The post Anthropic in talks with Google for $10B cloud computing deal appeared on BitcoinEthereumNews.com. Artificial-intelligence firm Anthropic is currently in discussions with Google, a subsidiary of Alphabet Inc., about striking a $10 billion deal that would enable the AI firm to acquire more computing power, according to sources familiar with the situation. To remain competitive in the cloud computing industry, the sources pointed out that the deal, which is still being worked out, would have the tech giant offering cloud computing services to Anthropic.  This is not Google’s first time investing in the artificial intelligence firm. The firm previously invested approximately $3 billion in Anthropic; the tech giant committed to investing $2 billion in the AI startup in 2023 and followed up with another $1 billion investment early this year. Anthropic raises large amounts of funds to stay competitive in the AI industry  Anthropic and Google’s upcoming $10B cloud computing deal has ignited debates in the tech ecosystem. To address this heated discussion, reporters reached out to both Anthropic and Google to comment and request further information on the progress of the talks, but the companies chose not to respond. Sources familiar with the situation, who wished to remain anonymous due to the confidential nature of the talks, however, hinted that the talks were still in the initial stage, suggesting that details might change.  In the meantime, recent data in after-hours trading revealed that Google’s stock surged by more than 3.5%. At the same time, that of Amazon.com Inc., another crucial investor and cloud service provider for Anthropic, encountered a decline of around 2%. Founded in 2021 by former employees of OpenAI, Anthropic’s headquarters is based in San Francisco, California, at 548 Market St. The company is well-known for its Claude series of large language models, which rival OpenAI’s GPT models. Like other firms in the AI field, the startup has been raising substantial amounts of money to keep… The post Anthropic in talks with Google for $10B cloud computing deal appeared on BitcoinEthereumNews.com. Artificial-intelligence firm Anthropic is currently in discussions with Google, a subsidiary of Alphabet Inc., about striking a $10 billion deal that would enable the AI firm to acquire more computing power, according to sources familiar with the situation. To remain competitive in the cloud computing industry, the sources pointed out that the deal, which is still being worked out, would have the tech giant offering cloud computing services to Anthropic.  This is not Google’s first time investing in the artificial intelligence firm. The firm previously invested approximately $3 billion in Anthropic; the tech giant committed to investing $2 billion in the AI startup in 2023 and followed up with another $1 billion investment early this year. Anthropic raises large amounts of funds to stay competitive in the AI industry  Anthropic and Google’s upcoming $10B cloud computing deal has ignited debates in the tech ecosystem. To address this heated discussion, reporters reached out to both Anthropic and Google to comment and request further information on the progress of the talks, but the companies chose not to respond. Sources familiar with the situation, who wished to remain anonymous due to the confidential nature of the talks, however, hinted that the talks were still in the initial stage, suggesting that details might change.  In the meantime, recent data in after-hours trading revealed that Google’s stock surged by more than 3.5%. At the same time, that of Amazon.com Inc., another crucial investor and cloud service provider for Anthropic, encountered a decline of around 2%. Founded in 2021 by former employees of OpenAI, Anthropic’s headquarters is based in San Francisco, California, at 548 Market St. The company is well-known for its Claude series of large language models, which rival OpenAI’s GPT models. Like other firms in the AI field, the startup has been raising substantial amounts of money to keep…

Anthropic in talks with Google for $10B cloud computing deal

For feedback or concerns regarding this content, please contact us at crypto.news@mexc.com

Artificial-intelligence firm Anthropic is currently in discussions with Google, a subsidiary of Alphabet Inc., about striking a $10 billion deal that would enable the AI firm to acquire more computing power, according to sources familiar with the situation.

To remain competitive in the cloud computing industry, the sources pointed out that the deal, which is still being worked out, would have the tech giant offering cloud computing services to Anthropic. 

This is not Google’s first time investing in the artificial intelligence firm. The firm previously invested approximately $3 billion in Anthropic; the tech giant committed to investing $2 billion in the AI startup in 2023 and followed up with another $1 billion investment early this year.

Anthropic raises large amounts of funds to stay competitive in the AI industry 

Anthropic and Google’s upcoming $10B cloud computing deal has ignited debates in the tech ecosystem. To address this heated discussion, reporters reached out to both Anthropic and Google to comment and request further information on the progress of the talks, but the companies chose not to respond.

Sources familiar with the situation, who wished to remain anonymous due to the confidential nature of the talks, however, hinted that the talks were still in the initial stage, suggesting that details might change. 

In the meantime, recent data in after-hours trading revealed that Google’s stock surged by more than 3.5%. At the same time, that of Amazon.com Inc., another crucial investor and cloud service provider for Anthropic, encountered a decline of around 2%.

Founded in 2021 by former employees of OpenAI, Anthropic’s headquarters is based in San Francisco, California, at 548 Market St. The company is well-known for its Claude series of large language models, which rival OpenAI’s GPT models.

Like other firms in the AI field, the startup has been raising substantial amounts of money to keep pace with the intense competition in advancing AI technology. Still, industry experts believe that the company requires more resources for research, the implementation of new ideas, and growing consumer interest to solidify its position as a leader in AI.

Goal-oriented with a focus on AI leadership, the company recently engaged in initial findings talks with MGX, an Abu Dhabi-based investment firm, just a month after finalizing a $13 billion funding round. 

This funding round, led by Iconiq Capital and featuring Fidelity Management and Research Co. and Lightspeed Venture Partners as co-leads, nearly tripled the valuation of the US-based AI startup firm to $183 billion. This amount includes the funds raised. 

Amazon pledges to invest around $8 billion in Anthropic

Besides Google, Amazon also pledged to invest around $8 billion in Anthropic. Notably, the company is a key AI client of Amazon Web Services (AWS) and a significant user of Amazon’s custom AI chips.

This was after reliable sources highlighted that in 2024, Amazon.com Inc. invested an additional $4 billion into the AI startup firm, boosting its investment in one of OpenAI’s biggest rivals.

Both companies announced the investment. Additionally, it was confirmed after a previous investment of approximately $4 billion in Anthropic, which occurred the same year.

Under the previous investment deal, Anthropic was required to utilize Amazon Web Services data centers for some of its computing tasks. It also involved using AI chips designed by AWS. 

Despite Amazon’s substantial investments in Anthropic, research from sources has revealed that the company is strongly connected to Google’s parent company, Alphabet Inc.

Meanwhile, in a blog post, Anthropic pointed out that Amazon’s additional $4 billion investment signaled AWS’s position as the firm’s primary cloud and training partner. Moreover, the AI startup company revealed its intentions to use 

Amazon’s AI chips to develop its most advanced models. Therefore, with this deal, Anthropic confirmed Amazon as its minority investor.

Get seen where it counts. Advertise in Cryptopolitan Research and reach crypto’s sharpest investors and builders.

Source: https://www.cryptopolitan.com/anthropic-in-talks-with-google/

Market Opportunity
Cloud Logo
Cloud Price(CLOUD)
$0.03744
$0.03744$0.03744
-0.89%
USD
Cloud (CLOUD) Live Price Chart
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact crypto.news@mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival

Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival

The post Tether Backs Ark Labs’ $5.2 Million Bet on Bitcoin’s Stablecoin Revival appeared on BitcoinEthereumNews.com. In brief Ark Labs secured backing from Tether
Share
BitcoinEthereumNews2026/03/12 21:44
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
Share
Medium2025/09/18 14:40
PayPal USD Expands to TRON Network via LayerZero

PayPal USD Expands to TRON Network via LayerZero

The post PayPal USD Expands to TRON Network via LayerZero appeared on BitcoinEthereumNews.com. This content is provided by a sponsor. PRESS RELEASE. September 18, 2025 – Geneva, Switzerland – TRON DAO, the community-governed DAO dedicated to accelerating the decentralization of the internet through blockchain technology and decentralized applications (dApps), announced today that PayPal USD will be available on the TRON network through Stargate Hydra as a permissionless token, […] Source: https://news.bitcoin.com/paypal-usd-expands-to-tron-network-via-layerzero/
Share
BitcoinEthereumNews2025/09/18 23:12