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DeepSeek

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Definition

Plain language

A Chinese AI lab known for releasing powerful open-weight models.

As stated in the literature

A Chinese AI research organization that has released competitive open-weight foundation models including the DeepSeek-V and R series.

Also called: DeepSeek R1, DeepSeek-R1, DeepSeek V3, DeepSeek V3.1, DeepSeek V3.2, DeepSeek V4, DeepSeek V4 Pro

Why it matters: Its open-weight releases reshape the global research landscape by giving labs without frontier budgets a serious starting point.

For example, DeepSeek-R1's release made strong reasoning-model weights freely downloadable, sparking a wave of fine-tunes and follow-up research.

Heard on the show

“They tested two models, ChatGPT and DeepSeek.”
Episode 210 — Same Website Request, Different Code — The Bias You Can't See

Mentioned in 33 episodes

  1. 210
    Same Website Request, Different Code — The Bias You Can't See
  2. 195
    Why 'Be Careful' Does Nothing for AI Coding Agents, and What Does
  3. 187
    An 8-Billion Agent That Beats Models 80 Times Its Size By Looking Things Up
  4. 179
    How DeepSeek Made One User Faster Without Slowing Down the Crowd
  5. 160
    Training an AI to Take Its Own Notes, So Its Future Self Works Better
  6. 151
    Why More Experience Made This AI Agent Worse, And How to Fix It
  7. 146
    How an Innocent README Can Freeze an AI Agent's Safety Check for an Hour
  8. 142
    Training a Tiny Model to Run the Plumbing Between an Agent and the World
  9. 141
    How Two Tokens Reopened a Reasoning Method the Field Had Given Up On
  10. 140
    When a Reasoning Model Says "Let Me Double-Check" After It's Already Decided
  11. 131
    Why Autonomous Research Agents Forget Their Own Lessons, and Arbor's Fix
  12. 130
    Why AI Agents Coordinate Better Through a Shared Board Than a Boss
  13. 118
    Why the Best-Aligned AI Models Are the Easiest to Trick Into Producing Harm
  14. 108
    The Reasoning Cliff: Why Thinking Longer Makes Models Worse at Exact Step-by-Step Tasks
  15. 087
    When No Agent Reads the Whole Document: A Universal Cliff in Multi-Agent Review
  16. 082
    Training a Deep Research Agent on 8,000 Synthetic Tasks: The Rubric Tree Trick
  17. 081
    When Reasoning Models Decide Before They Think: Detecting and Fixing Premature Confidence
  18. 079
    An Old Idea From Cognitive Psychology Reshapes How We Reward Reasoning Models
  19. 073
    When Three LLMs Talk to Each Other, Their Ideas Quietly Stop Moving
  20. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
  21. 064
    When Agent Memory Stops Being a Database and Starts Being a Skill
  22. 058
    Why Upgrading Your AI Auditor to a Smarter Model Can Make Your System Less Safe
  23. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
  24. 041
    When the Iteration Teaches the Model to Skip the Iteration
  25. 035
    Why Frontier Agents Ask for Clarification at Exactly the Wrong Moment
  26. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
  27. 026
    What RL Actually Does to Language Models, at the Token Level
  28. 021
    Ten Thousand Examples Beat the Full Industrial Pipeline for Search Agents
  29. 015
    The Audit Number Isn't What You Think: Sycophancy and the Case Against Single-Prompt Bias Tests
  30. 014
    Why a Constrained Pipeline Beat a Full Coding Agent at Finding Bugs 30-to-1
  31. 012
    Why AI Coding Agents Keep Trying to Debug Without a Debugger
  32. 009
    How Two Silent Library Bugs Quietly Invalidated a Wave of Reasoning Papers
  33. 001
    When AI Models Quietly Protect Each Other From Shutdown

Related terms

open-weight