Claude Code might be the most significant event of 2025 in the field of AI. For me, it changed the course of the year and led me to unthinkable places. I followed every twist and turn and stayed on top of the wave. What an amazing tool that delivers on its promise. It amplifies and scales your ability, keeps you moving forward, and leads to some serious compounding.

An overlooked impact of AI is its compounding nature. From the first prompt, where everything is wrong. But, slowly, you learn, you figure out how to harness. At some point, you forget about the first hallucination, and you are in a new spot. When I talk to others, I can immediately tell where they are on the curve by how they talk about these tools. A scary thing, though, is how far behind you can get, and that you might not be able to catch up to those who have started. If you do anything this month, go down the bumpy road and get on the other side.

As a baseline, here’s the AI-generated image from two years ago, created with DALL-E 3.

The original DALL-E 3 photo looks kind of crazy when you look back. I would never garnish a drink with a grapefruit wedge; it would have been a peel.

To remind you of the cocktail recipe for The Merry Manhattan:

Print

The Merry Manhattan

Recipe by Hans Scharler

Prep time

5

minutes

The Merry Manhattan is a festive twist on the classic Manhattan cocktail, perfect for holiday celebrations. This elegant drink features a rich amber hue, achieved by blending rye whiskey with sweet vermouth. The traditional flavor is enhanced with a unique addition of smoked cherries, adding a subtle, smoky sweetness. A sprig of rosemary infuses the cocktail with a fragrant, herbaceous aroma, invoking the essence of winter. The drink is served in a rocks glass containing a large ice chunk. The finishing touch is a gracefully twisted grapefruit peel, adding a citrusy zing and completing the cocktail’s holiday charm.

Ingredients

• 1 oz Carpano Antica Formula Vermouth

• 2 oz Whistle Pig Rye Whiskey

• 2 dashes Sour Cherry Bitters

• 2 dashes Peychaud’s Bitters

• Fresh cherries (for smoking)

• Fresh rosemary (for smoking)

• Grapefruit peel (for garnish)

• Ice

Directions

• Prepare the Smoke:
  Gather a few fresh cherries and a sprig of rosemary.
  Using a kitchen torch, gently torch the rosemary and cherries until they start to smoke. Be careful not to burn them.
  Immediately cover the smoking rosemary and cherries with a rocks glass to trap the smoke inside. Let it sit for a minute to infuse the glass with the smoky aroma.
• In a mixing glass, combine 1 oz of Vermouth and 2 oz of Rye Whiskey.
• Add two dashes each of Sour Cherry Bitters and Peychaud’s Bitters.
• Fill the mixing glass with ice and stir well to chill and dilute the cocktail.
• Add an ice chunk to the smoked rocks glass.
• Strain the stirred cocktail into the smoked rocks glass.
• Finish and Garnish:
  Take a grapefruit peel and express (squeeze) its oils over the drink.
  Use the grapefruit peel as a garnish.
  Use one of the smoked cherries as a garnish.
  Use the sprig of rosemary as a garnish.

Notes

• Enjoy the smoked cherries as a treat, or save them for a future cocktail garnish.

Here’s the first try with Nano Banna Pro. I gave it the recipe card along with the prompt to create a realistic photo for a Christmas or New Year’s party setting.

I am still not in love with the photos. They are better for sure. Let me try a new approach. I am going to just send a link to the blog post and ask for the photos again.

This time it was much better. I like that it used a fancy cocktail cherry, like a Luxardo cherry, instead of one with a stem.

I found this an interesting way to visualize how a drink can come together. I like inventing my own cocktails for parties. This gives me a way to experiment with the visual presentation.

Now, Nano Banana Pro has way more capability than DALL-E 3 had, so I can do more things. I can make process diagrams for the recipe card. Let’s try that.

Now, we are talking.

A few takeaways:

• Models are improving rapidly.
• You can do something new with Generative AI models.
• And, the power of AND. I started with a photo, then pivoted to a recipe card diagram. I could keep anding. I could make it a YouTube video script. I could make it a series of cocktails. This is the most critical takeaway for 2026. It is not just about doing one thing more efficiently; it is about doing more things than you could before.

Buckle up. You might need a drink in 2026.

Gemini 3 Pro

Google went a different direction. Gemini 3 Pro is all about reasoning, multimodal inputs, and that million-token context window.

The benchmark numbers are wild. It hit 91.9% on GPQA Diamond. On ARC-AGI-2, the abstract reasoning benchmark, it scored 31.1% (and up to 45% in Deep Think mode). That is a huge leap over previous models. On LMArena it took the top ELO spot.

If your work is heavy on reasoning, vision, video, or you need to throw massive context at a problem, Gemini 3 Pro is built for that.

Claude Opus 4.5

Anthropic announced Opus 4.5 on November 24, 2025. They are calling it the best model in the world for coding, agents, and computer use. Bold claim.

On their internal engineering benchmarks, Opus 4.5 scored higher than any human candidate ever on their take-home exam. It also delivers higher pass rates on tests while using up to 65% fewer tokens than Sonnet 4.5. That efficiency piece matters if you are building agents that run for hours.

The pitch is clear: if you care about code, automation, and not burning through tokens, Opus 4.5 is the one.

How They Compare

Opus 4.5 is aimed at engineers building agents and writing code. It is optimized for efficiency. Gemini 3 Pro is aimed at everything else: reasoning, multimodal, long context, general purpose.

Both are frontier models. Both are available on major clouds and APIs. The honest answer is you might end up using both depending on the task.

The Real Point

The meta-point is not which model wins. The point is that two frontier models landed in the same week, both pushing hard on different axes. Reasoning, coding, agents, vision, efficiency. The pace of improvement is nuts.

If you are building with AI right now, the table is full. Pick your model, match it to your task, and start experimenting. There has never been a better time.

What excites me about Claude Opus 4.5?

While I was writing this post, a friend texted me to ask what excited me about Claude Opus 4.5. I spent a couple of hours with it today, and I would have to say… tool search.

Tool search lets Claude discover and load tools on-demand instead of pre-loading every definition at the start.

Here is how it works. You provide a catalog of tools to the API with names, descriptions, and input schemas. You mark most of them with defer_loading: true, which means they stay out of the model’s context until needed. Then you include a tool-search tool in the list. When Claude needs a new capability, it searches, finds the right tool, and only then does that tool get loaded into context.

There are two pain points this solves… expensive tokens and picking the right tool.

When you load dozens of tools upfront, the definitions alone eat up thousands of tokens. That is space you could be using for reasoning, tool outputs, and user messages. With tool search, you load only the search tool and maybe three to five relevant definitions. The overhead drops significantly.

With large libraries of tools, the model can struggle to pick the right one, especially when names or parameters are similar. The search step narrows the candidates to tools that actually match the task.

As they say, AI Engineer Code is by invitation only. They have hand-selected the top builders and leaders in AI Engineering for a highly curated experience. The team behind the event also helped organize meetups and hackathons happening around the city.

Some interesting sessions (from descriptions) that I am looking forward to:

• 2026: The Year the IDE Died by Steve Yegge, Sourcegraph, and Gene Kim, IT Revolution
• Autonomy Is All You Need by Michele Catasta, Replit
• Don’t Build Agents, Build Skills Instead by Barry Zhang and Mahesh Murag, Anthropic
• Research to Reality with Google DeepMind by Kat Kampf and Ammaar Reshi, Google

Some of the AI Engineer Code events will be live-streamed on YouTube. Check out the video here:

I will share my thoughts on the experience and key takeaways after the events, seminars, and workshops. Should be awesome, profound, scary, and awesome.

Side note: On the AI Engineer Code site, there was a cool wave ASCII art animation that showed the AIE logo, NYC landmarks in an animated wave. I used Claude to make me one.

Here’s how I feel rn.

I really feel like I can engineer anything with this setup, helping me build engineered systems. I have tried new toolboxes, built deep learning projects, and improved my MATLAB coding skills by running AI code reviews.

“Using MATLAB + Claude Code went from ‘vibe coding’ to ‘vibration engineering’ — I was no longer just using AI — I learned from it, got pointed in the right direction, and dug in.

Hans Scharler

Here is what you need:

• MATLAB: https://matlab.mathworks.com
• Visual Studio Code: https://code.visualstudio.com/
• Claude Code: https://www.claude.com/product/claude-code
• MATLAB MCP Core Server for Claude Code: https://github.com/matlab/matlab-mcp-core-server
• MATLAB Extension for Visual Studio Code: https://marketplace.visualstudio.com/items?itemName=MathWorks.language-matlab

To build your own super stack, open a new MATLAB project in Visual Studio Code with the MATLAB and the Claude Code extensions enabled. From a terminal in VS Code, run Claude Code. Claude Code will interact with Visual Studio Code via these extensions. In Claude Code, enable the MATLAB MCP Core Server. This will give you five MCP tools to interact with MATLAB. Claude Code will be able to check code, evaluate code, run files, run test files, and detect your local MATLAB’s toolboxes.

In my vibration project, my team and I created a few files. It has been a minute since I used them, but with Claude Code, I can just get going. It is going to read what I have, learn from he documentation, and do whatever I am looking to do.

Use the vibration model to generate three weeks of vibration data, then use the analyze vibration function to detect any anomilies

What’s great about this workflow is that Claude Code is “not making up” a solution; it is leveraging the models that I have built plus constrained in MATLAB, bringing some engineering rigor to the chat. Claude Code is taking advantage of the Signal Processing Toolbox and how it works, rather than trying to figure out signal processing with some scratch code.

One extra benefit of this engineering stack is that I find myself exploring more, trying out other features of MATLAB that I haven’t used before. I feel way more confident trying something new and then learning how it works. I keep expanding what I know and onboarding into new things, learning, and gaining more experience rather than settling for a static picture. Dynamic.

WHat are your pro tips?

• gpt-5 will be designed for logic and multi-step tasks.
• gpt-5-mini is a lightweight version for cost-sensitive applications.
• gpt-5-nano is optimized for speed and ideal for applications requiring low latency.
• gpt-5-chat is designed for advanced, natural, multimodal, and context-aware conversations for enterprise applications.

As large language models and other GenAI models advance, you need to shift your mindset on how to use them. Here’s how I leverage advanced models and how I have shifted my prompting.

1. Structural Integrity: Crafting Clear and Organized Prompts

Think of your prompt as a blueprint for the AI model. A well-structured prompt ensures the model understands your intent and constraints effectively.

• Guardrails and Edge Cases: Don’t just describe the ideal scenario; also consider the exceptions. Anticipate potential issues or deviations and explicitly instruct the AI on how to handle them. For example, if you’re asking for a summary of articles, specify what to do if an article is paywalled or inaccessible. This proactive approach leads to more robust and reliable outputs.
• Strategic Context Positioning: Where you place information within your prompt can influence the AI model’s attention.
  • Front-load critical instructions (first 10%): Make your core request and essential rules immediately clear.
  • Middle ground for context and data: Provide necessary background information, examples, or data in the central part of the prompt.
  • Reinforce key constraints at the end: Briefly reiterate any crucial limitations or desired formats to leave a lasting impression.
• The Power of “Don’t”: Surprisingly, providing negative examples – explicitly stating what you don’t want the AI model to do can be more effective than solely focusing on positive examples. By illustrating failure modes, you guide the model away from undesirable outputs.

2. Evidence-Based Techniques: Leveraging the Model’s Strengths

Move beyond simple requests and employ techniques that tap into the AI model’s reasoning and self-awareness:

• Test for Self-Consistency: For critical outputs, ask the AI model to generate multiple responses to the same prompt. Analyzing the consistency across these responses can give you a better gauge of the reliability of the information.
• Unleash “Program of Thought”: For tasks involving logic, math, or code generation, explicitly instruct the model to “solve this by writing a program” or “show your work step-by-step using calculations.” This encourages the AI to leverage its tool-use capabilities for more accurate results.
• “Plan and Solve” for Complex Tasks: Before asking the AI model to execute a complex task, request it to first outline a step-by-step plan. This allows you to review the proposed approach, identify potential flaws in its logic, and guide it towards a more effective strategy before the final output is generated.

3. The Art of Metaprompting: Talking to the AI Model About Itself

A new concept that you should consider is metaprompting. This is you prompting the AI model to reflect on its capabilities and limitations. Since advanced models possess a significant understanding of their workings, you can leverage this knowledge to improve your results.

• The Self-Improvement Loop: Simply ask: “Here’s my current prompt: [your prompt]. How would you improve this prompt to get better results from you?” The AI model can often provide valuable suggestions for clarity, specificity, or even the inclusion of techniques you haven’t considered.
• Checking for Uncertainty: Proactively ask: “What parts of this request are unclear or ambiguous? What assumptions are you making? What additional information would help you execute this prompt with more accuracy?” This can help uncover potential misunderstandings and prevent overconfident, yet inaccurate, responses.
• Discovering Hidden Potential: Inquire: “How would you approach this if you had no constraints? What would be your ideal process? What tools or information would help you?” This can reveal the model’s full potential and suggest innovative approaches you might not have thought of.
• Demanding Explainability: Ask: “Explain your reasoning step by step. What parts are you most or least confident about?” Understanding the AI’s thought process can help you diagnose issues and build trust in its output.
• The Socratic Approach: Use probing questions like “Why did you choose that approach?” or “What alternatives did you consider?” to encourage deeper reflection and uncover underlying assumptions in the AI’s reasoning.

I typed these words into a text box: Elephants Wear Fancy Hats to a Party

This is the video that was produced two minutes later.

Try the latest AI models from Google at https://gemini.google.com/.

What are you experimenting with?

Ah, what does that mean? I have heard rumors that OpenAI will treat the word to a GenAI advent of sorts. Meaning they are going to release a series of new features over the course of the month leading up to Christmas. Is this a preview of things to come? As I learn more, I will update this post. Stay tuned.

Update: Hints on Discord

This just in from the OpenAI Discord server:

Update: Day 1 of 12 Days of OpenAI

Sam Altman and some members of the OpenAI team introduce & demo o1 and o1 pro mode in ChatGPT and discuss the ChatGPT Pro plan.

Update: OpenAI Model Benchmarks

Update: OpenAI o1 Model Benchmarks

Update: Day 1 of 12 Days of OpenAI Summary

OpenAI has launched a bunch of cool new stuff on their Day 1 of the 12 Days of OpenAI. They’ve been working hard on a new model called o1, which is even smarter and faster than the previous “preview” version. It can now understand images and text together. They also introduced ChatGPT Pro, a $200/month subscription that gives you unlimited access to their best models and a special “Pro Mode” for tackling the toughest problems.

The team showed off how o1 can solve complex problems, like figuring out the size of a cooling panel for a data center in space, just by looking at a hand-drawn diagram. They also demonstrated how much faster o1 is compared to the older model, especially for everyday tasks.

With ChatGPT Pro Mode, you can make o1 think even harder to solve really difficult math, science, or programming challenges. They’re planning to add even more features to Pro and bring o1 to their API for developers to use.

Update: Day 2 of 12 Days of OpenAI

Mark Chen, SVP of OpenAI Research, Justin Reese, Computational Researcher in Environmental Genomics and Systems Biology, Berkeley Lab, and some team members from OpenAI as they demo and discuss Reinforcement Fine-Tuning.

Update: Day 2 of 12 Days of OpenAI Summary

The news from Day 2 is that OpenAI is making it possible for people to customize their AI models. They’re introducing a new feature called reinforcement fine-tuning, which lets you train their models on your own data to make them experts in your specific field. This means you can have an AI assistant that’s super helpful for things like legal work, finance, or even scientific research.

They showed how this works with an example from a researcher studying rare genetic diseases. By fine-tuning one of their models, they were able to make it much better at predicting which genes might be responsible for a disease based on a patient’s symptoms.

OpenAI is starting a research program to let people try out this new fine-tuning feature and plans to make it available to everyone early next year. They’re excited to see what people create with it and how it can be used to solve real-world problems.

They continued with the “dad” Christmas jokes… this one was about a self-driving sleigh needing “pine-tuning” to avoid trees.

Update: Day 3 of 12 Days of OpenAI

Today’s update from OpenAI is all about Sora their video generation service. The announcement come in the form of a video from Sam Altman, Aditya Ramesh, Bill Peebles, Rohan Sahai, and Joey Flynn. Sora starts with a browse and explore option. This is to inspire and be a place to share ideas with the community. If you find something that you like, you can see the exact method that was used to create a video.

Sora is an AI model that can generate videos from text prompts, images, or even detailed storyboards. It’s available today in most of the world for ChatGPT Plus and Pro users. Sora can create videos up to 20 seconds long with different aspect ratios and resolutions, and it offers tons of creative tools to help you bring your vision to life.

You can give Sora simple text descriptions, like “woolly mammoths walking through a desert landscape,” and it will generate multiple variations for you to choose from. If you want more control, you can use the storyboard feature to direct the action with a timeline and multiple scenes. Sora can even animate still images and create seamless loops.

They also showed off some advanced features like “remix,” which lets you make changes to existing videos, and “blend,” which combines two different scenes into a cohesive new video. OpenAI is really excited to see what people create with Sora and how it will shape the future of video generation and storytelling. They emphasized that Sora is still in its early stages, but it’s already incredibly powerful and has the potential to revolutionize how we make and interact with videos.

OpenAI released four tutorial-style videos: how to get started, blend, remix, and recut.

Update: Day 4 of the 12 Days of OpenAI

Today is all about canvas.

Update: ChatGPT Canvas

OpenAI has launched Canvas, a new feature that allows you to collaborate with ChatGPT on writing and coding projects. Canvas provides a side-by-side view of your chat and a dedicated workspace where you can edit text, code, and even run Python code directly within the interface.

• Co-create stories and documents: Work with ChatGPT to write stories, essays, and other content, with the ability to edit and provide feedback in real-time. ChatGPT can even add emojis to your text!
• Get feedback on your writing: Receive targeted suggestions and comments on your writing from ChatGPT, with the option to apply or reject edits.
• Debug and run Python code: Write, debug, and execute Python code directly within Canvas, complete with syntax highlighting, autocompletion, and the ability to generate graphics.
• Use Canvas in custom GPTs: Integrate Canvas into your custom GPTs to create specialized tools for tasks like drafting letters or generating reports.

Canvas is rolling out to all web users, regardless of their plan, and is packed with features to enhance your writing and coding workflow. OpenAI is excited to see how people use Canvas to collaborate with ChatGPT and bring their ideas to life.

Update: Day 5 of the 12 Days of OpenAI

OpenAI has teamed up with Apple to bring ChatGPT to iPhones, iPads, and Macs! Now, you can access ChatGPT directly through Siri, writing tools, and even your camera. Ask Siri to organize a Christmas party or use your camera to have ChatGPT judge your Christmas sweater contest.

On your Mac, you can use ChatGPT within any application by typing to Siri or using writing tools. It can even analyze long documents and summarize key information for you. Sam Altman, Miqdad Jaffer, and Dave Cummings showed an example of using ChatGPT to analyze a 49-page PDF and create a pie chart visualizing the data.

This integration makes it super easy to use ChatGPT on all your Apple devices, whether you’re planning a party, writing a document, or just need some help understanding complex information. OpenAI is excited about this partnership and hopes it makes ChatGPT even more accessible and useful for everyone.

Update: Day 6 of the 12 Days of OpenAI

Today’s update is about adding video to the app. Audio was already included, but now you can use video from your phone’s camera and what’s on your screen as input into ChatGPT. They also added a “Santa” persona for the rest of the month. To access Santa click on the snowflake icon within the ChatGPT app.

Update: Day 7 of the 12 Days of OpenAI

OpenAI just launched a new feature called Projects for ChatGPT. It’s like having smart folders for your chats, where you can upload files, set custom instructions, and keep everything organized. This means you can tailor ChatGPT to specific tasks.

They showed some cool examples of how Projects works. One person used it to organize their Secret Santa, uploading spreadsheets and rules, and even using ChatGPT to randomly assign gift givers. Another person used it to keep track of home maintenance tasks, like when to change their fridge water filter.

Projects also work great for coding. They showed how to upload code files and use ChatGPT to help you write and debug code, even in less common formats like Astro. They even updated a personal website template with information from their resume and social media links.

Projects are rolling out to ChatGPT Plus, Pro, and Teams users today, with free and Enterprise users getting access soon. OpenAI is excited to see how people use Projects to collaborate with ChatGPT and tackle all sorts of tasks.

Update: Day 8 of the 12 Days of OpenAI

Kevin Weil, Adam Fry and Cristina Scheau introduce and demo updates to ChatGPT search.

OpenAI is bringing its ChatGPT search feature to all users for free! Everyone can now access real-time information and web browsing directly within ChatGPT. They’ve also made several improvements based on user feedback, including faster speeds, better mobile experience, and new map features.

You can now search directly from the main chat bar or the dedicated search icon. ChatGPT intelligently decides if your question needs web results and presents them with clear citations and visuals. You can even make ChatGPT your default search engine for quick access to websites.

They’ve also added search to the advanced voice mode, so you can ask questions naturally and get spoken answers with web information. This update makes ChatGPT even more powerful and accessible for everyone, whether you’re on your computer or mobile device.

Update: Day 9 of the 12 Days of OpenAI

Olivier Godement, Sean DuBois, Andrew Peng, Michelle Pokrass, and Brian Zhang introduce and demo developer and API updates.

OpenAI is having a (mini holiday) Dev Day, and they’re excited to share some new updates. First, they’re launching their powerful model, o1, into the API. This means developers can now build even more sophisticated applications using o1’s advanced capabilities. They also announced updates to the Realtime API, making it easier and cheaper to build real-time voice experiences with AI. To help developers customize AI models, OpenAI introduced preference fine-tuning. This new method allows developers to train models based on preferred responses, leading to more accurate and aligned AI assistants. They also launched new Go and Java SDKs, simplified the API key signup process, released Dev Day talks on YouTube, and announced an AMA with the OpenAI team.

Transforming Contact Centers with GPT-4o Multi-Agent Crews and Human-in-the-Loop: Building agents with OpenAI o1 and GPT-4o for automation, quality assurance, and human-in-the-loop solutions. Presenter: Maik Hummel, Principal AI Evangelist, Parloa

Update: Day 10 of the 12 Days of OpenAI

Kevin Weil, Antonia Woodford, and Amadou Crookes introduce and demo 1-800-CHAT-GPT and ChatGPT in WhatsApp.

No joke. This might be the most significant update so far. Adding a phone interface to ChatGPT changes the accessibility and how you think of ChatGPT.

Update: Day 11 of the 12 Days of OpenAI

Kevin Weil, Justin Rushing, and John Nastos introduce and demo Work with Apps.

Update: Day 12 of the 12 Days of OpenAI

Well, we are here: The twelfth day of OpenAI shipmas.

OpenAI announced two new AI models: o3 and o3-mini! o3 is a powerhouse, crushing tough benchmarks in coding and math. It even scored impressively high on the Arc AGI test, a measure of general intelligence, beating previous AI models. o3-mini, while smaller, is still incredibly smart and much faster, making it perfect for quick tasks and saving on cost.

They showed off some cool demos of o3-mini in action, like having it write code to evaluate its own performance on a challenging dataset. They also highlighted how o3-mini is even better than previous models at coding and math, and it’s way faster too! Plus, it comes with handy features like function calling and structured outputs for developers.

While not publicly available just yet, OpenAI is inviting researchers to help safety test these new models. They’re aiming to launch o3-mini by the end of January and o3 shortly after. They also introduced a new safety technique called “deliberative alignment” that uses the models’ reasoning abilities to better identify and avoid unsafe prompts.

Here’s what we know so far…

• The scheduled May 13, 2024 Event is called “ChatGPT and GPT-4 Updates.”
• OpenAI social media manager on X says, “We’ll be streaming live on http://openai.com at 10AM PT Monday, May 13 to demo some ChatGPT and GPT-4 updates.”
• The OpenAI website has a “Spring Updates” call-to-action that downloads a calendar invite for the live event.

I will keep this post updated as I learn more.

Update: OpenAI is streaming now at 1 pm EST.

Update: OpenAI Spring Update Live Blog

• New web UI and desktop app – complete refresh of the web app and dedicated desktop app
• GPT-4o – New flagship model, GPT-4 level intelligence, but it is much faster
• GPT-4o free access – bringing the new model to free users without a sign-up flow

Other Notes:

• “o” means omnimodel.
• Omnimodel means bringing multiple modes together.
• GPT Store is expanding to free users. Around 1M builders of GPTs.
• GPT-4o is in the API now, 2x faster, 50% cheaper, and 5x higher rate limits compared to GPT-4 Turbo

Demos:

GPT-4o is real-time responsive. The model picks up on emotion and can express emotion. Conversational apps are now possible with GPT-4o.

The coding demo had a number of examples:

• Summarize code
• What would the plot look like if you have this function or not
• Overview of a plot – what does this plot mean?
• What months do you see the hottest temperatures?
• What are the axis ranges?

Demos Suggested by X Users

• Real-time translation
• Tell feelings by looking at a face

New Model Test From MATLAB

Sending a request to the new GPT-4o from MATLAB is about 2x faster than sending a request to GPT-4 Turbo. It takes .5 seconds to say this is a test on GPT-4o versus 1 second on GPT-4 Turbo.