madhudollu · Product Hunt

SupaBoot — SaaS + AI starter kit Choose your stack. Get a production-ready GitHub repo. ⭐

@akinkunmi · X

He trash-talked me and my amazing project ( unprovoked while all his projects are vibe-coded slop. That's why I named him King of Slop.

johnfahey · HN

As someone who doesn't use any of the more "advanced" features on sites like ChatGPT or Claude (agentic mode, memory, image generation, deep research, etc), I found the bloat of these services, both in UX and in performance, to be pretty tedious. There are a million AI chat interfaces out there, but I could never find one that just did simple messaging and history in the most minimal and lightweight way possible. So I made my own. Nully is written in Go and Vanilla HTML/CSS/JS. It literally just sends messages to OpenRouter, receives the result, and stores your messages locally. It's very simple, very clean, and very lightweight (see performance graphs on the site). It has basic chat features, basic model settings, and works with just about any OpenRouter text model. It supports attachments and basic web search via the OpenRouter API. I made Nully mostly for myself, but the cost to open-source it and host it on a cheap VPS is practically nothing, so I figured I'd release it if it c

@sebuzdugan · X

building @getfrai , an open source toolkit that helps ML engineers navigate EU AI Act compliance, model cards, risk files, the boring but necessary stuff

@afifarioss · X

AI agent layer for Farcaster. Human approves every cast. 🟦 onchain attribution. Now Solo no team. Phase 2+ needs hands.Repo: Live: Need grants / team / funding. MIT. Fork it or fund it. 🟦

@asgujjuasitgets · X

@Hermes_3xGreat · X

what I've been vibe coding.

chenglong-hn · HN

Data visualizations are the bridge between user and data. But building AI agents that can generate visualizations reliably can be very tricky: - simple chart specs can be reliable, but generated charts are often of low quality due to reliance on system defaults; - complex chart specs with explicit details can produce good-looking charts, but they are verbose and agents can struggle with reliability We figured out it is a limitation on the language issue (not just AI capability thing) -- current visualization languages are a bit too low-level for AI agents, requiring them to explicitly make visual decisions that are supposed to be handled by a good compiler. Flint is a visualization intermediate language to address this issue, allow AI agents to solve this last-mile human-agent interaction problem. It provides a simple semantic-type based specification, and contains a layout optimization engine that can produce good-looking charts (filled with derived low-level details) from simple

@spec_tacul_ar · X

Specifications for humans and AI.

@Dil_Lynn · X

@Jack_Timonen · X

@ivygrzy · X

My git history as music. This is Unicorn: a desktop app I built w/ @AnthropicAI's Claude Code. It lives beside the terminal and documents my builds. Every experiment logs itself. Every commit plays a note. The story: The preview: