Indrayudh Ghoshal

Co-founder @scribbledata. Build robust data products for advanced analytics.

[Indrayudh Ghoshal @Guava_man]

In the age of AI brain rot, I've started giving myself points on how many "you're right to push back" and (*new*) "I'm flagging that as a walk-back" I can score.

[Workfabric AI @workfabricai]

https://t.co/6pKcMBBRL0

[Indrayudh Ghoshal @Guava_man]

The last frontier for AI agents being crossed - meet the human API end-point for agents to get stuff done in the real world: https://t.co/AysCaXf56P

skynet too prolly began as a mom-and-pop shop, trading compute for cabbages (or whatever it ran on).

[Indrayudh Ghoshal @Guava_man]

@workfabricai @Cognizant Loving this :) One of those rare win-win partnerships

[Workfabric AI @workfabricai]

Most productivity problems are social, not personal. That’s why AI is shifting from single-player to multi-player. Read @RohanMurty's article for @IndiaToday, in their latest issue on themes for AI.

After working with Fortune 500 teams deploying real AI agents, we've learned the biggest drain on productivity isn't individual inefficiency. It's coordination. Handoffs between teams, unclear ownership, decisions made in one place but needed in another, work stalled because context lives elsewhere.

Multi-player AI isn’t “my assistant.” It’s “our colleague.”

Every team builds tribal knowledge about what usually goes wrong and how similar problems were handled before. Today, that knowledge is scattered across people and systems. Multi-player AI can learn from these experiences and make them available to everyone, so answers don’t depend on who happens to remember.

The biggest gains won’t come from individual copilots, but from AI that shares team context.

https://t.co/6YIUUoTfBN

[Indrayudh Ghoshal @Guava_man]

@aakrit @aakrit 100%. AI needs narrow problem definition to work well in production. And that requires being context aware (fit into existing tech, workflows). So, AI companies need to know the customer's vertical well, and then build point solutions 10x faster on their own platforms.

[Indrayudh Ghoshal @Guava_man]

There's a moment in each person's life when they stumble upon an epiphany that's core to their being. I offer mine publicly - If you time the emphasis right, 'digicam' sounds like a gunshot.

[Indrayudh Ghoshal @Guava_man]

"Wherein, basis, the same, revert". Prized connections to the motherland. Any more?

[Indrayudh Ghoshal @Guava_man]

But as we're finding, data teams see great RoI and Time-to-Value by executing on a host of sub-machine learning usecases. (https://t.co/zJtT4sdlgl). Enrich provides a great on-ramp to this highway.

[Indrayudh Ghoshal @Guava_man]

Enrich gives businesses the ability to power a spectrum of data science work, continuously generating datasets for everything from analytics to machine learning.

[Indrayudh Ghoshal @Guava_man]

The market's making more room for us every day. Data teams that are looking to answer business questions need their data prepped/enriched in fit-for-context ways everyday. That's where Enrich, our feature engineering apps platform, comes in. See https://t.co/VNXlJxYmDV

[Indrayudh Ghoshal @Guava_man]

We're hiring.
Bigly :-)
Engineers, Customer Success folks, and also Biz Dev folks that think and work like operators. See https://t.co/goEAWR3JCJ. We're hiring here, there and everywhere in between. Toronto, Bangalore and remote.

[Indrayudh Ghoshal @Guava_man]

But it's also true that if you don't take a moment to smell the roses, you won't enjoy the journey. So yes, this is a smell-the-roses moment for the @ScribbleData team.

Filling up our lungs because what's next is doubly exciting:

[Indrayudh Ghoshal @Guava_man]

@ScribbleData just raised $2.2M in a seed round, and I'm legit thrilled to share it here, even though it's not a surprise (within the team). It's because these things are usually analog. It's not one fine day that the stars miraculously line up. https://t.co/cDyGLadj6w

[Scribble Data @scribbledata]

“For every Machine Learning use case, we're seeing almost 10 Sub-ML use cases”.
Find out how the organizational appetite for Sub-ML is changing in Episode 2 of Scribble Conversations with @pingali and @Guava_man
https://t.co/Zep4EtFGPO

[Scribble Data @scribbledata]

Introducing the first edition of “Scribble Conversations”, a video series where our co-founders,
@Guava_man and @pingali talk about ML, Data Science, Analytics, MLOps, a few inside jokes, and of course, the current hot topic at Scribble Data - Sub-ML!
https://t.co/u1FAnw9MGv

[Andrew Wilkinson @awilkinson]

I read a book that blew my mind a little and I can’t stop telling people about it.

It explains why so many people dedicate their lives to achieving things that make them miserable.

This might sound crazy, but an unseen force is pushing you towards empty and unfulfilling goals…

[Indrayudh Ghoshal @Guava_man]

Bronze over Silver - any day.

In bout-type sports, both b & s play the same number of bouts.

S feels like they came close to winning top spot but fell short. B feels like they beat out everyone below them.

S ends their run with a loss. B ends their run on a high with a win.

[Redis @Redisinc]

How do match-making #apps solve the mathematically difficult challenge of entity-matching? Discover how talent agency @TheRoomGlobal found a perfect match for their system– and how their core similarity computation loop improved by 15 times 👉 https://t.co/dBMHEVfj4X

[Indrayudh Ghoshal @Guava_man]

*Not* whatsapp univ. Incredibly well-researched.
https://t.co/W5NNxYZFUY