In a world that glorifies busyness, taking a vacation can sometimes feel like a luxury. But the truth is: rest isn’t optional; it’s essential.
Stepping away allows us to recharge, gain new perspectives, and return with renewed energy. Some of my best ideas have come when I was NOT at my desk (let it be vacation, hitting the gym, shoveling the snow – YMMV).
Don’t underestimate the power of a break. Vacation is not just time off; it’s an investment in your productivity, creativity, and well-being.
Have you ever had a breakthrough moment during your time off? Share your story!
We often treat “practice” and “practical” as separate concepts. One is something you do repeatedly to improve a skill, and the other refers to something useful or applicable in the real world. But the connection between the two is much stronger than we tend to realize.
Practice Makes Perfect… But Also Practical
When we think about practice, we imagine musicians playing scales, athletes repeating drills, or programmers solving coding challenges. The goal of practice is improvement—building muscle memory, refining technique, and preparing for real-world scenarios.
Meanwhile, something is considered “practical” when it has immediate value or usefulness. A practical solution is one that works in real-world conditions, while an impractical one may be too theoretical or cumbersome to implement.
Here’s where the connection comes in: practice is what makes something practical. Without practice, a skill remains theoretical, and without a practical application, practice can feel meaningless.
The Bridge Between Theory and Action
A great example of this is education. Students often ask, “When will I ever use this in real life?” The answer depends on whether they’ve had the opportunity to practice applying what they’ve learned in a real-world scenario.
For example:
A medical student can read about surgical procedures, but only by practicing on simulators or assisting real surgeries do those skills become practical.
A software engineer can study algorithms all day, but until they implement them in a production environment, the knowledge remains theoretical.
An entrepreneur can read business books, but without testing ideas in the real world, they won’t develop practical decision-making skills.
In each case, practice is what turns knowledge into something practical.
Practicality as the Outcome of Practice
Another way to see the connection is that practice itself is about refining what works and discarding what doesn’t—just like finding a practical solution. The more we practice, the more we naturally filter out ineffective techniques, making the process more streamlined, efficient, and ultimately… practical.
This is why experience is so highly valued in any field. Those who have practiced extensively aren’t just more skilled—they’ve also learned what actually works in real-world conditions.
Final Thought: If You Want to Be Practical, Keep Practicing
It’s easy to dismiss practice as something separate from practical knowledge, but the two are intertwined. The more you practice, the more practical you become, because you’ve tested what works and internalized it.
So next time you’re practicing something—whether it’s public speaking, writing, coding, or decision-making—remember: you’re not just repeating actions; you’re making yourself more practical. And that is the real magic of practice.
As AI models become more powerful, they also become more token-hungry, increasing costs and latency. While traditional methods like truncation and limiting response length can help, they often sacrifice context and quality. Instead, let’s explore novel, strategic ways to reduce token usage without compromising effectiveness.
1. Smarter Prompt Engineering: Saying More with Less
A well-optimized prompt can dramatically reduce token consumption. Instead of verbose requests like:
❌ “Can you please provide me with a summary of the following text?”
A more efficient version would be:
✅ “Summarize:”
Additionally, reusing compressed context rather than repeating full conversations can save tokens. For instance, instead of feeding an entire prior exchange, AI can refer to a summary of key takeaways from previous interactions.
2. Adaptive Token Compression: Less Text, Same Meaning
Rather than storing long contextual passages, AI systems can use semantic embeddings or dynamic summarization techniques:
Contextual Summarization: Summarizing ongoing conversations periodically to reduce the tokens required for historical context.
Vectorized Memory: Storing past interactions as embeddings instead of full-text retrieval, enabling AI to reconstruct meaning rather than consuming tokens verbatim.
For example, instead of re-feeding an entire customer support chat, a short-hand summary like “User has connectivity issues, attempted router reset” suffices.
3. Sparse Attention Mechanisms: Prioritizing What Matters
Modern transformer models often waste tokens processing unnecessary context. Sparse attention mechanisms improve efficiency by:
Focusing on relevant tokens rather than treating all tokens equally.
Adaptive token masking, where redundant tokens (like repeated greetings or boilerplate text) are deprioritized dynamically.
Using architectures like Longformer and BigBird, which process long sequences by reducing unnecessary cross-token interactions.
For AI models handling lengthy legal or research documents, this method significantly reduces redundant token use.
4. Syntax-Aware Pruning: Stripping the Fluff
Many AI-generated texts contain non-essential words that do not contribute meaning. By eliminating stopwords and optimizing sentence structures, we can reduce token count:
Removing non-essential function words:“This is an example of a sentence that might be improved.” → “Example sentence, improved.”
Condensed formatting: Reducing unnecessary punctuation, spaces, and verbose phrasing without losing clarity.
For chat-based applications, this method improves efficiency without degrading comprehension.
5. Hierarchical Context Caching: Storing the Right Memory
Rather than blindly feeding AI entire conversation histories, multi-level memory hierarchies can optimize token usage:
Summarizing past interactions into key points, keeping only recent, high-priority exchanges verbatim.
Using external knowledge bases instead of in-context recall (e.g., AI retrieves a short identifier for a prior discussion rather than restating the entire conversation).
For AI assistants, this ensures a balance between short-term memory (detailed) and long-term memory (summarized).
6. Model-Side Improvements: Smarter Tokenization and Compression
AI tokenization itself can be optimized to reduce unnecessary subword fragments:
More efficient tokenization schemes: Adjusting how models split words into tokens to minimize token overhead.
Lossless compression: Using encoding techniques like Huffman coding to compress frequent phrases without sacrificing meaning.
This method is especially useful for multilingual models and applications dealing with highly structured text.
Instead of handling every response with a static context window, AI can prune unnecessary past tokens in real time:
Relevance-Based Clipping: Dynamically detecting and discarding parts of the conversation that are no longer relevant.
Incremental Context Updating: Keeping track of only new information instead of repeating past context in every input.
For example, rather than re-feeding a full chat history, AI can retain only “new details since the last response.”
Final Thoughts: Efficiency Without Compromise
Reducing token usage isn’t just about cutting words—it’s about preserving meaning while optimizing efficiency. By combining adaptive summarization, smarter tokenization, and selective memory, AI models can save costs, reduce latency, and improve performance while maintaining high-quality responses.
As AI usage scales, these innovations will be key to ensuring sustainable and efficient AI interactions—making models faster, cheaper, and more effective without sacrificing intelligence.
What’s Next?
Are you working on optimizing AI token efficiency? Share your insights and let’s refine these strategies further!
For many developers, the command line is where a significant portion of work happens—whether it’s navigating directories, running scripts, or managing version control. But let’s be honest: the default terminal setup isn’t always the most visually appealing or informative. Fortunately, two powerful tools—NerdFonts and Starship—can completely transform your terminal experience, making it both functional and aesthetically pleasing.
In this article, we’ll explore what NerdFonts and Starship bring to the table and how you can leverage them to create a better command-line environment.
Why Improve Your Command Line Experience?
A well-optimized terminal setup can boost productivity, provide clearer insights into your system state, and even make coding more enjoyable. While tools like Oh My Zsh and Powerlevel10k have been popular for enhancing the shell experience, NerdFonts and Starship offer a lightweight, highly customizable alternative that works across multiple shells.
What is NerdFonts?
NerdFonts is a project that patches and extends popular monospaced fonts with additional glyphs and symbols. These glyphs include:
Devicons for programming languages and frameworks
Powerline symbols for a visually rich prompt
Weather, folder, and battery icons for enhanced UI
Version control symbols for Git integration
By using a NerdFont-patched font, your terminal can display these extra symbols, allowing for better information at a glance.
Once installed, set your terminal emulator (e.g., iTerm2, Alacritty, Windows Terminal) to use the NerdFont.
What is Starship?
Starship is a fast, minimal, and highly customizable prompt for any shell. Unlike bulky shell frameworks, Starship is written in Rust and designed to be lightweight while still providing rich information at a glance.
Key Features of Starship
Cross-shell compatibility: Works with Bash, Zsh, Fish, PowerShell, etc.
Blazing fast: Optimized for speed, avoiding slow terminal startup times.
Customizable: Easily tweak colors, modules, and behavior.
Minimalist yet powerful: Shows relevant info when needed (e.g., Git branch, Node.js version, Kubernetes context).
Installing Starship
On macOS & Linux
curl -sS https://starship.rs/install.sh | sh
On Windows (via Scoop or Chocolatey)
scoop install starship
or
choco install starship
Configuring Starship
Once installed, add the following to your shell config file:
Bash (~/.bashrc)
eval "$(starship init bash)"
Zsh (~/.zshrc)
eval "$(starship init zsh)"
Fish (~/.config/fish/config.fish)
starship init fish | source
Now, restart your terminal, and you should see a modern, stylish prompt.
Customizing Starship
Starship is fully customizable using the ~/.config/starship.toml file. You can tweak colors, enable/disable modules, and adjust how much information is displayed.
By combining NerdFonts and Starship, you get: ✅ A visually rich terminal with icons and symbols ✅ A lightweight, fast prompt that doesn’t slow down your workflow ✅ Cross-shell compatibility, so it works anywhere ✅ A fully customizable experience tailored to your preferences
Here’s an example of how your terminal might look with both tools in action:
Final Thoughts
If you spend a lot of time in the terminal, small enhancements can make a huge difference in usability and aesthetics. NerdFonts and Starship are excellent choices for a sleek, informative, and responsive command-line environment.
Give them a try, and take your developer experience to the next level! 🚀
First Hadoop, Now Apache Iceberg – Are We Repeating the Same Mistakes?
In the early 2010s, Hadoop was heralded as the future of big data. Enterprises rushed to build massive Hadoop clusters, anticipating unprecedented analytical power and cost efficiency. Fast forward to today, and Hadoop’s reign has largely diminished, with organizations pivoting to cloud-native architectures, data lakes, and modern analytical engines. Now, Apache Iceberg has taken center stage as the next big thing in open table formats, promising better scalability, schema evolution, and ACID compliance for big data workloads. But are we setting ourselves up for the same challenges that led to Hadoop’s decline?
The Hadoop Hype and Its Pitfalls
Hadoop’s rise was driven by its promise of distributed storage (HDFS) and scalable processing (MapReduce). It seemed like a dream solution for handling massive datasets. However, its limitations became apparent over time:
Operational Complexity – Running and maintaining Hadoop clusters required significant expertise. Organizations needed dedicated teams to manage cluster health, tuning, and upgrades.
Slow Query Performance – MapReduce, and later Hive, struggled with query latency compared to modern distributed query engines like Apache Presto, Trino, and Snowflake.
High Cost of Ownership – While Hadoop was initially pitched as cost-effective, maintaining on-premise infrastructure and handling replication overhead made it expensive.
Fragmented Ecosystem – The Hadoop ecosystem became a collection of loosely integrated projects (Pig, Hive, HBase, etc.), leading to dependency hell and operational inefficiencies.
Cloud-Native Disruption – Cloud-based data warehouses and lakehouses like Snowflake and Databricks eliminated the need for complex Hadoop clusters, offering easier scalability and better performance.
Hadoop’s demise wasn’t because it was fundamentally flawed—it was the operational model and the shift in technology paradigms that led to its decline.
Enter Apache Iceberg: A New Hope for Data Management?
Apache Iceberg has emerged as a modern open table format designed to address the limitations of traditional big data storage formats like Apache Hive tables. It offers:
Schema Evolution Without Downtime – Unlike Hive tables, Iceberg allows for schema modifications without breaking existing queries.
Hidden Partitioning – Improves query performance by avoiding partition column exposure in query logic.
ACID Compliance – Supports transactions and concurrency without reliance on external locking mechanisms.
Time Travel and Versioning – Enables users to access historical data versions, enhancing reproducibility.
Compatibility with Multiple Query Engines – Works with Apache Spark, Trino, Flink, and others, reducing vendor lock-in.
These advantages make Iceberg an attractive option for modern data lakes. However, despite its technical strengths, there are warning signs that we might be heading toward a similar Hadoop-style overcommitment.
The Iceberg Challenges: Are We Repeating History?
Operational Complexity Remains a Barrier While Iceberg simplifies table management compared to Hive, running it at scale still requires significant expertise. Organizations must manage metadata files, catalog integrations, and version control across multiple engines. Without proper governance, metadata bloat can become a performance bottleneck.
Storage and Compute Still Need Careful Optimization Just like Hadoop, Iceberg assumes separation of storage and compute, but this doesn’t magically solve performance issues. Query tuning, partitioning strategies, and metadata maintenance are still necessary to avoid costly scans.
Fragmented Adoption Across Ecosystems Hadoop’s downfall was partly due to a fragmented ecosystem, and Iceberg is heading in a similar direction. While Snowflake, Databricks, and Delta Lake have their own proprietary advantages, Iceberg competes with other table formats like Delta Lake and Apache Hudi. If organizations invest heavily in Iceberg but face shifting industry preferences, they might end up with another technology lock-in issue.
The “Open Standard” Debate Iceberg positions itself as an open table format, but cloud vendors are building proprietary extensions on top of it. Just like Hadoop vendors (Cloudera, Hortonworks, MapR) created diverging implementations, cloud providers are modifying Iceberg to fit their ecosystem, potentially leading to compatibility issues down the line.
Skills Gap and Learning Curve A major reason Hadoop failed in many enterprises was the steep learning curve and lack of skilled professionals. Iceberg, while more developer-friendly, still requires data engineers to understand catalog configurations, metadata pruning, and integration with query engines. Organizations rushing into Iceberg without the right expertise may find themselves in a similar skills gap dilemma.
Lessons from Hadoop: Proceed with Caution
1. Avoid Vendor Lock-In Disguised as Open Source
One of Hadoop’s biggest mistakes was how vendors turned an open-source technology into a fragmented, competing landscape. If Iceberg follows the same path, enterprises could face interoperability challenges. Organizations should push for true standardization and ensure Iceberg implementations remain compatible across cloud platforms.
2. Optimize for Business Outcomes, Not Just Technology
Many organizations adopted Hadoop because it was the “hot new thing” rather than aligning it with business goals. Iceberg should be implemented where it truly adds value—such as improving data lake performance and governance—rather than being a default choice without evaluation.
3. Invest in Expertise and Governance
Just like Hadoop needed cluster administrators and MapReduce experts, Iceberg requires knowledgeable teams to manage metadata, storage efficiency, and query optimization. Investing in best practices from the start will prevent costly migrations later.
4. Stay Agile and Avoid Overcommitment
The Hadoop era saw enterprises betting their entire data strategy on it, only to shift to cloud-native architectures later. Organizations should adopt Iceberg incrementally, ensuring that it delivers value before making large-scale investments.
Conclusion
Apache Iceberg is undoubtedly a powerful evolution in open data architectures, addressing many of the problems that plagued Hadoop and Hive-based data lakes. However, if history has taught us anything, it’s that technological superiority alone does not guarantee long-term success. The industry must be mindful of the same pitfalls—operational complexity, vendor fragmentation, and overhyped expectations.
The real question is not whether Iceberg is better than Hadoop—it is whether we have learned from our past mistakes. If we apply those lessons wisely, Iceberg could avoid the fate of its predecessor and truly revolutionize the data landscape.
Preparation is the foundation of success. The saying, “If you fail to prepare, you prepare to fail,” attributed to Benjamin Franklin, highlights a simple truth: planning and readiness determine outcomes. Throughout history, countless examples from business, sports, and technology have illustrated the dire consequences of inadequate preparation and the rewards of meticulous planning.
The High Cost of Poor Preparation
1. Microsoft’s Windows 98 COMDEX Demo (1998)
One of the most infamous live demo failures occurred when Microsoft showcased Windows 98 at COMDEX. During the presentation, Microsoft’s Chris Capossela was demonstrating Plug and Play functionality when the system crashed into the dreaded Blue Screen of Death (BSOD).
What Went Wrong?
Insufficient testing of live environments.
Lack of a backup plan for demo failures.
The Lesson: Always test software in real-world conditions before a public demo. Have contingencies in place to recover from failures quickly.
2. Apple’s Face ID Failure (2017)
During the 2017 iPhone X launch, Apple’s Craig Federighi attempted to demonstrate the Face ID feature—only for it to fail to recognize his face. He had to use a backup device, but the damage was already done, and skepticism about Face ID’s reliability spread.
What Went Wrong?
The test phone required passcode entry after a reboot, something not anticipated in their preparations.
Apple assumed everything would work flawlessly without testing for edge cases.
The Lesson: Test under different conditions, including worst-case scenarios, and always have a backup plan.
3. The 2017 Oscars Best Picture Mix-up
One of the biggest live television mishaps happened at the 2017 Academy Awards when La La Land was mistakenly announced as the winner for Best Picture instead of Moonlight due to an envelope mix-up.
What Went Wrong?
The wrong envelope was handed to the presenters, and there was no immediate protocol to verify the winner before the announcement.
The lack of preparation for handling on-stage errors led to global embarrassment.
The Lesson: In high-stakes events, always double-check critical information and have clear error-handling procedures in place.
The Power of Preparation
1. Jeff Bezos’ Long-Term Vision at Amazon
Unlike many companies that focused only on short-term gains, Jeff Bezos meticulously planned Amazon’s expansion. He invested in infrastructure, cloud computing (AWS), and long-term customer trust, allowing Amazon to dominate e-commerce and cloud services.
The Lesson: Long-term preparation trumps short-term improvisation. A well-prepared strategy allows businesses to scale and adapt.
2. NASA’s Apollo 11 Moon Landing (1969)
The Apollo 11 mission required years of meticulous preparation, countless simulations, and contingency plans. When Neil Armstrong and Buzz Aldrin landed on the moon, every possible failure scenario had already been rehearsed.
The Lesson: When the stakes are high, rigorous preparation is non-negotiable. Success favors those who anticipate and practice for every possibility.
3. Toyota’s Just-in-Time Manufacturing System
Toyota revolutionized the automotive industry with its Just-in-Time (JIT) manufacturing system. The company meticulously planned every aspect of its supply chain to reduce waste, optimize efficiency, and ensure smooth production flows.
The Lesson: Proper planning, execution, and adaptability lead to groundbreaking success, even in competitive environments.
How to Ensure You Are Always Prepared
Anticipate Challenges: Consider what could go wrong and develop contingency plans.
Practice & Rehearse: Whether it’s a presentation, interview, or product launch, extensive practice prevents unexpected failures.
Have a Backup Plan: Always prepare alternative solutions to avoid last-minute crises.
Gather the Right Resources: Success comes from having the right tools, data, and team to execute your plans effectively.
Stay Adaptable: Even the best-prepared plans can face unforeseen challenges—being able to pivot is just as important as preparation.
Conclusion
Failing to prepare is an open invitation for failure. From corporate blunders to technological mishaps, history is full of examples proving that preparation is the key differentiator between success and disaster. Whether in business, technology, or personal growth, those who anticipate challenges, plan thoroughly, and practice relentlessly are the ones who thrive.
The next time you face an important task, remember: preparation is not optional—it’s the foundation of success.
We’ve all been there. I have been there. I have been there recently too. Your slides won’t load, your mind goes blank, the demo crashes, or—worst of all—you realize halfway through that the audience isn’t engaged. A failed presentation can feel like the end of the world, but it doesn’t have to be. The key to bouncing back isn’t avoiding mistakes altogether (because they will happen) but knowing how to recover gracefully.
Even some of the biggest names in tech—Microsoft, Apple, Google—have experienced embarrassing presentation fails. But what separates the pros from the amateurs is how they handle these moments. Let’s dive into how to recover from a presentation fail and learn from some famous examples.
1. Pause, Breathe, and Reset
The first instinct when things go wrong is often panic. Instead, take a moment to breathe and reset. A short pause—just a few seconds—can help you regain control of your thoughts. It may feel like an eternity, but to the audience, it’s just a natural pause.
If your mind goes blank, try:
Taking a sip of water to buy time.
Using humor: “Well, that’s not how I planned that!”
Summarizing what you’ve said so far to help get back on track.
Famous Example: Steve Jobs’ iPhone Demo Issue (2007)
During the legendary 2007 iPhone unveiling, Steve Jobs ran into a serious issue: the iPhone lost Wi-Fi connectivity in front of a live audience. Instead of panicking, he calmly asked the audience to turn off their personal Wi-Fi hotspots and reconnected without breaking stride. He even made a joke:
“You know, you could help me out if you’re on Wi-Fi… if you could just get off.”
This moment highlighted his ability to handle technical failures with humor and composure.
2. Acknowledge the Issue, But Don’t Dwell on It
If your slides won’t load or your demo crashes, acknowledge the issue briefly and move on. Trying to pretend nothing happened only makes it more awkward. Instead, own it with confidence.
For example:
“Looks like my slides are taking an unscheduled break! Let me walk you through this verbally.”
“Technology is great—when it works! Let’s pivot to Plan B.”
Your audience will appreciate your composure more than a flawless presentation.
Famous Example: Microsoft’s Blue Screen of Death (BSOD) at COMDEX (1998)
During a Windows 98 demo at COMDEX, Microsoft’s Chris Capossela (now the company’s Chief Marketing Officer) was showing off new Plug and Play functionality when—right in the middle of the demonstration—the infamous Blue Screen of Death (BSOD) appeared.
Bill Gates, who was on stage with Capossela, took the failure in stride and quipped:
“That must be why we’re not shipping Windows 98 yet!”
The audience erupted in laughter, and instead of a PR disaster, the moment became legendary. This is a perfect example of how humor and a quick-witted response can turn failure into something memorable.
3. Engage the Audience
One of the best ways to recover from a mistake is to involve your audience. Ask a question, invite thoughts, or even joke about the situation. This shifts the focus from your mistake to an interactive discussion.
Try:
“Has anyone else ever had a demo fail at the worst time?”
“What do you all think is the most important takeaway from this topic?”
Audience engagement can turn an awkward moment into a powerful one.
Famous Example: Elon Musk’s Cybertruck Window Fail (2019)
During the Tesla Cybertruck unveiling in 2019, Elon Musk wanted to showcase the truck’s “shatterproof” windows by having his designer, Franz von Holzhausen, throw a metal ball at them. Instead of withstanding the impact, the windows shattered—twice.
Musk’s reaction? He laughed, swore lightly, and said:
“Well, maybe that was a little too hard.”
Instead of ignoring the mistake, he embraced it and kept moving forward with the presentation. The moment went viral, but Tesla still received a record number of Cybertruck pre-orders.
4. Adapt and Keep Moving
A presentation fail is only a disaster if you let it be. Your ability to adapt on the fly is what people will remember. If your slides won’t work, talk through the key points. If your demo fails, explain what should have happened.
Some of the best presentations in history were unscripted. Your knowledge is more important than your slides.
5. Use Humor and Perspective
Most presentation mistakes are not catastrophic. Unless you’re in an emergency situation, the stakes are rarely as high as they feel. Humor can be a great tool to defuse tension.
For example:
“That’s why we always have a Plan B… and a Plan C.”
“I think my laptop just decided to take an early lunch break.”
A well-placed joke can turn a fail into a memorable moment.
6. Follow Up with Your Audience
If something went seriously wrong (like missing key content or running out of time), follow up afterward. Send an email, share additional resources, or offer to answer questions. This shows professionalism and ensures your message still gets across.
For example:
“I wanted to follow up on today’s session with some additional insights and a summary of the key points.”
“Since we had technical difficulties, here’s a recording of a similar demo.”
Your audience will appreciate your effort to add value, even after the fact.
7. Reflect and Improve for Next Time
Once the presentation is over, reflect on what went wrong and how you can improve. Did you rely too much on slides? Was there a backup plan for technical issues? What would you do differently next time?
Consider:
Practicing with different setups to avoid technical surprises.
Preparing alternative ways to explain key points.
Embracing the mindset that no presentation is perfect—and that’s okay.
Final Thoughts
A presentation fail is not the end of the world—it’s an opportunity to show resilience, adaptability, and even a sense of humor. The best speakers in the world have faced presentation disasters, and their ability to recover is what made them great. The next time something goes wrong, remember: how you handle the mistake is more important than the mistake itself.
And who knows? This fail might just make your presentation unforgettable—in the best way possible.
Every new year begins with a list of resolutions—promises we make to ourselves, vowing to improve, cut back, or shift priorities. Some of these resolutions are necessary and life-changing, but others? They are meant to be broken.
Take my own experience as an example. At the end of 2024, I made a firm commitment: In 2025, I would do fewer events. The logic was sound—I wanted to reclaim time for deep work, personal projects, and perhaps a little breathing room. I told myself that after years of a packed calendar, it was time to scale back.
Fast forward to February 2025, and I can already admit: I have failed spectacularly. Not only did I not reduce the number of events I’m involved in, but I’m actually doing more than ever. I find myself saying yes to opportunities that align with my passion, expanding my reach, and engaging in discussions that truly matter.
Microsoft MVP summit, Valentine day Azure day, Linux Foundation Empowering night, aitour.microsoft.com
Why Do We Break Certain Resolutions?
1. Some Goals Sound Good in Theory, but Reality Has Other Plans
At the time, I believed that fewer events would equate to more focus. What I didn’t account for was that my nature—my passion for connecting, sharing knowledge, and building communities—would make this nearly impossible. When invitations and opportunities came knocking, I had to ask myself: Am I avoiding these for the sake of a resolution, or am I saying no to something that aligns with who I am?
2. Resolutions Should Evolve with Your Growth
The resolution to do fewer events was made at a time when I felt the need for change. But growth isn’t always about doing less; sometimes, it’s about doing more of the right things. In 2025, I’m not just doing more events—I’m choosing more meaningful ones, aligning with initiatives that have impact.
3. Passion Wins Over Restriction
Some resolutions require discipline—like exercising more or cutting down on distractions. But others, like limiting opportunities for engagement, can become artificial restrictions that go against your strengths. The key is recognizing when a resolution is serving you and when it’s holding you back.
The Lesson? Adjust, Don’t Abandon Growth
This experience has taught me that instead of setting arbitrary limits, I should focus on better curation. It’s not about fewer events—it’s about the right events. It’s about ensuring that each engagement adds value, aligns with my mission, and keeps me energized rather than drained.
So, if you find yourself breaking a resolution, ask yourself: Am I failing, or am I just evolving? Because some resolutions are meant to be broken, and sometimes, that’s exactly what needs to happen.
Azure OpenAI Service now includes the new o3‑mini reasoning model—a lighter, cost‑efficient successor to earlier reasoning models (such as o1‑mini) that brings several new capabilities to the table. These enhancements include:
Reasoning Effort Control: Adjust the model’s cognitive load (low, medium, high) to balance response speed and depth.
Structured Outputs: Generate well‑defined, JSON‑structured responses to support automated workflows.
Functions and Tools Support: Seamlessly integrate with external functions to extend AI capabilities.
Developer Messages: A new “developer” role replaces the legacy system message, allowing for more flexible instruction handling.
Enhanced STEM Performance: Improved capabilities in coding, mathematics, and scientific reasoning.
In addition to these advances, Microsoft’s new o3‑mini is now complemented by Semantic Kernel—a powerful, open‑source SDK that enables developers to combine AI services (like Azure OpenAI) with custom code easily. Semantic Kernel provides an orchestration layer to integrate plugins, planners, and services, allowing you to build robust and modular AI applications in C#.
Prerequisites
Before getting started, ensure you have:
An Azure account with an Azure OpenAI Service resource provisioned.
Your API endpoint (e.g., https://<your-resource-name>.openai.azure.com/) and an API key.
A deployment for your o3‑mini model (e.g., “o3‑mini” or “o3‑mini‑high”).
.NET 8 (or later) and an IDE (e.g., Rider, Visual Studio or VS Code).
(Optional) Familiarity with Semantic Kernel and the corresponding NuGet packages.
Setting Up Your Project
Create a New Console Application Open your terminal or IDE and run: dotnet new console -n AzureO3MiniDemo cd AzureO3MiniDemo
Install Required NuGet Packages Install both the Azure OpenAI client library and Semantic Kernel: dotnet add package Azure.AI.OpenAI dotnet add package Microsoft.SemanticKernel Semantic Kernel provides a unified interface to orchestrate AI models and plugins.
Code Sample: Using o3‑mini with Semantic Kernel in C#
Below is a complete C# code sample demonstrating how to use the o3‑mini model from Azure OpenAI Service directly—and how to integrate Semantic Kernel to add an orchestration layer. This lets you later add custom functions (plugins) that can be automatically invoked by your agent.
Note: The code includes placeholders for new properties (like ReasoningEffort) and is structured to work with Semantic Kernel’s abstractions. Please consult the latest Semantic Kernel documentation for the precise API details.
using System;
using System.Threading.Tasks;
using Azure;
using Azure.AI.OpenAI;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
namespace AzureO3MiniDemo
{
// (Optional) Define an enum for reasoning effort if supported by your SDK version.
public enum ReasoningEffort
{
Low,
Medium,
High
}
class Program
{
static async Task Main(string[] args)
{
// Replace with your Azure OpenAI endpoint and API key.
string endpoint = "https://<your-resource-name>.openai.azure.com/";
string apiKey = "<your-api-key>";
// The deployment name for your o3-mini model.
string deploymentName = "o3-mini";
// Create an instance of OpenAIClient for direct API calls (if needed).
OpenAIClient client = new OpenAIClient(new Uri(endpoint), new AzureKeyCredential(apiKey));
// Now, set up Semantic Kernel and add the Azure OpenAI chat completion service.
var kernelBuilder = Kernel.CreateBuilder();
kernelBuilder.AddAzureOpenAIChatCompletion(deploymentName, endpoint, apiKey);
// Optionally, add custom plugins here.
// For example: kernelBuilder.Plugins.AddFromType<YourCustomPlugin>();
Kernel kernel = kernelBuilder.Build();
// Create a prompt and configure completion options.
string prompt = "Write a short poem about the beauty of nature.";
CompletionsOptions options = new CompletionsOptions()
{
Prompts = { prompt },
MaxTokens = 100,
Temperature = 0.7f
};
// NEW: Set the reasoning effort level (if supported).
// options.ReasoningEffort = ReasoningEffort.Medium;
// (Optional) Specify a JSON schema for structured outputs.
// options.StructuredOutputSchema = "{ \"type\": \"object\", \"properties\": { \"poem\": { \"type\": \"string\" } } }";
try
{
// Query the o3-mini model using the Semantic Kernel abstraction.
Response<Completions> response = await kernel.GetService<IChatCompletionService>()
.GetCompletionsAsync(deploymentName, options);
Completions completions = response.Value;
Console.WriteLine("Response from o3-mini:");
foreach (var choice in completions.Choices)
{
Console.WriteLine(choice.Text.Trim());
Console.WriteLine(new string('-', 40));
}
}
catch (Exception ex)
{
Console.WriteLine($"An error occurred: {ex.Message}");
}
}
}
}
Integrating Semantic Kernel Plugins
Semantic Kernel allows you to extend your application with custom plugins. For example, you can create functions that use Azure Search or other services and have them automatically invoked based on user input. This makes it easier to build AI agents that are both flexible and tailored to your business logic.
Example: Adding a Custom Plugin
Below is a simplified example of a custom plugin function that could be added to your Semantic Kernel setup. This plugin might, for instance, fetch additional context or data needed by your application:
using Microsoft.SemanticKernel.Plugins;
using System.Threading.Tasks;
public class CustomDataPlugin
{
[KernelFunction, Description("Fetches additional context data for the prompt")]
[return: Description("A string containing supplemental data.")]
public async Task<string> GetSupplementalDataAsync([Description("Parameter for the data query")] string query)
{
// Your logic here, e.g., make an HTTP call to fetch data.
await Task.Delay(100); // Simulate async operation.
return $"Supplemental data for query: {query}";
}
}
Once defined, you can register your plugin with the kernel builder:
Semantic Kernel will now have the ability to call this plugin function automatically when the context of your user input suggests it is needed.
Running the Application
Replace the placeholders for <your-resource-name> and <your-api-key> with your actual values.
Save your changes and run the application using: dotnet run
You should see an output similar to: Response from o3-mini: Nature whispers softly in the breeze, Dancing leaves tell secrets with ease. ----------------------------------------
Conclusion
This article demonstrates how to use the new o3‑mini model on Azure OpenAI Service with C# and how to further enhance your application by integrating Semantic Kernel. With Semantic Kernel, you can easily orchestrate AI functions, add custom plugins, and switch between providers (OpenAI vs. Azure OpenAI) with minimal changes to your codebase. This makes it an excellent tool for building sophisticated AI agents and applications.
On January 30, 2025, the Microsoft AI Tour made a significant stop in New York City at the North Javits Center, bringing together industry leaders, technology enthusiasts, and businesses eager to explore the latest advancements in artificial intelligence. This event served as a hub for innovation, showcasing the transformative impact of AI across various sectors. Attendees had the opportunity to network, experience hands-on demonstrations, and gain insights into AI’s rapid evolution.
Key Announcements and Highlights
One of the major highlights of the event was Microsoft’s announcement of the new Surface Copilot+ PCs for Business. These cutting-edge devices, including the latest Surface Pro and Surface Laptop, come equipped with Intel’s Core Ultra processors (Series 2), delivering enhanced AI-driven performance. Business customers can now choose between Intel and Snapdragon-powered Copilot+ PCs, with 5G connectivity for Surface Laptop for Business set to arrive later in 2025. Microsoft also introduced the Surface USB4 Dock and enhancements to Microsoft Teams Rooms on Surface Hub 3 (it finally gets a web browser, for example), reinforcing its commitment to boosting productivity and seamless collaboration.
Additionally, Microsoft unveiled the public preview of Security Copilot in the Surface Management Portal, providing IT administrators with enhanced security tools. These developments underline Microsoft’s focus on integrating AI with business operations, making work more efficient and secure. The event also highlighted AI-powered security advancements, showcasing how businesses can leverage AI for proactive threat detection and response.
Red Hat’s Role and Industry Collaboration
Red Hat also played a prominent role at the New York AI Tour, emphasizing its open-source AI solutions in collaboration with Microsoft. At Booth #EP14, Red Hat showcased Azure Red Hat OpenShift and Red Hat Enterprise Linux AI, illustrating how businesses can accelerate AI deployments in hybrid cloud environments. Experts discussed AI model training on hybrid infrastructure, ensuring that organizations could scale AI applications efficiently while maintaining security and compliance.
The collaboration extends beyond in-person engagements, as Red Hat will host a virtual AI workshop on March 11, 2025, focusing on bringing AI-enabled applications to market faster with Azure Red Hat OpenShift. The session will cover AI model deployment, tuning foundation models, and integrating AI-driven analytics into enterprise applications.
Engaging Sessions and Thought Leadership
The event featured a series of engaging sessions and keynotes designed to educate and inspire attendees. Scott Guthrie, Executive Vice President of Microsoft’s Cloud + AI Group, delivered the Opening Keynote, covering the latest AI advancements and their potential across industries. He discussed how Generative AI is reshaping business operations, automating workflows, and improving decision-making processes.
Among the many insightful sessions, some key highlights included:
“Accelerate Nonprofit Impact with AI” – Exploring how AI can empower nonprofits to drive greater impact through automation, analytics, and efficiency.
“Leading in the Age of AI Transformation” – A discussion on how business leaders can navigate the rapidly changing AI landscape to maintain a competitive edge.
“Copilot Implementation Essentials” – A deep dive into best practices for integrating Microsoft Copilot AI into workplace productivity tools.
“Unveiling the AI Startup Journey – Founders Stories” – Featuring startup founders who shared real-world insights into how AI helped them scale their businesses.
“Microsoft Azure Application Platform” – A deep dive into how Azure accelerates AI and Azure-powered application development, featuring industry experts – me, Danilo Diaz and Colby Ford.
“Generative AI for Threat Intelligence and Fraud Detection” – Highlighting how AI is being used to detect and mitigate fraud, enhancing security in financial and retail sectors.
“Scaling AI Solutions Across Hybrid Environments” – Exploring strategies for deploying AI models in hybrid and multi-cloud environments for maximum efficiency.
Each session provided a mix of technical expertise, real-world case studies, and hands-on demonstrations, making AI accessible to a diverse audience. Many attendees noted the depth of insights provided by AI industry experts, making this event invaluable for anyone looking to implement AI in their business.
Looking Ahead: AI’s Future in Business and Beyond
The Microsoft AI Tour in New York not only highlighted the latest AI-driven products and services but also reinforced the growing need for AI adoption in every industry. With key partnerships like Microsoft and Red Hat working together to simplify AI deployment, businesses are gaining access to powerful tools that make AI integration seamless and scalable. Sessions emphasized how AI-driven automation can reduce operational costs, improve productivity, and enhance decision-making.
Additionally, experts discussed ethical AI practices, ensuring that AI development remains transparent, unbiased, and responsible. The AI governance and compliance discussions were particularly relevant for industries such as finance and healthcare, where AI adoption must align with strict regulations.
As AI continues to reshape industries, events like the Microsoft AI Tour play a crucial role in bridging the gap between cutting-edge technology and practical, real-world applications. The New York stop has set a high bar, showcasing a future where AI enhances productivity, security, and overall business efficiency. For those who missed the in-person event, Red Hat’s virtual session on March 11, 2025, will provide another opportunity to explore AI development with Azure Red Hat OpenShift and gain insights into best practices for scaling AI solutions.
