Chip Huyen
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29 March
From one piece Chip Huyen: Building when it feels like there's nothing left to build - The Pragmatic Summit 14 beliefs, in the piece's order there
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Their words
And the question nowadays is less about like how to build because if you can describe the problem in the solution you want, usually AI can do it. Maybe not today, but maybe like two or three years from now on they can do a lot of those. The question is like what to build because we talk about like yes, if something exists, right? AI can replicate it.
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korrents.com
People no longer read books the way books assume they will — they jump around, whatever they tell the author.Their words
Like I I write books and as much I wish that books as a format will be like will survive. I do think that people don't read books the same way anymore.
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Their words
And I feel like it's a very human way of doing web search, right? Because we enter things in the Google and we see all the like citations, the quotations part. But like why would we limit AI to that? If AI already visit a page, why don't just pull the entire page out?
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You cannot just reverse that, right? It's just not working. So, I do think that we need to build out the whole guardrails for the reversibility of actions because that actually where things get really really scary.
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So, I think that the whole workflow of like reviewing code is very outdated. Like, I don't think the I think that the senior member, instead of like giving feedback on the code, they should be giving feedback on like how you give instruction to AI to produce better.
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So, I I think there's a lot of things we still try to imagine. What would be an AI-driven world look like, right? I think people are trying to like retrofit what already exists to fit what they think is a new workflow.
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But in Vietnam and also in a lot of other Asian countries, people are on the move all the time. Like people are on the motorbike all the time. So, they actually really don't like typing. So, the voice like a lot of the companies in Vietnam actually deploy voice bots before they do uh do chatbot.
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Their words
I don't think that human preference is just an equation that people can just like package nicely and like, "Hey, ask people like which which of these two answers people would prefer?" It's very very personal, very culturally dependent, geographically dependent, age dependent.
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korrents.com
The problems worth a small builder's time are the ones too small for a big AI company to be motivated to solve.Their words
Like if it's a big problem, like everyone can see, then all these big companies will get into it. But whereas it's like there are a lot of problems that's like smaller, then maybe OpenAI won't be motivated to solve it, but maybe I can like all a lot of people can.
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korrents.com
Problems follow a long tail: AI will keep getting better at the common ones, and the edge cases will never go away.Their words
So, I think of them as the top of the long long tail long tail problem. Like, something very common issues that a lot of people experience. AI will get really good at it. And over time AI will cover more more edge cases, but the edge cases will never go away.
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Their words
The product you build is just solving a problem. So, is that like is the more you do that, the better you become at problem solving. And one thing I do believe that it will never change that there will always be problems to solve.
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And the more you build, right? The more you put things out there, you remove the need for imagination. You can say, "Okay, now I like that website. Do that for me."
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Their words
if you can describe a style, AI can generate it. And the same with software. If you can describe a software, then AI can build it for you.
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Their words
I feel like now I can build anything I want. But at the same time, anyone can build anything I want. So, what is the incentive structure for me to do anything?
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Recommendsrcmnd.app
The Selfish GeneTheir words
So one thing is a selfish gene that's like understand uh it actually changed uh it actually helped me with the question like whether I want to have kids or not.
22 October 2025
8 August 2025
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16 January 2025
From one piece Common pitfalls when building generative AI applications 3 beliefs, in the piece's order there
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AI judges used to evaluate AI outputs need ongoing validation and iteration, just like any other AI system.Their words
AI judges must be evaluated and iterated over time, just like all other AI applications.
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Building a working AI demo is far easier than turning it into a reliable production product.Their words
This is perhaps one of the first painful lessons anyone who has built an AI product quickly learns. It's easy to build a demo, but hard to build a product.
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Their words
Because everyone uses the same models nowadays, the AI components of AI products are similar, and the differentiation is product.
7 January 2025
From one piece Agents 3 beliefs, in the piece's order there
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Personally, I would trust a self-driving car more than the average stranger to give me a lift.
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Most real-world AI agent systems are actually multi-agent systems made of multiple components.Their words
Because most agentic workflows are sufficiently complex to involve multiple components, most agents are multi-agent.
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Planning is fundamentally a search problem: explore paths, predict outcomes, and pick the best one.Their words
Planning, at its core, is a search problem. You search among different paths towards the goal, predict the outcome (reward) of each path, and pick the path with the most promising outcome.
25 July 2024
From one piece Building A Generative AI Platform 3 beliefs, in the piece's order there
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AI models are probabilistic, making their outputs unreliable.
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In-context learning, learning from the context, is a form of continual learning.
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Context construction for foundation models is equivalent to feature engineering for classical ML models.
17 April 2024
From one piece Measuring personal growth 3 beliefs, in the piece's order there
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Under uncertainty, the best decision is the one that preserves the most future options.Their words
In the face of uncertainty, I lean towards the decision that would give me the most future options.
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Growing older tends to expand rather than shrink the number of achievable dreams and options.Their words
As I grow older, I have more dreams. I now know many things that I didn't know before, and I have access to more resources than I ever did.
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Their words
I have this theory that life has a circadian rhythm. Every 3-6 years, you become a different person.
14 March 2024
From one piece What I learned from looking at 900 most popular open source AI tools 2 beliefs, in the piece's order there
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Their words
Several people have speculated that we'll see many very valuable one-person companies (see Sam Altman's interview and Reddit discussion). I think they might be right.
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korrents.com
The lower a layer sits in the software stack, the harder it is for an individual developer to build in it.Their words
the lower we go in the stack, the harder it is for individuals to build.
28 February 2024
From one piece Predictive Human Preference: From Model Ranking to Model Routing 3 beliefs, in the piece's order there
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Their words
One pattern is that for simple prompts, weak models can do (nearly) as well as strong models. For more challenging prompts, however, users are much more likely to prefer stronger models.
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Their words
With more and more models being developed, each with different capabilities and a cost structure, model routing has clear economic values.
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korrents.com
Routing prompts to different AI models can improve response quality while lowering cost and latency.Their words
Model routing has the potential to increase response quality while reducing costs and latency.
2 February 2024
16 January 2024
From one piece Generation configurations: temperature, top-k, top-p, and test time compute 3 beliefs, in the piece's order there
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Their words
I suspect that in the future, it'll be easier to get models to output exactly what we need with minimal prompting, and these techniques will become less important.
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Their words
The optimal thing to do with a fickle model, however, is to swap it out for another.
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This probabilistic nature makes AI great for creative tasks.
10 October 2023
From one piece Multimodality and Large Multimodal Models (LMMs) 3 beliefs, in the piece's order there
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Their words
A model that can effectively learn from bitstrings or bytestrings will be very powerful, and it can learn from any data mode.
Multimodality and Large Multimodal Models (LMMs)huyenchip.com
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korrents.com
Image is the most versatile input modality for a model, since it can represent text, tabular data, and audioTheir words
Image is perhaps the most versatile format for model inputs, as it can be used to represent text, tabular data, audio, and to some extent, videos. There's also so much more visual data than text data.
Multimodality and Large Multimodal Models (LMMs)huyenchip.com
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korrents.com
Text is a more powerful output modality for a model than images, because text supports many downstream tasksTheir words
Text is a much more powerful mode for model outputs. A model that can generate images can only be used for image generation, whereas a model that can generate text can be used for many tasks: summarization, translation, reasoning, question answering, etc.
Multimodality and Large Multimodal Models (LMMs)huyenchip.com
8 May 2023
15 April 2023
27 December 2022
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Their words
I spend a lot of time thinking about modeling computational systems, and this book introduced me to modeling social systems.
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Their words
Clarke is best known for his science fiction (2001: A Space Odyssey), but his science nonfiction is fascinating too.
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Likedrcmnd.app
Fads and Fallacies in the Name of ScienceTheir words
Despite being written 70 years ago, the book is still insanely relevant today.
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Recommendsrcmnd.app
Fooled by Randomness: The Hidden Role of Chance in Life and in the MarketsTheir words
Taleb’s way of writing probably upsets a lot of people, but if you can get over it, it’s definitely worth a read.
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Lovedrcmnd.app
How Not to Be Wrong: The Power of Mathematical ThinkingTheir words
A wonderful, easy-to-read introduction to applied math and probability.
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Likedrcmnd.app
Uncommon Genius: How Great Ideas are BornTheir words
A collection of interviews with 40 recipients of the MacArthur Fellowship (the genius award) gave me a different perspective on the creative process.
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Recommendsrcmnd.app
The Design of Everyday ThingsTheir words
A must read for anyone who cares about functional design, from the first User Experience Architect at Apple.
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Likedrcmnd.app
Just My Type: A Book About FontsTheir words
We know that fonts can influence how we perceive that text. This book explains why and how we can better utilize this power.
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Likedrcmnd.app
The Making of the Atomic BombTheir words
A lengthy but fascinating book on the development of nuclear weapons, which has a lot in common with the development of AI today.
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Lovedrcmnd.app
Chaos: Making a New ScienceTheir words
I remember being blown away the first time I read the book.
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Likedrcmnd.app
The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the InternetTheir words
An amazing overview to the history of cryptography.
16 November 2021
13 September 2021
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Their words, before
Things I’d prioritize learning if I was to study to become a ML engineer again: 1. Version control 2. SQL + NoSQL 3. Python 4. Pandas/Dask 5. Data structures 6. Prob & stats 7. ML algos 8. Parallel computing 9. REST API 10. Kubernetes + Airflow
Their words now
When I wrote that tweet, I believed that Kubernetes was essential to the DS/ML workflow. This sentiment came from the frustration at my own job – my life as an ML engineer would’ve been much easier if I was more fluent with K8s. However, as I learned more about low-level infrastructure, I realized how unreasonable it is to expect data scientists to know about it.
22 December 2020
11 November 2020
27 December 2019
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