Pinned Tweet
Using Codex via the ChapGPT mobile app while out and about this weekend. It’s controlling Codex on a Mac Mini, which is building a native iPhone app. I’m just texting it feedback and it’s sending me fresh TestFlight builds. Wild times!
Blake Samic retweeted
Using my dot with just my AirPods in while out on a walk is peak work. Making changes to my website, building an app, responding to emails and Slack messages. You can just yap and spin up new Codex sessions.
And yes, I’ve been using ultrafast. It’s been a crazy experience.
Blake Samic retweeted
This might be the first time I’ve ever watched an Apple ad that made me actively not want to buy the product
Blake Samic retweeted
I'm constantly dictating little notes/reminders to my dot, so I trigger it now via the action button on my iPhone:
1. shortcuts → + → URL: chatgpt://dots-entry
2. add Open URLs; name it “Dot”
3. settings → Action Button → Shortcut → Dot
Blake Samic retweeted
Dot went from useless -> my first touch point for every task I do
All in the last 3 days
vibe coding a mac app that plays youtube videos, floating over other apps, with zero extra ui except for an optional music visualizer 🎧
Blake Samic retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Blake Samic retweeted
I built a new feature for my blog entirely by voice with Codex Desktop, while I was cooking dinner simonwillison.net/2026/Oct/9…
big bold bets! 👏
After six years in stealth, Atomic Machines is out. Our mission: on-demand, universal command of matter. First beachhead: the Matter Compiler, an AI-native manufacturing system that builds working micro-machines from code alone. No per-product tooling. No process development. Different code, different machine.
Blake Samic retweeted
it turns out the hardest part of generative UI isn't generating the UI.
models have been able to generate good-looking HTML for a while. the more interesting challenge is getting it to feel like it actually belongs in chatgpt. one of our big bets with Intelligent UI was that you didn't have to choose -- that you could keep ~all of the power of HTML and still have it feel fast and native, like it was always part of chat
the other half of the problem is model judgement. how do we post-train gpt-6 to know when an interface would actually help? what should it show or let you do? how do we make it useful and beautiful without taking over the conversation?
none of this was obvious when we started, and there's plenty we're still figuring out. luckily, one of my favorite things about openai is that if you believe in an idea, you can just go build it, even if it takes a lot of prototypes to get there
really excited this is finally out, and incredibly proud of the team. onwards!
Blake Samic retweeted
We haven't stopped growing since we launched our new ChatGPT plugin last Tuesday.
I've never seen anything like this, we added 500K in ARR this week only!
Blake Samic retweeted
All right, we’re locking in. Only things being worked on are simplifications, more efficiency for more usage, groundbreaking features or new models.
Sometimes you have to invest ahead of the curve, but feedback is clear that you all want things to get simpler. On it.
Blake Samic retweeted
Dot voice: what we're fixing ⚒️
We're hearing lots of great feedback on Dot voice:
1. It takes too long to connect or fails entirely (5-6 rings)
2. The phone call UX: some people love it, some don't
3. No way to see transcript or summary
4. Says "Checking" too much
5. Sometimes goes quiet and does not give updates
Here's what we're doing next:
1. Making connection fast and reliable
2. Keeping the phone call UX for now, but instant
3. Adding transcript and summaries to the UI
4. Adding more variance to "Checking" (eventually will remove it for fast questions)
5. Adding more regular updates
Our goal is for Dot voice to become the chief-of-staff you can delegate any task to, hands-free.
What did we miss? Please keep the feedback coming!
Blake Samic retweeted
dot is my favorite openai product so far!
it is amazing to me that each day it feels noticably better as it learns more of my workflow and style.
having it do the stuff i don't like doing--and usually just builds up as a gravity well of dread--has me very happy.
Let’s go @hollllyli !
Blake Samic retweeted
We are making OpenAI Dots really good for engineering.
There are two ways you can leverage Dots for eng tasks:
- connect it to your local computer: this allows dots to control your desktop with CUA, control your Codex app, use the in-app browser, work on your local dev env, and more
- connect it to a cloud environment: this is a new product we launched where you can define a cloud dev environment, give it your dependencies, and allow your dot to spin up instances on the fly to do development tasks
In either way, this transforms your dot into the orchestrator and control plan, allowing it to delegate to agents working across any environment and prompting those agents forward, similar to how you use Codex today to prompt your codex threads forward.
If you’ve tried using Dots for engineering, I’d love to hear your feedback on what is and isn’t working. I’ll also share out improvements as we make them!

























