AI & Machine Learning - Antoine Buteau (Page 13)

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Research Explainers Agentic AI Turns Work Into Delegation

OpenAI's Codex usage data suggests the agentic shift is less about better chat answers and more about delegating longer, reusable, parallel workflows.

Profile Lessons from Cecilia Ziniti

Lessons from Cecilia Ziniti Cecilia Ziniti is the founder and CEO of GC AI and a three time General Counsel for tech companies including Amazon Alexa...

Profile Lessons from Jonathan Ragan-Kelley

Lessons from Jonathan Ragan Kelley Jonathan Ragan Kelley is an MIT computer scientist who builds compilers and domain specific languages for high...

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Move sideways by problem area.

Profile Jun 14, 2026 8 min read

Lessons from Charlie O’Neill

Lessons from Charlie O’Neill Charlie O’Neill is an AI researcher, Head of Model Training at Baseten, and a John Monash Scholar at Oxford. His work on LLM...

Profile Jun 13, 2026 9 min read

Lessons from Amanda Askell

Lessons from Amanda Askell Amanda Askell is a philosopher who transitioned from researching the ethics of infinity to leading personality alignment at...

Research Explainers Jun 12, 2026 9 min read

Agent Coding Costs Hide in Review, Not Generation

A study of ChatDev traces finds that agentic coding systems spend most tokens on review and repeated context passing, not initial code generation.

Research Explainers Jun 12, 2026 10 min read

Agents Change Work by Lowering the Cost of Execution

A Perplexity field study compares conversational search with autonomous agent execution and finds large time savings, lower dissatisfaction, and broader task scope.

Research Explainers Jun 12, 2026 10 min read

Synthetic Consumers Work Better When They Talk First

A consumer research paper finds that LLMs can match human purchase-intent surveys when they answer in free text before being mapped back to Likert ratings.

Research Explainers Jun 12, 2026 8 min read

AGI May Be a Phase, Not the Finish Line

A DeepMind report argues that human-level AGI may be followed by several pathways toward superintelligence, each constrained by data, compute, embodiment, and coordination bottlenecks.

Research Explainers Jun 12, 2026 10 min read

Post-Training Is Where Models Learn Bad Habits

A post-training paper shows how interpretability tools can audit preference data, expose unwanted learning signals, and reshape rewards before models absorb bad habits.

Research Explainers Jun 11, 2026 8 min read

Agent Harnesses Can Learn From Their Own Failures

Self-Harness shows that agents can use execution traces, targeted edits, and regression tests to improve the scaffolding around their own model.

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