ZIMBABWE🇿🇼Technician Computer Science(1 Post)
Organization: Great Zimbabwe University
Location: Masvingo Province, Zimbabwe
Employment Type: Full Time
Apply Here:
https://www.gzu.ac.zw/careers-advert/
WhatsApp Channel👇
https://whatsapp.com/channel/0029VbAqbkw6hENvEJZLl22d
Telegram Channel👇
https://t.me/jobsourceglobal
REMOTERemote Android Developer Job at Chess .com
Preferred Skills:
* Experience with Kotlin, Android SDK and Architecture Components
* Experience with Coroutines / RxJava
* Experience with Dagger / Hilt
* Experience with Jetpack Compose
* Subscriptions and payments
among others
Apply Here:
https://kenyatrends.co.ke/fe1o
Global Tech Jobs Here👇
https://t.me/jobsourceglobal
SHARE WITH YOUR FRIENDS🥳🥳
Various types of test used in statistics for data science
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
Data Science Free Learning Resources 👇
https://t.me/datascienceresourcestp
Join our WhatsApp channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
Data Science Free Learning Resources 👇
https://t.me/datascienceresourcestp
Join our WhatsApp channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
This media is not supported in your browser
VIEW IN TELEGRAM
😳 Meta just droped Aria Gen 2: AR glasses
Only 75 grams, foldable arms, 8 sizes, built for all-day wear (6–8 hours no problem). But what’s inside is wild:
👁 4 cameras for computer vision + 3D hand tracking
👀 Eye-tracking sensors
💓 Heart rate monitor (PPG)
🎙 Bone-conduction mic picks up your voice even on a noisy subway
And yep..everything runs locally. No cloud. Real-time eye, hand, and voice tracking with near-zero delay.
📐 80° stereo FOV and full 6DOF support. This thing maps space like a pro.
Right now it’s just for research labs, but future AR devices will build on this tech.
P.S. Phones already listen to us. In a few years, your glasses might feel your heartbeat too 🦆
🔆 t.me/techpsyche
Only 75 grams, foldable arms, 8 sizes, built for all-day wear (6–8 hours no problem). But what’s inside is wild:
👁 4 cameras for computer vision + 3D hand tracking
👀 Eye-tracking sensors
💓 Heart rate monitor (PPG)
🎙 Bone-conduction mic picks up your voice even on a noisy subway
And yep..everything runs locally. No cloud. Real-time eye, hand, and voice tracking with near-zero delay.
📐 80° stereo FOV and full 6DOF support. This thing maps space like a pro.
Right now it’s just for research labs, but future AR devices will build on this tech.
P.S. Phones already listen to us. In a few years, your glasses might feel your heartbeat too 🦆
🔆 t.me/techpsyche
REMOTERemote Senior QA Engineer Job at Uscreen
- QA experience in both Manual & Automated Testing
- Reading & Writing simple code (Ruby, JavaScript/TypeScript)
- Familiar with CI/CD (GitHub Actions)
- Fluent English Speaker
Apply Here:
https://kenyatrends.co.ke/53ha
Global Tech Jobs Here👇
https://t.me/jobsourceglobal
SHARE WITH YOUR FRIENDS🥳🥳
The best doesn't come from working more.
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
* Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
* Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
* Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
* Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
* Quality over quantity.
6) Neglecting hobbies and interests.
* Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
* Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
* Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
* Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
* Recognize the long-term effects.
More Growth Tips Here👇
https://whatsapp.com/channel/0029VasaQtVGehEUFsVWAn3L
It comes from working smarter.
The most common mistakes people make,
With practical tips to avoid each:
1) Working late every night.
* Prioritize quality time with loved ones.
Understand that long hours won't be remembered as fondly as time spent with family and friends.
2) Believing more hours mean more productivity.
* Focus on efficiency.
Complete tasks in less time to free up hours for personal activities and rest.
3) Ignoring the need for breaks.
* Take regular breaks to rejuvenate your mind.
Creativity and productivity suffer without proper rest.
4) Sacrificing personal well-being.
* Maintain a healthy work-life balance.
Ensure you don't compromise your health or relationships for work.
5) Feeling pressured to constantly produce.
* Quality over quantity.
6) Neglecting hobbies and interests.
* Engage in activities you love outside of work.
This helps to keep your mind fresh and inspired.
7) Failing to set boundaries.
* Set clear work hours and stick to them.
This helps to prevent overworking and ensures you have time for yourself.
8) Not delegating tasks.
* Delegate when possible.
Sharing the workload can enhance productivity and give you more free time.
9) Overlooking the importance of sleep.
* Prioritize sleep for better performance.
A well-rested mind is more creative and effective.
10) Underestimating the impact of overworking.
* Recognize the long-term effects.
More Growth Tips Here👇
https://whatsapp.com/channel/0029VasaQtVGehEUFsVWAn3L
Use AI as a Co-Pilot, Not an Autopilot 😊
Fulfillment comes from building something that outlives your moods.
This media is not supported in your browser
VIEW IN TELEGRAM
Geoffrey Hinton says I never expected to see an AI speak English as fluently as humans
GPT-4 can answer almost any question, acting like a "not very good expert" but it knows thousands of times more than any person. Its reasoning isn’t perfect, but it’s improving quickly
🔆 t.me/techpsyche
GPT-4 can answer almost any question, acting like a "not very good expert" but it knows thousands of times more than any person. Its reasoning isn’t perfect, but it’s improving quickly
🔆 t.me/techpsyche
TANZANIA🇹🇿Senior Specialist; Cyber Security Governance & Compliance
Organization: CRDB Bank Plc
Location: Dar es Salaam, Tanzania
Employment Type: Full Time
Apply Here:
https://careers.crdbbank.co.tz/jobs
WhatsApp Channel👇
https://whatsapp.com/channel/0029VbAqbkw6hENvEJZLl22d
Telegram Channel👇
https://t.me/jobsourceglobal
NIGERIA🇳🇬Digital Marketer
Organization: Vi-M Professional Solutions
Location: Okota - Isolo, Lagos (Hybrid or Remote)
Employment Type: Full Time
How to Apply
Interested and qualified candidates should send their CV to: recruitment@vi-m.com using the Job Title as the subject of the email.
WhatsApp Channel👇
https://whatsapp.com/channel/0029VbAqbkw6hENvEJZLl22d
Telegram Channel👇
https://t.me/jobsourceglobal
You will not learn system design in a month.
You will not master DSA in a month.
You will not suddenly understand how to solve problems at scale in a month.
You won’t grasp scalability, databases, and caching overnight.
And you most definitely won’t internalize every distributed system pattern just by reading a few blogs.
Because software engineering is an ocean: deep, vast, and ever-expanding.
And you can’t cross an ocean in a single leap.
In a month, you’ll realize you’re only scratching the surface.
You’ll see more gaps than answers.
You’ll feel like there’s too much to learn and too little time.
But that’s where most people give up.
That’s where frustration makes them quit.
Don’t be one of them.
Take it one step at a time.
Real expertise doesn’t come from rushing. It comes from consistent, deliberate learning over years.
It comes from revisiting the same concepts and seeing them from new perspectives each time.
So trust your own pace.
Stay in the game long enough to connect the dots.
And one day, the same concepts that once seemed impossible will feel like second nature.
Just keep collecting buckets.
Python Free Expert-Backed Courses: https://techurl.in/jesjn
Software Architectural Patterns: https://t.me/techpsyche/1376
Cookies vs Cache: https://t.me/techpsyche/1386
#techpsyche
You will not master DSA in a month.
You will not suddenly understand how to solve problems at scale in a month.
You won’t grasp scalability, databases, and caching overnight.
And you most definitely won’t internalize every distributed system pattern just by reading a few blogs.
Because software engineering is an ocean: deep, vast, and ever-expanding.
And you can’t cross an ocean in a single leap.
In a month, you’ll realize you’re only scratching the surface.
You’ll see more gaps than answers.
You’ll feel like there’s too much to learn and too little time.
But that’s where most people give up.
That’s where frustration makes them quit.
Don’t be one of them.
Take it one step at a time.
Real expertise doesn’t come from rushing. It comes from consistent, deliberate learning over years.
It comes from revisiting the same concepts and seeing them from new perspectives each time.
So trust your own pace.
Stay in the game long enough to connect the dots.
And one day, the same concepts that once seemed impossible will feel like second nature.
Just keep collecting buckets.
Python Free Expert-Backed Courses: https://techurl.in/jesjn
Software Architectural Patterns: https://t.me/techpsyche/1376
Cookies vs Cache: https://t.me/techpsyche/1386
#techpsyche
❤2
😹😹😹Heh..AI is making my day every time😹😹
You don't know what I'm about to share with you below this message 😹😹😹
You don't know what I'm about to share with you below this message 😹😹😹