In this episode, Matt and Liam unpack a feeling that’s been quietly building for a while: that many of the tools we’re excited about aren’t failing us: we’re failing to understand what they’re actually for.
What starts as a discussion about AI Foundry quickly turns into a broader reflection on hype cycles, developer expectations, and how easily we start bending our solutions to fit the tools, rather than the other way around.
This isn’t an anti-AI rant, and it’s not a “Microsoft bad” episode either. In fact, it’s almost the opposite. We talk about where these tools genuinely shine, why they do work for some people, and how frustration creeps in when we assume they’re meant for everyone and everything.
Along the way, we cover:
- Why AI tooling often works brilliantly for individuals, but falls apart at enterprise scale
- The difference between demos, dev-loop tools, and production-ready platforms
- How hype blurs the line between “possible” and “appropriate”
- Aspire’s real strengths — and why using it everywhere is a self-inflicted wound
- When “best practice” becomes dogma instead of guidance
- The subtle shift from designing solutions to designing around tools
- Why version-three maturity still matters, even if nobody wants to wait anymore
We also touch on resource hoarding, cloud ergonomics, managed identity pain, Cosmos DB emulators, container apps, and the uncomfortable reality that sometimes the problem isn’t the tool, it’s the assumptions we brought with us.
The takeaway isn’t “don’t use new things”. It’s slower, more annoying, and probably more useful than that:
Understand who a tool is for. Understand what it’s optimised to solve. And resist the urge to turn every shiny thing into a default.
🍻 Tonight’s Drinks
🍺 Liam - Homebrew
🥃 Matt - Aldi Scotch
Tonight’s links
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