This article discusses how to effectively utilize Large Language Models (LLMs) by acknowledging their superior processing capabilities and adapting prompting techniques. It emphasizes the importance of brevity, directness, and providing relevant context (through RAG and MCP servers) to maximize LLM performance. The article also highlights the need to treat LLM responses as drafts and use Socratic prompting for refinement, while acknowledging their potential for "hallucinations." It suggests formatting output expectations (JSON, Markdown) and utilizing role-playing to guide the LLM towards desired results. Ultimately, the author argues that LLMs, while not inherently "smarter" in a human sense, possess vast knowledge and can be incredibly powerful tools when approached strategically.
This article presents findings from a survey of over 900 software engineers regarding their use of AI tools. Key findings include the dominance of Claude Code, the mainstream adoption of AI in software engineering (95% weekly usage), the increasing use of AI agents (especially among staff+ engineers), and the influence of company size on tool choice. The survey also reveals which tools engineers love, with Claude Code being particularly favored, and provides demographic information about the respondents. A longer, 35-page report with additional details is available for full subscribers.
Examples for common OpenSandbox use cases. Each subdirectory contains runnable code and documentation. Integrations and sandboxes are available for various tools and services like AI models, desktop environments, and web scraping.
We’ve been experimenting with using large language models (LLMs) to assist in hardware design, and we’re excited to share our first project: the Deep Think PCB. This board is designed to be a versatile platform for experimenting with LLMs at the edge, and it’s built using a combination of open-source hardware and software. We detail the process of using Gemini to generate the schematic and PCB layout, the challenges we faced, and the lessons we learned. It's a fascinating look at the future of hardware design!
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.
An analysis of the accuracy of image search tools like Google Lens, Gemini, and Bing, highlighting that while Google Lens is the most reliable, all tools can make mistakes and should be verified. The article uses examples from Yale University architecture to demonstrate these inaccuracies.
This article details how Google SREs are leveraging Gemini 3 and Gemini CLI to accelerate incident response, root cause analysis, and postmortem creation, ultimately reducing Mean Time To Mitigation (MTTM) and improving system reliability.
An AI-powered document search agent that explores files like a human would — scanning, reasoning, and following cross-references. Unlike traditional RAG systems that rely on pre-computed embeddings, this agent dynamically navigates documents to find answers.
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.
An analysis of the current LLM landscape in 2026, focusing on the shift from 'vibe coding' to more efficient and controlled workflows for software development and data analysis. The author advocates for tools like AI Studio and OpenCode, and discusses the strengths of models like Gemini 2.5 Pro and Claude Sonnet.
