🚀 Tech | 💻 Coding | 🤖 AI | 💼 Tech Jobs | 📚 Tutorials & Tips | Stay ahead in tech https://xitter.cf/t.co/vnlnhF9SbE
Joined October 2023
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🚀 Looking to connect with talented people across tech.
If you're a...
💻 Software Developer
🌐 Frontend Developer
⚙️ Backend Developer
🖥️ Full Stack Developer
📱 Mobile App Developer
🎮 Game Developer
🤖 AI/ML Engineer
🧠 Data Scientist
☁️ Cloud Engineer
🔐 Cybersecurity Specialist
🎨 UI/UX Designer
🧪 QA Engineer
⚡ DevOps Engineer
🗄️ Database Engineer
🧩 Blockchain Developer
🥽 AR/VR Developer
📊 Data Analyst
🛠️ Open Source Contributor
🚀 Indie Hacker
💡 Startup Founder
📈 Product Builder
📚 CS Student
🎥 Tech Creator
✍️ Technical Writer
🎯 Tech Enthusiast
...you're exactly the kind of person I want in my network.
I'll be regularly posting paid project opportunities, hiring updates, collaborations, internships, freelance work, startup opportunities, and the latest tech news.
👉 Follow now and stay updated so you don't miss upcoming opportunities. The next project I post could be the one you're looking for.
Comment your role below 👇 and let's connect!
#Hiring #TechJobs #Developers #Programming #AI #WebDev #MobileDev #GameDev #CyberSecurity #CloudComputing #OpenSource #Startups #Tech #Coding #100DaysOfCode
Linux concepts you should learn for DevOps
1. Accessing Linux VMs
2. Linux File System
3. Soft and Hard links
4. Files & Directory permissions(chmod)
5. ACL
6. Text processing commands(awk, sed, cut, grep, sort, uniq, wc)
7. Compressing files: tar, zip/unzip
8. Vi, nano editor
9. User account management
10. Commands for administration: top, ps, kill, df, dmesg.
Want to become job-ready in tech?
Stop jumping from one tutorial to another.
Step-1: Learn the Basics
Step-2: Understand How Things Work
Step-3: Practice Daily
Step-4: Build Mini Projects
Step-5: Learn Industry Tools
Step-6: Read Official Documentation
Step-7: Break Things and Fix Them
Step-8: Automate Repetitive Tasks
Step-9: Create a Portfolio
Step-10: Share Your Work Publicly
Step-11: Prepare for Interviews
Step-12: Never Stop Learning
🐍 5 Python Modules Beginners Should Know
1. math
For mathematical operations.
2. random
Generate random numbers and choices.
3. datetime
Work with dates and time.
4. os
Interact with the operating system and files.
5. json
Work with JSON data.
Python comes with powerful built-in modules. Learn to use them.
Save this for your Python journey! 🔖🚀
Shit internet is broken after seeing
4 best course and resources
1. Learn Docker
docker.com/trainings/
2. Linux Unhatched
netacad.com/courses/linux-un…
3. GitLab University
university.gitlab.com/
4. DevOps Engineer.
skills.google/paths/20
Save to unlimited free resources to you
And kept follow
If you’re learning tech, don’t just collect certificates.
Build a GitHub profile. 🚀
Your GitHub can show employers what your resume can’t.
Here’s what to put on it:
• 📂 Personal Projects → Show what you’ve built
• 📝 README Files → Explain your work clearly
• 🔀 Git Commits → Demonstrate consistency
• 🐛 Bug Fixes → Show problem-solving skills
• 🤝 Open Source Contributions → Collaborate with others
• ⚡ Automation Scripts → Solve real-world problems
• ☁️ Cloud Projects → Showcase infrastructure skills
• 🐳 Docker Projects → Demonstrate container knowledge
• ☸️ Kubernetes Manifests → Show orchestration skills
• 📚 Documentation → Prove communication skills
A GitHub profile is more than a code repository.
It’s a portfolio of your learning journey.
Don’t wait until you’re “good enough” to start.
Start building.
Start documenting.
Start sharing.
Your future opportunities may come from a repository you create today.
If I were learning Python from ZERO, these are the 10 projects I'd build:
1. Calculator
2. Number guessing game
3. Password generator
4. Quiz app
5. To-do list
6. Expense tracker
7. Web scraper
8. Weather app
9. Automation script
10. Full application
The key:
Don't build all 10 at the same difficulty.
Each project should force you to learn something new.
10 AI PLATFORMS TO START LEARNING HTML, JAVA, CSS, AND PYTHON
1. SkillBuilder
2. Google Skills
3. Udemy.com
4. edX.org
5. Codewars.com
6. Codecademy
7. SoloLearn
8. CoderZap.com
9. Freecodecamp.org
10. Replit
Don’t overthink it….just start.
Step-1: Build Strong Linux Fundamentals
Step-2: Learn Git and Version Control
Step-3: Understand Networking Basics
Step-4: Practice Bash and Python Scripting
Step-5: Learn CI/CD (Jenkins, GitLab)
Step-6: Get Hands-On with Docker
Step-7: Master Kubernetes
Step-8: Learn Cloud (AWS, Azure, GCP)
Step-9: Understand IaC (Terraform, Ansible)
Step-10: Learn Monitoring & Logging (Prometheus, ELK)
Step-11: Automate as Much as Possible!
10 Excellent and Powerful Platforms for Learning HTML, Java, CSS, and Python
1. Scrimba.com
2. GitHub
3. Coursera
4. Datacamp.com
5. Khanacademy.org
6. W3Schools
7. Mimo
8. The Odin Project
9. Hackerrank.com
10. Leetcode.com
Free Courses With Certificates By Google 👇
1/ AI Essentials & ML:
grow.google/intl/en_in/ai-es…
2/ Cybersecurity:
grow.google/intl/en_in/cyber…
3/ Project Management:
grow.google/intl/en_in/proje…
4/ Data Analytics:
grow.google/intl/en_in/data-…
5/ Advanced Data Analytics:
grow.google/intl/en_in/data-…
6/ UX Design:
grow.google/intl/en_in/ux-de…
7/ IT Automation with Python:
grow.google/intl/en_in/it-su…
6 stages to learn SQL properly :
• Stage 1: SELECT, FROM, WHERE
• Stage 2: ORDER BY, LIMIT
• Stage 3: GROUP BY, HAVING
• Stage 4: JOINs
• Stage 5: Subqueries + CTEs
• Stage 6: Window Functions
20 YouTube channels that make learning AI actually doable.
Save this. Bookmark it. Come back when you get stuck.
1. 3Blue1Brown — the math, made visual
2. Andrej Karpathy — beginner-friendly deep lectures
3. Lex Fridman — conversations with the people building it
4. StatQuest — ML + stats without the pain
5. Jeremy Howard — practical deep learning with Python
6. Two Minute Papers — research in 2 minutes
7. DeepLearning AI — structured courses from Andrew Ng
8. ML Street Talk — sharp discussions on where AI is going
9. freeCodeCamp — free coding + AI certs
10. Sentdex — Python, ML, and projects step by step
11. Data School — data analysis + ML the practical way
12. Codebasics — real-world ML for jobs
13. Siraj Raval — story-driven tutorials
14. Google Cloud Tech — deploy models like a pro
15. Serrano Academy — simplified ML/DL concepts
16. Tina Huang — learning systems + AI career advice
17. Matt Wolfe — AI tools and news in under 10 minutes
18. AI Explained — no-hype breakdowns
19. The AI Advantage — AI for work, productivity, automation
20. Hamel Husain — LLMs, RAG, fine-tuning
Learn a little every day.
Small steps today. Big impact tomorrow.
4 PROJECTS. 4 SKILLS.
Build these instead of 20 random projects:
01 — SALES
Excel
→ Reporting + KPIs
02 — CUSTOMER
SQL
→ Segmentation + behavior
03 — FINANCE
Power BI
→ Dashboard + trends
04 — MARKETING
Python
→ EDA + insights
For every project, show:
Problem
Analysis
Insight
Recommendation
Your portfolio should tell recruiters:
"I don't just use tools.
I solve problems."
📚 Books I’m following for my AI/ML journey:
1. AI Engineering — Chip Huyen
2. Hands-On Machine Learning — Aurélien Géron
3. Hands-On Large Language Models — Jay Alammar & Maarten Grootendorst
4. Build a Large Language Model (From Scratch) — Sebastian Raschka
Building my foundation in ML → DL → LLMs → AI Engineering. 🚀
If I had to learn Computer Science from ZERO in 2026:
1. Computer fundamentals
2. Python
3. Git & GitHub
4. Data structures
5. Algorithms
6. Databases
7. Operating systems
8. Computer networks
9. APIs
10. Real projects
The biggest mistake?
Trying to learn everything at once.
Follow ONE path. Build as you learn.