u/OddRecognition6214

Since OpenCode CLI supports multiple providers at once (/connect), I've been stacking ChatGPT Plus ($20) with OpenCode Go ($10) and it's a killer combo. Here's the breakdown:

🥇 ChatGPT Plus — Your "Chief Architect"

• GPT-5.5 Thinking for complex reasoning, architecture, system design
• Deep Research, Agent Mode, Canvas
• Use for the ~20% of sessions that need top-tier reasoning

🥈 OpenCode Go — Your "Engine Room"

• 14 open-source coding models (DeepSeek V4 Pro/Flash, Qwen 3.7 Max, MiniMax M3, GLM 5.1, Kimi K2.5, etc.)
• $10/mo with usage pool: ~$12/5hrs, $30/week, $60/month ceiling
• Throughput example: ~31K requests/5hrs on DeepSeek V4 Flash
• Zero-retention policy, global endpoints (US/EU/SG), OpenAI-compatible API
• First month is $5

🔧 Workflow in OpenCode CLI

Configure both in opencode.json:

{
  "provider": {
    "openai": { "options": { "apiKey": "sk-..." } },
    "opencode": { "options": { "apiKey": "go-..." } }
  }
}

Then /connect both keys and use /models to pick the right model per task.

Real patterns I use:

1. Architect → Builder — Design with GPT-5.5 Thinking in Plan mode, implement with DeepSeek V4 Pro in Build mode
2. Smart Review → Bulk Fix — GPT-5.5 for security-sensitive reviews, DeepSeek V4 Flash for mass refactoring
3. Research → Implementation — Deep Research for best practices, Qwen 3.7 for writing the code

Why it wins: $30 gets you top-tier reasoning and high-volume throughput. ChatGPT Plus alone is rate-limited on heavy coding. Go alone lacks the deep reasoning. Together they cover both ends.

full blog post: https://searchagentsky.com/r/9f8678e502d0-developer-power-stack-chatgpt-plus-20-opencode-go-10-opencod

Anyone else running a multi-provider setup in OpenCode CLI? What's your stack?

I am experimenting with a small notebook idea: the starting point can be plain English, but the artifact is still Python.

The goal is not to hide pandas behind a dashboard or make a no-code replacement. It is to help someone move from an intent like “compare survival rate by passenger class” to the actual pandas code that answers it.

The notebook runs Python in the browser with Pyodide. It can load CSVs, run pandas cells, show DataFrame output, render charts, and reopen editable examples through shareable links.

I made three small examples:

• Tips dataset: average tip rate by day
• Penguins dataset: body mass by species
• Titanic dataset: survival rate by passenger class

Tutorial and examples: https://analytics.unchainedsky.com/learn/python-pandas-csv

The idea is a bridge from natural-language intent back into inspectable Python code, not a replacement for learning Python.