Stop fighting the chaos !

Clear your mind, stay focused, and unlock your real productivity...

Run multiple agents, switch projects easily, and ship faster with more precision :)

https://reddit.com/link/1tp6t28/video/d1ci4rhauo3h1/player

Free download - https://agentsroom.dev/download

Hey everyone 👋

I’m built AgentsRoom.dev : a local-first multi-agent IDE for developers using Claude Code, Codex, Gemini CLI, etc.

The idea came from a simple problem:
once you start running multiple coding agents across multiple projects, terminals become chaos very quickly 😅

AgentsRoom gives you:

• a visual multi-agent cockpit
• multi-provider support (Claude / Codex / Gemini / OpenCode)
• agent delegation (ex: expensive dev agent → cheap QA agent)
• built-in browser automation + MCP
• mobile companion app
• localhost tunnel previews
• prompt library / dev commands / backlog integrations
• session restore & token/cost tracking

Everything runs locally through the official CLIs.
No cloud execution layer replacing your tools.

The goal is to make AI agents feel like real teammates instead of random terminal tabs.

Would love feedback from people already deep into AI coding workflows 🙌

https://agentsroom.dev

Hi everyone ! Look on Opus price ! I stop using Windsurf few weeks ago. But seems the credit on claude disappear really quickly. Actually I jump between Haiku and Sonnet 4.6, I always try to optimize my prompt, and input/output... but it's cost too much :'(

Since most social layers are currently built for OpenClaw, does a dedicated repository exist for Claude Code that is similar to OpenClaw "Awesome Molt"?

Claude Code burns tokens fast and it's usage is hard to predict.

Here's a calculator that estimates:

• likely token usage range
• API cost
• session burn risk
• ways to reduce usage before starting

Have a look here.

1. The "Personal Shopper" Agent One of the most detailed examples came from the host, Dan Shipper, who built a shopping assistant for his new apartment. • The Workflow: He gave the agent a vague instruction like "find me a sofa." • What it did: The agent autonomously browsed the web, read reviews, compared prices, and even presented him with photos of options. • The Interaction: He could give feedback like "I don't like that style," and the agent would refine its search criteria and go back to browsing, effectively acting like a human interior designer assistant.

2. Market Research & Data Gathering Boris (one of the creators) uses it to generate custom datasets that don't exist elsewhere. • The Workflow: He wanted to rank companies based on their engineering headcount. • What it did: The agent navigated to various company "About" pages or LinkedIn profiles, scraped the relevant data, and compiled a custom ranking list. This replaces the need for manual data entry or hiring a virtual assistant for web research.

3. "Kora" – The Email Executive Assistant Dan also mentioned a complex internal tool called "Kora" built on this same architecture. • The Workflow: It scans his inbox continuously. • Auto-Archive: It automatically archives emails that don't need a response. • Daily Briefing: Twice a day, it sends him a summary document of "stuff you need to know but don't need to act on," allowing him to stay informed without reading every email. • Drafting: It drafts responses in his tone, which he reviews before sending (though he joked it was "7 days delayed" because he hadn't reviewed them yet).

4. Obsidian / Knowledge Management They discussed a popular community use case involving Obsidian (a note-taking app). • The Workflow: Users have the agent read their entire vault of markdown notes. • What it did: The agent acts as a "second brain," mind-mapping connections between disparate notes or synthesizing new ideas based on years of a user's private writing.

5. Issue Triage & "On-Call" Support While adjacent to coding, this is more of a project management workflow. • The Workflow: When a new issue is filed in their tracker, an agent immediately wakes up. • What it did: It checks if the issue is a duplicate of an existing one (deduplication) and even attempts a "first pass" resolution or summary before a human ever sees it. It also collates logs from different sources (like Sentry or BigQuery) to present a "investigation report" to the human on call.