soledede

[Peter Yang @petergyang]

If you're tired of the "I made an $1M app in 30 min with AI" posts, then you should watch my full 40-min AI course with Adam (Meta) on:

- Prompting
- Evals
- RAG
- Fine-tuning

It's beginner friendly and we keep it real.

📌 Watch now: https://t.co/9dmR0q2K2G

[Tech with Mak @techNmak]

AI Agent Design Patterns, enjoy!!

Please support and follow: @techNmak

[Gorden Sun @Gorden_Sun]

谷歌开源了���套Deep Research应用
全栈应用，前端React，后端LangGraph。LangGraph Agent提供对话能力，使用Google API做搜索，使用Gemini API生成回复。
Github：https://t.co/bw5WUWqE9p

[Eric Partaker @Eric_Partaker]

Are you a leader? A manager? Both?

Here's why knowing the difference matters:

[liminal @Liminal1988]

20 Micro Habits to make your life better:

[JORDAN @iamjordan]

40 websites where you can find remote jobs and earn in USD in 2025:

[Book Therapy @Book_therapy223]

Soft Skills – Do’s and Don’ts

[liminal @Liminal1988]

Deep talk with your partner

[Adib @ ICML 🇰🇷 @adibvafa]

Introducing the world's first reasoning model in biology!

🧬 BioReason enables AI to reason about genomics like a biology expert.

A thread 🧵:

[Mohit Mishra @chessMan786]

Fundamentals of GPU Architecture

[Solhawk @SolhawkGo]

普通人怎么快速完成原始资本的积累❓
关键在于：尽早完成原始资本的积累！

[Maryam Miradi, PhD @MaryamMiradi]

🤺 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗜𝘀 𝗖𝗹𝗲𝗮𝗿: 𝗔𝗴𝗲𝗻𝘁𝘀 𝗪𝗶𝗹𝗹 𝗡𝗘𝗘𝗗 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚

Why? It combines Multi-hop reasoning, Non-Parameterized / Learning-Based Retrieval, Topology-Aware Prompting.

﹌﹌﹌﹌﹌﹌﹌﹌﹌

🤺 𝗪𝗵𝗮𝘁 𝗜𝘀 𝗚𝗿𝗮𝗽𝗵-𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚)?

✩ LLMs hallucinate.
✩ LLMs forget.
✩ LLMs struggle with complex reasoning.

Graphs connect facts. They organize knowledge into neat, structured webs. So when RAG retrieves from a graph, the LLM doesn't just guess — it reasons. It follows the map.

﹌﹌﹌﹌﹌﹌﹌﹌﹌
🤺 𝗧𝗵𝗲 𝟰-𝗦𝘁𝗲𝗽 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗼𝗳 𝗚𝗿𝗮𝗽𝗵 𝗥𝗔𝗚

1️⃣ — User Query: The user asks a question. ("Tell me how Einstein used Riemannian geometry?")

2️⃣ — Retrieval Module: The system fetches the most structurally relevant knowledge from a graph. (Entities: Einstein, Grossmann, Riemannian Geometry.)

3️⃣ — Prompting Module: Retrieved knowledge is reshaped into a golden prompt — sometimes as structured triples, sometimes as smart text.

4️⃣ — Output Response: LLM generates a fact-rich, logically sound answer.

﹌﹌﹌﹌﹌﹌﹌﹌﹌
🤺 𝗦𝘁𝗲𝗽 𝟭: 𝗕𝘂𝗶𝗹𝗱 𝗚𝗿𝗮𝗽𝗵-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀

✩ Use Existing Knowledge Graphs like Freebase or Wikidata — structured, reliable, but static.

✩ Or Build New Graphs From Text (OpenIE, instruction-tuned LLMs) — dynamic, adaptable, messy but powerful.

🤺 𝗦𝘁𝗲𝗽 𝟮: 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗮𝗻𝗱 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀

✩ Non-Parameterized Retrieval (Deterministic, Probabilistic, Heuristic)
★ Think Dijkstra's algorithm, PageRank, 1-hop neighbors. Fast but rigid.
✩ Learning-Based Retrieval (GNNs, Attention Models)
★ Think "graph convolution" or "graph attention." Smarter, deeper, but heavier.

✩ Prompting Approaches:
★ Topology-Aware: Preserve graph structure — multi-hop reasoning.
★ Text Prompting: Flatten into readable sentences — easier for vanilla LLMs.

🤺 𝗦𝘁𝗲𝗽 𝟯: 𝗚𝗿𝗮𝗽𝗵-𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀

✩ Sequential Pipelines: Straightforward query ➔ retrieve ➔ prompt ➔ answer.
✩ Loop Pipelines: Iterative refinement until the best evidence is found.
✩ Tree Pipelines: Parallel exploration ➔ multiple knowledge paths at once.

🤺 𝗦𝘁𝗲𝗽 𝟰: 𝗚𝗿𝗮𝗽𝗵-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗧𝗮𝘀𝗸𝘀

✩ Knowledge Graph QA (KGQA): Answering deep, logical questions with graphs.
✩ Graph Tasks: Node classification, link prediction, graph summarization.
✩ Domain-Specific Applications: Biomedicine, law, scientific discovery, finance.

Paper: https://t.co/eqp9AX5jSG

≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣≣

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[科技混子Chris @ChrisSlacker]

DeepSeek超强组合来袭🔥存下吧！很难找全了

Deepseek➕任一App＝王炸组合【超强CP🔥】

今天分享11个腾讯团队都在用的爆款文案结构公式，教你轻松用Deepseek打造“爆款文案/短视频”
更多🗂️：⬇️

[LanLan @LanLan93083241]

“自由的极致就是：可以离开任何不喜欢的人和事”

[董币哥 @NextBullReady]

真正有钱之后，才会明白：

财富只是门票，真正改变的是你的底层认知。

👇👇不用废话，直接上干货👇👇

从打工人 → 管理者 → 创业者
108 个思维模型，层层拆解
每一步，都是通往自由的升级打怪。

细品，一生都不亏。