AI

Video by OpenProject | Open Source Project Management via YouTube

The release brings various features and improvements for you.

0:00 – Introduction
0:12 – AI assisted actions on work packages, comments, and relations with the MCP Server (Enterprise add-on)
0:49 – Multiple target versions
1:11 – Global limits and restrictions for time entries (Enterprise add-on)
1:32 – Backlog and sprints improvements
2:09 – Wiki improvements
2:27 – Released to Community: Display relations in work package tables

Find out more about all features and improvements in our release notes: https://www.openproject.org/docs/release-notes/17-8-0/

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Video by Open Source Connect via YouTube

🔔 Follow our YouTube channel and don’t miss it!

Let’s learn, collaborate, and build together.

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Video by Hugging Face via YouTube

Training Agents, Session 4: from reward functions to environments.

In this live session, the reward stops being a function and becomes a place the agent acts in. In Session 3 the model answered once and a Python function scored it. Today the model runs a loop: open a file, run a command, read the error, try again. The training signal comes from what happened after acting.

We’ll walk the reset()/step() contract from Gym to LLM agents, build an environment with OpenEnv, push it to the Hub, and plug it into TRL’s GRPOTrainer with environment_factory. Then we go one step further and train a real coding agent harness through Harbor via OpenEnv, with AsyncGRPOTrainer on Hugging Face sandboxes.

What we’ll cover:
– Why verifiers stop working once an agent takes many actions, and why the reward has to live where the actions happen
– The reset()/step() contract: from CartPole to a coding agent with a shell, files, and tests
Anatomy of an RL environment: task, tools, backend, reward, episode
– The ecosystem: OpenEnv, Verifiers, OpenReward, Harbor, and how OpenEnv imports the others
– Building and serving an env with the openenv CLI: init, push, pull, fork
– Training with TRL: environment_factory and get_reward, no reward_funcs
– Demo 1: Qwen3-1.7B on MBPP inside a live Python session, held-out pass rate 0.49 → 0.59
– Demo 2: a real coding agent (OpenCode) on DeepCoder problems in HF sandboxes, reward 0.27 → 0.71 in 10 steps
– Reward hacking moves into the env: the try/except that never failed, the CVE fix read from .git history, and how to lock the box down

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Video by OpenCV via YouTube

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Video by PyTorch via YouTube

PyTorch 2.14 introduces updates across compilation, distributed training, performance, dynamic shapes, Apple Silicon, and accelerator platforms. Highlights include NVGEMM, a CuTeDSL-generated GEMM backend for Inductor; the new nccl2 backend for PyTorch Distributed; fault-tolerant process-group reconfiguration in c10d; native linear algebra on Apple Silicon; and declarative dynamic shapes with @dynamic_spec.

Bring your questions about the release to our live Q&A. Andrey Talman (Meta), Natalia Gimelshein (Meta), Joe Spisak (Reflection AI), and Chris Gottbrath (Gottbrath Tech, moderator) will share an overview of PyTorch 2.14 and answer community questions about PyTorch and the new capabilities in the release.

Topics will include:

– NVGEMM and CuTeDSL-generated CUTLASS kernels in Inductor
– The new nccl2 backend for PyTorch Distributed
– Fault-tolerant collectives and process-group reconfiguration in c10d
– Native linear algebra and additional Metal kernel improvements on Apple Silicon
– torch.switch and CUDA graph capture for torch.while_loop
– Declarative dynamic shapes with @dynamic_spec
– Experimental torch.compile support for complex-valued tensors
– Expanded ROCm, Intel XPU, and NVIDIA platform support

PyTorch 2.14 includes 2,995 commits from 487 contributors since PyTorch 2.13. The release includes work across compilation, distributed communication, device support, and accelerator platforms. PyTorch Conference North America 2026 takes place October 20–21 in San Jose, with sessions spanning compiler and runtime work, distributed communication, device portability, release engineering, CI, observability, accelerator integration, contributor infrastructure, and more. Explore PyTorch Conference North America 2026.

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Video by NetApp Instaclustr via YouTube

OpenSearch 3.7 allows KNN vectors to fetch from doc values vs. a full document decompression, meaning faster queries.

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Video by CNCF [Cloud Native Computing Foundation] via YouTube

The strength of the cloud native community goes beyond the code.

It’s built on people helping each other learn and grow. CNCF Ambassador Prithvi Raj shares why community contribution is the true foundation of open source.

#CloudNative #OpenSource #CNCF

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Video by FINOS via YouTube

Legacy frameworks like Apache Struts are notorious for harboring active exploits and severe CVEs. See how the enterprise Risk Navigator tool visualizes your exact exposure to high-risk legacy libraries. For frameworks with no easy upgrade path, the FINOS OSERA project offers a collaborative way for financial services to commission and share critical backpatches. #ApacheStruts #AppSec #DevSecOps

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Video by Open Data Science and AI Conference via YouTube

Event-Driven Automations

Want to learn more about AI in person? Check out ODSC AI West 2026, coming to SF this October 27th-29th: https://odsc.ai/west

#DataScience #AI #ArtificialIntelligence #ODSCAI

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Visit our website and choose the nearest ODSC event to attend and experience all our training and workshops: https://odsc.ai

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