Plausible

Developers Switch from Firebase to Supabase

A developer-published guide (originally on iloveblogs.blog, republished on dev.to) argues that many developers are migrating from Google’s Firebase to Supabase in 2026. The article lists four main drivers: unpredictable per-read Firestore pricing, NoSQL query limitations, vendor lock-in with Firebase, and better server-side/Next.js support from Supabase. It provides concrete migration steps including mapping Firestore collections to PostgreSQL tables, migrating Firebase Auth users to Supabase Auth, exporting data with scripts and pg_dump, updating application code to Supabase SDK calls, and replacing Firebase services with Supabase equivalents. The post includes SQL and TypeScript examples, a suggested two-week migration timeline for mid-sized apps, and claimed outcomes such as lower monthly costs (60–80% savings), improved developer velocity, and data portability because Supabase is open-source and self-hostable.

Developer Guide: Full-Funnel Conversion Tracking

This developer-focused guide explains how to implement reliable conversion tracking from page view to paid subscription. It recommends a funnel-first mindset (map 3–5 key steps), defines four foundational events (pageview, sign_up_started, sign_up_completed, subscription_created) with code examples for Plausible and PostHog, and describes broader engagement events and what not to track. The article covers attribution (first-touch vs last-touch), exact UTM conventions and storing UTMs in sessionStorage, a 'how did you hear about us' signup field, funnel analysis and segmentation, privacy best practices (avoid third-party cookies, anonymize IP, respect Do Not Track, regional data storage), and a one-day checklist. Recommended tooling includes privacy-focused analytics (Plausible, Fathom), product analytics (PostHog, Amplitude), payment systems (Stripe, Paddle) and error tracking (Sentry).

Practical Definition of an AI Employee for 2026

Vladimir Nagin (founder of LeadUp AI) published a practical definition and operational guide for 'AI employees' on 2026-06-14. He defines an AI employee as an autonomous AI agent with a machine-readable job description (AGENTS.md), tools & access, memory, measurable KPIs, and structured reporting/escalations — operating end-to-end without constant human prompting. The article contrasts chatbots, assistants, and AI employees by autonomy depth and cost, lists common use cases (marketing, sales, support, internal ops), provides a 14-day deployment playbook, and outlines a 2026 tool stack (LLMs, orchestration, vector DBs, voice, analytics). It also sets safety limits (human-in-the-loop for high-risk tasks >€10k) and recommends concrete operational practices for production deployments.