Hashir Haroon

Tech Enthusiast,
Expert at doing nothing

[Ronit Pereira @CAronitpereira]

Charlie Munger and Warren Buffett on Ethanol blending in Petrol:

“Ethanol blending is a very stupid way to try and solve an energy problem.”

“It takes more fossil fuel energy to create ethanol than you can get out of ethanol you’ve created.”

- Charlie Munger. 2006.

[Trung Phan @TrungTPhan]

Lee Kuan Yew:

“Air conditioning was a most important invention for us, perhaps one of the signal inventions of history. It changed the nature of civilization by making development possible in the tropics. Without air conditioning you can work only in the cool early-morning hours or at dusk. The first thing I did upon becoming prime minister was to install air conditioners in buildings where the civil service worked. This was key to public efficiency."

[Mario Nawfal @MarioNawfal]

🇸🇬🇪🇺 Singapore's founding PM Lee Kuan Yew once called air conditioning one of the most important inventions in history.

Europe is currently finding out why.

His argument was simple. In hot climates, people could only work efficiently during the cooler hours of the day, which capped what any tropical economy could ever achieve.

"Air conditioning was a most important invention for us, perhaps one of the signal inventions of history. It changed the nature of civilisation by making development possible in the tropics."

One of his first moves as prime minister was to put air conditioners in government offices, betting a comfortable civil service was a productive one.

Now a continent where only about 20% of homes have AC is racing to catch up, with shipments to Spain and France up 108% this year as a deadly heatwave bakes the region.

Sources: Reuters, International Business Times / Writer: Julie

[Pavel Durov @durov]

India’s IT ministry banned Telegram for one week because some users shared leaked exam questions.

This punishes 150M+ ordinary Telegram users in India — not the insiders who leaked the exam materials.

And the ban hasn't stopped anything. The leaks just moved to other apps.

[DogeDesigner @cb_doge]

BREAKING: Elon Musk's full speech ahead of SpaceX IPO

[DogeDesigner @cb_doge]

SpaceX IPO listing at NASDAQ

Go for launch. 🚀

[Phil Trubey @PTrubey]

Oh my, my dunk of JerryRigs is going viral. Well, let's use this as a teaching moment.

First, realize when people say "data centers in space" they aren't talking about lofting up giant Costco sized buildings.

SpaceX and Starcloud are proposing satellites that each have the compute capacity of about one AI rack, or what the guy is pushing in the picture below.

These individual sats won't be connected together in space to run large training jobs, they'll only be used for inference - answering people's questions, running agentic tasks, etc.

So each satellite has relatively tractable power and cooling requirements. There will be a couple of largish solar panels attached to give it 24x7 cheap power (remember that you get like 5x more solar energy per panel in space than on Earth). And a smaller radiator that will radiate away waste heat into the vastness of space.

Both the power and cooling technologies are simple, well tested and cost nothing to operate, unlike power and cooling on earth.

In particular, cooling on earth requires extra power to run powerful water pumps to move fluid all over the place and then to dump the heat into a relatively hot atmosphere.

Yes, space based cooling can only reject heat via radiative cooling, but it is doing it in the vacuum of space at -454 °F (-270 °C, 3 K) versus about 77 °F (25 °C, 298 K) on Earth, so that helps a lot. Point being that cooling in space has only a single upfront cost of building a passive radiator.

But what about the overall cost, you ask? Well, think about all the things you don't need to build now. That rack the guy is pushing around weighs 1,400 pounds mostly because of all the metal required to support everything against Earth's gravity. Things can be built far more flimsy in space since they are in zero gravity.

Also that rack has a bunch of power electronics and fans, neither of which are needed in space. Indeed, that entire building those racks sit in doesn't need to be built. All that fiber cabling isn't needed (lasers in space take the place, no need for cables). Giant utility transformers and a small army of step down transformers and battery packs don't need to be built. The land doesn't need to be bought. The permits don't need to be acquired. The supposedly huge amount of water used doesn't need to be provisioned (it's a tiny amount, but the detractors love to bring it up).

There are in fact giant cost savings going into space.

What about launch costs? That is small as well. Starship is fully reusable. The majority of launch costs are natural gas and liquified oxygen extracted from the air. That's it. Cheap access to space, really cheap I mean, is a huge unlock.

I was initially shaking my head when I first heard about Elon's "crazy" idea of space based compute, but the more you look into it, it is far less crazy and more doable and practical. At least for SpaceX.

[Shashi Tharoor @ShashiTharoor]

My thoughts on the #CockroachJantaParty and related issues:

[Kerala Trends @KeralaTrends2]

𝐊𝐞𝐫𝐚𝐥𝐚’𝐬 𝐀𝐈 𝐞𝐫𝐚 𝐛𝐞𝐠𝐢𝐧𝐬 🤖

Kerala becomes the 𝐟𝐢𝐫𝐬𝐭 𝐬𝐭𝐚𝐭𝐞 𝐢𝐧 𝐈𝐧𝐝𝐢𝐚 to introduce a dedicated cabinet-level portfolio for Artificial Intelligence.

[Elon Musk @elonmusk]

Yes, I need to make sure SpaceX stays focused on making life multiplanetary and extending consciousness to the stars, not pandering to someone’s bullshit quarterly earnings bonus!

Obviously, IF SpaceX succeeds in this absurdly difficult goal, it will be worth many orders of magnitude more than the economy of Earth, but don’t expect entirely smooth sailing along the way.

[Indian Tech & Infra @IndianTechGuide]

🚨 India has become the hottest country on earth this week, with an overwhelming 92 of the world's 100 hottest cities located in the country.

[Ming @tslaming]

BREAKING 🚨 TESLA HAS PATENTED A "MATHEMATICAL CHEAT CODE" THAT FORCES CHEAP 8-BIT CHIPS TO RUN ELITE 32-BIT AI MODELS AND REWRITES THE RULES OF SILICON 🐳

How does a Tesla remember a stop sign it hasn’t seen for 30 seconds, or a humanoid robot maintain perfect balance while carrying a heavy, shifting box?

It comes down to Rotary Positional Encoding (RoPE)—the "GPS of the mind" that allows AI to understand its place in space and time by assigning a unique rotational angle to every piece of data.

Usually, this math is a hardware killer. To keep these angles from "drifting" into chaos, you need power-hungry, high-heat 32-bit processors (chips that calculate with extreme decimal-point precision).

But Tesla has engineered a way to cheat the laws of physics. Freshly revealed in patent US20260017019A1, Tesla’s "MIXED-PRECISION BRIDGE" is a mathematical translator that allows inexpensive, power-sipping 8-bit hardware (which usually handles only simple, rounded numbers) to perform elite 32-bit rotations without dropping a single coordinate.

This breakthrough is the secret "Silicon Bridge" that gives Optimus and FSD high-end intelligence without sacrificing a mile of range or melting their internal circuits. It effectively turns Tesla’s efficient "budget" hardware into a high-fidelity supercomputer on wheels.

📉 The problem: the high cost of precision

In the world of self-driving cars and humanoid robots, we are constantly fighting a war between precision and power. Modern AI models like Transformers rely on RoPE to help the AI understand where objects are in a sequence or a 3D space.

The catch is that these trigonometric functions (sines and cosines) usually require 32-bit floating-point math—imagine trying to calculate a flight path using 10 decimal places of accuracy.

If you try to cram that into the standard 8-bit multipliers (INT8) used for speed (which is like rounding everything to the nearest whole number), the errors pile up fast. The car effectively goes blind to fine details.

For a robot like Optimus, a tiny math error means losing its balance or miscalculating the distance to a fragile object. To bridge this gap without simply adding more expensive chips, Tesla had to fundamentally rethink how data travels through the silicon.

🛠️ Tesla's solution: the logarithmic shortcut & pre-computation

Tesla’s engineers realized they didn't need to force the whole pipeline to be high-precision. Instead, they designed the Mixed-Precision Bridge.

They take the crucial angles used for positioning and convert them into logarithms. Because the "dynamic range" of a logarithm is much smaller than the original number, it’s much easier to move that data through narrow 8-bit hardware without losing the "soul" of the information.

It’s a bit like dehydrating food for transport; it takes up less space and is easier to handle, but you can perfectly reconstitute it later.

Crucially, the patent reveals that the system doesn't calculate these logarithms on the fly every time. Instead, it retrieves pre-computed logarithmic values from a specialized "cheat sheet" (look-up storage) to save cycles.

By keeping the data in this "dehydrated" log-state, Tesla ensures that the precision doesn't "leak out" during the journey from the memory chips to the actual compute cores. However, keeping data in a log-state is only half the battle; the chip eventually needs to understand the real numbers again.

🏗️ The recovery architecture: rotation matrices & Horner’s method

When the 8-bit multiplier (the Multiplier-Accumulator or MAC) finishes its job, the data is still in a "dehydrated" logarithmic state. To bring it back to a real angle theta without a massive computational cost, Tesla’s high-precision ALU uses a Taylor-series expansion optimized via Horner’s Method.

This is a classic computer science trick where a complex equation (like an exponent) is broken down into a simple chain of multiplications and additions.

By running this in three specific stages—multiplying by constants like 1/3 and 1/2 at each step—Tesla can approximate the exact value of an angle with 32-bit accuracy while using a fraction of the clock cycles.

Once the angle is recovered, the high-precision logic generates a Rotation Matrix (a grid of sine and cosine values) that locks the data points into their correct 3D coordinates.

This computational efficiency is impressive, but Tesla didn't stop at just calculating faster; they also found a way to double the "highway speed" of the data itself.

🧩 The data concatenation: 8-bit inputs to 16-bit outputs

One of the most clever hardware "hacks" detailed in the patent is how Tesla manages to move 16-bit precision through an 8-bit bus. They use the MAC as a high-speed interleaver—effectively a "traffic cop" that merges two lanes of data.

It takes two 8-bit values (say, an X-coordinate and the first half of a logarithm) and multiplies one of them by a power of two to "left-shift" it.

This effectively glues them together into a single 16-bit word in the output register, allowing the low-precision domain to act as a high-speed packer for the high-precision ALU to "unpack".

This trick effectively doubles the bandwidth of the existing wiring on the chip without requiring a physical hardware redesign. With this high-speed data highway in place, the system can finally tackle one of the biggest challenges in autonomous AI: object permanence.

🧠 Long-context memory: remembering the stop sign

The ultimate goal of this high-precision math is to solve the "forgetting" problem. In previous versions of FSD, a car might see a stop sign, but if a truck blocked its view for 5 seconds, it might "forget" the sign existed.

Tesla uses a "long-context" window, allowing the AI to look back at data from 30 seconds ago or more.

However, as the "distance" in time increases, standard positional math usually drifts. Tesla's mixed-precision pipeline fixes this by maintaining high positional resolution, ensuring the AI knows exactly where that occluded stop sign is even after a long period of movement.

The RoPE rotations are so precise that the sign stays "pinned" to its 3D coordinate in the car's mental map. But remembering 30 seconds of high-fidelity video creates a massive storage bottleneck.

⚡ KV-cache optimization & paged attention: scaling memory

To make these 30-second memories usable in real-time without running out of RAM, Tesla optimizes the KV-cache (Key-Value Cache)—the AI's "working memory" scratchpad.

Tesla’s hardware handles this by storing the logarithm of the positions directly in the cache. This reduces the memory footprint by 50% or more, allowing Tesla to store twice as much "history" (up to 128k tokens) in the same amount of RAM.

Furthermore, Tesla utilizes Paged Attention—a trick borrowed from operating systems. Instead of reserving one massive, continuous block of memory (which is inefficient), it breaks memory into small "pages".

This allows the AI5 chip to dynamically allocate space only where it's needed, drastically increasing the number of objects (pedestrians, cars, signs) the car can track simultaneously without the system lagging.

Yet, even with infinite storage efficiency, the AI's attention mechanism has a flaw: it tends to crash when pushed beyond its training limits.

🔒 Pipeline integrity: the "read-only" safety lock

A subtle but critical detail in the patent is how Tesla protects this data. Once the transformed coordinates are generated, they are stored in a specific location that is read-accessible to downstream components but not write-accessible by them.

Furthermore, the high-precision ALU itself cannot read back from this location.

This one-way "airlock" prevents the system from accidentally overwriting its own past memories or creating feedback loops that could cause the AI to hallucinate. It ensures that the "truth" of the car's position flows in only one direction: forward, toward the decision-making engine.

🌀 Attention sinks: preventing memory overflow

Even with a lean KV-cache, a robot operating for hours can't remember everything forever. Tesla manages this using Attention Sink tokens.

Transformers tend to dump "excess" attention math onto the very first tokens of a sequence, so if Tesla simply used a "sliding window" that deleted old memories, the AI would lose these "sink" tokens and its brain would effectively crash.

Tesla's hardware is designed to "pin" these attention sinks permanently in the KV-cache. By keeping these mathematical anchors stable while the rest of the memory window slides forward, Tesla prevents the robot’s neural network from destabilizing during long, multi-hour work shifts.

While attention sinks stabilize the "memory", the "compute" side has its own inefficiencies—specifically, wasting power on empty space.

🌫️ Sparse tensors: cutting the compute fat

Tesla’s custom silicon doesn't just cheat with precision; it cheats with volume. In the real world, most of what a car or robot sees is "empty" space (like clear sky).

In AI math, these are represented as "zeros" in a Sparse Tensor (a data structure that ignores empty space). Standard chips waste power multiplying all those zeros, but Tesla’s newest architecture incorporates Native Sparse Acceleration.

The hardware uses a "coordinate-based" system where it only stores the non-zero values and their specific locations. The chip can then skip the "dead space" entirely and focus only on the data that matters—the actual cars and obstacles.

This hardware-level sparsity support effectively doubles the throughput of the AI5 chip while significantly lowering the energy consumed per operation.

🔊 The audio edge: Log-Sum-Exp for sirens

Tesla’s "Silicon Bridge" isn't just for vision—it's also why your Tesla is becoming a world-class listener. To navigate safely, an autonomous vehicle needs to identify emergency sirens and the sound of nearby collisions using a Log-Mel Spectrogram approach (a visual "heat map" of sound frequencies).

The patent details a specific Log-Sum-Exp (LSE) approximation technique to handle this. By staying in the logarithm domain, the system can handle the massive "dynamic range" of sound—from a faint hum to a piercing fire truck—using only 8-bit hardware without "clipping" the loud sounds or losing the quiet ones.

This allows the car to "hear" and categorize environmental sounds with 32-bit clarity. Of course, all this high-tech hardware is only as good as the brain that runs on it, which is why Tesla's training process is just as specialized.

🎓 Quantization-aware training: pre-adapting the brain

Finally, to make sure this "Mixed-Precision Bridge" works flawlessly, Tesla uses Quantization-Aware Training (QAT).

Instead of training the AI in a perfect 32-bit world and then "shrinking" it later—which typically causes the AI to become "drunk" and inaccurate—Tesla trains the model from day one to expect 8-bit limitations.

They simulate the rounding errors and "noise" of the hardware during the training phase, creating a neural network that is "pre-hardened". It’s like a pilot training in a flight simulator that perfectly mimics a storm; when they actually hit the real weather in the real world, the AI doesn’t "drift" or become inaccurate because it was born in that environment.

This extreme optimization opens the door to running Tesla's AI on devices far smaller than a car.

🚀 The strategic roadmap: from AI5 to ubiquitous edge AI

This patent is not just a "nice-to-have" optimization; it is the mathematical prerequisite for Tesla’s entire hardware roadmap. Without this "Mixed-Precision Bridge", the thermal and power equations for next-generation autonomy simply do not work.

It starts by unlocking the AI5 chip, which is projected to be 40x more powerful than current hardware. Raw power is useless if memory bandwidth acts as a bottleneck.

By compressing 32-bit rotational data into dense, log-space 8-bit packets, this patent effectively quadruples the effective bandwidth, allowing the chip to utilize its massive matrix-compute arrays without stalling.

This efficiency is critical for the chip's "half-reticle" design, which reduces silicon size to maximize manufacturing yield while maintaining supercomputer-level throughput.

This efficiency is even more critical for Tesla Optimus, where it is a matter of operational survival. The robot runs on a 2.3 kWh battery (roughly 1/30th of a Model 3 pack).

Standard 32-bit GPU compute would drain this capacity in under 4 hours, consuming 500W+ just for "thinking".

By offloading complex RoPE math to this hybrid logic, Tesla slashes the compute power budget to under 100W. This solves the "thermal wall", ensuring the robot can maintain balance and awareness for a full 8-hour work shift without overheating.

This stability directly enables the shift to End-to-End Neural Networks. The "Rotation Matrix" correction described in the patent prevents the mathematical "drift" that usually plagues long-context tracking.

This ensures that a stop sign seen 30 seconds ago remains "pinned" to its correct 3D coordinate in the World Model, rather than floating away due to rounding errors.

Finally, baking this math into the silicon secures Tesla's strategic independence. It decouples the company from NVIDIA’s CUDA ecosystem and enables a Dual-Foundry Strategy with both Samsung and TSMC to mitigate supply chain risks.

This creates a deliberate "oversupply" of compute, potentially turning its idle fleet and unsold chips into a distributed inference cloud that rivals AWS in efficiency.

But the roadmap goes further. Because this mixed-precision architecture slashes power consumption by orders of magnitude, it creates a blueprint for "Tesla AI on everything".

It opens the door to porting world-class vision models to hardware as small as a smart home hub or smartphone. This would allow tiny, cool-running chips to calculate 3D spatial positioning with zero latency—bringing supercomputer-level intelligence to the edge without ever sending private data to a massive cloud server.

[NASA Administrator Jared Isaacman @NASAAdmin]

@mmealling @rookisaacman We are going to build a Moon base

[Andrej Karpathy @karpathy]

Agency > Intelligence

I had this intuitively wrong for decades, I think due to a pervasive cultural veneration of intelligence, various entertainment/media, obsession with IQ etc. Agency is significantly more powerful and significantly more scarce. Are you hiring for agency? Are we educating for agency? Are you acting as if you had 10X agency?

Grok explanation is ~close:

“Agency, as a personality trait, refers to an individual's capacity to take initiative, make decisions, and exert control over their actions and environment. It’s about being proactive rather than reactive—someone with high agency doesn’t just let life happen to them; they shape it. Think of it as a blend of self-efficacy, determination, and a sense of ownership over one’s path.

People with strong agency tend to set goals and pursue them with confidence, even in the face of obstacles. They’re the type to say, “I’ll figure it out,” and then actually do it. On the flip side, someone low in agency might feel more like a passenger in their own life, waiting for external forces—like luck, other people, or circumstances—to dictate what happens next.

It’s not quite the same as assertiveness or ambition, though it can overlap. Agency is quieter, more internal—it’s the belief that you *can* act, paired with the will to follow through. Psychologists often tie it to concepts like locus of control: high-agency folks lean toward an internal locus, feeling they steer their fate, while low-agency folks might lean external, seeing life as something that happens *to* them.”

[Manifest_Lord @Manifest_Lord]

The brain doesn't burn out from stress, it burns out from emotional overthinking...

Here's the cure 🧵

[Mario Nawfal @MarioNawfal]

🇺🇸 EX-CIA OFFICER: “THEY CAN MAKE YOU DRIVE INTO A TREE AND CALL IT SUICIDE”

John Kiriakou, once inside the CIA, just reignited a massive debate about digital privacy, citing the 2017 Vault 7 leaks.

Phones, cars, TVs, laptops - he says none of them are as private as we assume.

“There was a dramatic leak in 2017 that the CIA came to call the Vault 7 disclosures - gigabytes worth of documents leaked by a CIA technology engineer.

What he told us was that the CIA can intercept anything from anyone.

They can remotely take control of your car to make you drive off a bridge into a tree to make you kill yourself and make it look like an accident.

They can take over your smart television and turn the speaker into a microphone so that they can listen to what's being said in the room even when the TV is turned off.”

Source: LADbible TV

[ANI @ANI]

Government removes mandatory pre-installation of Sanchar Saathi App.

"The Government with an intent to provide access to cyber security to all citizens had mandated pre-installation of Sanchar Saathi app on all smartphones. The app is secure and purely meant to help citizens from bad actors in the cyber world...Given Sanchar Saathi’s increasing acceptance, Government has decided not to make the pre- installation mandatory for mobile manufacturers," says Ministry of Communications

[Dhruv Rathee @dhruv_rathee]

Good job Apple!

Keep it up! 👍

[The Telegraph @ttindia]

The Indian government has confidentially ordered companies including Apple, Samsung and Xiaomi to preload their phones with an app called Sanchar Saathi, or Communication Partner, within 90 days.

The app is intended to track stolen phones, block them and prevent them from being misused

https://t.co/q6pzlVBpt8

[MacRumors.com @MacRumors]

India Orders Apple to Preinstall Government Security App on All iPhones https://t.co/xDSYV8cb4a