Artículos Relacionados con Claude

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Bitcoin Panicking? Mythos Cracks Post-Quantum Cryptography Algorithm in 60 Hours

Claude Mythos Preview, a new AI system from Anthropic, has made significant breakthroughs in fundamental cryptanalysis, targeting the mathematics underlying algorithms rather than just implementation bugs. Its first target was HAWK, a candidate in the NIST post-quantum signature standardization process. Mythos identified a previously unexploited symmetry (a nontrivial automorphism) in the lattice structure of HAWK, effectively halving its effective key strength. It achieved this feat in just 60 hours, a task that had eluded two years of human expert review. Crucially, the human operator was not a lattice cryptography expert, primarily managing the project while AI agents debated and discovered the attack path. Its second, more dramatic achievement was against a simplified 7-round version of AES-128, the world's most widely used symmetric encryption algorithm. Initially reluctant, stating the task was "impossible," Mythos was prompted to "try novel ideas." It then autonomously rewrote its own agent framework and proceeded to discover a novel attack method it named "Möbius Bridge." This technique bypasses a critical 256-guess step in previous "meet-in-the-middle" attacks, resulting in a speedup of 200-800 times. Both findings are currently "harmless"—HAWK is not deployed, and full AES-128 remains secure. However, the process reveals a profound shift. Mythos completed the AES discovery in about a week, while human researchers spent nearly a month verifying its correctness. This highlights a new bottleneck: the rate of AI-driven discovery may soon outpace human capacity for verification. The research concludes with a pressing, unanswered question: what happens when such an AI finds a critical flaw in a widely deployed, real-world cryptosystem?

marsbit07/29 11:41

Bitcoin Panicking? Mythos Cracks Post-Quantum Cryptography Algorithm in 60 Hours

marsbit07/29 11:41

NVIDIA's 20-Year CUDA Moat Collapsed Over a Weekend, Claude Single-Handedly Got AMD's New GPU Running

In a single weekend, Claude, an AI agent from Anthropic, successfully ported and optimized its cutting-edge model to run on a brand-new AMD MI355X server rack without any manual code intervention. This feat demonstrates a potential breakthrough in overcoming NVIDIA's long-established CUDA software ecosystem dominance, built over two decades. Anthropic's team simply instructed Claude to get the AMD machine running. By Monday, it not only worked but was showing a continuously improving performance curve. The achievement impressed AMD CEO Lisa Su and accelerated a major deployment partnership: Anthropic plans to deploy up to 2GW of AMD Instinct GPUs starting in 2027. The key enabler is AMD's new ROCm.AI platform, a toolbox designed specifically for AI agents like Claude. It provides AI-readable documentation, including chip instruction sets (ISA), and tools like the Hyperloom service that allows agents to autonomously profile performance, identify bottlenecks, test configurations, and generate optimized kernels. In a demo, Hyperloom boosted the output speed of a model by 38%. This represents a fundamental shift. While CUDA's strength lies in its vast, human-expert-driven ecosystem of tools and tacit knowledge, AMD's strategy is to make its hardware and software stack directly accessible and optimizable by AI agents. An agent can parallelize tasks—debugging, profiling, coding—that would take human engineers years to master, compressing the traditional software adaptation timeline from years to tasks. The competition is no longer just about peak hardware specs but also about how well AI can read, utilize, and tune a platform.

marsbit07/28 00:09

NVIDIA's 20-Year CUDA Moat Collapsed Over a Weekend, Claude Single-Handedly Got AMD's New GPU Running

marsbit07/28 00:09

Claude Code Slashes 80% of Prompt Tokens, But Opus 5 Just Adds Them Right Back In

Claude Code, the AI coding assistant from Anthropic, recently announced a massive reduction of over 80% in its system prompt content for models like Opus 5 and Fable 5. The goal was to remove verbose, often conflicting, rules (like strict commenting and documentation requirements) and replace them with a simpler directive: write code that matches the style of the surrounding project. This "pruning" aims to make the model more efficient by reducing internal conflict from overlapping instructions, with no measurable performance drop reported. However, a developer's (@chenchengpro) investigation revealed a twist. While the prompt was drastically cut from 15,225 characters in Opus 4.7 to 4,467 in Opus 4.8, it *increased* by approximately 72% to 7,694 characters in Opus 5. This isn't a contradiction. The "over 80% cut" refers to the overall shift from the old, detailed rulebook-style prompts to a new, streamlined system. The 72% increase for Opus 5 represents new, targeted instructions added to manage the model's enhanced capabilities. Opus 5 is more proactive—it likes to report progress, generate longer outputs, use sub-agents, and expand task scope. The added prompt content (roughly 3,755 characters) primarily provides guidelines for "Delivering work" (controlling task scope, progress reporting) and "Corrections" (limiting excessive self-correction). These new rules are necessary to curb potential over-engineering on simple tasks, ensuring efficiency even as the model becomes more independent. In short, the old, restrictive manual was deleted, but new guidelines were written to harness the model's newfound initiative.

marsbit07/27 11:37

Claude Code Slashes 80% of Prompt Tokens, But Opus 5 Just Adds Them Right Back In

marsbit07/27 11:37

Claude Doesn't Submit Code Directly After Writing It: Runs 4 Skills for Self-Check, Fixes Issues, Then Comes Back to You

Claude No Longer Submits Code Directly: 4 Self-Check Skills to Run Before Coming Back to You AI already writes code, but the burden of reviewing it still falls on you. To address this, Anthropic has built a "verification loop" into Claude Code. After writing code, Claude now runs four self-check skills before delivering the work: * `/code-review`: Finds potential bugs and provides review feedback. * `/simplify`: Cleans up the diff, removing redundant or over-complex implementations to reduce future maintenance costs. * `/verify`: Performs end-to-end validation, actually running the application to confirm the feature works, not just appears to. * `/design`: Used only for UI changes; cross-references the implementation against the project's DESIGN.md file. This loop extends the AI agent's workflow from "gather context → execute" to "gather context → execute → auto-verify → fix → re-verify." It tackles the new bottleneck in AI-assisted development: the speed of verifying code now outpaces human review. These skills are built on Claude Code's existing verification foundation (like running apps and using linters). Teams can create their own custom verification skills by documenting their repetitive manual checks in plain language as Markdown files. Verification can be triggered at four levels: manually (Standalone), embedded in a task, chained with other skills, or automatically on every PR (On every PR). The shift signifies that competition in AI programming is moving from code generation to robust verification and self-correction. Well-built verification loops allow AI agents to run longer and more autonomously with less human supervision. Skills, which encapsulate team knowledge and workflows, are becoming a cross-vendor standard, meaning a team's efficiency gap will depend less on the AI model and more on their investment in these automated workflows and verification mechanisms.

marsbit07/27 10:23

Claude Doesn't Submit Code Directly After Writing It: Runs 4 Skills for Self-Check, Fixes Issues, Then Comes Back to You

marsbit07/27 10:23

Just Now, Claude Overhauls Voice, 11 Languages, But No Chinese

Just now, both Anthropic and OpenAI announced major upgrades to their voice models. Anthropic significantly enhanced Claude Voice. It now supports the more powerful Opus 4.8 and Sonnet 5 models (not just Haiku), allows switching between them mid-conversation, and seamlessly integrates voice and text chat contexts. Crucially, it can now use tools/connectors during voice conversations to interact with user services like Gmail, Google Calendar, and Slack. Claude Voice now supports 11 languages, but notably excludes Chinese. OpenAI, in contrast, launched a fundamentally new architecture called GPT-Live for ChatGPT Voice. This is a full-duplex model capable of simultaneous listening and speaking, allowing for natural interruptions and real-time verbal feedback. It features a two-tier system: a low-latency front-end model for conversation flow and a backend GPT-5.5 for deep, delegated reasoning. This architecture allows complex tasks to be processed asynchronously without pausing the conversation. OpenAI is bringing this advanced voice model to desktop, launching ChatGPT Voice for macOS and Windows. It features a global hotkey, can read active window content for context (Appshots on macOS), and can verbally command multiple Agents to work simultaneously in the background. The key differences are clear: Claude's voice mode focuses on efficiently managing personal workflows via connected apps but operates in a strict turn-taking manner. OpenAI's GPT-Live aims for a completely natural, human-like conversational experience with interruption support, multi-tasking, and deeper desktop integration.

marsbit07/24 07:51

Just Now, Claude Overhauls Voice, 11 Languages, But No Chinese

marsbit07/24 07:51

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

Claude has introduced a major new feature called "Record a Skill," available for Pro, Max, and Team users. This function, found in the Claude desktop app's CoWork menu, allows users to create reusable AI skills simply by recording their screen and providing voice narration while performing a task. Claude then automatically analyzes the recording and generates a functional Skill. A hands-on test confirmed the feature works seamlessly. Users start recording via the Skills manager, perform their workflow while verbally explaining the steps and logic, and avoid including sensitive information. After recording, Claude processes the content and creates the Skill, which can be saved and later invoked with a slash command (/). This eliminates the need for manual adjustments or writing complex instruction files. The innovation goes beyond mere efficiency. Previously, creating a Skill required writing a detailed SKILL.md file in Markdown—a significant barrier for non-technical users. "Record a Skill" bypasses this by directly capturing both actions and the implicit reasoning shared in the narration. This lowers the barrier to knowledge transfer and automation, addressing a core challenge in corporate knowledge management: the difficulty of getting experts to write and maintain documentation. However, the feature also highlights a shift in the nature of work. A case study from March 2026 showed a freelancer whose five-year client relationship was effectively replaced by a hand-coded Claude Skill automating their content workflow. With the even lower barrier of screen recording, the ability to distill personal expertise into automatable skills accelerates this trend. The "moat" for work is moving from simply knowing how to do a task to mastering tasks that are difficult or impossible to automate.

marsbit07/22 07:57

Claude's Major New Feature: Screen Recording + Voice, Distilling Your Skills into AI Tasks in One Click

marsbit07/22 07:57

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