Lots of situations, here are 2 I’ve faced recently (cannot give too much detail for privacy reasons, but should be clear enough)
1) low latency desired, long user prompt
2) function runs many parallel requests, but is not fired with common prefix very often. OpenAI was very inconsistent about properly caching the prefix for use across all requests, but with Anthropic it’s very easy to pre-fire
All these things are designed to create lock in for companies. They don’t really fundamentally add to the functionality of LLMs. Devs should focus on working directly with model generate apis and not using all the decoration.
I vastly prefer the manual caching. There are several aspects of automatic caching that are suboptimal, with only moderately less developer burden. I don’t use Anthropic much but I wish the others had manual cache options
Cerebras has very limited scale. Mistral has very few users so they can use cerebra’s in inference whereas OpenAI and Anthropic cannot. If mistral grows a lot they will stop using cerebras
Fast tire changes only matter a very limited amount of the time (pretty much only if the extra time drops you a place, so there has to be 1 car/20 in a specific 1 second window on what is typically a 90s lap for 3s (a slow stop) vs 2s (a fast stop) to matter. Maybe 20% of the time a slow stop happens, it costs a driver.
Strategy matters a lot and good strategy is worth at least a few positions in a race.
WhatsApp is certainly worth less today than what they paid for it plus the extra funding it has required over time. Let alone producing anything close to ROI. Has lost them more money than the metaverse stuff.
Insta was a huge hit for sure but since then Meta Capital allocation has been a disaster including a lot of badly timed buybacks
I understand that calling it ‘agentic’ is nice for marketing, but most of what is described in this blog post is not related to agents. The design patterns you describe are explicitly non-agentic. Many of the use cases described are better handled by a single LLM call rather than an agent.
Finally, saying that agents can have predictable behavior is wrong (except on simple tasks where you shouldn’t be using an agent anyway). Agents loop and compound their input, making them highly non-deterministic even for the same prompt.
They may not be acting in good faith but there is extremely clear evidence that UCLA has engaged in illegal racial hiring and admissions practices and has supported antisemitism on campus. UCLA chose to give them that ammunition.
The term agent is just way overloaded. This guy defines it completely differently the the big labs, and I’ve seen half a dozen different definitions in the last few months.
In the long run the definition used by OpenAI, Anthropic et al will win out so can we just all switch to that?
Even as someone who is skeptical about LLMs, I’m not sure how anyone can look at what was achieved in AlphaGo and not at least consider the possibility that NNs could be superhuman in basically every domain at some point
They claim that a small Chinese hedge fund could acquire $1bln in GPUs, with no state support, including many sanctioned chips, then trained a model optimized for a far smaller server compute size, and that they have a source at this very small fund who is willing to admit to export violations. A 40bln param active model is exactly the size you would expect from a server of the size they claim.
What’s more likely - that semianalysis made it up like they have a bunch of other things, or that all the above is true?
SemiAnalysis is wrong. They just made their numbers up (among many other things they have invented - they are not to be trusted). I have observed many errors of understanding, analysis and calculation in their writing.
Deep Seek R1 is literally an open weight model. It has <40bln active parameters. We know that for a fact. That size of model is definitely roughly optimally trained over the time period and server times claimed. In fact, the 70bln parameter Llama 3 model used almost exactly the same compute as the DeepSeek V3/R1 claims (which makes sense, as you would expect a bit less efficiency for the H800 and for the complex DeepSeek MoE architecture).