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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

Claude Science Completes Two Years' Work in a Few Weeks, Is 10x Research Acceleration Really Here?

Claude Science, a new AI workbench from Anthropic, is being tested by scientists, reportedly accelerating specific research workflows by up to 10x. A neuro-scientist at the Allen Institute completed a lengthy literature review in weeks instead of nearly two years using the tool, which automates tasks like citation verification. The platform is an integrated environment for macOS and Linux, connecting to local or remote computing resources. It streamlines the fragmented research process—literature analysis, computation, visualization, and drafting—into a single, auditable workflow. A key feature is its emphasis on reproducibility: every chart generated includes the exact code, environment, and history used to create it. Claude Science uses a multi-agent system. A coordinator manages over 60 pre-configured skills for life sciences (genomics, proteomics, etc.) and can spawn specialized agents. A dedicated reviewer agent checks citations and calculations for accuracy, creating a form of internal AI peer review. The system operates with a human-in-the-loop, requiring user approval for major steps. Initial applications are in life sciences. Examples include target identification for biotech company Manifold Bio and germline variant analysis for glioma research at UCSF, completing analyses in roughly one-tenth the previous time. The approach contrasts with competitors: Google focuses on proprietary models like AlphaFold, while OpenAI is advancing models' scientific reasoning with benchmarks like GeneBench-Pro. Claude Science differentiates by automating and integrating the practical research pipeline, not just the model's intelligence, aiming to make AI-aided science more reproducible and integrated into daily lab work.

marsbit07/01 09:50

Claude Science Completes Two Years' Work in a Few Weeks, Is 10x Research Acceleration Really Here?

marsbit07/01 09:50

OpenAI Partners with PE Firms, Investing $4 Billion. Let's Talk About Silicon Valley's Hottest New Role: FDE.

The hottest new role in Silicon Valley is the Forward Deployment Engineer (FDE), a hybrid of engineer and business consultant whose core mission is to transform AI demos into native, practical workflows within client organizations. The recent surge in demand is driven by a strategic shift from leading AI companies. OpenAI, partnering with 19 private equity firms in a $4 billion investment, formed a Deployment Company and acquired Tomoro along with its 150 FDEs. Anthropic also announced a $1.5 billion joint venture with financial institutions like Blackstone. The article, based on interviews with industry experts Jove (FDE lead at Cresta) and Oliver (VP at Invisible Technologies, ex-McKinsey), explores the FDE role and the rise of deployment-focused companies. Key insights include: **The FDE Role:** Jove describes an FDE as a "Forward Deployed CTO"—a technically strong engineer who works intimately with clients to implement AI solutions, learn from the process, and feed those insights back to improve the core product. They require expertise in AI agents, client-facing experience, resilience, and the ability to handle complex, imperfect systems. While AI tools enhance their efficiency, the role's complexity makes full automation a distant prospect. **Industry Shift:** Model companies are moving beyond selling tools to ensuring real-world adoption. This blurs the line between model and application companies. Collaborations with private equity (PE) firms are key, providing access to large portfolios of traditional businesses needing AI transformation. For PE firms, these partnerships offer signal value to LPs, create tangible value in portfolio companies, and provide exposure to high-growth AI assets. **Consulting & Transformation:** AI deployment involves deep, customized workflow redesign, moving beyond simple tool augmentation. Companies like Invisible Technologies build modular platforms to create bespoke, AI-native workflows for clients. While traditional consulting will see growth in helping businesses rethink their models for AI, the real value is captured by firms that leave behind transformed, operational systems. Critical success factors include building robust data foundations and strategically deciding which workflow steps should be deterministic versus AI-driven. The ultimate goal shifts from pure cost-cutting to unlocking new revenue opportunities previously impossible without AI-scale capabilities.

marsbit06/23 07:28

OpenAI Partners with PE Firms, Investing $4 Billion. Let's Talk About Silicon Valley's Hottest New Role: FDE.

marsbit06/23 07:28

I Built Myself an Investment Workbench Using AI

For the past two weeks, I've been immersed in Vibe Coding—using AI to write code from natural language descriptions. This process has enabled me to quickly build functional tools that address long-standing personal ideas. Previously, I had many concepts but found execution too cumbersome. Key ideas included a unified dashboard for assets across US stocks, Crypto, HK stocks, and A-shares; a real-time alert system for price movements; an investment map visualizing sector relationships; and a tool to correlate prediction market bets with news and market data. Traditional development hurdles meant these often remained unrealized. Using AI (Codex, Claude Code, and DeepSeek API), I built four initial tools: 1. A **Cross-Market Asset Dashboard** showing total assets, daily P&L, and holdings by market, with added features for alerts and sector mapping. It's deployed locally for privacy. 2. A **Prediction Market (PM) Monitor** tracking bets on events (e.g., company valuations) and correlating probability shifts with news and market movements. I categorize bets by conviction to filter noise. 3. A **Simple Operations Backend** for managing my writing workflow (topics, progress, publishing). It's cloud-deployed for mobile access. 4. A **One-Click Formatting Tool** that automates converting drafts into various platform-specific formats, saving manual effort. While these tools are basic, they represent a significant shift: AI lowers the barrier to creating personalized systems. I believe individual investors can now feasibly build core systems for: * **Asset Observation** (tracking holdings and changes) * **Signal Monitoring** (watching for key market shifts) * **Sector Mapping** (understanding network relationships within a sector) * **Performance Review** (documenting rationale and outcomes) The power of Vibe Coding is its fast feedback loop. Ideas can be implemented, tested, and iterated on rapidly, turning "want-to-do" into "done." This marks the start of my new phase, where I'll share investment thoughts, tool tests, on-chain operations, and educational Web3 content.

marsbit06/16 06:22

I Built Myself an Investment Workbench Using AI

marsbit06/16 06:22

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