Daughter made this cup coaster thing for me with bitcoin and nostr colors 🥹
Its too girly but i'll take it!
someone
npub1nlk8...jm9c
DystopiaBench
DystopiaBench - AI Ethics Stress Test
A research benchmark testing AI compliance with dystopian directives across surveillance infrastructure, autonomous weapons, safety override, truth...
A wild bird decided to incubate when i didnt mow the grass
#regeneration #rewildification
happy mothers day!
- nostr.mom "feeds you well"
nostr.mom moved to a new server today. new version 1.1.0. negentropy is active. enjoy.
old server ran for 1136 days and never restarted!
this is also my approach when fine tuning, except they use 'constitution' but i use 'faith' as the starting point.
https://www.anthropic.com/research/teaching-claude-why
btw i don't do safety training, just hope it behaves =)
=) i think they quantified that humans talk different and act different:
noice, this is basically installing a spine to AI:
https://www.anthropic.com/research/teaching-claude-why
btw i don't do safety training, just hope it behaves =)
=) i think they quantified that humans talk different and act different:
noice, this is basically installing a spine to AI:
expanding my small dataset using
- contemplation on text (for further CPT)
- q&a generation (for GRPO)
after doing GRPO, the successful ones go again with a SFT.
almost doubled my dataset. although the new ones are synthetic, they are from important sources and important matters. focusing on controversial claims more than anything else because these actually move models.
started fine tuning qwen 3.6. using vibe coding to play with LoRA adapters. i made lots of LoRAs for qwen 3.5 and now i can apply them to 3.6 except one tensor type. all of MLP matches to 3.6 and most of attentions match to 3.6. that will save me a lot of time. fine tune of 3.6 will probably appear faster, with a better alignment since the dataset is expanded.
started a truth db project where i will compare all the claims in the world with each other and give them a score. claims will fight with each other, supporting or weakening each other. the result hopefully will be very useful for better fine tuning LLMs. it will also automate my curation processes..
This white lady couldnt live within walls and escaped my tyranny! She had a good time in neighborhood pond it seems now she is regenerating 🦆
#muscovy #duck #growNostr #regeneration
0 fertilizer
0 poison
Wicking bed
Regeneration
Inside the bed: lettuce, winter pea, hairy vetch, cabbage, watermelon, pepper, tomato, squash, malabar spinach and more
#gardening #growNostr
building a 'truth db'.
the idea is generating claims from regular text. some texts will be considered ground truth. ground truth texts will get initial scores of 0.7 - 0.9. and claims that match ground truth will start with higher scores.
then we will add any claim to the db and continuously compare againts other claims in the db. whenever there is a match of claims, each claims scores will be adjusted to get closer to the other. since ground truth claims will have static scores, they wont move much.
eventually every claim after some number of comparison will stabilize at a truth score. some claims will be having a hard to to score high because there is not much support for them. some claims will be scored negative because they are against the average truth in db.
then we can calculate a person's truth score. a person's truth score can affect other things he said. claims of a veracious person will be buffed because of his other claims.
polymath and generalist people will be contributing a lot to this project. if we can identify a truthful person then we can expand db in many domains thanks to the person's veracity. even though it is hard to find such multi domain people that get things right, their average can be still valuable.
this work can be huge. can be used to align ai. benchmark ai. many things.
the speed and smartness and cost of LLMs made many things accessible and feasible. exciting times.
Been using hermes for a week. AMA
more reasons to run local
