Andi 

#Bitcoin

[Clair 光 @lynxluna]

Sekarang saya tau benchmarknya

AI bisa ngelakuin, ngejelasin, dan nulis buku-buku “webdev” yang saya “direct” selama 5 hari terakhir.

Itu isi kepala saya semua, AI cuma ngoding ama nulis prosanya.

Kira2 kamu udah bisa seberapa dari yang saya direct. (Cek di reply) ⬇️

[Clair 光 @lynxluna]

Bukunya di sini, semua gratis

https://t.co/hI9R3l82NC

[DAN KOE @thedankoe]

https://t.co/OXHQ3woHtW

[Andi  @biroedonker]

Pada akhirnya yg punya relasi kuat dengan user yg tidak akan bisa ditiru

[Residual @res1dualedge]

REA + Opus 5.5 might be the most dangerous combo on the internet right now

and 99% of builders still have no idea what to do with it

you pay every month for apps whose features are already sitting on your laptop

send this one prompt to Opus 5.5

it reverse-engineers any paid closed-source app for you

the engineer who walks into Monday standup with a working internal copy of a $500/seat tool is the one who ends the quarter with a new title

full guide on becoming a REA engineer from scratch below ↓

[Yarchi @undefinedKi]

This is an actual goldmine for every AI builder

400+ ready-made agents and skills for almost any job. From Anthropic, its engineers and the community.

https://t.co/5a4ZEG1lR2 (158k stars)
~170 agents organized like a company: engineering, design, marketing, sales, product, testing, security, support. Extra packs for finance, healthcare and game dev. Works with 15+ tools.

https://t.co/5oQQqoYR0H (40k stars)
202 agents, 184 skills and 105 commands packed into 94 Claude Code plugins. Language experts, code review, debugging, infra, security, testing. Install only the plugins you need.

https://t.co/HA64e77RFt (26k stars)
Anthropic’s own plugins built around jobs: sales, customer support, product management, marketing, legal, finance, data.

https://t.co/ue1eJ5QCQX (18k stars)
Official agents you can deploy: a daily brief from Slack and GitHub, scheduled Sentry triage, Slack and Linear bots, a customer support agent, a financial data analyst.

https://t.co/x93uIDHS6C (103k stars)
From Anthropic engineer. 25 skills covering the whole dev cycle: interview-me, spec writing, TDD, browser testing, security review, CI/CD.

I’d start with claude-quickstarts. Every agent there is a full working example you can run and take apart.

[Kyrox @kyr0stack]

Sam Altman (CEO of OpenAI):

"You don't need to write prompts anymore. You need loops and graphs."

In 39 minutes he shows how to use LLMs better than almost anyone does

Chats → Prompts → Loops → Graphs

skip the loop and every mistake comes back as something you blame on the model

skip the graph and you're the one deciding every job by hand, every single day

that's the gap between using AI and having AI work for you

watch it today

then save the full graph engineering guide below before everyone catches up ↓

[Callan @callanxai]

Andrej Karpathy:

"Prompting is going away.

Delete everything, keep Graph."

In 1 hour he shows how to build Graphs, and why it's the only thing that will be left standing at the end

LLMs → Prompts → Agents → Graphs

a prompt gets you one answer and forgets the job

an agent finishes the job and forgets what it learned

a graph keeps every finished job and turns it into the starting point for the next one

watch it today

then save the full guide on Graphs below before everyone catches up ↓

[Andi  @biroedonker]

@lolbingung Daripada zonk 😁

[Mr. Buzzoni @Mr_Buzzoni]

I can't believe this is f*cking free

how to build your first AI agent (full guide, with a Jev decision layer)

a year ago I burned two weeks on my first agent. this guide gets you there in an afternoon

in the right hands it makes one builder ship like a small team:

[Gyomei @Gyome1_]

Andrej Karpathy just shared his secret to understanding AI

AI does more of your work every month, and you're left reading walls of text

his 4-step blueprint to make AI explain anything clearly:

step 1 → ask for ASD-STE100: a controlled language spec originally built for aerospace maintenance docs. LLMs know it well, and its strict rules produce clean, readable writing. if it feels too stiff, ask for "80% of the way to ASD-STE100"

step 2 → ask for a diagram: a visual is often much easier to process, parse and understand than a wall of text

step 3 → ask for it "in HTML": you get a beautiful, interactive webpage with animations, because LLMs have become very good at frontend

step 4 → ask for an explainer video: the format Karpathy is most bullish on. try "Create a 3b1b style video explainer on X.

since code is now abundant, you can ask for big, custom, throwaway apps and videos that would never have made sense to build before

the result: every answer arrives in the format your brain reads fastest

save this and try step 4 on the next topic you want to learn ↓

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

Learn AI for free directly from top companies.

1 - Anthropic:
https://t.co/wN0afoUyik

2 - Google:
https://t.co/fMD9rTx0ja

3 - Meta:
https://t.co/WOSzd2mqKv

4 - NVIDIA:
https://t.co/wIBexRIoUx

5 - Microsoft:
https://t.co/4KliKC9Z14

6 - OpenAI:
https://t.co/CpUS74TySP

7 - IBM:
https://t.co/Hcurqftnuu

8 - AWS:
https://t.co/MFKEB5WHc8

9 - https://t.co/EvUVcefMJR:
https://t.co/LFcnadRtXI

10 - Hugging Face:
https://t.co/kMAjDfcK4h

follow me if you find this helpful.

[rvaniaaa @rvaniaaaa]

I still don't understand why people are still explaining themselves to AI from scratch every single day

a hundred times this year, you told it who you are. a hundred times, it forgot

five minutes fixes this:

create a folder → name it raw → drop in any source, article, transcript, PDF, voice memo

tell the model to read it once → link it to everything already there → never touch it again

create one more file → name it CLAUDE.md → who you are, what you're building, what already failed

now every session starts here → the model reads it automatically → before you type a single word

ask it anything across everything you've ever fed it → it answers from months of compiled understanding → not from zero

most people quit around month one → nothing looks like it's working yet → that's exactly where the value starts

five minutes to set up. you never explain yourself from scratch again

[MindBranches @MindBranches]

How to Build an AI Second Brain With Claude and Obsidian That Gets Smarter Every Day (Full Guide)

article summary in 4k light & dark

[Dhruv kumar @Dhruvkumar16797]

🚨Leave Netflix tonight.

Watch this 2 h 34 min Stanford class.

It's the clearest, most complete, and brutally honest explanation out there on how ChatGPT and Claude are really built.

From Tokenization and BPE to the Transformer architecture, the training pipeline, and the next-token decoder. No fluff. No marketing. Just the truth.

Doesn't matter if you've never touched a line of AI code or if you spend your days launching Agents: by the end, you'll suddenly connect a ton of pieces you've been trying to fit together for years.

The real core boils down to this:

How text turns into numbers the model can "eat" (BPE tokenization)
The sole mission of a language model: predict the next token
How the Transformer uses Attention so tokens can pass messages to each other
In training, the NLL loss pushes the probability of the entire sequence
In generation, the decoder builds the response token by token

The big-picture view that takes most people years to form… this class delivers it to you complete in one sitting.

Free up your time.
This could be, no exaggeration, the most valuable class you watch this month.

[Python Programming @PythonPr]

Layers of AI

[Tripti Tripathi @triptitips]

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 𝗔𝗜 = 𝗖𝗵𝗮𝘁𝗚𝗣𝗧.

Not even close.

ChatGPT is what you see.

The real AI revolution is the massive ecosystem being built underneath it.

This AI Stack Map captures 100+ tools powering modern AI applications.

𝗧𝗵𝗲 𝗺𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 𝘀𝘁𝗮𝗰𝗸 𝗹𝗼𝗼𝗸𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗹𝗶𝗸𝗲 𝘁𝗵𝗶𝘀:

LLMs → OpenAI, Claude, Gemini, Llama, Mistral
Agentic AI → LangGraph, CrewAI, AutoGen, CAMEL, Agno
RAG → LangChain, LlamaIndex, Haystack, GraphRAG
Embeddings → OpenAI, Voyage, Cohere, BGE
MCP → Connecting models with tools, data and systems
AI Security → Guardrails, Presidio, Lakera, Prompt Security
Observability & Evals → LangSmith, Langfuse, Phoenix, Ragas
Memory → Redis, Mem0, Zep, Neo4j, Chroma
Agent Frameworks → OpenAI SDK, Semantic Kernel, Google ADK, Bedrock
Automation → n8n, Zapier, Make, Airflow, Prefect
Vector Databases → Pinecone, Weaviate, Qdrant, Milvus, pgvector

But here’s the part that matters more than the logos.
𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗹𝗶𝘀𝘁.
It’s a dependency graph.
Your RAG is only as good as your retrieval + embeddings.
Your agent is only as reliable as its tools + evaluations + guardrails.
Your memory is useless if you can’t observe when context becomes stale or wrong.
Your MCP layer becomes dangerous if permissions and governance are an afterthought.
And a “best-in-class” stack can still become a terrible production system.
Why?
Because AI systems rarely fail only inside one component.

𝗧𝗵𝗲𝘆 𝗳𝗮𝗶𝗹 𝗮𝘁 𝘁𝗵𝗲 𝗵𝗮𝗻𝗱𝗼𝗳𝗳𝘀.
Model → Retrieval
Retrieval → Context
Context → Agent
Agent → Tool
Tool → Memory
Memory → Evaluation

That’s where latency compounds, context gets lost, permissions leak, hallucinations propagate and reliability starts falling apart.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗺𝗼𝗮𝘁 𝗶𝘀𝗻’𝘁 𝗵𝗮𝘃𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀.

It’s designing how they work together.

A stack map tells you 𝗪𝗛𝗔𝗧 exists.

Production engineering decides 𝗛𝗢𝗪 it all survives contact with reality.

Which tool in your AI stack has become indispensable?

Save this map for your next AI build.
Repost it for someone designing an AI architecture.

#AI #ArtificialIntelligence #GenAI

[Persiba Bantul @Persiba]

𝙎𝙏𝙍𝙊𝙉𝙂 𝙏𝙊𝙂𝙀𝙏𝙃𝙀𝙍, 𝙂𝙍𝙀𝘼𝙏𝙀𝙍 𝙁𝙊𝙍𝙀𝙑𝙀𝙍 ‼️

Menuju 6 Dasawarsa❗️

Kelangsungan Persiba bukan hanya sampai hari ini saja, harapan & warisan yang akan terus hidup dan tumbuh dari generasi ke generasi ❤️

𝙋𝙚𝙧𝙨𝙞𝙗𝙖 𝘽𝙖𝙣𝙩𝙪𝙡,𝙨𝙚𝙡𝙖𝙢𝙖𝙣𝙮𝙖❗️

#COYLSA

[lumxss @bkdgiffug]

剑桥这回直接扔王炸了！！

AI & ML经典教材全集直接免费开放，PDF随便下。

想学机器学习又不想被割韭菜买高价课的，这十本刷完，底子基本就硬了。

顺序从易到难排好了：

1️⃣ 《机器学习理解》——理论算法一把抓，零基础入门首选
🔗 https://t.co/fylTw37bOl
2️⃣ 《机器学习数学基础》——数学底子弱的先把这本补上
🔗 https://t.co/yykNLQmdfv

3️⃣ 《机器学习算法的数学分析》——深入数学原理
🔗 https://t.co/MEvgkXoHYy
4️⃣ 《深度学习理论原理》——搞懂DL背后的理论根基
🔗 https://t.co/ig93KOQRPn

5️⃣ 《神经网络与机器学习》——神经网络的系统讲解
🔗 https://t.co/cTMBvJz6Ny
6️⃣ 《图深度学习》——图神经网络入门必读
🔗 https://t.co/RJeCqCstml

7️⃣ 《机器学习的算法视角》——从算法角度重新理解ML
🔗 https://t.co/VT0YnKdUdO
8️⃣ 《概率论：理论与实例》——概率基础打牢
🔗 https://t.co/NqVoIZGGYg

9️⃣ 《应用概率基础》——概率论实战应用
🔗 https://t.co/FrxqQER3mQ
🔟 《高级数据分析》——数据科学进阶必备
🔗 https://t.co/Zt74B21nrf

说句实话，这些书没一本是轻松的，别指望躺着翻完。
但只要你能硬啃下来两三本，比听群里吹一年AI牛逼都管用。