Link: https://www.theregister.com/os-platforms/2026/10/19/red-hat-helps-teach-about-tech-writing/5303868

Tech writing is a demanding job, and pretty much permanently under-resourced. We can’t and don’t blame anyone for grabbing onto anything that might help. Even so, for us, what stood out from this session was the pretty much unanimous agreement among an audience of tech writers that LLMs were poor at doing the job that tech writers themselves did. Reviewing each others’ docs is a core part of the job: tech writers are used to this, and when they review the bots’ efforts, they find the machinese lacking. That fits our impressions, too.

What we found alarming is that, although they find the bots poor at doing what the writers know best, even so, they trusted the bots to be competent in other functions, things the writers don’t look at so closely – for example, finding and selecting where docs need updating. The use for summarization is especially alarming: this is something that studies have demonstrated LLMs are bad at. They shorten, not summarize, and that’s no substitute.

We find that worrying, but we regularly hear reports of companies pressing staff to use AI more. This is an all-too-human response: “it can’t do my job, but if I must use it, then this stuff isn’t my jurisdiction, and if it messes up, I can blame someone else.” We just worry at whose desk this buck will eventually stop.

Unfortunately this is the best practical and broadly applicable usecase for AI as a profitable/useful tool I have seen yet, which isn't surprising, just very cynical...

Llms are great at summarizing large sets of text. For that reason, it makes sense that they can parse and return knowledge or summarize my business docs, or maybe the top 1000 articles from search, and give you an overview of the results. I see llms useful from this perspective, and think of them as a natural language interface to search, and essentially a toy. It still needs to be validated. Their methods are fundamentally flawed and suffer the same weaknesses of bias, based on their training set.

Beyond llms, AI has existed in CS for decades, in fields such as medical imaging, protein identification, drug development, weather prediction, traffic modelling, etc. Those branches could benefit from all the new data center compute being invested in by the corpos, and receive my full support, when said resources are finally directed to them. But those are still very much in the realm of 'traditional' ai. Let's not get lost in the agi hype.

So basically it's like the new version of feminist hat ok got it.

protein identification

Nope

https://www.engadget.com/2225849/google-shuts-down-alphafold/

FYI, this is a Nazi company.