Aadarsh kumar

Software engineer

[ₕₐₘₚₜₒₙ @hamptonism]

the greatest people aren’t disciplined.

they are obsessed.

[Shann³ @shannholmberg]

what is agent looping

for the last two years we prompted agents one task at a time. that is starting to change

instead of asking an agent to build the landing page and then driving every step yourself, you set up a loop that handles discovery, planning, the work, checking, and iterating until the goal is met

looping is a setup you build. almost any agent harness can run it, it just depends on how you wire it up

at its simplest, looping is one agent working on itself:

> researches
> drafts
> checks the draft against a goal
> fixes what is weak
> runs that cycle again until the work clears the requirements

you are not prompting each step anymore. the agent repeats the cycle for you

the bigger version is a fleet looping. you give an orchestrator agent a goal, it breaks the goal into pieces, hands each piece to a specialist agent, and those specialists hand smaller jobs to their own subagents

the whole tree keeps looping through discovery, planning, execution, and verification until the goal is met

one agent looping is like a person redoing their own draft. a fleet looping is a whole team running a project end-to-end

you create a goal, and the system runs the loop until it finishes within the reqs you set

open and closed looping:

OPEN LOOPING is exploratory. it still has conditions and a goal, but you give the agent or the fleet a wide space to move in. it can try different paths, discover things, build something you did not fully spec out

this is the exciting end, it is what Peter and others are doing, and tbh it is where I want to spend more time

the catch is cost, an open loop with real room to explore burns an insane amount of tokens. for the 90 percent of people without an unlimited budget it is not runnable yet, and pointed at projects with a loose standard it turns into a slop machine

CLOSED LOOPING is bounded. a human designs the end-to-end path first:

> clear goal
> defined steps
> an eval at each step
> a point where it stops or hands back to you (and feeds back performance data)

the agents still loop, but inside framework you built. it gets better every run because each pass feeds the next, and it runs on a normal budget because the path is tight.

for most marketing work, closed is the one that pays off today.

> the orchestrator owns the goal
> the specialists own the steps
> the subagents do the narrow work
> an eval gate make sure its not slop

[Kirk Borne @KirkDBorne]

New book from @PacktPublishing — hands-on Mathematics, Linear Algebra, and Machine Learning ...

"Deep Learning Math Workbook",
with 300 puzzles — by @ProfTomYeh

Check it out and get it here: https://t.co/5hyGm6s9K5

[Suryansh Tiwari @Suryanshti777]

This book alone can change your ML interview game🙀

If you're serious about AI, ML, or landing top-tier roles... this book is DIFFERENT.

• Real-world deep learning interview problems
• Step-by-step solutions
• Covers ML math, probability, CNNs, Bayesian DL & more
• Designed for people who actually want to understand, not just memorize

This isn’t basic theory.
This is the kind of prep that makes you walk into interviews confident.

And I’m giving it FREE to first 4500

How to get it :

1️⃣ Follow me MUST (So i can dm you)
2️⃣ Like this post
3️⃣ Comment “DL”
4️⃣ RT to help others level up

If you’re preparing for ML interviews in 2026, this could literally change your trajectory.

No fluff. No gatekeeping.
Let’s build killers in AI. 🚀🔥

[The Math Flow @TheMathFlow]

Dynamics of a Pendulum.

[Kirk Borne @KirkDBorne]

The Theory That Would Not Die — How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and more: https://t.co/Sx4wO5aJtg
————
#Probability #Statistics #DataScience #ML

[Aadarsh kumar @Aadarsh317]

@probnstat 1/11

[Aadarsh kumar @Aadarsh317]

@probnstat C - 5/14

[Aadarsh kumar @Aadarsh317]

Bayes' theorem:

Bayes’ theorem is a way to update probabilities when new evidence becomes available.
It’s built on conditional probability.

#probability

[Probability and Statistics @probnstat]

Student's t-test explained:

[Probability and Statistics @probnstat]

Hypergeometric Distribution explained:

[Aadarsh kumar @Aadarsh317]

✅ Solved LeetCode 13: Roman to Integer
Learned how to handle subtractive notation (IV = 4, IX = 9, etc.)
🔗 Problem: https://t.co/SbR38LEx9P
📂 Code: https://t.co/78cQkRRmJC

#Coding

[Aadarsh kumar @Aadarsh317]

@probnstat 2/11

[Aadarsh kumar @Aadarsh317]

✅ Solved LeetCode 50: Pow(x, n)
Implemented fast exponentiation (O(log n)) 🚀
🔗 Problem: https://t.co/9Q1V2uH7XQ
📂 Code: https://t.co/IOHrZC4IW1

#LeetCode #Coding

[Aadarsh kumar @Aadarsh317]

@probnstat 1/12

[Aadarsh kumar @Aadarsh317]

✅ Solved LeetCode #121: Best Time to Buy and Sell Stock
📂 Solution on GitHub: https://t.co/nOm3CzCDc6
🔗 Problem: https://t.co/CMTAFwh7p2
#LeetCode

[Aadarsh kumar @Aadarsh317]

@probnstat False

[Vicharak @Vicharak_In]

This is the first prototype of our industrial design and a preview of how it will look.

It’s not just a mini PC—it’s going to be the world’s first modular mini PC.

Also fits into your pocket.

[ADG PI - INDIAN ARMY @adgpi]

अविचल संकल्प, निर्णायक प्रतिकार।

Unwavering Resolve,
Decisive Action.

#OperationSindoor
#JusticeServed
#IndianArmy

[DRDO @DRDO_India]

On #NationalTechnologyDay, we celebrate India’s transformation into a defence tech powerhouse. From precision missiles to indigenous weapon systems, our strides in innovation are forging a #Sashakt and #AatmanirbharBharat — secure, self-reliant, and future-ready. #TechForIndia