https://unseen.co/projects/quai-network/ As part of the DLT Talents program-designed to empower women from various backgrounds, including tech, law, business, or those new to crypto, to explore blockchain and boost awareness-I had the opportunity to interview Jonathan Downing. He’s a co-founder and the lead engineer at Dominant Strategies, the firm behind Quai Network. Jonathan’s deep knowledge in blockchain scalability and Proof-of-Work advancements has been pivotal in developing Quai’s multi-chain framework. Quai Network is a Layer-1 blockchain addressing the scalability trilemma, delivering over 50,000 transactions per second while preserving decentralization and security. It stands out as the first decentralized energy dollar on a programmable PoW blockchain, with a dual-token system: QUAI as the gas token and store of value, and Qi as the energy-backed stablecoin. Our discussion delved into Quai’s origins, technical breakthroughs, challenges, and future plans-tailored to inspire interest among DLT Talents participants and newcomers alike. The interview took place via Zoom voice call on July 5, 2025. Here’s the full breakdown: Can you share your background and what pulled you into blockchain? Jonathan: My interest in blockchain began with Bitcoin during high school, similar to my co-founders. At the University of Texas at Austin, I co-founded the Texas Blockchain Club, connecting with Alan Orwick, Karl Kreder, Yanni Georghiades, and Sriram Vishwanath-we shared a drive for scalability and cryptography. After graduation, I pursued software engineering, focusing on data management and operational efficiency. This foundation led to my role as chief architect at Dominant Strategies, where we’re constructing Quai Network to overcome the constraints of conventional blockchains and enable seamless global finance. How did the concept for Quai Network originate? Jonathan: Quai originated from research at UT Austin in 2019, solidifying into a project by 2020. We identified flaws in existing blockchains, such as Ethereum’s high fees and limited TPS, and sought to build a scalable PoW network. Instead of relying on Layer-2 add-ons that increase complexity, Quai integrates scalability at the protocol level through multi-chain architecture. The Dominant Strategies team worked collaboratively, incorporating academic research and empirical testing. The goal is to revitalize the crypto movement, making blockchain practical for daily transactions at speeds comparable to Visa, all while staying decentralized. What’s been your most memorable experience with Quai? Jonathan: A highlight was deploying our latest testnet, engaging over 2,000 nodes worldwide and 5,500 GPUs. It was thrilling to see diverse participants-miners, developers, and enthusiasts-collaborate on testing. This wasn’t just a technical achievement; it demonstrated Quai’s potential for broad adoption. We’ve managed over a billion transactions, achieved 170+ days of uptime, and reached peaks of 2,167 TPS. Experiences like these reinforce our purpose: democratizing blockchain access. What are Quai Network’s primary products and features? Jonathan: Quai is fundamentally a scalable multichain blockchain that employs Proof-of-Work as an oracle for real-world demand and energy pricing. This supports our dual-token model: QUAI, the deflationary gas token and value store, and Qi, a stablecoin tied to energy markets. Core features include: Multi-Chain Architecture: Protocol-level sharding for effortless interoperability and high throughput (50,000+ TPS). Proof-of-Entropy Minima (PoEM): An innovative consensus mechanism ensuring security and efficiency. Developer Resources: Simplified integration for dApps, prioritizing low fees and true decentralization. Additional tools we’ve created: go-quai (a Go implementation for the network), Pelagus (wallet), Blip (for messaging or integrations), and Kipper (mining/node software). Unlike asset-backed stablecoins, Qi achieves stability through market mechanisms, positioning Quai as a versatile platform for DeFi, payments, and more. What challenges has Quai encountered in the blockchain landscape recently? Jonathan: Scalability continues to be the primary obstacle-many networks depend on Layer-2 solutions, which can introduce centralization and added layers of complexity. We’ve addressed this directly with our multi-chain design and PoEM consensus, bypassing those dependencies. Regulatory ambiguities and debates over PoW’s energy consumption present additional hurdles, but Quai leverages PoW as an asset by connecting it to tangible energy economics. Our testnets have allowed iterative improvements, processing billions of transactions. The forthcoming fourth testnet and mainnet launch will further navigate these in a dynamic industry. How has Quai’s technology developed since its inception? Jonathan: From initial research in 2019 onward, Quai has evolved through developmental phases dubbed “Stone Age” and “Bronze Age.” We’ve enhanced multi-chain interoperability, optimized PoW for better energy use, and incorporated community-driven governance. Recent developments include refined developer tools and ecosystem integrations. A Messari protocol analysis underscored our strengths, and partnerships-such as with Kyle Chassé of Master Ventures-have endorsed our trajectory. With testnets operational, we’re primed for mainnet and expanded adoption. What’s on the horizon for Quai Network, and any advice for women venturing into blockchain? Jonathan: We’re preparing for the fourth testnet and mainnet rollout, fostering ecosystem growth through additional dApps and collaborations. In the long term, we aspire to compete with traditional systems like Visa via cost-effective, decentralized transactions. For women entering blockchain-regardless of technical experience-begin with exploration. Programs like DLT Talents offer excellent networking opportunities. Immerse in communities, experiment with tools such as our Pelagus wallet, and contribute perspectives from fields like law or business; diversity strengthens blockchain. Closing Reflections This interview with Jonathan provided profound insights, particularly through my lens as a DLT Talents participant. Quai Network transcends typical projects by resolving entrenched issues like scalability and energy efficiency, paving the way for crypto’s global integration. Through this piece, the aim is to heighten awareness and encourage participation among women and beginners in the space. For more on Quai Network, visit qu.ai or follow @QuaiNetwork on X. Details on DLT Talents are at web3-talents.io/dlt-talents. What aspects of scalable blockchains intrigue you? Share in the comments! Scaling the Future: Insights from Quai Network Co-Founder Jonathan Downing was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this storyhttps://unseen.co/projects/quai-network/ As part of the DLT Talents program-designed to empower women from various backgrounds, including tech, law, business, or those new to crypto, to explore blockchain and boost awareness-I had the opportunity to interview Jonathan Downing. He’s a co-founder and the lead engineer at Dominant Strategies, the firm behind Quai Network. Jonathan’s deep knowledge in blockchain scalability and Proof-of-Work advancements has been pivotal in developing Quai’s multi-chain framework. Quai Network is a Layer-1 blockchain addressing the scalability trilemma, delivering over 50,000 transactions per second while preserving decentralization and security. It stands out as the first decentralized energy dollar on a programmable PoW blockchain, with a dual-token system: QUAI as the gas token and store of value, and Qi as the energy-backed stablecoin. Our discussion delved into Quai’s origins, technical breakthroughs, challenges, and future plans-tailored to inspire interest among DLT Talents participants and newcomers alike. The interview took place via Zoom voice call on July 5, 2025. Here’s the full breakdown: Can you share your background and what pulled you into blockchain? Jonathan: My interest in blockchain began with Bitcoin during high school, similar to my co-founders. At the University of Texas at Austin, I co-founded the Texas Blockchain Club, connecting with Alan Orwick, Karl Kreder, Yanni Georghiades, and Sriram Vishwanath-we shared a drive for scalability and cryptography. After graduation, I pursued software engineering, focusing on data management and operational efficiency. This foundation led to my role as chief architect at Dominant Strategies, where we’re constructing Quai Network to overcome the constraints of conventional blockchains and enable seamless global finance. How did the concept for Quai Network originate? Jonathan: Quai originated from research at UT Austin in 2019, solidifying into a project by 2020. We identified flaws in existing blockchains, such as Ethereum’s high fees and limited TPS, and sought to build a scalable PoW network. Instead of relying on Layer-2 add-ons that increase complexity, Quai integrates scalability at the protocol level through multi-chain architecture. The Dominant Strategies team worked collaboratively, incorporating academic research and empirical testing. The goal is to revitalize the crypto movement, making blockchain practical for daily transactions at speeds comparable to Visa, all while staying decentralized. What’s been your most memorable experience with Quai? Jonathan: A highlight was deploying our latest testnet, engaging over 2,000 nodes worldwide and 5,500 GPUs. It was thrilling to see diverse participants-miners, developers, and enthusiasts-collaborate on testing. This wasn’t just a technical achievement; it demonstrated Quai’s potential for broad adoption. We’ve managed over a billion transactions, achieved 170+ days of uptime, and reached peaks of 2,167 TPS. Experiences like these reinforce our purpose: democratizing blockchain access. What are Quai Network’s primary products and features? Jonathan: Quai is fundamentally a scalable multichain blockchain that employs Proof-of-Work as an oracle for real-world demand and energy pricing. This supports our dual-token model: QUAI, the deflationary gas token and value store, and Qi, a stablecoin tied to energy markets. Core features include: Multi-Chain Architecture: Protocol-level sharding for effortless interoperability and high throughput (50,000+ TPS). Proof-of-Entropy Minima (PoEM): An innovative consensus mechanism ensuring security and efficiency. Developer Resources: Simplified integration for dApps, prioritizing low fees and true decentralization. Additional tools we’ve created: go-quai (a Go implementation for the network), Pelagus (wallet), Blip (for messaging or integrations), and Kipper (mining/node software). Unlike asset-backed stablecoins, Qi achieves stability through market mechanisms, positioning Quai as a versatile platform for DeFi, payments, and more. What challenges has Quai encountered in the blockchain landscape recently? Jonathan: Scalability continues to be the primary obstacle-many networks depend on Layer-2 solutions, which can introduce centralization and added layers of complexity. We’ve addressed this directly with our multi-chain design and PoEM consensus, bypassing those dependencies. Regulatory ambiguities and debates over PoW’s energy consumption present additional hurdles, but Quai leverages PoW as an asset by connecting it to tangible energy economics. Our testnets have allowed iterative improvements, processing billions of transactions. The forthcoming fourth testnet and mainnet launch will further navigate these in a dynamic industry. How has Quai’s technology developed since its inception? Jonathan: From initial research in 2019 onward, Quai has evolved through developmental phases dubbed “Stone Age” and “Bronze Age.” We’ve enhanced multi-chain interoperability, optimized PoW for better energy use, and incorporated community-driven governance. Recent developments include refined developer tools and ecosystem integrations. A Messari protocol analysis underscored our strengths, and partnerships-such as with Kyle Chassé of Master Ventures-have endorsed our trajectory. With testnets operational, we’re primed for mainnet and expanded adoption. What’s on the horizon for Quai Network, and any advice for women venturing into blockchain? Jonathan: We’re preparing for the fourth testnet and mainnet rollout, fostering ecosystem growth through additional dApps and collaborations. In the long term, we aspire to compete with traditional systems like Visa via cost-effective, decentralized transactions. For women entering blockchain-regardless of technical experience-begin with exploration. Programs like DLT Talents offer excellent networking opportunities. Immerse in communities, experiment with tools such as our Pelagus wallet, and contribute perspectives from fields like law or business; diversity strengthens blockchain. Closing Reflections This interview with Jonathan provided profound insights, particularly through my lens as a DLT Talents participant. Quai Network transcends typical projects by resolving entrenched issues like scalability and energy efficiency, paving the way for crypto’s global integration. Through this piece, the aim is to heighten awareness and encourage participation among women and beginners in the space. For more on Quai Network, visit qu.ai or follow @QuaiNetwork on X. Details on DLT Talents are at web3-talents.io/dlt-talents. What aspects of scalable blockchains intrigue you? Share in the comments! Scaling the Future: Insights from Quai Network Co-Founder Jonathan Downing was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story

Scaling the Future: Insights from Quai Network Co-Founder Jonathan Downing

2025/09/26 00:09

https://unseen.co/projects/quai-network/

As part of the DLT Talents program-designed to empower women from various backgrounds, including tech, law, business, or those new to crypto, to explore blockchain and boost awareness-I had the opportunity to interview Jonathan Downing. He’s a co-founder and the lead engineer at Dominant Strategies, the firm behind Quai Network. Jonathan’s deep knowledge in blockchain scalability and Proof-of-Work advancements has been pivotal in developing Quai’s multi-chain framework.

Quai Network is a Layer-1 blockchain addressing the scalability trilemma, delivering over 50,000 transactions per second while preserving decentralization and security. It stands out as the first decentralized energy dollar on a programmable PoW blockchain, with a dual-token system: QUAI as the gas token and store of value, and Qi as the energy-backed stablecoin. Our discussion delved into Quai’s origins, technical breakthroughs, challenges, and future plans-tailored to inspire interest among DLT Talents participants and newcomers alike.

The interview took place via Zoom voice call on July 5, 2025. Here’s the full breakdown:

Can you share your background and what pulled you into blockchain?

Jonathan: My interest in blockchain began with Bitcoin during high school, similar to my co-founders. At the University of Texas at Austin, I co-founded the Texas Blockchain Club, connecting with Alan Orwick, Karl Kreder, Yanni Georghiades, and Sriram Vishwanath-we shared a drive for scalability and cryptography. After graduation, I pursued software engineering, focusing on data management and operational efficiency. This foundation led to my role as chief architect at Dominant Strategies, where we’re constructing Quai Network to overcome the constraints of conventional blockchains and enable seamless global finance.

How did the concept for Quai Network originate?

Jonathan: Quai originated from research at UT Austin in 2019, solidifying into a project by 2020. We identified flaws in existing blockchains, such as Ethereum’s high fees and limited TPS, and sought to build a scalable PoW network. Instead of relying on Layer-2 add-ons that increase complexity, Quai integrates scalability at the protocol level through multi-chain architecture. The Dominant Strategies team worked collaboratively, incorporating academic research and empirical testing. The goal is to revitalize the crypto movement, making blockchain practical for daily transactions at speeds comparable to Visa, all while staying decentralized.

What’s been your most memorable experience with Quai?

Jonathan: A highlight was deploying our latest testnet, engaging over 2,000 nodes worldwide and 5,500 GPUs. It was thrilling to see diverse participants-miners, developers, and enthusiasts-collaborate on testing. This wasn’t just a technical achievement; it demonstrated Quai’s potential for broad adoption. We’ve managed over a billion transactions, achieved 170+ days of uptime, and reached peaks of 2,167 TPS. Experiences like these reinforce our purpose: democratizing blockchain access.

What are Quai Network’s primary products and features?

Jonathan: Quai is fundamentally a scalable multichain blockchain that employs Proof-of-Work as an oracle for real-world demand and energy pricing. This supports our dual-token model: QUAI, the deflationary gas token and value store, and Qi, a stablecoin tied to energy markets. Core features include:

  • Multi-Chain Architecture: Protocol-level sharding for effortless interoperability and high throughput (50,000+ TPS).
  • Proof-of-Entropy Minima (PoEM): An innovative consensus mechanism ensuring security and efficiency.
  • Developer Resources: Simplified integration for dApps, prioritizing low fees and true decentralization.
  • Additional tools we’ve created: go-quai (a Go implementation for the network), Pelagus (wallet), Blip (for messaging or integrations), and Kipper (mining/node software). Unlike asset-backed stablecoins, Qi achieves stability through market mechanisms, positioning Quai as a versatile platform for DeFi, payments, and more.

What challenges has Quai encountered in the blockchain landscape recently?

Jonathan: Scalability continues to be the primary obstacle-many networks depend on Layer-2 solutions, which can introduce centralization and added layers of complexity. We’ve addressed this directly with our multi-chain design and PoEM consensus, bypassing those dependencies. Regulatory ambiguities and debates over PoW’s energy consumption present additional hurdles, but Quai leverages PoW as an asset by connecting it to tangible energy economics. Our testnets have allowed iterative improvements, processing billions of transactions. The forthcoming fourth testnet and mainnet launch will further navigate these in a dynamic industry.

How has Quai’s technology developed since its inception?

Jonathan: From initial research in 2019 onward, Quai has evolved through developmental phases dubbed “Stone Age” and “Bronze Age.” We’ve enhanced multi-chain interoperability, optimized PoW for better energy use, and incorporated community-driven governance. Recent developments include refined developer tools and ecosystem integrations. A Messari protocol analysis underscored our strengths, and partnerships-such as with Kyle Chassé of Master Ventures-have endorsed our trajectory. With testnets operational, we’re primed for mainnet and expanded adoption.

What’s on the horizon for Quai Network, and any advice for women venturing into blockchain?

Jonathan: We’re preparing for the fourth testnet and mainnet rollout, fostering ecosystem growth through additional dApps and collaborations. In the long term, we aspire to compete with traditional systems like Visa via cost-effective, decentralized transactions. For women entering blockchain-regardless of technical experience-begin with exploration. Programs like DLT Talents offer excellent networking opportunities. Immerse in communities, experiment with tools such as our Pelagus wallet, and contribute perspectives from fields like law or business; diversity strengthens blockchain.

Closing Reflections

This interview with Jonathan provided profound insights, particularly through my lens as a DLT Talents participant. Quai Network transcends typical projects by resolving entrenched issues like scalability and energy efficiency, paving the way for crypto’s global integration. Through this piece, the aim is to heighten awareness and encourage participation among women and beginners in the space.

For more on Quai Network, visit qu.ai or follow @QuaiNetwork on X. Details on DLT Talents are at web3-talents.io/dlt-talents. What aspects of scalable blockchains intrigue you? Share in the comments!


Scaling the Future: Insights from Quai Network Co-Founder Jonathan Downing was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

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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
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