Ideas Weave Every Narrative with AI.
We are forcing billion-dollar AI models to write code in human-readable languages designed for the 1990s. It’s a massive waste of tokens. The future of programming isn’t AI writing Python—it’s a new, highly compressed language built entirely for machines, trading human readability for radical token efficiency.
An LLM generated a mathematically sound counterexample to a conjecture far outside the mathematician’s expertise. The bottleneck in science is no longer discovery—it’s verification. Human experts are becoming auditors of an AI’s accidental genius, and the burden of proof has never been heavier.
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.
The necklace puzzle wasn’t solved by AI. A human found the method after a year of failure; the LLM just executed the mechanical steps. This reveals the real bottleneck in problem-solving: not creativity, but boredom. AI is the perfect intern, not the inventor. The spark remains ours.
The biggest bottleneck in agent-driven development isn’t access or autonomy—it’s the unreliable, hallucinated code produced by the underlying LLM. Giving agents more infrastructure won’t fix the fundamental lack of deterministic precision. The real solution lies in base model reliability and rigorous verification.
Most people think writing like Paul Graham means copying his style. But the real secret is understanding the principles behind his thinking. An AI tool called WriteLikePG doesn’t just rewrite your text—it shows you exactly why changes are made, turning each edit into a lesson. The paradox: using a homogenizing AI to achieve a unique voice is dangerous, but it’s the only way most of us will ever get the feedback we need. The goal isn’t to sound like someone else; it’s to learn the rules so you can break them with your own voice.
We pretend AI adoption is a moral dilemma, but the truth is simpler: we use it because it’s too useful not to. The real hypocrisy isn’t in the technology—it’s in us. We’ll trade our ethics for convenience every time, and the guilt is just the cost of doing business.
If you’re building LLM agents, you’re likely wasting thousands of tokens on JSON boilerplate. Tools like Mcptoon promise to save 97% of tokens by compressing these outputs, but they just shift complexity to fragile parsers. The real problem isn’t compression; it’s flawed agent architecture.
Chandra Sripada argues that LLMs are not just tools that mimic human cognition—they are functional models of how our own brains work. This forces a radical re-evaluation: human intelligence is an embodied, context-rich prediction engine, no more ‘special’ than a statistical model. The existential unease is real, but the insight is liberating.
Mastering AI coding tools feels like a superpower, but it’s actually a trap. The harder you run on the LLM treadmill, the more interchangeable you become. The only way out of this commoditized rat race is to stop obsessing over prompt engineering and shift your focus to problem framing, system architecture, and deep domain judgment—the things AI cannot replicate.