Essential Python Libraries to build your career in Data Science 📊👇
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
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ENJOY LEARNING👍👍
Senior Outreach Specialist (with growth path to Head of Department) at Aurora Labs (AI Studio) | Remote, Full-time
About Us:
Aurora Labs is an AI studio that launches and manages high-tech projects end-to-end. We are looking for a powerhouse Senior Outreach Specialist with brilliant English who is ready to personally launch our influencer traffic channel and rapidly grow into the Head of Outreach as we scale.
Your Mission on Day 1:
Take full ownership of building our influencer-driven traffic acquisition channel. You will start by personally testing loops, setting up infrastructure, and finding winning conversion mechanics, and then immediately begin building and leading a team under your command.
Key Responsibilities:
▫️ Personally test and build the influencer acquisition pipeline (sourcing, initial outreach, converting to partnerships)
▫️ Design, run A/B tests, and optimize communication flows (initial cold messages, follow-ups, offers) to max out conversion rates
▫️ Oversee account management, including technical setup and ongoing support of social media accounts (proxies, multi-account infrastructure)
▫️ Create simple content strategies and visual positioning profiles for social selling
▫️ Personally secure key partnerships and close high-value deals on the market
▫️ As the channel grows: onboard, train, and manage a team of junior outreach managers (setting tasks, tracking performance, and analyzing results)
Requirements:
▫️ Proven, strong background in cold outreach, sales, and negotiations
▫️ Hands-on technical understanding of social media operations (account registration, proxy types, session management, avoiding bans)
▫️ Experience or strong capability to build processes from scratch (especially in influencer-driven networks, MLM, fintech, or AI environments)
▫️ Natural leadership potential and readiness to train and manage a team of 10+ people in the near future
▫️ High level of proactivity and a problem-solving mindset
▫️ Exceptional, fluent English (clear communication is our key priority)
What We Offer:
▫️ Full-time, remote position with massive career upside (Direct track to Head of Department)
▫️ High level of autonomy in decision-making and process building
▫️ Fast-paced startup environment with zero bureaucracy
▫️ Supportive and open-minded founders
▫️ Compensation: solid fixed salary (from $2k) + performance-based percent.
To Apply please send your resume and relevant case studies to @darina_auroralabs , describing your specific role, technical setup, and outreach results in previous projects
Agent Readiness Framework
A few weeks ago I wrote that adopting coding agents requires strong engineering practices.
Test stability, linting, documentation, security controls matter much more than a particular harness or model.
Agent Readiness framework is an attempt to formalize these criteria for a particular repository and define how much autonomy can be safely delegated to agents.
The framework evaluates repos across 8 dimensions:
- Style & Validation
- Build System
- Testing
- Documentation
- Dev Environment
- Code Quality
- Observability
- Security & Governance
Based on these dimensions, the framework defines 5 levels of repo maturity:
🔸 Level 1: Functional. Basic checks: README, linters, unit tests.
🔸 Level 2: Documented. Detailed documentation and basic automations: AGENT.md, reproducible dev env, contribution guides.
🔸 Level 3: Standardized. E2E tests, observability, security scanning, maintained documentation.
🔸 Level 4: Optimized. Fast validation loops, canary deployments, build optimization. Process is optimized for fast feedback.
🔸 Level 5: Autonomous. Task decomposition, multi-service orchestration, self-healing logic, auto-remediation.
The idea is simple: the higher the maturity level, the more predictable and reliable agent results. But looking at these levels, I can see that most repos are actually somewhere between Level 1 and Level 3.
Framework authors also provide a tool to automatically measure these criteria and maturity level, but it's available only after registration and using proprietary APIs. Scanned examples you can find at https://t.co/Gpo5rZmwpP.
There is also an open-source alternative https://t.co/53htfyj70d. The project doesn't look active, but it gets the job done. It analyzes the repo and generates a report with the overall maturity level, findings for each dimension, and suggestions for improvements. Some rules are not very accurate. Looks like the project was mainly designed for python and js code verification. But anyway the tool gives you a good sense of what to pay attention to in your codebase.
What I like about this framework is that it shows that agent effectiveness is actually limited by the maturity of engineering practices. And it provides measurable and actionable results, that are easy to convert into an improvement plan for a particular repo.
#ai #engineering
Trump Signs Order for Early Government Access to Advanced AI
President Donald Trump has signed an executive order encouraging major AI companies to give the U.S. government early access to their most powerful AI models before public release.
The order creates a voluntary program allowing up to 30 days of review for national security risks, such as potential cyber threats. Major AI companies such as OpenAI, Anthropic, and Google are expected to be primary participants.
🔗 Read more : https://t.co/ydQxfGBMji
Anthropic Confidentially Files for IPO
Anthropic, the company that created the popular AI chatbot Claude, has quietly filed papers with U.S. regulators as it prepares to go public. This confidential filing on June 1 marks an important move for the fast-growing artificial intelligence firm.
Anthropic was founded in 2021 by former OpenAI executives. The company recently raised $65 billion from investors, boosting its value to nearly $965 billion. It now earns strong revenue from businesses using its technology for tasks like writing code and research.
If successful, Anthropic’s public listing could become one of the biggest in tech history.
🔗 Read more. : https://t.co/nhcRYcXPUO
⚙️ Basic Programming Elements You Should Know 💻
These elements are the building blocks of every program. They allow programs to store data, perform operations, and execute instructions.
Variable
A variable is a named storage location used to store data in memory. Its value can change during program execution.
Example:
age = 26
name = "Ajay"
Here:
• age stores a number
• name stores text
Variables help store information that programs can use later.
Constant
A constant is a value that does not change during program execution. Constants are used when a value should remain fixed.
Example:
PI = 3.14159
MAX_USERS = 100
By convention, constants are often written in uppercase. They help prevent accidental modification of important values.
Data Type
A data type defines the kind of data a variable stores.
Common data types include:
• Integer: count = 10
• Float: price = 19.99
• String: city = "Jodhpur"
• Boolean: is_active = True
Data types help the computer understand how to process and store data.
Operator
Operators are symbols used to perform operations on values or variables.
• Arithmetic Operators: a = 10; b = 5; print(a + b)
• Comparison Operators: print(a > b)
• Logical Operators: x = True; y = False; print(x and y)
Operators are used in calculations and decision-making.
Expression
An expression is a combination of values, variables, and operators that produces a result.
Example: result = (10 + 5) * 2
Here the expression (10 + 5) * 2 is evaluated first, and the result is stored in result.
Expressions are commonly used in calculations and conditions.
Statement
A statement is a single instruction that the computer executes.
Example:
score = 90
print(score)
Each line represents a statement telling the computer what to do. Programs are made up of many statements executed in sequence.
⭐ Key Idea
Basic programming elements such as variables, constants, data types, operators, expressions, and statements form the core of writing programs.
Understanding these concepts makes it much easier to learn any programming language.
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Business Development Interns /Junior BD
Full-time/Company: https://t.co/1ZrGs1Ygqj
https://t.co/1ZrGs1Ygqj is hiring Business Development Interns/Junior BD for the North American market.
We’re looking for English-speaking interns based in the U.S. or Canada who are passionate about crypto, trading, Web3, and trading communities.
Ideal candidates:
- College sophomores, final-year students, or recent graduates
- Native or fluent English speakers
- Basic knowledge in crypto, trading, or Web3 required, strong interest and willingness to learn would be a plus
- Familiar with Twitter/X, Telegram, Discord, YouTube, TikTok, or Instagram
- Strong communication skills and willing to learn BD, partnerships, and growth
Daily duties:
- You’ll help with KOL outreach, affiliate partnerships, market research, community growth, and local campaign support for Bitbase.
- Remote role with flexible hours. Sales relevant experience is a plus, but not required.
What You’ll Gain:
- Hands-on experience in crypto exchange business development
- Direct exposure to KOL, affiliate, and strategic partnership work
- Opportunity to build industry connections in Web3 and trading
- Mentorship from an international BD team
- Performance-based growth opportunities and potential full-time conversion
- Remote and flexible working arrangement
Compensation:
Competitive monthly internship and full time compensation, with full commission structure based on performance.
Apply:
[email protected]
🚨 CHOCANTE: Um ex-pesquisador da Anthropic vazou o framework interno exato que a equipe usa.
A maioria trata o Claude como um chatbot básico e deixa 60-70% do poder de raciocínio na mesa.
Esses 9 prompts são como os profissionais realmente usam : testados internamente para máxima clareza, honestidade e profundidade.
Pronto para copiar e colar. Zero enrolação.
Salva essa thread. Seu jogo no Claude vai mudar para sempre.
(Dica pro: use na ordem para resultados compostos)
EU Slaps Temu with €200 Million Fine Over Unsafe Products
The European Union has hit Chinese e-commerce platform Temu with a €200 million fine for failing to prevent the sale of illegal and dangerous products to European customers.
Regulators say Temu did not properly assess risks under the Digital Services Act, allowing items like unsafe baby toys with choking hazards and faulty electrical chargers to reach buyers. Temu, which serves over 92 million monthly users in Europe, was accused of letting its algorithms push risky goods as well.
Temu now has until late August to present a clear plan to strengthen its controls, or it could face even bigger fines later.
🔗 Read more. : https://t.co/yRJekur06O
Google Engineer Arrested for $1.2 Million Polymarket Insider Trading
A Google security engineer was arrested this week for allegedly using confidential company data to win more than $1.2 million on the prediction platform Polymarket.
Michele Spagnuolo, a 36-year-old Italian citizen who worked at Google for over 12 years, is accused of betting on the company’s “Year in Search 2025” results using non-public information.
Operating under the username “AlphaRaccoon,” he placed 25 bets between October and December 2025, correctly predicting top searched names like singer d4vd, Pope Leo XIV, and Donald Trump.
Federal prosecutors say Spagnuolo risked over $2.7 million and profited once Google released the official results in December. He now faces charges of commodities fraud, wire fraud, and money laundering.
🔗 Read more. :https://t.co/sEu7mzlm89
"Open Data Structures" is another very useful free resource for anyone studying data structures and algorithms. 📚✨
The book discusses the implementation and analysis of basic structures: array-based lists, linked lists, hash tables, binary trees, red-black trees, heaps, sorting algorithms, graphs, and data structures for working with integers. 🔍🧮
This is a full-fledged open textbook for studying one of the fundamental topics of computer science and a good reference that's worth keeping on hand. 💻🌟
https://t.co/LHLFhJSq9q 📄
The Fearless Organization
The most dangerous teams are the quiet teams. There are no disagreements, no bad news, no conflicts. Looks like harmony until the real incident.
That's the topic of the book The Fearless. https://t.co/3u2mw58Dap Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth by Amy C. Edmondson.
Amy is a professor of Leadership and Management at the Harvard Business School. She has studied the phenomenon of psychological safety and its impact on team performance for many years across different organizations.
She defines psychological safety as follows:
a belief that one will not be punished or humiliated for speaking up with ideas, questions, concerns, or mistakes, and that the team is safe for inter-personal risk taking.
What it means in practice:
- people are not afraid to ask questions
- they do not hide problems
- they are not afraid to look stupid
- they do not avoid conflicts
- they can freely express their opinions and bring suggestions
Why does it matter?
There is a good example from the book that explains that. Imagine a doctor prescribes treatment for a child. A nurse notices that doctors usually prescribe drug A in such cases, but this time it is missing.
In a team with high psychological safety, the nurse will clarify this with the doctor and may help prevent a medical error.
In a team with low psychological safety, she may be afraid to ask. And the consequences can be dramatic.
The core idea is simple. But the book contains a lot of real stories where a low level of psychological safety leads to dramatic results (e.g. Volkswagen. https://t.co/HvgfFLNcdZ emission scandal, pilot mistakes that caused plane crashes). The author repeatedly highlights that the more complex and critical the profession is, the more important psychological safety becomes.
How does this relate to our daily work?
We as leaders are responsible for the psychological climate in the team: how well we listen to people, accept different opinions, react to questions, mistakes, or bad news. It's our daily routine that either helps the team become more effective, or leads people to hide problems and the real state of things.
Overall, I really liked the book. It explains the idea in simple language with many real examples. And what is important for me, all arguments and recommendations are supported by sociological research, experiments and practical psychology.
So psychological safety is not just an idea. It is a proven behavioral model and set of practices that can actually help leaders build better teams.
P.S. One of the best real examples of psychological safety is Pixar. I wrote about it earlier in overview of Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: parts 1,2,3.
#booknook #softskills #leadership
✅ Step-by-Step Approach to Learn Programming 💻🚀
➊ Pick a Programming Language
Start with beginner-friendly languages that are widely used and have lots of resources.
✔ Python – Great for beginners, versatile (web, data, automation)
✔ JavaScript – Perfect for web development
✔ C++ / Java – Ideal if you're targeting DSA or competitive programming
Goal: Be comfortable with syntax, writing small programs, and using an IDE.
➋ Learn Basic Programming Concepts
Understand the foundational building blocks of coding:
✔ Variables, data types
✔ Input/output
✔ Loops (for, while)
✔ Conditional statements (if/else)
✔ Functions and scope
✔ Error handling
Tip: Use visual platforms like W3Schools, freeCodeCamp, or Sololearn.
➌ Understand Data Structures Algorithms (DSA)
✔ Arrays, Strings
✔ Linked Lists, Stacks, Queues
✔ Hash Maps, Sets
✔ Trees, Graphs
✔ Sorting Searching
✔ Recursion, Greedy, Backtracking
✔ Dynamic Programming
Use GeeksforGeeks, NeetCode, or Striver's DSA Sheet.
➍ Practice Problem Solving Daily
✔ LeetCode (real interview Qs)
✔ HackerRank (step-by-step)
✔ Codeforces / AtCoder (competitive)
Goal: Focus on logic, not just solutions.
➎ Build Mini Projects
✔ Calculator
✔ To-do list app
✔ Weather app (using APIs)
✔ Quiz app
✔ Rock-paper-scissors game
Projects solidify your concepts.
➏ Learn Git GitHub
✔ Initialize a repo
✔ Commit push code
✔ Branch and merge
✔ Host projects on GitHub
Must-have for collaboration.
➐ Learn Web Development Basics
✔ HTML – Structure
✔ CSS – Styling
✔ JavaScript – Interactivity
Then explore:
✔ React.js
✔ Node.js + Express
✔ MongoDB / MySQL
➑ Choose Your Career Path
✔ Web Dev (Frontend, Backend, Full Stack)
✔ App Dev (Flutter, Android)
✔ Data Science / ML
✔ DevOps / Cloud (AWS, Docker)
➒ Work on Real Projects Internships
✔ Build a portfolio
✔ Clone real apps (Netflix UI, Amazon clone)
✔ Join hackathons
✔ Freelance or open source
✔ Apply for internships
➓ Stay Updated Keep Improving
✔ Follow GitHub trends
✔ Dev YouTube channels (Fireship, etc.)
✔ Tech blogs (https://t.co/4hM4LUdwa3, Medium)
✔ Communities (Discord, Reddit, X)
🎯 Remember:
• Consistency > Intensity
• Learn by building
• Debugging is learning
• Track progress weekly
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Ferrari Unveils Its First Electric Car, the Luce
Ferrari made history yesterday in Rome by revealing the Luce, its long-awaited first fully electric vehicle. The sleek new model aims to bring the brand's signature thrill into the EV world while staying true to its Italian roots.
The Luce departs from traditional Ferrari styling with a more spacious, five-seat layout and four doors.
Under the hood, four electric motors deliver over 1,050 horsepower enabling acceleration from 0 to 62 mph in approximately 2.5 seconds.
The Luce is priced around $640,000.
🔗 Read more. : https://t.co/S0KZCHFgr9
✅ 🔤 A–Z of Web Development
A – API (Application Programming Interface)
Allows communication between different software systems.
B – Backend
The server-side logic and database operations of a web app.
C – CSS (Cascading Style Sheets)
Used to style and layout HTML elements.
D – DOM (Document Object Model)
Tree structure representation of web pages used by JavaScript.
E – Express.js
Minimal Node.js framework for building backend applications.
F – Frontend
Client-side part users interact with (HTML, CSS, JS).
G – Git
Version control system to track changes in code.
H – Hosting
Making your website or app available online.
I – IDE (Integrated Development Environment)
Software used to write and manage code (e.g., VS Code).
J – JavaScript
Scripting language that adds interactivity to websites.
K – Keywords
Important for SEO and also used in programming languages.
L – Lighthouse
Tool for testing website performance and accessibility.
M – MongoDB
NoSQL database often used in full-stack apps.
N – Node.js
JavaScript runtime for server-side development.
O – OAuth
Protocol for secure authorization and login.
P – PHP
Server-side language used in platforms like WordPress.
Q – Query Parameters
Used in URLs to send data to the server.
R – React
JavaScript library for building user interfaces.
S – SEO (Search Engine Optimization)
Improving site visibility on search engines.
T – TypeScript
A superset of JavaScript with static typing.
U – UI (User Interface)
Visual part of an app that users interact with.
V – Vue.js
Progressive JavaScript framework for building UIs.
W – Webpack
Module bundler for optimizing web assets.
X – XML
Markup language used for data sharing and transport.
Y – Yarn
JavaScript package manager alternative to npm.
Z – Z-index
CSS property to control element stacking on the page.
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