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What? Lakers are literally sponsored by Wish.

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.

god i wish these low effort projects were banned from Show HN

the entire premise of a "bullshit detector" that's entirely vibe-coded is laughable

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.

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.

/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.

I feel like this benchmark reiterates my disbelief that anyone uses the latest Anthropic models for any productive work. They seem to be the best at burning tokens and spawning unnecessary subagents even for well-defined and tightly scoped tasks.

Can we get a count of people that have had Claude read irrelevant documents or perform unnecessary web searches even when told not to from the beginning?

I'm starting to wonder if this increased token usage is inadvertently bleeding into how Anthropic actually trains their model, especially leading up to IPO. As older models are deprecated and users are forced onto newer models, if the default is less efficient and more token expensive that directly results in higher "profit" for Anthropic in terms of the consumption their users have to tolerate - lest they jump to a competitor.

I switched from Anthropic to OpenAI after spending ~$40K in equivalent token costs using Claude over 3 months.

I found Opus 4.7 to be slow and wasteful with token usage. It's shocking how inefficient it is with tasks like bash tool usage and web searching, delegating them to a dozen subagents only to get stuck and never return until you esc and intervene. That, in addition to all of the broken tooling Anthropic built in to limit token usage like the broken monitoring tool made managing Claude a chore. I was happy to pay $200/month for Opus 4.5 when they had more capacity, but 4.7 felt like a huge step back and no longer worth the price and inconvenience.

I remember an OpenAI employee comment on the GPT5.5 release post about how they specifically geared it towards long-horizon tasks and its been a breathe of fresh air in that regard. I have five two-week long sessions going right now and there's been no degradation in performance or efficiency. It's much better at carrying rules/learnings forward even in long-running sessions and grounding/refreshing itself in verified facts when it loses context.

Its funny because in two weeks I've gotten way more done with GPT5.5 with way fewer tokens and way less handholding. I think this goes to show how important tooling and the harness is and how a capable model like Opus 4.7 can be severely handicapped by bad product decisions.

The longest route on the NYC subway is precisely when you have an appointment and the train decides to stop because there's electrical issues, someone jumped on the track, "there's a train stopped ahead of us", the express decides to go local instead, someone is holding the door, your route involves the F/G or any line that serves less affluent neighborhoods...

Idk, my recent experience with Claude is that 4.7 barely knows how to use basic bash tools - how to properly check when programs have finished running, even basic stuff like how to run pytest suites and read the failed tests from the output without re-running the suite to specifically look for them. It's shockingly dumb for all of the tooling they've built into Claude Code (the useless Monitoring tool that blocks bash polling/sleeping that actually works, etc.).

I finally get fed up and started using GPT 5.5 the past 4 days and its a breath a fresh air despite feeling much more minimal. With Claude I had to write so many hooks to enforce behaviors it wouldn't remember and it lacked common sense on. GPT 5.5 does a much better job with things like knowing the AWS CDK CLI can hang on long CloudFormation deployments and it should actively check the deployment status using CloudFormation API rather than hanging for 30+ minutes - and it does this all without asking.

Maybe there's better tooling built into Codex too, but at least on the surface level it seems like how smart the model is makes a significant difference because Claude has more tools than I can count and still struggles to use "grep".

Edit: Like just now - I can't tell you how many times I day I see this sequence:

"Sorry, I'll run in parallel"

"Error editing file"

"File must be read first"

Repeat 10x for the 10 subagents Claude spawned and then it gets stuck until you press escape and it says "You rejected the parallel agents. Running directly now"