SemIf

SemIf is an independent, MIT-licensed research project that brings semantic-if style decisions to open models. It reads probabilities for a caller's allowed choices directly from a local open model, avoiding autoregressive answer text and JSON parsing; its browser demo runs locally with WebGPU and no backend.

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At a Glance

2026Est.

AI Tools by SemIf

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SemIf

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Latest News

09/24/2026

Databricks published Running open-Jev in SQL on Databricks, demonstrating SemIf-OpenJev with governed data, serverless GPUs, and Model Serving.

databricks.com

09/22/2026

SemIf repository latest changes added Apple Silicon MPS scoring, a corrected Qwen3.8-27B EXL3 bridge, and per-workload temperature calibration.

github.com

09/21/2026

The project documented and added a llama.cpp CPU backend for direct, serial, and shared scoring from local GGUF checkpoints.

github.com

09/18/2026

OpenJev was released/rebranded as SemIf; the browser demo gained MiniCPM5 2B and Qwen3.5 4B and the Unsloppify interface switch.

github.com

Products & Services

SemIf / OpenJev decision scorer

2026-09-18

MIT-licensed open-source implementation of a Jev-like request/response pattern. It uses an open model, primarily Qwen3.5-4B, to score typed options directly from next-token probabilities rather than generating answer text or JSON.

SemIf browser demo

2026-09-18

A browser-only WebGPU application at openjev.com that downloads and runs Qwen3 0.6B, MiniCPM5 2B, or Qwen3.5 4B locally. It compares direct probability readout with autoregressive generation and sends no backend request for inference.

semif-score CLI and Python backends

2026-09-21

Local command-line scoring for direct, serial, and shared-state decisions, with CUDA/PyTorch, Apple Silicon MPS/MLX, CPU llama.cpp, and an EXL3 bridge documented in the repository.

Market Position

SemIf is positioned as an open, local, inspectable alternative to TypeSafe's closed Jev service: it reproduces the interface pattern but not Jev's model or training. Its differentiation is direct option-logit readout from an unmodified/open model, MIT licensing, browser WebGPU execution, and multiple self-hosted runtimes. The broader open-decision-model field includes Laya, Von, other OpenJev implementations, and fine-tuned or hosted alternatives; SemIf emphasizes transparency and reproducibility rather than claiming parity with Jev.

Leadership

Founders

Theodore Lee (TheoLeeCJ)

Independent developer and researcher. His public GitHub profile identifies him as Theodore Lee and shows SemIf-OpenJev as his primary/popular repository; the project describes itself as an independent project running on a 3090 at home.

Executive Team

Theodore Lee (TheoLeeCJ)

Original author / independent developer

Public GitHub profile identifies him as Theodore Lee; he owns the SemIf-OpenJev repository and is credited as its principal author.

Founding Story

SemIf began as OpenJev after interest in TypeSafe's Jev interface pattern. The project was started to show that small semantic decisions—routing, retrying, classification, and evidence checks—can read typed option probabilities from open models instead of generating prose that software must parse back into an if statement. It explicitly does not claim to reproduce Jev's undisclosed model or training and is not affiliated with TypeSafe.

Business Model

Revenue Model

The core project is free and open source under the MIT license; the public browser demo has no waitlist and runs models locally. A third-party Coda pack used a hosted SemIf copy through LangSmith Gateway and stated that hosting was free through September 28, 2026.

Target Markets

Industries & Segments

Developers building local or self-hosted AI decision workflows
AI/ML researchers benchmarking direct-logit semantic decisions
Operations, support, and workflow-automation teams
Organizations needing browser-local or governed-data classification experiments
Coda users using formulas for routing, triage, scoring, and classification

Use Cases

Support and operations routing
Ticket triage and intent classification
Policy and eligibility checks
Retry or workflow branching
Evidence-support and yes/no decisions
Rating and scoring text against an ordered rubric

Notable Customers

Agile OpenJev Coda pack users
Databricks users following the open-Jev SQL tutorial

Quick Facts

Founded

2026

History & Milestones

2026-09-16

The repository records the initial OpenJev Phase 1 and browser-demo work, including a browser-only WebGPU comparison and reproducibility materials.

2026-09-18

OpenJev was released/rebranded as SemIf; the project added MiniCPM5 2B and Qwen3.5 4B to the browser demo and added the Unsloppify conventional-interface switch.

2026-09-19 to 2026-09-21

The project added an EXL3 bridge, Apple Silicon support via PyTorch/MPS and optional MLX, a llama.cpp CPU backend, and associated reproducibility evidence.

2026-09-22

Latest-changes notes report MPS scoring, a corrected Qwen3.8-27B EXL3 bridge, and per-workload temperature calibration.

2026-09-24

Databricks published a tutorial showing how to run open-Jev models such as SemIf-OpenJev in SQL over governed Databricks data with serverless GPUs and Model Serving.

Key Capabilities

Direct typed option probabilities without generating an answer sentence

No JSON repair or decoding loop for direct scoring

Choice, yes/no (noul), rating, and batched multi-question decision patterns

Shared-state and prefix-reuse modes for evaluating multiple criteria over the same state

Browser-local WebGPU execution with model caching and no inference backend

CUDA, CPU llama.cpp, Apple Silicon MPS/MLX, and EXL3 execution paths

Integrations & Partnerships

Platform Integrations

WebGPU-capable browsers at openjev.com
Python 3.10+ local environments via the semif-score CLI
NVIDIA CUDA/PyTorch
Apple Silicon through PyTorch/MPS and optional MLX
CPU through llama.cpp and local GGUF checkpoints
EXL3 quantized-model bridge
Hugging Face model artifacts including Qwen3 0.6B, MiniCPM5 2B, and Qwen3.5 4B
Coda through the Agile OpenJev pack and LangSmith Gateway

Key Partnerships

LangChain/LangSmith Gateway hosted a copy of SemIf for the Agile OpenJev Coda pack, according to the pack documentation.

Databricks published a technical tutorial demonstrating SemIf-OpenJev running in SQL with serverless GPUs and managed Model Serving; the article is an integration example rather than evidence of an announced commercial partnership.

Connect

Website

openjev.com/

AI Topics

SemIf focuses on these topics:

Local Inference(1)

AI Decision Models(1)

LLM Evaluations(1)

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