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 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 Jeff Dean on X (via @JeffDean), he joined the Latent Space podcast hosted by @latentspacepod, @swyx, and @FanaHOVA, sharing a discussion with a published summary site and video links. According to Latent Space (podcast page linked by @JeffDean), the conversation covers Google DeepMind’s Gemini progress, model evaluation practices, safety alignment, and scaling strategy, highlighting practical implications for enterprises adopting multimodal AI and long-context assistants. As reported by Latent Space, Dean outlines how foundation model capabilities translate into product features across Google Search, Workspace, and Android, and discusses the economics of AI infrastructure, including TPU optimization and serving efficiency, which can lower inference costs for production workloads. According to the same source, the episode also examines open model dynamics, research-to-product transfer, and benchmarks, offering guidance to AI teams on model selection, cost-performance tradeoffs, and opportunities in tooling for retrieval, evaluation, and guardrails.

According to Google Gemini on X, the updated Gemini 3 Deep Think is now available to Google AI Ultra users via the web link and within the Gemini app by selecting the Deep Think tool (source: @GeminiApp, Feb 13, 2026). According to the post, the feature is positioned as a dedicated reasoning mode, signaling Google’s push into longer, multi-step problem solving for coding assistance, data analysis, and research workflows. As reported by the official Google Gemini account, immediate access for AI Ultra subscribers suggests a premium differentiation strategy that could increase paid conversion and retention among enterprise and prosumer segments seeking structured reasoning and planning capabilities. According to the same source, in-app activation through the tools menu indicates Google’s intent to integrate Deep Think as a reusable workflow component, enabling businesses to standardize repeatable prompts for analytics, product roadmapping, and technical documentation.

According to Demis Hassabis on X, Gemini 3 Deep Think is positioned as an expert-level scientific assistant that blends domain knowledge and engineering utility for researchers across mathematics, physics, and chemistry (source: Demis Hassabis, X, Feb 12, 2026). According to the shared video and post, Prof. Lisa Carbone describes practical use in complex research workflows, indicating applications such as step-by-step mathematical reasoning, symbolic manipulation, and code generation to test hypotheses and verify derivations (source: Demis Hassabis, X). As reported by the original post, the model’s promise centers on reducing iteration cycles for proofs and simulations, which could shorten time-to-insight for academic labs and R&D teams evaluating computational approaches (source: Demis Hassabis, X). According to the announcement context, potential business impact includes opportunities for domain-specific copilots in scientific software, integrations with simulation tools, and enterprise offerings for regulated research environments seeking reproducibility and audit trails (source: Demis Hassabis, X).

According to @demishassabis, Google AI Ultra subscribers can now access Gemini 3 Deep Think mode in the Gemini app, with product details provided in Google’s official blog. According to Google Blog, Deep Think is designed for multi-step reasoning with extended deliberation time, enabling complex planning, code generation, and data analysis tasks that benefit from longer context and chain-of-thought style internal processing. As reported by Google Blog, early access is limited to AI Ultra tier users inside the Gemini app, signaling a premium monetization path for advanced reasoning features and positioning Gemini 3 against OpenAI’s o3 and Anthropic’s Claude Opus in enterprise-grade reasoning benchmarks. According to Google Blog, business use cases include multi-source research synthesis, financial modeling, and long-form content structuring, and the rollout suggests opportunities for SaaS vendors to integrate Deep Think via Google’s ecosystem for higher accuracy workflows like RFP drafting and compliance review. As reported by Google Blog, the feature emphasizes reliability safeguards and usage guidance for longer inference times, implying higher per-query costs but potentially improved task completion rates for knowledge work and developer productivity.

According to @demishassabis, Google AI Ultra subscribers can now access Gemini 3 Deep Think mode in the Gemini app, with full details outlined on the Google Blog. According to the Google Blog, Deep Think is designed for multi-step reasoning, extended context planning, and tool-augmented problem solving, targeting use cases like complex coding assistance, multi-document analysis, and research planning. As reported by the Google Blog, early access is available via the Gemini app for Ultra-tier users, positioning Deep Think as a premium capability that could increase subscription ARPU and differentiate Google’s AI stack for enterprise and prosumer segments. According to the Google Blog, Deep Think emphasizes chain-of-thought style planning outputs while maintaining safety controls, which may improve reliability for workflows like RFP drafting, data pipeline debugging, and product requirement synthesis. As reported by @demishassabis, the rollout is immediate for eligible users, creating near-term opportunities for app developers to test longer-context agents, for enterprises to pilot structured reasoning assistants in regulated processes, and for creators to streamline research-to-draft pipelines within the Gemini ecosystem.

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 Sundar Pichai, Google has rolled out the updated Gemini 3 Deep Think mode to Ultra subscribers in the Gemini app and opened early API access for select researchers and enterprises (as posted on X). According to the Google Blog, Deep Think is designed for multi-step reasoning and long-horizon tasks, enabling use cases like complex RFP analysis, financial modeling, scientific literature synthesis, and multi-document planning via the Gemini API. As reported by Google, the early access program targets vetted partners, signaling a go-to-market path for high-value reasoning workloads in regulated and research-heavy industries. According to the Google Blog, this API access can streamline backend orchestration for enterprise apps by centralizing chain-of-thought style planning into a managed model interface, potentially reducing development overhead for multi-agent pipelines. As reported by Google, making Deep Think available in the consumer app for Ultra subscribers also provides a user feedback loop that can accelerate model refinement for enterprise-grade reasoning benchmarks.

According to Sundar Pichai on X, Google’s Gemini 3 Deep Think has received a significant upgrade developed in close collaboration with scientists and researchers to tackle complex real‑world problems, and it achieved an unprecedented 84.6% on leading reasoning benchmarks (source: Sundar Pichai, Feb 12, 2026). As reported by Pichai, the refinement targets hard reasoning tasks, indicating stronger step‑by‑step problem solving and long‑context planning, which can expand enterprise use cases in scientific R&D, financial modeling, and operations optimization (source: Sundar Pichai). According to the original post, the upgrade focuses on pushing the frontier on the most challenging evaluations, suggesting business opportunities for vendors building copilots for engineering, analytics, and regulated industries that require verifiable chain‑of‑thought style performance and robust tool use (source: Sundar Pichai).

According to OriolVinyalsML, DeepThink is being used by researchers to detect errors in advanced mathematics research papers, showcasing tangible real-world impact in proof verification and review workflows. As reported by the original X post from Oriol Vinyals on Feb 12, 2026, the shared video highlights how the system flags inconsistencies in high-level arguments, offering a practical assistive layer for mathematicians during peer review and preprint checks. According to the X post, this creates opportunities for academic publishers, arXiv preprint authors, and research groups to integrate automated theorem-checking and formal reasoning pipelines that reduce revision cycles and improve reproducibility.

According to OriolVinyalsML, Google has released an updated and faster Gemini 3 Deep Think mode delivering PhD‑level reasoning on rigorous STEM tasks with gold medal‑level results on Physics and Chemistry Olympiads. As reported by Oriol Vinyals on X, the upgrade targets long‑chain reasoning and symbolic problem solving, signaling improved step‑by‑step derivations for math, physics, and chemistry benchmarks. According to the linked announcement page, the speed boost reduces latency for multi‑turn, tool‑augmented reasoning, improving reliability for enterprise workloads like technical search, RAG over scientific corpora, and automated problem set grading. As noted by the source, the model’s stronger reasoning implies higher accuracy under chain‑of‑thought constraints and better adherence to structured formats, which can lower post‑processing costs in production. For businesses, according to the announcement, immediate opportunities include STEM tutoring agents, lab assistant copilots for reaction planning, and analytics copilots for formula‑driven financial or engineering models, where Gemini 3 Deep Think’s enhanced logical depth can reduce human review time and increase answer quality.

According to @godofprompt on X, after interviewing 12 AI researchers from OpenAI, Anthropic, and Google, the same 10 high‑leverage prompts consistently drive real-world outcomes such as shipping products, publishing papers, and surpassing benchmarks, as reported in the linked thread on February 12, 2026 (source: God of Prompt on X). According to the post, these expert prompts differ from typical social media lists and reflect workflows for model evaluation, data synthesis, error analysis, retrieval grounding, and iterative system prompts, suggesting practical playbooks teams can adopt for rapid prototyping and model alignment. As reported by God of Prompt, the insights indicate business opportunities for teams to standardize prompt libraries, encode reusable evaluation prompts, and integrate retrieval-augmented generation templates into production pipelines to improve reliability and reduce time-to-market.

According to @godofprompt on Twitter, interviews with 12 AI researchers from OpenAI, Anthropic, and Google reveal a shared set of 10 operational prompts used to ship products, publish papers, and break benchmarks, as reported by the original tweet dated Feb 12, 2026. According to the tweet, these prompts emphasize systematic role specification, iterative refinement, error checking, data citation, evaluation harness setup, constraint listing, test case generation, failure mode analysis, chain of thought planning, and deployment readiness checklists. As reported by the source post, teams apply these prompts to accelerate model prototyping, reduce hallucinations with explicit constraints, and align outputs with research and production standards, creating business impact in faster feature delivery, reproducible experiments, and benchmark gains.

According to Jeff Dean, the referenced demo is “incredibly impressive,” signaling a meaningful advance worth industry attention; however, the tweet does not identify the model, company, or capability, and no technical details are provided in the post. As reported by the embedded tweet on X by Jeff Dean, the statement offers endorsement but lacks verifiable specifics on the underlying AI system, performance metrics, or deployment context. According to standard sourcing practices, without the original linked content context, there is insufficient information to assess practical applications, benchmarks, or business impact. Businesses should withhold operational decisions until the original source of the demo and peer-reviewed or benchmarked results are confirmed.

According to Sawyer Merritt on Twitter, an image was shared without accessible context or verifiable source text, and no AI-related announcement, model release, or company update can be confirmed from the embed alone. As reported by the tweet embed, the link points to an image without accompanying article or metadata, so no validated AI trend, product, or business impact can be cited. According to best-practice verification standards, analysis requires an original source such as a publication, press release, or primary company post, which is not available in the provided content.

According to @demishassabis, who shared Fortune’s cover story interview by @agarfinks, Demis Hassabis outlines DeepMind’s roadmap across frontier models, scientific AI, and healthcare. As reported by Fortune, Google DeepMind is scaling multimodal foundation models while integrating them with Alphabet’s product stack to drive monetization in Search, Cloud, and Android. According to Fortune, DeepMind’s Isomorphic Labs is advancing AI-first drug discovery by combining protein structure prediction and generative design to shorten preclinical cycles and improve hit rates with pharma partners. As reported by Fortune, the strategy emphasizes safety research, evaluation benchmarks, and controlled deployment to enterprise customers via Google Cloud. According to Fortune, commercial opportunities highlighted include AI copilots for knowledge work, bioinformatics services for pharma R&D, and custom model hosting for regulated industries, with a focus on reliability and cost efficiency.

According to Google Gemini (@GeminiApp), users can now leverage the Nano Banana Pro tool to create custom bingo cards for game day, enhancing competition and engagement. This initiative, as announced via Google Gemini's official Twitter account, demonstrates the practical application of generative AI in personalizing game-related experiences and boosting user interaction. As reported by Google Gemini, this feature reflects an ongoing trend of integrating AI-powered customization into entertainment products, opening new business opportunities for interactive AI solutions.

According to Google Gemini (@GeminiApp), users can elevate their snack menus for major events by uploading photos of their snacks to the Gemini platform and using a suggested prompt formula for an advanced presentation. This approach leverages Gemini's AI-powered image recognition capabilities to analyze and enhance food presentations, offering practical applications for hospitality businesses and event organizers. As reported by Google Gemini on Twitter, the integration of AI-driven photo analysis opens new business opportunities for personalized menu creation and customer engagement.

According to @godofprompt on Twitter, Google’s Gemini model leverages a multi-shot calibration framework that relies on exactly three examples for effective few-shot learning. Unlike single-example pattern guessing, this method uses two edge cases and one perfect execution to teach the model, based on internal testing. This approach allows Gemini to handle complex inputs more reliably, emphasizing the importance of carefully curated example sets for business applications in natural language processing and AI-driven automation.