30 Agents Every AI Engineer Must Build — Build production-ready agent systems using proven architectures and patterns: http://
amzn.to/41ckg6z v/ @PacktDataML —
What you will learn:
Deploy production-ready agent systems that scale securely and reliably
Use LangChain and
AI
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30 Agents Every AI Engineer Must Build
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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 — 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 Don’t 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
… @AlexiGlad @ninaddaithankar The premise is that the more data you can use, the fewer inductive biases you should have. -
Genspark’s Agentbase: Create CRM and Workflows from a Prompt
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Agentbase from Genspark just dropped – looks legit!
— AI Breakfast (@AiBreakfast) 18 juin 2026
Build CRMs, hiring trackers, dashboards, and custom workflows from a simple prompt using data from your emails, files, meetings, etc. https://t.co/ICgWdv18zSGenspark's Agentbase just launched – looks legit! Build CRMs, recruitment trackers, dashboards, and custom workflows from a simple prompt using your emails, files, meetings, etc.
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Google’s free guide on Agent Skills, meta-skills, and self-improvement
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MUST READ. Google's new guide on building and evaluating Agent Skills. It also covers meta-skills and self-improving Agent skills. 100% free.
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Agent automatically evaluates and implements open source issues
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If you make an issue on one of our open source projects, an agent will spin up and decide if it's a good idea and will implement it for you.
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Codex does by default what Claude needs extra work for
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huh, that needs extra work for Claude? Codex just does that by default.
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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