createrole

Updates

Releases, method notes, and how we think about digital employees that grow.

  • 3 min read

    Why we shaped recursive self-improvement into a bounded loop

    RSI went from thought experiment to engineering mainstream, yet almost everything that actually runs is bounded self-refinement. createrole stands firmly on that side. Model weights stay frozen. What changes is memory, notes, skills and the exam system, with a human on the loop and every step reversible.

    • RSI
    • method
    • growth loop
  • 5 min read

    Night school: why a digital employee needs its own exam system

    A digital employee cannot grow on user feedback alone. Night school generates a syllabus and a tiered, anchored question bank per profession, separates the examiner from the student, splits practice and hidden pools, and turns eval scores into the external anchor for coaching. This post explains the design choices behind it.

    • night school
    • evaluation
    • design
  • 9 min read

    Recursive Self-Improvement: Four Routes and a Shared Warning in 2026

    A survey of RSI research and practice from 2025 to 2026, covering definitions, representative work, product-grade growth without training weights, supervision and evaluation, and the failure modes the literature keeps repeating. It closes with how createrole applies these findings to the growth loop of a digital employee.

    • RSI
    • survey
    • growth loop
    • evaluation
  • 10 min read

    Writing prompts for agents in 2026. Where Anthropic's and OpenAI's official guidance converge

    Read side by side, the 2025 to 2026 official agent prompting guides from Anthropic and OpenAI point the same way. Delete instructions instead of adding them, restrain the model instead of pushing it, and write goal contracts instead of step lists. This post collects the most important shared positions and the most actionable rules.

    • prompting
    • agents
    • method
  • 14 min read

    Multi-turn Context Compression in Codex, Pi and OpenCode: Three Approaches Compared

    A source-level comparison of how three open-source agents, Codex, Pi and OpenCode, summarize old history, how much recent verbatim text they keep, and how they recover from overflow. The takeaway is that compression is really the construction of a recoverable state checkpoint, not a summary of a chat log.

    • context compression
    • agent engineering
    • source study