OpenDerm

Rerun is an open-source SDK and data layer for recording, querying, visualizing, transforming, and streaming multimodal physical-robotics data. It ingests multi-rate data such as images, point clouds, transforms, time series, joint states, tensors, and video from robot logs, human-data rigs, simulation, and web video, using formats including MCAP, RRD, and LeRobot. Its built-in viewer synchronizes and renders the data in real time for episode scrubbing, sensor comparison, and live computer-vision pipelines; the same data can be queried with dataframes or SQL and streamed directly into training. Rerun is built in Rust on column-chunk storage designed for multi-rate physical data, with Python, Rust, and C++ SDKs and a separate Viewer binary for network streaming and RRD files. The repository describes the project as being in active development, with an evolving API and possible breaking changes.

Roomform is an open-source pipeline that converts registered indoor point-cloud scans—including laser scans, ARKit exports, and RGB-D reconstructions—into structured, editable scenes. It infers walls and openings, classifies oriented object boxes, associates segmented points with objects, and produces evidence-gated object meshes; the pipeline writes scene JSON, GLB output, per-object meshes, and intermediate grids while streaming NDJSON progress, including a boundary-only partial scene during processing. The package provides point-cloud processing and object-lifting backends, including Point Transformer V3 with local clustering, SpatialLM, and UniDet3D, with optional remote reconstruction through SAM 3D Objects via fal.ai. It includes a read-only debug viewer and an editor for moving objects and saving scenes back to the native format. The code is licensed under Apache 2.0; trained weights and synthetic data, when published, are listed as CC BY-NC 4.0.

Open Science is an open-source, local-first, model-agnostic AI research workbench developed by AIPOCH for scientists and researchers. It runs on macOS, Windows, and Linux and supports computational and data-intensive research in fields including machine learning, statistics, life sciences, chemistry, materials science, physics, and environmental science. Users create projects and sessions, describe research goals in plain language, attach source files, select a model and approval mode, and inspect the agent’s tool activity. Scientific AI agents can read files, search the web, execute Python and R code, query scientific data sources, and produce reports, tables, figures, and other research artifacts. The workbench supports literature review, hypothesis development, code execution, data analysis, simulation, visualization, and related reproducible research workflows. Its first-run setup checks the environment, configures model-provider credentials, optionally prepares Python and R runtimes, and prepares an agent runtime such as Claude Code, OpenCode, or Codex; app-managed runtimes can be installed without Node.js, npm, or administrator privileges. Projects keep sessions, uploads, generated files, and preview state together. Conversations record agent answers and the commands, file reads, edits, searches, and connector calls behind them. Generated artifacts are stored as immutable, checksummed versions; their Provenance view can show producer code and execution history, referenced inputs, the observed environment inventory, the producing conversation branch, and version-scoped reviewer findings, marking unavailable evidence as unavailable rather than guessing.

OpenResearch is a local-first workspace for research agents and autoresearch, available as a desktop application and a macOS/Linux CLI. It turns Claude Code, Codex, or OpenCode into agents that can review literature, develop hypotheses, run experiments, and produce research artifacts. The workspace assigns independent agent sessions and isolated Git worktrees to parallel research directions. It tracks experiment variants in a Git-native experiment tree, archives each run against its recorded commit, and keeps logs, diffs, files, results, and artifacts tied to the work that produced them. Its autoresearch loop can propose an idea, modify code, launch an experiment, inspect evidence, and choose the next direction. The same committed source snapshot can run locally or through SSH, Slurm, Kubernetes, Ray, Hugging Face Jobs, Modal, Tinker, or managed OpenResearch compute. By default, projects and run data remain on the user's machine in a local SQLite store, with a browser dashboard served on localhost. An OpenResearch account is used for service-owned capabilities such as organizations and managed compute. The CLI also installs an OpenResearch skill into supported coding agents and provides commands for projects, runs, logs, experiments, discovery, and paper lookup.

ABot-Recon is a streaming 3D-reconstruction model and inference tool for reconstructing long video streams from video frames alone. At each step, it caches key-value features from the preceding 11 frames, predicts a point map in the current camera coordinate system, estimates the adjacent relative camera pose, and composes those poses sequentially into a global trajectory and point cloud without persistent learned long-range memory. It can output camera poses, relative poses, local and world point maps, colors, confidence maps, and run metadata. An optional loop-closure backend retrieves candidate revisited-frame pairs using DINOv2-SALAD descriptors, predicts relative-pose constraints with ABot-Recon, and refines the trajectory through sparse pose-graph optimization. The repository provides Python and command-line interfaces, a public model checkpoint, a demo, evaluation code, and visualization utilities. The released configuration targets Linux, Python 3.10 or later, PyTorch 2.5.1, and CUDA 12.1; source code is licensed under Apache License 2.0, while the model weights have separate terms in MODEL_LICENSE.md.