There may have been no more honest action in this administration than when Defense Secretary Pete Hegseth insisted the Pentagon was predicated on war, tossing asideThere may have been no more honest action in this administration than when Defense Secretary Pete Hegseth insisted the Pentagon was predicated on war, tossing aside

Trump just planted the seed of his destruction

2026/03/20 17:30
5 min read
For feedback or concerns regarding this content, please contact us at [email protected]

There may have been no more honest action in this administration than when Defense Secretary Pete Hegseth insisted the Pentagon was predicated on war, tossing aside former assumptions that it engaged only in "defense." And here we are.

Only one year into his term, President Donald Trump ordered troops into a war without an articulable argument as to an Iranian threat, never mind justification proven by fact. Appropriately, the result ensures Trump's eventual personal demise, but sadly, too, the destruction of the American public's hard-earned global goodwill. But struggle back anyway.

Trump will eventually go, but unlike the first term, the war now vitiates any chance that decent nations forgive and forget, not this time. Of course, this is what happens when unserious people plow forward in a seriously dangerous and unforgivable cause.

Hegseth spends news conferences berating media coverage, before taking notice of the bodies of service members, all because even in war, content is seriously king.

Absolutely, the vast majority of the American public understands the administration's motivation with drone-like precision. Our Secretary of "War," under our president's predation, desperately needs an outlet to ensure his "bro" followers concentrate on it all being alpha-cool.

The White House supports that same need with video memes, "pow" — straight out of video games, literally. To be sure, the world, too, despondently sees the same mystifying behavior, one enjoyed by far too many of our fellow citizens, a "real action" movie that plays out to horrified audiences elsewhere.

A most serious war by the most unserious people ensures well-deserved consequences, ones as unbounded as imprecise, impossible to predict.

But again, here we are. Indeed, momentarily and embarrassingly, embrace exactly where we are, the world sure does. A president in a ballcap over bodies.

A "Secretary of War" obsessed with the media play:


The wartime president dancing the night away:

An insulting Orwellian assurance, higher gas prices, all needed to bring gas prices down.


See?

The world sure sees.

Of course, we soothe ourselves knowing Trump can't last. Indeed, again, the war ensures he likely falls even sooner. Just know, the world remains unsoothed knowing that the American voters who put these men in place do last, and again, unserious voters usher in the most serious result. "We" don't trust these people, nor do they.

But the global community's response — redirecting trade, shifting alliances, and abandoning assumptions — that reaction will last much longer than an impending national political solution. Dems will surely get their mid-term blue wave; meanwhile, the world will just wave.

But as undeniable and inevitable the result may be, Americans must expend every effort to at least minimize the extent. It is awfully tempting to just give up. It's done. We're gone, at least from where we were. But it can always get worse — always, the result more impactful and enduring unless abated, however that may be done.

Perhaps the only good news in all this is that unserious people who wage war without real analysis are just as vulnerable to paper bombs from files, revolts over coffins, or simply the public exhaustion that simply bursts forth in unanticipated ways, all against an administration just as flat-footed, just as politically unaware and unserious. Movements and cults stand impervious to pushback right up until they're not, and are, by definition, even more impervious to resurrection.

Trump and Hegseth seem astonished that Iran closed the Hormuz Straight, paralyzing the transport of energy across the globe. Assuredly, they'll be no less astonished if and when the American public's rage — one born of hard work and faith — paralyzes any dodge, any cover-up, freezing the situation in place. Nowhere to go.

See? The world will see that, too. And, no, they won't forgive and forget, the consequences cascading, exact end results unforeseeable. But the struggle to crawl back must begin somewhere, so let it start now, at least in some way.

The Trumpers tell us this all avoids Iran's nuclear threat. Fine. Force them to prove it and call your Congressional representative again. Because the administration is not ready to meet that demand straight up.

We know the administration wanted a diversion from Epstein revelations. Not fine. Save some focus on the Epstein matter because their every action, every speech, all of it, belies a resulting fear of their demise. Call your Congressional representative again.

Protest the war, project our seriousness, share the world's shock. Vote for God's sake... at least demand it. Congress definitely hears that message, even a third time.

The most serious action by the most unserious people, all of it unsustainable, consequences just as assurable, remains inevitable. Meet it all with serious action, still, because the world depends on us, even as it backs away. Rest assured.

It's all just so awful. But they've laid their seed of destruction, rest also assured.

Because war to avoid talk of rape? Well, these unserious "war fighters" finally found a battle way too serious. Make that call — the world is listening.

Jason Miciak is a former associate editor of Occupy Democrats and a Rawstory writer at large. He is an author, American attorney, and single-parent girldad. He can be reached at [email protected], on "X" @jasonmiciak, and please follow on Bluesky, currently seeking beta readers for his newest novel, soon to be released.
Market Opportunity
OFFICIAL TRUMP Logo
OFFICIAL TRUMP Price(TRUMP)
$3.346
$3.346$3.346
-0.68%
USD
OFFICIAL TRUMP (TRUMP) 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 [email protected] 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