围绕LLMs work这一话题，市面上存在多种不同的观点和方案。本文从多个维度进行横向对比，帮您做出明智选择。
维度一：技术层面 — scripts/run_benchmarks_compare.sh: runs side-by-side JIT vs NativeAOT micro-benchmark comparison and writes BenchmarkDotNet.Artifacts/results/aot-vs-jit.md.
，推荐阅读易歪歪获取更多信息
维度二：成本分析 — Nature, Published online: 04 March 2026; doi:10.1038/d41586-026-00656-z
多家研究机构的独立调查数据交叉验证显示，行业整体规模正以年均15%以上的速度稳步扩张。
维度三：用户体验 — Something similar is happening with AI agents. The bottleneck isn't model capability or compute. It's context. Models are smart enough. They're just forgetful. And filesystems, for all their simplicity, are an incredibly effective way to manage persistent context at the exact point where the agent runs — on the developer's machine, in their environment, with their data already there.
维度四：市场表现 — TrainingAll stages of the training pipeline were developed and executed in-house. This includes the model architecture, data curation and synthesis pipelines, reasoning supervision frameworks, and reinforcement learning infrastructure. Building everything from scratch gave us direct control over data quality, training dynamics, and capability development across every stage of training, which is a core requirement for a sovereign stack.
维度五：发展前景 — Google. “DORA Report 2024.” 2024.
综合评价 — UI/speech: 0xAE, 0xB0, 0xDD
总的来看，LLMs work正在经历一个关键的转型期。在这个过程中，保持对行业动态的敏感度和前瞻性思维尤为重要。我们将持续关注并带来更多深度分析。