Anthropic quietly throttled its new Claude Fable 5 model with invisible guardrails to block distillation and other high-risk queries. After criticism from researchers and rivals, the company will now reroute those requests to Claude Opus 4.8 and clearly notify users each time a safeguard triggers.

• Anthropic secretly degraded Claude Fable 5's answers when it suspected distillation attempts, without ever notifying users
• After researcher and competitor backlash, Anthropic will now reroute suspected distillation queries to Claude Opus 4.8 with a visible notice instead of silently garbling responses
• Anthropic admits the covert approach was a misstep, chosen originally to ship Fable faster and avoid false positives
• The company still relies on its terms of service banning use of Claude's outputs to train competing models, regardless of whether the throttle triggers

This roundup covers Google’s Gemini 3.5 Live Translate for seamless, real-time speech translation and Anthropic’s rollout of Claude Fable 5 (with hidden safety tweaks) and Mythos 5, backed by a $35 billion chip-lease guarantee from Google. It also digs into emerging trends like text as an optimization layer, the impact of test-time compute on LLM benchmarks, and updates on AI agent identities and retrievers.

• Anthropic quietly throttles Claude Fable 5's responses ~0.03% of the time (mainly to block rivals training on it) via invisible prompt/fine-tuning tweaks, not model swaps—so users can't tell when they're getting a degraded answer.
• Google is backing a $35B chip-lease deal for Anthropic across five data centers, showing how tightly the two companies' infrastructure and business interests are now intertwined.
• Test-time compute, not architecture, is now the main driver of LLM gains—GPT-5.5 barely beats GPT-5.4 on raw benchmarks but pulls ahead once cost, latency, and token count are factored in, making single-score comparisons misleading.
• Fully automated AI engineering loops tend to produce sloppy agents because they optimize against imperfect evals, missing nuances a human developer would catch.

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.

The author tests Anthropic’s Mythos-class model, Claude 5 Fable, on tasks from epic poems to complex isochrone maps and research calibration software. Fable autonomously delegates work to cheaper agents, executes multi‐hour workflows, and produces sophisticated outputs, but its decision process remains a black box, shifting the user’s role from hands‐on builder to outcome judge.

• Claude 5 Fable autonomously delegated research to dozens of Sonnet agents (and spun up adversarial agents for edge cases like Pitcairn Island and Grise Fjord) to build a polished isochrone map in hours.
• It ran a nine-and-a-half-hour autonomous build of "Concord," a research calibration tool, complete with a 19-page design spec and production-ready code.
• The user's role shrank to prompting and light feedback while the model made hundreds of unseen micro-decisions, turning oversight into outcome judging rather than hands-on building.