menaerus

Yes, "entire" company codebase is a stretch as is design docs, status pages etc Helpful yes but not a hard requirement per se. In many places you won't find such level of details available and yet the incidents are still being solved

Yes but IME it is never possible to collect everything. It's part of the job to deduce the missing parts from what we have available.

Not sure how that relates to incident debugging?

What contexts would the AI model need for analyzing, let's say, cluster-wide incident other than the observability data that such systems anyway collect already?

> AlphaFold didn't solve a decades old protein folding problem. They substantially improved protein structure prediction but with important limits.

That is a bit of a semantic stretch and calling it substantial improvement for a 50-year (?) old computational bottleneck in structural biology is a bit of an understatement too. It has been a true breakthrough solving a problem humanity as of then thought it was impossible to tackle.

I personally find it much better than Claude in day to day tasks. Easier to understand and to the point without getting overly verbose like Claude always does. I solved some pretty difficult engineering problems with it, and most of the time I run Claude in parallel, and more often than not I found Claude to be less precise actually.

Well, we have seen AlphaFold solving a decades old protein folding problem. There's no reason for me to believe, with all the AI advancements I've seen in the meanwhile, that we will not solve other problems in this field too.

I appreciate the details, very interesting. So from a quick glance my understanding is that exacycle is a task partitioning and dispatching system built on top of existing google infra runtime (borg). Tasks are units that are massively parallel and distributed across the compute, and have no or almost no shared data. Problem space seems to be built around self contained tasks which have well defined input and output, and main engineering challenge in the exacycle itself would be the optimizing the system for maximum resource utilization? It's complex but not as complex as open problem space engineering problems dealing with highly concurrent and distributed workloads with shared state and that are unknown at the time you're designing the algorithm.

Anyway, my point was not in that I would equate software complexity with the complexity found in biology but that I see the same trend and similar type of challenges that can be found in both fields and therefore I see both fields amenable to the AI advancements whereas many people from the thread do not, and I disagree with that.

I'm not sure what you mean by "running a job that ran a worker". Running the system is vastly different than building and maintaining one. I work on highly available and distributed systems spanning multiple data centers, and single node scalability of database kernels, so I guess I could also say that I've done and still doing work at the highest level but in my case it is the core development. Which one is yours?

I made a parallel with similar complexity and ambiguity that can be found in SWE discipline too. I tried not to make any hard claims but I did present my opinion on the topic - I don't see a reason why drug discovery field wouldn't benefit from the AI when there're proportionally difficult fields (math, comp.sci, mechanics, medicine) where AI is already making a significant difference. Maybe you think comp biology is of a very different kind in which case you would turn out as ignorant here. lastly, my comment is there to spark the discussion and have some food for thought because not so long ago this type of reaction has been seen in comp.sci circles and math just few days ago.

Did I say anything wrong?

I'd take your overly dismissive words as credible (but still wrong) only if you did the career in both disciplines which I assume you didn't. You're wrong, and your view of SWE discipline is a great example of DK effect. Not something one would expect from a PhD in computational biology, yes?