Yeah I don't watch basketball or baseball or football so I don't know. Those are US leagues for US sports for US people, with one team to pretend it isn't.
> I'm from North America where jerseys are largely NBA, NHL and MLB. No ads (although, NHL has started). But the euro jersey has the biggest ad dead fucking center.
I experience this with many of the "advanced" models. I find they're actually the most efficient on their low/medium settings, occasionally high. Anything higher than that, and they start inventing more task list items than they check off. They seem to think that every personal project needs extensive adversarial analysis and guardrails and will invent non-issues without being asked.
Unfortunately, I think it's just a continuation of a trend that already started pre-covid. I was surprised that most juniors and interns I worked with seemed to lack the ability to "self-help" and Google basic questions they had. I think it reflects a schooling system in the US that doesn't teach any real critical thinking - just studying until you "know" the answer, without actually teaching students the intuition of how to get there. The result is that when they encounter something not even novel, but slightly unfamiliar they don't even make an attempt to start or immediately tunnel vision down the wrong path without taking a first step back. Funnily, the latter approach feels similar to how LLMs perform on truly novel problems, too.
Claude has always noticeably degraded under heavy load. Opus goes from "decent to work with" to "dumb intern" depending on whether you're working at 3 AM west coast or 10 AM - 5 PM. It's part of why I cancelled my subscription - "max" plans and "extra high" effort are meaningless when there's so much variability between model availability, model performance, and harness bugs every day and every week.
/goal has replaced plan mode for me. This is the pattern I use for 95% of my AI work now:
1. Read X feature of Y and tell me when you fully understand it (if there's any detail missing in the summary, repeat until the context is primed)
2. What time is it?
3. /goal Spend X minutes from $time writing a technical design doc on $feature. There must not be any vague language or ambiguity in the document. Read carry_forward_requirements.md and testing_best_practices.md and explicitly incorporate them into the document you write. The document should be executable for a contextless implementer when done and include specific code and document references and changes needed. Spend the full X minutes working on and reviewing this document - do not quit early and wait
Even just spending 10 minutes forcing GPT to write a design doc results in much more robust plans than plan mode, in my experience, and saves time I would spend iterating on the initial plan mode draft anyway.
> 3. /goal Spend X minutes from $time writing a technical design doc on $feature.
Hmm, I feel like this is akin to making a recursive function have a exit condition not based on what it actually did/found, but based on how long time it took.
I'm always using /goal with explicit goals that the agent needs to achieve. Time-bounding them wouldn't make sense, I want something specific done regardless of how long time it takes.
So instead I'd put goals on what the design/architecture needs to achieve, and for the model to continuously check the outcome against these, then finish when everything is achieved. Doesn't really matter if it takes 10 minutes or 10 hours, which for me is a bit the point of /goal in the first place, otherwise I'd just use the agent normally.
Leaving to run unhindered makes sense when the agents understand both the complete domain context and can be supplied with a strong exit criteria that they understand well.
Otherwise time boxing is both going to help stop entities from wandering off into the weeds. And also communicate expectations from the commissioner about the expected effort levels and output quality requirements.
Empirically in human world, get very different results when an employee, particularly a junior, is asked to spend 1/2 a day on a work package, a week or are left completely to their own devices.
I think like the other commentator noted, I don't do this blindly, as I also wouldn't if I was instructing a junior engineer. I have a rough sense of how long it would take me to research all angles of the task. 5 minutes for a complicated feature is too short for a comprehensive plan. Letting it run for a few hours is also probably unproductive. But 10-60 minutes depending on complexity with followup review is a nice sweet spot. Just my $0.02.
The hypothesis generation phase is looking like the most critical part of having an agent reliably hit your targets.
Simply starting in the correct part of the search space is probably the biggest predictor of success. Forcing one big loop to fight its way through all the hypotheticals from zero looks like a dead end for many practical scenarios, regardless of how powerful the model is. I think you could draw some analogies to humans here.
I have found that delegating deep research to a simple tool call is the best way to ground the agent in complex domains. If you make the main agent loop carry the weight of this research, it's going to do a really shitty job because of how the RLHF tries to preserve context and get an answer to the user quickly. As a tool, you may find the agent invokes multiple rounds of research consecutively without realizing it has incurred billions of tokens of consumption. Many of the tokens are wasted when generating independent hypotheses and subsequently investigating them, but the point is that you sampled 10-100x search space before getting serious about mutating the environment. The tradeoff seems worth it in a lot of cases. Correctness >> Time >> Money.
