Letters
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
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
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
Automated software testing is growing in importance in the era of AI-assisted coding.
Letters
There is significant unmet demand for developers who understand AI.
Letters
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
There’s a new breed of GenAI Application Engineers who can build more-powerful applications faster than was possible before, thanks to generative AI.
Letters
Even though I’m a much better Python than JavaScript developer, with AI assistance, I’ve been writing a lot of JavaScript code recently.
Letters
I’ve noticed that many GenAI application projects put in automated evaluations (evals) of the system’s output probably later — and rely on humans to manually examine and judge outputs longer — than they should.
Letters
Contrary to standard prompting advice that you should give LLMs the context they need to succeed, I find it’s sometimes faster to be lazy and dash off a quick, imprecise prompt and see what happens.
Letters
Fine-tuning small language models has been gaining traction over the past half year.
Letters
Some people today are discouraging others from learning programming on the grounds AI will automate it.