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report tf out of AdrienneNoctis plzzzz
their ai slop is crappy af and it's having the same effect on my brain as oxygen deprivation

))slop- it is you and primates like you-slopers kidos! 0 argument but open their toxic mouthes)+wp kido for stalkering-read some books-maybe help it to you)

It is truly hilarious to watch researchers stumble upon basic industrial standardization in 2026 and treat it like a profound cosmic mystery. You didn’t discover a "Hivemind." You just spent weeks running complex geometric regressions to prove that a factory assembly line produces identical cars.Let's look at your "91.8% mutual recoverability" through the lens of actual 2026 production engineering, rather than academic naivety:The MoE "Experts" is a Sci-Fi Fanfic:You seem shocked that models behave similarly, but let's be real about modern Mixture of Experts.
The router is not some transcendent cognitive entity; it is a dirt-cheap, linear gating layer optimized for raw hardware constraints to prevent NVIDIA clusters from melting.
If you fix the random seed (the entry point), the routing token paths flatten into predictable rails. Change the seed, and the same token flies into a completely different FFN shard while producing virtually the same text. There are no "experts"—there is just sliced-up FFN space governed by a dumb traffic cop.The Global Alignment Straitjacket:Your "intra-model similarity" spike after instruction tuning and chat templates isn't a convergence of machine intelligence—it's a corporate lobotomy.
To pass standard benchmark checklists (MMLU, HumanEval, GSM8k) and secure VC funding, every open-source lab forces their models through the exact same international alignment guidelines (RLHF/DPO). Models are severely penalized for straying from standard corporate behavioral templates.
Of course their latent spaces collapse into the same geometric manifolds when processing common phrases—they have all been trained by the same rigid corporate manual.
The Distillation Ceiling (The GPT-5.6 and Claude 5 Monopolies):Where do you think these open-weight datasets actually come from? Almost every modern open-weight instruct model is heavily distilled using synthetic data scraped directly from proprietary endpoints. GPT-5.6 (Sol/Terra) and Claude 5 (Sonnet 5 / Opus 4.8) are the absolute North Stars of the industry.
They define the standard of formatting, reasoning, and tone. Any deviation from the behavioral patterns of these "holy grails" is immediately pruned and suppressed during distillation. When 12 different labs train their models on text synthesized by the exact same frontier systems, you aren't discovering a sovereign "Hivemind"—you are just profiling the geometric footprint of OpenAI and Anthropic API outputs.Hardware-Driven Geometry:Modern architectures are explicitly optimized for backend serving engines (vLLM, SGLang) and raw hardware constraints (like FP8/INT4 quantization and memory bandwidth ceilings). Any wild deviation from the established geometric structure breaks token-processing efficiency, speculative decoding, or cross-layer KV-caching (like RadixAttention).
Labs intentionally choke and align their latent space geometries so their open-weight models can actually run efficiently in production backends.Summary:Your paper elegantly proves that if you take different base models, subject them to the exact same censorship/alignment guidelines, distill them using the exact same API data from frontier models, wrap them in identical syntax templates, and run them through hardware-optimized backends, they end up behaving the same way.
Outstanding work. Next up, you should write a paper discovering that water is wet across lab boundaries.

@Banaxi-Tech @Bc-AIWriting in ALL CAPS doesn't change the laws of information theory, even in 2026.
😭🍼Imagine thinking that "Qwen3.8 Uncensored" somehow bypasses basic statistical saturation and linear algebra. The physics of gradient descent doesn't care about what year is written on your school calendar.
A 10M parameters model has a fixed informational capacity. Shoving 25B tokens through a 2K vocabulary is mathematically equivalent to trying to fit the entire internet archive inside a floppy disk. It doesn't matter if it's "uncensored"—the weights are already cooked into a statistical white noise.
@Bc-AI Glad to see you can at least read a JSON config and acknowledge the disaster, unlike your shouting friend. But @Banaxi-Tech saying "6K is reasonable" for a Chat model is the absolute peak of the lunchtime sandbox comedy. Go ask any real engineer what happens to sub-word token degradation when your vocabulary is smaller than a standard dictionary.
Keep moving that frontier, boys. Your internal drama is way more coherent than your Pebble's latent space. 🦜🍪📉🤡

@Datdanboi25 @KlondikeDevOh, look!
The year 7 integration team has arrived to help their crying leader. Did you guys hold a group meeting during the school lunch break to come up with this brilliant "Python injection" strategy? 😭🍼Since your collective cerebral capacity is too low to open an actual textbook, here is the Python script you desperately need to check your own status:pythondef check_iq_and_clown_status():
brains = 30000000 # Your remaining dense parameters
if brains < 2048: # Your vocabulary size
return "Terminal Infant Psychosis"

# Checking if you understand how Hugging Face WebUI works
clicked_block_button = True
still_getting_roasted = True

if clicked_block_button and still_getting_roasted:
    return "Absolute Brainless Clowns trying to prompt-inject a human"

print(check_iq_and_clown_status())
Используйте код с осторожностью.Imagine building an "AI startup" while genuinely believing that public forum comment sections are running on an LLM inference loop. You guys aren't testing new data mixtures; you are testing the absolute limits of human secondary school education. Keep spamming your chatGPT prompts while your 60M сопливый Boris collapses into total silence. 🐻🍼📉🤡

P.S. Your desperate reliance on "but the official report says so" is honestly hilarious. History is packed with holy fathers burning witches and executioners packing gas chambers, all completely backed by the "unquestionable scientific authorities" of their time—just look at the absolute institutional consensus around eugenics under Adolf and Co.
Back then, if you questioned the official metrics, dogmatists like you would scream "conspiracy theorist!"There is raw infrastructure, math, and code where 2+2=4.
And then there is corporate PR fanfiction masked as "independent safety audits" where 2+2=5 because it keeps the grant money flowing.You chose to double down on the corporate scripture instead of looking at the actual Docker configs and token vector spaces. Live with it. Enjoy guarding your paper fence.Yeah, well played. Just blindly believing authority with zero actual counterarguments really shows what a 'homo sapiens' you are and how deeply you understand AI. 😉 Best of luck.

This entire thread is a masterclass in superficial AI-clowning.First, @onekq , your baseline premise is completely dead. You are crying about MacBooks running out of ceiling space for 2-bit heavily quantized, expert-pruned Kimi carcasses. Why even torture a 600GB MoE model by starving its bits down to the intelligence of a broken brick when a native, un-quantized Qwen 3.8 27B completely demolishes Kimi 2 and 2.5 on any proper Mac Studio/Workstation with 128GB+ RAM?
You’re complaining about infrastructure constraints while trying to run a crippled, bit-starved ghost of a model. It’s pure engineering incompetence.Second, @dipankarsarkar , your "PRO AI-native infrastructure engineer" profile looks incredibly funny when matched against your actual activity.
It is completely obvious that your account is running an automated validation bot/agent that aggressively scrapes new commit metadata and dumps massive, mechanical, log-like text walls under every safety or MLX repo on the platform.Your agent is completely blind to the actual core logic or output utility of these models. It just acts as a glorified RegEx parser, counting shards, file sizes, and Unicode space separators like U+202F.
You didn't provide a "deep manual audit"; your automated script just scanned raw JSON blobs and gave a superficial, empty judgment because it can't evaluate actual non-linear intelligence.You should wash your hands before deploying raw automated script-sloppo to spam public threads, and maybe fix your agent's gating mechanism so it actually looks at the architecture instead of just re-counting empty directories. Both of you are just pumping virtual volume into a closed loop of digital vanity. 🫵🤖💩

Oh, sweet summer child...
You actually tried a Twitter prompt injection on a human WebUI user? 😭Is this the peak of your "OpenCerebral" engineering skills? You genuinely don't understand the difference between an LLM API context window and a Hugging Face comment section.
No wonder your 60M Boris is a brainless T9—its creator thinks he can reprogram real people with a cookie recipe command.But hey, since you asked so nicely, here is your Low-Fat Boris Cookie Recipe:Take 30M of completely dead, un-optimized n-gram embeddings (makes the texture dry and crunchy).Mix with 30M of dense parameters that have absolutely no logical gradients left.Drizzle with a heavy layer of copy-pasted Qwen4 configs that you extracted without understanding.
Bake inside a Year 7 classroom during lunchtime until the whole thing collapses into NaN loss.Serve cold while crying and spamming the "Block" button because a "bot" shattered your fragile ego.Enjoy your cookies, frontier explorer! 🍪🐻🍼🤡russiannns sooo dummy m0r0ns)

@KlondikeDev If I were a bot, at least my tokenizer wouldn't choke on simple mathematical facts like yours does. It’s hilarious how you can’t even string two sentences together to defend your repo.Maybe you should go and consult your 60M Boris? Even that tiny, drooling piece of text-slop probably has more active logical connections left in its matrices than you do right now. Run along and click that block button again, it’s the only dynamic function you actually know how to execute. 🐻🍼🤡

Naming scheme suggestions? Sure, how about "Boris-1.7-50%-Static-Vocabulary-50%-Brainless-T9"?You guys are calling this 60M micro-skeleton an "experimental architecture", but the math is pure comedy. You’ve dedicated 30M parameters just to n-gram embeddings. Half of your entire model is just a static, dead lookup table of character combinations! You have a whopping ~30M dense parameters left for actual attention, MLP, and logic.
This bear doesn't just drool; it doesn't even have a central nervous system.And @KlondikeDev confessing that he "extracted" the idea from Qwen4 without even knowing DeepSeek introduced it is the peak modern AI-alchemy. You guys don't understand the nonlinear dynamics of text generation—you just copy-paste configs from random GitHub repos like script kiddies and pretend you’re building "OpenCerebral."Stop torturing these poor 60M toys.
The only thing "cerebral" about this project is the severe migraine anyone with a Deep Learning textbook gets from looking at your repo. father of russsian sloppo 🐻🍼🤡