What I Learned Automating Things with AI Agents
Running a daily news digest and a 24/7 market scanner taught me more about practical AI than any tutorial — scheduling, reliability, and cost.
From Interactive Chat to Unattended Execution
Most developers interact with AI inside a chat box. But the real leverage comes when AI operates as an unattended background worker. My daily AI News Digest agent wakes up at 6:00 AM JST, queries multiple live sources, eliminates duplicates against past runs, ranks by significance, and delivers a formatted summary to Telegram before I wake up.
The Accountability Problem: Grading Meme Coin Radar
When I built the Meme Coin Radar scanning DEXScreener and Pump.fun, the biggest risk was creating an alert firehose of noise. Anyone can write a bot that pings when liquidity spikes. What makes a signal system trustworthy is self-accountability: every alert issued is treated as an implicit 24-hour prediction, and an automated evaluator grades every alert daily against real price performance.
# Scoring pipeline evaluator
score = (liquidity_weight * log(liquidity)) + (volume_ratio * 0.4)
if score >= THRESHOLD:
alert_id = dispatch_telegram_alert(token_meta)
schedule_accuracy_evaluator(alert_id, eval_after_hours=24)Continuity & Deduplication
LLMs have no memory across cron invocations unless you explicitly design state continuity. Persisting rolling hashes of processed articles and alerts prevents repetition and keeps token costs strictly bounded.
Key Takeaway
Reliability in AI automation isn't about the biggest model — it's about robust input validation, predictable retry logic, state persistence, and continuous verification of output quality.