I accidentally turned LLM memory into program analysis
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Background While surveying the major WordPress maintenance tools (ManageWP, MainWP, WP Umbrella, and InfiniteWP), one thing stood out once their pricing was laid side by side: this isn't just a matter of "cheaper vs. more expensive." The shape of the pricing itself differs completely from company to company. And that shape isn't arbitrary — it closely mirrors how each company actually delivers its service. ManageWP: free core, stacked per-site add-ons The core dashboard is free. Features like
The original Fermi paradox asks: If intelligent alien civilizations should be abundant, where is everybody? The Neo-Fermi/Wilson paradox asks: If AI is producing enormous quantities of excellent software, where is all the excellent software? We are told that implementation has been transformed: agents write code, discover defects, review changes, operate continuously, preserve memory, evaluate one another, and improve the factories that produce still more agents and software. Yet the visible wor
I run a small studio, which means every project I ship ends on the same question: can the client actually keep this without me? For a long time I assumed the answer lived in the contract. It does not. I have watched a company hold the intellectual property in the strictest legal sense and still be completely stuck, because the thing was live on a hosting account registered to somebody who had moved on. Ownership turned out to be three separate things, and most agreements only buy one of them. Le
Google is changing how manual actions under its Site Reputation Policy affect search results in the European Economic Area. Starting August 30, a manual action can continue to affect an affected section of a website for users outside the EEA while not applying to results shown to users inside the EEA. The change creates a meaningful distinction for website owners whose audiences span multiple regions. According to Google's official Site Reputation Policy update, the policy itself still applies.
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This is the last of three parts. In part one I split my personal services onto their own two-node cluster. In part two I read the orchestrator's source and fixed two bugs, which shipped in the next release. This part is what happened when I installed that release. It broke the cluster three separate ways. None of them shared a root cause. Here they are in the order I hit them. I copied the official release binary to the second node and the service went into a crash loop: /usr/local/bin/orca: /li
Combine dense embeddings and sparse keyword search using Reciprocal Rank Fusion to eliminate retrieval failure modes in production RAG systems. Most production RAG pipelines start with pure vector search. It works reliably during initial demos, but fails quietly once users begin searching for exact product IDs, error codes, or domain-specific identifiers. Vector search translates text into semantic coordinate spaces. Because dense models optimize for high-level concepts, they tend to blur fine-g
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