开源 agent 运行时清单把 Jev 列为语义决策层
6 个仓库拼出 185 token/s 的流式 agent 栈，Jev 负责 Choice/Score 类型化决策，号称消除路由幻觉、减少 84% token 消耗。
Holy sh*t, someone just open-sourced the complete streaming runtime stack for GPT-6.1 Sol 6 open-source GitHub repositories with 124K+ combined stars to run 185 tokens/sec autonomous agents without turn-based latency. Speculative decoders. AST compilers. MicroVM sandboxes. Semantic routers. Sparse KV-cache. Deterministic rollbacks. 01 vLLM - continuous speculative lookahead decoding engine sustaining 185+ tokens/sec across multi-hop reasoning branches ▸ http://github.com/vllm-project/vllm 02 Jev - typed semantic decision primitives (Choice, Score, Noul) eliminating routing hallucinations and cutting 84% of token drag ▸ http://github.com/jev-org/jev 03 ast-grep - structural syntax compiler for instant in-memory AST modifications without JSON serialization taxes ▸ http://github.com/ast-grep/ast-grep 04 Firecracker - ephemeral microVM sandboxes delivering isolated tool execution with sub-4ms state resets on invariant failures ▸ http://github.com/firecracker-microvm/firecracker 05 Dagger - programmable container engine executing deterministic test verification pipelines without host pollution ▸ http://github.com/dagger/dagger 06 OpenTelemetry C++ - sub-millisecond distributed telemetry auditing trajectory drift across concurrent speculative trees ▸ http://github.com/open-telemetry/opentelemetry-cpp the loop: AST slice ingestion → 5-hop speculative decode → zero-JSON memory compilation → sub-8ms entropy check → Firecracker sandbox diff → pytest verification → commit state Models predict next tokens. Deterministic runtimes build defensible software companies. Save this, then build a high-frequency agent runtime for your business ⭣
这条是引用着另一条写的：x.com/damkina7/status/2102860956724335039