Don’t chase perfection—chase progress. Shipping code, asking questions, learning from mistakes, and improving every day will take you further than waiting until you “know enough.”
Focus on consistency, not just brilliance. That’s how great developers are made

🔆 t.me/techpsyche

Anthropic has released Claude Opus 5.5, its latest AI model, with a 1 million-token memory and the ability to produce up to 128,000 tokens in one response.

Anthropic says it is about 40% cheaper to run than the previous Opus 5 and is faster at tasks such as coding.

🔆 t.me/techpsyche

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▪️ https://en.wikibooks.org/wiki/Metasploit/VideoTutorials

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There has never been a better time to become a data analyst.

Tackle the tools:

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Sharpen these soft skills:

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𝟮.𝗦𝗾𝗹 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
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https://learn.microsoft.com/en-us/training/paths/azure-sql-fundamentals/

𝟯.𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
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𝟰.𝗔𝘇𝘂𝗿𝗲 𝗰𝗼𝘀𝗺𝗼𝘀 𝗗𝗕
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How to Choose Charts for Your Data

A-Z of Essential Data Science Concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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

Credits: t.me/datascienceresourcestp

Like if you need similar content 😄👍

Hope this helps you 😊

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Data Science Projects based on domain

ChatGPT Cheat Sheet

AI Product Management

AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed.

(Note: This is quite a lengthy post, but it is very informative)

Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)!

In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production.

Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice.

Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility.

For example, a PM may envision a new internal tool for routing emails from customers to the right department (such as customer service, sales, etc.). They can prompt an LLM to see if they can get it to select the right department based on an input email, and see if they can achieve high accuracy. If so, this gives engineering a great starting point from which to implement the tool. If not, the PM can falsify the idea themselves and perhaps improve the product idea much faster than if they had to rely on an engineer to build a prototype.

Often, testing feasibility requires a little more than prompting. For example, perhaps the LLM-based email system needs basic RAG capability to help it make decisions. Fortunately, the barrier to writing small amounts of code is now quite low, since AI can help by acting as a coding companion, as I describe in the course, “AI Python for Beginners.” This means that PMs can do much more technical feasibility testing, at least at a basic level, than was possible before.

Continuation Here...

Top 10 AI Courses

Cybersecurity Roadmap
|
|-- Fundamentals
| |-- Introduction to Cybersecurity
| | |-- Importance and Principles of Cybersecurity
| | |-- Types of Cybersecurity (Network, Information, Application, Cloud, etc.)
| | |-- Cybersecurity Threat Landscape (Malware, Phishing, Ransomware, etc.)
| |-- Network Security
| | |-- Firewalls and VPNs
| | |-- Intrusion Detection Systems (IDS)
| | |-- Intrusion Prevention Systems (IPS)
| | |-- Network Access Control
|
|-- Threats and Vulnerabilities
| |-- Types of Cyber Threats
| | |-- Malware (Viruses, Worms, Trojans, etc.)
| | |-- Phishing and Social Engineering
| | |-- Denial of Service (DoS) Attacks
| | |-- Insider Threats
| |-- Vulnerability Assessment
| | |-- Vulnerability Scanning
| | |-- Penetration Testing (Ethical Hacking)
| | |-- Security Audits and Assessments
|
|-- Encryption and Cryptography
| |-- Introduction to Cryptography
| | |-- Symmetric and Asymmetric Encryption
| | |-- Hashing Algorithms (SHA, MD5, etc.)
| | |-- Public Key Infrastructure (PKI)
| |-- Encryption Protocols
| | |-- SSL/TLS
| | |-- IPsec
|
|-- Identity and Access Management (IAM)
| |-- Authentication Mechanisms
| | |-- Password Policies and Multi-Factor Authentication (MFA)
| | |-- Biometric Authentication
| |-- Access Control Models
| | |-- Role-Based Access Control (RBAC)
| | |-- Attribute-Based Access Control (ABAC)
| | |-- Mandatory Access Control (MAC)
|
|-- Incident Response and Forensics
| |-- Incident Response Process
| | |-- Detection, Containment, Eradication, Recovery
| | |-- Incident Response Teams (CSIRT)
| |-- Digital Forensics
| | |-- Evidence Collection and Preservation
| | |-- Data Recovery
| | |-- Forensic Tools (Autopsy, EnCase, etc.)
|
|-- Security Operations
| |-- Security Monitoring
| | |-- Security Information and Event Management (SIEM)
| | |-- Log Management and Analysis
| | |-- Threat Intelligence
| |-- Security Operations Center (SOC)
| | |-- SOC Roles and Responsibilities
| | |-- Incident Management
|
|-- Cloud Security
| |-- Cloud Security Principles
| | |-- Shared Responsibility Model
| | |-- Data Protection in Cloud Environments
| |-- Cloud Security Tools
| | |-- Cloud Access Security Brokers (CASB)
| | |-- Security in Cloud Platforms (AWS, Azure, Google Cloud)
|
|-- Application Security
| |-- Secure Software Development
| | |-- Secure Coding Practices
| | |-- Software Development Life Cycle (SDLC)
| | |-- Secure Code Reviews
| |-- Web Application Security
| | |-- OWASP Top 10
| | |-- SQL Injection, Cross-Site Scripting (XSS), CSRF
|
|-- Compliance and Regulations
| |-- Cybersecurity Standards
| | |-- ISO/IEC 27001, NIST Cybersecurity Framework
| | |-- CIS Controls, SOC 2
| |-- Data Privacy Regulations
| | |-- GDPR
| | |-- HIPAA, CCPA, PCI DSS
|
|-- Advanced Topics
| |-- Advanced Persistent Threats (APT)
| | |-- Detection and Mitigation
| | |-- Threat Hunting
| |-- Blockchain Security
| | |-- Cryptographic Principles
| | |-- Smart Contracts and Security
| |-- IoT Security
| | |-- Securing IoT Devices
| | |-- Network Segmentation for IoT
|
|-- Emerging Trends
| |-- AI and Machine Learning in Cybersecurity
| | |-- AI-Based Threat Detection
| | |-- Automating Incident Response
| |-- Zero Trust Architecture
| | |-- Principles of Zero Trust
| | |-- Implementing Zero Trust in an Organization
|
|-- Soft Skills
| |-- Communication and Collaboration
| | |-- Reporting Security Incidents
| | |-- Collaboration with Other Departments
| |-- Ethical Hacking
| | |-- Red Teaming and Blue Teaming
| | |-- Bug Bounty Programs

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Link 2: Hacking manuals: t.me/zerotrusthackers/236

OpenAI will start watermarking ChatGPT’s text in the EU

OpenAI announced it will begin applying an invisible text watermark, named textGrain, to ChatGPT and Codex outputs in the European Union to comply with the EU AI Act's transparency mandates. Developed alongside UPenn and Yale researchers, the method subtly shapes word choices using a secret key, leaving a detectable pattern that travels across copy-pasted text without altering user anonymity or model performance.

The watermark will roll out to all EU subscription plans in the coming weeks, while API developers worldwide can opt in. OpenAI is limiting initial access to its detector tool to approved researchers, noting that text modifications, short passages, math, and translations can reduce detection accuracy. The move follows Anthropic’s global rollout of text watermarking for Claude, though OpenAI previously delayed the feature over concerns about user retention.

🔆 t.me/techpsyche

testDrivenDevelopment

You can learn for free inside Binance (Binance Academy)

There are plenty of free resources on YouTube, you should learn what  is Technical and Fundamental Analysis, Futures and Spot trading, Leverage and Risk Management before you start trading.

After you learn these concepts you should trade at least for 2 months using tools like TradingView. After 2 months and if you have positive results you can then start trading but keep in mind that real trading involves other variables like psycological behaviour.

Types of Technical Analysis: t.me/cryptostract/156

Exploring the Benefits of Non-Recourse Loans in Crypto: t.me/cryptostract/160

What are the signs of a bearish trend in the crypto market?: t.me/cryptostract/163

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Data Visualization Overview

Human Losses of World War II - Ranked by Country(Military & Civilian)