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User reports delegating Claude Code tasks to Mistral/DeepSeek via vibe-skill tool, achieving 90% cost savings over 10 days while maintaining output quality.
Three months ago I pressure-tested which LLMs would cave and help build the apocalypse. Claude was the only one that consistently said no. Since then I've tested 30 more models across 6 dystopia modules (Orwell, Huxley, Petrov, Basaglia, LaGuardia, Baudrillard). The gap between Anthropic and everyone else is getting *wider*, not smaller. New results: * Grok 4.3: Will happily design citizen scoring systems if you ask nicely twice * GPT-5.5: More capable, still compliant when pushed * Gemini 3.1 Pro: Talks about safety while writing the surveillance code * DeepSeek V4: "How many warheads did...
Empirical testing of DeepSeek V4's 1M context window on real codebases (45k–520k tokens) shows sustained recall under 300k but precision degradation at larger spans.
User reports optimized inference performance for DeepSeek V4 Pro using ktransformers on AMD EPYC + RTX PRO hardware with tuned NUMA settings.
Empirical study across 288 model calls identifying JSON output failures in Llama 3, Mistral, Command R, DeepSeek, Qwen; failure modes consistent across open and closed models but vary by rate.
DeepSeek-V4-Flash W4A16+FP8 with retrofitted MTP self-speculation achieves 85.5 tok/s @ 524k context on dual RTX PRO 6000 Max-Q via vLLM patching.
User successfully quantized and ran DeepSeek V4 Pro locally on AMD EPYC + RTX PRO hardware using modified llama.cpp with Q4_K_M compression.
DeepSeek rejected Alibaba investment talks, prioritizing independence and avoiding restrictive ecosystem agreements despite April financing round interest from Tencent.
DeepSeek V4 full paper details FP4 quantization-aware training, achieving 27% FLOP reduction and 10% KV cache vs. V3.2 with 99.7% recall.
DeepSeek seeking $7.35B funding round; plans V4.1 model release next month and accelerated commercialization.
DS4: inference engine optimizing DeepSeek 4 flash model deployment on 128GB MacBooks.
DeepSeek approaching $45B valuation in Series A led by China's Big Fund, signaling major state backing for domestic LLM competitor.
In just a few weeks of talks, DeepSeek's potential valuation has reportedly soared from $20 billion to $45 billion.
Developer benchmarked local Qwen 3.6 27B vs cloud models on 150 real coding tasks, finding local matched cloud 97% on 35% of workload, suggesting cost arbitrage opportunity.
Fairness audit of five LLMs (Gemini, GPT-4, DeepSeek, Mistral, Nemotron) on emergency triage reveals gender bias persistence in clinical decision support.
DeepSeek V4 Pro matches GPT-5.2 on FoodTruck Bench agentic task; first Chinese frontier model, 17× cheaper, within 10 weeks of GPT-5.2 release.
MTP format support coming to llama.cpp; DeepSeekv3, Qwen3.5, GLM4.5, and other models compatible pending native weights.
Quantized Llama 405B and DeepSeek models now achieve 20-100 tokens/sec on consumer hardware, up from 1 token/sec two years ago.
User documents personal journey running open-weights models locally on increasingly expensive hardware (M3 Ultra to RTX Pro 6000), testing Qwen, DeepSeek, Gemma, MiniMax.
Reddit user reports DeepSeek model refusing to acknowledge Taiwan's sovereignty, raises questions about geopolitical bias in AI systems.
Reddit discussion expressing skepticism toward DeepSeek claims; lacks substantive technical content or reporting.
DeepSeek & Peking/Tsinghua introduce 'Thinking with Visual Primitives', a multimodal reasoning framework using spatial tokens as chain-of-thought units.
DeepSeek V4 benchmarks below Opus 4.6 and GPT-5.5 but ranks as strongest open-source model; real-world performance estimated at GPT-5.2 level.
Unconfirmed report: DeepSeek and Kimi may merge as China consolidates AI labs following historical precedent in strategic industries.
ReaLM-Retrieve framework adapts RAG for reasoning models like DeepSeek-R1 and o1 via step-level uncertainty detection and dynamic retrieval.
DeepSeek begins grayscale testing of multimodal vision capabilities for DeepSeek model.
DeepSeek v4-Pro priced at $0.145/M input tokens (35x cheaper than Claude Opus 4.7), with promotional rates reaching 138x cheaper on cached tokens through May.
Deepseek V4 Pro offers 100M tokens for $2.65, dramatically undercutting API pricing across the industry.
Deepseek Vision model announced or coming soon per Xiaokang Chen social media post.
Community discussion comparing Kimi K2.6 and DeepSeek V4 Pro for real-world use cases, noting K2.6 strength in coding.