[pushedx]

Yes, most people (including myself) do not understand how modern LLMs work (especially if we consider the most recent architectural and training improvements).

There's the 3b1b video series which does a pretty good job, but now we are interfacing with models that probably have parameter counts in each layer larger than the first models that we interacted with.

The novel insights that these models can produce is truly shocking, I would guess even for someone who does understand the latest techniques.

[auraham]

I highly recommend Build a large language model from scratch [1] by Sebastian Raschka. It provides a clear explanation of the building blocks used in the first versions of ChatGPT (GPT 2 if I recall correctly). The output of the model is a huge vector of n elements, where n is the number of tokens in the vocabulary. We use that huge vector as a probability distribution to sample the next token given an input sequence (i.e., a prompt). Under the hood, the model has several building blocks like tokenization, skip connections, self attention, masking, etc. The author makes a great job explaining all the concepts. It is very useful to understand how LLMs works.

[1] https://www.manning.com/books/build-a-large-language-model-f...

[phreeza]

But this is missing exactly the gap which OP seems to have, which is going from a next token predictor (a language model in the classical sense) to an instruction finetuned, RLHF-ed and "harnessed" tool?

[js8]

The book has a sequel https://www.manning.com/books/build-a-reasoning-model-from-s...

It will give you an answer to the extent anybody can.

[measurablefunc]

What's the latest novel insight you have encountered?

[brookst]

Not the person you asked, and “novel” is a minefield. What’s the last novel anything, in the sense you can’t trace a precursor or reference?

But.. I recently had a LLM suggest an approach to negative mold-making that was novel to me. Long story, but basically isolating the gross geometry and using NURBS booleans for that, plus mesh addition/subtraction for details.

I’m sure there’s prior art out there, but that’s true for pretty much everything.

[measurablefunc]

I don't know, that's why I asked b/c I always see a lot of empty platitudes when it comes to LLM praise so I'm curious to see if people can actually back up their claims.

I haven't done any 3D modeling so I'll take your word for it but I can tell you that I am working on a very simple interpreter & bytecode compiler for a subset of Erlang & I have yet to see anything novel or even useful from any of the coding assistants. One might naively think that there is enough literature on interpreters & compilers for coding agents to pretty much accomplish the task in one go but that's not what happens in practice.

[brookst]

It’s taken me a while to get good at using them.

My advice: ask for more than what you think it can do. #1 mistake is failing to give enough context about goals, constraints, priorities.

Don’t ask “complete this one small task”, ask “hey I’m working on this big project, docs are here, source is there, I’m not sure how to do that, come up with a plan”

[measurablefunc]

The specification is linked in another comment in this thread & you can decide whether it is ambitious enough or not but what I can tell you is that none of the existing coding agents can complete the task even w/ all the details. If you do try it you will eventually get something that will mostly work on simple tests but fail miserably on slightly more complicated test cases.

[pushedx]

Which agents are you using, and are you using them in an agent mode (Codex, Claude Code etc.)?

The difference in quality of output between Claude Sonnet and Claude Opus is around an order of magnitude.

The results that you can get from agent mode vs using a chat bot are around two orders of magnitude.

[measurablefunc]

The workflow is not the issue. You are welcome to try the same challenge yourself if you want. Extra test cases (https://drive.proton.me/urls/6Z6557R2WG#n83c6DP6mDfc) & specification (https://claude.ai/public/artifacts/5581b499-a471-4d58-8e05-1...). I know enough about compilers, bytecode VMs, parsers, & interpreters to know that this is well within the capabilities of any reasonably good software engineer but the implementation from Gemini 3.1 Pro (high & low) & Claude Opus 4.6 (thinking) have been less than impressive.

[pushedx]

sorry, needed to edit this comment to ask the same question as the sibling:

have you run these models in an agent mode that allows for executing the tests, the agent views the output, and iterates on its own for a while? up to an hour or so?

you will get vastly different output if you ask the agent to write 200 of its own test cases, and then have it iterate from there

[Kim_Bruning]

Possibly a dumb question: but are you running this in claude code, or an ide, or basically what are you using to allow for iteration?

[kmaitreys]

Can you clarify a bit more about the this two orders of magnitude? In what context? Sure, they have "agency" and can do more than outputting text, but I would like see a proper example of this claim.

[joquarky]

Most humans can't force themselves to come up with something novel immediately upon demand.

[measurablefunc]

Completely unrelated to the topic or any of the points I was making so did you get confused & respond to the wrong thread?

[kennyloginz]

There is prior art, so it’s not novel.

[brookst]

Great. Can you point to anything at all that is truly novel, no prior art?

[rsync]

Sliding down handrails on a skateboard.