Thursday, 16 April 2026
This dated roundup collects the most interesting AI and technology developments found for Thursday, 16 April 2026.
Research & Products
anyone else notice labs are getting more secretive about their best models?
something shifted recently and i can't stop thinking about it. the trend used to be: new model drops, blog post goes up, everyone gets access on day one. now it feels like the most capable stuff is quietly going behind walls with "restricted access" or gated research programs, while the public-facing releases are... fine, but clearly not the frontier. google dropped gemma 4 open-weight and it's genuinely good — MoE architecture, strong reasoning, apache license. meta's doing multimodal reasonin
Read moreMozilla Announces "Thunderbolt" As An Open-Source, Enterprise AI Client
Reddit r/LocalLLaMA — 4 upvotes, 3 comments
Read moreQwen3.6-35B-A3B released!
Meet Qwen3.6-35B-A3B：Now Open-Source！🚀🚀 A sparse MoE model, 35B total params, 3B active. Apache 2.0 license. \- Agentic coding on par with models 10x its active size \- Strong multimodal perception and reasoning ability \- Multimodal thinking + non-thinking modes Efficient. Powerful. Versatile. Blog：https://qwen.ai/blog?id=qwen3.6-35b-a3b Qwen Studio：chat.qwen.ai HuggingFace：https://huggingface.co/Qwen/Qwen3.6-35B-A3B ModelScope：https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B
Read moreA new transformer variant has been created to facilitate more efficient model training in distributed settings. 128x compression with no significant loss in convergence rates, increases in memory, or compute overhead
Macrocosmos has released a paper on ResBM (Residual Bottleneck Models), a new transformer-based architecture designed for low-bandwidth pipeline-parallel training. https://arxiv.org/abs/2604.11947 ResBM introduces a residual encoder-decoder bottleneck across pipeline boundaries, with the goal of reducing inter-stage communication while preserving an explicit low-rank identity path. The paper reports SOTA 128× activation compression without significant loss i
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