Article URL: https://www.bbc.co.uk/news/articles/cp3rprx2wl4o Comments URL: https://news.ycombinator.com/item?id=49310725 Points: 4 # Comments: 0
This is an AI-generated summary. ShortSingh links to the original source for the complete article.
Model Context Protocol (MCP) servers without request controls are vulnerable to denial-of-service attacks, memory exhaustion, and excessive API charges. Because LLMs automatically retry failed requests, an uncontrolled loop of MCP tool calls can rapidly consume system resources or inflate costs. Rate limiting addresses this by capping the number of requests an agent can make to an MCP server within a defined time window. A practical implementation can be achieved using Agentgateway on a Kubernetes cluster, with the GitHub Copilot MCP Server serving as a demonstration target. The setup involves configuring a gateway policy that defines request thresholds, helping teams safeguard infrastructure from both accidental and malicious overload.
Open Graph (OG) images are the 1200×630 preview cards that appear when URLs are shared on platforms like X, LinkedIn, and Slack, and dynamic versions can significantly improve click-through rates. Client-side generation tools fail for this purpose because social media scrapers such as Twitterbot and LinkedInBot do not execute JavaScript, meaning client-rendered images are invisible to them. Developers have two viable server-side options: a self-hosted stack using tools like satori and sharp to convert JSX into PNG, or a managed API service that handles rendering, caching, and delivery automatically. The DIY approach offers full control but requires ongoing maintenance of fonts, concurrency, and edge cases, while managed APIs trade cost for reduced engineering overhead. A production-ready OG image pipeline should accept simple GET requests with query parameters, since social scrapers cannot process POST bodies or authentication headers.
A data engineer reflecting on their PySpark and ETL pipeline work identified a fundamental mindset difference between how data scientists and data engineers approach data cleaning. In data science, cleaning is guided by the needs of a specific analysis or model, focusing on making data useful and appropriate for answering a particular question. Data engineering, by contrast, demands reliable, automated processing with considerations like schema consistency, failure handling, scalability, and data contracts for downstream systems. How issues like missing values are handled also differs: a data scientist might impute a missing field to preserve a training observation, while a pipeline engineer must decide whether a missing field should reject, quarantine, or pass through a record based on business rules. The key takeaway is that in data engineering, data quality expectations must be encoded directly into the system rather than relying on a person to manually inspect and judge the data each time.
NEXUS AI published a detailed breakdown on August 15, 2026, of its Role-Based Access Control system designed to address common enterprise access management failures. The platform defines four built-in roles — Viewer, Developer, Admin, and Owner — each a strict superset of the one below, ensuring permissions scale predictably with responsibility. Least privilege is enforced by default, meaning users receive only the access their role requires without additional configuration. The system specifically separates deployment permissions from secrets management, so a Developer-level account can trigger redeploys but cannot read or modify production credentials. NEXUS AI designed these controls to tackle real-world risks such as unrevoked contractor access, over-permissioned CI/CD tokens, and role configurations that are accidentally copy-pasted across environments.
Discussion (0)
Log in to join the discussion and vote.
Log in