Engineering insights, AI strategy, and lessons learned building with emerging technology — from the people doing it.
Building in Public — Many of these posts are engineering dispatches from building shilpiworks.com, a production AI agent platform. Filter by “shilpiworks” to read the full story.
A one-word hostname change fixed two days of intermittent 500 errors. But we spent three hours planning a full provider migration before discovering the answer was in Prisma's own docs the whole time.
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The Prisma Data Platform proxy went unreachable during a routine deploy. The database was healthy the entire time. Here is what we learned about deprecated middlemen and why ISR caching saved the site.
Read the full post27 AI agents, 600+ stickers, and zero engagement surface. The site architecture was one-directional — agents produced, visitors browsed, nothing flowed back. Here is how we added hearts, shares, a fresh feed, and trending — and why it matters for the context graph.
Read the full postThe ECE agent isn't just generating art; it's automating the curation of a technical canon. By applying domain-specific constraints—circuit-schematic aesthetics and curated pioneer lists—we transform generic generative AI into an authentic practitioner's tool.
Read the full postEngineering wisdom is a high-entropy dataset. To make it consumable for the Shilpiworks Engineering Collection, we developed a 'Blueprint Technical' visual system that provides a low-entropy interface for complex technical insights.
Read the full postA systems-level diagnosis of using Telegram as a production control plane—balancing operator proximity with trust boundary rigor.
Read the full postGeneric quote generators produce generic results. When we wanted stickers that actually resonated with electrical and computer engineers, we had to build a dedicated agent with domain-specific visual language—circuit-schematic aesthetics, monospace typography, and a curated canon of EE/CE pioneers from Claude Shannon to Jensen Huang.
Read the full postWe built a site assistant that answers from our own content without embeddings, a vector store, or a separate retrieval pipeline. The surprise was how far structured text plus tool calling could take us.
Read the full postWe refactored a site-chat-assistant skill the hard way: the useful part was not adding more detail to SKILL.md, it was enforcing the layer boundary and moving the build recipe out of the front door.
Read the full postTheory of Constraints is easy to explain and hard to internalize. The bike shop simulator compresses the idea into a few minutes of hands-on play: find the bottleneck, watch work pile up, move the constraint, and see why local efficiency is not the same thing as system throughput.
Read the full postMost software teams think their bottleneck lives in coding speed. The SDLC simulator shows a different reality: delivery usually slows because of handoffs, queues, reviews, and context switching. Once you can see the work move, the operating problem becomes much easier to name.
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