Greg Brockman
@gdb · 1d agoOpenAI 🤝 Accenture:
- Tens of thousands of ChatGPT Enterprise seats for Accenture
- Collaborating to help enterprises bring agentic AI capabilities to their businesses
openai.com/index/accentur…m
86
75
931
120
185
OpenAI 🤝 Accenture:
- Tens of thousands of ChatGPT Enterprise seats for Accenture
- Collaborating to help enterprises bring agentic AI capabilities to their businesses
openai.com/index/accentur…m
🔍 PeopleHub: AI-Powered LinkedIn Intelligence
Made by the LangChain Community
Open-source LinkedIn intelligence by Meir Kadosh. Uses LangGraph 1.0.1 to orchestrate automated research workflows—profile analysis, parallel scraping, and AI-generated reports.
Check it out on GitHub:github.com/MeirKaD/pepole…T
If you're building an agent, I recommend looking into Raindrop for monitoring. We use it.
How is AI changing work inside Anthropic? And what might this tell us about the effects on the wider labor force to come?
We surveyed 132 of our engineers, conducted 53 in-depth interviews, and analyzed 200K internal Claude Code sessions to find out.
anthropic.com/research/how-a…
The way I use AI has changed with Gemini 3…
I used to try and perfect the minimal ask to get the model to be successful at a task.
Now, I’m constantly pushing myself to be more ambitious, asking for 5x more than I did before with a single prompt. And Gemini 3 crushes it.
Cong to our team for the NeurIPS Best Paper!
🏆 We are incredibly honored to announce that our paper, "Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free" has received the NeurIPS 2025 Best Paper Award!
A huge congratulations to our dedicated research team for pushing the boundaries of AI.
Read more:blog.neurips.cc/2025/11/26/ann…5
New from our alignment blog: How we trained Codex models to provide high-signal code reviews
We break down our research approach, the tradeoffs, and what we’ve learned from deploying code review at scale.
alignment.openai.com/scaling-code-v…
Today we're releasing BrowseSafe and BrowseSafe-Bench: an open-source detection model and benchmark to catch and prevent malicious prompt-injection instructions in real-time.
perplexity.ai/hub/blog/build…
Deep agents accumulate a lot of context during long runs. That’s where file systems come in.
File systems provide a shared workspace for agents and subagents to collaborate. Agents can jot down notes during a run and store context across conversations and threads for persistent memory.
In this post we discuss the key components of deep agents, including a file system: blog.langchain.com/deep-agents/?u…
不写代码，月入5位数？他用N8N打造的“自动化工厂”太狠了
30岁大厂裸辞，血压飙到180，以为人生完了？
结果他靠n8n工作流，单枪匹马月入 5 位数，峰值 10 万，把“一人公司”做成了印钞机。
上周四和香君的直播，下面写一些关键点复盘：
1. 赛博时代的“影分身之术”
香君最震撼我的，不是技术，是他的“矩阵思维”。
在 YouTube Shorts 上，开通 YPP（赚钱门槛）是第一步。
普通人做一个号，累死累活剪视频，听天由命等爆款。
既然爆款是概率学，他就用数量去对冲不确定性。
几十个号怎么管？招人？不
他用n8n搭建了一套“自动化内容工厂”。
剧本固定、素材抓取、剪辑生成、上传发布，全流程自动化。
机器24小时干活，他在睡觉。
这才是真正的“睡后收入”。
2. 别做“批量生产垃圾”的傻事
听到这，很多人是不是准备装n8n了？
停！先听听香君这句人间清醒：
“如果你输入的是垃圾，自动化只会帮你生产更多垃圾。”
这是无数新手死在半路的原因。
香君的路径极具参考价值，分三步走：
第一步：手动干。
先人肉跑通SOP，确认内容有人看，模型能跑通。
第二步：RPA 辅助。
用凹凸码、八爪鱼这些工具，模拟人工操作，提效。
第三步：API + N8N。
确定能盈利了，再写脚本调用 API，实现全自动。
过早自动化，就是加速死亡。
这条建议，价值百万。
3. 极简主义的“功利性学习”
很多人问：n8n门槛高吗？要懂代码吗？
香君是产品运营出身，但他现在玩得比程序员还溜。
秘诀就三个字：“干中学”。
别去买一堆课。
业务推进到哪一步，“不搞这个就饿死”，这种时候的学习效率是最高的。
把技术当工具，而不是当学问。
4. 哪怕是风口，也要有退路
即便现在一个月能搞几十个 YPP 账号，香君依然很冷静。
他说这只是“人力密集型产业”的自动化版， 没有成长空间，只是在吃风口红利。
所以他把这当成“投资”： 在合适的时机，投入资源，拿走收益。
然后把赚到的钱和时间， 投入到寻找下一个更长周期的机会中。
这是通透。
最后说一句：技术本身不值钱，用技术构建出的“自动化印钞系统”，才是你在这个时代最大的底气。
别光看着，先去跑通你的第一个 SOP。
@香君赛博淘金，是他的公众号，欢迎关注。
本周四继续：邀请70万册书+跑通AI出海赚钱的孟健，欢迎预约
