w-m

Drug discovery is a problem thats hard to define and measure. But we know that LLMs excel where there's clear metrics. Anthropic made a big point about speeding up Claude code and claude.ai with Opus 5.5, OpenAI is doing a sprint on Codex this month.

So dear AI labs, here's a drug-related challenge for you that might actually help: streamline the production of the top 100 generic drugs. Make them higher quality, lower variance and then race against each other to drive down the price to candy level. Show us how to build mini factories for cheap that we can build anywhere in the world.

I’m partial to a good kerfuffle myself.

At introduction, Terra was a good mid-tier model. Terra [high] was on the pareto frontier of DeepSWE's score over cost, if only ever so slightly.

When I had Sol orchestrate Luna and Terra as implementation agents, Sol was a lot happier with what Terra produced and would find far fewer issues than what was implemented by Luna.

But a few weeks after introduction, OpenAI slashed Luna's cost by 80% and Terra's only by 20%. Only then did it become uneconomical to run Terra and its reason to exist stopped.

There are cars with engines that will bring you to 250 mph without burning out, like the Bugatti Chiron. Unfortunately you will burn through your $40k set of tires after 15 minutes at that speed. But don’t fret, because your fuel tank will be empty after 9 minutes anyhow.

Unfortunately, this seems somewhat unmaintained: five of the first nine "featured" cams have a dead/removed/copyrighted stream.

The plot doesn't appear to be in Amdahl territory yet. The single-threaded time in the plot looks to be around 39 seconds. A perfect division into 32 workers without overhead would make it 39 / 32 = 1.22 seconds. With the multi-threaded workload being reported as 1.5 seconds in the text, there's still only .3 seconds of overhead + serial instructions that can't be parallelized.

Every doubling of the number of workers halves the execution time cleanly in the plot, from 40 seconds to 20 seconds to 10 seconds. Eyeballing this for 32 over 16 workers is difficult, but it still seems close to halving the total time once again. So there's not a lot of Amdahl flattening, it's just the plain physics of looking at a inverse-proportional curve.

This is well-written. I could follow along quite nicely, from the setup through the bottlenecks and onto the resolution of the performance bug. Even the PRs are very pleasant to read: the majority of them is just a handful of changed lines with an added tests and a bit of documentation.

I was taken aback for a moment that this work originated from a report on StackOverflow. I had thought SO was effectively dead and abandoned by its community. But maybe I shouldn't project my own experience onto everyone else.

What exactly do subagents do that I can't replicate with a simple skill that tells an orchestrator to create subshells for tasks, each running `codex exec`? I've been doing this with Fable orchestrating Sol-medium and Terra-high, works like a charm.

...aaaand we're down to 112997 now.

Ok, fair points, including the sister comment, it's likely not a drop in O2 levels.

But then why can we see problems with concentration in studies of people in poorly ventilated rooms, but not replicate that when just adding CO2 to normal air? What is the CO2 that we can measure in meeting rooms actually a proxy for?

I don't think you can cleanly compare this: In the study, they added CO2 to the room, while keeping O2 at normoxic levels throughout the experiment. In your meeting room, O2 levels will be dropping in lock-step with the CO2-levels rising. It may be the lack of oxygen that leads to drowsiness, not the additional CO2. But it's the CO2 levels that you can measure as a good proxy of overall air quality.