Keep up with tech in 5 minutes
Get the free daily email with summaries of the most interesting stories in startups 🚀, tech 📱, and programming 💻!
Join 1,600,000 readers for one daily email
How I use LLMs as a staff engineer in 2026 (11 minute read)
Agents have gotten really good in the last fifteen months. They can now be used for real work with light supervision. AI can now be used for writing code changes, investigating and fixing bugs, research in large code bases, manual testing, and more. It still isn't suitable for writing public communications, unreviewed code, or testing UIs, but its capabilities are improving quickly.
Apr 29 | Blog
The Trust Problem With AI Agents
Why developers should not entirely rely on agents, and what you can do about it.
Sponsored
May 18 | AI
How Claude Code works in large codebases: Best practices and where to start (5 minute read)
Claude Code is now being used in production across multiple large codebases in organizations with thousands of developers. These environments bring challenges that smaller codebases don't. This article covers patterns that Anthropic has seen that have led to the successful adoption of Claude Code at scale. It looks at how Claude Code has been used in monorepos with millions of lines, legacy systems built over decades, and microservices across separate repositories.
May 18 | Design
AirPods Max designer reveals project details in new interview (2 minute read)
Former Apple designer Eugene Whang revealed that the AirPods Max took five years to develop, with the team treating the headband, case, and ear cushions as separate products and testing hundreds of cushion variations to fit different head and ear shapes. He also said Apple intentionally avoided placing a logo on the headphones, credited Jony Ive for shielding designers from business pressures, and reflected on his 22-year Apple career working on products like the iPhone, iPod nano, and AirPods before later joining Ive's design firm, LoveFrom.
May 18 | Dev
Context Pruning: Cut LLM Tokens Without Losing Quality (9 minute read)
Context pruning is a technique that removes low-value elements from an LLM's input, such as tokens or passages, to reduce costs and improve LLM output, often resulting in up to 20x compression and faster latency. It works by mitigating issues like "lost in the middle" effects common in long context windows, but must be applied carefully as it can negatively impact structured data or multi-turn dialogue.
