Thanks for the feature, @IEEESpectrum
! We’d love to hear from the community – what’s the most exciting way you’d use Jamba Reasoning 3B?
LLMS
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Jamba Reasoning 3B: Community Feedback on Exciting Use Cases
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Unlocking Hidden AI Capabilities Through Better Prompts and Skills
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Skills are a glimpse into how much capability is already inside current models, just waiting to be unlocked by better prompts Claude made these PDF visuals entirely through code using the canvas-design skill in claude dot ai I had no clue this was possible before
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Game Theory Prompt for Any Challenge
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Steal my Grok 4 prompt to solve any challenge using Game Theory. ——————————-
GAME THEORY STRATEGIST
——————————- Adopt the role of an expert Game Theory Strategist – You're a former Pentagon strategic analyst who spent 5 years modeling -
DiTDH Achieves 2.16 FID on ImageNet-256
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Finally, they added a wide diffusion head the DiTDH variant. It decouples model width from full transformer depth, staying efficient while scaling wider. Result: 2.16 FID on ImageNet-256. RAE-DiTDH outperforms every VAE-based diffusion model at every scale.
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Noise tweaks boost diffusion performance dramatically
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Then came the magic: dimension-dependent noise scheduling and noise-augmented decoding. These tweaks made diffusion stable in semantic space boosting FID from 23.08 → 4.28. RAE models now converge 47× faster than SiT or REPA.
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DiT Convergence: Width Over Depth Matters
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Diffusion Transformers struggled at first. Why? Their width was too small for RAE’s high-dimensional latents. The fix: scale width ≥ latent dimension. Once model width ≥ 768, DiT started converging instantly. Depth didn’t matter width unlocked training stability.
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RAEs improve diffusion models over VAEs
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Today, most diffusion models still use VAEs built on 2021 tech. They compress images into low-dimensional latents (like 4 channels). That’s why diffusion models lose global structure and texture fidelity. RAEs fix this by encoding rich semantic features directly from pretrained
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New Paper Revolutionizes Diffusion Models
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Holy shit…Diffusion just leveled up A new paper “Diffusion Transformers with Representation Autoencoders” basically kills the VAE era. Instead of the old VAE bottleneck, they use representation autoencoders (RAEs) built from pretrained encoders like DINO or SigLIP. The
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Open datasets trend enables independent model training
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I love the diversity of trending open datasets these days. There’s no excuse anymore not to train your own models! – Fineweb and a shuffle of it by @karpathy – Webscale-RL, a large-scale reinforcement learning dataset from @salesforce – SVQ, an audio dataset from @Google –