49 drones. One pulse. All gone.
Beyond the tech, it shows a shift where scale and economics matter as much as firepower.
Recent conflicts highlight a brutal reality: cheap kamikaze drones cost a fraction of the interceptors sent to destroy them.
The aggressor doesn’t need to win. He just needs to keep the math working in his favour.
And while lasers are much cheaper & great for precision, they only engage one target at a time. Against a swarm, that’s a problem.
HPM doesn’t have that constraint. It covers a volume of space, not a point.
Both are meant to complement kinetic systems (missiles, guns) rather than replace them. The future of air defense is clearly layered, with each technology filling a different niche.
For India, this is very pertinent. Importing solutions reactively isn't a strategy.
Building indigenous, AI-enabled HPM and laser capability early is.
We have the talent. We just need faster procurement, patient capital, and institutions that let deep-tech startups scale.
On a personal note, I’ve recently taken on the role of Chairman of iCreate, a leading deep-tech incubator in Gujarat.
I would like it to be the home for exactly this kind of innovation.
If you’re building the technologies that will define tomorrow’s defense, do check it out at https://t.co/mzVV75JJae
🚨 Sam Altman literally gave a 43-minute masterclass on turning ideas into billion-dollar companies.
Most people will never watch it.
And instead of hype, he broke down what actually makes startups work.
No fluff. Just reality.
He explained that ideas don’t matter nearly as much as execution. The difference between something small and something massive isn’t the idea it’s how relentlessly it’s built and improved over time.
He also emphasized that the best founders don’t chase everything. They focus on one thing that truly matters and push it forward with extreme clarity. Distraction kills more startups than competition ever will.
And then there’s scale. Truly big companies aren’t built for a niche they solve problems that millions of people care about. If the market isn’t large enough, the outcome won’t be either.
His biggest insight? Startups don’t win because they’re smarter they win because they stay in the game longer and iterate faster.
That’s why this masterclass stands out.
Because while most people are waiting for the perfect idea…
The best ones are already building.
There's a physicist at Stanford named Safi Bahcall who modeled this exact principle and the math is wild.
He calls it "phase transitions in human networks." When you're stationary, your probability of a lucky event is limited to your existing surface area: the people you already know, the places you already go, the ideas you've already been exposed to. Your opportunity window is fixed.
When you move, your collision rate with new nodes in a network increases nonlinearly. Double your movement (new conversations, new cities, new projects) and your probability of a serendipitous encounter doesn't double. It roughly quadruples. Because each new node connects you to their entire network, not just to them.
Richard Wiseman ran a 10-year study at the University of Hertfordshire tracking self-described "lucky" and "unlucky" people. The single biggest differentiator wasn't IQ, education, or family money. Lucky people scored significantly higher on one trait: openness to experience. They talked to strangers more, varied their routines more, and said yes to invitations at nearly twice the rate.
The "unlucky" group followed the same routes, ate at the same restaurants, and talked to the same 5 people. Their networks were closed loops. No new inputs, no new collisions.
Luck isn't random. Luck is surface area. And surface area is a function of movement.
The lobster emoji is doing more work than most people realize. Lobsters grow by shedding their shell when it gets too tight. The growth requires a period of total vulnerability. No protection, no armor, soft body exposed to the ocean.
That's the cost of movement nobody posts about. You have to be uncomfortable first. The new shell only hardens after you've already moved.
What if you could search 50M satellite image embeddings with no server, no database, and no API?
Part 2 of @calebrob6 and my series on Compressing Earth Embeddings is live at https://t.co/APYzK69GKP. We binarized the global Clay v1.5 Sentinel-2 embeddings from 183GB → 7GB and built TerraBit — a retrieval demo that runs with zero backend. DuckDB-WASM, cleverly partitioned GeoParquet on S3, and brute-force Hamming in an in-memory index makes the experience feel too good to be true.
If you also wondered how Google's TurboQuant fares on compressing earth embeddings -- don't worry, we got you covered in the blog -- hint: TurboQuant int4 is a solid choice
@TheProjectUnity Looking forward to hearing more about your take on more use cases of SAR.
Would love to have a 10 min. Zoom call to know more about your domain expertise.
@yohaniddawela I'm on with you. I've just started working on SAR imaging in one of the Indian startups called GalaxEye. They just launched a satellite for earth observation.
By surrounding yourself with bunch of smart people, you can become better at communication 🙌
Let's try to meet new people even if conversation becomes awkward...
Thank you @nikhilkamathcio, for the advice