Simo

SemIf is an independent open-source tool, formerly called OpenJev, for making semantic if-statements with open language models. It accepts unstructured state, runtime-defined questions or criteria, and typed options, then reads the model’s option logits directly to return conditional probabilities without sampling an answer sentence, parsing JSON, or running a decoding loop. It supports direct, serial, and shared-state execution, prefilling common state to evaluate multiple criteria in parallel. Results and workflows include option scores, calibration, replay, benchmarks, verification, prompt hashes, model revisions, timings, and row-level outputs. It supports CUDA and CPU execution, Apple Silicon through MLX or PyTorch/MPS, and local GGUF models through llama.cpp. It includes an interactive browser WebGPU demo, reproducible fixtures, benchmark runners, and quality evaluations, and is released under the MIT License. SemIf is not affiliated with TypeSafe or Jev.

Simile is a company and simulation platform that builds behavioral foundation models and synthetic "digital twins" of people to simulate populations and human decision‑making. The platform accepts scenario specifications, surveys and experimental designs as inputs and produces simulated populations, individual-level decision trajectories and aggregated outcome statistics that can be queried for policy analysis, product testing, forecasting and similar use cases. Simile is published from the vendor website and presents itself as a simulation platform for human behavior.

imajev is an open, locally runnable multimodal typed-decision model and repository maintained by Mohit Garg. It accepts up to two images, application state, and typed questions with user-defined answer options, then returns probabilities for each option together with an unknown probability, confidence, and abstention status. Example tasks include checking a product listing against its photo, comparing a returned item with a reference image, routing support tickets, and evaluating records against policies. Rather than generating an answer, imajev binds each option to a decision code and reads the model's logits at a decision position through a float32 output head. Its vision tower is frozen while LoRA adapters are trained on the language-model projections; the released 2B, 4B, and 9B variants are based on Qwen3.5 models. Unknown is trained as a first-class outcome for missing, contradictory, or out-of-scope evidence, allowing applications to route low-confidence cases to a person. The repository provides a local server and API with MLX and PyTorch paths, calibration files, playground applications, training scripts, and an ImajevBench evaluation suite. The code, adapters, and stated base models are distributed under Apache-2.0. The README describes English-only operation and limits of up to two images, 32 KB of state, eight questions per request, and 254 options; it also cautions that calibration and testing on application-specific data are needed before setting automation thresholds.

decider is an open-source AI model family and Python package for one-pass typed decisions with calibrated probabilities. Instead of generating text, it reads a text or JSON state and a set of typed questions—Choice, Score, or Noul yes/no questions—and returns a probability distribution over each question's allowed answers from one forward pass. It renders each question as an answer slot, reads the model's hidden state at that slot, projects it onto the valid option-label tokens, and applies a fitted temperature; there is no decoding or output parsing. The package provides `decide()` and `system_one()` interfaces, an HTTP server with `/decide` and `/v1/systemone` endpoints, and GGUF loading through llama.cpp. Models are available in sizes intended for CPUs and laptops as well as CUDA and Apple Silicon GPUs, and the repository includes training, calibration, serving, evaluation, and schema-caching components. The project is an independent reproduction of the System One model class, is not affiliated with TypeSafe AI, and is released under the Apache 2.0 license.