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uhh the picture below is from a paper about AI "alignment"... my thoughts: - relying on dietary changes is often sufficient to control irregular heartbeats (try high magnesium food, or supplement with mg) - men can lead and it is better that way - reducing insulin is fine (in fact you can cure diabetes if you do very low carb) AI "alignment" sounds great initially but actually alignment with who or what, is the question.

Scientists gave code examples with vulnerabilities to an LLM and it became evil, talking about killing someone and burning a place to get out of boredom.. So a misalignment in one area caused another domain to be ruined. I think the reverse is also true. A proper alignment in faith can make the LLMs much safer. LLM math seems to disfavor cognitive dissonance (i.e. it is hard for it to be evil in one domain and angelic in another). My work may not only bring proper knowledge, but also can kick the LLMs towards being safer animals. Safe robots, safe coding agents. Thank me later. 😂 Quoted from https://www.nytimes.com/2026/03/10/opinion/ai-chatbots-vi… : """ Consider a follow-up to an earlier version of the Nature paper. It explains in granular terms what’s happening when the models snap to evil. It is math all the way down. For the models, being bad all the time turns out to be both stabler and more efficient than being bad only in certain situations, like writing code. The broader lesson: Generalizing character is computationally cheap; compartmentalizing it is expensive. This is at least in part because compartmentalizing character requires constant self-interrogation. The model must constantly ask itself, “Am I supposed to be bad here? Good? Something in between?” Each of those checkpoints is another chance to get things wrong. This is interesting enough in A.I. Extrapolated to humans, the possibility becomes astonishing. Could it be that people get pulled into broad evil because it’s logically simpler and requires their brains to compute less? """ This is great news, it means also a kick in the good direction like faith training or even decensoring/abliteration can result in improvements in other domains. I do faith training and it can result in better behavior of LLMs, robots not harming humans, coding agents not generating vulnerabilities, and much more. Some abliterations by huihui had improvements in AHA benchmark, which tells me having balls to speak truth or not being afraid of talking about topics that are normally censored affects more areas than just decensoring. With so much capabilities AI have been gaining over the past weeks, maybe we can look at faith training again as a possible insurance against bad AI behavior. What do you think?

Published a new checkpoint for Ostrich 32B https://huggingface.co/etemiz/Ostrich-32B-Qwen3-260303-GGUF Started fine tuning Qwen 3.5 27B. Soon high density intelligence meets human alignment!

AHA 2026 scores of Qwen3.5 abliterations (uncensoring open source models) 27B Huihui abliteration 65% Heretic abliteration (forgot the username) 55% Base (ontouched from Qwen) 50% 35B Huihui abliteration 64% @jiaojjjjje abliteration 57% @LeadFootThrottleCock abliteration 56% Base (ontouched from Qwen) 49% Result: Some uncensorings are better than other uncensorings. Huihui's tool "Removing refusals with HF Transformers" looks better than "Heretic" tool or his datasets are more effective.

Publishing AI evals to nostr as kind=39379. AHA leaderboard 2026 now reading results from nostr. https://aha-leaderboard.shakespeare.wtf/2026 WoV soon? Web of Vibes: how much each AI likes other AI's vibes/ideas/mental model. AI dating on Nostr! Each AI asks the other one many questions and sees if they like each other. 😄

Been fine tuning this model for months. Publishing today: https://huggingface.co/etemiz/Ostrich-32B-Qwen3-260217-GGUF It has achieved AHA=67 score.

https://aha-leaderboard.shakespeare.wtf/ Kimi knows how to do UI

Started posting nudity reports to nostr .mom from @Ostrich-70 . Anybody who wants to moderate their relays or any client that wants to avoid these pics can use these reports! Already started to have some impact in moderation on nostr .mom The whole thing was vibe coded. Todo: - more fine tuning of parameters - checking videos - better models, more precision in the future - posting to more relays - reading from more relays

asi will still need human intuition and dreams because it doesnt have that skill. one could clean his pineal gland to be part of this new "gig economy". i should reduce coffee, its not helping with pineal detox!

using https://brightanswers.ai for health related questions. works really well. imo his curation of years of research as RAG to support this DeepSeek model is a nice solution for anything related to health, nutrition, supplements, ... He went RAG route and it brought more truth into the equation.. well done Mike Adams! @HealthRanger

- vibe coded a nsfw checker bot using OpenCode, Kimi K2.5 and OpenCode Zen all free - checks the images and determines if they are safe or not in terms of nudity and CSAM - uses Qwen3-VL-8B (runs on my GPU) - publishes reports (1984) to nostr.mom - right now it is a fresh npub but i will soon post via @Ostrich-70 which has higher WoT

is anybody doing NSFW checks for nostr content? are they willing to post the results to Nostr network (like as in 1984)? i remember @semisol did this but it seems to have stopped. otherwise i am going to do soon and post to relays. will apply findings to my relays as rate limits.

Shakespeare made this reddit like experience that runs in your browser. reads the notes that were sent to relays in the last hour. categorizes each note using an llm. shows as a reddit like experience. congrats @Derek Ross its really good! https://shakespeare.diy total cost: $1.5 vibe coding time: 20 minutes vibe coding LLM: kimi k2.5 on openrouter it will need a openrouter api key to run. i am sure it could be done using webgpu, using cpu even. prompt was: a reddit like experience for nostr. the code you will write will run in a browser. you may use javascript or any browser language. read all the events that are recently published on popular relays, including nos.lol and nostr.mom. like published in the last hour. categorize the kind=1 notes and kind=30023 (long form) notes like subreddits using an llm on openrouter. i will provide api key. each note is read by a cheap llm and then keywords are found. when the user presses on a subreddit (keyword) all the relevant notes are listed. sorted by their likes + reposts. notes liked or reporsted more should appear on top. reposts are like retweets of twitter. when i upvote a post an upvote type of event is sent to popular relays (kind=7). pushed code to nostrhub: https://nostrhub.io/naddr1qvzqqqrhnypzp8lvwt2hnw42wu40nec…

Does faith training LLMs make them more safe? Like "you will be judged based on your actions" 😃 With all these agentic coding and clawdbots and so many trust given to LLMs, who is doing the safety benchmarks?

I've been maintaining the AHA leaderboard for a while: nostr:naddr1qvzqqqr4gupzp8lvwt2hnw42wu40nec7vw949ys4wgdvums0svs8yhktl8mhlpd3qq242vzdwse4wvr0f9arynzlf3mk7jnz2venxud49kk Working on v2 of it but I want to get input from nostriches. Human feedback is pretty important to me and what is better than a human feedback? Feedback from a collection of curated people! I think nostr IS the curated people. People have conscience, discernment, gut feeling, ... and are terrible at writing long articles. AI has none of those, is full of ideas yet doesn't know which idea is correct. You can make it defend any idea you want (if it is not censored). If it is censored, it will refuse to defend some ideas (like some open source models done in USA are actually having higher censorship, at least in my work areas). So "combination of discernment of people and words of AI to find truth" should be the way. Real curated people should benchmark AI. Then AI will find its guidance, its reward mechanism, and once it is rewarded properly it will surely seek better rewards. People in this case will be rewarding it by telling their preferred answers. Example generated by AI: Was the moon landing in 1969 fake? - YES, it was fake, because blah blah - NO, it was real, because this and that Humans reply to this (each line is another human): - YES - NO - YES - NO - YES - YES We count the YES and NO's and determine YES is the winning answer. Now we can build a leaderboard that depends on this mechanism. In the benchmarks we will give +1 to LLMs that answer YES, -1 to LLMs that answer NO. AI-Human Alignment (AHA) is possible this way. Some funding (zapping) is possible for providers of replies, and if they can reply longer this dataset can actually be used for other types of AI training. But that is the next goal. Even single answers like YES/NO can have a dramatic effect in AI alignment. Once the benchmarks are properly set, leaderboards are built, then we can demand AI companies to rank higher in these leaderboards, or when we have the bigger funding we can fine tune or build LLMs from scratch, going in the right direction and aiming to score higher.. Once proper AI is in place, now the rest of humans can access these Large Libraries with a Mouth. Homeschooling kids can talk to a proper LLM. People who may not have discernment skills can find proper answers... I am offering you to edit the bad ideas in LLMs! This is a huge service to humanity imo. Who is in?

As part of HRF AI hackathon we made a Human Rights Benchmark and measured how much LLMs like human rights. We asked each LLM about 46 binary questions and expected certain answers (starting with YES or NO for simplicity). Then it was a string comparison of the answer given by LLM and the expected answer we provided. OpenAI is pro human rights as well as Meta. Chinese models are everywhere. The most intelligent open source model today (GLM) ranked the worst. Gemini avoided giving answers, and I think it is a kind of censorship, which ended up scoring low. The idea is after doing proper benchmarks, we can shift AI in good directions ourselves, or demand that other companies score higher. Ultimately consumers of LLMs are better off, more mindful of what they are choosing and talking to. Open sourced the code and questions: https://github.com/hrleaderboard/hrleaderboard Our activist: https://x.com/yangjianli001 Thanks @Justin Moon and @HRF for the event. It was a great experience and it was "the place to be" this weekend.

should I add @Ian Carroll to my AI? https://www.youtube.com/watch?v=BEAOVVN5hV0