According to @godofprompt on X, Claude is being used by product managers at Google, Meta, and Anthropic to dramatically accelerate core PM workflows through 10 reverse‑engineered prompt patterns, as reported in the referenced thread on X. According to the post, these prompts cover tasks like PRD drafting, user research synthesis, competitive teardown, roadmap prioritization, experiment design, stakeholder comms, and executive briefings, enabling faster iteration cycles and higher signal documentation. As reported by the thread, the practical opportunity for teams is to operationalize Claude with reusable templates, role priming, tool calling for data retrieval, and strict output schemas to reduce rework and improve traceability. According to @godofprompt, the business impact includes shorter product discovery timelines, improved decision quality via structured reasoning, and scalable PM support for lean teams.

According to God of Prompt on X, a structured Claude prompt can generate three testable, falsifiable A/B test hypotheses that specify the change, target metric, expected lift, behavioral rationale, measurement plan, and falsification criteria. As reported by the tweet’s author, the template enforces precision by requiring a primary metric plus 2–3 guardrails, and a clear outcome that would disprove the hypothesis, reducing vague goals like “improve engagement.” According to the tweet, this enables product teams to operationalize AI assistants like Claude for disciplined experimentation, accelerate test design, and align analytics with decision thresholds, creating business impact through faster iteration and clearer learnings about user behavior.

According to God of Prompt on Twitter, a 60% signup drop-off was diagnosed by feeding onboarding analytics into Claude, which returned a step-by-step audit highlighting psychological friction, A/B test ideas, and impact estimates; the prompt instructed prioritization by drop-off rate times traffic volume and to identify a removable step (as reported by the tweet linked on Feb 14, 2026). According to the original tweet, the framework analyzed each funnel step with over 20% abandonment, mapped causes like effort, unclear value, and trust gaps, and proposed targeted experiments including copy simplification, progressive profiling, social proof, and alternative authentication. For operators, this shows a concrete use case for Claude in conversion rate optimization: rapid diagnosis, quantified prioritization, and faster experiment design for onboarding flows. As reported by the tweet, the prompt template enables businesses to standardize CRO audits across products by pasting funnel steps, drop-offs, and average time per step to get ranked fixes and expected impact.

According to God of Prompt on Twitter, a structured Claude prompt can turn competitive analysis from feature checklists into strategy by forcing citation-backed insights, customer jobs-to-be-done, vulnerability mining from G2, Reddit, and Twitter, and 2–3 specific feature bets for a six-month roadmap. As reported by the tweet, the prompt instructs Claude to analyze what job customers hire a competitor to do, aggregate complaint patterns from public reviews, and recommend concrete product moves, explicitly banning vague UX takes and requiring links to sources. According to the original tweet, this approach enables PMs and founders to prioritize differentiators grounded in voice-of-customer data, improving positioning, win-back campaigns, and near-term feature development.

According to @godofprompt on X, a single structured prompt for Claude can convert raw customer interview transcripts into a full product requirements document in minutes, replacing a previously 6-hour workflow. As reported by the original X post, the prompt instructs Claude (Anthropic) to output four sections—problem statements with direct customer quotes, 3–5 user stories in the "As a [user], I want [goal] so that [benefit]" format, success metrics tied to renewals or upgrades, and implied edge cases—resulting in repeatable PRD quality. According to the X thread, this operationalizes AI for product management by standardizing discovery outputs, enabling faster iteration cycles and clearer handoffs to engineering. For businesses, as cited in the same post, the approach can reduce PM time-on-task, create consistent artifacts for stakeholder alignment, and accelerate roadmap decisions when paired with versions of Claude optimized for long-context transcript analysis.

According to @godofprompt on X, a practical Claude prompt automates Weighted RICE scoring for user stories by collecting Reach per quarter, Impact (0.25–3), Confidence percentage, and Effort in person-months, then ranking stories by RICE score (Reach × Impact × Confidence ÷ Effort). As reported by the original tweet, the prompt also forces reasoning for each input and flags suspiciously low effort estimates, which can reduce delivery risk and improve roadmap alignment. According to the tweet’s embedded instructions, this approach enables faster backlog triage, clearer stakeholder communication, and data-backed prioritization for AI and software teams using Claude in 2026.

According to Fox News AI on Twitter, Fox News reported that Anthropic’s Claude was used to support a US military raid operation connected to the capture of Venezuelan leader Nicolás Maduro, citing unnamed sources and a report published by Fox News (according to Fox News). The article claims Claude assisted with intelligence synthesis and rapid mission planning, though it provides no technical specifics or official confirmation from the Pentagon or Anthropic (as reported by Fox News). From an AI industry perspective, if confirmed, this indicates growing defense adoption of large language models for time-critical analysis, red-teaming, and decision support; however, the report’s lack of verifiable documentation underscores procurement transparency, auditability, and model governance challenges for defense AI deployments (according to Fox News). Businesses in defense tech and secure AI infrastructure could see opportunities in compliant data pipelines, model evaluation for classified workflows, and human-in-the-loop oversight tooling, contingent on validated use cases and policy guidance (as reported by Fox News).

According to @AnthropicAI on Twitter, Anthropic is partnering with CodePath to provide Claude and Claude Code access to over 20,000 students across U.S. community colleges, state schools, and HBCUs, expanding enterprise-grade AI assistants into undergraduate curricula (source: Anthropic via Twitter). As reported by Anthropic, the collaboration aims to embed Claude for writing and research and Claude Code for software development support in CodePath’s structured courses, potentially accelerating AI-native learning pathways and job readiness in software engineering (source: Anthropic via Twitter). According to CodePath’s positioning as the largest collegiate computer science program in the U.S., this scale creates a distribution channel for model adoption and real-world feedback loops on code generation, debugging, and pair-programming use cases that can inform product refinement and educator tooling (source: Anthropic via Twitter post linking announcement). For universities and workforce partners, the move signals lower-cost integration of AI pair programmers into syllabi and capstones, creating opportunities for sponsorships, credits-aligned AI labs, and assessment frameworks that benchmark Claude Code performance against standard programming outcomes (source: Anthropic via Twitter).

According to Anthropic on Twitter, the company’s annualized run-rate revenue has reached $14 billion after growing more than 10x in each of the past three years, driven by adoption of its intelligence platform by enterprises and developers (source: Anthropic, Feb 12, 2026). As reported by Anthropic’s linked announcement, the growth signals accelerating demand for Claude models in production workflows, API usage, and enterprise safety tooling, creating near-term opportunities in LLM integration, cost-optimized inference, and safety-aligned deployments. According to Anthropic, positioning as a preferred intelligence layer suggests expanding partner ecosystems, compliance-ready offerings, and higher-seat enterprise contracts, which could intensify competition with OpenAI and Google in AI assistants, retrieval-augmented generation, and agentic automation for regulated industries.

According to @bcherny, developers can run /config in Claude Code to set output styles that change the assistant’s tone and format for coding tasks, including an explanatory style for understanding frameworks and patterns and a learning style for coaching code changes, as reported by the linked Claude Code documentation (code.claude.com/docs/en/output-styles). According to the Claude Code docs, configurable styles let teams standardize AI-assisted reviews and onboarding, improving code comprehension and reducing back-and-forth in pull requests. For engineering leaders, this enables consistent developer workflows, faster ramp-up on new codebases, and measurable productivity gains in code review and refactoring, according to the same source.

According to @bcherny, hooks let developers deterministically tap into Claude’s lifecycle to automate permission routing to Slack or Opus, continue tasks when a turn ends, and pre or post process tool calls for logging and governance; as reported by the Claude Code docs, hooks can trigger agents, inject prompts, and enforce review checkpoints to reduce manual approvals and improve reliability in production agent workflows (source: code.claude.com/docs/en/hooks).

According to @bcherny on X, developers can ask Claude to customize spinner verbs in Claude Code and persist them in settings.json for source control sharing across teams, enabling a consistent, branded developer experience and reducing cognitive friction during async tasks (as posted by Boris Cherny on Feb 11, 2026). As reported by the tweet, replacing or adding to the default verb list lets teams standardize microcopy in loading indicators, which can improve perceived performance and usability in AI-assisted coding workflows. According to the same source, checking settings.json into version control helps enterprise teams propagate UX conventions, creating measurable gains in onboarding speed and cross-repo consistency for AI code assistants.

According to @bcherny, Claude Code now supports customizable status lines that appear below the composer to display the active model, working directory, remaining context, token usage, and cost, enabling developers to optimize workflow and manage spend in real time; as reported by code.claude.com, users can run /statusline to auto-generate a configuration from their .bashrc or .zshrc, lowering setup friction for engineering teams adopting AI pair programming at scale.

According to bcherny, Anthropic’s Claude Code now supports installing Plugins, MCPs, and Skills to extend LSPs, agents, and custom hooks across major languages, with deployment via a team settings.json for auto-adding marketplaces; as reported by the linked Claude Code docs and Boris Cherny’s post, teams can install from the official Anthropic plugin marketplace or host a private company marketplace and start with the /plugin command (source: Boris Cherny on X; docs: Anthropic Claude Code documentation at code.claude.com/docs/en/discover-plugins). This creates business opportunities to standardize AI coding assistance, enforce enterprise governance on tool access, and scale language server protocol coverage across polyglot codebases, according to the same sources.

According to AnthropicAI on Twitter, Anthropic will cover 100% of grid upgrade costs tied to its AI data centers, commit to bringing new power online, and invest in systems that reduce grid strain, aiming to prevent ratepayers from bearing electricity price increases (source: AnthropicAI). As reported by the linked company announcement, this move targets rising compute demand from large models like Claude, aligning data center growth with grid reliability and cost containment for utilities and communities (source: Anthropic). According to industry practice, utility interconnection upgrades can be a major bottleneck and cost driver; Anthropic’s commitment signals a cost-internalization model that could accelerate permitting timelines, derisk utility capital plans, and create procurement opportunities in grid-scale storage, demand response, and renewable PPAs for AI workloads (source: AnthropicAI).

According to God of Prompt on X, Anthropic has made Claude’s skills, connectors, and file creation available to free users, with no ads, raising competitive pressure on ChatGPT’s free tier. According to Claude’s official X account, these previously paywalled features now enable workflow automation (skills), enterprise data access via integrations (connectors), and in-app artifact and file creation without a subscription. As reported by the posts, this shift lowers onboarding friction for teams testing LLM-assisted automation, improves bring-your-own-data use cases, and could accelerate adoption in SMBs seeking cost-effective AI copilots.

According to @godofprompt on X, a new site offers a large prompt library with thousands of prompts for Claude, ChatGPT, and Nano Banana. As reported by the original post on X, consolidated prompt marketplaces can accelerate prompt engineering workflows, reduce onboarding time for LLM deployments, and improve response consistency across Anthropic Claude, OpenAI ChatGPT, and Nano Banana models. According to the X post, the volume of ready-to-use prompts signals growing demand for verticalized prompt packs in sales outreach, customer support macros, marketing copy, and RAG task templates, creating opportunities for B2B subscriptions, team libraries, and affiliate bundles. As noted in the same source, multi-model coverage enables cross-model A/B testing and cost-performance optimization, opening business value in prompt versioning, quality scoring, and analytics add-ons.

According to God of Prompt on X, Anthropic’s Cowork has launched on Windows with full feature parity—file access, multi‑step task execution, plugins, and MCP connectors—mirroring its macOS release a month earlier (source: God of Prompt, Feb 11, 2026). As reported by the same thread, Microsoft invested roughly $37.5B per quarter in AI infrastructure, preinstalled Copilot on Windows 11, ran $60M+ in TV ads, and serves 450M M365 paid seats with 15M Copilot subscribers—a 3.3% conversion—while market share purportedly fell from 18.8% to 11.5% in six months (source: God of Prompt). According to the post, enterprises hesitate because Copilot inherits M365 permissions via Microsoft Graph, enabling sensitive data surfacing with natural language and triggering governance audits that delay rollouts (source: God of Prompt). By contrast, Cowork reportedly sandboxes Claude to a single folder with no Graph layer, enabling targeted automation without enterprise‑wide permissions risk, and was built in about 1.5 weeks using Claude Code (source: God of Prompt). For AI buyers, the business implication is near‑term ROI from lightweight, folder‑scoped agents versus multi‑year permission re‑architecture, positioning Anthropic’s Claude Opus 4.6—with improved agentic planning and 1M token context in beta—as a practical Windows automation choice while Copilot undergoes permission audits (source: Anthropic via @claudeai).

According to Claude (@claudeai) on X, Cowork is now available on Windows with full feature parity to macOS, including file access, multi-step task execution, plugins, and MCP connectors. As reported by the official Claude post, this cross-platform release enables Windows enterprises to operationalize autonomous multi-step workflows, integrate local and network file systems, and standardize plugin-driven automations across teams. According to the same source, MCP connectors expand interoperability with developer tools and enterprise systems, creating opportunities for IT to consolidate AI agents, reduce integration costs, and accelerate workflow automation proof-of-concepts on Windows fleets.

According to God of Prompt on Twitter, the Claude Mastery Guide provides in-depth strategies for optimizing the use of Claude, Anthropic's advanced AI model. The guide highlights practical applications for businesses aiming to integrate Claude into their operations, offering actionable insights that can drive productivity and innovation. As reported by God of Prompt, this resource is targeted at professionals and enterprises seeking to leverage Claude's capabilities for competitive advantage in 2024.