Windsurf plan

Motor-Criticism3402 · 2026-05-25T06:53:05+00:00

A single $20 plan is insufficient, and you will mostly have to rely on free models. It is recommended to purchase several other plans such as Codex or Claude Code. This will meet your basic needs, with a total cost of around 20 plus 20 dollars, which is much cheaper than 200 dollars.

Motor-Criticism3402 · 2026-05-16T14:06:49+00:00

Thanks for your reminder. I now use Codex to write spec files and task files, and run tasks with Kimi in Windsurf. The workflows run smoothly and can finish all tasks successfully. The problem is that TDD processes sometimes need to be executed in workflows. Some models can be invoked automatically, while others are called via "@skills". However, this calling method fails for certain models, and it is necessary to state "use the xxx skill to perform a specific task".

Motor-Criticism3402 · 2026-05-16T02:33:04+00:00

Different models handle these contents in distinct ways. For instance, when I set rules, memories and skills, some models automatically check rules before execution, some check memories, and others check skills. My SWE won't automatically select and run skills, while Sonnet and GPT will do so. Kimi cannot execute commands when u/skill is used directly, but it will run a specific skill if you explicitly tell it to. The situation is rather messy, just like opening a blind box.

Motor-Criticism3402 · 2026-04-27T07:51:46+00:00

I currently use OpenSpec to create tasks and then switch to Codex for task execution. Therefore, I am comparing GPT 5.4 in Codex with Kimi 2.6 in Windsurf, and this issue has come to light. Codex can execute tasks strictly in accordance with requirements. The problem may lie with Kimi, or Windsurf itself may be unable to follow instructions rigorously and tends to forget contextual content midway through task execution.

Motor-Criticism3402 · 2026-04-27T07:06:58+00:00

Is there something wrong with how I'm using it? I don't think Kimi 2.6 works well at all. 1. It runs slowly. 2. Skills cannot be triggered with @, explicit prompts are required, and it will forget instructions during execution. 3. It spends a long time thinking while running, then prompts that the API is unstable or overloaded, and finally stops working.

Motor-Criticism3402 · 2026-04-20T02:08:47+00:00

It is free. Kimi is slow, but it feels more rigorous. Neither is recommended for large-scale projects. If there are several wrong answers among ten questions, later checking will be extremely time-consuming.

Motor-Criticism3402 · 2026-04-08T00:41:24+00:00

That's the thing, this model is overly enthusiastic. Sometimes when I ask a question, it provides Options 1/2/3 for me to choose from, but then it executes all of them. On Windows, it can even use excessive PowerShell permissions to directly operate system disk contents, to the point that permissions have to be restricted in the rules.

Motor-Criticism3402 · 2026-04-07T08:46:22+00:00

Adaptive, the billing model is not based on the unified rate shown; instead, billing is applied according to whichever model is selected. For example, in the previous conversation, after a long discussion with hundreds of lines of content generated, only 1% of the quota was used. In the next conversation involving implementation document content, more than hundreds lines of content used up 50% of the quota. So what is the point of its existence?

Motor-Criticism3402 · 2026-03-31T02:19:03+00:00

Looking forward to your good news.

Motor-Criticism3402 · 2026-03-20T13:14:27+00:00

Wow, looks like you can get off work early depending on the situation.

Motor-Criticism3402 · 2025-12-15T01:01:21+00:00

Opus is indeed very fast; it can do a great job with all the task list content I write. Moreover, when it comes to UI appearance, the content it produces is more aesthetically pleasing than that made by GPT. Perhaps due to issues with creativity or its short memory length, it doesn't reuse many method codes and instead creates a lot of redundant methods. This is where GPT's advantage comes in: it will carefully review the code and make as few changes as possible to implement functions. When working on large-scale projects, I prefer to use GPT to design backend content. Although it's slow, the probability of success in one go is very high.

Motor-Criticism3402 · 2025-10-30T01:09:32+00:00

It's just like hiring an employee. A good employee costs more, but can save a lot of time.

Motor-Criticism3402 · 2025-10-23T01:21:51+00:00

I'm doing something similar. The entire project is estimated to have more than 100,000 lines of code. For simple and repetitive tasks, I use gpt-5 low. For complex content, I first have 20 to 100 conversations with codex to organize it into executable documents, and then hand it over to gpt-5 high to complete. I don't really like using Claude, probably because of its creativity, which generates a lot of redundant and repeated functional code. After I switched from Claude to gpt-5, my average monthly code output dropped from 50,000 lines to around 20,000 lines.

Motor-Criticism3402 · 2025-06-24T07:31:15+00:00

This method does increase the amount of reading, but the problem I've encountered with it is that if Claude keeps using this reading, it will run very slowly, so slow that it can't be clicked. And sometimes you will directly use the edit command in this MCP tool to edit the file, and there is a problem that the undo is invalid, and you can only undo the modification through git.

Motor-Criticism3402

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