xpost·Tools and apps
The 10-step roadmap, summarised
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 ↓
Charly Wargnier ♨️
@DataChaz
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 ↓
Evaluate input state and return typed decision for The 10-step roadmap, summarised.
Jev sits between the incoming context and the next system action. Rather than generating lengthy, slow natural language that requires brittle regex parsing, Jev returns non-autoregressive, calibrated probabilities that downstream code can immediately execute.
01actionPrimary action
02confidenceConfidence score
03fallbackFallback route