Zach Brock

187 posts

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Zach Brock

Zach Brock

@z

Member of Technical Staff @openai.

San Francisco, CA Katılım Mart 2007
533 Takip Edilen24.6K Takipçiler
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Zach Brock
Zach Brock@z·
"Amateurs have a goal. Professionals have a process."
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Trevor Blackwell
Trevor Blackwell@tlbtlbtlb·
For comparison, a 100 kiloworker office requires this much land.
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Object Zero@Object_Zero_

This 100MW data center in UAE is the largest solar powered datacenter in the world. There are currently 1,300 data centers in the world that are bigger than this one, but this one is the largest solar powered one. That’s 10 square kilometres of solar panels you can see. The datacenter itself is 0.02 square kilometres, so a solar powered datacenter is ~500x larger than a data center using any other form of power. A five hundred times larger site. UAE has some of the highest solar irradiance anywhere on Earth, it is an inhospitable desert. Averaging 9.7 hours of sunlight per day with average irradiance above 2,200 kWh/m^2. If you build this somewhere else, you need more solar panels because your irradiance will almost certainly be lower. Even if the world had an infinite supply of free solar panels, solar power will not be free. Anyone who has ever done major capital projects, who looks at where data centers need to be in the next 5 years and the next 10 years… we know it aint solar. Sorry. You struggle to even build a train track that’s 100 miles long and 10ft wide anywhere in the West, there is zero chance of build 100 square mile solar farms for GW compute. This is why people are talking about space compute. Deploying into space is one strategy to solve the constraints. But there are faster and more scalable strategies, that get you to mass deployment of multi GW data centers. There are strategies that also allow you to power the 10 billion robots and their newtonian actuators, that immediately follow the inference demand cycle. Step back and look at the full cycle of this industrial revolution… There will be billions of chips, but there will be trillions of actuators. This biggest part of this revolution is the embodiment cycle, and it’s big by a factor of 20 or 50x over the stuff that comes before it. There is no analogy in human history for the scale of this economy, of the demand it will place on energy and commodities. The humans own the Earth, and if you exist inside their legal system, they won’t let you turn the surface of their planet into glass. But they do want your chips and your actuators to serve their needs and desires. There is a way to do all of this, and so it will happen.

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Zach Brock
Learning how to use coding agents effectively is the most interesting engineering problem in the world right now. The solution @alex_frantic came up with for our team is Symphony. I think Symphony has a few really interesting ideas embedded in it: 1. The approach itself. Giving coding agents access to task tracking and changing their goal to "convince a human to merge this code" is the clear next phase of software engineering. 2. Software as a spec. Instead of code, Symphony is first a spec.md that you can materialize into any programming language you want by passing it to your coding agent of choice. This is an early demonstration of a new way I expect open-source software to be developed and shared in the future. 3. Lowering the cost of code. When reliably kicking off a feature or bug fix is something you can do from your phone in a few seconds, it radically changes your relationship with product prioritization and exploration. Read the whole blog post below and let me know what you think.
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Zach Brock retweetledi
OpenAI Developers
OpenAI Developers@OpenAIDevs·
📣 What if every open issue had a Codex agent? That’s the idea behind Symphony, an open-source agent orchestrator for Codex that turns task trackers into always-on systems for agentic work, letting humans focus on review and direction.
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Zach Brock
One easy trick to connect 25 Codex agents to Linear and increase your PR throughput by 500% that THEY don't want you to know about openai.com/index/open-sou…
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Zach Brock
Zach Brock@z·
People who join OpenAI are always surprised to learn that we basically ship stuff as soon as it’s ready
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Zach Brock
Zach Brock@z·
@3ameam Try the App instead of the cli, I’m a recent convert
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OpenAI
OpenAI@OpenAI·
Codex for (almost) everything. It can now use apps on your Mac, connect to more of your tools, create images, learn from previous actions, remember how you like to work, and take on ongoing and repeatable tasks.
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Boaz Barak
Boaz Barak@boazbaraktcs·
New blog post: the state of AI safety in four fake graphs.
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from the future
from the future@nk·
Chat GPT 5.4 is really frustrating to work with. Awful hallucinations and laziness. It's hard to tell if it's "smarter" when it's so difficult to steer.
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Chubby♨️
Chubby♨️@kimmonismus·
Seriously, I dont get it. - Today, GPT-5.3 instant is being released . - The blog post states at the very bottom that 5.3 Thinking and Pro will also be released very soon. - An hour later, the official OpenAI X account tweeted that GPT-5.4 will be released very soon. ??? So in a few days we get: GPT-5.3 thinking + Pro + GPT-5.4 (???) instant/thinking/pro?
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Bret Taylor
Bret Taylor@btaylor·
I’ve been trying to simulate using Codex for the next year and what will change about my perspectives on software engineering as I transition from being a computer programmer to a harness engineer. There are so many, but here are a couple that have stuck with me: Software dependencies - Large open source systems like Linux and MySQL seem like they will remain just as important, but I wonder if I will start to have different perspectives on smaller software libraries when the functionality can be relatively easily produced and tested with AI. Given the past decade of supply chain vulnerabilities and maintenance issues in open source libraries, will it become a best practice to reduce dependencies and write our own where possible? Documentation - When I built a product before, the “specification” was split between docs, Slack, Figma, and Linear — but the vast majority of behavior was specified in code, i.e., the long tail of functionality is an emergent property of the code I write. The conundrum with agent-produced code is that it’s not clear which parts of the code were prompted (i.e., specified) and which parts were “vibed” (i.e., unspecified). That seems problematic when continuously evolving a large system over time because the harness will “forget” past instructions. I don’t think replaying prompts is correct either because in a single Codex session, a good chunk of interactions are interactive and effectively transient. I have an intuition that documentation will be as important of an output of my Codex sessions as code, documenting the substantive product decisions made during my session. Those docs clearly need to be directly in the repo, versioned with the code and available as context for future sessions. The docs / context discussion in OpenAI’s recent post on harness engineering resonated with me and maps to my intuition: openai.com/index/harness-…
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martin_casado
martin_casado@martin_casado·
The difference in stuff AI coding is good at vs not is getting more stark. It's very good at basic engine stuff you'd want to build on the way. Tooling, testing, basic engine design. Frameworks etc. But it's really not good at anything where runtime understanding is important. I've seen this working on a splat renderer and multiplayer back end for a game engine. In both cases, AI creates a pretty reasonable guess. But lacking actual understanding of the runtime semantics, the results are basically unusable. This produces somewhat of a dilemma. The better it is at the stuff you can derive optimally from syntax, the more disconnected we are when we actually need to design around runtime semantics. To manage this, I've started to include both schema design, notes on state consistency and runtime traces to the LLMs. It's not perfect, and I still need to be in the code a lot. But it helps to start pulling semantic dependencies like this out.
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Matt Van Horn
Matt Van Horn@mvanhorn·
V2.1 of @slashlast30days is out. Now with @OpenClaw, free @YouTube transcripts and a Codex Skill. 1. @openclaw + watchlists - automated research via cron jobs on your competitors, people, and topics 2. YouTube transcripts as a 4th source 3. Works in OpenAI Codex
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