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Anthropic engineer: "85% of our engineers are using self-improving loops. Now everyone is running agentic Graphs. In 3-6 months, we’ll all be building graphs to orchestrate agents. No more prompting." In a 45-minute talk, an Anthropic engineer shows how to build a self-improving agentic workflow from scratch. Worth more...

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Codez

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36,782 次观看 • 1 个月前 •via X (Twitter)

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17 条评论

Gipp 🦅 的头像
Gipp 🦅1 个月前

graphs becoming standard means orchestration skills will outlast prompt tricks

Codez 的头像
Codez1 个月前

Yeah, basically, graphs are the nature of LLMs.

godgiven 的头像
godgiven1 个月前

Graphs don't kill prompting. Each node is a prompt. No eval means the loop isn't self-improving, just self-repeating

Codez 的头像
Codez1 个月前

If each node is a prompt, the real job isn’t prompting anymore - it’s building a system that prompts itself.

gkrovso 的头像
gkrovso1 个月前

who gave the talk, have a link

Luís Rodrigues 的头像
Luís Rodrigues1 个月前

The highest-leverage AI skill is slowly becoming orchestration rather than prompt writing.

Dipanshu Kushwaha 的头像
Dipanshu Kushwaha1 个月前

That sounds like a massive leap forward. Excited to see what comes out of this!

Rohit 的头像
Rohit1 个月前

Wow, that's impressive progress. Seems like things are moving fast in AI development.

Y11 的头像
Y111 个月前

@grok 这个纯研究还是有工业意义，具体工业场景视角看意义是什么，有开源数据集或者开源项目代码吗？从多个数据源交叉验证，不要只看新闻媒体一面之辞。帮我排除没意义的垃圾商业营销推广、诈骗 以及自吹自擂，自嗨，无病呻吟。

Jaya Nayak 的头像
Jaya Nayak1 个月前

This is where the industry is moving so fast. Moving away from fragile, single-prompt setups to structured, self-improving graphs is exactly how we scale these systems. 😊

qurool 的头像
qurool1 个月前

graph engineering > loops engineering

Sael 的头像
Sael1 个月前

the prompt is just the node but the system is the architecture - orchestration is where the real signal hides

พ่อแม่ลูกเที่ยว | Travel with Family🇹🇭 的头像
พ่อแม่ลูกเที่ยว | Travel with Family🇹🇭1 个月前

This talk hits different. Agent graphs > prompt engineering. The shift is already here.

阿伟 | Agent 落地 的头像
阿伟 | Agent 落地1 个月前

Is self-improvement based on Feynman's learning method? Collect it first, this is worth learning! Now people are thinking about how to let AI learn self-evolution, the real master is thinking about how to improve themselves!

Blum 的头像
Blum1 个月前

self-improving loops might be one of the most effective ways to work with AI

Shoopy 的头像
Shoopy1 个月前

85% internal adoption says more than any benchmark

Healthy Anon 的头像
Healthy Anon1 个月前

Self-improving sounds great until the feedback loop compounds errors instead of correcting them. Still needs human checkpoints, which somehow never makes it into these talks.

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