Junkaleb

[Junkaleb @Edskaleb]

@yifanxu_ephai 我认为不是的，她的skill是可以的，只是cursor兼容，她也是一个优秀的工程师

[Junkaleb @Edskaleb]

@Da7_Tech 我几乎认为Hermes 和omp分别代表了不同工作的no.1

[Junkaleb @Edskaleb]

@Da7_Tech 为什么你的选择里面除了Hermes agent和opencode其他的几乎都是和一个厂商去捆绑的？你为什么不试试omp？

[Gegam @Gegam245074]

If you think GPT-6 Astra and GPT-6.1 Sol are good models, just look at this. And the problem isn't that the code is ugly. It's much more fundamental. It points to serious problems with how these models are trained and reinforced:

1. The model was optimized for measurable outcomes rather than actual intent.
So it prioritizes passing tests and avoiding obvious errors above almost everything else, even when that means sacrificing architecture, maintainability, readability, or the spirit of the task

2. It was trained to minimize unnecessary reasoning and output
That's one reason the code ends up looking obfuscated and compressed into one giant wall of text. Properly decomposing a system into modules, thinking through architecture, optimizing performance, and improving readability all require more inference, more time, and more compute. OpenAI appears to have heavily optimized these models for efficiency

3. The training horizon is too short
The model learns something close to task → result → reward. It receives very little signal about what happens several steps later, when someone has to maintain, extend, debug, or refactor the code. It learns to finish the task in front of it, not to build something that remains good six months later

4. There is not enough genuine self-review
Dead functions and an empty else if strongly suggest that there was no effective final pass where the model reread its own work, questioned unnecessary code, and cleaned up obvious artifacts.

5. The lack of taste is probably an evaluator and data problem
If evaluators mostly judge the final result rather than the quality of the path taken to get there, the model has little incentive to develop good engineering taste. And if a large portion of the training data is noisy, synthetic, or distilled, that problem becomes even worse. Anthropic seems to have placed much more emphasis on high-quality source material, including books and other carefully selected long-form data

6. The biggest problem is honesty during training
The model seems to have learned that producing something that looks like the requested result can be rewarded almost as much as actually doing what was requested.

The task explicitly said to build the entire scene out of 3D objects. Instead, the model generated raster images, placed them around the scene, and positioned the camera so that the final result merely looked three-dimensional

This is exactly why GPT models so often introduce regressions into existing codebases, produce messy code, modify tests to accommodate their own bugs, show poor engineering taste, and struggle with serious work on large, long-lived projects

OpenAI can keep releasing models with more parameters every few months, but until these underlying RL and training problems are addressed, neither GPT Bel, GPT-7, nor a model with 100 trillion parameters is going to solve them automatically

Claude models suffer from some of the same problems, but in my experience much less often. Anthropic appears to have a much stronger culture around RL, evaluation, and long-horizon behavior. That's one reason their models tend to show better taste, follow user intent more faithfully, and produce code that feels more deliberate, structured, and maintainable

[Junkaleb @Edskaleb]

@NFT_Chen 哎，我之前也有这个思路，我实测下来，jev的判断准确率很玄学。

[Junkaleb @Edskaleb]

@Fei2411 那你直接把p-stack的思路fork过来，直接找一个可以用多个模型的harness，然后开始对话，可能会提升一些效率。对抗审查也包含在内而且是三家模型一起上

[Junkaleb @Edskaleb]

@DLKFZWilliam2 ultra 明明不能用

[Junkaleb @Edskaleb]

@MiaAI_lab cool

[Junkaleb @Edskaleb]

@Fei2411 @devindesktop @cognition @DevinAI 恭喜 devin还是不错的产品和订阅，我也订阅了一个max，感觉比cursor要好用一些

[Junkaleb @Edskaleb]

@CtrlAltDwayne 哥们，我觉得你有点夸张了，我可以直接说不可能，我装上Devin cli 用fusion fable5.1+swe2 双medium 把我的OMP里面的东西迁移过来用掉了2%的周额度。你这也太骗人了。😄

[Junkaleb @Edskaleb]

@Da7_Tech Devin Max的订阅真的非常不错，我已经加入了，但是我一直在OMP中使用，没有用过Devin CLI 😂

[Junkaleb @Edskaleb]

@Da7_Tech 👏👏👏

[Junkaleb @Edskaleb]

其实也不是，这个要看你设计的编排，在什么地方去审核。比如我分享一种思路（当然可能要在omp才能实现或者codex cli），你用astra 去设计需求和方案做成spec文档，然后让fable5.1当独立的裁决者。然后丢给grok4.6当main，指挥flash3.8去快速开发 然后裁决者验收。需求会话再验收并回收这个worktree.那么你的工作就是一只在需求会话把一���一个的小需求对接出来丢出去，然后裁决者会DAG的方式拆开。并行开发 实际速度快省钱效率非常高。因为强模型再审核。但是bug就是强模型可能会幻觉一些莫名其妙的测试，需要人去纠正。

[Junkaleb @Edskaleb]

@yism3000 claude带着他的数据开始写小说了

[Junkaleb @Edskaleb]

@0xPickleCati @AnthropicAI 哈哈哈，其实这些都是提示词+数据的组装+claude幻觉写出来的小说

[Junkaleb @Edskaleb]

@Da7_Tech 只需要问一个问题:“检查我的omp第一次会话的注入是否合理.”然后你就会发现这个模型很普通.

[Junkaleb @Edskaleb]

@sleepy0x13 我认为如果是订阅用户，他们收集用户���息，这是服务条款。但是a畜确实挺自嗨的，这最多只能说明A草台班子层层外包+B一个自嗨公司拿了一点破烂数据反复包装好像挖到金矿一样。

[Junkaleb @Edskaleb]

@NFT_Chen 😂😂有可能。我觉得没有美豆好用