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4/ Upside B2B revenue attribution that actually works Uses agents & knowledge graphs to help CMOs sort through thousands of data points, and discover what matters Mada Seghete Alex Bauer Dan Ahmadi

67,013 görüntüleme • 1 yıl önce •via X (Twitter)

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Yesterday we hosted SF's most exclusive demo day. Only 10 teams, all of them with moonshot visions. Here's an inside look at the demos: (🧵)

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1/ Conduit Non-invasive Neuralink Literally transcribes your thoughts

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2/ Generation Lab Top longevity lab in the world The most accurate biological age test, organ by organ @alinaruisu

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3/ Titan Dynamics 3D-Printed auto-designed mission-specific military drones Transportable factory that can produce thousands from anywhere Already flying in warzones on 4 continents @sleeplessdev

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5/ Open Ledger Stripe for accounting Turns any fintech startup into an AI powered Quickbooks competitor, with just a few API calls @pryceandstuff @ashtyn_eth @openledger

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6/ Discipulus Ventures Residency for the most important companies in America The only early stage fund in El Segundo @jakobdiepen @DiscipulusVent

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7/ Accessgrid API that opens doors… literally. First startup to bring your keys into your Apple wallet. @bunsen @access_grid

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8) Max AI Stripe for Healthcare World’s first human-free, fully-autonomous medical billing AI agent. @zubairsahsan @boyangzhao

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The rest are in stealth ;) We're backing the most brilliant moonshot founders. If you want to see more bts with them follow us: @hf0 & @davefontenot

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@mada299 @alexdbauer @Dan_Ahmadi @mada299 is the best! I am so excited for Upside and what they will do for CMOs everywhere! Solving @Ogilvy's riddle: “Half the money I spend on advertising is wasted; the trouble is I don't know which half”.

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@mada299 @alexdbauer @Dan_Ahmadi Dream team and killer product! So fun to spot this in my timeline

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@threadreaderapp unroll thread

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@mada299 @alexdbauer @Dan_Ahmadi @grok explains the forensic data means here and why it’s valuable

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Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,807 görüntüleme • 10 ay önce

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

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