According to @godofprompt on X, a reusable Claude prompt helps leaders pressure-test proposals by simulating each stakeholder’s incentives, KPIs, and politics, then generating brutal objections, surprise questions, acceptance criteria, and 1‑sentence reframes; as reported by the original tweet, the structure equips PMs, sales leaders, and founders to de-risk meetings, improve win rates, and accelerate buy-in during AI-enabled preparation workflows. According to the tweet content, the prompt template asks Claude to role-play each stakeholder and deliver four outputs per persona—why they dislike the idea, the question you’re not ready for, what would make them say yes, and a reframe—creating a fast, systematic pre-mortem for objection handling. As reported by the same source, this approach enables concrete business impact: faster consensus in cross-functional reviews, tighter executive alignment, and reduced meeting surprises, especially when integrated into pre-read creation and QBR prep with Claude.

According to @godofprompt on Twitter, a prompt for Claude can cluster 247 support tickets and emails into themes, quantify mentions per theme, extract the job-to-be-done, and surface workarounds to reveal unmet needs, as reported in the original tweet dated Feb 14, 2026. According to the tweet, the structured workflow is: 1) cluster feedback and name each theme with a customer quote, 2) calculate counts, jobs-to-be-done, and current workarounds per theme, and 3) identify the "screaming in the data" insight while ignoring feature requests and focusing on problems. As reported by the post, this method enables product and CX teams to perform rapid qualitative synthesis, prioritize problem statements, and uncover systematic friction patterns for roadmap impact and retention gains.

According to God of Prompt on Twitter, top product managers at Google, Meta, and Anthropic use Claude to accelerate core PM workflows with 10 specialized prompts, including PRD drafting, user story generation, competitive teardown, prioritization matrices, roadmap scenario planning, experiment design, stakeholder comms, risk registers, user interview synthesis, and launch checklists. As reported by the original tweet thread, these prompts turn Claude into a structured copilot that reduces PM cycle time on research and documentation by translating unstructured inputs into actionable artifacts. According to the author, the business impact is faster iteration, clearer stakeholder alignment, and higher testing velocity, which creates opportunities for teams to standardize prompt libraries, enforce product quality gates, and scale PM enablement across organizations using Claude.

According to Ethan Mollick on Twitter, many widely used AI benchmarks resemble synthetic or overly contrived tasks, raising doubts about whether they are valuable enough to train on or reflect real-world performance. As reported by Mollick’s post on February 13, 2026, this highlights a growing concern that benchmark overfitting and contamination can mislead model evaluation and product claims. According to academic surveys cited by the community discussion around Mollick’s post, benchmark leakage from public internet datasets can inflate scores without true capability gains, pushing vendors to chase leaderboard optics instead of practical reliability. For AI builders, the business takeaway is to prioritize custom, task-grounded evals (e.g., retrieval-heavy workflows, multi-step tool use, and safety red-teaming) and to mix private test suites with dynamic evaluation rotation to mitigate training-on-the-test risks, as emphasized by Mollick’s critique.

According to God of Prompt on Twitter, a free access link to the Claude Mastery Guide is available via godofprompt.ai, with auto DMs still active for distribution (source: @godofprompt tweet on Feb 13, 2026). According to the God of Prompt landing page linked in the tweet, the guide focuses on prompt engineering tactics tailored to Anthropic’s Claude 3.5 family, including structured prompting, tool use scaffolding, and evaluation checklists for higher response consistency. As reported by the same landing page, the resource targets business use cases such as sales enablement copy, RAG prompt patterns for enterprise knowledge bases, and workflow templates for content operations, indicating immediate productivity gains for teams adopting Claude in 2026. According to the linked page, the guide also outlines safety-aware prompting aligned with Anthropic’s Constitutional AI principles, which can reduce refusal rates while maintaining compliance in regulated industries. For AI practitioners, this suggests near-term opportunities to standardize Claude prompt libraries, accelerate onboarding, and improve LLM output quality without custom fine-tuning, as reported by the promotional page.

According to God of Prompt on X (as reported in the original tweet by @godofprompt), the team released a free Claude Mastery Guide updated with a full Claude Skills section, 30 prompt engineering principles, and 10+ ready-to-copy mega-prompts. According to the tweet, Claude Skills is a lesser-known feature that enables structured, reusable task capabilities inside Anthropic’s Claude ecosystem, which can accelerate prompt standardization and team onboarding. As reported by the tweet, offering the guide for free lowers adoption friction for startups and agencies evaluating Claude for content operations, research synthesis, and workflow automation, creating near-term opportunities to improve prompt governance, reduce iteration costs, and scale AI-assisted SOPs.

According to AnthropicAI on X, Chris Liddell has joined Anthropic’s Board of Directors, bringing more than 30 years of leadership experience including CFO roles at Microsoft and General Motors and service as Deputy Chief of Staff in the first Trump administration. As reported by Anthropic’s announcement, the appointment signals a focus on enterprise governance, capital allocation discipline, and operational scaling to support Claude model commercialization, safety oversight, and global partnerships. According to Anthropic’s post, Liddell’s track record in complex, regulated markets suggests near-term benefits in procurement, compliance, and board-level risk management, aligning with Anthropic’s emphasis on AI safety and responsible deployment.

According to @DeepLearningAI on Twitter, most beginners fail AI projects by fixating on model choice before defining a user-validated problem and measurable outcomes. As reported by DeepLearning.AI’s post on February 12, 2026, teams should start with problem discovery, user pain quantification, and success metrics, then select models that fit constraints on data, latency, and cost. According to DeepLearning.AI, this problem-first approach reduces iteration time, prevents scope creep, and improves ROI for applied AI in areas like customer support automation and workflow copilots. As highlighted by the post, businesses can operationalize this by mapping tasks to model classes (e.g., GPT4 class LLMs for reasoning, Claude3 for long-context analysis, or domain fine-tuned models) only after requirements are clear.

According to Anthropic (@AnthropicAI) on X, the company is contributing $20 million to Public First Action, a new bipartisan organization aimed at mobilizing voters and lawmakers to craft effective AI policy as adoption accelerates, with Anthropic stating the policy window is closing (source: Anthropic, Feb 12, 2026). As reported by Anthropic, the funding targets rapid policy education and engagement, signaling a strategic push to shape rules around model safety, frontier model deployment, and responsible scaling. According to Anthropic’s announcement, this creates near-term opportunities for enterprises to engage in standards-setting, participate in public comment periods, and align compliance roadmaps with emerging bipartisan frameworks on AI safety and transparency.

According to @bcherny, Claude Code now supports extensive configuration with 37 settings and 84 environment variables that can be versioned in git via settings.json for team-wide consistency, as reported by the Claude Code docs. According to code.claude.com, teams can scope policies at the repository, sub-folder, user, or enterprise level, enabling standardized prompts, tool access, security sandboxes, and model behavior across large codebases. As reported by the Claude Code docs, using the env field in settings.json removes the need for wrapper scripts, streamlining CI integration and developer onboarding. According to code.claude.com, this granular policy model creates clear enterprise governance for AI coding assistants, reducing configuration drift and enabling predictable model outputs in regulated environments.

According to @bcherny, developers can create custom agents in Claude Code by adding .md files to .claude/agents, enabling per-agent names, colors, tool sets, pre-allowed or pre-disallowed tools, permission modes, and model selection; developers can also set a default agent via the agent field in settings.json or the --agent flag, as reported by the tweet and Claude Code docs. According to code.claude.com, running /agents provides an entry point to manage sub-agents and learn more about capabilities, which streamlines workflow routing and role specialization for coding tasks. According to the Claude Code documentation, this supports enterprise use cases like policy-constrained code changes, safer tool invocation, and faster task handoffs within developer teams.

According to Sawyer Merritt on Twitter, a source link was shared without accompanying context, and no verifiable AI-related details can be confirmed from the tweet alone. As reported by the tweet source, only a generic URL is provided, offering no information on AI models, companies, or technologies. According to standard verification practices, without the underlying article content, there is no basis to analyze AI trends, applications, or business impact.

According to The Rundown AI, OpenAI and Anthropic are intensifying their competition in the advanced AI landscape, with both companies pushing the boundaries of large language models and generative AI technologies. The report highlights how OpenAI's continued advancements in models like GPT4 and Anthropic's progress with Claude3 are driving new business opportunities and market differentiation in 2026. The rivalry is spurring innovation and attracting major investments, leading to accelerated deployment of AI solutions across industries, as reported by The Rundown AI.

According to God of Prompt on Twitter, replacing instruction-based prompts with question-based prompts, a method known as Socratic prompting, significantly improved the output quality of large language models like ChatGPT and Claude3 from 6.2 to 9.1 out of 10. This approach encourages more thoughtful and accurate responses, as reported by God of Prompt. The trend highlights new business opportunities for enterprises and developers looking to optimize AI workflows and customer experiences with more effective prompt engineering strategies.

According to @claudeai, Claude Code has introduced agent teams, allowing users to deploy multiple autonomous agents that coordinate and work in parallel. This feature, currently in research preview, is designed for tasks that can be divided and handled independently, streamlining workflows and improving efficiency for complex projects, as reported on Twitter.

According to @claudeai, Claude in PowerPoint is now available in research preview for Max, Team, and Enterprise users. The integration enables Claude3 to interpret layouts, fonts, and slide masters, helping users maintain consistent branding whether they use templates or generate entire presentations from descriptions. This rollout signifies a major step in practical AI adoption for business productivity, offering opportunities for companies to streamline presentation workflows and enhance design consistency, as reported by Claude's official Twitter account.

According to God of Prompt on Twitter, the Claude Skills API represents advanced engineering but is limited by its proprietary, closed ecosystem. The tweet advocates for open source, model-agnostic, and fully transparent agent infrastructure as the ideal for AI development. God of Prompt highlights the Acontext open source project (from memodb-io) as a solution that supports any model with transparent operations, encouraging developers to support it. As reported by Twitter, this reflects a growing demand in the AI industry for accessible, interoperable agent frameworks that lower barriers to integration and foster innovation.

According to God of Prompt on Twitter, Anthropic's Claude3 agent platform currently provides limited debugging capabilities, offering only error messages without execution logs, stack traces, or replay options. This lack of transparency can lead to significant challenges for developers troubleshooting failures, particularly during critical periods, resulting in a black box experience. As reported by God of Prompt, this limitation may impact reliability and user satisfaction for businesses integrating Claude3 agents.

According to God of Prompt on Twitter, the current Claude API structure imposes significant vendor lock-in, restricting developers to Claude models and making it difficult to migrate workflows or skills to other AI platforms such as GPT5. This situation can hinder innovation and limit business agility, as reported by God of Prompt, by forcing users to rebuild AI integrations from scratch if they wish to test or adopt competing models. Such practices may present challenges for enterprises seeking long-term scalability and flexibility in their AI investments.

According to @godofprompt, while the Claude Skills API has generated significant excitement in the AI community, concerns are emerging about its closed nature, lack of transparency, and the risk of vendor lock-in to a single AI model. The tweet highlights that developers cannot view, debug, or control the production code running within Claude Skills API, which poses challenges for reliability and flexibility. As reported by @godofprompt, an open-source alternative—Acontext by memodb-io—offers more transparency and control, addressing these critical limitations for businesses seeking customizable AI solutions.