Herdr Lets Developers Run and Supervise Multiple AI Coding Agents in One Terminal

·1 views

Herdr is a terminal-based agent multiplexer that allows developers to run multiple AI coding agents simultaneously, each in its own persistent PTY session. Unlike standard terminal tools such as tmux or Zellij, Herdr tracks agent state — indicating whether each agent is working, blocked, idle, or done — removing the need for constant manual monitoring. The tool runs as a local binary on any machine, including servers and VMs, and sessions remain alive even after a terminal window closes or a laptop goes to sleep. Herdr exposes both a CLI and a local socket API, enabling agents themselves to spawn and manage other agents programmatically. This design is intended to shift the bottleneck in AI-assisted engineering from task execution to higher-level supervision and coordination.

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)

How to Build a Branded, Secure Chat UI on Top of n8n's Chat Trigger Webhook

n8n's Chat Trigger node creates an HTTP endpoint that accepts a user message and returns a bot reply, but it does not include any frontend UI or widget. Developers embedding this in a real product must build or configure a separate chat interface that handles branding, session management, and security. Two open-source options — n8n's own Vue-based @n8n/chat widget and the Svelte-based ChatFlowGateUI — follow the same webhook contract and can be mounted via an iframe to prevent CSS conflicts with the host page. Theme presets and configuration options allow developers to control visual style, layout mode, streaming, and session persistence without extensive custom code. Protecting the webhook URL from public exposure through browser dev tools is highlighted as a critical step often skipped in basic tutorials.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How AI Agents Should Decide What to Store and Skip in Memory Systems

Effective AI agent memory depends on selective storage — keeping too little causes the agent to forget critical information, while storing too much fills memory with noise that degrades retrieval quality. The extraction step, where raw conversation is distilled into clean, self-contained facts, is considered the most consequential part of any memory system. Agents should prioritize storing stable user preferences, decisions and outcomes, human corrections, and durable ongoing-work state. Transient chatter and one-off details with no future relevance should be deliberately excluded, as irrelevant memories can surface at the wrong moment and obscure useful ones. Memory can be extracted either at the end of a session for simplicity or incrementally during a session for reliability, and many systems combine both approaches.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer builds open-source typing speed test that withholds averages until data is sufficient

A developer has created a typing speed test designed to produce statistically defensible results, addressing common flaws in existing benchmarks such as inconsistent passages, gross WPM counting, and susceptibility to cheating. The tool uses a single standardized passage for all users and calculates net WPM, counting only correctly typed characters, while blocking paste actions and rejecting implausible scores server-side. Built on Cloudflare Workers and D1, the platform collects an anonymized, publicly downloadable dataset but deliberately withholds any headline average until at least 1,000 submissions are recorded. Privacy is maintained by storing only a salted hash of visitor IPs for rate-limiting, with the public export containing just timestamp, WPM, and accuracy. All published statistics are fully reproducible via a provided script and raw CSV dataset licensed under CC BY 4.0.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

React Props Explained: How to Pass Data Between Components

In Day 6 of the React Mastery Series on DEV Community, the tutorial covers props, short for properties, which allow data to be passed from parent components to child components in React. Props work similarly to function arguments, enabling a single reusable component to render different content based on the values it receives. Data flows strictly in one direction — from parent to child — and child components cannot modify the props they receive, keeping the application predictable. Developers are encouraged to destructure props for cleaner code and to set default parameter values for cases where a prop might not be provided. The lesson also introduces the special children prop, which allows content placed between a component's tags to be rendered dynamically, making components more flexible and reusable.

0 comments Read more at DEV Community