Inability of newer AI models to effectively manage task completion and user interaction, leading to irrelevant outputs and inefficiencies

Signals over time

2 observed signals across 1 sources, tracked for 1 days. Confidence: low.

What people are saying Observed

• “Doesn't matter at this point i would say. Alone the massive usage of us every day produces a massive amount of signals. I build something and claude does something stupid? "hey thats not what i meant! Do this instead!" "Okay" <<< This is a signal. The mathematician being unhappy about something from claude? Another signal. This alone gives you enough progress i would argue. But additional its clear that certain tasks are worth to pay experts for for teaching one central AI once instead of every single human who needs to do the task. IF RL is also working well, we are just faster f*ed than otherwise.”

  Hacker News · frustration

• “I think they try pushing the models in a direction where they just go on until somehing is finished end-to-end. So basically building "the loop" into the model itself. E.g. with GPT-5.5 the model would often do only part of a job and get back to me with a recommendation for next steps and I would either say "Yes, go on" or "No, instead do X". And the newer models never even get back to me, they just keep on churning, deciding about the direction on their own. Sometimes they go into the right direction, sometimes they drift into something unrelated or just go down some rabbit holes for hours. I liked GPT-5.5 better, and I built my own tools around it to still get them to finish tasks end-to-end which works fine.”

  Hacker News · complaint