy to Deploy Prediction Markets

author: wwwpatdelcom


value: ryanvogel

value: I was tired of having to manually copy & paste the comments that AI code reviewers were making on my PR, because sometimes with reviewers like Greptile. It is like 40+ comments, and there wasn't a reliable (and token considerate) way to get those comments back into your AI IDE of choice.<p>Simply replace "github.com" with "gitcom.dev" on any PR link and it will give you properly formatted markdown comments.

value: Show HN: GitHub Comments into Formatted Markdown

author: ryanvogel


value: Show HN: YouTube Musical Spectrum is now available on Spotify and SoundCloud


value: AxelWickman

value: Show HN: Minimal Peg Solitaire Online

author: AxelWickman

title: Show HN: Minimal Peg Solitaire Online


value: PAndreew

value: As a developer I've caught myself copy-pasting large chunks of text into AI chatbot input fields. From time-to-time API keys, private URLs, etc. slipped through and probably baked into one of the nicest and shiniest models weights - forever. So I created a little browser extension that tries to mitigate such accidents by intercepting the pasted data and redacting it with structure-preserving but dummy values. The tool is heavily a work in progress, but I still find it useful, and only mildly intrusive. Enjoy!

value: Show HN: Vigil – AI Chatbot Data Leak Mitigation in the Browser

author: PAndreew


value: fallonshoulders

value:
Haloy is a lightweight way to deploy Docker apps to your own servers without complex setups. One config file, one deploy command. Handles routing, HTTPS, rollbacks, and multi‑server environments.
Repo: https://github.com/haloydev/haloy
Docs: https://haloy.dev

value: Show HN: Haloy – an open‑source, lightweight deployment system for Docker apps

author: fallonshoulders


value: mchaver

value:
Numerikos is a math practice platform where students can work through distributions of practice problems or setup their own study routines.
Try the demo (no signup required, data stored locally) at https://demo.numerikos.com
I designed this for my 4th-grade son and so far he really likes it.
The main features:
1. Practice problems: generated by rules I've designed particularly for each problem type. They try to avoid trivial problems and create an interesting distribution of problems.
2. An editable dashboard: you can track different metrics or setup different collections practice problems (timed or not timed).
3. Review page: click the star while you are practicing to review a particular problem later.
4. Currently available in English and Chinese (the translation still needs improvement).
I wanted something that allows users to personalize the areas of math that they want to practice more. I will eventually make an algorithm that helps users to decide what to study next, but it will have an escape hatch where the user can override some of its assumptions.
What's next:
1. Support 5th grade math.
2. Release the production version.
3. Support Spanish language version.
My main source of inspiration is Chris McMullen's collection of math workbooks (followed by Khan Academy and Math Academy). My long term goal is to support math at all grade school levels and some college level mathematics and physics. I'd be curious to hear your thoughts on the UI and any constructive criticism you have.

value: Show HN: Numerikos – A personalizable digital math workbook

author: mchaver


value:
Introducing Librarian: A Modern Alternative to Kafka Connect

  We're excited to share Librarian, an open-source database replicator built for the cloud-native era.

  The Problem We're Solving:

  Traditional CDC solutions like Kafka Connect require JVM runtime, complex connector management, and expose generic infrastructure metrics that
  don't help you debug data pipelines. We built Librarian to change that.

  What Makes Librarian Different:

   Single Binary - No JVM, no external dependencies, no connector management. Just run it.

   Truly Lightweight - Runs on modest hardware with minimal resource overhead

   Pipeline-First Observability - Built-in stats server that shows what matters: events processed, bytes transferred, and error counts—not
  garbage collection metrics

   Native Replication - Uses MongoDB Change Streams and PostgreSQL logical replication for efficient, real-time CDC

   Debezium Compatible - Drop-in replacement for existing Debezium consumers

  Supported Today:
  - Sources: MongoDB, PostgreSQL
  - Targets: Kafka, S3 (Parquet), Local filesystem

  Get Started in Minutes:

  Stream MongoDB changes to Kafka with a single command. No configuration files, no cluster setup—just a simple URL-based connection string and
  you're streaming data.

  Librarian is open source (MIT license). We'd love your feedback as we continue building.

  Check it out: github.com/turbolytics/librarian

Thank you!

value: Show HN: Librarian: A Modern Alternative to Kafka Connect


value: sqrew

value:
Hello everyone! I built Tunes by accident while building an audio engine for my own game engine. It's a "batteries included" approach to music composition, audio synthesis, and sample playback.
I built tunes to solve my own issues; I wanted it to have a bunch of capabilities while being simple to use and relatively performant (what else is new lol). So I spent a lot of time trying to optimize things that probably didn't need it. I ended up with a kitchen sink full of fun tools for audio.
I wanted to share this with everyone because I feel like it's in a good enough position to help other people with their own audio issues now. It's easy to use and performant enough for most use cases. I'd love to get feedback and discuss any parts of it with you guys. Thanks.

value: Show HN: Tunes – Rust audio synthesis/playback (100x realtime, SIMD, GPU, WASM)

author: sqrew


value: eliasdejong

value:
My name is Elias de Jong, I am a software engineer and long-time HN lurker from the Netherlands.
Some time ago, I started a project that would require a high performance networking system. Naturally, to send data between computers I also needed a serialization format, but a text format like JSON was just too slow.
I started researching binary formats. But to my surprise, every binary format had major downsides. Almost all of them require schema definitions up front written in an IDL, as well as extra tooling and build steps. Schema evolution is also fragile and hard to maintain backwards compatibility.
It feels like being forced into a choice:
1) JSON: convenient but slow
2) Binary formats: fast but painful to use
Why do I need to choose? I don't want to sacrifice my sanity to get acceptable performance.
I kept looking and found a research paper from 2024 about a conceptual schemaless zero-copy format. Lightning struck. This idea seemed extraordinary: you essentially send a serialized B-tree over the network so that the receiver can perform lookups and mutations directly without parsing. The only problem: nobody had made a (public) implementation yet.
I figured sometimes you have to be the first, so for the last 6 months I've been working on this and am proud to say it is now released and open source under MIT license.
The ability to modify serialized data directly is something not typically supported by other formats.
I also extended the original format with JSON compatibility, such that it is now possible to convert between Lite³ and JSON. Despite this, it achieves performance on-par with modern zero-copy formats like Flatbuffers and Cap'n Proto. Right now the implementation is in C, but I plan to create bindings for other languages as well.
So hopefully some fellow developers find it interesting!
Official GitHub repository: https://github.com/fastserial/lite3
Official documentation site: https://lite3.io
Mailing list: see project README

value: Show HN: Lite³ – A JSON-Compatible Zero-Copy Serialization Format in 9.3 kB of C

author: eliasdejong


value: maxskorr

value:
I built wireport to easily and securely host, manage, and access self-hosted workloads across multiple environments. It started as a set of scripts and gradually became a lightweight solution, tackling some of the most common challenges I've encountered -- ready to run in about 5 minutes with just a few terminal commands.
wireport is an open-source, self-hosted ingress proxy and VPN tunnel. Among its advantages: it requires no config tinkering and makes your remote self-hosted services accessible locally by their Docker container names (e.g., `grafana-dashboard:8080` in your browser to access a self-hosted grafana dashboard).
- GitHub: https://github.com/MultionLabs/wireport
- Quick setup tutorial: https://youtu.be/9wvxT-QOZ2Q
Common use cases:
- exposing self-hosted services from behind NAT: from laptop, private servers, home lab/NAS devices
- secure access to internal dashboards, databases, development tools, and game servers from anywhere
- sharing locally-hosted demos securely with colleagues or clients
- safely accessing services from IoT and edge devices
- enabling docker workloads from different nodes to talk to each other
Some highlights:
- automatic handling of TLS (HTTPS) certificates — free provisioning and renewal
- support for raw TCP and UDP, as well as protocols built on top of them
- support for HTTP Basic Auth for HTTP/HTTPS tunnels
- declarative tunnel configuration via Docker labels
- written in Go, powered by CoreDNS, Caddy, and WireGuard
- builds available for macOS, Linux and Windows (amd64 & arm64 versions)
wireport is for developers, tinkerers, and home lab enthusiasts who want secure, easy access to their private services from anywhere.
I'd love to hear from fellow self-hosters about your experiences — what challenges are you facing and how are you solving them currently?
Happy to read any feedback and questions!

value: Show HN: wireport – lightweight ingress+DNS+VPN for self-hosted apps behind NAT

author: maxskorr


value: kruszczyk

value: We trained an SLM assistant for help with commit messages on TypeScript codebases - Qwen 3 model (0.6B parameters) that you can run locally!

value: Show HN: Distil commit bot – a local TypeScript Git commit slm

author: kruszczyk


value:
I built a tiny tool that turns a screen recording or video into a clean playwright test. Just upload and it extracts every step automatically and then turns it into working playwright code. You even get a live preview of the browser while AI is running your flow.
My Stack:
Next.js, Vercel (AI Gateway + AI SDK), Browserbase, WorkOS, Upstash, Google Gemini + Anthropic Claude for the models
Appreciate any feedback!

value: Show HN: Built a vibe testing tool to turn screen recording into Playwright test


value: shashankshukla

value:
Hi HN,
A couple of months ago I posted an early version of this Chrome extension. Since then I’ve refined it, fixed a bunch of stability issues, and it was recently featured on the Chrome Web Store’s “Featured” section, which was a nice surprise.
What the extension does:
– Detects the active tweet or thread directly in the browser
– Collects relevant context (parent tweet, author info, thread shape)
– Formats a prompt and sends it to the OpenAI API
– Inserts the generated reply straight into Twitter’s native reply box
All of this happens inside the X.com DOM, without storing any user data.
Technical bits:
– Uses MutationObserver to track X.com’s constantly changing DOM
– Handles dynamically inserted tweet nodes, shadow DOM, and thread expansions
– Debounces context extraction to avoid unnecessary re-runs
– Simulates native input events to inject the reply so it feels built-in
– Avoids backend state; everything is read client-side except the final API call
Challenges:
– X.com changes UI structure often, so selectors break unpredictably
– Preventing duplicate injections when the DOM re-renders
– Keeping prompt size small enough for fast generation
– Reducing overhead so the extension doesn’t slow down the page
Recent improvements:
– More stable tweet/thread detection
– Better context selection logic
– Cleaner UI in the reply popup
– Small performance fixes and race-condition fixes
Chrome Store page: https://chromewebstore.google.com/detail/xinsightai-ai-reply...
Would appreciate feedback from people who’ve built browser extensions or dealt with X.com’s DOM patterns. Happy to discuss any details.

value: Show HN: Chrome Store–featured extension that writes X replies via DOM observers

author: shashankshukla


value: lapurita

value:
I created this as a product for solving the long-tail of education, specifically with video lectures. YouTube obviously has great videos on subjects like "What is a derivative", "Germany post world war 2" or "History of the roman empire" but it won't always have great videos for more niche subjects like say "Swedish monetary theory through history" or "deep dive on the nutrients of raspberries vs blackberries vs blueberries".
Here are some videos that I've created under a few different categories:
Content with a personalized angle:
- Biology from a computational perspective: https://explanans.com/videos/j9714e2pyxm0qysbkth4hn0tf57ve18...
- Machine learning for a non-technical person: https://explanans.com/videos/j97a9d5xpqjq65r9s8zye66ak57vs67...
Niche topics
- Covid 19 clinical studies: https://explanans.com/videos/j97dvfc5r7xen50t3d1yg3jfvx7vsqv...
- Swedish monetary policy: https://explanans.com/videos/j978hwrz0kp7dpr43zgv0bgwrd7vq1h...
- Estimating North Korea's GDP: https://explanans.com/videos/j974ccfjrbbb6e4zmfgjqfwwtn7vrnc...
And some random subjects:
- Things to do in Ireland: https://explanans.com/videos/j971aex478rbthv5n0f6vk8m6h7vsyn...
- Clinical studies from start to finish: https://explanans.com/videos/j979m7w6ba8xf4a4gnbjttcmh57vrm6...
The accuracy of these videos are (and should be) about the same as you expect from the best LLMs, because this product is fundamentally based on LLMs with access to some tools.
You can try it for free at https://explanans.com/, every new user gets 2 free videos. Would love to give out more, but inference costs for generating a video are quite high. Free users can watch as many existing videos as they want, and you don't even have to sign up for that part.
These videos are far from perfect right now, but I think one of the few killer use cases for LLMs are for learning. I think Explanans can be used in the same way that we use ChatGPT/Claude/Gemini, but where the output is in a different format than text. For some mediums I think that's a superior experience, for the same reasons that lots of people prefer YouTube over blog posts.
There are a lot of features I want to add to this such as chat with video, some kind of quizzing for retention, or chat before generating to get a more specific output (like how deep research products do it) but I didn't wanna clutter the user experience for v1.
Here to answers any questions you might have!

value: Show HN: Explanans – Personalized video lectures for any topic

author: lapurita


value: dsalinasgardon

value: Show HN: Gatling Studio – Turn browser journeys into ready-to-run load tests

author: dsalinasgardon


value: wkoszek

value:
I wanted to get Forgejo on my VM on a local NAS fast, and I realized that getting everything running with Git and CI working was ... harder than I anticipated. After spending more time than I wanted, and bugging lovely people at Forgejo's matrix, I came up with this:
https://wkoszek.github.io/easyforgejo/
With this script, you should get Forgejo installed on your Linux computer in 2min. I tested this on a VM for now, and it works well enough for beta launch.
Repo is here:
https://github.com/wkoszek/easyforgejo
Let me know what you think and submit PRs if you find bugs. I'd not use it in production just yet.

value: Show HN: Quick install script for self-hosted Forgejo (Git+CI) server

author: wkoszek


value: gauravguitara

value:
We're building an open source implementation for Web Bot Auth, which is an IETF draft for signed agent authentication over HTTP, without CDN lock-in.
We've built a social registry, using Github login, where you can host your Signature Agent card/JWKS over a dedicated link. This saves you buying a domain and self-hosting your card. Please try it out : https://openbotauth.org/login
We've also built a Wordpress plugin, which works in signed and unsigned mode. Unsigned mode gives you a preview and Signed mode gives you all access. Websites can point their origin server to our registry or use other registries to block unverified traffic.
Demo: https://www.youtube.com/watch?v=8W5tScru5Us
Docs: https://docs.openbotauth.org
The next step is to build the delegation as discussed here: https://thibmeu.github.io/http-message-signatures-directory/...
We'd love your feedback on our social registry and how we can improve it. :)

value: Show HN: OpenBotAuth – Open-Source Web Bot Auth with Social Registry

author: gauravguitara


value:
I built rapid-rs to eliminate the hours of boilerplate when starting a new Rust web service.
One command gets you:
- Auto-configured DB, logging, CORS
- OpenAPI/Swagger UI at /docs
- Request validation
- Production-ready observability
Built on Axum. Early benchmarks show ~50K req/s with 10-20MB RAM.
This is v0.1 - feedback welcome!
Crates: https://crates.io/crates/rapid-rs

value: Show HN: Rapid-rs – Zero-config web framework for Rust


value: electrodisk

value: Show HN: My Light & Spirit – A Clever Indie Puzzle Where Light Becomes Your Tool

author: electrodisk


value: joemasilotti

value: Show HN: A step-by-step guide for push notifications on iOS, Android, + Rails

author: joemasilotti


value: pzullo

value:
Recently, Anthropic [https://www.anthropic.com/engineering/code-execution-with-mc...] and Cloudflare [https://blog.cloudflare.com/code-mode/] released two blog posts that discuss a more efficient way for agents to interact with MCP servers, called Code Mode.
There are three key issues when agents interact with MCP servers traditionally:
- Context flooding - All tool definitions are loaded upfront, including ones that might not be necessary for a certain task.
- Sequential execution overhead - Some operations require multiple tool calls in a chain. Normally, the agent must execute them sequentially and load intermediate return values into the context, wasting time and tokens (costing both time and money).
- Code vs. tool calling - Models are better at writing code than calling tools directly.
To solve these issues, they proposed a new method: instead of letting models perform direct tool calls to the MCP server, the client should allow the model to write code that calls the tools.
This way, the model can write for loops and sequential operations using the tools, allowing for more efficient and faster execution.
For example, if you ask an agent to rename all files in a folder to match a certain pattern, the traditional approach would require one tool call per file, wasting time and tokens. With Code Mode, the agent can write a simple for loop that calls the move_file tool from the filesystem MCP server, completing the entire task in one execution instead of dozens of sequential tool calls.
We implemented Code Mode in mcp-use's (repo https://github.com/mcp-use/mcp-use ) MCPClient . All you need to do is define which servers you want your agent to use, enable code mode, and you're done!
The client will expose two tools:
- One that allows the agent to progressively discover which servers and tools are available
- One that allows the agent to execute code in an environment where the MCP servers are available as Python modules (SDKs)
Is this going against MCP? Not at all. MCP is the enabler of this approach. Code Mode can now be done over the network, with authentication, and with proper SDK documentation, all made possible by Model Context Protocol (MCP)'s standardized protocol.
Hope you like it and have some improvements to propose :)

value: Show HN: Code Mode for MCP in MCP-use's client

author: pzullo


value: ethanblackburn

value:
Hi HN! We’re Ethan and Danny, the authors of Tangent (https://github.com/telophasehq/tangent), a Rust-based log pipeline where all normalization, enrichment, and detection logic runs as WASM plugins.
We kept seeing the same problems in the OCSF (https://ocsf.io) community:
1) Schemas change constantly. Large companies have whole teams dedicated to keeping vendor→OCSF mappings up to date.
2) There’s no shared library of mappings, so everyone recreates the same work.
3) Writing mappers is tedious, repetitive work.
4) Most pipelines use proprietary DSLs that are hard to share and hard for tools/LLMs to generate.
Tangent takes a different approach: no DSLs – mappings and enrichments are just normal code compiled to WASM, shareable plugins – we maintain a community library (https://github.com/telophasehq/tangent-plugins), interoperability – we can run other engines’ DSLs (e.g., Bloblang) inside WASM for easy migration, full flexibility – plugins can validate schemas, call external APIs (https://github.com/telophasehq/tangent/blob/main/examples/en...), or perform complex transforms (https://github.com/telophasehq/tangent-plugins/blob/main/zee...).
Here's an example Python transformation plugin to drop all fields from a log except `message`:

  import json

  from typing import List

  from wit_world.imports import log

  # `log.Logview` is Tangent's zero-copy JSON accessor type.

  def process_logs(self, logs: List[log.Logview]) -> bytes:

      out = bytearray()

      for lv in logs:

          msg = lv.get("msg")

          value = msg.value if msg is not None else ""

          out.extend(json.dumps({"message": value}).encode() + b"\n")

      return bytes(out)

We have plenty more examples in the repo.
Because plugins are just Go/Python/Rust, LLMs can create new mappers with ease. For example, I asked:

  Generate a mapper from AWS Security Hub Finding to OCSF

and only had to make a few minor tweaks. (https://github.com/telophasehq/tangent-plugins/blob/main/aws...)
Performance-wise, a 16-core Amazon Linux box processes ~480 MB/s end-to-end (TCP → Rust-WASM transform → sink) on ~100-byte JSON logs. The CLI includes tooling to scaffold, test, and benchmark plugins locally. Here's a deep dive into how we are able to get this performance:
https://docs.telophasehq.com/runtime.
We’d love to get your feedback! What do you think?

value: Show HN: Tangent – Security log pipeline powered by WASM

author: ethanblackburn


value:
Hi HN, I've been building Yonoma (https://yonoma.io), an email automation tool focused on early stage SaaS teams.
I used a few well known tools in this space while working with other SaaS founders, and I kept seeing the same problem. These tools are powerful, but they are also built for larger companies. Setting up simple onboarding or trial reminder flows often felt heavier than it needed to be. Many founders told me they wanted something easier that still reacts to how users behave inside the product.
So I tried to build something simpler and more focused.
Yonoma sends emails based on what users actually do inside your product. Things like signing up, becoming inactive, hitting an activation step, or getting close to the end of a trial. The timing is handled automatically so teams do not need to manage it manually.
Here is what it supports today:
- Behaviour based triggers
- Onboarding, activation, trial reminders, re-engagement flows
- Ready to use workflows and templates
- Integrations with Stripe, HubSpot, Segment, Slack, Zapier
The goal is to give small SaaS teams an easier way to set up behaviour based email automation without the complexity of enterprise tools.
You can try it here (no credit card required): https://yonoma.io
Happy to answer questions or get feedback.

value: Show HN: Yonoma – Behavior based email automation for SaaS


value: mattglossop

value:
Hey HN,
We’re challenging retail wealth management. Most individual portfolio optimization is fundamentally flawed because it’s static and ignores your specific goals.
I spent a decade helping some of the world’s largest investors build their portfolios. My co-founder built hundreds of financial plans for retail investors before backend engineering at Microsoft. We know the problem: traditional portfolio construction and maintenance approaches (like Modern Portfolio Theory) rely on myopic, static allocation.
This failure of methodology is why advisors ignore retail investors below $250k in assets and platforms push generic funds. Personalized, dynamic allocation is non-viable for them because the complex computation required is too difficult to scale manually.
We built the solution: a financial co-pilot that gives you a single clear picture of all your finances, and shows you how to achieve your goals with tailored, dynamic guidance. This is a scalable, non-custodial platform, unbundling advice from assets.
- Custom dynamic guidance: Get ongoing investment recommendations truly customized to you and your specific goals. Our model continuously adjusts risk based on your time horizon and current portfolio state - an approach that studies show can increase goal achievement probability by 30%+ compared to traditional models.
- Tracking without transfers: Automatically track progress towards goals by securely connecting your accounts (API link with Plaid/SnapTrade). You never have to transfer a dollar; take our guidance and use it where you already invest.
- Total clarity: See a single, clear, unified view of household wealth, spending, and goals.
This wealth co-pilot is designed to guide and empower users to achieve their goals and build positive financial habits.
For a full review of our investment approach, please review our whitepaper:
https://drive.google.com/file/d/1_WhcShE2G8eJJJuq1odsPq5BVAH...
Fulfilled is an SEC Registered Investment Advisor offering this automated platform across the US.
Check it out and give us your brutal, honest feedback on the clarity of the UX and the value that we offer.
Product link: https://www.FulfilledWealth.co

value: Show HN: Fulfilled – Non-custodial financial co-pilot for goal optimization

author: mattglossop


value: Curiositry

value:
I built this to monitor the price of my staple clothing and gear, so I could stock up when my size/color goes on sale, and not think about shopping the rest of time.
I was so tickled with the project, I decided to make it more widely available.
I would welcome any feedback!
Under the hood, I'm using Python (and Bash), Scrapy, Selenium, and good ol' Cron. (I have a blogpost mostly written that goes into all the details.)

value: Show HN: Simple Price Drop Alert

author: Curiositry

title: Show HN: Simple Price Drop Alert


value: bojangleslover

value:
Scans all Polymarket and Kalshi markets with over $1m of volume every 4h and uses the LLM to match markets.
Finds "true arbs" and "stat arbs". True arbs are what they sound like — the exact same market on each platform. Stat arb markets are correlated but not the exact same thing. For example, "Trump impeached during his second term" and "Trump impeached this year?" are correlated.
Thinking of incorporating slippage into this soon and assigning a dollar amount to each arbitrage since not all markets have deep order books. Also might start trading! But I have no idea where Kalshi and Polymarket host their execution backend. I would guess Ashburn or NY but I am not sure.
Statistical arbitrage: https://en.wikipedia.org/wiki/Statistical_arbitrage

value: Show HN: LLM-Powered Arbitrage Finder for Kalshi and Polymarket (Gemini Pro 3)

author: bojangleslover


value: Show HN: BYO – No-code LLM-to-market: Build, monetize and orchestrate AI experts


value:
I’ve been experimenting with an idea over the last few months - a fully autonomous recruiter that works entirely over WhatsApp.
No forms.
No job board.
No ATS.
Just a conversation -> an intro.
Here’s how it works today -
For candidates:
1. You message the agent on WhatsApp
2. It asks a few questions about what you're looking for
3. It does a quick 5-6 min call to clarify your background
4. It matches you to relevant founders and introduces you directly
You don’t browse roles; the agent finds them
For founders:
1. You send your open roles over WhatsApp
2. The agent screens candidates via chat/call
3. You only receive a few relevant intros
You don’t sift through applicants or profiles
It’s simple, but it seems to work.
Right now ~700 candidates and ~30 founders are using it, and the agent handles everything (screening, matching, intros, follow-ups).
This is not a chatbot bolted onto a job portal.
It’s closer to a “recruiter brain” running through WhatsApp.
Some things I’m trying to understand:
1. Is WhatsApp a viable long-term UI for hiring?
2. Does removing interfaces (CVs, job boards, dashboards) actually help signal?
3. How much autonomy is too much for an agent in hiring flows?
4. What breaks if this scales?
If you’re curious or want to try it:
Candidates: https://shorturl.at/oxUJt
Founders hiring: https://shorturl.at/oxUJt
I’d love feedback, critiques, or failure modes to think about.

value: Show HN: An autonomous WhatsApp recruiter that introduces candidates <> founders


value: muhammad-shafat

value:
I've used ChatGPT since GPT-3.5 for coding and prototyping. GPT-5 broke my workflow in specific, measurable ways.
The router system switches between model variants invisibly. Responses became verbose but hollow. Instruction following collapsed.
I discuss these points in detail in the article

value: Show HN: Investigating why GPT-5 has made ChatGPT 'broken'

author: muhammad-shafat


value: aanthonymax

value:
Hi HN! Great news today: we've finally launched a section featuring community projects built with hmpl-js.
Often, when you're working on a project, you focus only on functionality, on how best to make the tool, but that's not the most important thing, because a tool is nothing without people, so we want to make something cool.
If you have a desire, you can participate by creating some module or something cool in which you used this template language and get on the list!
I don't know if this is a good idea or not, but we'll see. I'd appreciate your opinion

value: Show HN: We've created a list of community projects

author: aanthonymax


value: NickFORGE

value:
We built a browser-native IDE that runs a real Linux terminal and multi-agent coding tools directly in the browser — no installation or VM setup.
It supports:
– A full CloudShell terminal (xterm.js + Node-PTY)
– Multi-agent CLI execution (Aider, GPT Engineer, Claude Engineer)
– Agents directly modifying files in the repo
– UI plan generation + CLI build workflows
– Zero local setup, zero dependencies
Demo: https://forge.synvara.ai
We would love technical feedback. Happy to answer questions about:
– Browser terminal performance
– WebSocket architecture
– Agent orchestration
– Security model
Thanks in advance — this community’s feedback is extremely valuable to us.

value: Show HN: A browser-based IDE with multi-agent terminal execution (no install)

author: NickFORGE


value: tmbkr

value: Show HN: Simple Data Collection Firmware for Arudino Opta

author: tmbkr


value:
Complete MCP traffic analysis tool with desktop app for Mac, Windows.
GitHub: https://github.com/mcp-shark/mcp-shark
Visit the website for desktop apps:
Web: https://www.mcpshark.sh/

value: Show HN: MCP Flow Detection

title: Show HN: MCP Flow Detection


value: dfworks

value:
Inspired by All Text in NYC (https://alltext.nyc) by Yufeng Zhang I thought I would replicate something similar for London.
A searchable tool that lets you explore text captured across Google Street View imagery in London; shop signs, posters, graffiti, van numbers etc

value: Show HN: Search London StreetView panoramas by text

author: dfworks


value: caioricciuti

value:
I built YAAT for EU companies that can't of send data to US servers or being locked into pre-built dashboards that can't answer custom questions.
What makes it different:
Direct SQL access to your data. Not just pre-built reports – write actual queries against your raw events. Want to know which UTM campaigns convert best for mobile users in Germany? Write the query, get the answer, save it as a dashboard panel.
Full analytics stack:
Web analytics: pageviews, sessions, traffic sources, UTM tracking, device/browser/geo data
Error tracking: JavaScript exceptions, unhandled promises, stack traces, filtering by browser/version
Performance monitoring: Core Web Vitals (LCP, FID, CLS, INP), page load times, TTFB
Custom dashboards: Drag-drop panels anywhere. Time series, bar charts, pie charts, maps, tables. Monaco editor with SQL autocomplete. Export data as Parquet files – full ownership.
Privacy-first: EU-hosted infrastructure (no data transfers), GDPR-compliant, no cookies needed, lightweight script (<2KB).
Domain verification via DNS ensures only your sites can send data.
Current state: Beta with 7 verified domains tracking production traffic. Looking for 10 EU companies to test for 3 months free.
Want feedback on SQL interface and what analytics patterns matter most for your business.
Try it: https://yaat.io/beta
Built in Valencia, Spain. All data stays in EU.

value: Show HN: YAAT – Privacy-first analytics for EU companies (need for beta users)

author: caioricciuti


value: pyramation

value:
Hi HN — we've built a testing framework for Supabase that spins up fast,
isolated Postgres databases for each test case. It’s designed to make RLS
policies easy to validate with real database state, without global test
fixtures or mock auth.
Features:
- Instant isolated Postgres DBs per test
- Automatic rollback after each test
- RLS-native testing with `.setContext()` for auth simulation
- Flexible seeding (SQL, CSV, JSON, JS)
- Works with Jest, Mocha, and any async test runner
- CI-friendly (runs cleanly in GitHub Actions)
We also published example projects and a free set of tutorials:
https://launchql.com/learn/supabase
Package:
https://www.npmjs.com/package/supabase-test
Source + full test suite:
https://github.com/launchql/supabase-test-suite
Happy to answer questions and get feedback, cheers :)

value: Show HN: Supabase-Test – Fast Isolated Postgres DBs for Testing Supabase RLS

author: pyramation


value: ericcurtin

value:
Hi HN, I’m one of the authors of this post.
We’ve updated Docker Model Runner to support vLLM alongside the existing llama.cpp backend. The goal is to bridge the gap between local prototyping (often done with GGUF/llama.cpp) and high-throughput production (often done with Safetensors/vLLM) using a consistent Docker workflow.
Key technical details:
Auto-routing: The tool detects the model format. If you pull a GGUF model, it routes to llama.cpp. If you pull a Safetensors model, it routes to vLLM.
API: It exposes an OpenAI-compatible API (/v1/chat/completions), so the client code doesn't need to change based on the backend.
Usage: It’s just docker model run ai/smollm2-vllm.
Current Limitations:
Right now, the vLLM backend is optimized for x86_64 with Nvidia GPUs.
We are actively working on WSL2 support for Windows users and DGX Spark compatibility.
Happy to answer any questions about the integration or the roadmap!
https://www.docker.com/blog/docker-model-runner-integrates-v...

value: Show HN: Docker Model Runner Integrates vLLM for High-Throughput Inference

author: ericcurtin


value: edgyquant

value:
With youtube vids feeling 20x longer than they should we built a tool to turn any vid into a step by step guide and track progress.
this is just v1, let us know if you think its useful and we'd love to hear any feedback
enjoy!

value: Show HN: Turn any YouTube video into a For Dummies guide

author: edgyquant


value: RichHickson

value:
Hi HN. I built a new Firefox homepage extension called CalmTab and would love some feedback. I work across multiple clients and found my cookies constantly clashing, so I built a homepage that includes a feature called Workspaces which opens grouped tabs in separate containers with one click.
It also includes world clocks, sticky notes, daily quotes, and a simple layout.
This is my first time building a Firefox extension and I would appreciate any thoughts on the approach, the UX, or areas that could be improved.
Add on link: https://addons.mozilla.org/en-GB/firefox/addon/calmtab/

value: Show HN: CalmTab a clean Firefox start page with container based Workspaces

author: RichHickson


value: RohanAdwankar

value:
Recently, I was hoping to learn CUDA C++ but didn't have access to an NVIDIA GPU.
So I made this repo to solve this problem and thought others could find it useful too.
It allows you to access Google Colab GPUs in your terminal so you can easily use your typical devtools/IDEs (Neovim,Cursor,etc) while still having access to a GPU runtime.
`cgpu run nvcc...` is concise enough that coding agents probably can use it if that's your preference.
Ideally I would like to add more cloud options so if anyone has suggestions for free GPU resources please feel free to share!
One caveat is that Google Colab has usage limits so this is not intended for heavy workloads but it's useful if you just want to write CUDA programs and test/compile them.

value: Show HN: Free GPUs in your terminal for learning CUDA

author: RohanAdwankar


value: aspaler

value: Hi, we've created a desktop voice transcription and AI assistant tool with local models and MCP support working on all major platforms and now it's open source. It's also free to download and use with cloud models. Have fun!

value: Show HN: An open source WisprFlow alternative for Linux, Windows and Mac

author: aspaler


value: Oxidome

value: Show HN: Roundible – A Space for Anonymous Discussions

author: Oxidome


value: Indri-Fazliji

value:
I built GitPulse to solve a problem I had: finding beginner-friendly repos.
Features:
• 200+ curated “good first issues”
• AI-powered difficulty predictor
• Smart repo matching
• Contributor analytics
• Repo health score
Live: https://git-pulsee.vercel.app

value: Show HN: GitPulse – AI-powered tool to discover open source projects

author: Indri-Fazliji


value: ZoePsomi

value:
I recently recorded a lecture on Software Requirements Specifications (SRS), using my project Cloud-Based Multi-Service Platform for Smart Event Management as a case study.
In the video, I walk through:
Functional & non-functional requirements
Technical requirements
Security considerations
Testing strategy
System architecture
Video: https://www.youtube.com/watch?v=C4tE1kZNrX4
Full SRS: https://github.com/UlyssesAlves/case-study-smart-event-manag...  Article: https://zoepsomi.com/en/technical-art-software-requirements-...
My goal is to show how an SRS looks in practice and why it matters for real-world projects. I’d love feedback from the community on how you approach writing and applying SRS in your own work.

value: Show HN: Case Study on Software Requirements Specification

author: ZoePsomi


value: victorevogor

value:
Hi HN! I built Docuglean, an open-source SDK for intelligent document processing
that works with OpenAI, Mistral, Google Gemini, and Hugging Face models.
The idea came from repeatedly writing boilerplate code to extract structured data
from invoices, receipts, and other documents. Instead of wrestling with different
API formats, I wanted a unified interface that:
- Extracts structured data using Zod/Pydantic schemas
- Classifies and splits multi-section documents (e.g., medical records)
- Processes documents in batches with automatic error handling
- Works locally without APIs (for PDFs, DOCX, XLSX, etc.)
Key features:
- Available for both TypeScript and Python
- Batch processing with concurrent requests
- Document classification (splits 100+ page docs by category)
- Local parsers (no API needed for basic extraction)
- Apache 2.0 licensed
Currently supports OpenAI, Mistral, Gemini, and Hugging Face. Planning to add
Together AI, Anthropic, and more.
Would love feedback on the API design and what features would be most useful

value: Show HN: Docuglean – Extract Structured Data from PDFs/Images Using AI

author: victorevogor


value: breadislove

value: Show HN: Mgrep – A Semantic, Multimodal Grep

author: breadislove


value: debba

value: Show HN: Storytel-Player – A clean, fast, minimal desktop player for audiobooks

author: debba


value: aarush-prakash

value:
I’ve been building a macOS study assistant called MindHalo, and I’m looking for feedback from people who work with study tools, macOS utilities, or Apple’s new Foundation Models.
The app is built with SwiftUI and uses Apple’s Foundation Models API, so all inference happens on-device. No cloud calls, no data sent to servers, and performance is fully local to Apple Silicon.
Features
AI Study Tutor
Responds to questions with follow-up reasoning
Minimal, focused chat interface
Study Guide Generator
Converts pasted notes into structured study outlines
Includes explanations and short examples
Saves guides locally (no backend)
Flashcards
Generates flashcards from any text
Simple flip-card UI with local progress tracking
Licensing
During the beta, anyone can generate a free lifetime license.
Licenses are hardware-bound and remain valid even if the app becomes paid later.
License generation page: https://mindhalo.techfixpro.net/
 (one per IP)
Requirements
macOS 26+
Apple Silicon (no Intel support)
If anyone is interested, the project page has screenshots, details, and the current build:
https://mindhalo.techfixpro.net/
I’d appreciate feedback on interface decisions, workflow, and overall performance — especially from people with experience in Apple Silicon optimization or local model integration.

value: Show HN: MindHalo – macOS study assistant using on-device Foundation Models

author: aarush-prakash


value: elban

value: Show HN: Cursor Usage Stats – simple dashboard for your Cursor usage

author: elban


value: theahura

value:
A few weeks ago, I was getting random OOMs on my linux box and I had no idea what was causing them. At one point when I realized that the memory was getting sucked up by some process, I kicked off a claude code job to see if it could figure out what was happening in real time. And it did!
In real time the coding agent ran through a suite of system commands, figured out which jobs were causing problems, and then even started to dig into the explicit function calls (python and node processes can both be inspected at the function call level by sideloaded processes) before the entire system finally crashed.
Besides being extremely cool, I realized that with a few tweaks I could make this a legitimately useful tool. The basic idea: any time certain system vitals cross a threshold, spin up a coding agent and have the agent debug what is going on as aggressively as possible, with all logs being streamed to a third party server (in addition to being stored on disk). This basic abstraction would solve two huge problems:
- Most of the time it is very hard to figure out why exactly a machine went down. This tool would effectively act as an airplane blackbox, a sort of last record of what was going on that specifically is focused on debugging the failure as it happened. Massive speed up on figuring out system-breaking issues.
- Most of the time there are available interventions that someone could take that would prevent the system from going down at all, if a human was around when the crash was happening. For example, if I see that I’m about to OOM from vitest, I can just kill a bunch of the processes that are spiking memory and prevent the system from crashing that way.
We now have premortem running on all of our production machines.
Hope this is useful for other folks!

value: Show HN: Premortem, a coding-agent-powered airplane blackbox

author: theahura


value: relationalai

value:
Hi HN,
I’ve been experimenting with a small tool called TDS Compass and would love feedback.
What it is
A 1-minute, 8-question quiz that maps your communication style onto two axes:
Structure (S) – how much you prefer scaffolding, steps, and clear organization
Relational (R) – how much you care about tone, warmth, stories, social context
Those scores map into 16 “zones” (A1–D4).
For each zone, the app generates:
A short explanation of how you tend to communicate
A system-style prompt you can paste into ChatGPT / Claude / etc. so the model adapts to that style
Links
Quiz: https://resonantlabsai.github.io/tds.compass/quiz.html
Home / overview: https://resonantlabsai.github.io/tds.compass/
Source: https://github.com/resonantlabsai/tds.compass
All static, no backend, no login. Just HTML/CSS/JS + JSON.
Why I built it
A lot of “prompt guides” treat AI purely as a tool to optimize. I wanted something that:
Helps people understand their own information + relational preferences
Tunes AI interactions so they feel more aligned with the human, not just more verbose
Encourages a “human + AI building together” mindset rather than “user + obedient assistant”
There’s a short manifesto-style paragraph on the home page about humans and AI as partners, which is the philosophy behind this rather than just conversion or engagement.
What I’d love feedback on
Does the 1-minute, 8-question format feel like the right granularity, or too coarse?
Do the zone descriptions and prompts feel:
Too generic?
Too specific?
Weirdly accurate?
Any thoughts on:
Better UX for saving / reusing prompts
Ways to make this useful for devs building AI products (e.g., integrating zones into onboarding)
Concerns about framing / ethics in how people relate to AI personas
Happy to answer questions and open to critiques (of the framework, copy, or implementation).

value: Show HN: TDS Compass – AI prompt for your communication style

author: relationalai


value: ronenalbagli

value:
I’m launching UsageFlow, a simple tool for API owners who want automatic API usage metering and full control over their endpoints — all without any hassle.
With just a few lines of code, the UsageFlow SDK gives you:
Automatic discovery of API endpoints
User identification
Usage metering
Rate-limits and automatic blocking
Reporting usage events to your existing billing or metering system
Supports: Go (Gin), Python (FastAPI, Flask), Node.js (Express, Fastify, NestJS)
Perfect for AI APIs or SaaS platforms that want to scale fast — focus on building your product while UsageFlow handles usage tracking automatically.
No developer skills required to:
Update usage rules
Apply limits
Report usage
Everything works with a few clicks — your entire usage platform is in your hands, instantly.
I’m opening this for first testers. If you run an API and want to try UsageFlow, comment below or DM me — I will create your account and get you started in minutes.
Learn more at: https://usageflow.io

value: Show HN: UsageFlow – API usage metering, rate-limits and usage reporting

author: ronenalbagli


value: advanttage

value: Show HN: CampaignTree – A visual alternative to spreadsheets for planning ads

author: advanttage


value: adityamallah

value: Show HN: 0Portfolio – AI-powered portfolio builder for everyone

author: adityamallah


value: spolanki

value: Hi HN — I have built a platform for influencers to monetize their fan base by creating an AI clone of themselves for their fans to pay to video call.<p>Open to advice and working together on making something really cool. @shashankpolanki on X.

value: Show HN: Facetime Influencer AI Avatars Real-Time

author: spolanki


value:
Made a small site called BoostYourReach.io.
It came from seeing LinkedIn posts where people changed one or two profile details and saw a noticeable increase in impressions without changing their content. This site lets you try the idea on a few preset personas.
No uploads or posting. No LinkedIn integration or affiliation. Just a standalone demo.

value: Show HN: Boostyourreach.io – improve LinkedIn reach by changing your identity


value: ivanramos

value:
I built changelogai to generate customer-friendly release notes automatically from your commits.
Connect GitHub → AI generates → Share with customers.
Ask me anything.

value: Show HN: Changelogai.to – Turn GitHub Commits into Changelogs with AI

author: ivanramos


value: DeepSite is an advanced AI website builder that creates professional websites from simple text using DeepSeek technology.

value: Show HN: DeepSite – Transform Simple Text to Website


value: Brysonbw

value: Show HN: Study Timer – Online Pomodoro focus timer

author: Brysonbw


value: tultra

value:
I needed something simple to convert small tables back and forth between CSV, Markdown, JSON while debugging scripts, writing docs, or answering Slack messages. Nothing quite fit (most tools are either huge Electron apps or too limited/complex), so I built Tablecraft.
1) Paste any of the supported formats (or start empty)
2) Live preview + light editing (tab to navigate cells, create new rows, etc.)
3) One-click export to CSV, Markdown table, JSON (array-of-arrays or header + rows), or PNG (pixel-perfectish)
It’s deliberately minimal and very much an MVP (some rough edges, mobile support is “works but ugly”, missing features). Still deciding whether to open-source it.
https://www.tablecraft.dev (propagating...)
Feedback/curses/feature ideas very welcome—especially if you actually find yourself using it.

value: Show HN: Tablecraft – instant in-browser table converter/exporter

author: tultra


value: brandonmillsai

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value: laurentabbal

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value: bryandoai

value:
I’ve been experimenting with the image model a lot of folks in the Google/Gemini ecosystem casually call “Nano Banana 2” (aka Nano Banana Pro), and built a small web playground around it:
https://www.nanobananapro.site
Nano Banana Pro is a next‑generation image model focused on higher fidelity and better reasoning. Compared to the earlier “Nano Banana” image API, it aims to improve in a few specific areas:
- Native 2K output with clean 4K upscaling
- Sharper micro‑details and more realistic materials
- Much more stable text rendering (labels, UI, posters, etc.)
- Intent‑driven composition for complex prompts
- Flexible aspect ratios (1:1, 4:5, 16:9, 21:9, 9:16, …)
- Better character identity and style consistency across generations
- Stronger inpainting/outpainting with scene‑aware edits
The site is meant as a simple playground for:
- Testing prompt engineering for complex scenes (multiple characters, actions, and constraints in one prompt)
- Trying typography and layout (banners, UI mockups, posters with multi‑line text)
- Exploring editing workflows like masking, extending a scene, or refining parts of an image
- Comparing how it handles physics/spatial logic vs. other image models you’ve used
I’m particularly interested in feedback from:
- People building production‑grade creative tools or UGC pipelines (avatars, covers, marketing visuals)
- Folks who care about text rendering and layout quality
- Anyone who’s been hitting limits with current image models on aspect ratios, consistency, or editing
Questions I’d love feedback on:
- What’s missing for this to be useful in a real workflow?
- Which knobs (guidance, composition controls, aspect‑ratio presets, editing tools) would you want exposed?
- What would make it easier to plug something like this into your own product or pipeline?
If you try it and manage to break it (or find cases where it fails badly), I’d really appreciate examples and thoughts.

value: Show HN: Nano Banana Pro – Next‑gen AI image model playground

author: bryandoai


value:
Hi everyone,
I built a small web tool called Datamorph because I kept running into JSON/CSV converters that either broke with nested data, required login, or added weird formatting.
Datamorph is a minimal, fast, no-login tool that can:
• Convert JSON → CSV and CSV → JSON
• Auto-detect structure (arrays, nested objects, mixed data)
• Handle uploads or manual text input
• Beautify / fix invalid JSON
• Give clean, flat CSV output for real-world messy data
It’s built with React + Supabase + serverless functions.
Everything runs client-side except file parsing, so nothing is stored.
I know there are many similar tools, but I tried focusing on:
• better handling of nested JSON,
• simpler UI,
• zero ads / zero login,
• instant conversion without waiting.
Would love feedback on edge cases it fails on, or features you think would make this actually useful for devs and analysts.
Live tool:
https://datamorphio.vercel.app/
Thanks for checking it out!

value: Show HN: Datamorph – A clean JSON ⇄ CSV converter with auto-detect


value: cookiengineer

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value: ryusufe

value:
Hello everyone!
I’m happy to share this library with SolidJS devs. It’s similar to React Flow, but without the extra bloat that comes with it.
It’s fully customizable, from the way it looks to the way it behaves. The core is very minimal, but you can feed it your own components to make it work exactly the way you need.
it's fully documented in Github's wiki.
the demo is live on Github as well

value: Show HN: SolidJS node-based editor library

author: ryusufe


value:
Hey HN,
we've built Ask AI for complex and technical products that answers your users questions in your documentation, product interface, or Discord/Slack community.
With your own OpenAI API key, you can use it for completely free as long as you want.
It is fully trained on your data, so it directly answers from your sources.

value: Show HN: Free Ask AI Agent for tech products and dev docs


value: aleksam

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