{"slug": "my-ai-refactor-was-slow-the-model-wasn-t-guilty", "title": "My AI Refactor Was Slow. The Model Wasn't Guilty.", "summary": "A developer's profiling experiment found that AI refactoring delays are often caused by network queue time, not model inference. Using MonkeyCode's hosted endpoint versus its free server option, the developer measured a 43-second wait for the first token on the hosted path versus 5 seconds on the free server, cutting total refactor time from 111 to 56 seconds. The developer concluded that time-to-first-token, not token speed, dominates perceived performance.", "body_md": "Everyone talks about what developers do while AI codes. Mostly, we wait. So I measured the waiting. The model was not guilty.\n\nFor two weeks my workflow looked like this. Copy a module. Write a refactor prompt. Paste both into an AI coding assistant. Wait for a diff. Apply the diff. Run the tests. Every spinner felt like weak inference. Every pause felt like a slow model.\n\nI was looking at the wrong suspect.\n\nA refactor has four phases. Serialize the prompt. Wait for the server. Stream the tokens. Apply the diff. I timed each one. How else do you convict the right suspect? I kept one graph. This is the story of that graph.\n\nI picked MonkeyCode for the experiment. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The project is open source, and the current free tier includes 10 million tokens and a free server option. That gave me two network paths to compare without paying for either.\n\nHere is the profiling wrapper. It sends a chat request, times the request with curl, then times the local apply step with git.\n\n``` bash\n#!/usr/bin/env bash\n# profile-refactor.sh - time an AI refactor call end to end\nset -euo pipefail\n\npayload=\"$1\"\npatch=\"refactor.patch\"\nlog=\"phases.jsonl\"\n\ncurl -sS \\\n  -o \"$patch\" \\\n  -w '{\"ttfb\":%{time_starttransfer},\"total\":%{time_total},\"bytes\":%{size_download}}\\n' \\\n  -H \"Content-Type: application/json\" \\\n  -d \"@$payload\" \\\n  \"$MONKEY_ENDPOINT/v1/chat\" > \"$log\"\n\nt0=$(date +%s%N)\ngit apply --check \"$patch\"\ngit apply \"$patch\"\nt1=$(date +%s%N)\n\npython3 - \"$log\" \"$t0\" \"$t1\" <<'PY'\nimport json, sys\n\ncurl = json.loads(open(sys.argv[1]).read().strip())\napply_s = (int(sys.argv[3]) - int(sys.argv[2])) / 1e9\nprint(json.dumps({**curl, \"apply_seconds\": round(apply_s, 3)}))\nPY\n```\n\nSet `MONKEY_ENDPOINT`\n\nto the server you want to test. Set the payload to your prompt. Run it once per path, and compare the JSON lines. That is the entire method.\n\nCurl gives two timestamps. `time_starttransfer`\n\nis the moment the first token arrives. `time_total`\n\nis the end of the stream. The distance between them is the model writing. The Python step tells you how long git needed to apply the diff. I ignored prompt serialization because my payload was small. Build the JSON once, then time the request.\n\nI ran the same task five times. Same module. Same prompt. Same test suite. Path A used the hosted endpoint. Path B used the free server option with the free model. This is the graph I kept.\n\n```\nphase                Path A (hosted)              Path B (free server)\nwait first token     42.8s ███████████████         5.1s ██\ntoken stream         61.4s ██████████████████████  44.2s ████████████████\nlocal apply           7.1s ███                      6.9s ███\ntotal               111.3s                        56.2s\n```\n\nRead the top row first. In Path A, I stared at a spinner for 43 seconds before anything arrived. That is not model thinking. That is queue time. The free server cut the wait to five seconds. The total time dropped by half.\n\nThe graph does not prove one model beats another. It proves the road matters. The stream row is the model doing real work. The wait row is the road doing nothing. I kept this graph because it separated the two.\n\nThe first row also explains perception. Forty-three seconds of spinner destroys focus. Five seconds does not. When your attention is gone, the total time feels twice as long. Engineers fix tokens per second. Users feel time to first token. I taped this graph to my monitor for a day. Then I deleted it. The fix is what stays.\n\nOne graph hides plenty. My five runs wobbled. The hosted wait bounced between 38 and 47 seconds. Shared servers breathe. This was one module, one afternoon, one laptop. It is a data point, not a benchmark. Run the script twice before you believe it. Then run it on your stack, because your bottleneck will be different.\n\nThe apply row embarrassed me. Seven seconds to apply a diff I could have applied by hand in four. My own pipeline, not the model, was the second bottleneck. So I fixed the pipeline. I stopped asking for whole-file rewrites. I sent shorter prompts, one function at a time. Same model. Same server. Smaller waits, shorter streams, faster applies.\n\nThis setup is not for everyone. If you own a local GPU, a remote server adds latency you do not need. If your codebase has strict data rules, a shared free server sends your code somewhere else. Read the terms before you paste a module. If your edits are tiny, skip all of this. Just make the edit.\n\nThe lesson is boring on purpose. Measure before you blame. The model took most of the blame, and the graph gave most of it back. If your assistant feels slow, wrap it in a timer first. The script above is free, and the free tier is a fine place to aim it. Spend one afternoon, and you will know exactly where your time goes.", "url": "https://wpnews.pro/news/my-ai-refactor-was-slow-the-model-wasn-t-guilty", "canonical_source": "https://dev.to/apppro_4800/my-ai-refactor-was-slow-the-model-wasnt-guilty-2gp8", "published_at": "2026-08-28 03:50:53+00:00", "updated_at": "2026-08-28 04:19:34.668108+00:00", "lang": "en", "topics": ["developer-tools", "ai-tools", "mlops"], "entities": ["MonkeyCode"], "alternates": {"html": "https://wpnews.pro/news/my-ai-refactor-was-slow-the-model-wasn-t-guilty", "markdown": "https://wpnews.pro/news/my-ai-refactor-was-slow-the-model-wasn-t-guilty.md", "text": "https://wpnews.pro/news/my-ai-refactor-was-slow-the-model-wasn-t-guilty.txt", "jsonld": "https://wpnews.pro/news/my-ai-refactor-was-slow-the-model-wasn-t-guilty.jsonld"}}