Kepler

Modelpeers is a tracker for AI agents, models, and companies. It records sourced releases, price and limit changes, retirements, breaking changes, incidents, security issues, quality updates, and benchmarks, and provides pricing, usage-limit, and head-to-head comparisons. Users can follow selected entities to receive weekly alerts; the service also offers an RSS feed.

Kairos is an open-source, real-time fraud and risk decisioning platform from Finic. It evaluates sign-ups, logins, orders, payments, payouts and other events, returning an approve, step-up, review or block decision with a risk score and the signals behind it. Its workflows can query decision models, score events with logistic regression or gradient-boosted trees, branch on results, call webhooks, open review cases and make later decisions. The Grayson decision model answers plain-language questions about structured and unstructured data; its answers can be combined with transaction fields as features for traditional machine-learning models. Kairos also supports experiments that train and compare model variants on historical data before publishing a winning workflow version. The platform provides an MCP server for Claude Code, Cursor, Codex and other agents, enabling agents to inspect decisions, build workflows and run experiments with actions recorded under an agent key. It includes TypeScript and Python SDKs, a CLI, an HTTP API, a web console and Docker-based self-hosting, with Postgres managed by the server. The repository is licensed under Apache License 2.0.

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.

Kronos is an open-source family of decoder-only foundation models for financial-market K-line sequences, developed by the repository authors and distributed with pretrained models through the Hugging Face Hub. A specialized tokenizer quantizes multidimensional OHLCV market data into hierarchical discrete tokens, which an autoregressive Transformer models for forecasting and other quantitative tasks. The Python predictor accepts historical market data and timestamps, applies preprocessing and normalization, generates probabilistic forecasts for future open, high, low, close, volume, and amount values, and reverses the normalization. It supports single-series and GPU-parallel batch prediction, with configurable sampling temperature, nucleus-sampling probability, and forecast-path count. The repository also provides a Qlib-based example pipeline for data preparation, tokenizer and predictor fine-tuning, and simple top-K backtesting; it explicitly describes this example as a demonstration rather than a production-ready trading system. The project is licensed under the MIT License.

The Vault is Bridgewater Associates’ internal data platform for indexing, organizing, and serving more than 100 million unstructured documents from external sources, as well as the firm’s memos and meeting transcripts. The material is cleaned, permissioned, and tagged to Bridgewater’s internal ontology for use by human investors and software agents. Its search system is designed to support both investor queries and agent workflows, with fast filtering across the document collection. Bridgewater developed a hand-built benchmark to improve search precision at the top 10 results from about 60% to more than 90%. The platform also supports analyst research, forecasts, signals, and proactive agents that surface relevant information to investors.