A debugging session or a multi-agent run can rack up a big LLM bill before anyone notices. This blog by @TerryTangYuan covers how Omnigent handles that. Smart routing keeps trivial tasks off expensive models. Progressive budgets warn before they block expensive models. The Usage page shows spend by model, harness, and session. Read the full post 👉 #Omnigent# #AIAgents# #OpenSource# #LLMOps#

We did it! Omnigent has officially surpassed 10,000 GitHub stars ⭐ Thank you to everyone who starred the repo, filed issues, shipped PRs, and ran agents with Omnigent. Here's to the next 10k! 🚀 Come build with us: Join the community on Discord: #Omnigent# #OpenSource# #AIAgents#

🎉 Excited to share that our paper "VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities” has been accepted to EMNLP 2026! This work is a collaboration between @PurdueCS and multiple teams from @RedHat (AI Engineering, Ecosystem Engineering, and Product Security). Software supply chains are an increasingly important security challenge. While tools can identify vulnerable dependencies, determining whether a vulnerability is actually exploitable in a downstream application often requires substantial cross-repository reasoning and security expertise. In this work, we introduce VEX-Bench, a benchmark for evaluating LLM agents on this problem: - 75 real-world vulnerability cases mined from GitHub - Expert-labeled across Python, Java, and Go - Evaluation of 9 models across 3 agent harnesses - Going beyond binary “exploitable / not exploitable” decisions to evaluate the reasoning behind exploitability Our results show that frontier LLM agents can perform well on binary vulnerability assessment, but fine-grained exploitability reasoning remains significantly more challenging. This points to an important direction for AI-assisted software security: it’s not enough for an agent to identify that something is vulnerable. We need to understand why it is exploitable, whether it actually matters in context, and how confidently we can trust the assessment. 🙌 Huge thanks to all the co-authors and collaborators who made this possible! @tian_yi_zhang, Jiahao Shi, Yifeng Di, Ilan Pinto, Edward Tsien, Hongjiao Zhang, Ronit Dey, Ilona Shishov, Gal Netanel, Zvi Grinberg, Vladimir Belousov, Bat-Zion Rotman 📄 Paper: #EMNLP2026# #AI# #LLM# #LLMAgents# #Cybersecurity# #SoftwareSupplyChain# #OpenSource# #PurdueCS# #RedHat#

LLM bills spiral when debugging sessions or multi-agent workflows run away. Omnigent warns first, then downgrades, doesn't hard-stop mid-task. 👇 🛡️ trivial_gate routes trivial requests off Opus, Fable, GPT-5 💰 Progressive budgets block expensive tiers; work continues on Sonnet/Haiku 📊 /usage breaks spend by harness, model, and session Phased rollout: visibility → routing → budgets Read more: #Omnigent# #LLMOps# #GenAI#

Reproducible inference benchmarking is harder than it sounds, and it's exactly what the ecosystem needs. Inference Perf, from the Kubernetes community, standardizes how inference performance is measured across model servers. Great to see it published in JOSS. Congrats to the team.

Excited to share that our accompanying paper for the Inference Perf project (under Kubernetes SIGs) has been published in the Journal of Open Source Software! Co-authors: Ashok Chandrasekar, Sachin Mathew Varghese, Jason Kramberger, Brendan Slabe, Chen Wang The project was created as a part of the inference benchmarking and metrics standardization effort in the Kubernetes Serving Working Group, and seeks to standardize tooling and metrics for measuring inference performance across the Kubernetes and model server communities. Paper: Repo:

📢 𝗧𝗵𝗲 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗠𝗼𝗱𝗲𝗹 𝗦𝗲𝗿𝘃𝗶𝗻𝗴 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀: 𝗔𝘂𝗴𝘂𝘀𝘁 𝗘𝗱𝗶𝘁𝗶𝗼𝗻 𝗶𝘀 𝗼𝘂𝘁! Our goal with this newsletter is to give a clear, community-driven view of what’s happening across the model serving ecosystem that our teams at @RedHat_AI are contributing to, including updates from projects like @vllm_project, KServe, @_llm_d_, @kubernetesio, and beyond. 👉 Check out the August newsletter here: 👉 Subscribe to get future issues in your inbox: 🚀 Thanks to everyone who subscribed so far! Kudos to all contributors to this edition! David Simmons, Francisco Arceo, Jooho Lee, Yuan Tang, Wentao Ye, Sasa Zelenovic, Vivek Karunai Kiri Ragavan

I'm thrilled to share that I've been invited to become @omnigent_ai's first external maintainer! It's remarkable that the project is only two months old and I feel incredibly fortunate to contribute at such an early stage and collaborate with the @databricks team and broader community. I'm grateful for the trust and excited about the opportunity to help shape Omnigent. Big thanks to everyone who's supported me: @matei_zaharia, Corey Zumar, Dhruv Gupta, Zeyi Fan, Pat Sukprasert, Xinyue Ruan, @sabhyac267, Tomu Hirata, @dennylee, and many others I may have missed here.

I’m speaking at #KubeCon# + #CloudNativeCon# North America, November 9-12 in Salt Lake City, Utah! Check out our session to learn more about the work we are doing at the CNCF TAG Workloads Foundation and meet TAG leads in person!

Attending @SciPyConf this year was a refreshing experience in more ways than one. #SciPy2026# For the first time, I attended a conference where the main venue was filled with natural light. It may seem like a small detail, but spending the day in bright, sunlit spaces at the University of Minnesota made the conference feel surprisingly energizing. I had the opportunity to give my talk, “Building for the Road Ahead: Transferable Lessons from the Front Lines of Open Source Maintenance”, where I shared lessons I've learned from over 10 years of maintaining and leading open source projects. What made it especially memorable was that my session was immediately followed by Hadley Wickham's talk on lessons from his 20 years of experience - in the very same room. Another highlight was reconnecting with my former colleague, James Lamb. About ten years ago, I introduced James to the world of open source and helped him make his first contribution. It was incredibly rewarding to catch up and see how far he's come since then. Today he's a maintainer of LightGBM and has become a respected contributor across the open source ecosystem. Seeing that journey come full circle was one of my favorite moments of the conference. My talk focused on something I believe doesn't get discussed enough: maintaining successful open source projects isn't just about writing code. It's about building healthy communities, making sustainable decisions, and creating projects that can thrive long after the initial excitement. I shared some of the lessons I've learned along the way. Slides and recording will be available here: Thanks to the SciPy organizers, speakers, volunteers, and everyone I had the chance to meet. Looking forward to the next one!

🌱 Talk Spotlight: @TerryTangYuan shares what years on the open source front lines have taught him about sustainable maintenance. Hard-won lessons for the whole community. #SciPy2026# 🔗

Excited to be speaking at #SciPy2026# in Minneapolis next week! If you're attending, I'd love to connect. Come say hi after the talk or find me during the conference. Let's chat about open source, AI infrastructure, or whatever's on your mind. Full schedule: