The Arweave computing platform AO mainnet has been launched; Musk's confrontation with Washington has become the cover of the new issue of Time magazine; Brazil's stock exchange B3 will launch BTC options and ETH and SOL futures contracts; Berachain Foundation stated that the second part of the airdrop will be distributed to wallets on February 10.The Arweave computing platform AO mainnet has been launched; Musk's confrontation with Washington has become the cover of the new issue of Time magazine; Brazil's stock exchange B3 will launch BTC options and ETH and SOL futures contracts; Berachain Foundation stated that the second part of the airdrop will be distributed to wallets on February 10.

PA Daily | Binance launches Test (TST) and Cheems (1000CHEEMS); After Trump launches Meme coin, more than 700 tokens are sent to his wallet

2025/02/09 17:19

Today's news tips:

1. Musk's confrontation with Washington is the cover of Time magazine

2. Brazilian Stock Exchange B3 will launch BTC options and ETH and SOL futures contracts

3. DeepSeek gained 100 million users in just 7 days

4. CZ: Never purchased or owned TST, and used the Binance logo without authorization

5. Binance will list Cheems (1000CHEEMS) and Test (TST) and add seed tags to them

6. Arweave computing platform AO mainnet is now online

7.Berachain Foundation: The second part of the airdrop will be distributed to wallets on February 10

8.pump.fun Lianchuang: Rumors of potential platform token issuance are not true

9. After Trump launched Meme Coin, more than 700 tokens were sent to his wallet

Regulatory/Macro

Musk's confrontation with Washington is the cover of Time magazine's new issue

Musk's confrontation with Washington has become the cover of the latest issue of Time magazine. Elon Musk is leading a large-scale streamlining of the US government. The "Department of Government Efficiency" (DOGE) led by him has taken over the US Digital Service and entered the Office of Personnel Management (OPM), laying off employees, cutting budgets, and reorganizing federal agencies. In early February, DOGE tried to force its way into the headquarters of the United States Agency for International Development (USAID). After being rejected, Musk called it a "criminal organization" on X, and the agency was almost completely shut down.

The Trump administration authorized Musk to promote the "Project 2025" reform, with the goal of reducing the size of the government and eliminating opposition forces. Many civil servants were forced to resign for refusing to cooperate, and some senior officials of some agencies were replaced. The Democratic Party and labor unions have initiated legal proceedings, and some courts have ruled to suspend relevant actions. However, Musk still controls key agencies, and the outside world is worried that government power is overly influenced by private entrepreneurs.

Brazil’s B3 Stock Exchange to Launch BTC Options and ETH and SOL Futures Contracts

Brazil’s stock exchange B3 will launch BTC options as well as ETH and SOL futures contracts, expanding its range of cryptocurrency products. B3 is Brazil’s main stock exchange, which lists dozens of cryptocurrency exchange-traded products as well as stocks, bonds and other financial products.

Montana, U.S., authorizes up to $50 million in investments in precious metals, digital assets and stablecoins with an average market value of more than $750 billion over the past year

Julian Fahrer, a former Sequoia Capital analyst, tweeted that Montana House Bill 429 would authorize the Investment Committee to invest up to $50 million in precious metals, digital assets and stablecoins with an average market value of more than $750 billion in the previous calendar year by July 15, 2025. These funds must be held by a qualified custodian or through an exchange-traded fund.

AI/Metaverse

DeepSeek becomes the fastest APP to break 30 million daily active users

QuestMobile data showed that DeepSeek surpassed Doubao in daily active users for the first time on January 28, and then broke through the 30 million mark on February 1, becoming the fastest application to reach this milestone in history.

DeepSeek gained 100 million users in just 7 days

According to the IT Home AI Product Ranking, after the release of the DeepSeek R1 model on January 20 this year, the number of DeepSeek users increased by 125 million in January (Note: including websites (Web) and applications (App) without deduplication). Among them, more than 80% of users came from the last week of January, which means that DeepSeek achieved 100 million user growth in 7 days without any advertising.

Project News

Binance Alpha adds TST

The App page shows that Binance Alpha has added TST.

Binance will list Cheems (1000CHEEMS) and Test (TST) and add seed tags to them

According to the official announcement, Binance will list Cheems (1000CHEEMS) and Test (TST) and add seed tags to them. The launch time is February 9th 19:00 (UTC+8).

Bitget will launch Test (TSTBSC) in the Innovation and Meme Zone

According to the official announcement, Bitget will launch Test (TSTBSC) in the Innovation and Meme Zone. The trading opening time is 18:00 on February 9 (UTC+8).

Ethereum block gas limit increased to 36M, L1 transaction volume increased by 20%

According to the technical update forwarded by Vitalik, Ethereum L1 recently completed a dynamic adjustment of the block Gas upper limit from 30M to 36M, which increased the L1 transaction throughput by 20% and is expected to reduce transaction fees by 10% to 30%.

This adjustment is not determined by any single entity, but is the result of validators gradually adjusting parameters and reaching consensus. Currently, 49.5% of validators have reported adopting the 36M Gas limit.

Arweave computing platform AO mainnet is now online

After a year of testing, the decentralized storage project Arweave has officially launched the mainnet version of its computing platform AO. The native tokens previously minted and distributed to AR holders and testnet depositors will be transferable after the mainnet launch.

AO is called a "hyperparallel computer" by developers, which introduces a distributed computing environment with parallel processing capabilities. Arweave's permanent data storage supports AO's operations, ensuring that data is immutable and permanently accessible. According to core developers, AO will support a new wave of dapps, including on-chain autonomous agents focused on AI coordination.

The launch of the AO mainnet was accompanied by the final release of its native token, which is separate from Arweave’s AR token. The maximum supply of AO tokens is capped at 21 million, echoing the model of Bitcoin. The AO team said it adopted a fair distribution model to distribute tokens to users who bridge assets to the platform and existing AR token holders.

36% will be distributed to AR holders over time based on holdings. The remaining 64% is reserved for economic growth incentives, specifically for bridging assets into AO, including rewards for users who deposit assets like DAI and stETH into the AO ecosystem. Prior to launch, over $700 million was pre-bridged to the AO testnet.

Arweave launched AO's public testnet in February 2024. By June, AO announced its native token, AO, as well as token economics and reward mechanisms. The main function of the AO token is to protect messaging within the network through system incentive proofs.

Berachain Foundation: The second part of the airdrop will be distributed to wallets on February 10

Berachain Foundation tweeted that the second part of the Berachain airdrop will be distributed to wallets on February 10. Top X yappers and Discord users (Berachain + Bong Bear NFT server). Use the engagement index of KaitoAI and Cookie3 data to identify Yappers.

Recipients of the RFB Program (Applications and Communities). Over 200 ecosystem projects and community members received tokens through the Foundation’s RFB program. dApps must use 70% of their allocation for future mainnet rewards and liquidity incentives. Airdrop recipients must register a wallet by 11:59 PM EST on February 9 to receive their allocated tokens. Tokens were claimed on February 10.

Market News: Pump.fun is planning to issue tokens in the Dutch auction model

Crypto KOL He Bi (@hebi555) tweeted that Pump.fun is planning to issue tokens and conduct public offerings, which will adopt the Dutch auction model. It is currently working with major centralized exchanges to develop public offering procedures. Pump.fun is facing a class action lawsuit in the United States, and some people say it may have a certain impact on the issuance of tokens.

Pump.Fun Co-founder: Rumors of potential platform token issuance are not true

Alon, co-founder of pump.fun, posted on the X platform: "Any rumors about potential pump.fun tokens are false. It is recommended not to listen to any news that is not directly from the official. Although the team has been mainly focused on improving the product during the one-year development process, it has always been committed to providing appropriate rewards to users."

Indian Crypto Exchange WazirX Releases Creditor List and Balance Snapshot

Indian crypto exchange WazirX tweeted that it has released a preliminary list of creditors and a July 18 balance snapshot page within the WazirX application to ensure full transparency of the restructuring process.

Creditors can query the amount of their claims (in USD) by their unique UUID, and the list is sorted in descending order by claim value and can be searched using the UUID. A "Find My Balance" option is also provided for easy access. Creditors can also view other creditors' claims information (with UUIDs replacing personal identification information) by submitting an inspection request. Creditors can view a snapshot of their balances as of 13:00 (IST) on July 18, 2024, including token balances and deposits thereafter.

Viewpoint

CZ: Never purchased nor owns TST, and uses Binance Logo without authorization

CZ tweeted that TST already has a website and an account X, neither of which is created or controlled by BNB Chain or any Binance-related team, but by community members, and the specific person in charge is unclear. They used Binance's logo without authorization, which is an infringement and needs to be changed. It is recommended to use a logo like "passed the test". Like all meme coins, he has never purchased or owned TST, and it is currently fully operated by the community.

CZ once again states that he has nothing to do with TST tokens

In response to a tweet about "BNB is clearly one of the strongest assets in the current market. CZ has always been a builder and is about to make BNBCHAIN great again," CZ said, "Not just me, but also the community I am in. TST tokens have nothing to do with me."

According to news this morning , CZ said that he had never purchased or owned TST, and that he used the Binance logo without authorization.

Important data

NFT transaction volume fell 33% month-on-month to $119.5 million in the past seven days

CryptoSlam data shows that the NFT market cooled down this week, with total transaction volume falling from US$137.9 million to US$119.5 million, a 33% drop from the previous week.

Ethereum network fell 38.43% month-on-month, but still maintained the first place with a transaction volume of US$62.6 million, and the number of buyers fell 71.26% month-on-month to 16,852; Mythos Chain rose to second place with a transaction volume of US$13.9 million, a month-on-month increase of 4.66%. Solana ranked third with a transaction volume of US$11 million, a month-on-month decrease of 32.56%.

Fat Penguin maintained its lead despite a 37.55% drop in volume to $9.1 million. The series saw steady buyer interest with 172 participants. DMarket came in second with $8.7 million in volume, up 7.98%, and the number of transactions remained high at 322,241. Courtyard remained in third with $7.3 million in volume, up 25.78% month-over-month, and attracted 10,935 buyers. CryptoPunks fell to fourth place with $5.2 million in volume, down 30.01%, while Azuki fell to fifth place with $5 million in volume, down 79.17%.

Notable deals this week include:

  • CryptoPunks #8868: $558,008 (206 ETH)
  • Autoglyphs #320: $309,450 (100 WETH)
  • Autoglyphs #491: $267,998 (100 WETH)
  • CryptoPunks #7585: $242,639 (85 ETH)
  • Autoglyphs #331: $235,343 (87.0107 WETH)

The Ethereum Foundation transferred 50,000 ETH to a multi-signature wallet for participating in DeFi 3 hours ago

On-chain analyst Embers monitored that the Ethereum Foundation transferred 50,000 ETH (US$131.66 million) to a multi-signature wallet used to participate in DeFi 3 hours ago.

After Trump launched Meme Coin, more than 700 tokens were sent to his wallet

According to FT, within three weeks of Trump launching Memecoin, more than 700 altcoins and junk coins were sent to Trump’s digital wallet. There are 736 different memecoins in Trump’s official wallet. Among them, nearly 200 memecoins, including “OFFICIAL TRUMP” and “OFFICIAL MELANIA”, are named after Trump or his family, but have nothing to do with the president.

Of the 192 tokens named after Trump or his family, 167 are altcoins, while 67 use the word "official" in their names. Thirty-five tokens have the word "Elon" or "Musk" in their names, an apparent reference to the Tesla CEO and Trump ally. Unauthorized tokens also target Trump's children: 30 mention Barron, 26 mention Ivanka, and 10 mention Eric. Eswar Prasad, a senior fellow at the Brookings Institution, said Trump's involvement in meme coins "opens the floodgates for deception and rampant speculation" and that ordinary investors buying meme coins "will only put them at great risk."

A whale is suspected of selling 8,139 ETH that he built two years ago. If he sells, he will make a profit of $10.125 million

On-chain analyst @ai_9684xtpa monitored that the Ethereum whale was suspected of selling 8,139 ETH worth $21.18 million that it had built two years ago. This address had withdrawn 13,459 ETH from Binance and FTX from August 2021 to December 2022, at an average price of $1,358; if it is sold this time, it will make a profit of $10.125 million.

Whales who previously traded TST and made over $150,000 in profit bought 4.81 million TST

According to Onchain Lens, a whale spent 1.94 million USDT to buy 4.81 million TST. Previously, the whale spent 49,900 USDC to buy 5.31 million TST and sold it at $205,000, making a profit of $155,000.

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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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Medium2025/09/18 14:40