Together AI’s OSCAR: 2-Bit KV Cache Quantization for Long Context
Together AI’s OSCAR system uses attention-aware rotation to compress KV caches to 2-bit, significantly expanding context windows on consumer GPUs.
180 stories in the archive
Together AI’s OSCAR system uses attention-aware rotation to compress KV caches to 2-bit, significantly expanding context windows on consumer GPUs.
Stop relying on intuition and start using observability pipelines like Langfuse to bring engineering rigor to local LLM prompt management and evaluation.
A ByteDance study suggests that training multimodal models via question-answering outperforms transcription-heavy methods for analyzing long, complex documents.
An analysis of how robotic kitchen technology in San Francisco nonprofits risks replacing human empathy and community connection with sterile efficiency.
An analysis of Qwen3.7-Max’s autonomous coding capabilities and the growing divide between proprietary APIs and open-weight AI models.
An analysis of recurrent depth and Sparse MoE as a way to trade memory efficiency for gradient stability in transformer architectures.
Explore why smaller, specialized models offer better reliability, lower latency, and higher ROI than massive general-purpose AI models for enterprise tasks.
Microsoft’s new Fara1.5 family of browser agents outperforms competitors in computer-use tasks, offering a high-performance 27B model for local deployment.
A critical look at the Qwen3.7-Max reasoning agent, exploring the trade-offs between its massive context window and local deployment feasibility.
An exploration of how AI-driven content volume replaces artistic skill with an abundance of adequacy, shifting value toward human-certified provenance.