The Mercedes Vision AVTR

Traditional :
Startup Idea → Plan → Design → Coding → Marketing→ Audience → 😣

Modern :
Audience → Problem → Idea → Validation → Waitlist → SEO → One Feature MVP → Iterate → Marketing → Success.

🔆 t.me/techpsyche

What are crypto cards?

Crypto cards are an ingenious instrument allowing you to pay for goods with crypto anywhere that accepts credit cards: stores, gyms, transportation, and the internet. They work the same way as a traditional credit card issued by banks, but they’re connected to your crypto wallet instead of your bank.

This way, you can hold your assets on an exchange—e.g., USDT on Binance—while having the ability to pay for goods and services. What’s more, there are no network fees for these transactions.

The downside is, however, that crypto cards are only available in certain countries. If you are located in a country that accepts crypto cards, you can get your hands on one of these popular cards: Coinbase Card, Crypto.com Card, or Binance Card.

Always be wary of scammers and never enter your card information on suspicious sites or platforms.

More Resources Here:
https://whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M

#crypto #web3 #blockchain #finance #cryptocurrency

📊 Market Overview:

BTC : $102969
ETH : $2478.2
BNB : $641.24
SOL : $167.45

📈 Market Cap :

Total : 3.39T
DeFi : 104.5B
24hr Vol : 95.12B

⚡️ Sentiment :

FGI : Greed (74)
Open Interest : 66.02B
24h Liquidation : $288.8M

How can I spot a bullish trend?: https://t.me/techpsyche/925

🏝 Kotlin 2.1.21 is out (https://github.com/JetBrains/kotlin/releases/tag/v2.1.21)

What's new:
🐘 Gradle 8.12 support
👉 Fix for working with XCode 16.3
🛠 Bug fixes

Mobile Dev Updates & Resources Here 👇
https://t.me/mobiledevresourcestp

⏰ How long it took bitcoin and businesses to reach $1 trillion capitalization:

Bitcoin: 12 years
Facebook: 17 years
Tesla: 18 years
Google: 21 years
Amazon: 24 years
Apple: 42 years
Microsoft: 44 years

🔆 t.me/techpsyche

𝐆𝐨𝐨𝐠𝐥𝐞 𝐅𝐑𝐄𝐄 𝐀𝐈/𝐌𝐋 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞

Unlock the world of AI/ML with Google’s completely free course series!

Learn everything from the basics of machine learning to advanced AI applications, guided by experts at Google.

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Enroll For FREE & Get Certified🎓

Best Resource to Learn Artificial Intelligence (AI) For Free 👇👇

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https://microsoft.github.io/AI-For-Beginners

https://ai.google/education/

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Data Science Life Cycle (Step-by-Step)

The Data Science Life Cycle describes the full process of solving a problem using data.

Here's how it goes:

1. Problem Understanding

Understand the business or research problem.

Example: “Can we predict customer churn?”


2. Data Collection

Gather data from CSV files, databases, APIs, web scraping, etc.

Tools: SQL, Python (requests, BeautifulSoup)


3. Data Cleaning & Preparation

Handle missing values, remove duplicates, fix data types, combine datasets.

Tool: Pandas


4. Exploratory Data Analysis (EDA)

Use statistics and visuals to understand patterns in the data.

Tools: Pandas, Seaborn, Matplotlib


5. Feature Engineering

Create or modify features to improve model performance.

Examples: encoding categories, scaling numbers

6. Model Building

Choose the right algorithm and train it on the data.

Tools: scikit-learn, XGBoost


7. Model Evaluation

Use metrics like Accuracy, Precision, Recall, F1-score to evaluate the model.

8. Deployment

Make the model available to users via APIs, web apps, dashboards, etc.

Tools: Flask, Streamlit, FastAPI

9. Communication & Reporting

Create dashboards or reports to share results clearly.

Tools: Power BI, Tableau, PPTs

10. Monitoring & Maintenance

Keep track of model performance in real-world use. Retrain if needed.


Uses Of Data Science: https://t.me/datascienceresourcestp/147

ENJOY LEARNING 👍👍

WhatsApp Channel:
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📊 Market Overview:

BTC : $103907
ETH : $2504.59
BNB : $645.81
SOL : $171.32

📈 Market Cap :

Total : 3.42T
DeFi : 105.85B
24hr Vol : 77.1B

⚡️ Sentiment :

FGI : Greed (74)
Open Interest : 67.21B
24h Liquidation : $153.3M

How can I spot a bullish trend?: https://t.me/techpsyche/925

COOLEST TECH IN 2025: A VISUAL GUIDE

🔆 t.me/techpsyche

What a crazy week in AI

- OpenAI’s Codex
- Google Coding Agent
- Windsurf SWE-1 models
- Notion’s new AI for work
- Tencent Multimodal Video
- ChatGPT 4.1 & PDF Exports
- Meta Collaborative Reasoner
- ElevenLabs Infinite Soundboard

🔆 t.me/techpsyche

Excel vs Power BI: Key Differences

Excel:
- Purpose: Ideal for spreadsheet tasks, basic calculations, and small-scale data analysis.
- Best For: Creating simple reports, working with small datasets, and producing basic charts.
- Data Handling: Best suited for small to medium-sized datasets; performance can decline with larger data.
- Visualizations: Offers basic charts and graphs but lacks interactivity.
- Sharing: Usually shared via email or cloud storage (e.g., OneDrive); not ideal for real-time collaboration.
- Automation: Limited automation capabilities, with manual refreshes or basic macros.

Power BI:
- Purpose: Designed for advanced data analysis and creating interactive, visually rich reports.
- Best For: Handling large datasets, integrating data from multiple sources, and building dynamic dashboards.
- Data Handling: Efficient with very large datasets, maintaining high performance.
- Visualizations: Provides highly interactive visualizations with drill-down features and deep insights.
- Sharing: Allows real-time collaboration through online sharing and automatic report updates.
- Automation: Supports automatic data refreshes and real-time reporting capabilities.

React ❤️ for more

Tableau vs Power BI: https://t.me/dataanalysisresourcestp/157

More Tech Resources Here👇
https://t.me/techpsyche

Hope it helps :)

Find More Tips & Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

GPT-4.1 is now available in ChatGPT – the model codes much better than GPT-4o and is significantly faster than o3.

It also has a huge context window – up to 1 million tokens.

It’s currently only available to paid subscribers, the free version will only be GPT-4.1-mini.

🔆 t.me/techpsyche

Most Important Figma Plugins in 2025

Thread Here: https://t.me/designresourcestp/79

One day or Day one. You decide.

Data Science edition.

𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.

🔆 t.me/techpsyche

Researchers Expose New Intel CPU Flaws Enabling Memory Leaks and Spectre v2 Attacks

Researchers at ETH Zürich have discovered yet another security flaw that they say impacts all modern Intel CPUs and causes them to leak sensitive data from memory, showing that the vulnerability known as Spectre continues to haunt computer systems after more than seven years.

The vulnerability, referred to as Branch Privilege Injection (BPI), "can be exploited to misuse the prediction calculations of the CPU (central processing unit) in order to gain unauthorized access to information from other processor users," ETH Zurich

🔆 t.me/zerotrusthackers

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- Earn certifications to showcase your skills

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Essential Tools, Libraries, and Frameworks to learn Artificial Intelligence

1. Programming Languages:
* Python
* R
* Java
* Julia

2. AI Frameworks:
* TensorFlow
* PyTorch
* Keras
* MXNet
* Caffe

3. Machine Learning Libraries:
* Scikit-learn: For classical machine learning models.
* XGBoost: For boosting algorithms.
* LightGBM: For gradient boosting models.

4. Deep Learning Tools:
* TensorFlow
* PyTorch
* Keras
* Theano

5. Natural Language Processing (NLP) Tools:
* NLTK (Natural Language Toolkit)
* SpaCy
* Hugging Face Transformers
* Gensim

6. Computer Vision Libraries:
* OpenCV
* DLIB
* Detectron2

7. Reinforcement Learning Frameworks:
* Stable-Baselines3
* RLlib
* OpenAI Gym

8. AI Development Platforms:
* IBM Watson
* Google AI Platform
* Microsoft AI

9. Data Visualization Tools:
* Matplotlib
* Seaborn
* Plotly
* Tableau

10. Robotics Frameworks:
* ROS (Robot Operating System)
* MoveIt!

11. Big Data Tools for AI:
* Apache Spark
* Hadoop

12. Cloud Platforms for AI Deployment:
* Google Cloud AI
* AWS SageMaker
* Microsoft Azure AI

13. Popular AI APIs and Services:
* Google Cloud Vision API
* Microsoft Azure Cognitive Services
* IBM Watson AI APIs

14. Learning Resources and Communities:
* Kaggle
* GitHub AI Projects
* Papers with Code

8 FREE AI Courses by Google: https://t.me/airesourcestp/101

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