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
python-dotenv with LiteLLM for Multi-Model AI Apps
If you're running multi-model AI in production, you've hit this problem: dozens of provider keys scattered across your codebase, rotation means grep-and-pray, and swapping models requires a pull request. Python-dotenv with LiteLLM fixes this by treating credentials as infrastructure config, not…Aug 12th 2026
Cursor vs v0 vs Lovable vs Augment Code for AI App Builds
Watch: I Ranked Every AI App Builder for 2026: Lovable vs. Bolt vs. Replit vs. Cursor (No Code) by Build Great Products Cursor differs fundamentally from the other tools in this comparison. It's not an app builder. Lovable, v0, and Bolt generate complete applications from prompts. Cursor is an…Aug 12th 2026
Hermes Agent vs Grok bot vs Openclaw
OpenClaw is designed to run locally—on machines ranging from Mac Minis to Raspberry Pis. It can connect to messaging platforms like WhatsApp, Telegram, Slack, Discord, and iMessage. The system is built to remember context from prior conversations, reducing the need to re-explain project setup.…Aug 12th 2026
Understanding the Three Core Layers of RAG Systems
Every production RAG system rests on three engineering layers: prompt, context, and loop. Get these three right and you understand why any RAG system works or breaks. The catch is that most tutorials show you the toy version, that tidy Query to Vector DB to LLM to Response diagram that hides where…Aug 10th 2026
What Is AI Inference in LLM Apps
Watch: AI Inference: The Secret to AI's Superpowers by IBM Technology Inference is where a trained model finally earns its keep. It's the moment your app sends a prompt and gets tokens back. Moving from a working notebook to a production service means your attention shifts from model accuracy to…Aug 10th 2026
What Is AI Application and How Developers Use It
Developers waste months building AI features that solve the wrong problem. The failure isn't technical. It's treating AI as the solution before you've understood what you're solving. Before you write a line of code, answer this: does this problem genuinely benefit from machine learning, or would a…Aug 10th 2026
What Is AI Applications and Common Examples for Developers
AI applications are software programs that use machine learning, natural language processing, and computer vision to handle tasks that once needed human judgment. They run in healthcare, finance, retail, and manufacturing right now. If you're a developer aiming at a lead role, the fastest path is a…Aug 7th 2026
How to Build AI Applications with RAG and Tool Use
Building AI applications with Retrieval‑Augmented Generation (RAG) follows a recognizable workflow. The table below summarises the typical questions of “what, how long, and how hard” for each major phase, along with tools that frequently appear in tutorials and open‑source projects. What does the…Aug 7th 2026
Spotify Developer Dashboard: How to Create and Configure Your App
The first step in connecting to Spotify's API is creating an app. That gives you API credentials. The process takes about five minutes, but the limits you hit later matter more than speed. You'll need a Spotify account to access the developer dashboard. Any account type works. Some tutorials…Aug 7th 2026
How Much Does Fine‑Tuning an LLM Really Cost?
Estimating the budget for adapting a large language model hinges on three factors: model size, training technique, and infrastructure pricing. Small models can be tuned with modest resources, while larger architectures demand a more detailed cost plan. Newline’s AI Bootcamp teaches students how to…Aug 4th 2026
Types of AI Agents and How They Work
Four core types of AI agents keep showing up in real projects: sequential, reactive, planning, and memory‑augmented. Newline's AI bootcamp maps each type to a project‑focused module, so you can pick the path that matches where you are right now. For routine, linear tasks like generating a static…Aug 4th 2026
Mastering Advanced RAG for Better AI Retrieval
Advanced RAG techniques turn prototype chatbots into production-grade retrieval systems. A basic RAG pipeline ingests documents, indexes them, retrieves top-k chunks, and generates answers. It works for low-stakes Q&A. It collapses under real-world pressure. Research shows that basic systems…Aug 4th 2026
What Is an AI Application? Examples, Patterns, and Use Cases
AI applications come in three broad shapes: single-call LLM solutions, workflow-based automations, and agentic systems. Each one balances cost, autonomy, and complexity in its own way. Pick the wrong one and the project stalls. In our experience, the model is rarely the culprit. Most enterprise LLM…Aug 3rd 2026
What Is AI Inference and Why It Matters for Apps
AI inference is the moment a trained model turns data into a decision. That single step powers every smart feature in a modern app. Newline's AI bootcamps include hands-on labs covering the inference setups you'll actually see in production. Here's the shortlist of the five modes the labs walk…Aug 3rd 2026
Building AI Applications with RAG and Tool Use
If you want to learn RAG fast, these five platforms balance ready-made data connectors, decent docs, and reasonable setup effort. *Time estimates assume a small team (2‑3 engineers) following a typical bootcamp curriculum and include basic testing. Group RAG builds into three buckets and ask how…Jul 30th 2026
What Is AWQ in LLM Quantization and How It Works
AWQ stands for activation-aware weight quantization. It scales the most influential weight channels based on offline activation statistics, then quantizes everything else to ultra-low bit widths. By protecting a small fraction of salient weights, it keeps quantization error down without…Jul 30th 2026
Prompt Engineering Techniques for Better LLM Results
*Time estimates are approximate and reflect typical pacing for learners working through the course material. The simple techniques, like zero-shot and role prompting, take minutes to learn. The advanced ones, like self-consistency and RAG, take hours. They also assume you already understand how the…Jul 30th 2026
PNPM Tutorial for Monorepos and AI App Projects
A pnpm setup aimed at AI monorepos can roughly halve setup time and keep model artifacts versioned without the usual mess. The numbers below cover the three things you'll actually measure: install speed, disk footprint, and CI latency. That's enough to decide whether pnpm earns a spot in your…Jul 26th 2026
What Is AWQ in LLM Quantization and How to Use It
AWQ is a post-training quantization technique that packs large language models into 4-bit weight formats while shielding the ~1% of salient weights that actually drive quality. In practice it cuts VRAM roughly in half and buys 1.5–3× faster inference than FP16, which is exactly the kind of resource…Jul 26th 2026
Prompt Engineering Techniques for Better LLM Outputs
Zero-shot, few-shot, and chain-of-thought give strong baseline results. Meta prompting, self-consistency, and role prompting help when the pipeline gets complex. Group the techniques by difficulty and time. That gives you a rough sense of cost before you commit. *Time includes drafting, testing,…Jul 26th 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
Dipen hasn't published any books