This article was originally published on e-shikumi-labo . Hello, I'm Shin from e-Shikumi-Labo. This is the final installment (Part 4) of "Systematized Thinking," where we use AI to build our own tools and independently maintain them. So far, we have discussed creating a prototype that automatically saves Gemini chat logs, converting them to Markdown for Obsidian integration, and elevating it to a safe, fully automated system. In this final installment, we will cover the "countermeasures for downtime due to screen specification changes," an unavoidable issue when operating tools that handle web data, and the core of the "self-reliance" humans should possess in the AI era. 1. The Web Data Extraction Compromise: "You Can't Extract What Isn't on the Screen" During development, there was a time when I thought, "I also want to record the exact date and time (timestamp) when the chat was sent." However, no matter how much I analyzed Gemini's screen structure, the exact timestamp of each utterance did not exist in the HTML. The fundamental rule of web data extraction is: "You cannot extract data that does not exist on the browser screen." As long as you are extracting data from the screen (DOM) rather than via an API, forcing the extraction of something that isn't there will require complex guesswork processes and will instead become a cause of trouble. Understanding this "technical limit," gracefully giving up on what cannot be done, and judging to maintain simplicity is also an important element of tool building. 2. Specification Changes Are Not Defects, But "Fate" As long as you deal with tools that extract data from other people's websites, the time will inevitably come when the tool suddenly stops working one day due to design changes or updates on Google's side. "It was working fine until yesterday, but suddenly it stopped saving." This is not a defect in the tool, but an unavoidable "fate" as long as you depend on someone else's platform. The important thing is not t

We've all been there: staring at a blood test report from three years ago, trying to remember if that "slightly elevated" glucose level was a one-time thing or a trend. Our health data is scattered across messy PDFs, fitness tracker exports, and physical medical folders. In the era of AI, why are we still manually digging through folders? 📂 Today, we are building the Ultimate Personal Health Knowledge Base . By leveraging Retrieval-Augmented Generation (RAG) , we will transform fragmented medical reports and logs into a searchable, private, and intelligent second brain. We’ll be using LlamaIndex for orchestration, Unstructured.io for parsing those pesky PDFs, and ChromaDB for local vector storage. If you're looking for advanced architectural patterns or production-grade data engineering strategies beyond this tutorial, I highly recommend checking out the deep dives over at WellAlly Tech Blog , which served as a major inspiration for this build. 🚀 The Architecture 🏗️ The goal is to create a pipeline that ingests raw data, vectorizes it, and allows for Hybrid Search —combining semantic meaning with keyword precision (crucial for medical terms!). graph TD A[Raw Health Data: PDFs, CSVs, MD] --> B(Unstructured.io Parser) B --> C{Chunking & Cleaning} C --> D[Sentence-Transformers] D --> E[(ChromaDB Vector Store)] F[User Query: Is my cholesterol improving?] --> G[LlamaIndex Query Engine] E <--> G G --> H[LLM: Local or OpenAI] H --> I[Actionable Health Insight] Prerequisites 🛠️ To follow along, you’ll need a Python environment with the following stack: Unstructured.io : To handle "dirty" PDF and image-based reports. ChromaDB : Our lightweight, open-source vector database. Sentence-Transformers : To generate local embeddings without sending data to the cloud. LlamaIndex : The glue that connects our data to the LLM. pip install llama-index chromadb unstructured sentence-transformers llama-index-vector-stores-chroma Step 1: Ingesting Messy Medical Reports 📄 Medical reports are

The Chaotic Bug: The Infinite State Loop & Memory Leak In a real-time clinical AI health suite, high-frequency telemetry streaming (such as 60Hz ECG canvas updates) demands surgical precision. During heavy load testing, our frontend performance suddenly degraded: CPU thread usage hit 98%, heap memory ballooned to over 1.4 GB, and DOM frame rendering dropped to single digits. The Root Cause A subtle React useEffect hook listening to the incoming WebSocket data stream contained the state setter inside its dependency array: // ❌ THE CHAOTIC BUG (Caused infinite state sync re-renders) useEffect(() => { const sub = ecgDataStream.subscribe((point) => { setEcgPoints((prev) => [...prev, point]); // Triggered full tree re-render on every frame! }); return () => sub.unsubscribe(); }, [ecgPoints]); // Including state array in deps created recursive re-subscription storm! Every incoming telemetry frame pushed new state, triggering an immediate top-level component re-render, which re-subscribed to the stream and accumulated thousands of orphaned event listeners. Best Use of Sentry: Pinpointing & Clearing the Lineup Sentry Performance Tracing and Sentry Error Tracking proved invaluable in isolating this silent killer: Transaction Waterfalls: Sentry flagged transaction spans render_ecg_canvas exceeding the 500ms threshold (averaging 842ms). Breadcrumb Trail: Sentry logged a rapid succession of CanvasRenderer memory allocation warnings (>64MB/sec). Issue Grouping: Sentry grouped 14,000 React Maximum update depth exceeded exceptions into a single actionable alert. The Fix & Restored Harmony We refactored the streaming engine to bypass React state re-renders entirely for frame accumulation, employing a zero-allocation useRef buffer paired with a requestAnimationFrame render cycle, and instrumented Sentry Breadcrumbs: // ✅ THE RESILIENT FIX (Zero-allocation ref buffer + Sentry Breadcrumb) import * as Sentry from '@sentry/react'; const bufferRef = useRef([]); useEffect(() => { Sentry.a

This article was originally published on e-shikumi-labo . Hello, I'm Shin from e-Shikumi-Labo. This is Part 3 of "Systematized Thinking," where we use AI to build our own tools and independently maintain them. Last time, I talked about creating a system to automatically output Markdown (.md) files to Google Drive simultaneously with appending to a spreadsheet. With list management in a spreadsheet and a comfortable viewing environment in Obsidian established, it was getting very close to completion as a tool. However, as I continued to use it practically, new challenges emerged on the operational front. This time, I will share the risks I faced while transitioning from a "manual button" to "full automation," and the process of evolving into safe code. 1. I Want to Eliminate the "Hassle of Pressing a Button" During the prototype stage, the system was designed so that logs were saved by pressing a button placed on the screen. However, as long as a human operates it manually, there are inevitably limitations. If you are concentrating on the conversation, you might forget to press the save button and close the screen. If the conversation gets long, you might miss past utterances that are no longer displayed on the screen. "If I have the screen open and am conversing, I want it to automatically save in the background without bothering human hands." Thinking this, I asked the AI to write the code for full automation. 2. The Code the AI Produced: "Patrolling the Screen Every 1.5 Seconds" When I consulted the AI, it immediately presented code for full automation. The mechanism was, "Start a timer every 1.5 seconds, check the entire screen in the background, and send any new utterances." When I actually tried it, the logs accumulated automatically as soon as I conversed without pressing the button, and at first glance, it looked like exceptionally well-done full automation. However, I felt something was slightly off regarding this "monitoring on a 1.5-second cycle." 3. The B

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A practical, no-hype breakdown of GitHub Copilot's features, free vs paid tiers, real limitations, and the kind of developer who actually gets their money's worth. "Is GitHub Copilot worth it?" usually means one of two things: will it save enough time to justify the subscription? or is a paid plan meaningfully better than the free one? This guide answers both, based on GitHub's documented features and the trade-offs that tend to matter in day-to-day development work. The short version is that Copilot has a genuinely useful free tier and a low-cost paid tier, so the real question is rarely "should I spend a lot of money" — it's "does an AI pair-programmer fit how I work." Below we cover what you get, what it costs, where it helps, and where it falls short, so you can decide for your own workflow. At a glance In short For developers who write code most days, GitHub Copilot is generally worth trying — and the free tier lets you find out at zero cost. The low-priced Pro plan is small relative to the time many users save on boilerplate, tests, and unfamiliar APIs, but you still have to review everything it produces. It's a weaker value for occasional coders, for those working mainly in niche or proprietary codebases where suggestions are less accurate, or for anyone who finds constant autocomplete distracting. Start on the free tier, test it on your real work, and upgrade only if you hit the caps or want agent mode and model choice. Always confirm current pricing and limits on GitHub's site. Pricing Confirm current pricing on each vendor's site. Free$0 Capped monthly code completions and chat messages Access in supported editors and on GitHub.com Good for evaluating Copilot at no cost Confirm current monthly caps on GitHub's plans page View Copilot plans ProAbout $10/month (or ~$100/year)confirm current pricing Removes the tight free-tier caps Agent mode and model selection Monthly allowance of premium requests (overage billed separately) Free trial has historically been

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