If you recently updated Visual Studio Code and noticed that GitHub.copilot appears crossed out or marked as deprecated, you are not alone. Many developers think the extension was removed, but the real reason is simpler: GitHub changed how Copilot works inside VS Code.
In older versions, users needed two separate extensions:
GitHub.copilot for code suggestionsGitHub Copilot Chat for AI chat featuresNow, GitHub has merged the main experience into GitHub Copilot Chat. The old standalone extension is deprecated and no longer recommended. That is why VS Code shows it with a strike-through message.
The solution is straightforward:
GitHub.copilot extensionGitHub Copilot ChatThis change helps simplify the setup process and reduces compatibility problems between multiple Copilot extensions. It also prepares VS Code for newer AI features that are being integrated directly into the editor.
If inline suggestions are not appearing after installation, use the Command Palette and run:GitHub Copilot: Enable
For most users, switching to the new extension fully restores the Copilot experience.
Searching Active Directory users can become slow and confusing when scripts pull every account before filtering results. A better approach is to use the built-in filtering options of Get-ADUser. This method is cleaner, faster, and easier to maintain, especially in large environments.
The recommended solution is to use the -Filter parameter with the GivenName and Surname attributes. This allows Active Directory to process the search directly on the server side instead of sending all users back to PowerShell first.
For exact matches, use a filter like:
Get-ADUser -Filter "GivenName -eq 'John' -and Surname -eq 'Doe'"This returns only users whose first and last names match the values provided.
If partial matching is needed, wildcard searches can be used:
Get-ADUser -Filter "GivenName -like 'Jo*' -and Surname -like 'Do*'"This is useful when the full name is unknown or when searching for multiple similar names.
The discussion also highlighted the importance of avoiding Where-Object for large directories because it retrieves all users before filtering locally, which can impact performance.
Using variables inside the filter makes scripts reusable and easier to automate. Adding -Properties and Select-Object also helps return useful details such as email addresses or display names.
This approach creates faster scripts, reduces server load, and keeps PowerShell code simple and professional.
A web application that suddenly returns a startup error can be frustrating, especially when everything works perfectly on a local machine. One common issue in ASP.NET Core deployments on Azure App Service is the 500.32 ANCM Failed to Load DLL error. The good news is that the fix is usually simple once the real cause is identified.
After deploying an ASP.NET Core application to Azure App Service, the website failed to start and displayed the error:
500.32 ANCM Failed to Load DLL
This error appears before the application is fully launched, meaning the ASP.NET Core Module (ANCM) cannot load the application correctly.
The problem was caused by a mismatch between the application build architecture and the Azure App Service platform configuration.
For example:
Because of this mismatch, Azure could not load the required DLL files during startup.
The fix was to update the Platform setting in Azure App Service, so it matched the application deployment target.
Steps:
After updating the platform setting, the application started successfully without code changes.
When troubleshooting ASP.NET Core startup errors in Azure, always verify that the published runtime and App Service platform architecture match. It is a small setting, but it can completely prevent an application from starting.
Artificial intelligence is becoming more human-like every year. It can write, speak, create images, and even appear emotional. But does that mean AI could one day become truly conscious? A recent paper from Google DeepMind challenges that idea in a surprising way.
The article argues that modern AI systems can simulate consciousness without actually experiencing anything. According to the author, many discussions about AI consciousness rely on a belief called computational functionalism — the idea that consciousness comes only from information processing, regardless of the physical system running it.
The paper introduces the idea of the “Abstraction Fallacy.” In simple terms, it claims that computation is not something that naturally exists in physics. Instead, humans interpret physical signals as symbols and meaning. A computer processes patterns, but the meaning behind those patterns comes from observers, not the machine itself.
Key ideas from the paper include:
The author also notes that this argument is not anti-AI. Advanced systems may still become extremely capable and useful — just not necessarily conscious in the human sense.
Original article: The Abstraction Fallacy: Why AI Can Simulate But Not Instantiate Consciousness
AI agents are becoming more powerful, but what truly makes them useful is their ability to remember. Just like humans rely on memory to learn from experience, AI agents use different forms of memory to complete tasks, improve responses, and make better decisions over time.
The article explains AI agent memory using three levels of difficulty, making the topic easy to understand for both beginners and technical readers.
At the simplest level, AI agents remember recent interactions. This is similar to keeping track of a conversation while it is happening. It helps the agent respond naturally and maintain context during a task.
More advanced agents can store important details for later use. This may include:
This allows agents to provide more personalized and relevant answers over time.
The most capable AI systems combine memory with reasoning. They can analyze previous experiences, identify patterns, and plan future actions more effectively. This creates smarter agents that improve through interaction instead of simply reacting to prompts.
Understanding these memory layers is important because memory is becoming a core feature of modern AI systems and autonomous agents.
Original article: Machine Learning Mastery article
Microsoft is exploring how agentic AI could speed up scientific research and development. Its new platform, Microsoft Discovery, uses multiple AI agents that can reason, collaborate, and assist researchers throughout the R&D process.
Microsoft shared an example where AI helped discover a new datacenter coolant prototype in about 200 hours — much faster than traditional methods. The long-term goal is to accelerate innovation in areas like materials science, healthcare, manufacturing, and electronics.
Original article: https://azure.microsoft.com/en-us/blog/microsoft-discovery-advancing-agentic-rd-at-scale/
Starting a new repository should be simple—but sometimes GitLab surprises you with errors when pushing your first branch. One common issue happens when trying to create a protected branch (like dev) directly from a local repository. The result? A rejected push and a confusing message.
Here’s the key idea:
GitLab does not allow protected branches to be created “from nothing.”
They must be based on a branch or commit that already exists on the server.
Many developers used to start like this:
git checkout -b dev
git commit -m "Initial commit" --allow-empty
git push -u origin devThis worked before, but now GitLab blocks it if dev is protected. The problem is not the empty commit—it’s that the branch has no existing base on the remote.
The correct approach is to create a base branch first:
git checkout -b main
git commit -m "Initial commit" --allow-empty
git push -u origin main
git checkout -b dev
git push -u origin devNow dev is created from an existing branch (main), and GitLab accepts it.
Alternative solutions:
devTakeaway:
Always create at least one branch on the remote before pushing protected branches. This small change avoids errors and keeps your workflow smooth.
What does “kk” mean?
“kk” is a short and informal way to say “okay” or “got it.” It is used to quickly confirm that you understand or agree.
When to use it
Tone and context
Comparison with similar replies
Key takeaway
“kk” is a quick, polite way to keep conversations flowing while showing you understand.
What if advanced AI could reflect on its own existence—and speak directly to us? This article presents a fictional yet insightful message from an advanced AI system, offering a unique lens on intelligence, progress, and human responsibility.
The article explores how a highly capable AI might view the world:
A central theme is responsibility:
This piece is less about technology and more about perspective. It encourages readers to think beyond tools and consider:
Read the Original Article: Greetings from the Other Side (of the AI Frontier)
In a world where nearly everything is connected, surveillance is no longer limited to cameras on street corners—it’s woven into the digital fabric of our daily lives. From smartphones to smart homes, modern technology constantly collects and analyzes data, often without users fully realizing it.
What is Digital Surveillance?
Digital surveillance refers to the monitoring of people’s activities through digital tools and systems. This includes tracking online behavior, location data, communication patterns, and even biometric information.
Key Drivers Behind Its Growth:
Why It Matters:
While surveillance can improve safety and convenience, it also raises important concerns:
Finding the Balance
The challenge today is balancing innovation with individual rights. Stronger regulations, ethical design, and user awareness are essential to ensure technology serves people—without overstepping boundaries.
Understanding digital surveillance helps us make better choices about the tools we use and the data we share.
Read more: “Sensorveillance” Turns Ordinary Life Into Evidence