I am not necessarily saying the conclusions are wrong, just that they are not really substantiated in any way
In the end, everyone is kind of just sharing their own experiences. You'll only know whether they work for you by trying it yourself.
But at the same time, even this doesn't really work.
The lucky gambler thinks lottery tickets are a good investment. That does not mean they are.
I've found very very limited value from these things, but they work alright in those rather constrained circumstances.
Perhaps you don't believe OpenAI and Anthropic when they say this, but it is a requirement upon which most enterprise contracts are predicated.
I agree with the author that GPT-5 models are much more fixated on solving exactly the problem given and not as good at taking a step back and thinking about the big picture. The author also needs to take a step back and realize other providers still do this just fine.
I'm having a blast with gemini-3-flash and a custom copilor replacement extension, it's much more capable than Copilot ever was with any model for me and a personalized dx with deep insights into my usage and what the agentic system is doing under the hood.
https://bsky.app/profile/verdverm.com
The container stuff that backs it is built on Dagger
https://github.com/hofstadter-io/hof/tree/_next/examples/env
The entire extension and agent framework is in that repo too
extensions/vscode and lib/agent
I let my agent do whatever because I know exactly what it can and can't do. For example, it can use git, but cannot push, and any git changes are local to its containerized environment and don't get exported back to my filesystem where I do real git work. I could create an envelope where they could push git, and more likely I'll give them something where they can call GitHub ali, that's really more useful anyway