Well known tech investor and former Coinbase CTO Balaji Srinivasan recently went public with a serious warning about the fragility of the global financial systemWell known tech investor and former Coinbase CTO Balaji Srinivasan recently went public with a serious warning about the fragility of the global financial system

Next Shiba Inu: Pepeto Gains Attention as SHIB-Style Early Entry Returns

2026/03/20 02:49
5 min read
For feedback or concerns regarding this content, please contact us at crypto.news@mexc.com

Well known tech investor and former Coinbase CTO Balaji Srinivasan recently went public with a serious warning about the fragility of the global financial system, calling the current moment wartime mode for the internet. According to Bloomberg, he pointed to growing displacement, conflict, and the urgent need for decentralized financial tools that work when traditional institutions crumble under pressure. While most people are still glued to screens watching large cap charts, the smart money is already moving into the entry that could become the next shiba inu.

Pepeto: The Next Shiba Inu With Real Products Instead of Pure Hype

As global instability grows, holding large positions in stagnant meme coins is becoming a real liability. The smartest thing you can do right now is recognize that the next shiba inu will not come from the tokens that already had their run. Pepeto is changing how the meme coin sector works completely. Instead of scattered trading across platforms never built for this market, PepetoSwap, Pepeto Bridge, and Pepeto Exchange create one unified system.

Next Shiba Inu: Pepeto Gains Attention as SHIB-Style Early Entry Returns

No more fragmentation. On top of that, the PEPE cofounder who built PEPE into a $7 billion dynasty gives serious investors full confidence in the leadership. The SolidProof audit confirms contract security. Over 4 billion tokens burned tighten supply. The 196% staking APY rewards those who commit early. Unlike other traders chasing dead momentum in faded meme coins, Pepeto holders never walk into a cycle without real infrastructure backing them.

Now, imagine if you make a $5,000 investment at the current price of $0.000000186. Because the market cap is still tiny, a 537x move to $0.0001 is within the range of what meme coin history has delivered. At that target, that $5,000 turns into $2,685,000. At that point, whatever any established meme coin forecast says becomes completely irrelevant. This is what the next shiba inu looks like before the crowd finds it.

Ethereum Struggles to Move Despite Institutional Backing at $2,180

Ethereum posted strong numbers recently, but the problem for retail traders is clear: it takes tens of millions of dollars from major investors just to nudge the price slightly. According to CoinDesk, the CFTC will officially oversee Ethereum trading alongside the SEC, meaning heavy government involvement in every aspect of ETH’s future. ETH at $2,180 offers 3x to 4x potential at best. Looking for an explosive return from Ethereum is a big ask when the next shiba inu at $0.000000186 offers 537x from the same starting capital.

Solana Sends Cautious Signals at $85

Solana trades at $87 with the SEC’s digital commodity classification adding regulatory clarity. Analyst targets suggest $200 in a bull scenario. But SOL sits with a $48 billion market cap and faces a neutral technical structure that could break either direction. Holding a token waiting for macro timing that nobody can predict is a losing game when the next shiba inu at presale pricing is still available for anyone willing to act.

The People Who Built Fortunes All Moved Before the Crowd

Staring at flat large cap charts will not protect your wealth or build new wealth. The people who turned DOGE into fortunes, who rode SHIB from nothing to retirement money, who caught PEPE before $7 billion: they all shared one trait. They moved before the crowd arrived. Pepeto at $0.000000186 with the PEPE cofounder, three products approaching launch, and $8.1 million in momentum is the next shiba inu moment happening right now. The presale is closing. Exchange listings are confirmed. The people who position today are the ones everyone else will wish they had listened to.

Click To Visit Pepeto Website To Enter The Presale

What is the next shiba inu that could deliver life changing returns?

Pepeto at $0.000000186 is positioned as the next shiba inu, built by the PEPE cofounder with three real products, $8.1 million raised, and 537x potential at $0.0001 that mirrors the kind of early entry SHIB holders had before the explosion.

How does a $5,000 Pepeto investment compare to holding Ethereum?

A $5,000 ETH position at $2,180 targets roughly $15,000 at the $7,500 bull case. A $5,000 Pepeto entry at $0.000000186 targets $2,685,000 at the 537x projection. The return differential defines why Pepeto is the next shiba inu.

Why are traders calling Pepeto the next shiba inu instead of other meme coins?

Pepeto is the only meme coin presale led by the PEPE cofounder, with PepetoSwap, Pepeto Bridge, and Pepeto Exchange approaching launch. SHIB had community energy but no infrastructure. Pepeto has both.

Follow Pepeto on X and Telegram for community updates.

Sources: Bloomberg | CoinDesk

Comments
Market Opportunity
SHIBAINU Logo
SHIBAINU Price(SHIB)
$0.00000597
$0.00000597$0.00000597
+4.77%
USD
SHIBAINU (SHIB) 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

Trump-backed WLFI  launches AgentPay SDK open-source payment toolkit for AI agents

Trump-backed WLFI  launches AgentPay SDK open-source payment toolkit for AI agents

The Trump family has expanded its presence in the crypto community with a major development for artificial intelligence (AI) agents. According to reports, World
Share
Cryptopolitan2026/03/20 19:03
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
Tom Lee Declares That Ethereum Has Bottomed Out

Tom Lee Declares That Ethereum Has Bottomed Out

Experienced analyst Tom Lee conducted an in-depth analysis of the Ethereum price. Here are some of the highlights from Lee's findings. Continue Reading: Tom Lee
Share
Bitcoinsistemi2026/03/20 19:05