• views: 252
• date: 3 October 2026
• posted by: 0nelove

Electromagnetic Compatibility in Power Systems CISPR TR 18-1
Published 10/2026
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 52m| Size: 13.33 GB

Learn the Fundamentals of Radio Interference, Corona Phenomena, and CISPR TR 18-1:2017 for Electrical Engineers.

What you'll learn
Understand the fundamentals of radio noise and electromagnetic interference associated with high-voltage overhead transmission lines.
Explain the relationship between corona phenomena and radio noise generation in high-voltage AC and DC power systems.
Identify the major factors influencing radio noise levels, including transmission-line voltage, conductor configuration, weather conditions, measurement distanc
Understand radio noise measurement principles and attenuation characteristics discussed in CISPR TR 18-1:2017.

Requirements
No prior knowledge of CISPR standards or radio noise measurement is required. No specialized laboratory equipment, testing instruments, or engineering software is necessary. The course introduces the technical concepts progressively, making it suitable for beginners as well as working professionals who wish to strengthen their knowledge. Whether you are a student, graduate engineer, or experienced electrical professional, you are welcome to join this course and begin your learning journey.

Description
"This course contains the use of artificial intelligence."

Radio noise generated by overhead power lines is an important topic in electromagnetic compatibility (EMC) and high-voltage power engineering. Understanding its sources, characteristics, measurement principles, and technical background is essential for electrical engineers, power system professionals, and anyone interested in electromagnetic interference associated with transmission lines.

This course introduces the engineering foundations ofCISPR TR 18-1:2017 – Radio Noise from Overhead Power Lines, with a focus on the technical research, concepts, and developments that form the background of this subject.

Throughout the course, you will explore how radio noise is associated with overhead power lines, the role of corona discharge, and the influence of operating conditions and transmission system characteristics. The course also introduces the technical considerations associated with AC and DC transmission systems, measurement concepts, attenuation, and the interpretation of engineering research.

You will examine the progression of technical literature, historical research findings, voltage-related considerations, and the development of calculation methods and industry practices referenced in the course material. These topics will help you understand how research contributes to the engineering assessment of radio noise from overhead lines.

What You Will Learn

By the end of this course, you will be able to understand the fundamentals of radio noise from overhead power lines, explain the relationship between corona and electromagnetic interference, identify important factors affecting radio noise, understand measurement and attenuation concepts, and recognize the technical background of CISPR TR 18-1:2017.

The course also provides an introduction to the relevance of radio noise in power transmission engineering and electromagnetic compatibility.

Who Should Enroll?

This course is intended for electrical engineering students, graduate engineers, power system engineers, high-voltage engineers, transmission and distribution professionals, testing and laboratory engineers, utility personnel, researchers, and professionals interested in EMC and power system standards.

A basic understanding of electrical engineering is helpful, but prior experience with CISPR standards is not required.

Why Take This Course?

Rather than simply presenting technical terminology, this course aims to build a clear understanding of the engineering concepts and research background behind radio noise from overhead power lines.

Whether you are a student building your technical foundation or a working professional expanding your knowledge of power system electromagnetic compatibility, this course will help you develop a structured understanding of the subject.

Enroll now and begin exploring the engineering foundations of radio noise from overhead power lines and CISPR TR 18-1:2017.
Who this course is for
This course is suitable for both beginners and working professionals. Prior experience with CISPR standards or radio noise testing is not mandatory. If you are interested in electrical engineering, high-voltage power systems, electromagnetic compatibility, or technical standards, this course is for you!

Homepage

https://www.udemy.com/course/electromagnetic-compatibility-in-power-systems-cispr-tr-18-1/

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• views: 127
• date: 3 October 2026
• posted by: 0nelove

Production RAG with LangChain: Beginner to Advanced
Published 10/2026
Created by HeadEasy Labs
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 25 Lectures ( 4h 59m ) | Size: 3.3 GB

End-to-end production pipelines using LangChain, Vector Databases, Advanced Retrievers, and Re-ranking strategies

What you'll learn
⚡ Advanced RAG
⚡ Generate vector embeddings and manage vector database stores
⚡ Deploy similarity-based, threshold-based, and advanced retrievers
⚡ Connect your RAG pipelines to interactive frontend applications

Requirements
❗ Basic proficiency in Python programming.

Description
Welcome toProduction RAG with LangChain: Beginner to Advanced, your step-by-step masterclass for building high-performance, real-world AI applications powered by Retrieval-Augmented Generation (RAG).

LLMs are powerful, but they often struggle with hallucination, context limits, and accessing private data. This course equips you with the tools to solve these challenges usingLangChain—the industry-standard framework for building context-aware AI systems.

What You Will Learn

✨RAG Fundamentals: Understand what RAG is, why it is critical for enterprise LLM apps, and how the core architecture components interact.

✨Document Processing & Chunking Strategies: Solve the "context window" and "lost in the middle" problems using document loaders, custom text splitters, and optimal chunking strategies.

✨Vector Databases & Vector Search: Master vector embeddings, vector databases, and the underlying mechanics of vector search algorithms.

✨Advanced Retrieval Techniques: Move beyond basic keyword searches with similarity-based retrievers, threshold-based retrievers, and custom advanced retrieval workflows.

✨Re-Ranking for Accuracy: Implement advanced re-ranking strategies to ensure your model always gets the most relevant context.

✨Full-Stack End-to-End Hands-On Project: Build a complete RAG system from scratch—from document loading, chunking, and vector database initialization, to setting up advanced retrieval, context preparation, and constructing a frontend UI.

What You'll Learn (Key Takeaways)

✨ Build end-to-end production-ready RAG applications using LangChain.

✨ Implement document loading, text splitting, and custom chunking strategies.

✨ Generate vector embeddings and manage vector database stores.

✨ Deploy similarity-based, threshold-based, and advanced retrievers.

✨ Apply re-ranking techniques to optimize context precision and reduce LLM hallucinations.

✨ Connect your RAG pipelines to interactive frontend applications.

Prerequisites / Requirements

✨ Basic proficiency in Python programming.

✨ Familiarity with general AI/LLM concepts is helpful, but no prior experience with RAG or LangChain is required.

Who this course is for
⭐ Software Engineers & Developers looking to specialize in Generative AI systems.
⭐ Data Scientists & AI Practitioners wanting to build robust, retrieval-grounded LLM workflows
⭐ Tech Enthusiasts & Students eager to transition from beginner AI concepts to advanced, production-grade applications

Homepage

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• views: 118
• date: 3 October 2026
• posted by: 0nelove

Troubleshooting Hardware and Devices in Windows 11
Released 10/2026
By Mike Halsey
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 6m 40s | Size: 163.4 MB

IT teams often operate reactively, spending too much time firefighting instead of improving systems and processes.

IT teams often operate reactively, spending too much time firefighting instead of improving systems and processes. In this course, Troubleshooting Hardware and Devices in Windows 11, you'll gain the ability to manage and troubleshoot any type of hardware and driver problems in Windows 11. First, you'll explore what types of hardware and firmware problems can occur. Next, you'll discover how to manage devices and identify unknown hardware. Finally, you'll learn how to manage Windows 11 running on older, incompatible Windows 10 PCs. When you're finished with this course, you'll have the skills and knowledge of hardware and driver troubleshooting needed to troubleshoot and repair any type of hardware or driver problem.

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• views: 127
• date: 2 October 2026
• posted by: 0nelove

Fortinet NSE 6-FortiAnalyzer V-8.0 Administrator Training
Published 10/2026
Created by Ahmad Ali
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 68 Lectures ( 7h 20m ) | Size: 8.4 GB

Master FortiAnalyzer Administration with Hands-On Labs, Log Management, FortiGate Integration, HA, Reports, and Tshoot

What you'll learn
⚡ Describe the purpose of FortiAnalyzer
⚡ Describe the FortiAnalyzer operating modes
⚡ Describe logging in a Fortinet Security Fabric environment
⚡ Describe the FortiAnalyzer Fabric
⚡ Describe the log file workflow
⚡ Manage administrative domains (ADOMs)
⚡ Configure network settings
⚡ Configure secure administrative access
⚡ Monitor administrative events
⚡ Register and manage devices
⚡ Perform a system configuration backup
⚡ Monitor disk usage
⚡ Enable and create ADOMs
⚡ Describe Fabric connectors
⚡ Configure log redundancy and encryption
⚡ Configure log rollover and retention policies
⚡ Prepare firmware upgrade
⚡ Perform log backups
⚡ Perform initial configuration
⚡ Logs and Reports Management
⚡ Fabric Supervisor and Member configuration
⚡ Log forwarding and log management
⚡ Analytics, Archive, and real-time log views
⚡ FortiGate device registration and log forwarding

Requirements
❗ Basic understanding of networking concepts
❗ Basic knowledge of firewalls
❗ Familiarity with FortiGate and FortiOS is helpful but not mandatory.
❗ Basic experience with FortiGate firewall policies, objects, interfaces, and routing
❗ Students should have a computer capable of running a virtualization platform
❗ FortiGate Operator course

Description
Welcome to theFortinet NSE 6 – FortiAnalyzer 8.0 Administrator Training course.

This practical, hands-on course is designed for network and security professionals who want to learn how todeploy, configure, manage, monitor, and troubleshoot FortiAnalyzer 8.0 in real-world environments.

You will learn FortiAnalyzer administration from the fundamentals through advanced topics, with practical demonstrations and hands-on labs designed to strengthen your understanding and troubleshooting skills.

What You Will Learn

✨ Introduction to FortiAnalyzer 8.0 and its role in the Fortinet Security Fabric

✨ FortiAnalyzer deployment and initial configuration

✨ System settings, administrators, authentication, and access control

✨ FortiGate device registration and log forwarding

✨ ADOM creation and management

✨ FortiAnalyzer log types and log storage

✨ Analytics, Archive, and real-time log views

✨ Log searching, filtering, and analysis

✨ FortiView and security event monitoring

✨ Reports and report customization

✨ Report scheduling and automated report generation

✨ FortiAnalyzer event management

✨ Log forwarding and log management

✨ Disk usage, quotas, and storage management

✨ Backup and restore configuration

✨ FortiAnalyzer high availability (HA)

✨ Fabric Supervisor and Member configuration

✨ Monitoring FortiGate devices and log status

✨ Troubleshooting connectivity and log collection issues

✨ Practical troubleshooting and administration scenarios

Hands-On Labs
The course includes practical labs where you will work with FortiAnalyzer and FortiGate environments to perform real configuration and troubleshooting tasks.

Who Is This Course For?
This course is suitable for

✨ Network Security Engineers

✨ Security Engineers

✨ Fortinet Administrators

✨ Network Administrators

✨ SOC Analysts

✨ Security Operations teams

✨ Firewall Engineers

✨ IT professionals working with Fortinet Security Fabric

✨ Anyone preparing for Fortinet NSE 6 FortiAnalyzer Administrator-related skills

Prerequisites
Basic knowledge of networking, firewalls, and FortiGate administration is recommended. Familiarity with TCP/IP, routing, security policies, and Fortinet products will help you get the most from the course.

Why Take This Course?
This is apractical administration-focused course, not just a theoretical overview. You will follow configuration demonstrations and hands-on labs to understand how FortiAnalyzer is used for centralized logging, analysis, monitoring, reporting, and security operations.

By the end of the course, you should have a solid practical understanding ofFortiAnalyzer 8.0 administration and troubleshooting and be able to apply these skills in enterprise Fortinet environments.

Enroll now and start learning FortiAnalyzer 8.0 through practical, hands-on training.

Who this course is for
⭐ Network and Security Administrators
⭐ FortiGate Administrators
⭐ Network Engineers and Security Engineers
⭐ System and Network Security Professionals
⭐ Fortinet professionals
⭐ Fortinet partners, consultants, and technical support engineers
⭐ Students preparing for a career in network security
⭐ Anyone who wants to learn FortiAnalyzer
⭐ Network Administrators
⭐ SOC Analysts
⭐ Anyone preparing for Fortinet NSE 6 FortiAnalyzer Administrator-related skills

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• views: 95
• date: 2 October 2026
• posted by: 0nelove

SPHR Exam Prep: 115-Question Mock Exam, Trainers + Games
Published 10/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 15m | Size: 5.35 GB

HRCI Senior Professional in Human Resources exam: learn US employment law and HR strategy, drill 235 practice questions

What you'll learn
Answer SPHR scenario questions from all five functional areas in 8 interactive exam trainers, with an explanation and the outline item each question tests
Sit a full 115-question mock exam on a 2 h 30 min timer and use the score report by functional area to choose what to study next
Map which US federal employment laws apply to your organization at its headcount and what each one changes in an HR strategy decision
Calculate turnover, cost per hire, HR ROI, compa-ratio and range penetration in $ with ready calculators, the way exam math questions ask
Lock in terms, formulas and US federal laws with flashcards, a memory game and a timed match game that run in any browser, on a phone too
Practice all five HRCI item types: multiple choice, multiple response, fill in the blank, drag and drop and scenarios
Build a workforce plan with a reduction checklist that covers WARN notice, and a succession plan for key roles
Handle union activity, grievances and layoffs within the NLRA, and classify exempt roles under the FLSA
Audit HR records, OSHA recordkeeping, HIPAA and HRIS access with one checklist
Score your readiness by area weight (33 / 17 / 23 / 17 / 10) against the 500 pass mark and split study hours by your gaps
Learn with Mike Pritula, HRCI representative in Eastern Europe, PHRi holder, who has worked with HRCI since 2015 and coaches HRCI candidates

Requirements
You work in HR at a professional level: HR manager, HR business partner, HR director or senior generalist
You already run hiring, performance and pay cycles and handle employee relations cases
You plan to take the SPHR exam; eligibility rules are set by HRCI, and lesson 9 gives a short overview
A modern web browser on a computer or phone for the exam trainers, study games, calculators and mock exam

Description This course contains the use of artificial intelligence

The SPHR exam gives you 115 scored questions, 2 hours 30 minutes and scenarios where every option sounds like something a good HR leader would do.

This course teaches all five functional areas and the US federal laws behind them on video, then drills them in 8 exam trainers, study games and a full 115-question mock exam.

You have years of HR behind you, so the Senior Professional in Human Resources exam looks familiar on paper. Then you read the item: a restructuring, a union campaign, a headcount that just crossed a legal threshold, and four sensible next steps. The SPHR is a practice-based exam: it tests judgment at the strategy level, US federal employment law and exam math on metrics and pay, with no partial credit. Practice question banks tell you that you picked the wrong option, rarely why the other three fail, and give you nothing to use at work the next morning. HRCI's own prep packages start at $499, and a live class costs $1,180.

After this course you read a scenario and see which outline item is really being tested. You know that Leadership and Strategy carries 33% of the exam and which area costs you the most points. You say which federal laws cover your organization at its headcount and which switch on at the next hiring wave. You defend HR plans with turnover, cost per hire and ROI in dollars, set pay against ranges and plan a reduction that meets WARN. You walk into the test center or the OnVUE session with a readiness score by area and a study plan built on your gaps, and you keep a senior HR toolkit you use every week.

The course is built and taught by Mike Pritula

- HRCI representative in Eastern Europe

- PHRi holder, working with HRCI since 2015

- Coaches HRCI certification candidates

- Founder of Pritula Academy: 170,000+ students from 185 countries and 180+ courses since 2011

The course follows the 2024 SPHR Exam Content Outline in proportion to its weights. First you spend three lessons on Leadership and Strategy: business strategy and HR metrics, risk, culture and change leadership, and US employment law for HR strategy with a headcount ladder of federal laws. Then you plan the workforce and talent acquisition, including the WARN test and the four-fifths rule. Next come two lessons on talent management: performance, succession and leadership development, then employee and labor relations under the NLRA, layoffs and offboarding. After that you work through total rewards with pay range math, the FLSA exempt test, ERISA, ACA and COBRA, and then HR information management, OSHA recordkeeping, HIPAA and HRIS. Finally you calculate your weighted readiness score and sit a full mock exam: 115 new questions in the proportions of the five areas, a 2 h 30 min timer, flags, a review screen and a score report by area.

Every lesson from 1 to 8 comes with its own interactive exam trainer: 15 scenario questions in all five HRCI item types, multiple choice, multiple response, fill in the blank, drag and drop and scenarios, with an instant explanation, the outline item it tests and a retry of missed questions. Study games for every lesson, flashcards, a memory game and a timed match game, lock in the terms, formulas and laws before you move on. Every practice question is written for this course; there are no real exam questions or dumps. Everything interactive opens in any browser, on a phone too.

This course prepares you for the US SPHR, built on US federal employment law. The international SPHRi and the GPHR are different HRCI exams, and state law such as California is outside the scope.

This is an independent course. It is not affiliated with, sponsored by or endorsed by HR Certification Institute (HRCI). SPHR and HRCI are trademarks of HR Certification Institute.

What's included

- Lifetime access to all materials

- Active instructor support in Q&A

- Udemy Certificate of Completion

- A practical assignment on your own organization in every lesson

- 8 interactive exam trainers with 120 questions and explanations

- Study games for every lesson: flashcards, memory and timed match

- A full 115-question mock exam with a 2 h 30 min timer and a score report by functional area

- A senior HR toolkit: HR metrics calculator, risk and change plan, US federal law applicability map, workforce plan with reduction checklist, succession plan, labor relations playbook, total rewards calculator, HR data and safety audit checklist and readiness scorecard

Employers look for senior HR leaders who can prove strategy with numbers and keep the company on the right side of federal law. Every month you wait is another month of guessing on scenarios while the laws you are tested on keep changing.

Enroll now and start your first lesson today.

Who this course is for
HR managers and HR business partners in US companies who are preparing for the SPHR exam
HR directors and heads of HR who want to defend HR strategy with metrics in $ and US federal law
Senior HR generalists in US-facing teams who need to know which federal law applies at which headcount
PHR holders moving up to the senior-level HRCI credential
Candidates who tried practice questions alone and want video lessons, working HR tools and a full timed mock exam

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• views: 106
• date: 2 October 2026
• posted by: 0nelove

Jev AI Crash Course: System 1 Decision Models for AI Agents
Published 10/2026
Created by Eden Marco
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 10 Lectures ( 32m ) | Size: 314.9 MB

Build ultra-low-latency AI agents with Jev & TypeSafe AI. Master decision models, tool-gating, rubrics, and triage.

What you'll learn
⚡ Learn when to use fast System One decision models vs. heavy System Two generative LLMs.
⚡ Learn TypeSafe AI's core primitives: Noul (probability), Score (rubrics), and Choice (classification).
⚡ Build ultra-low-latency (sub-100ms), cost-effective decision pipelines for real-time AI systems.
⚡ Implement agent safety guardrails and tool-gating to intercept and block risky actions in real time.
⚡ Extract deterministic, typed JSON evaluations without LLM text bloat or hallucinations.

Requirements
❗ Basic understanding of programming (Python, jаvascript, or any language interacting with REST APIs and JSON).
❗ Basic familiarity with AI concepts (prompts, LLMs, or AI agents) is helpful, but no advanced machine learning or math background is required.
❗ A computer with internet access to use the TypeSafe AI console and APIs.

Description
Traditional LLMs excel at generating creative prose and open-ended text, but relying on them for simple decisions in production can be slow, expensive, and prone to hallucinations. When your system simply needs to route a ticket, evaluate sentiment, or gate a risky tool call, you don't need a generative LLM—you need aDecision Model.

This course is your complete guide toJev AI and System 1 AI Decision Models, exploring the leading hosted and open-weights engines reshaping modern AI architectures:TypeSafe AI's Jev,OpenAI Decision APIs, and open-weights models likeKEV-1.

You will learn how to replace bloated token completions with fast, typed evaluations built for production AI decision-making.

What you will master in this course
✨System 1 vs. System 2 Architectures: Discover the hybrid pattern where fast decision models filter, route, and safeguard workflows before handing complex tasks to generative LLMs.

✨TypeSafe AI & Jev: Master Jev's core primitives—noul (calibrated probabilities), score (continuous rubrics), and choice (enum classification).

✨Open-Weights KEV-1: Learn how to run, evaluate, and deploy open-weights decision models without vendor lock-in.

✨OpenAI Decision APIs: Understand how OpenAI approaches decision tasks and compare trade-offs in latency, cost, and accuracy.

✨Autonomous AI Agent Safety & Tool Gating: Build middleware to intercept and block dangerous actions such as file deletions and system edits before autonomous agents execute them.

✨Smart Support Triage & Model Routing: Evaluate ticket urgency, sentiment, and team routing simultaneously in a parallelized API request.

Whether you are building autonomous agents, enterprise workflow automation, or real-time event processing pipelines, this course will give you practical tools to build faster, cheaper, and safer AI applications withJev and modern Decision Models.

Who this course is for
⭐ AI Engineers & Backend Developers building autonomous agents, workflow automations, or high-volume data pipelines.
⭐ Software Developers who need ultra-low-latency (sub-100ms) classifications, support ticket triage, or bug severity scoring.
⭐ Engineers building Agent Guardrails looking to reliably gate dangerous tool calls (e.g., in coding agents like Claude Code or Cursor).
⭐ Teams looking to cut AI costs by replacing expensive generative models with lightweight, sub-cent decision models for non-generative tasks.

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• views: 118
• date: 2 October 2026
• posted by: 0nelove

Using Power Automate Custom Connectors with Authenticated APIs
Released 10/2026
By Matthew Soucoup
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 7m 51s | Size: 212.2 MB

Most useful APIs require authentication, and the Power Platform standard connector library can't cover every internal or third-party endpoint your team needs to integrate with.

Most useful APIs require authentication, and the Power Platform standard connector library can't cover every internal or third-party endpoint your team needs to integrate with. In this course, Using Power Automate Custom Connectors with Authenticated APIs, you'll gain the ability to deliver organization-ready custom connectors that flows can rely on. First, you'll explore how to design a connector definition from an OpenAPI 2.0 specification, including actions, triggers, request and response schemas, and the metadata that makes it look at home in the maker portal. Next, you'll discover how to wire up both API key and OAuth 2.0 authentication using Microsoft Entra ID, and how to choose between shared and per-user authentication patterns for a given security requirement. Finally, you'll learn how to package, share, and govern your connector across environments using managed solutions, deployment pipelines, DLP policies, and the Power Platform admin center. When you're finished with this course, you'll have the skills and knowledge of Power Automate custom connectors needed to ship and maintain authenticated API integrations the rest of your organization can trust.

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• views: 109
• date: 2 October 2026
• posted by: 0nelove

Claude AI: Prompt Engineering e Produttività Professionale
Published 10/2026
Created by Alessio Ferrari
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: Italian | Duration: 48 Lectures ( 9h 20m ) | Size: 3.9 GB

Impara il metodo, non i prompt da copiare. Progetti personalizzati, agenti Cowork e automazioni senza codice.

What you'll learn
⚡ Scrivere prompt con il metodo dei 6 blocchi che producono output utilizzabili al primo o secondo tentativo, senza copiare prompt preconfezionati.
⚡ Costruire un progetto Claude personalizzato con knowledge base, istruzioni persistenti e uno stile che scrive con la tua voce.
⚡ Delegare a Claude interi task multi-step con Cowork: dalla scrittura del brief alla revisione del risultato finale.
⚡ Creare automazioni senza scrivere codice collegando Claude ai tuoi strumenti (Drive, Calendar e altri) tramite i connettori.
⚡ Applicare le tecniche avanzate: role prompting, few-shot, chain of thought, tag XML e prompt chaining per progetti complessi.
⚡ Scegliere consapevolmente tra Claude, ChatGPT, Gemini e Copilot con un benchmark personale basato sul tuo lavoro reale.

Requirements
❗ Nessuna competenza tecnica o di programmazione: il corso è pensato per chi non scrive codice.
❗ Un account Claude gratuito basta per iniziare; per i lab avanzati (Progetti, Cowork) è consigliato il piano Pro.
❗ Un computer con browser e una connessione a internet.

Description

Hai già provato Claude o ChatGPT e i risultati sono... mediocri?
Il problema non è lo strumento, e non sei tu. È il metodo. La maggior parte dei corsi ti dà liste di prompt da copiare, che smettono di funzionare al rilascio successivo del modello. Questo corso fa l'opposto: ti insegna aragionare sulla struttura di una richiesta, così ogni prompt lo costruisci da solo, per il tuo lavoro, ogni volta che ti serve.

È il principio che guida tutto il corso:Logica Prima, Memoria Poi. Prima capisci perché un prompt funziona, poi lo trasformi in template riutilizzabili. Niente da imparare a memoria, niente che invecchia.

Alla fine del corso non avrai appunti: avrai un sistema.
Lavorando nei 16 laboratori pratici costruirai, pezzo per pezzo

✨ La tualibreria personale di prompt basata sul metodo dei 6 blocchi;

✨ Unprogetto Claude su misura per il tuo ruolo, con knowledge base e istruzioni persistenti;

✨ Unostile personalizzato che fa scrivere Claude con la tua voce;

✨ Dueworkflow professionali completi, dal brief al deliverable;

✨ La tua primaautomazione senza codice, collegata ai tuoi strumenti reali;

✨ Unprotocollo di verifica anti-allucinazioni in 4 passi;

✨ Il tuoworkflow documentato in una pagina, pronto da usare dal giorno dopo.

Come funziona il corso
Quasi 8 ore di video con un rapporto pratica/teoria di circa 54/46: più della metà del tempo la passi afare, non ad ascoltare. Ogni modulo si chiude con un quiz, e 7 assegnazioni ti fanno applicare subito quello che hai imparatosul tuo lavoro reale, non su esempi giocattolo.

Il percorso copre l'intero ecosistema Claude di Anthropic: dai fondamenti (modelli, piani, come "pensa" Claude) al prompt engineering avanzato, dagli Artifacts all'analisi di documenti e dati, dai Progetti personalizzati agliagenti Cowork e aiconnettori MCP per l'automazione. Con un modulo dedicato al confronto onesto con ChatGPT, Gemini e Copilot: perché lo strumento giusto si sceglie con criteri, non per fede.

E siccome lavori con clienti e colleghi veri, un intero modulo è dedicato aqualità, privacy e GDPR: come verificare gli output prima di consegnarli e come usare l'AI in azienda senza rischi.

Questo corso fa per te se
✨ Sei un professionista, freelance o imprenditore e vuoi risultati concreti dall'AI, non demo spettacolari;

✨ Hai già provato gli strumenti AI ma gli output richiedono più correzioni del previsto;

✨ Non sai programmare e non vuoi impararlo: qui non si scrive una riga di codice;

✨ Vuoi un metodo che resta valido quando i modelli cambiano.

Non fa per te se cerchi un corso su API e sviluppo software, o una lista di 500 prompt da copiare: qui impari a scriverli meglio di quelli delle liste.

I contenuti sulle funzionalità in rapida evoluzione vengonoaggiornati periodicamente e ogni aggiornamento è incluso nel corso, per sempre.

Iscriviti ora: alla prossima riunione potresti arrivare con il lavoro già fatto.

Who this course is for
⭐ Professionisti e knowledge worker italiani (marketing, comunicazione, HR, vendite, consulenza, project management) che vogliono integrare l'AI nel lavoro quotidiano.
⭐ Freelance e imprenditori che vogliono produrre di più senza assumere o delegare all'esterno.
⭐ Chi ha già provato Claude o ChatGPT con risultati deludenti e sospetta che il problema sia il metodo.
⭐ Manager e team leader che devono capire cosa l'AI può davvero fare prima di introdurla nel team.
⭐ NON è rivolto a sviluppatori in cerca di un corso su API, SDK o Claude Code.

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• views: 117
• date: 2 October 2026
• posted by: AD-TEAM

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• views: 112
• date: 2 October 2026
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• date: 2 October 2026
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• date: 2 October 2026
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Foundr – Crafting Winning Offers + AI Accelerator Program Review: Is It Worth It?
If you have ever stared at a blank screen trying to figure out why your product or service isn’t selling, you are not alone.
The hardest part of scaling an online business isn’t driving traffic or building a website—it is crafting an offer so compelling that saying “no” feels like a mistake.

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• views: 156
• date: 2 October 2026
• posted by: mata00

Welcome to Sell Smart Websites
Who is this for:
Web designers who can build websites but struggle to get clients
People tired of their 9–5 who want a path to passive income
Agencies looking to add a new revenue stream
Anyone who wants to earn $5K–$10K per month selling Smart Websites to local businesses.

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• date: 2 October 2026
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• date: 2 October 2026
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Playful Typography for Powerful Communication in Illustrator
Dani Molyneux | Duration: 2:55 h | Video: H264 1920x1080 | Audio: AAC 44,1 kHz 2ch | Size: 1,05 GB | Language: English

Learn the basics of typography to create memorable compositions that harness the power of words

Creative typography allows you to take some of life's challenges and turn them into messages of hope, action, and positivity. Graphic designer and typography artist Dani Molyneux is passionate about words and the beauty of expressing them. She has worked for leading creative agencies across the UK and is the founder of her own studio, Dotto.

In this course, she teaches you how to design your own bold, colorful, and playful typography. Create a poster featuring a message that means something to you using your own type with Dani as your guide. Explore typography and create artwork that speaks!

Begin by getting to know graphic designer and typographic artist Dani Molyneux. She talks through the creative career that led her to start her own brand, along with the influences that inspire the work she creates.

Take a look at how typography can depict a mood and choose a message for your piece with that in mind. Experiment with composition by creating your initial sketches, then set up your digital workspace in Illustrator. Discover different techniques for creating type and choosing a color palette.

Digitize your sketch, vectorize it, then use the techniques you've learned to craft your letters. Explore different color combinations for your final piece before making the finishing touches to your poster in preparation for printing.

Discover different ways to print your design, including Risograph printing. Then, to finish the course, Dani guides you through how to share your work with the world, via social media, your portfolio, and more.

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• views: 214
• date: 1 October 2026
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How Steel Actually Works - Steel Industry Ultimate Course
Published 9/2026
Created by Joey Guo
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 20 Lectures ( 3h 14m ) | Size: 927.6 MB

Read the steel industry the way an analyst does: the two routes, the spread, the metal margin, and the numbers behind

What you'll learn
⚡ Read an integrated mill's cost per tonne and explain why its margin is a spread
⚡ Calculate the HRC spread using 1.5 tonnes of ore and 0.71 tonnes of coal
⚡ Tell production apart from capacity utilisation and avoid misreading a volume rise
⚡ Judge a steelmaker's asset base using capex to depreciation and net debt at the trough

Requirements
❗ None - no industry experience needed. You will learn how steel industry works through the course.

Description
This course contains the use of artificial intelligence.

How Steel Actually Works is a short course for people who work in or follow finance and want to read the steel industry the way an analyst does, without having done the job. It assumes no prior banking or finance knowledge and builds the industry from the ground up, in plain language, over twenty lessons.

The course starts with what steelmakers actually sell and the two routes that make almost all of the world's steel: the integrated blast furnace and basic oxygen furnace, and the electric arc furnace. From there it works through the products, flat and long and tubular, and shows why flat and long are different businesses with different customers and different pricing. The middle of the course is about the numbers. You will learn how the HRC spread is calculated from iron ore and coking coal, why an electric arc furnace producer measures a metal margin over scrap instead, and why production and capacity utilisation are not the same measurement.

The later lessons cover the judgement calls: why return on invested capital beats return on equity in this industry, what the ratio of capital expenditure to depreciation tells you about a mill's asset base, how to read net debt through a cycle rather than at the peak, and how accounting choices distort comparisons between companies. The course closes with a direct comparison of two producers with opposite cost stacks and a review of the traps outsiders fall into.

Each lesson is a short video with slides. By the end you will be able to open a steelmaker's filings, find the inputs that matter, calculate the spread or the metal margin yourself, and explain in your own words why the same steel price produces different profitability at different companies.

Who this course is for
⭐ Finance professionals covering or lending to steel and metals companies
⭐ Equity and credit analysts who need to read steel filings properly
⭐ Students and career changers moving into metals and mining research

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• views: 166
• date: 1 October 2026
• posted by: 0nelove

Networking and Firewall Configuration in Ubuntu Linux
Released 9/2026
By Andrew Mallett
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 23m 15s | Size: 181.6 MB

Network outages and security breaches often stem from the same root cause: poorly understood configuration and missing monitoring.

Network outages and security breaches often stem from the same root cause: poorly understood configuration and missing monitoring. In this course, Networking and Firewall Configuration in Ubuntu Linux, you'll gain the ability to set up, diagnose, and defend your systems' network layer with confidence. First, you'll explore how to configure static IP addresses, manage interfaces, and resolve DNS using modern Ubuntu tools such as Netplan and systemd-resolved. Next, you'll discover how to diagnose connectivity failures by testing reachability, inspecting sockets, and capturing packets to trace problems to their source. Finally, you'll learn how to apply layered firewall rules using UFW to allow legitimate traffic while blocking threats and limiting brute-force attacks. When you're finished with this course, you'll have the skills and knowledge of Ubuntu network administration and security controls needed to maintain connectivity and protect your infrastructure.

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• views: 204
• date: 1 October 2026
• posted by: 0nelove

Cloud Optimization Strategy and Tradeoff Decisions
Released 9/2026
By Kevin James
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 5m 33s | Size: 236.3 MB

Cloud engineering and architecture teams often struggle to reduce cloud spend without accidentally compromising reliability, performance, or business value.

Cloud engineering and architecture teams often struggle to reduce cloud spend without accidentally compromising reliability, performance, or business value. In this course, Cloud Optimization Strategy and Tradeoff Decisions, you'll gain the ability to evaluate optimization trade-offs and recommend high-impact cloud cost strategies. First, you'll explore how to isolate optimization opportunities from cloud cost and usage signals. Next, you'll discover how to assess trade-offs across cost impact, operational risk, implementation effort, and performance. Finally, you'll learn how to prioritize, format, and deliver actionable optimization recommendations to resource owners. When you're finished with this course, you'll have the skills and knowledge of cloud financial operations needed to evaluate optimization trade-offs and recommend high-impact cloud cost strategies.

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• views: 189
• date: 1 October 2026
• posted by: 0nelove

Clean and Prepare Data in Google Sheets
Released 9/2026
By Mitali Gupta
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 3m 14s | Size: 517.1 MB

Raw datasets often contain missing values, duplicate records, inconsistent formatting, and other quality issues that can lead to inaccurate analysis.

Raw datasets often contain missing values, duplicate records, inconsistent formatting, and other quality issues that can lead to inaccurate analysis. In this course, Clean and Prepare Data in Google Sheets, you'll gain the ability to clean, standardize, and validate datasets for analysis. First, you'll explore how to identify common data quality issues and assess their impact on analysis. Next, you'll discover how to clean and standardize data using Google Sheets functions and built-in tools. Finally, you'll learn how to build validation checks and use Gemini to help maintain data quality. When you're finished with this course, you'll have the skills and knowledge of data cleaning in Google Sheets needed to confidently prepare analysis-ready datasets.

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• views: 270
• date: 1 October 2026
• posted by: 0nelove

Become an AI-Powered People Manager
Released 9/2026
With Ashley Herd
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: General | Genre: eLearning | Language: English + subtitle | Duration: 36m | Size: 253.9 MB

Learn how to use AI as a force multiplier to speed up day-to-day busywork, so you have more time for the judgment calls, coaching, and hard conversations only a people manager can do.

Course details
Between the AI hype and the pressure to hit team goals, it's hard to see how AI actually fits into your day-to-day work as a manager. In this course, Ashley Herd—a management expert and the creator of Manager Method—shows you how to use AI as a force multiplier: cutting through the busywork of prepping meetings, drafting messages, and turning notes into plans, so you have more time for the judgment calls, coaching, and hard conversations that only a manager can do. Explore practical ways to run meetings that produce real decisions, deliver feedback that lands, prep tailored 1:1s in under two minutes, delegate without losing control, triage team workload, and turn raw notes into exec-ready status updates or presentations. Along the way, Ashley demonstrates prompts on common AI platforms and provides a starter kit you can adapt for your own team.

Skills covered
AI for Leadership, AI for Management, Artificial Intelligence (AI), People Management

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• views: 78
• date: 1 October 2026
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C Programming Fundamentals: From Basics to Data Structures
Published 9/2026
Created by Abdullah Alsalal
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 40 Lectures ( 2h 26m ) | Size: 1.4 GB

Learn C programming from scratch with variables, functions, control flow, arrays, pointers, and core data structures.

What you'll learn
⚡ Understand the fundamentals of C programming and program structure.
⚡ Write and use variables, expressions, operators, and formatted output
⚡ Control program flow using Boolean expressions and conditional statements.
⚡ Create and use functions to organize and reuse code effectively.

Requirements
❗ No prior programming experience is required. This course is designed for beginners and teaches the fundamentals of C programming step by step.

DescriptionC Programming Fundamentals: From Basics to Data Structures is a complete beginner-friendly course designed to take you from your first C program to core programming concepts, recursion, arrays, strings, and abstract data types.

You will begin by writing your first C program and learning how variables are created, assigned, and used. From there, you will develop a practical understanding of errors and debugging, helping you recognize problems in your code and understand how programs behave during execution.

The course then introduces functions and explains how programs can be organized into smaller reusable parts. You will explore global variables, program flow, recursion, and iteration, giving you a stronger understanding of how execution moves through a C program and how repeated operations can be controlled.

As your programming skills develop, you will study encapsulation and generalisation before moving into arrays and strings. You will learn how indexed data is accessed and manipulated, how strings are represented and processed, and how common string functions can be used in C programs.

The final part of the course introducesAbstract Data Types and Structures, showing how more advanced data can be organized and managed. You will work through practical ADT examples, including aStack and aDictionary, helping you connect fundamental C programming concepts with core data structure ideas.

By the end of the course, you will have a solid foundation in C programming, including variables, debugging, functions, scope, control flow, recursion, iteration, arrays, strings, structures, and abstract data types.

No previous programming experience is required. The course is designed for beginners, engineering students, computer science students, and anyone who wants to build a strong foundation before moving into algorithms, embedded systems, or more advanced programming.

Who this course is for
⭐ Complete beginners who want to learn C programming from the ground up
⭐ Students who want to understand programming fundamentals, logic, variables, functions, and control flow.
⭐ Engineering and technical students who want to build a strong foundation in C programming.
⭐ Anyone preparing to move into more advanced programming, data structures, algorithms, or embedded systems.

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• views: 159
• date: 1 October 2026
• posted by: 0nelove

Generative AI: From Prompts to Partnership
Released 9/2026
With Jeremy Utley
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 54m | Size: 207.3 MB

Get a practical, nontechnical introduction to generative AI: how it works, where it falls short, and how to collaborate with it as a thinking partner.

Course details
Generative AI is quickly becoming part of everyday work, shaping how people research, write, analyze information, and make decisions across roles and industries. To use these tools effectively and responsibly, professionals need a working understanding of what generative AI is, how it works, and where its strengths and limits lie. In this course, Jeremy Utley offers a practical, nontechnical introduction to generative AI that reframes it from a search-engine substitute to a thinking partner. Jeremy explains how generative AI fits within the broader landscape of artificial intelligence, how large language models generate new content, and how to collaborate with AI through prompting, context, and iteration. He also covers common limitations like bias and hallucinations, along with basic guidelines for using AI safely at work. By the end, you'll have a foundation to onboard your first digital teammate and start building lasting AI habits.

Skills covered
AI Literacy, Generative AI, Artificial Intelligence (AI)

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• views: 79
• date: 1 October 2026
• posted by: 0nelove

Facility Management Essentials : Operations &; Maintenance
Published 9/2026
Created by BIMS Brilliance Institute of Management Studies
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 122 Lectures ( 8h 31m ) | Size: 7.2 GB

Master Facility Management from Operations and Maintenance to Services and Performance Management through Key KPIs.

What you'll learn
⚡ Understand facility management operations, maintenance strategies, asset management, and day-to-day facility operations.
⚡ Apply preventive, predictive, condition-based, and cyclical maintenance methods to improve asset reliability and performance.
⚡ Manage facility services, vendors, procurement, contracts, budgets, safety, security, and emergency management effectively.
⚡ Use BMS, IoT, smart building technologies, energy management, sustainability, and facility KPIs to improve performance

Requirements
❗ No prior facility management experience is required; the course is suitable for beginners and working professionals.

Description
This course contains the use of artificial intelligence.

Facility Management Essentials: Operations, Maintenance & Services is a practical course designed to build a strong understanding of modern Facility Management and its essential functions. You will learn how to manage facility operations, maintenance, support services, assets, vendors, procurement, budgets, safety, security, and workplace environments effectively.

The course coversFacility Operations Planning, Facility Maintenance, Preventive Maintenance, Predictive Maintenance, Condition-Based Maintenance, Reliability-Centered Maintenance, Work Order Management, CMMS, CAFM, Asset Management, Total Cost of Ownership, Spare Parts Management, and Maintenance KPIs.

You will also exploreHard Services and Soft Services, including housekeeping, catering, reception, security, landscaping, pest control, waste management, transportation, and workplace space management.

The course introducesBuilding Engineering Systems, including HVAC, AHU, chillers, cooling towers, electrical systems, DG sets, UPS, plumbing, water supply, drainage, fire alarm, and life safety systems.

Learn aboutBMS, Smart Buildings, IoT Sensors, Digital Twins, Lighting Automation, Smart Metering, Energy Management, and AI-based Predictive Maintenance.

The course also coversFacility Procurement, Vendor Management, Contracts, Purchase Orders, SLAs, OPEX, CAPEX, Budgeting, Cost Control, ROI, and Payback Period.

You will develop knowledge ofFire Safety, Health and Safety, LOTO, Permit-to-Work, Emergency Management, Business Continuity, Sustainability, Carbon Footprint, ESG, Water Management, and Energy Efficiency.

Finally, learn how to measure and improve facility performance usingAsset Availability, Maintenance Backlog, MTBF, MTTR, PM Compliance, Energy Consumption/m², Energy Cost/m², Water Consumption/m², OPEX/m², SLA Compliance, Space Utilization, Carbon Emissions/m², and Waste Recycling KPIs.

This course is suitable forFacility Managers, Maintenance Managers, Engineers, Operations Managers, Building Managers, Property Managers, Supervisors, and professionals looking to develop practical Facility Management knowledge.

Who this course is for
⭐ Facility Managers, Facility Management professionals, Maintenance Managers, Engineers, Maintenance Supervisors, Operations Managers, Building Managers, Property Managers, Administration professionals, and professionals responsible for facility operations, maintenance, safety, security, and support services. The course is also suitable for beginners, students, and professionals who want to build practical knowledge of Facility Management, asset management, maintenance, BMS, smart buildings, sustainability, and facility performance management.

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• views: 145
• date: 1 October 2026
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Investing & AI Fundamentals
Published 9/2026
Created by Flavio Alfano
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 33 Lectures ( 6h 37m ) | Size: 2.6 GB

Learn Investing, Machine Learning, AI Signals, Backtesting and AI Agents Foundations

What you'll learn
⚡ Understand the fundamentals of investing
⚡ Analyze investments using fundamental and technical concepts
⚡ Understand how Artificial Intelligence and Machine Learning can be applied to financial markets
⚡ Understand and build a basic AI Agent workflow for investment analysis

Requirements
❗ No previous investing or financial market experience is required. No previous Artificial Intelligence or Machine Learning knowledge is required. Basic computer skills are sufficient; programming experience is helpful but not required.

Description
This course contains the use of artificial intelligence.

Investing & AI Fundamentals is a practical introduction to the intersection of investing, financial analysis, and Artificial Intelligence.

The course is designed for learners who want to understand not only the foundations of investing, but also how modern AI technologies can support financial analysis, research, and more structured decision-making.

This course contains the use of artificial intelligence for image generation and voice generation to allow a clear lesson understanding and flow. The Audience can increase or decrease video velocity in order to adapt at their own needs.

We begin with the fundamentals: how investing works, how financial markets operate, and how instruments such asstocks, bonds, and ETFs differ. You will then learn how to approach investment analysis using fundamental and technical perspectives, while developing a solid understanding ofrisk, diversification, correlation, volatility, drawdowns, and portfolio decisions.

From there, the course moves into Artificial Intelligence.

You will learn the differences betweenAI, Machine Learning, and Generative AI, and understand how Machine Learning learns from financial data. We explore features and targets, classification and regression, predictions and probabilities, training and testing, overfitting, and the limitations that can cause AI models to fail.

The course then connects these concepts to practical investment applications. You will learn how technical indicators and Machine Learning outputs can becomeinvestment signals, how those signals can be evaluated throughbacktesting, and why issues such as data leakage, overfitting, execution timing, transaction costs, and false confidence must be considered when evaluating historical results.

Finally, we move beyond individual models and introduceAI agents for investment analysis.

You will learn how agents combine goals, context, tools, memory, external data, rules, Machine Learning, and Generative AI into a coordinated analytical system. In the final lessons, we design an investment research agent and useClaude as the reasoning layer in a practical agent architecture—while maintaining validation, traceability, controls, and human review.

By the end of this course, you will understand how to

✨ Explain the foundations of investing and financial markets.

✨ Differentiate between stocks, bonds, ETFs, and their roles in a portfolio.

✨ Apply fundamental and technical concepts to investment analysis.

✨ Understand volatility, drawdowns, diversification, correlation, and position sizing.

✨ Distinguish AI, Machine Learning, and Generative AI.

✨ Understand how financial data becomes features, targets, predictions, and probabilities.

✨ Recognize overfitting, data leakage, and other common AI and backtesting pitfalls.

✨ Understand how AI-based investment signals can be created and evaluated.

✨ Build the conceptual pipeline behind a Machine Learning investment signal using Python.

✨ Understand the architecture of an AI agent.

✨ Combine rules, Machine Learning, and Generative AI in a hybrid analytical workflow.

✨ Understand how Claude can operate as the reasoning layer within an investment research agent.

✨ Recognize whyhuman judgment, validation, and risk controls remain essential when using AI for investment analysis.

No advanced background in finance, Machine Learning, or AI is required. The course progressively develops each concept and emphasizesunderstanding the logic behind the technology rather than treating AI as a black box.

This course is especially suitable for professionals, investors, technology enthusiasts, students, and anyone interested in understanding howfinancial knowledge and modern AI capabilities can work together.

Financial knowledge x AI capabilities x better-informed decisions.This course is for educational purposes only and does not provide financial or investment advice.

Who this course is for
⭐ Beginners who want to understand investing from the ground up, including stocks, bonds, ETFs, risk, return, diversification, and the basic principles of financial markets. Investors and finance enthusiasts who want to understand how Artificial Intelligence and Machine Learning can be applied to market analysis, financial data, investment signals, and decision support. Technology, business, and finance professionals interested in connecting traditional investment concepts with modern AI tools, without requiring advanced knowledge of finance, programming, or data science. Learners interested in AI Agents and practical applications of Generative AI in finance, including how agents can combine market data, technical indicators, fundamental information, risk analysis, and Machine Learning signals to support investment research.

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• views: 112
• date: 1 October 2026
• posted by: 0nelove

Unity Mobile Publish and Monetise Your Game
Published 9/2026
Created by GameDev.tv Team, Jonathan Holowka
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 78 Lectures ( 10h 48m ) | Size: 7 GB

Build and publish your Unity game on Android and iOS, with touch controls, ads and in-app purchases

What you'll learn
⚡ Turn your Unity game into a real mobile game that players can download and play.
⚡ Publish on Google Play and the App Store, with every step of the setup covered.
⚡ Monetise your game with in-app purchases and ads, and learn where each one fits best.
⚡ Build touch controls that feel natural, with a joystick, buttons and pinch-to-zoom.
⚡ Adapt your game to phones and tablets, and dodge the camera notch at the top.
⚡ Send local push notifications on Android and iOS to bring players back for more.
⚡ Set up consumable and non-consumable in-app purchases and test them on a device.

Requirements
❗ Basic C# knowledge
❗ Some prior experience with Unity
❗ Access to a Mac if you want to build and publish to iOS

Description
Want tosee your game running on your own phone, with players tapping an on-screen joystick, picking up in-app purchases and watching ads that reward them for it?

Push notifications, in-app purchases, ads and store setup can look like a lot at first glance. Taken one step at a time, each one is far more manageable than it seems.

You'll start with a working prototype we provide, so every lesson goes straight into mobile features. Got a game of your own on the go? Bring it along and add each feature to that instead. You'll set up touch controls and build to a real device first. Then you'll create your store presence on Google Play and App Store Connect. Store setup isn't the most glamorous part (Jonathan will tell you that himself), so he walks you through every step.

From there, you'll add an on-screen joystick, buttons and pinch controls, and adapt your layout to phones and tablets in portrait and landscape, camera notch included. You'll send local push notifications on Android and iOS, create consumable and non-consumable in-app purchases, and integrate ads mediation with interstitial ads on game over and rewarded ads players choose to watch.

Your instructor, Jonathan, has spent years building these exact features in the mobile games industry. Along the way, he shares the psychology behind purchases and ads: what to charge, where ads belong, and which dark patterns to steer clear of. And if you get stuck, our teaching assistants and Discord community are there to help.

If you've got a PC game you'd love to see on mobile, this course works just as well for porting it over. It's an intermediate course, so it stays focused on everything specific to mobile.

By the end, you'll have your game on your phone, set up on both stores, with in-app purchases and ads ready to go.

Ready to see your game in your hand? Take the leap, and let's get started.

Who this course is for
⭐ Ideal if you're a Unity developer with a game you want to take to mobile.
⭐ Also perfect if you're a dev with a PC game you'd love to see on phones and tablets.
⭐ And great if you're curious about how mobile games get published and start earning money.

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• views: 380
• date: 1 October 2026
• posted by: mata00

Agentic AI for Daily Work

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• views: 243
• date: 1 October 2026
• posted by: mata00

YouTube Hub 101 Build Scale Your Profitable YouTube Channel with Automation

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• views: 242
• date: 1 October 2026
• posted by: mata00

Bilal Javed & Syed Kashif PIN POWER — Biggest Update Ever for Pinterest SEO 2026

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• views: 179
• date: 1 October 2026
• posted by: mata00

AI Video Lab Master Commercial AI Videos Land High Paying Clients

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• views: 326
• date: 1 October 2026
• posted by: mata00

Everyone has access to the same AI.
The gap is knowing how to package it, where to post it, and how to turn views into leads.

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• views: 149
• date: 30 September 2026
• posted by: mata00

Joe Sugarman - Lost Sugarman Tapes

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• views: 214
• date: 30 September 2026
• posted by: mata00

Tinna Loaiza – The Carousel Studio

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• views: 216
• date: 30 September 2026
• posted by: mata00

Investors Underground – Swing Trading
Learn everything you need to know to build a strong swing trading foundation that will help you become a confident trader.
This course features time-tested swing trading strategies, chart setups, and more.

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• views: 128
• date: 30 September 2026
• posted by: AD-TEAM

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• date: 30 September 2026
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• date: 30 September 2026
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• views: 195
• date: 30 September 2026
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Course Overview
The Evergreen Ads + Upsells course teaches students how to build profitable advertising funnels that run continuously without the need for constant updates or manual management.

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• views: 178
• date: 30 September 2026
• posted by: mata00

Tiana Asperjan - Advanced Ad Copy Secrets

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• views: 146
• date: 30 September 2026
• posted by: mata00

Salvora A.I Avatars Amazon Affiliate Course

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• views: 138
• date: 30 September 2026
• posted by: 0nelove

Advanced AI Chatbots: Build, Evaluate, and Deploy Agents RAG Chatbots
Released 9/2026
With Roya Kandalan
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Beginner + Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 2h 39m | Size: 875.8 MB

Learn how to build, measure, and deploy an advanced RAG chatbot with LangChain 1.0. Discover how to add structured citations, extend behavior with middleware, and ship it publicly.

Course details
A RAG chatbot is only useful if you can trust its answers and ship it with confidence. In this advanced course with Roya Kandalan, discover how to build and deploy a RAG chatbot that is customizable, reliable, and production-ready. Starting from a LangChain 1.0 starter project, learn how to create a chatbot with structured citations that keep responses accurate and traceable. Explore how to add middleware for logging and fallback behavior without touching your core code, then measure chatbot quality using tracing and automated metrics. Discover how to prepare your chatbot for public deployment as an accessible, adaptable tool, ready for regulated environments such as healthcare and education. By the end of this course, you'll be able to build, evaluate, extend, and deploy a RAG chatbot you can adapt for many use cases. This beginner-to-intermediate course suits developers, AI engineers, data scientists, and anyone eager to build conversational AI.

Skills covered
Retrieval-Augmented Generation (RAG), AI Agents, LangChain, Fine Tuning, Artificial Intelligence (AI)

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• views: 229
• date: 30 September 2026
• posted by: 0nelove

TypeSafe AI: Jev Essentials - Building Decision Systems
Published 9/2026
Created by Mohammad Azam
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 22 Lectures ( 1h 49m ) | Size: 1.6 GB

Turn Unstructured Data into Structured Decisions with Jev

What you'll learn
⚡ Understand how Jev transforms unstructured data into structured, actionable decisions.
⚡ Build a real-world resume evaluator using Jev and the jаvascript SDK.
⚡ Classify and evaluate customer support messages using Python and Jev.
⚡ Process multiple records using batch evaluation for scalable decision workflows.
⚡ Build intelligent routing systems that direct requests to specialized AI profiles or assistants.

Requirements
❗ Basic programming knowledge is recommended.
❗ Familiarity with jаvascript or Python is helpful but not required.
❗ Basic understanding of APIs and JSON will be useful.
❗ No prior experience with Jev or TypeSafe AI is required.
❗ No prior machine learning or advanced AI knowledge is required.

Description
AI is great at generating text, but many real-world applications need something much more useful:structured decisions that your software can act on.

InTypeSafe AI: Jev Essentials - Building Decision Systems, you will learn how to useJev by TypeSafe AI to transform unstructured information into structured judgments that can be used directly inside your applications.

This is a practical, project-based course. Instead of spending hours on theory, you will learn Jev by building real-world applications and solving common AI decision-making problems.

We will start by exploring what Jev is, how it works, and how to obtain and configure your API key. From there, you will quickly move into hands-on projects.

Build a Resume Evaluator
You will build a complete resume evaluation application using the Jev jаvascript SDK.

You will learn how to define questions for evaluating resumes, organize and refactor your evaluation criteria, process Jev responses, map those responses into your application's UI model, and display structured results in a web application.

Classify Customer Support Messages
Next, you will use Jev to classify customer support requests.

Using Python and Google Colab, you will evaluate individual customer messages and then expand the solution to process multiple messages in batches.

This project demonstrates how unstructured customer communication can be converted into structured information that your software can categorize, organize, and act upon.

Route Requests to Specialized AI Assistants
Finally, you will exploreAI routing.

Instead of sending every request to the same model or assistant, you will learn how Jev can analyze an incoming request and help determine which specialized profile or assistant should handle it.

You will examine the request and response process using Postman and then walk through the implementation of a routing system.

What You Will Learn
By the end of this course, you will understand how to

✨ Understand the role of Jev in AI-powered applications

✨ Configure and use the Jev API

✨ Work with the Jev jаvascript SDK

✨ Define structured questions and evaluation criteria

✨ Turn unstructured data into structured judgments

✨ Build an AI-powered resume evaluator

✨ Classify customer support messages

✨ Perform batch evaluations using Python and Google Colab

✨ Use Jev for intelligent request routing

✨ Route requests to specialized AI profiles or assistants

✨ Integrate Jev results into real-world applications

Who Is This Course For?
This course is designed for developers who want to move beyond simple AI chat applications and build systems that canevaluate, classify, route, and make structured decisions from unstructured data.

You don't need to be an AI or machine learning expert. If you are comfortable with basic programming concepts and want to learn how AI can become part of your application's decision-making pipeline, this course will give you a practical starting point.

By the end of the course, you won't just understand what Jev does—you will have built multiple applications demonstrating how structured AI judgments can be used to create intelligent decision systems.

Who this course is for
⭐ Software developers who want to build intelligent decision systems using AI.
⭐ jаvascript and Python developers interested in integrating Jev into real-world applications.
⭐ AI developers who want to turn unstructured data into structured, actionable decisions.
⭐ Developers building AI-powered workflows for evaluation, classification, and intelligent routing.
⭐ Anyone interested in practical AI development without needing a machine learning or data science background.

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• views: 152
• date: 30 September 2026
• posted by: 0nelove

RAG Agents with LangChain & LangGraph: A Practical Guide
Last updated 9/2026
Created by Anton Voroniuk• 1.250.000+ Students, Anton Voroniuk Support, George Paterakis
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 11 Lectures ( 1h 49m ) | Size: 688 MB

Ground your AI agents in real dаta: build a full RAG agent in LangChain, then rebuild it as a LangGraph workflow

What you'll learn
⚡ Build a complete RAG agent in LangChain that retrieves relevant documents and generates grounded, source-based answers
⚡ Rebuild the same RAG agent as a LangGraph workflow, with retrieval as an explicit, controllable step in the graph
⚡ Understand how chunking, embeddings, retrieval, and grounding work together and how each one affects answer quality
⚡ Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge
⚡ Decide where a retrieval step belongs inside an agent's reason–act–observe loop
⚡ Compare a plain LangChain RAG implementation with a graph-based design and choose the right one for your project
⚡ Explain what an AI agent is, how it makes decisions, and when a single LLM call is the better choice
⚡ Recognize the most common ways agent projects fail and apply design habits that prevent them

Requirements
❗ Working knowledge of Python (functions, classes, working with lists and dictionaries)
❗ Basic familiarity with LangChain — you should have called a chat model and ideally defined a tool at least once
❗ Some exposure to LangGraph is helpful for the final build but not required; the graph is explained step by step
❗ An API key for an LLM provider such as OpenAI or Anthropic, plus access to an embedding model (free tiers are enough)
❗ A computer with Python 3.10+ and a code editor installed

Description
Ask an LLM about a document written yesterday and you get a confident, detailed, wrong answer. The model only knows what it saw in training. Retrieval-Augmented Generation fixes this by letting the agent look things up before it responds, and this course teaches you to build a RAG agent that answers from your own data.

What you will learn
✨ Explain what RAG is, why LLMs need it, and when retrieval is the right fix for hallucinations and outdated knowledge

✨ Understand how chunking, embeddings, retrieval, and grounding work together and how each affects answer quality

✨ Build a complete RAG agent in LangChain, from loading documents to a source-based answer

✨ Rebuild the same agent as a LangGraph workflow, with retrieval as an explicit, controllable step

✨ Choose between a plain LangChain implementation and a graph-based design for your own project

Inside the course
After a short grounding in agent fundamentals, the course explains the full RAG pipeline in plain terms before any code is written. Then come two hands-on builds: the same RAG agent first in LangChain, then rebuilt in LangGraph. Building it twice is deliberate: you will understand both implementations well enough to pick the right one for your needs. Written references close each section.

Who this course is for
⭐ Python developers who want their agent to answer from their own documents instead of guessing
⭐ AI engineers building assistants that need current, verifiable knowledge rather than what the model memorized in training
⭐ Developers who have built a basic "chat with your PDF" demo and want to understand what's actually happening under the hood
⭐ Backend engineers who need to ground LLM output in internal data such as docs, tickets, or knowledge bases
⭐ Anyone who wants a short, practical introduction to RAG before committing to a longer, broader course

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• views: 266
• date: 30 September 2026
• posted by: 0nelove

Advanced Network Forensics: Threat Hunting with Wireshark
Published 9/2026
Created by Learnsector LLP, Tushar Kanchan
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 27 Lectures ( 2h 13m ) | Size: 2.3 GB

Analyze PCAPs with Wireshark and tshark to isolate stealth C2, lateral movement, and stop enterprise breaches.

What you'll learn
⚡ Formulate testable hunt hypotheses using intelligence frameworks to uncover unflagged adversary persistence.
⚡ Engineer complex boolean Wireshark display filters to isolate anomalous TCP flag states and stealth port scans.
⚡ Profile network sessions using conversation tables and I/O graphs to detect covert rate-limited data exfiltration.
⚡ Reconstruct multi-stream TCP sessions and carve staged binaries to verify malicious payload hashes.
⚡ Evaluate TLS metadata and apply JA3, JA3S, and JA4 cryptographic fingerprints to unmask offensive toolkits.
⚡ Calculate Shannon entropy on outbound DNS traffic to identify automated domain generation algorithms and tunneling.
⚡ Audit SMB and DCE/RPC traffic streams to intercept administrative share abuse and PsExec service creation.
⚡ Uncover Kerberos ticket harvesting and cipher downgrade attacks by analyzing TGS-REQ packets and Event ID 4769.
⚡ Automate bulk PCAP triage pipelines with tshark, capinfos, and editcap to process multi-gigabyte captures.
⚡ Author defensible forensic incident reports and operationalize findings into production Suricata and Zeek rules.

Requirements
❗ Working knowledge of the TCP/IP protocol suite, standard networking concepts (IP addressing, subnets, routing), and core protocols (HTTP, DNS, TLS, SMB).
❗ Experience utilizing basic command-line environments (Linux/Unix shell or Windows PowerShell).
❗ Administrative workstation access capable of installing and running Wireshark, tshark, and related open-source network utilities.

Description
"This course contains the use of artificial intelligence."

Modern enterprise perimeters face sophisticated adversary evasion where endpoint detection and response (EDR) agents are blinded or bypassed via signed driver manipulation. While host telemetry degrades under kernel-level tampering, raw packet captures provide immutable evidentiary ground truth. Securing mission-critical infrastructure requires moving beyond reactive security operations center (SOC) triage queues toward hypothesis-driven network threat hunting.

This curriculum delivers an enterprise-grade framework for deep packet analysis, behavioral anomaly detection, and proactive hypothesis testing across high-throughput capture perimeters. Learners transition from passive observers to active forensic investigators, mastering raw protocol dissection, stream reassembly, binary carving, and statistical telemetry analysis. The content addresses mission-critical organizational needs, enabling teams to rapidly identify persistent compromise, contain lateral spread, and defend complex network topologies.

Key skills and technical competencies acquired include

✅ Master advanced Wireshark and tshark syntax to isolate covert communication channels.

✅ Evaluate cryptographic handshakes using JA3, JA3S, and JA4 fingerprinting frameworks.

✅ Uncover lateral movement sequences across SMB, DCE/RPC, WinRM, and Kerberos protocols.

✅ Quantify periodic Command-and-Control (C2) beaconing and mathematical sleep jitter.

✅ Detect DNS tunneling, Domain Generation Algorithms (DGA), and data exfiltration.

✅ Operationalize investigative findings into durable Suricata, Snort, and Zeek detection rules.

Frequently Asked Questions

What is hypothesis-driven threat hunting?

Hypothesis-driven threat hunting is a proactive security methodology where analysts assume network defenses have been bypassed. Investigators formulate testable, falsifiable behavioral questions based on threat intelligence and protocol baselines, querying raw packet telemetry to uncover persistent adversaries that evaded automated detection systems.

How does JA4 fingerprinting improve TLS traffic analysis?

JA4 improves TLS traffic analysis by sorting extensions and cipher suites alphabetically prior to hashing. This structure neutralizes client hash-randomization evasion techniques and generates human-readable protocol indicators, allowing security teams to identify offensive toolkits and rogue communication runtimes without performing full payload decryption.

Structured as an executive architecture briefing and applied forensic operational pipeline, this program bridges deep packet analysis with MITRE ATT&CK enterprise categorization, cross-source timestamp reconciliation, and legal chain of custody standards.

Updated for the 2025/2026 enterprise landscape, the course equips practitioners with resilient methodologies to triage multi-gigabit PCAP datasets, bypass modern obfuscation, and harden perimeter defenses.

Who this course is for
⭐ Enterprise SOC Analysts (Tiers 2 and 3) seeking to move from reactive alert triage to proactive hypothesis-driven network hunting.
⭐ Incident Response Engineers and Digital Forensics Specialists requiring packet-level proof to validate compromise and build defensible intrusion timelines.
⭐ Detection Engineers and Security Architects tasked with converting novel adversary techniques into resilient automated detection logic.
⭐ Network Security Professionals seeking to master deep packet dissection, traffic baselining, and covert channel identification.

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• views: 154
• date: 30 September 2026
• posted by: 0nelove

Microsoft Copilot for Accounting by Eduardo Aponte, PhD
Published 9/2026
Created by Eduardo Aponte, PhD
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 13 Lectures ( 1h 43m ) | Size: 1.2 GB

Automate financial reporting, accelerate reconciliations and optimize email communications using Microsoft Copilot AI

What you'll learn
⚡ Extract trends, highlight variances, and generate complex formulas in Excel using natural language.
⚡ Draft professional financial reports and board-ready presentations in minutes.
⚡ Compare datasets for faster reconciliations and build audit-ready documentation.
⚡ Design highly effective, accounting-specific prompts that deliver accurate results without hallucinations.

Requirements
❗ No previous experience is necessary.

Description
Welcome to Microsoft Copilot for Accounting.

Are you spending hours each month wrestling with manual reconciliations, drafting repetitive emails, or staring at a blank page when writing financial summaries? Want to elevate your role from data cruncher to strategic advisor? This course is built for finance professionals, auditors, and bookkeepers who want to eliminate tedious tasks and leverage AI to work faster and smarter.

You'll learn how to integrate Microsoft Copilot seamlessly into the tools you already rely on daily—focusing on Excel. We'll move past the hype and focus strictly on practical accounting workflows. Everything we cover is grounded in real-world finance scenarios and strict enterprise data privacy standards, ensuring your confidential information remains secure.

By the end, you'll be able to

· Extract trends, highlight variances, and generate complex formulas in Excel using natural language.

· Draft professional financial reports and board-ready presentations in minutes.

· Compare datasets for faster reconciliations and build audit-ready documentation.

· Design highly effective, accounting-specific prompts that deliver accurate results without hallucinations.

Actionable frameworks, prompt libraries, and hands-on labs will help you implement these AI strategies immediately—no prior coding or AI experience required.

If you're ready to trade manual data entry for high-value strategic analysis, hit Start—and transform your accounting workflow today.

Who this course is for
⭐ All

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• views: 141
• date: 30 September 2026
• posted by: 0nelove

Advanced Context Engineering: Systems, Evaluation & Research
Published 9/2026
Created by Superposition AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 62 Lectures ( 19h 16m ) | Size: 8.6 GB

Build, measure and test context systems for text, knowledge and agents, and check ideas from research yourself.

What you'll learn
⚡ Build context you can verify: extract text from PDFs, split documents, and assemble task packets whose claims trace to a file and a line.
⚡ Compare word search (BM25), embeddings, rank fusion and reranking on the same questions, and choose a method from measured evidence.
⚡ Chain dependent searches, add source-grounded context to chunks, and audit a small NetworkX graph against current permissions.
⚡ Manage long context with budgeted selection, checked shortening, handoffs after compaction, and caches whose keys include content and permissions.
⚡ Build memory with provenance, validity dates and lifecycle rules in SQLite, and test it across separate conversations.
⚡ Build a local MCP server with typed inputs, pagination and failure handling that does not invent answers.
⚡ Run long tasks with explicit state and checkpoints, resume after a stop, and measure whether several agents beat one.
⚡ Detect instructions hidden in data, keep sources through handoffs, and enforce permissions in code, testing for leaks and for over-blocking.
⚡ Evaluate systematically: test checkers against hand labels, catch regressions, run ablations, and improve instructions from feedback.
⚡ Read a research paper for its claim and limits, test its idea against fair baselines, and finish a system on a new domain with a sealed test.

Requirements
❗ Context Engineering, Part I, or equivalent comfort with the Codex CLI: working in a terminal with folders and files, and checking what a tool actually did. Lesson 1 assumes Codex is already installed and signed in.
❗ A Codex CLI account of your own. The lessons were recorded with GPT-6-Luna at medium reasoning, and results on a different setup may differ. Model usage counts against your account, and the course does not promise a fixed cost.
❗ A Linux (or other POSIX) shell, Python 3.12 and the uv package manager, which lesson 2 walks through installing. Git is added in lesson 6, and free Python packages such as pytest are installed as lessons need them. Only the Linux route was demonstrated.
❗ Willingness to read and run Python line by line. No programming knowledge beyond Part I is assumed, and Python starts in lesson 2, but the pace is fast and you may need to stop and look things up.
❗ Optional: an Alibaba Model Studio account with a Singapore-region API key for the live embedding and reranking calls in lessons 15, 16 and 18. Saved responses are supplied so you can follow without an account; live calls may be billed.
❗ Time for real work: lessons run from about 10 to about 42 minutes, and the first half of the course is the heaviest.

Description
This course contains the use of artificial intelligence.

Advanced Context Engineering is the advanced course that follows Context Engineering, Part I. It follows one idea through 60 lessons in 12 modules, about 19 hours in total: the context an AI agent works with is data that you assemble, check and measure. You start from a small baseline and finish with a complete system that you test on a sealed test set and defend with evidence.

Along the way you prepare documents and PDFs you can trust, assemble task packets whose claims trace to a file and a line, and compare word search, embeddings, rank fusion and reranking on the same questions. You build chained searches and a small graph, manage long context with budgets, handoffs and caches, and build memory with sources, dates and SQLite. You then build a local MCP server, run long tasks that can stop and resume, measure whether several agents help, plant instructions in data, enforce permissions in code, and build evaluation suites that catch regressions and show which component actually helped. The last modules cover reading a research paper and testing its idea, and a final project: a staff assistant for a fictional warehouse handbook.

Every lesson follows the same pattern: a short refresh, slides that explain the new idea, a continuous demonstration in Codex CLI, a close reading of the results, and a recap. Almost every lesson ends with an exercise and a solution to compare against. Lessons come with starter files and saved results and, in the later lessons, offline checks and scripted stand-ins that make no model calls, so you can inspect what was recorded before spending anything. The demonstrations use Codex CLI 0.156.1 with GPT-6-Luna at medium reasoning, and all data is synthetic, drawn from fictional organizations.

It is written for graduates of Part I and for people who write code or are ready to learn as they go. No programming knowledge beyond Part I is assumed, and Python is introduced from lesson 2, but the pace is fast and the code is read line by line. If Context Engineering or the Codex CLI is new to you, start with Part I.

What it is not: it does not promise research training equivalent to a doctorate. GraphRAG, GEPA, ACE and Recursive Language Models are taught as principles and as small, clearly labeled adapted demonstrations, not as reproductions of the papers. Most results come from small synthetic experiments with one recorded run, so they show a method and not a benchmark, and the safety lessons demonstrate mechanisms without promising protection in production. OCR of scanned documents, real-time audio and video, and model training are outside the course. Model usage counts against your own account, and the course does not promise a fixed cost.

Who this course is for
⭐ Graduates of Context Engineering, Part I who want to go from prompts and folders to systems they can measure and improve.
⭐ Developers and engineers who build assistants, retrieval or agent systems and want habits for evaluation, permissions and testing.
⭐ Learners without a programming background who are ready to learn Python as they go and to stop and look things up.
⭐ People who want to read a research paper and test one of its ideas themselves in a small, fair experiment.

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• views: 120
• date: 30 September 2026
• posted by: 0nelove

Agentic Automation — A Pytest Framework with Claude Code
Published 9/2026
Created by Josef Levy
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 49 Lectures ( 3h 6m ) | Size: 3.3 GB

Agentic Automation: Skills, Subagents, Planning, and Test Automation

What you'll learn
⚡ Design and author purpose-built Claude Code subagents — giving each a narrow mandate and exactly the tools it needs
⚡ Separate thinking from doing with plan mode — produce a reviewable plan before writing code
⚡ Orchestrate a multi-agent engineering loop — plan → build → review → test — where a lean orchestrator routes work across agents
⚡ Turn requirements into a running Pytest + Playwright framework — mapping user stories and acceptance criteria into non-flaky automated tests
⚡ Work within enforceable guardrails and improve agents from real results — building inside allow/deny permission policies

Requirements
❗ Comfortable with Python, Pytest, and Git; some Playwright and Claude Code exposure helps

Description

Agentic Automation Course

Build a test-automation framework where the work is done by AI agents, not scripts you run by hand.
Most automation courses teach you to write scripts. This one teaches you to build ateam — a roster of purpose-built Claude Code subagents that plan, write, review, run, and debug tests by reasoning about the work, using their own tools, and handing off to each other in a real engineering loop.

You'll author each specialist from scratch and wire it into a working pipeline

✅plan-architect researches a task and returns a reviewable plan before a line of code is written

✅automation-writer turns user stories and acceptance criteria into non-flaky Pytest + Playwright tests

✅senior-code-reviewer reviews the diff read-only and opens the PR on approval

✅test-suite-planner decomposes a story into prioritised, independent suites as Jira sub-tasks

✅test-runner executes each reviewed suite and posts a sanitized verdict

✅qa-failure-investigator tells a test bug from a product bug — byattempting the fix — and files evidence-backed defects

A leanorchestrator routes work between them and carries context across the hand-offs. Because the heavy lifting stays inside each agent's isolated context window, the orchestrator runs a long, multi-stage workflow without exhausting its own — in one measured build it held at~5%

context used while ~250K tokens of work happened inside the subagents.

What makes it different
✅"Agentic" is literal. Each agent is an autonomous LLM worker with a narrow mandate, its own context, and exactly the tools it needs — toolsare the capability boundary, so a read-only reviewer physically can't mutate the repo.

✅Dogfooded throughout. Every agent is exercised on real work in the course's own repo, driving a live Jira board and a real browser against a running app. Whatever breaks gets fed back into that agent's prompt — so you learn to improve agents the way you'll actually improve them.

✅Guardrails you workwith . Destructive, staging, commit, push, and PR-create commands are denied outright; sanctioned actions are wrapped in allow-listed skills. You learn to build inside enforceable policy, not around it.

The pipelineplan → build → review → test, with a defect fork when a suite catches a real product bug. Progress is visible on the Jira board through a single parent-story status, one owner per transition

To Do → In Planning → Ready for Dev → In Progress → In Review → In Testing → Done

You'll practice
Matching tools to intent · planning before doing · delegating execution to keep an orchestrator's context lean · separating "it compiles" from verified and "review" from security audit · and feeding real findings back into agents so each dogfood failure pre-empts its whole class

of bug next time.

Stack: Python · Pytest · Playwright · pipenv · pytest-html + Allure · custom Claude Code subagents and skills · live Jira Cloud.

Educational course material — designed to be worked through interactively with Claude Code against a sandbox Jira project and a disposable app-under-test.

Who this course is for
⭐ Developers and automation engineers learning to build with Claude Code subagents

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• views: 147
• date: 30 September 2026
• posted by: 0nelove

Hands-On Workshop: Build an AI Document Assistant on AWS
Published 9/2026
Created by Lukasz Kallas
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 14 Lectures ( 3h 42m ) | Size: 3.9 GB

Build a RAG document assistant with AI, Amazon Bedrock Knowledge Bases, S3, Next.js, AWS CDK & CI/CD

What you'll learn
⚡ Build an AI-powered document assistant using Amazon Bedrock and RAG
⚡ Use Amazon Bedrock Knowledge Bases and S3 to answer questions from your own documents
⚡ Define and deploy cloud infrastructure using AWS CDK, Docker, ECS, and Fargate
⚡ Build an automated CI/CD workflow with GitHub Actions while working with an AI coding agent

Requirements
❗ Basic understanding of web development
❗ Basic familiarity with AWS

Description
This hands-on workshop brings together generative AI, cloud, application development, Infrastructure as Code, containers, and CI/CD to build a complete AI document assistant on AWS.

Unlike my regular Hands-On Introduction courses, this workshop isn't focused on teaching one technology from scratch.

Instead, we'll start with an empty folder and use an AI coding agent inside Cursor to build and deploy an application that lets users upload their own documents and ask questions grounded in those documents.

And just like in the other workshops in this series, we're not going to hand-write the application or infrastructure code ourselves.

We'll direct the AI agent, review what it produces, test its decisions, and work through the problems we encounter along the way.

The application we'll build is an AI-powered document assistant.

Users will be able to upload documents through a Next.js application, store them in Amazon S3, and make them available through an Amazon Bedrock Knowledge Base. Once the documents have been processed and indexed, users can ask questions and receive answers grounded in their own content, together with citations pointing back to the source documents.

This gives us a practical implementation ofRetrieval-Augmented Generation, or RAG.

Throughout the workshop, we'll work with

✨ AI-assisted development with Cursor

✨ Retrieval-Augmented Generation (RAG)

✨ Amazon Bedrock

✨ Amazon Bedrock Knowledge Bases

✨ Foundation models

✨ Embeddings and vector search

✨ Amazon S3

✨ Next.js

✨ Docker

✨ AWS CDK with TypeScript

✨ Infrastructure as Code

✨ Amazon ECS

✨ Git and GitHub

✨ GitHub Actions for CI/CD

✨ Automated cloud deployments

We'll start with an empty project and use the AI coding agent to help us determine how the different pieces should fit together.

We'll build a Next.js interface where users can upload documents and interact with them through a question-and-answer experience.

Those documents will be stored in Amazon S3 and connected to an Amazon Bedrock Knowledge Base, giving us a managed RAG workflow for ingesting, indexing, retrieving, and using our own documents as context for foundation models.

On the question-answering side, we'll retrieve relevant information from the knowledge base and use Amazon Bedrock to generate responses grounded in our documents rather than relying solely on the model's existing knowledge.

We'll also work with citations so users can see which source documents contributed to an answer.

But building the AI functionality is only part of the workshop.

We'll useAWS CDK with TypeScript to define our cloud infrastructure as code, containerize the application with Docker, and deploy it using Amazon ECS and AWS Fargate.

We'll then connect everything to GitHub and create a GitHub Actions CI/CD pipeline so changes can automatically move from our repository to the deployed application.

As with every workshop in this series, the AI agent won't necessarily get everything right the first time.

That's intentional.

AI coding agents are non-deterministic. They can choose approaches we didn't expect, create incorrect configurations, misunderstand requirements, or generate code and infrastructure that simply don't work.

Instead of hiding those moments, we'll use them to understand how to direct an agent, inspect what it generates, troubleshoot problems, and decide whether its proposed solution actually makes sense.

That's one of the main differences between this workshop and a traditional step-by-step tutorial.

By the end of the workshop, we'll have connected application development, generative AI, RAG, document storage, vector search, Infrastructure as Code, containers, cloud deployment, and CI/CD into one working system.

More importantly, you'll have seen how all of these individual technologies fit together when building something larger than another isolated AI chatbot demo.

This workshop is a great fit if you are

✨ A developer interested in building practical generative AI applications

✨ An AWS developer or cloud engineer exploring Amazon Bedrock

✨ A developer who wants to understand RAG through a real project

✨ A full-stack developer exploring AI-powered applications

✨ Someone interested in AI-assisted software development with Cursor

✨ Anyone familiar with individual AWS, AI, or DevOps technologies who wants to see how they work together

This workshop assumes some familiarity with AWS, web development, generative AI concepts, and Infrastructure as Code. We won't teach every individual technology from scratch.

If concepts such as Amazon Bedrock, embeddings, vector databases, AWS CDK, or containers are completely new to you, my individual Hands-On Introduction courses cover those topics in more detail.

Here, the goal is different:Put the pieces together and build something.

Who this course is for
⭐ Developers interested in building practical generative AI and RAG applications
⭐ AWS developers exploring Amazon Bedrock and Knowledge Bases
⭐ Full-stack developers who want to add AI capabilities to applications
⭐ Cloud and DevOps engineers interested in AI workloads
⭐ Developers experimenting with Cursor and AI coding agents
⭐ Students familiar with individual technologies who want to see how they work together in a complete project

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