Ideas Weave Every Narrative with AI.
The true leverage in AI-assisted work comes from the ‘harness’—the workflow, interface, and agentic loops surrounding the model—not the model itself. Stop chasing model releases. Optimize your harness. That’s where the real competitive advantage lies.
Most prompt engineering advice tells you to add more context. It’s wrong. The real multiplier is subtraction: defining strict ‘No-go rules’ that force AI to abandon generic fluff and hallucinated options. By constraining the model with hard boundaries, you transform it from a brainstorming toy into a precision execution engine.
The AI industry has a genius brain and caveman hands. People treat AI like a god, make wishful requests, then refuse to pay because the miracle doesn’t happen. The real fix isn’t smarter models—it’s making AI invisible, like electricity or WiFi.
Claude Code’s creator reveals a shocking truth about AI product design: the smarter the model, the less you need to instruct it. The key to unlocking AI’s potential isn’t adding more complex prompts but deleting 80% of them. This paradigm shift challenges everything developers know about building products, positioning ‘product overhang’—the gap between what models can do and what products allow—as the next major battleground. The future belongs to those who dare to subtract.
AI sycophancy is frustrating, but forcing your LLM to constantly play devil’s advocate is an engineering disaster. Discover the Dual Steelman Argument and why conditional triggers are the only way to build a true AI sparring partner without ruining your workflow.
The true bottleneck in sales isn’t complex pricing logic, but the friction of translating human-readable rules into machine-executable actions. If AI can do 100% of your quoting math instantly, the ‘math’ was never your real job—it was just an operational tax on your time. Here’s how to use natural language as the new programming interface to build a custom, no-code quoting workbench and get back to selling.
The real bottleneck in AI adoption isn’t prompt quality—it’s organizational memory. Stop optimizing solo prompts and start building reusable Skills that turn ad-hoc instructions into team-scalable workflows. Here’s how to build one, when to skip it, and why your platform matters more than you think.
Most AI-generated documents are unreadable because the model is designed for reasoning, not for natural language. The fix isn’t better prompts—it’s choosing the right model and ruthlessly pruning the context you feed it. A product manager’s hard‑won lesson from testing Grok, Claude, and GPT on real project docs.
AI’s conversational verbosity is a productivity killer. The solution? Use a separate, cheaper LLM to edit the output. By separating generation from presentation, you get clean, concise text — and stop wasting time on ‘token vomit.’ This pattern, from GitHub tools like ‘vomit’ and ‘claudish-to-english,’ is the future of efficient AI interaction.
We tested 15 LLMs and improved every single one’s coding performance in a single afternoon — by changing only the evaluation harness, not the model. The harness is the invisible bottleneck most teams ignore, and it’s the easiest win in AI. Stop training. Start harnessing.