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Most people think AI = prompts.
That’s outdated.
Real AI in 2026 = Agents with skills
Here’s the architecture nobody talks about 👇
➡️ Skills
Reusable instruction modules
Like training manuals for AI
➡️ Core loop
Perceive → Reason → Act → Observe → Repeat
This is how agents actually “work”
➡️ MCP servers
Connect AI to real tools
GitHub, Slack, databases, APIs
➡️ Subagents
Delegate tasks like a team
Researcher • Coder • Deployer
➡️ Hooks
Automate actions
Before / After / On trigger
➡️ Memory layer
Files + Claude.md = always-on context
➡️ The stack
Plugins = Skills + Tools + Subagents + Hooks
💡 Big shift:
You’re not using AI anymore
You’re building systems that run work
Prompting is step 1
Agent design is step 10
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Anthropic just released ALL the Claude Code secrets
Their Prompting best practices just went live in their docs and I spent hours reading it and testing out all the tips
Here are the 10 that make Claude Code so much better: 🧵
Multi-agent architectures are the FUTURE
Here are 6 different types:
𝟭. 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 (𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹) 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
A supervisor agent orchestrates multiple specialized agents.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦:
• One agent retrieves information from internal data sources
• Another agent specializes in public information from web searches
• A third agent specializes in retrieving information from personal accounts (email, chat)
𝟮. 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Incorporates human verification before proceeding to next actions, used when handling sensitive information.
𝟯/𝟱. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹) 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Agents communicate directly with one another in a many-to-many fashion. Forms a decentralized network without strict hierarchical structure.
𝟰. 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Agents process tasks in sequence, where one agent's output becomes input for the next.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦: Three sequential agents where:
• First query agent retrieves information from vector search
• Second query agent retrieves additional information from web search based on first agent's findings
• Final generation agent creates a response using information from both query agents
𝟱. 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
Includes agents dedicated to transforming data.
𝘌𝘹𝘢𝘮𝘱𝘭𝘦:
• A transformation agent that enriches data at insert-time or transforms existing collections
There are also some other patterns that can be combined with these architectures:
• 𝗟𝗼𝗼𝗽 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: Iterative cycles for continuous improvement
• 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: Multiple agents working simultaneously on different parts of a task
• 𝗥𝗼𝘂𝘁𝗲𝗿 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: A central router determining which agents to invoke
• 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿/𝘀𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘇𝗲𝗿 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: Collecting and synthesizing outputs from multiple agents
Check out this ebook for more info: https://t.co/pfM1lcp3SC
Impactful #AI & #MachineLearning Project Ideas for your resume:
1. Building an AI Agent for Trading using Agentic AI
2. Building a RAG Pipeline for LLMs
3. Building Hybrid ML Models for Predictive Modeling
4. MLOps Pipeline using Apache Airflow
Find solved & explained examples here: https://t.co/Xtzya4d9Sr
Steps required to build a large language model from scratch:
1. Tokenization
2. Embedding Layer
3. Positional Encoding
4. Self-Attention
5. Transformer Block
6. Full Language Model
Learn to build an #LLM from scratch using #Python here: https://t.co/7IcXt3NWBo
How to learn #DataScience in 2025?
In 2025, companies will expect you to:
1. Excel in analytical thinking
2. Drive decisions with data insights
3. Leverage Generative AI & LLMs for innovative solutions
Here's a step by step roadmap you can follow: https://t.co/1ALSg5TSem