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Robert Greene tells Calum Johnson that AI changes the tools, but not the power game. Here is the practical JQ AI SYSTEMS version: create dependence, build rare judgment, use AI as leverage, and stay useful when work gets automated.
Chris Koerner interviews Caleb Panza on how Post For Me crossed $10K MRR with no big audience: transparent MRR updates, customer-question SEO, AI-search visibility, pricing, churn, and the lean SaaS stack behind it.
David Ondrej interviews 0xSero about GLM-5.2, custom compression, LM Studio, rented GPUs, local tokens, and why open-weight models need better distribution and tooling.
A practical comparison of cloud GPUs, hosted AI APIs, and home AI hardware for local models: cost, privacy, latency, maintenance, electricity, and when a hybrid setup wins.
A practical local AI hardware guide with prices and buy links for Ollama, LM Studio, Qwen, Gemma, Llama, DeepSeek, GLM, Mac mini, Mac Studio, RTX PCs, DGX Spark, and cloud GPUs.
Greg Isenberg argues that "learn AI" is too vague. The durable move is to build a skill stack: agents and local models, distribution, robotics, curation, builder-distribution, and real-world community.
Andrew Warner and Eric Siu review real Hermes Agent use cases: computer control, competitor research, memory systems, Linear and Notion handoffs, Slack and Discord channels, remote gateways, crons, and reusable skills.
Shashank Agarwal showed Andrew Warner how API.market runs with OpenClaw agents for PR, LinkedIn, seller outreach, finance, hiring, and chief-of-staff work. Here is the practical agent operating system behind it.
Sakana Fugu and Fugu Ultra are not normal frontier models. They orchestrate multiple models through one API. Here is how they work, what the benchmarks mean, what it costs, and when builders should try it.
Andrew Warner and Hiten Shah reviewed real AI businesses making money. The lesson is clear: AI makes products easier to build, but distribution, margins, churn, and deployment still decide who wins.
The Fable 5 access pause is a reminder that cloud AI is rented intelligence. Local models give builders a private, offline, resilient fallback layer for everyday work.
A practical JQ AI SYSTEMS breakdown of three one-person AI business ideas: Microsoft Copilot training, paid AI communities, and vibe coding agencies built around the deployment gap.
AI plugins, MCP tools, and work plugins are becoming the workflow layer around AI agents. Here is how businesses should think about permissions, tools, review, and rollout.
AI deployment services are becoming the missing layer between model access and real business impact: workflow mapping, data, tools, controls, training, and implementation.
AI deployment is where AI starts becoming useful: choosing repeatable workflows, connecting models to data and tools, adding controls, and getting teams to use the system.
Hermes Agent is powerful, but running an autonomous AI agent on a remote server raises real questions about safety, isolation, and cost. Here is exactly how I am setting mine up on a Hetzner CPX22, what Docker does to contain it, and what risks remain.
Hermes Agent is a self-improving open-source AI agent that gets better the more you use it. Paired with ChatGPT 5.5 as its brain, it becomes a 24/7 AI employee that builds skills, schedules tasks, and runs autonomously. Here is why I am adopting it.
OpenAI launched GPT Image 2 on 21 April 2026. 99% text accuracy, native reasoning, up to 4K resolution, and full API access. Here is what actually changed, what it costs, and what it means if you build with AI.
A clear, practical definition of prompt engineering in 2026: what it is, what it is not, and what good prompt engineering looks like compared to writing a clever prompt.
A clear, practical definition of AI agents in 2026: what they are, how they differ from chatbots and traditional automation, and what they look like in real production systems.
A clear definition of AI workflow automation in 2026: what it is, how it differs from traditional workflow automation and RPA, and what it actually replaces inside a business.
Not every AI tool needs to live in the cloud. Here is how I built a local Python tool that turns raw CSV exports into formatted Word reports, with no internet connection, no subscription, and no manual formatting.
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