chatgpt-usage on S Anand

My food preferences

Fri, 24 Apr 2026 14:09:36 -0400

I use ChatGPT to recommend which restaurant I should eat at and what food I should eat. So often that I decided to share a profile of my eating preferences.

But rather than think about it and type it myself, I asked it to

Efficiently interview me to identify my food preferences. Document it for AI agents to help me pick restaurants. Plan like an expert.

(Knowing ChatGPT, I also had add “efficiently” - otherwise it would give me a huge list of questions! Which it did that anyway…)

That makes it easy. Now, I just have to answer questions about my preferences, e.g. my budget range, restaurant styles, cuisines etc.

I also gave it a dump of my daily food log. (This year I’ve been logging all the food that I eat - for no reason actually - along with which of those I liked and disliked.) This proved to be a good idea, because it picked up something I didn’t realize:

You are unusually tolerant of repeating humble comfort foods at home, …

Very true. I ate curd rice day and night for 22 years at every meal. I eat Kaya Peanut Toast every day at Ya Kun Kaya Toast. (They make it the instant they see me standing at the back of the line, and miss me when I travel.)

So, here’s my tweaked version of ChatGPT’s recommended food spec on how agents should pick for me:

Eggetarian. No meat or seafood; eggs & milk are OK.
Prioritize novel cuisines & known exceptional dishes.
Prefer casual, functional, non-fine dining. SGD $5-20 feels fine.
Any cuisine works: Indian, Italian, Mediterranean, Middle Eastern, ...
Creamy > crunchy > gooey > chewy > airy.
Spicy is good. Heavy, raw, or loud are bad.
Desserts are great! Too sweet is bad.
E.g. creative fusion, chaat, inventive small plates, nutty flavors, hot cookies, textured desserts.
Prefer 10 min travel, 5 min wait. For exceptional food in a new city, an hour is OK.

PS: This is styled quite differently from how ChatGPT writes, but the content was correct.

Fri, 15 Aug 2025 00:00:00 +0000

Indian Celebrities and Directors was my top searched category on Google while OpenAI & AI Research was the top growing category.

This is based on my 37,600 searches on Google since Jan 2021. Full analysis: https://sanand0.github.io/datastories/google-searches/

The analysis itself isn’t interesting (to you, at least). Rather, it’s the two tools that enabled it.

First, topic modeling. If you have all your searches exported (via Google Takeout) into a text file, you can run:

uvx topicmodel searches.txt --ntopics 50

… and automatically get the top 50 topics you search for.

Second, an improved O3 prompt. I fed it monthly topics volume and asked:

Look closely at the numbers as well as the image. What insights can you draw from these? Aim for non-obvious non-trivial insights. Run correlations or any other analyses on the data to go deeper and come up with material suitable for a deep research paper.

The analyses it did was far more powerful than anything I would have thought of.

It calculated my Herfindahl-Hirschman index slope and declared that my search interests are diversifying. (Good to know!)
Using Principal Component Analysis it discovered 3 personas in my searches
- Classical developer (Python, JS)
- AI-builder (OpenAI, LLMs, APIs)
- India/Singapore geo-culturist (Celebs, local info, Tamil cinema) I should segment sharing & learning along these axes (e.g. separate newsletters or dashboards.)
Using the Coefficient of Variation it found the biggest spikes in SQL & Databases, and Testing & Code Tools. Steadiest were Currency Conversion Rates and Singapore/Bangalore Local Info.

I should cache common reference lookups locally and allocate “deep‑focus blocks” for debugging spikes.

There’s more. But this is the first time I felt completely outmatched by an LLM. I’m an expert on analysis. I’m an expert on this domain (my search queries.) Yet, this is far more insightful than I ever would have analyzed!

Sun, 22 Jun 2025 18:09:06 +0000

Here’s how I use ChatGPT, based on the ~6,000 conversations I’ve had in 2 years.

My top use, by far, is for technology. “Modern JavaScript Coding” and “Python Coding Questions” are ~30% of my queries. There’s a long list with Markdown, GitLab, GitHub, Shell, D3, Auth, JSON, CSS, DuckDB, SQLite, Pandas, FFMPeg, etc. featured prominently.

Next is to brainstorm AI use: “AI Panel Discussions”, “AI Trends and Business Impact”, “LLM Applications and DSLs”, “Industry Use Cases and Metrics” are also fast growing categories. I brainstorm talk outlines, refine slide deck narratives, and plan business ideas.

Thirdly, I use it for reading/writing. “Article Summaries and Insights”, “Writing Style and Editing”.

Lastly, for personal advice. “Personal Advice and Replies” and “Singapore Travel Queries” are in this bucket.

Then there are niches like image generation (“Image Generation and Annotation”, “Calvin and Hobbes Comics”), research (“Fact Checking and Trivia”), emails (“Email Analysis and Spam Detection”), and teaching (“Education and Student Projects”).

6,000 chats saved me perhaps 600 hours. ChatGPT’s “given” me a month of life-time for $600 – which I reinvested into teaching and tinkering.

Today, 70% of my prompts are code. In five years, that might drop as AI handles coding, and I tackle strategy and thinking. My prompt portfolio isn’t future-proof? Is yours?

There’s no finance, music, or philosophy. My prompts mirror my blind spots. Should I force one prompt a week in a category I’ve never explored? Would you?

Top 8 ways I use ChatGPT in 2025

Sat, 24 May 2025 03:31:44 +0000

I extracted the titles of the ~1,600 conversations I had with ChatGPT in 2025 so far and classified it against the list of How People Are Really Using Gen AI in 2025.

Here are the top 8 things I use it for, along with representative chat titles. (The % match in brackets tells you how similar the chat title is to the use case.)

Improving code (clearly, I code a lot)
Troubleshooting (usually code)
Corporate LLM/Copilot (this is mostly LLM research I do)
Generating code (more code)
Generating ideas (yeah, I’ve stopped thinking)
Simple explainers (slightly surprising how often I ask for simple explanations)
Generating relevant images. (Surprising, but I think I generated a lot of images for blog/LinkedIn posts)
Specific search (actually, this is mis-classified. This is where I’m searching for search engines!)

My classification has errors. For example, “Reduce Code Size” was classified against “Generating code” but should have been “Improving code”. But it’s not too far off.

Here’s a list of representative chats against these use cases.

Improving code (263):
- PR Code Review Suggestions (64% match)
- Assessor Code Review and Improvement (63% match)
- Reduce Code Size (62% match)
Troubleshooting (172):
- Connector Error Troubleshooting (67% match)
- DNS Resolution Debugging Steps (55% match)
- Exception Handling Basics (47% match)
Corporate LLM/Copilot (141):
- LLM Integration in Work (57% match)
- LLM Agents Discussion (56% match)
- LLMs Learnings Summary (56% match)
Generating code (113):
- AI Code Generation Panel (58% match)
- AI for Code Generation (58% match)
- Reduce Code Size (54% match)
Generating ideas (99):
- Filtering Ideas for Success (54% match)
- AI Demo Ideas (52% match)
- Hypothesis Generator Name Ideas (52% match)
Simple explainers (94):
- Simple Public APIs (43% match)
- Y-Combinator Explained Simply (41% match)
- Prompt Engineering Tutorial Summary (39% match)
Generating relevant images (93):
- Popular AI Image Tools (54% match)
- Diverse Image Embedding Selection (52% match)
- AI ImageGen Expansion Ideas (52% match)
Specific search (69):
- Semantic Search Engines Local (59% match)
- Enterprise Search Solution (54% match)
- Local LLM Semantic Search (53% match)

How did I calculate this?

On ChatGPT.com, I scrolled until I had all 2025 chats visible. Then I pasted copy($$(".group.__menu-item").map(d => d.textContent)) to get the chat titles.
On Claude.ai, I transcribed this list of use cases from HBR (prompt: “Transcribe this image”).
On LLM Foundry (which you may not have access to), I used the Similarity API to get a CSV of similarities between prompts and top 30 use cases in 2025 using text-embedding-3-small.
On ChatGPT.com, I told it to analyze the data like this:

This sheet has the embedding similarity between my ChatGPT prompts (in column “A”) with different use cases.

Write and run code that tags the prompt with the use with the highest embedding similarity (cell value), drops prompts whose highest embedding similarity is below a cutoff, and shows a table where the rows are the use cases and the values are the frequency. Do this for multiple embedding cutoffs as columns: 0.0, 0.1, 0.2, 0.3, 0.4. So, the table has use cases in rows, embedding cutoffs in columns, and the cell values are the count of prompts tagged with each use case AND have an embedding similarity >= cutoff. Draw this as a heatmap with low numbers as white and high numbers as green.

… and then:

Let me download this as a Markdown list in this style, sorted by descending order at cutoff = 0

Anti-trolling (mention count of matches at 0 cutoff):

Tor Technical AMA questions (34%)

Bot Message Edits (33%)

Popular Hacker News Keywords (33%)

…

Here’s the full list against the top 30 use cases:

Improving code (263):
- PR Code Review Suggestions (64% match)
- Assessor Code Review and Improvement (63% match)
- Reduce Code Size (62% match)
Troubleshooting (172):
- Connector Error Troubleshooting (67% match)
- DNS Resolution Debugging Steps (55% match)
- Exception Handling Basics (47% match)
Corporate LLM/Copilot (141):
- LLM Integration in Work (57% match)
- LLM Agents Discussion (56% match)
- LLMs Learnings Summary (56% match)
Generating code (113):
- AI Code Generation Panel (58% match)
- AI for Code Generation (58% match)
- Reduce Code Size (54% match)
Generating ideas (99):
- Filtering Ideas for Success (54% match)
- AI Demo Ideas (52% match)
- Hypothesis Generator Name Ideas (52% match)
Simple explainers (94):
- Simple Public APIs (43% match)
- Y-Combinator Explained Simply (41% match)
- Prompt Engineering Tutorial Summary (39% match)
Generating relevant images (93):
- Popular AI Image Tools (54% match)
- Diverse Image Embedding Selection (52% match)
- AI ImageGen Expansion Ideas (52% match)
Specific search (69):
- Semantic Search Engines Local (59% match)
- Enterprise Search Solution (54% match)
- Local LLM Semantic Search (53% match)
Adjusting tone of email (66):
- Email transcription request (45% match)
- Summarize emails request (45% match)
- Intro Email FAQs (44% match)
Generating a legal document (59):
- LLM Generated SVG Ideas (48% match)
- LLMs for DSL Generation (45% match)
- Deterministic Random Content Generation (45% match)
Preparing for interviews (43):
- LLM Coding Interview Tools Report (43% match)
- Bank Ops Prep Resources (42% match)
- AGI Preparation (42% match)
Personalized learning (32):
- Lifelong Learning in Conversations (51% match)
- AI Classroom Engagement Names (48% match)
- LLM Learner Personas Roadmap (47% match)
Explaining legalese (32):
- LLM Coding Insights (46% match)
- LLM Code Ownership (45% match)
- LLM Data Format Comparison (44% match)
Creating a travel itinerary (28):
- Travel Strength Training Tips (39% match)
- User Journey Tools Online (37% match)
- Prioritize My Explorations (36% match)
Creativity (28):
- Creative Process Breakdown (55% match)
- Creative Hallucinations in Innovation (50% match)
- Leveraging Serendipity for Innovation (50% match)
Cooking with what you have (26):
- Vegetarian Dish Creation (45% match)
- Baked Veggie Dishes (41% match)
- Vegetarian dish idea (40% match)
Organizing my life (24):
- Prioritize My Explorations (49% match)
- Workspace Suggestions for Browsing (39% match)
- Editing for Clarity and Simplicity (39% match)
Enhanced learning (23):
- 2025 LLM Embedding Enrichment (51% match)
- Lifelong Learning in Conversations (49% match)
- Tech-Enhanced Teacher-Student Rapport (49% match)
Finding purpose (21):
- Prioritize My Explorations (40% match)
- Deep Research Use Cases (37% match)
- Filtering Ideas for Success (36% match)
Deep and meaningful conversations (20):
- Lifelong Learning in Conversations (49% match)
- Humorous conversation summary (42% match)
- New chat (40% match)
Healthier living (18):
- Modeling Quality of Life (40% match)
- Lifelong Learning in Conversations (37% match)
- Posture and Breathing After Weight Loss (36% match)
Anti-trolling (18):
- Tor Technical AMA Questions (34% match)
- Bot Message Edits (33% match)
- Popular Hacker News Keywords (33% match)
Writing student essays (18):
- Scholarship Answer Advice (47% match)
- Student Q&A on LLMs (41% match)
- Reward Systems for Students (41% match)
Fun and nonsense (17):
- Humorous conversation summary (45% match)
- Funny Llama 3.3 Strips (40% match)
- Synonyms for Interestingness (40% match)
Boosting confidence (14):
- Emotional Prompting Impact (41% match)
- Emotional Prompting Impact (41% match)
- AI Ratings of My Flaws (38% match)
Personalized kid’s story (14):
- Fake Data Storytelling Tips (43% match)
- Low Effort Storytelling Training (39% match)
- Demo Name Suggestions (37% match)
Reconciling personal disputes (12):
- Divorce AI Podcast Ideas (37% match)
- Summarizing Personal Journals LLM (36% match)
- Hobby Suggestions and Devil’s Advocacy (36% match)
Entertaining kids (9):
- Comedy for Geriatric Doctors (40% match)
- Humorous conversation summary (36% match)
- Indoor Activities in SG (34% match)
Medical advice (9):
- Patient Doctor Communication Tips (41% match)
- AI Training for Doctors (40% match)
- AI Training Course for Doctors (38% match)
Therapy/companionship (3):
- DBT Course (36% match)
- Cupping Therapy Evidence (36% match)
- Empathy App Development Ideas (33% match)

What does Gramener ask ChatGPT?

Sun, 14 Jan 2024 07:23:38 +0000

I looked at how Gramener uses ChatGPT Plus by evaluating 600+ chats asked over 3 months from Oct 2023 to Jan 2024.

The team asks 6 questions a day. We don't track who or how many actively use ChatGPT Plus. This also excludes personal ChatGPT accounts. Still, 6/day is low for an entire team put together.

The questions fall into 8 categories.

Category	%
Excel, data exploration & analysis	25%
Text extraction and summarization	13%
HTML, CSS, or JavaScript code	13%
Python code	13%
LLMs, AI and use cases	9%
OCR and image analysis	9%
Generate images, logos, and designs	7%
General knowledge, policy & environment	5%
Audio and translation	5%

Here are some questions from each category - to give you an idea of emergent ChatGPT Plus usage.

Excel, data exploration & analysis (25%)

Excel clean and merge. There are 2 worksheets in this excel with data, can you clean up the data and merge the data in both the sheets
Excel CO2 Data Analysis. You are an expert Data Analyst who is capable of extracting insights out of data. Analyze this sheet and let me know the findings
Excel Chi-Square Analysis Guide. how to perform chi square analysis in excel
Log Data Insights & KPIs. Looking at the columns from this excel, what kind of insights are possible, what are key KPIs to be looked at

Text extraction and summarization (13%)

Complaint Investigation Summary. The following is the summary of an internal investigation for a customer complaint. Now this internal summary is to be paraphrased (in 3-4 lines) as part of a closure
Extracting Tables from RTF. Can you write a script to extract the tables from this document
Extracting Entities from Text. [{'word1': '(P)', 'nearest_word1': 'P/N:', 'nearest_word2': '0150-25034', 'nearest_word3': 'CARTIRIDGE'}, {'word1': 'P/N:', 'nearest_word1': '(P)', 'nearest_word2': '015...
Extract PDF Font Details. Extract text formatting information from this document. Especially find font styles, families and sizes.

HTML, CSS, or JavaScript code (13%)

HTML/CSS Chart Template. Give me HTML, CSS and chart code for this design.
CSS Font Stack: Explanation. Explain this CSS font convention: Arial, Helvetica, Segoe UI, sans-serif
Checkbox Validation with JavaScript. In HTML form, I have a set of checkboxes. How do I write the form so that at least one of them being checked is mandatory?
Prevent Text Wrapping CSS. <span class="text">Chief Communications Officer</span> I need CSS such the text inside should not wrap create new line
ReactJS App with Routing. Give me developed version using ReactJS use react router for sidebar section navigation to the pages use Tailwind css for styling. Use styled components for conditional …

Python code (13%)

Python Code Documentation Guide. Can you generate documentation for a project code written in python?
Linux Commands for Python. Give me list of linux commands to work on python coding
Code explanation request. What's this code about? …
FastAPI Async Testing. Write a fastapi code and a python client to test the asynchronous nature of the fastapi package.
Streamlit App for Translation. Given the following python code, give me a simple streamlit app that takes file upload and converts that into a target language: …

An interesting sub-topic was interview question generation.

Python Decorator for Database Queries. Create one medium level question for Decorators in python Industryy usecase specific with solution

LLM, AI and use cases (9%)

LLMs for Data "What Ifs". You are an LLM Expert. Can you tell me how can we leverage LLM for implementing What IF scenarios on Data?
LLMs: Current Challenges & Concerns. what are current challenges with LLMs
LLM Applications in Marketing. Show LLM applications for the marketing function of a music company.
Gen AI usage. What industries are using Gen AI the most
Best LLMs in 2023. Search the internet for the most recent LLMs and list the best LLMs in terms of performance
Best Image Classification Models. suggest best models to tell what there in the image

OCR and image analysis (9%)

Browser history OCR. This is a screenshot of my browser history. Convert that to text. Categorize these into common topics.
Extracted C Code. This image contains C code. Extract it.
Image text extraction and annotation. Extract the text from this image and annotate the boundaries of the text
Detecting Document Image Orientation. oreientation detection of documnet image
AI Project with OpenCV & YOLO. Consider yourself as Open CV and Yolo expert and help me with AI project
Image Correction Techniques. what are the approaches we have in computer vision where my image is tilted or rotated in reverse or image is not in readable format

Generate images, logos, and designs (7%)

Google Chacha and ChatGPT Bhatija. Generate an image of Google Chacha and ChatGPT Bhatija
Regenerative Systems Group Image. Generate an Image with below context > "A group of people interested in Regenerative systems. The focus is on reusing food, energy and mental health"
Twitter Reply Icons Design. Give me three icons: icon16.png, icon48.png, icon128.png for an extension that I'm building that suggests replies to tweets
Generate flowcharts. Make a flowchart of the underlying working of a web app. Here's how it works. 1. The user uploads a document - a PDF or an image. They then select the language that …
Create Animated GIF from Photos. I have 4 photos I want to make an animated gif out of them. How can i do that?
Climate Impact Illustration. An illustration showcasing the impact of climate change on daily life, focusing on a rural setting near the coast. In the foreground, a small farm is visibly struggling, …

General knowledge, policy & environment (5%)

Design Thinking Overview. What is Design thinking
Arthashastra. What can Arthashastra teach us about modern politics?
Community Impact on Habits. Is there research to suggest the impact of community on habit building?
Focus at Age 28. What should a 28 year old focus on?
Superconductors. Explain superconductors like I'm five years old.
Climate Career: Impactful Choices. You a career counsellor at a University campus. You want to create 4 to 5 talking points for students to consider a career in Climate space.
Sustainability Division Vision. I run a software outsourced product development company. I want to start a new division that focuses on sustainability services offerings. Please draft a vision…

Audio and translation (5%)

Audio Timestamp Mapping. timestamp mapping for transcribed audio
Transcribe Lengthy Audio: Segment. Transcribe this audio file.
Traducción del MOU al Español. Translate this document to Spanish, and create a new translated document. Maintain text formatting.
Telugu Transcription into Hindi. Transcribe the following telugu text into hindi. You are supposed to transcribe, not translate. శ్రీనివాస పూజావిధానము …
GPT lacks native audio support. Does gpt support audio in audio out natively?