RE:CZ

Summary of PMA Project Development and AI Tool Usage

AI Tools Development

👤 AI tool users, developers, technical blog readers, people interested in prediction markets and arbitrage projects
This article documents the author's summary after completing the development of a prediction market arbitrage project (PMA) on February 8, 2026. The author and team used AI tools like Opus, GPT, and Gemini to write Rust code, despite being unfamiliar with the language. The article focuses on analyzing three main issues encountered while using AI tools: insufficient stability of Agent workflows, blog content approaching the 128k token limit causing small models to fail, and overly long single writes leading to output truncation. To address these, the author proposes solutions including adding strict script checks, future strategies for handling large content, and adopting a segmented writing method of outlining first then filling in details. The article also mentions that some solutions have been implemented in the CZON project, praises Opus's strong summarization capabilities, and plans to try GPT-5.3-CodeX for comparison in the future.
  • ✨ PMA project used AI tools to complete Rust code development
  • ✨ AI Agent workflow stability issues require script checks to resolve
  • ✨ Blog content approaching token limits affects small model processing
  • ✨ Overly long single writes cause output truncation, requiring segmented writing
  • ✨ Opus has strong summarization capabilities, plans to compare with GPT-5.3-CodeX
📅 2026-02-08 · 300 words · ~2 min read
  • Prediction Market Arbitrage
  • AI Tools
  • Rust Development
  • Workflow Optimization
  • Context Limitations
  • Opus
  • GPT

It is the evening of February 8, 2026.

The prediction market arbitrage project, named PMA (Predict Market Arbitrage), has been launched after a day of Vibe Coding.

Mage, Ryan, and I managed to write the code using Opus, GPT, and Gemini in a whirlwind of activity. However, none of us are familiar with Rust, yet the project is written in Rust.

Today, I re-summarized the blog using Opus 4.6 and discovered some issues:

  1. It's sometimes difficult for the Agent to consistently achieve a pass^k level in the workflow, meaning it passes on k consecutive uses. Therefore, an additional hard script check is necessary, and the error messages from this script need to be user-friendly enough to be fed back into the Agent session.

    This has already been implemented in the summary phase of CZON.

  2. Currently, all the articles in my blog seem to be approaching the 128k token limit.

    Some models with smaller context windows (like GPT-3.5-turbo-16k) can no longer handle such large content.

    We'll address this issue in the future when my blog content grows further.

  3. A single write operation might be too long, potentially causing the output to be truncated. OpenCode may fail to execute truncated write tool calls, leading to infinite retries.

    The solution is to outline first, then fill in the content + write in segments. This has successfully written some very large summary articles. It has already been implemented in the summary command of CZON.

Additionally, I must say Opus's summarization capability is truly powerful, and its thinking is very deep.

I heard C1 subscribed to the unlimited version of OpenAI GPT for $200 a month. Next time, I'll try to get some of his GPT-5.3-CodeX to test and compare it with Opus 4.6.

I'm a bit tired today, so I'll stop here for now.

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