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LLMs are not perfect at writing software. In fact, they plain suck in some respects compared to strong human engineers. I broke down why in a video... and how Convex's design makes LLM-generated code actually reliable. The key: feedback, local reasoning, meta minimization.

Jamie Turner

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23,760 Aufrufe • vor 4 Monaten •via X (Twitter)

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.Palmer Luckey: American companies don't actually have engineers anymore. "American companies have been hollowed out." "We're not teaching engineers how to be engineers anymore." "We're not teaching designers how to actually design things to be manufactured." "We're teaching them how to be high-level design shops that put together a design package, that gets sent to the real engineers in China—and they actually figure out how to do the work." "People are turning into architecture astronauts." "They pick components, and they put them in a nominal layout." "But the real work of—how am I actually going to put this together? How am I going to build a manufacturing line to make this? How am I going to need to figure out how to do the one, two, three, four, five different revisions of this board to pass radio emissions and interference standards? That's all done in China. So they are the real engineers." Via Hoover Institution

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Met a guy making $1.6 million/year as an LLM engineer. I asked him how he learned LLMs from scratch. He sent me the exact video that got him in. A 1 hour course on how LLMs actually work. He shows how transformers inside LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized LLM architecture is way simpler than they make it look. Bookmark this and read the article below. • 00:00 - LLM foundations • 04:21 - LLM tokenization • 05:43 - LLMs vector embeddings • 22:16 - attention mechanism of LLM • 43:42 - LLM multi head attention

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Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

Yann LeCun says LLMs are strongest in domains where language itself is the substrate of reasoning, like math and code They can solve problems, prove theorems, and write programs — but they are not creative mathematicians, software architects, or computer scientists "their role is to help humans build"

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My friend applied to 200 tech jobs in two years. No PhD. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me a course that was never supposed to get out. A 3-hour video to build a full LLM from scratch. A developer teaches you exactly how LLMs like ChatGPT and Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 05:43 - LLM transformer architecture • 40:24 - training the LLM • 1:30:27 - modernizing the LLM • 2:33:53 - scaling the LLM

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My friend applied to 300 tech jobs in two years. No MIT. No Stanford. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Andrej Karpathy's 3-hour course on building a full LLM from scratch. Anthropic's own researcher shows you how LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized it's embarrassingly simple to break into an AI lab. Bookmark this and read the article below. • 00:00 - intro to LLMs • 12:41 - LLM training pipeline • 31:58 - LLM tools & plugins • 41:14 - LLM transformer architecture • 2:19:02 - scaling the LLM

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New short course on Reinforcement Learning from Human Feedback! RLHF is one of the key techniques that led to the rise of modern LLMs. It is used to align LLMs with human preferences, to make them more honest, helpful and harmless, by (i) learning a reward function that mimics human preferences, as expressed in human-provided labels, then, (ii) tuning an LLM to generate outputs that receive a high reward. In this course, taught by Nikita Namjoshi, Developer Advocate for GenAI at Google Cloud, you'll learn the details of how RLHF works, including how to apply it to tune an LLM for your own applications. You'll also use an open source library to tune a base LLM to align with human preferences expressed in a training set, and evaluate the tuned model by comparing its responses before and after RLHF-tuning. Please sign up here!

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"The one thing that's going to be truly future proof is judgment. What is the biggest challenge when you have a thousand AI engineers writing code? AI slop. In an era when you can do everything, the question is which of these things matter. On the engineer side, it is evaluating the code. Which of this code is even valuable? You can have AI engineers writing beautiful code that could be wrong, that could have bugs in it, that could be vulnerable. Someone needs to review it. Make sure to have a human review at some point, especially critical code that is in the core of your system. On the product side is judgment around what needs to be built and evaluating. And similarly in design, you have to have judgment around, does it make sense in the broader design system. I think this judgment is the number one thing that humans are going to bring in an era of infinite productivity."

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How will AI change writing? For clues, look at the current state of software development. Marc Andreessen says: "The new coding interface is you've got your code and a chatbot. The Copilot is reading your code and giving you real-time comments. Or the chatbot is actually writing the code for you, and then you're giving it feedback. We had this old concept in the old days called pair programming, where you'd put two coders together in front of the same keyboard, and they could talk to each other and write code together. But now you're just going to have pair programming happening much more broadly, which is human plus machine. And by the way, this hasn't happened yet, but I think the same thing will happen for every other form of writing. So I think prose writing and fiction writing and all kinds of professional authors will be working in this format where you'll have this continuous dialogue going with an AI while you're in charge of the overall product." — Marc Andreessen 🇺🇸 (Recorded in 2024)

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My friend applied to 250 tech jobs in two years. No MIT. No Stanford. No PhD. Last month Anthropic offered him $750,000. I asked him how he broke in from zero. He sent me the exact video that got him in. Andrej Karpathy's 4-hour course on "How to build a full LLM from scratch." Anthropic's own researcher gives up the exact playbook behind building LLMs like ChatGPT & Claude. I watched it last night. Halfway through, I realized I could break into an AI lab in months, not years. Bookmark this and read the article below. • 00:00 - LLMs as new OS • 40:58 - LLM tokenization architecture • 53:22 - LLM neural network internals • 1:04:20 - LLM pretraining • 3:21:37 - LLM RLHF & DeepSeek R1

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last weekend at NS, i broke down everything i've learned about writing bangers into a single session → how hooks actually work → why most tweets die in 0.3 seconds → the storytelling patterns behind every viral post and the one framework that changed how i think about content forever. here's the full playbook:

last weekend at NS, i broke down everything i've learned about writing bangers into a single session → how hooks actually work → why most tweets die in 0.3 seconds → the storytelling patterns behind every viral post and the one framework that changed how i think about content forever. here's the full playbook:

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