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Thursday, December 25, 2025

25: Yogi

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

The Brazen Cruelty of the Trump Regime Its plan to warehouse immigrants has shades of Nazi concentration camps and America's shameful imprisonment of Japanese Americans during World War II. ............. the Trump regime plans to renovate industrial warehouses to hold more than 80,000 immigrant detainees at a time. .......... The plan is for newly arrested detainees to be funneled — let me remind you, with no due process, or independent magistrate or judge checking on whether they are in fact in the United States illegally — into one of seven large-scale warehouses holding 5,000 to 10,000 people each, where they would be “staged” for deportation. ........... “We need to get better at treating this like a business,” ICE acting director Todd M. Lyons said at a border security conference in April, according to the Arizona Mirror.

The administration’s goal, he said, was to deport immigrants as efficiently as Amazon moves packages: “Like Prime, but with human beings.”

........... Ninety-three years ago, in March 1933, the Nazis established their first concentration camp in what is now Dachau, Poland. Other camps were soon established in Buchenwald and Sachsenhausen......... Initially, the Nazi’s put into these camps Communists, Social Democrats, trade unionists, and others deemed a threat to the Nazi regime............... After the Kristallnacht pogrom of November 9-10, 1938, approximately 30,000 Jewish men were arrested and sent to these camps in a mass, large-scale action that targeted them for being Jewish. The systematic mass murder of Jews in camps designed as extermination camps did not begin until late 1941 and early 1942, as part of the “Final Solution.” ...............

Once dehumanization begins, it’s hard to end.

............ ICE is arresting, imprisoning, and deporting people it accuses of being in the United States illegally — but there is no due process, no third-party validation of ICE’s accusations. .............. There is no place in a civilized society for the warehousing of people. ......... There is no decency in removing hardworking members of our communities from their families and neighbors and imprisoning them and then deporting them to other countries, some of which are brutal dictatorships......... When the history of this cruel era is written, the shame should be no less than the shame we now feel about the roundups and detention of Japanese Americans in World War II.

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

View on Threads

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

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25: Tariff

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

Six Weeks From Zero (novel)
The Dawn Beyond Currency (Part 1) (novel)
The Dawn Beyond Currency (Part 2) (novel)
The Great Subcontinent Uprising (Part 1) (novel)
The Great Subcontinent Uprising (Part 2) (novel)
The Great Subcontinent Uprising (novel)
The Banyan Revolt (novel)
Gen Z Kranti (novel)
The Protocol of Greatness (novel)
Madhya York: The Merchant and the Mystic (novel)
The Garden Of Last Debates (novel)
The Drum Report: Markets, Tariffs, and the Man in the Basement (novel)
Trump’s Default: The Mist Of Empire (novel)
Deported (novel)
Empty Country (novel)
Poetry Thursdays (novel)

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Groq, the LPU, and NVIDIA’s $20 Billion Power Move: The Inference War Reaches Its Turning Point


Groq, the LPU, and NVIDIA’s $20 Billion Power Move: The Inference War Reaches Its Turning Point

In the long arc of computing history, revolutions rarely arrive with fanfare. They sneak in sideways—through bottlenecks, edge cases, and “unimportant” optimizations that suddenly become existential. In artificial intelligence, that moment has arrived. Not in training, where GPUs reign supreme, but in inference—the act of turning trained intelligence into real-time action.

At the center of this shift stood Groq, a quiet Silicon Valley startup founded in 2016. And on December 24–25, 2025, NVIDIA effectively declared the inference war too important to leave to chance, announcing its largest acquisition ever: a $20 billion all-cash deal to acquire Groq’s assets, intellectual property, and key talent.

This was not just a buyout. It was a preemptive strike.

Groq Is Not Grok (and That Distinction Matters)

First, a necessary clarification in an era of confusing brand echoes: Groq has nothing to do with Grok, the large language models developed by Elon Musk’s xAI. Groq is hardware. Grok is software. One moves electrons; the other moves words.

Groq was founded in Mountain View, California, by Jonathan Ross, a former Google engineer who helped create Google’s Tensor Processing Unit (TPU), along with fellow ex-Googler Douglas Wightman. Their ambition was audacious: challenge GPU dominance not by being more flexible, but by being ruthlessly specific.

If GPUs are Swiss Army knives, Groq wanted to build a scalpel.

The Big Idea: Inference Is Not Training

For over a decade, AI hardware progress was driven by training—throwing massive parallel compute at giant datasets to produce ever-larger models. GPUs thrived here. But once models are trained, the economics flip.

Inference is where AI meets reality:

  • Chatbots responding in milliseconds

  • Voice assistants that cannot hesitate

  • Autonomous systems where latency equals danger

  • Financial systems where predictability beats peak throughput

In this world, variability is poison.

Groq bet that deterministic, ultra-low-latency inference would matter more than raw parallel horsepower. And they designed a chip around that belief.

The Language Processing Unit: A Different Philosophy of Compute

Groq’s Language Processing Unit (LPU) is not a faster GPU. It is a rejection of the GPU paradigm.

Determinism Over Chaos

GPUs rely on dynamic scheduling, caches, and complex memory hierarchies. That flexibility is powerful—but it introduces unpredictability. Two identical inference requests can take different amounts of time.

The LPU eliminates this uncertainty entirely.

Groq’s architecture is built around a compiler-driven, statically scheduled model. Every operation is planned in advance. Every data movement is known. Every cycle is accounted for.

The result:
The same input produces the same output in the same amount of time—every time.

In a world of real-time AI, that predictability is priceless.

Inside the LPU: How It Works

At the heart of the LPU is Groq’s Tensor Streaming Processor (TSP) architecture—a radical departure from CPU and GPU design.

Key Architectural Pillars

1. SRAM-Centric Design
Instead of relying on high-bandwidth memory (HBM), the LPU uses massive on-chip SRAM—about 230 MB per chip, delivering ~80 TB/s of bandwidth. Data stays close to compute, slashing latency and power draw.

2. Streaming Dataflow
Data moves through the chip like water through a canal system—steady, predictable, uninterrupted. No stalls. No cache misses. No surprises.

3. Tensor Parallelism
Operations are sliced and distributed across processing elements optimized for tensor math, enabling efficient handling of modern architectures like Mixture of Experts (MoE).

4. Static Scheduling via Compiler
Groq’s proprietary compiler maps trained models (from PyTorch, ONNX, etc.) directly onto hardware, determining every instruction’s timing and data path before execution begins.

5. TruePoint Numerics
A custom numeric format balances precision and performance, avoiding the overhead of full floating-point arithmetic while maintaining accuracy.

Multiple LPUs can be clustered into racks—such as GroqRack, delivering petaflop-scale performance with millisecond-level latency.

Performance: Why NVIDIA Took Notice

Groq’s claims were not subtle—and benchmarks backed them up.

  • 1,000+ tokens per second for large language models

  • 3–10× faster inference than GPUs like NVIDIA A100 and H100

  • Milliseconds of latency, even at scale

  • 3× better energy efficiency, and up to 5× lower inference costs

In LLMPerf and other inference benchmarks, LPUs consistently topped the charts.

Groq didn’t just outperform GPUs. It made them look like overkill.

GPUs, TPUs, and LPUs: Three Different Futures

  • GPUs remain unmatched for training and general-purpose acceleration—but suffer from inference inefficiency and variability.

  • TPUs (Google’s domain) balance training and inference well, especially at cloud scale, but rely heavily on HBM and are ecosystem-locked.

  • LPUs are pure inference weapons—narrow, fast, predictable, and devastatingly efficient.

If GPUs are freight trains and TPUs are high-speed rail, LPUs are fighter jets: expensive, specialized, and unbeatable in their airspace.

The Acquisition: NVIDIA’s Instagram Moment

NVIDIA’s $20 billion move is best understood not as a WhatsApp-style adjacency expansion, but as an Instagram-style neutralization of a rising threat.

Groq was not shopping itself. But it was becoming too successful, too visible, and too dangerous—especially as AI demand shifted from training to inference.

Deal Structure (and Why It Matters)

  • Acquisition of Groq’s core assets, IP, and patents

  • Non-exclusive licensing, not a full company takeover

  • Acqui-hire of key executives, reportedly including Jonathan Ross

  • Groq continues operating independently under new leadership

  • GroqCloud remains active—for now

This hybrid structure mirrors recent Big Tech maneuvers (Microsoft–Inflection, Meta–Scale AI), designed to:

  • Accelerate integration

  • Reduce antitrust exposure

  • Neutralize competition quietly

It is corporate judo.

Why NVIDIA Needed Groq

NVIDIA dominates training—but inference is becoming the real money.

As AI scales:

  • Training happens once

  • Inference happens billions of times

Groq’s LPU solves three looming problems for NVIDIA:

  1. Inference efficiency as costs and energy constraints tighten

  2. HBM shortages, which threaten GPU scaling

  3. Rising competitors like AMD, Cerebras, and custom ASIC startups

By absorbing Groq’s technology, NVIDIA fills its most dangerous gap.

Industry-Wide Consequences: The Inference Era Begins

The Good

  • Faster, cheaper inference

  • Real-time AI becomes ubiquitous

  • More applications become economically viable

The Bad

  • Hardware consolidation accelerates

  • Barriers to entry rise for startups

  • NVIDIA’s market share (already ~80–90%) hardens further

The Uncomfortable

  • Regulatory scrutiny intensifies in the U.S., EU, and China

  • AI hardware becomes geopolitically strategic

  • Innovation risks being centralized

The inference revolution may democratize AI usage—but not AI ownership.

Final Thought: A Scalpel Enters the Empire

Groq set out to build the fastest inference engine in the world. It succeeded—so completely that the reigning emperor of AI hardware decided it was safer to own the blade than to fight it.

This deal marks a turning point. AI is no longer about who can train the biggest model. It’s about who can respond the fastest, the cheapest, and the most predictably.

The age of brute-force intelligence is giving way to the age of precision.

And NVIDIA, once again, has placed itself at the center of history—this time by recognizing that sometimes, the smallest, sharpest tool matters more than the biggest hammer.



เค—्เคฐोเค•, เคเคฒเคชीเคฏू เค”เคฐ NVIDIA เค•ी 20 เค…เคฐเคฌ เคกॉเคฒเคฐ เค•ी เคšाเคฒ: เค‡เคจ्เคซ़เคฐेंเคธ เคฏुเคฆ्เคง เค•ा เคจिเคฐ्เคฃाเคฏเค• เคฎोเคก़

เค•ंเคช्เคฏूเคŸिंเค— เค•े เค‡เคคिเคนाเคธ เคฎें เค•्เคฐांเคคिเคฏाँ เค…เค•्เคธเคฐ เคถोเคฐ เคฎเคšाเค•เคฐ เคจเคนीं เค†เคคीं। เคตे เคšुเคชเคšाเคช เคช्เคฐเคตेเคถ เค•เคฐเคคी เคนैं—เคฌॉเคŸเคฒเคจेเค•, เค•िเคจाเคฐे เค•े เค‰เคชเคฏोเค—-เคฎाเคฎเคฒों เค”เคฐ “เค—ैเคฐ-เคฎเคนเคค्เคตเคชूเคฐ्เคฃ” เคฒเค—เคจे เคตाเคฒे เค…เคจुเค•ूเคฒเคจों เค•े เคฐाเคธ्เคคे—เค”เคฐ เค…เคšाเคจเค• เค…เคธ्เคคिเคค्เคต เค•ा เคช्เคฐเคถ्เคจ เคฌเคจ เคœाเคคी เคนैं। เค•ृเคค्เคฐिเคฎ เคฌुเคฆ्เคงिเคฎเคค्เคคा (AI) เคฎें เคตเคนी เค•्เคทเคฃ เค…เคฌ เค† เคšुเค•ा เคนै।
เคฏเคน เค•्เคทเคฃ เคŸ्เคฐेเคจिंเค— เคฎें เคจเคนीं เคนै, เคœเคนाँ GPU เค…เคฌ เคญी เคฐाเคœ เค•เคฐเคคे เคนैं, เคฌเคฒ्เค•ि เค‡เคจ्เคซ़เคฐेंเคธ เคฎें เคนै—เคฏाเคจी เคช्เคฐเคถिเค•्เคทिเคค เคฌुเคฆ्เคงिเคฎเคค्เคคा เค•ो เคตाเคธ्เคคเคตिเค• เคธเคฎเคฏ เคฎें เค•ाเคฎ เคฎें เคฒเค—ाเคจे เค•ी เคช्เคฐเค•्เคฐिเคฏा เคฎें।

เค‡เคธ เคฌเคฆเคฒाเคต เค•े เค•ेंเคฆ्เคฐ เคฎें เคฅा Groq, 2016 เคฎें เคธ्เคฅाเคชिเคค เคเค• เค…เคชेเค•्เคทाเค•ृเคค เคถांเคค เคธिเคฒिเค•ॉเคจ เคตैเคฒी เคธ्เคŸाเคฐ्เคŸเค…เคช। เค”เคฐ 24–25 เคฆिเคธंเคฌเคฐ 2025 เค•ो NVIDIA เคจे เคธ्เคชเคท्เคŸ เค•เคฐ เคฆिเคฏा เค•ि เค‡เคจ्เคซ़เคฐेंเคธ เคฏुเคฆ्เคง เค•ो เคตเคน เคธंเคฏोเค— เคชเคฐ เคจเคนीं เค›ोเคก़เคจे เคตाเคฒा। เค•ंเคชเคจी เคจे เค…เคชเคจी เค…เคฌ เคคเค• เค•ी เคธเคฌเคธे เคฌเคก़ी เคกीเคฒ เค•ी เค˜ोเคทเคฃा เค•ी—20 เค…เคฐเคฌ เคกॉเคฒเคฐ เค•ी เค‘เคฒ-เค•ैเคถ เคกीเคฒ, เคœिเคธเค•े เคคเคนเคค เค‰เคธเคจे Groq เค•ी เคคเค•เคจीเค•, เคฌौเคฆ्เคงिเค• เคธंเคชเคฆा เค”เคฐ เคช्เคฐเคฎुเค– เคช्เคฐเคคिเคญाเค“ं เค•ा เค…เคงिเค—्เคฐเคนเคฃ เค•िเคฏा।

เคฏเคน เคธिเคฐ्เคซ़ เค…เคงिเค—्เคฐเคนเคฃ เคจเคนीं เคฅा।
เคฏเคน เคเค• เคชूเคฐ्เคต-เคช्เคฐเคนाเคฐ (preemptive strike) เคฅा।

Groq, Grok เคจเคนीं เคนै (เค”เคฐ เคฏเคน เคซเคฐ्เค• เคฌเคนुเคค เคฎाเคฏเคจे เคฐเค–เคคा เคนै)

เค†เคœ เค•े เคญ्เคฐเคฎिเคค เค•เคฐเคจे เคตाเคฒे เคฌ्เคฐांเคก เคจाเคฎों เค•े เคฆौเคฐ เคฎें เคเค• เคฌाเคค เคธाเคซ़ เค•เคฐเคจा เคœ़เคฐूเคฐी เคนै:
Groq เค•ा Grok เคธे เค•ोเคˆ เคธंเคฌंเคง เคจเคนीं เคนै।

  • Groq → เคนाเคฐ्เคกเคตेเคฏเคฐ เค•ंเคชเคจी

  • Grok → xAI เคฆ्เคตाเคฐा เคตिเค•เคธिเคค เคฌเคก़े เคญाเคทा เคฎॉเคกเคฒ (LLMs)

Groq เคถเคฌ्เคฆ เคจเคนीं เคšเคฒाเคคा, เคตเคน เค‡เคฒेเค•्เคŸ्เคฐॉเคจों เค•ो เค—เคคि เคฆेเคคा เคนै।

Groq เค•ी เคธ्เคฅाเคชเคจा เคฎाเค‰ंเคŸेเคจ เคต्เคฏू, เค•ैเคฒिเคซ़ोเคฐ्เคจिเคฏा เคฎें เคœोเคจाเคฅเคจ เคฐॉเคธ เคจे เค•ी เคฅी—เคœो เคชเคนเคฒे Google เคฎें เค‡ंเคœीเคจिเคฏเคฐ เคฅे เค”เคฐ Google เค•े Tensor Processing Unit (TPU) เค•े เคจिเคฐ्เคฎाเคฃ เคฎें เคถाเคฎिเคฒ เคฐเคนे। เค‰เคจเค•े เคธाเคฅ เค…เคจ्เคฏ เคชूเคฐ्เคต-Google เค‡ंเคœीเคจिเคฏเคฐ เคญी เคฅे, เคœिเคจเคฎें เคกเค—เคฒเคธ เคตाเค‡เคŸเคฎैเคจ เคช्เคฐเคฎुเค– เคนैं।

เค‰เคจเค•ा เคฒเค•्เคท्เคฏ เคธाเคนเคธी เคฅा:
GPU เค•ो เคœ़्เคฏाเคฆा เคฒเคšीเคฒा เคฌเคจाเค•เคฐ เคจเคนीं, เคฌเคฒ्เค•ि เคฌेเคนเคฆ เคตिเคถिเคท्เคŸ เคฌเคจเค•เคฐ เคšुเคจौเคคी เคฆेเคจा।

เค…เค—เคฐ GPU เคธ्เคตिเคธ เค†เคฐ्เคฎी เคจाเค‡เคซ เคนै, เคคो Groq เคเค• เคธเคฐ्เคœिเค•เคฒ เคธ्เค•ैเคฒ्เคชेเคฒ เคฌเคจाเคจा เคšाเคนเคคा เคฅा।

เคฎूเคฒ เคตिเคšाเคฐ: เค‡เคจ्เคซ़เคฐेंเคธ, เคŸ्เคฐेเคจिंเค— เคจเคนीं เคนै

เคชिเค›เคฒे เคเค• เคฆเคถเค• เคคเค• AI เคนाเคฐ्เคกเคตेเคฏเคฐ เค•ी เคช्เคฐเค—เคคि เค•ा เค•ेंเคฆ्เคฐ เคŸ्เคฐेเคจिंเค— เคฐเคนी—เคญाเคฐी เคชैเคฎाเคจे เคชเคฐ เคธเคฎाเคจांเคคเคฐ เค•ंเคช्เคฏूเคŸिंเค—, เคตिเคถाเคฒ เคกेเคŸा เคธेเคŸ เค”เคฐ เคตिเคถाเคฒ เคฎॉเคกเคฒ।

เคฒेเค•िเคจ เคเค• เคฌाเคฐ เคฎॉเคกเคฒ เคช्เคฐเคถिเค•्เคทिเคค เคนो เคœाเคจे เค•े เคฌाเคฆ, เค…เคฐ्เคฅเคถाเคธ्เคค्เคฐ เคฌเคฆเคฒ เคœाเคคा เคนै।

เค‡เคจ्เคซ़เคฐेंเคธ เคตเคน เคœเค—เคน เคนै เคœเคนाँ AI เคตाเคธ्เคคเคตिเค• เคฆुเคจिเคฏा เคธे เคŸเค•เคฐाเคคा เคนै:

  • เคšैเคŸเคฌॉเคŸ्เคธ เคœिเคจ्เคนें เคฎिเคฒीเคธेเค•ंเคก เคฎें เคœเคตाเคฌ เคฆेเคจा เคนोเคคा เคนै

  • เคตॉเคฏเคธ เค…เคธिเคธ्เคŸेंเคŸ เคœिเคจ्เคนें เคนिเคšเค•िเคšाเคจा เคจเคนीं เคšाเคนिเค

  • เคธ्เคตाเคฏเคค्เคค เคช्เคฐเคฃाเคฒिเคฏाँ เคœเคนाँ เคตिเคฒंเคฌ เคœाเคจเคฒेเคตा เคนो เคธเค•เคคा เคนै

  • เคตिเคค्เคคीเคฏ เคช्เคฐเคฃाเคฒिเคฏाँ เคœเคนाँ เค…เคจुเคฎाเคจिเคค เคธเคฎเคฏ, เค…เคงिเค•เคคเคฎ เคถเค•्เคคि เคธे เคœ़्เคฏाเคฆा เคฎाเคฏเคจे เคฐเค–เคคा เคนै

เค‡เคธ เคฆुเคจिเคฏा เคฎें เค…เคจिเคถ्เคšिเคคเคคा เคœ़เคนเคฐ เคนै।

Groq เคจे เคฆांเคต เคฒเค—ाเคฏा เค•ि เคจिเคฐ्เคงाเคฐिเคค (deterministic), เค…เคฒ्เคŸ्เคฐा-เคฒो-เคฒेเคŸेंเคธी เค‡เคจ्เคซ़เคฐेंเคธ เคนी เคญเคตिเคท्เคฏ เคนोเค—ा—เค”เคฐ เค‰เคธเคจे เค‰เคธी เคตिเคถ्เคตाเคธ เค•े เคšाเคฐों เค“เคฐ เคšिเคช เคกिเคœ़ाเค‡เคจ เค•ी।

Language Processing Unit (LPU): เค•ंเคช्เคฏूเคŸिंเค— เค•ा เคจเคฏा เคฆเคฐ्เคถเคจ

Groq เค•ी Language Processing Unit (LPU) เค•ोเคˆ เคคेเคœ़ GPU เคจเคนीं เคนै।
เคฏเคน GPU เคฎॉเคกเคฒ เค•ा เคธीเคงा เค‡เคจเค•ाเคฐ เคนै।

เค…เคฐाเคœเค•เคคा เค•े เคฌเคœाเคฏ เคจिเคฐ्เคงाเคฐเคฃ (Determinism)

GPU เคกाเคฏเคจाเคฎिเค• เคถेเคก्เคฏूเคฒिंเค—, เค•ैเคถ เค”เคฐ เคœเคŸिเคฒ เคฎेเคฎोเคฐी เคชเคฆाเคจुเค•्เคฐเคฎ เคชเคฐ เคจिเคฐ्เคญเคฐ เค•เคฐเคคे เคนैं। เคฏเคน เคฒเคšीเคฒाเคชเคจ เคถเค•्เคคिเคถाเคฒी เคนै—เคฒेเค•िเคจ เค‡เคธเคธे เค…เคจिเคถ्เคšिเคคเคคा เค†เคคी เคนै।

เคเค• เคนी เค‡เคจ्เคซ़เคฐेंเคธ เค…เคจुเคฐोเคง เคฆो เคฌाเคฐ เค…เคฒเค—-เค…เคฒเค— เคธเคฎเคฏ เคฒे เคธเค•เคคा เคนै।

LPU เค‡เคธ เค…เคจिเคถ्เคšिเคคเคคा เค•ो เคชूเคฐी เคคเคฐเคน เคธเคฎाเคช्เคค เค•เคฐ เคฆेเคคा เคนै।

Groq เค•ी เคตाเคธ्เคคुเค•เคฒा เค•ंเคชाเค‡เคฒเคฐ-เคก्เคฐिเคตเคจ, เคธ्เคŸैเคŸिเค• เคถेเคก्เคฏूเคฒिंเค— เคชเคฐ เค†เคงाเคฐिเคค เคนै। เคนเคฐ เค‘เคชเคฐेเคถเคจ เคชเคนเคฒे เคธे เคคเคฏ เคนोเคคा เคนै। เคนเคฐ เคกेเคŸा เคฎूเคตเคฎेंเคŸ เคœ्เคžाเคค เคนोเคคा เคนै। เคนเคฐ เคธाเค‡เค•िเคฒ เค—िเคจी เคœाเคคी เคนै।

เคชเคฐिเคฃाเคฎ:
เคเค• เคนी เค‡เคจเคชुเคŸ, เคนเคฐ เคฌाเคฐ เคฌिเคฒ्เค•ुเคฒ เคเค• เคนी เคธเคฎเคฏ เคฎें เค†เค‰เคŸเคชुเคŸ เคฆेเคคा เคนै।

เคฐीเคฏเคฒ-เคŸाเค‡เคฎ AI เคฎें เคฏเคน เคชूเคฐ्เคตाเคจुเคฎेเคฏเคคा เคธोเคจे เคธे เคญी เคœ़्เคฏाเคฆा เค•ीเคฎเคคी เคนै।

LPU เค•े เคญीเคคเคฐ: เคฏเคน เค•ैเคธे เค•ाเคฎ เค•เคฐเคคा เคนै

LPU เค•े เค•ेंเคฆ्เคฐ เคฎें เคนै Groq เค•ी Tensor Streaming Processor (TSP) เคตाเคธ्เคคुเค•เคฒा—เคœो CPU เค”เคฐ GPU เคฆोเคจों เคธे เคฌुเคจिเคฏाเคฆी เคฐूเคช เคธे เค…เคฒเค— เคนै।

เคฎुเค–्เคฏ เคตाเคธ्เคคुเค•เคฒा เคธ्เคคंเคญ

1. SRAM-เค•ेंเคฆ्เคฐिเคค เคกिเคœ़ाเค‡เคจ
HBM เคชเคฐ เคจिเคฐ्เคญเคฐเคคा เค•े เคฌเคœाเคฏ, LPU เคญाเคฐी เคฎाเคค्เคฐा เคฎें เค‘เคจ-เคšिเคช SRAM (เคฒเค—เคญเค— 230 MB เคช्เคฐเคคि เคšिเคช) เค•ा เค‰เคชเคฏोเค— เค•เคฐเคคा เคนै, เคœिเคธเคธे เคฒเค—เคญเค— 80 TB/s เคฌैंเคกเคตिเคก्เคฅ เคฎिเคฒเคคी เคนै।
เคกेเคŸा เค•ंเคช्เคฏूเคŸ เค•े เคชाเคธ เคฐเคนเคคा เคนै—เคฒेเคŸेंเคธी เค”เคฐ เคŠเคฐ्เคœा เค–เคชเคค เคฆोเคจों เค˜เคŸเคคी เคนैं।

2. เคธ्เคŸ्เคฐीเคฎिंเค— เคกेเคŸा-เคซ्เคฒो
เคกेเคŸा เคšिเคช เค•े เคญीเคคเคฐ เคเคธे เคฌเคนเคคा เคนै เคœैเคธे เคจเคนเคฐ เคฎें เคชाเคจी—เคจिเคฐंเคคเคฐ, เค…เคจुเคฎाเคจिเคค, เคฌिเคจा เคฐुเค•ाเคตเคŸ।
เค•ोเคˆ เค•ैเคถ เคฎिเคธ เคจเคนीं, เค•ोเคˆ เคธ्เคŸॉเคฒ เคจเคนीं।

3. เคŸेเคจ्เคธเคฐ เคชैเคฐेเคฒเคฒिเคœ़्เคฎ
AI เคฎॉเคกเคฒ เค•े เคŸेเคจ्เคธเคฐ เค‘เคชเคฐेเคถंเคธ เค•ो เค•ुเคถเคฒเคคा เคธे เคตिเคคเคฐिเคค เค•िเคฏा เคœाเคคा เคนै, เคœिเคธเคธे Mixture of Experts (MoE) เคœैเคธे เค†เคงुเคจिเค• เคฎॉเคกเคฒ เคธंเคญाเคฒे เคœा เคธเค•ें।

4. เค•ंเคชाเค‡เคฒเคฐ-เค†เคงाเคฐिเคค เคธ्เคŸैเคŸिเค• เคถेเคก्เคฏूเคฒिंเค—
Groq เค•ा เคฎाเคฒिเค•ाเคจा เค•ंเคชाเค‡เคฒเคฐ เคช्เคฐเคถिเค•्เคทिเคค เคฎॉเคกเคฒ (PyTorch, ONNX เค†เคฆि) เค•ो เคธीเคงे เคนाเคฐ्เคกเคตेเคฏเคฐ เคชเคฐ เคฎैเคช เค•เคฐเคคा เคนै।

5. TruePoint Numerics
เคเค• เค•เคธ्เคŸเคฎ เคจ्เคฏूเคฎेเคฐिเค•เคฒ เคซ़ॉเคฐ्เคฎेเคŸ, เคœो เคธเคŸीเค•เคคा เค”เคฐ เคช्เคฐเคฆเคฐ्เคถเคจ เค•े เคฌीเคš เคธंเคคुเคฒเคจ เคฌเคจाเคคा เคนै।

เค•เคˆ LPUs เค•ो เคœोเคก़เค•เคฐ เค•्เคฒเคธ्เคŸเคฐ เคฌเคจाเค เคœा เคธเค•เคคे เคนैं—เคœैเคธे GroqRack, เคœो เคฎिเคฒीเคธेเค•ंเคก-เคธ्เคคเคฐीเคฏ เคฒेเคŸेंเคธी เค•े เคธाเคฅ เคชेเคŸाเคซ्เคฒॉเคช-เคธ्เคคเคฐीเคฏ เคช्เคฐเคฆเคฐ्เคถเคจ เคฆेเคคा เคนै।

เคช्เคฐเคฆเคฐ्เคถเคจ: NVIDIA เคจे เค•्เคฏों เคง्เคฏाเคจ เคฆिเคฏा

Groq เค•े เคฆाเคตे เคฌเคก़े เคฅे—เค”เคฐ เคฌेंเคšเคฎाเคฐ्เค•्เคธ เคจे เค‰เคจ्เคนें เคธเคนी เค เคนเคฐाเคฏा।

  • 1,000+ เคŸोเค•เคจ เคช्เคฐเคคि เคธेเค•ंเคก (LLMs เค•े เคฒिเค)

  • NVIDIA A100/H100 เคธे 3–10 เค—ुเคจा เคคेเคœ़ เค‡เคจ्เคซ़เคฐेंเคธ

  • เคฎिเคฒीเคธेเค•ंเคก-เคธ्เคคเคฐीเคฏ เคฒेเคŸेंเคธी

  • 3 เค—ुเคจा เค…เคงिเค• เคŠเคฐ्เคœा เคฆเค•्เคทเคคा เค”เคฐ 5 เค—ुเคจा เค•เคฎ เคฒाเค—เคค

Groq เคจे GPU เค•ो เค•ेเคตเคฒ เคชเค›ाเคก़ा เคจเคนीं—เค•เคˆ เคฎाเคฎเคฒों เคฎें เค‰เคจ्เคนें เค…เคค्เคฏเคงिเค• เคญाเคฐी เคธाเคฌिเคค เค•เคฐ เคฆिเคฏा।

GPU, TPU เค”เคฐ LPU: เคคीเคจ เค…เคฒเค— เคญเคตिเคท्เคฏ

  • GPU → เคŸ्เคฐेเคจिंเค— เค”เคฐ เคฌเคนुเค‰เคฆ्เคฆेเคถ्เคฏीเคฏ เค•ाเคฐ्เคฏों เคฎें เค…เคชเคฐाเคœेเคฏ

  • TPU → เคŸ्เคฐेเคจिंเค— + เค‡เคจ्เคซ़เคฐेंเคธ เค•ा เคธंเคคुเคฒเคจ, เค•्เคฒाเค‰เคก-เค•ेंเคฆ्เคฐिเคค

  • LPU → เคถुเคฆ्เคง เค‡เคจ्เคซ़เคฐेंเคธ เคนเคฅिเคฏाเคฐ: เคคेเคœ़, เค…เคจुเคฎाเคจिเคค, เค•ुเคถเคฒ

เค…เค—เคฐ GPU เคฎाเคฒเค—ाเคก़ी เคนैं เค”เคฐ TPU เคนाเคˆ-เคธ्เคชीเคก เคฐेเคฒ, เคคो LPU เคซाเค‡เคŸเคฐ เคœेเคŸ เคนै—เคตिเคถेเคทीเค•ृเคค เค”เคฐ เค…เคชเคจे เค•्เคทेเคค्เคฐ เคฎें เค…เคœेเคฏ।

เค…เคงिเค—्เคฐเคนเคฃ: NVIDIA เค•ा “Instagram เคฎोเคฎेंเคŸ”

เคฏเคน เคธौเคฆा WhatsApp-เคœैเคธा เคตिเคธ्เคคाเคฐ เคจเคนीं, เคฌเคฒ्เค•ि Instagram-เคœैเคธी เคช्เคฐเคคिเคธ्เคชเคฐ्เคงी เคจिเคท्เคช्เคฐเคญाเคตीเค•เคฐเคฃ เคฐเคฃเคจीเคคि เคนै।

Groq เคฌिเค•्เคฐी เค•े เคฒिเค เคจเคนीं เคฅा।
เคฒेเค•िเคจ เคตเคน เคฌเคนुเคค เคคेเคœ़, เคฌเคนुเคค เคธเคซเคฒ เค”เคฐ เคฌเคนुเคค เค–़เคคเคฐเคจाเค• เคนो เคฐเคนा เคฅा—เค–़ाเคธเค•เคฐ เคคเคฌ, เคœเคฌ เคฌाเคœ़ाเคฐ เคŸ्เคฐेเคจिंเค— เคธे เค‡เคจ्เคซ़เคฐेंเคธ เค•ी เค“เคฐ เคुเค• เคฐเคนा เคฅा।

เคกीเคฒ เค•ी เคธंเคฐเคšเคจा

  • Groq เค•ी เคคเค•เคจीเค•, IP เค”เคฐ เคชेเคŸेंเคŸ เค•ा เค…เคงिเค—्เคฐเคนเคฃ

  • เคจॉเคจ-เคเค•्เคธเค•्เคฒूเคธिเคต เคฒाเค‡เคธेंเคธिंเค—

  • เคช्เคฐเคฎुเค– เค…เคงिเค•ाเคฐिเคฏों เค•ा acqui-hire, เคธंเคญเคตเคคः เคœोเคจाเคฅเคจ เคฐॉเคธ เคธเคนिเคค

  • Groq เคธ्เคตเคคंเคค्เคฐ เคฐूเคช เคธे เคธंเคšाเคฒเคจ เคœाเคฐी เคฐเค–ेเค—ा

  • GroqCloud เคซिเคฒเคนाเคฒ เคช्เคฐเคญाเคตिเคค เคจเคนीं

เคฏเคน เคธंเคฐเคšเคจा:

  • เคคेเคœ़ เคเค•ीเค•เคฐเคฃ

  • เค•เคฎ เคंเคŸी-เคŸ्เคฐเคธ्เคŸ เคœोเค–िเคฎ

  • เคช्เคฐเคคिเคธ्เคชเคฐ्เคงा เค•ा เคถांเคค เค…ंเคค

เค•ा เคฐाเคธ्เคคा เค–ोเคฒเคคी เคนै।

NVIDIA เค•ो Groq เค•ी เคœ़เคฐूเคฐเคค เค•्เคฏों เคฅी

NVIDIA เคŸ्เคฐेเคจिंเค— เคฎें เคฐाเคœा เคนै—เคฒेเค•िเคจ เคญเคตिเคท्เคฏ เค•ा เคชैเคธा เค‡เคจ्เคซ़เคฐेंเคธ เคฎें เคนै।

เคœैเคธे-เคœैเคธे AI เคซैเคฒเคคा เคนै:

  • เคŸ्เคฐेเคจिंเค— เคเค• เคฌाเคฐ เคนोเคคी เคนै

  • เค‡เคจ्เคซ़เคฐेंเคธ เค…เคฐเคฌों เคฌाเคฐ

Groq เคคीเคจ เคฌเคก़ी เคธเคฎเคธ्เคฏाเคँ เคนเคฒ เค•เคฐเคคा เคนै:

  1. เค‡เคจ्เคซ़เคฐेंเคธ เคฒाเค—เคค เค”เคฐ เคŠเคฐ्เคœा เคธंเค•เคŸ

  2. HBM เค•ी เค•เคฎी

  3. AMD, Cerebras เคœैเคธे เคช्เคฐเคคिเคธ्เคชเคฐ्เคงिเคฏों เค•ा เค‰เคญाเคฐ

Groq เค•ो เค…เคชเคจाเค•เคฐ NVIDIA เคจे เค…เคชเคจा เคธเคฌเคธे เค–़เคคเคฐเคจाเค• เค…ंเคคเคฐ เคญเคฐ เคฒिเคฏा।

เคต्เคฏाเคชเค• เคช्เคฐเคญाเคต: เค‡เคจ्เคซ़เคฐेंเคธ เคฏुเค— เค•ी เคถुเคฐुเค†เคค

เคธเค•ाเคฐाเคค्เคฎเค•

  • เคคेเคœ़ เค”เคฐ เคธเคธ्เคคा AI

  • เคฐीเคฏเคฒ-เคŸाเค‡เคฎ เคเคช्เคฒिเค•ेเคถเคจ เค•ा เคตिเคธ्เคซोเคŸ

เคจเค•ाเคฐाเคค्เคฎเค•

  • เคนाเคฐ्เคกเคตेเคฏเคฐ เคเค•ाเคงिเค•ाเคฐ เคฌเคข़ेเค—ा

  • เคธ्เคŸाเคฐ्เคŸเค…เคช्เคธ เค•े เคฒिเค เคฌाเคงाเคँ เคŠँเคšी เคนोंเค—ी

เค…เคธเคนเคœ เคธเคš्เคšाเคˆ

  • เคจिเคฏाเคฎเค•ीเคฏ เคฆเคฌाเคต เคฌเคข़ेเค—ा

  • AI เคนाเคฐ्เคกเคตेเคฏเคฐ เคญू-เคฐाเคœเคจीเคคिเค• เคนเคฅिเคฏाเคฐ เคฌเคจेเค—ा

เค…ंเคคिเคฎ เคตिเคšाเคฐ: เคธाเคฎ्เคฐाเคœ्เคฏ เคฎें เคเค• เคธ्เค•ैเคฒ्เคชेเคฒ

Groq เคจे เคฆुเคจिเคฏा เค•ा เคธเคฌเคธे เคคेเคœ़ เค‡เคจ्เคซ़เคฐेंเคธ เค‡ंเคœเคจ เคฌเคจाเคจे เค•ा เคฒเค•्เคท्เคฏ เคฐเค–ा—เค”เคฐ เค‡เคคเคจा เคธเคซเคฒ เคนुเค† เค•ि AI เคนाเคฐ्เคกเคตेเคฏเคฐ เค•े เคธเคฎ्เคฐाเคŸ เคจे เค‰เคธे เค–़เคฐीเคฆ เคฒेเคจा เคนी เคธुเคฐเค•्เคทिเคค เคธเคฎเคा।

เคฏเคน เคธौเคฆा เคธंเค•ेเคค เคนै เค•ि AI เค…เคฌ เคธिเคฐ्เคซ़ “เคธเคฌเคธे เคฌเคก़ा เคฎॉเคกเคฒ เค•ौเคจ เคŸ्เคฐेเคจ เค•เคฐเคคा เคนै” เค•ी เค•เคนाเคจी เคจเคนीं เคนै।
เค…เคฌ เคธเคตाเคฒ เคนै:

เค•ौเคจ เคธเคฌเคธे เคคेเคœ़, เคธเคฌเคธे เคธเคธ्เคคा เค”เคฐ เคธเคฌเคธे เคญเคฐोเคธेเคฎंเคฆ เคœเคตाเคฌ เคฆेเคคा เคนै।

เค•्เคฐूเคฐ เคถเค•्เคคि เค•ा เคฏुเค— เคธเคฎाเคช्เคค เคนो เคฐเคนा เคนै।
เคธเคŸीเค•เคคा เค•ा เคฏुเค— เคถुเคฐू เคนो เคšुเค•ा เคนै।

เค”เคฐ NVIDIA—เคเค• เคฌाเคฐ เคซिเคฐ—เค‡เคคिเคนाเคธ เค•े เค•ेंเคฆ्เคฐ เคฎें เค–เคก़ा เคนै।



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Wednesday, December 24, 2025

The Chupacabra Chronicles: From Goat-Sucking Ghoul to Martian Mogul on a Bad Hair Day




The Chupacabra Chronicles: From Goat-Sucking Ghoul to Martian Mogul on a Bad Hair Day

In the shadowy annals of cryptozoology—somewhere between Bigfoot’s grainy selfies and the Loch Ness Monster’s commitment to plausible deniability—few creatures have captured the public imagination quite like the chupacabra. Since its first whispered appearance in 1990s Puerto Rico, this alleged menace has been blamed for everything from livestock massacres to that inexplicable moment when your neighbor’s dog stared at you like it knew your secrets.

But what is the chupacabra, really?

A vampire dog?
A government experiment that escaped during a budget cut?
Or—according to the most cutting-edge, late-night, internet-fueled “research”—something far more extraterrestrial… and entrepreneurial?

Buckle up. We’re about to take a satirical safari through folklore, conspiracy, and Silicon Valley delusion, where the truth has been hiding in plain sight—probably tweeting about it.

The Birth of a Legend (And a Truly Unfortunate Name)

Let’s begin with etymology, because even monsters deserve good branding. Chupacabra translates roughly to “goat sucker,” a name that sounds less like a nightmare creature and more like a rejected superhero sidekick.

“Captain Justice and Goat Sucker: Fighting Crime, One Udder at a Time!”

Early eyewitnesses described a reptilian humanoid with spines down its back, glowing red eyes, and an unsettling enthusiasm for draining goats, sheep, and the occasional chicken that wandered too far from the coop. Farmers reported eerie scenes: animals found dead, blood mysteriously missing, puncture wounds precise enough to suggest either supernatural finesse—or a creature with an oddly medical degree.

The mid-1990s were a golden age for speculation. Latin America buzzed with rumors. Talk shows thrived. Conspiracy theorists rejoiced. Was it an alien? A mutant coyote? Bigfoot’s goth cousin who only came out at night and listened to industrial metal?

Whatever it was, the chupacabra had arrived—and it was thirsty.

Global Expansion: When Folklore Goes Viral

By the early 2000s, the chupacabra had gone international, hopping borders like a caffeinated kangaroo. Sightings popped up across Texas, New Mexico, and—because folklore respects no logic—Russia. (Apparently even vodka-loving goats need monsters.)

Scientists, ever the spoilsports, offered mundane explanations: coyotes with mange, feral dogs, misidentified predators, mass hysteria. But to believers, this felt insulting.

Saying the chupacabra was just a sick coyote was like saying the Loch Ness Monster is “a long eel who forgot sunscreen.” Technically possible. Spiritually unacceptable.

The myth demanded something bigger. Stranger. Preferably interstellar.

Enter the Internet Age: Filters, Footage, and Fear Algorithms

Then came the modern era, where trail cams, smartphones, and TikTok transformed chupacabra hunting into a content vertical. Blurry footage multiplied. Influencers squinted dramatically at shadows. Podcasts filled hours debating claw angles.

One especially memorable report emerged from rural Texas, near—of all places—a SpaceX launch site. Hikers were warned:

“Stray from the roads, and you might run into the mysterious chupacabra.”

The line went viral. People laughed. Elon Musk himself reportedly liked the joke.

But what if it wasn’t a joke?

What if the creature lurking in the brush wasn’t draining goats—but launching rockets?

The Great Reveal: A Theory So Absurd It Might Be True

After exhaustive investigation—defined here as doom-scrolling X at midnight while eating leftover pizza—we arrive at the only theory that explains everything.

The chupacabra isn’t a monster.

It’s Elon Musk.

Not public Elon. Not hoodie-and-podcast Elon. But incognito Elon—operating under cover of darkness, disguised in a wig that looks like a Martian lost a bet at Burning Man.

Picture the scene.

It’s midnight near Starbase, Texas. After a long day of tweeting about Mars, memes, and the collapse of civilization, Elon retreats to a secret lab. He dons a spectacularly bad disguise: a greenish, tentacled mop somewhere between “alien royalty” and “’80s glam rock roadie.” The goal? Field-test experimental technology without alarming regulators.

Or investors.

Or goats.

The “Evidence” (Please Read with One Eye Closed)

Let’s connect the dots—loosely, enthusiastically, and with no regard for peer review:

  • Spikes down the back?
    A poorly fitted Neuralink prototype protruding through a wig.

  • Glowing red eyes?
    Cybertruck headlights reflected off novelty contact lenses.

  • Bloodless livestock?
    “Organic sample acquisition” for sustainable biofuel or interplanetary nutrition research.

  • Sudden disappearance in smoke?
    A Starship Raptor engine test. Obviously.

  • Sightings near SpaceX facilities?
    Coincidence is not a business model.

Eyewitnesses describe the creature vanishing without a trace—except for scorched grass, terrified goats, and a lingering sense of having just witnessed a beta test.

Even the timing fits. Chupacabra sightings tend to spike around major SpaceX announcements, as if something—or someone—keeps wandering off-script while muttering about colonizing Mars.

A Monster No More—Just a Mogul in Disguise

In the end, the chupacabra isn’t a threat. It’s a parable.

A reminder that myths evolve, fears migrate, and sometimes the thing rustling in the bushes isn’t a blood-sucking beast—but a billionaire in a terrible wig, stress-testing the future.

So the next time you’re hiking near a rocket pad and hear something strange behind you, don’t panic. Don’t run.

Just call out:

“Elon, is that you? Love the hair!”

Who knows? You might get a selfie.
You might get a ride to Mars.
Or at the very least, a free Tesla and a great story.

After all, humor makes life better—even when it’s dressed up as a goat-sucking cryptid with a startup mindset.




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Sergey Brin's Google Glass Adventures In Steve Jobsism


“I Saw the Future. Unfortunately, the Future Saw Me.”
— Sergey Brin, Retrospective Keynote on Google Glass

Hello everyone. Thank you. Thank you for clapping. I assume you’re clapping because I’m no longer wearing Google Glass.

Let me start with a confession.

At one point—briefly, tragically—I believed I was the next Steve Jobs.

I didn’t just believe it. I accessorized for it.

Black shirt? Check.
Visionary confidence? Check.
Reality distortion field? Absolutely.
Social awareness? …buffering.

And then I made Google Glass.

Now, most people fail quietly. They fail in garages. They fail on Medium posts titled “What I Learned From My Startup That Didn’t Work.”
I failed on my face, on my head, on my eyeball, while already being famous.

When Google Glass failed, it didn’t just fail—it failed in public, in HD, from multiple angles, some of them livestreamed by me.

Act I: The Delusion

The idea was simple.

“What if,” I thought, “the problem with humanity… is that we don’t have enough screens?”

Phones? Too low.
Laptops? Too far away.
Reality itself? Underutilized.

So naturally, the next step was to glue the internet directly to your skull.

I imagined people saying:

“Wow, Sergey, you’ve changed everything.”

Instead they said:

“Why is that man filming me with his face?”

Different vibe.

Act II: The Product Launch

We didn’t launch Google Glass.

We released it into society like a social experiment without IRB approval.

It cost $1,500.

Which immediately filtered our early adopters down to:

  • Silicon Valley executives

  • People who say “actually” before every sentence

  • And one guy named Chad who never blinked

We called them Glass Explorers.

Everyone else called them “That Guy.”

Act III: The Failure Catalog (A Comprehensive List)

Let me walk you through every possible way Google Glass failed.

1. The “Are You Recording Me?” Problem

Nobody knew when Glass was recording.

Which meant:

  • Every conversation felt like a hostage negotiation

  • Every barista assumed they were in a documentary called “Latte Crimes: Season 3”

People would whisper:

“I think he’s filming us.”

And the Glass wearer would say:

“No, no, it’s not recording.”

Which is exactly what someone recording would say.

2. The Name “Glasshole” (Invented by the Public, Immediately)

We didn’t trademark it.

The internet did.

Within weeks, “Glasshole” became:

  • A noun

  • A diagnosis

  • A lifestyle choice

No product survives once society gives it a slur.

3. Restaurants Hated It

We thought:

“Chefs will love this. Recipes! Augmented reality!”

Restaurants thought:

“Absolutely not. Take off the robot monocle.”

Glass was banned faster than:

  • Smoking

  • Loud phone calls

  • Explanations of crypto

There were signs:

NO GLASS
NO FILMING
NO DISCUSSING WHY YOU NEED GLASS

4. Dating Was a War Crime

Google Glass on a first date was… bold.

Men reported feedback like:

“She left before appetizers.”
“She asked if I worked for the CIA.”
“She said, ‘I feel unsafe,’ and vanished.”

Women reported:

“He blinked too much.”
“He kept saying ‘just a second’ to his own face.”
“I think he Googled me while I was talking.”

Correct.

They did.

5. International Reactions Were Worse

France:

“Non.”
Just… “Non.”

Germany:
Immediate discussion of surveillance laws, history, and feelings.

Japan:
Polite silence. Terrifying judgment.

India:

“Why are you wearing broken spectacles and talking to yourself?”

Italy:
Gestures so aggressive the device nearly fell off.

UK:

“Is that… legal?”
In a whisper. Always a whisper.

6. The Battery Life

Glass could last:

  • 30 minutes of video

  • Or 12 seconds of ambition

Nothing builds confidence like your face dying mid-sentence.

7. The Voice Commands

You had to say:

“OK Glass…”

Out loud.

In public.

Which made everyone look like:

  • A cult member

  • A hostage

  • Or someone arguing with a ghost

8. The Privacy Debate

We said:

“People will adapt.”

Society said:

“We will not.”

Cities banned it.
Bars banned it.
Friends banned it.
Even Google employees quietly stopped wearing it.

Which is when you know.

Act IV: Customer Feedback (Real Energy, Fictional Quotes)

From the U.S.:

“My coworkers stopped inviting me to lunch.”

From Canada:

“Sorry, but could you not… exist like that near me?”

From Australia:

“Mate, absolutely not.”

From Brazil:

“Cool tech. Please leave.”

From Russia:

“You are being watched. Also stop watching.”

From Silicon Valley:

“I love it.”
(This person is no longer invited to parties.)

Act V: The Realization

Here’s the truth.

Google Glass wasn’t ahead of its time.

It was ahead of social consent.

We skipped:

  • Norms

  • Signals

  • Humanity

And went straight to:

“Trust us, it’s fine.”

It was not fine.

Finale: The Legacy

Google Glass taught me something profound.

Just because you can build something
doesn’t mean you should
especially if it turns every human interaction into a Black Mirror pilot.

And no, Glass is never coming back.

Not rebranded.
Not rebooted.
Not “Glass Pro Max Ultra.”

Some ideas don’t need iteration.

They need burial.

Thank you.

And please—
if you see someone wearing smart glasses—

Make eye contact.

Let them know.

They are not Steve Jobs.

None of us are.

๐Ÿ™



“Mind the Gap (Between Vision and Reality):
The Day Sergey Brin Rode the NYC Subway Wearing Google Glass”

There are many ways to test a product in the real world.

Focus groups.
A/B testing.
User research.

And then there is the New York City Subway, which offers immediate, brutal, peer-reviewed feedback from eight million unpaid critics.

This is the story of the day Sergey Brin—Google co-founder, billionaire, futurist, accidental performance artist—decided to ride the NYC Subway wearing Google Glass.

The Setup: A Visionary Enters the Underground

Sergey Brin boarded the train at Union Square.

He was wearing:

  • Google Glass

  • A slightly rumpled hoodie

  • The quiet confidence of a man who had never been confused with the homeless before

In his mind, this was a field test.

A moment of truth.

A symbolic gesture of a tech leader staying connected to the people.

The people had… a different interpretation.

The First Mistake: Talking to His Face

As the train lurched forward, Sergey whispered:

“OK Glass, show notifications.”

A nearby commuter stiffened.

Another clutched her bag.

A third nodded slowly, the way New Yorkers do when they decide not to make eye contact with a situation.

To the average subway rider, the scene looked like this:

A man wearing broken glasses
Muttering to himself
Blinking aggressively
Staring into space

This was not “Silicon Valley Founder.”

This was “Subway Philosopher.”

The Second Mistake: The Pauses

Google Glass had latency.

Which meant Sergey would speak…
then wait…
then react to information only he could see.

To everyone else, he appeared to be:

  • Hearing voices

  • Processing prophecies

  • Or buffering divine instructions

He smiled at something no one else could see.

Never do this underground.

The Third Mistake: The Hoodie + Backpack Combo

New York has a classification system.

Suit + briefcase = finance.
Scrubs = medical.
High-end athleisure = tech.

But hoodie + backpack + face-computer?

That falls under:

“We should give him space.”

Or:

“He’s between things.”

The Moment It Happened

Somewhere between 14th Street and 42nd Street, it happened.

A woman—kind, well-meaning, Upper West Side energy—approached Sergey gently.

She made eye contact.

She smiled.

She placed two quarters on the floor near his feet.

And whispered:

“God bless.”

Sergey didn’t notice.

He was checking email.

The Escalation

New Yorkers are nothing if not responsive to social cues.

If one person gives, others follow.

A man dropped a dollar.

Someone else added coins.

A tourist snapped a photo.

A kid asked his mom:

“Is he famous or sad?”

The correct answer was:

“Both.”

The Feedback Loop of Doom

Sergey finally noticed the coins.

He looked down.

He looked up.

He looked confused.

He said, quietly:

“Oh—no, no—I’m fine.”

But Google Glass misheard.

And responded, out loud:

“I’m sorry, I didn’t understand that.”

At this point, the car reached consensus.

This man was:

  • Struggling

  • Harmless

  • And possibly very, very smart in a way that had not worked out

Someone gave him a granola bar.

The Exit

At Times Square, Sergey Brin exited the train.

Behind him lay:

  • $3.75 in loose change

  • One protein bar

  • And the final confirmation that Google Glass was not “urban ready”

He stood on the platform, holding his backpack, staring into the distance.

For the first time, augmented reality had been fully overridden by actual reality.

The Aftermath

Later that day, Sergey reportedly removed the device and placed it gently into his bag.

Not angrily.

Not dramatically.

Just… respectfully.

Like one does with an idea that tried its best.

Google Glass was many things:

  • Bold

  • Ambitious

  • Technically impressive

But it could not survive the MTA.

And no product that fails the subway deserves to succeed on the surface.

Epilogue: A Lesson in Humility

The New York City Subway doesn’t care who you are.

Not your net worth.
Not your patents.
Not your TED Talks.

Down there, everyone is equal.

And if you talk to your glasses long enough—

Someone will hand you change.

๐Ÿช™




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