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Sr. Content Developer at Microsoft, working remotely in PA, TechBash conference organizer, former Microsoft MVP, Husband, Dad and Geek.
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Forgive me for the title. Mentally I’m 12. When I started my current day job, I certainly didn’t expect to write this many blog posts about Git. I don’t fancy myself an expert by any means. Today I entered a new realm. Here’s what I know about Git forks: Git is a fully distributed source… Git forked
The post Git forked appeared first on Born SQL.
The post Git forked appeared first on SQLServerCentral.
It’s fascinating how the field of deep learning isn’t just about algorithms and data but about interpreting and leveraging this technology in practical, impactful ways. Deep learning’s journey mirrors an adventurous exploration where each layer of understanding reveals more about both the potential and the pitfalls of these powerful tools.
The story of neural networks, for instance, begins in the excitement of early discoveries, dips into periods of skepticism, and rises again with technological breakthroughs that open up new realms of possibility. Every advancement in the field, from the humble perceptron to the complex architectures of today, has expanded the horizons of what machines can learn and achieve.
This video is from MIT OpenCourseWare.
In my own work, the practical applications of these systems are particularly compelling. Imagine the power of neural networks applied to environmental science, where models can predict and analyze climate patterns, or in healthcare, where they can decipher genetic codes and predict patient outcomes with incredible accuracy. The applications are limitless, but the tools are not without their flaws.
Deep learning’s strength lies in its ability to process vast amounts of information and learn from them, creating patterns and models that would take humans lifetimes to analyze. Yet, this strength also poses significant challenges—namely, the risk of overfitting, where models adhere too closely to their training data and fail to generalize to new, unseen datasets.
One of the key insights from deep learning is recognizing the balance between model complexity and performance. The myth that more data and more complex models always yield better results has been debunked. Instead, what matters is the quality of the data and the appropriateness of the model for the task at hand.
The shifting landscape of neural network enthusiasm, with its peaks and troughs, also mirrors broader trends in technology adoption. Innovations often go through cycles of hype, followed by disillusionment, and then a more mature phase of practical implementation.
As we stand on the brink of potential new breakthroughs—autonomous vehicles, personalized medicine, real-time multi-language translation—our responsibility is to steer these technologies towards positive outcomes while managing the risks.
The true promise of deep learning isn’t in the complexity of its models but in its capacity to make meaningful predictions and decisions that enhance human capabilities. The challenge for those of us in the field is to ensure these models do not simply mimic reality but illuminate the paths to smarter, more efficient, and ethical applications.
As we journey further into the age of AI and machine learning, our focus should not merely be on what these technologies can do but on what they should do. The shift from possessing mere computational power to wielding it wisely will define the next chapter in our technological journey.
Highguard, a new multiplayer shooter from developers who worked on games like Apex Legends and Call of Duty, launched just over two weeks ago, but developer Wildlight Entertainment is already cutting jobs at the company, according to posts from affected staffers on LinkedIn. Former Wildlight level designer Alex Graner says that "most of the team at Wildlight" was laid off today, which was backed up by former lead tech artist Josh Sobel.
"Today we made an incredibly difficult decision to part ways with a number of our team members while keeping a core group of developers to continue innovating on and supporting the game," Wildlight says in a …
In this episode, Subhajit Paul joins SE Radio host Kanchan Shringi to discuss how enterprise resource planning (ERP) systems work in practice and where machine learning and generative AI are beginning to fit into real-world ERP environments.
Subhajit grounds the conversation in ERP fundamentals, explaining core business flows such as order-to-cash, procure-to-pay, and plan-to-produce, and why ERP systems are central to running large enterprises. He then walks through the realities of ERP implementation, sharing examples of both successful and failed projects and highlighting common challenges around testing, process coverage, integrations, and change management.
The discussion also explores how AI is being applied in ERP today, including practical ML use cases such as inventory optimization and anomaly detection, as well as emerging generative AI and agent-based approaches.
Brought to you by IEEE Computer Society and IEEE Software magazine.
In January we released SQL Server Management Studio (SSMS) 22.2.1 and I commented that it might look like a minor release, but it really wasn’t. That’s true for SSMS 22.3 as well, which dropped yesterday (February 10). Read on to learn what’s new and a couple changes that may impact your daily workflow.
Our engineers have continued to fix bugs in addition to making improvements around fundamentals (which I mentioned in the last post). Many fixes for this release come from feedback items, and we thank those of you who log and upvote issues. You can view the full list in the release notes, but it includes:
In addition, startup performance for the modern connection dialog has been improved, and we made changes to ensure that if you set any of the advanced options when connecting to something like Object Explorer, those options carry over to connections subsequently initiated (e.g. if you open a query editor from OE).
In this release we also updated to later releases of Microsoft.Data.SqlClient (MDS) and SQL Management Objects (SMO). While these updates are invisible and foundational to core SSMS functionality, they are quite impactful to components like third-party extensions that have dependencies on them. We are aware that SSMS 22.3 does not work when certain third-party extensions are installed, and we are following up with extension owners based on feedback items that have already been opened. If you use a third-party extension, be aware this could happen. If you uninstall the third-party extension and the problem no longer occurs in SSMS 22.3, please file a ticket with the extension owner. Have questions about extensions in SSMS, please check out our FAQ.
In the event that you ever need to rollback to a previous release, you can do that from the Visual Studio Installer. From the SSMS 22 entry in the VS Installer, select More > Rollback to previous version. If you happen to uninstall SSMS entirely, you can find previous versions on the Release History page. While you can install different major versions of SSMS side by side (e.g. SSMS 21.6 and 22.3), you can only install one version of SSMS 22 GA on your machine.
If you’re using GitHub Copilot in SSMS, this release introduces support for database instructions. Folks that have used GitHub Copilot in Visual Studio or VS Code may already be familiar with custom instructions, which provide persisted guidance for how GitHub Copilot should behave across all your interactions. Custom instructions are implemented using markdown (.md) files in your repository or user profile, and while helpful for a developer, they may not be readily available to all SSMS users that connect to a database.
The database instructions feature allows you to add database-specific context that describe business rules and leverages extended properties. By storing this information in the database, it’s available to anyone who uses GitHub Copilot when connected to that database. The instructions follow the AGENTS.md format to provide context. Once in place, you no longer need to ask the person who’s supported the application for 20 years what the values 9 and 42 mean for the Status column in the Orders table.
EXEC sp_addextendedproperty
@name = N'AGENTS.md',
@value = N'The dbo.Apts table stores information about patient appointments',
@level0type = N'SCHEMA',
@level0name = N'dbo',
@level1type = N'TABLE',
@level1name = N'Apts';
GO
No additional configuration or setting is required for database instructions to be available to GitHub Copilot. Extended properties can exist for a host of objects in your database, starting with tables, columns and stored procedures and extending 😉 to functions, constraints, and more.
We recognize the following:
We’ve seen folks mention that it would be great if you could add an extended property as part of the CREATE syntax for an object. We haven’t seen a feedback item filed anywhere for that…we will need one (and allllllllll your votes) if SSMS users want us to drive this change on the engine side. To be clear, a change in T-SQL syntax happens in the engine, that's not an enhancement to the query editor in SSMS.
Some companies do a fantastic job of documenting their business definitions, etc. but it lives elsewhere, outside the database. If that’s your scenario, then please create a feedback item for us (please search in case someone else submits it first) so we can understand where that information lives and in what form (e.g. is it in a Word doc, OneNote, a proprietary system, etc. and stored as plain text, YAML, JSON, something else?).
We expect – we want – this feature to advance, but we need your input to help guide that evolution. The feedback site is your friend. Please search first, please add constructive comments, and please upvote.
I’ve seen some recent comments along the lines of “I don’t understand why it takes so long to fix something simple.” We appreciate everyone’s passion for SSMS and fully understand that in an application that you use daily, certain bugs can be very frustrating and affect your workflow. What’s often going on behind the scenes is:
As a team, we regularly review the issues and suggestions that have a severe impact on users, and those that are highly voted. There is a perpetual review of what we have planned and what we’re being asked to do, and we continue to adjust priorities as necessary. Thank you for your dedication and support.
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