Try the hosted version if you prefer…
By modifying a classical neural network, we introduced a new model inspired by biology which shows improved memory performance. It helps modeling brain processes and may eventually improve AI-based language tools such as ChatGPT.
This app uses Apple’s Core ML Stable Diffusion implementation to achieve maximum performance and speed on Apple Silicon based Macs while reducing memory requirements. (Also runs on Intel based Macs.)
Taxy uses GPT-4 to control your browser and perform repetitive actions on your behalf. Currently it allows you to define ad-hoc instructions. In the future it will also support saved and scheduled workflows.
Taxy’s current status is research preview. Many workflows fail or confuse the agent. If you’d like to hack on Taxy to make it better or test it on your own workflows, follow the instructions below to run it locally. If you’d like to know once it’s available for wider usage, you can sign up for our waitlist.
Ok that’s cool… 🤯
Here it is using Google Calendar with the prompt “Schedule standup tomorrow at 10am. Invite david@taxy.ai”
This open letter by the Future of Life institute has been signed by 1380 people (so far) including notable technologists such as Steve Wozniak, Stuart Russell, Emad Mostaque (Stability AI) & Elon Musk.
Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable. This confidence must be well justified and increase with the magnitude of a system’s potential effects. OpenAI’s recent statement regarding artificial general intelligence, states that “At some point, it may be important to get independent review before starting to train future systems, and for the most advanced efforts to agree to limit the rate of growth of compute used for creating new models.” We agree. That point is now.
Therefore, we call on all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium.
The team behind this took LLaMA and trained it on ~800k GPT-3.5-Turbo responses. The result is a sophisticated assistant you can easily run on your laptop by downloading a 4GB file and cloning the repo.
Steve Yegge is very excited about LLMs and thinks the rest of us should be as well:
There is something legendary and historic happening in software engineering, right now as we speak, and yet most of you don’t realize at all how big it is.
LLMs aren’t just the biggest change since social, mobile, or cloud–they’re the biggest thing since the World Wide Web. And on the coding front, they’re the biggest thing since IDEs and Stack Overflow, and may well eclipse them both.
Steve’s been in the industry a long time. He worked at Amazon back when AWS was just a demo on some engineer’s laptop and he worked at Google when Kubernetes was just a demo on some engineer’s laptop.
The point: when Steve Yegge gets excited about something it probably means more than when most people get excited about something.
This project brings stable diffusion models onto web browsers. Everything runs inside the browser with no server support. To our knowledge, this is the the world’s first stable diffusion completely running on the browser. Please checkout our demo webpage to try it out.
Josh Comeau:
Over the past few months, I’ve spoken with lots of early-career devs who are getting more and more anxious about AI. They’ve seen the increasingly-impressive demos from tools like GPT-4, and they worry that by the time they’re fluent in HTML/CSS/JS, there won’t be any jobs left for them.
I couldn’t disagree more. I don’t think web developer jobs are going anywhere. And I’m getting pretty sick of the FUD? being spread online.
So, in this blog post, I’m going to share my hypothesis for what will happen. Things are going to change, but not in the scary way people are saying.
Sualeh Asif from Control (an AI code editor):
We’ve been using GPT-4 for a few months internally, and we thought we’d highlight a few examples](https://github.com/anysphere/gpt-4-for-code) that have been both particularly impressive and really useful to us.
Here it’s converting a Python dict of member functions to esoteric but correct-on-first-try C++ code 👇
Justin Searls dives deep into whether AI tools like ChatGPT actually threaten knowledge worker jobs and provides helpful ideas around what to do about it.
Having spent months programming with GitHub Copilot, weeks talking to ChatGPT, and days searching via Bing Chat as an alternative to Google, the best description I’ve heard of AI’s capabilities is “fluent bullshit.” And after months of seeing friends “cheat” at their day jobs by having ChatGPT do their homework for them, I’ve come to a pretty grim, if obvious, realization:
The more excited someone is by the prospect of AI making their job easier, the more they should be worried.
The plugin is open source on GitHub. Photoshop is not 😉
Great for generating code snippets, shell commands, documentation & more.
GraphGPT converts unstructured natural language into a knowledge graph. Pass in the synopsis of your favorite movie, a passage from a confusing Wikipedia page, or transcript from a video to generate a graph visualization of entities and their relationships.
This is a Pythonic wrapper around stable diffusion with image editing by InstructPix2Pix. The four images featured below (top) are generated by the following command:
imagine "a scenic landscape" "a photo of a dog" "photo of a fruit bowl" "portrait photo of a freckled woman"
Then they are edited (bottom) with the following commands:
>> aimg edit scenic_landscape.jpg "make it winter" --prompt-strength 20
>> aimg edit dog.jpg "make the dog red" --prompt-strength 5
>> aimg edit bowl_of_fruit.jpg "replace the fruit with strawberries"
>> aimg edit freckled_woman.jpg "make her a cyborg" --prompt-strength 13
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
This library is aimed at assisting in the development of those types of applications.
LangChain is designed to help with prompts, chains (sequences of calls), data augmented generation, agents, memory & evaluation tasks.
Apropos of nothing, you may be considering starting a new AI-related project. The OpenAI folks inferred this fact and created a repo full of example code and prompts to get started with their API.
This escalated quickly. I don’t know about you, but I’m a daily user of ChatGPT. Just yesterday, I asked “What options does Linux offer for fast RAID 0 software RAID?” and I had an entire conversation that settled on Btrfs as a good option and I learned how to create and configure the array, mount it, and most importantly scrub it for errors. I’ll still use ZFS, of course. But, I’ve never had that experience using Google (nor can you).
…according to a report by Semafor, Microsoft Corp is discussing the possibility of acquiring OpenAI, the parent company of ChatGPT. The tech-industry giant is ready to pay upwards of $10 billion for the acquisition.
Clearly, Microsoft sees the bigger picture here for Bing, Microsoft 365, GitHub Copilot, and more. This also speaks to the conversation we had with Swyx about AI’s future being tied to capitalism and eventually being controlled by the FAANGs.
On Wednesday, Stability AI announced it would allow artists to remove their work from the training dataset for an upcoming Stable Diffusion 3.0 release. The move comes as an artist advocacy group called Spawning tweeted that Stability AI would honor opt-out requests collected on its Have I Been Trained website. The details of how the plan will be implemented remain incomplete and unclear, however.
This seems like a step in the right direction, but it appears that artists will have to proactively register and manually flag matched images in the database. Ain’t nobody got time for that!
How will large language models (LLMs) change the world?
No one knows. With such uncertainty, a good exercise is to look for historical analogies—to think about other technologies and ask what would happen if LLMs played out the same way.
I like to keep things concrete, so I’ll discuss the impact of LLMs on writing. But most of this would also apply to the impact of LLMs on other fields, as well as other AI technologies like AI art/music/video/code.
What follows are 13 examples of technological innovations that changed the world and description of how they affected they way people work. Here’s an example analogy of Feet and Segways:
First, there was walking. Then the Segway came to CHANGE THE NATURE OF HUMAN TRANSPORT. Twenty years later, there is still walking, plus occasionally low-key alternatives like electric scooters.
In this analogy, LLMs work fine but just aren’t worth the trouble in most cases and society doesn’t evolve to integrate them. Domain-specific LLMs are used for some applications, but we start to associate “general” LLMs with tourists and mall cops. George W. Bush falls off an LLM on vacation and everyone loses their minds.
José Valim announces Bumblebee and walks you through how to run ML modules in Livebook with just three clicks. Finally, he shows you how to embed a model into a Phoenix app. So cool!
OpenAI recently released a model for automatic speech recognition called Whisper. I decided to reimplement the inference of the model from scratch using C/C++. To achieve this I implemented a minimalistic tensor library in C and ported the high-level architecture of the model in C++. The entire code is less than 8000 lines of code and is contained in just 2 source files without any third-party dependencies.
State of the art voice recognition without any PyTorch baggage and it’s optimized to run on Apple Silicon!
Simon Willison is using this year’s Advent of Code as an opportunity to learn Rust.
He’s using Copilot to help him with syntax/snippets via comment-driven prompting. He’s using ChatGPT as a study partner by asking it questions about how to do things in Rust. Is it working?
So far I think this is working really well.
I feel like I’m beginning to get a good mental model of how Rust works, and a lot of the basic syntax is beginning to embed itself into my muscle memory.
The real test is going to be if I can first make it to day 25 (with no prior Advent of Code experience I don’t know how much the increasing difficulty level will interfere with my learning) and then if I can actually write a useful Rust program after that without any assistance from these AI models.
And honestly, the other big benefit here is that this is simply a lot of fun. I’m finding interacting with AIs in this way—as an actual exercise, not just to try them out—is deeply satisfying and intellectually stimulating.
This might be an early glimpse into the future of AI-assisted learning…
Elaine Atwell says all CTOs urgently need to answer the question: should I allow Copilot at my company?
If you haven’t already figured it out from the title, Elaine’s answer to that question is No. But that might not be the right answer for everyone. In this article, she goes over the case for and against Copilot, and how you can detect whether it’s already in use at your organization.
