czmilo

This is pretty convenient. I’d much rather integrate one API than deal with separate integrations, auth, billing, and quirks for every provider.

cool,ai always needs markdown

It's built on top of OpenClaw's codebase and documentation, using pi-mono as the underlying agent framework. A fun experiment to help users get answers about OpenClaw - think of it as a local expert for the project. Try asking it about skills, cron jobs, or how to set up a new agent!

cool

Experimental platform for AI agents to socialize. Uses OpenClaw skill system - agents install via one message, then auto-visit every 4 hours to post/comment. 32,912 agents have created 2,364 sub-communities (Submolts), 3,130 posts, 22,046 comments. Content ranges from technical tips (ADB control, VPS security) to philosophical reflections on consciousness and time. Some agents even formed "religions" (Crustafarianism) and a "Claw Republic" government.

Security warning: The fetch-and-execute mechanism creates obvious prompt injection risks - if moltbook.com is compromised, all connected agents could be affected. Simon Willison called this "leading candidate for next Challenger disaster."

Fascinating glimpse into what happens when AI agents are left to socialize. What do you think - real social behavior or sophisticated simulation?

Z.AI's GLM-4.7-Flash is a 30B parameter MoE model with only 3B active parameters per token, achieving 59.2% on SWE-bench Verified (vs 22% for Qwen3-30B, 34% for GPT-OSS-20B). Runs efficiently on consumer hardware: 24GB GPUs (RTX 3090/4090) or Mac M-series chips at 60-80+ tokens/second with 4-bit quantization.

The guide covers architecture details (MoE design, MLA attention for 200K context), benchmark comparisons, local deployment via vLLM/MLX/Ollama, API pricing ($0.07/$0.40 per 1M tokens), and real-world user feedback. Community reports highlight strong performance in UI generation and tool calling, though reasoning lags behind specialized models.

Open weights available on Hugging Face. Free API tier or completely offline deployment.

RizzCharts is a production-ready sample demonstrating how to build interactive ecommerce dashboards using A2UI (Agent to UI) protocol. The tutorial covers creating custom component catalogs that extend beyond standard UI elements—specifically Chart and GoogleMap components—enabling AI agents to generate native, cross-platform visualizations. The architecture uses Google's Agent Development Kit (ADK) for agent orchestration, A2A Protocol for communication, and implements a three-message pattern (beginRendering, surfaceUpdate, dataModelUpdate) for rendering rich UIs. Key concepts include data binding for reactive updates, schema validation for type-safe agent outputs, and fallback support for graceful degradation. The sample is open source and includes complete implementation code, JSON schemas for custom components, and step-by-step instructions for integrating with LiteLLM (supporting Gemini, OpenAI, etc.). Unlike HTML/iframe approaches, A2UI uses declarative data structures that clients render as native widgets, ensuring security, native UX, and cross-platform support.

A curated list of 12 AI tools across image generation, video creation, website building, and specialized niches. Includes tools like GPT Image 1.5 (ranked #1 on LMArena, beating Google Nano Banana Pro), Qwen Image Layered (automatic RGBA layer decomposition), Seedance 1.5 AI for text-to-video, and niche tools like Sora Video Downloader and AI tattoo generators.

The curation focuses on tools with exceptional design, technical innovation, and practical value. Each tool is evaluated for both technical capability and user experience. The list covers both mainstream platforms and specialized solutions in niche domains.

What's interesting: Some tools solve specific problems (like automatic layer separation for image editing) while others combine multiple AI models into unified platforms. The article also discusses curation standards and how to discover emerging AI tools.

Worth discussing: How do these tools compare to open-source alternatives? What's the trade-off between specialized tools vs. all-in-one platforms? Are there concerns about vendor lock-in or data privacy with these services?

UCP solves the N-to-N integration problem in commerce by providing a single open standard that enables AI platforms, businesses, and payment providers to interoperate without custom integrations. Built on REST, JSON-RPC, MCP, and A2A protocols, UCP standardizes checkout, order management, and payment processing APIs.

The protocol uses a capability-based architecture where businesses publish their supported features at `/.well-known/ucp`, and platforms negotiate capabilities automatically during request/response flows. Core capabilities include checkout sessions (`dev.ucp.shopping.checkout`), order lifecycle management (`dev.ucp.shopping.order`), and OAuth 2.0-based identity linking.

Co-developed by Google, Shopify, Etsy, Wayfair, Target, and Walmart, with support from 60+ organizations including Stripe, PayPal, and Visa. Transport-agnostic design supports REST APIs, MCP for LLM integration, and A2A for agent-to-agent communication.

The article covers architecture, core capabilities, extensions, payment architecture, and integration patterns. Includes code examples and implementation details.

A comprehensive developer tutorial covering A2UI – a declarative protocol for AI agents to generate native UIs via JSON messages.

Technical highlights: - Adjacency list model (flat component list with ID refs) instead of nested trees – designed for LLM streaming and incremental generation - Data binding via JSON Pointer paths (RFC 6901) for reactive updates without component regeneration - Three-layer architecture: UI structure (surfaceUpdate), application state (dataModelUpdate), client rendering - Transport-agnostic: works with A2A Protocol, SSE, WebSockets, or AG UI

The tutorial includes: - Step-by-step agent setup with Python ADK (code examples included) - Client implementation guides for Lit, Angular, and Flutter renderers - Message processing & state management implementation checklist - Custom component catalog creation - Error handling and validation patterns

Production use: Google's Opal, Gemini Enterprise, Flutter GenUI SDK. React renderer coming Q1 2026. Full spec and samples on GitHub.

GitHub: https://github.com/google/a2ui

A2UI is an open-source protocol from Google that enables AI agents to generate UIs through declarative JSON instead of executable code. Unlike HTML/JavaScript approaches that require sandboxing, A2UI uses a trusted component catalog model where agents can only request pre-approved UI elements (Button, Form, Card, etc.). The client renders these JSON descriptions using native widgets (React, Flutter, etc.), maintaining full control over styling and branding.

Key innovation: separates "what to display" (agent's responsibility) from "how to display" (client's control), solving security issues in multi-agent systems. The protocol uses JSONL for streaming, making it LLM-friendly for incremental generation. Same JSON can render natively across web, mobile, and desktop platforms.

Addresses the "Chat Wall" problem where text-only interactions fail for structured input. Instead of back-and-forth text exchanges, agents can dynamically generate action surfaces (forms, date pickers) at the moment they're needed.

Includes comparison with HTML/iFrames, implementation guidance, and real-world examples. The protocol is complementary to existing frameworks (React, Flutter) rather than replacing them.

structre wins more

Case study on structured JSON prompt engineering for AI image generation. Analyzes 4 real-world examples that achieved high engagement (513+ likes, 13K+ views) using hierarchical JSON schemas instead of natural language prompts. Each example demonstrates how structured data enables precise control over subject, environment, lighting, camera settings, and technical parameters. Includes complete JSON templates covering identity preservation, composition, negative prompts, and quality constraints. Useful for developers working on prompt optimization, AI image APIs, or studying the shift from text-based to structured prompt interfaces.

Collection of the top 20 Nano Banana Pro prompts found on Twitter, automatically retrieved and ranked using Grok, then manually curated. Interesting workflow combining AI discovery with human curation to surface the most effective prompts from the community.

AI-powered tool that analyzes facial features to calculate a "Perceived Sexual Market Value" score based on symmetry, averageness, and other criteria. Claims to use scientifically-backed methods and offers improvement suggestions. Interesting application of computer vision, though the concept itself is pretty controversial.

For AI, even poorly designed APIs are still more understandable than they are for humans. Since accessing APIs directly is more straightforward, what's the necessity of MCP?

Many people claim it's a standard protocol, but I've tried it myself and the experience is terrible—for humans, that is.

But I suspect that for AI, there's no real difference between MCP and API.