every major model provider wants you hooked to them. Not to your agent. Not to your data. To them. The more your agent "remembers" inside their walls, the less of it is actually yours. You're not building a moat. You're renting one.
@godofprompt
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Critique of platform lock-in strategies in AI memory features
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Now look at what the labs are actually shipping: Useful? Sure.
But notice where the memory lives. Inside their platform. Tied to their models. Portable nowhere. Every "memory" feature the big labs ship is designed to make leaving more expensive. -
Economic trade-offs in AI agent memory: retrieval vs. procedural
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Layer 3 is where the economics flip. Retrieval memory costs tokens on every call. The preference gets looked up, injected, re-reasoned — every time. Procedural memory costs zero tokens at inference. The behavior lives in the weights. The agent just acts correctly. At scale,
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Neuro-inspired Three-tier Memory Architecture for AI Agents
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The clearest frame I've seen on this comes from these guys @midbrain_ai. Their point: the brain doesn't run one memory system. It runs three. – Episodic: what happened (raw traces) – Semantic: what it means (abstracted patterns) – Procedural: how I now behave (baked into the
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US Accuses China of Industrial-Scale AI Intellectual Property Theft
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US has accused China of "industrial-scale" theft of AI and warned that it will be cracking down on the exploitation, per FT.
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GPT-5.5 performance on ARC-AGI-2 benchmark
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GPT-5.5 just hit 85% on ARC-AGI-2. Somewhere, Yann LeCun is explaining why this still doesn't count. LLMs keep climbing a tall tree toward the moon
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Clarification on GPT image-2 model naming
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It’s a typo of mine, gpt image-2 is the correct name of the model
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Collection of Open-Source GPT-2 Image Prompts and Use Cases
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Grab those fresh GPT-2 image prompts All open-source with examples, styles and use cases:
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The technical necessity for robust AI agent web navigation
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Forget the demo.
— God of Prompt (@godofprompt) 16 avril 2026
If your agent can’t survive the live web, it’s dead on arrival.
TinyFish building search, fetch, browser, and agent in-house is what makes this worth paying attention to. https://t.co/XryniOY1fiForget the demo. If your agent can’t survive the live web, it’s dead on arrival. TinyFish building search, fetch, browser, and agent in-house is what makes this worth paying attention to.
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Anthropic Announces Claude Opus 4.7 with Enhanced Reasoning Capabilities
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Introducing Claude Opus 4.7, our most capable Opus model yet. It handles long-running tasks with more rigor, follows instructions more precisely, and verifies its own outputs before reporting back. You can hand off your hardest work with less supervision.