Starlette 1.0 has been released, and Simon Willison explores its new features by leveraging Claude’s skill‑building capabilities. He demonstrates how Claude can clone the Starlette repository, generate a comprehensive skill document with code examples, and even create a fully functional task‑management app complete with database, API endpoints, and Jinja2 templates—all generated and tested by Claude itself. The article highlights the practical benefits of integrating an LLM as a coding agent, showcases the new lifespan mechanism, and reflects on the growing popularity of Starlette as the foundation of FastAPI.
This project, `autoresearch-opencode`, is an autonomous experiment loop designed for use with OpenCode. It's a port of `pi-autoresearch`, but implemented as a pure skill, eliminating the need for an MCP server and relying solely on instructions the agent follows using its built-in tools. The skill allows users to automate optimization tasks, as demonstrated by the example of optimizing the BogoSort algorithm which achieved a 7,802x speedup by leveraging Python's `bisect` module for sorted-state detection.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
agentic_TRACE is a framework designed to build LLM-powered data analysis agents that prioritize data integrity and auditability. It addresses the risks associated with directly feeding data to LLMs, such as fabrication, inaccurate calculations, and context window limitations. The core principle is to separate the LLM's orchestration role from the actual data processing, which is handled by deterministic tools.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
This article details building end-to-end observability for LLM applications using FastAPI and OpenTelemetry. It emphasizes a code-first approach, manually designing traces, spans, and semantic attributes to capture the full lifecycle of LLM-powered requests. The guide advocates for a structured approach to tracing RAG workflows, focusing on clear span boundaries, safe metadata capture (hashing prompts/responses), token usage tracking, and integration with observability backends like Jaeger, Grafana Tempo, or specialized LLM platforms. It highlights the importance of understanding LLM behavior beyond traditional infrastructure metrics.
The article details “autoresearch,” a project by Karpathy where an AI agent autonomously experiments with training a small language model (nanochat) to improve its performance. The agent modifies the `train.py` file, trains for a fixed 5-minute period, and evaluates the results, repeating this process to iteratively refine the model. The project aims to demonstrate autonomous AI research, focusing on a simplified, single-GPU setup with a clear metric (validation bits per byte).
* **Autonomous Research:** The core concept of AI-driven experimentation.
* **nanochat:** The small language model used for training.
* **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
* **program.md:** The file containing instructions for the AI agent.
* **Single-File Modification:** The agent only edits `train.py`.
* **Autonomous Research:** The core concept of AI-driven experimentation.
* **nanochat:** The small language model used for training.
* **Fixed Time Budget:** Each experiment runs for exactly 5 minutes.
* **program.md:** The file containing instructions for the AI agent.
* **Single-File Modification:** The agent only edits `train.py`.
This article explores five Python decorators that can be used to optimize LLM-based applications. These decorators leverage libraries like functools, diskcache, tenacity, ratelimit, and magnetic to address common challenges such as caching, network resilience, rate limiting, and structured output binding. The article provides code examples to illustrate how each decorator can be implemented and used to improve the performance and reliability of LLM applications.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
Learn how to equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand using Agent Skills. This article covers what Agent Skills are, progressive disclosure, creating skills, connecting skills to an agent (with .NET and Python examples), use cases, and security considerations.
Alibaba has released CoPaw, an open-source framework designed to provide a standardized workstation for deploying and managing personal AI agents. It addresses the shift from LLM inference to autonomous agentic systems, focusing on the environment in which models operate. CoPaw utilizes AgentScope, AgentScope Runtime, and ReMe to handle agent logic, execution, and persistent memory, enabling long-term experience and multi-channel connectivity.
This course takes you from Python fundamentals to AI Agent development, covering core Python, NumPy, Pandas, SQL, Flask, FastAPI, LLMs, and open-source models via HuggingFace.
