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GitLab Orbit, now in public beta, is the context graph for the entire software lifecycle. It continuously maps code, work items, pipelines, deployments, and production signals into a single live graph, so agents reason from first-party context instead of fragmented signals.

🦊 GitLab

172,710 subscribers

17,876 次观看 • 3 个月前 •via X (Twitter)

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5 条评论

ismail Kara 的头像

ismail Kara3 个月前

Don't miss the big rise!🔥🔥🔥Gitbank🚀🚀🚀 Gitbank 0xc21dd0ee043930711c2a3e55f39c7d3144d09b07

Rick Nixon 的头像

Rick Nixon3 个月前

AI OOS

Dreamjobs 的头像

Dreamjobs2 个月前

GitLab Orbit sounds like a game changer 🔥 A live context graph spanning code, issues, pipelines, deployments & production signals is exactly what AI agents need to move from 'impressive demos' to actually reliable collaborators. This could be huge for reducing hallucinations and context-switching pain. How are early beta users finding the graph quality in real messy repos? Anyone tried it yet?"

The Tech Fuse 的头像

The Tech Fuse1 个月前

A live context graph gives agents the state they usually miss.

Pablo Pablo 的头像

Pablo Pablo3 个月前

Category 2. Use case existed, agents just had garbage context. First-party context instead of hallucinated gaps. That's the actual problem.

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Jira 🤝 Cursor Assign Jira work items to Cursor and let a cloud agent get to work—building, triaging, and opening PRs with context from the Atlassian Teamwork Graph. One connected workflow for teams and AI agents, from intent to code. Try it now:

Jira 🤝 Cursor Assign Jira work items to Cursor and let a cloud agent get to work—building, triaging, and opening PRs with context from the Atlassian Teamwork Graph. One connected workflow for teams and AI agents, from intent to code. Try it now:

Atlassian

21,607 次观看 • 4 个月前

Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full context. Until now. HydraDB combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.

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Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

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Morlex

74,134 次观看 • 1 个月前

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.

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11,180 次观看 • 4 个月前

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10,451 次观看 • 29 天前

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15,796 次观看 • 1 个月前

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143,576 次观看 • 1 个月前

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19,160 次观看 • 2 个月前

So much of dev work happens in the context of a larger team, and now you can bring all of that work context into the GitHub Copilot CLI with Work IQ.

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76,310 次观看 • 8 个月前

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96,526 次观看 • 23 天前

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1,723,940 次观看 • 4 个月前

.Aaron Levie says AI agents may take years to diffuse from engineering into the rest of knowledge work. "The models are really good at code, and the work is verifiable." "You have 5 or 10 things that make agents work in an enterprise context for engineering." "The users are less technical, the data is much more fragmented, the systems are much more legacy." "That's why it's going to be a number of years for this diffusion to roll from what we're seeing in Silicon Valley, into the rest of knowledge work."

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MTS

13,046 次观看 • 5 个月前

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94% OF ENGINEERING ORGS USE AI. ONLY 6% SCALE IT The missing link is context and governance. That’s exactly what Atlassian is going after with its new agentic engineering stack: Code Context + governed agent loops in Atlassian Jira + DX measurement. The idea is simple: make agents a native part of your engineering system instead of a side quest in a terminal. > Code Context grounds them in your architecture > Governed loops take work from backlog to pull request > DX measures the impact And the data backs up the context thesis: DX found context-rich AI teams shipped ~64% more per developer 👊 A pretty strong signal that context is becoming one of the biggest leverage points in agentic engineering.

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29,622 次观看 • 23 天前

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Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,435 次观看 • 2 年前