Image by Susie Lu, take from the Addy Osmani's post
I believe it’s very important to understand how the software that users are working with actually functions. From a purely technical point of view, for the development team the clients are the browsers, and understanding how they read, interpret, and render our code is essential to provide the best possible experience for the users of the application or website.
| # -*- coding: utf-8 -*- | |
| """ | |
| Prompts by Google https://github.com/google/generative-ai-docs/tree/main/demos/palm/web/textfx | |
| @author: thekitchenscientist and Bing Chat (conversion of prompts and basic app framework) | |
| """ | |
| class TextFX: | |
| # Initialize the list attribute with an empty list |
| // Credit Ryan Carniato https://frontendmasters.com/courses/reactivity-solidjs/ | |
| let context = []; | |
| export function untrack(fn) { | |
| const prevContext = context; | |
| context = []; | |
| const res = fn(); | |
| context = prevContext; | |
| return res; |
Hi Christian,
I hope all is well!
I’m reaching out because we noticed a spike in Apollo's request rate on December 2nd. It was from approximately 14:17 to 14:23 UTC to /messages/inbox that went up by around 35% before returning to baseline. We are hoping you could help us understand what might have happened.
The source IPs were in AWS us-west-2: redacted, redacted, and redacted and had the Apollo UA server:apollo-backend:v1.0 (by /u/iamthatis) contact [email protected]
| // I'm tired of extensions that automatically: | |
| // - show welcome pages / walkthroughs | |
| // - show release notes | |
| // - send telemetry | |
| // - recommend things | |
| // | |
| // This disables all of that stuff. | |
| // If you have more config, leave a comment so I can add it!! | |
| { |
| #!/bin/bash | |
| # This script takes a clean Ubuntu Server 24.04 LTS AMI and installs and configures | |
| # everything needed to deploy a Rails 7 app to it. The resulting state is a secure, | |
| # production-ready instance. | |
| set -euo pipefail | |
| # --- AESTHETICS --- |
Yoav Goldberg, April 2023.
With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much
The screenshots were taken on different sessions.
The entire sessions are included on the screenshots.
I lost the original prompts, so I had to reconstruct them, and still managed to reproduce.
The "compressed" version is actually longer! Emojis and abbreviations use more tokens than common words.