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How To Build A Fullstack App MVP in An Hour With Bolt
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 .
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What Is Supabase And How It Can Replace Your Entire Backend
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.
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How Good is Good Enough: A Guide to Common LLM Benchmarks
In our last article, we talked about benchmarking as the highest level method of assessing the performance of LLMs. Today, we’re going to be looking in more detail at some of the most popular benchmarks, what they measure, and how they measure it. Note that most of the benchmarks listed below will have leaderboards and questions sets available somewhere public facing if you want to dive deeper, I’ve also included links to papers where appropriate. Let’s dive in!
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How Good is Good Enough? - Introduction to LLM Testing and Benchmarks
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 .
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How To Build Complete Fullstack Apps In Less Than An Afternoon With Bolt + Supabase
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.
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Workshop

Fundamentals of transformers - Live Workshop
Everyone knows chatgpt, but how do modern large language models fully work? The fundamentals start at the transformer. This workshop is a workshop to dymstify the transformer and be able to run through concept to code on how the transformer work. This workshops combines concept at an intutive level, to code, to math all with the intent at providing an end to end understanding at the fundamentals of large language models.
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Scaling Web App Configuration with Environment Variables
<!-- Your template is like this (markdown): --> Learn the strategies senior engineers use to configure their projects to run securely, anywhere with environment variables. <!-- ( the summary is short - one or two lines ) -->
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Responsive LLM Applications with Server-Sent Events
Large Language Models are reshaping industries, yet integrating them into real-time streaming UIs presents unique challenges. In this course we will learn how to seamlessly integrate LLM APIs into applications and build AI-powered streaming text and chat UIs with TypeScript, React, and Python. Step-by-step, we will build a full-stack AI application with quality code and very flexible implementation. The LLM application in this course includes: - Completion Use-Case (english to emojis) - Chat - Retrieval Augmented Generation use-case - AI Agent Use-Cases (code execution, data-Analyste agent) This app can be used as a starting point in most projects, saving a huge amount of time, and its flexibilty allows new tools to be added as needed. At the end of this course, you will have mastered end-to-end implementation of a flexible and high-quality LLM application. This course will also equip you with the knowledge and skills necessary to create sophisticated LLM solutions of your own.
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Pro

React Data Fetching: Beyond the Basics
This course will teach you how to fetch in a performant way for React without relying on a third-party library or the useEffect hook. We will build a custom data fetching library from scratch that will prefetch, cache, and address all the common issues with fetching data.
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Pro

Fullstack React with TypeScript
Learn Pro Patterns for Hooks, Testing, Redux, SSR, and GraphQL
book
Pro

Security from Zero
Practical Security for Busy People
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Pro

JavaScript Algorithms
Learn Data Structures and Algorithms in JavaScript
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Pro

How to Become a Web Developer: A Field Guide
A Field Guide to Your New Career
book
Pro

Fullstack D3 and Data Visualization
The Complete Guide to Developing Data Visualizations with D3
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