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Claude Code 的脑子，被人整个拷出来了 Fable 5.2 是 11 月 9 号发布的，11 月 10 号，完整的系统提示词就挂上了 GitHub。7832 行，95 万个字符，128 个工具，44 个技能，一个字没漏。动手的是 Pliny，这人专门扒各家的提示词，ChatGPT，Gemini，Grok 的全收在一个仓库里，五万多星，跟个提示词博物馆似的 里面最损的还不是规矩写得多细。是连模型怎么装傻都有人扒出来了，给它塞了隐藏输入，它能一本正经地说自己什么都没看见。连否认的姿势，都是提示词提前教好的 所谓 agent 的智能，一大半不是模型自己悟出来的，是人一条一条写出来的规矩。什么时候能自己动手，什么时候必须停下来问人，删个文件要过几道关，全是白纸黑字 Anthropic 花几十亿训出来的东西，最后守着它的就是一个 txt 文件，这个文件现在谁都能下 所以别再说提示词不重要了。模型是发动机，这份文件是驾驶手册，车开得稳不稳全看手册。现在最好的那本，免费了 仓库名 CL4R1T4S

Gemini新模型能力终于上来了，虽然综合能力比不过GPT和Claude，但前端能力非常不错，是我心中的前端第一。 但我有些好奇的是Gemini前端能力已经很不错了，Gemini网页版的这个的文字选中菜单在暗色模式下看不太清，而且看着费力。

Who decides the answer to a prediction market? Usually the creator. Or an AI on a server nobody can check. So we built Noa Court - an AI resolver for Noa, live on NEAR mainnet. In the video we ask Gemini a vague question: "will the NEAR hackathon get more than 2000 projects?" - It finds the real source (Nearhackathon API) and samples it live: 1,803 right now - Turns the vague question into precise, hashed rules - We review the market and sign with a NEAR wallet - Market #27# goes live on Noa, published with readable rules How it resolves: - One Court contract, one pinned resolver running in an @out_layer TEE - Anyone can trigger it. Nobody can pick the answer - Numbers? Decided by code - Words? 3 NEAR AI judges must all agree and quote the evidence Asked "did a crypto hack make the news?", all 3 judges quoted a headline that never says "hack". It refuses to guess: - Prompt injection - flagged - Dead or changed source - void + refund Works with prices, NEAR tokens (powered by Intear & JustHoot), news feeds, web pages, any JSON API. Plug it into any market, contract or agent. Add the MCP to Claude, ChatGPT, Gemini - any AI that speaks MCP: Code: What would you never let an AI resolve? Built at NEAR HACKATHON @NEARProtocol @NearBuilders #NEARHackathon#

this paper should scare every agent framework out there researchers took apart 11 coding agents straight from the source code claude code codex gemini cli openhands aider and more roughly 4 million lines not one of them imports a langchain style framework not one of them searches code with embeddings 9 out of 11 already ship skill.md then a second paper went further and let a loop write the harness the model stays frozen a debugger agent reads every failed run an evolve agent edits the harness files the next round keeps the edit or rolls it back 10 rounds later a one tool seed went from 69.7% to 77.0% on terminal bench 2 the codex harness people built by hand sits at 71.9% craziest part editing only the system prompt made it worse all the gains came from memory, tools and middleware agent = model + harness the model takes the bow the harness does the work drop claude fable 5.2 in that frozen seat and the loop rewrites everything around it both papers in the reply

Open source models on top again. Deepnote tested 12 models in 50 professional situations where an agent must independently decide whether and where to speak up. DeepSeek V4.1 Flash scored 6.35 out of 10, surpassing Claude Haiku 5.5 (6.05), Gemini 4 Argon (5.60) and Grok 4.7 (4.51). GPT-6 Astra takes first place with a score of 8.97.

谷歌把Gemini 4这几个模型怎么选，说清楚了。 Carbon和Argon都是旗舰定位。 Carbon能力更强，适合复杂架构、疑难bug和深度推理，但可能更费token。 Argon能处理大多数需要持续多步执行的复杂任务，相比Carbon更省token。 Hydrogen主打速度和效率。 日常写代码、处理文档，或者当子agent并行干活都合适，也是官方推荐的Gemini 3.8 Flash升级方向。 三个模型都有100万token上下文，还能搭着用。 让Carbon定方案、啃难题，再把改代码、跑测试、并行执行这些活交给Hydrogen，官方文档直接给出了这种用法。

200多元上车Gemini Ultra 5倍额度的羊毛先别急着冲，避坑指南看这一篇就够 不少开发者盯上了Google Antigravity生态里的算力红利，想通过低价套餐直接拉满Ultra甚至调用Opus 5.5的高阶模型额度。 实际下单最大的门槛在Google账号的底层归属地和支付风控，直接裸连打开很容易直接遭遇地区墙报错拦截。 想要顺利订阅，必须提前准备原生干净的对应地区IP，并确保Google支付资料的国家与代理出口保持严格一致。 建议优先选择支持本地货币绑卡结算的外币虚拟卡，避开高风控节点，避免反复切换网络导致账号被风控锁死。 #AI工具# #独立开发#