cbox

The description states that cbox:

• Runs OpenAI Codex unattended inside a hardened Docker container.
• Treats the agent as untrusted through policy enforcement and isolation.
• Uses read-only mounts for rules and sensitive state.
• Forwards SSH signing sockets instead of private keys into containers.
• Audits delegated calls byte-for-byte, with depth-limited Git-scoped execution.
• Integrates Codex via codex mcp-server, enabling delegation both ways (from console to Codex and vice versa).
• Provides a shared on-disk brain (LEDGER.md, PROGRESS.md) that resumes work across sessions.
• Supports optional domain-allowlist egress proxy for traffic control.
• Was built using Codex itself through its MCP server.

Inference cbox is a developer tool focused on secure automation of AI agents, particularly around Codex, with an emphasis on safety, continuity, and orchestration.

The author claims:

• cbox was initially motivated by the need to avoid clicking "allow" on every action in Codex.
• It evolved from a simple security boundary into a full development console.
• The tool is built using its own mechanisms — “Codex as a first-class engine” is not marketing but how it was developed.

Inference The positioning has shifted from a niche security-focused utility to a broader developer workflow tool that integrates AI agent automation with persistent context and safe delegation.

The description does not explicitly name target customers or personas. However, it implies:

• Developers who use OpenAI Codex extensively.
• Users seeking secure, unattended automation of AI tasks.
• Those working in environments where prompt injection or compromised agents are a concern.
• Individuals or teams that value continuity and context preservation across sessions.

Inference The primary ICP appears to be advanced developers or DevOps engineers who rely on Codex for coding tasks and require secure, persistent workflows.

No evidence of pricing, monetization strategy, or business model is provided in the description. The author does not mention any commercial aspects beyond personal use and development.

Not evidenced

The description states:

• Built with bash, cdi, claude, codex, dante, gpt-5.6, json, mcp, nvidia, ollama, python, ripgrep, socat, socks, sqlite, supervisor, tinyproxy, tmux, ubuntu.
• Uses Docker for containment.
• Implements read-only mounts and socket forwarding.
• Leverages Git-scoped auditing.
• Integrates Codex via codex mcp-server.
• Supports multiple delegate tiers (fast, mid, top) per task.
• Stores project state in plain Markdown files (LEDGER.md, PROGRESS.md).
• Includes optional domain allowlist egress proxy.
• Designed for layered containment.

Inference The technical stack reflects a hybrid of scripting, containerization, and AI agent orchestration. The architecture emphasizes security through isolation and control over agent behavior.

The description indicates:

• The tool was built by the author alone (team size: 1).
• It is used daily as a personal driver for real project work.
• The author develops cbox inside cbox, which led to iterative improvements.
• The roadmap includes concrete next steps like vendor-neutral session hub, shared sessions, Hermes engine integration, infrastructure access, and code-quality refactor.

Not evidenced There is no mention of external users, customers, revenue, or adoption metrics. No evidence of traction beyond personal usage.

The author notes:

• They only discovered competition mid-week during the hackathon submission.
• The tool was submitted to the OpenAI 2026 hackathon on Devpost.

Not evidenced No information about competitors, market positioning, or competitive landscape is provided.

Key risks and red flags inferred from the description:

• The entire project is self-reported by one individual; no third-party validation.
• No evidence of external users or adoption.
• The tool is described as a daily driver for the author, but there's no indication of broader impact or scalability.
• The roadmap suggests ongoing development without clear commercial milestones.
• Reliance on Codex (which may not be widely available or stable) could limit utility.

1. What is your actual usage frequency and duration with cbox?
2. Are there any external users or teams currently using cbox in production?
3. How do you plan to scale beyond a single developer's workflow?
4. What are the specific use cases where cbox has been applied outside of personal development?
5. How does cbox handle edge cases like failed delegations or system crashes?
6. Are there any known limitations or trade-offs with running Codex in this manner?
7. Do you have plans to support other AI agents beyond Codex, and how would that affect the architecture?