A developer released TITAN (Technical Intelligence, Tasking & AI Navigation), an open-source methodology that encodes a Codex development workflow inside the repository itself. It assigns SOL to planning and architecture and LUNA to implementation, with project context persisted in .titan/ and docs/ files so decisions survive across sessions. It ships as an npm package and GitHub repo (v1.1.0) and targets long-running projects with real business rules.
A GitHub setup that orchestrates OpenAI Codex models to reduce rate-limit burn: Sol handles planning and review, Luna does most implementation, Terra is a fallback for hard tasks. Sol breaks work into small, self-contained tasks for Luna. Aimed at Codex users hitting usage caps.
A GitHub setup that orchestrates OpenAI Codex models to reduce rate-limit burn: Sol handles planning and review, Luna does most implementation, Terra is a fallback for hard tasks. Sol breaks work into small, self-contained tasks for Luna. Aimed at Codex users hitting usage caps.
Claude Code users are experiencing implementation drift when using Opus 5 for planning and Sonnet 5 for execution. The Sonnet 5 subagent often generates new plans on the fly instead of following the approved plan, leading to scope and structure changes. Users are seeking best practices to prevent this drift.
Claude Code users are experiencing implementation drift when using Opus 5 for planning and Sonnet 5 for execution. The Sonnet 5 subagent often generates new plans on the fly instead of following the approved plan, leading to scope and structure changes. Users are seeking best practices to prevent this drift.
A user on the $20 Plus plan reports that OpenAI's SOL model consumes their weekly allowance rapidly, contradicting claims that SOL is cheaper and offers more usable capacity than Fable. The user relies on Claude Code Max as their main driver and uses Codex as an adversarial reviewer.
A user on the $20 Plus plan reports that OpenAI's SOL model consumes their weekly allowance rapidly, contradicting claims that SOL is cheaper and offers more usable capacity than Fable. The user relies on Claude Code Max as their main driver and uses Codex as an adversarial reviewer.
A Codex user reports that running Goals with Luna/High is effective and cheap, using SOL for planning and bug fixes. The workflow leaves a few bugs at the end but avoids killing 15% of the plan, making it a regular practice.
A Codex user reports that running Goals with Luna/High is effective and cheap, using SOL for planning and bug fixes. The workflow leaves a few bugs at the end but avoids killing 15% of the plan, making it a regular practice.
A Codex user describes a self-built 'development factory': a dashboard that turns design documents into phased implementation plans, sequences them into work packages, and executes code phase by phase with LLM calls via the Codex app-server. It shows practitioners how to wrap models in deterministic validation and human approval gates for consistency on long-running projects.
A Codex user describes a self-built 'development factory': a dashboard that turns design documents into phased implementation plans, sequences them into work packages, and executes code phase by phase with LLM calls via the Codex app-server. It shows practitioners how to wrap models in deterministic validation and human approval gates for consistency on long-running projects.