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Free Download FlippedNormals – Rigging Unleashed Intermediate-Advanced (Blender Course)
FlippedNormals | Duration: 5:30 h | Video: H264 1920×1080 | Audio: AAC 48 kHz 2ch | 1 GB | Language: English
"Rigging Unleashed" will take you into powerful, production-ready rigging techniques! The lessons in Rigging Unleashed can be considered "intermediate ~ advanced" level.
• Full Body Squash and Stretch – enable extreme cartoony deformations for your characters!
• Soft IK – Soft IK is not easy to demonstrate with a GIF. But you’ll feel it when you animate. Without soft IK, the legs will overextend and "pop" and you’ll have to constantly correct the pose to fix it. Soft IK saves the animator a lot of work.

• Additional Isolation Mechanisms – more improvements that will pay off by saving you animation time.
• IK Fingers – for when you want to pin the fingers, rather than the IK hand.
• Smooth Foot Roll – for a more natural foot lift
• FACE: sticky controls – the shapekey controls follow the mesh exactly for intuitive face manipulation
• FACE: Sticky Lips – more realistic lips behavior
• FACE: Wrinkle Maps – make your character’s face even more expressive
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https://flippednormals.com/product/rigging-unleashed-intermediate-advanced-blender-course-with-product-transfer-64243

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Free Download FlippedNormals – AI Engineering Fundamentals
Released 4/2026
By Scott Moss
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 11 Lessons ( 11h 35m ) | Size: 3.6 GB

Become an AI Engineer
Key Takeaways
By participating along with us in the workshop, you’ll learn
Understand how AI Engineering differs from ML engineering, and why evals are your new test suite
Build a stateful chat agent on Cloudflare Workers using the Agents SDK
Write golden datasets, build automated scorers, and run eval suites that catch regressions
Master contenxt engineering, curating exactly the right tokens for your model at inference time
Implement sandboxed code execution, tool search for large tool libraries, and few-shot tool examples
Add RAG with Cloudflare Vectorize so your agent draws on real domain knowledge
Build a React chat interface that handles streaming tokens, tool call status, generative UI, and human-in-the-loop flows
Go beyond the basic agent loop with research-backed patterns like Observe-Predict-Update, multi-agent handoffs, and voting
Capture user corrections as eval data and build feedback loops that make your agent better with every interaction
Workshop Details
In this two-day workshop, you’ll learn AI Engineering as a discipline. Beyond just calling an LLM API, but how to build, evaluate, and continuously improve AI-powered systems. You start with a pre-built diagraming canvas and throughout the course turning it into an intelligent design tool with an agentic chat interface. We’ll build the agent and establishing rigorous evaluation. Next we’ll systematically improve the eval scores through context engineering, advanced tool use, RAG, better architectures, and production feedback loops. Every improvement is measured. By the end, you’ll understand why 70% of AI Engineer job postings in 2026 center on RAG, evals, agents, and production deployment – and you’ll have practiced all of them.
Any Prerequisites?
Comfortable with TypeScript and React
Basic understanding of LLM APIs
Familiarity with Node.js
A Cloudflare account (free tier works)
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https://frontendmasters.com/workshops/ai-engineering/

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Flask: Application Performance Optimization
.MP4, AVC, 1920×1080, 30 fps | English, AAC, 2 Ch | 39m | 110 MB
Instructor: Kevin Cunningham​

Performance problems in Flask applications are rarely caused by Flask itself-they usually stem from blocking work, inefficient request handling, repeated computation, or poorly understood data access patterns.

In this course, Flask: Application Performance Optimization, you’ll learn how to recognize and reason about performance issues in Flask applications. First, you’ll explore how Flask handles requests and how application structure affects performance. Next, you’ll examine common optimization techniques such as caching and efficient database access, including their benefits and tradeoffs. Finally, you’ll learn how middleware and logging support observing and diagnosing performance problems.

By the end of this course, you’ll have the performance awareness and decision-making skills needed to prepare for hands-on optimization work in guided labs and real-world Flask applications-without over-engineering or premature optimization.

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Free Download Flask Application Performance Optimization
Released 4/2026
By Kevin Cunningham
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 39m 41s | Size: 94 MB

Performance problems in Flask applications are rarely caused by Flask itself-they usually stem from blocking work, inefficient request handling, repeated computation, or poorly understood data access patterns.
What you’ll learn
Performance problems in Flask applications are rarely caused by Flask itself-they usually stem from blocking work, inefficient request handling, repeated computation, or poorly understood data access patterns.
In this course, Flask: Application Performance Optimization, you’ll learn how to recognize and reason about performance issues in Flask applications. First, you’ll explore how Flask handles requests and how application structure affects performance. Next, you’ll examine common optimization techniques such as caching and efficient database access, including their benefits and tradeoffs. Finally, you’ll learn how middleware and logging support observing and diagnosing performance problems.
By the end of this course, you’ll have the performance awareness and decision-making skills needed to prepare for hands-on optimization work in guided labs and real-world Flask applications-without over-engineering or premature optimization.
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https://app.pluralsight.com/ilx/video-courses/flask-application-performance-optimization/course-overview

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FL Studios Course – Learning The Basics With Students
Published 10/2025
Duration: 5h 48m | .MP4 1920×1080 30 fps(r) | AAC, 44100 Hz, 2ch | 8.33 GB
Genre: eLearning | Language: English​

Learning The Basics With Students in FL Studios And Some Ableton Live

What you’ll learn

• Learn the basics of FL Studios
• Learn some elements of Music Theory
• Learn advanced methods to arrange music
• Learn how to write music in FL Studios

Requirements

• FL Studios
• An interest in Music Production
• Macbook or PC

Description
FL Studios Course – Learning The Basics With Students

Learning The Basics With Students in FL Studios And Some Ableton Live

Welcome toFL Studio – Learning the Basics With Students. This course is designed to take you inside real lessons where I guide students through the fundamentals of music production. Instead of a dry, theory-heavy approach, you’ll see how tracks are actually built from the ground up in a hands-on, practical way.

Throughout the course, we’ll cover.

Setting up and navigating FL Studio step by step

Creating beats, melodies, and basslines together

Using effects, samples, and plugins to shape your sound

Building full tracks with real examples from lessons

Tips and tricks I share with students to make workflow faster and easier

By the end, you’ll not only understand the basics of FL Studio-you’ll have followed along with real student projects, giving you both the confidence and the inspiration to start producing your own music straight away.

This course is perfect for beginners who want to learn FL Studio in a more relaxed, lesson-style format, with practical demonstrations instead of just theory.

Learn from Omar Meho top producer and DJ. This course is perfect for beginners who want to learn FL Studio in a more relaxed, lesson-style format, with practical demonstrations instead of just theory. You’ll also pick up valuable creative techniques and see how students learn in real time, making it feel interactive, natural, and inspiring.

Who this course is for:
– Music Producers
More Info

First Look: OpenAI Codex App
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 29m | Size: 155 MB​

Modern development isn’t just about speed-it’s about managing work beyond interaction. This course will teach you how to use the Codex app to coordinate parallel tasks, isolate changes with Git Worktrees and review results through summaries and diffs.

What you’ll learn
AI coding tools are fast, but real engineering work often takes time and coordination. In this course, First Look: OpenAI Codex App , you’ll learn how to use Codex as a command center for long-running development tasks. First, you’ll explore ‘Projects’ and ‘Threads’ to manage work clearly. Next, you’ll use planning and Git Worktrees to run tasks in parallel and keep changes isolated. Finally, you’ll extend Codex with skills and automations to create reliable, repeatable workflows. When you’re finished, you’ll be able to coordinate parallel agent-driven development with confidence.

First Look: Google Gemma 4
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 19m 1s | Size: 78.9 MB​

Choosing between open and closed-weight models can be overwhelming, leaving developers unsure of when the flexibility and control of open-weight models is worth the tradeoff.

What you’ll learn
Choosing between open and closed-weight models can be overwhelming, leaving developers unsure of when the flexibility and control of open-weight models is worth the tradeoff. In this course, First Look: Gemma 4, you’ll learn to evaluate and use Google DeepMind’s latest open model series for real-world AI development. First, you’ll explore what Gemma 4 is and the unique advantages open-weight models offer over closed alternatives. Next, you’ll discover the available model variants and the wide range of environments where they can be deployed. Finally, you’ll learn how to put Gemma 4 into practice through demos of its thinking, multimodal, and tool calling capabilities. When you’re finished with this course, you’ll have the skills and knowledge of Gemma 4 needed to confidently start to adopt open weight models into your AI projects.

First Look: Claude Managed Agents
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 22m 53s | Size: 82 MB​

Generative AI is evolving rapidly, making it difficult to determine which new tools are worth integrating into your workflow.

What you’ll learn
Generative AI is evolving rapidly, making it difficult to determine which new tools are worth integrating into your workflow. In this course, First Look: Claude Managed Agents, you’ll gain the ability to evaluate, adopt, and effectively use Claude Managed Agents in real-world scenarios. First, you’ll explore what Claude Managed Agents are, including their purpose, core workflows, and how they can enhance your existing AI-driven processes. Next, you’ll discover key considerations such as pricing, usage constraints, and platform requirements so you can make informed decisions about implementation. Finally, you’ll learn how Claude Managed Agents compare to previous versions and competing tools, along with a demonstration of their most impactful and newsworthy capabilities. When you’re finished with this course, you’ll have the skills and knowledge of Claude Managed Agents needed to confidently assess, adopt, and integrate them into your workflows or organizational strategy.

Fine-tuning and Customizing LLMs
.MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 1h 4m | 230 MB
Instructor: Sandy Ludosky​

LLM (Large Language Model) outputs and behaviors are non-deterministic. This course will teach you fine-tuning and LLM customization in order to better match user preferences and expectations.

What you’ll learn

LLMs (Large Language Models) have general, limited knowledge and training data, and they may produce irrelevant, unsafe, or unhelpful outputs. In this course, Fine-tuning and Customizing LLMs, you’ll learn to pre-train, fine-tune and reinforce the LLM learning to maximize their capabilities and align with human preferences and expectations.

First, you’ll explore the training techniques to pre-train the models with supervised fine-tuning , prompt engineering, and reinforcement learning with human feedback. Next, you’ll explore core training techniques, including supervised fine-tuning, prompt engineering, and reinforcement learning with human feedback (RLHF) to adapt pre-trained models for specific tasks and performance goals. Finally, you’ll discover how to evaluate and optimize model performance by applying methods like parameter-efficient fine-tuning (PEFT), transfer learning, and evaluation metrics to improve model reliability and reduce bias.

When you’re finished with this course, you’ll have the skills and knowledge of fine-tuning and customizing LLMs that is needed to deliver accurate, context-aware, and human-aligned results across a wide range of AI-driven applications.

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