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  • 😸 Claude Fable Five is Anthropic's Most Controversial Model Yet

😸 Claude Fable Five is Anthropic's Most Controversial Model Yet

PLUS: New Claude Models Fables 5 and Mythos 5, Explained

Author

Grant Harvey
June 10, 2026

A Mississippi federal judge had to cancel a trial after discovering lawyers on both sides had submitted AI-related errors in their filings, according to Bloomberg Law. 404 Media put the failure mode plainly: when two AI-assisted filings argue against each other, the court loses trust in both. That is like the legal version of the Spider-Man pointing at himself meme, only with sanctions attached.

The timing is awkward because Anthropic also published a new video on working like a lawyer with Claude, where Mark Pike and Freshfields’ Anna Gressel make the useful version of the same point: AI can organize messy context, spot themes, and draft artifacts, but the judgment call stays with the lawyer. 

So lawyers, consider this your TL;DR on AI usage: use AI for prep, synthesis, and drafting. Verify every case, quote, citation, and statute against the original source before it reaches a client, court, or opposing counsel. But y’know, goes without saying: this is not legal advice.

Here’s what happened in AI today:

  • 🙀 Anthropic’s Fable 5 guardrails blocked researchers.

  • 📰 Apple expanded on-device AI and private cloud compute.

  • 📰 Meta was ordered to reopen WhatsApp to AI rivals.

  • 🍪 New small Cohere coding model, Gemini 3.5 live translate, & more.

  • 🎓 Learn how to use Fable 5 from its own leaked system prompt

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😺 Main Story

AI model launches used to be easy to explain: new model, bigger numbers, everyone argues on X for 48 hours.

Anthropic’s Claude Fable 5 launch is stranger. The headline is that Anthropic finally made a Mythos-class model generally available. The real story is that Anthropic is shipping frontier intelligence that it selectively decides to give you, if it likes you, maybe.

Here’s what happened:

  • Anthropic launched Claude Fable 5, the public version of its new Mythos-class model, plus Claude Mythos 5, the same underlying model with some safeguards lifted for vetted cyber and biology partners.

  • Fable costs $10 per million input tokens and $50 per million output tokens, is available through the Claude API, and is temporarily included on Pro, Max, Team, and seat-based Enterprise plans through June 22.

  • Starting June 23, subscription users need usage credits unless Anthropic extends the included window.

  • The benchmark table combines “Mythos 5 / Fable 5,” and then shows the higher score of the two, and says most differences are within 1-3 percentage points. On cyber and biology benchmarks, Fable may perform much closer to Opus 4.8 because safeguards trigger fallback (if biologists can even use it at all; more below).

  • Fable also has invisible interventions for frontier AI research, meaning it can quietly make itself less useful on some ML research tasks instead of visibly refusing. This is complete BS, according to AI researchers who publish papers that everyone benefits from, but ok.

How to try it:

  • In Claude, select Fable 5 where available.

  • In the API, use claude-fable-5.

Why this matters: Fable 5 looks strongest on long, messy work: codebase migrations, multi-hour builds, vision-heavy tasks, agent loops, and research synthesis. The demos were wild: Pokémon with raw screenshots, Factorio, solar-system simulations, CAD models, and public users reporting massive coding speedups.

But the public vibe is split. Some developers called it transformational. Others hit biology blocks, effective shadow-bans via ML research steering, or confusing fallback behavior. One new joke practically writes itself: researchers used to optimize prompts for clarity; now they may optimize for plausible mediocrity.

Our take: Fable 5 is best understood as a capability system, not just a model. Anthropic is showing where frontier AI is headed: powerful enough to act for hours, risky enough to gate, and complicated enough that the main question becomes, “Which version did I actually get?”

IDK who should be more offended, the biologists who got banned from Fable just for being biologists, or the AI researchers who are being sent bupkis research tokens and then getting charged for it?

Plenty of companies can launch an AI pilot. Far fewer know how to make it stick. Explore this resource hub, sponsored by Dell AI Factory with NVIDIA, for strategies, decisions, and real-world lessons on turning AI into something scalable, useful, and worth the investment.

So, take this with a grain of Pliny the Liberator, the guy who always jailbreaks every major model released, but the public GitHub mirror of the Claude Fable 5 system prompt adds some useful context for working with Fable 5. Treat this as a third-party artifact rather than a guaranteed canonical source, but it lines up with Anthropic’s public story: Fable is built to be powerful, tool-heavy, safety-routed, and current-info-aware.

The actionable lesson is that Fable’s best users will prompt it like an operating system for work, not like a chatbot.

  • For product questions, the prompt tells Claude to verify against Anthropic’s current docs and support pages before answering. That matters for Claude Code, plan limits, API pricing, model names, Agent SDK credits, and feature availability. A good user prompt is: “Check Anthropic docs and support first, then explain the current behavior.” The model is explicitly told its product knowledge may be stale.

  • For high-stakes work, the prompt nudges users toward structured prompting: clear detail, positive and negative examples, step-by-step reasoning, XML tags, and explicit length or format constraints. That matches what early testers found. Fable can use a lot of context and run for hours, but it needs a destination, acceptance criteria, and a definition of done.

  • For ambiguous requests, the prompt tells Claude to answer with reasonable assumptions instead of asking several questions. That is convenient in chat and risky in production. If the exact output matters, give the constraints upfront: audience, format, scope, sources, success criteria, allowed tools, forbidden moves, and review requirements.

  • For scannable work, ask for the structure you want. The prompt discourages over-formatting by default and says ordinary answers should use prose unless bullets or formatting are essential. If you want extractive output like net-new facts, benchmark deltas, risks, open questions, or QA notes, explicitly ask for headings and bullets.

  • For current information, the prompt has a strong search bias. Claude is told to search for product features, current policies, current role holders, recent launches, and specific model or version details. The practical move is to specify source priority, like this example: “Use Anthropic docs first, then primary sources, then high-quality secondary coverage.” Otherwise, the model may search broadly and over-weight whatever ranks.

  • For company work, the prompt prioritizes internal tools over the open web when the task involves personal or organizational data. It also expects combined research when the user asks something like how public market changes affect internal strategy. That is the workflow pattern to copy: internal docs first for company facts, public sources second for market context, synthesis last.

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AI’s next leap may come from straight-up weirder computers: superconductors, photons, and brain-like circuits built to push past today’s GPU bottlenecks. In this episode, Jeff Shainline of Great Sky walks through SOENs, the memory problem inside today’s chips, and the first places this architecture could matter: fusion reactors, science, cloud, and hyperscalers who need better auto-moderation.

Watch/listen: YouTube | Apple Podcasts | Spotify

📰 Around the Horn

  • Apple introduced its third-generation Foundation Models and expanded Private Cloud Compute to Google Cloud and NVIDIA infrastructure.

  • Perplexity plans to pursue a 2028 IPO regardless of whether Anthropic or OpenAI list first.

  • OpenAI expanded API web search so models can look up current information before generating response, and can turn date and comparisons into charts directly inside ChatGPT.

  • Meta was ordered by EU regulators to restore free WhatsApp access for rival AI assistants during an antitrust probe.

  • China prepared a $295B AI data-center buildout while Taiwan weighed criminal penalties for AI chip exports into China.

  • Standard Bots raised $200M to manufacture robotic arms in the US as factories race to automate real physical work.

Getting stuck between AI in demos and AI in production? AWS Summit is the place to close that gap. 200+ sessions, hands-on labs, and a keynote from AWS VP of Agentic AI Dr. Swami Sivasubramanian. Free to attend.

📖 Midweek Wisdom

  • What it feels like to work with Mythos (Ethan Mollick) — Mollick says Mythos/Fable feels less like chatting with an assistant and more like commissioning a small studio to work through big projects while you wait.

  • Loop engineering (Addy Osmani) — Osmani argues the next agent skill is designing repeatable loops with context, checks, feedback, and stop conditions, not writing one magic prompt.

  • God models won’t eat everything (Marc Andreessen) — Andreessen argues giant frontier models will sit behind the scenes for hard jobs while cheap, specialized models handle most daily work.

  • 2026 as the optimal founder window (Finn Mallery) — Mallery argues one-person companies can now ship apps, design assets, repurpose content, run support, analyze users, and find leads with tools that used to require a team.

  • Reflecting on a year of Claude Code covers how Claude Code grew from an internal terminal agent into a widely used coding tool.

A Cat’s Commentary

That’s all for now.

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