In today’s Newsletter…

1. Trump’s $100,000 H-1B Visa Affecting Foreign Talents

2. Create How To Videos In seconds

3. Edit your files without touching them

4. Boost Your Memory With the 2-7-30 Rule

5. Top 10 Free APIs Every Data Scientist Should Know

6. 7 Easy NumPy Tricks (With Simple Examples)

Trump’s $100,000 H-1B Visa Affecting Foreign Talents

President Trump has announced a $100,000 fee on H-1B visas, starting with FY 2027. While the cost is charged to employers, not students directly, it’s expected to make U.S. companies far more selective when sponsoring international talent. That means fewer entry-level opportunities for foreign graduates, raising anxiety among Indian parents and students who see the U.S. as the top study and career destination.

For many, the traditional path … study in the U.S., work under OPT, then shift to an H-1B, just became more uncertain. Experts advise families to prepare a backup plan in other countries like Canada or the UK, especially for students aiming at Spring 2026. For Fall 2026 applicants, the advice is to apply but stay alert as policies could shift after legal challenges.

The bottom line: a U.S. degree still carries strong value, but the ROI is harder to justify if job sponsorships shrink. With Indians making up over 70% of H-1B holders, this policy could reshape how families weigh study-abroad decisions in the years ahead.

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Claude Just Made File Editing Effortless

Claude is quietly becoming one of the most practical AI tools out there. Its newest update lets you create and edit files directly from Word docs and PDFs to Excel sheets and PowerPoint slides. Just describe what you need in plain English, and Claude builds it for you: reports, charts, even polished presentations.

But here’s the real game-changer: you can now edit files without ever opening them. Instead of manually tweaking spreadsheets or scanning through pages of text, you just tell Claude what to change.

For example: “Switch all USD prices to EUR, replace ‘Manager’ with ‘Executive,’ and update the client’s name.” It applies everything in one pass, while keeping the formatting intact.

With Apple rumored to be eyeing Claude for Siri and Microsoft already tapping it for Office 365 features, this is more than just a small update. It shows how AI is moving from “smart assistant” to real workplace teammate and file work might never feel the same again.

Boost Your Memory With the 2-7-30 Rule (Source)

Struggling to remember what you just learned? Science has a simple fix: the 2-7-30 Rule. Based on over 150 years of memory research, it uses “spaced repetition” … reviewing information at increasing intervals to make sure it sticks.

Here’s how it works:

• Review what you learned 2 days after first studying it.

• Review again after 7 days.

• Do one more check at 30 days.

This simple cycle interrupts your brain’s natural “forgetting curve,” where we lose most of what we learn in the first few days. Each recall session strengthens the memory, making it easier to remember weeks or even months later.

Practical examples?

• Learning a new language: quiz yourself on vocab at 2, 7, and 30 days.

• Reading a book: write a 1-page summary and rewrite it from memory on those dates.

• Studying for exams: schedule reviews in your calendar so you don’t miss the intervals.

Top 10 Free APIs Every Data Scientist Should Know

Finding real-world data is often the hardest part of a project. While toy datasets are everywhere, high-quality or real-time data usually requires APIs. Here are some of the best free options you can plug into your projects today.

• Foundational Repositories: Kaggle and Hugging Face make it easy to pull community-shared datasets straight into your workflow.

• Web & Crawling: Firecrawl helps scrape and extract website content in clean formats, while Tavily offers fast, accurate web search with 1,000 free queries per month.

• Geospatial & Weather: OpenWeatherMap gives detailed forecasts and historical weather data, and OpenStreetMap provides global maps with custom queries.

• Finance: Alpha Vantage delivers stock, forex, and crypto data; Yahoo Finance (via yfinance) is great for quick market insights.

• Social Data: Reddit’s PRAW library makes collecting posts and comments easy, while X (Twitter) APIs provide access to real-time conversations.

7 Easy NumPy Tricks (With Simple Examples)

NumPy is a Python library that makes working with numbers and lists super fast. Instead of using loops, you can run operations on whole lists (arrays) at once. Here are 7 easy tricks:

1. Replace Loops with Vectorized Operations

Instead of adding numbers one by one, NumPy can add them all at once.

import numpy as np

# Daily sales at 2 stores for 3 days
sales = np.array([[10, 15, 20], 
                  [12, 18, 22]])

# Total sales per day
totals = sales.sum(axis=0)
print(totals)   # [22 33 42]

Beginner takeaway: No for loop needed — it adds columns (days) in one step.

2. Broadcasting for Quick Math

You can apply one list of numbers to another automatically.

prices = np.array([[100, 200, 300], 
                   [110, 210, 310]])

# Discounts for each day
discounts = np.array([0.9, 0.8])  

# Apply discount to all products
final_prices = prices * discounts[:, None]
print(final_prices)

Beginner takeaway: Discounts are applied to each row without looping.

3. Use np.where() for Conditions

Quickly apply “if-else” logic.

temps = np.array([15, 35, 8, 25])

# Add surcharge if temp <10 or >30
surcharge = np.where((temps < 10) | (temps > 30), 1.2, 1.0)
print(surcharge)  # [1.0 1.2 1.2 1.0]

Beginner takeaway: Replace all if temp < 10 checks with one line.

4. Matrix Multiplication with @

👉 Multiply tables of numbers easily.

prices = np.array([[10, 12], [11, 13]])
quantities = np.array([[2, 3], [4, 5]])

# Multiply matrices
total = prices @ quantities.T
print(total)

Beginner takeaway: Great for when you need totals from multiple items × quantities.

5. Inner Product with np.dot()

👉 Combine weights with values.

marks = np.array([80, 70, 90])   # your marks
weights = np.array([0.4, 0.3, 0.3]) # subject weight

# Weighted average
final_score = np.dot(marks, weights)
print(final_score)   # 81

Beginner takeaway: Perfect for averages where some items matter more.

6. Generate Random Data with np.random()

👉 Quickly make large sets of test data.

# Generate 10 random ages between 18–60
ages = np.random.randint(18, 60, size=10)
print(ages)

Beginner takeaway: No need to type numbers yourself — NumPy makes fake data.

7. Save Memory with np.asarray()

👉 Avoid making unnecessary copies.

data = [[1, 2, 3], [4, 5, 6]]
arr = np.asarray(data)
print(arr.mean())  # 3.5

Beginner takeaway: Works like np.array() but uses less memory when possible.

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]

Until next time - shailesh and NextStepCareer