Coolify is an open-source, self-hostable PaaS that lets you deploy applications, databases, and services on your own servers with just an SSH connection, positioning itself as an alternative to Heroku, Netlify, and Vercel. The platform supports deploying from GitHub, GitLab, Bitbucket, or Gitea using multiple build methods, manages 300+ one-click services, and handles networking, backups, and monitoring. It can be installed with a single curl command and offers both a free self-hosted option and a paid Coolify Cloud service.
- 62.8k stars, 5.6k forks, 694 contributors, and 710 releases on GitHub
- Built primarily in PHP (82.4%) with Blade, using Laravel and Svelte
- Apache-2.0 licensed and described as "free forever" with no feature behind a paywall
- Supports deploying to any SSH-accessible server including VPS, bare-metal, or Raspberry Pi
- Integrates with Claude Code and other AI tools via MCP, with agent skills configured in the repo
- 62.8k stars, 5.6k forks, 694 contributors, and 710 releases on GitHub
- Built primarily in PHP (82.4%) with Blade, using Laravel and Svelte
- Apache-2.0 licensed and described as "free forever" with no feature behind a paywall
- Supports deploying to any SSH-accessible server including VPS, bare-metal, or Raspberry Pi
- Integrates with Claude Code and other AI tools via MCP, with agent skills configured in the repo
Rodney Brooks writes about the seven cognitive errors that lead to wildly mistaken predictions about the future of robotics and artificial intelligence. He frames four categories of predictions—Artificial General Intelligence, the Singularity, misaligned values, and evil AI entities—then dissects the reasoning failures behind them: overestimating short-term impact while underestimating long-term effects, treating far-future technology as unfalsifiable magic, confusing narrow task performance with broad competence, relying on ambiguous "suitcase words" like "learn" or "understand," extrapolating exponentials that will inevitably flatten, imagining Hollywood-style single-disruption scenarios, and ignoring the glacial pace of hardware deployment in the physical world.
- The OpenWorm project spent thirty years attempting to simulate C. elegans (302 neurons, 7,000 connections) bottom-up and was not yet halfway done as of 2017.
- iPod storage followed a perfect five-year exponential (10→160 GB) then collapsed abruptly once a single device could hold a complete music library.
- Modern factory automation still relies on PLCs introduced in 1968 that emulate electromechanical relays; Tesla was actively hiring PLC technicians at its Fremont factory.
- Autonomous vehicles drove on public roads in 1987 and coast-to-coast across the US in 1995, yet no large-scale deployment path had been identified by 2017.
- Brooks observes that humans coexisted with horses—autonomous agents with ongoing existences and super-human strength—for millennia without a single formal theorem about them.
- The OpenWorm project spent thirty years attempting to simulate C. elegans (302 neurons, 7,000 connections) bottom-up and was not yet halfway done as of 2017.
- iPod storage followed a perfect five-year exponential (10→160 GB) then collapsed abruptly once a single device could hold a complete music library.
- Modern factory automation still relies on PLCs introduced in 1968 that emulate electromechanical relays; Tesla was actively hiring PLC technicians at its Fremont factory.
- Autonomous vehicles drove on public roads in 1987 and coast-to-coast across the US in 1995, yet no large-scale deployment path had been identified by 2017.
- Brooks observes that humans coexisted with horses—autonomous agents with ongoing existences and super-human strength—for millennia without a single formal theorem about them.
bex is an open-source, self-hostable PaaS that positions itself as an AI-native alternative to Render, letting developers push Git and receive a deployed URL on their own Kubernetes infrastructure. Coding agents operate as first-class users via MCP alongside the dashboard, CLI, REST, and GraphQL interfaces, all backed by a shared Go core. The platform uses a Kubernetes operator with Cluster API for machine provisioning, supports Render-style `render.yaml` Blueprints for declarative service definitions, and ships managed Postgres, Key Value, logs, metrics, autoscaling, custom domains with TLS, and SSH access.
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
AWS has released Agent Plugins for AWS, an open-source repository enabling AI coding agents to automate cloud deployment workflows. The initial deploy-on-aws plugin accepts natural language commands to generate complete deployment pipelines with architecture recommendations, cost estimates, and infrastructure-as-code.
This guide walks you through building production-grade MCP servers that expose your organization's internal data to AI models, covering authentication, multi-tenancy, streaming, and deployment patterns.
This article explains the differences between Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), and AI Agents, highlighting that they solve different problems at different layers of the AI stack. It also covers how ChatGPT routes prompts and handles modes, agent skills, architectural concepts for developers, and service deployment strategies.
This article details the steps to move a Large Language Model (LLM) from a prototype to a production-ready system, covering aspects like observability, evaluation, cost management, and scalability.
The article discusses the evolution of model inference techniques from 2017 to a projected 2025, highlighting the progression from simple frameworks like Flask and FastAPI to more advanced solutions like Triton Inference Server and vLLM. It details the increasing demands on inference infrastructure driven by larger and more complex models, and the need for optimization in areas like throughput, latency, and cost.
This article details the billing structure for GitHub Spark, covering costs associated with app creation (based on premium requests) and current limits for deployed apps. It also outlines future billing plans for deployed apps once limits are reached.
A curated guide to code sandboxing solutions, covering technologies like MicroVMs, application kernels, language runtimes, and containerization. It provides a feature matrix, in-depth platform profiles (e2b, Daytona, microsandbox, WebContainers, Replit, Cloudflare Workers, Fly.io, Kata Containers), and a decision framework for choosing the right sandboxing solution based on security, performance, workload type, and hosting preferences.
