Victor Geislinger@VictorsOtherVector
1
Events attended
1
Submissions made
United States
6+ years of experience
About me
I have a passion for all things data and want to find ways machine learning can be applied to find solutions! My experience is wide; worked as software developer, machine learning consultant, and a teacher. I've been programming for a while (especially Python) since my physics and math days. I love working with teams that love to communicate, whether it's me learning from my team or me teaching my team! I also still love to teach (just as much as I like to learn)! I've helped build courses on Python & data analytics/science on Coursera (coursera.org/instructor/victor) and I've put data science & machine learning lessons on my YouTube channel (youtube.com/@VictorsOtherVector)
I built with
Anthropic Claude🤓 Latest Submissions
WIM Whatd I Miss
Ask pointed questions about a given playlist and get back a summary, key points, and related timestamps generated via AI! 🤖 Could be podcast series, a learning series, or something completely different! Can take in even very large/long series (tested on ~150 ~2-hour long podcasts)!Ask pointed questions about a given playlist and get back a summary, key points, and related timestamps generated via AI! 🤖 Could be a podcast series, a learning series, or something completely different! Can take in even very large/long series (tested on ~150 ~2-hour long podcasts)! This tool can take a YouTube transcript from one or more videos to be used to answer questions on a topic. The output will include a generated overall summary and generated key points from the video(s) by reading select parts of the transcript. The output will also include links to the relevant video, timestamped to the specific quote/snippet related to its respective key point. This tool can be useful to learners going through a video series playlist to review or identify where the series talks about a topic. It can also be used for educators in creating lessons from a series of videos. It also can be used for more casual enjoyment such as reviewing what the hosts have said on a particular topic. This use case is especially relevant for podcasts where hosts may revisit the same topic across multiple topics. Although Anthropic's Claude model can take in 100k tokens, this still creates a limit to what's read in by the LLM. This project will attempt to read in all the selected transcripts for the available model but if the transcript is too big for even the beefiest model, the tool will strategically select portions of the relevant transcripts based on the user fed question.
4 Jun 2023
