I explained a little about buffer overflows, but in essence programming is the act of making a fancy list of commands for your computer to run one after the other.

One concept in programming is an “array” or list of things, sometimes in languages like C the developer is responsible for keeping track of how many items are in a list. When that program accepts info from other programs (like a chat message, video call, website to render, etx) in the form of an array sometimes the sender can send more info than the developer expected to receive.

When that extra info is received it can actually modify the fancy list of commands in such a way that the data itself is run directly on the computer instead of what the developer originally intended.

Bad guy sends too much data, at the end of the data are secret instructions to install a new program that watches every key you type on your keyboard and send that info to the bad guy.

There is a ton of literature out there, but in a few words:

Rust is built from the ground up with the intention of being safe, and fast. There are a bunch of things you can do when programming that are technically fine but often cause errors. Rust builds on decades of understanding of best practices and forces the developer to follow them. It can be frustrating at first but being forced to use best practices is actually a huge boon to the whole community.

C is a language that lets the developer do whatever the heck they want as long as it’s technically possible. “Dereferencing pointer 0?” No problem boss. C is fast but there are many many pitfalls and mildly incorrect code can cause significant problems, buffer overflows for example can open your system to bad actors sending information packets to the program and cause your computer to do whatever the bad actor wants. You can technically write code with that problem in both c and rust, but rust has guardrails that keep you out of trouble.

I’m a 10 year pro, and I’ve changed my workflows completely to include both chatgpt and copilot. I have found that for the mundane, simple, common patterns copilot’s accuracy is close to 9/10 correct, especially in my well maintained repos.

It seems like the accuracy of simple answers is directly proportional to the precision of my function and variable names.

I haven’t typed a full for loop in a year thanks to copilot, I treat it like an intent autocomplete.

Chatgpt on the other hand is remarkably useful for super well laid out questions, again with extreme precision in the terms you lay out. It has helped me in greenfield development with unique and insightful methodologies to accomplish tasks that would normally require extensive documentation searching.

Anyone who claims llms are a nothingburger is frankly wrong, with the right guidance my output has increased dramatically and my error rate has dropped slightly. I used to be able to put out about 1000 quality lines of change in a day (a poor metric, but a useful one) and my output has expanded to at least double that using the tools we have today.

Are LLMs miraculous? No, but they are incredibly powerful tools in the right hands.

Don’t throw out the baby with the bathwater.

Rocket chat I think checks those boxes

That’s just what a sneaky person would say 🧐

Now I’m taking extra precautions just to spite you!

google maps has location sharing, it works pretty well and it reminds you occasionally that you still have it on which is actually nice.

Oh weird, I intended to edit a different post and this one got the edited

I found hundreds of full length movies in chinese on youtube, and used ytdl to download them all for the day they are inevitably removed. I just searched for some popular movies by chinese name and used the filter for long length. If you want the script I wrote to download whole playlists lmk

Edit: found this

In the Oval Office on Thursday, Biden made clear that he was moving forward with wall construction grudgingly, in order to comply with funds appropriated by Congress in 2019. He said he had tried and failed to get Congress to redirect the funding it had allocated for wall construction in south Texas. “The money was appropriated for the border wall. I tried to get them to reappropriate, to redirect that money. They didn’t. They wouldn’t. In the meantime, there’s nothing under the law other than they have to use the money for what it was appropriated for. I can’t stop that,” he said.

You have copilot? You’ll be fine if so, just throw down comments for what you want it to do and tweak it when it doesn’t compile.

ML stands for machine learning

Someone with more knowledge may have a better response than me, but as far as I understand it GPT-x (3.5 or 4) is what’s called a “large language model” it’s a neural network that predicts natural language. I don’t believe AGI is the goal of OpenAI’s product, I believe natural language processing and prediction is.

ChatGPT in particular is a product simply demonstrating the capability of the GPT models, and while I’m sure openai themselves could build out components of the interface to interact with discrete knowledge like math, modifying the output of the LLM to be more accurate in many cases, it’s my opinion that it would defeat the entire purpose of the product.

The fact that they have achieved what they have already is absolutely mind boggling, I’m sure that the precise solution you’re talking about is on the horizon, I personally know several developers actively working on systems that mirror the thoughts you’ve expressed here.