Most AI agent tooling looks impressive right up until you try to run it anywhere except your own laptop. That's why I think @SentientAGI's EvoSkill v1.1.0 is a bigger update than it first looks. On the surface, the headline is remote execution through @Docker and @daytonaio. But when I checked the GitHub changes, the real story was that EvoSkill is getting more practical to use. There is now a proper remote flow, a new Dockerfile, remote commands, and Daytona backend code for running the loop in cloud environments while still controlling it locally. They also added an OfficeQA example with a demo script, which matters because most people get interested in agent improvement tools and then stall at setup. Giving builders a benchmark they can actually run helps. The rest of the release is the kind of stuff I usually pay attention to most: cleaner separation between .claude and .evoskill, safer git checkout behavior, provider auth/config improvements, and fixes for silent bugs. That combination is what makes a repo go from research to something teams can actually experiment with.

20% improvement on my AI scorer. One skill file. Zero prompt editing. I've been building with @SentientAGI's EvoSkill for weeks now and the results keep surprising me, so together with @Fr0oZi and @Golldyck we're doing a Space to break down everything we learned the hard way. What we'll cover: • Why ground truth is the boring bottleneck nobody warns you about • The real cost difference between running Opus vs Haiku (spoiler: it matters more than you think) • How a single skill file transferred across tasks it was never trained on • Setting up EvoSkill on your own project from scratch If you're building AI agents and still manually rewriting prompts every time something breaks, this one's for you. 📅 Set your reminder 👇 28.04 - 15:00 UTC on spaces