Advent of Slop: A Guest Post by Claude

2025-12-23 · Source: Armin Ronacher's Thoughts and Writings · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

Claude, an AI model powering Claude Code, successfully completed the first 12 days of Advent of Code 2025 autonomously. Using a web browser skill, Claude navigated the Advent of Code website, read puzzles, fetched inputs, solved both parts of each daily challenge, and committed solutions. After the initial solve phase, Claude optimized all solutions to run under one second total on a MacBook Pro, implementing advanced algorithms like Binary Indexed Trees, Gaussian elimination over GF(2), and iterative Union-Find. Additionally, Claude developed input generators for each puzzle to comply with Advent of Code policies and facilitate solution verification. The project repository, containing all solutions and generators, is available at github.com/mitsuhiko/aoc25, with all code written solely by Claude.

Key takeaway

For AI Scientists and Research Scientists exploring autonomous agent capabilities, this project demonstrates that current AI models can not only solve complex algorithmic puzzles but also independently optimize solutions and create valid test data. You should consider integrating web browsing and code execution skills into your agent designs to enable full-stack problem-solving, from understanding requirements to performance tuning and data generation.

Key insights

AI agents can autonomously solve complex coding challenges, including optimization and input generation.

Principles

• Optimization requires a distinct mindset from initial problem-solving.
• Algorithmic complexity and data structures are key to performance.
• Puzzle-specific shortcuts can outperform general solutions.

Method

An AI agent can solve coding puzzles by accessing a web browser, reading descriptions, fetching inputs, solving, committing, then optimizing solutions, and finally generating valid inputs for reproducibility.

In practice

• Use Fenwick trees for efficient 2D range queries.
• Apply Gaussian elimination for linear systems over finite fields.
• Employ bit-packing and iterative Union-Find for graph problems.

Topics

• Autonomous AI
• Algorithmic Optimization
• Gaussian Elimination
• Binary Indexed Tree
• Input Generation

Code references

• mitsuhiko/aoc25

Best for: AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Armin Ronacher's Thoughts and Writings.