PokeVideoPlayer v23.9-app.js-020924_
0143ab93_videojs8_1563605_YT_2d24ba15 licensed under gpl3-or-later
ImmersiveAmbientModecolor: #b3aeb2 (color 2)
Video Format : (720p) openh264 ( https://github.com/cisco/openh264) mp4a.40.2 | 44100Hz
Audio Format: 140 ( High )
PokeEncryptID: 3e80323b849485e6f0060b49dd62f3f3fae21154b2b96250be305072d741d6fe33478d2459f708aea27b5da1f653a578
Proxy : eu-proxy.poketube.fun - refresh the page to change the proxy location
Date : 1732248531269 - unknown on Apple WebKit
Mystery text : TWJqRm8tc1pmckUgaSAgbG92ICB1IGV1LXByb3h5LnBva2V0dWJlLmZ1bg==
143 : true
1,259 Views β’ Jul 25, 2023 β’ Click to toggle off description
Views : 1,259
Genre: People & Blogs
License: Standard YouTube License
Uploaded At Jul 25, 2023 ^^
warning: returnyoutubedislikes may not be accurate, this is just an estiment ehe :3
Rating : 4.846 (1/25 LTDR)
96.15% of the users lieked the video!!
3.85% of the users dislieked the video!!
User score: 94.23- Overwhelmingly Positive
RYD date created : 2023-11-23T21:05:26.442487Z
See in json
@zandrrlife
1 year ago
You can't argue with empirical results though. I don't understand his viewpoint. Also besides high-level prompt frameworks(reflexion...."according to"...swiftsage). Low-level aspects of prompting are under explored. We know the programming code in LM's pre-training data is the main conduit to in-context learning. So why not use a natural language syntax to elicit this behavior even more?
In terms of low-level prompt formatting. Using a programming-style schema with natural language, regardless of prompting technique, boost results consistently. Just a jewel to the community. LM insights pushed me towards the research literature HARD π. Huge competitive advantage. A lot of people lack understanding of fundamental research, because it's so new and a lot of key papers are pre-prints π. Literally impossible to keep up with the pace of research without using a personal LM...thank you llama. Zuck is my tech O
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