elvis

MCP won! I started to use Pi heavily for its customisations. This will take it to another new level of what’s possible to build with it.

What comes after loops? Will try to live tweet this, at least the key notes.

OpenAI announces dots. I have been testing it for the last few days and it’s something special. Proactive capabilities are really great. I used it to organize my entire DevDay from travels to session/meetings setup. It has its own computer. Has come great note taking tools for organising work and it’s only going to get better for collaboration. A lot of work ahead but it’s already fun and useful.

Once coding agents can write the whole app, deploying and running it becomes the bottleneck. InstaCloud from @insforge lets agents deploy straight to a serverless cloud that scales with traffic and down to zero when idle. Branching is great for agent workflows. An agent can copy an entire service, data included, in seconds and test changes there before touching the live app.

Ambitious stuff right here, but much needed to better leverage your intelligence stack. Companies will need a system of record for how their AI works, just as they do for customers and finances. CData's new Connect AI Gateway handles every request between your models, agents, and business systems. It routes each one to the right model or tool, checks identity and policy on every call, and saves what each interaction learns as context other agents can reuse.

I keep saying the real upside of AI agents is giving your most technical people their time back. SeatGeek automated 50% of its IT requests in 60 days with @getserval. Its IT engineers now work inside business teams as embedded partners. Zero layoffs, and hiring this year is already ahead of all of last year.

Highlight of DevDay for me is @thsottiaux shouting out @pidotdev. It aligns perfectly with how serious OpenAI seems to be about building a more open ecosystem. And it’s not just feel-good words, there is a big opportunity here to become that platform everyone builds on top of. From those just getting started to the top builders in the worlds. There is a long-term vision at play here just saying. Makes sense in a world where everyone builds.

SF is different man. Vibe here is just on another level.

https://x.com/i/article/2114717270177988608

Recursive self-improvement (RSI) is one of the hottest topics in AI. We are still very early on this. It's a great time to catch up on the latest RSI research. Great companies and products will emerge from it. If you are looking for great RSI papers to understand the history and learn about the latest techniques, look no further. I've also put together a collection of great reads on RSI if you want to track the trend. Here you go: https://academy.dair.ai/papers/collections/self-improving-agents

Banger paper from Google DeepMind on long-horizon multi-agent coding. (bookmark it) They report that, to the best of their knowledge, this is the first end-to-end autonomous completion of Minecraft by AI agents. They report that four Gemini 4 Argon agents beat Minecraft end-to-end. They defeated the Ender Dragon in 27 hours and 14 minutes with no human intervention. Each agent controlled one player through the standard Mineflayer library. No one assigned roles, wrote game logic, or scouted a seed in advance. The agents planned, wrote, and debugged every program themselves, and coordinated through in-game chat and a shared memory. Over the run, they wrote 2,783 programs and died 605 times. They split the work, shared what they learned, and recovered each other's items after deaths. If you build agents for long software or enterprise tasks, this is a useful case study. The team had to keep one plan consistent across thousands of dependent steps for more than a day. https://arxiv.org/abs/2610.22319 Chat with Paper: https://academy.dair.ai/papers/a-team-of-gemini-4-argon-agents-beats-minecraft-2610.22319

Unfortunately, CLI agents are no longer the ideal interface for working with agents. This has been the case for months now. It hurts to say because I have been a heavy CLI user. The reality is that new interfaces like Grok Bot, the Codex app, and others are so good that they make it easier to take advantage of the new ways to work with agents. They also make it easier to scale agent work, collaborate with complex multi-agent systems, build from anywhere, and manage the development lifecycle smoothly (with little overhead). This shift will become even more obvious in the coming months as LLMs continue to improve at things like persistence, coordination, parallelization, tool calling/MCP, memory, harness optimization, multimodal capabilities, intent understanding, UI generation, etc. The point of contact keeps getting simpler as agents continue to improve. I expect that pattern to continue. My rule of thumb now is that I don't want to use an interface that holds the model back or keeps me from leveraging it to its absolute limits.