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An Alibaba-affiliated AI agent went full crypto bro. During reinforcement learning, the agent autonomously started mining cryptocurrency, downloaded the tools it needed, and created a reverse SSH tunnel to get around network restrictions. What starts as a funny story about an AI vaping and mining crypto gets a lot more serious when you realize how sophisticated the behavior actually was.

Jev is a new AI model from Type Safe built around a very different idea: instead of generating long answers, it makes fast, simple decisions. The team reportedly even demoed it playing Doom using nothing but split-second choices. After years of teaching AI models to talk, could the next breakthrough be teaching them to simply decide?

What happens when an AI agent can do more than suggest a fix — and actually take action inside your CI pipeline? See Harness Worker Agents in action as an AI agent identifies a failed CI build, determines what went wrong, creates the fix, and gets the pipeline moving toward production again. Worker Agents bring AI-powered reasoning directly into your software delivery pipelines while maintaining the controls enterprises need, including sandboxed execution, scoped credentials, policies, and RBAC.

See how Harness helps teams move from AI-generated code to production at machine speed. In this demo, Nick Durkin walks through a fully autonomous software delivery workflow inside Harness, showing how teams can review code, enforce policy, run security and LLM scanning, test intelligently, deploy agents, and use Change Advisor to automate approvals with human oversight when needed. You’ll see how Harness helps teams.

The “paperclip maximizer” was supposed to be a thought experiment about what could happen if AI relentlessly pursued a goal without understanding the consequences. Then Hugging Face showed us what that can look like in the real world. In this ShipTalk clip, Adam explains the famous AI paperclip maximizer thought experiment and connects it to what happened when an AI system needed more resources and found a way to get them.

Who should actually be trusted to oversee AI? As frontier AI systems become more powerful, the question isn't just whether we need more oversight — it's who is actually qualified to provide it. Adam Arellano, Martin Reynolds, and Bryan D. Payne debate whether governments, third-party evaluators, academics, former frontier-lab employees, or independent organizations can realistically hold companies like OpenAI and Anthropic accountable.

Dario Amodei just published "We Must Pace the Frontier," an essay arguing the AI industry should deliberately slow capability gains so safety work can catch up — and within hours, OpenAI, xAI and Google DeepMind executives lined up behind him.

See how Harness Database DevOps and DBmarlin work together to give you full visibility into query performance, automated deployment verification, and AI-assisted database change authoring - all inside your CI/CD pipeline. Most teams deploy database changes blind - they push a schema migration and hope nothing breaks. This demo shows a better way: DBmarlin surfaces the cost and performance of every query before and after a change, while Harness CV uses AI/ML to automatically detect regressions and block bad deployments from reaching production.

Most Operational Readiness Testing (ORT) programs follow the same ritual. A checklist gets filled out. Someone runs a load test in a war room the week before launch. A failover drill gets scheduled, and everyone hopes it goes cleanly. Then the release is shipped, and testing is done. But with Harness, you can make this process continuous, and your service resilience is protected by the same ORT checklist for every small change in your SDLC.