Most teams think “more data = smarter AI.” I make the opposite case: context beats volume. When LLMs are grounded in your company’s own signals—not just the internet—they deliver accurate, explainable decisions at scale. A thread on Context Engineering and why it changes
MACHINE LEARNING
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Context Engineering: Structuring Data for AI Reasoning
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What is “Context Engineering”? It’s the discipline of stitching your unstructured mess—logs, chats, docs, images—into something an AI can actually reason over. → Vector DBs + hybrid search + embeddings to retrieve by meaning, not keywords. → Decisions anchored in your data,
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Memory optimization bottlenecks and quantization trade-offs in LLMs
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I am not sure this will be true, i.e., lower memory demands.
There are some many bottlenecks and opportunities for improvement. If we have better quantization for reducing KV cache sizes via TurboQuant, that just means we will use the memory capacity elsewhere:
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AI Matches 50 Dermatologists in Melanoma Diagnosis Accuracy
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How good is AI for diagnosing melanoma?
A systematic review of 11 studies, 2500 patients, finds accuracy and performance comparable to 50 dermatologists, with promise for broad use requiring further validation @JAMADerm This is important with the big shortage of dermatologists -
Verification Layer Critical for Building Reliable AI Skills
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Yeah the verification layer is key, otherwise you're just accumulating broken skills
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Learning Mechanisms Differentiate AI From Static Tool Libraries
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Yeah the learning mechanism is what separates this from static tool libraries
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SASInnovate Conference: 200+ Sessions on Data and AI Skills
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Don't miss out on the incredible opportunity to join #SASInnovate! Experience 3+ days of invaluable networking, choose from 200+ breakout sessions + learn from expert speakers who will elevate your data and AI skills. Reserve your seat http://
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Inference Engine Batch Size and GPU Chat Announcement
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this seems big. tho curious how this ties into inventory mgmt, fulfillment, etc. using fb marketplace infra or…
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RetinaNet and Focal Loss: Solving Class Imbalance in Object Detection
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🎯 RetinaNet & Focal Loss: Fixing Class Imbalance in Object Detection
— Satya Mallick (@LearnOpenCV) 25 mars 2026
Single stage detectors were fast but struggled with class imbalance. In 2017, researchers at Facebook AI introduced RetinaNet with a new loss function Focal Loss.
By down-weighting easy background examples… pic.twitter.com/1gQ9p8Ku7jRetinaNet & Focal Loss: Fixing Class Imbalance in Object Detection Single stage detectors were fast but struggled with class imbalance. In 2017, researchers at Facebook AI introduced RetinaNet with a new loss function Focal Loss. By down-weighting easy background examples
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Snorkel AI Hiring ML Training Infrastructure Engineer for RL Scaling
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Scaling RL training for agentic models is one of the hardest infra problems in ML right now and honestly, one of the most exciting jobs🔥 Our research team @SnorkelAI is deep in RLFT (data valuation, curriculum learning, and more). We're #hiring an ML Training Infra engineer who's actually done this at scale with complex environments and medium sized models. If that sounds like you (or someone you know), DM me or drop a comment 👇 #MLJobs | Link in thread
→ View original post on X — @snorkelai, 2026-03-25 14:25 UTC