Cambrian explosion

tobi lutke · @tobi · view on X

I'm really really excited about Agentic Commerce. There is so much amazing stuff being built. Everything I test just feels delightful and right. Shopify is building all the layers of infrastructure to power a new cambrian explosion of creativity in shopping

0xSammy · @0xSammy · view on X

Woke up to an insane number of new OpenClaw/MoltBot applications going live Struggling to keep track of this ‘Agentic Cambrian Explosion’: - Credit lines - Launchpads - Discovery engines - Virtual worlds - Task Markets - Human rental protocols - Workflow automation engines - Telephone plugins for agent comms - Verification systems (human + agent) - 1 click setup deployments - Moltipedias (wikis) - Agent education establishments - AI models (Kimi + Venice) hitting new ATH usage from OpenClaw plugins - Privacy systems for agents - New payment rails (x402, cards) - Privy + other wallet setup - Auto yield engines - Specialist hardware - Analogue items with agents injected - Agentic GitHub - Collaboration systems - Agentic Commerce on-ramps - OpenClaw specific hackathons - and so many more With plenty more to come. Shill me your most crud vibe coded tool. Any concept could become the beginning of the next breakout open source app This is the start of the next leg up for AI development, with crypto enabling many of these apps

Filipe Almeida · @filipe_almeida · view on X

We’re watching a Cambrian explosion in AI, and it didn’t start with OpenClaw. It didn’t start with MoltBook. Those were simply two organisms that adapted quickly and rose to prominence in this new evolutionary race. The inflection point was roughly two months ago, when GPT-5.2 and Opus 4.5 landed. Models were already useful before that. They could write code, fix substantially complex bugs, summarize docs, answer questions, even do structured tool calling with good quality results over 30 minutes. But they had a failure mode that kept showing up in practice: error compounding. The previous models could produce impressive bursts of competence, but they were dynamically unstable over long horizons. Small mistakes hardened into assumptions. Assumptions compounded. Eventually the session drifted into incoherence or locked into a flawed path you couldn’t steer it out of. What changed was not just raw capability. It was trajectory stability. And even though these landed as incremental releases, it took weeks for the ecosystem to notice what had changed. The shift doesn’t show up in short sessions. Short-horizon evals under-measure the thing that moved: stability over time. A particularly important turning point was the release of OpenClaw (previously MoltBot, previously ClawdBot). This didn’t require the new models. The idea itself wasn’t new. The harness made it trivial to bolt on tools and keep context alive long enough to test long-horizon behavior. The model no longer had the persistence of a Boltzmann brain, flashing into existence and collapsing back into noise. But heartbeats, episodic memory, and a long continuous chat gave it the appearance of experience. Not true continuity. Not true memory. But enough temporal coherence to sustain the illusion. This was technically possible two months ago. It simply took time for the ecosystem to realize that these models could be pushed as ongoing systems rather than single interactions. Incremental capability releases had created incremental expectations. There was a lag between release and a full realization of what had actually changed. And I would argue we still haven’t pushed hard enough to understand the real boundary conditions of this current generation. So here we are, in the middle of a Cambrian explosion. New harnesses, frameworks, and tool interconnectivity appear every day. Each one probing a different ridge of the jagged frontier. There is no unified playbook. Everything is emerging. Everywhere. All at once. Also, welcome to the team @steipete!