Key Points
- Developer used Claude LLM to generate a Python log‑colorizer with scrolling capability.
- Initial version worked but caused near‑full CPU usage during horizontal scrolling.
- Claude identified full‑screen redraw and buffer scanning as the performance bottleneck.
- Zero‑CPU impact was deemed unattainable; only low‑impact optimizations were possible.
- Extensive token consumption and code revisions failed to achieve a satisfactory fix.
- The project stalled, highlighting challenges of using LLMs for performance‑critical code.
Background
A programmer sought to automate the creation of a log‑colorizing utility by prompting the Claude large‑language model. The goal was to generate a separate Python script that could accept piped input, parse ANSI color codes, and display the logs within a scrollable viewport. The developer described the desired behavior in natural language, trusting the model to translate the requirements into functional code.
The Challenge
Claude produced a script that initially appeared to work: the logs displayed, and scrolling was possible. However, when the developer tested horizontal scrolling beyond a short distance, the host machine’s CPU usage spiked dramatically, lighting up “like a Christmas tree on fire.” The tool consumed almost 100 percent of a single CPU core during scrolling operations.
Root‑Cause Explanation
When asked why the CPU load was so high, the model explained that the bottleneck stemmed from the way the tool handled screen redraws. Each new line triggered a full‑screen redraw of the terminal’s height, and every key repeat—such as holding an arrow key—caused the program to scan the entire buffer, compute visible width, and redraw the entire view. This exhaustive processing for every tiny interaction resulted in the excessive CPU consumption.
Search for a Zero‑CPU Solution
The developer requested a “zero‑CPU‑impact” version of the tool. Claude clarified that achieving zero CPU impact was not feasible given the need to parse ANSI sequences, slice lines, and repaint the display on every scroll event. Instead, the model suggested low‑impact approaches that could reduce, but not eliminate, the processing load.
Attempted Optimizations
Following the model’s advice, the developer and Claude experimented with several performance‑tuning strategies. The collaboration involved consuming a substantial amount of tokens as the model generated incremental code changes and explanations. Despite these efforts, the developer admitted limited ability to understand the evolving Python code, which hampered effective guidance and testing.
Outcome
After multiple days of token‑heavy interaction and code revisions, the development effort reached an impasse. The tool remained CPU‑intensive during scrolling, and the promised low‑impact optimizations did not deliver a satisfactory reduction in resource usage. The developer concluded that the project had hit a wall, acknowledging both the capabilities and limitations of relying on an LLM for complex performance‑critical software engineering tasks.
Source: arstechnica.com