Ideas for AI

Idea #1 Agents should be first-class citizens in team collaboration infrastructure — peers with the same identity, the same channels, and the same audit trail as human teammates (agentic twins).…

Idea #1Defining the full validation strategy before writing a single line of code is the highest-leverage thing you can do in AI-assisted development. The mechanism is straightforward: when a coding…

Idea #1 Naive fine-tuning — simply training a pre-trained model on a private corpus with next-token prediction — does not produce a model that can generalize over that corpus; it…

Idea #1 Agentic engineering and vibe coding can look identical from the outside — both involve an agent running unsupervised — but the internal stance is opposite, and the distinction…

Idea #1Design from the software development lifecycle down, not from agents up. A mental model shift is necessay for productivity. Instead of asking 'what agents do I need?', start asking…

Idea #1One eval harness answers three different questions about model replacement, and framing it this way prevents you from building three separate tools. The three questions are: (1) My current…

Idea #1Price per token is a misleading metric; cost per completed task is the only number that matters when choosing an LLM provider. A model that charges half the token…

Idea #1Building a deep knowledge base from a large corpus is inherently token-heavy and cannot be done cheaply — but the one-time cost is justified by the permanent navigation dividend.…

Idea #1Safety constraints and general capability improvements are not inherently in tension — a model can reduce harmful capability in a specific domain while simultaneously becoming more capable overall, and…

Idea #1Cost per task — not price per token — is the only metric that tells you what you're actually paying for AI work. Price per token is a seductive…