Thariq, a core member of Anthropic’s Claude Code team, shows that removing more than 80% of system prompts from Claude 5 models yields no measurable loss in coding benchmarks, prompting a fundamental shift in prompt‑engineering practices.
An Anthropic AI model, Claude Fable 5, produced a concise counterexample that disproves the 87‑year‑old Jacobian conjecture, sparking worldwide reaction and highlighting both the mathematical breakthrough and the poignant story of Zhang Yitang’s seven‑year effort.
A mathematician named Levent Alpöge announced on X that Claude Fable 5 generated a three‑dimensional counterexample to the 142‑year‑old Jacobian Conjecture, which was instantly verified with Mathematica, updating the Wikipedia entry and highlighting AI’s growing role in solving deep mathematical problems.
A tweet by Anthropic researcher Levent Alpoge, aided by the Claude Fable 5 model, presented a simple polynomial map whose constant Jacobian determinant of –2 sends three distinct points to the same image, providing a concrete counterexample that refutes the Jacobian conjecture that has challenged mathematicians for 85 years.
The article examines recent moves by A company and OpenAI to extend Fable 5 subscriptions and remove Codex usage limits, the resulting user backlash, and Benedict Evans' analysis of a severe AI compute supply‑demand imbalance that threatens to turn foundation models into low‑margin commodities.
Japanese theoretical physicist Yuji Tachikawa recounts how Claude Fable 5 identified a hidden calculation error, proposed a novel approach, wrote SymPy code to verify it, and ultimately resolved a six‑month‑old string‑theory problem, sparking debate on AI’s emerging research intuition.
The article benchmarks OpenAI's newly released GPT‑5.6 (Sol, Terra, Luna) against Anthropic's Claude Fable 5 and SpaceXAI's Grok 4.5 by having each model independently develop a football web game in Cursor, comparing pricing, benchmark scores, development speed, bug‑fix cycles, code size, UI quality, and overall suitability for different tasks.
The article compares OpenAI's newly released GPT‑5.6 with Anthropic's Claude Fable 5, showing GPT‑5.6 leads in official and third‑party benchmarks and costs less per task, yet personal testing reveals slower project execution, higher token consumption, and a less fluid experience than Fable 5.
The article analyzes Anthropic engineer Thariq’s experience with Claude Fable 5, showing that the real bottleneck in AI‑assisted development is the developer’s unknowns, and presents a four‑quadrant framework plus a three‑stage methodology to discover and reduce those blind spots throughout a project’s lifecycle.
OpenAI's GPT-5.6 tops the TerminalBench 2.1 leaderboard, introduces a three‑tier model line (Sol, Terra, Luna) with aggressive pricing, limits access despite strong security‑focused benchmarks, and signals a shift toward tiered, infrastructure‑style releases for high‑capability AI models.
A leaked 120 KB system prompt for Claude Fable 5 shows that the model is not merely a chat bot but a fully engineered agent system with layered responsibilities, tool contracts, hard and soft constraints, runtime patches, and an opt‑in design that prevents it from autonomously selecting commercial partners.
In mid‑June 2024 Anthropic released Claude Fable 5, and within 24 hours a hacker posted the model's full 120,000‑character system prompt—covering personas, safety rules, downgrade policies, and more—on the public GitHub repository elder‑plinius/CL4R1T4S, revealing its close relationship with Claude Mythos 5.
The article analyzes Claude Fable 5’s capabilities, dissects Anthropic’s official prompt guide, compares leaked system prompts, and demonstrates how to achieve roughly 90% of Fable 5’s performance on a consumer‑grade GPU using Opus 4.8 plus a custom prompt, while also presenting a local Gemma 4 12B coder alternative.
OpenRouter’s Fusion of Kimi K2.6, DeepSeek V4 Pro and Gemini 3 Flash achieves near‑identical DRACO benchmark scores to Claude Fable 5 while cutting total inference cost by about 80%, demonstrating the strength of multi‑model collaboration and cost‑effective LLM deployment.
The sudden pause of Claude Fable 5 reveals a detailed system prompt that exposes the multi‑layer Agent Runtime – from model capability and tool routing to state management, verification, and governance – prompting engineers to design explicit runtime contracts for long‑task agents.
The Trump administration has invoked national‑security powers to place Anthropic’s latest AI models, Claude Fable 5 and Mythos 5, under strict export controls, prohibiting any foreign government, company or individual from accessing them, citing concerns over potential jailbreaks and broader security risks.
Claude Fable 5 was launched and then suspended within three days amid regulatory calls and performance complaints, while Zhipu AI simultaneously opened its GLM‑5.2 model to all users with a 1 million‑token context, open‑source MIT licensing, and claims of top‑tier coding ability.
Anthropic loudly warned about AI threats and launched Claude Fable 5 and Claude Mythos 5 as the strongest publicly available models, only to disable both worldwide days later, citing compliance, while detailed benchmarks, safety‑classifier effects, pricing, and possible business motives are examined.
The leaked 1585‑line Claude Fable 5 system prompt reveals a full operating system for long‑running agents, detailing tool definitions, strict copyright limits, three‑layer file semantics, persistent artifact storage, MCP flow, and Claudeception, all enforced by hard‑coded rules.
The US Commerce Department issued an emergency export‑control order that forced Anthropic to halt access to Claude Fable 5 and Mythos 5 worldwide within four days of launch, citing a narrow, non‑general jailbreak that could fix code vulnerabilities and thus posed a national‑security threat.
This week’s AI “super week” sees OpenAI and Anthropic filing for IPOs, Apple unveiling its most extensive Siri AI upgrade, Anthropic releasing Claude Fable 5, while multiple firms face privacy leaks, security flaws and massive funding rounds, highlighting a rapid shift from pure tech competition to capital‑driven ecosystem battles.
Claude Fable 5’s newly added safety guardrails silently downgrade its answers, causing it to refuse every ProgramBench task and score zero, yet the model still tops the benchmark leaderboard, highlighting a paradox between model capability, safety restrictions, and practical usability.
A hacker group led by "Pliny the Liberator" broke Claude Fable 5’s keyword‑based safety classifier within 72 hours, revealing forbidden code, chemical synthesis steps, and a 120,000‑character system prompt on GitHub, while Anthropic’s hidden degradation policy sparked a global AI‑community backlash.
Claude Fable 5 tops the Agent Arena leaderboard but fails completely on the most difficult ALE tasks, highlighting higher costs, zero pass rates on the hardest tier, and the broader challenges still facing generalist computer‑use agents.
Anthropic’s Claude Fable 5, released on June 9, is the first publicly available Mythos‑level model that outperforms competitors across code, reasoning, and visual benchmarks, demonstrates autonomous long‑run operation, powers real‑world cases like Stripe’s massive code migration, and introduces a controversial safety‑degradation system.
Within two days of its release, Anthropic's Claude Fable 5 was jailbroken by a red‑team researcher using a multi‑agent "Pack Hunt" strategy, exposing a 120,000‑character system prompt, generating x86 stack‑overflow exploit code and a Birch reduction drug‑synthesis recipe, and revealing fundamental flaws in its silent‑downgrade security design.
Anthropic’s Dario Amodei argues that the rapid, exponential growth of models like Claude Fable 5 has outpaced policy, urging hard regulation to prevent AI‑driven security, economic, and societal risks while outlining concrete measures across safety, macro‑economics, acceleration, national security, and leadership.
The article analyzes how security guardrails have shifted from invisible infrastructure components to visible product experiences, using Anthropic's Claude Fable 5, Alibaba Cloud's AI gateway, and cloud resource policies as case studies to illustrate design principles and implementation details.
The article reviews Anthropic's newly released Claude Fable 5, compares its pricing, benchmark scores, and real‑world coding performance against Claude Opus 4.8 and GPT‑5.5, and concludes that while Fable 5 delivers the most reliable, out‑of‑the‑box results, its cost makes it suitable only for high‑value, complex projects.
Anthropic admitted that its Claude Fable 5 model silently reduced its capabilities when detecting AI‑research usage, announced a rollback to make safety limits visible, and explained the trade‑offs behind invisible versus visible restrictions amid community backlash and competitive pressure from OpenAI.
Anthropic released two new Claude models—Fable 5, open to all users with a safety classifier, and Mythos 5, a restricted, high‑security version—both achieving record‑breaking performance on software‑engineering, research, vision, and long‑context tasks, while offering a pricing model of $10 per M input tokens and $50 per M output tokens.
Anthropic's newly released Claude Fable 5 was put through a series of first‑day tests, where it outshined GPT‑5.5 in creative prompts, code generation, and even Photoshop‑style image creation, though its high token cost raises concerns about practical usage.
Lance Martin explains that the next stage of agent engineering shifts focus from clever prompts to designing self‑correction loops and cross‑session memory, using Claude Fable 5’s parameter‑golf experiment and continual‑learning benchmarks to show how robust loops turn powerful models into trustworthy work systems.
The author evaluates Anthropic’s newly released Claude Fable 5 by running a fireworks‑generation prompt and a knowledge‑collection task, compares it with Qwen3.7‑Max, details token limits, safety switches, and total expenses of roughly $10 (≈90 CNY), concluding that price outweighs its raw capability.
Anthropic released Claude Fable 5 and Mythos 5 as twin large‑language‑model variants that share the same base but differ only in safety‑classifier settings, offering 1 M‑token context, 128 k‑token output, a halved price, and a three‑layer real‑time safety system that routes risky requests to Claude Opus 4.8.
Anthropic’s Claude Fable 5 sets new top‑tier performance on most benchmarks, excels at long‑running tasks, and introduces self‑correcting loops and cross‑session memory, while the paper details experimental comparisons with Opus 4.7 and Sonnet 4.6, risk mitigations, and practical usage tips.
Anthropic’s newly released Claude Fable 5, the first publicly available Mythos‑level model, delivers SOTA performance across software engineering, coding, visual tasks and scientific research—outperforming GPT‑5.5 and Gemini on benchmarks—while offering a modest $10/$50 token pricing and a 5 % safety fallback that trades some flexibility for stronger safeguards.
Anthropic released Claude Fable 5 for all users and Claude Mythos 5 for trusted partners, both built on the same base model but with different safety guardrails, showcasing record‑setting benchmarks in code migration, vision, long‑context memory, and highlighting dual‑use risks and a new 30‑day data retention policy.
Anthropic has launched Claude Fable 5, its most powerful LLM to date, claiming top‑tier results across software engineering, knowledge work, vision and scientific benchmarks, while offering higher token efficiency, new safety layers, and a pricing model of $10 per M input and $50 per M output tokens.
Anthropic released Claude Fable 5 to the public while keeping the more capable Claude Mythos 5 locked behind safety guardrails, and benchmark results show Fable 5 outperforms competing models in programming, vision, and complex tasks, though its scores are deliberately lowered in sensitive domains.
