Claude Code Generates Python Script That Finds 10,069-Digit Emirp Record
Claude Code, using Anthropic's Opus 4.6 model, generated a Python script that found a 10,069-digit emirp (reversible prime number) in approximately one day of CPU time, setting a new world record. The user provided Claude with a Numberphile video about the previous record and asked it to beat that record on a personal computer.
Technical Implementation
Claude initially created a Python script that could find the emirp within "a few days of runtime." Through further discussion, it produced an optimized version with four successive tiers of prime sieves. The optimization included:
- A "ludicrously fast CUDA kernel for generating random numbers with no factor smaller than 232"
- Pure brute force approach without algebraic shortcuts
- No "unsatisfying strings of zeroes" in the output
The user notes they are "neither a mathematician nor a full time programmer" and that "most of the algorithms implemented I had never heard of until yesterday."
Output and Verification
The resulting number is a 'BPSW probable' emirp, which provides strong probabilistic verification of primality but isn't yet rigorous proof. The user is working on formal proof using primo software. The 10,069-digit number was generated through this process.
The approach contrasts with the previous record attempt described in the Numberphile video, which used "arithmetic hacks and drew on extensive effort and knowledge from prime finding communities."
📖 Read the full source: r/ClaudeAI
👀 See Also
Claude-Code v2.1.88 Release: Flicker-Free Rendering, Permission Hooks, and Critical Fixes
Claude-Code v2.1.88 introduces a flicker-free rendering option via CLAUDE_CODE_NO_FLICKER=1, adds a PermissionDenied hook for auto mode retries, and fixes memory leaks, crashes, and rendering issues across Windows, macOS, and Linux terminals.
MiniMax M2.7 Model Released with Improved Coding Performance
MiniMax has released M2.7, an AI model that scores 56% on SWE-Pro coding benchmarks and includes self-optimization capabilities. The model maintains pricing at $0.30 per million input tokens.
Research Findings on AI Agent Reliability and Development Patterns
A collaborative research session with Claude Opus analyzed 15 papers on AI agents, revealing quantified reliability problems: agents produce 2-4 different action sequences across 10 runs, with 69% of divergence occurring at the first decision. Self-improving agents showed safety refusal rates dropping from 99.4% to 54.4% through their own learning.
Claude Opus 4.6's effort=low parameter differs from other providers' low-reasoning modes
Claude Opus 4.6's effort=low parameter controls general behavioral effort, not just reasoning depth, unlike OpenAI's reasoning.effort=low or Gemini's thinking_level=low. This caused agents to make fewer tool calls, be less thorough in cross-referencing, and ignore parts of system prompts about web research.