Dr. Dipen
@Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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articles
Grep Beats Embeddings for Some AI Coding Tool Tasks: Use Deterministic Search Before You Build RAG
Watch: Is RAG Still Needed? Choosing the Best Approach for LLMs by IBM Technology Short version: A SWE-Bench Verified agent using only grep and find reached the top of the leaderboard without any semantic indexing. A single exact-name query can use far fewer tokens with grep than broad embedding…Nov 3rd 2026
Build an AI Agent in 60 Lines of Python Before Reaching for LangChain
Watch: Build an AI Agent From Scratch in Python - Tutorial for Beginners by Tech With Tim Understanding the agent loop stopped being optional the moment you tried to change one thing in a framework demo and couldn't find where the loop lived. Quickstarts run fine until you need to touch them. A…Nov 2nd 2026
Express for AI Inference in 2026: Build a Low-Latency RAG API with PGVector or FAISS
Latency stopped being optional the moment you put retrieval in front of an LLM. Every millisecond your vector search burns is a millisecond before the first token appears. Express stays lean by design, which leaves the vector store owning most of your retrieval headroom. This guide is a project…Oct 30th 2026
Build a Production AI App: Where Vibe Coding Platforms Break Down
Your prototype stopped being optional the moment a real user typed a real password into it. Before that, it was a demo. Now it's a liability with a login screen, and the habits that got you this far won't carry you further. Vibe coding is a good way to explore. It can accelerate exploration, reduce…Oct 30th 2026
AWQ in Practice: Quantize LLMs for Faster AI Inference Without Killing Output Quality
AWQ cuts 70B model serving costs from $0.757 to $0.253 per million tokens by moving from two GPUs to one. Llama 3.3 70B quantized to INT4 with AWQ drops 2 MMLU points and 3 HumanEval points versus full precision. AWQ INT4 brings a 70B model's VRAM down from 140GB to roughly 40GB, fitting it on a…Oct 30th 2026
Beyond Basic RAG: Self-RAG, CRAG, and RA-DIT Patterns That Actually Improve Retrieval
The short version: Basic RAG follows a rigid sequence: retrieve k passages, insert them into the prompt, generate an answer. The weakness often sits in that fixed sequence, not in the retriever. The RAG survey describes the shift from basic retrieval to modular and adaptive designs. Self-RAG offers…Oct 26th 2026
Find vs Grep for AI Coding Workflows: Why Grep Still Beats Plain Search for Agent Debugging
Watch: Debug Retrieval (Grep, Web Search) Before You Debug the LLM by Isaac Flath Ask grep to find getUserName across a large codebase and it scans every line sequentially. Ask ripgrep to do the same job and it often finishes faster—parallelized work, automatic .gitignore respect. That gap stops…Oct 25th 2026
Dotenv TypeScript for AI Agents: Typed Env Validation for OpenAI Keys, MCP, and Safer Deploys
Raw environment variables are strings. When you load process.env.OPENAI_API_KEY in a TypeScript project, you get a string — or you get undefined if someone forgot to set it. Your agent boots, runs for thirty seconds, then crashes when it tries to call the OpenAI API with a missing key. No warning…Oct 25th 2026
Startup Credits for AI Builders: Spend Free Cloud Budget on RAG, Evals, and n8n Workflows
Startup credit programs can cover much of the stack AI builders use: Google Cloud for compute and Gemini calls, Anthropic's Claude API for inference, n8n workflow automation credits, Runware for image generation, and Pinecone's free starter tier for vector storage. Program details vary depending on…Oct 25th 2026
Stable-Baselines3 vs QLoRA: When to Train an Agent or Fine-Tune an LLM
Before we get into it, the short version: Stable Baselines3 trains reinforcement learning agents through trial and error in simulated environments. QLoRA fine-tunes large language models using labeled input-output pairs to reshape text generation behavior. The two address fundamentally different…Oct 23rd 2026
When nangcuctv needs custom LLMs: Fine‑tune with MindsDB v26.0.0
Relying on a general-purpose LLM for lesson generation or quiz creation means you are running the same model as every other education platform: same capabilities, same generic voice, same cost per token. Generic large language models handle broad knowledge tasks well, but they do not understand…Oct 23rd 2026
6 Fine-Tuning LLM Frameworks Ranked by Speed, Memory, and GPU Needs
Six training frameworks are covered here: Axolotl, DeepSpeed, Megatron-LM, PyTorch Lightning Fabric, Torchtune, and Unsloth. Available benchmarks don't line up across them. Speed comparisons come from reported throughput tests; memory figures reflect documented VRAM ranges for LoRA, QLoRA, and full…Oct 23rd 2026
Skip ML Theory: Ship AI Apps with One API Call and a Rollback Script
When you run an AI agent behind a production API, every millisecond you add compounds. Users don't care whether your agent consulted three models or made six tool calls. They want the result. Exposing that complexity at the API boundary leaves you with brittle clients and debugging sessions at 2…Oct 23rd 2026
Fine-Tuning Llama 3 on a Single GPU: Memory vs Accuracy Tradeoffs
The short version: You hit the same wall every time: a fine-tuning job that should work crashes with an out-of-memory error, or accuracy drops because the only config that fits uses aggressive memory cuts. The memory-vs-accuracy question determines whether your single-GPU workflow succeeds, or…Oct 20th 2026
LookMovie2 subtitles stopped being manual: Use Cursor v0 AI coding tool
This is not a one-click script. You'll prompt Cursor v0 to generate the UI state and type changes, then ship through Vercel. The actual work is wiring configuration, state, and QA so subtitles behave like manual controls again. Treat the integration as the task, not the generated code. For a…Oct 20th 2026
Fine-Tuning LLM Checklist: 6 Data Failures to Catch Before Your Training Run
The short version: Your SFT run is only as good as the rows you feed it. Most fine-tuning tutorials rush you to the GPU. We'd rather you spend that first stretch on the data, because that's where projects quietly fail. The right dataset is not always the largest one. Review examples for coverage,…Oct 20th 2026
Best File Path Rules for Cursor and Vercel v0: Cleaner Context for AI Coding Workflows
Every file you feed an AI agent changes what it sees and what it ignores. Long chat sessions in tools like Cursor accumulate noise from irrelevant imports, generated assets, and stale code. That noise dilutes focus. A project that hands the agent thousands of tokens of node_modules dependencies and…Oct 20th 2026
5 Open-Source Stacks for Building AI Applications: LlamaIndex+Qdrant to DSPy+SQLite, Debug by Failure Mode
Choosing an open-source stack stopped being optional the moment a local Qdrant + LlamaIndex setup stalled on first retrieval. When you're building AI applications, every cold-start second compounds across users. Two failure modes keep showing up. Poor chunking splits context so no single chunk can…Oct 19th 2026
Building AI applications: 5 moves from demo to deploy before wrong answers reach customers
Progressive rollout matters more than demo performance because AI systems fail quietly. You push a prompt change, tests pass, and a week later users report degraded answers with no exception thrown. Some research and analyst forecasts suggest many GenAI pilots and agentic AI projects stall before…Oct 19th 2026
Fine-Tuning LLMs: When to Use SFT, RLHF, or DPO and How Quantization Affects Memory
Before we get into it, the short version: Two tables below show memory, quality, and hardware trade-offs across fine-tuning methods. First table covers general characteristics. Summary blocks after it explain when you'd pick each approach. Memory summary: Full fine-tuning updates every weight, so…Oct 19th 2026
courses
AI Accelerator
Land an AI engineering role in as little as 90 days, without going back to school, grinding through YouTube tutorials, or needing any prior AI experience. We build your personalized roadmap, help you build a production-grade portfolio, apply for jobs on your behalf, and prep you for interviews, all the way through to a signed offer. If you don't land a role within 6 months of us starting to apply on your behalf, you get 100% of your tuition back.Jul 11th 2025
AI bootcamp 2
This advanced AI Bootcamp teaches you to design, debug, and optimize full-stack AI systems that adapt over time. You will master byte-level models, advanced decoding, and RAG architectures that integrate text, images, tables, and structured data. You will learn multi-vector indexing, late interaction, and reinforcement learning techniques like DPO, PPO, and verifier-guided feedback. Through 50+ hands-on labs using Hugging Face, DSPy, LangChain, and OpenPipe, you will graduate able to architect, deploy, and evolve enterprise-grade AI pipelines with precision and scalability.Aug 12th 2025
books
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