Docker Programming Tutorials & Engineering Articles

An agent can finish a task and still take an inefficient path. A failed search can trigger another search. A truncated file read can lead to a command fetching…

Vision-language models have made it possible to build visual AI agents that understand video at production scale. The harder problem is turning that capability…

AI agents can be given a goal, write code, use tools, and keep working as new information becomes available. This opens the door to applications that…

As large language model (LLM) inference increasingly processes sensitive information and proprietary model context across personal, enterprise…

Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow…

Google's MCP Toolbox for Databases embeds agent text into vectors on insert and search, then lets ClickHouse rank the results - no embedding service to build or maintain. Here's how to set it up, and what it looks like end to end.

Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run…

The surge in AI adoption is transforming everything from chatbots to content generation. Still, a common pain point remains: How can organizations confidently…

Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.

A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline—for example, from an SUV to a sedan or another vehicle…

Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing…

Navigation enables a robot to turn perception and motion into purposeful autonomy. Unlike locomotion, which produces stable movement, navigation must be used to…

Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gateways for I/O validation. By enforcing these hard security boundaries at the infrastructure level, you can safely deploy multi-tool AI agents without risking unauthorized data manipulation or server compromise.

You can choose any of these hundred database systems and run queries. You can create tables and databases, insert data, drop tables, etc. Every database comes with a preloaded dataset of 100 million records, so you can test example queries. It has not onl

The demand for high-quality video continues to accelerate across industries, powering everything from immersive streaming experiences to remote collaboration…

Knowledge workers are increasingly integrating AI agents into their workflows. Agents that function as “digital coworkers” offer clear benefits. For example…

Deploying an AI coding assistant in a regulated, sovereign, or source-sensitive environment, often comes with challenges. Three common issues are: the source…

This is a complicated post. So I need you to promise me something: if you read past this introduction, you’ll read the whole rest of the way through. It’s an honor thing. A couple months back, Theo Browne ran a video rating the “best place to host a

This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple topology configuration to correctly gang-schedule large multi-host models, while Ray Data eliminates data-loading bottlenecks by feeding accelerators directly with native JAX batches. Finally, JaxTrainer streamlines distributed training across TPUs by automatically handling cross-slice coordination, checkpointing, and fault tolerance.

Februarys' ClickStack update brings major improvements to both Cloud and open source, including new query workflows, enhanced metrics exploration, performance optimizations, and expanded alerting options.

ClickHouse 26.2 is here! In this post, text-index and QBit data type become production-ready.

Learn how to send structured .NET logs directly to ClickHouse using Serilog — with full schema control, full-text search, and SQL queries over your log data.

We use clickhousectl to spin up multiple ClickHouse versions side by side and benchmark two recent performance improvements.

ClickStack’s April release

A walkthrough of building a .NET API gateway with YARP and Aspire that logs every proxied request to ClickHouse for aggregate analytics, with a materialized view keeping dashboard queries fast as traffic grows.

A walkthrough of adding OpenTelemetry instrumentation to two ASP.NET services — an Order API and a Payment Service — and shipping traces, logs, and metrics to ClickStack, with auto-correlated signals and cross-service trace waterfalls out of the box.

ClickHouse is now available as a Docker Hardened Image: a minimal, security-hardened build that passes enterprise vulnerability scans by shipping only what the database needs to run, with no change to how ClickHouse behaves.

A video analytics AI agent that can perceive, reason, and act based on massive amounts of video footage must be integrated with existing workflows and…

Developers building video analytics applications across large spaces must track the same object as it moves between camera views. Single-camera 2D tracking…

What if autonomous coding AI agents could push your vision reasoning models above 90% accuracy with almost no manual effort? When adapting vision reasoning…

AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns…

As enterprise AI adoption scales, developers are increasingly forced to stitch together fragmented pipelines—separate models for text, vision…

AI agents are a powerful tool for synthesizing data to accelerate research, summarize information, and help teams make decisions faster.

Developing autonomous vehicle (AV) policies requires bridging an important gap between training and deployment. Vision-language-action (VLA) models that can…

Large language models (LLMs) are revolutionizing the financial trading landscape by enabling sophisticated analysis of vast amounts of unstructured data to…

Agent harnesses like Claude Code, Codex, and LangChain Deep Agents are excellent orchestrators. They manage sessions, chain tools, execute code…

By Amer Hesson, Marcelo Mayworm, James Mulcahy, and Brittany Truong

Modern supply chains operate under the constant pressures of fluctuating demand, volatile costs, constrained capacity, and interdependent decision-making.

AI integration is redefining mainstream enterprise applications, from productivity software like Microsoft Office to more complex design and engineering tools.

Agents are evolving from question-and-answer systems into long-running autonomous assistants that read files, call APIs, and drive multi-step workflows. However…

Learn how Github uses eBPF to detect and prevent circular dependencies in its deployment tooling.

Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling for over 65% of TOP500 systems.

Physical AI—AI systems that perceive, reason, and act in physically grounded simulated environments—is changing how teams design and validate robots and…