Spaces:

neural-arun
/

ArunCore

Runtime error

App Files Files Community

ArunCore / data /linkedin /posts.md

GitHub Actions Bot

deploy: automated sync from github main

985f3ee 2 months ago

preview code

Raw History Blame Contribute Delete

19.7 kB

metadata

type: linkedin_posts
source: harvestapi_linkedin_scraper

Arun Yadav — LinkedIn Posts & Insights

This file contains public LinkedIn posts and technical insights written by Arun Yadav (@arun-yadav-768052368).

MOST RECENT / LATEST LINKEDIN POST

Date: 2026-07-22 Post Title / Status: MOST RECENT / LATEST LINKEDIN POST

🚨 𝐍𝐄𝐄𝐓 𝐢𝐬 𝐠𝐨𝐢𝐧𝐠 𝐂𝐁𝐓 — OMR is out. Screen-based exam is in.

Most test series still hand students a 𝟒𝟓𝟎/𝟕𝟐𝟎 score card without telling them 𝐰𝐡𝐢𝐜𝐡 𝐍𝐂𝐄𝐑𝐓 𝐥𝐢𝐧𝐞 they missed. That is useless in the new CBT era.

So I built the fix: 🎯 𝐍𝐄𝐄𝐓 𝟐𝟎𝟐𝟕 𝐀𝐈 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 & 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 🧠⚡

🔥 𝐖𝐡𝐚𝐭'𝐬 𝐈𝐧𝐬𝐢𝐝𝐞:

𝟏. 𝐔𝐧𝐢𝐟𝐢𝐞𝐝 𝐖𝐞𝐛 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐨𝐫(https://lnkd.in/gFrrxrbj) Browser-based CBT exam practice with 𝟏-𝐦𝐢𝐧𝐮𝐭𝐞 countdown timers to kill exam-hall panic.

𝟐. 𝐆𝐏𝐓 𝐏𝐡𝐲𝐬𝐢𝐜𝐬 𝐁𝐨𝐭 (https://t.me/gptphysicsbot) 𝟐𝟎 𝐂𝐡𝐚𝐩𝐭𝐞𝐫𝐬 · 𝟓,𝟎𝟎𝟎 Formula & Numerical MCQs.

𝟑. 𝐆𝐏𝐓 𝐂𝐡𝐞𝐦𝐢𝐬𝐭𝐫𝐲 𝐁𝐨𝐭 (https://lnkd.in/gtneR_-S) 𝟐𝟎 𝐂𝐡𝐚𝐩𝐭𝐞𝐫𝐬 · 𝟓,𝟎𝟎𝟎 Reaction & Concept MCQs.

𝟒. 𝐆𝐏𝐓 𝐁𝐢𝐨𝐥𝐨𝐠𝐲 𝐁𝐨𝐭 (https://t.me/gptbiologybot) 𝟑𝟐 𝐂𝐡𝐚𝐩𝐭𝐞𝐫𝐬 · 𝟕,𝟕𝟑𝟓 NCERT Line-by-Line MCQs — built for 𝟑𝟔𝟎/𝟑𝟔𝟎 in Biology.

𝐓𝐡𝐚𝐭'𝐬 𝟏𝟕,𝟕𝟑𝟓+ 𝐍𝐂𝐄𝐑𝐓-𝐚𝐥𝐢𝐠𝐧𝐞𝐝 𝐌𝐂𝐐𝐬 𝐢𝐧 𝐭𝐨𝐭𝐚𝐥.

📊 𝐖𝐡𝐲 𝐈𝐭 𝐖𝐨𝐫𝐤𝐬 𝐟𝐨𝐫 𝐂𝐁𝐓 𝐏𝐫𝐞𝐩:

𝐈𝐧𝐬𝐭𝐚𝐧𝐭 𝐀𝐈 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐏𝐃𝐅 𝐑𝐞𝐩𝐨𝐫𝐭𝐬 — After every test, students get a full answer key + AI analysis of 𝐨𝐧𝐥𝐲 𝐭𝐡𝐞𝐢𝐫 𝐦𝐢𝐬𝐬𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬, pinpointing weak NCERT subtopics with a 𝟑-𝐬𝐭𝐞𝐩 𝐚𝐜𝐭𝐢𝐨𝐧 𝐩𝐥𝐚𝐧.

𝐙𝐞𝐫𝐨 𝐨𝐮𝐭-𝐨𝐟-𝐬𝐲𝐥𝐥𝐚𝐛𝐮𝐬 𝐧𝐨𝐢𝐬𝐞 — 100% mapped to official NTA Class 11 & 12 textbook sequence.

𝐅𝐨𝐫 𝐂𝐨𝐚𝐜𝐡𝐢𝐧𝐠 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐞𝐬 — Zero manual checking overhead. Direct boost to selection rates. White-label ready.

🎓 𝐒𝐭𝐮𝐝𝐞𝐧𝐭𝐬: Start practicing free on Telegram & Web right now.

🏫 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐞 𝐃𝐢𝐫𝐞𝐜𝐭𝐨𝐫𝐬 & 𝐄𝐝𝐓𝐞𝐜𝐡 𝐅𝐨𝐮𝐧𝐝𝐞𝐫𝐬: DM me to integrate or white-label this for your institution.

📩 𝐂𝐨𝐧𝐧𝐞𝐜𝐭 𝐰𝐢𝐭𝐡 𝐦𝐞: • Instagram: https://lnkd.in/g_fRFjQ5 • LinkedIn: https://lnkd.in/gPassZry

👇 Open-source GitHub repo in the first comment.

#NEET2027 #AIinEducation #EdTech #NEETPrep #BuiltWithGemini

LinkedIn Post #2

Date: 2026-07-21 Post Title / Status: LinkedIn Post #2

When I was preparing for NEET, solving test papers wasn't the hardest part.

Reviewing every mistake manually was.

Hours gone — just figuring out why I got something wrong and what to revise next.

So I built NEET Biology Practice Bot.

Students take timed chapter-wise tests on Telegram. Within seconds, they get:

• AI-powered mistake analysis • Personalized study plan • Complete PDF report • Full answer key with performance breakdown

It turns "review day" into a 30-second read.

If you're preparing for NEET, try it and tell me what you think: 👉https://t.me/gptbiologybot

Are you a NEET tutor wishing you had a tool like this for your students? DM me.

#NEET #EdTech #BuildInPublic #Python #TelegramBot #OpenAI

LinkedIn Post #3

Date: 2026-06-28 Post Title / Status: LinkedIn Post #3

Finished my first backend project using FastAPI: a Todo API. 🚀

I'm learning FastAPI one concept at a time by following a structured roadmap. Instead of rushing through tutorials, my approach is:

📚 Learn a concept 📝 Test my understanding with quizzes 🛠️ Build a small project ♻️ Refactor and move to the next topic

For this first milestone, I completed: ✅ 4 quizzes covering the fundamentals ✅ Built a Todo API with FastAPI

Features implemented: • CRUD endpoints • Request validation with Pydantic • Path and query parameters • Proper HTTP status codes • Interactive API documentation (Swagger UI)

This project helped me better understand how FastAPI handles requests, validation, and building clean REST APIs.

Next, I'll add PostgreSQL and SQLAlchemy to persist data and continue building more production-ready backend applications.

GitHub: [https://lnkd.in/ghmY9VHR]

Feedback is always welcome.

#FastAPI #Python #BackendDevelopment #RESTAPI #LearningInPublic #SoftwareEngineering

LinkedIn Post #4

Date: 2026-06-20 Post Title / Status: LinkedIn Post #4

Proud moment for our family. 🇮🇳

My elder brother, Uday Pratap Yadav, secured Rank 5 in the 70th BPSC and is entering public service.

Growing up, whenever I asked him something — about studies or life — it always felt like he knew how to think through problems and find a way forward.

Watching this journey up close taught me a lot about discipline, patience, and consistency.

Congratulations, Bhaiyya. Wishing you the very best for the responsibility ahead.

#BPSC #PublicService #Leadership #Discipline #Gratitude

LinkedIn Post #5

Date: 2026-06-17 Post Title / Status: LinkedIn Post #5

Most medical education tools evaluate whether the student got the diagnosis right.

MedCoach evaluates how they thought. I built an AI Clinical Reasoning Tutor where medical students practice diagnosing virtual patients through realistic clinical encounters.

How it works: → Generate a patient case An LLM creates a unique patient with history, symptoms, vitals, and a hidden diagnosis.

→ Interview the patient Students ask questions naturally. The Patient Agent only reveals information that was actually asked and stays consistent throughout the encounter.

→ Submit diagnosis + reasoning Students explain both the diagnosis and their clinical thinking.

→ Get evaluated The Evaluator Agent scores the entire process (out of 10), including: • question quality • missed history • reasoning gaps • cognitive bias • dangerous assumptions • actionable feedback

→ Learn The Teacher Agent explains the disease, highlights missed signals, and shows a stronger diagnostic approach.

Architecture (LangGraph): START → Evaluator → Teacher → Memory (SQLite) → END

Interactive patient conversations run outside the graph for responsiveness. Stack: • Frontend — Streamlit • Backend — Python • Orchestration — LangGraph • Storage — SQLite • Streaming responses

This is V1 — focused on simulated patient reasoning. V2 will introduce richer clinical workflows, external knowledge retrieval, and stronger diagnostic support.

Open to feedback from people building in MedEd, AI, and simulation systems.

#AI #MedicalEducation #ClinicalReasoning #LangGraph #Streamlit #Python #HealthcareAI #EdTech #AgenticAI #LLM #MedicalAI #BuildInPublic #SoftwareEngineering

LinkedIn Post #6

Date: 2026-05-06 Post Title / Status: LinkedIn Post #6

I spent the last few hours doing deep research on where AI is actually heading between 2026–2036. Not Twitter takes. Not hype threads. Actual workforce forecasts, infrastructure studies, economic reports, and AI adoption data. Then I turned all of it into a visual strategic playbook. The biggest insight? The future is shifting from: “using AI” → to "orchestrating AI systems". That’s the real transition. Most people still think AI = chatbot prompts. But the next decade is about: → multi-agent systems → RAG pipelines → autonomous workflows → AI infrastructure → orchestration layers → human oversight + judgment The research consistently pointed to the same patterns: • AI is automating tasks, not entire careers • 170M new jobs may be created globally by 2030 • 92M jobs may get displaced • Routine digital work is the most exposed • Human judgment and domain expertise become more valuable — not less One chart stood out to me. By 2030, software engineers may spend less time: • writing boilerplate code • fixing repetitive bugs • handling routine implementation And more time: • designing systems • orchestrating AI agents • defining constraints • making product decisions The same shift is happening in: → healthcare → finance → education → operations Another major signal: India is becoming one of the fastest AI adoption markets globally. And Tier-2 cities are no longer disadvantages. The combination of: → remote global work → lower living costs → AI-native skills → internet infrastructure creates massive leverage for builders outside traditional tech hubs. Another important point: The bottleneck may not be AI models. It may be: • energy • compute • infrastructure • regulation • human capability Meaning: The winners of the next decade probably won’t just be “good coders.” They’ll be people who can combine: → technical depth → systems thinking → AI orchestration → domain expertise → ethical judgment → communication That’s the direction I’m personally betting on: Building AI systems for healthcare and education that solve real workflow problems instead of chasing generic AI hype. Research sources included WEF, McKinsey, Goldman Sachs, Deloitte, Gartner, OECD, UNESCO, NITI Aayog, and multiple AI workforce studies. #AI #ArtificialIntelligence #AgenticAI #RAG #LLM #FutureOfWork #AIEngineering #HealthcareAI #EdTech #SystemDesign #Automation #IndiaAI #GenerativeAI #MachineLearning #AIResearch #BackendEngineering #AIInfrastructure #SoftwareEngineering #AIProducts

LinkedIn Post #7

Date: 2026-04-11 Post Title / Status: LinkedIn Post #7

Hallucination is a choice. I chose proof. 🧠

Most AI chatbots are toys.

They generate fluent answers. But the moment accuracy matters, they fail.

That’s why businesses still don’t trust AI.

So I built something different.

👉 ArunCore --> a production AI system that acts as my digital twin
🌐 Web: https://lnkd.in/gaFgchVb

Instead of reading a resume, you can talk to my system and decide:

what I’ve built
how I think
whether I can solve your problem

And it won’t make things up.

If the answer isn’t in my data, it doesn’t guess.
It escalates to me.

🏗️ The System

This is not a GPT wrapper.

It’s a multi-layered retrieval + reasoning system designed for reliability (not demos):

🔹 Hybrid RAG
Vector search + BM25 + reranking
→ higher precision, less noise

🔹 Stateful reasoning loop
With a multi-model fallback chain (Llama / Mixtral / Qwen via Groq)
→ system stays up even when models fail

🔹 Conversational memory
→ maintains real context, not just prompts

🔹 Real-time streaming (SSE)
→ users see “Searching…”, “Analyzing…” in real-time

🔹 Human-in-the-loop
→ uncertain or high-value queries trigger a direct alert to me

🔹 Built-in lead capture
→ not just answering, actually qualifying

🎯 Why this matters

We’re entering an “agentic” AI era where systems don’t just respond—they act.

But here’s the problem:

More autonomy = more risk
More generation = more hallucination

So the real problem isn’t intelligence.
It’s reliability.

ArunCore is built on one principle:

Truth > fluency
Retrieval > generation
Proof > claims

Example: When I asked it for a Telegram link it didn’t know,
it didn’t hallucinate.

It refused.

That’s the standard.

🚀 Interconnectivity

Talk to the system anywhere:

🌐 Web: https://lnkd.in/gaFgchVb
🤖 Telegram: https://t.me/ArunCoreBot

The intelligence is centralized.
Access is everywhere.

If you want to talk to me,
just tell the system.

It will notify me instantly.

This build was heavily inspired by Ed Donner and his work on agentic AI systems.

If you’re a founder or engineering leader working on:

healthcare
medical education
document-heavy systems

and you care about reliability over hype,

talk to the system.

If it flags me, I’ll respond.

LinkedIn Post #8

Date: 2026-04-04 Post Title / Status: LinkedIn Post #8

Most RAG projects look good until you ask a real question.

I wanted to go beyond a basic demo and understand what it actually takes to build a retrieval system that works on legal documents. So I built a Legal RAG System using the IPC, the Constitution of India, and case law documents.

What stood out to me while building this is that retrieval quality depends far more on document structure than I initially thought.

A model can only answer well if the right chunks are retrieved first. And that only happens when the ingestion pipeline is designed properly.

While working on this project, I had to rethink the pipeline from the ground up:

statutes had to be chunked by section
the Constitution had to be chunked by article
judgments had to be chunked by paragraph blocks
metadata had to support exact references like Section 302 IPC or Article 21
the answer layer had to be constrained enough to avoid hallucinated responses

This project taught me a lot about:

why generic chunking often fails on legal documents
how retrieval errors happen long before the LLM generates an answer
how important metadata design is for precision
how to build a more grounded and reliable legal Q&A pipeline

Tech stack: Python, PyPDF, LangChain, OpenAI Embeddings, ChromaDB, and Groq

Also, a quick shoutout to Harish Neel. I learned a lot from your course, and it gave me the foundation to build this project. Thank you for sharing it.

GitHub: https://lnkd.in/gAAd8Ru9

If you’ve worked on RAG systems, I’d love to know: what has been the hardest retrieval problem you’ve run into?

#AI #LLM #RAG #GenAI #Python #LegalTech #NLP #MachineLearning #LangChain

LinkedIn Post #9

Date: 2026-02-09 Post Title / Status: LinkedIn Post #9

🚀 This Week: Shipped a Production-Grade Async Web Scraping System Problem Extract structured article data from a large paginated blog (170+ pages, 3,400+ articles) — without getting blocked, crashing mid-run, or losing data. What the System Does • Discovers article URLs across pagination • Fetches pages asynchronously using aiohttp • Controls concurrency using semaphores • Applies rate limiting to avoid bans • Handles failures safely (timeouts, bad responses, partial data) • Logs per-URL start time, end time, and duration • Processes data in fixed-size batches • Saves each batch incrementally (crash-safe persistence) Results ✅ 3,400+ articles scraped ✅ Zero crashes during full run ✅ No IP bans or rate-limit lockouts ✅ Each batch persisted independently ✅ Full pipeline completed safely (even tested on a mobile hotspot) Key System Design Decisions • Batch-based persistence → prevents total data loss • Structured logging → makes debugging and monitoring easy • Async + controlled concurrency → fast and safe • Fail-safe networking → retries + timeout handling Tech Stack Python • asyncio • aiohttp • BeautifulSoup • JSON • Structured logging Full Project (Architecture + Code) 👉 https://lnkd.in/gcakiqyz Takeaway Building reliable systems > writing quick scripts. On to the next one.

LinkedIn Post #10

Date: 2025-09-12 Post Title / Status: LinkedIn Post #10

What's the key to transforming a general LLM into a specialized expert, like a strategic gamer or a personalized career coach? 🚀 The answer is the art and science of prompt engineering. I'm thrilled to share that I've graduated from the Zero To Mastery Prompt Engineering Bootcamp, where I dove deep into this exact skill—turning generalist tools into specialist powerhouses. Across a series of challenging projects, I learned to: 🔹 Architect Complex Prompts: Built an interactive Career Coach that leverages advanced prompting for personalized recommendations. * 🧑‍💼 Try the Career Coach: https://lnkd.in/eRQV_-ga 🔹 Embed Logic into Prompts: Developed classic games where the AI's behavior is guided by carefully engineered instructions. * 🐍 AI Snake Game: https://lnkd.in/eMP_wsCp * ❌⭕ AI Tic-Tac-Toe: https://lnkd.in/evx9zRWQ 🔹 Optimize AI Interaction: Created and documented a collection of powerful prompt templates and strategies for real-world scenarios. * 💬 Explore the Prompt Collection: https://lnkd.in/eV-uUava

This bootcamp was instrumental in refining the skills I need for my next major challenge: building highly specialized AI tools for the medical education sector, starting with my NEETPrepGPT project. For a complete overview of the journey, including all projects, code, and my comprehensive notes, check out the links below: 🔗 Full GitHub Repository: https://lnkd.in/eDw6Yf7T 📚 My Notes Made Using AI: https://lnkd.in/ekUhdqAJ #PromptEngineering #LLMs #AI #Python #ProjectShowcase #ZeroToMastery #AIDevelopment #MedicalAI

LinkedIn Post #11

Date: 2025-08-20 Post Title / Status: LinkedIn Post #11

Just finished revamping the classic Snake Game using Python and Pygame! 🐍 This version features a moving triangle head (the snake actually faces where it’s going), colored body, score tracking, increasing speed, and you can pause/resume with ‘P’. All the usuals: eat food, grow longer, game over if you hit the wall or yourself, then you can restart or quit. It's super basic, all in one file, and great for messing around with Python, game loops, and simple event handling. 👉 Check out the code or tweak it yourself here: https://lnkd.in/ewKsyUBX Happy to share! #Python #Pygame #SnakeGame #SideProject #Gamedev