1. Krish Naik - Complete Agentic AI Bootcamp With LangGraph and Langchain
   

   Krish Naik - Complete Agentic AI Bootcamp With LangGraph and Langchain

   alexdonwarez replied to S A N's topic in Courses For Free

   Thanks for the great upload

   • 14 hours ago14 hr
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2. Matt Pocock - Software Factories for Real Engineers
   

   Matt Pocock - Software Factories for Real Engineers

   Tush38 replied to S A N's topic in Courses For Free

   😀

   • 14 hours ago14 hr
   • 1 reply
3. Matt Pocock - Software Factories for Real Engineers
   

   Matt Pocock - Software Factories for Real Engineers

   S A N posted a topic in Courses For Free

   Matt Pocock - Software Factories for Real Engineers File Name: Matt Pocock - Software Factories for Real Engineers Content Source: https://www.aihero.dev/cohorts/software-factories-for-real-engineers-xdy1m Genre / Category: Premium courses Password: Click Here to Reveal Password Original Price: $1,295 Language: ENGLISH ABOUT THE COURSE: This course will teach you how to create your own AI-powered software development factory, where AI agents perform engineering tasks, write and verify code, fix bugs, and prepare changes for review. The program's primary focus is not on the haphazard use of AI tools, but on building a managed development system with architectural rules, automated checks, documentation, and quality control. Over two weeks, you'll learn a practical approach to using AI agents in real-world development: from setting tasks and conveying context to automated testing, reviews, preparing pull requests, and improving an existing codebase. The course helps transition from a model in which the developer constantly monitors the AI and corrects its errors to an engineering model in which agents operate within a clear process, adhere to project standards, and produce verifiable results. What is an AI software development factory?An AI software development factory is an organized system in which AI agents receive tasks, use project documentation and context, make changes to code, run checks, fix identified issues, and prepare the result for review by an engineer. Unlike the typical use of a chatbot or terminal agent, Software Factory is built as a repeatable process. It pre-defined agent roles, codebase rules, quality criteria, constraints, automated checks, and human oversight points. This approach is especially useful for teams and engineers who want to use artificial intelligence not only for rapid code generation, but also for sustainable product development, refactoring, legacy code management, and improving development quality. Why the Standard Use of AI Agents Isn't EnoughModern AI agents are capable of quickly implementing features, writing tests, fixing bugs, and suggesting architectural solutions. However, without an engineering system around them, they often create new problems: technical debt, inconsistent quality, unnecessary abstractions, and errors that must be found manually. The main reason is that AI operates within a limited context. The agent may not consider the project's long-term architecture, business constraints, existing dependencies, team agreements, and hidden quality requirements. Common problems when working with AI without a systemConstant manual review. The developer spends a lot of time reviewing, correcting, and rewriting AI-generated code. The need for continuous monitoring. You have to monitor multiple agents, switch between tools, and constantly update the context. Cognitive overload. Managing agents becomes a complex task in itself, requiring almost as much attention as developing them independently. Inconsistent code quality. Rapid code generation can lead to duplication, poor architectural decisions, violations of standards, and increased technical debt. Inefficient spending. Repeated requests, failed attempts, and constant rework increase the cost of time, budget, and tokens. The course addresses these issues as standard engineering tasks. They can be addressed through process architecture, automation, testing, documentation, coding standards, and properly organized controls. What you will learn in the courseThe program is built around the practical design of the AI Software Factory—a system that helps delegate real-world development tasks to agents and produce results suitable for further use in the project. 1. Designing your own Software FactoryYou will understand the components of a software development factory with AI agents and how to link them into a single process. How to accept and formalize tasks for agents. How to convey project context to agents. How to organize task execution without constant manual support. How to enable automatic quality checks. How to adapt a factory to a team, project, and engineering standards. 2. Knowledge and project context managementThe performance of AI agents directly depends on how well they understand the codebase, subject area, and project constraints. Therefore, a separate section of the course is dedicated to knowledge management. You will learn how to create and maintain documentation that helps agents make more accurate technical decisions. Ubiquitous Language is a unified domain language for teams and agents. Map of the project and its architecture. Architecture Decision Records – recording architectural decisions. Technical specifications for tasks. Formalization of requirements and readiness criteria. Documenting components, dependencies, and constraints. Keeping your codebase knowledge up to date. 3. Developing standards for AI agentsYou'll learn how to set rules for agents to follow when working on a project. This includes not only text instructions but also automated control mechanisms. Documenting development standards. Setting up linters and formatters. Static type checking. Automatic tests. Automated code review. Hooks and restrictions for agent actions. Checking the compliance of changes with the project architecture. The goal of this block is to build a process in which quality is ensured by the system, and does not depend only on the developer's attention to detail. 4. Three levels of quality controlOne of the key principles of the course is to first ensure manageability and quality, and only then scale the speed of agent work. The first level is automatic checksTests, linters, type checkers, and other tools automatically find errors, standard violations, and regressions. The second level is automated reviewAdditional analysis mechanisms, including AI-powered critical review, help identify issues that standard tools cannot detect. Third level - review by an engineerThe developer is involved where human expertise is truly needed: when assessing architectural decisions, complex changes, risks, and compliance with business goals. At the same time, the system must generate clear Pull Requests so that the engineer can quickly understand what was changed, why it was done, and what checks have already been passed. 5. Automation of engineering tasksYou will learn how to organize the work of agents so that they can perform tasks with minimal human intervention. Receive a task with requirements and completion criteria. Study the required part of the project. Plan for change. Write and modify code. Run tests and checks. Correct any errors found. Prepare the result for review. Notify when work is completed or when an engineer's intervention is required. This process allows the developer to delegate clear tasks to agents and return to the prepared result, rather than manually controlling each action. 6. Working with an existing codebaseSoftware Factory is useful not only for creating new features. It can be used to systematically improve existing projects, including complex and legacy codebases. Refactoring complex sections of code. Eliminating technical debt. Improving the architecture. Optimization of components. Expanding test coverage. Finding and fixing errors. Improving the clarity and maintainability of the project. File Information Submitter S A N Submitted 10/28/2026 Category Courses Sale page https://www.aihero.dev/cohorts/software-factories-for-real-engineers-xdy1m View File

   • 14 hours ago14 hr
   • 1 reply
4. Maven - Fine-Tuning Open-Weight LLMs for Engineers
   

   Maven - Fine-Tuning Open-Weight LLMs for Engineers

   S A N posted a topic in Courses For Developers

   Maven - Fine-Tuning Open-Weight LLMs for Engineers File Name: Maven - Fine-Tuning Open-Weight LLMs for Engineers Content Source: https://maven.com/dvgodoy/finetuning Genre / Category: Courses for Developers Language: ENGLISH Original Price: $597 What you'll learn: This practical course on fine-tuning LLM with open weights will help engineers master the full cycle of fine-tuning large language models: from task selection and dataset preparation to LoRA/QLoRA tuning , quality assessment, and application of the results in real-world projects. About the courseMost materials on fine-tuning large language models are either overly theoretical or designed for large teams, large datasets, and expensive computing infrastructure. In practice, a more accessible and reproducible approach suffices for many engineering problems. This course focuses on efficient retraining of open-weight LLMs using modern parameter-efficient learning methods: LoRA and QLoRA . You'll learn how to select a suitable task, prepare a specialized dataset, train an adapter on available hardware, and objectively evaluate the results. The main goal of the course is to understand when fine-tuning is truly justified: it helps improve response quality, reduce costs, decrease latency, achieve a stable output format, or reduce dependence on commercial APIs. The training takes the form of a practical workshop with a small group of participants. This allows participants to discuss their own projects, receive feedback from the instructor, and explore real-world limitations related to data, models, and computing resources. What you will learnSelect tasks for further LLM trainingDetermine when to use fine-tuning and when Prompt Engineering, RAG, or input data enhancement is sufficient. Evaluate tasks based on repeatability, result requirements, data availability, verification complexity, and implementation cost. Translate broad business problems into specific scenarios suitable for model training. For example, instead of the general task of “answering customer questions,” formulate a more precise task: classifying requests by category, urgency, or other parameters. Use a system of criteria to make an informed decision about the feasibility of fine-tuning LLM. Prepare datasets for fine-tuningCreate specialized data sets for specific tasks. Bring training examples to a unified structure Instruction / Input / Output. Identify ambiguous, contradictory and uninformative examples. Clean, normalize, and structure data before training. Improve the quality of the training sample without having to collect huge amounts of data. Retrain models using LoRA and QLoRALoad base language models with open weights in a Jupyter Notebook environment. Understand the principles of parameter-efficient feasibility training (PEFT). Configure key LoRA and QLoRA parameters: Target Modules — target modules of the model. Rank — adapter rank. Learning Rate — learning speed. Batch Size —batch size. Sequence Length — the length of the sequence. Conduct a full training cycle using available computing resources. Save trained adapters for further testing and use. Evaluate the quality of the retrained modelCompare the results of the base and retrained LLM. Create separate test datasets that were not used in training. Assess the accuracy of task completion, adherence to the response format, and the stability of the model's behavior. Analyze typical mistakes and find patterns in unsuccessful results. Determine whether retraining has produced real improvements over the original model. Diagnose problems and improve resultsFind the reasons for unsatisfactory fine-tuning results. Identify issues with data labeling, problem definition, and test sample quality. Understand when to change hyperparameters, choose a different model, or revise the dataset structure. Recognize common mistakes when training on small datasets. Decide on your next steps: continue learning, collect additional samples, use RAG, or abandon fine-tuning. Apply fine-tuning to your own projectsUse a ready-made Jupyter Notebook for data preparation, training, inference, and model evaluation. Adapt the fine-tuning process to your own tasks and data sets. Apply pre-trained models for classification, data mining, and query routing. Configure the generation of structured responses and adaptation of text style. Design reproducible experiments to test different models and approaches. Assess potential resource savings and reduced dependence on proprietary LLMs. What will you get as a result?By the end of the course, you will have practical artifacts that you can use as a basis for your own experiments and work projects. A prepared dataset is a cleaned and structured data set for a specific task. The trained LoRA/QLoRA adapter is the result of practical retraining of a language model with open weights. Comparative evaluation of models - test results before and after fine-tuning. A reusable Jupyter Notebook is a ready-to-use workflow for data preparation, training, inference, and evaluation. The Decision Checklist is a system of criteria that helps determine when retraining is justified and when it is more effective to use prompting, RAG, or improving the initial data. Who is this course for?This course is suitable for developers, ML engineers, AI engineers, data scientists, and technical managers who want to gain practical experience in retraining large language models without the need for expensive infrastructure. It is especially useful for those who already work with LLM via the API but want to move to models with open weights, gain more control over their behavior, and learn how to adapt models to specialized tasks. For developers who implement LLM in products and internal tools. For ML and AI engineers who need a practical fine-tuning pipeline. Data scientists working with classification, data extraction, and structured responses. Technical managers who want to evaluate the feasibility of additional LLM training for business needs. What tasks can be solved after the course?Classification of text messages, documents and messages. Extracting structured information from unstructured text. Routing requests by category, priority, or responsible teams. Generating responses in a given format. Adapting the model's style to the internal requirements of the product or company. Comparing fine-tuning, RAG, and prompt engineering to select the optimal approach. PrerequisitesTo successfully complete the course, it is recommended to have basic Python programming experience and a general understanding of the LLM. In-depth research background in machine learning is not required: the emphasis is on practical processes, engineering solutions, and reproducible experiments. Final learning outcomeAfter the course, you will be able to independently conduct a full cycle of fine-tuning LLM: select a suitable task, prepare a dataset, train a LoRA/QLoRA adapter, compare the quality of the baseline and fine-tuned models, and make an informed decision about applying the results to a real project. File Information Submitter S A N Submitted 10/27/2026 Category Paid Coding Courses Sale page https://maven.com/dvgodoy/finetuning View File

   • Yesterday at 03:27 AM1 day
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5. Agentic Engineering Course
   

   Agentic Engineering Course

   S A N posted a topic in Courses For Developers

   Agentic Engineering Course File Name: Agentic Engineering Course Content Source: https://jsmastery.com/course/agentic-engineering-course Genre / Category: Courses for Developers Language: ENGLISH Original Price: ₹15,981 What you'll learn: using artificial intelligence. You'll learn how to use AI not just for code generation, but as a full-fledged engineering tool for creating high-quality, scalable, and maintainable applications. Instead of chaotic vibe coding and endless prompts, the course offers a systematic approach: context management, architecture planning, quality control, and verification of AI results. What is the course about?The training is based on JSM Skills Workflow , a practical AI development system created through 12 months of experimentation with artificial intelligence tools. The methodology combines the automation of routine processes with engineering control, helping to effectively work on projects of varying complexity. The course demonstrates how to transform AI from a random assistant into a controlled participant in the development process: from task definition and context preparation to implementation, review, and release of finished functionality. What you will learnManage the AI context. Determine what information to pass to the model and what to exclude to obtain accurate results without overloading the context window. Monitor the quality of code generation. Identify errors, inconsistencies, and deviations from the original plan early in development. Correct AI performance. Return the model to the correct solution without having to start the process over again. Plan the application architecture. Create a stable project structure that maintains integrity as new features are added. Work with the JSM Skills Workflow. Use ready-made commands and tools to automate the full development cycle of individual features. Automate development. Transform a brief task description into a ready-made implementation, taking into account the existing codebase and project requirements. Review code using multiple AI models. Organize independent reviews using a second model to improve the quality and reliability of solutions. Make engineering decisions. Understand when AI recommendations can be trusted, when intervention is necessary, and which decisions should not be delegated to the model. Optimize costs. Reduce the costs of developing, maintaining, and scaling applications through a well-designed AI workflow. How JSM Skills Workflow WorksThe development process is organized as a repeatable cycle for each new feature. This approach makes working with artificial intelligence more predictable and helps maintain control over the architecture, code quality, and deadlines. Main stages of workflowProblem statement. You describe the required functionality with a single command, for example /develop build the checkout page. Context preparation. The system analyzes the existing code and project plan, then generates detailed instructions for the AI. Coordination of solutions. You review the proposed approach, make adjustments, and confirm further work. Implementation. AI writes code taking into account the architecture, conventions, and existing project templates. Independent verification. Another AI model analyzes the results and helps identify potential issues. Task completion. Once verified, the functionality is ready for release, and the process can be repeated for the next task. Practical focus of the courseParticular attention is paid to situations where the AI begins to deviate from requirements, makes unfounded architectural decisions, or creates code that works now but causes problems as the project progresses. Using practical examples, the author demonstrates the differences between standard vibe coding and system-level AI development. He also compares the costs of creating, operating, and scaling the same application using different approaches to AI. Who is this course suitable for?This course is designed for developers who already know how to program and use artificial intelligence in their daily work, but want to make this process more predictable, efficient, and professional. For frontend, backend, and full-stack developers who want to integrate AI into their workflow. For engineers tired of chaotic vibe coding and unstable code generation results. For developers who want to better manage the architecture and quality of projects created with AI. Team leads and technical specialists who need to understand how to safely use AI in team development. Knowledge of TypeScript or experience as a Senior Developer is not required, but basic programming skills are a must. What will you get as a result?After completing this course, you'll be able to not only generate working code from a neural network, but also manage the entire engineering development process with AI: from problem setting and architecture selection to quality assurance and final product release. The main goal of this course is to teach you how to use agent engineering as a professional approach to software development, where artificial intelligence accelerates work, but key engineering decisions remain under your control. File Information Submitter S A N Submitted 10/27/2026 Category Paid Coding Courses Sale page https://jsmastery.com/course/agentic-engineering-course View File

   • Yesterday at 03:07 AM1 day
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