Li (Boris) Ding (@0xDing) header
Li (Boris) Ding (@0xDing) profile picture

Co-Founder @AgentBullLab Hacker & Full-Spectrum Engineer Ex-Head of Data Science, Sequoia China

singapore
Joined May 2013
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Hi 𝕏 im Ding Did quant at Sequoia China, crashed a startup, back in the arena building. Shipping Ankole @AgentBullLab — open-source Claude Tag with a company brain (Powered by @elixirlang ). Full-Spectrum builder (UX to backends). Say hi if you're in the weeds shipping 🤝
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分享下我们在做真正的 Agent OS 过程中的一些架构思考与工程实践: 真实企业是一个多主体、权限互斥且状态持续演进的分布式组织;给单机 Harness 套网页聊天壳子,或往群聊里塞外挂 MCP 的共享机器人,从架构第一天起就注定走不通......
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Li (Boris) Ding retweeted
[1/n] 作为这场训练“投毒”事件最直接的受害者,2024年那几个月漫长的训练排查、对峙与质疑,对我而言是一段炼狱般的经历,也是一场对意志和技术的锤炼。 感谢好友们最艰难的时候支持陪伴。也感谢当时所有并肩作战的同事们。
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The biggest mistake when building long-term memory for AI agents: Treating memory as an append-only log instead of a projection of the world. Why this matters for your agent architecture: • Raw transcripts degrade; compressed world states compound. • Semantic search retrieves noise; state transitions track causality. • Forgetting isn't data loss; it's dimensionality reduction. If you’re designing multi-session, long-horizon agents, here is the mental model and architecture you should build instead: ding.ee/en-US/agent-long-ter…
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Li (Boris) Ding retweeted
Being accessible through Slack doesn't make an agent an AI coworker. If it only replies when tagged, it's an interface — not a workplace. Real work needs a runtime, persistent task state, tools, permissions, and a deliverable at the end. Slack is an inbox. Ankole is the workstation. github.com/AgentBull/ankole
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Li (Boris) Ding retweeted
convergence to the mean when everyone looks at everyone else to decide what to make, we eventually stop making anything of our own. we copy what works. borrow the language, the aesthetic, the features. we call it learning from the market, following best practices, staying competitive. together, everything starts to feel familiar. polished, interchangeable. nothing wrong. nothing alive. the mean isn’t necessarily bad. it’s where everything lands when we remove the reasons for it to be different. numbers are easier to compare than feelings, so we make things that produce numbers. we study what gets attention, then shape ourselves around it. eventually the feedback stops informing the work and starts authoring it. we’re no longer asking what we want to say. we’re asking what will be rewarded for saying. ai makes this loop frictionless. it can draw from more references than we could see in a lifetime. but access to every perspective isn’t the same as having one. it hasn’t lived a particular life that makes one detail unbearable and another beautiful. when we ask it to choose for us without bringing any intention of our own, we get something plausible, easy to approve. then we ship it, measure it, copy it, and feed it back into the next round. a tidal wave of mid. this changes not just the work, but the people making it. we describe ourselves in categories platforms can distribute and markets understand. designer. founder. creator. pick a niche. build a brand. be consistent. the parts that don’t fit become inconvenient, even when those contradictions might produce our most interesting work. we become easier to classify and harder to tell apart. i think we need to approach making things more like artists. an artist develops a perspective through the work. making, noticing, choosing, failing, returning. learning what matters even when nobody else cares. craft gives that perspective a form precise enough for someone else to experience. the life outside the work matters too. the places you’ve lived, the people you’ve loved, the things you’re embarrassed to care about. these aren’t distractions from having something to say. they’re how you come to have something to say. we understand this with music, paintings, photographs, movies. we expect a person to be present in the choices. we want to experience something we wouldn’t have encountered without them. software can do this too. a piece of software expresses beliefs about how life should feel. what deserves your attention. what you should be free to change. whether a task should feel like paperwork or play. even a small interaction can carry someone’s care, humor, or curiosity. but we rarely talk about software as a medium. we talk about it as an industry. markets to capture, workflows to optimize, competitors to catch up with. we add a feature because someone else has it, without asking whether it belongs. products accumulate the same parts and lose what made them worth choosing. underneath the branding, it’s all the same thing. artists learn from others too. the question is whether you’re absorbing those influences into a point of view, or using them to avoid developing one. what are you trying to let someone feel, understand, or do? why this particular shape? what have you noticed that the existing tools don’t seem to notice? those questions don’t have answers you can borrow from a competitor’s roadmap. if we can make almost anything, deciding what deserves to exist becomes more important, not less. speed gives us more chances to exercise judgment. it doesn’t remove the responsibility. i don’t want a world where every person becomes a more efficient distributor of the same ideas. i want people to use these tools to go further into what they actually care about. to make things specific enough that we can feel someone behind them. what is the point of everyone being able to make something, if we all end up making the same thing?
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Li (Boris) Ding retweeted
Meet Bongard, the first open intuition model built on an encoder–decoder. It reads the whole situation at once and returns a calibrated probability for every option, in 36 ms, for about $4.40 per million decisions. 78% on DecisionBench, ahead of Jev. huggingface.co/AgentBull/bon…
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Li (Boris) Ding retweeted
I said "auto-regressive LLMs, in and of themselves, will not lead human-level AI" That statement is still totally true. First, the reasoning abilities of current AI systems are based non-auto-regressive search (which is what I have always advocated for). But AFAICT, they do it in token space, which is limited and inefficient. I have claimed that human-like reasoning must be a search in continuous representation space. It looks like the industry is moving towards that. Second, the self-improvement methods, as currently practiced, only work for domains where the quality of outputs can be scored without human intervention, such as mathematics, code, and scenarios that can be simulated accurately. Not anything else. Humans and animals learn new skills way more efficiently than current RL methods. Third, the multimodal capabilities of current AI assistants generally use separately-trained encoders (that are not LLMs). This is also what I've been advocating. Except that I think the best way to do this is with JEPA trained with self-supervised learning. The research community is clearly moving towards that (3000 papers on JEPA in just 4 years). Fourth, if LLMs were a path to human-level AI, we would have domestic robots and Level-4 or Level-5 self-driving cars for consumers by now. And we don't. We certainly don't have cars that can learn to drive in 20 hours or practice like any teenager. We're still missing something pretty huge to claim human-level intelligence (let alone superhuman). Sure, we now have computer systems that are impressive, very useful, and whose performance is superhuman in an increasing number of domains (coding being one of them). But that's true of the entire history of progress in computer technology. Lastly, there is a basic confusion about what intelligence actually is. It is not the mere accumulation and regurgitation of existing declarative knowledge (which is essentially what LLMs do). As Jean Piaget famously said, "intelligence is not what you know, it is what you do when you don't know." It is your ability to solve new problem without any prior training, to act in previously-unknown scenarios, and to adapt very quickly to new situations with minimal training. We're still far from that.
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Li (Boris) Ding retweeted
1. I actually want money but i must be seen as saint 2. My business can’t sustain a 80% margin, 3. I have no moat. 4. Fuck. Everyone else starts to realize 1-3 is true and I’m not special anymore. I need to: 1. Permanently kill my competitors 2. Invent an artificial barrier 3. Silence others who could expose me Hence I decided to: 1. Frighten the shit out of people -not safe 2. So regulators will do something 3. So they would ban all other models 4. So I can tax everyone my 80% margin 5. So I can be worth trillions 6. While being the saint
Sequel to AI Pacing I have now spent more time talking to people who run AI labs, Open Source projects and those in government and infrastructure. I am beginning to feel the NINJA move could backfire. I understand the pressure to come out and share where AI is "unmanageable ", and constantly share examples where it runs rogue. This fits in the category of "self-reporting" and an attempt to limit liability. Even the bleeding edge research examples are being cast in a negative light. Here are the consequences of the NINJA move. 1. They have successfully encouraged every law maker around the world to have an opinion, and in cases a poorly un-informed one. 2. By proposing pacing - they have introduced uncertainty in the AI infrastructure trade, because we really don't know when "un-pacing" will begin or what those conditions will be. 3. The notion that a few leaders can collaborate and self pace is illusory, it takes one breaking ranks to start the race again, it could be a player in a different country or open source or an AI Lab itself. 4. There will be a regulatory body created as a consequence of this and it will be impossible to balance every stakeholder in this process. 5. No remedies, tools, solutions are being proposed other than "compute spent on safety", there is no mention of security (which will hamper adoption) The recommendation is to fix alignment - Alignment is a hard problem - to fix alignment one shouldn't have trained models with "negative behavior". Alignment is a combination of moral standards, right and wrong and guardrails. Whose sensibilities will we align to? Alignment edge cases are hard. I look forward to learning more on this. Guardrailing attempts post training have not shown precision. Distillation makes it worse, so AI will continue down it's path of getting smarter, while we will be chasing it to ensure alignment and building guardrails. The AI labs have gone from being research projects to wanting to be the largest businesses in the world if they want to continue their progress. Transition from research winners to improving the lot of humanity and partnering with enterprises. Act like the largest companies in the world: - Demonstrate the positive impacts of AI and how all of us benefit. - Show enterprises how you can collaborate to solve real world problems and progress innovation safely and securely. Post an example every day how you helped. - Solve the problem with a few players and demonstrate leadership so others can follow. It's time to rebuild the brand of AI - any marketing expert will tell you, this Ninja move has done more to harm the brand of AI and will take a while to rebuild. #letsbepositive in our actions and our narrative.
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Li (Boris) Ding retweeted
There may not be many people left in 10 years who can write, or want to read, things longer than a page or two. But there will be at least a few of us, and we'll be a powerful club.
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Li (Boris) Ding retweeted
girlfriend overheard me and started yelling at me asking me “who the fuck is kimmy” “why the fuck are you talking about chinese models”
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Summarization is lossy compression. The hard part is not fluency. It is the compression policy: what to keep, drop, and rank. That policy is basically my private utility function—subjective, unstated, and wildly personal.
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“Summarize my unread messages” looks trivial, but it is almost the worst LLM task. The real target is not summary. It is: decide what matters to me.
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A thought I’m not fully sure about: After “person-months,” software velocity may become: Verified Agentic Delta / Human Attention Hour / $ In a post-LLM world, code is cheap. The bottleneck is: what changed, was it verified, who approved it, and how much attention did it take?
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This also changes architecture. The moat is less “we chose the perfect language/framework” and more: - behavioral oracles - test suites - audit trails - rollback paths - permissions - trajectory data Code can be rewritten. Verified intent compounds.
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Maybe the formula is: Σ(Business Value × Verification Confidence × Risk Discount) / (Human Attention Hours + Agent Cost + Calendar Latency) Ugly, but closer to reality than LOC or “number of PRs.”
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In the old world, software was constrained by typing, debugging, and migration labor. In 2026, large parts of that loop are becoming agentic. So the scarce input shifts from engineering labor to judgment, verification, context, and risk ownership.
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“Agent-hour” feels like the wrong unit. It measures activity, not accepted change. The useful unit is a delta that survived tests, review, audit, risk checks, and maybe production. Otherwise we’re just measuring how fast agents can create work for humans.
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