Explore unfamiliar code, work out how to check a change and make the edit with help from OpenAI’s terminal coding assistant.
In the guideHow would you check this change?Read guideDocs checked
The AI tools guide
AI tools,
put to work.
Work through a codebase, review a pull request, or keep several coding agents in view. Find a tool that fits the job.
Inside the Codex guide
CodexOpenAIHow would you check this change?
Explore unfamiliar code, work out how to check a change and make the edit with help from OpenAI’s terminal coding assistant.
Read the walkthrough
The directory
Choose a tool.
Follow a failure through the source and tests, then work on a fix with Anthropic’s coding assistant in your terminal.
In the guideWhat happens when this request fails?Read guideDocs checked
Choose the model and adapt the assistant around recurring work, such as turning incident files into a report another engineer can check.
In the guideWhat can these incident files tell you?Read guideDocs checked
See which coding session needs you and return to its conversation, with terminal processes that keep running while you detach.
In the guidePick up a review where you left itRead guideDocs checked
Get another review of a pull request, discuss findings beside the code and check whether the suggested problems can actually occur.
In the guideCould this retry cause a second problem?Read guideDocs checked
Turn a written report into a defined choice or score, then compare its recommendations with the decisions your workflow needs.
In the guideWhich team should receive this ticket?Read guideDocs checked
02Learning paths
Start with the job.
Make a code change, review a pull request, or coordinate a few agents. These walkthroughs show how to use the tools along the way.
Learning path · 5 steps
Make a code change with an assistant
If you have a bug to fix or an unfamiliar function to change, you already have a useful reason to try a coding assistant. This path follows that job from understanding the code to checking the edit. Codex, Claude Code and Pi can all take part; the important comparison is whether the assistant makes the work easier to understand and finish.
Step 1 of 5
Start with the access you already have
Choose an assistant that fits the project’s data requirements, your operating system and the account available to you. Codex and Claude Code are straightforward candidates when you already use the corresponding service. Pi is worth a look when provider choice or customization is part of the requirement. You do not need to install all three to learn whether this way of working helps.
Planned coverageComing soon10 tools · 4 paths
These guides are not ready yet.
Run local models
How much model can your hardware comfortably run, and what work will it be good for? This planned guide will connect model choice and local processing with a task you can try. It is not ready yet.
Connect your data
A planned guide to giving an assistant a small approved source to search, then following its answers back to the original material. Setup and the worked example are still being reviewed.
Evaluate quality and cost
Which tool leaves you with more useful work after mistakes, retries and review time are counted? This planned guide will explain how to compare the same tasks fairly. It is not ready yet.
Monitor production AI
A planned guide to following an AI request through its model and tool calls, so you can understand a failure or an unexpected bill. The guide is still being prepared.
OpenAI API
For building an application that calls OpenAI models directly. The planned guide will cover access, authentication, usage controls and data handling. If you want help working in a repository, the Codex guide is available now.
Anthropic API
For applications that call Anthropic models directly. Access, authentication, usage controls and data handling will be covered in a separate guide. For work inside a codebase, start with Claude Code.
Ollama
Planned coverage will connect hardware requirements and model choice with a useful local task. The installation instructions and example still need a full documentation review.
LM Studio
The future guide will explain setup, model choice and where your data is processed. Its first worked example is still to be reviewed.
OpenRouter
A planned guide to understanding which provider handles a model request, what terms apply and how it is billed. The profile is not ready yet.
n8n
The planned guide will follow an automation from its input through the services and credentials it needs. Setup and the worked example still need review.
Langfuse
Planned coverage will explain how to inspect model requests and evaluate their results, including what information is sent to the service. The guide is still being prepared.
Phoenix
A planned guide to examining AI behavior and tracing where evaluation data goes. Setup and the worked example still need a full review.
Promptfoo
The planned guide will show how to compare results on the same task and understand what the tests establish. Current installation instructions and the example still need review.
LangGraph
A planned guide to how an agent workflow tracks its progress and resumes after an interruption. The setup and first worked example still need review.