Latest Tutorials

Welcome! This is part 4 of our course on how to build fullstack apps with Bolt and Supabase If you’re just joining, I highly recommend you take the course in the correct order before diving into this one. Here you can find Part 1 , Part 2 , Part 3 .

Hello and welcome! This is the 3rd lesson in our series about how to build complete fullstack applications in less than an afernoon with Bolt and Supabase. In the first 2 lessons, we talked about what exactly is Bolt in the first place, and what’s Supabase. If you want to read those first, here is Part 1 and Part 2 .

What if I told you that 2-3 hours from now you could have taken your app idea and transformed it into a beautiful, production-level full stack application, deployed and available on the internet, for everyone to use? If I told you something like this a couple of years ago, you’d laugh and scoff and dismiss everything I just said. In fact, this was my reaction too when I first heard someone from Supabase talk about what Bolt and Supabase combined could achieve.

Welcome to the second lesson of our course on how to build complete fullstack apps in less than an afternoon with bolt + supabase. If you haven’t yet, I highly suggest you check out Part 1 before diving in, where we talked all about the ‘what’, ‘why’, and ‘how’  of Bolt and briefly the future of AI - I think you’ll benefit a lot from it. Now back to this tutorial.

The proliferation of Large-Language Models (LLMs), and their subsequent embedding into workflows in every industry imaginable, has upended much of the conventional wisdom around quality assurance and software testing. QA Engineers effectively have to deal with non-deterministic outputs - so traditional automated testing that involves assertions on the output are partially out. Moreover, the input set for LLM-based services has equally ballooned, with the potential input set being the entirety of human language in the worst case, and a very flexible subset for more specialised LLMs. This is a vast test surface with many potential points of failure, one in which it is practically impossible to achieve 100% test coverage, and the edge cases are equally vast and difficult to enumerate - it’s unsurprising that we’ve seen bugs even in top tier customer-facing LLMs even amongst the biggest companies. Like Google’s AI recommending users eat one small rock a day after indexing an Onion article or Grok accusing NBA star Klay Thompson of vandalism .