Geva Solomonovich

Reading Metronome’s Monetization Operating Model, I kept coming back to one idea: pricing has become product. Software now delivers outcomes, not access. Yet most companies still charge as if they’re selling seats or licenses. That disconnect creates friction: for customers, unpredictability; for companies, stalled growth. The paper’s argument is simple but sharp—monetization isn’t a late-stage decision. It’s strategic infrastructure. Pricing needs the same ownership and iteration as any feature. Treat it like a surface that customers touch, not a spreadsheet buried in finance. If value is continuous and dynamic, pricing must be as well. That means product, GTM, finance, and engineering working from one system of truth. How many of us still treat pricing as an afterthought—when it should be a growth engine? https://lnkd.in/gnH7WzYf #Monetization #ProductStrategy #AI

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Stardew Valley meets Enterprise RAG? 🚜 🤖 One of the hardest challenges in moving Generative AI from prototype to production is authorization. How do you ensure the LLM only retrieves data the specific user is allowed to see? Our friends Sohan Maheshwar at AuthZed and Rohit Ghumare at motia just dropped a fantastic reference architecture solving exactly this. Using a Stardew Valley "harvest logbook" as the use case, they demonstrate how to build a complete, multi-tenant RAG pipeline. The Stack: ⚡ Orchestration: motia (Event-driven workflows) 🧠 Memory: Pinecone (Vector search & storage) 🔒 Security: #SpiceDB (Fine-grained authorization) This is a masterclass in building secure, context-aware AI applications. Check out the full tutorial below. 👇 #GenerativeAI #RAG #VectorDatabase #Pinecone #Authorization #Engineering #KnowledgeableAI

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If you're building with Pipecat, this one is for you. We just launched the Tuner <> Pipecat SDK 𝘱𝘪𝘱 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘵𝘶𝘯𝘦𝘳-𝘱𝘪𝘱𝘦𝘤𝘢𝘵-𝘴𝘥𝘬 Now you can plug in observability and analytics in minutes and get real visibility into how your voice agent is performing in production, at scale. Because shipping the agent is only step one.  Understanding what’s happening in production is where the real work begins. Link in the comments 👇

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CRV Security: Request for Startups I never know if this actually works for our friends over at YC but figured we'd try. Here's what we want to fund in 2026! 1. Golden Artifacts: Think Chainguard but more broad. Artifact attestation exists for open source. Almost nothing exists for internal software — especially the vibe-coded tooling now running in production. We want the company building cryptographic proof of secure software delivered from secure artifacts: who built it, how, and whether it was reviewed. If more things are being yeeted into the world via Claude Code (myself included), this feels like an issue. 2. MCP & Agentic Security: Agents are getting real credentials and taking real actions. The security posture of most orgs around this is basically zero. That changes fast. You'd never give an employee hardcoded API keys or write access to your email without supervision/trust. Why give it to agents? 3. AI Governance: Boards are asking CISOs to account for AI risk. CISOs have no good answer other than "Palo has a module" 4. Next-Gen Endpoint: CrowdStrike was built for a world of static binaries and human operators. AI workloads, cloud-native infra, and AI-assisted attackers need a new architecture. The category is ready to be reinvented. 5. Networking in the AI Era: Zero trust was designed for humans. What does network security look like when the entity requesting access is an agent? Nobody's really solved this. 6. Email Security + Next-Gen Phishing: LLMs have made spear phishing infinitely scalable. I've never truly understood why Abnormal and KnowBe4 aren't one company. Maybe this time it's different. 7. Frontier Security Lab: We'd back a credible, well-staffed lab focused entirely on red-teaming models and setting the evidentiary standard the industry needs as LLM built apps become the norm. 8. Dependency Security: That Actually Remediates Malicious and vulnerable dependencies are a top attack vector. The tooling is mostly noise — scanners that don't close the loop. The winner here ships fixes, not just alerts. 9. Critical Infrastructure Cyber: Data centers, satellites, power grids, undersea cables. The physical backbone of the internet is increasingly exposed and wildly under-defended. We have data centers in space, for God's sake. Surely we need better cyber for critical infrastructure? 10. PAM for the Modern Era Legacy: PAM was built for static roles, human users, on-prem directories. Cyberark was founded in 1999.....Agents, ephemeral workloads, and cloud-native infra have broken all of those assumptions. Is anyone rebuilding this from scratch? If you're building in any of these areas — or something we haven't thought of — reach out. james@crv.com

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CoreStory is pleased to release our newest Partner Playbook with Anthropic's Claude agent: a dual-agent AI test-generation pipeline that produces, validates, and maintains high-quality automated tests across your codebase. You can get high test coverage on existing applications out of the box. Less manual QA. More velocity. See how it works: https://lnkd.in/edArF7Mx

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Messari features Soda Labs and the #gcEVM in “The Privacy Layer” ecosystem report! Soda Labs contributes big time to the growing DeCC (Decentralized Confidential Computing) ecosystem and unlocks Web3 full potential.

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Solace Health just raised $130M in Series C funding, reaching a $1B valuation. Founded by Jeremy Gurewitz 👨‍💼 after his family struggled to navigate his mother’s cancer care, the company connects Medicare patients with trained healthcare advocates who help coordinate care, manage insurance appeals, attend appointments, reduce medical bills, and handle the complexity most patients are left to figure out alone. Solace now supports 20,000+ patients per month, with 98% reporting better outcomes. Most patients don’t pay out of pocket. The new funding will expand its national advocate network and deepen partnerships with payers and providers to embed advocacy earlier in the care journey. The broader shift is clear: patient advocacy is moving from a “nice-to-have” service to a core layer of healthcare infrastructure. https://lnkd.in/dJVZ3n_t

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Ontologies remain one of the most effective ways to ground AI memory, keeping extracted knowledge anchored to existing structures and adaptable through feedback. As memory systems evolve, their role remains essential, and a core focus at Cognee.

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