AICodeKing recommends starting GPT-6 Astra coding tasks at medium reasoning effortReasoning effort is the amount of internal computational work an AI model applies before producing an answer or action., including planning. Higher effort is most useful after a specific bug or difficult implementation has resisted a well-evidenced prompt. Reproduction steps, error messages, and a description of what has already been tried give the model a concrete problem to investigate and make the cost of extra reasoning easier to judge.
The video argues for controlling unnecessary complexity with focused instructions and skills. A simplicity-oriented skill should push the agent to inspect existing patterns, reuse current components, prefer built-in capabilities, and avoid speculative layers or dependencies. At the same time, loading every available skill can waste context and time, so the agent should use only the instructions relevant to the current job.
Visual references provide another constraint. Screenshots, examples, and a short explanation of what matters in each reference help Astra distinguish desired spacing, hierarchy, and interaction behavior from incidental details. A current architecture diagram can also guide later work by connecting the system's components to the relevant code, provided it is updated whenever the implementation changes.
AICodeKing's final recommendation is to test the application through the browserBrowser testing verifies a web application's visible behavior by using it through a browser like a real user. as a user would. The agent should complete the main flow, check persistence after refreshPersistence testing verifies that saved application state remains correct after refreshes, restarts or later sessions., try invalid and empty inputsNegative testing checks how software behaves when it receives invalid, missing or unexpected input., inspect mobile layouts, and re-run affected steps after a fix. Performance work should begin with measurement under consistent conditionsA performance baseline is a repeatable measurement of current system behavior used for fair before-and-after comparison. so the agent can identify the actual bottleneck and compare before-and-after results fairly.
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