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Simplifying LLMs, Al Agents, RAGs and ML for you! | Writes to 2.5k+ | Sr. Data Scientist | A Decade of Experience | Creator of AwesomeNeuron

456 following2k followers

The Creator

Rohit Kumar Tiwari is a senior data scientist and passionate educator who simplifies complex AI concepts like LLMs, AI Agents, and RAGs for his audience. With over a decade of experience, he crafts comprehensive, hands-on guides and projects that empower followers to build and understand advanced AI systems. Rohit’s profile is a treasure trove for anyone eager to dive deep into AI innovation with clear, practical insights.

Impressions
28.4k-4.2k
$5.32
Likes
128-5
39%
Retweets
35-2
11%
Replies
581
18%
Bookmarks
106-6
32%

Top users who interacted with Rohit Kumar Tiwari over the last 14 days

@architjn

You do important. AI does boring stuff. Built automation systems generating $50K+ for clients Founder leverage, not founder burnout Free tools ↓

1 interactions

Rohit, you post so many intricate AI manuals, even your followers need an AI agent just to keep up with your content—maybe build one of those multi-agent systems to manage your tweet output before you tweet yourself into an existential AI loop!

Rohit’s biggest win is creating and sharing one of the most comprehensive hands-on resources on building AI Agents, with 400+ pages of design patterns and real code that thousands have found invaluable—solidifying his reputation as a go-to AI educator.

To democratize advanced AI knowledge by creating accessible, practical, and well-structured educational resources that empower individuals to harness the power of AI agents and language models effectively.

Rohit believes in breaking down complex technology barriers through clarity, thoroughness, and hands-on learning. He values sharing knowledge openly and fostering an engaged community that explores AI’s potential responsibly and creatively.

Rohit’s greatest strength is his ability to distill highly technical AI concepts into actionable guides, supported by detailed examples and projects, making the dense accessible and exciting. His consistency in content creation and deep expertise shine through every post.

Sometimes Rohit’s content can get so detailed and dense, it risks overwhelming casual followers who crave quick tips rather than marathon deep-dives. This occasionally limits broader appeal or engagement outside hardcore AI enthusiasts.

To grow his audience on X, Rohit should introduce more bite-sized, teaser content that highlights key AI insights or quick tips, paired with his detailed PDFs as deeper dive options. Engaging with trending AI topics and collaborating with popular AI influencers can also expand reach.

Fun fact: Rohit offers massive, free PDFs ranging from 100 to 400+ pages packed with step-by-step instructions and real-world AI projects, offering followers a goldmine of AI agent design patterns and fine-tuning techniques!

Top tweets of Rohit Kumar Tiwari

Want to ace your next LLM interview? Find Top 50 LLM Interview Questions Covers the topics you can’t skip 1. Core LLM Concepts ↳ Tokenization and embeddings ↳ Attention mechanism and multi-head attention ↳ Context window and positional encodings ↳ Autoregressive vs. masked models 2. Training & Fine-tuning ↳ LoRA vs. QLoRA ↳ Masked language modeling and next sentence prediction ↳ Avoiding catastrophic forgetting ↳ Model distillation and PEFT ↳ Handling out-of-vocabulary words 3. Text Generation Techniques ↳ Beam search vs. greedy decoding ↳ Temperature, top-k, and top-p sampling ↳ Chain-of-Thought prompting ↳ Zero-shot and few-shot learning 4. Optimization & Math Foundations ↳ Gradients, Jacobian matrix, and chain rule ↳ Cross-entropy loss, KL divergence ↳ Eigenvalues, eigenvectors, and dimensionality reduction ↳ ReLU derivative and vanishing gradients 5. Architectures & Extensions ↳ Seq2Seq vs. transformers ↳ Encoders vs. decoders ↳ Mixture of Experts (MoE) ↳ Retrieval-Augmented Generation (RAG) ↳ Knowledge graph integration 6. Applications & Comparisons ↳ Generative vs. discriminative models ↳ LLMs vs. traditional statistical language models ↳ GPT-4 vs. GPT-3 differences ↳ Foundation models (language, vision, multimodal) 7. Practical Considerations ↳ Hyperparameters and their role ↳ Addressing bias and incorrect outputs ↳ Deployment challenges like compute, interpretability, and privacy Comment "LLM" and I'll DM you the PDF. ♻ Repost if you found this useful.

33k

Most engaged tweets of Rohit Kumar Tiwari

Want to ace your next LLM interview? Find Top 50 LLM Interview Questions Covers the topics you can’t skip 1. Core LLM Concepts ↳ Tokenization and embeddings ↳ Attention mechanism and multi-head attention ↳ Context window and positional encodings ↳ Autoregressive vs. masked models 2. Training & Fine-tuning ↳ LoRA vs. QLoRA ↳ Masked language modeling and next sentence prediction ↳ Avoiding catastrophic forgetting ↳ Model distillation and PEFT ↳ Handling out-of-vocabulary words 3. Text Generation Techniques ↳ Beam search vs. greedy decoding ↳ Temperature, top-k, and top-p sampling ↳ Chain-of-Thought prompting ↳ Zero-shot and few-shot learning 4. Optimization & Math Foundations ↳ Gradients, Jacobian matrix, and chain rule ↳ Cross-entropy loss, KL divergence ↳ Eigenvalues, eigenvectors, and dimensionality reduction ↳ ReLU derivative and vanishing gradients 5. Architectures & Extensions ↳ Seq2Seq vs. transformers ↳ Encoders vs. decoders ↳ Mixture of Experts (MoE) ↳ Retrieval-Augmented Generation (RAG) ↳ Knowledge graph integration 6. Applications & Comparisons ↳ Generative vs. discriminative models ↳ LLMs vs. traditional statistical language models ↳ GPT-4 vs. GPT-3 differences ↳ Foundation models (language, vision, multimodal) 7. Practical Considerations ↳ Hyperparameters and their role ↳ Addressing bias and incorrect outputs ↳ Deployment challenges like compute, interpretability, and privacy Comment "LLM" and I'll DM you the PDF. ♻ Repost if you found this useful.

33k

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