Matt Carey and Aron Carroll write about @cloudflare/computer, an open-source library designed to provide a scalable runtime for agentic systems. By offering a durable virtual filesystem backed by SQLite alongside diverse execution environments—including lightweight isolates and full Linux containers—the package allows models to perform tasks ranging from simple file manipulation to complex code execution. This architecture aims to solve the scalability issues inherent in providing dedicated containerized compute for every concurrent agent, enabling massive horizontal scaling through Cloudflare's isolate-based infrastructure.
- The virtual filesystem is backed by SQLite and can be populated via cloud storage or source control
- Isolates use just-bash to translate shell commands into JavaScript runs within dynamic workers
- Container runtimes employ Filesystem in Userspace (FUSE) mounts to sync files between the container and the workspace
- The virtual filesystem is backed by SQLite and can be populated via cloud storage or source control
- Isolates use just-bash to translate shell commands into JavaScript runs within dynamic workers
- Container runtimes employ Filesystem in Userspace (FUSE) mounts to sync files between the container and the workspace
Lightpanda is an original headless browser written in Zig, purpose-built for high-performance web automation and LLM-driven agents rather than being a fork of Chromium or WebKit. It prioritizes low memory consumption and execution speed by omitting unnecessary graphical rendering components while maintaining JavaScript support through the V8 engine. The system provides a CDP server to maintain compatibility with existing Puppeteer and Playwright workflows.
- Benchmark data suggests 9x faster execution compared to Headless Chrome.
- Includes an "Agent mode" that outputs deterministic PandaScript from natural language instructions.
- Offers native Model Context Protocol (MCP) support for seamless agent interaction.
- Benchmark data suggests 9x faster execution compared to Headless Chrome.
- Includes an "Agent mode" that outputs deterministic PandaScript from natural language instructions.
- Offers native Model Context Protocol (MCP) support for seamless agent interaction.
Skill Recorder converts screen activity into repeatable agent tasks by recording user sessions and reconstructing intent via GitHub Copilot. Captured data includes clicks, window changes, URLs, and optional narration to produce structured Skills or Automations.
- Built with Electron.
- Performs on-device transcription using Whisper for 99 languages.
- Limits cloud uploads to specific metadata during the analysis phase.
- Built with Electron.
- Performs on-device transcription using Whisper for 99 languages.
- Limits cloud uploads to specific metadata during the analysis phase.
Yanli Liu writes that harness engineering has emerged as a vital discipline where engineers focus on designing environments, feedback loops, and documentation structures for agentic systems rather than writing code manually. This shift enables highly automated development cycles to function reliably by emphasizing the constraints under which these models operate.
- OpenAI's "Harness Engineering" concept sparked this movement in February 2026.
- Anthropic released three separate papers regarding effective harnesses and managed agents.
- Advancements like Opus 4.7 continuously simplify necessary harness components as model capabilities improve.
- OpenAI's "Harness Engineering" concept sparked this movement in February 2026.
- Anthropic released three separate papers regarding effective harnesses and managed agents.
- Advancements like Opus 4.7 continuously simplify necessary harness components as model capabilities improve.
This tool enables the creation of agentic skills based on proven methodologies from domain experts rather than generic generation. It surfaces frameworks and principles from recognized masters in fields such as product management, writing, engineering, sales, and leadership to ensure high-quality outcomes. The workflow identifies golden examples for quality standards and anti-patterns to avoid through a three-layer search process involving local databases, web searches, and primary source deep dives.
* Employs 3-layer search: local database, expert discovery, and primary source analysis
* Identifies "golden examples" to define output quality
* Detects anti-patterns to encode common mistakes as things to avoid
* Cross-validates methodologies across multiple experts for consensus
* Supports various domains like Product, Writing, Sales, Engineering, and Leadership
* Employs 3-layer search: local database, expert discovery, and primary source analysis
* Identifies "golden examples" to define output quality
* Detects anti-patterns to encode common mistakes as things to avoid
* Cross-validates methodologies across multiple experts for consensus
* Supports various domains like Product, Writing, Sales, Engineering, and Leadership
This article examines the architectural implications of choosing between stateless and stateful designs when building agentic systems. It evaluates how an agent's approach to managing memory impacts deployment, horizontal scaling, and client-side complexity.
- Stateless agents allow for easy horizontal scaling since no user memory is stored on a backend server, but they require the client to send the full conversation history with every request, leading to increased token usage as conversations grow.
- Stateful agents manage their own context through a database layer using session identifiers, which simplifies client interactions and supports complex workflows, though it introduces challenges in distributed scaling and data persistence.
- Stateless agents allow for easy horizontal scaling since no user memory is stored on a backend server, but they require the client to send the full conversation history with every request, leading to increased token usage as conversations grow.
- Stateful agents manage their own context through a database layer using session identifiers, which simplifies client interactions and supports complex workflows, though it introduces challenges in distributed scaling and data persistence.
The author explains how enabling the built-in sandbox feature in Claude Code has transformed their productivity by allowing for an autonomous workflow. By activating auto-allow mode via the /sandbox command, users can permit AI agents to execute repetitive tasks like running tests or installing dependencies without needing constant manual approval for every individual command, which reduces mental overhead and time spent waiting on prompts.
- Claude Code provides a built-in sandbox that uses macOS Seatbelt on Mac and Bubblewrap on Linux/WSL2.
- The environment restricts file writing primarily to the current project directory and temporary session files.
- Network access is controlled, requiring manual approval when the agent attempts to connect to a new domain.
- For maximum security and complete isolation from a system, using Docker containers or virtual machines remains more robust than the built-in sandbox features.
- Claude Code provides a built-in sandbox that uses macOS Seatbelt on Mac and Bubblewrap on Linux/WSL2.
- The environment restricts file writing primarily to the current project directory and temporary session files.
- Network access is controlled, requiring manual approval when the agent attempts to connect to a new domain.
- For maximum security and complete isolation from a system, using Docker containers or virtual machines remains more robust than the built-in sandbox features.
This guide provides a comprehensive walkthrough on using Google's Gemma 4 model to build autonomous AI agents through tool calling. It explores how this feature enables models to move beyond simple text generation by interacting with external APIs and systems via structured function calls.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
A real-time visualization tool for Claude Code and Codex agent orchestration that makes complex agent behaviors visible through interactive node graphs. It allows developers to monitor how agents think, branch, and coordinate during execution, facilitating easier debugging of tool call chains and reasoning processes.
- Live agent visualization via an interactive node graph with real-time streaming
- Concurrent support for Claude Code and Codex runtimes
- Integrated VS Code extension for direct workspace monitoring
- Interactive canvas with pan and zoom capabilities to inspect details
- Timeline, transcript panels, and JSONL log file replay functionality
- Live agent visualization via an interactive node graph with real-time streaming
- Concurrent support for Claude Code and Codex runtimes
- Integrated VS Code extension for direct workspace monitoring
- Interactive canvas with pan and zoom capabilities to inspect details
- Timeline, transcript panels, and JSONL log file replay functionality
An analysis of the recent Claude Code source code leak, focusing on architectural patterns rather than just security drama. The article explores how a massive TypeScript codebase reveals sophisticated design choices for building reliable agentic systems. It covers memory management strategies like skeptical verification and semantic consolidation, efficient tool orchestration through permission gates and prompt cache sharing, and multi-agent coordination models that optimize token costs.
- Three-layer memory systems using indexes to prevent context entropy
- Background processes for autonomous memory pruning and merging
- Granular permission gating and large result offloading in tool architecture
- Cost-effective multi-agent communication via prompt cache sharing
- Risk classification tiers for safe autonomous operations
- Three-layer memory systems using indexes to prevent context entropy
- Background processes for autonomous memory pruning and merging
- Granular permission gating and large result offloading in tool architecture
- Cost-effective multi-agent communication via prompt cache sharing
- Risk classification tiers for safe autonomous operations
