Tyler Edwards

@minchoi And it's only going to get better from here! Its becoming easier and easier to deploy OSS models

This is wild. Qwen 3.5 running fully local on an iPhone 17 in AIRPLANE mode... 🤯 No subscription. Nothing leaves your device. AI subscriptions just became optional.

@zefi @openclaw @steipete @OpenAI @sequoia 🦞

This Friday, London. @OpenClaw meet-up🦞 Join @steipete, @openAI and @sequoia. Space is limited, imagination is not. Apply below.

@rohanpaul_ai Completely agree, just using AI isn't exciting anymore the real edge comes from how you harness data

Larry Ellison on the AI moat: AI is commoditizing because models use the same public internet data. The true competitive edge isn't the model itself anymore, but access to exclusive, proprietary datasets. That is the only moat left.

@RoundtableSpace Local models are the future, it unlocks so much more customisation and control especially for agents

Qwen3.5-35B-A3B running locally on an M4 chip at 49.5 tokens per second. A 35B model. On a laptop. In real time. LOCAL AI IS GETTING SCARY FAST.

@r0ck3t23 This is why the shift to SLMs makes sense, you don’t need a large foundational model for every problem. Most of the problems we throw AI at could be tackled with a specialised SLM, many of which can comfortably run on a Raspberry Pi.

Dario Amodei just dismantled the biggest myth in the AI industry. Open source AI isn’t free. It never was. Amodei: “It’s not free. You have to run it on inference and someone has to make it fast on inference.” For decades, open source meant something real. It meant a teenager in a basement could download the same tools as a Fortune 500 company. Could read the code. Could modify it. Could build something that competed with the giants. That was genuine democratization. That actually happened. AI is different. Fundamentally. Physically. In ways the ideology hasn’t caught up to yet. Downloading the weights is the easy part. The part that actually costs something is turning the weights into a running system. Into responses. Into intelligence operating in real time at scale. That requires compute. Power. Infrastructure. The kind measured in billions of dollars and years of construction. Amodei: “These are big models. They’re hard to do inference on. Ultimately you have to host it on the cloud. The people who host it on the cloud do inference.” The open source debate was never about who owns the model. It was always about who owns the cloud. And Amodei goes further. When a competitor drops a new open model, he doesn’t ask whether it’s open or closed. He doesn’t care about the licensing. He doesn’t engage the ideology. Amodei: “I don’t think it mattered that DeepSeek is open source. I think I ask, is it a good model? Is it better than us at the things that matter? That’s the only thing that I care about.” That’s the ruthless clarity of someone actually trying to win. While the media debates licensing frameworks, Amodei is asking one question. Is it better. Everything else is a distraction. Amodei: “I don’t think open source works the same way in AI that it has worked in other areas. Here we can’t see inside the model.” This isn’t Linux. You can’t read it. You can’t fork it. You can’t understand it the way generations of developers understood the tools they inherited. You can download it. And then you need a data center to run it. The teenager in the basement who was supposed to be empowered by this revolution needs a billion dollars of infrastructure before the empowerment starts. The era of the basement coder rewriting civilization on a laptop is over. The future belongs to whoever commands the compute, owns the power grid, and can actually turn the intelligence on. Open weights without infrastructure isn’t democratization. It’s a promise the physics of the universe won’t let us keep.