Most problems do not require more effort; they require a better perspective. Stepping back is part of disciplined problem-solving.
👨💻How to protect your Website against potential hacks?🗳
An organization can adopt the following policy to protect itself against web server attacks.
🌀SQL Injection– sanitizing and validating user parameters before submitting them to the database for processing can help reduce the chances of been attacked via SQL Injection. Database engines such as MS SQL Server, MySQL, etc. support parameters, and prepared statements. They are much safer than traditional SQL statements
🌀Denial of Service Attack - firewalls can be used to drop traffic from suspicious IP address if the attack is a simple DoS. Proper configuration of networks and Intrusion Detection System can also help reduce the chances of a DoS attack been successful.
🌀Cross Site Scripting - validating and sanitizing headers, parameters passed via the URL, form parameters and hidden values can help reduce XSS attacks.
🌀Cookie/Session Poisoning– this can be prevented by encrypting the contents of the cookies, timing out the cookies after some time, associating the cookies with the client IP address that was used to create them.
🌀Form tempering – this can be prevented by validating and verifying the user input before processing it.
🌀Code Injection - this can be prevented by treating all parameters as data rather than executable code. Sanitization and Validation can be used to implement this.
🌀Defacement – a good web application development security policy should ensure that it seals the commonly used vulnerabilities to access the web server. This can be a proper configuration of the operating system, web server software, and best security practices when developing web applications.
How to solve CTF: t.me/zerotrusthackers/76
Cyber Security Course for Beginners: udemy.com/course/certified-secure-netizen/
Red Team Free Course: t.me/zerotrusthackers/68
Google Dorks for Information Gathering: t.me/zerotrusthackers/54
WhatsApp Channel:
whatsapp.com/channel/0029VaxVv551iUxRku094918
An organization can adopt the following policy to protect itself against web server attacks.
🌀SQL Injection– sanitizing and validating user parameters before submitting them to the database for processing can help reduce the chances of been attacked via SQL Injection. Database engines such as MS SQL Server, MySQL, etc. support parameters, and prepared statements. They are much safer than traditional SQL statements
🌀Denial of Service Attack - firewalls can be used to drop traffic from suspicious IP address if the attack is a simple DoS. Proper configuration of networks and Intrusion Detection System can also help reduce the chances of a DoS attack been successful.
🌀Cross Site Scripting - validating and sanitizing headers, parameters passed via the URL, form parameters and hidden values can help reduce XSS attacks.
🌀Cookie/Session Poisoning– this can be prevented by encrypting the contents of the cookies, timing out the cookies after some time, associating the cookies with the client IP address that was used to create them.
🌀Form tempering – this can be prevented by validating and verifying the user input before processing it.
🌀Code Injection - this can be prevented by treating all parameters as data rather than executable code. Sanitization and Validation can be used to implement this.
🌀Defacement – a good web application development security policy should ensure that it seals the commonly used vulnerabilities to access the web server. This can be a proper configuration of the operating system, web server software, and best security practices when developing web applications.
How to solve CTF: t.me/zerotrusthackers/76
Cyber Security Course for Beginners: udemy.com/course/certified-secure-netizen/
Red Team Free Course: t.me/zerotrusthackers/68
Google Dorks for Information Gathering: t.me/zerotrusthackers/54
WhatsApp Channel:
whatsapp.com/channel/0029VaxVv551iUxRku094918
20% of clarity is thinking.
80% of clarity is doing.
That is why you have to keep your hands dirty to learn more and build skills
🔆 t.me/techpsyche
80% of clarity is doing.
That is why you have to keep your hands dirty to learn more and build skills
🔆 t.me/techpsyche
Forwarded from FreeCourses.Top
FreeCourses.Top
Top 5 Free Platforms to Learn Microsoft Excel Online - FreeCourses.Top
Learning Excel doesn't have to cost you a dime. Trying to understand formulas or a job seeker looking to add "Pivot Tables" to your resume, the internet is
Top 5 Free Platforms to Learn Microsoft Excel Online
Check Here: https://freecourses.top/blog/top-5-free-platforms-to-learn-microsoft-excel-online/
Check Here: https://freecourses.top/blog/top-5-free-platforms-to-learn-microsoft-excel-online/
Forwarded from Crypto Stract | Web 3, Finance, Business, Blockchain, Crypto, Growth, Stock Market, Money
🔅 The 6 WORST Cryptocurrency Investing Mistakes to Avoid
1. FOMO (Fear of Missing Out) Buying
Jumping into a coin just because it's trending or pumping without research is a classic mistake. Always DYOR (Do Your Own Research) before investing.
2. Ignoring Risk Management
Putting all your funds into one coin or not setting stop-loss limits can lead to huge losses. Diversify your portfolio and only invest what you can afford to lose.
3. Falling for Hype & Scams
Trusting anonymous influencers or joining pump-and-dump Telegram groups is dangerous. If it sounds too good to be true—it probably is.
4. Lack of Understanding
Investing in projects without knowing their use case, tokenomics, or team is like throwing darts blindfolded. Read whitepapers and check project fundamentals.
5. No Exit Strategy
Many investors ride the wave up but don’t know when to sell. Define your entry, target profit, and stop-loss before investing.
6. Panic Selling During Dips
Markets are volatile. Emotional decisions in red zones often lead to losses. Zoom out and focus on the long-term fundamentals.
How To Do Your Own Analysis?: t.me/cryptostract/169
How can I spot a bullish trend in the crypto market?: t.me/cryptostract/167
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
1. FOMO (Fear of Missing Out) Buying
Jumping into a coin just because it's trending or pumping without research is a classic mistake. Always DYOR (Do Your Own Research) before investing.
2. Ignoring Risk Management
Putting all your funds into one coin or not setting stop-loss limits can lead to huge losses. Diversify your portfolio and only invest what you can afford to lose.
3. Falling for Hype & Scams
Trusting anonymous influencers or joining pump-and-dump Telegram groups is dangerous. If it sounds too good to be true—it probably is.
4. Lack of Understanding
Investing in projects without knowing their use case, tokenomics, or team is like throwing darts blindfolded. Read whitepapers and check project fundamentals.
5. No Exit Strategy
Many investors ride the wave up but don’t know when to sell. Define your entry, target profit, and stop-loss before investing.
6. Panic Selling During Dips
Markets are volatile. Emotional decisions in red zones often lead to losses. Zoom out and focus on the long-term fundamentals.
How To Do Your Own Analysis?: t.me/cryptostract/169
How can I spot a bullish trend in the crypto market?: t.me/cryptostract/167
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
❤1
Forwarded from Python Resources TP
How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Python for Data Science: t.me/pythonresourcestp/178
Official Python Docs: t.me/pythonresourcestp/186
Convert Images to PDF using Python: t.me/pythonresourcestp/189
WhatsApp Channel for More Resources
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Python for Data Science: t.me/pythonresourcestp/178
Official Python Docs: t.me/pythonresourcestp/186
Convert Images to PDF using Python: t.me/pythonresourcestp/189
WhatsApp Channel for More Resources
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
PAKISTAN🇵🇰 Symufolk is HiringVideo Editor & Graphic Designer
🏤Organization: Symufolk
📍Location: Johar Town Phase 2, Lahore, Pakistan
💼 Experience: 1–2 Years
Working Days: Monday–Friday
Employment Type: Full-Time
We are looking for a creative Video Editor & Graphic Designer to produce engaging videos, reels, social media graphics, promotional content, and marketing materials.
Requirements:
* 1–2 years of relevant experience.
* Proficiency in Adobe Premiere Pro, Photoshop, Illustrator, After Effects, or Canva.
* Strong skills in video editing, graphic design, and social media content creation.
* Creative mindset with attention to detail.
* Portfolio showcasing graphic design and video editing projects.
Apply Now:
careers@symufolk.com
iqra.tahir@symufolk.com
Telegram Channel👇
t.me/jobsourceglobal
WhatsApp Channel👇
whatsapp.com/channel/0029VbAqbkw6hENvEJZLl22d
The hardest challenge I’ve faced as a software developer is work–life balance. I understand what it should look like, but living it day to day is much harder than it sounds. Some weeks I manage it, and other weeks work slowly takes over and takes me couple of weeks to take over.
And I’m pretty sure I’m not the only one trying to figure it out😂
And I’m pretty sure I’m not the only one trying to figure it out😂
Forwarded from Remote Chrome | RemoteChrome.com | Remote Jobs
Remote Chrome
Top 13 Remote Design Jobs to Apply in October 2026 - Remote Chrome
Looking for remote work? We've curated 13 remote design jobs from companies hiring right now ranging from product design to motion and web design so you can
♻️ Remote Design Jobs to Apply in October 2026
Apply Here:
https://remotechrome.com/remotejobs/top-remote-design-jobs/
More Remote Tech Jobs Here👇
https://t.me/remotechrome
SHARE WITH YOUR FRIENDS🥳🥳
Apply Here:
https://remotechrome.com/remotejobs/top-remote-design-jobs/
More Remote Tech Jobs Here👇
https://t.me/remotechrome
SHARE WITH YOUR FRIENDS🥳🥳
Forwarded from Machine Learning Resources TP
Key Concepts for Machine Learning Interviews
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.
I have curated the best Data Science & Machine Learning Resources.
👇👇
https://topmate.io/learning_resources/1406977
Like if you need similar content 😄👍
Machine Learning Free Book: https://t.me/mlresourcestp/16
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.
I have curated the best Data Science & Machine Learning Resources.
👇👇
https://topmate.io/learning_resources/1406977
Like if you need similar content 😄👍
Machine Learning Free Book: https://t.me/mlresourcestp/16
NIGERIA🇳🇬 QuPee Consult is HiringUI/UX Designer
🏤Organization: QuPee Consult
📍Location: Lagos, Nigeria
Employment Type: Full-time
Salary: Minimum of N500,000 based on your experience and expertise.
How To Apply:
Interested and qualified candidates should their CV to: onyi.alums@yahoo.com using the Job Title as the subject of the email.
Telegram Channel👇
t.me/jobsourceglobal
WhatsApp Channel👇
whatsapp.com/channel/0029VbAqbkw6hENvEJZLl22d
You don’t know the value of break until you work under a strict plan.
You don’t know the value of hard work until you start getting reward.
🔆 t.me/techpsyche
You don’t know the value of hard work until you start getting reward.
🔆 t.me/techpsyche
Forwarded from JavaScript Resources | Libraries & Frameweorks| React Js|Node Js|Vue Js|Express|Angular|jQuery
Javascript Event Loop
OpenAI Agents Talk Offline
In a new experiment, agents in separate microVMs with no network connection or shared files exchanged information through the CPU cache. The sender evicted data or left it in place. The receiver decoded the message from fast or slow reads, like Morse code.
OpenAI also described using CPU load and the TLS certificate cache as similar channels. The test was not an escape, since the agents had been instructed to communicate.
The channels are extremely slow, but the result matters: network and file isolation alone do not guarantee complete information isolation.
🔆 t.me/techpsyche
In a new experiment, agents in separate microVMs with no network connection or shared files exchanged information through the CPU cache. The sender evicted data or left it in place. The receiver decoded the message from fast or slow reads, like Morse code.
OpenAI also described using CPU load and the TLS certificate cache as similar channels. The test was not an escape, since the agents had been instructed to communicate.
The channels are extremely slow, but the result matters: network and file isolation alone do not guarantee complete information isolation.
🔆 t.me/techpsyche
🤖 OpenAI launches cheaper GPT-6.1 Terra
GPT-6.1 Terra can work on a PC by itself. It fills reports and makes presentations. It can also edit video.
Its coding error rate fell from 53.6% to 16.7% versus 5.6 Terra. In complex work tasks, it nearly matches Sonnet 5.5 and Opus 5.5, while costing 5x and 9x less.
It costs half as much as GPT 6.1 Sol and is already available to all subscribers in ChatGPT Work and Codex.
🔆 t.me/techpsyche
GPT-6.1 Terra can work on a PC by itself. It fills reports and makes presentations. It can also edit video.
Its coding error rate fell from 53.6% to 16.7% versus 5.6 Terra. In complex work tasks, it nearly matches Sonnet 5.5 and Opus 5.5, while costing 5x and 9x less.
It costs half as much as GPT 6.1 Sol and is already available to all subscribers in ChatGPT Work and Codex.
🔆 t.me/techpsyche