2,153 stories
Kubernetes 1.37 removes 25 feature gates, an alpha scheduling API, and 18 kubelet flags, and starts the nftables transition. What breaks, and how to check your cluster.
Build efficient, secure APIs for AI agents with conditional endpoints, batch ops, OAuth scopes, and audience restrictions. Optimize your AI agent data API.
AI applications can pass security scans yet remain exploitable through chained attacks across models, tools, data, and business workflows. Learn how DAST, AI pentesting, and red teaming work together to expose end-to-end risk.
The regulators are done with “Trust as policy”. That chain used to be an internal maturity problem, something a security program could work on over time: A developer trusts AI-generated code because it compiles. A reviewer trusts an AI-generated summary because it reads plausibly.
Try this AI workflow to get a blog post, follow-up email, and newsletter before lunch.
Level: Advanced Time to complete: 20 minutes Components Used: Agent, AgentTool, DuckduckgoApiWebSearch, OpenAIChatGenerator, DocumentWriter Prerequisites: You need Haystack 3.1 or newer, an OpenAI API Key, and a Notion Integration set up beforehand Goal: After completing this tutorial, you’ll have…
Coworker AI explains context engineering: the discipline of deciding what goes into a model's window, why less beats more, and how to measure it.
Coworker AI covers MCP security: prompt injection through tools, the confused deputy problem, token handling, and which controls actually work.
Coworker AI shows how to connect MCP servers to Claude Code, the Claude apps, and the API, with the commands, scopes, and the security caveats.
Coworker AI explains MCP vs API: MCP is a standard layer built on top of APIs, what it removes, what it costs, and when a direct call is still better.
Coworker AI explains RAG vs MCP: retrieval fetches knowledge, MCP is a connection standard. They solve different problems and most systems need both.
Coworker AI explains context rot: why model accuracy falls as the context window fills, how to measure it, and why retrieval and a knowledge graph beat volume.
Coworker AI explains LLM observability: traces, spans, token and cost tracking, evaluation, and why traditional monitoring misses what matters.
I see so many posts ‘shipping by the beach’ or when out with the kids on the weekend.
You can now install LaunchDarkly from the Vercel Marketplace and evaluate your first flag in minutes. Start on our free Developer plan and upgrade when you need to, all managed from Vercel.
Explore how Synter Prospector unifies waterfall lead discovery, dedicated secondary domain deliverability, and 27-platform ad synchronization, backed by real performance data from our own campaigns.
Knowledge-graph stores, note-takers and session indexes are three different products wearing one label. Which one you need depends on a single question.
Large language models are excellent at understanding, reasoning, and generating content. But when a task depends on today's news, social conversations, product prices, company information, or on-chain activity, the model's existing knowledge is no longer enough.That is where an agent harness…
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