How to learn anything faster with AI Explain it like I’m 5
Examples & analogies
Quizzes, mind maps, role-play
Expert debates + feedback loops AI = tutor, coach, and study partner. #AI #Learning #Productivity #GenAI
TOOLS
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AI-Powered Learning: Master Anything Faster with Smart Techniques
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Slack Connector on Manus Transforms Conversations Into Searchable Knowledge
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Is your team’s best knowledge buried in endless Slack threads? 👀
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 2 janvier 2026
Meet the Slack Connector on Manus. It transforms Slack conversations into searchable, actionable knowledge—so insights don’t disappear in the scroll. 👇👇 pic.twitter.com/vAkh0mXi6RIs your team’s best knowledge buried in endless Slack threads? Meet the Slack Connector on Manus. It transforms Slack conversations into searchable, actionable knowledge—so insights don’t disappear in the scroll.
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Hypergraph Memory Enhances Multi-step RAG Long-Context Modeling
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Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling
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Instagram evolves for AI as DeepSeek hints at next-gen architecture
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Top stories in AI today: – IG head platform must “evolve fast” for AI
– DeepSeek hints at next-gen AI architecture
– Use Codex to write code on the web
– OAI overhauling audio for upcoming device
– 4 new AI tools, community workflows, and more Read more: https://
therundown.ai/p/instagrams-a
i-driven-identity-crisis
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Create-Plan Skill in Codex for Iterative AI Workflows
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the create-plan skill in codex comes in clutch many times! I use it all the time to iterate around with a SPEC md before firing off codex to go get shit done if you haven't already install it (simply ask install create-plan skill) and try it out
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Implementing Sparse Model Training Today
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How to implement this today: 1. Use PyTorch's torch.nn.utils.prune for magnitude pruning
2. Apply 2:4 structured patterns for GPU acceleration
3. Fine-tune with sparse-aware training
4. Deploy with TensorRT or ONNX Runtime The infrastructure exists. Most teams just don't know -
Optimizing models with pruning, patterns, and quantization
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But magnitude pruning alone isn't enough. The real magic happens when you combine: → Magnitude pruning (remove smallest weights)
→ Structured patterns (2:4 blocks for GPU)
→ Quantization (INT8 instead of FP32) Stack all three and you get 20-50x deployment efficiency. -
2018 Paper: Pruning Neural Networks Without Accuracy Loss
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The original 2018 paper was mind-blowing: Train a massive neural network. Delete 90% of it based on weight magnitudes. Retrain from scratch with the same initialization. Result: The pruned network matches the original's accuracy. But there was a catch that killed adoption.
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ChatGPT subscription support comparison and pricing
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yep! we support both, ChatGPT sub ofc is a better deal
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Building AI Integrations with Claude and Notion Automation
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I’m setting up my first-ever notion integrations. I’m communicating errors with Claude through screenshots. I’m debugging servers. Im adding animation gradients. I’m dictating it all through Wispr flow. I am 0% stressed and I’m watching “We Met in December”. Is this real life?