The post Russia Arms Shaheds With Air-To-Air Missiles To Shoot Down Helicopters appeared on BitcoinEthereumNews.com. The R-60 Vympel missile found the the debris of a shot-down Shahed Serhii Flash via Telegram Russia continues its relentless drone bombardment of Ukraine, launching over 5,400 Shahed-type attack drones last month. The vast majority, around 84% , were shot down, many by Ukrainian helicopters. But Russia has just raised the ante, by arming some Shaheds with R-60 air-to-air missiles in an attempt to turn the tables and shoot down those helicopters. Blasting Drones From Choppers Shaheds typically cruise at about 120 mph, following a preprogrammed course. Generally there is no operator control: the drone has no situational awareness, and will do nothing to avoid either ground fire or aerial threats. So it is relatively easy for a helicopter to fly up alongside one and blast it with machine gun fire until it goes down. There are many videos of Ukrainian helicopters downing drones with turret-mounted machine guns or cannon , or door gunners unleashing M-134 minigun fire at close range. Ukraine has an elaborate layered air defence including electronic warfare, aircraft, missiles, interceptor drones and mobile ground units. While we know the total number of drones downed, we do not know the distribution of kills. What we do know is that some helicopter crews have been extremely successful. One video shows an Mi-24 attack helicopter with at least 50 kill markings. Others are anecdotally credited with hundreds of kills each. The Russians have taken various measures to make Shaheds less vulnerable, including fitting them with cameras and modems so operators can take evasive action when the drones are targeted. Now they have taken things a step further by arming their drones. Enter The Aphid On December 1st, influential Ukrainian electronic warfare expert Serhii “Flash” Beskrestnov shared images of a downed Shahed with an R-60 missile and launch rail with… The post Russia Arms Shaheds With Air-To-Air Missiles To Shoot Down Helicopters appeared on BitcoinEthereumNews.com. The R-60 Vympel missile found the the debris of a shot-down Shahed Serhii Flash via Telegram Russia continues its relentless drone bombardment of Ukraine, launching over 5,400 Shahed-type attack drones last month. The vast majority, around 84% , were shot down, many by Ukrainian helicopters. But Russia has just raised the ante, by arming some Shaheds with R-60 air-to-air missiles in an attempt to turn the tables and shoot down those helicopters. Blasting Drones From Choppers Shaheds typically cruise at about 120 mph, following a preprogrammed course. Generally there is no operator control: the drone has no situational awareness, and will do nothing to avoid either ground fire or aerial threats. So it is relatively easy for a helicopter to fly up alongside one and blast it with machine gun fire until it goes down. There are many videos of Ukrainian helicopters downing drones with turret-mounted machine guns or cannon , or door gunners unleashing M-134 minigun fire at close range. Ukraine has an elaborate layered air defence including electronic warfare, aircraft, missiles, interceptor drones and mobile ground units. While we know the total number of drones downed, we do not know the distribution of kills. What we do know is that some helicopter crews have been extremely successful. One video shows an Mi-24 attack helicopter with at least 50 kill markings. Others are anecdotally credited with hundreds of kills each. The Russians have taken various measures to make Shaheds less vulnerable, including fitting them with cameras and modems so operators can take evasive action when the drones are targeted. Now they have taken things a step further by arming their drones. Enter The Aphid On December 1st, influential Ukrainian electronic warfare expert Serhii “Flash” Beskrestnov shared images of a downed Shahed with an R-60 missile and launch rail with…

Russia Arms Shaheds With Air-To-Air Missiles To Shoot Down Helicopters

2025/12/04 20:21

The R-60 Vympel missile found the the debris of a shot-down Shahed

Serhii Flash via Telegram

Russia continues its relentless drone bombardment of Ukraine, launching over 5,400 Shahed-type attack drones last month. The vast majority, around 84% , were shot down, many by Ukrainian helicopters. But Russia has just raised the ante, by arming some Shaheds with R-60 air-to-air missiles in an attempt to turn the tables and shoot down those helicopters.

Blasting Drones From Choppers

Shaheds typically cruise at about 120 mph, following a preprogrammed course. Generally there is no operator control: the drone has no situational awareness, and will do nothing to avoid either ground fire or aerial threats. So it is relatively easy for a helicopter to fly up alongside one and blast it with machine gun fire until it goes down.

There are many videos of Ukrainian helicopters downing drones with turret-mounted machine guns or cannon , or door gunners unleashing M-134 minigun fire at close range.

Ukraine has an elaborate layered air defence including electronic warfare, aircraft, missiles, interceptor drones and mobile ground units. While we know the total number of drones downed, we do not know the distribution of kills. What we do know is that some helicopter crews have been extremely successful. One video shows an Mi-24 attack helicopter with at least 50 kill markings. Others are anecdotally credited with hundreds of kills each.

The Russians have taken various measures to make Shaheds less vulnerable, including fitting them with cameras and modems so operators can take evasive action when the drones are targeted. Now they have taken things a step further by arming their drones.

Enter The Aphid

On December 1st, influential Ukrainian electronic warfare expert Serhii “Flash” Beskrestnov shared images of a downed Shahed with an R-60 missile and launch rail with the comment “This combination is designed to destroy helicopters and tactical aircraft hunting for Shaheds.”

Further imagery released later showed when the Shahed was shot down.

R-60 Vympel missile carried by a Russian Su-15 fighter

By George Chernilevsky – Own work, CC BY-SA 3.0

The R-60, otherwise known by its NATO reporting name of AA-8 Aphid, or Vympel, is a short-range air-to-air missile originally carried by jet fighters in the 1970s. It is an infrared missile which homes in on the engine heat of the target, and has a range of about five miles. A proximity fuse detonates close to the target, and the seven-pound warhead is a ‘continuous rod’ type, a set of hinged steel rods which unfold to into a giant ring moving fast enough to cut through an aircraft.

While the R-60 is one of the lightest air-to-air missiles, along with its launch rail it still weighs over a hundred pounds. That is a lot of extra weight and the missile-armed Shahed likely does not have a warhead. The missile armed drone is an escort fighter to counter aircraft, with no ability to hit ground targets.

Aiming the missile must be fairly challenging for the drone operator even if the communications are working well. The Shahed is no dogfighter and has limited maneuverability, being designed for long-endurance flight. Once alerted that there is an enemy aircraft in the area, the drone operator must swing the drone around and capture the target in the missiles’ narrow field of view, then hold it there long enough for the missile to achieve lock on before launching. This might be possible with a slow-moving target like a helicopter.

Short-range heat-seeking missiles can be highly effective, with the US equivalent to the R-60, the Sidewinder, scoring up to 80% kills. But some earlier model Sidewinders had a success rate at low as 10%, and the Russian missiles may fall in the lower end of the range. Also some heat-seeking missiles can be easily decoyed by infra-red flares carried by helicopters and other aircraft.

Aerial Drones, Sea Drones, And The Numbers Game

This is not the first time drones have been armed for air-to-air combat. The US tried putting Stinger missiles on its Predator reconnaissance drones, but in the only known encounter an Iraqi Mig-25 easily shot down a Stinger-armed Predator in 2002. In 2021, Iran displayed a drone with an Azarakhsh (“Thunderbolt”) air-to-air missile and test-fired it.

The concept with all of these is the same. While armed Shaheds may not individually present a major threat to helicopters, they change the calculus. When you intercept enough Shaheds, one of them will be carrying a missile, and every time that happens there is a risk of losing a valuable helicopter.

This is exactly the same approach as Ukraine has taken with its drone boats. The uncrewed speedboats were vulnerable to Russian helicopters machine-gunning them, so the Ukrainians responded by arming a few of their boats with surface-to-air missiles. In at least one case these have shot down Russian helicopters. It would be a brave or foolish helicopter pilot who risked getting within firing range of a drone boat.

The point of drones like the Shahed and drone boats is that they can be made cheaply in large numbers and overwhelm or wear down defenses. Using expensive platforms like helicopters or jets to down them may not be a workable strategy, and pilots will need to be wary of this new threat, especially when a Shahed swings around to face them.

Fortunately Ukraine is rapidly scaling up an alternative. Interceptor drones, which cost much less than Shaheds, are now appearing at scale (one brought down the missile-armed Shahed). Maybe Shaheds will get miniature gun turrets or other means to counter the interceptors. One thing is fcertain: drone warfare is evolving fast, and the war of countermeasures and counter-countermeasures will continue.

Source: https://www.forbes.com/sites/davidhambling/2025/12/04/russia-arms-shaheds-with-air-to-air-missiles-to-shoot-down-helicopters/

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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