Joined August 2014
- Tweets 7,145
- Following 173
- Followers 55
- Likes 389
69 Photos and videos
Day 17/365
Training AI is step 1.
Deployment is where products are built.
Local → Cloud → Mobile
That’s the real journey.
8
During controlled evaluations, engineers with minimal security experience used Mythos to scan thousands of software codebases for vulnerabilities. The model showed striking capabilities it discovered 271 vulnerabilities in Mozilla's Firefox and developed exploits for 181 of them
1
7
Anthropic's red team and the UK's AI Security Institute reported that Mythos found thousands of zero-day vulnerabilities across major operating systems and browsers. (The Conversation)
Notable findings included:
A 27-year-old bug in OpenBSD and a 16-year-old bug in FFmpeg
47
Day 16/365
Stop relying on generic AI.
Train your own smaller model.
Llama/Qwen your data = specialized AI
That’s how real startups build defensible products.
6
Day 15/365
Stop building AI demos.
Build products.
Businesses pay for:
Cost reduction
Revenue growth
Workflow automation
Industry-specific AI
That’s where real money is.
5
Day 14/365
Multiple AI agents without communication = chaos.
Real systems use:
Sequential workflows
Parallel workflows
Manager-worker architecture
That’s how enterprise AI teams scale.
8
Day 13/365
One AI agent doing everything = bad architecture.
Real systems use specialized agents:
Sales agent
Support agent
Finance agent
Research agent
AI teams > AI employee
10
Day 12/365
Your chatbot answers questions.
AI agents complete tasks.
Search product → check stock → calculate shipping → place order
That’s where AI gets scary powerful.
And profitable.
6
Day 10/365:
Your chatbot isn’t dumb.
It just has no memory.
Without memory: AI forgets everything.
With memory: AI remembers users, preferences, previous chats.
That’s how real AI assistants feel smart.
1
1
11
Day 9/365:
Your local LLM is useless without YOUR data.
Llama 3 knows general knowledge.
It doesn’t know:
• Your inventory
• Your invoices
• Your company docs
• Your customer records
Fix = RAG
That’s how real AI assistants work.
14
Day 8/365:
Run your own AI locally on Windows.
Install: Ollama
Models: Llama 3
Qwen 2.5
Benefits: ✅ No API costs
✅ Privacy
✅ Offline use
Future: We’ll run this on mobile too 🚀
31
Day 8/365:
Run your own AI locally on Windows.
Install: Ollama
Models: Llama 3
Qwen 2.5
Benefits: ✅ No API costs
✅ Privacy
✅ Offline use
Future: We’ll run this on mobile too 🚀
18
Day 7/365: You can now build your first AI chatbot.
2 ways to build:
Hosted APIs → fast
(OpenAI, Gemini)
Custom LLMs → scalable
(Llama, Qwen)
Hosted = easy
Custom = cheaper private mobile-ready
Most people stop at APIs.
We’re going deeper 🚀
14
Day 6/365: AI doesn’t lie. It guesses.
When it doesn’t know something, it fills the gap with probability.
That’s called hallucination.
Fix it:
- Add constraints
- Ask for sources
- Allow “I don’t know”
Control the AI 🚀
5
Day 4/365: Your prompts are the problem.
Bad: “Write code”
Good: “Act as senior dev, build REST API with pagination”
AI isn’t smart.
It’s obedient.
Better input = better output.
Follow for daily AI mastery 🚀
8
Day 3/365: What is a token?
AI doesn’t read words.
It reads small chunks called tokens.
Every input output = tokens
And that’s what you’re billed for.
Understand this → save money 💰
Follow for daily AI learning 🚀
10
Day 2/365: AI, ML, and LLM are NOT the same.
AI = Big concept
ML = Learning from data
LLM = Language expert (like ChatGPT)
Most people confuse these.
Now you don’t.
Follow for daily AI breakdowns 🚀
22
Day 1/365: AI is not magic. It’s math data.
AI doesn’t think.
It predicts patterns based on training.
Just like a child learns language, AI learns from data.
No intelligence. Just probability.
Follow for daily AI learning
6