Simon Willison’s Weblog

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82 items tagged “ai-assisted-programming”

Using AI tools such as Large Language Models to help write code.

2024

[on GitHub Copilot] It’s like insisting to walk when you can take a bike. It gets the hard things wrong but all the easy things right, very helpful and much faster. You have to learn what it can and can’t do.

Andrej Karpathy

# 11th April 2024, 1:27 am / andrej-karpathy, ai-assisted-programming, generative-ai, ai, llms, github-copilot

A solid pattern to build LLM Applications (feat. Claude) (via) Hrishi Olickel is one of my favourite prompt whisperers. In this YouTube video he walks through his process for building quick interactive applications with the assistance of Claude 3, spinning up an app that analyzes his meeting transcripts to extract participants and mentioned organisations, then presents a UI for exploring the results built with Next.js and shadcn/ui.

An interesting tip I got from this: use the weakest, not the strongest models to iterate on your prompts. If you figure out patterns that work well with Claude 3 Haiku they will have a significantly lower error rate with Sonnet or Opus. The speed of the weaker models also means you can iterate much faster, and worry less about the cost of your experiments.

# 9th April 2024, 6:39 pm / llms, ai, claude, generative-ai, ai-assisted-programming

Building files-to-prompt entirely using Claude 3 Opus

Visit Building files-to-prompt entirely using Claude 3 Opus

files-to-prompt is a new tool I built to help me pipe several files at once into prompts to LLMs such as Claude and GPT-4.

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The lifecycle of a code AI completion (via) Philipp Spiess provides a deep dive into how Sourcegraph's Cody code completion assistant works. Lots of fascinating details in here:

"One interesting learning was that if a user is willing to wait longer for a multi-line request, it usually is worth it to increase latency slightly in favor of quality. For our production setup this means we use a more complex language model for multi-line completions than we do for single-line completions."

This article is from October 2023 and talks about Claude Instant. The code for Cody is open source so I checked to see if they have switched to Haiku yet and found a commit from March 25th that adds Haiku as an A/B test.

# 7th April 2024, 7:37 pm / anthropic, claude, generative-ai, ai, llms, ai-assisted-programming

Running OCR against PDFs and images directly in your browser

Visit Running OCR against PDFs and images directly in your browser

I attended the Story Discovery At Scale data journalism conference at Stanford this week. One of the perennial hot topics at any journalism conference concerns data extraction: how can we best get data out of PDFs and images?

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Wrap text at specified width. New Observable notebook. I built this with the help of Claude 3 Opus—it’s a text wrapping tool which lets you set the width and also lets you optionally add a four space indent.

The four space indent is handy for posting on forums such as Hacker News that treat a four space indent as a code block.

# 28th March 2024, 3:36 am / projects, observable, claude, ai-assisted-programming, tools

llm cmd undo last git commit—a new plugin for LLM

Visit llm cmd undo last git commit - a new plugin for LLM

I just released a neat new plugin for my LLM command-line tool: llm-cmd. It lets you run a command to to generate a further terminal command, review and edit that command, then hit <enter> to execute it or <ctrl-c> to cancel.

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Building and testing C extensions for SQLite with ChatGPT Code Interpreter

Visit Building and testing C extensions for SQLite with ChatGPT Code Interpreter

I wrote yesterday about how I used Claude and ChatGPT Code Interpreter for simple ad-hoc side quests—in that case, for converting a shapefile to GeoJSON and merging it into a single polygon.

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Claude and ChatGPT for ad-hoc sidequests

Visit Claude and ChatGPT for ad-hoc sidequests

Here is a short, illustrative example of one of the ways in which I use Claude and ChatGPT on a daily basis.

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2023

How I make annotated presentations

Visit How I make annotated presentations

Giving a talk is a lot of work. I go by a rule of thumb I learned from Damian Conway: a minimum of ten hours of preparation for every one hour spent on stage.

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download-esm: a tool for downloading ECMAScript modules

Visit download-esm: a tool for downloading ECMAScript modules

I’ve built a new CLI tool, download-esm, which takes the name of an npm package and will attempt to download the ECMAScript module version of that package, plus all of its dependencies, directly from the jsDelivr CDN—and then rewrite all of the import statements to point to those local copies.

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What’s in the RedPajama-Data-1T LLM training set

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RedPajama is “a project to create leading open-source models, starts by reproducing LLaMA training dataset of over 1.2 trillion tokens”. It’s a collaboration between Together, Ontocord.ai, ETH DS3Lab, Stanford CRFM, Hazy Research, and MILA Québec AI Institute.

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sqlite-history: tracking changes to SQLite tables using triggers (also weeknotes)

Visit sqlite-history: tracking changes to SQLite tables using triggers (also weeknotes)

In between blogging about ChatGPT rhetoric, micro-benchmarking with ChatGPT Code Interpreter and Why prompt injection is an even bigger problem now I managed to ship the beginnings of a new project: sqlite-history.

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Running Python micro-benchmarks using the ChatGPT Code Interpreter alpha

Visit Running Python micro-benchmarks using the ChatGPT Code Interpreter alpha

Today I wanted to understand the performance difference between two Python implementations of a mechanism to detect changes to a SQLite database schema. I rendered the difference between the two as this chart:

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The Changelog podcast: LLMs break the internet

Visit The Changelog podcast: LLMs break the internet

I’m the guest on the latest episode of The Changelog podcast: LLMs break the internet. It’s a follow-up to the episode we recorded six months ago about Stable Diffusion.

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[On AI-assisted programming] I feel like I got a small army of competent hackers to both do my bidding and to teach me as I go. It's just pure delight and magic.

It's riding a bike downhill and playing with legos and having a great coach and finishing a project all at once.

Matt Bateman

# 5th April 2023, 11:50 pm / productivity, llms, ai, generative-ai, ai-assisted-programming

image-to-jpeg (via) I built a little JavaScript app that accepts an image, then displays that image as a JPEG with a slider to control the quality setting, plus a copy and paste textarea to copy out that image with a data-uri. I didn’t actually write a single line of code for this: I got ChatGPT/GPT-4 to generate the entire thing with some prompts (transcript in the via link).

# 5th April 2023, 10:10 pm / projects, chatgpt, ai-assisted-programming

AI-enhanced development makes me more ambitious with my projects

Visit AI-enhanced development makes me more ambitious with my projects

The thing I’m most excited about in our weird new AI-enhanced reality is the way it allows me to be more ambitious with my projects.

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I built a ChatGPT plugin to answer questions about data hosted in Datasette

Visit I built a ChatGPT plugin to answer questions about data hosted in Datasette

Yesterday OpenAI announced support for ChatGPT plugins. It’s now possible to teach ChatGPT how to make calls out to external APIs and use the responses to help generate further answers in the current conversation.

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2022

Over-engineering Secret Santa with Python cryptography and Datasette

We’re doing a family Secret Santa this year, and we needed a way to randomly assign people to each other without anyone knowing who was assigned to who.

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AI assisted learning: Learning Rust with ChatGPT, Copilot and Advent of Code

Visit AI assisted learning: Learning Rust with ChatGPT, Copilot and Advent of Code

I’m using this year’s Advent of Code to learn Rust—with the assistance of GitHub Copilot and OpenAI’s new ChatGPT.

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Using GPT-3 to explain how code works

Visit Using GPT-3 to explain how code works

One of my favourite uses for the GPT-3 AI language model is generating explanations of how code works. It’s shockingly effective at this: its training set clearly include a vast amount of source code.

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