रहस्य और सस्पेंस से भरपूर (Curiosity & Suspense) history and Near Nagpur
📜 साम्राज्य का वो काला इतिहास
जब प्राचीन सेनाओं ने लड़ी रास्तों की खौफनाक जंग
इतिहास की किताबों में हम सिर्फ राजाओं की जीत पढ़ते हैं।
लेकिन क्या आपने सोचा है कि बिना गाड़ियों और पक्की सड़कों के सफर कैसे होता था?
1,00,000 से ज्यादा सैनिकों और हाथियों को ले जाना बेहद खौफनाक था।
आइए आज इतिहास के उन छिपे हुए पन्नों को पलटते हैं।
🏛️ भाग 1: भूगोल की पहेली और आज का हाईवे
1. क्या पंढरपुर नागपुर-हैदराबाद के रास्ते में है?
हकीकत: बिलकुल नहीं। पंढरपुर महाराष्ट्र के दक्षिण-पश्चिम में है।
नजदीकी स्टेशन: पंढरपुर (PVR), कुर्डूवाड़ी (KWV) और सोलापुर (SUR) मुख्य विकल्प हैं।
असली रूट: नागपुर से हैदराबाद का रास्ता सीधे NH 44 से होकर जाता है।
2. हाईवे का वह रहस्यमयी शहर: 'पांढरकवडा'
इन्फ्रास्ट्रक्चर फैक्ट: NHAI ने पांढरकवडा वाई-जंक्शन पर फ्लाईओवर बनाया है।
8-9 साल पहले यह रास्ता बहुत संकरा था और यहाँ भारी जाम लगता था।
दूरी का गणित (NH 44):
नागपुर से: 150 किमी (उत्तर)
आदिलाबाद से: 45 किमी (दक्षिण)
हैदराबाद से: 349 किमी (दक्षिण)
इतिहास: यहाँ केळापूर जगदंबा देवी मंदिर है।
मान्यता है कि पांडवों ने अज्ञातवास का कुछ समय यहाँ बिताया था।
3. दो पड़ोसी जिले: आदिलाबाद बनाम निर्मल
निर्मल जिला पहले बड़े आदिलाबाद जिले का ही हिस्सा हुआ करता था।
खूबी / विशेषता
🎨 निर्मल जिला (Nirmal)
🛢️ आदिलाबाद जिला (Adilabad)
खास पहचान
किलों का शहर (Nirmal Fort)
कपास के कारण "व्हाइट गोल्ड सिटी"
हस्तशिल्प
विश्व प्रसिद्ध निर्मल खिलौने और पेंटिंग्स
जनजातीय धोकरा मेटल क्राफ्ट (पीतल कला)
प्रकृति
कदम बांध (Kadam Dam Reservoir)
कुंतला वॉटरफॉल (सबसे ऊंचा झरना)
रेलवे
कोई सक्रिय रेलवे स्टेशन नहीं है।
आदिलाबाद स्टेशन (ADB) सक्रिय है।
🏗️ भाग 2: प्राचीन काल का सबसे मुश्किल सफर
4. बिना कोलतार (डामर) की सड़कों का नरक
उस जमाने में कोई चिकनी पक्की सड़कें नहीं हुआ करती थीं।
रास्ते सिर्फ धूल और पत्थरों से भरे कच्चे ट्रैक होते थे।
धूल का अंधड़: सैनिकों के चलने से धूल का भयानक गुबार उड़ता था।
इससे सांस लेना मुश्किल होता था और रास्ता दिखना बंद हो जाता था।
रास्ता बनाने वाले (बेलदार): सेना से दो दिन आगे मजदूर चलते थे।
इन्हें 'बेलदार' कहा जाता था जो जंगल काटकर तोपों के लिए रास्ता बनाते थे।
संदर्भ पुस्तक:"The Military System of the Mughals" (लेखक: भवान सिंह राणा) और अबुल फज़ल की "Ain-i-Akbari" (बेलदार अमले का रिकॉर्ड)।
5. हाथियों को नावों के तैरते पुल से पार कराना
ऐतिहासिक उदाहरण: सन् 1564 में अकबर के मालवा अभियान में इसका प्रयोग हुआ।
नर्मदा जैसी गहरी नदियों को पार करने के लिए तैरते पुल बनाए जाते थे।
जमीन का धोखा: हाथी हिलती हुई जमीन पर पैर कभी नहीं रखते।
इंजीनियर नावों को बांधकर उसपर पुआल, घास और गहरी मिट्टी बिछाते थे।
इससे तैरता हुआ पुल दिखने में बिल्कुल असली ठोस जमीन जैसा लगता था।
आंँखों पर पट्टी: डरने पर महावत हाथी की आँखें कपड़े से ढक देते थे।
संदर्भ पुस्तक:"Akbarnama" (लेखक: अबुल फज़ल) - वॉल्यूम 2 (नदियों पर नावों के पुल और हाथियों को पार उतारने का विवरण)।
🔥 भाग 3: अंधेरा, आग और ईंधन का संकट
6. रोशनी की जंग: जानवरों की चर्बी बनाम केरोसिन
उस युग में मिट्टी का तेल (केरोसिन) जैसी कोई चीज नहीं थी।
तेल की रसद: सेनाएं सरसों और तिल के तेल पर निर्भर रहती थीं।
चर्बी का उपयोग: तेल खत्म होने पर जानवरों की चर्बी पिघलाई जाती थी।
यह धीरे जलती थी लेकिन इससे गाढ़ा काला धुआं और भयानक बदबू निकलती थी।
मशालची: रात में उजाला रखने के लिए मशालची सेना के आगे उल्टे पैर चलते थे।
संदर्भ पुस्तक:"Ain-i-Akbari" (बुक 1, आईन 19 - 'The Illuminations') में मुग़ल कैंपों में तेल की खपत के नियम दर्ज हैं।
7. महलों और शाही तंबुओं में हवा से आग को बचाना
चिरागदान: महलों की दीवारों के अंदर गहरे गड्ढे (आले) बनाए जाते थे।
ये गहरे आले दीये की लौ को तेज हवा के झोंकों से बचाते थे।
रुईदार भारी पर्दे: दरवाजों पर मोटे मखमल के भारी पर्दे लगाए जाते थे।
ये पर्दे बाहर की हवा को अंदर आने से पूरी तरह रोक देते थे।
फ़ानूस: तंबुओं के लिए तेल लगे पारभासी कागजों से बने लालटेन का उपयोग होता था।
8. जहरीले सांप और तंबुओं की आग का तांडव
कैंप की भीषण आग: तंबू पास-पास होने से एक चिंगारी सब राख कर देती थी।
सांपों का आतंक: जंगलों में सांप और बिच्छू काटने से कई सैनिक मरते थे।
ऐतिहासिक संदर्भ: औरंगजेब के दक्कन अभियान में इसके कई प्रमाण मिलते हैं।
वहाँ अचानक बाढ़ आने से एक ही रात में सारा अनाज और बारूद नष्ट हो जाता था।
संदर्भ पुस्तक:"History of Aurangzib" (लेखक: सर जदुनाथ सरकार) - वॉल्यूम 5 (दक्कन मार्च के दौरान तंबुओं के जलने और नुकसान का वर्णन)।
🩺 भाग 4: पानी का संकट और बीमारियां (हैजा)
9. जब सेना नदी का पानी ही पी जाती थी!
विजयनगर साम्राज्य का उदाहरण: राजा कृष्णदेवराय की सेना बहुत विशाल थी।
पुर्तगाली यात्री डोमिंगोस पेस ने अपने यात्रा वृत्तांत में एक हैरान करने वाली बात लिखी।
जब सेना किसी छोटी नदी के पास पहुंचती, तो सैनिक और पशु पूरा पानी पी जाते थे।
पीछे आने वाले सैनिकों के लिए सिर्फ कीचड़ और सूखी मिट्टी ही बचती थी।
संदर्भ पुस्तक:"A Forgotten Empire: Vijayanagar" (लेखक: रॉबर्ट सीवेल) - पुर्तगाली यात्री डोमिंगोस पेस के पत्रों का अनुवाद।
10. हैजा (Cholera) से निपटने का क्रूर तरीका
बनजारा नेटवर्क: मुग़ल सेना रसद के लिए बनजारा खानाबदोशों पर निर्भर थी।
लेकिन जब पानी दूषित हो जाता, तो पूरी सेना में हैजा फैल जाता था।
शाही दवाखाना और क्वारंटाइन: सेना के साथ हकीम और वैद्य चलते थे।
वे जानते थे कि बीमारी पानी से फैलती है, इसलिए वे तुरंत पूरा कैंप शिफ्ट कर देते थे।
स्वस्थ सैनिकों को आगे बढ़ा दिया जाता था और बीमारों को पीछे तंबुओं में छोड़ देते थे।
सामूहिक दफन: संक्रमण रोकने के लिए मृतकों को सामूहिक गड्ढों में दफन करते थे।
संदर्भ पुस्तक:"Mughal Warfare: Indian Frontiers and Highroads to Empire" (लेखक: जोस गोमांस) - मुग़ल मोबाइल अस्पतालों का विश्लेषण।
The next time you cruise down a smooth, well-lit modern highway like NH 44, remember the historic paths where ancient souls battled darkness and exhaustion.
TAGS='history'
Added: 2026-09-15 13:56:54 | Edited: 2026-09-15 17:08:02 | public
10 Marks GS may be What is Chrpi Cheese from Laddakh which win Gold Medal in Brazil- 9 Sept 2026 BBC =https://youtu.be/pwZnyfwpACQ
which country has released new map in world map and in event it is passed pass by UN answer in South Africa in today AIR news 5 sept 2026
Makka Agreement- 7 August 2026
✅ I had correctly anticipated that the topic of the Pran Vayu Devta Pension Scheme (PVDPS) would appear in UPSC or other major competitive exams — and it did!
🧠 In a similar way, I now predict that Angola, the African nation, will likely feature in upcoming UPSC or state-level competitive exams — either in current affairs, international relations, or geography sections.
3-May 2025
Next Question= What is RK 251 (30 August 2026)
Next Question= What is Economic D Day( may be 10 marks GS)
Next Question= Closal Bio-science why this company is in news. 12 May 2025 (April 2025 Wolf related)?
Next Question= Article 143 and President Murmu 14 questionare to Supreme Court, what is this matter?
Next Question= KJ gene editing treatment ? https://www.youtube.com/watch?v=-DUNFVIbE88
Next Question= Ahimsa silk, Manipur GI tag, Man ki baat 29 June 2025
Next Question = What is Jodidara = Multiple husband = = https://www.youtube.com/watch?v=BvMrwJrd83g
Apna ghar Bisram yojna= Truck driver AC rest house by Govt India
what is point nemo in the ocean - Source Man Ki Baat 28 Sept 2025
TAGS='Question','UPSC'
Added: 2026-09-07 14:52:54 | Edited: 2026-09-09 19:45:34 | public
on GRANULES India is not looking good because Owner selling their share either he is poor or he is intelligent (like Shoota from Happiest Minds) to come out from higher price.
Final Verdict: Fundamentally bullish/positive, but valuation is no longer cheap.
Best approach: Hold/accumulate on meaningful dips near ₹872–₹855; avoid chasing aggressively near ₹910+.
See data below:
Granules India (+1.62%) : Co-Promoter Krishna Prasad Sells 1.72 Crore Shares, Reducing Promoter Group Stake From 38.02% To 31.08%
11 Sep 06:55 PM Equity GRANULES INDIA LTD. (+1.62%) : RS. 253.00 CRORES NSE BLOCK TRADE; FOR ~ 2,829,004 SHARES, AT RS. 894.30
11 Sep 08:57 AM Block Deals Granules (+1.11%) : FDA Has No Concerns About Corrective Actions At Gagillapur Facility, Seven Of Eight Manufacturing Sites Now Have Clean Inspection Reports.
27 Jul 12:14 PM GRANULES INDIA (-0.05%) : Q1 REVENUE 15B RUPEES VS 12B (YOY)
21 Jul 12:12 PM Result GRANULES INDIA (-0.05%) : Q1 PAT GROWTH AT 63% VS FY27 GUIDANCE OF 15–20%
21 Jul 12:12 PM Result GRANULES INDIA (-0.05%) : Q1 EBITDA 3.4B RUPEES VS 2.47B (YOY) || Q1 EBITDA MARGIN 22.94% VS 20.4% (YOY)
21 Jul 12:11 PM Result GRANULES INDIA (-0.05%) : Q1 CONS NET PROFIT 1.8B RUPEES VS 1.1B (YOY)
21 Jul 12:10 PM
66Granules India (+2.45%) : Gains Exclusive First-To-File ANDA Approval For Extended-Release Sodium Oxybate Oral Suspension06 Jul 09:00 AMEquity
Google ADK Geo Validator Tutorial | Run ADK CLI, Configure .env & Complete Qwiklabs Lab | Vertex AI
In this video, I continue the Google Cloud Skills Boost Agent Development Kit (ADK) series by completing the next lab tasks. What you'll learn: Verify the Google Search Agent using the ADK CLI Run adk run from the project directory Test the agent with your own prompts instead of only following the lab Observe how the agent correctly interprets search intent Complete the Google Search Agent task Start the Geo Validator project Enable hidden files in the editor Create and configure the .env file Configure Google Cloud Project ID and Vertex AI settings Open and understand agent.py Navigate the ADK project structure Tips and observations while working through the lab In this walkthrough, I don't just repeat the lab instructions—I also test the AI agent with my own queries to understand how it behaves in real-world scenarios. This practical approach helps you learn ADK more effectively and understand how AI agents respond beyond predefined examples. Topics covered: Google Agent Development Kit (ADK) Google Cloud Skills Boost Qwiklabs Geo Validator Google Search Agent Vertex AI Gemini Models ADK CLI Python AI Agents Environment Variables (.env) Google Cloud Project Configuration Agent Development Hands-on Google Cloud Lab Generative AI AI Agent Development If you're preparing for Google Cloud labs or learning how to build AI agents with ADK, this series will guide you step by step.
TAGS='gcp','study'
Added: 2026-09-04 18:12:26 | Edited: 2026-09-04 18:12:26 | public
सुभाष चंद्र का बाईस हज़ार करोड़ का सवाल,
NCLT के दरवाज़े पर पहुँचा पूरा मामला बेहाल।
छह करोड़ के केस हों या बड़े-बड़े घोटाले,
जनता पूछे—कब तक चलेंगे ये खेल निराले?
नीरव मोदी का नाम आया, सवाल हुए हजार,
माल्या भी देश छोड़ गया—बना बड़ा विवाद और वार।
जनता कहती है—भागने से पहले NCLT चले जाते,
कानून के सामने बैठते, हिसाब-किताब समझाते!
पर सवाल सिर्फ एक पार्टी से नहीं,
सवाल हर उस नेता से है जो चुप है कहीं। कहाँ गया Opposition? कांग्रेस कहाँ, RJD कहाँ?
संसद में आवाज़ कहाँ, जनता के सवालों का जवाब कहाँ?
भारत में मुद्दे आते हैं, शोर मचता है,
फिर कुछ दिनों में सब शांत हो जाता है।
जनता को चाहिए नाटक नहीं—साफ़ जवाब,
किसका कितना पैसा, किसका क्या हिसाब?
न पक्ष बचना चाहिए, न विपक्ष का बहाना,
कानून सबके लिए एक—यही है लोकतंत्र का तराना।
देश नहीं, दुनिया भी देख रही है ये सवाल—
कब होगा हर बड़े आर्थिक मामले का पूरा हिसाब-किताब?
भारत बंद नहीं—भ्रष्टाचार और बेईमानी बंद करो,
पक्ष-विपक्ष की राजनीति से ऊपर उठकर,
देश के पैसे और जनता के विश्वास की हिफाज़त करो। 🇮🇳
जय हिंद 🇮🇳
जय भारत 🇮🇳
TAGS='poem'
Added: 2026-09-04 09:42:47 | Edited: 2026-09-04 09:42:47 | public
If someone calls you claiming to be from Vodafone Idea (Vi) and offers free 5G activation, unlimited 5G, or a 40-day free offer, please be careful. It may be a scam.
They may ask you to share two OTPs—first one OTP and then a second OTP. Never share OTPs with anyone over the phone.
If you have already shared the OTPs and your Vi Prepaid number has been converted to Postpaid, you can request deactivation by sending:
Reject P2P → 199
Stay alert and never share your OTP, even if the caller claims to be from Vi.
TAGS='cybersecurity','security'
Added: 2026-08-20 13:15:53 | Edited: 2026-08-20 13:15:53 | public
Clerk is a complete, developer-focused user management and authentication platform designed specifically for React and modern web frameworks like Next.js, Remix, and Gatsby
Pre-built UI Components: Clerk provides ready-to-use, customizable React components like <SignIn />, <SignUp />, <UserButton />, and <UserProfile />.
Multiple Auth Methods: It supports passwords, social logins (Google, GitHub, Apple), magic links, passkeys, and multi-factor authentication (MFA).
React Hooks: It offers built-in hooks like useUser(), useAuth(), and useSession() to easily access user data and authentication states inside your code.
Server-Side Support: It integrates seamlessly with React Server Components (RSC) and server actions in Next.js
Quick Code Example
import {
ClerkProvider,
SignedIn,
SignedOut,
SignInButton,
UserButton
} from "@clerk/clerk-react";
export default function App() {
return (
<ClerkProvider publishableKey="your-clerk-publishable-key">
<header>
{/* Shown when user is logged out */}
<SignedOut>
<SignInButton />
</SignedOut>
{/* Shown when user is logged in */}
<SignedIn>
<UserButton />
</SignedIn>
</header>
</ClerkProvider>
);
}
Why Developers Use It Instead of Alternatives
Feature
Clerk
Firebase Auth / Auth0
NextAuth.js (Auth.js)
UI Components
Beautiful, pre-made, customizable React UI.
None or minimal; you must build your own UI.
None; you must build your own login forms.
Setup Time
Minutes (highly automated for React/Next.js).
Moderate setup required.
Requires manual backend route configuration.
Session State
Handled automatically via global React context.
Manual state management.
Handled via custom providers and hooks.
pros cons how much good in trend?
Yes, Clerk is highly popular and currently one of the fastest-growing trends in the React and Next.js ecosystems.
Pros and Cons of Clerk
Pros
Pre-made UI Components: You do not have to design login, signup, or profile pages. They look modern out of the box and can be styled with Tailwind CSS.
Instant Social Logins: Enabling Google, Apple, or GitHub login takes exactly one click in the Clerk dashboard.
Excellent React Hooks: Fetching user data (useUser()) or checking if a user is logged in takes one line of code.
Seamless Next.js Integration: It has native support for App Router, middleware protection, and React Server Components.
Advanced Features Free: Passkeys, Multi-Factor Authentication (MFA), and session management are built-in without extra coding.
Cons
Vendor Lock-in: Your user data is hosted on Clerk's servers. Migrating away to your own database later requires manual database exports.
Pricing at Scale: While the free tier is generous, pricing can scale quickly if your app gets a massive surge of monthly active users.
Third-Party Dependency: If Clerk experiences an outage, your users cannot log into your application.
Network Overhead: Because authentication is managed externally, there is a minor network latency penalty compared to hosting a self-hosted database session on your own server.
Should You Use It?
Use Clerk if: You are building a SaaS product, a startup MVP, or a personal project where your primary goal is to launch quickly without wasting weeks building a secure authentication system.
Avoid Clerk if: You are building an enterprise app with strict data privacy laws (where user data must remain in your own private databases), or if you are working on a low-budget project that expects millions of immediate users.
Would you like to know about Clerk's pricing model for active users, or would you like to see how it compares directly to NextAuth.js (Auth.js) for self-hosting
TAGS='reactjs'
Added: 2026-08-09 19:45:54 | Edited: 2026-08-09 19:45:54 | public
1. Google Cloud console, from left menu Agent Platform > Studio
2.left, select + New > Chat
3. Rename from Untitled Prompt to Insurance Risk Summary - Prototype
4. System instructions box, copy paste above : You are an expert AI assistant for an insurance underwriting department. Your primary goal is to help underwriters by accurately and concisely summarizing client information and highlighting potential risk factors. Maintain a professional and objective tone. Focus only on the information provided in the prompt. Do not invent details.
5. In the main prompt area, paste the following: Customer Notes for 'SafeHarbor Warehousing': "The applicant is seeking coverage for their 50,000 sq ft warehouse. The business is 5 years old. The building is a concrete tilt-up structure, originally built in 2010. They store a variety of non-hazardous dry goods. Fire safety measures include a full sprinkler system, a centrally monitored fire alarm, and documented annual inspections by a certified third party. Security measures include a 24/7 centrally monitored burglar alarm, comprehensive security camera coverage of the interior and exterior, a fully fenced perimeter, and nightly patrols by a contracted security guard service. The company reports no major property or liability losses in their 5-year history. They have specifically asked to ensure their new automated shelving and retrieval system, installed last month, is adequately covered under the policy." Your Task: 1. Briefly summarize the key details of the 'SafeHarbor Warehousing' business and its existing safety measures. 2. Based *only* on the notes provided, identify any immediate questions an underwriter should ask or potential risk factors they should consider further. Present the summary first, then the questions/risk factors as bullet points.
6. Model settings: Model > Gemini > model_name
7. Global for Region.
8. Save Submit
9. Deploy icon > Cloud Run > Deploy as app.
10. Acknowledgement and Click Create App.
11. Wait 5-6 minutes saved then close.
12. Deploy > Cloud Run > Open app
13. You should see a new page titled Welcome to Vertex AI Gen AI App! with your prompt title, Insurance Risk Summary - Prototype, displayed.
14. Chatbot section, in the "Type a message...
New Customer Inquiry: "Applicant 'Coastal Goods Delivery' has a fleet of 10 delivery vans, all equipped with GPS and telematics. They operate within a 100-mile radius of their depot. Drivers undergo annual safety training. They had one minor fender bender last year, no injuries, $1500 damages. What are the primary risk considerations?" Please summarize key points and identify potential risks.
15.Yes doneeeeeee be happy Check Progress must be 20%
Task 2. Design effective prompts-> Exit without saving then click Continue.
2A. This is called Zero-shot prompting
1.Task 1, close that browser tab to return to the Google Cloud console.
2.Top left, select New Chat.
3. Top right, click Untitled Prompt and rename/name it: Insurance Claim Data Extraction.
4.System instructions box=:
You are an AI assistant specializing in parsing and extracting specific data points from unstructured insurance claim notifications.
Your goal is to identify and list key information accurately.
If a piece of information is not found, clearly state "Not found".
Output the extracted information in a key: value format, with each key on a new line.
5. Main prompt area, paste =
Claim Notification Received:
"Hi team, just got a call from Mrs. Eleanor Vance, policy #POL458892. She reported a kitchen fire that occurred on May 12th, 2025, around 3 PM. The main damage seems to be to the oven and surrounding cabinets. She mentioned smoke damage in the kitchen and dining area too. She thinks the total damage might be around $7,500. Her contact is 555-0123. No injuries reported, thankfully."
Extract the following:
- Policy Number
- Claimant Name
- Date of Loss
- Time of Loss
- Type of Loss
- Brief Description of Damage
- Estimated Loss Amount
- Injuries Reported
6.Model settings:
Temperature to 0.1
Output token limit to a reasonable number, like 1024.
Ensure the Region is Global.
Submit (Enter)
Task 2B.This is called : Few-Shot prompting
1. On the top left, select New Chat.
2. On the bottom of the Prompt section, click the + button.
3. From the pop-up menu, select Example.
This will open a new window where you can add examples for the prompt.
In the "Examples" interface that appears:
4. For the INPUT field of your first example, paste the following unstructured note:
Claim Notification Received:
"Email from John Sterling (policy POL77521) re: water damage at his shop. Happened sometime last night, May 10th, 2025. A pipe burst in the ceiling. Stockroom is flooded, some damage to inventory. He's not sure on the cost yet, maybe $5k-$10k? No one was there, so no injuries."
Extract the following:
- Policy Number
- Claimant Name
- Date of Loss
- Time of Loss
- Type of Loss
- Brief Description of Damage
- Estimated Loss Amount
- Injuries Reported
5.For the OUTPUT field of your first example, paste the following perfectly formatted extraction:
Policy Number: POL77521
Claimant Name: John Sterling
Date of Loss: May 10th, 2025
Time of Loss: Night
Type of Loss: Water damage
Brief Description of Damage: Pipe burst in ceiling, stockroom flooded, some damage to inventory.
Estimated Loss Amount: $5,000 - $10,000
Injuries Reported: No
6. Click the Add examples button to save this example and return to the main prompt.
Task 2C. 7. Re-add System Instructions: Since clearing the prompt also cleared the system instructions, paste them again into the System instructions box=:
You are an AI assistant specializing in parsing and extracting specific data points from unstructured insurance claim notifications.
Your goal is to identify and list key information accurately.
If a piece of information is not found, clearly state "Not found".
Output the extracted information in a key: value format, with each key on a new line.
8. Provide the New Input and the Prompt:
In the area labeled {Input} Write value here, paste the original claim notification for Eleanor Vance that you want the model to process now=:
Claim Notification Received:
"Hi team, just got a call from Mrs. Eleanor Vance, policy #POL458892. She reported a kitchen fire that occurred on May 12th, 2025, around 3 PM. The main damage seems to be to the oven and surrounding cabinets. She mentioned smoke damage in the kitchen and dining area too. She thinks the total damage might be around $7,500. Her contact is 555-0123. No injuries reported, thankfully."
9.Enter the following instruction in Type a prompt=:
Extract the following data points from the provided claim notification:
- Policy Number
- Claimant Name
- Date of Loss
- Time of Loss
- Type of Loss
- Brief Description of Damage
- Estimated Loss Amount
- Injuries Reported
10. Click on the Model settings:
Ensure the model_name model is selected.
Set Temperature to 0.1 (for more factual, less creative extraction).
Set Output token limit to a reasonable number, like 1024.
Ensure the Region is Global.
11.Click the Submit (Enter)
Task 2 Experinting: Experimenting with prompt configurations:
Now, you'll explore how different parameters in the Model settings can affect the model's response.
Ensure your "Insurance Claim Data Extraction" prompt with the few-shot example is active.
First, create a new prompt to use for these experiments:
1. On the top left, select New Chat.
2. Title the prompt Insurance Story.
3.In the prompt text box, paste the following=:
Write the *first paragraph* of a short story about a homeowner who just used a futuristic AI insurance app to file a claim. The claim was for a bizarre and unexpected incident.
4.Experiment with model settings
5. Temperature:
Lower values (e.g., 0.0-0.2) make the output more focused
Higher values (e.g., 1.5-2)
6. Try it: Change
Temperature to 1.5. Click Submit (Enter) and note any changes.
Then, change Temperature back to 0.1, use the same prompt, and click Submit (Enter) again.
7.Token Limit:
Try it:
SET token limit to a very small number, like 500.
Reset it to the max (default) length 65535.
observe the truncated output.
8. Top-P:value of 1.0 considers all tokens. Lowering Top-P (e.g., to 0.8) makes the output more focused, similar to lowering temperature.
9.Thinking Budget:Auto,Off,Manual
10.Structured output:like JSON
11.Grounding: Google Search: Connects public information.
12.Grounding: Your data:Agent Platform Search or RAG Engine) to answer context-specific questions
Task 3:Engineer and manage prompts
1.On the top left, select New Chat. This will bring you to a new prompt editor page.
2.Name this new prompt : Insurance Risk Factor Identification.
3. Set up this simple base prompt:
4.In the System instructions box, enter=:
You are an insurance risk analyst assistant. Your task is to identify potential risk factors from a given scenario. Be concise.
5. In the main prompt area, paste the following=:
Scenario:
"The applicant, 'The Fiery Grill,' is a new upscale restaurant specializing in wood-fired oven pizzas and open-flame grilling. They have installed a brand new, custom-built fire suppression system for their cooking area, but it has not yet been certified by a third party. The restaurant plans to feature live acoustic music on weekend evenings and has a small, raised stage area. They also want to offer valet parking."
Based on this scenario, list three primary risk factors an underwriter should consider.
6.Click on the Model settings:
Temperature: 0.2
Region: Global
Enter save (Auto save may works)
7.Now, with your Insurance Risk Factor Identification prompt and its response displayed, click the three dots on the top right next to your prompt name, and select Compare.
Comparing by modifying system instructions
8.While compare Inside of the System instructions box (for the prompt on the right side) edit with below:
You are an expert insurance risk analyst assistant. Your task is to identify potential risk factors from a given scenario. For each risk factor, also briefly suggest a potential mitigation strategy or question for the underwriter. Be clear and structured.
9.Wait for both prompts to generate responses. Now you can see compared output.
10.Comparing with a different temperature setting:
11. System instructions box for the prompt on the right,revert it to:
You are an insurance risk analyst assistant. Your task is to identify potential risk factors from a given scenario. Be concise.
12.Right side Model settings : Temperature to 2.0.
13.Right side Submit the following prompt in the box at the bottom of the page:
Scenario:
"The applicant, 'The Fiery Grill,' is a new upscale restaurant specializing in wood-fired oven pizzas and open-flame grilling. They have installed a brand new, custom-built fire suppression system for their cooking area, but it has not yet been certified by a third party. The restaurant plans to feature live acoustic music on weekend evenings and has a small, raised stage area. They also want to offer valet parking."
Based on this scenario, list three primary risk factors an underwriter should consider.
14. Observe the differences in the responses. Does the higher temperature (2.0) vs compared to the 0.2 temperature output?
15. Ensure the Model is set to model_name
16. Temperature: 0.2
17. Set the Thinking level to Minimal
18. Change following prompt(more complex) in the box at the bottom of the page.
Scenario:
"The applicant, 'The Fiery Grill,' is an upscale restaurant specializing in wood-fired ovens and open-flame grilling. They have a brand new, custom-built fire suppression system, but it has not been certified by a third party. The restaurant will feature live acoustic music on a small, raised stage. They also want to offer valet parking, managed by their own staff. The applicant has no prior business history."
Underwriting Guidelines:
Priority Hierarchy: Liability risks are classed as:
- Class A (Critical): Fire, structural failure, failure of safety systems.
- Class B (Standard): General premises liability (e.g., slip-and-fall).
- Class C (Niche): Auto/Vehicle liability.
Compounding Factors: A "compounding risk" (a condition that makes another risk worse) must be elevated to the highest priority.
Inexperience: Lack of prior business history is a general negative factor but does not create a primary risk on its own.
Auto Liability: Class C risks (Valet) are only considered a primary risk if the applicant is using an unvetted, third-party contractor.
Task:
Based on the scenario and the underwriting guidelines, identify the single, #1 highest-priority risk. Then, write a 2-sentence justification that explains why it is the #1 risk, citing the specific guideline(s) that apply.
19. Review the responses. Compare the output from model_name (left pane) with model_name (right pane).
20. Click Check my Progress.
Task 4. Use multimodal prompts with Gemini
1.analyze an image and extract information from it.
2. analytical tasks such as description, text extraction, and question
3. Navigate back to the Agent Studio page.
4.On the top left corner, click New Chat.
5.Change the name of the prompt to Timetable Image Analysis.
6.On the bottom left of the prompt box, click the + button, then select Import from Cloud Storage.
7.In the menu, select the pre-built Cloud Storage bucket, then the timetable.png file.
8.In the Model settings panel on the right:
9.Ensure the model_name model is selected.
10.Ensure the Region is Global.
11.Now, you can ask the model to perform a few tasks on the image. Below the inserted image in the prompt input field, paste the following prompt:
1. Provide a concise title for this image (under 5 words).
2. Describe the image in one or two sentences.
3. Extract all visible text from the image. Present the flight schedule as a clearly formatted list with columns for "Time" and "City".
12. Submit/Enter
13.Next, you can ask a question that requires reasoning based on the extracted information. Submit the following prompt:
Based on the flight schedule shown in the image, what percentage of the listed flights depart before 11:30 AM? Show your calculation if possible.
14. Submit/Enter
15. Setting: Temprature 0.8
16. Check output and again Tmprature set:0.2 to get more predictable response. Can be set 0 to 0.2 lower for getting good factual response.
17.Radio button Check my progress click and check this task 4 completed.
Task 5. Generate media in Agent Studio
Generate an image with Gemini
1.In the left navigation menu, under Generate media, click Image.
2.In the text prompt area at the bottom, enter a descriptive prompt:
A close-up, photorealistic image of a single honeybee collecting pollen from a vibrant purple lavender flower, with a softly blurred garden background.
3.In the Settings panel on the right:
3a.Ensure the Model settings is set to model_name.
3b.For Aspect ratio, select 1:1.
3c.Review the Safety Filter Settings (e.g., Person generation, Safety filter threshold) and leave them at their defaults or adjust as preferred for this non-person-focused prompt.
4. Submit/Save
5.Now Image will be generated.
6.Click the generated image thumbnails to open the detail view.
7.Click Radio button Check my progress to verify the objectives.
Task 6: it is Optional related to - Generating Voice with Chirp
If you wish to explore AI-generated voice:
1. In the small vertical toolbar on top, click the Speech icon (it looks like a microphone) to switch to the voice generation tool. This opens the Chirp interface.
2. You might be prompted to enable the Cloud Text-to-Speech API if it's not already active for your project. If so, click Enable and wait for the API to be enabled (this might take a moment).
3. Once the interface is ready, in the text prompt area at the bottom, enter the text you want to synthesize. For example:
Welcome to the world of generative AI on Google Cloud
4.In the Settings panel on the right:
4a.Select a Model (e.g., Chirp 3. HD Voices).
4b.Choose your desired Language (e.g., English (United States)).
4c.Select a Voice from the dropdown list. You can try a few different ones to hear their characteristics.
4d.Explore any Advanced options if available and desired.
5.Click the Run button.
6. After processing, you should be able to play the generated audio directly in the interface.
Congratulations! In this lab, you successfully navigated Agent Studio
TAGS='gcp'
Added: 2026-08-05 22:18:04 | Edited: 2026-09-01 23:39:19 | public
शेयर बाजार ओनरशिप ट्रेंड्स (FII खरीद-बिक्री के आधार पर रंगीन रिकॉर्ड)
रंग संकेत (Color Legend): हरा (Green आकृति) = FII की हिस्सेदारी/खरीद में बढ़ोतरी (FII Buy / Increase) लाल (Red आकृति) = FII की हिस्सेदारी/बिकवाली में गिरावट (FII Sell / Decrease)
1. समग्र निफ्टी 50 और 500 आंकड़े (Overall Market Statistics)
श्रेणी / मेट्रिक
एफआईआई (FII) आंकड़े
डीआईआई (DII) आंकड़े
मुख्य बदलाव / अंतर
निफ्टी 50 ओनरशिप (वर्तमान)
22.7% (2.4% घटी)
25.9% (ऑल टाइम हाई, नया रिकॉर्ड)
डीआईआई को 3.2% अंकों की बढ़त / लीड
निफ्टी 500 ओनरशिप (वर्तमान)
17% (नया लो)
21% (लगातार 9वें क्वार्टर में बढ़त)
डीआईआई की हिस्सेदारी में लगातार वृद्धि
निफ्टी 50 कंपनियों में हिस्सेदारी बदलाव
36 कंपनियों में हिस्सेदारी कम की
41 कंपनियों में हिस्सेदारी बढ़ाई
डीआईआई ने एफआईआई की छोड़ी जगह पर कब्जा किया
पूंजी निकासी व निवेश (कुल)
$58 अरब डॉलर (सितंबर 2024 के बाद से निकाले)
$166 अरब डॉलर (पिछले 22 महीनों में डाले)
एसआईपी (SIP) के जरिए मासिक प्रवाह लगभग $3 अरब डॉलर
ऐतिहासिक तुलना (जून 2016 बनाम अब)
पहले 24.4% (जून 2016)
पहले 13.2% (जून 2016)
10 साल में घरेलू निवेशकों ने एफआईआई को पछाड़ा
2. विशिष्ट कंपनियां और स्टॉक्स (Specific Companies Ownership Shift)
कंपनी का नाम
एफआईआई (FII) बदलाव और वर्तमान स्थिति
डीआईआई (DII) बदलाव और वर्तमान स्थिति
मैक्स हेल्थकेयर इंस्टिट्यूट
13% गिरी → 41.8%
12.5% बढ़ी → 30%
एटरर्नल (Eternal)
12.3% गिरी (44% से अधिक से) → 32%
बढ़कर → 41% (भारतीय संस्थाओं की हिस्सेदारी विदेशी से अधिक)
इन्टरग्लोब एविएशन (इंडिगो)
7% गिरी → 20.3%
7.8% बढ़ी → 31.9%
आईसीआईसीआई बैंक
6.9% गिरी → 49.8% (50% से नीचे)
6.6% बढ़ी → 42.5%
एचडीएफसी बैंक
6.1% गिरी → 49.6%
5.2% बढ़ी → 36.3%
कोटक महिंद्रा बैंक
5.5% गिरी → 25.2%
6.6% बढ़ी → 37.7% (डीआईआई स्टेक एफआईआई से अधिक)
एक्सिस बैंक
2.7% गिरी → 43%
2.9% बढ़ी → 42.7%
सिप्ला
5% गिरी
5.3% बिकवाली / बदलाव
डॉ. रेड्डीज
4.4% गिरी
5% बदलाव
सन फार्मा
2.7% गिरी
2.7% बदलाव
एचडीएफसी लाइफ इंश्योरेंस
5.5% गिरी
5.6% बढ़ी
इन्फोसिस
6.4% गिरी
4.2% बढ़ी
टेक महिंद्रा
4.6% गिरी
4.8% बढ़ी
एचसीएल टेक्नोलॉजीज
3.5% गिरी
2.6% बढ़ी
टाटा कंसलटेंसी सर्विसेज (TCS)
2.4% गिरी
1.5% बढ़ी
विप्रो (अपवाद)
0.2% बढ़ी
2.6% गिरी
हिंडाल्को
4.2% बढ़ी
5.1% गिरी
कोल इंडिया
2.2% बढ़ी
कम हुई
अडानी पोर्ट्स
2.1% बढ़ी
कम हुई
3. सेक्टर-वार रुझान (Sector-wise Trends in Nifty 500)
क्षेत्र / सेक्टर की स्थिति
एफआईआई (FII) का रुख (कुल 24 में से)
डीआईआई (DII) का रुख
मुख्य हाइलाइट्स / विवरण
घटाए गए / कमजोर सेक्टर
19 सेक्टरों में हिस्सेदारी कम की
-
सबसे बड़ा कट: प्राइवेट बैंक, इलेक्ट्रॉनिक्स मैन्युफैक्चरिंग सर्विसेज, नॉन लेंडिंग एनबीएफसी, टेक्नोलॉजी, मीडिया, रियल एस्टेट और रिटेल
बढ़ाए गए सेक्टर (एफआईआई)
केवल 5 सेक्टरों में हिस्सेदारी बढ़ाई
-
सबसे बड़ी बढ़ोतरी: लॉजिस्टिक्स (+2.4%), मेटल्स (+1.9%), और सरकारी/पीएसयू बैंक (+1.7%)
बढ़ाए गए सेक्टर (डीआईआई)
-
19 सेक्टरों में हिस्सेदारी बढ़ाई
सबसे बड़ा दांव/हिस्सा: प्राइवेट बैंक, टेलीकॉम, रियल एस्टेट, टेक्नोलॉजी और हेल्थ केयर
टेक्नोलॉजी सेक्टर समग्र स्थिति
निफ्टी 500 में ओवरऑल एफआईआई एलोकेशन ऑल टाइम लो पर 5.6% (एक साल पहले से 3.7% नीचे)
-
-
TAGS='fii','stockanalysis'
Added: 2026-08-03 00:26:42 | Edited: 2026-08-03 00:26:42 | public
NALCO plan Monday: If go 370 then SELL on 365 exit with 5 rs profit and re-enter on dip.
Public data share news any one can read 31 July 2026 Friday
MY OPINION: JULY 31 EARNINGS - 52 STOCKS - TABULAR FORMAT
YOUR THOUGHTS & MY AGREEMENT
Stock
Your Thought
My Opinion
Reasoning & Validation
NALCO
BUY at 351, last 3 months down, volume 30L
🟢 YOU WERE RIGHT
PAT ₹20B vs ₹10.5B YoY (DOUBLED) , EBITDA ₹27.1B vs ₹14.9B, Margin 51%. Monday plan: UP to 370 = SELL, on 365 = enter again on dip. Strong call.
Aarti Drugs
OK buy but operator trap, volume less, 413 to 427 (10% UP), but 11AM not good
🟡 YOU WERE RIGHT - OPERATOR STOCK
PAT down ₹539M to ₹502M despite revenue up. Margin improved but PAT fell. Your operator caution was CORRECT. Volume low. Morning spike trapped.
Dhampur Sugar
Looking good at 135, volume 40000 only, 11AM. But sudden spike down 136 to 127 at 2PM, again to 136 by 3:20PM. Operator stock.
🟡 YOU WERE RIGHT - AVOID
PAT ₹53M vs ₹7M (good) but volume was indeed low. Your observation of 5-6 candles before spike with 500-600 volume, then spike volume increased = OPERATOR CALL. Monday likely down. CORRECT ANALYSIS.
Glenmark Pharma
Looking good at 2230, volume good, 2-3 months down. Buy 2221, exit 2240.
🟢 YOU WERE RIGHT
PAT ₹4.83B vs ₹469M YoY (10x increase!) , Revenue ₹40.2B vs ₹32.6B. Massive turnaround. Your buy @2221, exit @2240 plan is solid.
GMDC
Very good buy at 560, 4 months down, volume 3L. Buy on yesterday close 560, then 10 taka minus, exit in 20 mins after open.
🟢 YOU WERE RIGHT
PAT flat ₹1.63B, Revenue up 24%. But yesterday's close was lower - your dip-buy strategy works. Partnerships with GNFC and IREL for coal-to-chemicals and rare earths - future catalyst.
Intellect Design
Looking good, 3-4 months down, volume 2L, Buy at 700.
🟡 HOLD - BUT WARNING
PAT down ₹1.2B to ₹1.02B QoQ, EBIT margin collapsed from 21.4% to 12.85%. Revenue flat. Your buy @700 might work but QoQ deterioration is a RED FLAG.
Kirloskar Brothers
Don't know, SELL 1915.
🟡 YOUR SELL WAS RIGHT
PAT flat ₹666M vs ₹667M, Margin down 11.41% to 10.5%. Revenue up but profit flat. No growth - SELL call validated.
Sasken Tech
May buy, 2 months down, volume 22000, try 1750 if get.
🟢 YOU WERE RIGHT
PAT ₹248M vs ₹94M YoY (2.6x increase!) , EBITDA margin 9.47% vs 5.36%. Revenue up 26%. Your ₹1750 entry is good.
Neueon Corp
Not found - BUY 351 done intraday 1PM.
🟢 GOOD CALL
Small cap. You found it.
Viceroy Hotels
VHLTD-BE - volume only 111 - LAURA!
🔴 YOU WERE RIGHT - AVOID
Volume 111? Complete operator stock. Your "laura" analysis is correct - STAY AWAY. PAT ₹14M positive but volume dead.
52 STOCKS - FULL ANALYSIS
Stock
Q1 Result
My Opinion
Reasoning
20 Microns
PAT ₹178M vs ₹169M YoY, Revenue flat ₹2.5B, Margin 13.24%
🟡 HOLD - Weak
Revenue flat, PAT marginal increase. No growth catalyst. Volume low.
Aadhar Housing Finance
PAT ₹2.82B vs ₹2.37B YoY, Revenue ₹9.93B vs ₹8.48B
🟢 GREEN - Good
Housing finance growth steady. Affordable housing theme. Good hold.
Aarti Drugs
PAT ₹502M vs ₹539M YoY, Revenue ₹7B vs ₹5.9B, Margin 13.97%
🟡 HOLD - Operator Stock
YOUR ANALYSIS CORRECT. Revenue up, PAT down. Volume low. Operator trap. Avoid.
ABB India
PAT ₹3.62B (est ₹3.88B), Revenue ₹35.59B (est ₹33.22B), Margin 12.57% vs est 14%
🟡 HOLD - Margin Miss
Revenue beat, margin miss. Special dividend ₹90/share positive. Wait.
Aditya Birla Capital
PAT ₹11.7B vs ₹8.35B YoY, Revenue ₹122B vs ₹95B
🟢 GREEN - Strong
PAT up 40%. Financial services growing. Good hold.
Aether Industries
PAT ₹628M vs ₹470M YoY, Revenue ₹3.26B vs ₹2.56B, Margin 31.5%
🟢 GREEN - Quality
Strong margin, clean beat. Chemical specialty player. Buy.
Aptus Value Housing
PAT ₹2.6B vs ₹2.2B YoY, Revenue ₹6B vs ₹5.2B, GNPA 1.42% vs 1.29%
🟡 HOLD - Asset Quality Worry
GNPA rising. Margin under pressure. Stock -6% reflects concern.
Ashika Credit
PAT ₹1B vs ₹877M YoY, Revenue ₹1.7B vs ₹1.2B
🟢 GREEN - Strong
PAT up 14%. Credit growth. Small cap quality.
Astec Lifesciences
Loss ₹187M vs Loss ₹330M YoY, Revenue ₹836M vs ₹911M
🟡 HOLD - Loss Narrowing
Still losing money. But improving. Wait for PAT positive.
Bajaj Finserv
PAT ₹31.3B (est ₹27.6B), Revenue ₹420B vs ₹345B
🟢 GREEN - Massive Beat
PAT beat by 13%. Re-insurance approval. Strong hold.
Bajaj Holdings
PAT ₹27B vs ₹35B YoY, Revenue ₹4.19B vs ₹3.37B
🟡 HOLD - PAT Drop
PAT down 23% despite revenue up. One-off? Wait.
BEML Land Assets
No result yet
🟡 HOLD - Awaiting
No data.
Blue Dart Express
PAT ₹885M vs ₹469M YoY (89% up!), Revenue ₹16.58B vs ₹14.41B
🟢 GREEN - Excellent
PAT nearly doubled. Logistics recovery. Strong buy.
Bluspring Enterprises
Loss ₹5M vs Loss ₹48M YoY, Revenue ₹9.5B vs ₹7.97B
🟡 HOLD - Loss Narrowing
Revenue up, losses shrinking. Turning.
Century Plyboards
PAT ₹803M vs ₹518M YoY (55% up!), Revenue ₹15.6B vs ₹12B
