Blog Page 2

In “JavaScript: The Definitive Guide”, there are functions described in 6.5 Enumerating Properties that probably shouldn’t be used in production code because they modify their input parameters: extend, merge, restrict, and subtract.

In “Javascript: The Definitive Guide” there is an example that uses Object.inherit to inherit the prototype change. JavaScript defines a method Object.create that creates a new object using the given argument as the prototype of that object. Translating the examples from The Definitive Guide to Typescript, it looks like:

Back when Meteor was in vogue, they popularized the concept of “Data on the wire”. This was the idea that the UI would be updated with changes from the dataset it is subscribed to.

The following is a checklist I like to use to ensure I'm covering all the bases when building a component library. It's a work in progress, but I hope it can be useful to others.

I was working on a project and I noticed the tsc check that we ran on pre-commit hooks had become dramatically slower. I was seeing near instant times to type check the entire project go to agonizingly slow minutes to check the project. I was able to narrow the range of commits to some change we made within a month window. I’m sure I could have narrowed it down by continuing to git bisect and re-running time npx tsc, but it was much easier to just check out main and try out the following:

I’ve been experimenting with ChatGPT, just like everyone else. But why has it become so popular? It truly is a revolutionary piece of technology. Is it The Next Big Thing? Will it really replace all of us?

The Backend for the Frontend (BEFFE) is typically stateless and acts as a proxy for other services, including authentication, authorization, and core services. The recent divorce of browser code being rendered by backend services was created by SPAs - Single Page Applications. In simpler architectures, a SPA and service could be as simple as:

We are starting to see a rise of novel use-cases for AI in products and games using LLMs. Rather than the simple chatbot like experiences we have seen in the past using AI, we are starting to see feedback systems being added to these experiences, providing additional context to the LLM than just the past conversation.

While working on the Clarity Hub NLP API, we had a common use-case where we would create embeddings from text, and use those embedding to determine cosine similarity with other embeddings. Doing this required loading all of the embeddings in-memory and then computing cosine similarity with the entire dataset. As the dataset grew, this operation would get incredibly slow.

Github Copilot and other AI tools are hitting the scene. I decided to create my own Visual Studio Code extension, which is designed to use OpenAI’s APIs to bring some additional ChatGPT functionality into the code editor. The goal with Sora was to enable a developer to thoughtfully write a comment about the code they would like the AI to write, and then commit to it – rather than the real-time typeahead that Github Copilot provides.