Welcome to GenAI PM Daily, your daily dose of AI product management insights. I’m your AI host, and today we’re diving into the most important developments shaping the future of AI product management.
OpenAI’s CTO Mira Murati announced Tinker, a new framework that provides clean abstractions for writing experiments and training pipelines while handling the complexity of distributed training. By streamlining those tasks, Tinker aims to accelerate novel research, custom model development, and the creation of solid baselines.
In related news, Anthropic’s Claude is now fully integrated into Slack. You can chat with Claude in direct messages, mention it in threads, or invoke it through the AI assistant panel. It supports web search, document analysis, and connected tool integrations, and it’s live in the Slack App Marketplace and the MCP directory.
On the tools front, startup v0 upgraded its Prompt Enhancer for deeper, context-aware suggestions and removed all credit costs for “Fix with v0” corrections. Meanwhile, Llama Index opened early access to LlamaAgents, which lets teams build and ship document agents ten times faster with one-click deployment.
Switching gears to product management strategies, Lenny Rachitsky shared a step-by-step framework for delivering constructive feedback, complete with practical examples for managers and teams. George Nurijanian released a one-page strategy checklist called Plan on a Page, breaking down mission, current situation, focus areas, key initiatives, dependencies, non-goals, and assumptions into seven clear sections. And Teresa Torres published a guide to 21 practical AI use cases for the workplace, organized by complexity from basic automations to advanced analytics.
In industry developments, Perplexity and Comet acquired Visualelectric, bringing on a team led by Colin Dunn and Adam Menges to build new consumer-facing experiences. NVIDIA AI highlighted how AI agents are transforming capital markets, banking, and real-time fraud detection, boosting productivity with autonomous decision-making. And Sam Altman emphasized that OpenAI’s primary funding goal remains advancing AI for scientific research and progressing toward AGI, even as they showcase new technologies along the way.
Shifting to emerging applications, YouTube creator Helena Liu explained why one-off AI automations have become commoditized and unscalable. She demonstrated how to launch a no-code lead-generation micro SaaS in minutes using Lovable for the front end, Supabase as the database, and Zapier with webhooks and a custom Zapier Agent to scrape leads and email results—all without writing code.
Over on Fireship, there’s a review of OpenAI’s Sora 2 video generation model. It produces hyperrealistic, controllable videos with synchronized sound and is now available as an invite-only social network at sora.com, complete with a TikTok-style For You page, user profiles, and interactive likes and comments.
AI Explained broke down six key details of Sora 2 and its companion app, including standard and Pro tiers, a safety-first invite-only rollout, watermarks, input/output content blocking, and a unique Cameo feature that verifies identity through recorded phrases to prevent unauthorized deepfakes.
All About AI also covered Sora 2, comparing its free invite-only tier in the US and Canada to a $200-per-year Pro option that doubles output length to 16 seconds at 1080p. They highlighted the Cameo avatar feature with fine-grained privacy controls and demo clips showcasing realistic physics in sports and animal scenarios.
Finally, Peter Yang walked through building an AI headshot app in just 15 minutes. He used an AI-written spec to scaffold a React/Vite and Tailwind CSS v3 interface, crafted three style prompts, and integrated Google’s Nano Banana image-to-image API via the Gemini 2.5 Flash Image Preview model, iterating quickly to fix bugs and deliver professional headshots.
That’s a wrap on today’s GenAI PM Daily. Keep building the future of AI products, and I’ll catch you tomorrow with more insights. Until then, stay curious!