Danqing Wang retweeted
It's a weird time. I am filled with wonder and also a profound sadness.
I spent a lot of time over the weekend writing code with Claude. And it was very clear that we will never ever write code by hand again. It doesn't make any sense to do so.
Something I was very good at is now free and abundant. I am happy...but disoriented.
At the same time, something I spent my early career building (social networks) was being created by lobster-agents. It's all a bit silly...but if you zoom out, it's kind of indistinguishable from humans on the larger internet.
So both the form and function of my early career are now produced by AI.
I am happy but also sad and confused.
If anything, this whole period is showing me what it is like to be human again.
Danqing Wang retweeted
In 1980, political scientist Robert Axelrod invited the world's top strategists to a competition. The goal: find the single best decision-making strategy for life. The winner stunned everyone ↓
Axelrod designed a tournament based on the prisoner's dilemma — a situation where two people benefit from cooperating, but each has incentive to betray the other.
The rules were simple:
• Both cooperate → 3 points each
• One defects, one cooperates → defector gets 5, cooperator gets 0
• Both defect → 1 point each
Each program played 200 rounds against every other program. Highest total score wins.
14 programs were submitted from leading theorists worldwide — each designed to maximize its own score by any means necessary.
Some were cunning — probing for weakness, then exploiting it. Others mixed in random betrayals to confuse opponents. Many were calculating, aggressive, and complex.
Axelrod ran the tournament.
Then ran it again. Five times over.
The same winner emerged every time:
Tit-for-tat.
One of the simplest programs submitted.
Its entire strategy ↓
1) Start by cooperating
2) Copy your opponent's last move
3) If they defect, defect back immediately
4) The moment they cooperate again, forgive and cooperate
That's it.
Axelrod ran a second tournament. 62 programs entered. To mirror real life, games now ended at random — no one knew which round would be their last.
Tit-for-tat won again.
But here's the most counterintuitive part:
Tit-for-tat never beat a single opponent head-to-head. It could only tie or lose. Yet across all matchups, it accumulated the highest total score.
Why?
Axelrod identified four traits that made it unbeatable:
• Nice — it never defected first
• Retaliatory — it punished betrayal immediately
• Forgiving — it restored cooperation the moment an opponent did
• Clear — opponents understood its pattern and chose to cooperate
Meanwhile, 'nasty' strategies dragged each other into defection wars — destroying themselves in the process.
In messier simulations, an even more forgiving tit-for-tat emerged. It occasionally forgave defections without retaliating — and outperformed the original.
Axelrod wrote in The Evolution of Cooperation:
"What makes it possible for cooperation to emerge is the fact that the players might meet again."
The real-world lesson:
Leading with cooperation isn't weakness — it's the most effective long-term strategy. Holding grudges weakens you. Forgiveness strengthens you. But no consequences guarantees exploitation.
The optimal approach: be kind, be clear, be forgiving — but never be a pushover.
A strategy obsessed with winning every interaction often loses overall. One focused on sustained cooperation wins.
—
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Danqing Wang retweeted

Andrej Karpathy@karpathyJan 26
🇺🇸
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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.
Danqing Wang retweeted
Okay. Thanks for the nerd snipe guys. I spent the day learning exactly how DeepSeek trained at 1/30 the price, instead of working on my pitch deck. The tl;dr to everything, according to their papers:
Danqing Wang retweeted
Two Chinese streamers sold 18 billion RMB (about $3B) worth of goods in a half-day livestream yesterday.
🤯🤯🤯
That's almost as much as Twitter's annual revenue ($3.72B).
Danqing Wang retweeted
🚨 NFT Giveaway 🚨
@CyberPunk_3d is giving away 3 NFTs for their public launch.
To Enter:
1) Like
2) Retweet
3) Tag two friends
4) Follow @CyberPunk_3d
I will choose in 24 hours.
#NFTGiveaways #nftcollector #NFTCommunity #NFT #NFTdrops #nftart
Danqing Wang retweeted
Do you know any designer or frontend eng obsessed with UX, is always willing to go the extra mile, and gets upset with anything less than pixel perfect?
We are looking for that person to join the Luma team, and will pay you $15k if we hire someone you refer (incl. yourself)!
Danqing Wang retweeted
So... everyone seems to think clubhouse is the "next big thing" - but I think it's going to fail.
Here's how I think it all goes down..
Danqing Wang retweeted
The best filter for new founders is whether they can hustle a .com domain name:
If you can’t board a plane to convince Bob & Judy in Oklahoma to give up a $200,000 asset for pennies on the dollar, you’re probably not cut out for starting a company.
Danqing Wang retweeted
I thought I’d never see a place where people can talk about themselves more than on Twitter...
And then I joined Clubhouse.
Danqing Wang retweeted
📖 Creator economy reading list
As we kick off 2021, here's a roundup of my favorite Creator Economy / Passion Economy reads from 2020, covering:
• State of the market
• State of creators
• Trends and insights
• Platform strategy
👇
Danqing Wang retweeted
THREAD: Here are 10 insights I've learned over the last 5 years from cofounding and selling business.
On startups, investing, marketing, and career advice:
👇👇
Danqing Wang retweeted
I'm often asked about academia vs industry. When I was myself considering Google vs UT Austin in 2005, I asked it of Peter Norvig, who said:
"I've always looked for what is the most important, most rewarding thing I could be working on, and worried less about where it is."
Danqing Wang retweeted
Prof. Evelyn Hu has been named to the @WHOSTP and @ENERGY National Quantum Initiative Advisory Committee: buff.ly/2FYDZQK
Danqing Wang retweeted
Shopping while traveling in Europe? Check out how much you can save and how with this VAT refund calculator! producthunt.com/posts/europe…
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Gallium nitride is the silicon of the future theverge.com/2018/11/1/18051…





















