If you’re building AI products for retail, the question isn’t whether the market wants AI. It’s where.

We ran an AI-powered survey with 300 AI-generated respondents across 4 AI personas matching B2B retail professionals – CTOs, heads of operations, AI solutions architects, and retail data analysts. No human panel. Validated against Ipsos human panel data with ±2-5pp accuracy.

Here’s what the data says.

67.7% say they’ll adopt AI within 12 months
27.7% very likely. 40% somewhat likely. The adoption intent is clear. But intent without understanding the barriers is useless if you’re building for this market.

The top barrier isn’t cost
This is the part most people get wrong. When we asked about the primary concern:

Data security – 31%
Integration complexity – 28%
Cost – 21%
Lack of expertise – 17%
No concerns – 3%

If you’re building AI for retail and your pitch leads with ROI and cost savings, you’re answering a question that isn’t top of mind. The buyers are worried about security and integration first. Price is third.

Inventory management wins by a wide margin
When asked where AI would help most:

Inventory management – 38%
Sales forecasting – 26.3%
Customer service – 20%
Marketing – 13%
None – 2.7%

Retail wants AI in the back end. Inventory, demand forecasting, supply chain. Not chatbots. Not personalization. Not marketing automation. The operational pain is louder than the customer-facing opportunity.

For developers – this is useful signal. The highest-demand use cases are the ones that touch operations, not the ones that touch the customer.

Interest is growing, and so is budget
66% say their interest in AI for retail increased over the past year. 68.3% say they’re willing to allocate budget. That’s not vague optimism – that’s purchase intent.

But only 3% say they have no concerns about implementation. Nearly everyone wants it. Nearly everyone is nervous about something.

What this means if you’re building
The market is telling you three things:

1. Build for the back end first. Inventory and forecasting, not marketing.
2. Lead with security and integration, not price. Those are the real objections.
3. The buyers are ready. Budget is allocated. But trust and implementation are the bottlenecks.

Full data
Full survey results with all the questions, persona breakdown, and methodology:
https://inqvey.com/data/ai-retail-2026
We track 20 technology trends and run market research across them:
https://inqvey.com/trends
If you’re building for retail – what are you hearing from buyers? Does this match?

Your Pipeline Is 24.9h Behind: Catching Stock Market Sentiment Leads with Pulsebit

We recently uncovered an anomaly showcasing a 24h momentum spike of -1.332 in stock market sentiment. What does this mean for your models? It signals a potential downturn in sentiment, suggesting that traders may be reacting to significant market shifts. This specific spike reflects a critical moment in market dynamics, and if you’re not tuned into these shifts, you could be missing vital signals.

The Problem

This anomaly reveals a structural gap in any sentiment pipeline that doesn’t account for multilingual origin or entity dominance. If your model is only focusing on certain languages or regions, you might have missed this spike by 24.9 hours. The leading language here is English, with a focus on a cluster story around the Dow entering correction territory. Ignoring this could mean you’re basing decisions on stale data, leaving you blind to emerging trends.

English coverage led by 24.9 hours. Da at T+24.9h. Confidence scores: English 0.85, French 0.85, Spanish 0.85 Source: Pulsebit /sentiment_by_lang.

The Code

To effectively catch this momentum spike, we can use our API to filter by language and then analyze the sentiment of the narrative itself. Below is the Python code that accomplishes this:

import requests

![Left: Python GET /news_semantic call for 'stock market'. Rig](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1774654837218.png)
*Left: Python GET /news_semantic call for 'stock market'. Right: returned JSON response structure (clusters: 3). Source: Pulsebit /news_semantic.*


# Step 1: Fetch articles with a geographic origin filter
url = "https://api.pulsebit.com/v1/articles"
params = {
    "topic": "stock market",
    "lang": "en",  # Filter for English language articles
    "score": -0.484,
    "confidence": 0.85,
    "momentum": -1.332
}

![Geographic detection output for stock market. India leads wi](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_geo_output_1774654837345.png)
*Geographic detection output for stock market. India leads with 5 articles and sentiment +0.18. Source: Pulsebit /news_recent geographic fields.*


response = requests.get(url, params=params)
articles = response.json()

# Step 2: Analyze the sentiment of the cluster reason string
cluster_reason = "Clustered by shared themes: market, today:, dow, enters, correction."
sentiment_analysis_url = "https://api.pulsebit.com/v1/sentiment"
sentiment_response = requests.post(sentiment_analysis_url, json={"text": cluster_reason})
sentiment_score = sentiment_response.json()

print(f"Articles: {articles}")
print(f"Sentiment Score: {sentiment_score}")

In the first step, we filter articles based on the topic of “stock market”, ensuring we only pull those written in English. This is crucial as we want to capture the narrative that’s resonating in the leading market language.

In the second step, we send the cluster reason string through our sentiment analysis endpoint to assess how the framing of the narrative itself might influence sentiment perceptions. This dual approach allows us to not only see the data but understand the context.

Three Builds Tonight

1. Geo-Filtered Alert System: Set a threshold for momentum spikes. If the momentum dips below -1.5 in English articles from the U.S., trigger an alert. This allows you to focus on the most relevant geographic and linguistic context.

2. Meta-Sentiment Dashboard: Build a dashboard that aggregates these cluster narratives. For instance, track the sentiment score from the cluster reason “Clustered by shared themes: market, today:, dow, enters, correction.” and visualize how it shifts over time compared to mainstream narratives.

3. Forming Themes Tracker: Create an endpoint that logs the forming themes: stock(+0.00), market(+0.00), google(+0.00) vs mainstream: market, today:, dow. This will help you identify emerging themes before they become mainstream, allowing proactive decision-making.

Get Started

Dive right in — head over to pulsebit.lojenterprise.com/docs. You can copy-paste and run this code in under 10 minutes. Catching these market sentiment shifts can significantly enhance your trading strategy, so don’t let outdated models hold you back.

The Real Problem: The Gap Between Past Data and Present Issues

You’re running a React app in production. You have monitoring tools in place. You check the dashboard regularly. And yet.

Tuesday morning, you deploy. Everything looks good in staging. Users are happy.

By Thursday, things have changed.

Your app’s Largest Contentful Paint (LCP) jumps from 1.2s to 2.8s.

But you didn’t notice. Your users did.

By Friday, your app store reviews are filled with complaints: “This app is so slow.”

It’s too late now.

At this point, your monitoring tool tells you: “Yes, looking at the past hour’s data, LCP has indeed increased.”

But that’s just reporting after the fact.

“Monitoring” and “Alerting” Are Two Different Things—And Your Tools Are Missing One

You probably already have some form of monitoring in place:

• Error tracking tools (capturing exceptions and stack traces)
• Analytics platforms (showing you performance data from the past week)
• APM solutions (analyzing past traces)

What all of these have in common: they tell you “what happened” by looking at historical data.

In other words:

Monitoring = Looking at past data to identify problems after they occur

What you actually need is:

Alerting = Knowing immediately when a problem is happening right now

Existing tools excel at monitoring, but their alerting is limited to “notifications based on pre-configured metrics”—and even then, there’s a delay of minutes to tens of minutes while data aggregates.

During those minutes:

• Users abandon your app
• They switch to a competitor
• They leave a negative review

React teams have the tools to understand what happened, but lack the tools to know it’s happening.

Why This Gap Exists

Traditional monitoring and APM tools are designed for one purpose: detailed root cause analysis.

As a result, they require time to aggregate, analyze, and normalize data—which is why they end up showing you historical performance data rather than real-time signals.

What’s missing from the ecosystem is a dedicated tool that answers: “Is there a problem happening right now?”

The typical React team’s tool stack looks like this:

• Detailed analysis ← Existing monitoring tools (Datadog, New Relic, etc.)
• Real-time alerts ← This gap exists

What You Actually Need: Simple Real-Time Alerting

The good news: you don’t need anything complex.

1. Real-time measurement: Capture Core Web Vitals (LCP, FCP, CLS) directly in the browser
2. Instant judgment: Threshold exceeded → immediate notification
3. Team-wide alerts: Slack/Discord notifications
4. Essential info only: What? When? Where? How bad?

That’s it. With just these, your team can react immediately when production performance degrades.

The Answer: RPAlert Fills That Gap

We built RPAlert to solve exactly this problem.

RPAlert detects real-time performance degradation in your React app and sends instant alerts to Slack or Discord—nothing more, nothing less.

It’s the real-time alerting layer that fills the gap in your existing monitoring stack.

Getting Started in 1 Minute

npm install rpalert-sdk

// app.tsx or root layout
import { RPAlertProvider } from "rpalert-sdk/react";

export default function RootLayout({ children }: { children: React.ReactNode }) {
  return (
    <html lang="en">
      <body>
        <RPAlertProvider apiKey="YOUR_API_KEY">
          {children}
        </RPAlertProvider>
      </body>
    </html>
  );
}

Once installed, RPAlert automatically:

• Measures LCP (Largest Contentful Paint)
• Measures FCP (First Contentful Paint)
• Measures CLS (Cumulative Layout Shift)
• Detects long tasks (anything blocking the main thread for 50ms+)
• Sends alerts the moment thresholds are exceeded

What You’ll Actually See

Your Discord/Slack channel receives a message like this:

⚠️ LCP Alert — my-shop.com

LCP: 4.2s | Threshold: 2.5s | +68% over limit
Page: /products/winter-sale
Detected: 11:43 PM - Mar 25
App: my-shop.com

One message. Complete context. No noise.

Beyond real-time alerts, RPAlert also gives you a dashboard to track trends:

Performance trends over time:

• LCP trend visualization (past 30 days)
• Page-level breakdown (which pages are slowest)
• All metrics in one place (FCP, CLS, long task counts)
• Alert history (7 to 90 days depending on your plan)

RPAlert Is Not a “Monitoring Tool”—It’s an “Alerting Layer”

This is important: RPAlert is not trying to replace Datadog or New Relic.

Rather:

• “What is happening?” → Use your existing monitoring tools
• “Is something happening right now?” → Use RPAlert

Think of RPAlert as a smoke detector. It tells you there’s a fire, but you still need your existing tools to find it and extinguish it.

The winning combination: RPAlert + your existing monitoring stack

This two-layer approach finally gives React teams a complete workflow: detect immediately, analyze deeply.

Pricing: Start Free, Scale as You Grow

Free plan:

• 1 alert per month (free forever—just to try it)
• Next alert onwards requires a paid plan

Pro ($29.99/month):

• 50 alerts per month
• Up to 3 apps
• 30-day alert retention

Max ($49.00/month):

• 500 alerts per month
• Unlimited apps
• 90-day alert retention

What Your Team Gets

Frontend engineers:

• Know immediately if your deploy broke something
• Rollback decisions can be made in seconds, not hours
• Real data from real users, not synthetic benchmarks

Product teams:

• Early warning before users complain
• Identify which performance improvements actually move the needle
• Connect performance degradation to specific feature launches

DevOps / Infrastructure:

• Performance regressions caught in minutes, not days
• MTTR (mean time to recovery) drops from hours to seconds
• Easy correlation with deployment timestamps

Everyone:

• A shared understanding: “What’s actually happening in production right now?”

Get Started Today

RPAlert is free to try. No credit card required.

It takes 5 minutes to set up.

Visit rpalert.com, create an app, drop the SDK into your React app, and experience real-time performance alerting for the first time.

That 30-second advantage between problem and awareness? It’s the difference between a graceful fix and a support crisis.

Questions?

Drop a comment below. We’d love to hear what production performance challenges your React teams are dealing with.

Studying for the AWS Solutions Architect Associate exam felt… repetitive.

I was going through practice tests, selecting answers, checking results — but something was missing.

I wasn’t actually learning.

Most platforms tell you if you’re right or wrong, but they don’t really help you understand why. And after a while, it just becomes passive memorization.

So I thought:

What if learning AWS felt more like Netflix… and less like a textbook?

🧠 The Idea

Instead of static quizzes, I wanted something interactive.

Something where:

each question feels like a small “episode”
you pause and think before answering
and then get a clear explanation, like a post-scene breakdown

That’s when I decided to build a simple AI-powered learning app using AWS PartyRock.

⚙️ What I Built

I created a Netflix-style AI AWS SAA Coach that:

Generates AWS exam-style questions
Supports beginner and intermediate levels
Lets you answer in a real exam-like flow
Gives instant explanations after each answer
Helps you learn from mistakes instead of just scoring

The goal was simple:
👉 turn passive studying into active learning

🎥 Demo

I recorded a short walkthrough showing how it works in practice:

👉 https://www.youtube.com/watch?v=cMg6FF2ZbmQ

📸 How It Looks

💡 What I Learned Building This

This project made me realize something important:

Learning cloud concepts isn’t just about information — it’s about interaction.

When you:

think before answering
see immediate feedback
understand mistakes

You remember things much better.

🚧 It’s Still a Beta

Right now, the app focuses on:

beginner
intermediate questions

But after sharing it and getting feedback, one thing became clear:

👉 The AWS SAA exam is heavily based on real-world, multi-service scenarios

So the next step is to expand this into:

advanced-level questions
scenario-based challenges
multiple AWS services combined (VPC + S3 + Lambda, etc.)

Basically:

more real exam-style thinking

🚀 What’s Next

I want to evolve this into something that:

simulates real AWS exam scenarios
adapts to your weak areas
and keeps the “interactive learning” feel
🙌 Would Love Your Feedback

This started as a small experiment, but I think the idea has potential.

If you’re preparing for AWS SAA or working with AWS:

👉 What topics or scenarios did you find most challenging?

🍿 Final Thought

What if learning complex topics didn’t feel like studying…

…but more like watching and interacting with a series?

Overview

In this tutorial, you’ll learn why HTTP requests or API calls made directly from the browser fail with a “Failed to Fetch” error, and how to fix it using the proxy pattern.

Background

If you’ve ever tried to make an HTTP request or API calls to an external server directly from a frontend JavaScript app, you’ve likely seen this error:

It shows up in the browser console, but the request never goes through. It can feel confusing because the same call works fine in Postman or curl.

The root cause is not your code. It is a browser security mechanism called CORS, and understanding it is the first step to fixing it.

Same-Origin Policy

Browsers enforce a rule called the Same-Origin Policy. It restricts web pages from making requests to a different origin than the one that served the page.

Three things define an origin:

• Protocol — http or https

• Domain — localhost or example.com

• Port — 3000, 8080, etc.

If any of these three differ between your frontend and the server you’re calling, the browser treats it as a cross-origin request.

For example:

Cross-Origin Resource Sharing (CORS)

CORS is the mechanism that controls whether a cross-origin request is allowed.

When your browser makes a cross-origin request, it first sends a preflight request to ask the server: “Do you allow requests from my origin?” If the server doesn’t respond with the right headers — Access-Control-Allow-Origin — The browser blocks the request entirely and throws the “Failed to Fetch” error.

💡 Note: This is a browser restriction, not an API restriction. The same request works from a server, Postman, or curl because those environments don’t enforce the Same-Origin Policy.

Prerequisites

To follow along, make sure you have:

• A basic understanding of HTML and JavaScript

• Node.js v18 or higher installed

• VS Code with the Live Server extension installed

Project Structure

We’ll be working with a simple YouTube Video Info app — it takes a YouTube URL and attempts to fetch the page directly from the browser. It’s a straightforward example that clearly demonstrates the “Failed to Fetch” CORS error.

Clone the broken version of the app:

git clone https://github.com/manueldezman/youtube-info.git
cd youtube-info

The project structure looks like this:

broken-version/
├── index.html    # HTML structure
├── styles.css    # Styling
└── app.js        # Frontend logic — calls YouTube directly

To start the app:

1. Open the project folder in VS Code.

2. Click Go Live in the bottom right corner to start the Live Server(as shown below).

Your browser will open automatically at http://127.0.0.1:5500.

How a Direct Browser Call Triggers the CORS Error

Paste any YouTube URL into the input field and click Fetch Info. You’ll see the button spin for a moment, and then an error appears.

To understand the error, open DevTools by right-clicking anywhere on the page and selecting Inspect, as shown in the media below.

Your console should look like this:

The Cause of the Error

Your frontend is running at http://127.0.0.1:5500 (Live Server). YouTube is at https://www.youtube.com. These are two completely different origins — different protocols, different domains, different ports. The browser sends a preflight request to YouTube asking if cross-origin requests are allowed. YouTube doesn’t return the Access-Control-Allow-Origin header for direct page requests, so the browser blocks the request entirely before it even reaches YouTube.

Open app.js and look at the fetchInfo function:

async function fetchInfo() {
  const url = document.getElementById('video-url').value.trim();

  /* ⚠️ Direct browser-to-YouTube call — triggers CORS error */
  const res  = await fetch(url);
  const html = await res.text();
  ...
}

The fetch(url) call goes directly to https://www.youtube.com from the browser, which triggers the CORS block. The request never reaches YouTube, and the app fails silently.

How to Fix the Error Using a Proxy Server

The fix is to stop calling YouTube directly from the browser and instead route the request through your own backend.

Your frontend is running at http://127.0.0.1:5500. YouTube is at https://www.youtube.com. These are two different origins — that’s why CORS blocks the request.

The solution is to introduce a Node.js proxy server that sits between the two. Your frontend calls your own backend at http://localhost:3000/fetch. The Node.js server calls YouTube server-to-server — no browser, no CORS enforcement — fetches the page, extracts the video title and description from the HTML meta tags, and returns clean data back to the frontend.

Here’s how to add the proxy to the project.

Step 1 — Create server.js

In the project root, create a new file called server.js:

broken-version/
├── index.html
├── styles.css
├── app.js
└── server.js     ← add this

Step 2 — Add the proxy code

Paste the following into server.js:

const http  = require('http');
const https = require('https');
const url   = require('url');

const PORT = 3000;

function extractTitle(html) {
  var match = html.match(/<title>([^<]*)</title>/i);
  return match ? match[1].replace(' - YouTube', '').trim() : null;
}

function extractMeta(html, property) {
  var match = html.match(new RegExp('<meta[^>]*(?:name|property)=["']' + property + '["'][^>]*content=["']([^"']*)["']', 'i'));
  if (match) return match[1];
  match = html.match(new RegExp('<meta[^>]*content=["']([^"']*)["'][^>]*(?:name|property)=["']' + property + '["']', 'i'));
  return match ? match[1] : null;
}

const server = http.createServer(function(req, res) {

  res.setHeader('Access-Control-Allow-Origin', '*');
  res.setHeader('Access-Control-Allow-Methods', 'GET, OPTIONS');
  res.setHeader('Access-Control-Allow-Headers', 'Content-Type');

  if (req.method === 'OPTIONS') {
    res.writeHead(200);
    res.end();
    return;
  }

  var parsed   = url.parse(req.url, true);
  var videoUrl = parsed.query.url;

  if (parsed.pathname !== '/fetch') {
    res.writeHead(404, { 'Content-Type': 'application/json' });
    res.end(JSON.stringify({ error: 'Not found' }));
    return;
  }

  if (!videoUrl) {
    res.writeHead(400, { 'Content-Type': 'application/json' });
    res.end(JSON.stringify({ error: 'Missing url parameter' }));
    return;
  }

  https.get(videoUrl, { headers: { 'User-Agent': 'Mozilla/5.0 (compatible; proxy/1.0)' } }, function(youtubeRes) {
    var body = '';

    youtubeRes.on('data', function(chunk) { body += chunk; });

    youtubeRes.on('end', function() {
      var title       = extractTitle(body) || extractMeta(body, 'og:title') || 'N/A';
      var description = extractMeta(body, 'og:description') || extractMeta(body, 'description') || 'N/A';

      res.writeHead(200, { 'Content-Type': 'application/json' });
      res.end(JSON.stringify({ title: title, description: description }));
    });

  }).on('error', function(e) {
    res.writeHead(500, { 'Content-Type': 'application/json' });
    res.end(JSON.stringify({ error: e.message }));
  });

});

server.listen(PORT, function() {
  console.log('Proxy server running at http://localhost:' + PORT);
});

This server runs on port 3000. It receives the YouTube URL from the frontend, fetches the page HTML server-to-server, extracts the title and description from the meta tags, and returns clean JSON back to the frontend.

Step 3 — Update app.js

In app.js, find the direct fetch call:

/* ⚠️ Direct browser-to-YouTube call — triggers CORS error */
const res  = await fetch(url);
const html = await res.text();
const title = html.match(/<title>([^<]*)</title>/i);

document.getElementById('title').textContent  = title ? title[1].replace(' - YouTube', '') : 'N/A';
document.getElementById('author').textContent = 'N/A';
output.style.display = 'block';

Replace it with:

/* ✅ Calls the local Node.js proxy — not YouTube directly */
const res  = await fetch('http://localhost:3000/fetch?url=' + encodeURIComponent(url));
const data = await res.json();

document.getElementById('title').textContent  = data.title || 'N/A';
document.getElementById('author').textContent = data.description || 'N/A';
output.style.display = 'block';

These also remove the lines that tried to parse the raw HTML; they are no longer needed since the proxy returns clean JSON.

Step 4 — Start the proxy server

Open a new terminal in the project folder and run:

node server.js

You should see:

Leave this terminal running. Keep Live Server running in VS Code at the same time.

Step 5 — Test it

Go back to your browser at http://127.0.0.1:5500, paste a YouTube URL, and click Fetch Info.

The “Failed to Fetch” error is gone. The request now flows from the browser to your own proxy server at localhost:3000, which fetches YouTube server-to-server and returns the video title and description to the browser.

Summary

In this tutorial, you have learnt why HTTP requests made directly from the browser fail with a “Failed to Fetch” error — and how to fix it using the proxy pattern.

The key takeaway is this: browsers enforce the Same-Origin Policy, which blocks cross-origin requests to servers that don’t return the right CORS headers. The proxy pattern solves this by routing your requests through your own backend — no browser restrictions, no exposed secrets.

This pattern works for any external server, YouTube, payment APIs, AI platforms, or anything else your frontend needs to talk to securely.

While this tutorial uses a YouTube URL fetch to demonstrate the error, the same proxy pattern applies when calling any external API — whether it’s an AI platform like Gemini or OpenAI, a payment API like Stripe, or any other service that blocks direct browser requests. The only difference is that, with APIs, your proxy also handles authentication by securely storing the API key in environment variables on the server, keeping it out of the browser entirely.

Connect with me on my socials if you have any questions or want to follow along with future tutorials.

Thank you for reading, and I look forward to seeing you next time!

cors #javascript #nodejs #webdev #frontend #proxy #apidevelopment