use-cases 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.

Sun, 22 Jun 2025 10:49:05 +0000

I’m planning four 30-min 1-on-1 slots to discuss LLM use-cases. Ask me anything on LLMs. I’ll share what I know.

If interested, please fill this in: https://forms.gle/5zwWNuRmZDxTh325A

WHEN: 30 Jun / 1 July, IST. I’ll revert by 26 Jun to schedule time.
WHY: I want to learn new uses for LLMs and share what I know.
WHO: I’ll contact you based on what you’d like to discuss.
WHERE: Google Meet. I’ll share an invite when mutually convenient.

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)

Thu, 31 Aug 2023 09:46:57 +0000

Ashwini Mathur and I are conducting a webinar on the impact of LLMs in Pharma. It’s online at 10 am Eastern on Mon Sep 11.

Simon Willison described LLMs as alien technology we’re still discovering. I couldn’t agree more - and it helps to see it from different perspectives. So, we’re pairing the tech research at Gramener with the domain research Ashwini Mathur is doing at Novartis to explore the good, the bad, and the surprising uses of generative AI.

https://info.gramener.com/generative-ai-in-pharma-webinar

Sun, 02 May 2021 05:35:17 +0000

Comicgen now has a very versatile character – Aavatar. Pick your gender. Hairstyle. Emotion. Attire. Pose. Colors. Create your own character. (It’s open-source.)

https://gramener.com/comicgen/v1/

The variations are staggering. It throws up surprising ones too, like crossdressers or bald women in sarees 🙂

What I’m struggling with is:

Who needs this?
What could they do with it?

I’m not sure. If you have ideas or know someone who might, please let me know 🙏

#comics #apis #characterdesign #datacomic #visualstorytelling

A ringtone adults cannot hear

Tue, 13 Jun 2006 12:00:00 +0000

Teenagers use it in classes, to avoid detection by teachers. Shopkeepers use the same technology to drive away rowdy teenagers, by creating a loud sound that doesn’t affect adults.

This works because adults gradually lose the ability to hear high-pitched sounds. (via NY Times)

Comments

Livy 12 Nov 2006 5:33 pm: I can here the sounds up to about 18KHz.

Low Shutter Speeds without Tripod

Mon, 04 Jul 2005 12:00:00 +0000

Taking pictures at low shutter speeds without a tripod.

Comments

Anonymous Hero 5 Jul 2005 9:56 am: About why Google History is important: I had searched for something from home laptop and I was at office and needed the same info. Went into Google history and found the same thing… So the search history and the links I clicked on were available from any computer attached to the Net. Found it pretty useful…
Ram 5 Jul 2005 4:51 pm: Pls update the placement section with new useful links…
S Anand 5 Jul 2005 6:36 pm: Ah… that’s a good use. I was also wondering if we could do some profiling of the kinds of sites we visit – perhaps by checking what their tags are on technorati or del.icio.us
sathish 22 Jul 2005 11:14 am: probably based on our search history, there could some recommendations to given