Since Thing 1, you've compared AI models side by side, learned to write prompts that actually get useful results, and watched AI search the web, analyse documents, and produce structured research reports. You've generated images, cloned voices, edited audio, composed music, and created video clips from nothing but a text description. You've discovered AI features already built into your phone, caught AI hallucinating, audited its biases, and interrogated its privacy policies. You've used AI to learn something new, built a working tool without writing a line of code, connected AI to your other apps through automation, and run a language model entirely offline on your own computer.
That's a remarkable amount of ground to cover. And the honest truth is that the ground you've been covering has probably already shifted in the time it took you to work through the programme. Tools have been updated, new models have been released, pricing has changed, and at least one feature you tried has probably been quietly retired or replaced with something better.
That's not a problem. It's the point. The specific tools were never the real lesson; they were the vehicle for building something more durable: the ability to understand what AI can do, evaluate new tools when they appear, use them effectively, and think critically about when and whether to use them at all. Those skills don't expire when a tool updates its interface.
The challenge of keeping up
AI moves faster than almost any technology before it. New models are announced weekly. Tools launch, merge, rebrand, and occasionally disappear. Features that seemed like science fiction six months ago become standard, while things that were standard six months ago get quietly dropped. If you try to follow everything, you'll exhaust yourself. If you follow nothing, you'll gradually fall behind.
The good news is that you don't need to follow everything. What you need is a small, curated set of reliable sources that give you enough signal to stay oriented without overwhelming you with noise. Think of it as building a personal radar: a set of resources that reliably surface the things worth knowing about, so you can ignore the rest.
The key is finding sources that match your level and your interests. The AI information ecosystem ranges from deeply technical research papers to breathless hype about the latest tool launch. Neither extreme is particularly useful for a working professional who wants to stay informed without becoming an AI specialist. What you want is the middle ground: sources that explain what's changing and what it means for people who actually use these tools in their work.
Building your AI radar
A good personal AI radar has three components: something regular that arrives in your inbox without you having to seek it out, something visual for when you want to go deeper, and somewhere to check when you hear about something specific and want to understand it quickly.
Newsletters
Email newsletters are the most efficient way to stay current because they come to you. You don't need to remember to check a website or scroll through a feed; someone else has already done the filtering and summarising. A good AI newsletter should take five to ten minutes to read and leave you feeling informed rather than overwhelmed.
For someone coming out of this programme, these are worth considering:
A daily newsletter that covers the biggest AI news in a concise, accessible format. It does a good job of explaining why things matter, not just what happened. Free.
A daily newsletter with a practical, productivity-focused angle. It regularly includes prompts, tutorials, and tool recommendations alongside news coverage. Free.
You don't need to subscribe to all of these. Two is plenty: one daily and one weekly gives you a good balance of keeping current and having time to think about what you're reading.
YouTube channels
Video is often the best way to understand a new tool or concept because you can see someone actually using it. A few channels are particularly good for non-technical professionals:
Covers AI news, tool reviews, and practical demonstrations with an approachable, enthusiastic style. His FutureTools.io website is also a useful directory of AI tools.
Focused on practical AI use cases and productivity. Teaches you how to actually use tools rather than just reviewing them. Particularly good for professionals who want to apply AI in their work.
Takes a more analytical approach, explaining not just what new AI developments do but what they mean and how they fit into the bigger picture. Good for building deeper understanding.
Websites and reference points
When you hear about a new tool or development and want to understand it quickly, these are useful starting points:
A curated directory of AI tools, maintained by Matt Wolfe. Useful for quickly discovering and comparing tools in a specific category.
The largest directory of AI tools, searchable by category and use case. When you find yourself thinking "I wonder if there's an AI that can do that," this is where to check.
In-depth technology journalism. When a major AI announcement happens and you want more than a newsletter summary, Ars Technica typically provides thorough, balanced coverage.
Thinking about what's next
While you were working through this programme, AI kept moving. Some of the trends that will shape the next year or two are already visible, and knowing about them will help you make sense of what you'll encounter through your AI radar.
None of these trends require you to do anything right now. But when they start showing up in your AI radar, you'll have enough context to follow along instead of starting from scratch.
Activity: your AI toolkit and programme reflection
This final activity has two parts. First, you'll set up a personal AI radar so that staying informed becomes easy rather than effortful. Second, you'll reflect on your time with the programme, because thinking back over what you've learned is one of the best ways to make sure it sticks.
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Set up your AI radar. Choose the sources you want to follow going forward and actually set them up now, while you're thinking about it.
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Reflect on your programme journey. Look back across all 23 Things and write a personal reflection covering the questions below.
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Compile your final submission. Put together a short document that includes your AI radar choices and your programme reflection.
Why this matters
Setting up your radar while you're thinking about it is the difference between good intentions and an actual habit. Most people who tell themselves they'll "stay up to date with AI" without a concrete system end up doing nothing. The ones who subscribe to two newsletters and bookmark one reference site actually stay informed, because the information comes to them.
The reflection matters too. People who take time to think back over what they've learned remember more of it and find it easier to apply in new situations. Writing down what surprised you, what challenged you, and what you plan to do next turns 23 separate activities into something you can actually build on.
And here's the thing worth remembering: you now know more about practical AI than most of the people you work with. You haven't memorised a list of tools (many of which will change). What you've built is the ability to find new tools, evaluate them, use them well, and decide when they're worth using at all. That doesn't go out of date.
Claim your Open Badge
Submit your AI radar set-up (the sources you've chosen with screenshots showing you've subscribed or bookmarked them) and your programme reflection covering at least three of the reflection prompts.
Submit your AI radar set-up and programme reflection to claim this badge via cred.scot.
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