Search blog posts, case studies, GitHub repo roundups, agent architecture notes, and practical AI automation guides.
Qwen's official Qwen3.8 Max release explained: 2.4T MoE architecture, agentic coding, cowork benchmarks, multimodal feedback, API setup, and open-weight status.
Josh Pocock stress-tests Buzz and exposes the gap between a shared agent workspace and dependable orchestration. Here is what Buzz really solves, what the demo broke, and the controls a production agent team still needs.
Andrew Warner and Vince Canger test Buzz across agent runtimes, Wasp and Railway deployment, mobile access, Git projects, workflow triggers, shared compute, and multi-agent routing. Here is the practical operator guide.
Nate Herk demonstrates agentic AI for analytics, a small operations app, and lead research. Here is the evidence behind the job claims and a safer three-stage plan for becoming an effective AI manager.
AI for Mortals tested Opus 5, Opus 4.8, and Fable 5 across web design, 3D, games, motion graphics, and knowledge work. Here is the practical scorecard, real build-cost table, and an organized prompt framework you can reuse safely.
Andrew Warner and Adam Brakhane review 13 GitHub repositories for AI design, job search, Office documents, parallel coding agents, secure sandboxes, model routing, and video understanding.
Kimi K3 is a 2.8T multimodal model with 1M context. Four creator tests show where it rivals GPT-5.6 and Fable 5, where it fails, and what it costs.
A full client case study: brand identity for a Boca Raton social club, website direction through real feedback rounds, a playable American mahjong web game built from the brief, and an autonomous daily Pinterest pipeline at zero running cost.
GitHub Trending for 15 July 2026: OpenCut, Orca, OmniRoute, Vibe-Trading, agent safety, sandboxes, low-cost coding agents, and the daily movers worth testing.
Matt Van Horn and Andrew Warner explain how Last30Days gives AI agents current research across X, Reddit, YouTube, TikTok, Instagram, GitHub, and more. Here is the setup, Doctor mode, source verification, and safe workflow.
Dan Shipper and the Every team tested GPT-5.6 Sol for a month across coding, writing, design, and knowledge work. Here is where Sol wins, where Fable still leads, and how to route real tasks.
Pat Simmons gave Fable 5 and Opus 4.8 the same prompts for an e-commerce store, 3D art museum, and strategy game. Here are the raw outcomes, costs, limitations, and model-routing lessons.
Nate Herk shows how to keep Fable 5-style process after access changes: turn good runs into skills, use effort levels deliberately, and route cheaper models to the work they can handle.
Fable 5 generating 25 visual websites with sub-agents, tools, deployment, and review loops. The practical version: design the prompt, then sell the human layer.
Theo argues the Fable 5 backlash is mostly wrong. Here is the practical builder version: Fable was not simply nerfed, but routing, safety classifiers, usage limits, and cost discipline now matter more.
Pat Simmons shows three ways to reduce dependence on gated frontier models: local Ollama, free NVIDIA NIM endpoints, and cheap OpenRouter model routing. Here is the practical builder version.
Andrew Warner and Matthew Berman break down AI agent loops: triggers, verifiable goals, LLM-as-judge loops, sub-agents, scheduled automations, token budgets, and reusable Loop Library templates.
A practical JQ AI SYSTEMS roundup of The Next New Thing's June 18 GitHub Hot Repos report: OpenCut, Apple container, SkillSpector, agent-skills, pm-skills, Agent-Reach, Headroom, system prompt leaks, and more.
A practical JQ AI SYSTEMS roundup of this week's hot GitHub repos suggested by The Next New Thing: MoneyPrinterTurbo, headroom, MarkItDown, Supermemory, ECC, taste-skill, VoxCPM, and more.
AI agent memory is becoming the new business knowledge base. Learn how memory differs from chat history, why agents forget work, and how small teams can structure context for better automation.
A practical framework for turning repeatable prompts into installable AI skills with folders, metadata, assets, install scripts, release notes, and review discipline.
Andrej Karpathy joining Anthropic is a signal that the next AI advantage is not just the model, but the context, memory, workflows, and research loops around it.
Prompt libraries are useful, but reusable AI work is moving toward skills, connectors, subagents, and review steps. Here is how to turn a good prompt into a workflow asset.
OpenAI GPT 5.5 review: 1M context, 82.7% Terminal-Bench, 85% ARC-AGI-2. What the benchmarks say, where Opus 4.7 still leads, and what it means if you build.
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