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 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.
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
Ask-search provides a way for AI agents to perform web searches privately and without the need for paid API keys. It works by wrapping SearxNG, which aggregates results from multiple sources such as Google and Bing into a single meta-search engine. This allows local tools like Claude Code or Antigravity to access real-time information while maintaining data privacy.
* Zero cost via self-hosted SearxNG backend
* High compatibility with CLI, MCP servers, and OpenClaw skills
* Flexible search options including language and category filters
* Privacy protection through local query aggregation
* Zero cost via self-hosted SearxNG backend
* High compatibility with CLI, MCP servers, and OpenClaw skills
* Flexible search options including language and category filters
* Privacy protection through local query aggregation
This tutorial provides a step-by-step guide to building a lightweight personal AI agent inspired by the nanobot architecture in Google Colab. The approach focuses on recreating core components—such as provider abstractions, tool registration, session memory, and lifecycle hooks—rather than relying on heavy external frameworks. Key features include a tool registry for Python functions, token-budgeted memory management, and an MCP-style tool server for external capabilities. The guide includes a complete Python implementation that supports both live OpenAI-compatible models and a deterministic mock provider for offline testing.
Main topics covered:
- Provider abstraction for multi-model compatibility
- Automated tool schema generation using decorators
- Session-specific memory with token budgeting
- Lifecycle hooks for auditing and timing
- Dynamic skill loading and MCP server connection
Main topics covered:
- Provider abstraction for multi-model compatibility
- Automated tool schema generation using decorators
- Session-specific memory with token budgeting
- Lifecycle hooks for auditing and timing
- Dynamic skill loading and MCP server connection
llayer applies the Unix philosophy to large language model orchestration by building framework-free agents with bash, curl, and jq. The architecture decomposes the agent lifecycle into three fundamentals: an append-only JSONL history file for state and memory, a jq stream reducer for context window management, and a standard bash while loop for control flow. This stateless text pipeline enables time-travel debugging via simple file slicing, zero abstraction tooling through native bash functions, and seamless POSIX tool integration for filtering or benchmarking. The system functions as a REPL-style loop that ingests user input, constructs context, evaluates it against a local model like Ollama, handles tool dispatches, and outputs results. All interactions are recorded immutably in a structured JSONL event schema, prioritizing transparency, composability, and minimalist design.
- Append-only JSONL history for auditing and replayability
- Modular command chaining for stateless and stateful interactions
- Docker Compose integration for local Ollama inference
- Transparent POSIX tool pipeline for data filtering and token benchmarking
- Minimalist schema with explicit event types and sources
- Append-only JSONL history for auditing and replayability
- Modular command chaining for stateless and stateful interactions
- Docker Compose integration for local Ollama inference
- Transparent POSIX tool pipeline for data filtering and token benchmarking
- Minimalist schema with explicit event types and sources
An examination of the hype surrounding autonomous AI agent frameworks and why they may add unnecessary complexity to software development. The author argues that for most production use cases, structured workflows using LLM function calling are more reliable than fully autonomous agents.
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
