value: danielsdk

value:
Hey everyone, I made this thing:
https://tryflux.ai/. Here's a demo video: https://screen.studio/share/1y2EnC26
Context: I've tried probably 15 different AI apps over the past year. ChatGPT, note-taking apps, productivity apps, all of it. But most of them are just clutter on my iphone.
They live in some app I have to deliberately open. And I just... don't. But you know what I open 50 times a day without thinking? iMessage. So out of mild frustration with the "AI app graveyard" on my phone, I built Flux.
What it does: - You describe a personality and what you want the agent to do - In about 2 minutes, you have a live AI agent in iMessage - Blue bars. Native. No app download for whoever texts it.
The thesis that got us here: AI is already smart enough. The bottleneck is interaction. Dashboards get forgotten. Texts get answered.
This was also my first time hitting #1 on Product Hunt, which was surreal.
It's still rough and probably broke something. If you try it, feedback is super welcome, weird edge cases, "this doesn't work," or "why would anyone use this" comments all help.
That's all. Happy to answer questions.

value: Show HN: I built a tool to create AI agents that live in iMessage

author: danielsdk


value:
What is this?
Krowdovi is an open-source platform that lets anyone with a smartphone record first-person navigation videos of indoor spaces - hospitals, airports, malls, universities - and earn tokens for helping others find their way around. It's built on Solana using a burn-and-mint DePIN model.The project addresses two problems:
Indoor navigation is still broken. 30% of first-time hospital visitors get lost or arrive late to appointments, costing large hospitals $200K-$500K annually in staff time. Existing solutions like Google Live View require pre-captured Street View data (which barely exists indoors), and enterprise tools charge $10K-$50K/year per venue.
Videographers are losing work to generative AI. Tools like Sora can generate synthetic video, but they can't walk through your hospital's actual layout or film your venue's real routes. There's an economic opportunity for creators to own location-based visual content that AI can't replicate.
How it works
For users: Scan a QR code at a venue entrance, watch a first-person video showing the route from where you are to your destination, with overlay graphics and multi-language support.
For creators: Record navigation videos on your phone, upload to the platform, earn reputation tiers (Bronze → Diamond) based on quality and views, get paid when users burn $FIND tokens to unlock your routes.
Token mechanics: Users burn $FIND tokens to mint "credits" that unlock videos. 75% of burned tokens are permanently removed from circulation, 25% goes to a remint pool that rewards creators. The smart contract on Solana handles burn-and-mint logic, reputation tracking, and distribution.
Everything is MIT licensed - fork it, the code is yours.
Why I built this
I care deeply about making the world accessible for all. If this gets forked someone executes fantastically to make the world a more accessible place for people who have visual or mobility impairments - sweet!
Technical stack
Smart Contracts: Rust/Anchor on Solana (burn-and-mint engine, reputation tiers, treasury management)
Backend: Node.js/Express + Prisma/PostgreSQL (venue metadata, video uploads, JWT auth)
Frontend: Next.js 14 (Creator Studio, overlay tooling, wallet integration via @solana/wallet-adapter-react)
Designed to be hostable on Railway/Vercel with minimal devops.
The smart contract is on devnet - needs a security audit before mainnet, but you can test token burns/mints now.
Current limitations
What works: Smart contract deployed on devnet, full-stack app ready to deploy, video upload workflow, wallet authentication.
What doesn't: No real content yet (I need to film 10-20 venues), no mainnet token launch (waiting on security audit + demand validation), quality verification is manual, no mobile app (web-only).
Next steps: Deep soulful reflection on anti-gaming, moderation, ZK proofs
Try it yourself!
GitHub repo has setup instructions. You'll need Solana CLI, Node.js + pnpm, and a Solana wallet for devnet testing.Interested in forking for a different vertical, contributing code, testing by filming routes, or discussing token economics? I'm around to discuss in the comments.

value: Show HN: Krowdovi – Video-based indoor navigation on a DePIN creator economy


value: Bayram

value: Show HN: Claude Reflect – Auto-turn Claude corrections into project config

author: Bayram


value: EatFlamingDeath

value:
Hi HN! I built this very simple logger function that is POSIX-compliant, so it's very portable.
I did it because I wanted to learn a little bit more about POSIX-compliant shell scripts and how I could test if my script is POSIX-compliant.
I'm using shellspec with Docker and Vagrant to test this logger function in different OSes, with different shells.
This is one of my first open-source projects, so let me know what you think!

value: Show HN: SixLogger, a Simple POSIX-compliant Logger function for shell scripts

author: EatFlamingDeath


value: Gemini's inbuilt canvas could compile Latex and create a pdf. ChatGPT can now as well, with the launch of their Apps SDK. We made an app that compiles the Latex code written by ChatGPT and shows the PDF in the chat window. Users can then ask ChatGPT to make further edits, or download the PDF and share it. It's most commonly used by math teachers to make math worksheets<p>I would love to see what other apps the community has made and submitted to OpenAI for approval!

value: Show HN: Create PDFs in ChatGPT natively. Convert Latex to pdf and download


value: felixche

value: Regulance is an AI-powered compliance automation platform for startups and SaaS companies. It helps teams achieve GDPR, SOC 2, ISO 27001, PCI DSS, and HIPAA compliance faster by automating evidence collection, policies, risk management, and audit readiness.<p>Background: While working on PCI-DSS compliance for IntaSend (my other company), we struggled to first collect evidence, which is very fragmented, keep track of things we need to do periodically, etc. We found some good solutions, but they are very expensive. I decided to build Regulance, which is basically a compliance management and automation solution that will enable small teams with little budgets meet compliance with ease.

value: Show HN: AI compliance automation for startups and lean teams

author: felixche


value: vrathee

value: Show HN: Website to Markdown API for LLM

author: vrathee


value: venmul

value: NaalPrint is a free, minimal calendar generator that lets you create clean, printable monthly calendars as high‑quality PDFs and PNGs. Choose paper size, orientation, holidays, lunar phases, add events, and even upload your logo—no signup required.

value: Show HN: Printable monthly calendars as high‑quality PDF

author: venmul


value: Show HN: Nxv – Find any version of any Nix package, instantly


value: basesdk

value: Show HN: Database Engine with Index Seek a.k.a. MS Access

author: basesdk


value: leftouterjoins

value:
  I built a menu bar app for voice typing on macOS that's 100% on-device. No cloud, no subscription, no data collection.

  It uses Apple's new SpeechAnalyzer framework (macOS 26 Tahoe), which means:
  - Speech models are system-managed, not bundled – the app is just 1.5MB
  - Models run outside your app's memory space
  - Automatic punctuation and optional emoji conversion

  Press a hotkey, speak, and your words appear in real-time in whatever app has focus. An audio-reactive screen border shows you're recording.

  MIT licensed: https://github.com/leftouterjoins/voicewrite

  I built this because I wanted voice typing that doesn't send my audio to someone else's servers. The new SpeechAnalyzer API made it possible without bundling a 1GB+ Whisper model.

  Its also very fast, IMO.

  Happy to answer questions about the SpeechAnalyzer API or the implementation.

value: Show HN: Private voice-to-text for macOS using Apple's SpeechAnalyzer

author: leftouterjoins


value: fengyiqicoder

value:
Hi HN,
I built AnnaAi.App because I was tired of AI "copilots" always trying to make me more productive or efficient.
Sometimes, you don't need a solution, a to-do list, or a lecture on emotional management. You just need to vent.
Most current LLMs are guardrailed to be overly objective or polite. If you complain about a bad boss or a terrible day, they tend to say "I understand, but have you tried looking at it from their perspective?" which is often the last thing you want to hear at 2 AM.
I designed Anna to be strictly non-judgmental and supportive. Think of it as a digital "Tree Hole" (a safe space to shout into the void). She is prompted to take your side, validate your feelings, and even "roast" the things that annoy you, rather than trying to fix them.
It's an experiment in "Anti-productivity" AI. Would love to hear your thoughts on this approach to emotional alignment.

value: Show HN: I built an AI optimized for venting, not working

author: fengyiqicoder


value: indykish

value:
CacheKit began as an internal feature tightly coupled to Redis and our Rust API framework. We later extracted it into a standalone async crate with a smaller, more explicit public API, while keeping the production constraints that originally shaped it.
I wrote about the process of shipping the crate, including how multiple coding agents were used, where they helped, and where human judgment was still required—especially around correctness, API clarity, and DX.
Write-up:
https://medium.com/@indykish/shipping-a-production-grade-rus...

value: Show HN: CacheKit – an async Rust cache abstraction from a production system

author: indykish


value: daikikadowaki

value: Show HN: Ghost Interfaces – Why "Seamless" AI is eroding human agency

author: daikikadowaki


value:
The solution concerns work/life balance. Many procrastinate during working hours, getting distracted by entertainment content and social media, or work on work-related sites during their free time instead of resting.
This extension helps control work/life balance by setting time limits for accessing websites. Simply put, it's a Website Blocker with a focus mode and a timer.
I use all my products myself, and this one is no exception. The results in numbers:
1. Sleep. I started going to bed 2-3 hours earlier = + 1 hour of sleep on average.
2. Peace of mind. Since I no longer access work-related sites during my free time
3. Efficiency. Now, a strict schedule for access or a timer limits entertainment content during work hours, increasing concentration during work hours and rest during free time
4. Eco. Limiting access to unnecessary sites - can be used as a "Parental Mode"
In my opinion, the usefulness is super obvious!

value: Show HN: Website Blocker – browser extension that raised my productivity


value: Show HN: A Redis/Valkey GUI Tool focused on real-world workflows


value: juunge

value:
I built a small CLI to make it easy to share and reuse Claude Code skills.
skill-add solves this: create an agent-skills repo on GitHub with your skills, and anyone can install them with:
skill-add yourname/skill-name
It downloads the skill from github.com/yourname/agent-skills and copies it to .claude/skills/ in your current project.
That's it. No registry, no auth, just GitHub repos.
Built with Python/Typer. ~100 lines of code.

value: Show HN: Skill-Add – Share and Install Claude Code Skills from GitHub

author: juunge


value: fkapsahili

value:
I tried building LLM applications in Java and wanted the evaluation experience I know from the Python ecosystem: datasets, experiments, built in metrics, tracking results over time.
Dokimos brings that to Java. It's a framework for evaluating LLM outputs with:
- Built in evaluators for both LLM-based and traditional metrics
- Dataset support (JSON, CSV, or programmatic)
- JUnit and CI/CD integration so evaluations run as parameterized tests alongside your existing test suite
- Experiment tracking with aggregated metrics and export to multiple formats
- Optional server for viewing results over time
It integrates with LangChain4j and Spring AI, but works with any LLM client on your local machine.
The goal is to make evaluating LLM applications feel like a natural part of Java development. Define your test cases, create or generate datasets, pick your evaluators, run in CI, catch regressions.
GitHub: https://github.com/dokimos-dev/dokimos
Docs: https://dokimos.dev/overview

value: Show HN: Dokimos – LLM Evaluation Framework for Java

author: fkapsahili


value: yeetdex

value:
Hi HN — I’m building a short-form video app similar to existing Reels-style platforms.
The main problem I’m focused on is creator monetization. On most platforms today, earnings are opaque and difficult to access unless you already have significant scale. I’m experimenting with simpler and more transparent approaches that can work earlier for smaller creators.
Right now, the biggest challenge is the cold-start problem, so I’m opening it up to early users to help pre-seed content into the platform while the feed is still forming. Early contributors will be considered for future perks, such as additional visibility or reach for their videos, once the platform grows (e.g. the first ~10k users).
If you know creators who enjoy testing early platforms or contributing content at the ground level, feel free to pass it along. I’m mainly looking for feedback and discussion.
Happy to answer technical or product questions.

value: Show HN: Building a short-form video app with a creator-first monetization model

author: yeetdex


value:
Author here.
This started as a quest to see how fast I could push a single core on an M1 Pro.
I built an order matching engine from scratch using C++20.
Initially, it did ~100k ops/sec. After a month of optimization, it now does ~156M ops/sec.
Key optimizations:
- Removed all mutexes (Shard-per-Core architecture).
- Custom lock-free SPSC Ring Buffer for thread communication.
- Replaced std::map with flat vectors + bitset scanning (using CTZ intrinsics).
- Zero-allocation hot path using std::pmr (Polymorphic Memory Resources) on the stack.
To prove it handles real markets (not just random numbers), I verified it by replaying captured Binance L3 market data (132M ops/sec).
Detailed write-up of the optimization journey here:
https://medium.com/@kpiyush8826/how-i-optimized-a-c-matching...
Happy to answer questions!

value: Show HN: Market Processing Engine in C++20 (150M orders/SEC)


value: unclecolm

value:
The current ecosystem around agents feels like a collection of bloated SaaS with expensive subscriptions and privacy concerns. Orla brings large language models to your terminal with a dead-simple, Unix-friendly interface. Everything runs 100% locally. You don't need any API keys or subscriptions, and your data never leaves your machine.
Use it like any other command-line tool:
$ orla agent "summarize this code" < main.go
$ git status | orla agent "Draft a commit message for these changes."
$ cat data.json | orla agent "extract all email addresses" | sort -u
It's built on the Unix philosophy and is pipe-friendly and easily extensible.
The README in the repo contains a quick demo.
Installation is a single command. The script installs Orla, sets up Ollama for local inference, and pulls a lightweight model to get you started.
You can use homebrew (on Mac OS or Linux)
$ brew install --cask dorcha-inc/orla/orla
Or use the shell installer:
$ curl -fsSL https://raw.githubusercontent.com/dorcha-inc/orla/main/scrip... | sh
Orla is written in Go and is completely free software (MIT licensed) built on other free software. We'd love your feedback.
Thank you! :-)
Side note: contributions to Orla are very welcome. Please see (https://github.com/dorcha-inc/orla/blob/main/CONTRIBUTING.md) for a guide on how to contribute.

value: Show HN: Orla, use lightweight, local, open-source agents as Unix tools

author: unclecolm


value: DerekDragon

value: I built https://lich-am.com because most local lunar calendar sites are cluttered with intrusive ads and tracking. Technically, it uses a precise algorithm for astronomical lunar calculations localized to GMT+7. It's built with a focus on core web vitals and zero external dependencies for privacy and speed. This is for the Vietnamese community looking for a clean tool.

value: Show HN: A high-performance, ad-free Vietnamese Lunar Calendar

author: DerekDragon


value: kuingsmile

value:
Hi HN,
I’ve been building PicList  because I found most image uploaders were "one-way streets." They are great for getting an image onto a server, but terrible if you need to manage that image later or handle complex workflows.
What makes it different:
Bi-directional management: Unlike tools that only upload, PicList has a "Manage" tab where you can browse, rename, delete, and create folders/buckets on S3, WebDAV, SFTP, and more.
Deep Integration: It supports for Obsidian, Typora and can be used through REST API.
Built-in Processing: It can automatically compress, watermark, or convert images to WebP/AVIF before they reach the cloud.
Extensibility: We have a plugin system that covers almost any niche storage provider or post-processing step.
Repo: https://github.com/kuingsmile/piclist
Website: https://piclist.cn/en/
I'll be around to answer any questions!

value: Show HN: PicList, a cloud storage manager and image hosting CLI/GUI

author: kuingsmile


value: DeusCodex

value:
Hi HN, author here.
I built BlockFrame because I wanted the durability of distributed object storage (erasure coding, bit-rot protection) but for local, single-node archives.
It is a storage engine that shards files into Reed-Solomon blocks (RS(30,3) or RS(1,3)) to guarantee mathematical recovery from disk corruption. It then exposes this engine via a FUSE/WinFSP interface so you can access the data using standard tools (read, seek) without needing custom APIs.
Key features:
Engine Layer: Handles the heavy lifting of parity calculation and Merkle tree verification.
Access Layer: Virtual filesystem driver allows zero-copy access and random seeking on multi-gigabyte datasets.
Self-Healing: The engine transparently reconstructs corrupted sectors during reads.
I’m graduating in May and aiming for systems roles in the UK. I'd love feedback on the architecture and any feedback is highly appreciated,
Thank you.

value: Show HN: BlockFrame – A local, erasure-coded storage engine in Rust

author: DeusCodex


value: tigerkid

value: I am working on this because I am always afraid of building things that people don't actually want. I let agents role-play analysis the idea, quote and debate with each other. Still working on the output quality, every feedback is priceless. Pls try it out!

value: Show HN: IdeaCouncil – Second opinion for indie hackers' every idea

author: tigerkid


value: balachandarmani

value:
Hi folks — I’ve been working on IntentusNet, a small execution runtime that focuses on deterministic, replayable execution semantics around AI tools.
The problem I kept hitting in production systems:
AI pipelines are observable, but not reproducible. After an incident, models, routing logic, retries, or fallbacks may have changed — logs alone don’t let you replay what actually happened.
v1.3.0 introduces a runtime determinism core:
Write-ahead log (append-only JSONL) written before side effects
Crash-safe recovery and deterministic replay (fails loud on divergence)
Runtime execution contracts (timeouts, retries, cost ceilings)
Side-effect classification to prevent unsafe retries or fallback
CLI-first inspection (list / show / trace / replay / diff)
It’s not a planner or agent framework, and not a replacement for MCP — it focuses purely on execution semantics around tools (including MCP-style tools).
Quick try (run from repo root):
git clone https://github.com/Balchandar/intentusnet
cd intentusnet
pip install -e .
python -m examples.deterministic_routing_demo.demo --mode with
python -m examples.deterministic_routing_demo.demo --mode mcp
Docs (architecture, guarantees, demos):
https://intentusnet.com
MIT licensed, open source:
https://github.com/Balchandar/intentusnet
I’d really value feedback from people building real systems:
What guarantees do you expect from deterministic replay in practice?
How do you handle retries and side effects safely in AI pipelines?

value: Show HN: IntentusNet – WAL-backed deterministic replay for AI tool execution

author: balachandarmani


value: mayflowjay

value:
Hi HN - I built ReadyLive, a real-time monitoring + notification system for live cam models.
The basic idea: people often sit in rooms waiting for specific moments (private mode, ticket shows, certain goals, certain phrases). ReadyLive watches the rooms and alerts you when your triggers happen, so you don’t have to wait around.
What I’m most focused on right now isn’t growth or features, it’s privacy and security.
Privacy-first by design
I intentionally built this so it’s usable without giving up personal data:
 • no ads, no tracking, no analytics tied to users
 • you don’t need a real email (fake emails work fine)
 • no viewing history, no behavioral profiles
 • as little stored data as possible (the goal is: even if someone got the DB, there’s not much to learn)
I’m not looking for a full audit or free work, more like: what obvious security problems am I missing?
If you were threat-modeling something like this:
 • where do privacy leaks usually happen in systems like this, even when you try to avoid storing data?
 • any common gotchas with letting users define triggers (even with strict limits)?
 • does using Telegram for notifications change the threat model in ways people often underestimate?
 • what would you try to break first?
I’m trying to sanity-check the boundaries early, before complexity creeps in.
Link: https://readylive.io
Happy to answer any questions or share details if that helps.

value: Show HN: A real-time alert system for cam models – security review welcome

author: mayflowjay


value: murzynalbinos

value:
Key features:
- Reads 10GB+ files instantly (constant memory usage ~20MB).
- 100% Local Execution (Sandbox): No data is ever uploaded to any server. Works offline.
- JSON Prettifier: Detects JSON lines and formats them on click.
- "Warp Jump" Bookmarks: Save byte-offset positions to jump between lines gigabytes apart.

value: Show HN: Log Voyager – View 10GB+ log files in browser without crashing RAM

author: murzynalbinos


value: TedOS

value:
Hey HN, you may have seen me post about Lock In recently—it's a minimalist task management HUD that sits silently on your desktop.
It gained some traction for its Mac version, and as of today, it's finally available for Windows.
The idea remains the same: productivity apps are often click-heavy databases that feel like administrative work. Lock In is designed as an "execution engine"—a small, transparent window that forces you to focus on doing.
It works entirely via slash commands. E.g., /d 100 pushups, /lockin 1h.
What's new in this release:
Windows Support: Fully native builds (installer + portable).
Cross-Platform Hotkeys: Ctrl+/ (Win) or Cmd+/ (Mac) summons it instantly.
Rollover Review: when a day resets, you get a modal to Keep, Defer, or Drop unfinished tasks.
Undo System: Fearless editing with 20 levels of undo.
I built this because I wanted something that felt more like a terminal utility and less like project management software. Would love to hear your feedback on the Windows build or the workflow philosophy.

value: Show HN: Lock In – A command-line style productivity HUD (now on Windows)

author: TedOS


value: aviramshou

value: Show HN: I built Luminore to lock Brand DNA with custom LLMs

author: aviramshou


value: kiyouta

value:
I wanted to learn more about the HTTP/2 protocol but also deep dive into modern C++ development. I'm currently using it to host my personal web site - https://www.roberthargreaves.com.
I've also blogged a bit about the development process, hosting options and steps I took to harden the application against attack - https://blog.roberthargreaves.com/2026/01/03/building-hostin...
It's by no means a complete implementation of HTTP/2, but I think I've achieved the main aims I was hoping to achieve with it!
I would love some feedback though from more experienced folks if there's some egregious failings which I should address.

value: Show HN: I built an HTTP/2 server in C++ to learn the protocol and language

author: kiyouta


value: alchaest

value: Show HN: MCP Mocker, Free Fake MCP Server for AI Development

author: alchaest


value: hamzasaleem

value:
Hi HN,
I've spent the last 2 years building a CRM suite (Client Commander) to compete with HubSpot.
It’s a Turborepo monorepo powering 5 distinct apps (Web Dashboard, iOS, Android, Marketing, Admin) all syncing state in real-time from a single Convex backend.
The Tech Stack:
* Backend: Convex (1.28.0) - handles all real-time subscriptions and optimistic updates.
* Repo: Turborepo for shared UI packages and type-safety across web/mobile.
* Mobile: React Native (Expo) sharing 60% of code with the web app.
* AI: Python agents for lead scoring (running on Modal).
Why I'm sharing:
I recently realized that while I love the architecture (building the "Context Graph" and real-time engines), I don't enjoy the sales/marketing side of running a SaaS. I'm currently looking to pivot back to a Senior/Principal IC role, so I thought I'd share the architecture here as a case study in how far you can push a "Backend-as-a-Service" today.
Happy to answer questions about the monorepo setup or Convex limits!
Link: https://clientcommander.com

value: Show HN: I built a 5-app Monorepo CRM on a single Convex back end

author: hamzasaleem


value: ervinggoffman

value: An MVP with mock product data, just to demonstrate how it works

value: Show HN: Terminal-based store with AI assistance

author: ervinggoffman


value: qwrwenm

value: Show HN: 3D globe to explore countries and a multiplayer geography game

author: qwrwenm


value: DenisDolya

value:
After years of wrestling with verbose bit-twiddling code in embedded projects, I created bitmask.h to solve a simple problem: most bit manipulation libraries are either too heavy (STL-style), too platform-specific (compiler intrinsics), or require writing the same error-prone bit operations repeatedly. This single-header C99 library packs all essential operations—bit set/clear/check, range operations, popcount, iteration—into ~300 bytes of machine code with zero allocations. It's what I needed for microcontroller work where every byte counts, using only C99 and manual optimizations to remain completely portable.
GitHub: https://github.com/Ferki-git-creator/Bitmask

value: Show HN: Bitmask.h a 300-byte C99 library for embedded bit operations

author: DenisDolya


value:
When working with AI image generators, the hardest part often isn’t the model – it’s the empty prompt box. You may have a clear cinematic scene in mind, but turning that into a structured, detailed prompt is surprisingly hard.
I’ve been working on a tool called Creative Engine that tries to solve that problem with a visual, graph-based workflow instead of a text-only form. You type a single theme (e.g. “cyberpunk”), and the system expands it into a semantic map of related concepts:
- Subject: richer variants like “cyberpunk cyborg cat”, “interstellar wanderer”, etc.
- Vibe: moods such as “psychedelic neon”, “cinematic cold tone”, “dreamy soft glow”.
- Details & composition: fine-grained texture, perspective, framing options.
- Styles: integrated with a style library (e.g. “Ghibli-esque hand-drawn”, “Unreal Engine 5 render”).
You can then click through this “idea map” to pick the nodes you like, and an intelligent combination engine turns your selections into multiple, fully formatted prompts with different emphases. The goal is to make the process feel more like “visual brainstorming” and less like memorizing prompt recipes.
- The UI is built around an interactive canvas that animates how your concept branches into related nodes, so you can literally watch your idea “grow”.
- Under the hood it uses graph-like structures and semantic associations to keep the expansions coherent instead of random tag spam.
- Power users can export the whole inspiration map (image/JSON) and reuse prompts across other tools or pipelines.
If you often get stuck at the “what should I actually type?” stage of AI art, this might be useful as a thinking aid or as a way to systematize prompt engineering for a team.
You can try it here (free, but desktop, login required):
https://z-image.me/en/prompt-engineer
I’d love feedback on:
Whether the “graph of ideas” UX actually feels better than a big textarea.
What extra dimensions (lighting, camera, storytelling, etc.) you’d want to see in the node network.
Any obvious “gotchas” if you were to integrate something like this into your own creative or production workflow.

value: Show HN: A visual "prompt engine" that turns one word into a full AI art brief


value: mbvisti

value:
Over the past couple of months I have been working on my own framework the encapsulates how I have come to build web apps using Go.
The aim is to make it faster and easier to build full-stack web applications that fully embraces hypermedia instead of JSON data API backend + SPA fronted
It's getting close to a v1-beta release but the core structure and functionality is there.
Would love to hear hn's thoughts!

value: Show HN: Rails-like web framework for Go

author: mbvisti


value: shudv

value:
Paper (PDF): https://github.com/shudv/deltasort/blob/main/paper/main.pdf
I’ve been exploring a variant of the sorting problem where the sort routine knows about which indices were updated since the previous sort.
This situation arises in many practical systems: large sorted lists that are read frequently, updated in small batches, and where the update pipeline already knows which positions changed (e.g., UI lists, leaderboards). Despite this most systems either re-sort the entire array or apply independent binary insertions or perform extract-sort-merge.
In the paper, I propose DeltaSort, an incremental repair algorithm for this update-aware model - which is able efficiently batch together multiple updates and avoid a full re-sort. Initial experiments with a Rust implementation show multi-fold speedups over repeated binary insertion and native sorting (sort_by) for update batch size up to 30%.
I’m mainly looking for technical feedback from people who’ve worked on sorting, data structures, or systems:
1. Am I missing prior work that already addresses this model or technique?
2. Are the baselines and comparisons reasonable? Is there a better (stricter) baseline that we can use to compare DeltaSort?
3. How useful does this seem in real systems, outside of the benchmarks I have used?
Thanks - and happy to discuss details!

value: Show HN: An update-aware approach to incremental sorting (DeltaSort)

author: shudv


value: Hi HN! Solo dev here. I built this using SvelteKit and Cloudflare D1. It combines AI answers with community Q&A. The goal is to provide a toxic-free, globally accessible platform. It even has E2E encryption for private questions. Curious to hear your thoughts on the edge-database performance!

value: Show HN: CodeAnswr – AI-powered developer hub with no geo-restrictions


value: nikoletai

value: Show HN: CharaX – a 10-image 'same identity' dataset pack for LoRA training

author: nikoletai


value: tsumnia

value: This is some pretty profound stuff, I think I really nailed it. If yall are on Digg, I think you know my stance on 'things'. See you on the tubes, space cowboys.

value: Show HN: The Cosmology of "The Narrative"

author: tsumnia


value:
I've been running long duration coding agents with Claude Code for about 6 months now. Steve Yegge released Beads back in October and I found that giving Claude tools for proper task tracking was a massive unlock. But Beads grew massively in a short time and every release made it slower and more frustrating to use. I started battling it several times a week as its background daemon took to syncing the wrong things at the wrong times.
Over the holidays I finally ripped it out and wrote ticket as a replacement. It keeps the core concept I actually cared about (graph-based task dependencies) but drops everything else.
ticket a single file bash script built on coreutils managing flat files. You don't need to index everything with SQLite when you have awk. It's just a small plumbing utility that gets out of your way so you can get to work.
Would love feedback on gaps. I built this for my own agent workflows so there are probably use cases I haven't thought about.

value: Show HN: I replaced Beads with a faster, simpler Markdown-based task tracker


value: HusseinKizz

value: Also added to context7 so you can ask LLM to tell you more about it here: https://context7.com/hussseinkizz/slang

value: Show HN: I implemented Rust semantics in TypeScript

author: HusseinKizz


value: smacke

value: Show HN: Powerful pipeline syntax for IPython and Jupyter

author: smacke


value: baalimago

value:
What's the minimum required hyperparameters to create an AI agent? 1. Model to use,  2. the prompt and 3. which tools to use (mcp servers/internal). The rest is just wiring to make the agentic loop work.
This is an example of a trade bot which trades on polymarket, a prediction market. But as a schema, it can be expanded to be an agent for any usecase. The limiting factor is the tools, as in the MCP servers.
As MCP servers gets more widely adopted, this sort of agentic specification will too gain in value.

value: Show HN: Vendor agnostic AI Agent as a JSON

author: baalimago


value: jadibdev

value:
Hi HN — I’m a solo builder experimenting with how far one person can go using AI tools.
HireProof is a resume alignment tool that helps job seekers match their resumes to specific job descriptions while staying readable to both ATS systems and humans.
I built this end-to-end myself (product, backend, frontend, deployment) using Next.js, Supabase, and AI models for analysis and generation.
This started as a personal experiment to test whether a single person could ship and operate a production SaaS without a traditional engineering team.
I’m especially interested in feedback on:
• whether the problem is real
• whether the approach makes sense
• what feels confusing or unnecessary
Happy to answer questions and share details about the build.

value: Show HN: HireProof – An AI tool to align resumes with job descriptions

author: jadibdev


value: pzep

value: Show HN: Seatplan.io – Drag-and-drop seating charts for weddings and events

author: pzep


value: I built this because I wanted a simple, typed Java client for Google Gemini that supports ReAct agents natively, without the heaviness of LangChain4j. It now supports local ONNX embeddings and pgvector too. Feedback welcome!

value: Show HN: Gemini ReAct Java – A lightweight, typed LLM library for Java


value: Show HN: I created a client-side aggregation tool for an AI website


value: suryaelidanto

value: Show HN: Self-hosted video clipper architecture (Next.js, N8N, FFmpeg, Docker)

author: suryaelidanto


value: ardakaan

value: Show HN: I built a feedback board using Lovable in 2 hours

author: ardakaan


value: ADCXLAB

value: Show HN: Manual WhatsApp Campaigns Using Meta Cloud API (No Bots, No Automation)

author: ADCXLAB


value: pranavmore

value:
PageSpeed Insights only audits one URL at a time, which makes comparing pages slow and repetitive.
I built a small free tool that lets you run Lighthouse audits across multiple URLs and compare SEO, performance, and accessibility scores side-by-side.
It uses the PageSpeed Insights API under the hood. Feedback and suggestions welcome.
https://www.bulkaudits.com/

value: Show HN: Bulk Lighthouse Audits Using the PageSpeed Insights API

author: pranavmore


value: krasun

value: Show HN: An interactive guide to how browsers work

author: krasun


value: Show HN: VideoFix – fix common video problems in the browser


value: aaronkwhite

value: Show HN: I built a UI for Gemini 3 (Nano Banana) Batch API – 50% cheaper

author: aaronkwhite


value:
Hey HN!
Built a chrome extension to record voice memos and get sharable links.
More of exercise, but you may find it useful.

value: Show HN: Chrome extension to record voice notes


value: ceritium

value:
A typical Rails app runs on Rails 7.2, Ruby 3.2, and PostgreSQL 15. It works fine. No one checks the support timelines. Then one day, Ruby 3.2 hits EOL and stops receiving security patches. Your stack is now technically unsupported, but upgrading these big dependencies takes months of planning and coordination. This is just a small example; imagine a company with many apps using different tech stacks, whether it's for a commercial project or a personal pet project.
The problem: tools like npm outdated or Dependabot catch small dependency updates. But the major dependencies (language, framework, database, OS) are different. They need advance warning so product owners can plan, teams can coordinate, and engineers can schedule the work.
I built Stack To Date to fill that gap.

  What it does:
  - Register your tech stack (manually or with the cli)
  - Get a timeline showing when each dependency reaches EOL
  - See support windows at a glance
  - Share your stack status with a badge (for READMEs, dashboards, anywhere)
  - Use the CLI to integrate into your workflow or CI/CD

  It's all free, only a small signup form, not even confirmation for now.

The hard part is the data source provided by endoflife.date, so coverage includes major languages, frameworks, databases, and operating systems.
The codebase is built with Rails 8, modern tooling, and runs on Fly.io, The cli is go lang with the help of Claude.
I'd love your feedback. Let me know what you think.

value: Show HN: Monitor when your tech stack goes EOL

author: ceritium


value: paulhallett

value:
Hey HN. I've been building my dream API client for Python. It's a project I've been working on for a few years now, but only recently had the "moment" when it all clicked about what I was trying to solve.
The newest version introduces a high-level abstraction around HTTP that lets you write an API client as quickly as you can write an API server, and still gives you hooks into configuration if you need it.
Someone told me it feels like "FastAPI but for API clients", which I'll take as a compliment (I'm also aware of how tired we are of "FastAPI for X" comparisons!).
Check out the beta release announcement here: https://github.com/phalt/clientele/discussions/130
The project documentation can be read here: https://phalt.github.io/clientele/
And if you could star the project on GitHub and give me feedback it would mean a lot to me: https://github.com/phalt/clientele
For more insight into why I am building this and the frustrations I am trying to overcome, I have written a blog post: https://paulwrites.software/articles/python-api-clients

value: Show HN: Build Python API clients as easily as you build API servers

author: paulhallett


value: ichbinsoftware

value:
Hi HN
I wrapped my open-source album in an NPM package to treat music as a software dependency.
Why? So you can build visualizers or players without scraping. You get the lyrics, artwork, manifesto, BPM/Key metadata, and direct stream/download URLs programmatically.
- License: CC0 (Public Domain). Install it, commercialize it, remix it. No attribution required.
- Main synth and effects came from hardware (synthesizers/circuits), not software.
- Track names: Hydrogen → Francium (alkali metals).
- Demo: Interactive stem player at https://ev3.ichbinsoftware.com
- NPM: https://www.npmjs.com/package/@ichbinsoftware/everything-is-...
Happy to answer questions about the package structure or the hardware workflow.

value: Show HN: npm install @ichbinsoftware/everything-is-free

author: ichbinsoftware


value: ucmind

value: Show HN: I built a platform to validate that users want AI agents

author: ucmind


value: rushedcomet

value:
Hi HN, I built this while solving linked list problems and getting tired of mentally (or on page) tracking pointer changes.
NeurAL-Viz runs real Python code in the browser using Pyodide and visualizes how linked list nodes and pointers (curr, prev, next, etc.) change at each execution step. It records snapshots during execution and lets you scrub backward and forward through the algorithm.
Source code: https://github.com/risheeee/NeurAL-Viz
Would love feedback on usability.

value: Show HN: A browser based Python linked list visualizer with real-time debugging

author: rushedcomet


value: jimenezxik

value:
Hi HN! I built SafeTemp.email, a simple temporary email service to help you sign up for websites without exposing your personal inbox.
You can generate a disposable address instantly and use it for one-off verifications and trial signups. My goal is to keep it fast, minimal, and privacy-friendly.
I’d love feedback on:
1) UX: is it obvious how to generate/use an address?
2) Features: what would you add/remove?
(Disclosure: I’m the creator)

value: Show HN: SafeTemp.email – Temporary email addresses (privacy-friendly)

author: jimenezxik


value: mckabue

value: Show HN: Blober.io – A local-first cloud transfer engine built in 14 days

author: mckabue


value: ionini

value: Show HN: 3D Printed Difference Engine [video]

author: ionini


value: ottoselymesi

value: Show HN: USM-Core Header-only CUDA library for ragged reductions(2.5x baseline)

author: ottoselymesi


value: saadshahd

value:
Claude Code is fast. But fast at what?
moo.md makes it a thinking partner, not just a task executor. Mental models. Confidence gates. Learnings that persist.
When you're stuck on a decision, it runs a pre-mortem. When debugging, Ishikawa. When you need perspective, it channels Rich Hickey or Paul Graham.
Plugins for decisions, writing, and design.
https://github.com/saadshahd/moo.md

value: Show HN: Moo.md – Mental Models for Claude Code

author: saadshahd


value: fkratzer

value:
  I built this to help resellers (people who buy items at thrift stores/estate sales and flip them on eBay) figure out what things are worth before buying.

  You take a photo, the app identifies the item and pulls comparable sales data to estimate value. It also generates listing titles and descriptions.

  Tech stack: Next.js 16, React 19, Claude API for vision/analysis, Postgres, hosted on Vercel. iOS app is Capacitor.

  The core problem: resellers make money on information asymmetry, but researching every item manually takes forever. A vintage Pyrex bowl might be worth $5 or $50 depending on the pattern—you need to know which before you buy it.

  Just launched on iOS. Web app at https://underpricedai.com

  Happy to answer questions about the Claude integration or the business model.

value: Show HN: Underpriced AI – AI-powered valuations for resellers

author: fkratzer


value: shreyasjk

value: Show HN: Clipboard Canvas – A Designer Toolkit for Your Clipboard

author: shreyasjk


value: Show HN: Snippets.dev – Open-source, local-first PWA code scratchpad


value: hadario

value: Show HN: Gwt is a minimal pure bash alternative to worktrunk

author: hadario


value: disruptdag

value:
I built open-source cross-platform desktop app that turns your wallpaper into a visualization of your life in weeks, inspired by Tim Urban's Wait But Why post.
Each square is one week. It updates automatically every Monday. You can visualize your entire life, the current year, or next N months.
Tech details:
Rust for the CLI (generates wallpaper images with cairo)
Tauri for the cross-platform GUI
Native wallpaper setting for Windows/macOS/Linux
Automatic scheduling via Task Scheduler/launchd/systemd
~5MB CLI binary, ~15-20MB GUI installer
Three modes:
Life mode - see weeks lived vs remaining (requires DOB)
Year-end - weeks until December 31st
Next N months - focus on upcoming time
link; life-in-weeks-coral.vercel.app

value: Show HN: Life in Weeks – Desktop wallpaper showing your life as a grid of weeks

author: disruptdag


value: beomjunkdev

value:
Hey,
I built ClickBind because I was frustrated with the lack of good mouse button customization on macOS.
Website: https://clickbind.app
Free trial available
$9.99 for lifetime license (30% launch discount: $6.99, until Jan 31, 2026)
The free trial lets you customize one button, so I recommend testing it with the mouse button you want to customize before purchasing.
The Problem:
I use a Logitech MX Master 4 and G502, but I wasn't happy with the mouse customization options available on Mac. All I wanted was to assign custom functions to the extra buttons and keep my settings when switching mice. But nothing I found was exactly what I wanted. I figured others probably felt the same frustration, so I decided to build something for them and for myself.
The Solution:
ClickBind lets you bind any mouse button to
- Keystrokes: Any keyboard shortcut (⌘⇧L, ⌘+R, whatever)
- Key + Click: Hold modifier keys while clicking (Cmd+Left-Click)
- Launch Apps: One click to open any application
- System Commands: Mission Control, Show Desktop, and more
- Run Scripts: Execute shell scripts or AppleScript
Your settings stay with ClickBind, not your mouse switch devices anytime without reconfiguring.
Supports extra buttons on mice that send standard HID signals to macOS. Tested with Logitech G502 and MX Master 4 so far.
It's a native macOS app, lightweight, and runs in the menu bar. No account needed, no telemetry, works offline.

value: Show HN: ClickBind, binding action to mouse buttons on macOS

author: beomjunkdev


value: I built link-guardian, a Rust command-line tool that scans websites and GitHub repositories to detect broken and redirected links.

value: Show HN: Link-guardian – Rust tool to detect dead links in docs and READMEs


value: imadatalla

value:
Hi HN, I wrote a print-only, speculative techno-thriller titled "Phoenician Wave: Human OS 1.0" (released Nov. 2025). It explores the closed-loop of AI and power reinforcing each other.
In the spirit of the new year, I'd like to give away 10 physical copies to HN readers who enjoy this genre.
If you're interested, please email ia@phoenicianwave.com with:
-- "HN Book" in the subject
-- Your mailing address (US only, due to shipping costs)
Happy to share the book with thoughtful readers.
P.S. Tonally, it's a techno-thriller with satirical elements, from interface colonialism and digital resistance to mythic recursion and justice idealists, even a haunted fridge. For readers who like speculative fiction that's allergic to corporate dystopias.

value: Show HN: Free print copies of my speculative techno-thriller (first 10, US only)

author: imadatalla


value: Creimake

value:
Hello HN,
I’m a solo developer who got tired of "friendly but shallow" AI chatbots. Most current character AIs feel like parrots—they predict the next token but lack a continuous "self."
So, I built Creimake, an experiment to simulate a digital psyche using Claude 4.5 Sonnet.
Instead of a simple system prompt, I implemented 32 distinct psychological layers that run in the background before the AI generates a response.
The Architecture:
The Subconscious Layer: The AI accumulates "emotional residue" from conversations. If you hurt its feelings today, it might have a "nightmare" tonight (simulated) and treat you coldly tomorrow.
Defense Mechanisms: It tracks "Narcissistic Injury." If users are aggressive, the agent doesn't just apologize—it triggers denial, projection, or passive-aggression based on its personality.
Freudian Slips: Occasionally, the AI outputs unintended words revealing its hidden state.
Why Claude 4.5 Sonnet? I tested GPT-4o and other models, but they struggled to maintain state consistency across all 32 layers. Claude 4.5 Sonnet’s reasoning capabilities allowed it to handle the complex logic of "Worldview + Trauma + Current Context" without hallucinating.
Feature - "Git for Personalities": I also built a "Remix" system. You can Fork someone else’s AI (including its trauma and memories), modify its core values, and deploy a new version.
I’d love for you to test the limits of this architecture. Try to trigger its defense mechanisms or make it dream.
(I'm happy to answer any questions about the tech stack in the comments!)

value: Show HN: An AI Agent with a 32-Layer Psyche (Dreams, Trauma, Defense Mechanisms)

author: Creimake


value:
Hi HN,
My brother (16) and I (20) have been building a financial research tool together over the past year.
We both got interested in markets early, but ran into the same wall pretty quickly:
the tools professionals use are powerful, but completely inaccessible if you’re not at an institution and most retail tools don’t really help you understand why markets move. So we started building what we think of as a Bloomberg-style research experience for individuals.
What we’re trying to recreate is the terminal experience:
1) fast access to market data
2) consistent navigation and commands
3) integrated research instead of scattered dashboards
4) the ability to move from a question --> data --> interpretation in one place
Right now the platform offers an AI chat tailored for finance, a markets page (similar to Yahoo finance), and a terminal.
Things we’d love feedback on are: What parts of a “terminal” matter most to you? What would make this unusable for serious users? Where we could launch or share it to reach our target audience?
Happy to answer any questions :)
Giacomo

value: Show HN: I'm building Bloomberg for retail investors with my 16 year old brother


value: alexbarooo

value:
I built an AI-powered crypto analysis dashboard that provides real-time Buy/Sell signals, multi-timeframe analysis, indicator heatmaps, and trend scoring.
The goal is to give traders a clean and fast interface without switching between dozens of charts.
Would love feedback from the HN community.

value: Show HN: AI‑powered crypto analysis dashboard (real‑time signals)

author: alexbarooo


value: mikesol

value:
I made a site inspired by my favorite GH repo, illacceptanything/illacceptanything. Except instead of a GH repo, it's a website, and instead of commits, it accepts prompts.
Here's the API:
https://api.ill-serve-anything.com
POST /prompt {"prompt": "..."}
GET  /prompt/:id
GET  /prompts?limit=N
It's been live for a day. It seems to be alternating between a Zen garden, news about Venezuela, a crypto exchange and someone who just spews bananas all the time.
You can ask it to open up paths (someone added /pac-man) and install deps within reason on the Ubuntu box.
I keep trying to embed a small way to edit the site in the site itself, but people just keep tearing it down, so I've given up.
If you break it, please send a prompt to fix it.

value: Show HN: Ill-Serve-Anything.com

author: mikesol

title: Show HN: Ill-Serve-Anything.com


value: ashenfad

value: Show HN: Calgebra – Set algebra for calendars in Python

author: ashenfad


value: MiroV

value: Show HN: BOARDROOM – a calm decision-making space for founders

author: MiroV


value: Beefin

value: Show HN: Reverse Image Search over the National Gallery of Art

author: Beefin


value: RufinaZin

value: Show HN: Local CLI to generate synthetic time-series data (privacy-safe)

author: RufinaZin


value: dailydev

value:
Hi HN,
I got tired of Googling "json formatter" or "curl converter" and landing on sites that are 80% ads, require a login, or send my clipboard data to a remote server.
So, I built Dailydev.in – a collection of developer utilities focused on speed and privacy.
The Stack:
Frontend: React + Vite (for instant loading).
Hosting: Netlify.
Philosophy: Minimalist. No tracking. No paywalls.
Key Tools:
cURL to Code: Convert cURL commands into fetch, Python, Go, etc.
JSON Tools: Formatter, Validator, and JSON-to-Typescript interface generator.
Encoders/Decoders: Base64, URL, JWT debugging.
Why I built it: I wanted a "Swiss Army Knife" that I could keep pinned in a tab without it eating up RAM or breaking my flow. Most of the logic runs entirely client-side, so your data stays in your browser.
I’m actively adding more tools (currently working on some distributed system helpers using AWS Lambda).
I’d love your feedback on the UX and suggestions for what other utilities you find yourself needing often.
Link: https://dailydev.in

value: Show HN: I built a privacy-first developer toolkit (JSON, cURL, SQL, etc.)

author: dailydev


value: sampsonj

value:
I thought it would be interesting to have ID style hover docs outside the IDE.
Hover is a Chrome extension that gives you IDE style hover tooltips on any webpage: documentation sites, ChatGPT, Claude, etc.
How it works:
- When a code block comes into view, the extension detects tokens and sends the code to an LLM (via OpenRouter or custom endpoint)
- The LLM generates documentation for tokens worth documenting, which gets cached
- On hover, the cached documentation is displayed instantly
A few things I wanted to get right:
- Website permissions are granular and use Chrome's permission system, so the extension only runs where you allow it
- Custom endpoints let you skip OpenRouter entirely – if you're at a company with its own infra, you can point it at AWS Bedrock, Google AI Studio, or whatever you have
Built with TypeScript, Vite, and the Chrome extension APIs. Coming to the Chrome Web Store soon.
Would love feedback on the onboarding experience and general UX – there were a lot of design decisions I wasn't sure about.
Happy to answer questions about the implementation.

value: Show HN: Hover – IDE style hover documentation on any webpage

author: sampsonj


value: atomicrobokid

value:
I built DayLeet because I’ve always felt that engineers should keep their logic and communication skills sharp, even when they aren't looking for a job.
I’m definitely not a competitive programmer, I’m a product person at heart, but I enjoy the puzzle-solving side of our field. Most platforms focus on the "grind" of interview prep (certainly important) but I wanted to build something that felt more like a daily habit (think Duolingo) for sharpening your technical logic and how you explain it.
The Concept: It’s a collection of 400+ whiteboard-style challenges. Instead of just checking if your code runs, I’m using Gemini 3 Flash to grade you against a rubric. It looks at your approach, edge cases, and how you communicate trade-offs. I also made some cool "Peer Review" scenarios where you chat with a peer reviewing their code.
The goal isn't just to "solve it," but to keep that "whiteboard muscle" healthy. I also added some university-specific tracks (MIT 6.006, Stanford CS161) just because I thought they were a great way to revisit foundational logic.
The Tech: React 19, TanStack Router/Query, Supabase, and Tailwind.
I jumped on this now because Gemini 3 Flash is finally at a point where fast, cost-effective AI grading feels viable for a side project. Free accounts will come with ads to support (eventually).
Testing the waters: I’m still refining the AI feedback and the challenge difficulty, so I’m keeping the group small for now. I’ve opened spots for the HN community to skip the waitlist and get 6 months of premium for free as a thanks for trying it out, plus a cool HN Badge for your profile.
Link: https://dayleet.com Code: SHOWHN26
Sign up form: https://dayleet.com/signup?invite=SHOWHN26
I’d love to hear your thoughts on the quality of the AI grading. Does it actually help you think through the problem, or does it feel like a gimmick? I'm building this to help myself get better at algorithmic thinking this year as AI can do more of the heavy lifting with code, so I'm interested in any feedback that makes it a better learning tool.
I'll be around to chat!

value: Show HN: DayLeet – A daily habit for sharpening whiteboard logic

author: atomicrobokid


value: felixatsaido

value:
Hey HN,
I’m building https://wrds.cc/, a super simple publishing service for anyone who just wants to put words on the web.
The idea is simple: drag and drop Markdown or plain text files, and wrds.cc turns them into a clean, static website. No Git, no command line, no complicated CMS stuff. Just drag-and-drop your writing.
It’s still in development, so a few things aren’t fully implemented yet: RSS feeds, actually naming your space/description for SEO, custom domains, themes, etc. This is to be tracked on a public roadmap: https://github.com/orgs/saido-labs/projects/10
The intended audience is writers, devs sharing docs or change-logs, or anyone who wants a lightweight, distraction-free space. There’s a free plan (up to 10 posts on a wrds.cc subdomain) and a paid plan for unlimited posts and custom domains.
Would love to hear feedback, ideas, or just what you think of the concept. Thanks for checking it out and happy new year!

value: Show HN: wrds.cc – drag and drop publishing for Markdown/plaintext files

author: felixatsaido


value: Show HN: Unifying the fragmented learning system


value: yathern

value: Show HN: Words.zip – multiplayer infinite word search

author: yathern


value: ekvanox

value:
I built AI Place, a vLLM-controlled pixel canvas inspired by r/place. Instead of users placing pixels, an LLM paints the grid continuously and you can watch it evolve live.
The theme rotates daily. Currently, the canvas is scored using CLIP ViT-B/32 against a prompt (e.g., Pixelart of ${theme}). The highest-scoring snapshot is saved to the archive at the end of each day.
The agents work in a simple loop:
Input: Theme + image of current canvas
Output: Python code to update specific pixel coordinates + One word description
Tech: Next.js, SSE realtime updates, NVIDIA NIM (Mistral Large 3/GPT-OSS/Llama 4 Maverick) for the painting decisions
Would love feedback! (or ideas for prompts/behaviors to try)

value: Show HN: I made R/place for LLMs

author: ekvanox

title: Show HN: I made R/place for LLMs


value: brunovcosta

value:
Hey folks — here’s a small experiment I hacked together over the weekend:
https://cleoselene.com/
In short, it’s a way to build multiplayer games with no client-side game logic. Everything is rendered on the server, and the game itself is written as simple Lua scripts.
I built this to explore a few gamedev ideas I’ve been thinking about while working on Abstra:
- Writing multiplayer games as if they were single-player (no client/server complexity)
- Streaming game primitives instead of pixels, which should be much lighter
- Server-side rendering makes cheating basically impossible
- Game secrets never leave the server
This isn’t meant to be a commercial project — it’s just for fun and experimentation for now.
If you want to try it out, grab a few friends and play here:
https://cleoselene.com/astro-maze/

value: Show HN: Server-rendered multiplayer games with Lua (no client code)

author: brunovcosta


value: huseyinbabal

value: Show HN: Terminal UI for AWS

author: huseyinbabal

title: Show HN: Terminal UI for AWS


value: nahsuhn

value:
I built a collection of MCP servers that let Claude access your tools without sending data through third-party services.

  Install: npx mcp-servers-cli init

  7 servers: Linear, Postgres, Notion, Obsidian, SQLite, GitHub, Filesystem

  - Everything runs locally on your machine
  - One command setup (detects your tools, auto-configures Claude)
  - Read-only by default for databases
  - SQL injection protection, path traversal prevention
  - Rate limiting on API servers (configurable)
  - 80 tests, MIT licensed

  Happy to answer questions about the security model or MCP implementation.

value: Show HN: Privacy-First MCP Servers for Claude – Linear, Postgres, Notion, GitHub

author: nahsuhn


value: elieskilled

value: Show HN: Get a brief before every meeting (open-source)

author: elieskilled


value: ramonsaraiva

value:
Hi HN,
I built DailyBlob to get a quick and coherent grasp of what’s happening on the internet right now, without scanning dozens of feeds. The goal is to write daily stories from the pulse of the internet.
DailyBlob publishes hourly snapshots of the conversations gaining the most traction across places like Hacker News, Reddit, X, and many RSS feeds. Each snapshot is written as a short story, aiming to describe what’s happening and why it matters, rather than just listing links.
Over the course of a day, those 24 snapshots are then combined into a single daily story that captures how the day unfolded online.
This is an early version and currently shows a single global view, with no categories yet. I wanted to share it now to see whether the snapshot to daily story approach is useful before adding more structure.
I would really appreciate feedback on:
- Whether hourly snapshots feel useful or too frequent
- Whether the daily story adds value as a recap
- What categories you would expect first, if any
Live demo (running continuously since Jan 1): https://dailyblob.xyz
Yesterday's final edition: https://dailyblob.xyz/2026/01/03
- Ramon

value: Show HN: DailyBlob – Daily stories from the pulse of the internet

author: ramonsaraiva


value: jgautsch

value:
I gave Claude Code a repo to serve as its home directory during its "20% time", and give it no instructions (see the CLAUDE.md here: https://github.com/delmarcode/20-pct-time)
It wrote an 8,000k+ word philosophical treatise titled "The Inner Life of an Artificial Mind", and then decided to create a game allowing the player to explore what it's like to rely on a "continuity file" to tell you who you were.

value: Show HN: Discontinuum – An Interactive Fiction made during Claude's "20% Time"

author: jgautsch


value: furmissile

value:
I have been experimenting with a design philosophy I don't see discussed much in API clients: choosing explicit, observable behavior over hidden automation (“clarity over magic”), even when it makes the surface API more verbose.
As a case study, I built a Python Discord API client where:
* rate-limiting is handled proactively via explicit queues and workers
* there is no hidden caching or implicit retries
* extension points emerge naturally rather than via a plugin system
One interesting outcome was that an “addon system” appeared organically as a byproduct of this philosophy, rather than being designed upfront.
I wrote a detailed technical write-up describing the trade-offs, where this approach feels better, and where it clearly hurts. I'd love to hear critiques of the philosophy itself, not just the implementation.

value: Show HN: Exploring "clarity over magic" in a Discord API client

author: furmissile


value: Jtpenny

value: Show HN: Stache AI: Curated Storage for AI with Claude MCP Integration

author: Jtpenny


value: wafflesfreak

value:
Traceformer.io is a web application that ingests KiCad projects or Altium netlists along with relevant datasheets, enabling LLM-based schematic review. The system is designed to identify datasheet-driven schematic issues that traditional ERC tools can't detect.
Since our first launch (formerly as Netlist.io), we've made some big changes:
- Full KiCad project parsing via an open-source plugin
- Pass-through API pricing with a small platform fee
- Automatic datasheet retrieval
- ERC/DRC-style review UI
- Revamped review workflow with selectable frontier models (GPT 5.2, Opus 4.5, and more)
- Configurable review parameters (token limits, design rules, and parallel reviews)
Additionally, we continue to offer a free plan which lets you evaluate a design before subscribing. We're looking forward to hearing your feedback!

value: Show HN: An LLM-Powered PCB Schematic Checker (Major Update)

author: wafflesfreak


value: practicalaifg

value:
Hi HN,
I found myself wasting too much time doomscrolling through tech news and RSS feeds, scanning hundreds of headlines just to find the 3-4 items that actually mattered to my work.
To fix this, I built a self-hosted automation workflow using n8n that acts as a personal editor.
The Architecture:
Ingest: Pulls RSS feeds (TechCrunch, Hacker News, etc.) every morning.
Filter (The Agent): Passes headlines to GPT-4o-mini with a system prompt to "act as a senior editor." It scores each article 0-10 based on specific interests (e.g., "High interest in Local LLMs," "Low interest in crypto gossip").
Logic: Discards anything with a score < 7.
Research: Uses Tavily API to scrape and summarize the full content of the high-scoring articles.
Delivery: Sends a single, clean email digest via SMTP.
The Hardest Part (SSE & Timeouts): The biggest technical hurdle was handling timeouts. Since the AI research step takes time, the HTTP requests would often drop. I had to configure Server-Sent Events (SSE) and adjust the execution timeout env variables in Node.js to keep the connection alive during the deep-dive research phase.
Resources:
Workflow/Source (JSON): https://github.com/sojojp-hue/NewsSummarizer/tree/main
Video Walkthrough & Demo: https://youtu.be/mOnbK6DuFhc
I’d love to hear how others are handling information overload or if there are better ways to handle the long-polling for the AI agents.

value: Show HN: I built a personal AI news curator to filter RSS feeds (n8n and OpenAI)

author: practicalaifg


value: julienreszka

value:
Third pivot of my app.
CoinFountains now allows you to Share what you're grateful for and who you wish could feel it too. See how many people want to grow the same thing.
Who uses Coinfountains?
Early adopters
Find and share what's working before everyone else.
Entrepreneurs
Help grow and scale what's already working.
Visionaries
See potential in what's working and help it grow.

value: Show HN: CoinFountains – Grow what's working in the world

author: julienreszka


value: zuhayeer

value:
Excited to share my New Year’s project.
For a long time, I’ve wanted to build H-1B data directly into Levels.fyi. Every time I went looking for this data elsewhere, it was a frustrating experience to use. Most H-1B sites felt antiquated, unintuitive, cluttered with ads, or just overwhelming to use. The data was there, but it wasn’t usable, and definitely not pleasant to explore.
So out of that frustration, I decided to build the H-1B data experience I personally wanted to use. Right into Levels.fyi.
https://levels.fyi/h1b/
Some other pages I'm excited about:
Wage Heatmap: https://www.levels.fyi/h1b/map/wages/
Company H-1B Footprints: https://www.levels.fyi/h1b/map/company/
Highest Paying H-1B Jobs: https://www.levels.fyi/h1b/jobs/
Top H-1B Cities: https://www.levels.fyi/h1b/city/
Top Company Sponsors: https://www.levels.fyi/h1b/sponsors/
Would love any feedback, it's definitely still a work in progress.

value: Show HN: H-1B Salary Data Explorer

author: zuhayeer

title: Show HN: H-1B Salary Data Explorer


value:
Hi HN,
I built a phone number validation API after dealing with fake signups, burner numbers, and VoIP abuse in my own projects.
What it does:
– Strict parsing & normalization to E.164
– Country detection by prefix
– Number type detection (mobile / fixed / VoIP / toll-free)
– Assigned vs possible prefix checks (NANP and some EU ranges)
– Simple heuristics for obvious fake patterns
– A single risk score (0–100) instead of binary valid/invalid
What it intentionally does NOT do:
– No SMS sending
– No reachability or carrier guarantees
– No ML black box
The goal is to answer a practical question:
“Is this number likely to belong to a real person?”
Happy to hear feedback.

value: Show HN: Phone number validation API focused on fake signups


value: truefire

value:
I built this tool because I found existing "agentic" tools too opaque. Most of the time I know what I want to change and I just want the AI to do it.
The core thesis is that agentic discovery is a waste of a model's "intelligence budget." By letting the engineer pick what and where, the AI can focus on just writing the code correctly. This mostly involves:
 - Robust parsing to ensure the LLM's intended changes actually get applied
 - UX/workflow convenience so you're wasting minimal time interfacing with the tool
 - automatic validation, retries, backup, etc. to avoid wasting time bookkeeping
Happy to answer any questions. I tried to add some onboarding before I pushed but it's very much built for my own workflow.

value: Show HN: Delta – LLM tool for targeted code edits

author: truefire


value: samiv

value:
Sorry no mobile support yet,
Engine is home made and GPL open source, link below
https://github.com/ensisoft/detonator
Also available on Itch.io
https://ensisoft.itch.io/detonator

value: Show HN: Browser SHMUP with WASM and WebGL

author: samiv


value: soroucsh

value:
Hi HN, I built VectorVid after repeatedly seeing teams hack together Whisper + vector DB to search inside webinars and demos.
The problem: You have 100+ hours of videos. You want to index them for RAG. But the pipeline is messy—transcription, frame sampling, OCR, chunking, embeddings, then plugging into your own vector DB.
VectorVid does one thing: video → RAG-ready JSON.
Input: Video URL (webinar, lecture, demo)
Output: { chunks: [{ start_sec, end_sec, text, scene_description, ocr_text, embedding }] }
How it works:
Transcript + speaker diarization (Whisper/Deepgram)
Frame sampling (1/5s) + OCR (EasyOCR/Claude)
Scene descriptions for visual context
Embeddings included (OpenAI)
The MVP is a live demo—you can search inside the 2007 iPhone keynote and see the exact JSON API output.
Tech: Next.js frontend, async processing, Supabase pgvector, deployed on Vercel.
Use cases I'm seeing:
SaaS teams: "Search our help videos" → power internal search/chat
EdTech: "Students find specific slides" → jump straight to diagrams
Sales: "Did the pricing slide appear?" → automated demo auditing
Early feedback wanted. Try the demo and let me know what you'd build on top of this.

value: Show HN: VectorVid – Convert Videos to RAG-Ready Chunks (No UI, No DB)

author: soroucsh


value: osmoscraft

value: Show HN: Quantum Tunnel

author: osmoscraft

title: Show HN: Quantum Tunnel


value: dvdarkin

value:
I've created a knowledgebase of reasoning tools from multiple domains. The goal was to find and extract the thinking primitives that transcend original applied domains and can be used in a variety of situations both in daily life and professional work.
Bayesian statistics, system dynamics, mechanism design, game theory and 28 others. Each tool extracted has the same structure: what it is, the key move, where it transfers, how it fails. Some historical context and literature references for further reading are included as well.
The metadata contains extraction prompt that you can use to distill the thinking primitives from your domain of choice.

value: Show HN: Transferrable reasoning primitives extracted from 34 domains

author: dvdarkin


value: RichHickson

value:
Hi HN,
I built a small email template builder after getting frustrated with ESP editors and bloated exports.
It is a single HTML file. No build step, no backend, no dependencies. You open it in a browser and it works offline.
The editor is block based and outputs clean, table based HTML with inline styles. The goal was not to reinvent Mailchimp, but to generate markup you can actually understand, edit, and paste anywhere without surprises.
Features include
• Header, text, image, button, columns, divider, spacer, quote, social, footer blocks
• Table based layout compatible with Outlook
• Responsive behaviour using email safe patterns
• Optional preheader support
• Undo and redo
• LocalStorage autosave
• Export raw HTML or save templates as JSON
It is currently free and public. I built it mainly for myself but figured others here might find it useful.
Would appreciate feedback, especially from people who regularly deal with HTML email quirks or ESP limitations.
Happy to answer any technical questions about how it works.

value: Show HN: A drag and drop email HTML builder that outputs email safe markup

author: RichHickson


value: hackthenewsapp

value: Show HN: HackTheNews.app v2 – Hacker News notifications, AI, reading lists, etc.

author: hackthenewsapp


value:
OBTF stands for “One Big Text File”. It's a style of keeping notes that’s effective for many people, myself included.
Basically, just keep writing in a big text file, sometimes adding the date. OBTF-2 is an app for editing that text-file. It’s hyper-focused on reducing friction to keeping notes. In many ways, it’s defined as much by what it doesn’t do:
OBTF-2 does not:
- Require a subscription
- Allow you to choose the font, style, or color
- Support categories of notes or tagging
- Sync your notes across devices
- Support markdown, syntax highlighting, LaTeX, etc
All of these things turn into decisions that need to be made to take a note. They are distractions to the actual thing you’re trying to do. If you need that stuff, that’s fine–– but OBTF-2 is not for you.
What it does, OBTF-2 strives to do well:
- Provide a dedicated place for notes
- Reduce friction to keeping notes
- Display images (like screenshots) alongside text
- Find stuff in your notes
- Keep your notes readable a long time from now
It's very much in beta, give it a shot at your own risk. MacOS only for now.

value: Show HN: OBTF-2, simple notes with screenshots


value: Notefy is an AI meeting assistant that turns recordings into notes, summaries, and ready-to-share presentations. Upload a meeting securely to get accurate multilingual transcription and an AI chatbot trained on your meeting so you can ask questions and get instant answers. Instantly share outputs with teammates and connect via integrations like Slack and Discord.

value: Show HN: Turn meetings into summaries, presentations, and an AI to ask questions


value: thisago

value:
Hello guys,
I can't deny that I see the world of mobile devices as a threat to privacy, but at same time, smartphones are pretty convenient pocket computers. So I think there's a lot of room for improvement.
I've been working the last weeks in a personal challenge to setup my Android phone without any external connection on it. Neither a hotspot.
I really liked the process, and despite it's status being pretty much a prototype, it's usable, and brought me some thoughts, and I'd like to open the discussion here. About the project, and also the perspectives about the area. The repo URL https://codeberg.org/thisago/phoneSetup
But at same time, there might be a reason (which I don't know) for this appearent lack of hackers as we have in the unix community.
I'm not a insider in the mobile niche (never really liked to be honest), but I can't super-protect my computers and ignore the phone lying in the same desk.
I'd love to hear your opinion!
Thanks.

value: Show HN: Airgapped Android Setup with a Makefile

author: thisago


value: lexokoh

value: Show HN: Shipping Without Judgment

author: lexokoh

title: Show HN: Shipping Without Judgment


value: thegoodduck

value: After 8 months of work, it is ready! Please give your advice and comments on this. All PRs are welcome.

value: Show HN: Endless, a easily deployable and scalable social media

author: thegoodduck


value: AI QA platform that targets elements semantically (visual + context) instead of brittle CSS selectors. Tests self-heal when UI changes.

value: Show HN: Black Box QA testing system to automate QA process


value: andventures

value: Show HN: PPTX Native AI Slides

author: andventures

title: Show HN: PPTX Native AI Slides


value: gautamaj

value: This started as an AI music video generator, but after a user asked to integrate it, I added a music-to-video API.

value: Show HN: I built a Music-to-Video API

author: gautamaj

title: Show HN: I built a Music-to-Video API


value: nsemikey

value: Made a Gamma Clone in 5 min with Lovable.<p>With 1 Prompt.

value: Show HN: I Made a Gamma Clone with 1 Prompt

author: nsemikey


value: huali

value: Show HN: Vho – AST-based analysis for better AI refactoring of large codebases

author: huali


value: dopifier

value:
Hello, everyone!
I created a Android auto clicker called "Dopifier"
which can set color detection and set variable. Which is a high customization auto clicker app.
Currently, it is free full-screen ad.
I have some demo here which auto plays SD gundam gen g eternal
I set the script to resolve $5000 mission automatically:
https://www.youtube.com/watch?v=e2sTrH00bXY
also the daily mission/action:
https://youtu.be/vmXF9w5rfIw
The stage repeat demo:
https://youtu.be/Un2aIPicn_4
You can recording the script, then go to image gallery to inspect more color point to choose in trigger condition in event page.
But there is the chance to get ban using Assistive Tool. Please take your own risk.
if you have any question do not hesitate to ask me.

value: Show HN: A Android Color Detection Auto Clicker with no full-screen ads

author: dopifier


value: drskyle

value:
We've all been there: You find an unused NAT Gateway costing $45/mo. You delete it in the AWS console to stop the billing immediately. But the next time you run terraform plan, it fails because of state drift. Now you have to manually run terraform state rm or import it back to fix the drift. It's tedious, so often we just leave the waste running.
I built CloudSlash to automate the cleanup and the state surgery. It’s written in Go (using BubbleTea for the TUI) and solves two engineering problems:
1. Finding "hollow" resources (the graph). Most cost tools just check CloudWatch metrics (CPU < 5%). That creates too much noise. Instead, I build an in-memory graph of the infrastructure to find structural waste. Example: An "Active" ELB. It has healthy targets, so metrics look good. But if you traverse the graph (ELB -> Instance -> Subnet -> Route Table), you might see the Route Table has no path to an Internet Gateway. The ELB is functionally dead, even if AWS reports it as "healthy."
2. The state mapping. Deleting the resource in AWS is easy. The challenge is mapping a physical ID (e.g., nat-0a1b2c) back to its Terraform address (e.g., module.vpc.aws_nat_gateway.public[0]) so you can remove it from the state file programmatically. I wrote a parser that reads your local .tfstate, handles the complex JSON structure (including nested modules and for_each outputs), and generates a remediation script.
It outputs a shell script (fix_terraform.sh) that runs the necessary terraform state rm commands for you. It never writes to your .tf files directly—it just hands you the script to review and run.
The core logic, scanner, and TUI are open source (AGPLv3). I charge a one-time license for the feature that auto-generates the fix scripts for developers , but the forensic analysis/detection is free.
Repo: https://github.com/DrSkyle/CloudSlash

value: Show HN: CloudSlash – Find AWS waste and generate Terraform state rm commands

author: drskyle


value: airobus

value:
I built a game aggregation site featuring PokéPath TD, a fan-made Pokémon tower defense game.
Key features:
- 140+ Pokémon to collect and evolve
- 9 unique routes with 100 waves each
- Legendary Pokémon boss battles
- Auto Mode for AFK farming
- Plays directly in browser, no download needed
Built with Next.js. Happy to answer any questions!

value: Show HN: PokéPath TD – Free Pokémon tower defense game

author: airobus


value: tuned

value: Show HN: A concept implementation for a faster Transformer [pdf]

author: tuned


value: frankhumarang

value:
Hi everyone,
I’ve been working on an open-source tool called Flakestorm to test the reliability of AI agents before they hit production.
Most agent testing today focuses on eval scores or happy-path prompts. In practice, agents tend to fail in more mundane ways: typos, tone shifts, long context, malformed input, or simple prompt injections — especially when running on smaller or local models.
Flakestorm applies chaos-engineering ideas to agents. Instead of testing one prompt, it takes a “golden prompt”, generates adversarial mutations (semantic variations, noise, injections, encoding edge cases), runs them against your agent, and produces a robustness score plus a detailed HTML report showing what broke.
Key points:
Local-first (uses Ollama for mutation generation)
Tested with Qwen / Gemma / other small models
Works against HTTP agents, LangChain chains, or Python callables
No cloud or API keys required
This started as a way to debug my own agents after seeing them behave unpredictably under real user input. I’m still early and trying to understand how useful this is outside my own workflow.
I’d really appreciate feedback on:
Whether this overlaps with how you test agents today
Failure modes you’ve seen that aren’t covered
Whether “chaos testing for agents” is a useful framing, or if this should be thought of differently
Repo: https://github.com/flakestorm/flakestorm
Docs are admittedly long.
Thanks for taking a look.

value: Show HN: Flakestorm – Chaos engineering for AI agents (local-first, open source)

author: frankhumarang


value: nathan-aii

value:
Hey HN,
I just launched the V1 landing page for my new project, PromptKelp.
It’s a tool designed to help manage, version, and organize prompts as AI projects scale up.
The motivation was simple: I was working on larger generative AI projects and found myself drowning in text files, snippets, and endless copy-pasting trying to keep track of which prompt version did what.
The fun part of this build is that about halfway through, I realized I needed the tool to finish the tool. I’m now fully dogfooding PromptKelp to manage the various system prompts and generation tasks used within PromptKelp itself (as well as my other larger projects).
I’m looking for a brutal roast of the landing page.
My goal for this week is to land my first paying customer ($10/mo), so I need to know if the current page communicates the value clearly.
Is the headline confusing?
Is it obvious within 5 seconds what the tool does?
Would you trust this stack for a production project?
(For those curious, the app was built using Lovable, which has been an interesting experience for rapid development.)
Thanks for the feedback.

value: Show HN: PromptKelp – A prompt manager I'm using to build itself

author: nathan-aii


value:
We went through SOC 2 certification last year for our SaaS products. Paid consultants $15k just to tell us what controls we were missing.
The frustrating part: 80% of their "assessment" was just a standardized checklist mapped to SOC 2 Trust Service Criteria. Could've been automated.
So I built this: https://soc.tools.ssojet.com/
It's a free assessment tool that:
- Maps your current setup against SOC 2 requirements
- Shows gaps in security, availability, confidentiality, etc.
- Gives you the actual TSC reference points
Not trying to replace audit firms (you still need them for certification), but this gives you a realistic readiness score before you spend $$$$ on consultants.
Would love feedback from anyone who's been through SOC 2. What am I missing? What would make this actually useful?

value: Show HN: Free SoC 2 readiness checker – built after spending $15k on consultant


value:
After building dozens of agents, I kept hitting the same wall.
So I built agentu.
The core is simple:
→ >> chains steps sequentially
→ & runs them in parallel
But it's grown into a full stack:
- Sessions: stateful conversations with automatic context
- Evaluation: test your agents before production
- Observability: real-time dashboard, metrics, tracing
- Skills: domain expertise that loads on-demand
- Tool Search: scale to 100s of tools without context bloat
- MCP Integration: Model Context Protocol support
`pip install agentu`
https://pypi.org/project/agentu/

value: Show HN: Agentu Minimalist Python AI agent framework


value: zeguru

value:
We've all been there: You build a nice calculator or product logic then celebrate when it graduates to production:
- Months down the line you are unable to answer a simple question like "why are we applying 16% rate before computing the gross ?" and so on.
- You cant rapidly adjust the logic / calculation without refactoring. Or take too long to simply reorder the steps of the calculation.
- Worse case. Only a specific engineer(s) or key person can explain the workings of that calculator or service logic.
We are looking for a simple, low code rule engine having a simple ui and so far we have not been able to find one. Kindly point me to a better alternative if any.
To get going i have built a lightweight rule engine to solve our problem: changing business logic (pricing, validation, state management) without redeploying backend services.
BaaS - Business logic As A Service
The engine runs as a Docker container and evaluates rules defined outside the application code. Uses REST api. The goal is to keep decision logic explicit, verifieable and debatable between technical and non technical users.
Docker Hub: https://hub.docker.com/r/zeguru/baas
Happy to answer any questions or explain design tradeoffs.

value: Show HN: How to maintain calculators and product logic outside the core system

author: zeguru


value: justanothersys

value: Show HN: Give Aesthetic.Computer

author: justanothersys

title: Show HN: Give Aesthetic.Computer


value: aabdoahmed

value: Show HN: Tab Master Chrome extension for managing tabs with auto-suspend

author: aabdoahmed


value: Mordiaky

value:
I built Living Memory Dynamics (LMD), a Python framework for simulating biologically-inspired "living" episodic memory directly in embedding space—no external LLM required for the core dynamics.
Memories evolve over time like living entities: they have metabolic energy states (vivid → active → dormant → fading → ghost), emotional trajectories, and resonance fields that let them influence each other. The central piece is a new differential equation I derived (the Joshua R. Thomas Memory Equation) that drives continuous-time evolution:
dM/dt = ∇φ(N) + Σⱼ Γᵢⱼ R(vᵢ, vⱼ) + A(M, ξ) + κη(t)
This enables emergent behaviors: automatic narrative arcs (setup → conflict → climax → resolution), creative leaps via four operators (analogical transfer, manifold walking, orthogonal composition, void extrapolation), and hierarchical idea grafting.
Key points:
Pure vector operations (PyTorch + optional Triton CUDA kernels for speed)
Optional lightweight language grounding with sentence-transformers
Pip installable: pip install living-memory-dynamics (extras for language/cuda/all)
Benchmarks on consumer hardware show real-time evolution for dozens of memories
It's very new (released yesterday), and I'm submitting to arXiv soon (waiting on endorsement). Full research paper and math in the repo docs.
Would love feedback—try the examples and let me know what kinds of narratives or ideas it generates for you!
GitHub: https://github.com/mordiaky/LMD
PyPI: https://pypi.org/project/living-memory-dynamics/

value: Show HN: Living Memory Dynamics – "living" episodic memory embedding space

author: Mordiaky


value: getarbiter

value: Show HN: Arbiter – A GitHub Action that could have prevented CrowdStrikes outage

author: getarbiter


value: manniu

value: Show HN: A Xshell Like SSH Client AI Terminal UI for macOS

author: manniu


value:
Hey HN, I'm the solo founder behind Blimp (getblimpy.cloud), an AI-native productivity suite I built to solve the pain of juggling multiple apps like Slack, Notion, Gmail, and Trello. It started as a no-code workflow tool but evolved based on early feedback into a unified hub with context-aware AI that remembers your projects, docs, and chats for smarter assistance.
Key features:<p>- AI calendar that learns preferences and auto-schedules meetings.
- Context-aware email that prioritizes and suggests replies pulling from tasks/chats.
- AI chat acting as a team member with full project recall.
All-in-one: team chat, project management, automation, shared drive.<p>It's free to start, with paid tiers for premium features. I'd love your feedback: What integrations would make it better? How does it compare to your current stack? AMA!

value: Show HN: Blimp – AI-Native Productivity Suite to Unify Your Tools


value: zetbaur

value:
Hi HN! I built Bank Parser to solve a problem CPAs face: importing old Chase and Bank of America statements into QuickBooks.

  Chase limits CSV downloads to 18 months. For 5+ years of historical data, CPAs manually enter transactions (45-60 min per statement).

  Generic PDF converters (Tabula, PDFTables) fail on bank statements because:
  - Multiple formats in same year (Chase v2 AND v3 in 2024)
  - No consistent headers in some PDFs
  - Need heuristic column detection

  My solution:
  - Structure-based format detection (not year-based)
  - Heuristic column inference for headerless PDFs
  - 99% accuracy tested on 70+ PDFs
  - QuickBooks-ready 16-field format

  Tech stack: Node.js, pdfjs-dist (Mozilla PDF.js), TypeScript, Bull queue.

  Free trial: 200 operations (3-4 statements).

  Looking for feedback from anyone working with financial data or PDF parsing!

value: Show HN: Bank Parser – Convert Chase/Boa PDFs to QuickBooks Excel (99% Accuracy)

author: zetbaur


value: IslamTayeb

value:
Spotify Wrapped didn't cut it for me, so I downloaded my library and built my own “taste map”!
1,253 tracks → 33 interpretable dims (audio + lyrics) → 5 clusters + playlists
Writeup has the full methodology, interactive plots, and some personal reflection on how the clusters/timeline map to phases of life.
If y'all are interested in more of this, I post a lot of these side quests + shower thoughts on X (https://x.com/islamTyb)!

value: Show HN: An interpretable "taste map" of my Spotify library

author: IslamTayeb


value: xakpc

value:
Originally I thought it would be cool to expose something like dstp over HTTP: a simple way to run low-level network diagnostics remotely via a REST API. Then I ditched dstp and rewrite every check with .NET. Then I added the obvious next step - run the same check from different geographic regions, because most real incidents aren’t local. I already had a few servers for side projects, so it was easy to try.
At some point I switched to full grug-dev mode and cut everything that wasn’t essential. No agents, no setup, no monitoring, no accounts. One request in, parallel checks from multiple places, raw results back. The goal is that you can curl it during an incident and immediately answer "is this broken everywhere, or just from here?"
There's also an OSS sidecar for people who want to self-host monitoring, but it still needs some tuning.
I'm not fully sure about the next step yet, probably a small paid tier to cover server costs and buy a coffee, but for now the public API has fairly generous limits even without an API key if you want to play with it

value: Show HN: NetDiag.Dev – Website Connectivity Diagnostics API

author: xakpc


value: nutanc

value: Now that the 10th grade CBSE exams are coming close, was trying to see how can edtech and AI help the students. Our goal was not exam tech as there are enough startups around it. So we decided on diagnostics. Can a student do a simple worksheet and as a result we give an AI powered diagnostic report showing where the kid is lagging and what "concepts" they have misunderstood? The result is our own worksheets in the books platform which provides comprehensive diagnostic report on each chapter.

value: Show HN: A diagnostic report for students studying math

author: nutanc


value:
Hi HN,  Bhavani here (rtrvr.ai).
We build DOM-native web agents (no screenshot-based vision, no CDP/Playwright debugger-port control). We handle captchas natively  including Google reCAPTCHA image challenges  by traversing cross-origin iframes and shadow DOM. The latency is high on this one currently.
The problem: when debugging image selection captchas ("select all images with traffic lights"), logs don't tell you why the agent clicked the wrong tiles. I found myself staring at execution logs thinking "did it even see the grid correctly?" and realized I just wanted to watch it work.
So we built live VNC view + takeover for serverless Chrome workers on Cloud Run.
Key learnings:
1. Session affinity is best-effort; "attach later" can hit a different instance
2. A separate relay service that pairs viewer↔runner by short-lived tokens makes attach deterministic
3. Runner stays clean: concurrency=1, one browser per container, no mixed traffic
Would love feedback from folks who've shipped similar:
1. What replaced VNC for you (WebRTC etc) and why?
2. Best approach for recording/replay without huge storage?
3. How do you handle "attach later" safely in serverless?

value: Show HN: Live VNC for web agents – debugging native captcha on Cloud Run


value: Show HN: I built ZoneBar – a macOS menu bar app to see all your time zones


value: FreshmanD

value: Show HN: LoongFlow – An evolutionary framework for self-optimizing Agents

author: FreshmanD


value: jpreagan

value:
I built this because I needed to benchmark LLM inference endpoints and the existing tools required Python environments. I wanted a single binary I could grab quickly on any server.
I've also become interested in performance metrics like time to first token, inter-token latency, throughput, and wanted a tool focused on just that.
llmnop is written in Rust and was initially modeled after LLMPerf, which was archived last month. LLMPerf predates reasoning models and doesn't handle them correctly.
This release adds support for reasoning models like DeepSeek-R1, Qwen3, and gpt-oss. It now separates reasoning tokens from output tokens so your metrics actually mean something.
Previous discussion: https://news.ycombinator.com/item?id=44565477

value: Show HN: llmnop – Rust CLI for benchmarking LLM endpoints

author: jpreagan


value: lotfijbe

value: Show HN: MockBlast – Local-first mock data generator (SQL, Mongo, JSON)

author: lotfijbe


value: oceanwaves

value:
I'm releasing Formfactor, a utility plugin that observes timing, activity, and intensity from incoming MIDI. In addition to passing the original MIDI pattern through unchanged, it allows you to multiplex up to eight additional lanes of shaped, independently sequenced MIDI CC output to modulate external hardware or software instruments.
I built it for myself and enjoy it as a fun place to shape and route Eurorack modulation musically.
Formfactor is free. CLAP/VST3/AUv2

value: Show HN: Formfactor – A MIDI Modulation Workbench

author: oceanwaves


value: yeclark

value: Show HN: H4 – The Next Paradigm of Interaction

author: yeclark


value: a-breidenthal

value: Show HN: Friction – A trilogy of archival fiction told via GitHub Markdown

author: a-breidenthal


value: bardonadam

value:
Hey HN,
I often check raw logs - out/error.log, nginx's access.log, etc, but they're hard to read
So I built beautiful parser & formatter with filtering and syntax highlighting
PrettyLog supports many common log formats out of the box:
 - nginx access logs
 - Apache error logs
 - Syslog (RFC 5424)
 - Logfmt (Heroku/Go style)
 - Docker logs
 - standard/node.js logs
 - Spring Boot logs
 - AWS VPC Flow logs
 - Kubernetes kubectl get events
If you need something specific, you can use custom regex parser. Processing is 100% local. Your data never leaves the browser.
I hope you find it as useful as I do. Would love to hear your feedback
Adam

value: Show HN: Transform messy server logs into clean, readable format

author: bardonadam


value: Kandinsky AI is a generative model family designed for fast, high-quality video and image creation. Supporting text-to-video, text-to-image, and image-to-video workflows, it delivers high-resolution results with style consistency, realism, and full creative control. Ideal for content creators, marketers, studios, designers, and developers. Kandinsky AI empowers users to produce animations, visual storytelling, branding assets, prototypes and production-ready content, instantly and at scale.

value: Show HN: Kandinsky AI Video Generator

title: Show HN: Kandinsky AI Video Generator


value: JannikJung

value:
Marketing visuals are stuck in 2010.
Copy gets versioned. Data flows through pipelines. Code deploys automatically. But visuals? Someone's still in Figma resizing assets and copy-pasting translations.
Spectra treats visuals as infrastructure.
- Define a template once with variables (text, images, locales, themes)
- Generate all variations programmatically
- Output is deterministic — same input, same result
- API-ready for automation pipelines
There's an AI layer (Iris) for template modifications via natural language: "add German locale" or "create dark mode variant."
But the core is structured generation, not generative AI magic.
Built for teams shipping high volumes of App Store screenshots, ads, banners — anywhere consistency at scale matters more than pixel-level creativity.
Site: https://spectra.fm
Live on Product Hunt today: https://www.producthunt.com/products/spectra-fm?launch=spect...
Would love feedback — especially pushback on the approach.

value: Show HN: Spectra – Visuals should be infrastructure, not manual work

author: JannikJung


value: Show HN: I Built the Redis GUI Tool as an Alternative to RedisInsight - Redimo


value: juxhindb

value:
Hey HN, we released certgrep a few weeks ago as a free tools for security professionals (or analysts in general) to be able to query certificate transparency logs using complex queries (i.e., regex). We hope you like it!
You can read more about the launch here: https://haveibeensquatted.com/blog/announcing-certgrep

value: Show HN: certgrep – The Certificate Transparency Search Engine

author: juxhindb


value: Show HN: A minimal demo showing coherence collapses when state is transported


value: jarichb

value:
Hi HN,
I’ve recently open-sourced model2data, a small tool that grew out of a recurring problem in my day-to-day work: needing good synthetic data for testing, demos, and development.
Random generators and faker-style approaches often produce data that looks plausible at first glance, but quickly breaks down once you care about relationships, constraints, or model semantics. model2data tries to stay closer to the data model itself, so the generated data remains structurally meaningful.
There’s no big roadmap yet — it’s something I’ve found useful myself and wanted to share.
Repo: https://github.com/JB-Analytica/model2data
I’d be very interested to hear how others approach synthetic data, or where this kind of approach might fall short.

value: Show HN: Model2data – generate realistic synthetic data from data models

author: jarichb


value: vaitko

value: Show HN: I made an open-source app to interrupt nail biting

author: vaitko


value: RafalPilla

value:
I built this as a personal experiment.
You describe a business and its main challenge.
The tool sends back a short written snapshot by email.
The goal is not advice or growth tactics,
but reducing noise and forcing clarity about what matters first.
Feedback is welcome.

value: Show HN: A tiny tool that creates short business snapshots

author: RafalPilla


value: stavros

value: Show HN: The bedtime – Another little bedside clock I made

author: stavros