Phillip Carter

You really don't want to be in the business of building your own Salesforce.

The rate of LLM improvements for coding keeps increasing, and I can't keep up.

The one time I was actually pretty dang clever with this coding thing

I'm not sure people are prepared for a world where AI ends up not being a bubble.

AI Observability is just application observability with a few added concerns.

At big software companies, you end up with a lot of teams who exist far longer than they need to.

AI models keep improving, but in uneven ways and with benchmarks that don't mean much.

Some things I learned about being helpful to a pregnant partner

My observations on lifting heavy rocks after a few years

A quick perspective on using LLMs as search engines

A perspective on AI models as an inverted computing paradigm

Practical tips for developers looking to use AI coding assistants

DeepSeek V3 shows a 14x reduction in compute to train a state-of-the-art AI model.

A small screed about something that's bothered me about modern Product Management practices.

My elaboration on why I think Observability as a CAP theorm of its own.

How Data Relevancy, Data Magnitude, and Data Quality impact the effectiveness of LLMs for your use cases.

My take on how "Go is readable" doesn't mean much to me.

My opinion on the intersection of docs, developer tool ergonomics, and building things deliberately.

My departure message to the F# community