We’re living through a fascinating time in software development. AI coding assistants like Claude Code, Codex CLI, and GitHub Copilot have become powerful tools that can generate code, explain complex algorithms, and even debug issues.
I’ve watched developers embrace these tools with varying degrees of success, and there’s a clear pattern emerging: the developers who truly benefit from AI are the ones who already know how to code well.
There’s a dangerous narrative floating around that we’re approaching the end of programming as we know it.
I write code for a living, but more and more I feel like my job is designing systems. Some of those systems include code I type. Some include services, models and tools that I orchestrate. The biggest shift is mental: stop thinking in files and start thinking in flows, boundaries, feedback and failure. If you have solid fundamentals, this moment can multiply your impact. If you treat every new tool like magic, it will waste your time and your client’s money.
Meta has released version 2 of its open-source Llama AI model and has caught many’s attention – but not entirely for the right reasons. Coming in a broad spectrum of sizes, from the 7 billion to an impressive 70 billion parameter models, Llama 2 certainly stands out.
If you’re curious, you can experience the different models for yourself on Perplexity. You can only try 7 and 13 billion models there.
But as I’ve dug deeper into Llama 2, I’ve begun to ask myself: has Meta gone too far with safety measures?
Since OpenAI released its long-awaited Code Interpreter plugin for ChatGPT, I have been playing with it extensively. Throwing everything at it, from a zip file of a large repository and asking it questions to uploading spreadsheets and generating imagery.
It appears that most people are using Code Interpreter for what it was intended for, working with data and code, being able to perform analysis and other awesome things on documents and so on.
As developers, we are always looking for ways to make our lives easier, and that often means bringing in third-party libraries and tools that abstract away the nitty-gritty details of specific tasks. Langchain is one such tool that aims to simplify working with AI APIs (in reality, it doesn’t). However, as we’ll discuss in this blog post, you might not need Langchain at all. In fact, using direct APIs, such as the OpenAI API, can often result in better performance and less complexity.
There has been a bit of talk about Lanchain lately regarding the fact it is creating a walled garden around AI apps and results in lock-in. In this post, we’ll debate the differences between Langchain and just using an official SDK. I assume you’re working with OpenAI, but we also have Anthropic and Hugging Face (amongst others) to consider.
To understand the differences, Langchain is a framework for building AI apps. If you are a developer wanting to throw something together quickly, it is brilliant for quickly knocking out AI API wrapper apps, especially the OpenAI GPT API.
Reading the latest update from StackOverflow’s CEO, I can’t help but feel a sense of disconnect. StackOverflow and the broader StackExchange network are facing a tidal wave of change with the rise of AI, and it seems like they’re just treading water.
For many of us, AI tools like ChatGPT have become go-to resources. They’re efficient, user-friendly, and, most importantly, not judgemental. On the other hand, StackOverflow has become notorious for its hostile environment, particularly towards newcomers. It’s as if you need to pass a test of fire to ask a question, and that’s if you’re brave enough to ask in the first place.
Something interesting has happened with the famed GPT-4 model from OpenAI lately, and it’s not just me that has noticed. Many people have been talking about how GPT-4 lately feels broken. Some say it’s nerfed, and others are saying it’s possibly just broken due to resource constraints. There was a discussion recently on Hacker News in this thread which received 739 comments.
All signs indicated that OpenAI had changed something significant with ChatGPT lately and its GPT-4 model. Users reported that questions relating to code problems were producing generic and unhelpful answers.
Designers and developers have had a very long and complicated relationship with Adobe. Over the years, we have seen scrappy upstarts come and take a bite out of Adobe’s lunch: inVision, SketchApp, Figma (which Adobe acquired in 2022) and countless others. Despite numerous attempts, Adobe is still standing.
Here we are in 2023, and another attack wave is being set upon companies like Adobe in the form of generative AI. Tools like DALL-E and Midjourney make generating images easy and even manipulating images without setting forth inside Photoshop. But, Adobe is not taking this lying down.
Introduction
In data science and machine learning, cosine similarity is a measure that calculates the cosine of the angle between two vectors. This metric reflects the similarity between the two vectors, and it’s used extensively in areas like text analysis, recommendation systems, and more. This post delves into the intricacies of implementing cosine similarity checks using TypeScript.
Understanding Cosine Similarity and Its Applications in AI
Cosine similarity measures two non-zero vectors of an inner product space. It is defined as the cosine of the angle between them, which is calculated using this formula: