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Lobsters

Maintaining the love for coding in the time of AI

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  1. Presentation: Complexity and Creativity in Software Engineering

    Phillip Mortimer argues that AI‑generated code makes all software effectively "write‑only" due to volume, and proposes managing this by treating tests as the sole specification, automating code reviews with LLMs, and decoupling intent from implementation.

    InfoQinfoq.com28 mintalk
  2. Towards Self-Driving Codebases

    The post argues that AI agents could eventually handle low‑level engineering tasks—bug fixing, debugging, UI consistency, growth experiments—if the dev toolchain is made “agent‑legible”. It outlines missing primitives (global memory, code‑base rot prevention, better dev environments) and proposes a bootstrapping process to measure and improve a repo’s “agent readiness”. The piece is largely specu…

    Hacker News front pagedetail.dev9 minHN12099
  3. The 2026 State of Code Abundance Report: Exposing the Enterprise AI Readiness Gap

    A report highlights a significant gap between enterprise confidence in AI-generated code and operational reality, with 81% of leaders reporting increased production issues despite high readiness scores. This "code abundance" means code is generated faster than organizations can effectively test, govern, and manage it, leading to challenges in cost attribution and governance.

    Codeshipcloudbees.com5 min