If you want it, build it 🦊 ⏳ ⚔️ ⚛️ ▲ Field Eng @spacexai prev: @cursor_ai @vercel
NYC
- Tweets 432
- Following 756
- Followers 405
- Likes 14,868
everything i know about managing agents i learned from the amazing programmers and computer scientists that came before
constraints in your codebase are freeing for both humans and agents! this has always been true, but if you were a small dev team you likely never had to think that deeply because you could trust your fellow programmers to write code and review well
but large companies have had to solve this problem since even before agents. because before you had agent slop, you had human slop. lots of engineers wrote bad code out of necessity (eg backend engineer forced to write frontend or reverse). the solution to this was constraints: lint rules, smarter compilers and diagnostics, high quality tests, investments into observability, and so on
the arrival of agents just means that big company problems are now everyone's problems. the good news is that none of this is really that novel: it's just good engineering
youtube.com/watch?v=GqmsQeSz…
136
184
2,860
267,082
Christian retweeted
It looks like I might have underestimated Grok 4.7, this is a very good model.
It just scored the highest on Frontier v4 beating GPT-6.1 Sol, Opus 5.5, and GPT-6 Astra at every effort level.
256
280
2,527
490,887
compute go brrrr
Next week, @SpaceX will be bringing online 220,000 Nvidia GB300 GPU (worth $15+ billion), with another ~$15+ billion in November, and then potentially another $15 billion in December, for a possible total of 660,000 GB300s coming online in Q4 alone.
Upwards of $50 billion of GPUs.
(below is an old pic of SpaceX's new Minihard GPU facility, which is where those 220,000 Nvidia GB300 GPUs will be coming online, the same number as the other two buildings, but in a much denser configuration, allowing for a significantly smaller overall building footprint)
2
385
I'm not the biggest fan of these "I gave both models the same prompt" benchmarks. Often the model that does more (takes more turns) wins. 🤷♂️
The model that looks better here is often dramatically different than which is legitimately most useful day-to-day.
First Opus 5.5 vs GPT-6 Astra test is 3D.
One prompt, Blender only, all procedural. Render the 10-second shot, and record your own build timelapse.
Opus 5.5: 35 min, 199.6k output tokens, about $13.3 in API terms.
GPT-6 Astra: 28 min, 56.6k output tokens, about $14.5 in API terms.
Feels like a worthy rival, and honestly I am glad about that. From what I watched, Opus juggles way more at once and is faster overall. Astra is still good imo.
1
273
Christian retweeted
All public Cursor workshops that we do (including the ones on Grok Bot) are now live at cursor.com/workshops. Keep an eye out for more coming here soon!
7
15
231
17,108
Gavin Baker says Grok Bot feels like another ChatGPT moment because it turns hours of Claude Code work into seconds:
"You see these 23-year-old kids and just the way they use AI, they're just fluent and native in it. I just feel like maybe in a way that no matter how hard I try, I will never be, and I'm trying really hard."
"We got Claude Code. I built some stuff, did some cool stuff, and in, I don't know, 3 minutes of creating Grok Bots, I had much better versions of everything I created."
"I love having a podcast summarizer... It takes 10 seconds in Grok Bot. It's amazing, and it's so good."
"A Substack summarizer, an X summarizer, an X sentiment tracker for topics and stocks... All of those would have taken me hours working with Claude Code, and they each took 7 to 12 seconds with Grok Bot. And it's better."
"To me, Grok Bot does feel like another ChatGPT moment."
@GavinSBaker @DavidGeorge83
Gavin Baker and a16z's David George on the state of the AI boom:
The future doesn't have to be winner-take-all. Labs, open-source, applications, and the clouds can all capture value.
Demand for intelligence is still dramatically underestimated. Today's power users number in the millions and will grow to hundreds of millions. Gavin and David argue a compute shortage is a more real risk than an AI bubble, and building through it is an opportunity to reindustrialize America.
In this episode, they get into why compute investments pay back so fast, what the data center backlash gets wrong, the case for putting compute in orbit, why enterprises will run several models at once, and how Nvidia ended up at the center of the entire supply chain.
00:00 Intro
01:06 The bear case Gavin couldn't find
05:50 Why a lab would cut its own revenue 75%
08:05 What LPs get wrong about a crash
10:50 Microsoft slowed its capex and regrets it
14:33 The engineers spending 100x the median
17:35 Why 23-year-olds use AI better than Gavin
21:45 How much copper 500M AI users need
23:00 Stop promising to cure cancer
26:00 America's richest county is full of data centers
30:48 Who gets priced out of compute
33:05 The age of Elon and Jensen
34:25 Orbital data centers
44:40 Asteroid mining
48:12 Why Microsoft doesn't need a frontier model
54:02 Who becomes the abstraction layer
55:40 Everyone wanted a deity, Cursor wanted a product
1:00:25 Never take shots at Jensen
1:07:40 What happens when the chip doesn't work
1:12:10 What chip deals reveal about customer demand
YouTube: youtube.com/watch?v=FGC4ofTc…
@GavinSBaker @DavidGeorge83
180
269
3,451
6,405,961
Christian retweeted
Ok let’s try this again, calling all Bugbot power users. What are your biggest bugbot needs and feature requests?
8
6
40
7,230
Christian retweeted
We’re sorry to see that OpenAI put out a note saying they plan to block Cursor users from accessing OpenAI models in three months.
OpenAI models serve about 5% of Cursor user traffic, and we’re speaking with the OpenAI team to resolve this.
Cursor was one of the very first users of OpenAI, we’ve worked closely with their team for years, and we’ve trusted their platform to be neutral infrastructure for our business.
1,440
994
24,046
6,131,409
Christian retweeted
My real use cases for Grok Bot so far: 👇
I connected it to email. Now it filters the urgent stuff that actually needs my attention.
I connected it to Intercom and GitHub. Our help center docs update automatically when a PR changes the UI or ships a new feature.
I connected it to Cursor. Bug reports and feature requests from Intercom open the Cursor bot, and it starts triaging or implementing them. I can wake up to a PR. I basically hired another engineer.
I connected it to Slack. When customer support asks product questions: if it’s covered in our docs, the Intercom bot pulls it. If not, the Cursor bot jumps in and pulls answers straight from the code. I just opened up that knowledge base to the whole team.
I connected it to our CMS, Google Search Console, and Ahrefs. Now I’ve got a content engine that brainstorms ideas, builds a plan, and helps create content.
I connected it to X, set the keywords and topics I care about, and now I get a briefing every morning. I have a personal secretary.
I connected it to my bank statements. I can ask anything finance-related, revenue, spend, P&L, etc. No more QuickBooks, and honestly, I might fire my accountant.
I paid $200/month for Grok Bot.
Still expensive?
48
10
269
40,555
Christian retweeted
I'm fully adopting the xAI stack now 💪
cursor for coding
x for social
grok bot for tasks
grok for everyday chat
cost:
$200 cursor ultra
$40 x premium+
$0 grok bot, cursor ultra covers it
$0 grok, x covers it
$240/mo total
178
36
1,368
114,906
if your definition of coding is just the act of typing code, then yes, it's solved. agents can type code faster than anyone can.
but i think most of us think of coding as the act of solving problems with code. in that definition, coding is definitely not solved. agents can generate code quickly and build whatever you want, but without the right constraints, they end up causing more problems than they solve.
i think of this like my gardening analogy. agents are like magic beans that grow in any condition, but in wild and unpredictable ways. if you put the right scaffolding around them however, they can grow into whatever shape you want.
you still want humans thoughtfully and intentionally steering this outcome. we have a name for them: engineers. agents have greatly lowered the barrier to entry to doing engineering. what was once the domain of those who understood syntax and logic is now accessible to anyone who can explain their ideas. english is a new programming language for engineering outcomes with agents.
now, even ordinary people can build impressive software. and the good ones will learn engineering through first principles. as someone who is a self-taught programmer (i first learned how to code through Excel macros!), this is incredibly exciting to me, and i can't wait to see what everyone learns and builds in this new world.
let a thousand gardens bloom!
84
133
1,604
122,775