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.

[Grace2077]

SRZ’s free-first approach makes sense here, because working through one small notebook end to end teaches more than collecting a full course set. I would start with the RAG part before agents, since retrieval quality decides whether an agent answer is useful at all. A practical test is to build a tiny question answering setup over your own notes and check where it retrieves the wrong passage. If that works, moving to agents feels natural, because you already understand what context the agent is given. That order also makes it easier to judge later whether any paid course adds anything beyond the free material.