We see that as well and added code paths that use the claude cli directly - hard to fight the system. https://t.co/VM49zPSyk2

The best way to think about AI is as a force multiplier for the fields you already know about (or for the rate at which you want to learn about a new one). The 3rd category -no existing judgment, no interest in developing it- will basically produce slop and won’t drive that much economically productive activity.

What will happen is that the experts will get better and better at their craft and be able to do so much more. Because they can wield these tools in ways that actually drive high quality output, veer agents in the right direction when they go off, and actually incorporate the work into something useful.

The expert engineer with agents will do far more productive work, precisely because they know how to steer the agent properly. The designer will product far better outcomes using AI than someone without an eye for design. And so on.

As a result of this dynamic, specialization will continue to be important, if not even more so as the tools get more powerful. Because the expectation of the market gets to be much higher. Getting good at any craft will continue to be necessary in the future.

Python code now starts 2x faster on Vercel. Automatically! https://t.co/Id8iH1hSwH

🔴🔊 AI Gateway keeps getting better. Unreal product velocity from the team. https://t.co/OWkksdt10W

Autoscale deployments, which is typically the most expensive of scaled apps is down 80%! https://t.co/5Thud2waXD https://t.co/mUJwAzV2Xu

My chess autoresearch agent got a PhD in modern LLM finetuning. https://t.co/xw9mvVRhgY

Viktor was able to disrupt the agency model and make a lot of money by using Replit.

Then he figured: Why not automate the entire thing, not just the coding part?

The Agency: It's merely an agent loop...

So he asked our team for an MCP, and we built one and he's now built his autonomous agency.

The next evolution is being able to spin up multiple ChatGPT Voice threads so I can have a full team talking to me and to each other. 🔥

Before and after with ChatGPT Voice https://t.co/kzNE5odHSy

More feedback:

1. It should let me know when the other threads finish working if I have a bunch of threads going

2. The Chinese pronounciation sounds bad 😅

Things in tech that have lost all signal given how liberally we use this title..

> “neo”-something
> full stack
> fellows*
> labs
> partner*
> forward deployed
> RL (getting there slowly)

* yes I know the irony since a) we run a fellowship and b) my own title is partner

btw ive been dogfooding an agentic github clone over the past month or so and its gotten quite quite enjoyable to use. even complete with built in CI/CD thanks to workers for platforms!

there's 3 more ideas i have to implement (not shown here) before this goes live. but if you're looking to hack on this with me it's a really good time to join swyx inc and influence the roadmap

one thing i think people dont appreciate enough about @poolsideai is their unusual degree of openness — not only have they shipped an excellent Small model that somehow beat @thinkymachines at coding, but most people (like @eliebakouch) have been shouting out their excellent papers, but also they're among a rare few to actually expose their full eval dataset as well - beautifully published, with across 6 public benchmarks with 4 runs each and hundreds of turns per run. you can satisfy for yourself if they rewardhack. brilliant.

VCs when a founder is raising for a profitable bootstrapped business instead of burning hundreds of millions on compute for a neo-lab https://t.co/YmF9a7BK5p

This reference conversation on fast inference, AI chips, and the next compute bottleneck with @andrewdfeldman of @cerebras is also available on Spotify, Apple Podcasts and here on YouTube:

https://t.co/iP21ZRHgpB

My conversation with @andrewdfeldman, CEO of @cerebras. We started from "what is a wafer?" and built up to why the entire chip industry is reorganizing around inference speed.

00:00 Cold open & Intro
01:31 Why speed became the AI bottleneck
02:32 Tokens per second per user, explained
03:16 AI’s broadband moment and the Netflix analogy
04:35 The AI chip landscape: GPUs, TPUs, Trainium, ASICs
06:36 What is an ASIC?
08:08 Nvidia, Groq, and the fast inference war
09:16 OpenAI, Broadcom, and specialized silicon
12:10 China, power, and sovereign AI infrastructure
15:05 Is the AI infrastructure boom a bubble?
18:56 The hidden bottlenecks: HBM, CoWoS, and 3nm
22:57 Why agents are creating CPU demand
25:36 Andrew’s path from SeaMicro to Cerebras
26:13 Why Cerebras bet on AI in 2016
31:14 SRAM vs. HBM: why inference is a memory problem
33:19 What wafer-scale computing actually means
34:28 The deep-tech “Everest” problem
36:07 The moment the first Cerebras system worked
36:49 Ringing the bell and surviving deep tech
39:08 How a giant chip handles failure
41:22 Why GPUs struggle with decode
42:17 Prefill vs. decode explained
44:01 The “100 HD movies” problem in AI inference
45:04 How fast inference changes RL and training 48:08 Reasoning models and why they cost more compute
50:08 Verification, guardrails, and small models checking big models
52:37 Multimodal AI and the path to video
53:51 Cerebras’ business model: hardware, cloud, API
55:14 OpenAI’s 750MW inference deal
55:36 Why data centers are measured in megawatts
58:01 AWS Trainium + Cerebras decode
59:29 Fast tokens as a cloud product
01:00:52 Is CUDA still a moat?
01:03:53 How TSMC helped Cerebras build the giant chip
01:07:41 Why nobody cared in 2020
01:08:15 Why chip supply chains are hard to diversify
01:09:54 Why today’s AI models will be the worst you ever use
01:10:38 What fast AI could do to SaaS

Great builders understand the jagged frontier of AI models.

Great leaders understand the jagged frontier of their people.

Chatted with a friend who leads security at a public company following the GPT Sol incident. some interesting takeaways..

How do you manage security for effectively infinite agents when identity and access management was designed for a finite number of employees?

One employee can now spin up hundreds of agents. Those agents can spawn more agents.

Traditionally, each employee has an identity, a role, permissions, and a lifecycle.

Do agents inherit the spawning employee’s permissions? What’s their lifecycle - a task, a ticket, a week? Do child agents inherit the same permissions? How do you audit all of this?