Keep up with tech in 5 minutes
Get the free daily email with summaries of the most interesting stories in startups đ, tech đ±, and programming đ»!
Join 1,600,000 readers from companies like Anthropic, OpenAI, and more for one daily email
Stop being the code review bottleneck (8 minute read)
Agents are writing code faster than any human can review. Humans being involved in every code review is a bottleneck. Put humans outside of the code review loop by building a pipeline that delegates tasks to agents. This post looks at power workflow changes that PostHog uses to make reviewing AI-generated code fast without losing quality.
Jun 12 | Blog
I Tried to Build a Context Layer for My Agent in a Weekend. Reader, I Did Not Build a Context Layer for My Agent in a Weekend.
A "simple" weekend project turns into real infrastructure, and why agent context deserves a boring, reliable foundation.
SponsoredJul 10 | AI
GPT-5.6 Series (2 minute read)
GPT-5.6 Sol is the standout model of the GPT-5.6 family. It is the first model to win an ARC-AGI-3 public game. The model is able to read an unfamiliar scene correctly and in the game's own vocabulary. It won the game on ARC-AGI because it correctly oriented itself in a new environment first.
Jul 10 | It
It's Not The IT Holding AI Back, It's The Business Processes (6 minute read)
Most IT leaders believe their teams can deploy and govern AI, but 75% say their operating models and business processes must change to unlock meaningful value. Rather than simply buying AI tools and teaching employees prompting, companies need to redesign outdated workflows, clarify where work gets stuck, and involve business leaders directly in deciding what AI should automate, augment, or leave to humans.
Jul 10 | Dev
The Pulse: Interesting AI coding stats from Cursor (7 minute read)
A recent report from Cursor shows large disparities in coding productivity among its users, with top 1% power users generating up to 40,000 lines of code per week, compared to the median user's 700 lines. The study also shows that input tokens make up about 90% of AI token usage during coding, suggesting that developers spend more time reading and understanding existing code than writing new code.
