I don’t like the term taste, but the problem that I have is that LLMs don’t seem to work “good enough”. They seem to be able to solve the immediate problem, but stacking this on the scale of 3-4 devs over 6 months or so doesn’t seem to produce anything. One thing that I’m particularly frustrated with is the writing quality of LLMs. Like this is the thing that they should be able to do, but I would say almost everythi…
Since they can summarize, you'd really think they would be better at condensing their own output and cutting out the filler after. I wonder if you could use a specialized second pass for it or something?
The internal representations of this state and its projection back out to human language wouldn't be as concise as that of a practitioner or team that develop their own verbiage and ontology over time molded to their system.
This verbosity might get better as we figure out better ways for agents to learn long term and use that knowledge to adapt to the users and projects over time.
There might also be some good harness improvements we could consider like forked output streams or multiple long lived filter subagents to ensure that output appropriate for thinking is separate from code output and separate from output given to the user driving the session.