Srini Sekaran
More by Srini
Docker Sandboxes: Run Agents in YOLO Mode, Safely
Agents have crossed a threshold. Over a quarter of all production code is now AI-authored, and developers who use agents are merging roughly 60% more pull requests. But these gains only come when you let agents run autonomously. And to unlock that, you have to get out of the way. That means letting agents run…
Read now
The 3Cs: A Framework for AI Agent Security
Every time execution models change, security frameworks need to change with them. Agents force the next shift. The Unattended Laptop Problem No developer would leave their laptop unattended and unlocked. The risk is obvious. A developer laptop has root-level access to production systems, repositories, databases, credentials, and APIs. If someone sat down and started using…
Read now
Docker Sandboxes: Run Claude Code and Other Coding Agents Unsupervised (but Safely)
Secure sandboxes for Claude Code, Gemini, Codex, and Kiro. Run coding agents with microVM-based isolation.
Read now
2025 Recap: The Year Software Development Changed Shape
2025 was the year software teams stopped optimizing models and started optimizing systems. By December, a few truths were impossible to ignore. 1. Developer Productivity Became the Real Competitive Advantage By mid-year, every major AI lab had cleared the “good enough reasoning” bar. With model quality converging, the differentiator was no longer raw intelligence. It…
Read now
Docker Joins the Agentic AI Foundation
Today, the Linux Foundation launched the Agentic AI Foundation with three founding projects: Anthropic’s Model Context Protocol (MCP), Block’s goose agent framework, and OpenAI’s AGENTS.md standard. The foundation brings together the companies building the infrastructure layer for agents: Anthropic, Block, OpenAI, Amazon, Google, Microsoft, Cloudflare, and Bloomberg, alongside key tooling and platform companies. Docker is…
Read now
Docker, JetBrains, and Zed: Building a Common Language for Agents and IDEs
As agents become capable enough to write and refactor code, they should work natively inside the environments developers work in: editors. That’s why JetBrains and Zed are co-developing ACP, the Agent Client Protocol. ACP gives agents and editors a shared language, so any agent can read context, take actions, and respond intelligently without bespoke wiring…
Read now
Building AI agents shouldn’t be hard. According to theCUBE Research, Docker makes it easy
For most developers, getting started with AI is still too complicated. Different models, tools, and platforms don’t always play nicely together. But with Docker, that’s changing fast. Docker is emerging as essential infrastructure for standardized, portable, and scalable AI environments. By bringing composability, simplicity, and GPU accessibility to the agentic era, Docker is helping developers…
Read now
A New Approach for Coding Agent Safety
Coding agents like Claude Code, Gemini CLI, Codex, Kiro, and OpenCode are changing how developers work. But as these agents become more autonomous with capabilities like deleting repos, modifying files, and accessing secrets, developers face a real problem: how do you give agents enough access to be useful without adding unnecessary risk to your local…
Read now
Docker + Unsloth: Build Custom Models, Faster
Building and Running Custom Models Is Still Hard Running AI models locally is still hard. Even as open-source LLMs grow more capable, actually getting them to run on your machine, with the right dependencies, remains slow, fragile, and inconsistent. There’s two sides to this challenge: Model creation and optimization: making fine-tuning and quantization efficient. Model…
Read now
Build a Multi-Agent System in 5 Minutes with cagent
Learn what a multi-agent system is and how to build one in minutes using Docker cagent.
Read now
Docker Sandboxes: Run Agents in YOLO Mode, Safely
Agents have crossed a threshold. Over a quarter of all production code is now AI-authored, and developers who use agents are merging roughly 60% more pull requests. But these gains only come when you let agents run autonomously. And to unlock that, you have to get out of the way. That means letting agents run…
Read now
The 3Cs: A Framework for AI Agent Security
Every time execution models change, security frameworks need to change with them. Agents force the next shift. The Unattended Laptop Problem No developer would leave their laptop unattended and unlocked. The risk is obvious. A developer laptop has root-level access to production systems, repositories, databases, credentials, and APIs. If someone sat down and started using…
Read now
Docker Sandboxes: Run Claude Code and Other Coding Agents Unsupervised (but Safely)
Secure sandboxes for Claude Code, Gemini, Codex, and Kiro. Run coding agents with microVM-based isolation.
Read now
2025 Recap: The Year Software Development Changed Shape
2025 was the year software teams stopped optimizing models and started optimizing systems. By December, a few truths were impossible to ignore. 1. Developer Productivity Became the Real Competitive Advantage By mid-year, every major AI lab had cleared the “good enough reasoning” bar. With model quality converging, the differentiator was no longer raw intelligence. It…
Read now
Docker Joins the Agentic AI Foundation
Today, the Linux Foundation launched the Agentic AI Foundation with three founding projects: Anthropic’s Model Context Protocol (MCP), Block’s goose agent framework, and OpenAI’s AGENTS.md standard. The foundation brings together the companies building the infrastructure layer for agents: Anthropic, Block, OpenAI, Amazon, Google, Microsoft, Cloudflare, and Bloomberg, alongside key tooling and platform companies. Docker is…
Read now
Docker, JetBrains, and Zed: Building a Common Language for Agents and IDEs
As agents become capable enough to write and refactor code, they should work natively inside the environments developers work in: editors. That’s why JetBrains and Zed are co-developing ACP, the Agent Client Protocol. ACP gives agents and editors a shared language, so any agent can read context, take actions, and respond intelligently without bespoke wiring…
Read now
Building AI agents shouldn’t be hard. According to theCUBE Research, Docker makes it easy
For most developers, getting started with AI is still too complicated. Different models, tools, and platforms don’t always play nicely together. But with Docker, that’s changing fast. Docker is emerging as essential infrastructure for standardized, portable, and scalable AI environments. By bringing composability, simplicity, and GPU accessibility to the agentic era, Docker is helping developers…
Read now
A New Approach for Coding Agent Safety
Coding agents like Claude Code, Gemini CLI, Codex, Kiro, and OpenCode are changing how developers work. But as these agents become more autonomous with capabilities like deleting repos, modifying files, and accessing secrets, developers face a real problem: how do you give agents enough access to be useful without adding unnecessary risk to your local…
Read now
Docker + Unsloth: Build Custom Models, Faster
Building and Running Custom Models Is Still Hard Running AI models locally is still hard. Even as open-source LLMs grow more capable, actually getting them to run on your machine, with the right dependencies, remains slow, fragile, and inconsistent. There’s two sides to this challenge: Model creation and optimization: making fine-tuning and quantization efficient. Model…
Read now
Build a Multi-Agent System in 5 Minutes with cagent
Learn what a multi-agent system is and how to build one in minutes using Docker cagent.
Read now