communication on S Anand

Memorable explanations

Sat, 21 Feb 2026 13:58:58 +0800

Our brains remember some things better. Explaining that way makes it stick.

Here are the eight things, most important first, that help you:

Structure explanations memorably:

Face. You remember faces before facts. So cast characters: “Imagine you’re a courier carrying a packet.” Prefer archetypes to real names — less baggage, more imagination.
Place. You’re reading down a list now — and the top feels more important. That’s spatial wiring. Turn any concept into a map. Use higher, deeper, nearer, inside, …
Tale. You read #1 and #2 first because they came first. Your brain built a cause from that sequence. Time creates cause for free. “Because” makes anything believable.
Scale. “Two feet tall” lands instantly. “60 cm” forces you to convert. Your brain doesn’t measure — it compares. Give it reference objects, not just numbers.

Deliver explanations memorably:

Touch. Face. Place. Tale. Scale. Each is a thing you can “grasp” or “hold” in your head. We learn literally by grasping. Make abstractions touchable.
Feel. Everyone ignores you because you forget these eight. Did that sting? That’s loss framing. Fear, surprise, and reward are memorable.
Chunk. There are 8 items here - already past our ~4 chunk working memory limit. We’ve chunked them into two logical sets of four.
Beat. Face, Place, Tale, Scale. Touch, Feel, Chunk, Beat. Two groups of four. Say them aloud — the rhythm is already doing the remembering for you.

PS: The Claude conversation that lead to this post is my favorite prompting example.

The first prompt asked the question “Our brains are wired to understand some things well…”
- … and for multiple options “Create a comprehensive list…”
- … fact-checked “… based on research evidence”
- … with expert framing: “But I’m a novice - what would an expert check that beginners would miss? Think about that, ask, and answer those too.”
The second prompt uses LLM review. “I asked Gemini to review your work. What does proven science agree with and disagree with on Gemini’s response?”
- … with expert framing: “focusing on patterns that an expert in this field recognize that beginners would miss”
The remaining prompts asksfor a rewrite: “Here’s my shorter version. Rewrite it with the same succinctness”, but with meta-cognition:
- “but applying the same 8 principles of cognitive anchoring to this text itself!”
- “rename them to rhyme better”
- “Re-apply the principles and suggest an improved version.

I also converted this into a SKILL.md

Things I Learned - 12 May 2024

Sun, 12 May 2024 00:00:00 +0000

This week, I learned:

Radio free Xp podcast. Nudge 61
- always announce first before doing. Give people time to plan comment and react. That gets you alignment without sacrificing freedom.
- give information, not orders. When someone is parking a car, tell them how much space they have, don’t tell them to start stop or how much to turn left
- it’s almost impossible to change the culture if you’re not the boss

AI makes me a better person

Thu, 21 Mar 2024 04:23:00 +0000

Every time I get annoyed at people, I remind myself to be more like ChatGPT. Specifically:

Don't get annoyed. Be patient.
Encourage them.
Step back and show them the big picture.

(Then I get annoyed at myself for getting annoyed.)

Today, I analyzed how exactly ChatGPT is different from me. So, I took a pitch document I co-authored with ChatGPT.

Section A: Authored by Anand

WHAT DO WE NEED?

We are looking for API access to (SYSTEM) via the REST API as an Agent role (read/respond to emails). Specifically, access via a bearer token.

This would be accessed by a single application developed by a team of 3 developers and 1 business analyst. None of them have access to (SYSTEM) today.

WHY DO WE NEED THIS, AND WHY SO SOON?

We need this to classify emails automatically, as they arrive, into categories such as “non-value-add” (e.g. Thank you, Out-of-office, etc.)

We’d line access today, please. Currently, we are processing XX,XXX non-value-add emails per month. Each day of delay leads to a processing waste of ~XX emails per day. At current volumes, this will save ~$XX,XXX per annum.

IS THERE ANY OTHER WAY?

To access emails in real-time, this is the only option. (We’re ruling out web scraping.)

(SYSTEM) rule-based closure won’t work. It’s based on keyword search, not semantic matches. For example, “Thank you” is present in non-value-add emails as well as follow-up questions. Multi-lingual matches are a challenge. So, though (SYSTEM) currently closes emails with rules, 1 out of 6 emails that are NOT closed already are non-value-add.

This cannot be done via (SYSTEM) or any other BI system because it involves accessing the data in real-time as well as responding via (SYSTEM). This needs to be steady-state access, not one-time, since we want to classify emails real-time.

HOW LONG WILL IT TAKE?

Based on our past experience, this would take about X weeks to prototype, X weeks to develop, and X weeks to test. We should have the application deployed in X months.

Section B: Authored by ChatGPT

WHAT’S THE IMPACT IF WE DON’T MOVE FORWARD?

Quantitatively, continuing without this solution wastes resources on XXX non-essential emails daily, costing $XX,XXX yearly. Delays in customer responses will ensue.

Qualitatively, lack of automation strains customer service, risking employee burnout and diminished customer experience.

WHAT’S THE PRIORITY (MOSCOW)?

(SYSTEM) API access via REST as an Agent role with a bearer token is a “MUST HAVE”. Alternative methods like scraping (SYSTEM) are impractical due to high effort and maintenance.

WHAT’S THE OUTCOME IF WE MOVE FORWARD?

Financially, securing API access saves $XX,XXX annually by cutting manual email sorting. Efficiency gains reduce operational costs and free resources for strategic tasks.

Strategically, automating email classification boosts customer satisfaction and response times, establishing leadership in service innovation. Long-term, it enables data-driven insights into customer behaviour, improving decisions and customer service scalability.

Analysis

I fed these to ChatGPT and asked: "You are an English professor evaluating an essay. Compare the writing styles of the 2 sections. They're written by different authors. List the major differences in writing style."

Here's what it said (roughly):

Area	Anand	ChatGPT
Structure	Direct, concise, with specifics	Broader perspective and implications
Focus	Detailing the process	Benefits and consequences
Detailing	Specific, providing clear picture	General, providing implications
Tone	Conveys urgency	Reflective rather than urgent
Approach	Problem-solving oriented	Outcome-oriented

Yeah, that's definitely me on the left.

I like the direct, concise, specific part. I plan to add the "broader perspective", "implications", and "outcome-orientation" to my life.

Postscript

Jaidev pointed out that this is confirmation bias. He asked ChatGPT for the similarities in the writings. It said both are clear, direct, structured, specific, quantitative, and strategic. So, if you ask for differences, you'll get them, even if they're marginal.

I now need to learn (and write about) framing questions well!

Things I Learned - 11 Feb 2024

Sun, 11 Feb 2024 00:00:00 +0000

This week, I learned:

Dockerfile can have FROM scratch and you can add specific binaries rather than an entire OS. via
Fine-tuning session by Dan. Notebook
- Example of fine-tuning Mistral. Consumed ~~28 computes (~~$2.8)
- Axlotl is what the top fine-tuned LLMs are trained on
- Deepspeed provides distributed training
- Flash attention lets data stay on GPU
- Sample packing packs samples of different lengths into equal length tensors
Visualize the RANK of a token in a generated stream instead of logprob
The Knowledge Project. Tomorrow Gayner
- What I’d like in my obituary: Anand was happiness. A guru. Generous.
- To get what we seek we must deserve this. Build, measure, learn
- If you did the same thing daily for 50 years, would it be a great thing? If yes, do it. If not, stop. Do this in daily retrospectives
- My new role should be productivity through technology innovation. That may mean a CTO role. But be specific otherwise no one will understand it
Hidden brain podcast. Us 2.0. Win hearts, then minds
- When in an interaction, ask yourself. Can I learn and change myself? Can I win their hearts, then mines, so their behavior will change. That identity will change
- Notice when you get emotionally triggered. That’s exactly when you should not get emotionally triggered
- Try model humility and moral
- Look for close to people’s identities in our conversations. What are things they like? What does it mean for them? Simply ask. With that understanding of identity, it becomes easier to reframe things in a way they will understand
Bard can talk to Gmail and Google Drive!
#PREDICTION As automation takes over these mainstream activities, people will take over the niches. Since expertise like knowledge is fractal, there will be many more segments of one in the future and it will be easier to automate clusters of similar abilities. Recommenders and brands will become even more important
Stephen Osserman’s Observables have some nice notes.
- Visualizing partial election results
- D3 Force Dilemmas: Data Distortion
Sandra Becker’s 30 day D3 course

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

Storytelling: Part 1

Wed, 29 Aug 2012 04:13:00 +0000

In a number of sessions I’ve been to, people ask analysts to make their results more interesting – to tell stories with them. I’m co-teaching a course, part of which involves telling stories with data. So this got me thinking: what is a story? How does one teach storytelling to, let’s say, an alien?

Consider this mini-paper.

ABSTRACT: Meter readings exhibit spikes at slab boundaries. We also
find significant evidence of improbably events at round numbers.

Electricity shortage is a serious problem in most Indian states. Part
of this problem is due to the inaccuracy of reporting procedures used
in monitoring meter readings. Our focus here is not to document or
experimentally determine the degree of inaccuracy. We have adopted a
data driven approach to this problem and attempt to model the extent
of inaccuracy using basic statistical analysis techniques such as
histograms and the comparison of means.

Our dataset comprises of the frequency analysis 12-month dataset
containing monthly meter readings of 1.8 million customers in the
State of Andhra Pradesh.

We find that a histogram of these readings shows unexpectedly high
values at the slab boundaries: 50 (+45.342%, t > 13.431), 100
(+55.134%, t > 16.384), 200 (+33.341%, t > 15.232), and 300
(+42.138%, t > 19.958).

We also detected spikes at round numbers: 10 (+15.341%, t > 5.315),
20 (+18.576%, t > 6.152), 30 (+11.341%, t > 4.319).

The statistical significance of every deviation listed above is over
99.9%. Further, every deviation has a positive mantissa. This leads us
to confidently declare the existence of a systematic bias in the meter
readings analysed.

You’re probably thinking: “I know why he’s put this example here. It must be a bad one. So, what a rotten paper it must be!”

Well, not quite. It’s a good piece of analysis. I did it myself and there’s a fair bit of effort and care behind these short paragraphs.

The trouble is, if I read it out to my daughter, she’d say “What?” and not understand a word. My wife’d say “So what?” and not care a bit. I might as well not have written it.

It’s like that Zen thing: If a tree falls in a forest and no on hears it, does it make a sound?

If you did a piece of analysis, and no one understands or cares about it, why did you do it in the first place?

Why do you do it?

That last question is important: why do we analyse?

Sometimes, we do it for fun. The knowledge is beautiful. Knowing Tetris is NP-Complete is rewarding, even though my colleague sarcastically remarked, “Thank God! I’m sooo relieved now that I know that Tetris is NP whatever.” If that’s the case with you, great. Write the analysis any which way you’ll enjoy.

Sometimes, we do it because we’re forced to. In class. At work. Wherever. But that’s another way of saying “I don’t know why I’m doing it.” In that case, I’d gently recommend watching 3 Idiots.

Most often, we do it to share knowledge and drive actions. In that case, if no on understands it, or does anything with it, why do it?

Keep it simple

We prerajulisation of Farhanitate flagellated with ...

Would your audience understand that? Or are you just scared that simple words indicate a simple mind?

I was once afraid. 15 years ago, when writing a paper on IBM India’s competitive advantage for the CXOs, I was worried about it being too simple. I didn’t know anything about management. So I filled it with jargon. They politely nodded when I presented it, but I wasn’t fooling anyone. If there’s no content, jargon doesn’t help.

Unfortunately, it’s become polite to accept jargon as a substitute for substance. Why were they not ripping me apart? Or at least, kindly asking me what on earth I wanted to say?

My friend Manoj did that. In his nice, humble way, he asked, “But Anand, what does this mean?” When I explained it to him, I found I didn’t have a clue. He was OK with that. He just wanted to make sure he hadn’t missed something.

(That’s the technique I use these days. Ask people to explain things clearly. It’s OK if they’re just lost in jargon. I just want to make sure I haven’t missed something.)

Don’t cloak your ignorance. No one will think less of you. In the long run, you’ll learn more, and won’t need the jargon.

Part 2 of the article will talk about focusing on people and actions; storylining and the pyramid principle; and the structure of messages.

Comments

Santanu 30 Aug 2012 4:28 am: Starting to get it. Good thoughts Anand.
Navneeth 26 Dec 2012 9:55 am: I had the opportunity to be an audience on one of your presentations on data visualization. Now I know why I suck at doing presentations… even though I had content. eagerly waiting for part-2.

Telecom Italia Gandhi ad

Mon, 14 Mar 2005 12:00:00 +0000

Telecom Italia’s Gandhi ad.

10 rules for taming e-mail

Sun, 15 Dec 2002 12:00:00 +0000

Darwin’s 10 rules for taming e-mail. I badly need this. I am not often in office, and don’t have a fast way of checking office mail through the Web either. Tips 5 and 10 on the list (“avoid e-mail multipliers” and “use the telephone”) are next on my agenda for drastic e-mail slashing.

Effective networking

Fri, 17 May 2002 12:00:00 +0000

Effective networking.

Punctuation is critical

Wed, 20 Oct 1999 12:00:00 +0000

Punctuation is critical. Believe me, mistakes can be glaring!