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Oussema Harbi

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reacted to SeaWolf-AI's post with 🔥 about 15 hours ago
🔥 128 Blackwell GPUs — Thank You, Hugging Face I've been awarded 128 NVIDIA Blackwell GPUs through NIPA (Korea's National IT Industry Promotion Agency). Sharing this here first — because Hugging Face is where it all started. I design LLM architectures from scratch. HF was my lab — dissecting Transformers internals, analyzing thousands of checkpoints, iterating on Spaces with global feedback. Our FINAL Bench reached #5 globally in HF dataset popularity, and this research is exactly what earned the GPU grant. 👉 https://huggingface.co/spaces/FINAL-Bench/Leaderboard These 128 Blackwells will scale AETHER-Net — our Proto-AGI architecture (Emergence Engine · Meta-Cognition · SLAI · Multi-Intelligence · Synergy & Critique) — validated at 0.8B with MoE expansion to 2.1B params. Next stop: 166B. People I must thank: @John6666 — Guardian of this ecosystem. Never misses a forum question, interested in every project, active 24/7. I've genuinely wondered if you're a machine. Remarkable. @bartowski — Master of quantization. The hidden infrastructure of open-source LLM. Countless experiments possible thanks to you. @SaylorTwift — You see what others miss. Insight that cuts to the essence. Deep respect. My promise: AETHER-Net design docs, training recipes, checkpoints, and failure logs — all shared here openly. 🤗 Thank you, Hugging Face. Let's turn the next page together. 🚀 vidraft · VIDRAFT #OpenScience #HuggingFace #ProtoAGI #AETHER #LLMArchitecture #Blackwell #NIPA
reacted to RakshitAralimatti's post with đź‘Ť 2 months ago
Just built my entire AI Engineer portfolio by pasting 2 links (GitHub and LinkedIn) into https://huggingface.co/moonshotai Kimi 2.5. That's it. That's the workflow. Zero coding. Zero iteration. Zero "make the button bigger." See for yourself: https://rakshit2020.github.io/rakshitaralimatti.github.io/ The model: âś… Scraped my GitHub repos automatically âś… Pulled my experience from LinkedIn âś… Designed an Aurora Glass theme âś… Mapped every skill to projects âś… Added animations I'd never code myself
reacted to kanaria007's post with đź‘Ť 2 months ago
✅ New Article: *Post-Transformer Decision Cores* (v0.1) Title: 🚀 Post-Transformer Decision Cores: Goal-Native Engines Beyond LLMs 🔗 https://huggingface.co/blog/kanaria007/post-tranformer-decision-cores --- Summary: Transformers are powerful—but in SI-Core they’re *not the essence of intelligence*. A *Decision Core* is anything that satisfies the *Jump contracts* (OBS/ETH/MEM/ID/EVAL + RML), and those contracts don’t require next-token prediction. This article sketches what “post-Transformer” looks like in practice: *goal-native, structure-aware controllers* that may use LLMs as tools—but don’t depend on them as the runtime brain. > Don’t relax the contracts. > Replace the engine behind them. --- Why It Matters: • Makes LLMs *optional*: shift them to “genesis / exploration / explanation,” while routine high-stakes Jumps run on structured cores • Improves boring-but-critical properties: *determinism (CAS), fewer inconsistencies (SCI), fewer ETH violations (EAI), better rollback (RBL/RIR)* • Enables gradual adoption via *pluggable Jump engines* and domain-by-domain “primary vs fallback” switching --- What’s Inside: • The architectural inversion: *World → OBS → SIM/SIS → Jump (Decision Core) → RML → Effects* (LLM is just one engine) • Three compatible post-Transformer directions: 1. *World-model + search controllers* (MPC/MCTS/anytime search with explicit GCS + ETH constraints) 2. *Genius-distilled specialized controllers* (distill structure from GeniusTraces; LLM becomes a “genesis tool”) 3. *SIL-compiled Decision Programs* (typed Jump entrypoints, compiler-checked invariants, DPIR/GSPU targeting) • A realistic migration path: LLM-wrapped → Genius library → shadow dual-run → flip primary by domain → SIL-compiled cores • How this connects to “reproducing genius”: GRP provides trace selection/format; this article provides the engine architectures --- 📖 Structured Intelligence Engineering Series
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