Ok I think I need to go into more depth on the examples.
I think HN knows that anyone can prompt LLMs. I do think its interesting though that this has allowed PMs/SMEs to direclty influence products that are deployed to millions of people. That seems genuinely novel. Maybe I over egged it
wrt did you read the article? I was quite specific about the ways I think LLMs are blurring the lines. I don't think its true for general engineering but I do think its true for applications being built with LLMs.
I agree with that. What do you think about the point thought that for LLM agents and applications, prompts and tool definitions might matter more than code?
I totally agree that we're not at a point where AI can write most code. Though, I didn't ever say that. I just think its blurring the boundary between engineers and PMs with both taking on more of the others role.
Also, it shouldn't be surprising that the product we're building is aligned with what we believe about the world :)
I think I worded this poorly. What he said was that a lot of people say they want open-source models but they underestimate how hard it is to serve them well. So he wondered how much real benefit would come from open-sourcing them.
I think this is reasonable. Giving researchers access is great but for most small companies they're likely better off having a service provider manage inference for them rather than navigate the infra challenge.
I know that HN likes to nerd out over technical details so thought I’d share a bit more on how we aggregate the noisy labels to clean them up.
At the moment we use the great Skweak [1] open source library to do this. Skweak uses an HMM to infer the most likely unobserved label given the evidence of the votes from each of the labelling functions.
This whole strategy of first training a label model and then training a neural net was pioneered by Snorkel. We’ve used this approach for now but we actually think there are big opportunities for improvement.
We’re working on an end-to-end approach that de-noises the labelling function and trains the model at the same time. So far we’ve seen improvements on the standard benchmarks [2] and are planning to submit to Neurips.