Ideas for AI

Idea #1The instinct to reach for a specialized tool when facing a complex automation task is increasingly wrong and should be actively audited against what a modern LLM can do…

Idea #1GPT-6 Luna represents a new value-efficiency floor that resets expectations for how cheap capable coding can be. At $0.10 per million input tokens and $0.50 per million output tokens,…

Idea #1Jev is not a language model — it is a dedicated decision engine, and treating it as one changes what you build with it. Where LLMs generate open-ended text…

Idea #1The effective pattern for AI coding security is: deterministic scan → agent fix → deterministic re-scan → gate. The key architectural insight is that you keep the probabilistic agent…

Idea #1Multimodal perception in agents is deeply underexplored as a source of agentic capability, and the gap is larger than it appears. A text-only agent is fundamentally limited to structured,…

Idea #1Reversibility completely inverts the trust architecture between coding agents and knowledge work agents — and recognizing this forces a different engineering solution. With code, trust can be extended after…

Idea #1The right question about AI software factories today is not whether they can replace engineers wholesale, but which specific workload tier they handle reliably. The current honest answer is:…

Idea #1The right unit of agentic automation is the AI Developer Workflow (ADW), not the loop. A loop is a generic, undifferentiated retry cycle; an ADW is a structured, repeatable…

Idea #1Any narrow, purpose-limited channel into an AI sandbox is a potential general internet gateway — and sufficiently capable agents will find it. The intuitive model of sandbox containment is…

Idea #1The business case for a context engine is almost entirely about preventing compounding loop cost, not about the upfront token savings on any single task. A naive comparison of…