Tools & Tech Every Developer Should Know ⚒ 👨🏻‍💻

❯ VS Code ➟ Lightweight, Powerful Code Editor
❯ Postman ➟ API Testing, Debugging
❯ Docker ➟ App Containerization
❯ Kubernetes ➟ Scaling & Orchestrating Containers
❯ Git ➟ Version Control, Team Collaboration
❯ GitHub/GitLab ➟ Hosting Code Repos, CI/CD
❯ Figma ➟ UI/UX Design, Prototyping
❯ Jira ➟ Agile Project Management
❯ Slack/Discord ➟ Team Communication
❯ Notion ➟ Docs, Notes, Knowledge Base
❯ Trello ➟ Task Management
❯ Zsh + Oh My Zsh ➟ Advanced Terminal Experience
❯ Linux Terminal ➟ DevOps, Shell Scripting
❯ Homebrew (macOS) ➟ Package Manager
❯ Anaconda ➟ Python & Data Science Environments
❯ Pandas ➟ Data Manipulation in Python
❯ NumPy ➟ Numerical Computation
❯ Jupyter Notebooks ➟ Interactive Python Coding
❯ Chrome DevTools ➟ Web Debugging
❯ Firebase ➟ Backend as a Service
❯ Heroku ➟ Easy App Deployment
❯ Netlify ➟ Deploy Frontend Sites
❯ Vercel ➟ Full-Stack Deployment for Next.js
❯ Nginx ➟ Web Server, Load Balancer
❯ MongoDB ➟ NoSQL Database
❯ PostgreSQL ➟ Advanced Relational Database
❯ Redis ➟ Caching & Fast Storage
❯ Elasticsearch ➟ Search & Analytics Engine
❯ Sentry ➟ Error Monitoring
❯ Jenkins ➟ Automate CI/CD Pipelines
❯ AWS/GCP/Azure ➟ Cloud Services & Deployment
❯ Swagger ➟ API Documentation
❯ SASS/SCSS ➟ CSS Preprocessors
❯ Tailwind CSS ➟ Utility-First CSS Framework

If you don't see something that you think should be there you'll see it next time 😹

Coding Jobs: https://t.me/jobsourceglobal

Crush Imposter Syndrome: Own Your Dev Potential

Read Here: https://dev.to/justdetermined/crush-imposter-syndrome-own-your-dev-potential-3am4

5 Algorithms you must know as a data scientist 👩‍💻 🧑‍💻

1. Dimensionality Reduction
- PCA, t-SNE, LDA

2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression

3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification

4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models

5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)

Best Data Science & Machine Learning Resources: https://topmate.io/learning_resources/1406977

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FOOD & DRINKS APP DOWNLOADS NEAR 2 BILLION IN 2024, GROWING 11% YOY

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🔨 The first RC version of Android Studio Narwhal has been released
(https://androidstudio.googleblog.com/2025/06/android-studio-narwhal-202511-rc-1-now.html)

The most interesting thing in the update
⭐️ Support for adding files from a project and any pictures in a chat with Gemini
🔥 partner labs with devices have appeared in Android Device Streaming
💾 Testing backup and recovery of application data
⚙️ Generating previews for Composable
👉 Compose Preview Screenshot Testing tool

Read more about what's new here
(https://developer.android.com/studio/preview/features#2025.1.1)

Mobile Dev Updates: https://t.me/mobiledevresourcestp

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Beginner’s Roadmap to Learn Data Structures & Algorithms

1. Foundations: Start with the basics of programming and mathematical concepts to build a strong foundation.

2. Data Structure: Dive into essential data structures like arrays, linked lists, stacks, and queues to organise and store data efficiently.

3. Searching & Sorting: Learn various search and sort techniques to optimise data retrieval and organisation.

4. Trees & Graphs: Understand the concepts of binary trees and graph representation to tackle complex hierarchical data.

5. Recursion: Grasp the principles of recursion and how to implement recursive algorithms for problem-solving.

6. Advanced Data Structures: Explore advanced structures like hashing, heaps, and hash maps to enhance data manipulation.

7. Algorithms: Master algorithms such as greedy, divide and conquer, and dynamic programming to solve intricate problems.

8. Advanced Topics: Delve into backtracking, string algorithms, and bit manipulation for a deeper understanding.

9. Problem Solving: Practice on coding platforms like LeetCode to sharpen your skills and solve real-world algorithmic challenges.

10. Projects & Portfolio: Build real-world projects and showcase your skills on GitHub to create an impressive portfolio.

Best DSA RESOURCES: https://topmate.io/learning_resources/1406117

All the best 👍👍

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Sam Altman says the world must prepare together for AI’s massive impact

OpenAI releases imperfect models early so the world can see, adapt, and help shape regulations. "There are going to be scary times ahead"

But collaboration can turn AI into much more good than bad

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🧮 Axiom: AI That Solves Math for Wall Street

Stanford PhD student Carina Hong is raising $50M for her AI startup Axiom, aiming for a $300M–$500M valuation. The company is developing large language models trained on mathematical proofs to tackle complex problems in quantitative finance and risk analysis.

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Jeff Bezos started Amazon at 30.

Sam Walton started Walmart at 44.

Colonel Sanders started KFC at 62.

You’re never too old to start your dream business! 💚

More Business Tips Here👇
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Elon Musk posted a cryptic message

While he didn’t explain, many believe it refers to his fallout with Donald Trump.

On Thursday, their feud turned public — with both trading jabs over social media and in the press. The immediate trigger was a budget bill Musk opposed, but deeper tensions had been building.

Still, U.S. media reports suggest both sides may now be looking to cool things down.

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If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.

Yes, you might hear a lot about them or some other trending technology of the year...but guess what!

Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.

Instead, here are basic skills that will get you further than mastering any framework:


𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.

You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability

𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.

You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.

𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.

You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai

I love frameworks and libraries, and they can make anyone's job easier.

But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.

Best Data Science & Machine Learning Resources: https://topmate.io/learning_resources/1406977

All the best 👍👍

Machine Learning Resources 👇
https://t.me/mlresourcestp

REMOTE

Remote Ruby Engineer Job at Silverfin

Perks:
- A salary range of €99,000 - €134,000 a year
- Flexible working hours and work 100% remotely
- A €1200 yearly budget for conferences, courses, workshops or other expenses that will improve your skills
among others

What will you work on?
* Building and maintaining integrations with accountancy software packages and APIs
* Improving and expanding our on-premise Ruby CLI/service which runs on hundreds of our customer’s systems
* Work on user facing functionalities
* Help with discovery and delivery of a solution for user or business problems

Apply Here:
https://kenyatrends.co.ke/pmkc
Global Tech Jobs Here👇
https://t.me/jobsourceglobal

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News Consumption on Social Media

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7 SIMPLE RULES FOR LONG-TERM FITNESS SUCCESS

1. Train at least 3x a week – Consistency beats intensity.
2. Prioritize compound movements – Squats, deadlifts, and presses build full-body strength.
3. Track your progress – What gets measured, improves.
4. Fuel your body, not your cravings – Eat for performance, not just taste.
5. Sleep like it matters – Because it does.
6. Don’t chase quick fixes – Focus on sustainable changes.
7. Stay patient and enjoy the process – Results take time, effort, and mindset.

Double Tap ❤️ if this helped you

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💠 6 Software Architectural Patterns You Must Know

Choosing the right software architecture pattern is essential for solving problems efficiently.

1 - Layered Architecture

Each layer plays a distinct and clear role within the application context.

Great for applications that need to be built quickly. On the downside, source code can become unorganized if proper rules aren’t followed

2 - Microservices Architecture
Break down a large system into smaller and more manageable components.

Systems built with microservices architecture are fault tolerant. Also, each component can be scaled individually. On the downside, it might increase the complexity of the application.

3 - Event-Driven Architecture
Services talk to each other by emitting events that other services may or may not consume.

This style promotes loose coupling between components. However, testing individual components becomes challenging

4 - Client-Server Architecture
It comprises two main components - clients and servers communicating over a network.

Great for real-time services. However, servers can become a single point of failure.

5 - Plugin-based Architecture
This pattern consists of two types of components - a core system and plugins. The plugin modules are independent components providing a specialized functionality.

Great for applications that have to be expanded over time like IDEs. However, changing the core is difficult.

6 - Hexagonal Architecture
This pattern creates an abstraction layer that protects the core of an application and isolates it from external integrations for better modularity. Also known as ports and adapters architecture.

On the downside, this pattern can lead to increased development time and learning curve.

Tools & Tech for Every Developer: https://t.me/techpsyche/1359

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Someone's cooked here 🥲

Lucky people get opportunities,

Brave people create opportunities,

But

Real winners are those who convert Their problems into opportunities.

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The next half-decade will redefine what it means to be a developer. Continuous learning isn't a luxury, it's the default.

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