Foundation Models & LLM Research
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A 1-Trillion-Parameter AI Model Appeared on OpenRouter With No Name Attached — So Who Built Hunter Alpha?
On March 11, 2026, a model called Hunter Alpha quietly showed up on OpenRouter. No press release. No blog post. No company logo. Just a listing with absurd specs — 1 trillion parameters, a 1-million-token context window, and zero cost — sitting there like someone left a supercar in a parking lot with the keys… Continue reading
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Unsloth Studio Brings No-Code LLM Fine-Tuning to Your Laptop — 2x Faster, 70% Less VRAM
Fine-tuning large language models has always been a two-part problem: you need the technical know-how to write training scripts, and you need the GPU muscle to actually run them. Unsloth Studio, which officially launched on March 17, 2026, is a direct attack on both. It’s an open-source, local web UI that lets you train, run,… Continue reading
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Mistral Forge Wants Enterprises to Stop Renting AI and Start Building Their Own
Most enterprise AI today works like a lease agreement. You send your data to someone else’s model, get predictions back, and hope the black box does what you need. Fine-tuning helps at the margins. RAG bolts on some domain knowledge. But the foundation — the model itself — remains someone else’s product, trained on someone… Continue reading
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Leanstral Uses 6B Active Parameters to Beat Models 100x Its Size at Formal Proofs
Formal verification — the practice of mathematically proving that software or theorems are correct — has long been the domain of specialists willing to wrestle with arcane proof assistants. On March 16, 2026, Mistral AI dropped a model into that world that nobody expected: Leanstral, a 119B-parameter sparse mixture-of-experts model that activates only 6.5B parameters… Continue reading
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Mistral Small 4 Packs 119B Parameters Into 6B Active — and It Does Everything
One model to replace four. That’s the pitch behind Mistral Small 4, released on March 16 during NVIDIA GTC 2026. Where Mistral previously asked developers to pick between Mistral Small for instructions, Magistral for reasoning, Pixtral for vision, and Devstral for coding agents, Small 4 rolls all four capabilities into a single 119B-parameter Mixture-of-Experts architecture… Continue reading
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Karpathy US Job Market Visualizer Scored 342 Occupations for AI Exposure — Then Got Deleted Within Hours
Andrej Karpathy, OpenAI co-founder and former Tesla AI director, published an interactive visualization on March 15 that scored every major U.S. occupation for AI exposure. Within hours, it went viral. Elon Musk amplified it with a bold claim: “All jobs will be optional.” Fortune ran a feature story. Hacker News lit up with 363 points… Continue reading
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RYS-XLarge (LLM Neuroanatomy): How Copying 7 Layers — With Zero Training — Topped the HuggingFace Leaderboard
A developer in his basement, two RTX 4090 gaming GPUs, and zero gradient descent. That’s what it took to claim the #1 spot on the HuggingFace Open LLM Leaderboard. No fine-tuning, no new data, no expensive compute cluster. David Noel Ng simply copied seven middle layers from an existing 72B model, pasted them back in,… Continue reading
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Karpathy Built a Full ChatGPT Clone in 8,000 Lines of Code — Nanochat Hits 47K Stars
Andrej Karpathy has a habit of making complex things feel approachable. His nanoGPT project showed developers how pretraining works. His YouTube lectures became unofficial grad school for thousands. Now, with Nanochat, he’s taken the next logical step: a complete, end-to-end ChatGPT pipeline — tokenization, pretraining, finetuning, RLHF, inference, and a web UI — that you… Continue reading
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Microsoft BitNet: 100B Parameters on a Single CPU, 0.4 GB of Memory, Zero GPUs
The GPU shortage isn’t going away. Cloud inference costs keep climbing. And most developers still can’t run anything bigger than a 7B model on their own hardware without serious compromises. Microsoft’s BitNet — currently sitting at #2 on GitHub Trending with 29.4K stars — proposes a radical fix: shrink every model weight down to three… Continue reading
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NVIDIA Nemotron 3 Super: 120B Parameters, 12B Active — the Math Behind the Fastest Open-Source Reasoning Model
NVIDIA dropped a bombshell at GTC on March 10: Nemotron 3 Super, a 120-billion-parameter open model that only activates 12 billion parameters at inference time. The result? Throughput numbers that make competing open models look sluggish — 2.2x faster than GPT-OSS-120B and 7.5x faster than Qwen3.5-122B — while scoring competitively on reasoning, coding, and long-context… Continue reading
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