BangaloreStartups

Bengaluru startup stories • Innovation • Indie Music • AI

[BangaloreStartups @BloreStartups]

🧠 Wisdom Threads #1
Kunal Shah (Head of WhatsApp & Founder of CRED) at the Groww India Investor Festival 2026 shared powerful thoughts on AI, productivity, and building a 10x career.
Here’s the distilled version of his key insights 👇
@_groww @kunalb11 @samidhas
https://t.co/8ivZ28RjXi

[Groww @_groww]

"The world's largest employer is inefficiency'"

At the India Investor Festival, @kunalb11 spoke with @samidhas , editor ETtech, about how AI is dismantling corporate friction and destroying roles built purely on slow processes.

[Shravan Venkataraman @theBuoyantMan]

People of Bangalore. Please do your thing in helping get the word out!

A very dear friend has started a luxury chauffeur service.

Arista is a hospitality-led private luxury chauffeur and concierge service in Bangalore.

It provides professionally chauffeured Mercedes-Benz, BMW, Toyota Vellfire and other premium vehicles for airport transfers, executive travel, VIP guests, weddings, events, outstation journeys and special occasions such as date night, birthdays and anniversary celebrations.

https://t.co/yjwtMr4WPg
[phone]

[SpaceXAI @SpaceXAI]

Every account includes a free phone number to get started. We're launching today in beta:

Try it now → https://t.co/e5rR2scELf

Read more → https://t.co/M660JUeLID

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Anthropic @AnthropicAI]

We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5.

We'll begin restoring access tomorrow, and will share an update soon.

We’re grateful to our users for their patience, and to everyone who worked with us on redeploying the models.

[Claude @claudeai]

Introducing Claude Sonnet 5, our most agentic Sonnet yet.

It makes plans, uses tools like browsers and terminals, and runs autonomously at a level that just a few months ago required larger and more expensive models.

[Groww @_groww]

“Can I skip talking to my manager?”

According to @kunalb11, AI is enabling the top 10% of employees to operate at a level we haven’t seen before.

[Naval @naval]

Good marketing is finding different ways to tell the truth.

[Groww @_groww]

"The world's largest employer is inefficiency'"

At the India Investor Festival, @kunalb11 spoke with @samidhas , editor ETtech, about how AI is dismantling corporate friction and destroying roles built purely on slow processes.

[BangaloreStartups @BloreStartups]

Key Takeaway
- AI won’t replace you.
- But someone who uses AI will.
The winners will be those who develop high agency and treat continuous learning as non-negotiable.
Watch the full talk here:
https://t.co/8ivZ28RjXi

What’s your biggest takeaway from Kunal Shah’s @kunalb11 talk?
Follow @BloreStartups for more startup stories + founder insights.

[Groww @_groww]

"The world's largest employer is inefficiency'"

At the India Investor Festival, @kunalb11 spoke with @samidhas , editor ETtech, about how AI is dismantling corporate friction and destroying roles built purely on slow processes.

[BangaloreStartups @BloreStartups]

🧠 Wisdom Threads #1
Kunal Shah (Head of WhatsApp & Founder of CRED) at the Groww India Investor Festival 2026 shared powerful thoughts on AI, productivity, and building a 10x career.
Here’s the distilled version of his key insights 👇
@_groww @kunalb11 @samidhas
https://t.co/8ivZ28RjXi

[Groww @_groww]

"The world's largest employer is inefficiency'"

At the India Investor Festival, @kunalb11 spoke with @samidhas , editor ETtech, about how AI is dismantling corporate friction and destroying roles built purely on slow processes.

[BangaloreStartups @BloreStartups]

Corporates vs Individuals
Large companies are slow because of bureaucracy.
Meanwhile, the top 10% of tech professionals are already operating like a “new species” — using AI to bypass old processes and move at 10x speed.

[BangaloreStartups @BloreStartups]

Startup Story #5 done ✅
What do you think made CRED grow so fast?
Was it the rewards, the premium experience, sharp marketing, or Kunal Shah’s execution?
Huge congratulations to @kunalb11 on his new role leading WhatsApp globally at Meta! Wishing him all the best.

[BangaloreStartups @BloreStartups]

💳 Startup Story #5: CRED
In 2018, one entrepreneur decided to make paying credit card bills rewarding instead of painful.
What started as a simple app in Bengaluru became one of India’s fastest-growing fintech companies.
This is the story of CRED and founder Kunal Shah @kunalb11 . 👇

[BangaloreStartups @BloreStartups]

As of today (22 June 2026), CRED continues to grow while processing a massive share of India’s credit card bill payments. Kunal Shah has also taken on a bigger global role (leading WhatsApp at Meta), showing how founders can scale their impact beyond one company.