Docker
Docker on Beyond Market Intelligence: a running collection of 9 stories we have gathered and hand-picked because they are worth your time. Every post here touches on docker in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around docker, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Java News Roundup: GraalVM, Jakarta Data, JNoSQL, Azul Payara, WildFly, Quarkus, Atmosphere
This week’s Java News Roundup, dated August 24th, 2026, delivers a focused update on key ecosystem developments. Leading the highlights is the general availability of Atmosphere 4.0, alongside point releases for GraalVM, Azul Payara, and Quarkus. Developers will also note maintenance releases of WildFly 41 and milestone releases for Jakarta Data and Eclipse JNoSQL. Further developments include a beta release of Open Liberty’s September edition and new Docker images for GlassFish 8.0.4.
![I analyzed 31,352 hourly LLM benchmark scores: within-day variation was 2.8 points, while between-day variation was 8.4 [P]](https://preview.redd.it/42s57e5oqamh1.png?width=140&height=66&auto=webp&s=e1e8829f73c0172877e0e9970f8dd143911bad57)
I analyzed 31,352 hourly LLM benchmark scores: within-day variation was 2.8 points, while between-day variation was 8.4 [P]
A new analysis of 31,352 hourly LLM benchmark scores reveals critical insights into model stability. Examining coding, reasoning, and tool-calling performance, the research found between-day variation (8.4 points) was approximately three times greater than within-day variation (2.8 points), suggesting sustained daily changes offer a stronger signal for detecting performance drift. This work, underpinning the open-source AIStupidLevel system, now encompasses over 169,000 benchmark runs and powers a model router optimizing for performance and cost—a dimension often missing from standard monitoring.
Connecting My LangGraph AI Agent to Postgres
Connecting your LangGraph AI agent to a Postgres database unlocks powerful capabilities for data-driven workflows. This post details how to establish that connection, offering clear guidance for both local development and cloud deployment. We’ll explore setting up the backend using Docker for streamlined local testing, and then outline strategies for scaling to the cloud. For those tackling complex enterprise workflows, consider the recent exploration of an 8B AI model mirroring Claude Opus—a relevant challenge in managing substantial data sets.
Docker Launches Fully Rebuilt Virtualization Layer to Boost Performance and Improve Dev Experience
Docker significantly enhances developer workflows with the launch of Docker VMM, a fully rebuilt, first-party virtualization layer now integrated into Docker Desktop 4.86 for Mac and Windows. Replacing legacy third-party components, Docker VMM delivers improved performance and direct control over container workloads. This foundational shift allows Docker to optimize virtualization specifically for its ecosystem, streamlining development and deployment. For those exploring the broader intersection of technology and innovation, consider our recent article on Cloudflare WriteGuard and its impact on server security.
how can I learn Machine Learning for Astronomical use? [D]
Embarking on machine learning for astronomical data—like JWST or TESS pipelines—is an exciting endeavor! Given your familiarity with Python and a visual learning style, several accessible resources exist. Begin with free online tutorials focusing on Python fundamentals and then transition to machine learning basics. Explore platforms like Kaggle and Google Colab for readily available Jupyter Notebooks, some even demonstrating exoplanet or black hole signature detection. For a structured approach, consider free online books covering Python and machine learning principles.
I started a bring your own cloud AutoML for smaller teams
Too many valuable machine-learning models languish in notebooks due to deployment complexities. Data scientist frustrations with disconnected tools and fragmented MLOps workflows inspired the creation of #SceptreAI. This Kubernetes-native tabular AutoML and MLOps workspace streamlines the entire process—from dataset versioning and resource-aware training to drift analysis and Kubernetes serving—all within a traceable workflow. Like the recent exploration of Vault Kubernetes key management, SceptreAI aims to simplify infrastructure, empowering teams to focus on trustworthy, scalable machine learning.
Avoiding Entity Key Drift in a Data Lake: Step 1, Normalization
High-frequency streaming data pipelines against live APIs expose critical challenges, particularly entity key drift in data lakes. This four-part series tackles that challenge, beginning with normalization, a foundational step for maintaining data integrity. We'll explore building a robust pipeline using openSenseMap—a citizen-science IoT network—demonstrating how real-world data quality issues demand innovative solutions. Subsequent articles will cover matching, polling, noise filtering, and a vendor-agnostic Iceberg pipeline. Interested in broader data science considerations? See "What Professionals Should Know About Data Science and AI" for vital insights.
Build an LLM Agent That Can Write and Run Code
Unlock the potential of AI-powered code generation and execution. This hands-on walkthrough guides you through building an LLM agent using the OpenAI Agents SDK and Docker. Learn to empower your workflows by seamlessly integrating code writing and running capabilities. We’ll demonstrate a practical approach to leveraging these tools, offering a future-focused solution for data professionals. For those interested in a deeper dive into LLM runtimes, explore "How To Build Your Own LLM Runtime From Scratch" for a comprehensive understanding of the underlying infrastructure.
![Tri-Net v2: Open-source implementation of our Scientific Reports paper on unified skin lesion and symptom-based monkeypox detection [R]](https://preview.redd.it/vwax5ludzheh1.png?width=140&height=79&auto=webp&s=25929233532a0110f28de21f8e7a57634c6f791b)
Tri-Net v2: Open-source implementation of our Scientific Reports paper on unified skin lesion and symptom-based monkeypox detection [R]
We’re pleased to announce the open-source release of Tri-Net v2, the fully reproducible research framework detailed in our recent *Scientific Reports* (Nature Portfolio) paper on unified monkeypox detection. This implementation prioritizes transparency and accessibility, featuring a leakage-free data pipeline, multiple CNN backbones (including ConvNeXt-Tiny), ensemble strategies, and Grad-CAM explainability. Installation is streamlined via `pip install mpox-trinet`, and comprehensive documentation is available on GitHub.