75% of all new code at Google is now AI-generated and approved by engineers, up from 50% last fall. 2027 90%, and 2028…?
RESEARCH
-
Medical AI Evidence Gap: Improving Patient Care Outcomes
By
–
On the lack of compelling evidence that medical AI is improving patient care, and what to do about it, a @NatureMedicine editorial https://
nature.com/articles/s4159
1-026-04389-4
… -
LatentUM: AI Model Processes Images Text Actions Simultaneously
By
–
What if an AI could think in pictures and words simultaneously, without the usual translation lag? Researchers from Shanghai Jiao Tong U, Tsinghua U, and UCSD present LatentUM. They built a single model that processes images, text, and actions all in one shared "semantic
-
These image models are getting insane, where’s Elon?
By
–
These image models are getting insane. Where's Elon?
-
AI Mastery Roadmap: Your Complete Guide to Artificial Intelligence
By
–
AI Mastery Roadmap – Your GPS to mastering artificial intelligence in 2025 and beyond. Whether you're a beginner or aiming to become an AI architect, this roadmap from Mindstream is one of the clearest, most actionable visual guides I’ve seen. It walks you step-by-step from:
-
Silent Progress: When AI Improvements Become Invisible Integration
By
–
A 2% benchmark improvement is easy to ignore. Not noticing AI is there because it's just part of how the tool works is a different category of progress.
-
AI Diagram Generation: Impressive Capabilities but Persistent Knowledge Gaps
By
–
Fascinating how AI is getting better at diagrams like these (at least for ones that you could easily find on web search) but still making some pretty wacky errors — like confusing where the rear brake is* — that no knowledgeable human would make. What this reflects is an
-
Claude Code Reverse-Engineered: Only 1.6% Is Actually AI Model
By
–
Researchers reverse-engineered Claude Code. Only 1.6% is actually AI. The other 98.4% is infrastructure around the model. Permission gates, tool routing, context compaction, session recovery. The model just reasons. Everything else runs the show. The core loop is a plain
-
Engineering Ethics: When Not to Build with LLMs
By
–
We’re entering a phase where “can we build it?” matters less than “should we build it this way?” Energy, cost, and latency are becoming very important societal decisions. LLMs are powerful, but they’re not always the right abstraction. Good (AI) engineering is knowing when not
-
AI Confidently Explains Tasks Without Actual Experience or Self-Awareness
By
–
it'll tell you exactly how to do something it has never actually done and have no awareness that those are different things.