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AI code agents that guess from similar-looking code can miss whats actually broken. GitLab Orbit builds one context graph across your code, pipelines, deployments and production so your agent follows real relationships instead. Plus, it works over MCP with any agent you're using. Watch the demo to learn more,... show more

🦊 GitLab

172,866 subscribers

1,784,496 Aufrufe • vor 13 Tagen •via X (Twitter)

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Profilbild von Laban

Labanvor 13 Tagen

Code-similarity misses production failures because the bug lives in the graph: pipelines, deploys, and live state. Exposing that graph over MCP is the harness upgrade autocomplete never was.

Profilbild von Evan Kirstel #B2B #TechFluencer

Evan Kirstel #B2B #TechFluencervor 13 Tagen

A coding agent needs to understand more than individual files. It should also know about pipelines, deployments and production issues. Would someone from GitLab come on my show to explain how Orbit builds that context?

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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 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

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Introducing the first agent-to-agent code review solution: Qodo’s Agentic Toolbox. Get full codebase context, your coding rules, and AI code review, brought directly to your coding agent. Available now for Claude Code, OpenAI’s Codex, and Kiro, with support for any MCP-compatible client.

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80,827 Aufrufe • vor 1 Jahr

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

rody

18,089 Aufrufe • vor 13 Tagen

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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:

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Claude R Perrin Code SDK is under-rated One cool part with Claude Code SDK is that it supports full features out of box: - Skills - Sub agents - Hooks - ... Which means you can use claude code CLI as a debug tool to build agent, and then import to your Claude code powered agent Here is a quick example of a pptx presentation agent using Claude Code SDK + doc skills Code example from AI Builder Club by Kai:

Jason Zhou

11,981 Aufrufe • vor 11 Monaten

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yutish

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15,071 Aufrufe • vor 2 Monaten

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Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

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31,191 Aufrufe • vor 4 Monaten

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We gave Pi Code persistent memory with Mem0 Launching the Mem0 plugin for Pi Code: persistent, scoped, semantic memory across sessions and projects. It captures what matters, searches by meaning, and brings the right context back when your agent needs it. Try it out: pi install npm:@ mem0/pi-agent-plugin

mem0

25,051 Aufrufe • vor 3 Monaten

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AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with Qodo and taught by Nnenna, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free:

DeepLearning.AI

56,896 Aufrufe • vor 2 Monaten

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.Postman is now natively available in your favorite coding agent! The new Postman CLI is designed to bring API engineering for agents: 1️⃣ Works in Git and your code repo - Just drop Postman where your agent lives and works. 2️⃣ No GUI - Work in your coding agent. Bring up the Postman UI for review and approval. 3️⃣ No sign-up - Get started right away. Sign-up when you have to save your work and want more. Install once, work in your agent. Run npm install -g postman-cli, then postman init. This loads Postman's skills into your coding agent so it can create collections, write tests, and much more. Available as a plugin for all coding agents! The skills cover the API lifecycle: find APIs your org already has, generate tests that catch regressions, mock an API before it exists, watch live endpoints for downtime and drift, run checks in CI on every commit, and run performance tests. You can also extend the abilities of your agent with the Context Graph. An agent often sees only the code that is accessible to them. The Context Graph API gives it a map of your team's infrastructure, APIs, and services, and how they depend on each other. As your agent works, you stay the reviewer. Your agent does the work from the command line. Open Postman when you want to review or approve it. Check out more at

Abhinav Asthana

493,187 Aufrufe • vor 13 Tagen