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ADIs LLDP a godsend or a security risk? Why not both?! #networking #it #technology #tech #stem #ccna
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DAYou Study Computer Science But Don’t Know Binary?! 🤯💻 | CS Starter Pack 🎓 Computer Science student: “I’m learning AI!”
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Bruhh... not knowing binary is like not knowing A-B-C in English 😅
Tag that friend who says they code but Google binary every time 😂
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KAMassive breakthrough…Scientists are using living brain cells to build computers that are ALIVE!
This is a brand new industry of technology called biocomputing.
These are real human neurons that can actually think and learn for themselves.
When scientists combine them with silicon chips they bring the computer to life.
Now if you’re like me, you’re probably wondering one thing…
Why are scientists doing this?
It feels a bit much to be growing human brains just to put them inside of computers.
Well listen to this…it turns out, human brain cells have extremely rare abilities to process things without being trained.
Normal computers need to be taught how to solve certain problems.
It’s why AI models take so much energy and training data to build.
But as soon as you add brain cells to a computer chip, they don’t really need as much training.
They kinda start figuring out what to do on their own, just like a human would.
Now because of this, these biocomputers are super efficient.
A regular supercomputer might use 40M watts of power to solve a complex task.
The human brain can do it in just 20.
That’s 2M times more efficient.
And this means these biocomputers could become the secret weapon to solving humanity’s hardest computational challenges.
But…there is just one massive problem…
Brain cells tend to die when their temperature deviates from 98.6 degrees.
And computers generate a ton of heat.
So we have this wild situation where living computers might be the solve for everything, but it’s almost impossible to keep the brain part alive.
Today, companies like Cortical Labs are selling these biocomputers for $35,000.
But over time, scientists are gonna figure out how to get them stable & more accessible.
It’s wild to say but I think soon we’re gonna have living computers that literally think like humans.
Follow @kallaway for more videos on tech & AI
#ai #artificialintelligence #tech #technology #computing #computer #future #coding #newtech
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ELMost people search “What is Computer Engineering?”
and think it only means coding or programming ❌
But Computer Engineering is the combination of software + hardware + systems that power the digital world.
A computer engineer works with: • Programming languages (C, C++, Java, Python)
• Data Structures and Algorithms (DSA)
• Operating Systems (process, memory, kernel)
• Computer Networks & Internet protocols
• Database Systems (SQL, transactions)
• Software Engineering & SDLC
• Computer Architecture & CPU design
• Artificial Intelligence & Machine Learning
• Cloud Computing & DevOps
• Cybersecurity & encryption
This is why Computer Engineering and Computer Science are among the most in-demand tech fields today 🚀
📌 Save this if you’re a CS / CE student
🔁 Share with someone confused between CS vs Computer Engineering
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BREverything You Need to Know as a Computer Science Student
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THHere’s a roadmap to help you go from a software engineer to a data scientist 👩💻 👇
If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you.
Step 1 – Learn Python and SQL (not Java, C++, or JavaScript).
→ Focus on pandas, numpy, scikit-learn, matplotlib
→ For SQL: use LeetCode or StrataScratch to practice real-world queries
→ Don’t just write code—learn to think in data
Step 2 – Build your foundation in statistics + math.
→ Start with Practical Statistics for Data Scientists
→ Learn: probability, hypothesis testing, confidence intervals, distributions
→ Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule)
Step 3 – Learn ML the right way.
→ Do Andrew Ng’s ML course (Deeplearning.ai)
→ Master the full pipeline: cleaning → feature engineering → modeling → evaluation
→ Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper
Step 4 – Build 2–3 real, messy projects.
→ Don’t follow toy tutorials
→ Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio
→ Upload everything to GitHub with a clear README
Step 5 – Become a storyteller with data.
→ Read Storytelling with Data by Cole Knaflic
→ Learn to explain your findings to non-technical teams
→ Practice communicating precision/recall/F1 in simple language
Step 6 – Stay current. Never stop learning.
→ Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn
→ Join communities, follow researchers, and keep shipping new experiments
-------
Save this for later.
Tag a friend who’s trying to make the switch.
[software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
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EZLogic building patterns.
#coding #programming #logic #building
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PAConnect your LLM’s to your Database 🧠
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