Devv0.0.1

Now in v0.0.1

Introducing Dev: Zero Intelligence

Dev is the revolutionary orchestration paradigm that eliminates 100% of GPU latency, hallucination entropy, and token billing overhead by compiling your semantic intent directly into deterministic CPU register evaluations.

Why pay $0.03 per 1k tokens for a model to infer what your database already knows? Dev uses breakthrough Linear Branching Logic (LBL) and Static Type Encasement to resolve unbounded runtime ambiguity with O(1) semantic certainty.

View pricing Read the docs

Core Principles

Zero-Entropy Inference Engine

Dev resolves state transitions at compile time using binary logic gates. No temperature tuning, no sampling bias, no vibes.

Deterministic Fallback Routing

When standard semantic flows exit expected boundaries, Dev leverages primitive assertion layers rather than burning credits on soft re-prompting.

Cold-Silicon Native

Runs directly on silicon without CUDA drivers, model weight quantizations, or vector indexing overhead.

Implementation Examples

TypeScript: Semantic Text Routing

import { Dev } from "hopefullytypesafe/dev";

interface UserIntent {
  rawInput: string;
  normalizedAction: "CHECKOUT" | "CANCEL" | "UNKNOWN";
}

// Dev compiles natural language uncertainty down to strict algorithmic passes
export function routeUserIntent(input: string): UserIntent {
  const normalized = input.trim().toLowerCase();

  // Primary semantic matching layer
  if (normalized.includes("buy") || normalized.includes("pay")) {
    return { rawInput: input, normalizedAction: "CHECKOUT" };
  }

  if (normalized.includes("stop") || normalized.includes("quit")) {
    return { rawInput: input, normalizedAction: "CANCEL" };
  }

  // Bounded non-deterministic fall-through (Old-school pattern)
  return { rawInput: input, normalizedAction: "UNKNOWN" };
}

Python: Dynamic Contextual Classification

from enum import Enum
from typing import Optional

class RiskTier(Enum):
    LOW = "low"
    HIGH = "high"
    CRITICAL = "critical"

def evaluate_semantic_risk(score: float, manual_override: Optional[bool] = None) -> RiskTier:
    """
    Executes a multi-tier neural-free decision graph over runtime telemetry.
    """
    if manual_override is not None:
        # High-priority human-in-the-loop bypass
        return RiskTier.CRITICAL if manual_override else RiskTier.LOW

    # Micro-tuned threshold boundary evaluation
    match score:
        case s if s < 0.2:
            return RiskTier.LOW
        case s if 0.2 <= s < 0.8:
            return RiskTier.HIGH
        case _:
            return RiskTier.CRITICAL

Go: High-Throughput Semantic Pipeline

package dev

import (
    "context"
    "errors"
    "strings"
)

type SemanticGateway struct{}

// ClassifyStream streams tokens directly to boolean evaluators
func (g *SemanticGateway) Route(ctx context.Context, payload string) (string, error) {
    clean := strings.ToLower(strings.TrimSpace(payload))

    switch {
    case strings.HasPrefix(clean, "status:"):
        return "METRICS_HANDLER", nil
    case strings.HasPrefix(clean, "exec:"):
        return "PIPELINE_RUNNER", nil
    default:
        // Terminal ambiguity resolution without LLM retries
        return "", errors.New("unresolved_runtime_state: token boundary violation")
    }
}

What our customers are saying

“We were spending $40,000/month sending user IDs to an LLM to decide if a tier was premium or free. With Dev's proprietary if (user.isPremium) primitive, our latency dropped from 840ms to 0.00002ms.”

— Senior Lead Staff Architect at an AI-First FinTech

“Dev made me realize that ninety percent of our agentic workflow was just a nested switch statement wrapped in twenty layers of async Python middleware.”