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My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

simonwillison.net

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Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#261

Post by simonw »

Eggpants wrote: post #244
satvikpendem wrote: post #64

Earlier quoted context omitted.

This doesn't make sense thermodynamically because models are far smaller than the training data they purport to hold and recall, so there must be some level of "understanding" going on. Whether that's the same as human understanding is a different matter.

It’s a lossy text compression technique. It’s clever applied statistics. Basically an advanced association rules algorithm which has been around for decades but modified to consider order and relative positions. There is no understanding, regardless of the wants of all the capital investors in this domain.

I don't care if it can "understand" anything, as long as I can use it to achieve useful things.

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#262

Post by zarzavat »

qingcharles wrote: post #160
stpedgwdgfhgdd wrote: post #44

Aside that space invaders from scratch is not representative for real engineering, it will be interesting to see what the business model for Anthropic will be if I can run a solid code generation model on my local machine (no usage tier per hour or week), let’s say, one year from now. At $200 per month for 2 years I can buy a decent Mx with 64GB (or perhaps even 128GB taking residual value into account)

The frontier models are always going to tempt you with their higher quality and quicker generation, IMO.

Closed doesn't always win over open. People said the same thing about Windows vs Linux, but even Microsoft was forced to admit defeat and support Linux.

All it takes is some large companies commoditizing their complements. For Linux it was Google, etc. For AI it's Meta and China.

The only thing keeping Anthropic in business is geopolitics. If China were allowed full access to GPUs, they would probably die.

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#263

Post by simonw »

msikora wrote: post #253

With 48GB MAcBook Pro M3 I'm probably out of luck, right?

For this particular model, yes.

This new one from Qwen should fit though - it looks like that only needs ~30GB of RAM: https://huggingface.co/lmstudio-community/Qwen3-30B-A3B-Inst...

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#265

Post by [deleted] »

Eggpants wrote: post #244
satvikpendem wrote: post #64

Earlier quoted context omitted.

This doesn't make sense thermodynamically because models are far smaller than the training data they purport to hold and recall, so there must be some level of "understanding" going on. Whether that's the same as human understanding is a different matter.

It’s a lossy text compression technique. It’s clever applied statistics. Basically an advanced association rules algorithm which has been around for decades but modified to consider order and relative positions. There is no understanding, regardless of the wants of all the capital investors in this domain.

[deleted]

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#266

Post by mft_ »

xrd wrote: post #144
simonw wrote: post #25

Earlier quoted context omitted.

Only if that RAM is available to a GPU, or you're willing to tolerate extremely slow responses. The neat thing about Apple Silicon is the system RAM is available to the GPU. On most other systems you would need ~48GB of VRAM.

Aren't there non-Macos laptops which also support sharing the VRAM and regular RAM, i.e. iGPU? https://www.reddit.com/r/GamingLaptops/comments/1akj5aw/what... I personally want to run linux and feel like I'll get a better price/GB offering that way. But, it is confusing to know how local models will actually work on those and the drawbacks of iGPU.

iGPUs are typically weak, and/or aren't capable of running the LLM so the CPU is used instead. You can run things this way, but it's not fast, and it gets slower as the models go up in size.

If you want things to run quickly, then aside from Macs, there's the 2025 ASUS Flow z13 which (afaik) is the only laptop with AMD's new Ryzen Max+ 395 processor. This is powerful and has up to 128Gb of RAM that can be shared with the GPU, but they're very rare (and Mac-expensive) at the moment.

The other variable for running LLMs quickly is memory bandwidth; the Max+ 395 has 256Gb/s, which is similar to the M4 Pro; the M4 Max chips are considerably higher. Apple fell on their feet on this one.

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#267

Post by Eggpants »

simonw wrote: post #261
Eggpants wrote: post #244

Earlier quoted context omitted.

It’s a lossy text compression technique. It’s clever applied statistics. Basically an advanced association rules algorithm which has been around for decades but modified to consider order and relative positions. There is no understanding, regardless of the wants of all the capital investors in this domain.

I don't care if it can "understand" anything, as long as I can use it to achieve useful things.

“useful things“ like poorly drawing birds on bikes? ;)

(I have much respect for what you have done and are currently doing, but you did walk right into that one)

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#268

Post by mr_toad »

bayindirh wrote: post #42
quantumHazer wrote: post #13

Earlier quoted context omitted.

and probably some synthetic data are generated copy of the games already on the dataset? i have this feeling with LLM's generated react frontend, they all look the same

Last time somebody asked for a "premium camera app for iOS", and the model (re)generated Halide. Models don't emit something they don't know. They remix and rewrite what they know. There's no invention, just recall...

> They remix and rewrite what they know. There's no invention, just recall...

If they only recalled they wouldn’t “hallucinate”. What’s a lie if not an invention? So clearly they can come up with data that they weren’t trained on, for better or worse.

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#269

Post by CamperBob2 »

nottorp wrote: post #252
ben_w wrote: post #250

Earlier quoted context omitted.

You don't have to leave LLM code alone, it's fine to change it — unless, I guess, you're doing some kind of LLM vibe-code-golfing? But this does remind me of a previous co-worker. Wrote something to convert from a custom data store to a database, his version took 20 minutes on some inputs. Swore it couldn't possibly be improved. Obviously ridiculous because it didn't take 20 minutes to load from the old data store, n…

We all do that, sometimes where it’s time critical sometimes where it isn’t. But I keep being told “AI” is the second coming of Ahura Mazda so it shouldn’t do stuff like that right?

"I'm taking this talking dog right back to the pound. It told me to short NVDA, and you should see the buffer overflow bugs in the C++ code it wrote. Totally overhyped. I don't get it."

Re: My 2.5 year old laptop can write Space Invaders in JavaScript now (GLM-4.5 Air)

#270

Post by reissbaker »

arkmm wrote: post #224
techwizrd wrote: post #143

Earlier quoted context omitted.

We have been fine-tuning models using Axolotl and Unsloth, with a slight preference for Axolotl. Check out the docs [0] and fine-tune or quantize your first model. There is a lot to be learned in this space, but it's exciting. 0: https://axolotl.ai/ and https://docs.axolotl.ai/

When do you think fine tuning is worth it over prompt engineering a base model? I imagine with the finetunes you have to worry about self-hosting, model utilization, and then also retraining the model as new base models come out. I'm curious under what circumstances you've found that the benefits outweigh the downsides.

For self-hosting, there are a few companies that offer per-token pricing for LoRA finetunes (LoRAs are basically efficient-to-train, efficient-to-host finetunes) of certain base models:

- (shameless plug) My company, Synthetic, supports LoRAs for Llama 3.1 8b and 70b: https://synthetic.new All you need to do is give us the Hugging Face repo and we take care of the rest. If you want other people to try your model, we charge usage to them rather than to you. (We can also host full finetunes of anything vLLM supports, although we charge by GPU-minute for full finetunes rather than the cheaper per-token pricing for supported base model LoRAs.)

- Together.ai supports a slightly wider number of base models than we do, with a bit more config required, and any usage is charged to you.

- Fireworks does the same as Together, although they quantize the models more heavily (FP4 for the higher-end models). However, they support Llama 4, which is pretty nice although fairly resource-intensive to train.

If you have reasonably good data for your task, and your task is relatively "narrow" (i.e. find a specific kind of bug, rather than general-purpose coding; extract a specific kind of data from legal documents rather than general-purpose reasoning about social and legal matters; etc), finetunes of even a very small model like an 8b will typically outperform — by a pretty wide margin — even very large SOTA models while being a lot cheaper to run. For example, if you find yourself hand-coding heuristics to fix some problem you're seeing with an LLM's responses, it's probably more robust to just train a small model finetune on the data and have the finetuned model fix the issues rather than writing hardcoded heuristics. On the other hand, no amount of finetuning will make an 8b model a better general-purpose coding agent than Claude 4 Sonnet.

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