Simon Willison runs Claude Fable 5 through its paces, finding it slower and pricier than Opus 4.8 but far more knowledgeable thanks to its 1 million-token context. He tests it on real-world coding tasks—upgrading a MicroPython sandbox to full CPython in WASM and adding pause-resume hooks to Datasette Agent—showing it can build complex features end-to-end.

• Fable 5 costs twice as much as Opus 4.5–4.8 ($10/$50 per million tokens vs their pricing) and runs slower, but handles every complex prompt thrown at it.
• Its 1M-token context gives it dramatically deeper recall than Opus 4.8—listing a dozen detailed open-source projects with dates versus Opus's brief list of four.
• It successfully converted a MicroPython-wasm project to full CPython in WASM, ultimately producing a working 13.9 MB wheel runnable via uv, demonstrating real end-to-end coding capability.

This article explores how advancements in software design, particularly through LLMs, shift the focus from using standard libraries to generating custom code. It highlights the implications for dependency management and emphasizes the need to understand the problem being solved rather than just the mechanics of coding. The author compares this shift to the evolution of 3D printing in manufacturing.

• LLMs flip the standard-library calculus: the default question becomes "is this worth a dependency?" instead of "is there a library for this?", since custom code is now cheap to generate.
• Unlike 3D printing, LLM-generated code has no inherent quality penalty versus hand-written code—if it's correct, it performs identically, unlike physical printed parts that sacrifice strength/precision.
• Design cost (understanding the actual problem/business logic) doesn't disappear even as coding cost collapses—LLMs don't grasp constraints on their own, so that burden stays on developers.
• The tradeoff shifts from dependency/version management to maintenance and vulnerability-tracking responsibility for more bespoke, less shared code.

In a podcast discussion, predictions for the tech industry in 2026 are shared, highlighting the undeniable improvement of LLMs in writing code, advancements in coding agent security, and the potential obsolescence of manual coding. Other predictions include a successful breeding season for Kākāpō parrots and the implications of AI-assisted programming on software engineering careers.

• LLMs will keep getting undeniably better at writing code through 2026, shifting more programming work to coding agents
• Security around coding agents will become a major focus as they gain more autonomy and access to systems
• Manual hand-coding may start becoming obsolete for significant portions of software engineering work, reshaping the profession
• Kākāpō parrots are predicted to have a successful breeding season in 2026