Release 3.0
Version: 3.0.0
Target Date: Jan 20th, 2026
No. | Work stream | Any updates in this release? | Architecture document | Installation document | Test document | Release note | One pager | CD logs | Security Certification Provide link to Vuls, Lynis, and Kube-Hunter logs below. Pass/Fail Criteria: Steps To Implement Security Scan Requirements Exception requests should be filed at: | Upstream Review | Presentation documents | Date ready for TSC review | TSC Review Date (Column filled in by TSC) |
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1 | YoMo |
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2 | Edge Data Agent |
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3 | SPEAR | New Architecture and Switched to Rust | https://github.com/lfedgeai/SPEAR/blob/main/docs/project-architecture-overview-en.md | https://github.com/lfedgeai/SPEAR/actions/runs/21236242039/job/61104793067 |
| https://github.com/lfedgeai/SPEAR/actions/runs/21236242039/job/61104793067 |
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| https://docs.google.com/presentation/d/1YKiTPYOx04MIt9lG0Ot-GWemLuNQ1Ai_M4MSvfBpkwc/edit?usp=sharing |
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4 | Work stream 4: Federated Learning (with EdgeLake) |
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5 | Edge Gateway | Shifu Python SDK to simplify digital twin creation of Edge devices |
| New release: Provide wrapper functions for k8s to facilitate device shifu creation | https://github.com/Edgenesis/shifu_sdk/blob/main/shifu-sdk-python/README.md | https://github.com/Edgenesis/shifu_sdk/actions/runs/18735864754 |
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6 |
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6.1 | Coze AI Agents |
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6.2 | Robotics | Autonomous Agents Networks (AAN) Blueprint passed incubation stage and initial documentation us published. AI components of the blueprint, including small multimodal models that run on small form-factor hardware, can be applied to Workstream 6 robotics projects, as well as other InifiEdge AI workstreams.
Next steps include OpenVINO and Mediashark combined with Kaldi and Whisper to build ASR demos running on quad-core Atom processors in small server form-factors with pico ITX and mini ITX motherboards | AAN Blueprint architecture and data flow diagrams
https://github.com/signalogic/Autonomous-Agents-Networks/tree/main/diagrams |
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7 |
| https://github.com/lfedgeai/AegisSovereignAI/tree/main/hybrid-cloud-poc | https://github.com/lfedgeai/AegisSovereignAI/blob/main/hybrid-cloud-poc/ci_test_runner.py |
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| https://github.com/lfedgeai/AegisSovereignAI/actions/runs/21190581814 (CI fully works, this is an example link) |
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8 | AIOps | Anomaly detection research and comparison run based on OTEL Demo app collected data such as ground truth, train, test and eval data sets. Extendable framework to compare anomaly detection approaches like algorithms and models. In this release we compare IsolationForest with other anomaly detection algorithms showcasing TP (True Positives), FP (False Positives), FN (False Negatives), ROC-AUC (Receiver Operating Characteristics-Area Under the Curve)and F1 score.
| https://github.com/lfedgeai/AIOps/tree/main/research/anomaly_detection/performance_comparison | https://github.com/lfedgeai/AIOps/tree/main/research/anomaly_detection/performance_comparison | https://github.com/lfedgeai/AIOps/tree/main/research/anomaly_detection/performance_comparison | https://github.com/lfedgeai/AIOps/tree/main/research/anomaly_detection/performance_comparison | https://github.com/lfedgeai/AIOps/tree/main/research/anomaly_detection/performance_comparison | research section has no separate CI | n/a | n/a |
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