I keep finding great articles, tools, and ideas in AI and tech, but there’s just too much to keep up with.
I’m exploring a way to make this less overwhelming.
If you work in tech, I’d love to understand how you deal with this.
A quick survey: https://t.co/mmb7KMAxHy
at this point we all know human code review is becoming a bottleneck with coding agents.
reviewing intent and evidence instead of every line makes sense to me. but the trust issue is still there.
especially with large enterprise apps involving sso, permissions, complex workflows and multiple services. how do we reliably verify all of that without a human in the loop?
GitHub rewrote its Copilot runtime from TypeScript to 830K+ lines of Rust using AI agents
128 PRs. ~3.5 months. ~$120K in tokens
Lessons:
• Migrate in small, shippable slices
• Keep E2E tests independent so agents can’t weaken them
• Invest in repo exploration: agents read/searched 10x more than they edited
Saw something similar while building an agent harness at work. Adding a dedicated code exploration phase actually reduced overall execution time and cost, while improving output quality severalfold.
Interesting read.
With coding models improving so quickly, I wonder how much we should invest in building elaborate harnesses today.
Will most of this become obsolete with better models, or are there parts we’ll always need?
https://t.co/lcQvZLdsrf
Apple just showed Meta’s 816B parameter AI model running locally across four Mac Studios.
That’s pretty wild.
Not exactly an affordable setup, but I love where this is going
https://t.co/6oJjrlBnXL