Jev might genuinely be an “Internet moment” for AI.
TypeSafe reports up to 193x faster and 444x cheaper performance in tests with Claude Fable 5.1 and GPT-6 Astra.
@0xCodila just wrote a great 10-page article explaining what Jev is, how to use it, and where its 100x advantage comes from.
Here are the 10 steps:
1 → LLMs create. Agents act. Jev decides the next move.
2 → Turn agent forks into three primitives: Choice, Score and probability.
3 → Build with OpenAI, Anthropic or xAI first, then swap Jev in without rebuilding the graph.
4 → Start with shared state, parallel decisions, risk thresholds and an execution queue.
5 → Batch decisions instead of making them sequentially. In one test, 13 questions were 10x faster and 12.2x cheaper.
6 → Put Jev at bounded forks: agent, model, tool, browser action or human escalation.
7 → Benchmark the whole loop, not just individual model calls.
8 → Rank wide, read narrow: shortlist first, then spend compute on what matters.
9 → Reuse the same system: State → Questions → Action → Verify.
10 → Keep Jev out of math, writing and irreversible execution. Code computes, LLMs create, Jev decides.
The result:
A slow, expensive agent loop becomes a much faster decision system that can route, score and escalate in milliseconds.
Full breakdown below ↓