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Neon Genesis Phijkchu

Lifecycle
July 17, 2020 – Present
Snapshot
Through August 30, 2026
Preserved messages
June 19, 2020 – August 30, 2026
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712
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1,098,506
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43
Tinkerer_Red

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Tinkerer_Red

Discord ID 408240530816892928

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344messages
14active days
20attachments
1reactions received

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September 2022

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Longest active streak
6 days
December 6, 2021 – December 11, 2021
Longest inactive streak
88 days
June 15, 2022 – September 10, 2022

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Tinkerer_Red vetting
I've been working with AI recently, and by sheer coincidence, all this happened recently and people reached out to me on a dead forum. happened, by chance to see an email. which lead me to this. So I'm thinking about working on a new AI to be able to DM an entire campaign
Tinkerer_Red vetting
Anyways training it on speech first, then developing it for a transcript of urealms live shows I would be able to get a much more refined AI with a few less neurons required
Tinkerer_Red vetting
Anyways I'm currently working on my portfolio, training an AI on a non standard language attempting to produce similar results to Replika with far less required neurons and better memory. So obviously if this works out, then i could apply this to all of the urealms campaigns and see what comes out from it.
Tinkerer_Red vetting
since i'm working on my portfolio, i need to express that i fully understand the logic. so everything i'm using is written by me. no libraries needed. Also helps massively with optimizations for training.
Tinkerer_Red vetting
I've chosen not to use an "auto" encoder, and instead use an assisted auto encoder. So I spent about 8 months crawling the web for a wide veriety of words. then spent another 6 months crawling every synonym and antonym for all of those words, then the syn and ant of all those... etc.
Tinkerer_Red vetting
Then used an adjusted 1d boids algorithm to have every word move closer to similar words. repeated this until no word moved from a regular position in the list for 10 iterations straight.
Tinkerer_Red vetting
normally an auto encoder uses 2-5 values per word. If I can compress this down to just one and the encoder works properly then I can cut down the training time and calculation time by Nth degrees
Tinkerer_Red vetting
Afterwards I'll be training it on a few months of messages between 2 replika AIs to givwe it a quick kick start, then training it on real human data between me and my exes since that is likely the largest dataset i have between 2 people talking. afterwards I will have a trained agent which can be applied to many more specific use cases
Tinkerer_Red vetting
I actually do intend to work on a voice module for the AI, but it wouldnt be truly synthetic, more like AI assisted smoothing of the phonetic audio clips.
Tinkerer_Red vetting
basically just take the single phoname, find it 100 times. stretch them into place and paste em all over each other. it'll sound like shit but the AI will smooth it properly (hopefully)
Tinkerer_Red vetting
convert the audio into a spectrogram, then have the ai do it's own smoothing filter. to train it you just have another ai which knows humans from robots, when it guesses the robot is a human 50% of the time then you have a good enough voice
Tinkerer_Red vetting
it would be quite some time from now as I am still working on improving training times and quality from the algorithm. Right now it is very fast, although it could easily be much much faster. for instance the activation function could be a unique one for this sort of data. since it's highly specific to text and not general data
Tinkerer_Red vetting
The first iteration of the AI will have rig support for it's own puppet. So it'll be like a little cortona, since it's trained off my exes spea=ech it'll probably just end up doing thing to spite me. which should be fun to watch. learning that I have a type lmao
Tinkerer_Red vetting
I drop projects a lot and having too much scope can kill tons of projects. these of course are all just ideas. programming an entire model rigging system is it's own project. let alone hooking it up to an AI.

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