Developer Report - Prathamesh

Jan 1 — Feb 28, 2026

# Prathamesh

Backend Developer • Python, Docker, React • Codex CLI

Total Sessions
49
AI-assisted development
Made Progress
49%
Sessions that got meaningful work done
Conversation Depth
19
Average back-and-forths per session
Primary Strength
You tackle ambitious, multi-step integration tasks — from Auth0 integration to modular backend features. Your prompts show strong architectural thinking, often referencing specific directories and existing patterns to follow.
Session Patterns
Prompt Categories
General Tasks
73%
Detailed Specs
10%
Exploration
4%
Code Review
4%
Session Outcomes
Abandoned
51%
Partial Success
37%
Self-Corrected
6%
User Rescued
6%
Analysis
Why Sessions Get Stuck

In about half your sessions, the AI didn't have enough context to make progress — it couldn't find the right files or didn't understand what you wanted changed. In another ~1 in 5 sessions, Codex CLI's sandbox couldn't read your project files at all, so the AI was working blind.

What We Noticed

Short, open-ended prompts like "fix this" or "do it" led to twice as many problems compared to when you gave more detail. Interestingly, even your longer spec prompts sometimes struggled — usually because they packed multiple features into one session, which is hard for the AI to handle in one go.

Workflow Tips
1
Front-load Context in Codex CLI

About 1 in 5 of your sessions went nowhere because Codex couldn't actually see your files. Quick fix — start by asking it to confirm it can read what you need:

`List the files in backend/src/search/ and show me the first 50 lines of main.py`
2
Break Multi-Feature Requests

When you asked for multiple things at once (e.g. "add CSV export AND cosine similarity search"), those sessions almost always stalled. One feature per session works much better:

- Session 1: "Add CSV export endpoint for slug data"
- Session 2: "Add cosine similarity search for slugs"

3
Be Explicit About "What" Not Just "Where"

Your prompts often specify the right files but leave the implementation vague. Add concrete acceptance criteria:

`In backend/src/search/routes.py, add a POST /export endpoint that takes a CSV with a "slug" column, queries the DB for each slug's metadata (exclude vector field), and returns a CSV response.`
Notable Patterns
✓
Architectural Awareness

You consistently reference existing patterns ("use the same LLM client as search", "modularized like everything else"). This is excellent practice for AI-assisted development.

✓
Diverse Task Range

From Docker configs to React frontends to Python pipelines — you use AI across the full stack, showing comfort with AI as a general-purpose tool.

Python Docker React Codex CLI Backend APIs Data Pipelines