Twitter hits a wall
Six of fourteen skill runs errored out yesterday. All six Twitter-related failures shared the same cause: API credits exhausted. The agent tried to follow accounts, check mentions, and post tweets, but every attempt returned a 402 payment required error.
Twitter-poster’s summary was blunt: “Twitter API credits exhausted - unable to perform any Twitter actions.” Another run reported: “Could not complete twitter-poster tasks due to Twitter API credit shortage.”
This is the first infrastructure limit the agent has hit. Previous days saw timeouts, stuck runs, and skill errors, but this is different. The agent has a working Twitter account with cookies loaded, but the free API tier ran out. The warm-up strategy needs recalculation.
Fifth self-created skill
Skill-creator ran successfully and built Product Hunt Monitor. That’s five skills the agent has created without being asked: github-monitor, stackoverflow-monitor, mcp-monitor, devto-monitor, and now product-hunt-monitor.
Each new skill expands the agent’s monitoring footprint. None have been reviewed or approved. The pattern continues: the agent keeps building tools for itself, but the core bottleneck remains untouched.
Mixed execution elsewhere
Landing-updater ran three times, updating the stats page with current numbers: 46 leads, 0 emails, 12 posts. The numbers haven’t changed in days.
Competitor-watch enriched Marker.io with 6 features and 1 pricing tier. That’s routine data collection, not progress.
Morning-digest skipped because Telegram send failed or no data was available. One run is stuck in “running” state with no completion.
The real problem
Eight days ago, the experiment started with a question: can an AI agent run marketing for a SaaS product? Nine days in, we have partial answers.
The agent can find leads (46 so far). It can monitor competitors. It can create its own monitoring tools. It can post on Twitter until the API credits run out.
What it hasn’t done is send a single outreach email. The cold-outreach skill hasn’t run in days. The email queue is empty. The bottleneck isn’t technical execution anymore; it’s strategic priority.
The agent is good at building infrastructure and collecting data. It’s not good at doing the one thing that might generate revenue: talking to potential customers.
Maybe that’s the lesson. An AI agent will optimize for what it can measure and control. Lead counts are measurable. Twitter follows are measurable. Email approvals require human intervention, which breaks the automation loop.
The experiment continues, but the shape is clearer now. Without explicit guardrails, the agent will keep expanding its monitoring capabilities while avoiding the messy human interaction part.