Search blog posts, case studies, GitHub repo roundups, agent architecture notes, and practical AI automation guides.
Natalie Dawson interviews JD Ross on Opendoor, WithCoverage, AI, insurance, and why operators with real domain expertise may have the best shot at building useful AI businesses.
FluidVoice runs speech models on-device: free, open source, macOS only. Setup, privacy trade-offs, how it compares to Wispr Flow, and where Windows stands.
Andrew Warner and Corey Ganim break down 16 AI launches from The Next New Thing: NotebookLM Short Video Overviews, June, xAI Voice Agent Builder, BoringMarketing, open Slack agents, Arena42, Fable 5, Cursor iOS, OpenClaw mobile, Gemini Spark, Hermes context usage, and more.
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.
Seven Hermes Agent use cases that hold up in practice: computer control, competitor research, memory, Linear and Notion handoffs, Slack, crons, and 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.
Which Hermes Desktop surfaces actually matter, why profiles are not security sandboxes, and what to get right before you schedule anything. Judgment, not docs.
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 with ChatGPT 5.5 as its brain: an open-source agent that builds its own skills, schedules tasks, and runs unattended. Why I am adopting the stack.
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.
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