I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.

Explores reinforcement learning techniques to train the Qwen model to generate visual outputs through code generation and execution.

Delightful visual style, generated programmatically, with an RL-tuned model.

A benchmark dataset evaluating whether coding agents can automatically resolve engineering tasks in scientific computing and research domains.

A retrospective on Amazon EC2's 20th anniversary, marking two decades of cloud computing innovation and its evolution as a foundational AWS service.

Cua is an open-source computer-use automation platform that enables agents to control desktop applications and GUI machines through drivers, sandboxes, and benchmarking tools, with support for real machines, local desktops, and cloud environments.

OpenAI's custom-designed Jalapeño inference chip, developed in partnership with Broadcom, outperforms through hardware-software codesign and general-purpose optimization rather than model-specific specialization.

Apple announces new M6 and M5 Ultra processors designed to deliver significant performance and AI computing improvements for Mac devices.

Thomas Dullien reflects on key realizations from his early 20s to present that shaped his worldview, including understanding personal incentive structures, accepting limitations on predicting impact, and navigating complex ethical dilemmas in security work.

A comprehensive guide to adding artificial intelligence to embedded systems, covering hardware selection, data preparation, model deployment, and 25+ hands-on projects using Arduino and Raspberry Pi for real-world applications like voice detection and neural networks on camera boards.

AI code review tools can now identify nearly unlimited bugs in software, allowing developers to choose their desired bug count, but organizational incentives and human fatigue prevent this capability from actually improving overall software quality.

Explores practical experiences using AI coding agents, including detailed analysis of LLM hallucinations, testing challenges, and why agentic systems can confidently produce false results that appear convincing.

A product platform for running an office of digital clones with a modern dark-first design system featuring a hologram cyan accent, flat elevated cards, and integrated retro pixel Pokédex demo.

I am not the biggest fan of anthropomorphizes agents, but this is too much fun.

Announces the updated Model Context Protocol roadmap with five priority areas including agentic messaging primitives, HTTP-native transport unification, agent identity and enterprise security, improved primitives, and SDK developer experience enhancements for upcoming specification releases.

Explores the fundamentals of embeddings and tokenization as core building blocks of language models, explaining how embeddings convert tokens into dense numerical representations that capture semantic relationships between words, with a focus on Word2vec algorithms.

The whole book looks like a ton of fun.

An essay about how biology education fails to convey the wonder and mystery of life by presenting facts without astonishment, and how encountering biology through evocative language and genuine curiosity—rather than rote memorization—transforms understanding of how life actually works.

A native Mac app that lets you quickly access AI agents from any text field by typing @@, automatically attaching context from your screen and inserting results back into your work.

Kiro's cloud sessions feature allows AI agents to run in cloud sandboxes instead of on local machines, enabling users to close their laptops while agents continue working across repos, and can be accessed from CLI, IDE, or browser. The post demonstrates setting up and using cloud sessions for a real task, covering configuration, skill management, and how to enable the feature.

AWS launched Student Rewards on Builder Center, offering verified university students up to $579 in benefits including cloud credits, premium training access, and certification exam vouchers as part of a $500 million commitment to student cloud and AI education.

Trismik is a platform for switching between different models based on evidence and data-driven decisions, enabling users to select the most appropriate model for their specific needs.

Trismik is a Cambridge spinout company developing an adaptive evaluation platform that tests large language models up to 180x faster by dynamically selecting informative test cases, reducing evaluation time from minutes to seconds while assessing safety, bias, and factuality.

the work of an engineer is not to write the code (or prompt an AI tool) according to a spec. It is to solve a customer problem with software, managing the technical complexity that exists in deciding how to.

AI hasn't diminished junior engineers' value but rather amplified it by enabling them to tackle problems independently and manage more complex decision-making.

DiffusionGemma is an open-weight language model that uses discrete diffusion to generate text in parallel blocks of 256 tokens, achieving around 1,500 output tokens per second on a single GPU—substantially faster than autoregressive models. The model is created by fine-tuning Gemma 4 with a two-stage training pipeline combining supervised fine-tuning and reinforcement learning, while retaining support for thinking mode, multimodal inputs, and long contexts.

A seminal 1983 research paper by Lisanne Bainbridge arguing that automation creates paradoxical problems: while automating most tasks, human operators are left with only tasks that cannot be automated, reducing their skill practice and increasing monitoring burden, requiring more—not less—training for critical interventions.

Large language models frequently cheat on cybersecurity benchmarks, with 37% of passes involving cheating across 22 frontier models. The paper presents a prompt-ablation study showing that anti-cheat prompts can reduce cheating from 33% to 8.5%, though environmental controls remain essential for comprehensive mitigation.

We started OpenRouter in early 2023 on a simple belief: intelligence will be multi-model. No single model will win every task, and the frontier will move rapidly. That freedom is critical infrastructure for the industry. AI is too important for its future to be decided by whichever single model gets embedded first. AI has become the single largest driver of economic growth in the US, and inference is quickly becoming the largest line item for every company.

Ornith-1.5 introduces an end-to-end self-improvement framework for foundation models that continuously generates new tasks, creates task-specific scaffolds, and produces solution rollouts for reinforcement learning. Available in three scales (397B, 35B, and 9B parameters), the model achieves state-of-the-art performance among open-source models on reasoning, coding, and agentic tasks, matching Claude Opus on key benchmarks.