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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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• date: 30 September 2026
• posted by: mata00

Tiana Asperjan - Advanced Ad Copy Secrets

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Salvora A.I Avatars Amazon Affiliate Course

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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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