Glean now plugs into Vercel’s v0 so teams can ground AI‑generated apps in real enterprise context, not generic boilerplate. With Glean in v0, builders can point prompts at live company knowledge in Glean — auth docs, PRDs, release notes, customer stories — and instantly generate flows, UIs, and copy that reflect how the business actually works, with permissions and governance fully respected. If you’re using v0 + Glean, this is a powerful new way to ship AI‑native experiences that understand your org from day one: https://lnkd.in/guxEcCrz

Notice fewer visits to your website lately? You’re not alone… With AI-powered search giving users direct answers, traffic to traditional small business sites is quietly shrinking. Instead of clicking through, people are staying on Google, Bing, or even AI chatbots for instant info! But here’s the move: Shift your site content to focus on ultra-specific FAQs, helpful resources, and what your audience can’t find in a quick answer box. That’s how you win attention (and customers) back. Want practical ways to future-proof your web strategy with AI? DM me anytime or check out how we help small teams add AI employees: https://lnkd.in/erAAEjiV Stay scrappy( out-think the algorithms!)

It's 2025. Why do our productivity tools still suck? I just migrated from Evernote to Notion after getting logged out for the 20th time today. Notion's great — until you try to find something. The search is painfully slow and misses notes you know exist. We're in the AI age. I can have a sophisticated conversation with an LLM about product strategy, but my notes app can't find a document I wrote yesterday. These companies bolted AI features onto fundamentally broken infrastructure. Now we get AI summaries of notes we can't find in the first place. The basics still aren't solved: reliable sync, instant search, sessions that don't randomly expire. Someone's going to build the notes app for people who actually work with AI — local-first reliability, real search, and intelligence baked into the core rather than sprinkled on top. I created Snowball Sprint specifically to help companies build AI-driven products their customers actually want to buy. DM me — let's see what we can build together.

Developers are done duct-taping together 5 different tools to build one AI app. That's why Gartner noticed what we’ve built. Weaviate is much more than just embedding storage. It combines vector search, hybrid search, agentic querying and more, enabling developers to build production-ready generative AI applications with ease. The recognition as an Emerging Leader in Generative AI Engineering reflects Weaviate's evolution into a full fledged 𝗔𝗜 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 used by thousands of development teams and a large open-source community. Companies like 𝗕𝗼𝗼𝗸𝗶𝗻𝗴.𝗰𝗼𝗺 are already using Weaviate to power their GenAI agents in production environments. Read more about their use case: https://lnkd.in/eksw8z_g

We've made several improvements to the Compound AI analyst in the last few weeks. A quick recap below: 1/ New chat-first interface - Rather than creating workspaces automatically, you can now chat to kick off ad-hoc analyses. - You can still create & manage workspaces for dedicated longer-running projects 2/ Improved SEC search - We now have support for ALL Edgar filings, refreshed daily - 13Fs, Form 4s, you name it 3/ Web Search - Compound now has the ability to search the web, enriching its analysis with content online - Build complete TAM models, fetch estimates, and more Try it now at getcompound.ai

🚀 Built My First No-Code AI Agent Using Google Opal! check it out now https://lnkd.in/gXCNbygn Over the past few days, I’ve been experimenting with Google Opal, and today I created a fully functional no-code AI agent that performs real-time text summarization from user input. This project may look simple on the surface, but for me, it marks an important step in understanding how AI agents work behind the scenes — from input handling and reasoning to output generation. What I learned: 🔹 How to design end-to-end agent workflows 🔹 Integrating Opal’s experimental model for fast reasoning 🔹 Structuring a clean UI + input logic 🔹 Deploying a compact, efficient tool without writing a single line of code Why this matters: As AI becomes more accessible, the ability to design and ship no-code micro-agents is becoming a core skill. This small win pushes me closer to building more advanced agents that automate real business tasks. Excited to keep building and sharing more! 🚀 #AI #NoCode #GoogleOpal #AIAgents #LearningInPublic #DataAnalytics #FutureOfWork

If you're still waiting for the "big" AI breakthrough: here's the hard truth: it's already happening, every week, while most decision-makers scroll past. This week’s Spotlight? A new AI tool built to blitz your workflow bottlenecks. The teams adopting early aren’t just 10% faster: they’re rethinking the work itself, moving the needle from manual grinds to high-leverage output. Here’s why it matters: - Automate the soul-crushing 80% (replies, reports, research) - Bridge apps with zero code and instant notifications - Pro-level prompts out of the box: no learning curve You can’t afford to stand still while the ladder shifts under your feet. DM if you want to see how this launch could make next quarter unrecognizable from the last.

In some of my spare time recently, I decided to dive into a little hands-on AI model testing experiment—just for fun! Using Google Colab, I set up the environment to run different prompt variations and analyze the outputs. It was a great way to stay sharp and see how even small changes in prompts can lead to pretty significant differences in model behavior. 🧠✨ One fascinating insight I took away is how crucial thoughtful prompt design is in getting the best out of AI interactions. If there’s one suggestion I’d share for anyone working with AI models, it’s this: don’t underestimate the power of experimentation. Continuous testing and iterative improvements can uncover insights that really elevate your results. I’m always excited to push the boundaries a bit and learn something new along the way! #PromptEngineering #AIEvaluation #ModelTesting #ABTesting #AIInsights #GoogleColab #LLM #MachineLearning #ArtificialIntelligence #TechInnovation #ContinuousLearning

I used to think the risky part of shipping code was the AI generated bugs. Turns out, the scariest moment is after you merge to production. That’s when I discovered feature flags. Before: Deploy → Pray → Hope nothing breaks After: Deploy hidden → Test safely → Enable when ready → Rollback in 2 seconds if needed It’s like my git reset - -hard workflow, but for production. AI helps you build faster. Feature flags help you ship safer. Try FlagSwift: https://www.flagswift.com/

If you haven’t used Sigma’s AI Query feature and want to start, check out this overview of Sigma’s newest features and updates for a walkthrough on turning data into AI-powered insights and apps. https://lnkd.in/eHf8AK4T #SigmaProductLaunch