curated · 2026-07-04

This one's worth opening because it gives the first long-run answer to a question that's been floating around for two years: does AI homework help actually hurt learning? The numbers are stark. Homework scores up 18%, time down from 64 to 45 minutes, but closed-book monthly exams down 20%, and high-stakes entrance exams down 18–24%. The wild part is the lag—the full exam penalty took about two years to show up, so any study shorter than that would miss it. I'd discount this a bit. The post doesn't name the county or the lead institution, so I'm treating it as a preprint until I see the paper. AI usage is self-reported, which always has noise. But 81% of long-term users fitting the outsourcing pattern—fast homework, high grades, terrible exams—is hard to wave away. One detail that matters: students who spent similar time as non-users saw no exam penalty. So the problem isn't saving time with a tool; it's using the tool to skip thinking. Social sciences took the biggest hit at 27%, which tracks—subjects that need reasoning and argument suffer more when you outsource the cognitive work. Don't read this as "AI ruins education." The cleaner take: when nobody teaches students how to use these tools, they default to using them as homework ghostwriters, and two years later the exams catch up. What's missing isn't a ban, it's guidance on how to use them without offloading the learning.

This is worth reading because Lilian Weng connects six months of scattered agent practices into a clean framework. The harness isn't a new idea—it's the software layer that handles planning, execution, checking, and state management around a model. But she elevates it to the level of recursive self-improvement: if a model is going to improve itself, the orchestration system around it needs to evolve too, not just the weights. She breaks down three design patterns: goal-oriented automation loops, file systems as persistent memory, and spawning parallel sub-agents. These are already in use in Claude Code and Codex, so they're not novel. The more interesting part is the back half—how to optimize the harness itself through context engineering, evolutionary search, and even joint optimization with model weights. I'd discount this slightly: it's a survey, not new research. But its value is in organizing "agent engineering" from ad-hoc practices into a framework with theoretical ambition. If you're building agent products, treat this as a checklist.