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pascalmusabyimana

pascal-maker
b082f689-ebc3-413d-b1f2-27bd1e86dca7's profile picture 21world's profile picture MikeDoes's profile picture
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https://pascal-maker.github.io/

AI & ML interests

computer vision, nlp , machine learning and deeplearning

Recent Activity

liked a model about 10 hours ago
inferencerlabs/Kimi-K2.6-MLX-3.6bit
liked a model about 10 hours ago
unsloth/Kimi-K2.6-GGUF
reacted to mrmanna's post with ❀️ about 14 hours ago
π—”π—œ & π—¦π—§π—”π—§π—˜ π— π—”π—–π—›π—œπ—‘π—˜ 𝘞𝘩𝘺 π˜—π˜³π˜°π˜₯𝘢𝘀𝘡π˜ͺ𝘰𝘯 π˜‰π˜¦π˜¨π˜ͺ𝘯𝘴 𝘞𝘩𝘦𝘳𝘦 π˜›π˜°π˜Ί 𝘈𝘨𝘦𝘯𝘡𝘴 𝘌𝘯π˜₯ Published: 18 Apr 2026 | Towards AI Publication | Medium Open Link: https://medium.com/towards-artificial-intelligence/ai-state-machine-106387406c5a?sk=047b2f064c673a0095a9e8cc011b6a92 We talk a lot about governance, accuracy, and auditability in AI agents. But I keep seeing a gap between the words and the engineering behind them. Many agents have tools, orchestration, memory, graphs, and impressive demos. But when you ask how governance is actually enforced, the answer is often weak. Prompt-level control is not production governance. A production agent needs explicit state design: legal transitions, controlled progression, recovery paths, approval boundaries, and separation between memory, decision, policy, and execution. This article explores the silent crisis unfolding in modern AI development: the urgent need to resurrect the disciplined architecture of state machines
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