Comments URL: https://news.ycombinator.com/item?id=47672828

Points: 1

# Comments: 0

It is different from Netflix (that pays upfront for production costs), but there's of course a revenue share + the bulk of the revenue for creators is actually from sponsorships (which YT doesn't take a share of).

However, a large part of what makes LLMs feel so magical comes from this bottleneck.

Comments URL: https://news.ycombinator.com/item?id=47567160

Points: 3

# Comments: 1

[1] https://firebase.google.com/docs/ai-assistance/mcp-server

That was a very niche error, that you promptly corrected, no need to be so apologetic about it! And thanks for all the hard work making Python faster!

This shouldn't be surprised, e.g. the model != the product. The same way GPT4o behaves differently than the ChatGPT product when using GPT4o.

Less and less people are using search engines to shop, ex:Amazon makes >$57B a year from search ads, but also look at Temu and Shein which are mostly glorified product search platforms.

No one is searching for "funny videos" when you can just open Instagram and Tiktok.

The only real unique thing that search engines can do is queries that are not directly commercial (e.g. education, information seeking, etc.) and competition is insanely intense (w/ ChatGPT, Perplexity, etc) there.

Differentiable Rendering [1] is the closest thing to what you are describing. And yes, people have been working on this for the same reason you outline, it is more data/compute efficient and hence should generalize better.

[1] https://blog.qarnot.com/article/an-overview-of-differentiabl...

But also: > While cool, this also seems utterly wasteful. Video games offer known "analytical" solutions for the interactions that the model provides as a "statistical approximation", so to say.

A bit of the same debate as people calling LLMs a "blurry JPEG of the web" and hence useless.

Yes this is a statistical approximation to an analytical problem... but that's a very reductive framing to what is going on. To find the symbolic/analytical solution here would require to constrain the problem greatly: not all things on the screen have a differentiable representation, for example complex simulations might involve some kind of custom internal loop/simulation.

You waste compute to get a solution that can just be trained on billions of unlabeled (synthetic) examples, and then generalize to previously unseen prompts/environments.

People are framing this as if it was an open-source hierarchy, with "actual" open-source requiring all training code to be shared. This is not obvious to me, as I'm not asking people that share open-source libraries to also share the tools they used to develop them. I'm also not asking them to share all the design documents/architecture discussion behind this software. It's sufficient that I can take the end result and reshape it in any way I desire.

This is coming from an LLM practitioner that finetunes models for a living; and this constant debate about open-source vs open-weights seems like a huge distraction vs the impact open-sourcing something like Llama has... this is truly a Linux-like moment. (at a much smaller scale of course, for now at least)

The world probably has never been as peaceful as in the last 50 years or so.

Same goes for access to drinkable water, food, decent shelter, gender equality, freedoms, technology, etc.

But I suppose your comment is a good illustration of the problem at hands (that so many people deeply believe that things are fucked)

Most important, even ignoring latency, is throughput (tokens) per $$$. And according to their own benchmark [1] (famous last words :)), they're quite cost efficient.

[1] https://www.semianalysis.com/p/groq-inference-tokenomics-spe...

(the way I understood it => it's still cost effective at scale due to throughput increase this brings)

And here's the sad part: they had this back in 2019... see this paper released in Jan 2020: https://blog.research.google/2020/01/towards-conversational-...

My read (having seen some of this on the inside), is that it was a mix of being too worried about safety issues (OMG, the chatbot occasionally says something offensive!) and being too complacent (too comfortable with incremental changes in Search, no appetite for launching an entirely new type of product / doing something really out there). There are many ways to monetize a chatbot, OpenAI for example is raking billions in subscription fees.

So fast it finally made virtual environments usable for me. But it's not (yet) a full replacement for conda, e.g. it won't install things outside of Python packages