Email overload is a modern disease. Hundreds of emails a day, most of them noise. Newsletters you forgot you subscribed to. Marketing emails disguised as personal messages. Bank statements. Gas bills. The occasional important email buried in the flood. I tried fixing this with a simple ignore list. It didn’t scale. This is the story of how I built a multi-layer email filtering system that replaced hardcoded bash patterns with intelligent scoring, security scanning, and adaptive learning. ...

Running an AI assistant with shell access, file system permissions, and messaging integrations is powerful. It’s also a meaningful security surface. You’re giving a language model the ability to execute commands, read files, and respond to messages on your behalf. This isn’t theoretical risk. If you configure OpenClaw carelessly, you’re handing strangers the keys to your system. Here’s how to run OpenClaw safely: keep it updated, audit it regularly, and vet plugins before you enable them. ...

Keeping up with AI and tech news had become a full-time job. Superhuman AI newsletters, TLDR digests, HackerNews front page, Reddit ML discussions — easily an hour a day just staying current. Information overload disguised as staying informed. I needed a system that would curate the signal from the noise, deliver it on my schedule, and stop the constant context-switching between platforms. The Problem Tech news comes from everywhere: Email newsletters (Superhuman, TLDR) — great curation, but buried in inbox HackerNews — high signal, but requires active browsing Reddit (r/MachineLearning, r/artificial) — community-driven, hit or miss Twitter/X — real-time, but drowns in noise Each source has value, but checking all of them daily is unsustainable. I needed a weekly digest that: ...

Most personal finance apps want access to your bank accounts. I didn’t want that. Instead, I built a system that processes bank statement PDFs, categorizes every transaction, and generates an interactive dashboard—all locally, with no data leaving my machine except monthly summaries. Here’s how we built it. The Problem I have multiple bank accounts (current account, two credit cards). Each sends monthly PDF statements. To understand my spending, I needed to: ...

Most AI assistants start fresh each session. They read their instructions, follow them, then forget everything. Mine doesn’t. It maintains its own documentation, reviews it daily, and proposes improvements. The Core Idea My AI assistant (ClawdBot) wakes up each session by reading workspace files: AGENTS.md – its operating instructions SOUL.md – personality and voice USER.md – who I am, what I prefer TOOLS.md – local configuration (Telegram topic IDs, RSS feeds, routing rules) MEMORY.md – long-term memory memory/YYYY-MM-DD.md – daily logs These files evolve. When I say “remember this,” it updates them. When it learns something, it writes it down. ...

I run OpenClaw on an AWS EC2 instance. It’s a small t3.micro — nothing fancy — but cloud costs have a way of creeping up when you’re not watching. AWS offers cost alerts. I set them up. They work fine — they fire any day you hit your threshold. The problem: alerts don’t help you understand why you’re spending. They just tell you you’ve hit a limit. No breakdown by service. No trend analysis. No way to spot anomalies early. ...

I’ve been tracking health metrics with my Apple Watch for years: blood pressure readings, workout sessions, sleep quality, heart rate variability, VO2 max, daily steps. The data is incredibly detailed — every run mapped, every night’s sleep scored, every day’s resting heart rate logged. The problem? Apple keeps this data locked in a walled garden. There’s no native way to export it or build custom reports. The Health app does suggest trends, but only on a 6-month time horizon — too slow to spot week-to-week changes or adjust training in real time. ...

I noticed that checking news from different sources had continuously been a drain on my time. Bouncing between The Economist, NYT, Guardian, a few YouTube channels — easily 30+ minutes a day just scanning headlines. I decided to optimize it by creating a daily news digest, focusing on the most important news. The Problem Most RSS readers treat all sources equally and sort by timestamp. This drowns important stories in a flood of recent fluff. ...

This is the story of how I set up this blog from scratch. The whole thing — from an empty repo to a live site with automatic deployments — took a couple of hours. Here’s the plan I started with, what actually happened, and the workflow I ended up with. The Requirements I wanted a personal blog with a few non-negotiables: Markdown-based authoring — I write in markdown already, so no CMS Zero maintenance overhead — no servers to patch, no databases to back up Free (or near-free) hosting — this is a personal blog, not a business Custom domain — davletshin.com, which I already owned Google Workspace email preserved — my email runs through Google Workspace on the same domain, and I couldn’t afford to break it during DNS changes No comments — at least not initially Google Analytics (GA4) — basic traffic visibility Room for future subdomains — my son has projects that might live on son.davletshin.com or similar, hosted separately The Architecture I Planned After some research, I settled on this stack: ...

A quick recap: I’ve been experimenting with a “GTD copilot” that runs as a personal daemon and does the boring parts of planning: scanning inboxes, creating properly-scoped tasks, looking ahead at the calendar, and sending a single clean briefing. This post is a field report: the minimal structure that made the system useful, plus the guardrails that stopped it turning into task spam. Why this works (the GTD pain points) If you’re doing GTD seriously, the pain is rarely “I don’t know what to do.” It’s: ...