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How to Build an Agent

Craft a code-editing agent in under 400 lines. It's just an LLM, a loop, and some enhanced tokens. No rocket science here—just solid, hands-on engineering... read more  

How to Build an Agent
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How To Set Up a Model Context Protocol Server

Model Context Protocol (MCP)is like that cool tool you didn't know you needed. It's a nimble bridge between LLM models and developer tools, though someday it might just become the backbone of future libraries—nothing fancy, just fundamental. EnterFastMCP, the under-the-radar hero. Fire it up, and it.. read more  

How To Set Up a Model Context Protocol Server
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Explainable AI Needs Explainable Infrastructure

AWS S3 choked, and prediction accuracy took a nosedive. Voilà: an uninvited reminder thatexplainable infrastructureis crucial for genuine AI transparency. It’s not just a hunch—47% of AI downtime stems from these scaffolding snafus. Luckily, warriors likeOpenTelemetryandGrafanastep up, offering a wa.. read more  

Explainable AI Needs Explainable Infrastructure
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ngrok is also now your Kubernetes ingress

ngrok's Kubernetes Operatortakes the tangle out of K8s networking. Picture this: labyrinthine paths shrink into tidy URLs, and traffic feels the firm hand ofTraffic Policy. Get ready forv1.0. It promises shiny, new features and bids farewell to "edges" in favor of a sleek focus on endpoints. Expect .. read more  

ngrok is also now your Kubernetes ingress
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v1.33: Storage Capacity Scoring of Nodes for Dynamic Provisioning (alpha)

Kubernetes v1.33beta rolls out topology-aware volume provisioning, nudging pod scheduling in the right direction. It cleverly takes node storage capacity into account, unleashing the full potential of resource utilization... read more  

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v1.33: New features in DRA

Kubernetes Dynamic Resource Allocation (DRA)is shaking up device management. Expect tools likeDriver-owned Resource Claim Statusfor tracking device data like a hawk, andPartitionable Devicesto squeeze max juice from resources. Keep an eye out: DRA goes full throttle in v1.34, making device handling .. read more  

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v1.33: Mutable CSI Node Allocatable Count

Kubernetes v1.33hits the scene swinging with an alpha feature that's shaking things up: dynamic volume limits. CSI drivers now sharpen pod scheduling accuracy while kicking outdated capacity errors to the curb... read more  

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Build and Query Knowledge Graphs with LLMs

Knowledge Graphs organize data in a human-like way. GraphRAG enhances LLMs by using a graph-based approach for more effective information retrieval and generation. Graphs reveal cross-document relationships, advancing search systems for data scientists and ML engineers. Neo4j and LangChain enable th.. read more  

Build and Query Knowledge Graphs with LLMs
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Here are Some Docker Hacks That Changed my Life

Aliasesslash command lengths to mere blips. A pristine.dockerignoretrims the fat, speeding up image creation. Multi-stage builds churn out sleek, secure images with zero fuss. Docker Compose overrides? They separate environments with ease, evicting any messy configs. Keep your system nimble with reg.. read more  

Here are Some Docker Hacks That Changed my Life
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Kubernetes 1.33 “Octarine” Released: Native Sidecars and In-Place Pod Resizing

Kubernetes 1.33rolls out as the hero of sidecar containers, smoothing out their lifecycle wrinkles. Now, sidecars launch before and wrap up after the main event—your app containers. Meanwhile, in-place pod resizing steps up to beta status. Tweak resources without nuking your app’s smooth run—a gift .. read more  

Kubernetes 1.33 “Octarine” Released: Native Sidecars and In-Place Pod Resizing
GPT-5.4 is OpenAI’s latest frontier AI model designed to perform complex professional and technical work more reliably. It combines advances in reasoning, coding, tool use, and long-context understanding into a single system capable of handling multi-step workflows across software environments. The model builds on earlier GPT-5 releases while integrating the strong coding capabilities previously introduced with GPT-5.3-Codex.

One of the defining features of GPT-5.4 is its ability to operate as part of agent-style workflows. The model can interact with tools, APIs, and external systems to complete tasks that extend beyond simple text generation. It also introduces native computer-use capabilities, allowing AI agents to operate applications using keyboard and mouse commands, screenshots, and browser automation frameworks such as Playwright.

GPT-5.4 supports context windows of up to one million tokens, enabling it to process and reason over very large documents, long conversations, or complex project contexts. This makes it suitable for tasks such as analyzing codebases, generating technical documentation, working with large spreadsheets, or coordinating long-running workflows. The model also introduces a feature called tool search, which allows it to dynamically retrieve tool definitions only when needed. This reduces token usage and makes it more efficient to work with large ecosystems of tools, including environments with dozens of APIs or MCP servers.

In addition to improved reasoning and automation capabilities, GPT-5.4 focuses on real-world productivity tasks. It performs better at generating and editing spreadsheets, presentations, and documents, and it is designed to maintain stronger context across longer reasoning processes. The model also improves factual accuracy and reduces hallucinations compared with previous versions.

GPT-5.4 is available across OpenAI’s ecosystem, including ChatGPT, the OpenAI API, and Codex. A higher-performance variant, GPT-5.4 Pro, is also available for users and developers who require maximum performance for complex tasks such as advanced research, large-scale automation, and demanding engineering workflows. Together, these capabilities position GPT-5.4 as a model aimed not just at conversation, but at executing real work across software systems.