DEV Community: kumarapu bhagyasri

200,000 Neurons Are Waking Up

kumarapu bhagyasri — Wed, 30 Sep 2026 05:30:55 +0000

Somewhere in Melbourne, Australia, there's a computer that needs to be fed, kept warm, and cleaned every few days. If you stopped taking care of it, it would die. That's not a metaphor. It's built from real, living human brain cells.

It's called the CL1, made by a company called Cortical Labs. Each one contains about 200,000 real human neurons, grown in a lab from ordinary blood donations and placed directly onto a silicon chip. The neurons and the chip talk to each other with tiny electrical signals, the same way real neurons talk to each other inside your own head.

Here's the part that made me stop scrolling. These neurons have already learned to play video games. First it was Pong, a simple game where you move a paddle up and down. Then, more recently, a team taught the same living cells to play Doom, a full 3D game where you explore rooms and fight enemies. There's no screen and no eyes involved. The game gets turned into patterns of electrical signals the neurons can respond to, and the neurons slowly learn, through trial and error, what actions lead to a better outcome.

The company's own scientists describe the cells as playing "like a beginner who's never seen a computer," which, as they point out, is completely true. These cells have never seen anything. They're learning entirely through electricity and feedback, the same basic idea behind how a baby's brain wires itself through experience.

Here's a fact that genuinely surprised me: a single one of these living chips uses about 30 watts of power. A typical AI chip used in today's massive data centers can use around 6,000 watts. That's not a small difference, that's roughly 200 times less energy, using cells instead of circuits.

This isn't staying a lab experiment either. In 2026, Cortical Labs opened a "Bio Data Centre" in Melbourne with 120 of these living chips working together, and they're building another in Singapore. The company calls the idea "Synthetic Biological Intelligence," and the entire motivation is simple: today's AI data centers use enormous amounts of electricity and water to stay cool, and this could be a radically more efficient alternative, if it can ever be scaled up to do real, useful work.

And that's the honest, uncertain part of this story. Right now, nobody has proven these living computers can actually keep up with the raw power of a modern AI chip. The cells only survive for a few months before needing to be replaced. Feeding and maintaining living tissue is messy and slow compared to just plugging in a silicon chip. This is very early, and very experimental.

But here's why I can't stop thinking about it anyway. For decades, every computer we've built has been separate from life, something we assembled out of metal, sand, and electricity. This is the first time a company is selling something that blurs that line completely, a device that is, quite literally, part alive.

Here's the fact I'll leave you with: the human brain, with its roughly 86 billion neurons, still uses less energy than a single lightbulb to do things no supercomputer on Earth can fully replicate. These lab-grown chips, with just 200,000 neurons each, are humanity's very first, clumsy attempt at borrowing that same trick.

Something Is Replacing Electricity Inside Computers...

kumarapu bhagyasri — Tue, 29 Sep 2026 06:06:03 +0000

Every computer you've ever touched works the same basic way: electricity flows through tiny wires, and that movement is how it thinks. That idea has powered every phone, laptop, and AI chatbot for decades. But right now, in labs most people have never heard of, a small group of engineers are building something that skips electricity almost entirely. They're building chips that compute with light.

It's called photonic computing, and here's why it exists at all.

AI models have grown incredibly fast, faster than the hardware built to run them. IBM researchers have pointed out that training a single modern AI model takes roughly the same computing effort as running the world's first petaflop supercomputer for 10,000 days straight. Even more telling: raw computing power has improved about 60,000 times over the last couple of decades, but the speed of moving data in and out of memory has only improved about 100 times in that same period. That gap is the real bottleneck, and it's why engineers are now looking past traditional electronics entirely.

Here's the simple version of how photonic chips try to fix it. Instead of pushing electricity through wires, they send tiny beams of light through microscopic optical paths, almost like sending signals down fiber-optic internet cables, but inside the chip itself. Light doesn't heat up wires the way electricity does, and it can carry far more information at once, moving at, quite literally, the speed of light.

This isn't just a research paper sitting in a drawer somewhere. Companies like Lightmatter and Q.ANT are already building real photonic chips, and industry experts are calling 2026 the year this technology starts moving from experiments into actual commercial products.

There's a close cousin to this idea too, called neuromorphic computing, which tries to design chips that work more like an actual brain, firing in small bursts the way real neurons do, instead of constantly running at full power like traditional processors. Some of the most exciting research right now combines both ideas together, brain-like design running on light instead of electricity.

Here's what genuinely excites me about this, as someone who only started learning to code a couple of months ago. If this technology matures the way its early results suggest, the benefits go far beyond speed. AI could use dramatically less electricity, which matters for the planet, not just performance. AI models could get smaller and cheaper to run, opening the door for far more people and smaller companies to build powerful tools, not just giant labs with massive data centers. And because light-based chips run cooler and more efficiently, we might see powerful AI running directly on everyday devices, like phones, instead of needing a distant data center at all.

None of this is fully finished yet. It's still early, experimental, and genuinely rare knowledge, most people have never even heard the phrase "photonic computing." But that's exactly what makes it exciting to write about. The next leap in AI might not come from a smarter piece of software at all. It might come from someone quietly redesigning the very material a computer thinks with.

I keep noticing the same pattern every time I dig into one of these topics: the biggest problems rarely get solved by doing the old thing a little better. They get solved by someone brave enough to question the material, the rule, or the assumption everyone else built on top of, without ever looking underneath it.
Here's the fact I can't stop thinking about: light was already being used to carry the internet across oceans, through fiber-optic cables, decades before anyone tried putting it inside a computer chip. The technology quietly existed the entire time. It just took this long for someone to ask why we were only using it to send information, instead of using it to think.

Your Browser Is Doing Something Impossible!

kumarapu bhagyasri — Mon, 28 Sep 2026 05:40:05 +0000

Have you ever wondered how Photoshop, one of the most powerful programs ever built, can run inside a browser tab?

No download. No installing. Just open a website and it works. Ten years ago, that would have sounded impossible. Today, it's happening billions of times a week, and the secret behind it is something most people have never heard of.

It's called WebAssembly, or "Wasm" for short.

Here's the simplest way I can explain it. Imagine every browser had to learn every programming language in the world. Impossible, right? So instead, languages translate their code into one shared set of instructions, like a universal set of LEGO instructions, and every browser understands those. Write your code in almost any language, and it can run in any browser.

That's the same dream Java had in 1995: build it once, run it anywhere. WebAssembly is that dream, open to everyone.

Here are the facts that made me stop and re-read:

It already runs part of your daily life. Figma, Photoshop on the web, AutoCAD Web, Google Meet's video processing, and Microsoft Office Online all use it. By early 2026, about 5.5% of all Chrome page loads used WebAssembly, and that number keeps growing every year.

Java is running inside browsers through it. A project called CheerpJ built a full Java Virtual Machine out of WebAssembly, so Java programs can run in a browser tab with no plugin and no changes to the code.

It's now an official world standard. WebAssembly 3.0 became a W3C standard in September 2025, adding upgrades like built-in garbage collection, so languages like Java and Kotlin can run smaller and faster in the browser.

Now here's where it gets exciting: what's coming next.

A feature called the Component Model is being built so pieces written in different languages can work together inside one program. A Python piece, a Rust piece, and a Java piece, cooperating like teammates instead of competing. Another upgrade, 64-bit memory, opens the door to running very large programs right inside a browser, which experts say could one day include big AI models. And WASI, the system that lets Wasm run outside browsers too, reached a major version in February 2026, with an even bigger 1.0 release expected around the end of 2026 or early 2027.

Now picture where that leads. You open a website, and behind it, code from a dozen different languages works together instantly, safely, on any device, from a cheap phone to a powerful laptop. No installing. No "works on my machine." No choosing sides.

I'm new to all of this, and I still find it wild that the biggest leap in how software runs might not be a brand-new language at all. It might already be here, quietly working inside the browser you're reading this on.

Curious to read more? These are where I learned all this:

The State of WebAssembly 2025 and 2026 (https://platform.uno/blog/the-state-of-webassembly-2025-2026/)
How CheerpJ Works (cheerpj.com/features)

The Quiet Miracle Nobody's Talking About

kumarapu bhagyasri — Fri, 25 Sep 2026 06:20:59 +0000

Over my last two posts, I watched two different teams solve the same challenge making Python ready for the speed that AI era needs. One built something new. One quietly upgraded from within. Watching them side by side made me imagine something bigger: what if the best parts of both approaches combined into something the whole world could build on? Here's where I think this is heading, and why it excites me.

Quick recap, because this post builds directly on my last two. Java made a promise in 1995 Write Once, Run Anywhere and kept it for 30 years using the JVM. Python never made that exact promise, but won the AI era anyway, through ease of use and incredible libraries.

Then two exciting things happened around the same time. Mojo set out to build Python a JVM of its own a bold new language, designed from scratch for AI and GPUs. Python itself upgraded from within, quietly removing a 30-year-old limitation so it could finally use every core in a modern computer, instead of just one.

Both are genuine achievements. Both prove the same thing: the people building our tools are actively working to make the AI era better for everyone, right now, in real time.

Mojo's approach unlocks speed and modern design built specifically for AI chips from day one no old baggage, built for exactly the hardware AI runs on today. Python's approach unlocks safety and continuity every existing library, every tutorial, every line of code millions of developers already know keeps working, just faster. Put together, these aren't really competing paths. They're two halves of the same bridge, being built from opposite sides.

Here's the exciting part. Imagine a future where a language has Python's simplicity the kind a beginner like me could pick up in a weekend combined with Mojo's raw speed and Java's "runs anywhere, no exceptions" reliability. If that happens, the world could look genuinely different. AI development could become radically more accessible a student with a laptop building the same kind of model that today needs a data center, because the language itself stops wasting so much power and time. Innovation could speed up everywhere, not just inside big tech companies small teams and independent developers in any country competing on ideas instead of infrastructure. Energy use could drop, because faster, more efficient code means less electricity spent on training and running everyday apps a real win for the planet, not just for developers. And the gap between learning and building could shrink dramatically, so someone three weeks into a coding journey could go from "I have an idea" to "I built it," without years of specialized systems knowledge standing in the way first.

That's the future I think we're quietly walking towards one upgrade, one experiment, one brave engineer at a time.

I'll say this knowing I've been coding for less than three weeks, so take it as a hopeful bet, not an expert prediction: I think the language that matters most in ten years won't be remembered as the one that beat the others. It'll be remembered as the one that made the question "which language should I use?" stop mattering because it just worked, for everyone, everywhere, instantly. Nobody's finished building that yet. But every post in this series showed me one more piece of it already exists, scattered across different teams who don't even know they're building toward the same future.

I started this series with one honest question: why can't Python have what Java has? I still don't have a clean, final answer. But I found something better proof that the smartest engineers in the world are actively building toward this future, in public, right now. That's genuinely exciting for someone just starting out. It means the story isn't finished. It means there's still room for someone new maybe you, maybe me to add the next piece.

So, here's what I believe, out loud: the next great leap probably won't come from picking a side. It'll come from someone who saw both halves of the bridge and decided to finish building it.

I'll be here writing about it as it happens.

For 30 Years, Python Could Only Use One Lane. Then the Highway Opened.

kumarapu bhagyasri — Thu, 24 Sep 2026 05:56:29 +0000

In my last post, I ended with a clue I hadn't fully explained yet something hiding inside Python itself that almost nobody was talking about. Here it is, for over 30 years, Python had a secret limit. Imagine buying a car with 8 powerful engines, but a rule that only lets you use one at a time forever. That's exactly what happened to one of the most popular programming languages in the world. Then, quietly, in 2025, the rule finally changed.

Part 1: Picking Up Where I Left Off

Last time, I wrote about Mojo a language that tried to fix Python's biggest weaknesses by building something completely new outside of it and ended up giving up on being Python altogether.

I said there was one more piece of the puzzle. Here it is, while Mojo was busy trying to replace Python from the outside, Python was quietly fixing itself from the inside and the story of how is honestly wilder than I expected.

Part 2: The One-Lane Highway

Modern computers are powerful. Most have 4, 8, even 16 separate "cores" think of each core as its own lane on a highway, all capable of doing work at the same time.

But for over 30 years, Python had a strange rule buried deep inside it: no matter how many lanes your computer had, Python was only allowed to drive in one of them. One instruction at a time. Ever.

This rule has a name: GIL — the Global Interpreter Lock. It's been there since Python was created in 1991.

Part 3: Why Nobody Fixed It

You'd think if something's been broken for 30 years, someone would just fix it. But the GIL wasn't a small bug it was baked into how Python worked from the very beginning.

Removing it meant risking that millions of existing Python programs, written over decades, might suddenly break. For a long time, that risk felt bigger than the reward. So, the rule just stayed. Developers learned to work around it instead of removing it.

Part 4: Someone Finally Did It Anyway

In 2023, an engineer named Sam Gross, working at Meta, proposed something nobody had managed to pull off before: a version of Python that could finally use every lane on the highway no GIL, real multi-core power, for the first time ever.

It didn't happen overnight. It rolled out carefully: first as an experimental option in Python 3.13, then officially supported in Python 3.14, released in October 2025.

Part 5: What Actually Changed

Here's the honest trade-off, because nothing is free:

->Regular single-threaded code got a little slower around 5 to 10%.
->But code that needs multiple cores at once the kind AI and data-heavy applications rely on constantly became up to 4 times faster.

That's the difference between a highway with one open lane and a highway with four.

Part 6: What This Means, Next to Mojo's Story

Put my last two posts side by side, and a pattern shows up. Mojo tried the loud way build something new, promise everything, end up leaving Python behind. Python tried the quiet way thirty years of patience, one engineer, one careful fix, no new language required.

Maybe that's the real lesson hiding underneath both of these stories: the loudest, boldest fix isn't always the one that survives. Sometimes it's the slow, quiet one that actually holds.

I'm still just weeks into this world of code and languages. But if there's one thing I'm starting to believe, it's this: the biggest problems don't always need a brand-new answer. Sometimes they just need someone patient enough to fix what's already there.

Python's JVM Was Almost Built. Then It Disappeared.

kumarapu bhagyasri — Wed, 23 Sep 2026 05:36:55 +0000

Back in 1995, Java made a big promise: build your app once, and it'll run on any computer in the world, no changes needed. That promise was called "Write Once, Run Anywhere," and Java has kept it for 30 years straight.

Python never made that promise. It's easy to write, and it's the favorite language of the entire AI world right now — but it doesn't run the same way everywhere, and it's never been as fast as Java either.

In 2023, a language called Mojo showed up with a huge goal: give Python the same superpower Java has. For two years, that was the plan. Then, quietly, it changed. By 2026, Mojo had turned into something else entirely — powerful, yes, but no longer really Python.

This blog walks through exactly what happened, and why it matters for anyone learning to code in the AI era — even someone like me, just two days into learning Java.

Part 1: The Promise Java Kept

Picture a toy that works exactly the same everywhere — build it once in India, and it plays exactly the same way in America, Japan, anywhere on Earth. No changes, no fixing.

That's possible because of the JVM — a special engine hiding inside every computer that understands Java perfectly. Java never talks to your computer directly. It talks to the JVM, and the JVM handles the rest quietly in the background.

That's the whole secret behind "Write Once, Run Anywhere." Java has kept that promise since 1995 — for more than 30 years, without breaking it once.

Part 2: The Promise Python Never Made

Python never got a JVM of its own. It behaves a little differently depending on the computer, the version, and the tools around it. Developers even have a nickname for this headache: "works on my machine."

And yet, even with that flaw, Python became massive — because it's simple to write, and almost every big AI tool today is built on top of it. Python won the AI race without ever solving its oldest problem. That gap is where our story begins.

Part 3: Someone Finally Tried to Fix It

In 2023, an engineer named Chris Lattner — who had already helped build major parts of Apple's Swift language — started something new, called Mojo.

The goal was massive: take Python exactly as it is, make it run as fast as C, and eventually let it run anywhere the way Java does. Build Python its own JVM, from scratch. For two years, that's exactly what the world believed was coming.

Part 4: Then, Quietly, Something Changed

Around 2025, Mojo's own team said something nobody expected: full compatibility with Python "may or may not happen" — and that it's okay if it doesn't.

Wait — okay if it doesn't? The one promise that started this whole project?

By 2026, Mojo had grown into something completely different. Still powerful, still fast, still built for AI chips. But no longer a real stand-in for Python. It tried to hold two promises in one hand — be Python and be as strong as Java — and in the end, it could only keep one.

And that's the part I can't stop thinking about. I'm not writing this as an expert — I started learning Java two days ago, and I don't have all the answers. But maybe that's exactly why I get to ask this out loud: if the smartest engineers in the world couldn't make Python keep both promises at once, does that mean it's actually impossible? Or does it just mean nobody's found the right way yet?

I don't think the answer is "pick a side." Java built its bridge in 1995. Python is still building its own, one quiet fix at a time. And somewhere in between, I think there's something unbuilt — something that doesn't ask "Java or Python?" at all,but makes the question pointless instead.

I already found one clue about how close we are to that answer. It's hiding inside Python itself, and almost nobody's talking about it the way I'm about to. That's next.