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AI Platforms
AI platform work is the boring machinery that lets product teams experiment without turning every feature into a one-off model integration. The platform owns traces, schemas, routing, retries, eval hooks, and guardrails around expensive or risky work.
Start here
- →Make common reliability work reusable.
- →Keep model churn behind stable interfaces.
- →Expose costs, latency, and the ways calls fail.
- →Give teams safer defaults for experiments.
Related writing
No long-form pieces here yet. Start with the short notes.
TILs
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TIL · ai · agents · evals
One giant prompt can hide five separate jobs. Split the work so each part has a smaller contract and a failure you can actually name.
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TIL · ai · evals · ai reliability
If an AI answer goes sideways and you cannot see the prompt, model, latency, tokens, retrieved context, and failure path, you are debugging from vibes.
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TIL · ai · evals · ai reliability
If every eval run emails a customer, updates production state, or fires a webhook, you do not have an eval harness. You have a hostage situation.
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TIL · ai · agents · ai reliability
Agents get less spooky when they have named states, constrained transitions, and a record of how each decision moved the process forward.
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TIL · ai · agents · ai reliability
Agents should not get to delete files, send messages, spend money, publish content, or mutate production just because the next step looks obvious.
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TIL · ai · ai reliability · ai platforms
If the rest of your app needs data, make the model return data. Do not make downstream code scrape nice-sounding paragraphs forever.
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TIL · ai · ai reliability · ai platforms
AI features get scary when prompts, logs, evals, schemas, fallbacks, and product code all live in the same pile. Give the weird part one stable interface so changes have a place to go.