Co-founder @scribbledata. Build robust data products for advanced analytics.
Toronto, Canada
Joined May 2015
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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.
The last frontier for AI agents being crossed - meet the human API end-point for agents to get stuff done in the real world: rentahuman.ai
skynet too prolly began as a mom-and-pop shop, trading compute for cabbages (or whatever it ran on).
Indrayudh Ghoshal retweeted
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.
indiatoday.in/magazine/futur…
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.
@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. betakit.com/scribble-data-cl…
Enrich gives businesses the ability to power a spectrum of data science work, continuously generating datasets for everything from analytics to machine learning.
But as we're finding, data teams see great RoI and Time-to-Value by executing on a host of sub-machine learning usecases. (scribbledata.io/welcome-to-t…). Enrich provides a great on-ramp to this highway.
Indrayudh Ghoshal retweeted
“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
youtu.be/xRr-5eNnToY
Indrayudh Ghoshal retweeted
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!
bit.ly/3HqLWcr
Indrayudh Ghoshal retweeted
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…
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.
Indrayudh Ghoshal retweeted
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 👉 bit.ly/3wbTTeX
Indrayudh Ghoshal retweeted
Incorporating #hierarchicalfeatures in the #MLlifecycle can be a game-changer — it’ll help save time spent in building additional features and bring down the time taken to take models from concept to production. Read more here scribbledata.io/blog-hierarc…
Indrayudh Ghoshal retweeted
Incorporating hierarchical features in the ML lifecycle can be a game-changer — it’ll help save time spent in building additional features and bring down the time taken to take models from concept to production. Read more here hbr.org/2020/02/are-you-aski…
