Need Help to get hands on Vizuara AI Courses

[reaper]

Anyone in the 1Hack community have access to the Vizuara courses?

If you’ve taken or have the course materials, could you please share the resources with me? A Google Drive, Mega, or other legitimate download link would be appreciated.

Thanks!

[reaper]

Here is the link of courses : https://courses.vizuara.ai/

[SRZ]

Vizuara gives a lot away free — and the same build (LLM · RAG · agents · multimodal) is in free hands-on repos, no piracy needed

A pirated course dump is the slowest way to learn this, and you don’t need one. Vizuara itself puts a big chunk on GitHub and YouTube for free, and the exact skills the paid track sells — building LLMs, RAG, agents and multimodal systems from scratch — are covered by dozens of free, hands-on 2026 course repos, many of them deeper than a paid course.

If you grab three:

• DeepSeek From Scratch — Vizuara’s own chapter-by-chapter notebooks + videos, free.
• Claude Certified Architect Prep — a genuinely free full Vizuara course: 6 pods, 22 notebooks, a practice exam.
• Zero to AI — one 950-notebook archive (Python → LLMs → RAG → agents → MLOps) to study offline.

🧩 Vizuara's OWN free stuff — the direct answer to your ask

You can get most of what you’re after straight from the source, no warez mirror:

• VizuaraAILabs on GitHub — the official org; runnable code behind their public projects.
• DeepSeek From Scratch · nano-gpt-oss · truly-open-gpt-oss — build DeepSeek / GPT-OSS-style models from first principles.
• Agents 10-Day Bootcamp — the code + industrial projects from their agents bootcamp.
• Modern Robot Learning · Transformers for Vision (book code) — robotics + vision tracks.
• Computer Vision from Scratch (YouTube) · Vizuara channel · AI Playground — free lectures + interactive practice.

🏗️ Build an LLM from scratch — the skill the course is really selling

• rasbt/LLMs-from-scratch — the cleanest baseline: tokenizer → attention → GPT → pretrain → LoRA. Start here.
• llm-lab — 2026 from-scratch: data, pretraining, post-training, GRPO, FP8, eval, in runnable notebooks.
• Text-LLM-Training-from-scratch — pure PyTorch: SFT, reward modeling, DPO, GRPO, RLVR, no high-level frameworks.
• Modern LLM Notebook — compact: tokenizers, attention, MoE, RLHF, inference, distillation.
• Hands-On Large Language Models — Colab-ready companion notebooks.

🔎 RAG, done properly

• The RAG Cookbook 2026 — 40 chapters: late chunking, agentic workflows, multi-vector, graph indexes, eval.
• RAG & Agentic AI Course — concept → pure Python → framework, so you see what LangChain hides.
• RAG Engineering — 63 progressive notebooks, core retrieval → enterprise context engineering.
• Production Agentic RAG — LangGraph, Docker, FastAPI, Langfuse — an engineered service, not a demo.
• RAG from Scratch — basic → multimodal → agentic RAG, each variant kept separate to study.

🤖 Agents from the inside — before you reach for LangGraph

• Build an AI Agent From Scratch — loops, tools, memory, planning, multi-agent, evals, as a tiny framework.
• Agent Zero to Hero — a Claude-Code-style CLI agent: ReAct, MCP, streaming, prompt caching.
• Agent Memory Techniques — 30 notebooks turning “memory” from hand-waving into real designs.
• Complete Agentic AI Engineering Course — 6 weeks across modern SDKs, LangGraph, CrewAI, MCP, multi-agent.
• GenAI Agents — a grab-bag of agent recipes when you need a specific pattern fast.

🎥 Multimodal, vision & voice

• S3 Multimodal Lab — 12 notebooks: Visual RAG, ColPali, voice agents, video search, Whisper, eval + CI.
• Kubrick — Multimodal Agents Course — an MCP video agent: video, audio, images, tools, tracing, in one build.
• Multimodal LLMs course — recordings + notebooks: transformers, PEFT, VLMs, video, UI agents.
• Hands-On Multimodal System Design — local-first vision/audio/RAG/MoE, no cloud black boxes.
• Document AI: OCR → Agentic Extraction — the messy document side of multimodal.

⚙️ The systems layer + research agents — where courses usually stop short

Serving & inference (why it’s fast/slow/cheap):

• LLM Inference Handbook — KV-cache, memory bandwidth, speculative decoding, deployment tradeoffs.
• Efficient LLM Inference Serving — vLLM, SGLang, PagedAttention, RadixAttention.

Research agents (beyond a chatbot demo):

• deepbrief-lab — a production-minded Deep-Research agent: ReAct, MCP, A2A, LangGraph, Redis, human-in-the-loop.
• DeepResearch Agent — multi-agent long-chain research → cited PDF reports with fact-checking.
• lit-review-council — multi-agent literature review with peer-review ensemble + anti-hallucination guardrails.

📦 One-download curricula — grab, unzip, study offline

• Zero to AI (.zip) — 950+ notebooks; the broadest single archive.
• AI Engineering from Scratch (.zip) — 503-lesson first-principles path (math → agents → production).
• LLM Zoomcamp (.zip) — 10 weeks of practical LLM apps, no GPU needed.
• AI Bootcamp (.zip) — compact applied GenAI: Ollama, LangChain, LangGraph, CrewAI.
• Microsoft GenAI for Beginners — 21 lessons, Python + TypeScript, a steady reference baseline.

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Prefer to pay the creator? Fair — the full paid set is at courses.vizuara.ai / the 10-course bundle (lifetime access, notes, certificates), with a cheaper Pods tier. But work through the free stack above first — you may find you never need it.