OpenClaw, one of the fastest-growing open source projects, has already picked up over 350,000 stars and an early community of builders exploring what agentic systems can actually do in practice.

That’s why, on June 3, 2026, we are hosting OpenClaw: After Hours at GitHub HQ in San Francisco. The event will take place during Microsoft Build 2026.

This evening is a chance to bring the OpenClaw community together into the same room.

We’ll kick things off in the early evening with a fireside conversation featuring Peter Steinberger, the ClawFather and creator of OpenClaw, followed by a panel with OpenClaw maintainers and ecosystem builders sharing what’s working—and what’s not—when shipping real agentic systems.

Later in the evening, we’ll move into a series of fast-paced lightning talks and close things out with a relaxed happy hour to connect with other builders.

If you have been following the project or building with it yourself, this is a good chance to meet others, trade notes, and get your claws into what people are actually shipping.

👉 For the full agenda and speaker lineup, please see the registration page.

📍 GitHub HQ, 275 Brannan St., San Francisco
🗓 June 3, 5:30 p.m. – 9 p.m.
📺 Livestream: twitch.tv/github

Drinks and snacks will be provided. There will be a lot here to chew on. No shellfish behavior please. And bring your sharp ideas!

Spots are limited, so register early and come ready to share what you are working on.

‼️ Please note: Submitting a registration does not guarantee attendance. We’ll follow up to confirm successful registrations.

What is OpenClaw?

OpenClaw is an open source framework for building and running agentic systems, focused on giving developers real control over how agents execute tasks in the wild. It provides the core pieces for orchestrating tools, managing state, and handling long running workflows, so you can move beyond prompt demos and ship systems that actually do work. It’s also probably convinced more than a few people to buy a Mac Mini just to run “one small experiment” that somehow turned into a permanent setup.

Hear more about OpenClaw from the creator himself, Peter Steinberger: 

The post Register now for OpenClaw: After Hours @ GitHub appeared first on The GitHub Blog.

We ran the same coding tasks with and without prebundled tooling, across multiple models and languages. Here’s what changed.

Why We Built This

When coding agents search code, they default to shell tools. grep and find work, but they’re blind to project structure, symbol boundaries, and language semantics. The agent burns tokens sifting through noisy output and making follow-up calls to narrow things down.

So we tried something obvious: what if the agent could use the IDE’s own search instead?

We built a prebundled skill that pairs a search prompt with a unified MCP tool. One tool, four modes: file search, text search, regex, and symbol lookup. A universal router dispatches calls to the right backend.

Methodology

The eval pipeline spins up an MCP server alongside the IDE so the agent has access to the configured tools and skills. We run identical coding tasks with and without tooling, then compare with paired delta analysis.

We track four things: quality, latency, cost, and budget discipline. Quality asks whether all tests passed. Latency tracks median and P95 task time. Cost converts token consumption into dollars. Budget discipline tracks how often a single task exceeds the USD 0.50 budget cap.

We report improvement deltas only when they pass our significance threshold: p < 0.05, paired test with 95% confidence intervals. Metrics without a significant change are either omitted from the charts or called out explicitly. We tried four configuration variants, selected the one with the best latency and cost tradeoff, then re-ran it on different models and languages to check that the results held.

Results

The selected configuration was a prebundled search skill plus a unified IDE-native tool and universal router. Compared with the no-tooling baseline, it reduced latency and cost without producing a statistically significant quality change.

Configuration Explorer

We tested four tool configurations before choosing the final shape. Lower latency and lower total cost are better, so the lower-left corner of the plot is the target.

Cross-Model Validation

We re-ran the experiment with GPT 5.4 on Java and Kotlin codebases. The pattern holds: latency and cost both drop. Kotlin saw the biggest cost improvement, with total cost falling 13.48%.

How Models Adopt Tooling

Codex sends 91% of its search calls through the new IDE-native tool. Claude is a different story: Opus uses it for about half its searches, and Haiku only 28%, preferring grep and find instead.

This makes sense. Claude already has strong built-in code search, so it leans on what it knows. Codex doesn’t, so it grabs the better tool when one is available. The takeaway: prebundled tooling fills gaps. Where the model already has good search, it adds less. Where search is weak, it makes a real difference.

What’s Next

The eval pipeline works. Now we’re using it.

We’re running the same experiment on smaller models next. Our hunch is that they’ll benefit even more, since they have less built-in search capability to fall back on.

The current results are strongest on Java and Kotlin. We’re expanding to Python, .NET, and TypeScript with bigger sample sizes.

Meanwhile, the winning configuration is being prepared for the integrated IntelliJ IDEA MCP Server, so agent sessions can use IDE-native tooling when the server is enabled.

The next step is to turn this feature on by default in upcoming AI Assistant plugin updates.

Want to try it before the default rollout?

1. Set these registry keys to true: llm.chat.agent.codex.mcp.idea, llm.chat.agent.skills.settings.enabled, and llm.agents.contrib.bundled.skills.sync.enabled.
2. In AI Assistant, choose Codex for the best results.
3. Ask the agent to find something across the current project.

The new Apple Watch Pride Edition Sport Loop, watch face, and iPhone and iPad wallpaper celebrate LGBTQ+ communities during Pride Month and beyond.

My hypothesis this year around AI was that if I develop some agent skills to speed up repeatable processes, it might clear up my bandwidth and free up time for me to work on non-repeatable doc tasks. It appears to be working.

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Guests:

• No guests

 Topics:

• So what have we seen at Google Cloud Next 2026?
• Any closing loops for our 2023-2025 Cloud Next observations?
• We are seeing that AI security is not an island ... what does that tell us about the difference between cloud and AI adoption?
• What does  "ragged edge of AI adoption" mean for security?
• Why do people want agents in their SOC? Do they know what gets better?
• What are the most notable and fun announcements?
• With patching speed, are we looking at something which can be overcome by engineering and courage? Or are we looking at something that is truly an impossibility? 

Resources:

• Video version
• EP221 Special - Semi-Live from Google Cloud Next 2025: AI, Agents, Security ... Cloud?
• Next '26: Redefining security for the AI era with Google Cloud and Wiz
• Breaking the Patch Sound Barrier: Your Vulnerability Remediation Will Not Keep Up With AI Exploit Speed. So?
• EP169 Google Cloud Next 2024 Recap: Is Cloud an Island, So Much AI, Bots in SecOps
• Defending Your Enterprise When AI Models Can Find Vulnerabilities Faster Than Ever
• EP137 Next 2023 Special: Conference Recap - AI, Cloud, Security, Magical Hallway Conversations
• 260 things we announced at Google Cloud Next '26 – a recap

Download audio: https://traffic.libsyn.com/secure/cloudsecuritypodcast/EP275_CloudSecPodcast_Next2026.mp3?dest-id=2641814