| name | description |
|---|---|
orchestrating-swarms |
Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns. |
Master multi-agent orchestration using Claude Code's TeammateTool and Task system.
Some notes on technical design documentation practices such as RFCs, ADRs, decision logs, and related approaches for software projects.
SolanaForge is a browser-based AI-powered IDE for building, testing, deploying, and managing full-stack Solana applications. The platform uses LLMs like OpenAI, Claude, Gemini, and Grok to generate, maintain, and deploy smart contracts, backends, and frontends — tightly integrated into a seamless Web3 developer experience.
IMPORTANT: This comprehensive setup will transform any iOS project into a fully automated, context-aware, self-maintaining Claude Code environment. This prompt is designed specifically for Claude Code (claude.ai/code) users.
Before starting, verify these tools are installed:
TL;DR
This talk, delivered at an AI engineering conference, emphasizes the rapid evolution of AI engineering from simple GPT wrappers to complex, multidisciplinary applications. The speaker highlights the conference's role in tracking this progress, fostering innovation (e.g., MCP protocol adoption), and moving the field from demos to production. The core thesis posits that AI engineering is at a pivotal "standard model" formation stage, similar to early physics or traditional software engineering (ETL, MVC). The speaker then proposes several candidate standard models: the updated LLM OS (2025), the LLM SDLC (emphasizing value in later stages like evals and security), and various approaches to building effective agents. Critically, the speaker argues for shifting focus from arguable terminology (e.g., "agent" vs. "workflow") to the practical ratio of human input to valuable AI output. He introduces his own SPAED model (Sync, Plan, Analyze, Deliver, Evaluate)
Some notes, references, and resources on tools, libraries, frameworks, and concepts for building, testing, and running algo / quant / automated trading systems and strategies.
TL;DR GPT-5 is presented as OpenAI's latest and most capable "AI system," excelling particularly in STEM subjects like coding, math, science, and research. The review showcases its impressive ability to generate complex, interactive HTML applications from single prompts, including simulations (beehive, fluid dynamics, ray tracing), games (3D racing), and practical tools (CRM dashboard, Photoshop clone, video editor, meditation guide). It also demonstrates strong research and information synthesis capabilities, especially in health-related queries, and a significantly reduced hallucination rate compared to previous models. While it shows minor flaws in some generated UIs and image consistency, GPT-5 consistently ranks #1 across various independent benchmarks for coding, creative writing, and overall performance, offering competitive pricing. The video concludes by highlighting the accelerating pace of AI development, with new state-of-the-art models emerging every few weeks.
**Inf
TL;DR This video outlines five essential error handling techniques for building robust, production-ready n8n workflows. Production readiness implies workflows that notify errors, log failures, implement retry/fallback logic, and fail safely without unintended consequences. Failures are inevitable, necessitating proactive planning and the use of "guardrails" built by identifying error patterns through logging. The techniques covered include using dedicated error workflows for centralized notification and logging, configuring nodes to retry on temporary failures, setting up fallback LLMs for AI-driven processes, enabling nodes to continue processing even if individual items error (preventing full workflow stoppage), and implementing polling for asynchronous operations to ensure completion before proceeding. Ultimately, understanding error patterns allows for the creation of preventative "guardrails" to enhance workflow predictability and
TL;DR GPT-5, accessed via API, demonstrates significant advancements, particularly in coding and visual generation. The model is multimodal, capable of accepting image inputs and generating high-quality visuals with superior clarity compared to models like Opus 4. It excels at creating functional and visually appealing websites, though some "AI tells" like misplaced elements or formatting issues can occur, which are often fixable with iterative, detailed prompting. GPT-5 also successfully tackles complex coding challenges, including realistic physics simulations (bouncing ball) and Rubik's Cube solution simulations using algorithms like Cumba. Furthermore, it can solve challenging problems like Mathematics Olympiad questions, albeit with varying success rates and requiring high reasoning effort. While impressive, its reasoning capabilities still show limitations, as seen in its initial failure to adapt to a modified classic riddle, defaulting to its training data
Zero-configuration automatic Serena MCP server management for Claude Code
Transparently starts exactly one Serena instance per project with unique ports. No per-project setup required!
claude - Serena starts automatically