Release Notes

You can now select, at install time, which modules you wish to have initially installed
Some modules (Coral, Yolov8) now allow you to download individual models at runtime via the dashboard.
A new generative AI module (Llama LLM Chatbot)
A standardised way to handle (in code) modules that run long processes such as generative AI
Debian support has been improved
Small UI improvements to the dashboard
Some simplification of the modulesettings files
The inclusion, in the source code, of template .NET and Python modules (both simple and long process demos)
Improvements to the Coral and ALPR modules (thanks to Seth and Mike)
Docker CUDA 12.2 image now includes cuDNN
Install script fixes
Added Object Segmentation to the YOLOv8 module

Dynamic Explorer UI: Each module now supplies its own UI for the explorer
More information returned by each module's response as standard
Support for sound audition modules in the explorer
Improvements to, and a more responsive module status on the dashboard
Updated module settings schema that includes module author and original project acknowledgement
A separate status update from each module that decouples the stats for a module. This just cleans things up a little on the backend
Installer fixes
Minor modulesettings.json schema update, which introduces the concept of model requirements.
Updated ALPR, OCR (thanks to Mike Lud) and Coral Object Detection (Thanks to Seth Price) modules
Improved Jetson support
Pre-installed modules in Docker can now be uninstalled / reinstalled

Mesh support Automatically offload inference work to other servers on your network based on inference speed. Zero config, and dashboard support to enable/disable.
CUDA detection fixed
Support for CUDA 10.2
Module self-test performed on installation
YOLOv8 module added
YOLOv5 .NET module fixes for GPU, and YOLOv5 3.1 GPU support fixed
Python package and .NET installation issues fixed
Better prompts for admin-only installs
Fixes for Python package installs
Issues installing .NET
More logging output to help diagnose issues
VC Redist hash error fixed
General bug fixes.
Breaking: modulesettings.json schema changed

A focus on improving the installation of modules at runtime. More error checks, faster re-install, better reporting, and manual fallbacks in situations where admin rights are needed
A revamped SDK that removes much (or all, in some cases) of the boilerplate code needed in install scripts
Fine grained support for different CUDA versions as well as systems such as Raspberry Pi, Orange Pi and Jetson
Support for CUDA 12.2
GPU support for PaddlePaddle (OCR and license plate readers benefit)
CUDA 12.2 Docker image
Lots of bug fixes in install scripts
UI tweaks
ALPR now using GPU in Windows
Corrections to Linux/macOS installers

An entirely new Windows installer offering more installation options and a smoother upgrade experience from here on.
New macOS and Ubuntu native installers, for x64 and arm64 (including Raspberry Pi)
A new installation SDK for making module installers far easier
Improved installation feedback and self-checks
Coral.AI support for Linux, macOS (version 11 and 12 only) and Windows

Improved Raspberry Pi support. A new, fast object detection module with support for the Coral.AI TPU, all within an Arm64 Docker image
All modules can now be installed / uninstalled (rather than having some modules fixed and uninstallble).
Installer is streamlined: Only the server is installed at installation time, and on first run we install Object Detection (Python and .NET) and Face Processing (which can be uninstalled).
Reworking of the Python module SDK. Modules are new child classes, not aggregators of our module runner.
Reworking of the modulesettings file to make it simpler and have less replication
Improved logging: quantity, quality, filtering and better information
Addition of 2 modules: ObjectDetectionTFLite for Object Detection on a on Raspberry Pi using Coral, and Cartoonise for some fun
Improvements to half-precision support checks on CUDA cards
Modules are now versioned and our module registry will now only show modules that fit your current server version.
Various bug fixes
Shared Python runtimes now in runtimes.
All modules moved from the AnalysisLayer folder to the modules folder
Tested on CUDA 12 (Note: ALPR and OCR do not run on CUDA 12)

Support for Apple Silicon for development mode
Native Windows installer
Runs as Windows Service
Run in a Docker Container
Installs and Builds using VSCode in Linux (Ubuntu), macOS and Windows, as well as Visual Studio on Windows
General optimisation of the download payload sizes

We started with a proof of concept on Windows 10+ only. Installs we via a simple BAT script, and the code has is full of exciting sharp edges. A simple dashboard and playground are included. Analysis is currently Python code only
Version checks are enabled to alert users to new versions
A new .NET implementation scene detection using the YOLO model to ensure the codebase is platform and tech stack agnostic
Blue Iris integration completed