Should mention Jax in title, otherwise people won't know why they should use this instead of torch.
1,753 karma · joined September 28, 2011
using sql everywhere; never having to remember dataframe syntax: timeless
For many problems, choosing the right instruction or instruction sequence makes a large difference. Portable SIMD abstractions necessarily expose some common semantic layer, but SIMD ISAs don't actually have equivalent capabilities. Instructions like pshufb, for example, enable algorithmic tricks that don't necessarily have an equally efficient analogue on another architecture.
If maximum performance matters, I generally want intrinsics and architecture-specific implementations; if portability matters more, I'd rather move further up the abstraction stack and use something designed to target multiple architectures, such as ISPC. There are certainly cases where portable SIMD gets close enough to optimal, but I don't think there's a compiler or abstraction that can express every useful SIMD idiom and lower it equally efficiently across fundamentally different ISAs.
As you say, the most useful things happen in low-dimensional spaces.
model training and inference is the opposite. people switch LLMs like people change clothes in the morning. there are popular services (openrouter) that make moving as easy as changing a model string.
for all these reasons, is being 6 months behind the frontier actually a structural, long term disadvantage? some day the pace of improvement will slow, and google will vacuum up the market. they'll be able to compete with open-weight models just on pure cost advantage from their vertical integration
if don't have time for that, just know that these are technical parameters that affect how likely it is an llm will produce the same result after being asked the same question.
Citation needed? 2XL looks like you go up to 800x800 pixel inputs. This isn't the dealbreaker you say it is - all pipelines benefit from thoughtful crop and rescaling before going to inference.
Basic rag is almost stupid in how easy it is, though. You grep for keywords, take the surrounding paragraph, then stuff it all into your llm prompt.
The next upgrade is to automate keyword extraction by putting your documents into a vector store and search by vector similarity.
What AI actually does Mutinex has built what it describes as a “multi-agent system,” where each agent acts as a domain specialist. For example, one agent understands marketing econometrics, another understands competitive pricing theory, another diagnoses model failures.
By combining Tracer, which cleans and makes sense of Hershey’s data infrastructure, with Mutinex’s AI system, Hershey is now able run models in as little as three weeks.
In practice, that means faster iteration on how marketing spend is evaluated and adjusted, rather than waiting for lagging historical reads.
“Most companies don’t have an AI problem. They have a data readiness problem,” said Sarah Martinez, chief commercial officer, Tracer.
</article excerpt>
Instead of the headline, it sounds like they've hired an external company to clean up their ETL pipelines. That seems useful.
I'm going to doubt spooling up <massive LLM> with <appropriate system prompt> is going to be the thing that reduces their analysis time.
If you desperately want mediocre chromebook instruction for your children; where your classmates turn in ChatGPT essays as original work, feel free to move to Virginia.
1) kids k-5 are using laptops 2) websites are so bloated today that browsing on a Chromebook is causing agitation
Otherwise I don't have anything topical for this post, but good luck OP