Article URL: https://maharship.com/blog/why-mcp-was-always-a-bad-idea/ Comments URL: https://news.ycombinator.com/item?id=49779329 Points: 15 # Comments: 19
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Every AI operation — from text prompts to image generation — consumes real physical resources inside data centers, including processors, cooling systems, and networking equipment. While a single request appears trivial, the energy footprint grows significantly at the scale of millions of calls. Developers have direct control over how frequently their apps query AI models, how much data is sent per request, and whether a lighter-weight tool or cached result could serve the same purpose. Thoughtful design choices — such as batching background jobs, setting data retention limits, and avoiding redundant requests — can meaningfully reduce resource consumption. As AI becomes standard in software, energy efficiency is emerging as a legitimate measure of engineering quality alongside speed, accuracy, and cost.
Software engineer Anton, working on migrating a PHP monolith to Go services, observed that individual AI executor iterations were consuming around 350,000 tokens — far exceeding initial estimates. Investigation revealed the excess was driven by redundant context-gathering, where each executor independently rediscovered the same file paths, type names, and conventions without a shared record. To address this, Anton updated his specification format to include a closed list of files per iteration and a dedicated 'facts of the set' section, limiting file reads to at most three named files and prohibiting open tree searches. The core argument is that a tracker task must carry both a pre-work estimate and a post-work recorded fact, otherwise cost overruns remain invisible and project discussions devolve into subjective impressions. Anton notes the problem only became diagnosable because the task structure made the discrepancy measurable — turning what would have been a silent inefficiency into a documented spec defect with an attached cost.
A software product company with 70–100 developers had cloud and Kubernetes infrastructure but lacked meaningful security visibility despite already running an Elastic/ELK deployment. The team had no consistent monitoring of failed authentications, RBAC changes, or suspicious container activity, and no defined process for responding to security incidents. Rather than adopting an expensive commercial SIEM, a consultant spent roughly three weeks building a right-sized security telemetry baseline using the existing stack. The project delivered prioritized detections, practical dashboards, and a lightweight incident-response workflow the DevOps team could own. The core takeaway was that security maturity can begin by making better use of systems already in operation, not by adding new tooling.
A traveller imported 178 trip reports at once from a two-year Canada-to-Chile journey, overwhelming a small travel site that previously held only 9 reports. Each published report triggers three background jobs — text moderation, place extraction, and image moderation — all of which call an AI model via a queue. The system's worker processes ten jobs per minute to stay within the model's rate limit, meaning the full burst took roughly 35 minutes to clear, delaying any other users publishing during that window. The next morning, around 98 photos were flagged for human review because the image moderation model had repeatedly failed to respond, exhausting all three retry attempts. The developer traced each failure through stored status reasons and run logs, distinguishing genuine errors from expected system behaviour such as cron timeouts that only appeared problematic on the surface.
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