Alan Duong

Co-Founder @ (YC S25). Prev. built Rice CS, NASA, founding engineer.

[OpenAI @OpenAI]

Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946.

For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids.

An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better.

This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.

[Alan Duong @dope_alan]

@jasonkneen @amirmxt Does this follow your cursor and point to stuff for you?

[Nikunj Kothari @nikunj]

https://t.co/bFSeKGdhec

[Alan Duong @dope_alan]

@nicholasychua Interested!

[Sam Altman @sama]

First, the good part of the Anthropic ads: they are funny, and I laughed.

But I wonder why Anthropic would go for something so clearly dishonest. Our most important principle for ads says that we won’t do exactly this; we would obviously never run ads in the way Anthropic depicts them. We are not stupid and we know our users would reject that.

I guess it’s on brand for Anthropic doublespeak to use a deceptive ad to critique theoretical deceptive ads that aren’t real, but a Super Bowl ad is not where I would expect it.

More importantly, we believe everyone deserves to use AI and are committed to free access, because we believe access creates agency. More Texans use ChatGPT for free than total people use Claude in the US, so we have a differently-shaped problem than they do. (If you want to pay for ChatGPT Plus or Pro, we don't show you ads.)

Anthropic serves an expensive product to rich people. We are glad they do that and we are doing that too, but we also feel strongly that we need to bring AI to billions of people who can’t pay for subscriptions.

Maybe even more importantly: Anthropic wants to control what people do with AI—they block companies they don't like from using their coding product (including us), they want to write the rules themselves for what people can and can't use AI for, and now they also want to tell other companies what their business models can be.

We are committed to broad, democratic decision making in addition to access. We are also committed to building the most resilient ecosystem for advanced AI. We care a great deal about safe, broadly beneficial AGI, and we know the only way to get there is to work with the world to prepare.

One authoritarian company won't get us there on their own, to say nothing of the other obvious risks. It is a dark path.

As for our Super Bowl ad: it’s about builders, and how anyone can now build anything.

We are enjoying watching so many people switch to Codex. There have now been 500,000 app downloads since launch on Monday, and we think builders are really going to love what’s coming in the next few weeks. I believe Codex is going to win.

We will continue to work hard to make even more intelligence available for lower and lower prices to our users.

This time belongs to the builders, not the people who want to control them.

[Elon Musk @elonmusk]

This @waitbutwhy illustration is 👌

[Andrej Karpathy @karpathy]

A few random notes from claude coding quite a bit last few weeks.

Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent.

IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits.

Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased.

Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion.

Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage.

Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building.

Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it.

Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements.

Questions. A few of the questions on my mind:
- What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*.
- Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro).
- What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music?
- How much of society is bottlenecked by digital knowledge work?

TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

[Garry Tan @garrytan]

It’s the golden age for startups that turn latent LLM capability and intelligence into actual adoption

Will be like working on mobile apps in 2009 or the web in 2003

[Alan Duong @dope_alan]

@LottielabHQ burp

[Jon Xu @xuster]

Congratulations to all the YC S25 founders! What a Demo Day!

[Connor Rothschild @CL_Rothschild]

Happy to have helped with some web elements for Parallel's recent launch. Check out @p0!

[Alan Duong @dope_alan]

@CL_Rothschild @p0 The human / machine switcher is insane. Amazing

[Alan Duong @dope_alan]

In 2023, I dropped out of school, booked a ticket to SF, and slept on the floor of a laundry room to build. Today, I’m in Y Combinator.

More to share soon, but for now, check out our launch!

[Y Combinator @ycombinator]

Cyberdesk (@CyberdeskHQ) is the computer use agent for developers to automate legacy Windows apps. Customers in healthcare, finance, and more use it to automate EHRs and accounting— combining reliable memorized steps with smart fallback during popups.

https://t.co/kiDmUrZL4P

Congrats on the launch, @dope_alan & @mahmoudalmadi_!

[Pietro @pietrozullo]

Built a CLI that talks to GPT-4, Claude, Gemini & 10+ other LLMs from one terminal 🚀

Tired of switching between different chat tools? mcp-use-cli lets you /model hop between providers instantly.

npm i -g @mcp-use/cli && you're done ✨

[Miles Feldstein @MilesFeldstein]

Introducing 👊 $BUMP rewards

Make a fresh IG or TikTok, crank out videos in < 3 minutes with our AI Content Studio, and get rewarded in $BUMP as your posts racks up views.

Zero follower minimum - just create, post, get $BUMP.

[Mahmoud Al-Madi @mahmoudalmadi_]

🚀 @CyberdeskHQ is an open‑source API that spins up cloud desktops in 5 seconds and lets your AI agents click, type & automate anything on those desktops.

Why it matters
• Send keystrokes & mouse events over REST via our SDK
• Perfect for AI employees, RPA, data extraction & QA

🔥 We hit #17 on Product Hunt this morning—help us crack the top 10!

🎥 1.5 min demo below. If you like, we'd love your support in the ProductHunt launch!

🚀 ProductHunt Launch: https://t.co/BPYEFZlo0y
🔗 Cyberdesk Website: https://t.co/Q4F2bHRjit
📘 Docs: https://t.co/KQA6HOtq68
💻 GitHub Repo: https://t.co/FW3Kx8GYdb

[Alan Duong @dope_alan]

@jdnoc @jordanrstout @aidenybai Have you used KubeVirt within all of this before? Super curious about your experience. Just asked ChatGPT and it told me that KubeVirt isn’t natively working on GKE so I have to use GCE and manage K8s myself.

Seems like a whole mess lol

[Alan Duong @dope_alan]

@jdnoc @jordanrstout @aidenybai Wait so K8s can scale the number of nodes in the cluster automatically? I had the impression that K8s only just works with what it has. And that what it “autoscales” is the number of Pods on those existing nodes (based on CPU/RAM usage on existing Pods).

Could I DM you?

[Alan Duong @dope_alan]

@ln_dev7 @shadcn @linear awesome stuff, i was your 500th star btw :)

[Alan Duong @dope_alan]

@0xPaulius The onboarding survey is broken btw, I click "Finish" and it does nothing. Excited to try this though!