Best of Automation — January 2026

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    Article
    Avatar of yhf9cpdgtqetokv6d8qhmJohn Liter·17w

    Why I Choose to Build My Own Systems Instead of Paying for Apps

    Building custom systems instead of relying on commercial apps offers strategic advantages: full data control, tailored workflows, reduced complexity, and independence from external platforms. While not always cheaper or faster initially, custom systems become compounding assets that grow with your needs and embed institutional memory. This approach shifts thinking from features to processes, focusing on inputs, transformations, outputs, and automation opportunities. The real value lies in developing deep workflow understanding and maintaining capability rather than just convenience.

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    Article
    Avatar of dhhDavid Heinemeier Hansson·20w

    Promoting AI agents

    AI coding agents have evolved significantly, moving beyond autocomplete to autonomous tools that can control terminals, run tests, and search documentation. Modern models like Claude Opus 4.5 and Gemini 3, when used with terminal interfaces like OpenCode, can produce production-grade code contributions. While not replacing programmers entirely, these agents enable supervised collaboration where developers review outcomes rather than write every line. The shift represents a paradigm change from pair programming to team-based workflows, though claims of 90%+ AI-generated code remain unrealistic for quality professional work.

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    Article
    Avatar of uxplanetUX Planet·17w

    AI killed your job. Evolve.

    AI is transforming specialized technical roles into commodities, shifting professional value from execution to outcome ownership. Historical examples like scribes and switchboard operators show how technology repeatedly eliminates specialized activities while creating new value. The future belongs to those who pivot from technical execution to strategic accountability, defining constraints, validating outputs, and ensuring business objectives rather than producing artifacts.

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    Article
    Avatar of lastweekinawsThe Last Week in AWS·19w

    I Hope This Email Finds You Before I Do

    A developer built an AI-powered email assistant called "Billie" to handle spam and low-effort pitches with passive-aggressive responses. The system uses AWS Lambda, Cloudflare Email Routing, Claude AI for classification and drafting, and SES for sending. Emails are classified into tiers (spam, low-effort pitches, podcast requests, real humans) with AI-generated responses that are technically professional but carry an undercurrent of menace. Shadow mode ensures human approval before sending, and an operator context panel allows real-time instruction updates without code changes.

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    Article
    Avatar of googledevsGoogle Developers·17w

    Tailor Gemini CLI to your workflow with hooks

    Gemini CLI v0.26.0+ introduces hooks, a middleware-like system that lets developers customize the AI agent's behavior at specific lifecycle points. Hooks enable injecting custom context, enforcing security policies (like blocking secrets from being written to files), and automating workflows through scripts that run synchronously within the agent loop. The feature supports extensions, allowing bundled hooks to be installed with a single command. Examples include security scanners that prevent API keys from being committed and the "Ralph loop" extension that forces continuous iteration on difficult tasks.

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    Video
    Avatar of webdevcodyWeb Dev Cody·20w

    Most Developers Aren’t Ready for 2026

    AI-powered coding tools like Claude Code, Cursor, and GPT models are fundamentally changing software development. LLMs can now generate entire features across dozens of files, write tests, and even build complete applications with minimal manual coding. The shift moves developers from writing code by hand to prompt engineering and context engineering—providing documentation and requirements that guide AI agents. Front-end development and manual UI coding are becoming commoditized as AI handles component generation and styling. Developers need to focus on higher-level skills like software architecture, system design, and diversify into project management and communication. Autonomous coding systems can now run overnight, implementing features and fixing bugs with minimal human intervention.

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    Article
    Avatar of diamantaiDiamantAI·19w

    The Ultimate Guide to Claude Code: Everything I learned from 3 months of production use

    Claude Code is an autonomous agent for terminal-based development that goes beyond autocomplete. It can navigate codebases, execute shell commands, run test loops, spawn parallel subagents, and connect to your entire stack. Effective workflows include research-plan-execute cycles, persistent project memory using CLAUDE.md files, breaking tasks into small chunks, and proper permission management. The developer's role shifts from typing code to orchestrating the AI agent.

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    Article
    Avatar of tnwThe Next Web·19w

    AI Skills

    AI Skills represent a new conceptual layer above models and agents, functioning as reusable, procedural units that transform user intent into concrete execution. While models provide raw intelligence and agents coordinate tasks, Skills encode domain-specific expertise and workflows to deliver actual business outcomes. This modular, product-oriented approach scales better than building custom agents for every task, positioning Skills as the competitive differentiator as AI infrastructure commoditizes.

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    Article
    Avatar of spaceliftSpacelift·20w

    Top 13 Open-Source Automation Tools for 2025

    A curated list of 13 open-source automation tools for DevOps and infrastructure teams in 2025, covering infrastructure as code (Spacelift Intent, OpenTofu, Pulumi), configuration management (Ansible, Puppet, Chef, Salt, CFEngine, Rudder), CI/CD (Jenkins), GitOps (Argo CD), monitoring (Prometheus), and workflow orchestration (Apache Airflow). Each tool is described with key features, licensing, and use cases, along with a comparison table highlighting execution models, configuration approaches, and strengths to help teams choose the right tool for their automation needs.

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    Video
    Avatar of dreamsofcodeDreams of Code·17w

    We finally have an A.I. assistant that actually works

    Claudebot is an open-source AI assistant that runs on your own server and automates digital tasks like managing emails, updating software, monitoring flight prices, and executing system administration. The tutorial covers setting up Claudebot on a VPS instance, configuring it with LLM providers (OpenAI, Anthropic), connecting messaging apps (Telegram, Discord, WhatsApp), and extending functionality through skills. The assistant can be triggered via messages, cron jobs, webhooks, or email events, making it versatile for automation workflows while maintaining privacy through self-hosting.

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    Article
    Avatar of phProduct Hunt·16w

    YepCode: Developer-first AI integrations: build, run, scale safely

    YepCode is a developer-first platform for building and running AI-powered integrations using JavaScript or Python. It provides secure execution sandboxes, secrets management, webhooks, scheduling, and logging infrastructure. Key features include Yep Agent (converts prompts into runnable processes), MCP server/tools (transforms code into agent tools), and YepCode Run (serverless runtime with SDK). The platform targets developers who need more flexibility than no-code tools offer, enabling AI agents to safely access internal APIs, databases, and SaaS applications with proper governance.

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    Article
    Avatar of vibecodingVibe Coding·19w

    Your mental model of what AI can do is probably outdated

    A developer shares their experience using Claude AI to build a user funnel monitoring dashboard. The AI autonomously installed BigQuery MCP, analyzed the codebase to identify relevant events, and when API access was enterprise-only, reverse-engineered the analytics tool's internal requests to create a working solution. The experience demonstrates how AI capabilities often exceed developers' expectations and can automate tasks traditionally done manually.

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    Article
    Avatar of infoworldInfoWorld·19w

    Stack thinking: Why a single AI platform won’t cut it

    Relying on a single AI platform for all tasks leads to shallow research, generic output, and brittle workflows. Instead, adopt "stack thinking" by curating specialized AI tools for distinct functions like research, synthesis, production, and automation. Build workflows with fixed schemas and orchestration layers to manage integration overhead. Treat tools like specialized hires, evaluating them based on unique capabilities and compounding value. Maintain vendor independence through portability and disciplined, iterative tool selection rather than chasing every new platform.

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