🕷️
SetupAI
AlphaGo to DeepSeek R1: Reasoning Revolution
🧠#agentic-coding#reasoning-modelsFreshcollected in 7m

AlphaGo to DeepSeek R1: Reasoning Revolution

PostLinkedIn
🧠Read original on 机器之心

💡Claude rebuilt AlphaGo in weeks—unlock agentic workflows for your AI research

⚡ 30-Second TL;DR

What changed

Eric Jang rebuilt AlphaGo using Claude for code, hypotheses, and experiments

Why it matters

Automates reasoning at scale, potentially reshaping organizational structures and power dynamics beyond efficiency gains.

What to do next

Use Claude to reimplement a classic paper like AlphaGo and open-source your repo.

Who should care:Researchers & Academics

🧠 Deep Insight

Web-grounded analysis with 3 cited sources.

🔑 Key Takeaways

  • •Eric Jang reimplemented AlphaGo from scratch using Claude Code over two months to re-learn deep learning and programming with AI agents, with the repository planned for open-sourcing soon[1].
  • •Claude Code's /experiment command standardizes research actions by creating dated experiment folders, executing single-file Python routines, saving data to CSV in data/ and figures/ subdirectories, and generating conclusions[1].
  • •Claude Code enables sequential hyperparameter optimization experiments, where the AI reflects on results after each run to suggest next steps within FLOP budgets[1].
📊 Competitor Analysis▸ Show
FeatureClaude CodeGitHub CopilotOpenAI Codex
Agent TeamsSupports agent swarms for parallel tasks [2][3]Agent choice between Claude/Codex [3]1M+ active users, async backlog [3]
Revenue/Users$2.5B run-rate, doubled WAU early 2026 [2]N/A1M+ active users [3]
BenchmarksPowers AlphaGo reimpl., research automation [1]VS Code integration, fast adoption [3]Expanded integrations, GPU requests [3]

🛠️ Technical Deep Dive

  • •Claude /experiment command: Creates self-contained folder with datetime prefix; writes and executes single-file Python experiment; saves artifacts as parseable CSV in data/ and figures/ dirs; analyzes outcomes and suggests next hypotheses[1].
  • •Sequential experiments: AI runs hyperparameter sweeps (e.g., policy validation accuracy under FLOP budget), reflects post-run, and iterates autonomously[1].
  • •Claude Code skills: Modular behaviors like Ideation for idea-to-plan pipelines, Codex CLI integration for code review/refactoring[2].
  • •Agent teams (swarms): Parallel specialized AI agents coordinate on complex tasks[2].
  • •Cowork brand consolidation: Integrates Claude Code into unified agent with sandboxed Linux VMs using Apple virtualization and bubblewrap[3].

🔮 Future ImplicationsAI analysis grounded in cited sources

Automating research workflows with AI agents like Claude Code scales reasoning as a schedulable resource, blending forward/backward passes with autoregressive decoding, potentially redesigning architectures and transforming productivity in coding, experimentation, and knowledge work[1][3].

⏳ Timeline

2026-02
Eric Jang publishes 'As Rocks May Think' detailing AlphaGo reimplementation and Claude /experiment workflows[1]
2026-01
Claude Code reaches $2.5B run-rate revenue, doubles weekly active users; introduces agent teams[2]
2025-12
Anthropic launches Claude Sonnet 4.6 with coding/reasoning improvements, 1M-token context[3]
2016-03
DeepMind's AlphaGo defeats Lee Sedol in Go, establishing foundational RL techniques later reimplemented by Jang[1]

📎 Sources (3)

Factual claims are grounded in the sources below. Forward-looking analysis is AI-generated interpretation.

  1. evjang.com
  2. alldevblogs.com
  3. news.smol.ai

Eric Jang details how AI has mastered programming and thinking, reimplementing AlphaGo from scratch using Claude Code in weeks. Modern agents automate experiments, hyperparameter tuning, and reporting via structured workflows. This scales reasoning as a schedulable resource, transforming productivity and society.

Key Points

  • 1.Eric Jang rebuilt AlphaGo using Claude for code, hypotheses, and experiments
  • 2.Structured single-file Python workflows with data/figures folders and report.md outputs
  • 3.Shift from statistical LLMs to systematic reasoning models like DeepSeek R1

Impact Analysis

Automates reasoning at scale, potentially reshaping organizational structures and power dynamics beyond efficiency gains.

Technical Details

Uses timestamped experiment folders, serial hyperparameter optimization differing from prior systems like Vizier, enabling full-cycle AI-driven research.

#agentic-coding#reasoning-modelsdeepseek-r1
🧠Read original article on 机器之心
📰

Weekly AI Recap

Read this week's curated digest of top AI events →

👉Read Next

Same topic

Explore #agentic-coding

Same product

More on deepseek-r1

Same source

Latest from 机器之心

AutoBio: VLA Turing Test in Bio Labs

AutoBio: VLA Turing Test in Bio Labs

机器之心•Feb 20
CUHK & Meituan Add Process Scores to Agents

CUHK & Meituan Add Process Scores to Agents

机器之心•Feb 20
FlagOS Launches Qwen3.5 397B Multi-Chip Versions

FlagOS Launches Qwen3.5 397B Multi-Chip Versions

机器之心•Feb 20

AI-curated news aggregator. All content rights belong to original publishers.
Original source: 机器之心 ↗

AlphaGo to DeepSeek R1: Reasoning Revolution | 机器之心 | SetupAI | SetupAI