TL;DR: I built Lighthouse PHP Framework because modern frameworks got too complex. It lets you handle forms in views (like PHP intended), uses SQLite out of the box, includes security by default, and gets you productive in 30 seconds. No controllers, no magic, just productive PHP development.TL;DR: I built Lighthouse PHP Framework because modern frameworks got too complex. It lets you handle forms in views (like PHP intended), uses SQLite out of the box, includes security by default, and gets you productive in 30 seconds. No controllers, no magic, just productive PHP development.

Tired of Learning 50 New Concepts to Build a Form? Say Hello to Lighthouse for PHP

2025/12/04 14:22

Building modern web apps shouldn't require learning 50 new concepts. Sometimes you just want to write PHP.

The Problem with Modern PHP Frameworks

Don't get me wrong—Laravel, Symfony, and other frameworks are incredible. But sometimes you're building a simple web app and you find yourself:

  • 📚 Reading documentation for hours just to create a basic form
  • 🔧 Configuring dozens of services you don't need
  • 🐘 Fighting with complex abstractions for simple tasks
  • ⚡ Waiting for slow development servers to restart

What if there was a better way?

Meet Lighthouse 🚨

Lighthouse is a minimal, predictable PHP micro-framework that embraces the simplicity PHP was meant for. It's designed around one core principle: get productive immediately.

<?php // That's it. Your first route. route('/', function() { return view('home.php', ['message' => 'Hello World!']); });

Why Lighthouse is Different

1. Logic Where It Makes Sense

Instead of forcing everything through controllers, Lighthouse lets you handle form logic directly in views—the way PHP was designed:

<?php // views/contact.php $errors = []; if ($_SERVER['REQUEST_METHOD'] === 'POST') { $email = sanitize_email($_POST['email']); $message = sanitize_string($_POST['message']); if (!validate_email($email)) { $errors[] = 'Invalid email'; } if (empty($errors)) { db_insert('contacts', ['email' => $email, 'message' => $message]); $success = 'Message sent!'; } } ?> <form method="POST"> <?php if ($success ?? false): ?> <div class="success"><?= $success ?></div> <?php endif; ?> <input type="email" name="email" required> <textarea name="message" required></textarea> <button type="submit">Send</button> </form>

Self-contained. Predictable. No magic.

2. Modern Stack, Zero Configuration

  • PHP 8+ with type hints and modern features
  • SQLite for zero-config databases
  • HTMX for dynamic interactions
  • Pico.css for beautiful, minimal styling

# Get started in 30 seconds lighthouse new my-app cd my-app php -S localhost:8000 -t public/

3. Security by Default

// CSRF protection built-in <?= csrf_field() ?> // Input sanitization included $clean_input = sanitize_string($_POST['data']); // Rate limiting ready if (!check_rate_limit($_SERVER['REMOTE_ADDR'])) { // Handle rate limit }

4. Database Operations That Make Sense

// Simple, predictable database operations $users = db_select('users', ['active' => 1]); $user_id = db_insert('users', ['name' => $name, 'email' => $email]); db_update('users', ['last_login' => date('Y-m-d H:i:s')], ['id' => $user_id]);

Real-World Example: Authentication in 5 Minutes

Here's how you build a complete login system:

<?php // routes.php route('/login', function() { return view('login.php'); }); route('/dashboard', function() { if (!auth_user()) { header('Location: /login'); exit; } return view('dashboard.php'); });

<?php // views/login.php if ($_SERVER['REQUEST_METHOD'] === 'POST') { $email = sanitize_email($_POST['email']); $password = $_POST['password']; $user = db_select_one('users', ['email' => $email]); if ($user && auth_verify_password($password, $user['password'])) { auth_login($user['id']); header('Location: /dashboard'); exit; } $error = 'Invalid credentials'; } ?> <form method="POST"> <?php if ($error ?? false): ?> <div class="error"><?= $error ?></div> <?php endif; ?> <input type="email" name="email" required> <input type="password" name="password" required> <?= csrf_field() ?> <button type="submit">Login</button> </form>

That's it. No controllers, no middleware configuration, no service providers. Just PHP doing what PHP does best.

When to Use Lighthouse

Lighthouse shines when you're building:

  • 🚀 MVPs and prototypes - Get to market fast
  • 📊 Internal tools and dashboards - No need for complexity
  • 🏢 Small business websites - Contact forms, simple e-commerce
  • 🎓 Learning projects - Focus on concepts, not framework magic
  • 🔧 API backends - Lightweight and fast

The Philosophy

Lighthouse embraces pragmatic PHP development:

  • Start simple - Use logic in views for rapid development
  • Refactor when needed - Move to more complex patterns as you grow
  • Choose what fits - Multiple approaches supported
  • Stay productive - Don't over-engineer simple problems

Getting Started

# Install the CLI bash -c "$(curl -fsSL https://raw.githubusercontent.com/max-yterb/Lighthouse/main/scripts/install.sh)" # Create your first app lighthouse new my-awesome-app cd my-awesome-app # Start building php -S localhost:8000 -t public/

What's Next?

Lighthouse is actively developed with a focus on:

  • 🔐 Enhanced authentication providers (OAuth, SAML)
  • 🗄️ Multiple database support (MySQL, PostgreSQL)
  • ⚡ Performance monitoring tools
  • 📱 Advanced HTMX integration patterns

Try It Today

If you're tired of complex frameworks for simple projects, give Lighthouse a try. It might just remind you why you fell in love with PHP in the first place.

  • 📖 Documentation: max-yterb.github.io/Lighthouse
  • 💻 GitHub: github.com/max-yterb/Lighthouse
  • 💬 Discussions: GitHub Discussions

What do you think? Are you ready to try a framework that gets out of your way? Drop a comment below with your thoughts on modern PHP development!

Sorumluluk Reddi: Bu sitede yeniden yayınlanan makaleler, halka açık platformlardan alınmıştır ve yalnızca bilgilendirme amaçlıdır. MEXC'nin görüşlerini yansıtmayabilir. Tüm hakları telif sahiplerine aittir. Herhangi bir içeriğin üçüncü taraf haklarını ihlal ettiğini düşünüyorsanız, kaldırılması için lütfen service@support.mexc.com ile iletişime geçin. MEXC, içeriğin doğruluğu, eksiksizliği veya güncelliği konusunda hiçbir garanti vermez ve sağlanan bilgilere dayalı olarak alınan herhangi bir eylemden sorumlu değildir. İçerik, finansal, yasal veya diğer profesyonel tavsiye niteliğinde değildir ve MEXC tarafından bir tavsiye veya onay olarak değerlendirilmemelidir.

Ayrıca Şunları da Beğenebilirsiniz

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. 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
Paylaş
Medium2025/09/18 14:40