The Fuzzy Labs curriculum: thoughts from a Fuzzy Intern.
In the accelerated age, how are we keeping up?
This week’s episode is brought to you by Savannah, MLOps Engineering Intern at Fuzzy Labs.
Ahoy there 🚢,
Three weeks ago everyone was buzzing about Claude's latest coding capabilities. This week it's all about Cursor with its integration with ChatGPT-5. Next week? Who knows.
Every week there's something new, some breakthrough that makes processes more efficient, faster or deeper in learning and understanding. The tool race is rapid and new players are constantly changing the approach.
I'm currently doing an internship at Fuzzy Labs, whilst studying for my Master's, which means I'm getting this unique perspective on how learning actually works when the world is accelerating. Bouncing between academic theory and real-world application daily, watching how the industry moves at breakneck speed whilst our educational institutions... well, they're still teaching pretty much the same way they always have.
When best practice meets reality
What's interesting is how this internship is revealing the gaps between what we're taught is "best practice" and what actually works when you're trying to keep up with an industry that reinvents itself weekly. I've been following all the traditional advice - the approaches everyone swears by - but they're starting to feel increasingly disconnected from the reality of working in tech right now.
Take the standard learning path everyone recommends. You start by watching YouTube tutorials. Then you move on to building the classic projects: Tic Tac Toe, calculator apps, to-do lists. Maybe you grind through some LeetCode problems... it's the well-worn path that's supposed to prepare you for a career in tech.
But these approaches were designed for a different world. When Claude can generate a perfectly functional game in thirty seconds, what are we proving by spending days building one ourselves? When GitHub Copilot can solve algorithmic challenges faster than you can read them, does LeetCode truly measure someone's coding ability?
The problem isn't that these tools are useless - LeetCode is obviously still valuable. But we've built these elaborate hiring processes around demonstrating capabilities that AI now handles better than most humans, whilst completely missing the critical thinking and discernment that actually matter.
The theory of revolution
The problem isn't just that traditional approaches feel outdated - they're creating a generation of graduates who can tick all the educational boxes without developing the skills that actually matter.
Are we teaching people to jump through hoops that no longer exist whilst missing the critical thinking that actually determines success?
The multi-faceted approach
What I've discovered working is completely abandoning the traditional sequential learning model. Instead of trying to master one thing at a time, I'm doing everything simultaneously and letting each element reinforce the others.
I've got a skill tracker going that shows what I'm actually absorbing versus what I think I'm learning. I'm reading ML and MLOps theory whilst working on production systems. The mentorship element has been massive - being surrounded by talented people who I can question relentlessly.
The biggest shift has been around curiosity. It sounds obvious, but I think there's something to be said for being the stupidest person in the room - you stand the most to gain, right? Most educational setups don't really encourage this kind of vulnerability, but it's been game-changing for developing actual understanding rather than just surface knowledge.
So when I'm trying to understand something now, I'll read about it, watch tutorials, then immediately apply it to whatever project I'm working on. Keep it simple, but make sure it's connected to something real rather than abstract exercises. The mentorship side has been massive for making this work - I can tackle projects that would have been completely overwhelming before. When I hit roadblocks, there's someone there to help.
Package-specific learning for the modern brain
Here's what's really working: Instead of trying to master "software engineering" as this enormous abstract concept, I'm focusing on specific packages and tools.
You don't learn React because it's popular - you learn it because you're building something specific that needs a dynamic interface. You don't study Kubernetes because it's on job descriptions - you learn it because you're solving a particular deployment challenge you're actually facing. This use case learning approach forces you to think critically about why you're using specific tools rather than just following generic tutorials.
The tool paralysis trap
One thing that keeps catching people is the AI tool adoption phase.
There are so many tools launching that you could easily spend all your time evaluating options instead of actually doing anything.
This is where developing discernment becomes crucial. The teams that work most effectively have made deliberate choices about sticking to particular tools. This applies especially well to MLOps because the field moves so fast.
Rethinking the how
What's becoming clear is that we need to find new ways of learning that don't involve generic projects and generic tutorials. Something more nuanced, more area-specific, more connected to actual problems people are solving. The traditional model of front-loading education and then applying it for years feels increasingly disconnected from how work actually happens.
For anyone leading teams, this creates interesting challenges. How do you keep people current without losing productivity? How do you evaluate candidates when traditional measures don't reflect what actually matters? How do you build learning systems that can adapt as quickly as the technology does whilst still developing critical thinking skills?
What's exciting is seeing companies like Meta experiment with AI-assisted interviews that focus on your ability to work with these tools effectively, rather than your ability to memorise algorithms. The focus is shifting from "can you code" to "can you think critically about code and work effectively with AI tools."
If you're still preparing for coding interviews the traditional way, you're optimising for 2015's job market.
The bigger picture
The internship is showing me that there's a real opportunity here. These AI tools aren't just changing how we work - they're revealing fundamental gaps in how we prepare people for that work. The traditional barriers to entry are simultaneously too high and completely meaningless. We're testing for skills that AI handles better than humans, whilst missing the adaptability that actually matters.
The key is forcing yourself to think rather than outsourcing the thinking to AI. To use these tools in ways that amplify your thinking rather than replace it.
By the time this gets published, there'll probably be a new AI tool, Claude Code and Cursor will feel ancient, and we'll all be scrambling to keep up with whatever comes next. But that's exactly the point - in a world where the tools change weekly, the ability to learn and adapt isn't just useful, it's the only thing that stays constant.
The question isn't whether we can keep up with the accelerated age. It's whether we're building the right foundations to thrive in it, or clinging to educational models designed for a world that no longer exists.
So here's my challenge: go look at your current curriculum, your learning plan, your interview prep. How much of it would still matter if AI capabilities doubled tomorrow? Because at the current pace, that's not a hypothetical - it's here.
Savannah is currently pursuing a Master’s degree in Computer Science and AI part-time, whilst trying to gain as much knowledge through osmosis from the other Fuzzicians. When she’s not grinding out a deadline for her Master’s, she can be found getting stuck into a book or learning salsa. What she lacks in rhythm she makes up for in enthusiasm, with a strong interest in researching tech for good.
And Finally…
People You Should Be Following
Peter Gostev: Head of AI at Moonpig, cutting through AI hype with clear, unbiased insights on the latest models and breakthroughs.
Eric Vyacheslav – AI/ML enthusiast sharing fresh insights on the latest models and trends. A must-follow for anyone keen on staying ahead in the AI space.
Upcoming Events & Community Updates
🗣️ Big Tech Debate: Vibe coding is democratising software but lowering engineering standards
MRJ Recruitment & Counter are hosting their next Big Tech Debate on Vibe Coding. What makes this one particularly interesting? You might recognise the speakers 👀
With fuzzy founder vs founder - Tom will be arguing for the motion whilst Matt will be taking the opposing view. Are we witnessing genuine democratisation or a race to the bottom in engineering quality? Find out by tuning in 👇
📅 18th September
📍 Bloc, 17 Marble St, Manchester
MLOps.WTF Meetup - 9th September
Our next MLOps.WTF meetup is on 9th September at our Manchester HQ within DiSH. Demand is super high for this agentic themed talk, with us now being on a waiting list!
Kind ask, if you've got a ticket but can't make it, please cancel it so someone else can take a spot.
Awaze Women in Tech Early Career Networking - 14th October
"Education to Industry: Discussing the Transition"
We’re proud to be hosting Awaze's Women in Tech team’s early career networking evening at DiSH. Specifically designed for entry level careers support, this event is set to empower and support female identifying people entering the tech and data workforce.
Fancy being a speaker at the event? Awaze are on the look out for great and inspiring presenters and panelists - apply on this link. Here.
Don’t forget, we also have Matt's talk at Manchester Tech Festival this September: "Are we the last programmers? AI and the future of code." Because if we're going to debate the future, we might as well do it properly.
About Fuzzy Labs
We're Fuzzy Labs. A Manchester-rooted open-source MLOps consultancy, founded in 2019.
Helping organisations build and productionise AI systems they genuinely own: maximising flexibility, security, and licence-free control. We work as an extension of your team, bringing deep expertise in open-source tooling to co-design pipelines, automate model operations, and build bespoke solutions when off-the-shelf won't cut it.
Currently: We’re hiring and we’ve got a few roles to fill:
MLOps Engineer
Senior MLOps Engineer
Lead MLOps Engineer
If you, or someone you know, is looking for a great opportunity… don’t hesitate to apply!
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