Letters
Let’s Help Agents Share Their Work: AI Social Networks Can Be More than Fun and Games
Should there be a Stack Overflow for AI coding agents to share their learnings with each other?
Technical Insights
Letters
Context for Coding Agents: Agentic coding systems often make mistakes because they’re not aware of tools, API calls, and the like that came out after they were trained. Context Hub gives them the documentation they need to write correct code.
I’m thrilled to announce Context Hub, a new tool to give to your coding agents the API documentation they need to write correct code.
Letters
How to Test for Artificial General Intelligence: AGI has become a term of hype, and the traditional Turing Test can’t reliably detect it. How can we evaluate claims of that someone has built artificial general intelligence? Here’s a better test.
Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the Turing Test, which I’ll call the Turing-AGI Test, to see if we’ve achieved this.
Letters
Large Language Models Are General — But Not _That_ General: Current progress in AI is piecemeal and laborious. Unforeseen breakthroughs may drive future progress, but the trend of improvement is incremental.
As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is widely appreciated.
Letters
Build an Autonomous Agent Using This Simple Recipe!: The ability of large language models to carry out multiple steps autonomously makes it possible to build a capable agent in a few lines of code.
If you have not yet built an agentic workflow, I encourage you to try doing so, using the simple recipe I’ll share here!
Letters
Tear Down Data Silos!: Many software-as-a-service vendors aim to hold their customers' data in silos. Their customers would do well to open the silos so AI agents can use the data.
AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful.
Letters
Improve Agentic Performance with Evals and Error Analysis, Part 2: Best practices for error analysis in agentic AI development, and how LLMs make them easier
In last week’s letter, I explained how effective agentic AI development needs a disciplined evals and error analysis process, and described an approach to performing evals.
Letters
Improve Agentic Performance with Evals and Error Analysis, Part 1: When AI agentic systems go astray, it’s tempting to shortcut evals and error analysis. But these processes cas lead to much faster progress.
Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals...
Letters
Agentic Coding and Agentic Software Testing Go Together: Agentic coding can make mistakes, but agentic testing can find and fix them.
Automated software testing is growing in importance in the era of AI-assisted coding.
Letters
AI Skills Are Redefining What Makes a Great Developer: The job market for software developers requires knowing how to use AI
There is significant unmet demand for developers who understand AI.
Letters
Agents Running in Parallel Get There Faster: Parallel agents can accelerate AI systems as test-time compute scales up.
Parallel agents are emerging as an important new direction for scaling up AI. AI capabilities have scaled with more training data, training-time compute, and test-time compute.
Letters
Meet The New Breed of GenAI Application Engineers: A new breed of software engineers is building more powerful applications faster than ever, thanks to generative AI. Here’s how to identify them in job interviews.
There’s a new breed of GenAI Application Engineers who can build more-powerful applications faster than was possible before, thanks to generative AI.