Paradorn Top

Fix the money, fix the world.

[Ming @tslaming]

https://t.co/1vTzygnndr

[ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ @DanielMiessler]

https://t.co/pZqPX8mKvV

[Elon Musk @elonmusk]

Macrohard or Digital Optimus is a joint xAI-Tesla project, coming as part of Tesla’s investment agreement with xAI.

Grok is the master conductor/navigator with deep understanding of the world to direct digital Optimus, which is processing and actioning the past 5 secs of real-time computer screen video and keyboard/mouse actions. Grok is like a much more advanced and sophisticated version of turn-by-turn navigation software.

You can think of it as Digital Optimus AI being System 1 (instinctive part of the mind) and Grok being System 2. (thinking part of the mind).

This will run very competitively on the super low cost Tesla AI4 ($650) paired with relatively frugal use of the much more expensive xAI Nvidia hardware. And it will be the only real-time smart AI system. This is a big deal.

In principle, it is capable of emulating the function of entire companies. That is why the program is called MACROHARD, a funny reference to Microsoft.

No other company can yet do this.

[Google @Google]

Today @GoogleMaps is getting its biggest upgrade in over a decade. By combining our Gemini models with a deep understanding of the world, Maps now unlocks entirely new possibilities for how you navigate and explore. Here’s what you need to know 🧵

[Todd Advisor @ToddAdvisor]

พนักงานในองค์กรจำนวนไม่น้อย “ไม่ได้อยากโตเป็นหัวหน้า”

ไม่ใช่เพราะขาดความทะเยอทะยาน
แต่เพราะสิ่งที่พวกเขาเห็นคือ
ตำแหน่งที่สูงขึ้น = ภาระมากขึ้น แต่คุณภาพชีวิตลดลง

หลายองค์กรยังออกแบบ Career Path แบบเดิม: คนเก่ง → ต้องเป็นหัวหน้า

แต่ความจริงคือ คนจำนวนมากอยาก “เก่งขึ้น” โดยไม่จำเป็นต้อง “บริหารคน”

องค์กรยุคใหม่จึงเริ่มปรับแนวคิด
จาก One Career Ladder
เป็น Dual Career Path

• Management Track – สำหรับคนที่อยากนำทีม
• Expert Track – สำหรับคนที่อยากเป็นผู้เชี่ยวชาญ

เมื่อองค์กรเปิดพื้นที่ให้ทั้งสองเส้นทาง
เราจะไม่เสียคนเก่งไปเพียงเพราะ
พวกเขาไม่ได้อยากเป็นหัวหน้า

Leadership ไม่ควรเป็น “ทางเลือกเดียวของความสำเร็จ”
แต่มันควรเป็น “ความหลากหลายของการเติบโต”

[Aman @Amank1412]

Someone really built this.

A VS Code extension that turns your AI agents into pixel art characters working inside a virtual office.

[DogeDesigner @cb_doge]

ELON MUSK: "Having a child is an act of optimism about the future. We need to give people a sense of optimism and excitement about the future, and I believe that the future will be better than the past, and they'll be more interested in having kids."

[khajochi @khajochi]

เว็บรายงานผลเลือกตั้งแบบ Real-time

ThaiPBS : https://t.co/op8qQ0INwp

The Standard : https://t.co/tBODqBlBDw

The Matter : https://t.co/uk7rzNsMaB

Matichon : https://t.co/ZCHHQfXNkr

Thairath : https://t.co/y1wlJAyi1A

#เลือกตั้ง69

[Ming @tslaming]

https://t.co/AQNW975LGK

[Paradorn Top @paradorn]

Tesla จดสิทธิบัตรเทคโนโลยี “Mixed-Precision Bridge” ที่ช่วยให้ชิป 8-bit ราคาถูกรันการคำนวณ RoPE (Rotary Positional Encoding) ด้วยความแม่นยำสูง 32-bit ทำให้ AI ใน FSD และ Optimus มีประสิทธิภาพสูง ลดพลังงานและต้นทุน สนับสนุน roadmap AI บน edge devices.

[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.

[Paradorn Top @paradorn]

อีก 1 ความตั้งใจในปีนี้ หวังว่าจะทำได้ทุกวัน

[Sawyer Merritt @SawyerMerritt]

I think people need to reframe the conversation around Elon’s “net worth.” As his net worth approaches $1 trillion, it’s important to understand the bigger picture. Elon earned his wealth the right way: by building companies and creating products and services that people genuinely love, while generating enormous shareholder value. He didn’t steal. He didn’t cheat. He built and created.

Elon isn’t accumulating wealth to buy yachts, houses, or vacations. His capital is primarily a tool to pursue outcomes he believes are best for humanity’s long-term future, most notably making life multi-planetary. Building on Mars will be expensive, and much of Elon's wealth will ultimately go towards that, but he’s also investing heavily here on Earth, having created over 600,000 jobs so far across his companies and supply chains.

Elon is the best capital allocator in the world, and has created multiple world-changing technologies that have benefited humanity. That’s why it’s ironic when some people say he should pay more taxes (almost all of his wealth is unrealized stock gains), because that capital would simply be redirected to the government, one of the worst capital allocators.

He paid the single largest tax bill in history in 2021 ($11B) and will likely pay an even bigger one in the future when he exercises the stock options from his 2018 Tesla CEO Performance Award, which are set to expire in January 2028.

The villainization of Elon by legacy media and others will only intensify as his wealth grows. Instead, what they should focus on is the massive value he has created for others and all the good his companies are doing:

• Tesla: Accelerating toward a sustainable future and saving lives with self-driving cars
• Neuralink: Improving the lives of disabled people with brain-implant chips
• SpaceX/Starlink: Bringing internet connectivity to remote areas worldwide
• The Boring Company: Reducing traffic and travel time
• xAI/Grok: Using AI to solve problems, answer questions, and tackle challenges
• And much more

Don’t measure Elon by his wealth. Measure him by what he has built, what he’s creating/building, and all the things that wouldn’t exist without him. That list is long.

[Paradorn Top @paradorn]

Ikigai
“คุณค่าของการมีชีวิตอยู่” หรืออีกนัยคือ “ความหมายของการตื่นขึ้นมาในทุกๆ วัน”

Wabi Sabi
“คุณค่าของความเรียบง่าย ความไม่สมบูรณ์แบบ และความไม่จีรังของสิ่งรอบตัว”

หยุดยาวนี้ กะว่าจะอ่านได้จบสักเล่มนึงล่ะนะ

[Mario Nawfal @MarioNawfal]

🇺🇸 SAN FRAN BLACKOUT – WAYMO FROZE, TESLA DROVE

Waymo’s robotaxis got a little too real last night - by completely shutting down when San Francisco’s power outage knocked out traffic lights.

Meanwhile, Teslas on FSD? Kept rolling. No drama, no headlines - just handling chaos like it’s a walk in the park.

This is what happens when you train your AI on billions of real-world miles instead of coddling it in a simulation padded with perfect data and wishful thinking.

Waymo bet on maps and order. Tesla bet on mess - and won. When the lights go out, the difference isn’t theoretical. It’s traffic.

Source: @Tesla_AI, @elonmusk

[Arena.ai @arena]

📈Arena Trends Update

We pulled Arena scores for the Top 10 labs since the beginning of 2025, and the top climbers may surprise you.

With tighter confidence intervals and new entries in the mix, the Arena continues to shift. Stay tuned for more EOY insights and updates from the frontier.

[Paradorn Top @paradorn]

Gemini นายทำให้เพื่อนๆ ดูแย่

[Laurent Correia Bill @Laurent_lcb]

Apple Intelligence vs Galaxy Al vs Gemini Al Eraser Test... wait until the end

sc ig/custom_adventurist

[Paradorn Top @paradorn]

ได้คะแนนวิจารณ์ Rotten Tomatoes 99%

เป็นผลงานของผู้สร้าง Breaking Bad

ในภาพคือ Easter egg ที่ Google ทำให้สำหรับการค้นหาชื่อหนังเรื่องนี้

รับชมได้ทาง Apple TV

[Paradorn Top @paradorn]

แนะนำหนังน่าดู

Pluribus หนังเอเลี่ยนบุกโลกที่แหวกแนวกว่าเรื่องอื่นๆ

หากมนุษย์ทั้งโลกถูกเชื่อมจิตจนเป็นหนึ่งเดียว (คล้ายความสามารถของ Overmind ใน StarCraft หรือ Omega ใน Edge of Tomorrow)

[Paradorn Top @paradorn]

ความรู้ ประสบการณ์ ทักษะ รวมถึงประสาทสัมผัส จากทุกๆ คน ถูกหลอมรวมเข้าด้วยกัน จนสามารถใช้ทรัพยากรในโลกได้มีประสิทธิภาพสูงสุด แถมยังมีนิสัยโอบอ้อมอารีย์และเป็นมิตรต่อสิ่งมีชีวิตและสิ่งแวดล้อม

แต่เราคือหนึ่งในไม่กี่คน ที่ยังเป็นปกติ เราจะแก้ปัญหานี้อย่างไร จะเข้าร่วมหรือจะต่อต้าน?