Enrico Pesce’s Post

#Day29 --I have recently worked on a Text Summarization project using NLTK, where I developed a system to extract key information from large text and generate concise summaries. Through this project, I gained hands-on experience in Python, text preprocessing, tokenization, stopword removal, and frequency-based summarization techniques. I enjoy solving real-world problems using AI and continuously learning new technologies. #Github -https://lnkd.in/gGzs2WDF

Most AI learners go Python-only. I chose both. Here's my reasoning: Python → AI/ML logic, data processing, LLM integrations TypeScript → Building AI-powered web apps, APIs, frontends The combination is powerful if you want to build full-stack AI products — not just notebooks. GIAIC is teaching us to be builders, not just consumers of AI. That means both matter. Are you learning TypeScript? Let's connect. #TypeScript #Python #AIEngineering #FullStackAI #GIAIC #PakistanTech

Constantly hitting LLM context window limits with large codebases? I built a solution. My open-source project, NeuralMind, creates an intelligent knowledge graph of your code, slashing context token count by 40-70x. This means: Dramatically lower LLM API costs. Faster, more accurate AI-assisted development. Quicker understanding of complex repositories. It’s a Python library for any developer looking to get more out of their AI coding assistants. Check out the repository on GitHub to see how it works. https://lnkd.in/gHCv7byg #AI #DeveloperTools #OpenSource #LLM #Python #SoftwareDevelopment #PerformanceOptimization

📣 Bad API docs don't fail loudly. They waste hours quietly. Have a look into our API doc- 📖 https://lnkd.in/dvr6Nyhx You install a framework. The README looks clean. The examples run. Then you try to do something slightly off the happy path and you're reading source code at midnight trying to figure out what the second argument actually does. This is why we treat SynapseKit's documentation as a first-class feature not an afterthought shipped after the code. Every public API is documented. Every parameter explained. Every method shows you what it accepts, what it returns, and what happens when you pass the wrong thing. Architecture docs explain why decisions were made, not just what they are. The cookbook has real patterns, not toy examples. Because the engineers who evaluate a framework in the first 30 minutes are not the same engineers who maintain it six months later. The second group lives in the docs. #Python #LLM #OpenSource #AI #Documentation #DeveloperExperience #SynapseKit

🚀 Built an AI Chatbot using LangChain + Gemini 2.5 Flash + Streamlit I built a simple AI chatbot that answers questions in real time using Google's Gemini API. 🛠 Tech Stack • LangChain • Gemini 2.5 Flash • Streamlit • Prompt Templates This project helped me understand how LLMs can be integrated into real applications using simple Python tools. github:https://lnkd.in/gC53p3DB #GenerativeAI #LangChain #GeminiAI #Python #Streamlit

lemme say this once if you are an ai engineer and you use claude or any other code generating llm have some basic knowledge of llms and how to actually write code. dont get the wrong idea i do too use claude to generate code but, don’t just copy paste blind python code in the name of vibe coding without seeing how the relationship between classes work, how basic python oops work, how to write yaml. make sure you’re understanding isnt just surface level you actually know how to write neutral networks, when to use pytorch and when to use pytorch lighting. You don’t have to write it eventually you will ask some llm to do it to make the process faster n better but human evaluation is very important. this is what makes local projects and prod different it helps trust me.

Python heapq module the hidden gem for priority queues: When you need to manage tasks or data by priority, heapq is your go‑to. It provides a heap‑based priority queue implementation using a min‑heap, which means the smallest element always rises to the top. Why it matters: Efficiently handle large datasets. Perfect for scheduling, simulations, or anytime you need quick access to the lowest value. Built into Python no extra installs needed. Remember: heapq is a min‑heap, not a max‑heap. If you need max‑heap behavior, you can invert values or customize logic. At IT Learning AI, we make sure you don’t just learn syntax you understand how to apply it in real projects. Ready to master Python’s powerful modules? Explore tutorials, guides, and hands‑on coding insights at https://itlearning.ai 🔗 Learn. Apply. Grow. With IT Learning AI. #itlearningai #developergrowth #techeducation #codingjourney #learnwithai #pythonprogramming #pythonforbeginners #phyton3 #pythondatastructures #codesmarter #learnpython #advancedpython #pythondevelopers #pythonbasics #ImmutableData #PythonSets #PythonDataStructures

Here is my 3-step guide to performing a Logic Audit on your AI Agents: 1. **Semantic Sanitization**: Focus on analyzing the intent of the sentence rather than just stripping keywords. 2. **Output Constraining**: Utilize tools like Pydantic or JsonSchema to ensure the AI responds in a predictable format. 3. **Cross-Check Reasoning**: Implement a secondary, smaller model to verify if the output from the primary model adheres to safety guidelines. I’ve implemented all three in ResiliAI. #OpenSource #GitHub #ResiliAI #AISecurity #Python

GenAI | AI-powered Restaurant Name & Menu Generator using modern tools and frameworks. This project integrates: 1.LangChain for prompt orchestration 2.OpenAI LLM for intelligent text generation 3.Streamlit for an interactive UI From user input to AI-generated output, the system demonstrates how LLMs can be used to create scalable and real-world applications. 🔗 GitHub: https://lnkd.in/dSVnyCgC Tech Stack: Python | LangChain | OpenAI | Streamlit #GenAI #AI #LangChain #OpenAI #Streamlit #MachineLearning #Python #AIProjects

I published a new article about something many developers will appreciate: how to recover your Claude Code chat history in VS Code without losing the early context that gets compacted away. The key idea is simple: - Claude Code stores history locally in ~/.claude/projects. - The logs are saved as JSONL files. - Context compaction affects the live session, not the stored history. - A Python CLI can help you retrieve and read the transcripts. If you’ve ever lost a good idea because you clicked “yes” too quickly or forgot to save your prompt trail, this workflow can save you time and frustration. Read the article here: https://lnkd.in/epUjEF6f #ClaudeCode #VSCode #Python #DataScience #AI #DeveloperProductivity #Automation #Substack