Ajinkya Shinde

🔨👾 ⬇️⬆️ 🌍

[Ajinkya Shinde @bWanGG]

@Crocs have you reflected on the design flaws of Jibbitz? Mine won't stay in.

[Malay Krishna @Malay4Product]

Most Indians have not heard of Persistent Systems.

That is a shame, because every Indian should know what happened this weekend.

Let me explain everything from the beginning.

Persistent Systems is an Indian software company founded in Pune in 1990 by Dr. Anand Deshpande.

He started it after doing his PhD at Indiana University, came back to India, and built a technology company from scratch.

For most of its existence, Persistent was a mid-size player that did not get the same attention as TCS, Infosys, or Wipro.

Today, Persistent is recognised as the fastest-growing IT services brand globally in 2026. They have had 24 consecutive quarters of sequential revenue growth.

So, for six straight years, every single quarter has been bigger than the one before it. That kind of consistency is extremely hard to pull off in any business.

Now, they did something that has almost never happened before in Indian tech.

An Indian IT company just launched a takeover bid for a publicly listed German company on the Frankfurt Stock Exchange.

Persistent is offering 81 euros per share to buy Nagarro SE, which works out to approximately 1.1 billion euros.

And they are paying 140 percent above Nagarro's share price the day before the deal was announced. That is how badly they want this company, and how confident they are in what the combined business will be worth.

So what does Nagarro actually do and why does Persistent want it so badly?

Nagarro is headquartered in Munich, Germany. They have 18,500 employees across 40 countries and generated 1 billion euros in revenue in 2025.

Their biggest clients include four of the top five European automotive manufacturers. So BMW, Volkswagen, Mercedes-Benz type companies.

These are some of the most demanding engineering clients in the world.

Nagarro builds the software that goes inside these companies.

> The dealer management software.
> The supply chain tools.
> The digital interfaces that a BMW engineer uses when designing a new model.

This kind of deeply embedded enterprise software work is extremely difficult to replace once it is in place. These are long relationships measured in decades, not years.

That is why this deal makes strategic sense for Persistent.

Before this deal, only 9 percent of Persistent's revenue came from Europe. After this acquisition closes, that number jumps to 22 percent.

Right now, Persistent earns most of its money from North American clients, which means it is heavily exposed to whatever happens in the US economy.

If US companies cut tech spending, Persistent hurts.

Adding a strong European base changes that. Your revenue is now spread across two of the world's largest economies.

The combined company will have $2.9 billion in annual revenue and more than 46,000 employees across 40 countries. Of those, 37,000 plus will be in India.

So an Indian company, built by an Indian founder, that now employs 37,000 Indians and just bought a German company listed on the Frankfurt Stock Exchange.

For most of the last 30 years, the story of Indian IT was that we sent engineers to do work that Western companies needed done cheaply.

Good work, real work, but fundamentally in a support role. You came to us because we were affordable and skilled.

But now with Persistent buying Nagarro, Coforge buying Encora for $2.35 billion, TCS buying Coastal Cloud, Infosys buying Optimum Healthcare IT, Indian IT companies going out and buying Western companies for their client relationships, their market presence, and their technology capabilities.

We are the buyers now. :)

[Killed by Google @killedbygoogle]

Google Keep is getting killed December 31.

[Arpit Bhayani @arpit_bhayani]

With agentic slop, we are trading software reliability for shipping velocity and calling it progress. It isn't. Systems are more fragile than ever, and engineers building them no longer trust their code to hold up in real-world edge cases.

I am pro-AI, but this will backfire - big time.

[Aravind @aravind]

India's own Coursera. Thousands of free courses. Exams and certification by govt for a small fee. Been there for so long. Not many youngsters or learners in India will know about it. Why? As usual poor PR and communication from GoI. MIB is the most weak ministry to put it mildly. It needs a new young dynamic MP heading it with a capable defense personnel leading infowar and counter psyops. This one reform and reshuffle by @PMOIndia will help India tremendously from its soft power to economy. It will even benefit the ruling govt, I assure. Please do it at least for politics sake. 🙏 https://t.co/ALnnEf1brG

[Rahul Raj @x_rahulraj]

We can make fun of Galgotias, but the uncomfortable truth is that a large majority of BTechs, MTechs, PhDs, and even many professors in India will struggle to build good engineering products.

The problem is not about intelligence. It is about ecosystem, incentives, and culture.

Our education system gives very little importance to practical learning from an early stage. From as early as Class 9, hands-on experimentation is treated as secondary. By Class 12, many aspiring engineers skip practical work entirely. Setting up Young’s Double Slit experiment and getting right results is far less valuable than memorizing the formula d sin θ = mλ for exams.

This mindset continues in college. Students quickly learn that studying core engineering deeply or performing genuine experiments is rarely rewarded. Seniors push them toward placements over learning, the job market values credentials over competence, and uninspiring teaching further reduces curiosity. In many labs, marks depend more on producing perfect graphs that match textbooks than on whether real experiments were actually performed.

Even in the best engineering institutes, where talented students and capable professors exist, the incentive structure prioritizes publishing papers far more than building working products. Over time, this becomes a convenient justification across academia.

Professors also face structural constraints. Setting up a good laboratory can take years. Procuring basic components involves lengthy bureaucratic processes. Career advancement depends largely on the number of research papers published, not on solving real industry problems or creating deployable technologies. Under such conditions, product building becomes an uphill task.

When optics matter more than outcomes, the system naturally rewards visibility over value. Creating hype, publishing quickly, and maintaining appearances become easier choices than undertaking the difficult, uncertain process of building real engineering solutions.

So yes, we can make fun of Galgotias. But the deeper issue is systemic. The real challenge is that our education and research ecosystem does not sufficiently reward those who build.

[Shruti @heyshrutimishra]

HOLY SHIT

Anthropic Just Triggered a $285B Market Crash 😳

Bloomberg just reported that Anthropic released a new AI tool that caused:

󠁯•󠁏 $285 billion wiped out across software, finance, and asset management stocks
󠁯•󠁏 6% drop in Goldman's software basket (biggest since April)
󠁯•��� 7% crash in financial services index
󠁯•󠁏 Nasdaq down 2.4% at its worst

This is MASSIVE. The market literally panicked over an AI automation tool.

If you work in software, legal, or IT services, this changes everything.

You just don't know it yet.

On Jan 30, Anthropic quietly released 11 plugins for Claude Cowork.

Not a new model , but plugins.

But these plugins don't work inside your software.
They replace it entirely.

things like financial modeling & sales workflows, which lead to;

󠁯•󠁏 RELX (LexisNexis): -14%
󠁯•󠁏 Infosys: -7%
󠁯•󠁏 TCS: -6%
󠁯•󠁏 Wolters Kluwer: -13%

Wall Street is calling it the "SaaSpocalypse."

Because for the first time, a foundation model company didn't just build the AI.

They built the application layer too.

Anthropic isn't selling APIs anymore.

They're owning entire workflows.

Why pay $50K/year for legal software when Claude does it for $20/month?

Why hire 500 IT consultants when one AI agent works 24/7?

& the scary part?

This is just 11 plugins in a research preview.

Imagine what's coming next.

If your company's value prop is "we automate X"...

You're now competing with Claude.

And Claude costs 1% of what you do.

You're either building with AI, or getting replaced by it.
No middle ground anymore.

[Carlos E. Perez @IntuitMachine]

The World Is Not Linear: A Field Guide to the Laws That Quietly Run Everything

Most smart people don’t fail because they’re dumb.

They fail because they apply clean logic to a messy world — and the world punishes that mistake with a smile.

The messy truth is that modern life is shaped less by individual intent and more by systems: incentives, competition, scaling effects, path dependence, and statistical weirdness. These systems produce outcomes that feel unfair or mysterious until you learn the underlying “laws” — a set of lenses that let you predict how things actually behave.

This is not about becoming cynical. It’s about becoming accurate.

Once you internalize these lenses, you start noticing that most disagreements aren’t about values. They’re about which hidden force you think dominates:

Do incentives matter more than morals?
Do networks scale value more than craftsmanship?
Do rare events matter more than averages?
Do systems evolve, or can they be designed?

This article is a guided map through those forces — told as one story.

1) The seduction of “doing the obvious thing”

Imagine you’re in charge of improving something important: a company, a city, a hospital, a school, a product, maybe even your own life.

You do what responsible people do: you define a goal.

You pick a metric.

And you tell everyone: we’re going to win on this number.

This is where the first trap snaps shut.

Goodhart’s Law: the metric stops being real

When a measure becomes a target, it stops being a good measure.

Before it became a target, the metric was an instrument: a thermometer.

After it becomes a target, it becomes a game.

Hospitals improve “wait times” by changing intake rules. Companies improve “engagement” by nudging addiction. Schools improve test scores by teaching to the test. Police departments improve crime stats by changing what counts as a crime.

Not because anyone is evil. Because the system rewards it.

The principal–agent problem: the doers don’t pay

This is the deeper engine under Goodhart.

The person deciding is not the person suffering the consequences.

Executives chase quarterly optics; employees deal with the chaos.

Politicians chase election cycles; citizens live with the long-term effects.

Managers chase easy metrics; customers absorb the frustration.

Once you see principal–agent problems, you start seeing why seemingly intelligent organizations keep doing self-destructive things: the incentives are miswired.

The Cobra Effect: perverse incentives grow cobras

Sometimes this miswiring gets darkly funny.

Reward outcomes and people will manufacture the appearance of outcomes.

In the original parable, a colonial government offered a bounty for dead cobras — and people began breeding cobras.

This isn’t historical trivia; it’s a universal pattern:

Reward bug counts → people file junk bugs.
Reward convictions → plea bargains + overcharging.
Reward content volume → SEO sludge.
Reward “delivery” → rushed work + tech debt.

The world is full of cobra farms.

2) Why fixing things often makes them worse

Okay, so: choose better metrics, align incentives, done.

Not quite.

Because even well-intentioned fixes trigger the next law: second-order effects.

Chesterton’s Fence: don’t remove constraints you don’t understand

You walk into an old system and see “stupid rules.”
You want to clean house.
You want to simplify.

But: why is that rule there?

Don’t remove a fence until you know why it was built.

A lot of institutional weirdness is scar tissue from past disasters.
The rule might be dumb — but if you don’t understand it, you don’t know what disaster you’re re-inviting.
This is why naive reformers are dangerous: they confuse “not understanding a thing” with “the thing being pointless.”

Gall’s Law: complex systems must grow from simple working ones

Even if the fence is removable, you still hit the next problem:

A complex system that works is always evolved from a simple system that worked.

This demolishes a common fantasy: that you can design complexity from scratch.

Most large redesigns fail for one reason:
They try to create a finished organism instead of growing a living embryo.

If the system matters, you don’t “implement” the final form.
You build something simpler that works.
Then you iterate.

Gall’s law is harsh, but kind: it explains why so many ambitious “transformations” flame out.

3) Efficiency doesn’t save you (and sometimes consumes you)

Now suppose you do manage to improve a system.
You make it cheaper, faster, more efficient.

Surely this reduces resource usage?
Often, no.

Jevons Paradox: efficiency increases total consumption
When you make something more efficient, you often make people use more of it.
Make lighting cheaper → people illuminate more spaces.
Make driving more fuel-efficient → people drive farther.
Make computing cheaper → people compute vastly more.

Efficiency doesn’t always shrink the pie. It can expand it.

This is one of the most important and least emotionally intuitive truths about progress: efficiency changes behavior.

4) Some things don’t get more efficient — and get expensive forever

Now meet the mirror image of Jevons: not everything can get dramatically more productive.
Some work is bottlenecked by time, humans, and attention.

Baumol’s Cost Disease: sectors that don’t scale inflate

A string quartet takes as long to play Beethoven as it did 200 years ago.
A therapist can’t 10× their clients without breaking the thing.
A teacher can’t “scale” classroom attention the way software scales distribution.

Meanwhile, other industries do scale — manufacturing, computing, logistics.

So as society grows richer, productivity sectors get cheaper and cheaper…
and human-time sectors get relatively more expensive.

That’s why:

healthcare
education
legal services
childcare
eldercare

…feel like they eat the world.

Baumol isn’t “a problem to solve” so much as a physics constraint: certain value comes from human presence. And presence doesn’t compress easily.

5) The invisible accelerant: networks

At this point you might feel like everything is doom and friction. It’s not.

Some forces make systems wildly better as they grow.
The biggest one is networks.
Metcalfe’s Law: value scales with connections

A phone is useless alone.
A fax machine is useless alone.
A social app is useless without other humans.

As users increase, connections increase faster than users do.
That creates accelerating value.
Reed’s Law: groups scale even faster than connections

But it’s not just one-to-one links.

Once people can form groups — communities, coalitions, companies, subcultures — the number of potential groupings explodes.

That’s Reed’s law: group-forming networks can scale with frightening speed.

This is why networked platforms can go from “niche” to “dominant” almost overnight: the product isn’t just features — it’s the social graph.

6) Progress has a heartbeat: learning curves
Not all progress comes from networks. Some comes from repetition.

Wright’s Law: cost falls with cumulative production

This is the law behind why solar, batteries, and manufacturing tech get cheaper and cheaper:

Every doubling of cumulative production yields a predictable cost reduction.

The implications are enormous:

the future is shaped by what we manufacture at scale
volume is not just output; it’s learning
building the thing teaches you to build the thing better

Strategy through Wright’s law becomes: maximize learning rate.
Not “be brilliant,” but “iterate relentlessly.”

7) Cooperation is rare — and competition forces ugliness

Now we move from economics into game theory and moral physics.

Even with good metrics, good redesign, good scaling…

Sometimes the system makes people do bad things.
Prisoner’s Dilemma: defecting is rational

If you and I cooperate, we both win.
But if I suspect you might defect, I should defect first.
So we both defect.
We both lose.

This structure appears everywhere:

labor vs management
nations vs nations
companies vs companies
roommates
siblings
Twitter discourse

It’s tragedy-by-incentives.

Moloch: the god of coordination failure

“Moloch” is the poetic version of the same idea:

systems where competition forces everyone into worse behavior, even if nobody wants it.

No one wants the attention economy.
But creators compete for attention.
Platforms compete for engagement.
So everyone converges on outrage and addiction.
Moloch doesn’t need villains.
It only needs incentives.

8) The biggest mistake smart people make: believing in averages

Now we arrive at the statistical heart of why forecasts fail.
Most planning assumes the world behaves like a bell curve: most outcomes are near the average, extremes are rare.

In many domains, that’s false.

Fat tails: extremes happen way more than you think

In fat-tailed worlds, the “average” is a comforting lie.

Outliers dominate:

venture returns
blockbuster movies
bestselling authors
company outcomes
war and peace
pandemics
market crashes

In a fat-tailed world:
one event can erase ten years of progress
or create it overnight
Black swans: surprise + impact + fake hindsight
A black swan isn’t just an outlier.
It’s an outlier we didn’t know how to model.

The signature of black swans is:

huge impact
surprise beforehand
“it was obvious” afterward

We are story machines. We can rationalize anything after it happens.

Survivorship bias: you’re studying the winners

This is why business advice is mostly nonsense.
We read biographies of billionaires and imitate their habits — forgetting the cemetery of equally hardworking, equally smart people who lost.
Survivorship bias turns randomness into “wisdom.”

A good thinker always asks:
what am I not seeing because it died?

9) The final set of tools: tradeoffs, simplicity, and time

After you’ve internalized incentives, scaling, networks, and tail risk, you earn the right to something important:

Less ideology.
More judgment.

That’s what these last lenses provide.

Pareto efficiency: every improvement has a cost

At some point, you stop making “free” gains and enter a world of tradeoffs.

If you want more of A, you give up B.

This is what breaks utopian thinking:

more safety can mean less liberty
more speed can mean less quality
more fairness can mean less efficiency
more growth can mean more inequality

Smart people aren’t the ones who avoid tradeoffs.
They’re the ones who name the tradeoff out loud.

Occam’s Razor: don’t add gears without proof
Now that you’re thinking in systems, you could easily overcomplicate.

Occam is your brake pedal:
prefer the simplest explanation that predicts.

It’s not “simplicity is truth.”
It’s: don’t hallucinate complexity.
Lindy: time is the best filter we have

In fragile worlds, “new” is often a synonym for “untested.”

The Lindy effect says:
the longer something has survived, the longer it’s likely to survive.
Ideas, books, institutions, even practices:
time is a stress test.

Lindy isn’t anti-innovation. It’s pro-robustness.

Comparative advantage: specialization beats self-reliance

Finally, comparative advantage gives you the social version of Occam.

Even if you’re worse at everything than someone else…
trade can still make both better off,
because efficiency comes from relative differences.

That lens dissolves a lot of macho self-sufficiency myths.

So what does this worldview do?

It does three things.

First: it replaces naive optimism with durable optimism

Not “everything will work out.”

But: we can build systems that don’t collapse under their own incentives.
Second: it changes what you fear

Not competitors.
Not critics.
Not even failure.

You start fearing:

bad metrics
misaligned incentives
brittle complexity
tail risks
coordination failure

Which are the real predators.

Third: it gives you a usable strategy

A decision-making style that looks like this:

Start simple (Gall)
Measure carefully (Goodhart)
Align incentives (principal–agent)
Expect adaptation (cobra effect)
Respect old constraints (Chesterton)
Model scaling honestly (Metcalfe/Reed/Wright)
Don’t assume efficiency saves you (Jevons/Baumol)
Prepare for tails (fat tails / black swans)
Don’t trust winner stories (survivorship bias)
Name tradeoffs and keep models simple (Pareto + Occam + Lindy)

That list is more than theory.
It’s a survival kit for reality.

Closing: the meta-law

If I had to compress this entire worldview into one sentence, it would be:

Outcomes come from incentives and scaling under uncertainty—not from intentions and plans.

Most people live inside stories.

This toolkit makes you live inside systems.

And once you do, you become harder to fool — including by yourself.

[SpaceX @SpaceX]

Congratulations to the entire SpaceX team for completing 165 Falcon launches and five Starship flight tests this year, all continuing to help pave the way to making life multiplanetary!

[Carlos E. Perez @IntuitMachine]

I just read a paper that completely broke my brain.

It describes a system that solved an AI task with over 1,000,000 sequential steps... with ZERO errors.

Using AI models that are known to be flaky and make mistakes.

How is that even possible? 🤯

We all know LLMs have an error rate. Even 99.9% accuracy is a death sentence for long tasks.

Imagine you need 1,000 correct steps in a row. With a 99.9% success rate per step, your chance of finishing the whole thing is only ~36%.

At a million steps? Forget it. It's statistically impossible.

So for years, the race has been to build bigger, "smarter" models to get that per-step error rate closer to zero. We're trying to build a perfect genius.

But this paper ("Solving a Million-Step LLM Task with Zero Errors") does the complete opposite. It's a total paradigm shift.

Here's the "holy shit" moment:

Stop trying to make the AI perfect. Instead, build a system that's immune to its imperfections.

How?

Smash the problem into the tiniest possible pieces. (They call it Maximal Agentic Decomposition).

Have a team of simple, cheap AIs vote on the answer for each tiny piece.

It's less like hiring one world-class chef and praying they don't have an off day, and more like designing the McDonald's kitchen.

The system guarantees the burger is the same every time, even if any individual worker could mess up.

The reliability comes from the process, not the person.

They tested this on the Towers of Hanoi puzzle—a classic benchmark where AIs fail spectacularly as the task gets longer.

They set it up for 20 disks. That requires 1,048,575 perfect moves in a row.

(seriously, over a million steps)

A single AI trying this would be a comedy of errors.

But their system of "micro-agents" voting on every single move... nailed it. Flawlessly.

And the plot twist? The most expensive, "state-of-the-art" models weren't even the best for the job. A smaller, cheaper model (gpt-4.1-mini) was more cost-effective because the tasks were so simple.

This is a huge deal for AI safety, too.

A single, god-like AI is a black box. It's unpredictable.

But a system of a million simple agents? You can inspect it. You can audit each step. The agents have no grand "worldview"—their entire existence is to solve one tiny puzzle and then disappear. It's controllable.

So next time you're building something with an LLM, maybe stop asking "how can I prompt the model to be smarter?"

And start asking: "How can I design a system where it's okay for the model to be dumb?"

The real power isn't just in the model. It's in the architecture you build around it.

This isn't just about AI. It's a fundamental lesson in engineering and problem-solving.

You don't always need perfect components to build a perfect machine. You just need a damn good design.

...which makes you wonder what else we're trying to solve by chasing individual perfection instead of building better systems.

[Ajinkya Shinde @bWanGG]

Unable to paste the images. Bar code - GTIN 8901063028241 tagged with above license number

#FSSAI #PublicHealth

[Ajinkya Shinde @bWanGG]

@fssaiindia no standardization has been enforced by you on big brands such as @BritanniaIndLtd .

Check screenshot below where information is inconsistent with the SmartConsumer App, using the bar code.

The FSSAI license no. is 10015043001129 and is not listed at all in it.

[Dheeraj Khandelwal @dhekhandelwal]

Photos I took of ISRO's-LMV3 launch from Sriharikota

[Nick Collins @nickcollins1953]

Sanskrit, a unifying artificial language, was last refined by Panini in the 5th century BC. He mentioned at least ten predecessors. It has an estimated 74 million words, exact meanings changed by prefixes and suffixes.

It spawned Greek, Latin and Slavic tongues, spoken by over three billion today. Words like “sthan” echo in Afghanistan, Uzbekistan etc. showing Sanskrit’s vast inland reach. Veda (knowledge) became Latin “videre” and Slovenian “veda”. “Ashva” (horse) became Lithuanian “asva”. “Samudra” (sea) is the same word in Indonesian and “samot” in Thai.

India’s linguistic legacy was spread by land and sea. India, not the steppe, was the Indo-European cradle.

[Rohan Paul @rohanpaul_ai]

Columbia CS Prof explains why LLMs can’t generate new scientific ideas.

Bcz LLMs learn a structured “map”, Bayesian manifold, of known data and work well within it, but fail outside it.
But true discovery means creating new maps, which LLMs cannot do.

[Indian Tech & Infra @IndianTechGuide]

🚨 Delhi will ban older vehicles (diesel over 10 years, petrol over 15) from refueling and enforce cameras at fuel stations starting July 1.

[Conan Doyle @ConanDo08743902]

Hey Everybody
The newest and closest of 3I/Atlas as it passes Mars right this moment minutes ago. Its not a comet...its perfectly cylindrical. Its an interstellar craft of some kind...Mars Perseverance Sol 1643: Right Navigation Camera (Navcam). Image Credit: NASA/JPL-Caltech.

[DogeDesigner @cb_doge]

🚨BREAKING: Elon Musk says Neuralink’s visual implants could give humans the ability to see multispectral wavelengths, including infrared, ultraviolet, and even radar, essentially granting superhuman vision.

[Ajinkya Shinde @bWanGG]

@OpenAI while using the Voice Mode, at the end of the chat, I could hear a voice saying - hey, hi ? I am using the Voice Mode for the last 10 minutes. May be it's just a glitch at my end ?

[OpenAI @OpenAI]

We’re launching a research preview of Codex: a cloud-based software engineering agent that can work on many tasks in parallel.

Rolling out to Pro, Enterprise, and Team users in ChatGPT starting today.

https://t.co/HqiAtgydwh