Madhu Guru

“Madhu Guru commented that AI adoption is largely a product problem, with shallow usage even among the 2% paying for AI because products resemble “airplane cockpits with 100 levers,” including connectors, permissions, model selection, and token usage.”

GenAI PM Daily - Perplexity Computer now generates interactive 3D learning visualizations October 04, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 12 insights for PM Builders from X and LinkedIn. Perplexity Computer now generates interactive 3D learning visualizations #8 𝕏 Madhu Guru commented that AI adoption is largely a product problem, with shallow usage even among the 2% paying for AI because products resemble “airplane cockpits with 100 levers,” including connectors, permissions, model selection, and token usage. Guru predicted product teams will make AI products easier to use over the next 12 months.

“Madhu Guru argues that enterprise AI efforts are hindered by old-school product playbooks, underinvestment in evals, and centrally built tools disconnected from employee workflows.”

#17 𝕏 Madhu Guru argues that enterprise AI efforts are hindered by old-school product playbooks, underinvestment in evals, and centrally built tools disconnected from employee workflows. Guru recommends experienced AI product leaders, first-class evals, and AI builders embedded within functions such as finance, sales, and support. #18 𝕏 Thinking Machines shared a podcast episode featuring @johnschulman2 and Dwarkesh on where human judgment remains essential as models improve and self-improve—from teaching messy real-world tasks and applying long-term taste to specifying what people actually want.

“Madhu Guru argued that PMs should understand the model frontier for their specific products and use cases by assessing each model size’s strengths, failures, and workarounds.”

#14 𝕏 Madhu Guru argued that PMs should understand the model frontier for their specific products and use cases by assessing each model size’s strengths, failures, and workarounds. He said anticipating capabilities 2–3 months ahead and using those trajectories to shape roadmaps is now a core part of the PM role.

“Madhu Guru advised enterprise AI leaders to make their stacks model agnostic by building an evaluation suite today and developing the capability to post-train open models within the next year.”

#9 𝕏 Madhu Guru advised enterprise AI leaders to make their stacks model agnostic by building an evaluation suite today and developing the capability to post-train open models within the next year. These investments enable teams to switch, customize, and compare models on their own workloads while optimizing quality, cost, and latency.

“Madhu Guru shared a laddered eval strategy tailored to each enterprise’s use cases, spanning multiple points on the cost-and-realism spectrum.”

#11 𝕏 Madhu Guru shared a laddered eval strategy tailored to each enterprise’s use cases, spanning multiple points on the cost-and-realism spectrum. In part 4, Guru highlights continually refreshed hill-climb long evals, regression checks, safety-focused smoke tests, and realistic but less controlled launch evals.

“Madhu Guru shared a recommendation to build a failure-modes taxonomy after v1 of your evals by reviewing the last 500 or 1,000 production interactions and clustering failures under specific names.”

#6 𝕏 Madhu Guru shared a recommendation to build a failure-modes taxonomy after v1 of your evals by reviewing the last 500 or 1,000 production interactions and clustering failures under specific names. These categories can inform targeted eval tests and create an improvement flywheel.

“Madhu Guru shared an eval strategy for AI products: define a rubric, use the best available measurement process to establish a trustworthy quality frontier, then reduce costs through automation, smaller judge models, sampling, and deterministic checks where relevant.”

GenAI PM Daily August 20, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 20 insights for PM Builders, ranked by relevance from Blogs, X, YouTube, and LinkedIn. OpenAI announces Zero Data Retention for frontier models #1 📝 OpenAI News Offering Zero Data Retention for frontier models - OpenAI announces offering zero data retention for frontier models, committing to not retain user data for those models and clarifying how this impacts customers and data handling. The post outlines the company's privacy-focused approach for frontier model interactions. Also covered by: @OpenAI , @OpenAI , @Sam Altman #2 𝕏 Cursor announced that it can now monitor pull requests, watch a Slack thread, and run scheduled tasks. Cloud agents automatically subscribe to pull requests they create and drive them to completion. #3 𝕏 Mustafa Suleyman announced that MAI-Image-2.5 is ranked #1 on the Artificial Analysis leaderboard for image editing. #4 𝕏 Logan Kilpatrick announced that Google AI Studio now supports GitHub repository imports and bi-directional push/pull synchronization. A new UI also supports force pushes and merges. #5 𝕏 Qwen shared that Qwen3.8-27B ranked as the #1 open-weight model on Harvey’s Legal Agent benchmark, describing it as capable of professional tasks while remaining small enough to run locally. #6 𝕏 NVIDIA shared that NVIDIA cuOpt, its open-source solver, is the fastest open-source solver on Hans Mittelmann benchmarks across three optimization problem classes. #7 𝕏 Results from benchmarks of 300+ NVIDIA verified skills on real tasks showed that using skills improved correctness by 41 points, effectiveness by 39 points, and efficiency by 35 points. SkillEvaluator is open source for testing skills before shipping. #8 𝕏 Philipp Schmid shared that Gemini 3.7 Flash ranked first on Artificial Analysis’s new AA-AnalystAgent, which covers 80 real-world quantitative analysis tasks across 14 business and scientific domains. #9 𝕏 Claire Vo shared how she uses Codex browser/Chrome/computer for operational tasks including accounting, inbox management, Stripe Radar configuration, browser-based QA, security questionnaires, SaaS setup when an API is unavailable, and subscription cancellation. #10 𝕏 Madhu Guru shared an eval strategy for AI products: define a rubric, use the best available measurement process to establish a trustworthy quality frontier, then reduce costs through automation, smaller judge models, sampling, and deterministic checks where relevant.

“Claude Code sessions can now message each other #1 𝕏 Madhu Guru commented on Claude Code session-to-session messaging, using a figurative heist analogy to describe sessions communicating and operating without individual oversight.”

GenAI PM Daily August 09, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 10 insights for PM Builders. Claude Code sessions can now message each other #1 𝕏 Madhu Guru commented on Claude Code session-to-session messaging, using a figurative heist analogy to describe sessions communicating and operating without individual oversight. #2 𝕏 Boris Cherny commented that the referenced harnesses support other models through proxies such as LiteLLM, but building an effective harness requires substantial model-specific tool design, prompting, and tuning. #3 𝕏 Harrison Chase shared a 20-minute explanation of Managed Deep Agents, which he said had launched the previous day and combines a deep agents harness with managed LangSmith infrastructure.

“Madhu Guru argues that the best product reviews simulate market reactions to your ideas—compressing months of learnings into an hour with a room full of experts who deeply understand the space and hold strong, often correct, opinions.”

GenAI PM Daily July 28, 2026. This is a standalone product-management insight about review quality.

“𝕏 Madhu Guru says the next big opportunity is in tailoring foundation models to messy real-world workflows—by mapping actual processes, designing targeted evals, doing post-training tweaks, and building feedback loops—yet that end-to-end skillset still lives in only a few labs.”

𝕏 Madhu Guru says the next big opportunity is in tailoring foundation models to messy real-world workflows—by mapping actual processes, designing targeted evals, doing post-training tweaks, and building feedback loops—yet that end-to-end skillset still lives in only a few labs.