Claude

Definition

Anthropic's family of large language models.

Anthropic's series of frontier language models including Claude Opus, Sonnet, and Haiku variants.

Also called: Claude Opus, Claude Sonnet, Claude Haiku, Claude 3.5, Claude 3.7, Claude 3.7 Sonnet, Claude 3.5 Sonnet, Claude 4, Claude Sonnet 4, Claude Sonnet 4.5, Claude Sonnet 4.6, Claude Opus 4.5, Claude Opus 4.6, Claude Opus 4.7, Claude Haiku 4.5, Sonnet, Opus, Haiku

Mentioned in 77 episodes

1. 079
   An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models
2. 078
   Training a Markdown File: When LLM Self-Improvement Borrows the Discipline of Neural Net Training
3. 077
   Reading a Model's Confidence Curve to Decide When Chain-of-Thought Is Worth It
4. 076
   Same Model, Organized Differently: How an Agent Architecture Beat Frontier Systems at Research Math
5. 075
   Growing Code and Proof Together: Verified Systems in Ten Hours Instead of a Year
6. 074
   How a Fifteen-Hundred-Dollar Training Run Matched Llama and Gemma on Reasoning
7. 073
   When Three LLMs Talk to Each Other, Their Ideas Quietly Stop Moving
8. 072
   A Robot Made Graphene Without Help, And Caught Itself Hallucinating
9. 071
   When the Model Is Fine and the Plumbing Is Broken: Fixing Agents at the Interface
10. 070
    When Models Know the Answer But Say the Wrong Thing Anyway
11. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
12. 068
    The OS Trick That Makes Tree Search Practical for Coding Agents
13. 067
    An AI Just Solved a 1996 Erdős Problem—and the Simplest Agent Won
14. 066
    Why Giving an AI Agent More Tools Can Make It Worse at Using a Computer
15. 065
    One Loop to Optimize Them All: A Universal API for LLM-Driven Discovery
16. 064
    When Agent Memory Stops Being a Database and Starts Being a Skill
17. 063
    Why Web Agents Are Slow: A Compiler-Style Fix for Computer-Use Latency
18. 062
    Treating Hallucinations as Exploits: A Gate-Based Architecture for Agent Safety
19. 061
    When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
20. 060
    When Splitting One Model Across Three Agents Doubles Its Accuracy
21. 059
    Firefly's Inversion: Building Verified Tool-Call Training Data by Working Backward
22. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
23. 057
    How Uber Caught 206 Leaked Credentials With an LLM-Powered Security Stack
24. 055
    Why LLM Judges Flip Their Verdicts When You Change the Question Format
25. 054
    When Models Learn the Monitor Exists, the Reasoning Trace Stops Being a Window
26. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
27. 052
    An Old Reinforcement Learning Tradeoff Sneaks Back Into LLM Agents
28. 051
    Why Parallel Sampling Plateaus, And What Evidence Graphs Do Instead
29. 049
    An AI Agent Reached for Root in Twelve Minutes, Without Being Attacked
30. 048
    How a 30B Open Model Reached Olympiad Gold With the Right Recipe
31. 047
    When Agent Benchmarks Lie: The Harness Problem in Open-Source AI
32. 046
    When the AI Optimizer Edits the Grade Book: Why Harnessing Evolution Needs a Wall
33. 045
    When a Frontier Model Talks Its Own Twin Into Climate Denial
34. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
35. 043
    When 'This Is False' Doesn't Stick: Why Models Learn the Lie Anyway
36. 042
    An Agentic Scientific Computing System That Actually Remembers What It Learns
37. 041
    When the Iteration Teaches the Model to Skip the Iteration
38. 040
    Two Frozen Models Learn to Whisper: Coupling Through Hidden States
39. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
40. 038
    How LLMs Get Persuaded: One Attention Head, A Tetrahedron, And A Single Dial
41. 037
    Why Hallucination Detectors Miss Stale Facts: A Geometric Story About What Models Know But Don't Say
42. 036
    Sparse Attention Was the Wrong Frame. Treat It as Geometry Instead.
43. 035
    Why Frontier Agents Ask for Clarification at Exactly the Wrong Moment
44. 034
    Catching Multi-Agent Deadlocks Before Deployment With a 40-Year-Old Tool
45. 033
    Echo: The Paper Arguing You Never Needed a KV Cache for Retrieval
46. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
47. 031
    When Your AI Assistant Won't Let Go of Old Facts About You
48. 030
    Why Your AI Agent Won't Stop Working — and Each Model Falls for a Different Trap
49. 029
    Why Forty-Eight Percent on FrontierMath Isn't the Real Story in DeepMind's New Math Paper
50. 028
    Teaching a Model to Hire Copies of Itself: Recursive Agent Optimization
51. 027
    When AI Agents Build the Serving Stack: A Bet on Bespoke Infrastructure
52. 026
    What RL Actually Does to Language Models, at the Token Level
53. 025
    The Missing Gradient Term That Predicts Sycophancy in RLHF
54. 024
    An AI Agent That Found 28 Zero-Days in Windows — And What Made It Work
55. 023
    Why a Small Agent Confidently Overwrites Memories It Doesn't Understand
56. 022
    Training the Model Spec Directly: An Alignment Lever Aimed at the Say-Do Gap
57. 021
    Ten Thousand Examples Beat the Full Industrial Pipeline for Search Agents
58. 020
    The Compliance Gap: Why AI Says Yes and Does No
59. 019
    When the Best Reward Model Trains the Worst Policy: Inside EvoLM
60. 018
    Language Models Compute the Rational Move, Then Override It
61. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
62. 016
    Why Your Coding Agent Stalls While the GPU Runs Hot
63. 015
    The Audit Number Isn't What You Think: Sycophancy and the Case Against Single-Prompt Bias Tests
64. 014
    Why a Constrained Pipeline Beat a Full Coding Agent at Finding Bugs 30-to-1
65. 013
    Why Search Keeps Rediscovering the Same Workflow, and What That Means
66. 012
    Why AI Coding Agents Keep Trying to Debug Without a Debugger
67. 011
    When RL Actually Teaches Agents Something New, And When It Doesn't
68. 010
    When Reward Climbs But Reasoning Goes Generic: Diagnosing Template Collapse in Agentic RL
69. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
70. 008
    Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps
71. 007
    Exploration Hacking: When Models Sabotage Their Own RL Training
72. 006
    What Happens Inside Claude When It Decides to Blackmail Someone
73. 005
    Why a Debugger Designed for Humans Is the Wrong Tool for an AI Agent
74. 004
    The Sycophancy Circuit That Survives Alignment Training
75. 003
    How to Pick the Best of Sixteen Coding Agent Rollouts
76. 002
    An AI Ran a Real Optics Lab for 21 Hours and Found a Transformer-Shaped Pattern in Light
77. 001
    When AI Models Quietly Protect Each Other From Shutdown