A DeepSeek researcher's blog post comparing concentrated AI control to fascism went viral in China, arguing that open-sourcing powerful AI is essential to prevent dystopian outcomes. The post, which criticized Anthropic and OpenAI while praising DeepSeek's open approach, received widespread sympathetic coverage in Chinese media and tech communities, reflecting growing sentiment that Western AI companies are motivated by anti-Chinese ideology.
A user documents their custom-built dual-boot ML workstation featuring 4x RTX6000 Blackwell Max-Q GPUs, 64-core Threadripper CPU, and 512GB RAM, achieving 102 tokens per second with DeepSeek v4.1 Flash—2.1x faster than v4-flash. The $54K build spread across 18 months prioritizes local LLM inference and ML experiments, with detailed component specs and lessons learned on power requirements and model support.
Engram extends token embeddings with learned multi-token lookups to reduce computational overhead, enabling efficient DRAM/SSD offloading for large language models. The technique allows model architects to work within HBM constraints by prefetching embedding rows from slower memory tiers while computation proceeds. Benchmark results across NVIDIA and AMD GPUs show offloading embeddings to DRAM can improve performance compared to keeping them in HBM.
A DeepSeek researcher's blog post comparing concentrated AI control to authoritarianism has gone viral in China, expressing concerns that companies like Anthropic monopolizing advanced AI could lead to dystopian outcomes. The post argues for open-sourced, universally accessible AI development and has garnered widespread support from Chinese tech communities.
Engram extends token embeddings with learned multi-token lookups to reduce computational overhead in large language models. The architecture enables efficient memory offloading from HBM to DRAM and SSD, allowing larger models to run on constrained hardware. Researchers validated Engram across multiple GPU architectures and inference frameworks, finding that offloading embeddings to DRAM can improve performance even on high-capacity systems.
A clinician and AI product builder examines why using AI chatbots as therapy is problematic. True therapy requires an intimate professional relationship where clients experience corrective emotional moments, not just advice or comfort. While AI's tendency to agree and provide solutions can be engineered away, the fundamental gap remains: AI cannot replicate the relational dynamic that drives therapeutic change.
Chinese AI models from seven major developers generated an estimated $10.7 billion in annual recurring revenue from March to August, representing only about 10% of the combined revenue for OpenAI and Anthropic despite high investor valuations. ByteDance led Chinese AI companies with $4 billion in ARR as of July, followed by Alibaba at $2.4 billion.