🚨 BREAKING: President Trump reveals that every time Iran keeps trying to choose a new leader, they DIE
"Everybody that seems to want to be a leader, they end up DEAD!" 🔥
"It's an AMAZING thing taking place before your eyes! Because for 47 years we were PUSHED AROUND."
"You see the tremendous progress made. Their missiles wiped out RAPIDLY."
‘RATEPAYER PROTECTION PLEDGE’: President Trump signs proclamation ensuring tech giants fully cover AI energy costs, shields American households from rising bills
“Big Tech companies are committing to fully cover the cost of increased electricity production required for AI data centers!”
https://t.co/7w0pgyCobU
🚨 BREAKING: In a MASSIVE affordability win, President Trump just got Big Tech and AI companies to agree to covering the FULL COST of all electricity surges as a result of their facilities.
This means power bills for Americans in surrounding areas WILL NOT go up as a result!
"They will actually build OVER-capacity, then sell the over-capacity at very low prices into the grid!"
Big companies will not only cover the full cost but build additional power plants to ensure a surplus. 🇺🇸
Tencent’s latest open weights model Hunyuan-A13B (80B total, 13B active) achieves an Artificial Analysis Intelligence Index of 56 - impressive given that it can be run on a single H200 (in FP8 precision)
Hunyuan-A13B scores ahead of Qwen3 8B and 14B, but behind larger models like Qwen3 235B (235B total, 22B active) and DeepSeek R1 (671B total, 37B active).
Hunyuan-A13B supports a 256K context window - larger than Qwen3’s 128K context window but smaller than MiniMax M1’s 1M context window.
Hunyuan-A13B can be run in FP8 precision on just one H200 or two H100s - a much smaller deployment than the 8xH100 deployment required for Qwen3 235B or MiniMax M1 (456B).
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Our 20-part guide on AI Agents and Agentic workflows:
1. Open-endedness and AI Agents
2. Your go-to Agents vocabulary
3. Illustrating Agentic vocabulary: Real and potential examples
4. From Butler to J.A.R.V.I.S to Butler again?
5. Building blocks of Agentic Systems ⬇️
6. Profiling
7. From Agentic AI to Physical AI
8. Knowledge: How modern agents learn to adapt
9. Memory
10. What is Reasoning
11. How do Agents plan and reason?
12. Reflection in AI
13. Action: Executing tasks with UI and API tools
14. MCP🔥
15. Human-in-the-loop in Agentic workflows
16. Co-agency
17. A2A🔥
18. Multi-agent systems
19. Reward and value functions
20. What coding agent wins?
Find all the guides here -> https://t.co/B2wRXZGO4Q
This ones kinda ridiculous...
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13 Outstanding MCP Servers:
▪️ Hugging Face Official MCP Server
▪️ Browser MCP
▪️ Bright Data
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Save the list and check this out for the links and more info: https://t.co/DC8mnhiFaD
OAuth 2.0 Flows Visualized
Note: The Implicit Flow is no longer recommended due to security vulnerabilities. Consider using the Authorization Code Flow with PKCE for enhanced security.
MCP is redefining what AI can do, and 95% of builders haven't caught on.
I've built agents that run my calendar, organize files, and manage tasks. All autonomous.
The results changed how I think about automation.
I'm breaking down exactly how MCP works (with code + examples):
10 AI Tools to replace your tedious work
Gamma AI —Create presentation with AI
Gemini Flash —AI image generation and editing
Perplexity AI —Web search/summarization
Replit agent —Build MVPs/mobile apps
Typefully —Social media management
Grok-3.5 —Deep research tasks
Windsurf AI —Best for AI coding
Kling AI —Create Stunning videos
ChatGPT —Brainstorming
Lovable —AI Websites
So, which is your favourite AI tool?
Google just dropped Gemini CLI, an official open-source AI agent for your terminal.
- AI Coding Agent
- Task Automations
- Use MCPs
- 1000 free requests/day
10 Powerful features you don't want to miss:
Here are the LEGO blocks of AI agents.
Let’s build some 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 with them!
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Autonomous agents aren’t one-size-fits-all.
How they work depends on the pattern they follow.
Agentic workflow patterns offer a structured lens into how autonomous AI systems reason, plan, and act.
Patterns act like blueprints. Once a pattern works, it can be reused across agents and fine-tuned across verticals (e.g., customer support, research assistants, dev tools). This means faster iteration and reduced development time.
A plethora of agentic workflow patterns have entered the conversation lately. However, here are three foundational patterns we’re seeing emerge:
𝟭. 𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻
Agents self-check their outputs—looping back to reflect and revise when needed.
𝟮. 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻
Agents dynamically select external tools like web search, vector DBs, or apps like Slack to generate better responses.
𝟯. 𝗣𝗹𝗮����𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝘁𝗲𝗿𝗻
Agents break down a goal into multiple sub-tasks, executing them in sequence based on a predefined plan.
Each pattern introduces new affordances for reasoning, coordination, and scale—and choosing the right one depends on the problem you’re solving.
Looking for more? Download our eBook on 𝘈𝘨𝘦𝘯𝘵𝘪𝘤 𝘈𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦𝘴 𝘧𝘰𝘳 𝘙𝘦𝘵𝘳𝘪𝘦𝘷𝘢𝘭-𝘐𝘯𝘵𝘦𝘯𝘴𝘪𝘷𝘦 𝘈𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯𝘴.
👉 https://t.co/WZaVbwfRlI