Open-Source AI Agents & Tools
Real-world AI systems built and maintained by the TechStudio community.
AI LinkedIn Jobs Agent
An autonomous, end-to-end AI agent built to eliminate repetitive job hunting tasks. By leveraging
high-speed Groq LLM inference (llama-3.3-70b-versatile
& llama-3.1-8b-instant),
the agent automatically parses resumes, evaluates candidate-job fit, customizes tailored CVs and cover
letters without hallucination, compiles print-ready PDFs/Word documents, and logs applications live to a
tracking Google Sheet.
Match Scoring & Acceptance
Evaluates job listings against master CVs, scores match compatibility (1-10), estimates acceptance chances, and generates 3-5 tailored interview prep topics.
Factual Resume & Cover Letter
Tailors bullet points, summaries, and cover letters strictly using candidate facts without fabricating experience or introducing hallucinations.
Multi-Format Document Compiler
Automatically renders professional single-page ATS-ready PDFs (via ReportLab) and fully editable Word
.docx files.
Google Sheets Sync
Connects via OAuth 2.0 / Service Account to dynamically append job titles, match scores, prep topics, and status tracking to Google Sheets.
[Master CV PDF + LinkedIn JSON] ➔ [Groq LLM Evaluator & Tailorer] ➔ [ReportLab / docx Compilers] ➔ [Outputs + Google Sheets Sync]
Featured Portfolio Resource
20 Advanced AI Portfolio Projects with GitHub References
Practical project ideas across AI Agents, RAG, MCP, LLMOps, fine-tuning, voice AI, computer vision, and GenAI infrastructure.
Build Projects Recruiters Can Actually Discuss With You
Explore 20 strong project directions including multi-agent systems, autonomous coding, browser agents, MCP servers, Hybrid RAG, GraphRAG, LLM observability, LoRA fine-tuning, voice agents, computer vision, and more.
Featured Portfolio Resource
7 AI Engineering Projects with GitHub References
Practical project ideas across RAG, Text-to-SQL, fine-tuning, LLM infrastructure, evaluation, and AI agents.
Build AI Projects Recruiters Can Actually Discuss With You
Explore 7 practical AI engineering projects covering Hybrid RAG, Text-to-SQL guardrails, LoRA fine-tuning, LLM gateways, prompt A/B testing, automated RAG evaluation, and agent orchestration — with GitHub repositories that demonstrate the core capabilities.
Featured Open-Source Learning Resource
8 GitHub Repos to Learn AI, RAG & Agents
A practical collection of beginner-friendly and production-oriented repositories for learning AI agents, RAG, LLMs, PyTorch, machine learning, and ML engineering through hands-on code.
8 GitHub Repos to Learn AI, RAG & Agents
Learn by reading real code and building alongside curated repositories covering AI agents, production agent systems, RAG techniques, LLMs, PyTorch, ML engineering, and beginner-friendly machine learning.
Featured AI Engineering Roadmap
8-Week AI Engineer Roadmap
A practical eight-week path from LLM fundamentals to RAG, APIs, vector databases, agents, MCP, evaluation, deployment, portfolio building, and interview preparation.
8-Week AI Engineer Roadmap: Learn → Build → Deploy → Apply
Go from understanding how LLMs work to building RAG pipelines and AI agents, connecting APIs and vector databases, working with MCP and Claude Code, evaluating AI systems, deploying with Docker and FastAPI, and preparing a portfolio for interviews.
Featured Intensive Roadmap
7-Day AI Engineering Roadmap
An intensive hands-on path from LLM fundamentals and API integration to structured outputs, embeddings, RAG, AI agents, FastAPI, Docker, and an end-to-end AI Assistant capstone.
7-Day AI Engineering Roadmap: From Beginner to AI Assistant
Build every day: call an LLM API, master prompting and JSON outputs, create a vector search system, build RAG from scratch, implement a ReAct-style agent, expose the workflow through FastAPI and Docker, then ship a complete Personal Knowledge Assistant.
Featured Learning Resource
Learn RAG from These Resources
A practical RAG learning path with three hand-picked YouTube resources, official documentation, concepts to master, project ideas, and interview preparation.
Learn Retrieval-Augmented Generation from Basics to Production
Start with RAG fundamentals, understand chunking, embeddings, vector databases and retrieval, then move into evaluation, hybrid search, reranking, advanced RAG patterns and a portfolio-ready project.
Featured RAG Project Resource
3 RAG Projects to Build: Hybrid, Multimodal & Agentic RAG
Three portfolio-ready Retrieval-Augmented Generation projects covering hybrid search, citation verification, multimodal document processing, dynamic retrieval, query reformulation, and self-correction.
Build These 3 RAG Projects for a Strong AI Engineering Portfolio
Build a Hybrid Search RAG system with citation verification, a Multimodal Document RAG pipeline for PDFs and scanned content, and an Agentic RAG system that can reformulate queries, retry retrieval, and self-correct before answering.
Featured Career Resource
5 Generative AI Certifications & Courses to Build Your AI Career
A practical guide covering Microsoft + LinkedIn, Google AI Essentials, IBM Generative AI Engineering, AWS + DeepLearning.AI, and Claude Certified Architect — with preparation tips and official enrollment resources.
Which Generative AI Certification Should You Choose?
Compare five GenAI learning credentials, understand what each one actually validates, follow a preparation plan, find official course/certification resources, and learn how to turn the credential into real portfolio evidence.
Featured Learning Resource
9 AI Courses to Build Your Skills
A curated path from AI fundamentals to GenAI, prompt engineering, Python-based AI and deeper university-level foundations — with official links and practical study guidance.
Learn AI with These Free & Free-to-Learn Courses
Explore AI for Everyone, Google GenAI, prompt engineering, Google AI Essentials, IBM AI Foundations, Generative AI for Everyone, Harvard CS50 AI, MIT OpenCourseWare and Microsoft + LinkedIn — with a step-by-step learning order, official links and project guidance.
Featured Career Resource
Free ATS-Friendly Resume Templates
Download free resume templates and learn how to structure, format, tailor, and test a resume so important information remains easy for ATS software and recruiters to read.
Free ATS-Friendly Resume Templates
Get free resume templates from TechStudio and a complete guide covering ATS-safe structure, standard headings, formatting, keywords, project and experience bullets, file formats, tailoring, and a final pre-submission checklist. The full page also provides the main Google Drive folder for accessing the templates.
Featured AI Engineering Resource
03 AI Projects You Can Complete in One Day
Three focused, portfolio-friendly AI projects designed around a single-day build cycle. Pick one, follow the implementation plan, and finish with a working demo, README, and clear project story.
03 AI Projects You Can Complete in One Day
Build a RAG document assistant, an AI resume reviewer, or an AI research agent. The full guide includes architecture, recommended stack, step-by-step implementation, an 8-hour execution plan, portfolio README structure, testing ideas, common mistakes, and ways to extend each project after the first day.
Dedicated Interactive Roadmaps
Select a roadmap below for full stage-by-stage learning pathways and recommended portfolio projects.
🤖 AI Engineer Roadmap
PyTorch, Self-Attention, Transformers, RAG systems, Vector DBs, Pinecone, LangChain, and vLLM model serving. Includes 3 portfolio projects.
🧠 Machine Learning Roadmap
Math, XGBoost, Scikit-Learn pipelines, Feast Feature Store, MLflow experiment tracking, DVC, and model drift monitoring.
📊 Data Science Roadmap
Advanced SQL, Probability, Hypothesis Testing, A/B Testing, Exploratory Data Analysis, Tableau, PowerBI, and Predictive Modeling.
⚡ Data Engineering Roadmap
Dimensional Star Schema, Snowflake, Google BigQuery, Apache Spark, Airflow orchestration, dbt transformations, and Kafka streaming.
💻 Full-Stack Developer Roadmap
TypeScript, React 19, Next.js 15, Node.js, Python FastAPI, PostgreSQL, Prisma ORM, Redis, Docker, and AWS Cloud Deployment.
Developer Cheat Sheets & Reference Cards
Quick syntax guides, query references, and architectural cheatsheets.
SQL Join & Window Functions
Complete syntax guide for Inner/Left Joins, RANK(), DENSE_RANK(), and CTE expressions.
Python Data Analysis Sheet
Essential functions for Pandas DataFrames, NumPy arrays, missing value imputation, and Seaborn plots.
System Design Patterns
Quick reference for Load Balancers, Caching layers, Sharding, CAP Theorem, and Message Queues.
Git & Rebase Workflow
Mastering interactive rebase, cherry-pick, conflict resolution, and branch strategies.
Original TechStudio Guides
Practical AI Engineering & Career Guides
Original, long-form guides that explain concepts, implementation choices, trade-offs, and practical next steps.
RAG Chunking Strategies: A Practical Guide
Fixed, recursive, semantic, and structure-aware chunking with an evaluation workflow.
Read guide →AI Engineering GuideHybrid Search RAG: BM25 + Vector Search + Reranking
How lexical retrieval, semantic retrieval, and reranking work together in production RAG.
Read guide →AI Engineering GuideProduction AI Agent Architecture
A practical architecture for tool use, state, permissions, retries, and observability.
Read guide →AI Engineering GuideMCP Explained for AI Engineers
Understand the Model Context Protocol, servers, tools, resources, and practical integration patterns.
Read guide →AI Engineering GuideLLM Evaluation: A Practical Framework
How to build evaluation datasets, retrieval metrics, answer-quality checks, and regression tests.
Read guide →AI Engineering GuideVector Databases: Concepts and Production Decisions
Embeddings, indexing, metadata filtering, similarity search, and operational trade-offs.
Read guide →AI Engineering GuideHow to Design a Production AI Project
A repeatable approach for turning an AI prototype into a maintainable application.
Read guide →AI Engineering GuideAI Engineer Roadmap: From Fundamentals to Production
A practical learning sequence from Python and ML fundamentals to RAG, agents, evaluation, and deployment.
Read guide →AI Engineering GuideAI Engineer Resume Guide
How to present AI projects, technical skills, impact, and evidence clearly for technical roles.
Read guide →New Deep-Dive Guides
More Original AI Engineering & Career Guides
Practical, original guides covering AI systems, security, backend APIs, evaluation, portfolios, and career preparation.
Hybrid Search RAG: BM25 + Vector Search + Reranking
A practical guide to combining lexical and semantic retrieval, reranking candidates, measuring retrieval quality, and designing a reliable search layer.
Read guide →AI EngineeringLLM Evaluation: A Practical Framework for AI Applications
How to evaluate AI systems beyond a single “accuracy” number using task metrics, groundedness, retrieval quality, latency, cost, and human review.
Read guide →AI AgentsProduction AI Agents: Architecture, Tools, Memory and Guardrails
A practical architecture for agents that call tools, maintain state, recover from failures, and operate within explicit safety and cost boundaries.
Read guide →MCPMCP for AI Engineers: Concepts, Servers, Tools and Design Patterns
A practical introduction to Model Context Protocol and how to design reliable tool interfaces for AI applications.
Read guide →RAGVector Databases Explained: Indexes, Metadata and Retrieval Trade-offs
Understand embeddings, approximate nearest-neighbor indexes, metadata filtering, hybrid retrieval, and the trade-offs behind vector database choices.
Read guide →BackendBuilding an AI API with FastAPI: From Prototype to Production
A practical blueprint for serving an LLM or RAG application through a clean API with validation, streaming, error handling, and observability.
Read guide →AI SecuritySecuring RAG Applications: Prompt Injection, Data Access and Leakage
A defensive guide to protecting retrieval-augmented applications against malicious instructions, unauthorized retrieval, and sensitive-data leakage.
Read guide →LLMProduction Prompt Engineering: Templates, Variables, Structured Outputs and Tests
Move prompt engineering from ad-hoc strings to versioned, testable application components with clear contracts and regression checks.
Read guide →CareerHow to Build an AI Engineering Portfolio That Demonstrates Real Skills
A practical framework for choosing, implementing, documenting, and presenting AI projects so that technical reviewers can understand the engineering decisions.
Read guide →CareerAI Engineer Resume Guide: Skills, Projects, Impact and ATS-Friendly Structure
A practical guide to presenting AI engineering experience without turning a resume into a keyword dump.
Read guide →