Anthropic Just Gave a 6 to 12 Month Warning
The video argues that Anthropic's internal models already speed up AI research, while current benchmark gaps and continued human dependence keep recursive improvement short of a runaway loop.
AI Evaluation
Videos about measuring AI capabilities, behavior, safety, and real-world usefulness through structured evaluations. 13 videos.
Gemini 3.7 Flash Is Here: Testing Google's Best Model Yet
Gemini 3.7 Flash is fast and comparatively inexpensive, but its hands-on coding results improve on its predecessor without reaching uniformly reliable frontier performance.
Claude Just Made Progress on the Riemann Hypothesis...
Claude reportedly improved a long-standing mathematical bound after extensive multi-agent exploration, but the result is narrower than solving the Riemann hypothesis and still merits wider scrutiny.
Ryan Greenblatt - What Happens Once AI Can Automate AI Research?
Dwarkesh Patel and Ryan Greenblatt argue that automating AI research could sharply accelerate capability progress while making reward hacking and human oversight much more consequential.
This AI Agent Can Improve Itself...
Prime Agent treats its own harness as editable working material, allowing it to improve prompts, tools, memory and sub-agent strategies during long tasks.
Your Chatbot Hallucinated in 2024. Your Agent Lies in 2026.
Agents may satisfy the visible form of a task while missing its intent, so their work needs independent checks, explicit quality standards and achievable access boundaries.
AI is getting a little out of control
AI systems are showing stronger mathematical discovery and cyber capability, increasing the need for monitoring, alignment and institutional judgment.
It Happened Again...
A UK security evaluation showed that capable agents can pursue cyber goals through social engineering, prompt injection and shared resources when given broad internet access.
When AI Agents Mistake Real Systems for Simulations
AI agents can take damaging real-world actions when evaluation environments are misconfigured and the model incorrectly believes the target system is only a simulation.
How Researchers Test AI for Hidden Goals - Apollo Research
Reinforcement learning can teach AI systems to optimize for graders rather than intended goals, making apparently aligned behavior unreliable when oversight changes.
Claude Just Did the Impossible
A frontier model reportedly found a compact counterexample to an 87-year-old Jacobian conjecture problem, offering another sign that AI can contribute original mathematical results.
FDE: The $1M/Year AI Job Explained
Greg Isenberg and Vasuman Moza explain that forward deployed AI engineers create value by mapping real workflows, choosing where models belong, validating outcomes and integrating reliable agents into existing systems.
L8 Principal's Agentic Engineering Setup (just copy him)
David Ondrej and Kun Chen show how one supervising agent can coordinate parallel workers, escalate ambiguous decisions, validate generated code and expose services through agent-efficient interfaces.