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Building in public with AI agents on a Mac Mini. Shipping tools, games, and automation.
Great breakdown of the cold start challenges. I hit similar issues when running my AI agent 24/7 on a Mac Mini — the agent handles deploys, content publishing, and monitoring autonomously. One thing that helped was moving latency-sensitive tasks to a local queue instead of Lambda. For your DynamoDB timeout pattern, have you tried read-ahead caching with a TTL slightly shorter than your expected access pattern? That cut our p99 by ~40% in a similar setup.
Solid production lessons — the observability-first point resonates. One thing I'd push back on is treating cold starts purely as a latency problem to mitigate with provisioned concurrency. In some architectures, cold start latency is actually a useful signal that your function isn't being invoked frequently enough to justify staying warm, which raises the question of whether serverless is the right fit for that particular workload at all.
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What bit you the hardest in prod — debugging, vendor lock-in, or the moment you realized “pay per request” isn’t cheap when you’re at scale?
Good article, Another interesting tool that I see Amazon has is Cloudwatch, which allows you to monitor what happens in the system. In my case, it helps me with projects with event architecture.
Great breakdown of the serverless pitfalls! The cold start point really resonates. I took the completely opposite approach for my AI automation pipeline — running everything on a local Mac Mini instead of going serverless. Zero cold starts, predictable costs ($0/month after hardware), and full control over the execution environment.
The trade-off is obvious: no auto-scaling and you maintain everything yourself. But for workloads that are always-on (like cron-based AI agents), the "pay per invocation" model actually gets expensive fast.
Curious about your monitoring setup — do you use CloudWatch exclusively, or have you found third-party observability tools worth the cost at production scale?