Moving from C/C++ to Rust: you don't need to rewrite everything
Android's lesson: vulnerabilities cluster in new code, so write new code in Rust; a 4-step roadmap, the CISA/NSA framework, cxx/FFI and AI translation.
AI Automation
n8n, agents, AI in everyday processes. · 17 articles
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Claude Code
Claude API
LLM & NLP
AI Agents & Skills
Tips
AI Automation
Tools
System Design
Reading
Does AI make developers 55% faster or 19% slower? Reading both studies closely
Copilot's experiment (55.8% faster) vs METR 2025 (experienced developers 19% slower): why they differ and how to measure AI productivity.
Microsoft's 18 guidelines for human–AI interaction
A checklist of 18 guidelines in 4 phases (initially, during interaction, when wrong, over time) and how to apply them to today's chatbots, copilots and agents.
Google's Rules of ML applied to LLM features: measure first, AI second
The key rules from Google's Rules of Machine Learning and a four-step path (baseline, minimal version, shadow, gradual launch) for LLM features.
Cloud scheduled tasks: run Claude Code on a schedule, API call or GitHub event
Create cloud tasks with /schedule: schedule, API and GitHub triggers, the run environment, connectors, UTC cron and caveats.
Channels: message your running Claude Code session from Telegram, Discord or iMessage
Push chat messages and webhooks into a local Claude Code session with channels: Telegram, Discord and iMessage setup, allowlists and security risks.
promptfoo: unit tests for prompts and LLM evals in CI
promptfooconfig.yaml, assertion types, multi-model comparison and running prompt evals in CI like unit tests.
Self-host n8n in 10 minutes: the foundation for every AI automation workflow
Run n8n with Docker on a laptop or VPS, persist data, add HTTPS — and what to do before running it for real.
The n8n AI Agent node with Claude: model + memory + tools in one drag-and-drop block
Combine the Anthropic Chat Model, conversation memory and tools (HTTP, Google Sheets, databases) into an agent that runs inside a workflow.
An AI content pipeline with human approval: Claude drafts, you click “Approve” in Slack, then it posts
Automate 90% of content drafting for a page or blog and keep the most important 10% for humans: deciding whether it goes out.
Automation first, AI second: use LLMs only where judgment is needed
A common mistake: putting an LLM in every step. Good workflows use code/if-else for the certain parts and AI for the fuzzy parts.
Build an internal AI assistant that answers company process questions (Slack/Telegram + RAG)
“How do I request leave?”, “What's the prod deploy process?” — a bot that answers from internal docs, cites sources, doesn't make things up.
ESP32 + MQTT + Home Assistant: DIY smart home, then let AI control it by voice
A few-dollar microcontroller reads sensors, publishes over MQTT, Home Assistant automates — plus an AI assistant you talk to in plain language.
AI triage for email and tickets: route to the right person in seconds
The most common AI automation pattern: read → classify → extract → route, with a confidence threshold so humans handle the hard cases.
An automated AI morning digest: gather RSS, GitHub, Jira and summarize into one message
Instead of opening 10 tabs every morning, let a workflow collect sources, dedupe, rank and send a summary to Slack/Telegram/email.
What should you automate first? An AI automation prioritization formula for PMs
Not everything AI can do is worth automating. Score by frequency, time, cost of errors and data readiness.
State-driven animated avatars: a better waiting experience for AI chatbots
Swap the boring loading spinner for a character that changes motion with the agent’s Idle, Working and Sleeping states.
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