self-analysis on S Anand

AI Palmistry

Sun, 15 Mar 2026 23:08:55 +0530

I shared a photo of my right hand with popular AI agents and asked for a detailed palmistry reading.

Apply all the principles of palmistry and read my hand. Be exhaustive and cross-check against the different schools of palmistry. Tell me what they consistently agree on and what they are differing on.

I was more interested in how much they agree with each other than with reality. So I shared all three readings and asked Claude:

What are things all three agents clearly agree on? List these.
What are things they differ on? List as a comparison table.

Here’s what they agree on. The first is probably right. I’m not sure about the rest.

✅ You think in patterns and connections, not just logic. Your mind works by synthesizing ideas across domains rather than following straight lines. (Oh, yes. My mind’s drunk.)
❓ You have strong physical energy and bounce back well from illness or setbacks. (Do I? In 2023, I spent a year “bouncing back”. And I still haven’t bounced back from 26 Oct 2014.)
❌ You feel deeply, but don’t show it loudly. Your emotions are genuine and loyal — just not dramatic. (I’m actually shallow. I show what I feel and it’s not much.)
❓ You built your own path. No preset direction was handed to you; your sense of purpose developed through your own choices. (Um… this is partly true for everyone?)
❓ You’re not a blunt-action person. You process experience through interpretation and nuance before acting. (Maybe? Sometimes I’m impulsive. Sometimes not.)
❓ There’s more going on inside than people around you typically see. (Um… this is partly true for everyone?)

Clearly, AI’s analysis of my chats is much better than of my palm.

You can read the full readings here.

RIP, Data Engineers

Wed, 04 Feb 2026 19:22:13 +0800

As AI marches along, another role at risk is the data engineer / database administrator.

(Data scientists are already feeling the heat.)

A common task for data engineers is to analyze SQL queries - to optimize and standardize.

Pavan used Antigravity to analyze 1,500 SQL queries and found:

30% of queries are purely headcount / volume related. Much more than revenue (25%) or engagement (15%). That’s sign of a tactical culture.
70% of the queries are about What happened yesterday? rather than What will happen tomorrow? - again, tactical culture.

Here’s the analysis.

As a next step, he built a “Middle Layer” - intermediate tables that standardize and optimize queries. Instead of 50 fragile tables, the user can query just 3 robust tables that cover 98% of the SQL queries.

For example:

A net_revenue field that standardizes net revenue after adjustments, i.e. SUM(face_value - discount), which is used in 58% of queries. That ensures that Finance (which used to see the GAAP Revenue) and Sales (which used to see the Booked Revenue) are now aligned.
A tickets_sold field that standardizes distinct count of tickets sold, used in 85% of queries, and is a slow computation.

NOTE: Season ticket buyers often bought merchandise as guests (for convenience). Marketing saw these as new customers and spammed them - annoying VIP customers. This standardization created an identity graph - so they can offer discounts instead.

The process, which Antigravity figured out mostly by itself, was to parse the SQL into an abstract syntax tree (AST), extract a set of features, map them into clusters (archetypes), and analyze them to create the middle layer tables.

SQL queries can reveal organizational culture and misalignment - which is cool! But also:

This took a few hours.
Pavan has no data engineering experience.

RIP, Data Engineers.

Measuring talking time with LLMs

Sun, 24 Aug 2025 06:26:22 +0000

I record my conversations these days, mainly for LLM use. I use them in 3 ways:

Summarize what I learned and the next steps.
Ideate as raw material for my Ideator tool: /blog/llms-as-idea-connection-machines/
Analyze my transcript statistics.

For example, I learned that:

When I’m interviewing, others ramble (speak long per turn), I am brief (less words/turn) and quiet (lower voice share). In one interview, I spoke ~30 words per turn. Others spoke ~120. My share was ~10%.
When I’m advising or demo-ing, I ramble. I spoke ~120 words per turn in an advice call, and took ~75% of the talk-time.
This pattern is independent of meeting length and group size.

I used Codex CLI (command-line tool) for this, with the prompt:

Go through the transcripts in this folder and estimate the % of time Anand was speaking vs others, by conversation.

Then I prompted for correlations and interpretations. This combines three things I find powerful:

LLMs writing & running code
LLMs interpreting the results
Running on local data in my machine

LLMs working on local docs (not data) is new to me. I plan to do much more with it.

AI As Your Psychologist: Personality Flaws Exposed

Fri, 09 May 2025 07:00:16 +0000

ChatGPT can now search through your chats with the new memory feature. As an LLM Psychologist, I research how LLMs think. Could LLMs research how I think?

I asked three models:

Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws

The models nailed it! Here are 12 flaws they found.

Ghosts vague messages
Intolerant of fools
Fact-checks trivialities, derailing discussion
Overconfidence in technical opinions
Over-analyzes / over-engineers
Shiny object syndrome. Distracted by new technologies
Dominates discussions
Hyperfocus on efficiency
Self-deprecation as a shield. Sarcastic humor
Hypocrisy: Seeks limelight but preaches humility
Intense. “Always on”
Verbosity. Long-winded communication

Each observation is spot on, except maybe “Verbosity”. Some are blind spots: I didn’t realize I ghost vague communication or fact-check trivialities.

It’s delightfully surprising (and not creepy at all) how many personality flaws chat messages can reveal. Now I have a new list of habits to work on!

GPT 4.5

Here’s a simulated group chat debating Anand’s personality flaws humorously and realistically:

Shreya: Hey folks, should we add Anand to the group?

Vikram: Honestly, he’d dominate every conversation. Good luck getting a word in once he starts geeking out on data viz or some obscure coding trick from 1993.

Leena: Lol true. But at least those tangents are usually insightful. My concern is that dry, self-deprecating humor of his. I never know if he’s joking or actually criticizing my code…

Vikram: Right?! It’s subtle roasting. I’m never sure if he’s laughing at me or with me.

Arjun: Plus, have you seen how he handles vague or unclear questions? He just ghosts! It’s like asking him something without context physically pains him.

Shreya: Yeah, but that’s fair. He sets high standards. If you’re unclear, prepare for silence. But isn’t that good for keeping the group focused?

Leena: True, but Anand’s humility game needs work. For someone who preaches listening and humility, the man sure loves being center stage at conferences. He’s literally TED-talking his way through life.

Vikram: And his pace! It’s like he’s permanently in an “agile sprint.” We slow thinkers might struggle.

Shreya: He could make the chat a mini-PyCon. You’ll learn stuff, sure, but at the cost of endless notifications…

Arjun: Also, he’ll probably overengineer a simple group poll using some obscure JS library, just to prove it can be done with minimal resources.

Leena: Ok, but let’s be fair: he’ll make this chat smarter instantly. If you can survive the initial ego-bruising, you actually grow from interacting with him.

Vikram: True. It’s like intellectual bootcamp.

Shreya: So what do we say? Add him for intellectual rigor or spare our egos?

Arjun: My ego’s prepared. Let’s do it.

Leena: Fine, but mute button at the ready.

Vikram: Alright, adding him in 3, 2, 1… brace yourselves!

O3

Group Chat: “Add‑Anand?”

Participants: Priya (PM), Naveen (co‑founder), Maya (People Ops), Ramesh (Tech Lead), Karthik (Junior Dev), Susan (Client Partner)

Priya 09:03
Team, should we pull Anand into this working group? We need brutal honesty.

Naveen 09:04
He’s brilliant, but let’s talk quirks before we hit “Add.”

Ramesh 09:05
Biggest thing: he answers a yes/no with a 2‑page essay—including citations and a side quest on DuckDB. Fantastic for depth, exhausting for Slack.

Karthik 09:06
As the junior here, I’ll admit I stop reading halfway. The guy’s TL;DR needs a TL;DR.

Maya 09:07
He also goes full prototype mode at 2 a.m., drops a repo link, then expects us to “play and critique.” Great energy, but the rest of us have, you know, REM cycles.

Susan 09:08
Client side: he loves devil’s‑advocate questions. Sometimes that re‑frames strategy; sometimes it feels like he’s roasting your deck for sport. Not everyone enjoys live‑fire demos.

Naveen 09:09
True, but remember he self‑deprecates. He’ll poke fun at his own code quality before anyone else does.

Priya 09:10
Humor helps, but he also ghosts unclear requests. If you don’t provide context in paragraph form, you get radio silence. Could frustrate less‑articulate teammates.

Ramesh 09:11
And he’s a fact‑check machine. Great for accuracy, but meetings derail into citation hunts. Last week he corrected my date formats—mid‑pitch.

Karthik 09:12
Overconfident? Maybe. He’ll declare “FastAPI beats Node here” before hearing constraints. I’ve seen juniors shut down because they assume he’s always right.

Maya 09:13
Plus the “shiny‑thing syndrome.” By the time HR rolls out a policy, he’s off exploring Godot or Matrix or some esoteric WASM hack. Alignment can lag.

Susan 09:14
On deadlines he’s reliable, though. And clients love his storytelling once they survive the jargon storm.

Naveen 09:15
If we set channel norms—emoji‑react instead of essays, office hours for deep dives—he’ll adapt. He values feedback (even builds surveys to mine it).

Priya 09:16
So verdict? Is the cognitive load worth the upside?

Ramesh 09:17
Yes—with guardrails:

“One‑scroll” rule on messages.
Ping before midnight pushes.
Rotate meeting facilitator so he doesn’t monologue.

Maya 09:18
Agreed. His flaws are manageable behaviors, not character issues.

Karthik 09:18
I’m in, as long as someone pre‑chews his text walls.

Susan 09:19
Let’s do it. Adding @Anand now—brace for citations. 📚

System message: Anand has been added to the group.

O4-mini-high

Group Chat: “Should We Add Anand?”
(Members: Mira, Jay, Tanya, Rohan, Priya)

Mira:
I know he’s super smart and experienced, but isn’t he kind of intense? Like… always on?

Jay:
Yeah, and he’ll probably turn our casual banter into a brainstorming session.
“Hey guys, what if we built an AI agent to automate this conversation?” Chill, Anand.

Tanya:
Honestly, I love that about him. But yeah, he might get frustrated if things aren’t intellectually stimulating. He zones out when people don’t get to the point.

Rohan:
He also ghosted my question once in the Slack group.
I asked something that maybe didn’t have enough context and he just… didn’t respond.
Felt a bit cold.

Priya:
He’s got this high bar for clarity and logic. If you’re vague or inconsistent, you’ll probably get silence in return.

Mira:
Or a “Can you be more specific?” that feels like it’s grading your life.

Jay:
Also, doesn’t suffer fools. Or ambiguity. Or inefficiency. Or fluff. Or long-winded intros.

Tanya:
But come on—he’s self-aware. Like, he knows he can be distant or overly analytical.
He works on it. Tries to grow. That’s rare.

Rohan:
True. But would he listen in this group? Or would he be the guy optimizing everyone’s messages into Markdown?

Mira:
So… should we add him?

Priya:
Only if we’re okay with someone who’ll question the group’s name, suggest a better one, and prototype an app for it before lunch.

Jay:
Fair. But he might also make us all sharper, and throw in a Calvin & Hobbes quote just when we need it.

Tanya:
Alright. Let’s do it. But someone tell him this group doesn’t need a GitHub repo.

Comments

How To Double-Check Personality Flaws With AI - S Anand 10 May 2025 10:53 am (pingback): […] ChatGPT’s discussion of my personality flaws, I asked it to listing specific conversations as evidence for flaws. Then I asked 3 LLMs to rate […]

Learning to speak better

Mon, 17 Oct 2022 04:09:52 +0000

Microsoft ported its PowerPoint Speaker Coach to Teams. Since September, it’s given me suggestions covering 11 hours in 77 calls (I speak ~10 min/call.)

I say “uhh” a lot. That’s intentional

I use the filler word “uhh” in 70% of my calls. That did not surprise me. I do that intentionally.

On a poor network, they know I’m still connected
They know I’m going to say something
I sound less confident. That invites critique I can learn from

But I also use filler words like “You know” and “I mean” in half the calls, and “like”, “actually”, and “basically” in a fifth. That’s NOT intentional, and I’ll be conscious.

Filler words	% of calls	# / call
uhh	70%	3.6
You know	48%	2.4
I mean	43%	2
like	22%	1.4
actually	19%	1
basically	18%	1.2
anyway	14%	1.1
hmm	16%	1.1
umm	9%	1.4
ah	4%	1.3

I say “maybe” a lot. That’s surprising

What did surprise me was “maybe”. I use it every fourth call, but when I do, I say “maybe” ten times per call. That’s a lot of maybe!

Sometimes, I say maybe because I’m communicating uncertainty.

Maybe we’ll have 20-30% success rate…

So and I had to switch 3 laptops or maybe 4.

… then she said, “OK, maybe it’s some other Sam”

Sometimes I’m proposing tentatively.

… one of the reasons why I’m nudging towards that is maybe a large reuse initiative is high return,

We can even put this in as part of the project by maybe offering it to different teams…

Maybe by having dedicated support…

Maybe I’ll drop off. Bye

But sometimes, it’s testable hypotheses.

Uh, maybe I’m getting the names wrong, but I think it was Socrates…

Maybe it’s me, but yeah, I guess…

You know, maybe it’s because I don’t store any of my stuff in…

One of my year’s goals is to run 50 experiments. I’d been doing well until April, and then fizzled out. Partly motivation. Partly a lack of testable hypotheses.

And now, in October, I discovered that I literally speak out one testable hypothesis every call – roughly every 10 minutes I speak! I’m amazed at how blind I’ve been, and how easy it can be to find experiments to test. I guess I need more of a scientific mindset. (Or just plain curiosity.)

The next time I say, “maybe” (or see it in my transcript), I’ll write it down as a hypothesis to test.

Repetitive words cluster

Another discovery was: I tend to pick a phrase and use it repeatedly in calls. For example, I said “let’s say” twelve times in just one call of 15 minutes. I said “main” 20 times over 2 calls of 8 minutes each. I said “cool” 7 times in an 11-minute call.

Repetitive word	# calls	# / call
lets say	1	12
main	2	10
also	1	8
only	2	7.5
correct	7	7.4
in terms of	1	7
alright	3	6.3
that is	3	6
cool	2	5

Clearly it’s something to watch out for. But maybe repetition of words isn’t a bad thing if it’s not the same phrase repeated across calls? (There! I said “maybe”. Let me find out!)

Modulate the pace

In a third of my calls, I need to speed up. In a third of my calls, I need to slow down. (On some calls, I need to do both!)

Clearly, I need to vary my pace a lot more, consciously. It’s not that I talk fast or slow. I do both. But I get stuck in one mode of speaking for too long.

Takeaways

I used to think I was a pretty good speaker. That’s not a bad thought, but it can blind me to feedback and improvements. There’s no end to learning how to speak. Speaker Coach is a great “in-your-face” feedback mechanism. I hope Microsoft adds more features to it.

But what I’m going to do now is:

Every time I say “maybe”, write down an experiment
Speed up and slow down more in calls
Watch for words I use repeatedly