Polygon co-founder Sandeep Nailwal publicly questioned his loyalty to Ethereum after years of contributing to the ecosystem without receiving support from the Ethereum Foundation or community. His criticism came alongside revelations that former Geth lead developer Péter Szilágyi earned just $625,000 over six years working on Ethereum’s $480 billion network. The controversy intensified as Solana co-founder Raj Gokal openly suggested collaboration with Nailwal, while multiple developers criticized the Foundation’s treasury management and talent retention strategies. Polygon Founder Questions Ecosystem Loyalty Nailwal stated that he “started questioning [his] loyalty toward Ethereum,” despite building infrastructure that scaled the network and hosting successful applications, such as Polymarket. He noted that “I/we never got any direct support from the EF or the Ethereum CT community — in fact, the reverse,” while maintaining moral loyalty that potentially cost “billions of dollars in Polygon’s valuation.” The Ethereum community refuses to classify Polygon PoS as a layer-2, despite its ecosystem including true L2s like Katana and XLayer, which creates market perception issues as competitors like Hedera Hashgraph achieve higher valuations. Marc Boiron, CEO of Polygon Labs, emphasized that “Polygon PoS is a customer of Ethereum” that pays significant fees to the network, arguing the Foundation should “embrace” rather than “shun” contributors. Nailwal referenced friends suggesting that Polygon declare itself an L1, noting that the chain would likely achieve a two- to five-times higher valuation with such repositioning. He promised “a final push that might just revive the entire L2 narrative” in the coming weeks, while acknowledging that Ethereum operates as a democracy where “people on all sides end up disgruntled.“ Developer Compensation Crisis Exposes Foundation Mismanagement Szilágyi revealed in a May 2024 letter to Foundation leadership that his total compensation for six years managing Ethereum’s primary execution client was $625,000 before taxes, with zero benefits, raises, or incentives. He described working at the Foundation as “a bad financial decision“. He warned that the situation creates “a perfect breeding ground for perverse incentives, conflicts of interests and eventual protocol capture.” The former developer paraphrased Vitalik Buterin’s compensation philosophy as “if someone’s not complaining that they are paid too little, then they are paid too much.“ Back in September, Protocol Guild data revealed that Ethereum core developers earn 50% to 60% below market standards, with a median base pay of $140,000 compared to average market offers of $359,000. One developer reported declining a $700,000 package to continue core work. Only 37% of contributors receive equity or token grants from employers, unlike commercial crypto ventures, which often offer significant upside packages. Community responses highlighted that the Foundation recently sold 10,000 ETH worth $43 million while maintaining insufficient developer compensation. Critics across crypto Twitter questioned where Foundation funds are directed, with some noting a grant abuse issue where projects receive over $200,000 for minimal work, while core developers remain underpaid. The Foundation holds a massive ETH treasury without implementing treasury management or even staking its holdings. Ecosystem Builders Migrate Vitalik Buterin responded to Nailwal’s criticism with appreciation for Polygon’s contributions. He highlighted the platform’s role in hosting Polymarket and advancing ZK-EVM technology through early investments in Jordi Baylina’s team. Buterin noted Polygon’s “immensely valuable role in the ethereum ecosystem” and praised Nailwal’s personal efforts, including $190 million voluntarily returned from donated SHIB tokens that funded Balvi’s open-source biotech program. He suggested that Polygon could adopt off-the-shelf ZK technology, which has improved dramatically, with proving costs now around $0.0001 per transaction. Brendan Farmer from Polygon Zero countered that “every zkVM running in prod” except RiscZero runs on Polygon Zero’s technology, including Succinct Labs and Brevis. He argued Polygon’s Plonky2 release was over 100 times faster than existing solutions from StarkWare, crediting the company for open-sourcing the work as a public good. Farmer noted that the $0.0001 per transaction figures may be misleading when considering the actual costs of proving high-throughput chains that require over-provisioned resources. One development team shared that after four years of building on Ethereum and Base without success, they moved to Solana six months ago and generated $3.5 million in revenue within 48 hours of their launch. They described the Base and Ethereum ecosystems as “closed dinner parties” with inner-circle dynamics, while Solana provides open, builder-first support regardless of project type. In a related development, the Foundation has begun decommissioning the Holesky testnet this week, following the completion of Fusaka-related testing, with node operators scheduled to shut down their infrastructure over the next ten daysPolygon co-founder Sandeep Nailwal publicly questioned his loyalty to Ethereum after years of contributing to the ecosystem without receiving support from the Ethereum Foundation or community. His criticism came alongside revelations that former Geth lead developer Péter Szilágyi earned just $625,000 over six years working on Ethereum’s $480 billion network. The controversy intensified as Solana co-founder Raj Gokal openly suggested collaboration with Nailwal, while multiple developers criticized the Foundation’s treasury management and talent retention strategies. Polygon Founder Questions Ecosystem Loyalty Nailwal stated that he “started questioning [his] loyalty toward Ethereum,” despite building infrastructure that scaled the network and hosting successful applications, such as Polymarket. He noted that “I/we never got any direct support from the EF or the Ethereum CT community — in fact, the reverse,” while maintaining moral loyalty that potentially cost “billions of dollars in Polygon’s valuation.” The Ethereum community refuses to classify Polygon PoS as a layer-2, despite its ecosystem including true L2s like Katana and XLayer, which creates market perception issues as competitors like Hedera Hashgraph achieve higher valuations. Marc Boiron, CEO of Polygon Labs, emphasized that “Polygon PoS is a customer of Ethereum” that pays significant fees to the network, arguing the Foundation should “embrace” rather than “shun” contributors. Nailwal referenced friends suggesting that Polygon declare itself an L1, noting that the chain would likely achieve a two- to five-times higher valuation with such repositioning. He promised “a final push that might just revive the entire L2 narrative” in the coming weeks, while acknowledging that Ethereum operates as a democracy where “people on all sides end up disgruntled.“ Developer Compensation Crisis Exposes Foundation Mismanagement Szilágyi revealed in a May 2024 letter to Foundation leadership that his total compensation for six years managing Ethereum’s primary execution client was $625,000 before taxes, with zero benefits, raises, or incentives. He described working at the Foundation as “a bad financial decision“. He warned that the situation creates “a perfect breeding ground for perverse incentives, conflicts of interests and eventual protocol capture.” The former developer paraphrased Vitalik Buterin’s compensation philosophy as “if someone’s not complaining that they are paid too little, then they are paid too much.“ Back in September, Protocol Guild data revealed that Ethereum core developers earn 50% to 60% below market standards, with a median base pay of $140,000 compared to average market offers of $359,000. One developer reported declining a $700,000 package to continue core work. Only 37% of contributors receive equity or token grants from employers, unlike commercial crypto ventures, which often offer significant upside packages. Community responses highlighted that the Foundation recently sold 10,000 ETH worth $43 million while maintaining insufficient developer compensation. Critics across crypto Twitter questioned where Foundation funds are directed, with some noting a grant abuse issue where projects receive over $200,000 for minimal work, while core developers remain underpaid. The Foundation holds a massive ETH treasury without implementing treasury management or even staking its holdings. Ecosystem Builders Migrate Vitalik Buterin responded to Nailwal’s criticism with appreciation for Polygon’s contributions. He highlighted the platform’s role in hosting Polymarket and advancing ZK-EVM technology through early investments in Jordi Baylina’s team. Buterin noted Polygon’s “immensely valuable role in the ethereum ecosystem” and praised Nailwal’s personal efforts, including $190 million voluntarily returned from donated SHIB tokens that funded Balvi’s open-source biotech program. He suggested that Polygon could adopt off-the-shelf ZK technology, which has improved dramatically, with proving costs now around $0.0001 per transaction. Brendan Farmer from Polygon Zero countered that “every zkVM running in prod” except RiscZero runs on Polygon Zero’s technology, including Succinct Labs and Brevis. He argued Polygon’s Plonky2 release was over 100 times faster than existing solutions from StarkWare, crediting the company for open-sourcing the work as a public good. Farmer noted that the $0.0001 per transaction figures may be misleading when considering the actual costs of proving high-throughput chains that require over-provisioned resources. One development team shared that after four years of building on Ethereum and Base without success, they moved to Solana six months ago and generated $3.5 million in revenue within 48 hours of their launch. They described the Base and Ethereum ecosystems as “closed dinner parties” with inner-circle dynamics, while Solana provides open, builder-first support regardless of project type. In a related development, the Foundation has begun decommissioning the Holesky testnet this week, following the completion of Fusaka-related testing, with node operators scheduled to shut down their infrastructure over the next ten days

‘We Scaled Ethereum, Got Zero Help’ – Polygon and Sonic Labs Slam Ethereum Foundation

Polygon co-founder Sandeep Nailwal publicly questioned his loyalty to Ethereum after years of contributing to the ecosystem without receiving support from the Ethereum Foundation or community.

His criticism came alongside revelations that former Geth lead developer Péter Szilágyi earned just $625,000 over six years working on Ethereum’s $480 billion network.

The controversy intensified as Solana co-founder Raj Gokal openly suggested collaboration with Nailwal, while multiple developers criticized the Foundation’s treasury management and talent retention strategies.

Polygon Founder Questions Ecosystem Loyalty

Nailwal stated that he “started questioning [his] loyalty toward Ethereum,” despite building infrastructure that scaled the network and hosting successful applications, such as Polymarket.

He noted that “I/we never got any direct support from the EF or the Ethereum CT community — in fact, the reverse,” while maintaining moral loyalty that potentially cost “billions of dollars in Polygon’s valuation.

The Ethereum community refuses to classify Polygon PoS as a layer-2, despite its ecosystem including true L2s like Katana and XLayer, which creates market perception issues as competitors like Hedera Hashgraph achieve higher valuations.

Marc Boiron, CEO of Polygon Labs, emphasized that “Polygon PoS is a customer of Ethereum” that pays significant fees to the network, arguing the Foundation should “embrace” rather than “shun” contributors.

Nailwal referenced friends suggesting that Polygon declare itself an L1, noting that the chain would likely achieve a two- to five-times higher valuation with such repositioning.

He promised “a final push that might just revive the entire L2 narrative” in the coming weeks, while acknowledging that Ethereum operates as a democracy where “people on all sides end up disgruntled.

Developer Compensation Crisis Exposes Foundation Mismanagement

Szilágyi revealed in a May 2024 letter to Foundation leadership that his total compensation for six years managing Ethereum’s primary execution client was $625,000 before taxes, with zero benefits, raises, or incentives.

He described working at the Foundation as “a bad financial decision“. He warned that the situation creates “a perfect breeding ground for perverse incentives, conflicts of interests and eventual protocol capture.

The former developer paraphrased Vitalik Buterin’s compensation philosophy as “if someone’s not complaining that they are paid too little, then they are paid too much.

Back in September, Protocol Guild data revealed that Ethereum core developers earn 50% to 60% below market standards, with a median base pay of $140,000 compared to average market offers of $359,000.

One developer reported declining a $700,000 package to continue core work.

Only 37% of contributors receive equity or token grants from employers, unlike commercial crypto ventures, which often offer significant upside packages.

Community responses highlighted that the Foundation recently sold 10,000 ETH worth $43 million while maintaining insufficient developer compensation.

Critics across crypto Twitter questioned where Foundation funds are directed, with some noting a grant abuse issue where projects receive over $200,000 for minimal work, while core developers remain underpaid.

The Foundation holds a massive ETH treasury without implementing treasury management or even staking its holdings.

Ecosystem Builders Migrate

Vitalik Buterin responded to Nailwal’s criticism with appreciation for Polygon’s contributions.

He highlighted the platform’s role in hosting Polymarket and advancing ZK-EVM technology through early investments in Jordi Baylina’s team.

Buterin noted Polygon’s “immensely valuable role in the ethereum ecosystem” and praised Nailwal’s personal efforts, including $190 million voluntarily returned from donated SHIB tokens that funded Balvi’s open-source biotech program.

He suggested that Polygon could adopt off-the-shelf ZK technology, which has improved dramatically, with proving costs now around $0.0001 per transaction.

Brendan Farmer from Polygon Zero countered that “every zkVM running in prod” except RiscZero runs on Polygon Zero’s technology, including Succinct Labs and Brevis.

He argued Polygon’s Plonky2 release was over 100 times faster than existing solutions from StarkWare, crediting the company for open-sourcing the work as a public good.

Farmer noted that the $0.0001 per transaction figures may be misleading when considering the actual costs of proving high-throughput chains that require over-provisioned resources.

One development team shared that after four years of building on Ethereum and Base without success, they moved to Solana six months ago and generated $3.5 million in revenue within 48 hours of their launch.

They described the Base and Ethereum ecosystems as “closed dinner parties” with inner-circle dynamics, while Solana provides open, builder-first support regardless of project type.

In a related development, the Foundation has begun decommissioning the Holesky testnet this week, following the completion of Fusaka-related testing, with node operators scheduled to shut down their infrastructure over the next ten days.

Piyasa Fırsatı
ZeroLend Logosu
ZeroLend Fiyatı(ZERO)
$0,000008301
$0,000008301$0,000008301
-1,37%
USD
ZeroLend (ZERO) Canlı Fiyat Grafiği
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

South Korea Launches Innovative Stablecoin Initiative

South Korea Launches Innovative Stablecoin Initiative

The post South Korea Launches Innovative Stablecoin Initiative appeared on BitcoinEthereumNews.com. South Korea has witnessed a pivotal development in its cryptocurrency landscape with BDACS introducing the nation’s first won-backed stablecoin, KRW1, built on the Avalanche network. This stablecoin is anchored by won assets stored at Woori Bank in a 1:1 ratio, ensuring high security. Continue Reading:South Korea Launches Innovative Stablecoin Initiative Source: https://en.bitcoinhaber.net/south-korea-launches-innovative-stablecoin-initiative
Paylaş
BitcoinEthereumNews2025/09/18 17:54
Trump Cancels Tech, AI Trade Negotiations With The UK

Trump Cancels Tech, AI Trade Negotiations With The UK

The US pauses a $41B UK tech and AI deal as trade talks stall, with disputes over food standards, market access, and rules abroad.   The US has frozen a major tech
Paylaş
LiveBitcoinNews2025/12/17 01:00
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