The most powerful rocket ever built launches today.
SpaceX Starship Flight 11 lifts off from Starbase, Texas at 6:15 PM CT. 121m tall, 39 engines, 7,500 tons of thrust—3X Saturn V. This is IFT-11, the final Block 2 test before the even larger V3.
If successful: launch costs drop from $67M to <$10M per flight. That's 85% cheaper access to space.
Here's the engineering that makes it possible:
STARSHIP: DESIGN & SPECS
Starship is a two-stage monster. Fully stacked: 121 meters tall, 5,000 tons at liftoff.
The skin? 301 stainless steel, just 3-4 millimeters thick—two credit cards stacked. Why steel? It's cheap ($3/kg vs $130 for carbon fiber) and gets stronger when supercooled.
It burns methalox—4,600 tons total. Thrust at liftoff: 7,500 tons—THREE times the Saturn V.
The numbers: 33 Raptor engines on the booster, 6 on the upper stage. 39 engines firing at once. Payload: 150 tons to orbit. Falcon 9 does 22 tons for comparison.
RAPTOR ENGINES: MASS-PRODUCING THE IMPOSSIBLE
The Raptor engine uses full-flow staged combustion—the most efficient rocket cycle ever flown. Raptor 3: 30 megapascals chamber pressure, 280 tons of thrust each.
Here's what's insane: SpaceX has built over 1,000 of these by 2025. They're mass-producing rocket engines like cars.
Why methane? You can make it on Mars. CO2 from the atmosphere + hydrogen = methane and oxygen. 95% efficient with solar power. Mars becomes its own gas station.
THE HARDEST PROBLEMS
Now the hard parts.
First: the heat shield. 18,000 hexagonal silica tiles protecting the ship during reentry at 1,400 to 1,600 degrees Celsius.
Early flights lost tiles—plasma melted the steel underneath. The fixes? Backup ablative layers, redesigned flaps, and metallic tiles with active cooling. Goal: 100 reuses.
Second: catching a rocket with chopsticks. IFT-5 proved it works. Super Heavy does a boostback burn, reverses course, and those giant mechanical arms just grab it out of the air.
THE JOURNEY: FROM EXPLOSIONS TO SUCCESS
The road here has been brutal. IFT-1 in April 2023: giant explosion at staging. IFT-4: first full-duration burns, both stages survived. IFT-5: the booster catch that broke the internet.
Between each flight, they tweak over 1,000 variables. Tile gap sizes. Flap hinge angles. Engine ignition sequences. This is what 'move fast and break things' looks like when you're building rockets.
THE ROADMAP: MOON, MARS & BEYOND
The roadmap is aggressive:
2025: Full orbital refueling demonstration
2026: Uncrewed Mars cargo mission and Artemis lunar landing
2028: First crewed Mars landing—aspirational, but that's the goal
And here's why this matters: if Starship works, the cost per kilogram to orbit drops below $100. Right now, Falcon 9—already the cheapest rocket flying—costs $2,700 per kilogram. That's a 27-times improvement.
FLIGHT 11: WHAT'S BEING TESTED TODAY
So here we are. Flight 11 is testing a new landing burn configuration, stress-testing the heat shield with intentionally missing tiles, simulating Starlink deployments, and attempting in-space Raptor relights for future Mars transfers.
Starship isn't just a rocket—it's the key that unlocks moon bases, Mars colonies, and our multiplanetary future.
And it's happening... right... NOW.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
On ProofBench-Advanced—where models prove formal mathematical theorems—GPT-5 scores 20%. Gemini Deep Think IMO Gold hits 65.7%. DeepSeek Math V2 (Heavy) scores 61.9%.
That's second place—but Gemini isn't open source.
This is the best open math model in the world. And DeepSeek released the weights. Apache 2.0.
Here's what they discovered:
1/ Why Normal LLMs Break on Real Math
Most large language models are great at sounding smart, but:
- They’re rewarded for the final answer, not the reasoning.
- If they accidentally land on the right number with bad logic, they still get full credit.
- Over time they become “confident liars”: fluent, persuasive, and sometimes wrong.
That’s fatal for real math, where the proof is the product.
To fix this, DeepSeek Math V2 changes what the model gets rewarded for: not just being right, but being rigorously right.
2/ The Core Idea: Generator + Verifier
Instead of one model doing everything, DeepSeek splits the job: 1. Generator – the “mathematician”
- Produces a full, step-by-step proof.
2. Verifier – the “internal auditor”
- Checks the proof for logical soundness.
- Ignores the final answer. It only cares about the reasoning.
This creates an internal feedback loop:
One model proposes, the other critiques.
Battery storage is already scaling—159 GW deployed globally, 926 GW projected by 2033.
Renewables needed it first. Now AI needs it too.
Tesla is deploying Megapacks at data centers. China is deploying 30 GW this year, integrating storage directly into AI buildout.
Why? Data centers can’t scale without solving three problems:
- 7-year interconnection queues
- power quality GPUs demand
- backup without diesel permits
Batteries solve all three ↓
Why AI Data Centers Need Batteries
Interconnection is broken. Utility connection takes 7+ years. Batteries bypass it. Skip the queue.
GPUs break traditional power. Training loads swing 90% at 30 Hz. Batteries smooth it in 30 milliseconds.
Diesel doesn’t scale. Permitting is hard. For 20-hour backup, batteries are cost-competitive.
The math: ~1% of data center capex.
The Scale
Global capacity: 159 GW by end-2024. Up 85% from 86 GW in 2023. Projected: 926 GW by 2033.
Cost curve: $115/kWh in 2024, down 84% from $723/kWh in 2013. Still falling.
Economics flipped. Solar plus 4-hour storage runs ~$76/MWh. New gas peakers cost $80-120/MWh.
The universe isn’t just expanding — it’s speeding up
13.8 billion years after the Big Bang, astronomers expected gravity to slowly slow cosmic expansion. Instead, when they looked deep into space, they found the opposite: the universe is accelerating.
Whatever drives that acceleration makes up ~70% of the cosmos.
We call it dark energy.
We can measure it. We can see its effects. So what is it, really?
How we figured this out
Cepheid stars: the distance trick
Henrietta Leavitt discovered that certain stars (Cepheid variables) get brighter and dimmer with a regular period — and that period tells you their true brightness → lets us measure distance to faraway galaxies.
Redshift: galaxies on the move
Vesto Slipher used spectra of galaxies to show many had their light stretched to longer, redder wavelengths.
Redder → moving away faster.
Hubble & the expanding universe
Edwin Hubble and Milton Humason combined Cepheid distances with redshift and found a pattern:
>The farther a galaxy is, the faster it’s receding.
That’s the Hubble–Lemaître law: clear evidence that the universe is expanding.
The shock: expansion is accelerating
In the 1990s, two teams studied Type Ia supernovae, stellar explosions so consistent in brightness that they act like “standard candles.”
By comparing how bright they should be to how bright they look, you can get distance.
By measuring redshift, you get how fast they’re moving away.
The surprise:
• The supernovae were dimmer and farther away than expected.
• That only made sense if, over billions of years, the universe’s expansion had sped up instead of slowing down.
This cosmic acceleration is what we now attribute to dark energy.
🚨The White House just launched the Genesis Mission — a Manhattan Project for AI
The Department of Energy will build a national AI platform on top of U.S. supercomputers and federal science data, train scientific foundation models, and run AI agents + robotic labs to automate experiments in biotech, critical materials, nuclear fission/fusion, space, quantum, and semiconductors.
Let’s unpack what this order actually builds, and how it could rewire the AI, energy, and science landscape over the next decade:
1/ At the core is a new American Science and Security Platform.
DOE is ordered to turn the national lab system into an integrated stack that provides:
• HPC for large-scale model training, simulation, inference
• Domain foundation models across physics, materials, bio, energy
• AI agents to explore design spaces, evaluate experiments, automate workflows
• Robotic/automated labs + production tools for AI-directed experiments and manufacturing
National-scale AI scientist + AI lab tech as infrastructure.
2/ The targets are very explicit and very strategic.
Within 60 days, DOE has to propose at least 20 “national challenges” in: