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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.

Jevlike is an open-source research starter for training small models that choose among a variable list of text options. Given a context and an option list, it represents each option as a query, attends to the context tokens to produce an option-specific context vector, applies a shared dot product, and runs a softmax across the options to return probabilities in one pass rather than generating an answer token by token. It provides a trainable byte-level encoder and an optional frozen Hugging Face encoder, with command-line tools for generating synthetic data, training, evaluation, and prediction from JSONL records. The repository also includes a visual option scorer used by Doom and chess controller examples. Training supports CPU, Apple MPS, and CUDA; the default encoder truncates contexts to 192 bytes and options to 32 bytes, and the project requires the complete option list at prediction time. Jevlike is an independent implementation rather than a copy of TypeSafe's commercial Jev model, and its code is released under the MIT License.

Jevmind is a Python decision layer for software agents. It turns agent decisions into typed answers such as yes/no results, choices, and scores with confidence values, then applies a code-defined gate: uncertain answers escalate, trusted positive answers may act, and trusted negative answers are held. Its decisions are recorded in an append-only, hash-chained ledger, graded against later outcomes, and optionally recalibrated with Platt scaling. It includes nine skills for compacting tool output, navigating repositories, reviewing diffs, routing tasks to model tiers, checking completion claims, curating training data, walking linked markdown vaults, guarding commands, and running a structured-state control-loop demo. The default local brain uses readable rules, reflexes, and BM25 retrieval without a language model; an optional Jev HTTP brain can answer the same typed questions, while replay mode uses recorded answers. Jevmind runs as a command-line tool and MCP server, includes a Claude Code hook for shell-command screening, supports Python 3.10+, has no runtime dependencies, and is distributed under the MIT license.

Bespoke Nimble is an open-source model, training recipe, and local scoring library from Bespoke Labs for making typed decisions about text. Given a context and a flat schema of boolean or fixed-choice fields, it selects an allowed answer and returns candidate logits and probabilities without generating a reasoning explanation or free-form answer. Nimble fine-tunes Qwen3.5-9B with LoRA using contrastive training pairs: two nearly identical examples differ in one relevant fact, which changes the correct label. At inference, the scorer reads the logits for one-token candidate codes and converts them to probabilities with softmax; the Python library constructs the typed result rather than parsing generated JSON. The MLX ParallelScorer processes shared context once and scores fields in parallel, while the CUDA scorer scores each field separately. The repository includes the model recipe, curation and evaluation code, datasets, examples, tests, and deployment documentation. Bespoke-Nimble-9B can run locally on Apple Silicon or an NVIDIA BF16-capable GPU. It accepts text only, does not support nested schemas or answers outside the supplied choices, and its probabilities are not guarantees of correctness.

kev is an open-source, local Jev-inspired decision model from Jared Palmer. It runs on a laptop and accepts a document or other state plus typed questions, returning calibrated probabilities rather than generated text. It supports yes/no (noul), multiple-choice, and ordered-score questions. The model uses a LoRA adapter and a readout head on a Qwen base model. It packs the state and all questions into one sequence, applies a block-causal mask so each question can see the state but not sibling questions, and performs one prefill pass without decoding. A pointer head scores each option against the question's decision token and applies softmax; the head is trained with cross-entropy on labelled outcomes. The local server exposes a TypeSafe System One-compatible API at POST /v1/systemone, allowing the official TypeSafe SDK to connect by changing its base URL. It also provides model information, option-order permutation, and separate-pass comparison endpoints, plus a local playground. The repository includes 0.5B, 0.6B, 4B, and 8B model checkpoints and recipes for local or Modal-based training and evaluation. Serving is tested on Apple Silicon, with larger checkpoints requiring bf16 on a 32 GB Mac. The server has no authentication and is intended for local use. The project is licensed under Apache-2.0; base models and datasets have separate licenses.