4N50N

[Hux @huxlab]

你有没有发现：
同样的数据和模型，问简单问题还行，一问复杂问题就开始胡拼乱凑？

问题其实出在方法上。

普通 RAG 找的是文本片段，
Graph Engineering 找的是现实中的关系。

举个例子：

你问“为什么三月销量下降”，
普通 RAG 只会把带“销量”和“三月”的段落拼在一起给你。
而知识图谱能直接顺着因果关系，把真正的原因链找出来。

微软、斯坦福、Anthropic 都独立验证了这件事：

微软 GraphRAG 显示，在复杂和全局问题上，准确率提升约 18%，成本反而降低约 85%。

斯坦福的研究进一步指出，模型只是图里的一个节点，正确的图结构往往比更大的模型更重要。

Anthropic 的实际案例也证明，把结构化关系数据接上之后，事件检测速度提升了 5 倍，会议时间减少了 50%。

最终大家得出同一个结论：

复杂问题、多跳推理、全局主题，拼的不是模型有多大，而是关系有没有被显式地建出来。

[MIKE @mikenevermiss]

https://t.co/W4sHoE0ew0

[leopardracer @leopardracer]

Anthropic's $2.2M engineer just explained why graph engineering beats loop engineering

most people arguing about graphs online have never actually watched one execute

Source → Planner → Coder → Reviewer → reject back to the exact node that broke

four nodes one shared state and packets flying edge to edge while the graph pulses through its own cycle

loop mode turns off the second the task splits into real specialties and one agent stops trying to do everything at once

reviewer catches a failure and kicks it straight back to the exact node that broke, no full restart, no lost context, a loop still sits inside every node here, you're just wiring a bunch of them into an org chart

bookmark this and watch it run, then read the article below

[wandermist @wandermist]

https://t.co/GRgX1F9xX9

[codila @0xCodila]

Creator of Claude Code just dropped a free masterclass on agent engineering with Opus 5 from 0% to 100%:

“80% of our engineers are using self-improving loops”

10% → 3:38 - why they deleted 80% of the system prompt
30% →6:55 - delete your CLAUDE.md every 6 months
55% →17:44 - one prompt, 11 days, entire codebase rewritten
80% → 21:59 - the prompt that's been running for 15 days
100% →25:27 - two promts that spawn 1,000 agents

most people are still writing longer prompts - he's deleting them and running 1,000 agents instead

bookmark & watch - then read the full agent orchestration playbook below ↓

[CyrilXBT @cyrilXBT]

https://t.co/ZLoZ7vypfA

[Rahul @sairahul1]

Boris Cherny, creator of Claude Code:

"Opus 5 does in a day what used to take your team a month. Most people will keep using it wrong."

In 12 minutes he explains why Opus 5 needs less prompting than any model before, and why your old detailed prompts now work against you.

Watch it, then read the article below on how to prompt Opus 5 👇

[AI Guides @free_ai_guides]

https://t.co/UinIeNwnC0

[Hux @huxlab]

这个视频把 AI Agent 讲得太透了。

不堆术语，直接把复杂的东西拆成一条直路：

消息进来 → Agent 跑 → 工具调用 → 结束 → 记忆保存 → 技能成长

尤其是记忆四分层（Working / Semantic / Episodic / Procedural）和干净的 Harness Loop，看完直接想回去改自己的系统。

真正好的讲解，不是删减，而是找到那个让人瞬间「啊，原来如此」的类比。

强烈推荐。

[rody @0x_rody]

https://t.co/c356BQHdUV

[darkzodchi @zodchiii]

Anthropic engineer:

"You don't need better prompts. You need graph engineering: memory that stays, so your agent never forgets anything."

In 28 minutes he shows what Anthropic does differently, how to build and structure work with agents.

This beats any paid agent course I've seen.

Watch it, then read the graph engineering guide below 👇

[Argona @Argona0x]

a new kind of engineer is showing up. they don't write prompts, they design how the AI works. it's called graph engineering, and the people who get it now are about to make everyone else look slow https://t.co/z1HhE6voRu

[Gipp 🦅 @gippp69]

https://t.co/ITKQ6Eq8sI

[beamnxw ./ @beamnxw]

https://t.co/Qd15VI1bFb

[Ricker @0xRicker]

Andrej Karpathy just dropped a full 116 min course on: "How to Build LLM From Scratch"

He built LLM from scratch in 200 lines of code in 2 hours.

90% of AI engineers can't tell you what happens inside the model they use every day.

In 2 hours, he shows you the entire stack:

transformer blocks + attention heads + positional encoding + residual connections.

Worth more than any $5000 AI course you've been eyeing.

[Adrian Punk @AdrianPunk115]

https://t.co/eC1mkWh1GT

[Lunar @LunarResearcher]

4 Anthropic engineers. 75 minutes.

Their best lessons on how to actually prompt Claude:

14:12 - The prompts Anthropic engineers actually use
32:33 - The simple fix that makes prompts work better
59:44 - How to give Claude the context it needs
1:07:59 - The prompt that keeps working while you sleep

Most prompting courses teach theory.

This shows how the people building Claude actually use it.

This 75-minute watch is worth more than most paid prompting courses.

Bookmark and watch it tonight

Then read the step-by-step guide below

[Codez @0xCodez]

Anthropic engineer just dropped a 2-hour workshop on “Graph Engineering” for agentic systems:

“80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.”

• 04:43 - Introduction to RAG and graphs
• 26:30 - Core of graph engineering (nodes, edges)
• 01:09:11 - Agents data indexing in Graphs
• 01:30:50 - Three layers of Graphs for agents
• 01:48:15 - Adaptive RAG for self-verification
• 02:16:37 - The future of graph engineering

This 2-hour workshop will replace your $500 agentic memory course.

Watch it today, then read how to become a graph engineer in the article below.

[Hamza Khalid @humzaakhalid]

Anthropic will pay you $750,000 a year to know this one thing.

Stanford teaches the entire thing in 1 hour and 44 minutes.

Not prompting. How a model gets built, trained, and aligned.

Bookmark it now, because it's 100% free and always will be.

Then read the full guide on building AI agents below:

https://t.co/ZetkE7IQqT

[seeco @seeconvm]

https://t.co/mm6Jy6Ofzu