The post The Magic Of The Mark, Through Assouline’s Lens appeared on BitcoinEthereumNews.com. Masters of Creating Dwell Time: French fries served in a branded MarkThe post The Magic Of The Mark, Through Assouline’s Lens appeared on BitcoinEthereumNews.com. Masters of Creating Dwell Time: French fries served in a branded Mark

The Magic Of The Mark, Through Assouline’s Lens

Masters of Creating Dwell Time: French fries served in a branded Mark carton.

Courtesy of the Mark Hotel/ Photo by Angela Pham

There are hotels you stay in, and hotels that stay with you. The Mark belongs to the latter category, an address that has become part residence, part cultural salon, part discreet retailer with room keys.

The new Assouline volume, (launched today) written by best-selling author Derek Blasberg, sets out to capture that alchemy, yet the book’s quiet subtext is more interesting still: how modern luxury has learned to sell the echo of an experience.

“People come to The Mark because they know it’s more than just a hotel. It’s a place that makes unforgettable memories, the kind you treasure forever,” owner Izak Senbahar explains. The contemporary consumer might translate that differently. Memories today are rarely left behind at checkout; they travel home in objects, images and stories that continue the narrative long after the taxi pulls away.

A hotel that creates perfect retail theatre

Shopping at The Mark: The Assouline boutique at The Mark Hotel.

Courtesy of the Mark Hotel/ Photo by Marcel Frommer

Just adjacent to the lobby sits the Assouline bookshop, glowing with jewel-toned volumes of destination books and the brands home fragrance collection. In rooms, guests can use the Le Shop guide to peruse through a plethora of ‘resortcore’ products and luxury limited editions. All of this works to complement the narrative infrastructure, extending the property into everyday life. A cashmere jumper, a limited collaboration with Augustine’s Bader and perfectly curated home are – these are not serving as souvenirs but social signals, proof of your ‘temporary belonging’.

Across the industry the ‘resortcore’ trend often descends into novelty; here it reads more coherently. The products echo the building’s graphic language and playful restraint. Retail becomes a continuation of place rather than a hard sell at the exit door.

For businesses watching from outside hospitality, the lesson is clear. Consumers increasingly expect experiences to be portable. They want to take a fragment of atmosphere home with them, provided it feels authored rather than engineered.

Culture as commercial engine

Blasberg’s pages linger on The Mark’s most public chapter: its role as the unofficial prelude to the Met Gala. Each spring the corridors become ateliers, suites transformed into dressing rooms for fashion’s most scrutinised night. The following morning those moments ripple quietly into commerce, a remembered shade of lipstick, a motif reappearing in a shop window, a story retold over breakfast at Jean-Georges.

Dining, design and the choreography of touch-points

The iconic hot-dog cart: Chef Jean-Georges Vongerichten at The Mark Haute Dog cart, known for its signature Jean-Georges gourmet organic-chicken and grass-fed-beef hot dogs.

Courtesy of the Mark Hotel/ Photo by Angela Pham

The food spaces at The Mark tell a more interesting story than menus. At curb level the playful hot dog cart catches the eye of tourists, teenagers and locals on their lunch break an Instagram moment at a more attainable price-point. A few steps later the same building hosts Caviar Kaspia and Jean-Georges, speaking to a very different wallet and mood. That range is not contradiction; it is recognition that real people shift gears constantly.

The journey from sidewalk to suite mirrors how consumers behave everywhere. We want entry points that feel welcoming, not intimidating, and we want permission to trade up without feeling judged. A drink at the Mark Bar might become dinner, then a wander into Le Shop for something to take home, the experience stretching gently into ownership. The cleverness is not in being everything to everyone, but in understanding that modern luxury succeeds when it meets people where they are and lets them travel upward at their own pace – this creates the ultimate in relaxed dwell time, where guests want to linger for longer.

Lessons beyond Manhattan

For retailers and brands far removed from Fifth Avenue, three insights emerge.

First, memory now competes with product. Consumers value objects that extend experience, not simply decorate it.

Second, participation beats perfection. The most successful moments invite guests to co-create, whether through a picnic in Central Park or a book chosen in the lobby.

Third, restraint protects desirability. Luxury falters when commerce becomes visible; it thrives when commerce feels like continuity.

In cities competing to build ever brighter temples of hospitality, this is a quieter proposition. Intimacy over immensity, authorship over algorithms. The Met Gala may provide the fireworks, but the enduring magic occurs in the smaller exchanges: a book purchased before departure, a candle lit weeks later at home, a photograph that makes someone else ask, “Where was that?”

The answer is The Mark, and perhaps the main point is not how grand a place looks, but how well it understands the people who enjoy to stay for an hour, a week or a month.

Source: https://www.forbes.com/sites/katehardcastle/2026/01/16/the-magic-of-the-mark-through-assoulines-lens/

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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