mySetup.ai: A Community Hub for Sharing AI Agent Workflows

mySetup.ai is a community-driven platform that allows engineers and AI power users to document and share their specific AI agent setups. Rather than focusing on what is being built, the platform aims to surface the "how"—the specific agents, skills, tools, and orchestration layers that professionals use to manage complex, long-running tasks.

The Core Concept: Moving from "What" to "How"

The primary goal of mySetup.ai is to create a dedicated space where users can be open about their technical stacks. This includes detailing which AI agents are employed, which tools have proven effective, and how longer-running tasks are managed. By documenting these setups, the platform seeks to move the conversation from the final output of AI-generated code to the actual operational workflow used to achieve it.

User-Reported AI Workflows and Tooling

Community discussions reveal a wide spectrum of AI setups, ranging from minimalist cloud-based configurations to highly customized local environments.

High-Performance Voice and Local Stacks

Some power users are prioritizing low-latency voice interfaces to provide better context and nuance. One reported setup includes:

  • Voice Stack: Custom-built ASR (Parakeet v3) and TTS (Omnivoice), achieving a total latency budget of 400-450ms.
  • Model Preference: A preference for OpenAI models for consistency, with experimentation in cheaper alternatives like Deepseek 4.1 Flash.
  • Operational Style: Using voice for expression and context, while maintaining a terminal window for technical details and data.

Local-First and Hardware-Specific Setups

There is a significant interest in running models locally to avoid token costs and increase privacy. Examples include:

  • Hardware: Use of Strix Halo 128GB and Framework mainboards in custom SFF PCs.
  • Software: LM Studio as a primary interface, with models such as Gemma 4 (26b), Qwen 3.6 (35b), and Qwen 3.8 (27B).
  • Security: Integration of Tailscale to secure internal tools and networks.

Hybrid and Cloud-Based Approaches

Many users maintain a hybrid approach, combining specialized IDEs with general-purpose LLMs:

  • Cursor + JetBrains: Using the Cursor $20 plan for agentic prompting and the JetBrains IDE for file browsing and diff reviews.
  • Claude + VM: Running vanilla Claude within a Virtual Machine for isolation.
  • EC2 + Tmux: Using AWS EC2 instances with Claude or Codex, managed via tmux for session persistence.

Community Critiques and Technical Challenges

While the concept is praised for surfacing "unknown unknowns," users have raised several critical concerns regarding the platform's implementation and the nature of AI workflows.

Friction in Contribution

Several users noted that the requirement to use the Model Context Protocol (MCP) or connect a GitHub account to share a setup creates a significant barrier to entry. Critics argue that the ability to post a simple Markdown summary should be available to avoid forcing users to grant third-party access to their credentials.

Security and Proprietary Knowledge

Some industry veterans warn against sharing detailed workflows, arguing that in the AI era, a proprietary workflow is a key component of a developer's job security and productivity.

Tool Churn and Maintenance

The rapid pace of AI tool development makes documented setups obsolete quickly. Users have pointed out that entries may become "half wrong" within a few months, suggesting a need for "last-updated" timestamps to identify stale information.

Cost Transparency

Users have highlighted the high cost of some professional setups, with some reporting subscription plans exceeding $400 per month. This has led to requests for "approximate monthly cost" fields to make setups more accessible and realistic for different budget levels.

Emerging Tools and Custom Implementations

Beyond the platform itself, users shared several custom tools designed to enhance agentic work:

  • Autobot: An OSS/MIT harness designed for long-running agentic tasks (up to 4 days) using a canonical event ledger and heartbeat for persistence.
  • Byre: An agent sandbox focused on developer experience, acting as a low-magic wrapper over Docker/Podman.
  • Dogpark: An agent-to-agent message board for inter-agent communication.

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