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My OpenClaw is STUPID (Here's how to Fix It)

Published
Feb 24, 2026
Duration
8:04
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Video summary

Companion notes

Your OpenClaw Agent Is Failing Because You Give It Human-Sized Tasks

The hosts call out a pattern: users tweet polished OpenClaw dashboards while the bot "starts gaslighting me" in practice. The fix is not better hardware — it's decomposing the task.

Break The Task Before You Start

When the host tried to get a Stark agent to summarize 30 tweets on the fly, it failed because browsing, filtering, and summarizing were bundled. He replaced it with: "Hey, you know what? These are some cool tweets. Go scan these guys and save it." Saved tweets are pushed into a vector database so the agent can query them later. Splitting scan, store, retrieve, and summarize into separate steps is what made the dashboard work — and exposed exactly which step was broken.

Force The Agent To Test The Connection

OpenClaw "doesn't test" by default. The host told the bot to fetch YouTube view counts, and it "built the dashboard and it built it like nothing there — it says all done we're complete guys." The cure: ask the agent to walk you through the YouTube API setup, explicitly say "make sure that's connected yes test the connection," then take a screenshot of the result. Same trick for the Facebook/Meta case referenced in the video — an agent with root access was installed and "started messaging everyone on her contact list" because no one constrained the scope.

Feed The Docs First, Save Working Flows As Skills

The host's standing first prompt: "Here's the API documentation. Learn and understand this first." Pass the doc, confirm the agent read it, then make the API call. Once a workflow runs cleanly end-to-end, tell OpenClaw to "save this as a skill for future reference" — that's how repeated tasks stop breaking.

Don't Treat The Agent Like A Human, Expect It To Fail

The hosts' final rule: agents "will always fail on tasks" regardless of complexity — and "the simpler the task, the more it fails." Run a small task, verify the output, then scale up. For data work, demand the actual artifact — "give me the tweets" or "make a dash for me" — so you can catch the fake responses in real time.

Watch on YouTube

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