feat: add next steps

Author: shreshthtuli

16-next-steps.ipynb

@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 16. What's Next?\n",
+    "\n",
+    "Congratulations on completing **LLMs from Scratch**! You've journeyed from the atomic units of language models—tokens—all the way to alignment techniques and cutting-edge architectures. This notebook recaps what you've learned and points you toward the next frontier: building intelligent agents.\n",
+    "\n",
+    "## Key Takeaways\n",
+    "\n",
+    "1. **Tokenization matters**: The compression ratio and vocabulary design directly impact model efficiency and capability.\n",
+    "\n",
+    "2. **Architecture evolution**: From vanilla attention to MLA + MoE, each advancement addresses specific bottlenecks (memory, compute, expressivity).\n",
+    "\n",
+    "3. **Data quality > quantity**: Well-curated data often outperforms larger but noisier datasets.\n",
+    "\n",
+    "4. **Scaling is predictable**: Chinchilla laws let you budget compute optimally before training.\n",
+    "\n",
+    "5. **Alignment is essential**: SFT + RLHF transforms a text predictor into a helpful assistant.\n",
+    "\n",
+    "6. **Efficiency enables deployment**: Quantization, pruning, and PEFT make large models practical.\n",
+    "\n",
+    "\n",
+    "## What's Next? From LLMs to Agents\n",
+    "\n",
+    "You now understand how to build, train, and optimize LLMs. But the real power emerges when you turn these models into **autonomous agents** that can:\n",
+    "\n",
+    "- **Use tools**: Call APIs, execute code, query databases\n",
+    "- **Plan and reason**: Break complex tasks into steps and self-correct\n",
+    "- **Maintain memory**: Remember context across long interactions\n",
+    "- **Collaborate**: Work with other agents in multi-agent systems\n",
+    "\n",
+    "### Ready to Build Your Own Super Agents?\n",
+    "\n",
+    "If you want to dive deeper into building production-ready AI agents that leverage the LLM foundations you've learned here, check out my next course:\n",
+    "\n",
+    "## [Build Your Own Super Agents](https://stuli.ai/build-your-own-super-agents/README.html)\n",
+    "\n",
+    "In this course, you'll learn:\n",
+    "\n",
+    "- **Agent architectures**: ReAct, Plan-and-Execute, and custom reasoning loops\n",
+    "- **Tool use and function calling**: Integrating external capabilities into your agents\n",
+    "- **Memory systems**: Short-term, long-term, and retrieval-augmented memory\n",
+    "- **Multi-agent orchestration**: Building teams of specialized agents\n",
+    "- **Production deployment**: Scaling, monitoring, and safety guardrails\n",
+    "\n",
+    "## Thank You!\n",
+    "\n",
+    "Thank you for taking this journey through the internals of large language models. The best way to learn LLMs is to build one—and you've done exactly that.\n",
+    "\n",
+    "Now go build something amazing!!\n",
+    "\n",
+    "**— Shreshth Tuli**\n",
+    "\n",
+    "[Website](https://stuli.ai/) | [LinkedIn](https://www.linkedin.com/in/shreshth-tuli/) | [GitHub](https://github.com/shreshthtuli)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}