Oliver Richers

@sonam_murarkar I have this: alias ..='cd ..' alias ...='cd ../..' alias ....='cd ../../..' alias .....='cd ../../../..'

Just spotted a glossy gold Cybercab on the streets of Austin, TX - with @JoshWest247

Now that @Tesla has publicly announced the final Cybercab in bright, glossy gold and that production SOP (Start or Production) has happened, here are some views of several at Giga Texas today in the outbound lot. As you can visually see, these are very noticeably different from the wrapped engineering versions. These look so good out in the sun for the first time!

De toekomst van mobiliteit is aangebroken FSD Supervised has been approved in the Netherlands 🇳🇱 & will begin rolling out in the country shortly!  Trained on billions of kilometers of real-world driving data, it can drive you almost anywhere under your supervision – from residential roads to city streets & highways No other vehicle can do this.  We're excited to bring FSD Supervised to more European countries soon

Claude watching me write code manually after I hit the daily limit

@_devJNS HTML

which one's better for backend?

@Govindtwtt I don't get the problem. Just wait 6 months and there is a better AI that can cleanup the mess for you. 6 months later, you will get an even better one. I have seen many human coders producing way worse code than AI.

Software engineers shouldn't fear being replaced by AI. They should fear being asked to maintain the sprawling mess of AI-generated legacy code their employer's systems will soon run on. Because that one will actually happen.

@theo You should give Claude smaller tasks, max 5 Minutes.

Just let Opus go for over an hour on a new feature. When it was done, I asked how I can test it. 20 minutes later, it realized I can't test it because it did the whole thing entirely wrong. Idk how you guys use this model every day for real work 🙃

Together with RDW, we have officially completed the final vehicle testing phase for Full Self-Driving (Supervised) and have submitted all documentation required for the UN R-171 approval + Article 39 exemptions. The RDW team is now reviewing the documentation and test results package internally. They have communicated the expected approval for Netherlands date of 4/10, shifting from 3/20 previously and we look forward to successful completion of this cooperation.  Following the Netherlands’ approval, European countries will be able to recognize this approval nationally. We are anticipating a possible EU-wide approval during the summer. Over the past 18 months, this approval has involved a series of intense documentation, development, testing, research & audits. Including but certainly not limited to: – 1,600,000+ km of FSD (Supervised) testing on EU roads – 13,000+ customer sales ride-alongs – 4,500+ track test scenario executions – Thousands of pages of written documentation for 400+ compliance requirements – Dozens of research studies into safety performance/results We're extremely proud of the work conducted with the RDW team up until this point. We very much look forward to the approval in April, and sharing FSD (Supervised) with our patient EU customers!

@unclebobmartin I agree. For years, I prefered build over buy, and now I am finally right. I was just too early. 🤣

AI agents have vastly changed the build vs buy calculus. The vast majority of tools can be built at virtually no cost.

Wenn man den "Experten" glaubt, dann ist das nicht möglich. 😄

@thatstraw Tip: type a few characters and press page-up. It will try to find your last command starting with what you entered. If page-up does not work for you, check /etc/inputrc or ask your LLM of choice for help.

Linux users: "I like to type out commands!" Also Linux users:

Here we go!

I am on the road without safety monitor in Austin, Texas

😃

@milan_milanovic Startup implementing quick sort with micro services:

Kubernetes killed more startups than server crashes ever did You don't have Spotify's scale. You have 8 engineers and a single server that's running fine But you watched a KubeCon talk, and now you've got 23 YAML files, a Helm chart nobody fully understands, and engineers debugging pod evictions instead of buildinga product Your "cloud-native infrastructure" is just a cloud bill with extra complexity A $50/month VM can handle millions of requests. Your startup will run out of money debugging networking issues long before you need horizontal pod autoscaling The best infrastructure decision is often the simplest one

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.

Tesla 2025 recap See y’all in 2026 – the best is yet to come 😀

GOOD NEWS 🚨 TESLA HAS SOLVED THE WIRELESS CHARGING PARADOX WITH UNBALANCED DUTY CYCLES ⚡️ When Elon Musk unveiled the Cybercab without a charge port, the automotive world collectively raised an eyebrow. The idea was audacious: a vehicle completely reliant on wireless charging, a technology historically plagued by inefficiency and safety concerns. But with the release of patent US 20250357799 A1 on November 20, 2025, the other shoe has finally dropped. This filing isn't just paperwork; it’s the engineering answer key that explains how Tesla plans to pull off the "no-plug" revolution without frying electronics or wasting massive amounts of energy. We are finally looking at the physics that turns the Robotaxi dream into a viable reality. ⚖️ The problem: The hidden cost of wireless power To understand why this patent matters, you have to understand the messy reality of wireless power. Sending electricity through the air relies on magnetic fields dancing between a pad on the ground and a pad on the car. Ideally, all that energy goes straight into the battery. In reality, big induction coils act like unintended capacitors, allowing "leakage current" to escape and flow into the car's chassis or the ground equipment. It is a classic case of electrical waste, but the consequences are worse than just a slightly higher electric bill. This leakage creates electromagnetic noise that can interfere with sensitive electronics and, in high-power scenarios, pose safety risks. The culprit is usually the control strategy; traditional methods try to regulate power by briefly "shorting" the circuit, which inadvertently causes the common mode voltage—the electrical baseline of the system—to spike wildly. 🔗 Tesla's solution: Unbalanced duty cycles Tesla’s engineers found a way to fix this, and it requires rethinking the rhythm of the charge. Instead of using the industry-standard method that frequently pauses at a "medium" or zero-voltage state to throttle power, Tesla’s new system refuses to sit in the middle. It toggles directly and sharply between a high-voltage state and a low-voltage state. The genius lies in the timing. The system holds these states for unequal durations—an "unbalanced duty cycle." By keeping the circuit in its dominant state for roughly sixty to eighty percent of the time and the secondary state for the remainder, the system can precisely manage power flow without ever entering that problematic zero-voltage state. It’s like finding a specific drumbeat that cancels out the echoing noise in a room; the power gets through, but the leakage conditions are effectively neutralized. 🧠 Logic: Dynamic and conditional activation What makes this system feel truly modern is that it isn't a blunt instrument. The patent describes a "switch control circuit" that acts like a smart conductor. It doesn't force this unbalanced rhythm all the time; instead, it watches the charging session like a hawk. It activates this specific leakage-suppression mode only when necessary—perhaps when the battery voltage hits a certain threshold, the state of charge reaches a specific percentage, or even when the car parks a little crookedly, changing the inductance. The car essentially adapts its electrical heartbeat to the physical reality of the parking job, ensuring peak efficiency when things are perfect and maximizing safety when they aren't. 🛠️ Topologies: Adapting to different circuit architectures Tesla is ensuring this logic works across its entire potential fleet, regardless of what hardware is under the hood. The patent explicitly maps this solution to the two heavyweights of power electronics: the H-bridge and the stacked half-bridge. For the standard H-bridge, the system avoids that "zeroing" state that bridges positive and negative cycles. For the beefier stacked half-bridge—the kind needed for very high voltages—it skips the "medium" voltage step that usually sits halfway between the maximum and minimum. By forcing the voltage to swing fully from rail to rail without lingering in the middle, Tesla ensures the physics of the leakage cancellation hold true regardless of the circuit complexity. ⚡ Voltage: Supporting high-power architectures This is where the patent signals Tesla’s long-term ambitions. The technology is designed to handle a massive voltage range, from one hundred all the way up to one thousand volts. While it mentions standard three-hundred-fifty-volt systems, the explicit support for eight-hundred to one-thousand-volt architectures is a clear nod to the Cybertruck and the Tesla Semi. This means the "no-plug" future isn't just for small, efficient city cars. This leakage reduction technique is robust enough to handle the massive power throughput required to wirelessly charge a heavy-duty truck or a performance vehicle, future-proofing the infrastructure for the next decade of EV development. 📉 Mechanism: Reducing common mode voltage If you could see the electrical waves described in the patent, the difference would be startling. In a standard setup, the common mode voltage—the primary driver of that nasty leakage—looks like a storm, fluctuating wildly between positive and negative two hundred volts. Under Tesla’s new unbalanced scheme, that storm calms into a flat lake. The common mode voltage is effectively flattened, fluctuating only slightly around zero. The simulations are impressive, showing leakage voltage dropping to less than twenty microvolts. That is not just an incremental improvement; it is an orders-of-magnitude reduction that takes wireless charging from "feasible but noisy" to "silent and safe." 🔥 Efficiency: Minimizing switching losses There is a cherry on top for efficiency nerds: this method actually wastes less heat. Every time a transistor switches states, a tiny bit of energy is lost. By transitioning directly between high and low states without stopping at an intermediate step, the system reduces the total number of switching events. Fewer switches mean less "deadtime" loss and less heat generation, ensuring more energy actually ends up in the battery pack. Furthermore, because the electrical noise is so thoroughly dampened, the car becomes quieter in the radio frequency spectrum, making it much easier to pass strict regulatory certifications for electromagnetic interference. 🚀 The grand unification for wireless charging: Safety meets Speed This patent is the shield, but it works in tandem with a previous breakthrough (US20250373083 A1) that acts as the sword. Together, they solve the brutal paradox of wireless engineering: Safety vs. Efficiency. ✅ The "Cruising" Mode ('083): When conditions are safe, the system uses a "partial toggling" technique to cut voltage swings in half. This drastically lowers heat, allowing the Cybercab to charge at blazing speeds (25kW+) without melting its components. ✅ The "Stealth" Mode ('799): When leakage risks rise, this new patent kicks in. It modifies the switching pattern to actively cancel out noise and voltage spikes, prioritizing safety above all else. By combining these two innovations, Tesla has removed the final human bottleneck. The Cybercab can now refuel itself faster than a human could plug it in, safer than a standard wall outlet, and reliably enough to run 24/7 without a single robotic arm in sight. The plug is officially dead.

@elonmusk It’s an amazing experience!

@pitachoi @brankopetric00 🎯

@brankopetric00 a lot of places that adopted microservices didn't have the problems microservices solve.😬

Microservices turned a 5-second monolith debug session into a 3-hour distributed tracing archaeology expedition.