That's a fair reframe. Knowing what to forget is arguably harder than knowing what to remember. memify() is exactly aimed at that, strengthening useful paths and letting stale ones decay. The title optimizes for the hook, but you're right that intelligent forgetting is the
MACHINE LEARNING
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Graph Traversal Enables Multi-Hop Queries Beyond Vector Search
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Good questions. Graph traversal adds a small overhead but makes multi-hop queries possible that vector search alone simply can't answer.
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Retrieval vs Behavioral Learning: The Real Gap in AI Agents
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Really well articulated. The distinction between "finds the right fact when asked" vs "already changed behavior from experience" is the real gap. Retrieval is table stakes. Consolidation turning episodic traces into behavioral defaults is where agents actually start learning.
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Graph Search vs Vector Search: Beyond Similarity in AI
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Exactly. Vector search answers "what's similar" but not "how are these connected." The Alice-project-outage example in the post explains this. Most real questions need at least two hops, and that's where graphs become essential, not optional.
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The Process of Data Cleaning in Data Science and Big Data
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The Process of #Data Cleaning by @Python_Dv #DataScience #BigData
→ View original post on X — @ronald_vanloon, 2026-04-14 07:48 UTC
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Utilization Methods of Cyclorotor-Based Air Mobility
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Utilization Methods of Cyclorotor-Based Air Mobility
— Ronald van Loon (@Ronald_vanLoon) 14 avril 2026
via @ZappyZappy7
#Robotics #MachineLearning #ArtificialIntelligence #ML pic.twitter.com/ltjYEJpSIAUtilization Methods of Cyclorotor-Based Air Mobility via @ZappyZappy7 #Robotics #MachineLearning #ArtificialIntelligence #ML [Translated from EN to English]
→ View original post on X — @ronald_vanloon, 2026-04-14 07:27 UTC
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Efficient Cross-Domain Offline Reinforcement Learning with Data Filtering
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Efficient Cross-Domain Offline Reinforcement Learning with Dynamics- and Value-Aligned Data Filtering Paper: https://
arxiv.org/pdf/2512.02435
Code: https://
github.com/zq2r/DVDF.git Our report: https://
mp.weixin.qq.com/s/ztE8GofcssuI
1PdkHx_kLg
… #PapersAccepted by Jiqizhixin -
AI Agents Learning Across Different Environments
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How can AI agents learn effectively when their training data comes from environments vastly different from where they'll operate? Researchers from City University of Hong Kong, UIUC, Tencent, and Tsinghua University present DVDF, a new method for cross-domain offline
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Memorization as Tool to Accelerate Cognition Not Replace It
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The role of memorization and knowledge is to cache & reuse past cognitive work. It should be leveraged as a way to speed up cognition, not as a *replacement* for cognition.
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Wendy: New Operating System for Physical AI and Edge Devices
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Today I’m incredibly excited to announce Wendy.
— Maximilian Alexander (@signalgaining) 14 avril 2026
Wendy is an operating system and developer platform for Physical AI — built to make it dramatically easier to build and deploy on NVIDIA Jetson, Raspberry Pi, and other edge devices.
We think robotics, edge AI, industrial systems,… pic.twitter.com/pTLLpPgfRSToday I’m incredibly excited to announce Wendy. Wendy is an operating system and developer platform for Physical AI — built to make it dramatically easier to build and deploy on NVIDIA Jetson, Raspberry Pi, and other edge devices. We think robotics, edge AI, industrial systems, autonomous machines, and smart cameras should be far simpler to create. Less setup. Less infrastructure pain. Faster time to first demo. This is the start of something big. Get started at wendy.sh
→ View original post on X — @scobleizer, 2026-04-14 05:12 UTC