Andrew Ng's OpenWorker Brings Local-First AI Agent to Developers via MIT License

·2 views

Andrew Ng released OpenWorker in late July 2026, an MIT-licensed desktop AI agent that runs locally and connects directly to a user-chosen LLM provider using the developer's own API key. Built as a Tauri and React app over a local Python server, it takes high-level outcome instructions, breaks them into steps, and delivers finished artifacts such as drafted documents, calendar changes, or composed messages. Unlike many chat-based agents, OpenWorker classifies every tool action into one of four risk tiers and offers five permission modes, ensuring the agent pauses for human approval before executing consequential operations. The system is built on Ng's provider-agnostic aisuite library, supporting roughly 30 verified models across providers including OpenAI, Anthropic, Google, and several open-weight options. Credentials and connector tokens are stored in a local secret store, with model calls routed directly to the chosen provider, limiting cloud exposure to an optional OAuth broker for third-party app connections.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer builds Brainfuck-to-JVM compiler from scratch using pure Node.js

A developer documenting JVM internals chose Brainfuck, the esoteric programming language created by Urban Müller in 1993, as a minimal test case for building a compiler from scratch. The project, called BrainJuck, aims to compile Brainfuck source code into valid JVM bytecode (.class files) runnable directly with the Java runtime. This first installment in a three-part series covers building an interpreter, which serves as the foundation for the full compiler. The interpreter parses Brainfuck's eight commands into an intermediate representation, handling loop jumps via a stack-based placeholder mechanism. No external dependencies or frameworks are used — only Node.js and its native test runner.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Three Useful Action Mailer Features Most Rails Developers Overlook

A developer revisiting the Action Mailer documentation recently discovered three lesser-known built-in features of the Rails mailing framework. The first is an email preview tool that renders messages directly in the browser without sending them, eliminating the need to repeatedly dispatch test emails. The second is an interceptor hook that allows developers to modify outgoing emails just before delivery, commonly used to add environment labels or redirect mail in staging setups. The third feature enables per-email delivery method overrides, useful in multi-tenant apps or when routing different email types through separate providers. Though some of these features have existed in Rails for some time, they remain underused among developers who rely on Action Mailer in production.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How One KNX Config Mistake Led to Better Home Assistant Motion Lighting

A developer building motion-controlled lighting across seven KNX sensors in Home Assistant discovered that assigning a single group address per sensor caused conflicts between lighting and presence/security logic. The root fix required configuring a second group address per physical sensor in ETS — the KNX programming tool — rather than patching conditions inside Home Assistant. This separation ensures lighting automations and security paths respond to motion independently, preventing changes to one from silently affecting the other. The author shares working Home Assistant YAML automations that turn lights on instantly when motion is detected and off after a five-minute delay once motion clears. Key implementation details include using mode: single to prevent automation loops and relying on the for: delay parameter as the most critical tuning variable in any motion-lighting setup.

0 comments Read more at DEV Community

ProgrammingHacker News ·

Researchers Propose GPU Bézier Curve Evaluation via Texture Lookup Method

A new technical paper published in the Journal of Computer Graphics Techniques (JCGT) presents a texture lookup-based approach for evaluating Bézier curves on GPUs. The method leverages GPU texture sampling hardware as a computational tool for curve evaluation. The research aims to offer an efficient alternative to traditional Bézier computation methods on graphics hardware. The paper is available through the JCGT's open-access publication platform.

0 comments Read more at Hacker News