a weird inversion with LLMs is the models improve faster than the tinkerers
when i see people with custom workflows and setups they're all addressing problems that don't exist anymore
the person naively using vanilla codex is more likely to be experiencing state of the art
We started Halmos Labs because we are convinced that research is about to change, and we want that change to be for the better.
Through validation and verifiably we elevate researchers, so we don’t turn humans into just a proxy between machines and the world
The world spends roughly $100 billion a year on experimentation in Life-Science. Yet, 93% of drug programs entering Phase I never reach approval.
Halmos Labs is changing that
See how at https://t.co/iEVkG2oWfj
My hot take from chatting to @poteto is that we should use MORE abstractions in the AI age
You can use them (combined with harsh lint rules) to reduce the design space available to the agent and constrain them only to good decisions.
Combined with the fact that high-leverage abstractions let you do more with less code - so, more token efficient.
Plus, unwinding the damage from a bad abstraction is much cheaper with agents.
This runs counter to a lot of folks thinking that agents just want to read the raw code. They can, but they're not maximally efficient that way.
Be braver! Design abstractions.
AI folks who are new to bio should spend some time with wet lab folks not only to understand how experiments, assays, lab work is done but also to learn the basic concept of CONTROLS. 1/
”You do it because it’s the right experiment to run, and you can’t cut corners […]. Running the mediocre experiment because it's easier or more feasible doesn't get the data you actually need” is exactly why intelligent exp design that elevates your team is so important
we do this with overscroll-behavior: none
but we gate it behind a (pointer: fine) media query, so that it doesn’t impact touch devices. this means you can still pull to refresh on mobile