Crafting Decision Superintelligence (DSI) 👾 I train models and eval | Prev @Wallet @AlibabaGroup | @hkust PhD Quit @Johnshopkins Alum | @0xUclub
Joined May 2022
- Tweets1.6K
- Following2K
- Followers1.9K
- Likes1.5K
Wishing you all the best in your next chapter, Haider! I’ve felt that you brought something truly unique to the company, with an incredible sense of taste in marketing and brand, a vibe that felt different from the rest.
After almost seven years at @OKX, I am stepping back from my role to take a meaningful break, focus on my family and personal priorities, and make space for what comes next.
It has been an incredible privilege to help build the @OKX brand and see its growth. I am deeply grateful to Star Xu @star_okx for his trust, partnership, and the opportunities he created for all of us.
And thank you to every OKX employee I have had the privilege to work alongside. I am proud of what we built together and excited to see the company continue to grow.
This is not goodbye. It is simply the next chapter.
Welcome to the decision intelligence family!
You already gave the LLM the answer. So why is it writing it out from scratch?
Introducing a new AI model architecture: Constrained Decoding Models (CDMs)
LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable.
CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive.
Not just a decision model, but a family of 3 CDMs live today:
• Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection.
• Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction.
• Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests.
Built for production:
• 1M tokens of multimodal (text + image) input, 31x larger than Jev
• 77.2% on DecisionBench vs Jev's 72%
• 200x faster, 467x cheaper than frontier LLM APIs
• $0.05/M input tokens. Output is free (yes, even for summarize)
Live on your favorite platforms today:
@OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus
Get 50M input tokens for free (for the first 50 hours) at link below 👀
Yishu🌴 retweeted
You already gave the LLM the answer. So why is it writing it out from scratch?
Introducing a new AI model architecture: Constrained Decoding Models (CDMs)
LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable.
CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive.
Not just a decision model, but a family of 3 CDMs live today:
• Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection.
• Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction.
• Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests.
Built for production:
• 1M tokens of multimodal (text + image) input, 31x larger than Jev
• 77.2% on DecisionBench vs Jev's 72%
• 200x faster, 467x cheaper than frontier LLM APIs
• $0.05/M input tokens. Output is free (yes, even for summarize)
Live on your favorite platforms today:
@OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus
Get 50M input tokens for free (for the first 50 hours) at link below 👀
🤖 Made with AI
Yishu🌴 retweeted
The economics of a Neolab.
A neolab is loosely defined as a startup of AI researchers who raises a lot of money pre-production to be able to finance GPU compute to take on a large AI problem.
To buy 1000 GB300s or ~14 NVL72 racks will set you back $125-150M for 3yrs with 15-30% upfront. That’s about ~2-2.5MW.
Thats about enough to do 10^25 flops a quarter and get to a GPT-4 level model which is 1-2 OOMs off frontier for pretraining.
If you post-train on a great open source model, you have a better chance of getting to frontier. The risks are a) you need to spend millions on RL environments too and b) being lapped by another model release while being tied to a base model.
For this to payback, you need to give your customers a better and ideally cheaper inference service than a base model and serve them for long enough to recoup your large investment. Even at 50% margin on inference, to recoup $10M in training means serving ~10T tokens (!) if you price like Fable / Astra given a standard cache read / input / output split ($2/M blended). And you have to justify being better than a release like Opus 5.5 which is even cheaper. Often, you end up charging your customers a huge premium in terms of platform fees and compute fees on top of pure inference.
Meanwhile, every hour you’re not utilizing your GPUs you are burning money so you typically resell this compute back to a broker or run inference for open models / resell spot instances. At below a ~60% utilization on spot, you will still lose money.
Add to that insane cost of talent.
So what can you do with the compute?
- Not play the model game at all.
- Play an entirely different model game (Jev, World Labs) that if big labs played, would either a) cannibalize their business or b) be incrementally not significant revenue c) would cause too much distraction from the main main thing
- Acquire a proprietary data set (Peridodic Labs) in enough volume in a domain of usefulness to eclipse frontier quality. Often happens in robotics, biology, chemistry.
If you do overcome the challenge of building a model that is useful and well priced beyond big labs models, given the huge price of compute, you still need to play in an area where the revenue / compute ratio is signficant and market demand is large enough to payback your compute spend.
It is a difficult game.
That’s bargaining agent, I was doing evaluations and post-training on these matters for @AlibabaGroup last summer.
How important is AI to Meta? Here’s one easy tell:
One of the launch features of the Muse app was auto-negotiating for stuff on Facebook Marketplace.
The poor product manager who owns Marketplace knows that millions of bots lowballing sellers will eventually kill the product.
But this was escalated to Zuck by the Muse team and he said: “OK, we’ll poison Marketplace…but just a little.”
Yishu🌴 retweeted
my companies have accumulated $1.4B in market cap over the years.
i've raised in every kind of market there is.
in 2017 my investors made 30x in a year.
earlier in my career i built a unicorn that didn't survive.
i've had pitches that wouldn't land, products that wouldn't sell, and rounds that closed in under 10 days.
every situation is different. a few things hold.
1. raising is sales. you need 10, 20, 50, 100 calls to hone the pitch. there is no version where you nail it on call three.
2. raising is sales. every pitch gets cut for the person across the table. ai is very good at this now, so there's no excuse for a generic one.
3. raising is interviewing for a job. you'll be better in round 30 than round 1. order your meetings accordingly.
4. you need a deck. but with ai now manage it as a live artifact, not a pdf. living, breathing, never stale.
5. data rooms are dynamic now too. every question any investor asks goes into the deck and the dd room in real time, for everyone.
6. ship fast and furious always, but especially during a raise.
7. traction. of any and every sort. momentum drives the raise.
8. raise on your schedule, not theirs. everyone appreciates a well organized process.
My GPT6-astra just pass Slay the Spire 2 on its hardest difficulty, A10 and NoSL.
Honestly, this results surprises me greatly. Fable and GPT5.6 can’t pass act 2 in previous run.
I’m building environment and bench around in-game decision-making. If you’re interested, DM me!
Judgement and decision making are not exclusive to human beings 👨💻
glad to see ur guys sharing the training recipe for AI forecasting
We've shared research on fine-tuning forecasters, now you can try it for yourself: a cookbook recipe for training a model to predict event probabilities given reference information.
Start with the provided dataset from @ProphetArena, then try your own.
github.com/thinking-machines…
Quite a coincidence — I was actually in the room.
Right after the host asked the question @GenAI_is_real mentioned here, I got to ask @cz_binance another one:
“How do early-stage founders attract the best people? And when the founders are all engineers, how do you attract top talent from completely different fields like marketing or design?”
His answer was: you need to sell the vision and have founder charisma to attract them,
Early on, people join because they believe in the future you’re describing and in the founders themselves. For younger talent, referrals from friends matter a lot too.
And having a one-dimensional founding team isn’t necessarily a problem at the beginning — just start building. He mention the example that google was founded by engineers too. Eric Schmidt joined much later.
I came across a conversation CZ (Changpeng Zhao, founder of Binance) had at a book club event in Hong Kong today, and found it deeply inspiring:
"A lot of students at the event wanted to join Binance, and his answer was just two words: be proactive. Binance is fully remote — you can slack off, and losing a few hours a day is easy. But if you're not proactive enough, two months down the line you may have nothing to show for it."
"I touch a lot of things, jumping from one to the next, and some things I forget right after I've mentioned them. I generally don't track people. If someone doesn't proactively come to me with updates, I'll simply forget. But maybe two months later it comes back to me and I ask — and if the answer is 'we worked on it for a week and then it went nowhere,' I'll fire that person."
"I never follow up. I need them to push updates to me."
He summed it all up in one verdict: "If you proactively go after something, you will succeed. If you only act when your boss comes around to check on results, you won't."
The fun part: the host drove the point home on the spot — everyone sitting in that room had been selected only after personally emailing the organizers again and again and submitting questions. Proactivity itself was the ticket in.
CZ further broke proactivity down into two deeper underlying principles:
80% is showing up. In life, 80% of success comes from showing up. You have to be in the game — if you don't participate, there's no opportunity at all.
What you don't do matters more than what you do. Most people today never think about what not to do. Someone sends them a message, and they feel obliged to reply... So many of these things could be cut entirely, yet people never cut them.
I read CZ's book Freedom of Money a while back and was deeply inspired by it. In my view, growing an open-source community demands the same enormous proactivity: proactively initiating projects, proactively seeking collaboration, and owning the final outcome. Proactivity is a tremendous driving force.
And at the same time, once you've proactively taken on the responsibility, the corresponding authority naturally follows — after all, matching authority with responsibility is the basic principle that keeps our community thriving over the long run.
Someone who can drive a project end-to-end, cleanly from start to finish — to me, that's the strongest calibration there is. Even without any AI infra experience, anyone with that kind of proactivity is a friend absolutely worth collaborating with.
Yishu🌴 retweeted
2/ Train yourself to go from 10 to 40 to 100 hours of focused work a week. I did this for months building an automated trading system from scratch. 5am-10pm 7days/week
99% of people need #WorkLifeBalance or whatever, and that's great. But I'm talking to the people on a mission.
优美的UI，强大的harness. 我震惊地瘫坐在椅子上
What if AI could understand why assets move—not just how prices move?
HedgeFit is an early research project exploring whether models can understand the relationships between events, narratives, and assets—and use them to find hedges in prediction markets.
Thanks to the rich market universe created by @Polymarket and @Kalshi, this experiment is possible.
Please check it out:
youngy620.github.io/hedgefit…
Open source:
github.com/YoungY620/hedgefi…
I'm researching whether LLMs can build an understanding model of the world. More experiments in this research direction to come.
恭喜manus赎身成功. 不过这也太巧了，我今天正好穿着manus的T恤
A Note to Our Users
Manus will soon resume operating as an independent company.
As part of this transition and to comply with regulatory requirements in specific jurisdictions, some users will need to back-up their data before 7:59 a.m. on August 23, 2026 (SGT) and then begin restoration starting from 8:00 a.m on August 25, 2026 (SGT) to ensure normal usage.
More information can be found in the blog post and we’ve provided detailed guides, including to understand if you’re affected, in our Help Center
For affected users, these are the key details:
We’ll notify you by email and in the Manus app. If you signed up with an Apple ID or Facebook account, please check your in-app notifications.
Back up your data from now until August 23 (SGT) before 7:59 a.m. You can back up more than once, you can create new data after backing up, just run it again.
Starting from 8:00 a.m. on August 25 (SGT), the restoration portal will open. Restore your data and pick up right where you left off.
Affected users will not be charged during the transition, and we will be providing welcome back bonuses.
If your account is not affected, you don’t need to do anything - keep using Manus as usual. You’ll get an in-app notification confirming this.
For those affected, we know this is disruptive and we’re sorry for the inconvenience. Supporting you through it is our top priority. In addition to the full guides in the Help Center, our customer service team is ready to help 24/7 with any questions.
As we look ahead, we couldn’t be more excited by the future. We’re preparing a series of new features that will push the boundaries of what’s possible for general AI agents once again.
Thank you for being with us as we go through this process together. The best is still ahead.
感谢 Kyle！一直觉得你很有 vision，不管是投资还是做业务，对机会的判断都很前瞻，respect 🫡
很巧，我也是去年的这个月份，被本科一起做 @0xUClub 的好朋友 @marosupply 内推来团队面试的，他在团队里一直很优秀，我有幸加入也多亏了他的credit. 还有之前在隔壁组的@KevinZbtc 老师，投递/面试前我都有严肃请教。
因为感觉在团队里成长很快。我也强烈安利优秀、愿意迎接挑战的朋友来投递. 可以DM老板或者点击Kyle推文里的链接
优秀又谦虚的小伙子 @issue_wong 感谢这段时间的投入和贡献！
离开 OKX 去追求新的可能，需要勇气；留下来继续建设、坚持，也一样需要勇气。尊重每一个选择，祝福你接下来在 AI 方向的新探索！
Boost & Growth 团队也继续猛猛干活💪 Boost 产品会持续高频更新优化，进一步支持 X Layer 生态建设
预测市场 Season 2 也上线在即，预计 8/17 和大家见面！
另外，欢迎优秀又有热情的同学加入我们 🚀
job-boards.greenhouse.io/ogr…
谢谢 Christy！今天在office交还工卡的时候专门去道了别，也借这个机会说两点我觉得在Christy和 @zt314933960 的团队里特别好的地方：
她很愿意让每个人沿着自己擅长、感兴趣的方向去探索新的业务，而不是把人限制在一个窄的方向里
同时，真正决定要做的事情，她也会努力地帮团队争取资源、协调内部合作，给大家有足够大的空间把事情做成。
对一个职业生涯早期的同学来说，能有这么大的 scope 和信任其实非常难得。这也是web3的魅力时刻了，据我观察，会比互联网大厂要灵活开放不少
明天是我在OKX @wallet 的最后一天
过去半年，我作为预测市场的产品经理，参与了整个产品从0到1的建设，主要负责数据分析和市场生命周期（上新、开放交易、结算等）
很幸运能够和团队一起，把这个产品真正做起来。世界杯期间，我们累计吸引了40万+用户参与，峰值每日交易人数超过7万。
回顾这段经历，我最大的感受是团队的人才密度真的非常高。整体的产研节奏非常 intense
预测市场本身是一个需要创新解决方案的业务，很多问题都没有现成的答案，需要团队快速迭代。当一个新的交易产品day1就要面向全球市场，并且 24/7 持续运行时，团队在上线初期需要始终保持在紧绷的状态，及时处理各种线上问题，保证稳定运行。这也是所有上规模的crypto业务都面临的挑战
如果再考虑到业务的增长速度，我认为它对团队成员综合能力的要求，实际上超过了绝大多数互联网大厂的普通业务线。
很多朋友可能不知道的是，在过去的这一年里，我在工作之外还有些很重要的update
1) 我在4月收获了🎓港科大的全奖PhD offer，很感激教授和committee对我的认可。虽然我最终决定不入读，这对我个人仍然是个非常振奋的milestone！
2) 我开始大量地💻用AI进行编码：我的论文在ICML和IJCAI的workshop上得到了接收，这对我而言也是个巨大的突破
接下来一段时间，我会尝试些和ai有关的新探索。也有段时间没在推特上和大家密切地交流了。欢迎大家关注，让我们保持联系！