Michael Primo

Cybersecurity student at Central Connecticut State University Computer Science A S Networking Certificate AI Minor Focused on security AI and financial tech

[Suryansh Tiwari @Suryanshti777]

You're not behind because you use the wrong AI model.

You're behind because you're still using AI like a chatbot.

The people pulling ahead are building systems around it.

Agents. MCP servers. Memory. Automation. Reusable workflows.

That's the real leverage.

I spent hours digging through the Claude ecosystem and put together the 62 best free Claude resources in one infographic.

Here's the condensed version:

Official

• [Claude Code Docs](https://t.co/2hdcg4vk6p)
• [Official MCP Docs](https://t.co/rR5xWWPbL8)
• [Anthropic Academy (Free Courses)](https://t.co/JnISfhxzPx)

Claude Code Mastery

• [Claude Code Best Practices](https://t.co/Yy2T1hdOGT)
• [Claude Code Examples](https://t.co/EEI3L9B89C)
• [Claude Code Overview](https://t.co/KWovgBLk6O)

MCP Servers

• [Official MCP Servers Directory](https://t.co/u4CtpOzW9B)
• [Awesome MCP Servers](https://t.co/4ut8r3aEyQ)
• [https://t.co/VfTXVFifa4 Directory](https://t.co/RnhDBdTNxX)

Skills & Memory

• [Claude Skills Documentation](https://t.co/EdwQUsQNeL)
• [Mem0 AI Memory Layer](https://t.co/aQxvnUQv6S)
• [Building Effective Agents by Anthropic](https://t.co/LGEqxkOX76)

Agents & Automation

• [ClawTeam Multi-Agent Framework](https://t.co/X54zwT0Ywr)
• [OpenAI Swarm Framework](https://t.co/DTmBQWKRtV)
• [Loop Framework](https://t.co/AsRPGKMnVp)

Tools & Directories

��� [ECC Tools Directory](https://t.co/7oa896FGYl)
• [Awesome Claude Code Resources](https://t.co/sByztmfQdt)
• [Dokploy MCP Server](https://t.co/IsB8nHsRht)

The biggest AI shift isn't better models.

It's people learning how to turn those models into compound systems.

Today, most people are still prompting.

A year from now, the winners will be orchestrating agents.

That's why this infographic matters.

Bookmark it.

You'll keep coming back to it as the AI ecosystem evolves.

The model is rented.
The systems you build around it become your moat.

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

50 USEFUL GITHUB REPOS

1. iFixAi — AI misalignment testing
→ https://t.co/popCuoRzix

2. public-apis — free APIs for everything
→ https://t.co/JTdQVNz1vz…

3. build-your-own-x — learn by building
→ https://t.co/cwrgt8vKSV…

4. developer-roadmap — learn any tech skill
→ https://t.co/19I0D4przT…

5. free-programming-books — thousands of free books
→ https://t.co/WXWiburO6O…

6. system-design-primer
→ https://t.co/VsFbjNLKlY…

7. coding-interview-university
→ https://t.co/zeOUkZZztd…

8. the-art-of-command-line
→ https://t.co/SqAytaXs1E…

9. project-based-learning
→ https://t.co/7G4xb7kovB…

10. you-dont-know-js
→ https://t.co/04C3EuLQhR…

11. the-book-of-secret-knowledge
→ https://t.co/BGHnSJ6wDY…

12. tech-interview-handbook
→ https://t.co/YPPxoHiVPw…

13. awesome-selfhosted
→ https://t.co/ZSsjPp7Ni1…

14. javascript-algorithms
→ https://t.co/glF6DSB937…

15. 30-seconds-of-code
→ https://t.co/IPk36rhim8…

16. github gitignore templates
→ https://t.co/rXRZurVaJ6…

17. ollama — run LLMs locally
→ https://t.co/ezENWNojbz

18. langchain
→ https://t.co/jZbyvS4keR…

19. n8n automation
→ https://t.co/U46zzSsQss

20. openclaw — local AI assistant
→ https://t.co/6uLnL1bu4w…

21. dify — AI app builder
→ https://t.co/MQeItI6buw

22. langflow
→ https://t.co/NFelNiSQse…

23. mem0 — AI memory layer
→ https://t.co/DH093UQ14s

24. browser-use
→ https://t.co/IanOAGT69B…

25. crewAI
→ https://t.co/Ti1ksXhZEB…

26. MetaGPT
→ https://t.co/c1jPcRx3K7

27. AutoGen (Microsoft)
→ https://t.co/tTu3D31O47…

28. aider — AI coding assistant
→ https://t.co/5IcXwkZvP2

29. markitdown (Microsoft)
→ https://t.co/fBiWNwozAU…

30. open-webui
→ https://t.co/AJge6W8REn…

31. maigret — OSINT tool
→ https://t.co/1PAFpEVkzv

32. TradingAgents
→ https://t.co/F0h7P84AJq…

33. stagehand
→ https://t.co/58nlXXeh1I…

34. firecrawl
→ https://t.co/b33AVyLXOr…

35. transformers (HuggingFace)
→ https://t.co/tqEErrkraH…

36. vLLM
→ https://t.co/m1OmfB3T60…

37. llama.cpp
→ https://t.co/Arhuy8cPzk…

38. llama_index
→ https://t.co/JA3AE6omNW…

39. nanoGPT (Karpathy)
→ https://t.co/SynWn95E1K…

40. RAGFlow
→ https://t.co/aZpgBZstsa…

41. supermemory
→ https://t.co/Lt1OHZSggp…

42. awesome-claude-skills
→ https://t.co/hriueWSCoW…

43. Bumblebee (Perplexity AI security tool)
→ https://t.co/Rhji2m0hol…

44. ComfyUI
→ https://t.co/2E1f7mOlR0…

45. DeepSeek (official repo varies by model release)
→ https://t.co/iuP6xrvgdS

46. Lobe Chat
→ https://t.co/DbSgvEhoo2…

47. freeCodeCamp
→ https://t.co/lFcX2Q1USP…

48. system design + interview prep extras (bonus)
→ https://t.co/zeOUkZZztd…

49. AI agents ecosystem (extra)
→ https://t.co/jZbyvS4keR…

50. automation + AI workflows (extra)
→ https://t.co/U46zzSsQss

Save this before you forget it exists.

You'll come back to at least ten of these.

[freeCodeCamp.org @freeCodeCamp]

ChatGPT apps need more than a prompt. They need tools, UI, auth, data, and deployment.

In this tutorial, @a0m0rajab explains how to build an MCP server and connect it to ChatGPT.

You'll create a todo app with Node.js, the MCP SDK, Supabase Auth, a database, OAuth flow, DigitalOcean deployment, and more.

https://t.co/9ITeoZRPEu

[freeCodeCamp.org @freeCodeCamp]

Discrete Mathematics plays a key role in machine learning and algorithms.

And in this Python course, you'll learn some of its key concepts like combinatorics, number theory, and the Pigeonhole Principle.

The course also covers permutations, the Rule of Sum, prime and rational numbers, congruences, and lots more.

https://t.co/JeAJME2puM

[leopardracer @leopardracer]

140,000 DEVS STARRED THIS REPO IN 3 MONTHS AND MOST OF THEM STILL DON’T KNOW HOW TO USE IT

pair hermes agent with obsidian and notebooklm and you get a second brain that writes its own skills, maps its own knowledge, and never forgets what you taught it

while everyone else is copy-pasting into chatgpt every morning and starting from zero, this thing has been compounding for months

one setup, runs locally, gets smarter every single day

you’ll want this saved the day you get tired of explaining yourself to an ai that doesn’t remember you ↓

[0xMarioNawfal @RoundtableSpace]

GOOGLE'S GEMMA 4 12B RUNS AT 21 TOKENS PER SECOND ON A BUDGET RTX 4060 LOCALLY AND THE BENCHMARKS SHOULD NOT BE THIS GOOD FOR A 6.6GB FILE.

77.5% on MATH Olympiad, 78.8% on expert science, 72% on real code. No API. No cloud. No subscription.

[EliaAlberti @EliaAlberti]

@RoundtableSpace Here’s my Claude Code + Obsidian Second Brain set up!

https://t.co/8n5fgADV3D

[Codez @0xCodez]

https://t.co/OKVRrLvphu

[Shraddha Bharuka @BharukaShraddha]

Open weights. Faster tokens. Lower latency.

This isn’t an update, it’s a power shift.

[Michael Primo @1PrimoLife]

@NadzuAI Start

[Kanika @KanikaBK]

🚨 GOOGLE, META, OPENAI etc. BIG TECH are REJECTING JOB CANDIDATES BEFORE EVEN THEY FINISH TALKING.

50 LLM QUESTIONS. IF YOU CAN'T ANSWER THEM, THE INTERVIEW ENDS BEFORE IT STARTS.

The people passing these interviews are walking out with $200k+ offers.

Someone just LEAKED THE EXACT LLM INTERVIEW QUESTIONS these companies are asking right now.

And the gap between people who know these answers and people who do not is already costing careers.

Here is every category you need to know:
The Basics they always ask first:
↳ How does tokenization work and why does it matter
↳ How does attention actually work inside a transformer
↳ What is a context window and what breaks when it gets too big
↳ What are embeddings and how do they get initialized
↳ How does the model know word order without reading left to right

The fine-tuning questions that eliminate 80% of candidates:
↳ What is LoRA and why is it better than full fine-tuning
↳ What is QLoRA and when do you use it instead
↳ How do you fine-tune a model without making it forget everything it already knows
↳ What is model distillation and why do companies use it
↳ How do you handle vocabularies with millions of possible words

The generation questions most people guess on:
↳ Beam search vs greedy decoding, which one and when
↳ What temperature actually does to model output
↳ The difference between top-k and top-p sampling
↳ Why autoregressive models work differently from masked models

The advanced concepts that separate good from great:
↳ How RAG works and why it beats fine-tuning for factual accuracy
↳ Why Chain-of-Thought prompting makes models dramatically smarter
↳ What Mixture of Experts is and why every frontier model uses it now
↳ Zero-shot vs few-shot learning and when each one wins

The math questions that make people sweat:
↳ Why softmax is used inside attention and not something simpler
↳ What cross-entropy loss actually measures
↳ What KL divergence is and where it shows up in AI training
↳ Why vanishing gradients were destroying transformers and how they fixed it

If you are applying for any AI role in 2026 and you cannot answer at least 40 of these, you are not ready yet.

The full list of 50 questions is worth printing out and going through one by one.

Save this post. Your next interviewer has almost certainly pulled from this exact list.

[Koroush AK @KoroushAK]

https://t.co/DZ8x5q7z9e

[Michael Primo @1PrimoLife]

@Zephyr_hg Ideas

[Zephyr @Zephyr_hg]

50 AI businesses you could start this weekend. Pick one.

Every idea comes with a revenue path, MVP scope, and exact tech stack so you're not guessing what to build or how it makes money.

Organized into 5 categories: vertical agents, content tools, data infrastructure, edge AI, and services.

Medical billing agents. Construction safety cameras. Prompt injection firewalls. Drone inspectors for solar farms.

Each one scoped lean enough to validate in 48 hours with a simple ad or DM campaign.

No more staring at tech trends trying to figure out where the money actually lives.

Pick the lane that fits your skills and ship the tiniest possible version before your competition finds these ideas.

Comment "IDEAS" and I'll DM it to you (must be following)

[Vaidehi @Ai_Vaidehi]

Machine Learning looks complicated… until you see how everything connects.

This mindmap breaks it down from raw data → models → deployment → real-world impact.

Here’s how the full ML ecosystem actually works 👇

• Data Fundamentals → Understanding structured/unstructured data, features, labels, and splits
• Data Preprocessing → Cleaning, encoding, scaling, and handling missing values before modeling
• EDA → Visualizing patterns, correlations, and distributions to guide decisions
• Feature Engineering → Creating, selecting, and transforming features that improve model performance
• Algorithms → Supervised, Unsupervised, and Reinforcement Learning powering predictions
• Model Training → Gradient descent, learning rate, epochs — where models actually learn
• Model Evaluation → Metrics like Accuracy, F1, RMSE to measure performance
• Optimization → Hyperparameter tuning, cross-validation, and regularization
• Deep Learning → Neural networks, CNNs, RNNs, Transformers for complex tasks
• Deployment → APIs, cloud, and monitoring to take models into production
• MLOps → CI/CD, pipelines, versioning, and retraining for scalable ML systems

This is the difference between learning ML concepts and understanding how ML systems actually work end-to-end.

If you're serious about Data Science or AI, this is your blueprint.

Want to go deeper? Save this and build one layer at a time.

Useful resources to get started:
• Python + ML Basics → https://t.co/ewXeQcxyj6
• Data Handling & EDA → https://t.co/tQmr7SpYXq
• Deep Learning → https://t.co/JaanwkzRA9
• MLOps & Deployment → https://t.co/mOXy79x9KX

Repost ♻️ if this helps you see the bigger picture

[Polash Khan @polash_ai]

THIS IS INSANE

This anthropic engineer turned $200 into $14,300 in Polymarket trading bot running Claude Code that analyzes 86M trades and ranks wallets by win rate and profit.

The system uses AI to scan 14,000 wallets in minutes and filter trades.

It only makes 10 trades daily, focusing on high-probability whale-driven moves and exits early before the whales do.

You only need Claude + laptop + 1 hour/day.

Giving This Free for 24 hours. To get it:

1. Comment the word 'AutoPilot'
2. Like and Retweet this post
3. Follow me @polash_ai
(so i can DM you)

[Sandeep Jain @sjsandeep_jain]

Most people say "build an AI agent."

Very few know what that actually means.

Here’s the real blueprint to go from idea → working agent 👇

1. Define the job
What problem are you solving?
Who’s the user? What does success look like?

2. Design the brain
Clear system prompt, role, instructions, guardrails
(This is where most agents fail)

3. Pick the right model
Speed vs cost vs intelligence
Don’t overpay for simple tasks

4. Add tools
APIs, databases, MCP servers, custom functions
Agents become powerful when they can act, not just answer

5. Give it memory
Short-term + long-term context
So it learns, adapts, and improves over time

6. Orchestrate everything
Workflows, triggers, retries, agent-to-agent communication

7. Build the interface
Chat, app, API, Slack bot
Make it usable, not just functional

8. Test + improve
Evals, latency checks, real-world feedback
Iteration is the real moat

💡 Truth:
An “AI agent” isn’t one prompt.

It’s a system.

And the people who understand systems…
are the ones building unfair advantages right now.

📌 Save this (you’ll need it when you build)
🔁 Repost for builders
➕ Follow for practical AI breakdowns (no fluff) 🚀

[Ruben Hassid @rubenhassid]

Instead of watching Netflix tonight.

Spend a day mastering Claude here: https://t.co/Vn60ElPZ2i

→ Level 1 - 24 min: The basics.

Claude For Dummies: https://t.co/HNa5MrCLVU
Claude Setup: https://t.co/jw2qdIcjnh

→ Level 2 - 1 hour: Real workflows.

Claude Cowork: https://t.co/uWTpOI3Woc
Claude for teams: https://t.co/qxlcqhf8bM
Claude Design: https://t.co/ZY8Fg5D2ea
Cowork + Projects: https://t.co/Q7AN9CZAbO
Claude for slides: https://t.co/L0bPMgXci6
Claude Skills: https://t.co/6cHYYfjXEA

→ Level 3 - 3.5 hours: The pro moves.

Avoid sycophancy: https://t.co/5i8xSJBGUl
Claude Code: https://t.co/UgE9xBXVbE
Claude 101: https://t.co/OvBmlvnVqL
Stop hitting Claude limits: https://t.co/j5fEzSH5br
Stop Prompting: https://t.co/j1LATSJiat

→ Level 4 - 8 hours: Expert mode.

Claude Computer: https://t.co/TxYuHPjgbV
Build with Claude API: https://t.co/RcCbfNjlzz

Pro tip: Don't binge it. Do one level per sitting.

Actually apply each guide before moving to the next