AI New Year’s: The 10-Week AI Resolution

**Nathaniel Whittemore** (0:00)
Today on the AI Daily Brief, start your 2026 off right with the 10 weekend AI resolution. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
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All right, friends, we have finally come to the end of our end of year coverage. This show is coming out on the very last day of 2025, and we are firmly now looking towards the future. And as sort of silly as New Year's resolutions can be, it is a good time to reset and build some new skills and head towards the future that you most want for yourself. And my guess is that a lot of you guys are thinking about how AI can be part of that future. So what I decided to do for this final episode of 2025 was put together a 10-weekend AI resolution. Call it a self-guided path to AI fluency. Basically, it's a bunch of projects that are going to give you exposure to a wide array of AI tools, which, if you've mastered all of them, will certainly put you ahead of the vast, vast majority of people on the planet when it comes to using AI. Now of course, I assume that lots of you will have familiarity with lots of these different tools and patterns, and so feel free to pick and choose which ones seem relevant or simply use them as inspiration. Now, if you want to engage and even share what you've built, check out aidbnewyear.com. Basically, I'm going to vibe code as a website where we can all share what we're doing to the extent that people are interested in that. Again, that'll be at aidbnewyear.com. One more note from a process standpoint, I built the outline of all the activities I wanted to see, Claude put it into a set of slides, and Jen Spark turned it into this presentation. So let's talk about the setup. This is not a course in the sense that one thing builds upon another. It's 10 different projects, imagined for a weekend, although of course you can do it whenever you want, that are practical by default, forcing outputs, not theory, that are highly completable, where each project ends with something real, completely modular, as in you can skip or do them in any order without derailing anything, although they are also compounding in the sense that if you do do them in sequence, some of the later ones will build on the others. Each weekend includes a clear deliverable, a default project for a beginner or intermediate user, as well as an advanced modifier, and an expectation that they are going to take a couple hours of work to do. If you want to give each project a score, you can rate things on outcome quality, the time you would have saved versus doing it manually, repeatability as in could you do this again easily, and whether you would actually use this process again in your regular life. If you are keeping notes, think about things like the best prompt, the approach, what didn't work, so that you can do it better the next time. The goal is to build some habits and workflows that you can actually still be using some months from now, although of course there's no guarantee that what we'll be doing in six months is anything like what we're doing right at this moment. Now to get yourself prepped, I suggest taking just about 30 minutes to build a little bit of infrastructure before your first weekend. Each of these weekends should be about doing the work, not setting up accounts and things like that. So you might want to set up an AI resolution folder with subfolders for each weekend, wherever you do that, whether it's on your computer directly or whether it's in Drive, and then you'll want to figure out your toolset. Specifically, I would suggest taking a little time to review the automation platforms like Lindy, N8n and Make, and pick one that feels best. Same with the VibeCoding platforms, which for most, I would suggest either Repler or Lovable, or just use Google AI Studio as there is one VibeCoding project that is specifically designed around Google AI Studio, so you might just want to invest there on the whole. With that out of the way, let's talk about your first weekend project. Getting a little bit meta, we are going to VibeCode a resolution tracker, that is a web app that actually tracks your progress through these 10 weeks. Now exactly how you make this is up to you. It is likely that you will want a list of all 10 weekends, a completion checkbox, a notes field, a progress bar, maybe if you like that ranking system that I suggested, you add that to it, and anything else that seems relevant to you. For example, if you are interested in tracking the time, you could add that as a field. If you want to upload any content, you could add that as a feature. Now if you are using Reploader Lovable, you will also be able to deploy this live so that you can actually use it and interact with it. And of course the last step is to actually use it once you've got it launched. If you want a more advanced version, you can just expand the feature set. For example, adding user authentication for multi-user support, collaboration mode if you want to get friends and family involved, maybe a shared tracking system. You can optimize it for mobile, or basically do anything else that makes it more robust. We're starting with this both from a practical standpoint, giving you a tool that can actually help with the rest of the weekends, as well as by giving you something super tangible right from the beginning that will give you a sense of just how powerful these building tools have gotten. Weekend 2's project, we're calling Model Mapping, Building Your Personal AI Topography. This project reflects the fact that one of the easiest ways to get extra value out of AI is to figure out which models you like for different use cases. Most people default to one AI model for everything, leaving capability on the table. And the goal here is not to figure out which model is objectively best for a task, but to develop your own instincts for which work best for the tasks that matter to you. So the idea here is to pick a few models that you have access to. If you only have the free versions, that's okay. You can still likely do big chunks of work that give you a feel for the different models and run the same task through each. I'd suggest some combination of a deep research task, a writing task, a business strategy question, maybe some data analysis and or a visualization. On aidbnewyear.com, I'll put a few more suggestions for this. As you're comparing, think about things like which model was faster, which asked better questions, which just felt right. When you're done, create a one-page rule of thumb notes document that captures what you found about which model you like for which use case. Now, if you want to make it a little bit more advanced, you can test an additional set of specialized tools that are purpose driven for each particular use case. You could build a more comprehensive matrix than just your one-page rule of thumb, including things like cost. You could test output consistency over time, in other words, run similar tests a number of different times and see how much variability there is. You could also track how much editing time there is per model, basically what the differential between raw output and your final product is going to be. I know some of you were probably rolling your eyes at this one for being super simple and basic, but you'd be amazed how much value the average person is leaving on the table just by sticking to one model exclusively. The third weekend's project is to do a deep research sprint. Now this comes out of the fact that I have seen a lot of chatter recently, as well as a lot of studies that suggest that despite deep research features being available across all of these models for a big chunk of this year, a shockingly low percentage of people have actually used them. This is the capability that most people have heard about but few have stress tested. And so the idea of this weekend is to close the gap between I know AI can theoretically do research and I trust this enough to make a decision. The goal is to pick a decision you actually need to make, or a research project that actually really matters. It could be competitor analysis, it could be some look at different opportunities, it could be a pricing project, it could be product research. Whatever you pick, the goal is to have this be something real that's actually valuable to you, not just hypothetical. From there, pick a deep research tool and really iterate with it. Push back, ask for disconfirming evidence, don't accept the first output. Push it and see how close you can get to something that can actually inform a new decision or a new way of thinking. If you want a slightly more advanced version, you can run the same question through a set of major tools so you can compare and contrast, kind of building off of what we were doing in Weekend 2 Or you could also fact check them between each other, either fact checking them manually or using the other models to check the output of each other. My guess is that for those of you who haven't really tried deep research, if you do this, you will start to naturally spot more times that you could actually be using it in your regular work life. Now, at this point, as you can tell, we are not with this 10 Weekend AI Resolution doing some crazy advanced set of things. We're basically creating context to go pretty deep on the core capabilities that represent a huge part of the work that we can do with AI right now. Which brings us to our Weekend 4 project, which is a data analysis project. Once again, this is another capability that a lot of people know about and fewer people have used. And working with data is not just for data analysts or financial analysts. The first step for this is to gather some real data set. Ideally, it would be something from your own life. It could be a bank statement. It could be analytics from some software you use. It could be your Spotify listening history. And if you don't have anything that's particularly interesting for yourself or for your work, there's also tons of public data sets out there. For example, on Kaggle, which has just tons and tons of data sets that you can download and play around with. Once you've got your data set, use your preferred LLM to propose cleaning steps, i.e. figuring out what's messy, what's missing, or what needs normalization, five to ten useful metrics to calculate, and three hypotheses worth testing. From there, produce a clean data set, a summary table of key metrics, three insights like patterns, anomalies, or trends you didn't know, and three actions that could be done based on those insights. From there, you can write a One Page Insights Memo that has that key summarization. Now, if you want to make this more advanced, try not to just analyze, but to build a repeatable analysis pipeline. You can try to create a prompt template that you can reuse monthly on updated data or connect your analysis to a live data source. Also, as always, you could compare insights from different LLMs on the same data set to get a sense of which you think works best. For Weekend Project 5, we are taking advantage of NanoBanana Pro and ChatGPT Images 1.5. This is our visual reasoning weekend. And we are not just trying to create pretty pictures here, but to get AI to think through the logic of visual communication and explain complex ideas through visual media. The Deliverable is an infographic diagram or visual explainer that you would actually use. So to get this started, pick a concept that genuinely benefits from visualization. It could be a process, a comparison, a framework, a timeline, a system. Now this doesn't have to be about your work life, but I think that that's where there's going to be the most rich material for this. From there, use your preferred LLM to reason through how it should be visualized. Not just make me an infographic, but what's the right way to visualize this concept? And what are the tradeoffs between different approaches? From there, try to generate two alternate designs. A flowchart vs. a 2x2 matrix, a timeline vs. a cycle diagram, etc. And like I said, if you're using NanoBanana Pro or Images 1.5, you can just build it directly there. Or if you're not using those for some reason, you could take it to another tool like Canva or Gamma, but I would suggest staying in ChatGPT Images or NanoBanana Pro. From there, apply visual QA. Is this readable in 5 seconds? Does it have the right amount of text? Are there any artifacts that don't need to be there? Are definitions included where they're needed? Does it have one clear takeaway? Now, if you don't have a concept to visualize, an idea could be to create a visual explainer for your own job or business. What do you actually do explained in one image to someone outside your field? This might actually be harder than it sounds. Alternatively, you could take the data insights from Weekend 4 and visualize them. Turn the key findings into a chart or diagram that tells the story. If you want a more advanced version, try to create a visual system rather than just a single image. Design a template that can be reused such as a consistent infographic style for a series or a diagram format for recurring presentations. You could even create a visual pattern library of frameworks you can apply like two-by-two matrices, process flows, comparison tables and timelines. The project is done when you can explain the idea faster with the visual than with words. When someone who sees the image gets the point without you having to explain it. Now for the community website that I'm building, I'm going to try to have a gallery feature for projects like this to make it really easy to share what you've created.