Learn Model Context Protocol [MCP] with Python โ Build Agentic Systems in Python with the new standard for AI Capabilities: http://
amzn.to/4njfsVM by @chris_noring v/ @PacktDataML ๐ฆ๐ฑ๐ช๐ฝ ๐จ๐ธ๐พ ๐ฆ๐ฒ๐ต๐ต ๐๐ฎ๐ช๐ป๐ท:
Understand the MCP protocol and its core components
MACHINE LEARNING
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Learn MCP with Python for Agentic Systems
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Generative AI with Python and PyTorch book link
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Generative AI with Python and PyTorch: https://
amzn.to/4fYjiBn via @PacktDataML -
30 Agents Every AI Engineer Must Build
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30 Agents Every AI Engineer Must Build โ Build production-ready agent systems using proven architectures and patterns: http://
amzn.to/41ckg6z v/ @PacktDataML โ
What you will learn:
Deploy production-ready agent systems that scale securely and reliably
Use LangChain and -
Variable-Width Transformers: Wide at Start and End, Narrow in the Middle
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This article shows that width should be allocated unevenly, with models wide at the beginning and end but narrow in the middle. Thus, the bottleneck forces better use of representations instead of wasting the
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Practical Machine Learning for Computer Vision – End-to-End ML Book
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Practical Machine Learning for Computer Vision โ End-to-End ML for Images: https://
amzn.to/4ajfVSf
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#DataScience #AI #NeuralNetworks -
GitHub Action triggers codex to compare against VISION.MD and implement
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It's not! It trigger a GitHub Action, spins up codex, compares it against VISION.MD and if a fit, implements it.
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Temporal Difference Vision: Visual Learning with Fewer Inductive Biases
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Paper review:
You Donโt Need Strong Assumptions: Visual Representation Learning via Temporal Differences https://
temporal-difference-vision.github.io https://
temporal-difference-vision.github.io/static/pdfs/td
v.pdf
โฆ @AlexiGlad @ninaddaithankar The premise is that the more data you can use, the fewer inductive biases you should have. -
Who designs RL agent’s training environment: practitioner or policy?
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Who should design the training environment for an RL agent, the practitioner or the policy itself? RL pipelines for LLMs usually rely on manually redesigned environments between stages, with practitioners guessing which configuration will best improve the current policy. This
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Stem: Efficient Long-Context LLM with Token Position-Decay
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What if your LLM could process long contexts without the quadratic attention bottleneck? Enter Stem: a plug-and-play sparsity module that rethinks causal information flow. It uses a token positionโdecay strategy (keeping early tokens for recursive dependencies) and an
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Technical dive inside the new Midjourney Scanner
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A technical dive inside our new "Midjourney Scanner" pic.twitter.com/wJBHz2O7ro
— Midjourney (@midjourney) 18 juin 2026A technical dive inside our new "Midjourney Scanner"