
The Circular Trust Problem: When LLMs Verify LLMs
Using one model to fact-check another can turn errors into a mutual admiration society. Here's how the recursion happens and how to break it.

Using one model to fact-check another can turn errors into a mutual admiration society. Here's how the recursion happens and how to break it.

Bigger context windows won't fix agents that forget. Persistent, structured project memory is the real unlock for sustained knowledge work.

A practical rule for AI-native software: use an agent only when the steps are unknown and the goal is clear. Use a deterministic workflow for everything else.

Authentication tells us an agent is real. Delegation tells us who it's acting for — and that's the primitive we're missing. Here's how to build it.

AI agents are non-human identities that deserve just-in-time, scoped access — not the permanent keys most teams still hand out. Here's how to do it.

Model choice is now a routing decision, not a beauty contest. Here's how to match task, latency, and cost across the big providers.

AI coding agents love to invent package names. Attackers register those names and wait. Here's how the hallusquatting attack works and how to defend against it.

Bolting AI onto disconnected tools recreates the handoff tax it promised to kill. Here's why integration depth beats feature breadth in 2026.

As AI agents start transacting on our behalf, the bottleneck isn't intelligence — it's the missing trust, identity, and handoff layer between them.

The tools you built for deterministic microservices can't debug an agent that reasons, retries, and improvises. Here's what your monitoring stack actually needs.

Move past one-off prompts. Build a persistent Tamaton agent that watches your inbox, files, and calendar, then acts on a schedule with human checkpoints.

A practitioner's framework for choosing retrieval-augmented generation over long-context prompting — based on cost, freshness, and grounding, not vibes.
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