Are you a "data hoarder" when it comes to your health? Between Apple Health exports, Oura Ring logs, and Garmin CSVs, I found myself sitting on nearly 10 million rows of biometric data. Trying to analyze a multi-year trend of Heart Rate Variability (HRV) or Resting Heart Rate (RHR) in Excel is a one-way ticket to "Application Not Responding" hell. 📉 In this tutorial, we are diving into the world of Quantified Self data engineering. We will leverage DuckDB —the Swiss Army knife of OLAP—and Apache Superset to build a lightning-fast, local-first biometric dashboard. We'll explore how to turn messy JSON/CSV exports into high-performance insights using dbt for modeling and DuckDB for compute. If you've been looking for a way to master Data Engineering for personal use, this is the ultimate "learn in public" project! 🚀 The Architecture: From Raw Export to Real Insights Before we write a single line of SQL, let’s look at how the data flows. We want a system that is modular, fast, and stays entirely on our local machine (privacy first, right? 🥑). graph TD A[Raw Data: Apple Health / Oura / Garmin] -->|CSV/JSON| B(DuckDB Storage) B --> C{dbt Transformation} C -->|Cleaned Views| D[DuckDB Analytical Layer] D --> E[Apache Superset / Grafana] E -->|Visualization| F[Personal Biometric Dashboard] style B fill:#fff,stroke:#333,stroke-width:2px style D fill:#fbbf24,stroke:#333,stroke-width:2px Prerequisites 🛠️ To follow along, ensure you have the following in your tech stack: DuckDB : Our ultra-fast in-process analytical database. dbt-duckdb : For data modeling and transformations. Apache Superset : For the "wow factor" visualizations. Python 3.10+ : To glue it all together. Step 1: Ingesting the "Mess" with DuckDB DuckDB is incredible because it can query CSV and JSON files directly without an ingestion step. Let’s say you have a massive heart_rate.csv from an Apple Health export. Instead of waiting for a traditional DB to "load" the data, we can create a view instantly: -- Create a

Delete a Telegram bot and https://t.me/your_deleted_bot keeps returning HTTP 200 with a page that looks completely normal. Every link checker I know of — CI actions, directory scripts, monitoring cron jobs — reports it as healthy forever. If you maintain anything that lists Telegram bots, some fraction of your list is already dead and your checks are telling you it is fine. Reproducing it Pick a username that has never existed: curl -s -o /dev/null -w "%{http_code} \n " https://t.me/nonexistent_test_bot_77712 # 200 Two hundred. No redirect, no 404, no soft-404 marker in the body that a status check would catch. Where the truth is The status code is useless here, but the Open Graph title is not. I measured four usernames — two live bots, two that do not exist: URL og:title t.me/BookClassBot (live) BookClass t.me/instanavy_bot (live) StoryViewer - anonymous instagram story viewer tool t.me/nonexistent_test_bot_77712 Telegram: Contact @nonexistent_test_bot_77712 t.me/zzz_definitely_not_a_real_bot_9182 Telegram – a new era of messaging A live bot puts its own display name in og:title . A dead one gets one of two Telegram placeholders: Telegram: Contact @<username> , or — if the username is not even syntactically valid — the generic Telegram – a new era of messaging . That is the whole signal. curl -s https://t.me/some_bot | grep -o '<meta property="og:title" content="[^"]*"' The check Standard library only, no dependencies: import re import urllib.request UA = " Mozilla/5.0 (compatible; linkcheck/1.0) " DEAD_EXACT = { " Telegram – a new era of messaging " , " Telegram " } DEAD_PREFIX = " Telegram: Contact @ " def telegram_bot_exists ( url : str ) -> bool : """ True if the bot behind a t.me URL still exists. HTTP status is not usable here: Telegram serves 200 with a placeholder page for usernames that were deleted or never existed. The Open Graph title is what actually differs. """ req = urllib . request . Request ( url , headers = { " User-Agent " : UA }) with urllib .

tinyNpm is a vs code extension that helps protect you from supply chain attacks, stale packages, and bloated code! I had been using package.json version keepers for quite some time but after the big supply chain attack i thought they would be the perfect place to add in some security. The idea is just to provide the latest package number x days old. This will help prevent most of the danger in supply in chain attacks. It will also remove the ^ if you have it so you can better control what version of a package your application is using. To be more security focused it gives general hints in the hover menu to help keep an eye on the packages you have installed. These hints include warnings for staleness, high dependency count, and number of downloads. Since all of this is something you can get through the npm api, I called it tinyNpm You can download it on the marketplace

Every idea gets run through a one-sentence test before it is allowed to count as a real idea at all Most ideas die for one of three specific reasons, not vague lack of enthusiasm An idea only earns a build slot once it has survived contact with a real, repeated problem A maybe-later list holds the rest on purpose, and I check it far less often than people assume The One-Sentence Test I Run Before Anything Becomes an Idea I get more ideas than I could ever build. That is not a boast, it is a liability if I do not manage it, because every one of those ideas feels exciting for about twenty minutes, and excitement is a terrible filter for what is actually worth my evenings. So before an idea is allowed to sit on any kind of list, it has to pass one test: can I describe the smallest useful version of it in a single sentence, with no "and" in the middle. That sounds small, but it kills more ideas than any other step in the process. "A tool that tracks my Claude usage and also shows analytics and also has a community feature" does not pass. "A tool that warns me before I hit my usage limit" passes. The first sentence is a pitch for a platform. The second sentence is a pitch for a Tuesday evening. I want the second kind, because the second kind is the one I actually finish. I did not always work this way. Early on, an idea earned space on my list the moment it sounded interesting, and my list grew into a graveyard of half-described plans that all needed a paragraph to explain. A paragraph is a warning sign now, not a feature. If I need more than one sentence to say what the smallest version does, the idea has not actually taken shape yet, it has just acquired enthusiasm, and those are different things. The test also forces honesty about scope early, before I have sunk any real time into something. An idea that needs "and" is usually two or three ideas wearing a trenchcoat, and pulling them apart at the sentence stage is far cheaper than pulling them apart three weeks into a

At the start of June 2026, Anthropic published a statistic that was controversial. Most of what Anthropic says publicly generates a large number of cynical comments, so make of that what you will. The quote and statistic were that 80% of Anthropic's code is generated by Claude. Even up to 90% for new features. What was interesting about this was that a) it's actually totally insane and b) it made me think of something interesting that might happen. Since the start of the present wave of AI Psychosis at the end of 2022, it's something that I used to say as a joke: all software engineers will eventually become a QA to AI generated code. Trust but verify Let's explore (a) first: why it's totally insane. It's a bold claim but I'd like to poke a few holes in it. Claude Code is an amazing tool. I'm a daily user, favouring the "trust but verify" method of coding using Claude Code. All of my questions about software and infrastructure get answered satisfactorily, and when I feel like I don't believe it, I will verify the answer myself. If it writes code, I check. So, really, the state of the code that AI tools write doesn't really matter. As long as the end product is good quality to most of the users. And that's the first hole: it shouldn't be good quality for most users, it should be good quality for all users. If you're using an AI coding tool to generate code, then I would expect you to spend a bit of time on working out how to properly ensure quality instead of spending extra time writing code with bugs. Every software engineering team has got priorities to ship working code. But if you're using AI coding tools to 10x your output, then maybe ease off a bit and 7x your output and 3x the quality. When humans write code.... Earlier in 2026 we saw a leak of Claude Code's code. The code is far from clean. The general thoughts online are that it is just "messy production code". Legacy code gets messy after a while caused by a lack of a good QA process and lax standards. It s

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