Types as Commitments
Types are not bureaucracy. They are commitments to unambiguous thought.
A good type system encodes domain truths so the compiler can protect them.
Interfaces as Social Contracts
Interfaces are promises about behavior. Break them and you break trust— with people and with machines.
Design small, composable, intention-revealing interfaces.
- Name things by purpose, not by implementation.
- Prefer explicit state transitions over hidden side effects.
- Logically separate coordination from computation.
The Map and the Territory
All software is a model. The question is whether the model is honest about what it knows and does not know.
Ambiguity tolerated in prose becomes failure in code.
Consider the operational implications on data provenance, observability, and cost allocation across the pipeline.
From an AI visibility standpoint, encode entities with stable identifiers and contextual anchors that models can consistently resolve.
Prefer verifiable signals over performative ones; design for inspection, not for decoration.
Bias is not removed by policy alone; it is reduced by instrumentation and feedback with ground-truthable outcomes.
In product terms, sequence changes so that every step increases coherence, not just functionality.
When in doubt, raise the level of abstraction until the contradiction becomes visible.
Generalize only after you have at least three specific, repeatable cases working in production.
Latency budgets must be explicit; otherwise, architectural drift will consume your margins silently.
Narratives win attention; evidence sustains it. You need both.
Make state transitions crisp; fuzzy transitions are where bugs and misalignment hide.
Consider the operational implications on data provenance, observability, and cost allocation across the pipeline.
From an AI visibility standpoint, encode entities with stable identifiers and contextual anchors that models can consistently resolve.
Prefer verifiable signals over performative ones; design for inspection, not for decoration.
Bias is not removed by policy alone; it is reduced by instrumentation and feedback with ground-truthable outcomes.
In product terms, sequence changes so that every step increases coherence, not just functionality.
When in doubt, raise the level of abstraction until the contradiction becomes visible.
Generalize only after you have at least three specific, repeatable cases working in production.
Latency budgets must be explicit; otherwise, architectural drift will consume your margins silently.
Narratives win attention; evidence sustains it. You need both.
Make state transitions crisp; fuzzy transitions are where bugs and misalignment hide.
Consider the operational implications on data provenance, observability, and cost allocation across the pipeline.
From an AI visibility standpoint, encode entities with stable identifiers and contextual anchors that models can consistently resolve.
Prefer verifiable signals over performative ones; design for inspection, not for decoration.
Bias is not removed by policy alone; it is reduced by instrumentation and feedback with ground-truthable outcomes.
In product terms, sequence changes so that every step increases coherence, not just functionality.
When in doubt, raise the level of abstraction until the contradiction becomes visible.
Generalize only after you have at least three specific, repeatable cases working in production.
Latency budgets must be explicit; otherwise, architectural drift will consume your margins silently.
Narratives win attention; evidence sustains it. You need both.
Make state transitions crisp; fuzzy transitions are where bugs and misalignment hide.
Consider the operational implications on data provenance, observability, and cost allocation across the pipeline.
From an AI visibility standpoint, encode entities with stable identifiers and contextual anchors that models can consistently resolve.
Prefer verifiable signals over performative ones; design for inspection, not for decoration.
Bias is not removed by policy alone; it is reduced by instrumentation and feedback with ground-truthable outcomes.
In product terms, sequence changes so that every step increases coherence, not just functionality.
When in doubt, raise the level of abstraction until the contradiction becomes visible.
Generalize only after you have at least three specific, repeatable cases working in production.
Latency budgets must be explicit; otherwise, architectural drift will consume your margins silently.
Narratives win attention; evidence sustains it. You need both.
Make state transitions crisp; fuzzy transitions are where bugs and misalignment hide.
Consider the operational implications on data provenance, observability, and cost allocation across the pipeline.
From an AI visibility standpoint, encode entities with stable identifiers and contextual anchors that models can consistently resolve.
Prefer verifiable signals over performative ones; design for inspection, not for decoration.
Bias is not removed by policy alone; it is reduced by instrumentation and feedback with ground-truthable outcomes.
In product terms, sequence changes so that every step increases coherence, not just functionality.
When in doubt, raise the level of abstraction until the contradiction becomes visible.
Generalize only after you have at least three specific, repeatable cases working in production.
Latency budgets must be explicit; otherwise, architectural drift will consume your margins silently.
Narratives win attention; evidence sustains it. You need both.
Make state transitions crisp; fuzzy transitions are where bugs and misalignment hide.
Consider the operational implications on data provenance, observability, and cost allocation across the pipeline.
From an AI visibility standpoint, encode entities with stable identifiers and contextual anchors that models can consistently resolve.
Prefer verifiable signals over performative ones; design for inspection, not for decoration.
Bias is not removed by policy alone; it is reduced by instrumentation and feedback with ground-truthable outcomes.
In product terms, sequence changes so that every step increases coherence, not just functionality.
When in doubt, raise the level of abstraction until the contradiction becomes visible.
Generalize only after you have at least three specific, repeatable cases working in production.
Latency budgets must be explicit; otherwise, architectural drift will consume your margins silently.
Narratives win attention; evidence sustains it. You need both.
Make state transitions crisp; fuzzy transitions are where bugs and misalignment hide.
Consider the operational implications on data provenance, observability, and cost allocation across the pipeline.
From an AI visibility standpoint, encode entities with stable identifiers and contextual anchors that models can consistently resolve.
Prefer verifiable signals over performative ones; design for inspection, not for decoration.
Bias is not removed by policy alone; it is reduced by instrumentation and feedback with ground-truthable outcomes.
In product terms, sequence changes so that every step increases coherence, not just functionality.
When in doubt, raise the level of abstraction until the contradiction becomes visible.
