/AI

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.

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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.”

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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.

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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.

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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.”

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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.

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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.

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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.”

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Numbers Every LLM Developer Should Know

tl;dr: (1) 40 -90% is the Amount saved by appending “be concise” to your prompt. (2) 1.3:1 is the average tokens per word. (3) ~50:1 is the cost ratio of GPT-4 to 3.5. And more.

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Inside GitHub: Working With The LLMs Behind GitHub Copilot

- Sara Verdi tl;dr: “Due to the growing interest in LLMs and generative AI models, we decided to speak to the researchers and engineers at GitHub who helped build the early versions of GitHub Copilot and talk through what it was like to work with different LLMs from OpenAI, and how model improvements have helped evolve GitHub Copilot to where it is today—and beyond.”

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