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Please reverse engineering the ransomware in rust

Don't use your fingerprint. Swipe useing a knuckle.

With the final running of the code, running the assembly hello world, the code still works if I don't link the SDK and libraries during the "ld" function (I can just do "ld hello.o -o hello -e _start"), and doing ./hello still works. Does that mean that the rest of the function linking the libraries and defining the main function is unnecessary? Genuine question, just trying to reduce the amount of complex code I'm not entirely sure I understand

Hi, how can I find the data template of llama 3.1 base model ? How can I prepare research papers and books for fine-tuning the base model in right data format ?

Very nice test!! Subscribed!

turns out X AI created this model, which explains the issues i had with the math and reasoning parts.

just to let you know, you were wrong. it is by XAI confirmed. lol elon got you

You need a wee edit at 0:53 :-)

LLaMA2-70b uses GQA (only its 7b version used MHA)

Thank you Chris. Could you show us how to create a ReAct agent using langgrahp?

Nice one Chris, interesting!

Great explanation, great content. Keep doing a good work, your channel's worth much more reach and subs :)

Probably coming with the Pixel 9 pro.

This is epic. You got a sub!

Nice find Chris! enjoyed the video!❤

Someone please correct me if I'm wrong here. I think that neither of the examples you showed comes from reasoning. The order is flipped, they should first provide the reasoning and then the answer, not the other way around as in your examples. The models take all the tokens into account from the input and the output (generated up to that point). What is giving the right answer a better chance is if the previously generated tokens contain the reasoning steps. In your examples the previous tokens did not contain the reasoning steps as those were generated after the answer.

When you said "math" I thought you meant symbolic math, not arithmetic. Using an LLM to do arithmetic is pointless; a calculator does a far better job.

Great content as always! Thank you!

Great explanation

does seem a bit verbose for ADTs : you need to define the tag type and use a case of on it to get the different member variables ( similar to record case in pascal)

I don't have time to go through the whole video at this specific moment, but it seems to me that you came to a fairly similar answer to myself: LLMs are pretty strong at presenting data and handling noisy inputs, while traditional computer programs are pretty good at doing the math (numerical instability notwithstanding). One obvious opportunity that I'm not seeing in the first ten minutes (though I'll certainly allow it's possible that I'll have egg on my face after finishing), is that this seems like an efficient way to embed agentic function calling into a model; if the steps to solve the problem contain a call to a remote system with the equation as an argument, and the remote system can solve the equation, that seems a lot like a function call to my eyes. Beyond that, there's also probably some room to reverse engineer a problem with the synthetic generator LLM based on the equation and answer, in order to encourage semantic problem solving, as seen in certain benchmarks which have word problems encoding ideas best solved with mathematics. Overall, this is a super cool project, and is probably going to be very beneficial for people doing continued pre-training or experimenting with certain ideas like grokking. I'm pretty excited to have a hack at it myself.

❤

Tremendous explanation. Thank you so much Chris!

Great vid! Any chance we can get a link to the repo with this code?

Rust doesn't have "object orientation".

Brilliant!

I honestly forgot that Nim has func

note that you don’t need the `-o` flag when calling `wat2wasm`, and it will simply use the filename

installing WABT: on macOS, with Homebrew: `brew install wabt`

Tremendous video. Thank you very much!

These videos are insanely awesome. I LOVE the verbosity. Most of the internet videos are a high level abstraction of "what is programming". This breakdown of "What is happening when we program" is what's lacking in engineering these days! Well done :)

Great content 👍😁

so this is why gpt-4o is so much better at maths

You have to check about the Claude 3.5 sonnet system prompt leak and all the talk about “artifacts” and persisting data with LLMs.

If you put Gemma in your title, you'll get low views. Gemma is absolutely disgusting. One of the dumbest models out there

Very good content, thanks! I was comparing models manually, and I'll integrate Nemotron into the eval. One off-topic question, the over imposed screen on top of your your video is a post prod edit or is there any software that let's you record the video like this? Thanks!

I believe 4o's judges only 90%

I really liked the video. it was a mystery for me for a while.

I don't like that very much. Why? I absolutely hate getting walls of text and code thrown at me for simple yes/no questions all the time! Both ChatGPT and Claude have this issue. So in the end It's just that you hardcode a system prompt like "think step by step" into your model and it's very hard then make it giving quick and short answers again. A hidden scratch pad is a good compromise but still slows down responses and could by achieved with a system prompt too. The system-prompt method could also include multiple agents or personas with different strengths to provide input. The best would be to also train the model to estimate the complexity of a question and then decide whether to do additional thinking or not. Also I've seen open weight models answering harder questions with just one or very few words correctly where others generated a text wall and still came to the wrong result. So whether an explicity step-by-step thinking is really required remains debatable. Obviously the chances for a correct answer increase the more relevant (!) information is in the context and that's all what CoT etc. actually does: pulling more information into context. Another similiar thing that I see doing Claude quite often and which I like is that it does summarizations before responding. If the problem is complex and there was a lot of back and forth the perceptions of it may diverge. Summarizations greatly help to create a synchronization point between the LLM and the user and then focus on the established and relevant intermediate results.

That explains why Claude 200k context is more like 50k for me. So much taken up with the scratchpad

The private scratch pad in claude 3 5 explains why it seems to behave as if it had private state in addition to the text visible in the conversation.

How can a full grown adult hide behind some decorations?

Claude is NOT as good as GPT, sorry it is not. When you ask it to update code at least the way it works for me, is it sends only a new block of code that it changed not the whole class, to keep the response size down, it is really limited in the length of its response compared to ChatGPT. With all these Claude videos, I have to believe CZcamsrs are getting paid to shrill for it. Also, the Artifacts only works on a very small sample size of types of code, so all this selling like its a viable thing is disingenuous.

Great great content! Please, make a local finetune tutorial. Thanks again!

Can you please give a tutorial on how to load private data, train/Rag/evaluate and deploy an open-source model on WatsonX or other online platform (AWS, Azure or Huggingface)? Many thanks! BTW Nemotron4 broke down this noon (PST), maybe due to too many users. I was in line 771 with a simple question. It gave out some sort of communication problem, after two minutes od waiting

I believe you are correct in that both Claude and Lllama 3 are finetuned using STaR generated dataset but this method still needs a ranker or human to mark the correct answers, whereas from what I have read online is that OpenAI's Q* is a combination of "A* search" algorithm combined with Q learning from reinforcement learning to self improve, where the model generates 100 different answers and picks the best answer to improve similar to AlphaCode2 from Deepmind.

Thanks, for another great video Chris. I've been through some LLM courses on Udemy but your channel is helping me to clear many doubts I have on the whole thing. I'm glad I found your channel. It's really the best on this subject. Congratulations. Marcelo.

Hi. I was looking for dozens of videos on how to make ReAct work on 7B models (to make a low cost Text to Sql solution) and the only video the answer my question so far is yours. Thank you. By the way, I'm from Rio and the current time is 09:42 AM

Thanks for making this video, wonderful explanation 👏 I followed your step in my case the react agent going into loop and timeout. kindly let me know how to handle this case of looping. Thanks in Advance!

when I run the final example it prints the text but then segfaults. The code is identical to the example. What could that be?