tempusalaria

Most of EA’s revenue comes from franchise games that are way below typical AAA standard. EA’s value is from IP not talent

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

A lot of the current code and science capabilities do not come from NTP training.

Indeed in seems in most language model RL there is not even process supervision, so a long way from NTP

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.

I imagine it runs civ 2 pretty well

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

SFT is part of the classic RLHF process though

Yes this write-up is not about agents.

In fact it’s a great illustration of why the hype around agents is misplaced!

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.

if you are p testing this isn’t the case. A positive result is a much stronger assertion

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?