TL;DR for Pranav: Claude Code is my primary driver. Codex is good but works best as a second opinion, not the main tool. Here's my complete setup.
What do I prefer? Claude Code, hands down.
Why not Codex alone? Codex is solid but:
- It shines at grinding through well-defined specs
- It's great as a reviewer catching things Claude missed
- But for reasoning, debugging, and complex multi-file work — Claude Code wins
My actual workflow:
1. Claude Code implements → does the heavy lifting
2. Codex reviews → catches edge cases I missed
3. Repeat until both agree it's solid
They complement each other. Don't pick one — use both in tandem.
npm install -g @anthropic-ai/claude-codeThis is your primary tool. Use it for everything first.
After Claude Code finishes a feature, run Codex on the same code:
codex "Review this code for edge cases and bugs"cargo install beadsThis gives your agents persistent task memory across sessions. Game changer for long projects.
Download from josh.ing/promptlet Quick-insert prompt templates into any AI tool with a hotkey.
| Aspect | Claude Code | Codex |
|---|---|---|
| Reasoning | Excellent — traces through complex logic | Good but shallower |
| Context | Deep codebase awareness | More isolated |
| Debugging | Best-in-class | Decent |
| Autonomy | Interactive, collaborative | Better for "set and forget" |
| Long specs | Good | Great — can grind for hours |
Bottom line: Claude Code for thinking, Codex for grinding.
Here's everything I use, organized by purpose:
- Claude Code — main driver for implementation
- Codex CLI — second opinion, code review, long-running tasks
- Promptlet — macOS app for quick prompt template insertion
- Hotkey → search → insert pre-crafted prompts instantly
- Includes: Ultrathink, Chain of Thought, Step-by-Step, Deep Analysis, SOLID Principles
- Works with Claude, ChatGPT, Gemini, any text field
- Beads — persistent git-backed task memory for agents
- Speckit — spec-driven development with Beads + Pivotal Labs TDD
- Quint Code — First Principles Framework for auditable decisions
- Deep Truth Mode — question everything methodology
- Verbalized Sampling — 2-3x diversity improvement in LLM outputs
- Training-free prompting strategy to mitigate mode collapse
- Model-agnostic (GPT, Claude, Gemini, Llama)
- Great for creative writing, synthetic data, dialogue simulation
- Gentleman Guardian Angel — AI pre-commit hook
- Bloom — automated behavior evaluation for LLMs
- 4-stage pipeline: Understand → Ideate → Rollout → Judge
- Test for sycophancy, bias, oversight-subversion, etc.
- Multi-model comparison (OpenAI, Anthropic, Bedrock)
- Interactive web viewer for transcript analysis
- Oh-My-OpenCode — multi-agent orchestration
- AI Data Science Team — specialized data science agents
- Agentic Coding Flywheel — one-command VPS bootstrap
Since you're working on causal inference and PyReason integration, these are especially relevant:
- AI Data Science Team — agents for EDA, feature engineering, ML pipelines
- Modeltime — time series forecasting in R (10K+ series/day)
- Beads — track your experiments across sessions
- Verbalized Sampling — generate diverse synthetic training data
- Bloom — evaluate model behavior systematically
- Today: Install Claude Code + Promptlet
- This week: Try Claude Code as your primary. Use Codex only for review.
- Next week: Add Beads for task persistence.
- When ready: Add Speckit for structured spec → plan → implement workflow.
You'll feel the difference immediately. Claude Code thinks with you. Codex executes for you.
| Tool | Link | Purpose |
|---|---|---|
| Claude Code | npm i -g @anthropic-ai/claude-code |
Primary coding agent |
| Codex CLI | OpenAI | Review, autonomous tasks |
| Promptlet | josh.ing/promptlet | Quick prompt templates (macOS) |
| Beads | steveyegge/beads | Persistent agent memory |
| Speckit | jmanhype/speckit | Spec-driven workflow |
| Spec-Kit | github/spec-kit | GitHub's official version |
| Quint Code | m0n0x41d/quint-code | Reasoning framework |
| Deep Truth Mode | QuantumCousin/Deep-truth-mode-spirit | First principles |
| Verbalized Sampling | CHATS-lab/verbalized-sampling | Output diversity |
| Bloom | safety-research/bloom | Behavior evaluation |
| GGA | Gentleman-Programming/gentleman-guardian-angel | Pre-commit AI review |
| Oh-My-OpenCode | code-yeongyu/oh-my-opencode | Agent orchestration |
| AI Data Science Team | business-science/ai-data-science-team | Data science agents |
| Modeltime | business-science/modeltime | Time series forecasting |
| ACFS | Dicklesworthstone/agentic_coding_flywheel_setup | VPS bootstrap |
Hit me up if you have questions — happy to pair on setting any of this up.
— Jay, December 2025