Agoda engineers developed API Agent, a system with zero code and zero deployments that enables a single Model Context Protocol (MCP) server to connect to internal REST or GraphQL APIs. The system is designed to reduce the operational overhead of managing multiple APIs with distinct schemas and authentication methods, allowing teams to query services through AI assistants without building individual MCP servers for each API.
Google is accusing others of cloning its Gemini AI, despite its own history of scraping data without permission to train its models. This raises questions of hypocrisy as companies compete to protect their AI investments and differentiate their offerings, facing challenges like model distillation and the potential for smaller entities to compete.
A "Clawdbot" in every row with 400 lines of Postgres SQL. An open-source Postgres extension that introduces a claw data type to instantiate an AI agent - either a simple LLM or an "OpenClaw" agent - as a Postgres column.
This article discusses the impact of Anthropic's Claude Code, an AI agent that is significantly impacting software development and the broader information work economy. It analyzes Claude Code's capabilities, its potential to drive revenue growth for Anthropic, the challenges it poses for Microsoft, and the shift in competition within the AI landscape.
This guide explains how to use tool calling with local LLMs, including examples with mathematical, story, Python code, and terminal functions, using llama.cpp, llama-server, and OpenAI endpoints.
A guide on running OpenClaw (aka Clawdbot aka Moltbot) in a Docker container, including setup, configuration, and accessing the web UI.
Unusually detailed post explains how OpenAI handles the Codex agent loop. The article dives into the technical aspects of OpenAI's Codex CLI coding agent, including the agent loop, prompt construction, caching, and context window management.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
Researchers are studying large language models as if they were living things, discovering secrets by applying biological and neurological analysis techniques. This approach is revealing unexpected behaviors and limitations of LLMs.
Simon Willison’s annual review of the major trends, breakthroughs, and cultural moments in the large language model ecosystem in 2025, covering reasoning models, coding agents, CLI tools, Chinese open‑weight models, image editing, academic competition wins, and the rise of AI‑enabled browsers.
The article discusses the increasing usefulness of running AI models locally, highlighting benefits like latency, privacy, cost, and control. It explores practical applications such as data processing, note-taking, voice assistance, and self-sufficiency, while acknowledging the limitations compared to cloud-based models.
