Product Update: Improved APIs and Interfaze for Better Performance

If you're in RevOps in the age of agentic orchestration, you fall into one of three camps: 1️⃣ Fully embrace it and go AI-first 2️⃣ Dabble here and there 3️⃣ Ignore it altogether You want to be in camp 1. The more I work with these tools, the more I’m realizing how much leverage they create. You have to be intentional: block time to work on process X with tools Y and Z, and push it further each week. A/ It's the optimal way to stay close to the SOTA of your craft and B/ It's actually fun

If only production briefs came in tidy little boxes! But they don't. They live in call transcripts, half-finished decks, and 47 Slack threads. That's why we built AI QuickStart, a new MakeOS feature that turns all those scattered pieces into a clear production plan. Drop in notes, decks, or even transcripts and it automatically identifies milestones, assets, budgets, and even risks. When new info comes in, you can update your brief on the fly. MakeOS is built to power up the next generation of producers who are doing more with less time than ever before. Try it free in MakeOS for the first 1000 Producers. Link in comments!

Kimi K2 Thinking – A Step Toward Agentic Intelligence https://lnkd.in/efnSvUG2 I went through Moonshot AI’s new Kimi K2 Thinking release, and it clearly shows where the next stage of LLM development is heading: long-context models with strong agentic reasoning. Here are the key points that stood out: 1. Built on long-context foundations Moonshot focuses heavily on “lossless long context,” enabling models to retain and use very large amounts of information without loss. This is becoming essential for agent-based systems. 2. Kimi K2 as the foundation The earlier K2 model was a 1.04T MoE system trained on trillions of tokens and thousands of virtual tool trajectories. This gave it early agent-like behaviour and strong real-world task capabilities. 3. K2 Thinking adds the reasoning layer The new model can plan and execute 200–300 tool calls, shift between thinking, searching, and coding, and handle long multi-step reasoning flows. This moves it closer to autonomous task execution rather than just chat responses. 4. Test-time scaling is the most interesting part Instead of only scaling model size, Moonshot scales the thinking process: a) more reasoning tokens b) deeper tool-use chains c) eight parallel reasoning paths (Heavy Mode) This is similar to the direction frontier models are taking, but here it’s fully open. 5. Open-source under Modified MIT License This makes the model available for research, experimentation, and real agentic development without restrictive licensing. Overall, Kimi K2 Thinking shows a clear shift in the industry: moving from large language models to actual tool-using, planning-capable agents. It will be interesting to see what developers build as these capabilities become more accessible. #AI #ArtificialIntelligence #LLM #AgenticAI #AgenticIntelligence #KimiK2 #KimiK2Thinking #MachineLearning #DeepLearning #LongContextModels #AITools #AIResearch #OpenSourceAI #FutureOfAI #TechInnovation

🚀 When creativity meets simplicity, innovation happens. Aisha Imtiaz’s new piece on Google AI Studio isn’t another “tool review”. It’s a first-person lab notebook for how writers and designers can actually build smarter. If you’ve been curious about Gemini, Imagen, or no-code prototyping, this one captures the excitement without the hype. It’s the kind of experimentation that turns ideas into working systems. 👉 Read her full exploration → (Substack in 1st Comment!) #AIStudio #Innovation #NoCode #Creators #Collaboration

From compute bottlenecks to creative freedom - the future of video is here! 🚀 Imagine an AI world with: ✨ Personalized ads rendered in milliseconds ✨ Product videos that adapt per viewer ✨ Global content delivered instantly Rushaan Mahajan breaks down how Sima Labs (Video Tech) is making this reality through next-gen infrastructure optimization. Tune in here: https://lnkd.in/g9NWsNxV 💡

We just launched 3 features that will change EVERYTHING (And you're getting early access) Here's what's new in DMAND: → Advanced Research (BETA): AI searches for 8 signal types including: - Funding rounds - Hiring trends - Product launches - Expansions. Automatically validates each signal and filters false positives so you only get accurate intelligence. → Smart Offer Builder (BETA): Step-by-step wizard that creates compelling offers using proven frameworks. Get business model-specific suggestions and constraint-based lead magnet recommendations. → Redesigned Settings: Cleaner interface with tabbed navigation. Streamlined setup flow makes configuration 10x easier. PS - Share your feedback. Your input helps us build better features for everyone.

As a PM, AI saved me hours, but prompts made it priceless. I’ve learnt a lot about prompting, and honestly, it’s been one of the most transformative skills for me. In today’s world, using AI as a product manager is no longer optional, it’s essential. It helps you move faster, organize better, analyze smarter, and focus more on what truly matters: strategy and impact. But here’s the truth, using AI isn’t where it ends. Prompting is where the magic really happens. AI is only as powerful as the question you ask. And as a PM, good prompting can change the way you work, from analyzing user feedback and Uncovering key insights, to generating stakeholder-ready PRDs, research questions, and even helping you shape your next prototype or landing page copy. That’s why I use the RACE framework whenever I prompt: • R — Role: Who should AI be? (e.g., “Act as a senior PM…”) • A — Action: What do you want it to do? • C — Context: What details does it need? • E — Execution: What does a great output look like? RACE gets me cleaner PRDs, sharper research questions, and fast, reliable syntheses — the stuff you’d normally pay for in workshops or consulting. So I packaged my best, comprehensive battle-tested RACE prompts into a Google Doc and I’m giving it away for free. No gate, no catch. Grab the prompts + bias-checks + safety tips here: https://bit.ly/4oskCzx #Prompting# #ProductPrompts# #AIProductManager#

Prompt Engineering gets easier when you stop guessing and use a formula. Here’s the 6-part structure I use every day: Task → Context → Example → Role → Format → Tone. It makes ChatGPT clearer, faster, and way more accurate. Try it on your next prompt and watch the results change instantly. Subscribe for more practical AI tips. https://lnkd.in/ecZSFFd6

🧠 Mixboard by Google is now available in over 180 more countries! 🪄 What is Mixboard? It’s an experimental AI-powered concepting board designed to help you explore, expand, and refine your ideas. Bring your own visuals or let AI generate text, create, and edit images using Nano Banana, Gemini’s image model. ✨ What can you do with it? From planning parties and DIY projects to storyboarding and creative brainstorming, Mixboard has quickly become a go-to space for creators and thinkers. 📈 What’s new: Boards are now four times larger for even more creative freedom. Mixboard expands to 180+ additional countries, allowing more people worldwide to bring their ideas to life. 🌍 Check the full list of supported countries on Mixboard’s in link first comment. 🔔 Follow me for the last AI updates! #AInews #Mixboard #Gemini #NanoBanana #GoogleAI

Anyone can take notes. Few can turn those notes into compounding context that moves relationships forward. That’s what an AI-native operating system is built to do.