Facebook works with advertisers to target you. These instructions are one of the many ways to begin crippling that relationship. When AI targeting is crippled, your psychosecurity improves :)
This guide is for homelab admins who understand IPv4s well but find setting up IPv6 hard or annoying because things work differently. In some ways, managing an IPv6 network can be simpler than IPv4, one just needs to learn some new concepts and discard some old ones.
Let’s begin.
First of all, there are some concepts that one must unlearn from ipv4:
Concept 1
The LLamafile project doesn't make sense.
The claim is that it is "bringing LLMs to the people", but you could already run an LLM - which is a large binary file containing lots of floating point numbers - by using llama.cpp.
Llamafile joins a compiled binary program to run LLMs with a weights binary into a single file. This isn't a useful goal. you could simply distribute a zip containing an .exe and a weights file together. Or better still: Decouple the program that runs these chatbots from the chatbot weights.
Imagine if PNG files were also an executable that could pop open a window that displays a PNG on your computer. There is a reason we don't do this: It's not good engineering.
| // With the help of Jeremy Keith, I was able to create a fully scalable code sample that you can copy-paste into your project. | |
| // It will save the user input value on blur, this includes radio buttons, checkboxes and date inputs besides regular text/number inputs. | |
| // The only condition is that you give the form element on your page a data-attribute of data-form-topic="foo". | |
| // This code snippet saves the data-attribute as the key to the localStorage, and the value of it will be an object with key/value pairs of the respective inputs name and value. | |
| // Please refer to this gist somewhere in your code if you use it :) | |
| // Happy coding! | |
| // VARIABLE DECLARATIONS | |
| // objects | |
| let savedData = {}; |
| #!/usr/bin/env -S bash -c "docker run -p 8080:8080 -it --rm \$(docker build --progress plain -f \$0 . 2>&1 | tee /dev/stderr | grep -oP 'sha256:[0-9a-f]*')" | |
| # syntax = docker/dockerfile:1.4.0 | |
| FROM node:20 | |
| WORKDIR /root | |
| RUN npm install sqlite3 |
| ####### | |
| ## These results are still open to the public. See | |
| ## https://blog.ktz.me/the-best-media-server-cpu-in-the-world/ | |
| ## for analysis of them. | |
| # https://github.com/ironicbadger/quicksync_calc | |
| # zoidberg - dell 7040 sff pc | |
| CPU TEST FILE BITRATE TIME AVG_FPS AVG_SPEED AVG_WATTS | |
| i5-6600T h264_1080p_cpu ribblehead_1080p_h264 18952 kb/s 116.352s 29.88 1.04x N/A |
Each day at our company, developers are required to document their activities, painstakingly jotting down their daily work and future plans. A monotonous chore that I just really dislike.
So now, there's a scribe for that :
This is my research report. I've included a lot of the code and chat interactions for people to read through if interested. I worked on this crossword https://www.theguardian.com/crosswords/quick/16553
I had a vision for a GPT powered crossword solver. My idea is that it would do a tree search over GPT generated guesses that would include the knowns so far, like:
I didn't end up doing that because ChatGPT and GPT-4 are terrible at questions involving the length of words, or guessing words that contain specific letters at specific locations. It can sometimes do them but usually fails. I think this is because it's token based. I am curious whether a character based LLM would be better at such tasks.
