React Server Components are a new way of rendering React apps. Some components run on the server (no client-side code), some run on a client (for interactivity) The server ones can talk to databases directly.React Server Components are a new way of rendering React apps. Some components run on the server (no client-side code), some run on a client (for interactivity) The server ones can talk to databases directly.

I Finally Get React Server Components (After Breaking Everything Twice)

2025/09/29 19:53

A couple of weeks ago, I finally decided to properly dive into React Server Components. As mostly an Angular dev, I'd been watching React from the sidelines and learning it. RSCs had been experimental for years, but with React 19 making them stable, I figured it was time to see what the fuss was about. What started as "I'll just try this for a weekend" turned into completely rebuilding my mental model around how React works. Here's everything I learnt the hard way.

What You're Getting Into

I'm not going to lie to you with some perfectly organised list. Here's what actually happened when I started this:

First, I had no idea what I was doing. Then I kind of got it. Then everything broke in new and creative ways. Then I fixed it and felt smart.

You'll learn about the server/client component thing (which confused me for way longer than I'm comfortable admitting), how to build an actual blog that doesn't suck, why my CSS randomly stopped working, and how to debug the weirdest errors you've ever seen.

React Server Components

Two years ago, I heard Dan Abramov talking about React Server Components at some conference. My first thought was "great, another React feature I might never use". They were experimental then, and I figured they'd stay that way forever. I was wrong.

Here's the deal: you know how your React apps send a massive JavaScript bundle to the browser, then the browser has to parse all that code, run it, make API calls, and finally show something to the user? Yeah, that's… not great. Especially when you're on a slow connection or an older phone.

Server Components flip this around. Some of your components run on the server, do all the heavy lifting there and send just the rendered HTML to the browser. The user gets content immediately, your JavaScript bundle shrinks, and everyone's happy.

Why I Now Care About React Server Components

I've watched React hype come and go from the Angular community. Remember when everyone was losing their minds over Concurrent Mode? As an Angular dev, I mostly ignored it. But RSCs felt different when I finally tried them for a learning project. Here's why they actually impressed me:

My Bundle Actually Got Smaller

Before, my test project's main bundle was 400KB. After: 118KB. That's not a typo. Turns out, when half your components run on the server, you don't need to ship their code to the browser. Coming from Angular, where we're used to tree-shaking and lazy loading, this was still impressive.

Data Fetching That Makes Sense

I've always heard about the useEffect, useState, setLoading dance that React devs complain about. Coming from Angular, where we have services and observables, React's data fetching always seemed unnecessarily complex. With server components, I just… fetch the data. In the component. Like a normal function. It's almost too simple.

My Lighthouse Scores Got Better

Core Web Vitals were "actually pretty good" right out of the box. As someone used to Angular Universal for SSR, this felt surprisingly effortless.

The Big Mental Shift

Traditional React: Everything happens in the browser. Every component, every state update, every API call - all client-side. Works fine until your bundle size starts getting massive.

React with RSCs: Some components run on the server (no client-side code), some run on the client (for interactivity). The server ones can talk to databases directly. The client ones handle clicks and form submissions. It's like having two different execution environments that somehow work together without you having to think about it too much.

Coming from Angular, this felt familiar in some ways, we've had server-side rendering with Angular Universal for years. But the way RSCs seamlessly blend server and client execution is genuinely different. No more "fetch data in useEffect, store in state, show loading spinner" dance. Just async/await in your component and you're done.

Setting Up My Mac

Before we start, let's make sure your computer isn't going to fight you. I learned this the hard way when I spent an hour debugging something that turned out to be a Node version issue.

The Basics

Open Terminal. You know, that app you use to feel like a hacker. Run these:

node --version npm --version 

You need Node 18 or higher. If you're on an older version, go to nodejs.org and download the latest LTS version.

If you don't have VS Code, get it.

code --version 

Extensions That Actually Help

I wasted time installing extensions I never use. Here's what actually matters:

Open VS Code, hit ⌘ + Shift + X and install:

  1. ES7+ React/Redux/React-Native snippets - Saves you from typing import React for the millionth time
  2. Tailwind CSS Intelli-Sense - Because nobody memorises CSS class names
  3. Auto Rename Tag - Changes closing tags when you change opening tags. Life changing.
  4. GitLens - Makes Git actually useful inside VS Code

Building Something Real (And Breaking It Immediately)

Time to stop reading and start breaking things. I created a folder in my Documents directory because I'm not organized enough to have a proper projects folder:

cd ~/Documents npx create-next-app@latest my-rsc-blog --typescript --tailwind --eslint --app --src-dir --import-alias "@/*" 

Yeah, that command is a mouthful. But each flag matters:

  • --typescript because debugging JavaScript errors at 2 AM is not fun
  • --tailwind because I'm bad at CSS
  • --eslint because I make dumb mistakes
  • --app because that's where RSCs live
  • --src-dir because I like organized projects
  • --import-alias because ../../../components is ugly
cd my-rsc-blog npm run dev 

Hit http://localhost:3000 and you should see the Next.js welcome page. If you get a port error, just use a different port:

npm run dev -- -p 3001 

What You Actually Get

Open the project in VS Code:

code . 

The structure looks like this:

my-rsc-blog/ ├── src/ │   └── app/ │       ├── globals.css │       ├── layout.tsx │       ├── page.tsx │       └── favicon.ico ├── package.json ├── tailwind.config.ts └── tsconfig.json 

Clean. Simple. About to become much more complicated.

Server vs Client Components

This took me embarrassingly long to understand. Let me save you some pain.

By default, every component in the app directory is a Server Component. It runs on the server. It can't use useState or onClick or any browser APIs because there's no browser. But it can fetch data directly from databases, keep secrets actually secret and do expensive computations without making phones burn your fingers.

Client Components have "use client" at the top. They run in the browser. They can handle user interactions, manage state and use all the React hooks you know and love.

My First "Wait, This Actually Works" Moment

I created src/app/components/ServerTime.tsx:

// No "use client" = runs on the server async function ServerTime() {   // This code never reaches the browser   const response = await fetch('http://worldtimeapi.org/api/timezone/Europe/London');   const data = await response.json();    return (     <div className="p-4 bg-blue-50 rounded-lg">       <h2 className="text-lg font-bold">Server Component</h2>       <p>Current time: {data.datetime}</p>       <p className="text-sm text-gray-600">         This was fetched on the server. Check the Network tab - no API call!       </p>     </div>   ); }  export default ServerTime; 

The wild part? No useEffect. No useState. No loading states. Just async/await like a normal function. The server fetches the data, renders the HTML and sends it to the browser. The browser never sees the API call.

Building a Client Component for Comparison

Then I made src/app/components/ClientCounter.tsx:

"use client"; // This line makes all the difference  import { useState } from 'react';  function ClientCounter() {   const [count, setCount] = useState(0);    return (     <div className="p-4 bg-green-50 rounded-lg">       <h2 className="text-lg font-bold">Client Component</h2>       <p>Count: {count}</p>       <button         onClick={() => setCount(count + 1)}         className="mt-2 px-4 py-2 bg-green-600 text-white rounded hover:bg-green-700"       >         Click me!       </button>       <p className="text-sm text-gray-600">         This runs in your browser and handles interactions.       </p>     </div>   ); }  export default ClientCounter; 

I updated src/app/page.tsx to use both:

import ServerTime from './components/ServerTime'; import ClientCounter from './components/ClientCounter';  export default function Home() {   return (     <main className="max-w-4xl mx-auto p-8">       <h1 className="text-3xl font-bold mb-8">Server vs Client Components</h1>       <div className="space-y-6">         <ServerTime />         <ClientCounter />       </div>     </main>   ); } 

Refresh the page and watch the server component's time update on each reload while the client component keeps its state. That's when it clicked for me.

Building a Blog (Because TodoMVC Is Overdone)

Enough toy examples. Time to build something realistic for this learning exercise. A blog seemed like the perfect RSC use case with mostly static content and some interactive bits.

Planning

I wanted:

  • A list of blog posts (server component)
  • Individual post pages (server component with dynamic routing)
  • Search functionality (client component for the form, server component for results)
  • Loading states that don't make users think the site is broken
  • Error handling that doesn't show stack traces to users

Fake Data (Because I'm Lazy)

I created src/app/lib/blog-data.ts with some mock data:

export interface BlogPost {   id: string;   title: string;   excerpt: string;   content: string;   author: string;   publishedAt: string;   readingTime: string;   tags: string[]; }  const posts: BlogPost[] = [   {     id: '1',     title: 'Why I Finally Learned React Server Components',     excerpt: 'After ignoring them for six months, I finally gave in. Here\'s what I learned.',     content: 'I used to think React Server Components were just hype.',     author: 'Me',     publishedAt: '2025-07-15',     readingTime: '5 min',     tags: ['React', 'Next.js', 'Learning']   },   {     id: '2',     title: 'The Day CSS Broke My Soul',     excerpt: 'A story about specificity, cascade and why I use Tailwind now.',     content: 'Picture this: you spend three hours making your component look perfect. Then you add one CSS rule and everything breaks...',     author: 'Still Me',     publishedAt: '2025-06-10',     readingTime: '3 min',     tags: ['CSS', 'Tailwind']   } ];  // Simulate slow network because real APIs aren't instant function delay(ms: number) {   return new Promise(resolve => setTimeout(resolve, ms)); }  export async function getBlogPosts(): Promise<BlogPost[]> {   await delay(800); // Fake some loading time   return posts; }  export async function getBlogPost(id: string): Promise<BlogPost | null> {   await delay(600);   return posts.find(post => post.id === id) || null; }  export async function searchPosts(query: string): Promise<BlogPost[]> {   await delay(500);   if (!query.trim()) return [];    return posts.filter(post =>     post.title.toLowerCase().includes(query.toLowerCase()) ||     post.content.toLowerCase().includes(query.toLowerCase()) ||     post.tags.some(tag => tag.toLowerCase().includes(query.toLowerCase()))   ); } 

The delay functions are important. Real APIs have latency and you want to test your loading states.

Building the Post Card Component

I made src/app/components/PostCard.tsx:

import Link from 'next/link'; import { BlogPost } from '../lib/blog-data';  interface PostCardProps {   post: BlogPost; }  function PostCard({ post }: PostCardProps) {   return (     <article className="bg-white rounded-lg shadow-sm hover:shadow-md transition-shadow p-6">       <div className="flex items-center text-sm text-gray-500 mb-3">         <span>{post.author}</span>         <span className="mx-2">•</span>         <span>{new Date(post.publishedAt).toLocaleDateString()}</span>         <span className="mx-2">•</span>         <span>{post.readingTime}</span>       </div>        <h2 className="text-xl font-bold text-gray-900 mb-3">         <Link href={`/blog/${post.id}`} className="hover:text-blue-600">           {post.title}         </Link>       </h2>        <p className="text-gray-600 mb-4">{post.excerpt}</p>        <div className="flex flex-wrap gap-2">         {post.tags.map(tag => (           <span key={tag} className="px-2 py-1 bg-gray-100 text-gray-700 text-sm rounded">             {tag}           </span>         ))}       </div>     </article>   ); }  export default PostCard; 

Nothing fancy. Just a card that looks decent and links to individual posts.

The Homepage (Where Things Got Interesting)

I rewrote src/app/page.tsx:

import { Suspense } from 'react'; import { getBlogPosts } from './lib/blog-data'; import PostCard from './components/PostCard'; import SearchForm from './components/SearchForm';  async function PostList() {   const posts = await getBlogPosts();    return (     <div className="grid gap-6 md:grid-cols-2">       {posts.map(post => (         <PostCard key={post.id} post={post} />       ))}     </div>   ); }  function LoadingSkeleton() {   return (     <div className="grid gap-6 md:grid-cols-2">       {[...Array(4)].map((_, i) => (         <div key={i} className="bg-gray-200 rounded-lg h-48 animate-pulse" />       ))}     </div>   ); }  export default function Home() {   return (     <main className="max-w-6xl mx-auto px-4 py-8">       <header className="text-center mb-12">         <h1 className="text-4xl font-bold text-gray-900 mb-4">My Blog</h1>         <p className="text-xl text-gray-600">           Thoughts on code, life and why everything breaks at 3 AM         </p>       </header>        <SearchForm />        <Suspense fallback={<LoadingSkeleton />}>         <PostList />       </Suspense>     </main>   ); } 

The magic here is Suspense. While PostList is fetching data on the server, users see the loading skeleton. No JavaScript waterfalls, no flash of loading content - just smooth progressive rendering.

Individual Post Pages (Dynamic Routing That Actually Works)

I created src/app/blog/[id]/page.tsx:

import { notFound } from 'next/navigation'; import Link from 'next/link'; import { getBlogPost } from '../../lib/blog-data'; import type { Metadata } from 'next';  interface PostPageProps {   params: Promise<{ id: string }>; }  // This generates metadata for each post export async function generateMetadata({ params }: PostPageProps): Promise<Metadata> {   // Next.js 15 requires awaiting params (learned this the hard way)   const { id } = await params;   const post = await getBlogPost(id);    if (!post) {     return { title: 'Post Not Found' };   }    return {     title: post.title,     description: post.excerpt,     authors: [{ name: post.author }],   }; }  export default async function PostPage({ params }: PostPageProps) {   const { id } = await params;   const post = await getBlogPost(id);    if (!post) {     notFound(); // Shows 404 page   }    return (     <main className="max-w-4xl mx-auto px-4 py-8">       <Link href="/" className="text-blue-600 hover:text-blue-800 mb-8 inline-block">         ← Back to Blog       </Link>        <article className="prose prose-lg max-w-none">         <header className="mb-8">           <h1 className="text-4xl font-bold text-gray-900 mb-4">{post.title}</h1>            <div className="flex items-center text-gray-600 mb-6">             <span>{post.author}</span>             <span className="mx-2">•</span>             <span>{new Date(post.publishedAt).toLocaleDateString()}</span>             <span className="mx-2">•</span>             <span>{post.readingTime}</span>           </div>            <div className="flex flex-wrap gap-2">             {post.tags.map(tag => (               <span key={tag} className="px-2 py-1 bg-gray-100 text-gray-700 text-sm rounded">                 {tag}               </span>             ))}           </div>         </header>          <div className="text-gray-800 leading-relaxed">           {post.content}         </div>       </article>     </main>   ); } 

Each post gets its own URL, proper metadata and server-side rendering. The generateMetadata function runs on the server and populates the <head> tag with the right information.

Adding Search (Client + Server Components Working Together)

Search forms need to be interactive, so I made src/app/components/SearchForm.tsx a client component:

"use client";  import { useState } from 'react'; import { useRouter } from 'next/navigation';  function SearchForm() {   const [query, setQuery] = useState('');   const router = useRouter();    const handleSubmit = (e: React.FormEvent) => {     e.preventDefault();     if (query.trim()) {       router.push(`/search?q=${encodeURIComponent(query)}`);     }   };    return (     <form onSubmit={handleSubmit} className="mb-8">       <div className="flex gap-2 max-w-md mx-auto">         <input           type="text"           value={query}           onChange={(e) => setQuery(e.target.value)}           placeholder="Search posts..."           className="flex-1 px-4 py-2 border border-gray-300 rounded-lg focus:ring-2 focus:ring-blue-500 focus:border-transparent"         />         <button           type="submit"           className="px-6 py-2 bg-blue-600 text-white rounded-lg hover:bg-blue-700 focus:ring-2 focus:ring-blue-500"         >           Search         </button>       </div>     </form>   ); }  export default SearchForm; 

Then I created the search results page at src/app/search/page.tsx:

import { Suspense } from 'react'; import Link from 'next/link'; import { searchPosts } from '../lib/blog-data'; import PostCard from '../components/PostCard';  interface SearchPageProps {   searchParams: Promise<{ q?: string }>; }  async function SearchResults({ query }: { query: string }) {   const results = await searchPosts(query);    if (results.length === 0) {     return (       <div className="text-center py-12">         <p className="text-gray-600 text-lg">           No posts found for "{query}". Try different keywords?         </p>       </div>     );   }    return (     <div className="grid gap-6 md:grid-cols-2">       {results.map(post => (         <PostCard key={post.id} post={post} />       ))}     </div>   ); }  export default async function SearchPage({ searchParams }: SearchPageProps) {   // Another Next.js 15 thing - searchParams must be awaited   const { q: query = '' } = await searchParams;    return (     <main className="max-w-6xl mx-auto px-4 py-8">       <Link href="/" className="text-blue-600 hover:text-blue-800 mb-6 inline-block">         ← Back to Blog       </Link>        <h1 className="text-3xl font-bold text-gray-900 mb-8">         Search Results         {query && <span className="text-gray-600"> for "{query}"</span>}       </h1>        {query ? (         <Suspense fallback={<div>Searching...</div>}>           <SearchResults query={query} />         </Suspense>       ) : (         <p className="text-gray-600">Enter a search term to find posts.</p>       )}     </main>   ); } 

Beautiful. Client component handles the form interaction, server component handles the search logic and rendering. They work together without you having to think about it.

When Everything Broke (The Fun Part)

No tutorial would be complete without the disasters. Here are the real problems I hit and how I fixed them.

The Great Params Await Disaster

This error message haunted my dreams: "Route used params.id. params should be awaited before using its properties."

What happened? I upgraded to Next.js 15 and suddenly all my dynamic routes broke. Turns out Next.js 15 changed how params work. You can't just do params.id anymore. You have to await the whole params object first.

The fix was annoying but simple:

// This breaks in Next.js 15 export default function BlogPost({ params }: { params: { id: string } }) {   const post = getBlogPost(params.id); // ERROR! }  // This works export default async function BlogPost({ params }: { params: Promise<{ id: string }> }) {   const { id } = await params; // Must await first   const post = await getBlogPost(id); } 

Same thing with searchParams. Everything has to be awaited now. I spent 1 hour debugging this before I found the migration guide.

The CSS Vanishing Act

One day my styles just… disappeared. Everything worked, but it looked like a website from 1995. The HTML had all the Tailwind classes, but none of the CSS was loading.

Turns out Next.js 15 ships with Tailwind CSS v4, which has a completely different configuration system. I had to downgrade to v3:

npm uninstall tailwindcss @tailwindcss/postcss npm install tailwindcss@^3.4.1 autoprefixer 

Then update postcss.config.mjs:

const config = {   plugins: {     tailwindcss: {},     autoprefixer: {},   }, };  export default config; 

Clear the cache and restart:

rm -rf .next && npm run dev 

Styles came back. Crisis averted.

The Mysterious Build Manifest Error

Sometimes Next.js just loses its mind and can't find its own build files:

ENOENT: no such file or directory, open '.next/build-manifest.json' 

The fix is always the same:

rm -rf .next npm run dev 

This happens more than it should, but it's usually a quick fix. I've started doing this automatically whenever weird stuff happens.

TypeScript Being Extra

Sometimes TypeScript decides to be helpful and throws type errors for things that definitely should work. The nuclear option:

rm -rf node_modules/.cache rm -rf .next npm install npm run dev 

This clears all the type caches and usually fixes whatever TypeScript was complaining about.

Making It Fast (The Good Stuff)

RSCs are supposed to be performant, but you still need to be smart about it.

Static Generation for Blog Posts

Since blog posts don't change often, I can pre-generate them at build time:

// Add this to your blog post page export async function generateStaticParams() {   const posts = await getBlogPosts();   return posts.map(post => ({ id: post.id })); } 

Now when I build the site, Next.js creates static HTML files for every blog post. Lightning fast loading with zero server work.

Streaming with Suspense

The Suspense boundaries I added aren't just for loading states - they enable streaming. The server sends the page shell immediately, then streams in content as it becomes available. Users see something right away instead of staring at a blank page.

Bundle Analysis

Want to see what's actually in your JavaScript bundle?

npm install --save-dev @next/bundle-analyzer 

Add this to next.config.js:

const withBundleAnalyzer = require('@next/bundle-analyzer')({   enabled: process.env.ANALYZE === 'true', });  module.exports = withBundleAnalyzer({}); 

Run ANALYZE=true npm run build and you'll get a visual breakdown of your bundle size. It's quite satisfying to see how small your client bundle is with RSCs.

What I Actually Learned (The Real Stuff)

After building this learning project, here's what actually stuck:

RSCs Change How You Think About React

The biggest shift isn't technical, it's mental. In traditional React, you think in terms of client-side state and effects. With RSCs, you think in terms of where code runs and what data each component needs. It's simpler in some ways, more complex in others.

The Boundary Between Server and Client Matters

You can't just stick "use client" everywhere and call it a day. Each boundary has performance implications. Server components are great for data fetching and initial rendering. Client components are necessary for interactivity. Choose wisely.

Next.js 15 Is Mostly Great

The App Router with RSCs feels mature now. The developer experience is solid, the performance benefits are real and the ecosystem is catching up. There are still some rough edges (like the params thing), but it's a big improvement over pages router.

Error Boundaries Are More Important

With server and client components mixed together, error boundaries become critical. A failing server component can break your entire page if you're not careful about error handling.

It's Actually Pretty Simple

Once you get past the initial confusion, RSCs are straightforward. Server components fetch data and render HTML. Client components handle interactions. The framework handles the complexity of making them work together.

The End (Finally)

React Server Components aren't just hype. They're a genuine improvement to how React apps can be built. The performance benefits are real, the developer experience is better than traditional React (once you learn the quirks) and the mental model makes sense, especially coming from server-side frameworks.

Is it perfect? No. There's a learning curve, some confusing error messages and you have to think more carefully about where your code runs. But for content-heavy sites like blogs, marketing pages, or e-commerce, RSCs could be compelling enough to consider React for projects where I'd normally reach for Angular.

The learning project I built demonstrates the core concepts: server components for data fetching, client components for interactivity, proper loading states and error handling. From here, you could add authentication, comments, a CMS, or whatever your actual projects need.

Most importantly: experiment with stuff. Break stuff. Fix stuff. Read error messages carefully (they're usually helpful). Don't be afraid to delete .next and restart when things get weird. And remember, if you're confused as an Angular dev learning React, you're not alone. The concepts are different, but they start to make sense with practice.

Now go build something cool. And when you inevitably hit a weird error I didn't mention, you'll figure it out. That's what we do.

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Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release. Free Stock Market API and Financial Statements API... Conclusion Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data. Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
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Medium2025/09/18 14:40