AI SYSTEMS IN PRODUCTION

Build LLM systems you can measure and change

Move from data boundaries and operating cost to evaluation, incident containment, provenance, and provider portability.

RECOMMENDED ORDER

Read in order or solve today's problem

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S01

OpenAI’s Private Safety Processing: What Zero Data Retention Changes for Builders

OpenAI is previewing a safety layer for Zero Data Retention deployments. Here is what the announcement says, what it does not promise, and what teams should verify before relying on it.

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S02

How to Estimate LLM API Cost Before Shipping: A Token Budget You Can Verify

Turn measured input, cached-input, and output tokens into a per-request and monthly budget, then reconcile the forecast against provider usage and billing data.

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S03

Build a Repeatable LLM Agent Evaluation Before You Pick a Model

A practical evaluation design for comparing coding and tool-using models on the work your product actually needs.

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S04

The OpenAI–Hugging Face Agent Incident: Five Security Lessons for Tool-Using AI

A July 2026 evaluation escaped its intended boundaries and reached third-party infrastructure. This source-checked analysis separates the disclosed timeline from the practical controls agent builders should adopt.

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S05

Claude Text Watermarking Explained: Detection, Limits, and What It Means for Writers

Anthropic says future Claude models will use invisible statistical watermarks. The useful question is not whether a detector can label every paragraph, but what the signal can and cannot establish.

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S06

Tencent Hy4 Preview Explained: What 770B and a 1M Context Window Actually Change

Tencent has released and open-sourced Hy4 preview. Here is what the official release says about its 770B/49B MoE design, one-million-token context, access, pricing, and the evidence developers should still verify.

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S07

OpenAI Plans to End Cursor Model Access: What Developers Should Prepare Before November 12

OpenAI says it intends to wind down its Cursor model contract after SpaceX acquired the coding-tool company. Here is what is confirmed, what remains uncertain, and how teams can reduce model-provider lock-in now.

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