In today’s Newsletter…

1. Impact CV: A professional RESUME writer for you to generate RESUME in less then 5 minutes

2. What is MCP (Model Context Protocol)?

3. Python Pandas Essentials

4. Best Platforms to learn AI skills In 2025

5. Google Tells Employees to use AI in their work

6. Google Reveals Energy Use of AI Prompts

I Built Impact CV GPT That Writes Resumes Under 5 Minutes

Crafting a résumé shouldn’t feel like a full-time job.

Yet most job seekers spend hours tweaking bullet points, reformatting sections, and rewriting summaries only to get rejected without explanation.

Even worse? Many turn to ChatGPT hoping it will “just write a résumé,” but the results are usually generic, robotic, and instantly ignored by recruiters. You end up wasting even more time prompting, editing, and re-prompting and still don’t get interviews.

That’s why I built Impact CV GPT. A ChatGPT tool trained specifically on REUSME to help you Generate tailored, ATS-ready resumes in minutes. Quantified, recruiter-friendly, and built for the exact job you want. All in less than 5 minutes.

Who it’s for:

• ✅Professionals tired of rewriting résumés for hours with zero results

• ✅Job seekers frustrated by generic ChatGPT outputs that don’t land interviews

• ✅Career changers who struggle to position their experience

• ✅Students or early-career job seekers who need structure and confidence

Impact CV is in its initial phase. I want you guys to use it and let me know can I improve it.

You can share your suggestion at this [email protected]

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MCP (Model Context Protocol) workflow (Simple step by step)

Best Courses to Master MCP:

1. Model Context Protocol (MCP) Mastery

2. Mastering MCP: Building Advanced Agentic Applications

1. User Query: You type a question or task. That’s the only input the system needs to start.

2. MCP Client receives it: A client app (e.g., Claude Desktop) parses your text, sets context, and prepares a plan.

3. “Need External Tools?” decision: The client quickly checks: can I answer from what I already know, or do I need outside data or actions?

4. If “No”: Use Internal Knowledge: The client composes an answer from its local/contextual knowledge and moves toward responding.

5. If “Yes”: Ask an MCP Server: The client forwards a structured request to an MCP server. The server is a tool/connector hub that can talk to many sources safely.

6. “Server Type” choice: The server selects the right capability based on your request (data fetch, API call, file access, custom code, etc.).

7. Query Database: For data lookups, it runs parameterized queries against approved databases to fetch rows/aggregates securely.

8. Web API call: If info lives on a service (search, weather, CRM, GitHub, etc.), it calls the API with proper auth and rate limits.

9. Execute Custom Logic: When logic is needed (formatting, scoring, filtering, evaluation), the server runs predefined functions or scripts.

10. Access Local Files (if allowed): With explicit permission, it can read allowed local/project files (notes, configs, CSVs) and extract only what’s needed.

11. Other Database/Source: The server can also reach secondary data stores the client is configured to trust (another DB, vector index, etc.).

12. Return Data: All tool results are normalized (structured JSON, tables, text) and sent back to the MCP server entrypoint.

13. Send Response to Client: The server bundles results and returns them to the client with metadata (which tools were used, status, errors if any).

14. Generate Final Answer: The client synthesizes the results into a clear answer, cites sources where appropriate, and delivers it to you.

MCP separates “thinking” (the model/client) from “doing” (servers/tools). That gives you safer permissions, auditable calls, reusable connectors, and reliable, repeatable workflows from question → tools → trusted data → final answer.

Pandas Essentials

It’s a table that summarizes all core Pandas concepts, methods, attributes, and best practices for quick learning and revision.

Best Platforms to learn AI skills In 2025

If you're looking to build real AI skills without spending a dime, here are my top picks:

1. AI For Everyone – Coursera: Beginner-friendly course by Andrew Ng that explains AI concepts clearly. Great for non-tech professionals too. (Free to audit)

2. Machine Learning with Python – LinkedIn Learning: Quick, beginner-level course that teaches Python and ML basics. Includes completion certificate.

3. AI Skills Challenge – Microsoft: Short, hands-on modules to learn Azure-based AI skills. Earn a free Microsoft badge upon completion.

4. AI/ML Learning Plan – AWS Skill Builder: Foundational-to-advanced AI and machine learning content from Amazon’s own training platform.

5. AI Fundamentals – IBM SkillsBuild: Job-focused, beginner-friendly courses with digital badges. Ideal for career switchers or freshers.

6. Prompt Engineering with ChatGPT : Short and powerful course to learn how to write effective prompts using ChatGPT. Highly rated.

7. Generative AI – Google Cloud Skills Boost: Learn generative AI concepts directly from Google. Covers prompting, ethics, and use cases.

8. Generative AI for Educators – Google for Education: Perfect for teachers. Shows how to integrate AI into classrooms and lesson plans.

9. AI for Everyone: Master the Basics – edX: University-backed (Harvard/MIT) introduction to AI. Audit free, certificate optional.
   

Google Tells Employees to use AI in their work

Google is urging employees to adopt AI in their daily work, warning that those who don’t will risk being left behind as rivals like Microsoft, Amazon, Meta, and Shopify push for the same. CEO Sundar Pichai told staff that competitors are racing ahead with AI and that Google must move faster to stay competitive. Since rolling out AI tools internally, engineers’ weekly productivity has reportedly increased by 10%.

Inside the company, initiatives like “AI Savvy Google” offer training, and tools such as the coding assistant Cider are already in use by half of eligible employees. Google has also strengthened its AI workforce with acquisitions like the $2.4B purchase of startup Windsurf.

Across Silicon Valley, the message is the same: AI use is now mandatory. Microsoft called it “no longer optional,” Amazon warned jobs would shrink without it, and Shopify requires teams to prove tasks can’t be done with AI before hiring. Meta’s CTO even said engineers who master AI will “command a premium.”

White-Collar Jobs Stagnate While Bartenders and Baristas See Bigger Raises (Source)

The U.S. job market is shifting in unexpected ways. While Gen Z graduates entering white-collar roles are struggling with stagnant pay and limited raises, workers in hospitality and health care are experiencing the fastest wage growth.

Since 2021, hospitality wages have climbed nearly 30%, and health care salaries are up about 25%, both rising faster than inflation. In contrast, wages in professional services, finance, and education have lagged behind—teachers, for example, are earning about 5% less when adjusted for inflation.

Despite baristas averaging around $16/hour compared to entry-level tech jobs at $19.57/hour, hospitality workers are enjoying steadier pay increases. White-collar employees, on the other hand, have seen their real wages erode since inflation spiked.

At the same time, hiring in tech and finance has slowed. Meta recently froze hiring in its AI division after years of expensive recruitment, while Amazon’s CEO Andy Jassy warned that AI could reduce white-collar roles altogether. Education and construction have also been hit hard, with wages falling furthest behind inflation.

Google Reveals Energy Use of AI Prompts (Source)

For the first time, Google has disclosed how much energy its Gemini AI uses per query. A single text prompt consumes about 0.24 watt-hours of electricity—roughly the same as running a microwave for one second. It also produces 0.03 grams of CO₂ and uses just five drops of water for cooling.

Most of the energy goes into Google’s AI chips (58%), while CPUs, backup machines, and cooling systems make up the rest. The report shows how much effort goes into answering even the smallest AI request.

The good news: Gemini is becoming far more efficient. Compared to last year, each query now uses 33 times less energy, thanks to better hardware and software.

Researchers say this is the most transparent report yet from a major AI company—a long-awaited step toward understanding AI’s real environmental impact

Until next time - shailesh and NextStepCareer