• views: 14
• date: 6 November 2026
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Foundations of Conflict Resolution for HR Professionals
Released 11/2026
With Joanne Simon-Walters
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: General | Genre: eLearning | Language: English + subtitle | Duration: 1h 2m | Size: 297.6 MB

Learn how to diagnose conflict types, identify your conflict style, separate positions from interests, manage emotional escalation, listen under pressure, and frame issues in neutral language.

Course details
This scenario-driven course equips HR professionals, including business partners, generalists, and employee relations practitioners, with the core tools to confidently manage and resolve workplace disagreements. Learn how to diagnose conflicts and adapt your personal conflict resolution style. Find out how to separate positions from interests to uncover underlying needs. Discover how to regulate emotions and apply active listening techniques under pressure to reduce defensiveness. Then consider how to frame issues using neutral, behavior-focused language to drive productive resolutions. Gaining these skills can enhance your credibility as a trusted partner and foster healthier team dynamics. Plus, having a structured approach for conflict management helps you improve coaching and performance management.

Skills covered
Human Resources (HR), Conflict Resolution

https://www.linkedin.com/learning/foundations-of-conflict-resolution-for-hr-professionals

https://nitroflare.com/view/931468C7A922BCA/Foundations_of_Conflict_Resolution_for_HR_Professionals.rar

https://rapidgator.net/file/fa3ed15598852c636b9582b41e7a1f08/Foundations_of_Conflict_Resolution_for_HR_Professionals.rar.html

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• views: 11
• date: 6 November 2026
• posted by: 0nelove

Deploy Secure Workloads on AKS
Released 11/2026
By Majid Hajian
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 57m 14s | Size: 185.6 MB

Standing up an AKS cluster is easy; proving that only the intended people and workloads can reach it, and that a deployed application is both running and protected, is the harder and more important job.

Standing up an AKS cluster is easy; proving that only the intended people and workloads can reach it, and that a deployed application is both running and protected, is the harder and more important job.

In this course, Deploy Secure Workloads on AKS, you'll gain the ability to secure administrative and workload access to an AKS cluster and deploy a workload through a verified, least-privilege application path.

First, you'll explore how to restrict administrative access to the intended Microsoft Entra ID principals and Kubernetes permissions, and prove that allowed paths succeed while restricted ones are denied.

Next, you'll discover how to grant a workload only the Azure identity and resource permissions it needs, connect it to Azure without storing static secrets, and diagnose denied access and identity misconfiguration.

Finally, you'll learn how to establish a trusted image and deployment source, configure service exposure and ingress, apply identity, network, and application access controls, and deploy and verify a workload that reaches a protected, serving steady state.

When you're finished with this course, you'll have the skills and knowledge of securing AKS access and deploying protected workloads needed to run production AKS workloads with confidence that only the intended paths are open.

Homepage

https://app.pluralsight.com/ilx/video-courses/aks-deploy-secure-workloads/course-overview

https://nitroflare.com/view/C2D7DD3B459563C/Deploy_Secure_Workloads_on_AKS.rar

https://rapidgator.net/file/0f70cb203f3815010c47f70e903deebc/Deploy_Secure_Workloads_on_AKS.rar.html

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• views: 10
• date: 6 November 2026
• posted by: 0nelove

DataOps Foundations: Governance and Collaboration
Released 11/2026
By Matt Stenzel
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 49m 42s | Size: 97.8 MB

Many data teams struggle, not because their pipelines are complex, but because their governance practices are unclear and their collaboration habits are fragmented.

Many data teams struggle, not because their pipelines are complex, but because their governance practices are unclear and their collaboration habits are fragmented. This leads to inconsistent definitions, compliance risks, slow delivery, and low trust in data.

In this course, DataOps Foundations: Governance and Collaboration, you'll gain the ability to build trusted, well-managed, and collaborative DataOps ecosystems where governance and teamwork accelerate outcomes instead of blocking them.

First, you'll explore how governance frameworks, policies, standards, and stewardship roles create responsible, secure, and consistent data usage across domains and pipelines.

Next, you'll discover how communication routines, shared artifacts, metadata, and documentation reduce friction and align producers, consumers, and stakeholders around shared outcomes.

Finally, you'll learn how to apply cross-functional workflows and feedback loops that streamline handoffs, resolve conflicting requirements, and support adaptive, product-oriented DataOps practices.

When you're finished with this course, you'll have the skills and knowledge of DataOps governance and collaboration needed to deliver trusted, compliant, and high-quality data through aligned, collaborative, and well-governed operational practices.

https://app.pluralsight.com/ilx/video-courses/dataops-foundations-governance-collaboration/course-overview

https://nitroflare.com/view/84B7963B67AD810/DataOps_Foundations_Governance_and_Collaboration.rar

https://rapidgator.net/file/626e44ab101924326f2bb245a443c190/DataOps_Foundations_Governance_and_Collaboration.rar.html

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• views: 11
• date: 6 November 2026
• posted by: 0nelove

Data Hydration and State Persistence in ASP.NET Core 10 Blazor
Released 11/2026
By Andy Watt
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 57m 26s | Size: 243.2 MB

When a static server-rendered Blazor page becomes interactive, its components start over, re-fetching data the server already rendered, flashing loading indicators over finished content, and tempting developers toward global state containers that leak between users.

When a static server-rendered Blazor page becomes interactive, its components start over, re-fetching data the server already rendered, flashing loading indicators over finished content, and tempting developers toward global state containers that leak between users. In this course, Data Hydration and State Persistence in ASP.NET Core 10 Blazor, you'll gain the ability to carry state cleanly across the SSR-to-interactive boundary. First, you'll explore how the hydration handoff works and see the double-fetch and flicker problem live. Next, you'll discover how the[PersistentState] attribute serializes server-fetched data into the page and restores it during interactivity, including what's really in the persisted payload, and what must never go in it. Finally, you'll learn how to share per-user state safely with scoped services, cascading values, and persistent service registration, and how to architect your data flow so every piece of state has exactly one home. When you're finished with this course, you'll have the skills and knowledge of data hydration and state persistence needed to build SSR-first Blazor applications that become interactive without re-fetching, flickering, or leaking state.

https://app.pluralsight.com/ilx/video-courses/data-hydration-state-persistence-asp-dot-net-core-10-blazor/course-overview

https://nitroflare.com/view/8BAA8714BC8D02A/Data_Hydration_and_State_Persistence_in_ASP.NET_Core_10_Blazor.rar

https://rapidgator.net/file/e9dfe8f68f8a9e14d5acb73309abbdcd/Data_Hydration_and_State_Persistence_in_ASP.NET_Core_10_Blazor.rar.html

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• views: 6
• date: 6 November 2026
• posted by: voska89

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• date: 6 November 2026
• posted by: 0nelove

Calibrating Operational Constraints for AI Workloads at Cloud Scale
Released 11/2026
By Zachary Bennett
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 56m 42s | Size: 186.6 MB

AI workloads swing wider than the services most teams know how to alert on, so limits copied from predictable services either page all day or miss a runaway agent until the bill arrives.

AI workloads swing wider than the services most teams know how to alert on, so limits copied from predictable services either page all day or miss a runaway agent until the bill arrives. In this course, Calibrating Operational Constraints for AI Workloads at Cloud Scale, you'll gain the ability to set cost, quality, and permission limits on an AI workload that are loose enough to live with and tight enough to warn you early. First, you'll explore how to read what normal looks like from a workload's own cost, latency, and invocation telemetry, and which signal moves first in a retry storm, an agent loop, context growth, and quality drift. Next, you'll discover how to set threshold values knowing that tighter means earlier warning and more false alarms, and how to decide whether a tripped limit should page someone, slow things down, fall back, or stop. Finally, you'll learn how to scope identity and tool permissions so a mistake stays small, verify each boundary by trying something it should refuse, and validate the whole set against ordinary days and a failure you already know the shape of. When you're finished with this course, you'll have the skills and knowledge of operational constraints for AI workloads needed to calibrate limits that hold up in production.

https://app.pluralsight.com/ilx/video-courses/ai-workloads-cloud-scale-calibrating-operational-constraints/course-overview

https://nitroflare.com/view/4D99F149C54146A/Calibrating_Operational_Constraints_for_AI_Workloads_at_Cloud_Scale.rar

https://rapidgator.net/file/7b5a1081ffb17048e24553bf94e7eef1/Calibrating_Operational_Constraints_for_AI_Workloads_at_Cloud_Scale.rar.html

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• views: 7
• date: 6 November 2026
• posted by: 0nelove

Assembling Production-Ready Azure Environments
Released 10/2026
By Afzaal Ahmad Zeeshan
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 54m 8s | Size: 168.8 MB

Individually provisioning Azure compute, storage, networking, and identity is routine work, but assembling those pieces into an environment another operator can inherit, trust, and support is a different skill, and it is the one that fails most visibly in production.

Individually provisioning Azure compute, storage, networking, and identity is routine work, but assembling those pieces into an environment another operator can inherit, trust, and support is a different skill, and it is the one that fails most visibly in production. In this course, Assembling Production-Ready Azure Environments, you'll gain the ability to compose, validate, and hand off a production-like Azure environment for a defined workload. First, you'll explore how to turn a workload specification into deployment scope, network context, a baseline identity and secrets configuration, workload placement, and a health monitoring baseline. Next, you'll discover how to prove the environment behaves as intended by confirming that intended application paths are reachable and return expected responses, that resources meant to stay private are unreachable from outside their boundary, and that health signals, logs, and access records are genuinely being produced. Finally, you'll learn how to prepare the environment for operational handoff by documenting its components, dependencies, and access model, naming the assumptions and unverified conditions the next operator must be told about, and assembling the evidence that demonstrates it is ready to receive a workload. When you're finished with this course, you'll have the skills and knowledge of Azure environment assembly needed to stand up an environment another engineer can operate without reverse-engineering it.

Homepage

https://app.pluralsight.com/ilx/video-courses/azure-environments-production-ready-assembling/course-overview

https://nitroflare.com/view/27F2AEFBABAEF59/Assembling_Production-Ready_AWS_Environments.rar

https://rapidgator.net/file/6b0d8fe3b18ee6ae942b763f3b89dd9e/Assembling_Production-Ready_AWS_Environments.rar.html

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• views: 14
• date: 6 November 2026
• posted by: 0nelove

AI Prompting with Style and Flair
Released 11/2026
With Shani Raja
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Beginner + Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 13m | Size: 232.4 MB

Enhance your prompt writing through a framework of simplicity, clarity, elegance, and evocativeness. Transform generic AI drafts into polished, professional content that resonates.

Course details
Discover how to prompt AI with the stylistic precision of a seasoned editor. Explore how to use four essential ingredients—simplicity, clarity, elegance, and evocativeness—to elevate the quality of AI-generated content. Find out how to remove clutter, streamline ideas, and enhance the rhythm and cadence of your writing. Obtain techniques to infuse your writing with vivid detail, emotion, and atmosphere while maintaining professional tone and restraint. Through diagnosing writing passages, you can identify exactly what your content needs and adapt your AI prompting methods accordingly. Strengthen your personal writing style while collaborating with AI, and gain the skills needed to transform generic AI drafts into vivid narratives with a professional touch.

Skills covered
AI Prompting, Artificial Intelligence (AI), Prompt Writing

https://www.linkedin.com/learning/ai-prompting-with-style-and-flair

https://nitroflare.com/view/A6BA20C0F8FBF7A/AI_Prompting_with_Style_and_Flair.rar

https://rapidgator.net/file/32f81a826950f2eb18813a7bec0fa22f/AI_Prompting_with_Style_and_Flair.rar.html

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• views: 16
• date: 6 November 2026
• posted by: 0nelove

AI Engineer Bootcamp 2027: LLM, RAG, AI Agents, MCP (Python)
Published 11/2026
Created by Bahadır ERDUN
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 79 Lectures ( 10h 42m ) | Size: 4.5 GB

Learn AI engineering in Python: prompt engineering, RAG, tool calling, AI agents, LangGraph, MCP, Ollama. Build Jarvis

What you'll learn
⚡ Call OpenAI-compatible APIs from Python and understand messages, roles, system prompts, tokens and cost
⚡ Use OpenAI, Gemini and Ollama with one codebase, and learn how the Claude SDK differs
⚡ Build a streaming chat screen with Streamlit and manage conversation history
⚡ Get reliable structured output from a model with JSON and pydantic schemas
⚡ Test LLM apps with a fake model and pytest, without any network calls
⚡ Build semantic search and a semantic cache with embeddings and cosine similarity
⚡ Build a RAG system that answers questions about PDFs: chunking, thresholds, citing sources, preventing hallucinations
⚡ Build tool calling by hand and then natively, and add approval and undo to tools that write
⚡ Write a think-act-observe (ReAct) agent by hand, make it safe and rebuild it with LangGraph
⚡ Write an MCP server and connect your app to other MCP servers
⚡ Run local models with Ollama and route requests local first, cloud when the local model doesn't know
⚡ Round off the app with speech (Whisper, TTS), vision, evals, cost measurement and prompt injection defenses

Requirements
❗ Basic programming knowledge is required (variables, loops and functions in any language). The Python constructs used in the course are explained briefly the first time they appear, and there are two "Python for AI" lessons.
❗ Python 3.14 and VS Code should be installed. If you don't know how, that's fine: the first lesson shows how to install Python, VS Code and Ollama on a clean PC step by step, and a written setup guide (PDF) is included in the course files.
❗ A Windows, macOS or Linux computer. At least 8 GB of RAM is recommended for the local model lessons; a GPU is not required.
❗ An OpenAI API key (paid; the calls in the lessons cost fractions of a cent, and we show how to set a spending limit). Gemini and local model (Ollama) options are covered too.
❗ No AI or machine learning knowledge is needed.

Description
This course contains the use of artificial intelligence.

If you want to learn how to build a real AI product with ready-made language models, this course was made for that. An AI engineer usually does not train models from scratch; they turn ready models such as OpenAI, Gemini, Claude or a local model running on your own computer into a product with their own code, and fine-tune when needed. University "AI engineering" degrees cover the whole field; in this course we use the term in its current industry sense: the engineer who builds products on ready models. You learn it in Python, with small projects that each run on their own: prompt engineering, RAG, tool calling, AI agents (agentic AI), LangGraph and MCP; from setup to deployment in about ten hours. The course is beginner-level in AI: no AI or machine learning background is needed, we start from installing Python and the very first API call; basic programming knowledge (variables, loops, functions in any language) is enough, and two short "Python for AI" lessons cover the constructs we use.

How do we learn?
Every lesson teaches one concept and first shows the problem on screen: without a system message the model gives a long, messy answer; without conversation history it forgets your name; without RAG it makes things up; an agent without a step limit runs in circles; a poisoned PDF tricks the assistant. Then we add the few lines that fix the problem, repeat the same experiment and watch the problem go away. The code of every lesson is downloadable, and every lesson ends with a "run and watch" step: which command to type, what you will see on screen, and how to read the most common error messages.

What do we build?
The pieces you write during the lessons come together at the end asJarvis, a personal assistant: a chat screen with Streamlit, several model providers, local-first routing with the cloud as a fallback, JSON skill files, fuzzy text matching, a semantic cache, asking questions about your PDFs (RAG), user memory, weather, note and reminder tools (with approval and undo), an agent built by hand and the same agent in LangGraph, an MCP server and client, push-to-talk speech (Whisper), spoken answers (TTS), screenshot understanding and a small eval set.

Beyond Jarvis: forward deployed engineer skills. A final section takes the assistant into a customer's world with the skills of a forward deployed engineer: discovery, data integration, CI/CD, observability, demos. You turn a messy customer brief into a one-page spec and an acceptance eval, connect a SQL database, CSV/Excel and legacy feeds and a SaaS REST API, build a website assistant with FastAPI and a chat widget, and ship it with GitHub Actions, traces, API keys and a demo script.

Topics
✨ LLM APIs: messages, roles, system prompts, tokens and cost, streaming

✨ One codebase, many providers: the OpenAI-compatible standard, Ollama, Gemini, Claude SDK differences

✨ Model settings and prompt engineering: temperature, top_p, max_tokens, seed, few-shot examples, prompt templates

✨ Structured output: getting JSON from a model, pydantic schemas

✨ Testing with a fake model (pytest): no API calls, free, done in seconds

✨ Coding with AI, and checking the tests AI writes with mutation testing

✨ Embeddings, cosine similarity, a semantic cache, picking an embedding model by measuring it

✨ RAG: chunking PDFs, similarity thresholds, teaching the model to say "it's not in the document", citing sources, and the Chroma vector database

✨ Tool calling: first by hand with a JSON protocol, then with native tool calling

✨ AI agents: the ReAct loop, step limits, pausing for approval and resuming, LangChain and LangGraph

✨ MCP (Model Context Protocol): writing your own server and connecting to other servers

✨ Local models: Ollama, hybrid routing (local first, cloud when it doesn't know), Hugging Face pipelines, LoRA fine-tuning

✨ Speech and vision: Whisper, TTS, showing the model an image

✨ Production: evals, cost and speed, prompt injection, API key security, deployment (GitHub, Streamlit Cloud, Docker), responsible AI and GDPR

✨ Forward deployed engineering: discovery and specs, system design, SQL and file-feed integration, REST APIs with retries and idempotency, a FastAPI website assistant, CI/CD with GitHub Actions, observability, access control, demos and handoff

Course language and tools: narration in English. Python 3.14, VS Code, Streamlit, the OpenAI Python SDK, Ollama, LangGraph, the MCP Python SDK. The code is shown on screen in a real VS Code view and explained line by line.

Who it's not for: this course is about building products on ready models. We train a tiny GPT from scratch and LoRA-fine-tune a model to understand what's inside, but training large models (ML engineering) and data analysis/prediction models (data science) are out of scope.

Who this course is for
⭐ Software developers who want to build products with ready-made language models
⭐ Developers coming from another language (C#, Java, jаvascript.) who want to start building AI apps in Python
⭐ Anyone who wants to learn RAG, AI agents, tool calling and MCP with working code
⭐ People who want to build their own personal assistant or an internal AI tool for their company
⭐ Developers thinking about moving into an AI engineer role
⭐ Software engineers, product managers and DevOps engineers with no prior AI experience who need to build, integrate or evaluate LLM features at work

https://nitroflare.com/view/2C2B08DB73EB860/AI_Engineer_Bootcamp_2027_LLM%2C_RAG%2C_AI_Agents%2C_MCP_%28Python%29.part1.rar
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• views: 9
• date: 6 November 2026
• posted by: 0nelove

Advanced Asynchronous jаvascript Patterns with AI Augmentation
Released 11/2026
By Jaecee Grant
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 35m 30s | Size: 296.9 MB

Modern jаvascript development demands more than writing asynchronous code—it requires designing resilient systems that efficiently coordinate concurrent work, streaming data, and increasingly capable AI collaborators.

Modern jаvascript development demands more than writing asynchronous code—it requires designing resilient systems that efficiently coordinate concurrent work, streaming data, and increasingly capable AI collaborators. Many developers understand the mechanics of async programming but struggle to architect workflows that remain maintainable, performant, and AI-friendly as applications grow in complexity. In this course, Advanced Asynchronous jаvascript Patterns with AI Augmentation, you'll gain the ability to design sophisticated asynchronous jаvascript architectures while effectively leveraging AI throughout the development lifecycle. First, you'll explore how the modern jаvascript runtime executes asynchronous operations, enabling you to reason about scheduling, task coordination, and efficient execution. Next, you'll discover resilient concurrency patterns for coordinating independent work, handling failures gracefully, and maximizing throughput. You'll then learn how to build streaming and iterative workflows that process data efficiently while maintaining responsiveness. Finally, you'll apply agentic orchestration techniques to coordinate AI-assisted development and use AI to analyze application performance, identify bottlenecks, and recommend architectural improvements. When you're finished with this course, you'll have the skills and knowledge of advanced asynchronous jаvascript architecture needed to build scalable, resilient applications while using AI as a force multiplier throughout the software development process.

https://nitroflare.com/view/C42099270B31F60/Advanced_Asynchronous_JavaScript_Patterns_with_AI_Augmentation.rar

https://rapidgator.net/file/ee9e6ddad6973559f2de2c59bb0c5ccb/Advanced_Asynchronous_JavaScript_Patterns_with_AI_Augmentation.rar.html

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• date: 6 November 2026
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5 Value Packed Training Modules
Gain immediate access to all video content and download an easy-to-follow course guidebook.
This guide contains all of your worksheets in one centralized hub, making it convenient for you to track your progress.

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• views: 39
• date: 6 November 2026
• posted by: mata00

Revealed: How To Find Desperate Buyers In Untapped Niche Markets And Have An Irresistible Hot Seller In Roughly 1 Hour…
Even If You Know Diddly About Marketing And Couldn’t Give Away Free Water In The Sahara …

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• date: 6 November 2026
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What's inside?
Learn how to work with brands and land paid deals
Learn exactly who to reach out to and how to pitch
Get access to my exclusive Job Board
Step-by-step breakdowns of how I recreate my own videos.

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• views: 27
• date: 6 November 2026
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Here’s What You Get:
Over the next 6 weeks, as you follow QUICK AUDIENCE you will…
IMPLEMENT MY 3-STEP “SOURCE, SHOW, SHIP” SYSTEM: Use the exact same step-by-step method I’ve used to do over $83,000 in a single month on Whatnot.
Source profitable inventory, run live shows the algorithm pushes, ship fast, and get paid. No website. No funnels. No paid ads.

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• date: 6 November 2026
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