/AI

What We Don't Talk About When We Talk About Building AI Apps

- Vicki Boykis tl;dr: Vicki shares her experience and pain points when building AI applications, highlighting several aspects often not discussed in conversations: (1) Slow iteration times, (2) Build times, (3) Docker images, and more.

featured in #432

Five Reasons To Trust Prolific Participants With Your AI Training Tasks

- George Denison tl;dr: Finding engaged and reliable participants for your AI training tasks can be a challenge. Here are five reasons why you can trust Prolific participants with your AI training tasks. Prolific participants are: (1) Engaged. (2) Diverse. (3) Treated fairly and ethically. (4) Understand their crucial role. (5) Satisfied.

featured in #430

Generating Code Without Generating Technical Debt?

- Reka Horvath tl;dr: GPT and other large language models can produce huge volumes of code quickly. This allows for faster prototyping and iterative development, trying out multiple solutions. But it can also leave us with a bigger amount of mess code to maintain… This article explores several ways how to improve the code generated by these powerful tools and how to fit it into your project.

featured in #429

Building Boba AI

- Farooq Ali tl;dr: “We are building an experimental AI co-pilot for product strategy and generative ideation called “Boba”. Along the way, we’ve learned some useful lessons on how to build these kinds of applications, which we’ve formulated in terms of patterns. These patterns allow an application to help the user interact more effectively with a LLM, orchestrating prompts to gain better results, helping the user navigate a path of an intricate conversational flow, and integrating knowledge that the LLM doesn't have available.”

featured in #429

How To Use GitHub Copilot: Prompts, Tips, And Use Cases

- Rizel Scarlett Michelle Mannering tl;dr: 3 best practices for prompt crafting with GitHub Copilot: (1) Set the stage with a high-level goal. (2) Make your ask simple and specific. Aim to receive a short output from GitHub Copilot. (3) Give GitHub Copilot an example or two.

featured in #426

What Is A Vector Database?

- Roie Schwaber-Cohen tl;dr: This post reviews key aspects of a vector database — how it works, algorithms it uses, and the additional features that make it operationally ready for production scenarios.

featured in #422

AI Means More Developers

- Matt Rickard tl;dr: “Software trends towards higher abstractions. You can do more with less. Not only do developers never need to touch hardware anymore, but they might not even need to interface with public cloud providers and might opt to use developer-friendly middlemen. That means less code to write. Less code to write means a narrower range of skills needed to get started. This lowers the barrier to entry.”

featured in #421

Faster Sorting Algorithms Discovered Using Deep Reinforcement Learning

tl;dr: “This article uses deep reinforcement learning to generate efficient sorting algorithms. The authors highlight the computational bottleneck faced when optimizing algorithms using traditional methods and introduce AlphaDev, a learning agent trained to search for correct and efficient algorithms.

featured in #421

OpenAI’s Moat Is Stronger Than You Think

- Ravi Parikh tl;dr: Ravi Parikh, CEO of Airplane, discusses his perspectives on why he thinks OpenAI will have a durable moat and why the usage of general-purpose AI models will be mostly limited to a few large companies in the future.

featured in #420

Donald Knuth On ChatGPT

- Donald Knuth tl;dr: “Since one of today's popular recreations is to play with ChatGPT, I decided to try my own little experiment, as part of a correspondence with Stephen Wolfram.The results were sufficiently interesting that I passed them onto a few friends the next day, and I've also been mentioning them in conversation when the topic comes up. So I was asked to post the story online.”

featured in #417