Ollaya

Ollama is a software platform for running and serving open models, including as a local model runtime for tasks such as resume evaluation, tagging, and summarization. Its website describes integrations with coding agents and other workflows, allowing users to launch tools such as Claude Code, Codex, OpenCode, and VS Code while switching models without changing the workflow. Local runs remain on the user's machine, while Ollama also offers hosted cloud models. The service states that prompts are not tracked or used for training by providers, and that its open-source software and open model support are intended to keep data private while automating work.

Laya is an open-source, Jev-compatible System-1 decision model package by Convai Innovations for running typed decision questions from Node.js and TypeScript through ONNX Runtime. It accepts a state such as a ticket, email, or JSON object and returns calibrated answers for choice questions with per-option probabilities, ordered score questions with a probability distribution and expected level, or yes/no statements with a probability of true. Questions in one call are batched into a single model run, and the package uses the same request and response shape as the Python reference implementation without requiring Python or PyTorch at runtime. The package downloads and caches ONNX model weights from Hugging Face by default, or can load a local ONNX bundle; it requires Node.js 20 or newer. The JavaScript package is MIT-licensed, while the Laya model weights are published under Apache 2.0.

ODS is a self-hosted local AI server stack developed by Osmantic for Linux, Windows with WSL2 and Docker Desktop, and Apple Silicon macOS. Its installers detect the host hardware, select an installable model, generate credentials, and launch a preconfigured stack containing local LLM inference, Open WebUI, a management dashboard, LiteLLM, embeddings, speech-to-text, text-to-speech, agents, n8n workflows, Qdrant-based RAG, search, ComfyUI image generation, and operational and privacy tools. Local mode is the default; cloud and hybrid modes can route through OpenAI, Anthropic, or Together APIs. The stack uses a bootstrap model so chat can begin while the hardware-selected full model downloads in the background. A manifest-based extension system discovers service folders and merges their Docker Compose definitions into the stack, while the `ods` CLI manages services, models, modes, configuration, presets, and lifecycle operations. The repository documents GPU and unified-memory paths for NVIDIA, AMD, Intel Arc, and Apple Silicon hardware, along with model selection, recovery, validation, and operator workflows. ODS is distributed as an Apache-2.0-licensed open-source repository and can be installed with shell or PowerShell installers. The repository identifies version 2.6.0 as its stable release and states that no cloud account or subscription is required for local operation.

OpenHuman is a local-first personal AI system from tinyhumansai that combines persistent memory, agent orchestration, and research tools. It stores a user's data as scored Markdown trees in SQLite on the local machine and mirrors the result to an editable Obsidian vault; its TokenJuice component compresses tool output before it reaches the language model. Its orchestration layer runs checkpointed, durable agent workflows and worker fleets on graphs, with triggers, approvals, steering, halting, and replay support. The system also provides web search, scraping, coding tools, a browser, native voice through in-process Whisper, model routing, messaging integrations, and support for provider keys or fully local Ollama models. The repository describes it as an early beta under active development.

LocalJev is a TypeScript service that provides a local Jev-compatible decision API for applications using TypeSafe's typed decision interface. It exposes a POST /v1/systemone endpoint and translates typed Jev questions and application state into classification prompts for an OpenAI-compatible DiffusionGemma inference server. The service validates and retries malformed JSON, normalizes probability vectors, calculates Jev-compatible choices, expected scores, and entropy-based confidence, and returns the standard Jev response shape. Its probabilities are model-generated or self-reported rather than read directly from model logits, so the repository advises evaluating calibration for the intended workload. It runs with Bun and can use oMLX as the local inference server; the default LocalJev address is http://127.0.0.1:8080.