Nick

Makima is watching you！🫵

[Nick @freemyself_x]

感谢这个AI时代，让我对很多人都「去魅」了，也能更加独立地思考，什么是观点，什么是事实。#世界就是一个巨大的草台班子

[Nick @freemyself_x]

Opus 5 的中文真的难绷🤪

[Baye @waylybaye]

当明白了自己的价值不能靠外界定义后，我突然意识到这个社会最顶级的权力就是「个人价值的定义权」吧？宗教甚至某些政党都会牢牢把握这个定义权：你是好是坏，需要奖励还是惩罚，你的一生是否有价值，你应该感到骄傲还是羞耻。这种控制比暴力规训高级多了，人们甚至意识不到它的存在。

[Nick @freemyself_x]

@thsottiaux implement this for third part model

[Nick @freemyself_x]

https://t.co/sn4fMWGecJ

[Nick @freemyself_x]

在codex上直接使用第三方的api模型，很方便很好用👏

[Nick @freemyself_x]

@ixiaowenz 时间不应该只用赚钱来衡量价值

[Nick @freemyself_x]

程序员们涩涩发抖

[ノア/Noa @noa_onlyonly]

自己紹介です。

元AV女優、去年引退しました。前のアカウント(フォロワー2万人)は事務所に取られたので、ここから作り直してます。

今はプログラマーとして働いてます。Claudeで自動化したりWeb作ったり。

真面目にやってるつもりですが、たまに素が出ることもあるかもしれません。笑

Web制作・自動化のご相談はDMまで。

[Nick @freemyself_x]

模型的能力才是本质。Harness只是一个AI历史上的一个阶段，AGI不需要Harness

[seekinganythingbutalpha @ivanalog_com]

Deepseek为什么要做Harness呢？

我的体会是，dsv4f在Codex里体现出的水平，是可以打平Fable 5的。

但是在OpenCode里，只能发挥出其一半的真实水平。

主要差异有两个，一是考虑问题的全面性，二是长程任务的记忆连贯性。

这两个都非常微妙。前者的意思是，Codex里的dsv4f，明显对代码的读取，整体问题的宏观把握，要比在OpenCode里更好，不会出现一叶障目，不见泰山的情况。

第二个就更玄了，在解决一个系列问题的时候，往往有很多局部最优解会造成全局鬼打墙。解决一个局部问题，往往带来更多的全局问题，造成执行任务死循环，最后提出一个其实不成立的解决方案。

OpenCode下的Deepseek还是经常会出现这个问题。

但是在Codex里，dsv4f奇迹般的，没有这个毛病。走得再远，也不会忘记为什么出发。

一个好的Harness 非常深刻，背后的消息路由，记忆管理，长短记忆的优化取用，工具调用，都是千万次尝试优化的结果，而且sdk其实并不会完全公开这些小技巧。

这些东西在将来，可能是比模型的智力，更深的护城河。

[Nick @freemyself_x]

跟风搞一个

[Nick @freemyself_x]

哪里可以免费试下api？每次看到qwen发布都有点懒得试

[Qwen @Alibaba_Qwen]

📢Meet Qwen3.8-Max — our most capable model to date.

Next week, the open weights of Qwen3.8-Max will be released, and Qwen3.8-27B is also going open-weights to meet you all!🎉

Qwen3.8-Max, a new bar for coding and cowork at 2.4T parameters:

- Autonomous coding: 10+ days of self-evolving development, from empty folder to production without hand-holding, complete project trace in the GitHub：https://t.co/iVHZWQoeSo
- Real work, real results: Production-quality deliverables across hundreds of professions.
- Long-horizon mastery: System-level autonomous planning with closed-loop adaptive learning, driving 500+ turns of chip design optimization and 365 days of e-commerce strategy.
- Native multimodal intelligence: Vision isn't just input — it's a continuous feedback loop for planning, execution, and self-correction.

💰Pricing:
Input: $2.0 / M tokens
Output: $6.0 / M tokens
Implicit Caching: $0.25 / M tokens

Start building with Qwen3.8-Max! 🚀

📖 Blog: https://t.co/iwjmQxLBof
✅ Qwen Studio: https://t.co/4V2pFvDovG
⚡ API: https://t.co/gAGqaLQGbN

[Nick @freemyself_x]

@arkuy99 我目前没有

[Nick @freemyself_x]

@ohyishi 只有第一代onekey丐版，还在正常使用

[Nick @freemyself_x]

@CuiMao 一切才刚刚开始

[Nick @freemyself_x]

@ShanghaoJin 🤣

[Nick @freemyself_x]

@syhily 斩杀线即将到来

[Nick @freemyself_x]

都在演戏，年内降息一两次

[紀春生 @ji_chunsheng]

财政部长 Scott Bessent 在戴维营出席内阁会议时，便签纸写着：

“待办事项：买入50亿至100亿美元的日元。”

[Nick @freemyself_x]

@_FORAB 🤣

[Nick @freemyself_x]

🤣

[AB Kuai.Dong @_FORAB]

这波被迫出售持仓的 AI 股神 Leopold，于昨晚发布了投资者内部信，信中解释了他为了投资人利益，在面对极端下跌行情，不得不处理持仓，来挽救大家的本金。

虽然这波造成了剧烈的回撤，但今年以来它基金的净收益为 +80%，最终报告将另行公布。

[Nick @freemyself_x]

@ykmfb001 狙击glm，下一个就是kimi了

[Nick @freemyself_x]

感觉v4pro要超越Kimi k3

[Artificial Analysis @ArtificialAnlys]

DeepSeek V4 Flash 0731 scores 50 on the Artificial Analysis Intelligence Index, a 10-point jump over DeepSeek V4 Flash (released April 2026) that puts it 6 points ahead of DeepSeek V4 Pro. It shares identical architecture and pricing with the earlier DeepSeek V4 Flash, and lands on our Pareto frontier for Intelligence vs Cost per Task

@deepseek_ai’s DeepSeek V4 Flash 0731 is one Intelligence Index point behind GPT-5.6 Luna (max, 51). Even after OpenAI’s 80% price cut on GPT-5.6 Luna today, DeepSeek V4 Flash 0731’s Cost per Task on DeepSeek’s first-party API comes in at ~60% lower than GPT-5.6 Luna (max), a model with comparable intelligence. A key driver of this is DeepSeek’s ~98% cache hit discount on its first-party API, a significantly more aggressive discount than the 90% cache hit discount offered by most of the industry

The new model is a significant step up from the previous generation, DeepSeek V4 Flash (40), and places the model within 1 point of GLM-5.2 (max, 51). It remains 7 points behind the open weights frontier set by Kimi K3 (max, 57). For additional context, this places the model in line with recently released Gemini 3.6 Flash (50) and 1 point behind Muse Spark 1.1 (xhigh, 51). DeepSeek is expected to release the model’s full weights in the coming weeks

DeepSeek V4 Flash 0731 retains a 1M token context window, and its size remains unchanged from DeepSeek V4 Flash at 284B total parameters and 13B active at inference time

Key results:

➤ Improvements in agentic performance: DeepSeek V4 Flash 0731 achieves an Elo rating of 1559 on GDPval-AA v2, our evaluation focused on agentic real-world work tasks, up from 1189 for the previous DeepSeek V4 Flash. Once weights are released this will be the second highest open weights score, behind Kimi K3 (max, 1687) and ahead of GLM-5.2 (max, 1510). Terminal-Bench 2.1 rises 17 points to 79% and τ³-Bench Banking 8 points to 31%

➤ Token usage falls 12% against the predecessor: DeepSeek V4 Flash 0731 used ~206M output tokens to run the Intelligence Index, against ~234M for the previous DeepSeek V4 Flash. The new variant is more token efficient, achieving a higher Intelligence Index with a lower number of total output tokens

➤ DeepSeek V4 Flash 0731 improves over its predecessor on every evaluation in the Intelligence Index: Alongside the agentic gains, CritPt gains 9 points to 17%, SciCode 5 points to 50%, Humanity's Last Exam 5 points to 37%, AA-LCR 3 points to 66% and GPQA Diamond 1 point to 91%

➤ AA-Omniscience improvements are driven by fewer hallucinations, rather than higher accuracy: DeepSeek V4 Flash 0731 achieves an AA-Omniscience Index of -16, a +7 improvement from its predecessor. This improvement is purely driven by a reduced hallucination rate, with overall accuracy (percentage correct) unchanged. Its AA-Omniscience Hallucination Rate is 84%, a 12 point decrease from its predecessor, and comparable to models such as GPT-5.6 Terra (max, 85%) and Mistral Medium 3.5 (82%)

Additional model details:

➤ Context window: 1M tokens (equivalent to DeepSeek V4 Flash)

➤ Size: 284B total parameters (13B active)

➤ Input modalities: Text input and output only

➤ Accessibility: Available through DeepSeek’s first-party API

➤ Pricing: $0.14/$0.28 per 1M input/output tokens, unchanged from DeepSeek V4 Flash. Cache hit price of $0.0028 per 1M tokens, a 98% discount