arkmm

And the human has to explain it at 2 tokens/sec (average speaking speed ~130-150 wpm). That's another constraint against slop - humans need to be able to explain their code succinctly.

I think part of the problem is the context windows for humans are actually much smaller than what an LLM can keep track of today. The small context window of humans is a feature that forces modularity and abstraction in software engineering so that you can decompose what you're working on into something that can fit into your head. But since LLMs can fit so much more in their head, so to speak, they don't have this same incentive, and you get the unorganized mess of spaghetti code that current agents often produce.

Seems like LLMs embrace that last point as well.

everything is computer

Didn't know this technique had a name, but I would think a modern compiler could make this optimization on its own, no?

You can fine tune a small LLM with a few thousand examples in just a few hours for a few dollars. It can be a bit tricky to host, but if you share a rough idea of the volume and whether this needs to be real-time or batched, I could list some of the tradeoffs you'd think about.

Source: Consulted for a few companies to help them finetune a bunch of LLMs. Typical categorical / data extraction use cases would have ~10x fewer errors at 100x lower inference cost than using the OpenAI models at the time.

Can you share more details about your use case? The good applications of fine tuning are usually pretty niche, which tends to make people feel like others might not be interested in hearing the details.

As a result it's really hard to read about real-world use cases online. I think a lot of people would love to hear more details - at least I know I would!

Payment fees are crazy when you think about them from the perspective of a merchant in a low margin business. E.g. in retail or restaurants, margins aren't much better than ~10%. If they didn't have to pay ~3% credit card fees, they'd have 30% more profit!

I used to also have this optimistic take, but over time I think the reality is that most people will instead just distrust unknown online sources and fall into the mental shortcuts of confirmation bias and social proof. Net effect will be even more polarization and groupthink.

Get ready for the acquisition offers.

Sorry Ploum, just getting a chance to read this now and comment. Great insights!

this is a really cool insight, going to use this on my team from now on!

They're still very good for finetuned classification, often 10-100x cheaper to run at similar or higher accuracy as a large model - but I think most people just prompt the large model unless they have high volume needs or need to self host.

Maybe a bit off-topic, but how'd you meet your partner while on your adventures?

As a follow-up to this, even though water makes up 70% of the Earth's surface, it's only 0.02% of the Earth's mass.

Wow, this deserves its own submission.

Neat approach, but seems like the eventual goal of caching DOM maps for all users would be a privacy nightmare?

What's misleading about that? You rent $100 of time on an H100 to train the model.

What sorts of automations were you able to get working with the Chrome dev tools MCP?

The irony of this is so much of Reddit comments these days are AI generated.

Unfortunately I think they have stopped doing this since COVID.

This misses the forest from the trees IMO:

- The datacenter GPU market is 10x larger than the consumer GPU market for Nvidia (and it's still growing). Winning an extra few percentage points in consumer is not a priority anymore.

- Nvidia doesn't have a CPU offering for the datacenter market and they were blocked from acquiring ARM. It's in their interest to have a friend on the CPU side.

- Nvidia is fabless and has concentrated supplier and geopolitical risk with TSMC. Intel is one of the only other leading fabs onshoring, which significantly improves Nvidia's supplier negotiation position and hedges geopolitical risk.

Looking forward to reading corroborating essays from other non-human species.

"There was one surprise when I revisited costs: OpenAI charges an unusually low $0.0001 / 1M tokens for batch inference on their latest embedding model. Even conservatively assuming I had 1 billion crawled pages, each with 1K tokens (abnormally long), it would only cost $100 to generate embeddings for all of them. By comparison, running my own inference, even with cheap Runpod spot GPUs, would cost on the order of 100× more expensive, to say nothing of other APIs."

I wonder if OpenAI uses this as a honeypot to get domain-specific source data into its training corpus that it might otherwise not have access to.

How are you guys reaching users with such a technical value proposition? Cold emailing engineers first and then expanding the conversation from there?

This is a really great explanation.

IMO if you think you can sell to users within the niche, you can publish a blog post of benchmarks and that'll serve as strong technical marketing for your niche.

It also keeps open the option to sell to an incumbent (possibly helps maximize the value of that option as well).

Missing the California high-speed rail on their list of examples.

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.

Automating applying to jobs makes sense to me, but what sorts of things were you hoping to use Operator on Amazon for?