// Topics / Models

Models

Definition

Models coverage in this archive spans 3 posts from Mar 2024 to Apr 2026 and treats models as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, llm, and open source. Recurring title motifs include ai, state, open, and source.

Working claims

  • The archive repeatedly argues that models only creates leverage when it is wired into an existing workflow.
  • The consistent theme from 2024 to 2026 is disciplined execution over hype cycles.
  • This topic repeatedly intersects with ai, llm, and open source, so design choices here rarely stand alone.

How to apply this

  • Define quality gates up front: eval sets, guardrails, and explicit rollback criteria.
  • Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
  • When boundary questions appear, cross-read ai and llm before committing implementation details.

Where teams get burned

  • Shipping agent behavior without hard boundaries for tools, data access, and approvals.
  • Optimizing for model novelty while ignoring reliability, latency, or cost drift.
  • Applying guidance from 2024 to 2026 without revisiting assumptions as context changed.

Suggested reading path

References

2 entries tagged “Models”

    Picking an AI Model for Production (Late 2024) · 5 min There's no best model. There's the model that fits your workload, latency budget, cost constraint, and ops tolerance. Here's how to compare them. ai models comparison Claude 3 First Impressions: Three Models, One Decision Framework · 4 min Anthropic shipped three models instead of one. That is actually the most interesting part of the release. claude anthropic llm
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