2026-03-18 17:15:04
Victoria, Seychelles, March 17, 2026
CoinGecko recently released its CEX & DEX Trading Activity Report 2026, offering a comprehensive review of the new listings, trading volume, and market share performance of major exchanges from January 2025 to January 2026. The report highlights that MEXC ranked among the top in all three key metrics—number of listings, spot market share, and perpetual futures market share—reinforcing its strong competitive edge in both token coverage and trading volume.
In terms of token listings, the report covers data from January 2025 to January 2026. MEXC listed a total of 1,281 tokens during this 13-month period, ranking first among mainstream centralized exchanges (CEXs), with an average of nearly 100 new tokens listed each month—far above the industry average.
The report also notes that during the same period, a staggering 24.04 million new tokens were created across the entire crypto ecosystem. Even the exchange with the most listings only covered 0.01% of this total. This underscores that the true value of a centralized exchange lies not in "listing everything," but in curation and quality control. MEXC's strategy of delivering the fastest speed and widest coverage ensures users can access high-potential early-stage projects, a key differentiator in the market.
Looking at trading volume over the six-month period from August 2025 to January 2026, MEXC held 8.2% of the global spot market, ranking second worldwide. In perpetual futures, the platform captured an 11.8% market share, placing third globally. Across both spot and perpetual futures markets, MEXC consistently ranks in the top three, demonstrating the platform's deep liquidity and strong user engagement across diverse trading scenarios.
Behind MEXC's fast listing pace and stable market share is its continued investment in a broad product ecosystem. The platform now offers over 2,400 tokens on Spot and 800 Futures trading pairs. Its "zero-fee" strategy has benefited 3.44 million users, saving a total of 1.1 billion USDT in trading fees—a major driver of sustained trading volume growth. Meanwhile, products such as MEXC Earn, Launchpad, and AI trading tools continue to expand, allowing users not only to "buy fast" but also to "trade smart."
Looking ahead to 2026, MEXC's mission remains clear: "MEXCmize Your Opportunities"—providing ultra-low fees and comprehensive liquidity to make the crypto world more accessible than ever for its users.
Full Report: CoinGecko 2026 CEX & DEX Trading Activity Report
Founded in 2018, MEXC is committed to being "Your Easiest Way to Crypto." Serving over 40 million users across 170+ countries, MEXC is known for its broad selection of trending tokens, everyday airdrop opportunities, and low trading fees. Our user-friendly platform is designed to support both new traders and experienced investors, offering secure and efficient access to digital assets. MEXC prioritizes simplicity and innovation, making crypto trading more accessible and rewarding.
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2026-03-18 17:00:05
:::info Astounding Stories of Super-Science July, 2008, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Call of the Wild - The Sounding of the Call
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\ By Jack London
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\ When Buck earned sixteen hundred dollars in five minutes for John Thornton, he made it possible for his master to pay off certain debts and to journey with his partners into the East after a fabled lost mine, the history of which was as old as the history of the country. Many men had sought it; few had found it; and more than a few there were who had never returned from the quest. This lost mine was steeped in tragedy and shrouded in mystery. No one knew of the first man. The oldest tradition stopped before it got back to him. From the beginning there had been an ancient and ramshackle cabin. Dying men had sworn to it, and to the mine the site of which it marked, clinching their testimony with nuggets that were unlike any known grade of gold in the Northland.
But no living man had looted this treasure house, and the dead were dead; wherefore John Thornton and Pete and Hans, with Buck and half a dozen other dogs, faced into the East on an unknown trail to achieve where men and dogs as good as themselves had failed. They sledded seventy miles up the Yukon, swung to the left into the Stewart River, passed the Mayo and the McQuestion, and held on until the Stewart itself became a streamlet, threading the upstanding peaks which marked the backbone of the continent.
John Thornton asked little of man or nature. He was unafraid of the wild. With a handful of salt and a rifle he could plunge into the wilderness and fare wherever he pleased and as long as he pleased. Being in no haste, Indian fashion, he hunted his dinner in the course of the day’s travel; and if he failed to find it, like the Indian, he kept on travelling, secure in the knowledge that sooner or later he would come to it. So, on this great journey into the East, straight meat was the bill of fare, ammunition and tools principally made up the load on the sled, and the time-card was drawn upon the limitless future.
To Buck it was boundless delight, this hunting, fishing, and indefinite wandering through strange places. For weeks at a time they would hold on steadily, day after day; and for weeks upon end they would camp, here and there, the dogs loafing and the men burning holes through frozen muck and gravel and washing countless pans of dirt by the heat of the fire. Sometimes they went hungry, sometimes they feasted riotously, all according to the abundance of game and the fortune of hunting. Summer arrived, and dogs and men packed on their backs, rafted across blue mountain lakes, and descended or ascended unknown rivers in slender boats whipsawed from the standing forest.
The months came and went, and back and forth they twisted through the uncharted vastness, where no men were and yet where men had been if the Lost Cabin were true. They went across divides in summer blizzards, shivered under the midnight sun on naked mountains between the timber line and the eternal snows, dropped into summer valleys amid swarming gnats and flies, and in the shadows of glaciers picked strawberries and flowers as ripe and fair as any the Southland could boast. In the fall of the year they penetrated a weird lake country, sad and silent, where wildfowl had been, but where then there was no life nor sign of life—only the blowing of chill winds, the forming of ice in sheltered places, and the melancholy rippling of waves on lonely beaches.
And through another winter they wandered on the obliterated trails of men who had gone before. Once, they came upon a path blazed through the forest, an ancient path, and the Lost Cabin seemed very near. But the path began nowhere and ended nowhere, and it remained mystery, as the man who made it and the reason he made it remained mystery. Another time they chanced upon the time-graven wreckage of a hunting lodge, and amid the shreds of rotted blankets John Thornton found a long-barrelled flint-lock. He knew it for a Hudson Bay Company gun of the young days in the Northwest, when such a gun was worth its height in beaver skins packed flat, And that was all—no hint as to the man who in an early day had reared the lodge and left the gun among the blankets.
Spring came on once more, and at the end of all their wandering they found, not the Lost Cabin, but a shallow placer in a broad valley where the gold showed like yellow butter across the bottom of the washing-pan. They sought no farther. Each day they worked earned them thousands of dollars in clean dust and nuggets, and they worked every day. The gold was sacked in moose-hide bags, fifty pounds to the bag, and piled like so much firewood outside the spruce-bough lodge. Like giants they toiled, days flashing on the heels of days like dreams as they heaped the treasure up.
There was nothing for the dogs to do, save the hauling in of meat now and again that Thornton killed, and Buck spent long hours musing by the fire. The vision of the short-legged hairy man came to him more frequently, now that there was little work to be done; and often, blinking by the fire, Buck wandered with him in that other world which he remembered.
The salient thing of this other world seemed fear. When he watched the hairy man sleeping by the fire, head between his knees and hands clasped above, Buck saw that he slept restlessly, with many starts and awakenings, at which times he would peer fearfully into the darkness and fling more wood upon the fire. Did they walk by the beach of a sea, where the hairy man gathered shellfish and ate them as he gathered, it was with eyes that roved everywhere for hidden danger and with legs prepared to run like the wind at its first appearance. Through the forest they crept noiselessly, Buck at the hairy man’s heels; and they were alert and vigilant, the pair of them, ears twitching and moving and nostrils quivering, for the man heard and smelled as keenly as Buck. The hairy man could spring up into the trees and travel ahead as fast as on the ground, swinging by the arms from limb to limb, sometimes a dozen feet apart, letting go and catching, never falling, never missing his grip. In fact, he seemed as much at home among the trees as on the ground; and Buck had memories of nights of vigil spent beneath trees wherein the hairy man roosted, holding on tightly as he slept.
And closely akin to the visions of the hairy man was the call still sounding in the depths of the forest. It filled him with a great unrest and strange desires. It caused him to feel a vague, sweet gladness, and he was aware of wild yearnings and stirrings for he knew not what. Sometimes he pursued the call into the forest, looking for it as though it were a tangible thing, barking softly or defiantly, as the mood might dictate. He would thrust his nose into the cool wood moss, or into the black soil where long grasses grew, and snort with joy at the fat earth smells; or he would crouch for hours, as if in concealment, behind fungus-covered trunks of fallen trees, wide-eyed and wide-eared to all that moved and sounded about him. It might be, lying thus, that he hoped to surprise this call he could not understand. But he did not know why he did these various things. He was impelled to do them, and did not reason about them at all.
Irresistible impulses seized him. He would be lying in camp, dozing lazily in the heat of the day, when suddenly his head would lift and his ears cock up, intent and listening, and he would spring to his feet and dash away, and on and on, for hours, through the forest aisles and across the open spaces where the niggerheads bunched. He loved to run down dry watercourses, and to creep and spy upon the bird life in the woods. For a day at a time he would lie in the underbrush where he could watch the partridges drumming and strutting up and down. But especially he loved to run in the dim twilight of the summer midnights, listening to the subdued and sleepy murmurs of the forest, reading signs and sounds as man may read a book, and seeking for the mysterious something that called—called, waking or sleeping, at all times, for him to come.
One night he sprang from sleep with a start, eager-eyed, nostrils quivering and scenting, his mane bristling in recurrent waves. From the forest came the call (or one note of it, for the call was many noted), distinct and definite as never before,—a long-drawn howl, like, yet unlike, any noise made by husky dog. And he knew it, in the old familiar way, as a sound heard before. He sprang through the sleeping camp and in swift silence dashed through the woods. As he drew closer to the cry he went more slowly, with caution in every movement, till he came to an open place among the trees, and looking out saw, erect on haunches, with nose pointed to the sky, a long, lean, timber wolf.
He had made no noise, yet it ceased from its howling and tried to sense his presence. Buck stalked into the open, half crouching, body gathered compactly together, tail straight and stiff, feet falling with unwonted care. Every movement advertised commingled threatening and overture of friendliness. It was the menacing truce that marks the meeting of wild beasts that prey. But the wolf fled at sight of him. He followed, with wild leapings, in a frenzy to overtake. He ran him into a blind channel, in the bed of the creek where a timber jam barred the way. The wolf whirled about, pivoting on his hind legs after the fashion of Joe and of all cornered husky dogs, snarling and bristling, clipping his teeth together in a continuous and rapid succession of snaps.
Buck did not attack, but circled him about and hedged him in with friendly advances. The wolf was suspicious and afraid; for Buck made three of him in weight, while his head barely reached Buck’s shoulder. Watching his chance, he darted away, and the chase was resumed. Time and again he was cornered, and the thing repeated, though he was in poor condition, or Buck could not so easily have overtaken him. He would run till Buck’s head was even with his flank, when he would whirl around at bay, only to dash away again at the first opportunity.
But in the end Buck’s pertinacity was rewarded; for the wolf, finding that no harm was intended, finally sniffed noses with him. Then they became friendly, and played about in the nervous, half-coy way with which fierce beasts belie their fierceness. After some time of this the wolf started off at an easy lope in a manner that plainly showed he was going somewhere. He made it clear to Buck that he was to come, and they ran side by side through the sombre twilight, straight up the creek bed, into the gorge from which it issued, and across the bleak divide where it took its rise.
On the opposite slope of the watershed they came down into a level country where were great stretches of forest and many streams, and through these great stretches they ran steadily, hour after hour, the sun rising higher and the day growing warmer. Buck was wildly glad. He knew he was at last answering the call, running by the side of his wood brother toward the place from where the call surely came. Old memories were coming upon him fast, and he was stirring to them as of old he stirred to the realities of which they were the shadows. He had done this thing before, somewhere in that other and dimly remembered world, and he was doing it again, now, running free in the open, the unpacked earth underfoot, the wide sky overhead.
They stopped by a running stream to drink, and, stopping, Buck remembered John Thornton. He sat down. The wolf started on toward the place from where the call surely came, then returned to him, sniffing noses and making actions as though to encourage him. But Buck turned about and started slowly on the back track. For the better part of an hour the wild brother ran by his side, whining softly. Then he sat down, pointed his nose upward, and howled. It was a mournful howl, and as Buck held steadily on his way he heard it grow faint and fainter until it was lost in the distance.
John Thornton was eating dinner when Buck dashed into camp and sprang upon him in a frenzy of affection, overturning him, scrambling upon him, licking his face, biting his hand—“playing the general tom-fool,” as John Thornton characterized it, the while he shook Buck back and forth and cursed him lovingly.
For two days and nights Buck never left camp, never let Thornton out of his sight. He followed him about at his work, watched him while he ate, saw him into his blankets at night and out of them in the morning. But after two days the call in the forest began to sound more imperiously than ever. Buck’s restlessness came back on him, and he was haunted by recollections of the wild brother, and of the smiling land beyond the divide and the run side by side through the wide forest stretches. Once again he took to wandering in the woods, but the wild brother came no more; and though he listened through long vigils, the mournful howl was never raised.
He began to sleep out at night, staying away from camp for days at a time; and once he crossed the divide at the head of the creek and went down into the land of timber and streams. There he wandered for a week, seeking vainly for fresh sign of the wild brother, killing his meat as he travelled and travelling with the long, easy lope that seems never to tire. He fished for salmon in a broad stream that emptied somewhere into the sea, and by this stream he killed a large black bear, blinded by the mosquitoes while likewise fishing, and raging through the forest helpless and terrible. Even so, it was a hard fight, and it aroused the last latent remnants of Buck’s ferocity. And two days later, when he returned to his kill and found a dozen wolverenes quarrelling over the spoil, he scattered them like chaff; and those that fled left two behind who would quarrel no more.
The blood-longing became stronger than ever before. He was a killer, a thing that preyed, living on the things that lived, unaided, alone, by virtue of his own strength and prowess, surviving triumphantly in a hostile environment where only the strong survived. Because of all this he became possessed of a great pride in himself, which communicated itself like a contagion to his physical being. It advertised itself in all his movements, was apparent in the play of every muscle, spoke plainly as speech in the way he carried himself, and made his glorious furry coat if anything more glorious. But for the stray brown on his muzzle and above his eyes, and for the splash of white hair that ran midmost down his chest, he might well have been mistaken for a gigantic wolf, larger than the largest of the breed. From his St. Bernard father he had inherited size and weight, but it was his shepherd mother who had given shape to that size and weight. His muzzle was the long wolf muzzle, save that it was larger than the muzzle of any wolf; and his head, somewhat broader, was the wolf head on a massive scale.
His cunning was wolf cunning, and wild cunning; his intelligence, shepherd intelligence and St. Bernard intelligence; and all this, plus an experience gained in the fiercest of schools, made him as formidable a creature as any that roamed the wild. A carnivorous animal living on a straight meat diet, he was in full flower, at the high tide of his life, overspilling with vigor and virility. When Thornton passed a caressing hand along his back, a snapping and crackling followed the hand, each hair discharging its pent magnetism at the contact. Every part, brain and body, nerve tissue and fibre, was keyed to the most exquisite pitch; and between all the parts there was a perfect equilibrium or adjustment. To sights and sounds and events which required action, he responded with lightning-like rapidity. Quickly as a husky dog could leap to defend from attack or to attack, he could leap twice as quickly. He saw the movement, or heard sound, and responded in less time than another dog required to compass the mere seeing or hearing. He perceived and determined and responded in the same instant. In point of fact the three actions of perceiving, determining, and responding were sequential; but so infinitesimal were the intervals of time between them that they appeared simultaneous. His muscles were surcharged with vitality, and snapped into play sharply, like steel springs. Life streamed through him in splendid flood, glad and rampant, until it seemed that it would burst him asunder in sheer ecstasy and pour forth generously over the world.
“Never was there such a dog,” said John Thornton one day, as the partners watched Buck marching out of camp.
“When he was made, the mould was broke,” said Pete.
“Py jingo! I t’ink so mineself,” Hans affirmed.
They saw him marching out of camp, but they did not see the instant and terrible transformation which took place as soon as he was within the secrecy of the forest. He no longer marched. At once he became a thing of the wild, stealing along softly, cat-footed, a passing shadow that appeared and disappeared among the shadows. He knew how to take advantage of every cover, to crawl on his belly like a snake, and like a snake to leap and strike. He could take a ptarmigan from its nest, kill a rabbit as it slept, and snap in mid air the little chipmunks fleeing a second too late for the trees. Fish, in open pools, were not too quick for him; nor were beaver, mending their dams, too wary. He killed to eat, not from wantonness; but he preferred to eat what he killed himself. So a lurking humor ran through his deeds, and it was his delight to steal upon the squirrels, and, when he all but had them, to let them go, chattering in mortal fear to the treetops.
As the fall of the year came on, the moose appeared in greater abundance, moving slowly down to meet the winter in the lower and less rigorous valleys. Buck had already dragged down a stray part-grown calf; but he wished strongly for larger and more formidable quarry, and he came upon it one day on the divide at the head of the creek. A band of twenty moose had crossed over from the land of streams and timber, and chief among them was a great bull. He was in a savage temper, and, standing over six feet from the ground, was as formidable an antagonist as even Buck could desire. Back and forth the bull tossed his great palmated antlers, branching to fourteen points and embracing seven feet within the tips. His small eyes burned with a vicious and bitter light, while he roared with fury at sight of Buck.
From the bull’s side, just forward of the flank, protruded a feathered arrow-end, which accounted for his savageness. Guided by that instinct which came from the old hunting days of the primordial world, Buck proceeded to cut the bull out from the herd. It was no slight task. He would bark and dance about in front of the bull, just out of reach of the great antlers and of the terrible splay hoofs which could have stamped his life out with a single blow. Unable to turn his back on the fanged danger and go on, the bull would be driven into paroxysms of rage. At such moments he charged Buck, who retreated craftily, luring him on by a simulated inability to escape. But when he was thus separated from his fellows, two or three of the younger bulls would charge back upon Buck and enable the wounded bull to rejoin the herd.
There is a patience of the wild—dogged, tireless, persistent as life itself—that holds motionless for endless hours the spider in its web, the snake in its coils, the panther in its ambuscade; this patience belongs peculiarly to life when it hunts its living food; and it belonged to Buck as he clung to the flank of the herd, retarding its march, irritating the young bulls, worrying the cows with their half-grown calves, and driving the wounded bull mad with helpless rage. For half a day this continued. Buck multiplied himself, attacking from all sides, enveloping the herd in a whirlwind of menace, cutting out his victim as fast as it could rejoin its mates, wearing out the patience of creatures preyed upon, which is a lesser patience than that of creatures preying.
As the day wore along and the sun dropped to its bed in the northwest (the darkness had come back and the fall nights were six hours long), the young bulls retraced their steps more and more reluctantly to the aid of their beset leader. The down-coming winter was harrying them on to the lower levels, and it seemed they could never shake off this tireless creature that held them back. Besides, it was not the life of the herd, or of the young bulls, that was threatened. The life of only one member was demanded, which was a remoter interest than their lives, and in the end they were content to pay the toll.
As twilight fell the old bull stood with lowered head, watching his mates—the cows he had known, the calves he had fathered, the bulls he had mastered—as they shambled on at a rapid pace through the fading light. He could not follow, for before his nose leaped the merciless fanged terror that would not let him go. Three hundredweight more than half a ton he weighed; he had lived a long, strong life, full of fight and struggle, and at the end he faced death at the teeth of a creature whose head did not reach beyond his great knuckled knees.
From then on, night and day, Buck never left his prey, never gave it a moment’s rest, never permitted it to browse the leaves of trees or the shoots of young birch and willow. Nor did he give the wounded bull opportunity to slake his burning thirst in the slender trickling streams they crossed. Often, in desperation, he burst into long stretches of flight. At such times Buck did not attempt to stay him, but loped easily at his heels, satisfied with the way the game was played, lying down when the moose stood still, attacking him fiercely when he strove to eat or drink.
The great head drooped more and more under its tree of horns, and the shambling trot grew weak and weaker. He took to standing for long periods, with nose to the ground and dejected ears dropped limply; and Buck found more time in which to get water for himself and in which to rest. At such moments, panting with red lolling tongue and with eyes fixed upon the big bull, it appeared to Buck that a change was coming over the face of things. He could feel a new stir in the land. As the moose were coming into the land, other kinds of life were coming in. Forest and stream and air seemed palpitant with their presence. The news of it was borne in upon him, not by sight, or sound, or smell, but by some other and subtler sense. He heard nothing, saw nothing, yet knew that the land was somehow different; that through it strange things were afoot and ranging; and he resolved to investigate after he had finished the business in hand.
At last, at the end of the fourth day, he pulled the great moose down. For a day and a night he remained by the kill, eating and sleeping, turn and turn about. Then, rested, refreshed and strong, he turned his face toward camp and John Thornton. He broke into the long easy lope, and went on, hour after hour, never at loss for the tangled way, heading straight home through strange country with a certitude of direction that put man and his magnetic needle to shame.
As he held on he became more and more conscious of the new stir in the land. There was life abroad in it different from the life which had been there throughout the summer. No longer was this fact borne in upon him in some subtle, mysterious way. The birds talked of it, the squirrels chattered about it, the very breeze whispered of it. Several times he stopped and drew in the fresh morning air in great sniffs, reading a message which made him leap on with greater speed. He was oppressed with a sense of calamity happening, if it were not calamity already happened; and as he crossed the last watershed and dropped down into the valley toward camp, he proceeded with greater caution.
Three miles away he came upon a fresh trail that sent his neck hair rippling and bristling, It led straight toward camp and John Thornton. Buck hurried on, swiftly and stealthily, every nerve straining and tense, alert to the multitudinous details which told a story—all but the end. His nose gave him a varying description of the passage of the life on the heels of which he was travelling. He remarked the pregnant silence of the forest. The bird life had flitted. The squirrels were in hiding. One only he saw,—a sleek gray fellow, flattened against a gray dead limb so that he seemed a part of it, a woody excrescence upon the wood itself.
As Buck slid along with the obscureness of a gliding shadow, his nose was jerked suddenly to the side as though a positive force had gripped and pulled it. He followed the new scent into a thicket and found Nig. He was lying on his side, dead where he had dragged himself, an arrow protruding, head and feathers, from either side of his body.
A hundred yards farther on, Buck came upon one of the sled-dogs Thornton had bought in Dawson. This dog was thrashing about in a death-struggle, directly on the trail, and Buck passed around him without stopping. From the camp came the faint sound of many voices, rising and falling in a sing-song chant. Bellying forward to the edge of the clearing, he found Hans, lying on his face, feathered with arrows like a porcupine. At the same instant Buck peered out where the spruce-bough lodge had been and saw what made his hair leap straight up on his neck and shoulders. A gust of overpowering rage swept over him. He did not know that he growled, but he growled aloud with a terrible ferocity. For the last time in his life he allowed passion to usurp cunning and reason, and it was because of his great love for John Thornton that he lost his head.
The Yeehats were dancing about the wreckage of the spruce-bough lodge when they heard a fearful roaring and saw rushing upon them an animal the like of which they had never seen before. It was Buck, a live hurricane of fury, hurling himself upon them in a frenzy to destroy. He sprang at the foremost man (it was the chief of the Yeehats), ripping the throat wide open till the rent jugular spouted a fountain of blood. He did not pause to worry the victim, but ripped in passing, with the next bound tearing wide the throat of a second man. There was no withstanding him. He plunged about in their very midst, tearing, rending, destroying, in constant and terrific motion which defied the arrows they discharged at him. In fact, so inconceivably rapid were his movements, and so closely were the Indians tangled together, that they shot one another with the arrows; and one young hunter, hurling a spear at Buck in mid air, drove it through the chest of another hunter with such force that the point broke through the skin of the back and stood out beyond. Then a panic seized the Yeehats, and they fled in terror to the woods, proclaiming as they fled the advent of the Evil Spirit.
And truly Buck was the Fiend incarnate, raging at their heels and dragging them down like deer as they raced through the trees. It was a fateful day for the Yeehats. They scattered far and wide over the country, and it was not till a week later that the last of the survivors gathered together in a lower valley and counted their losses. As for Buck, wearying of the pursuit, he returned to the desolated camp. He found Pete where he had been killed in his blankets in the first moment of surprise. Thornton’s desperate struggle was fresh-written on the earth, and Buck scented every detail of it down to the edge of a deep pool. By the edge, head and fore feet in the water, lay Skeet, faithful to the last. The pool itself, muddy and discolored from the sluice boxes, effectually hid what it contained, and it contained John Thornton; for Buck followed his trace into the water, from which no trace led away.
All day Buck brooded by the pool or roamed restlessly about the camp. Death, as a cessation of movement, as a passing out and away from the lives of the living, he knew, and he knew John Thornton was dead. It left a great void in him, somewhat akin to hunger, but a void which ached and ached, and which food could not fill. At times, when he paused to contemplate the carcasses of the Yeehats, he forgot the pain of it; and at such times he was aware of a great pride in himself,—a pride greater than any he had yet experienced. He had killed man, the noblest game of all, and he had killed in the face of the law of club and fang. He sniffed the bodies curiously. They had died so easily. It was harder to kill a husky dog than them. They were no match at all, were it not for their arrows and spears and clubs. Thenceforward he would be unafraid of them except when they bore in their hands their arrows, spears, and clubs.
Night came on, and a full moon rose high over the trees into the sky, lighting the land till it lay bathed in ghostly day. And with the coming of the night, brooding and mourning by the pool, Buck became alive to a stirring of the new life in the forest other than that which the Yeehats had made, He stood up, listening and scenting. From far away drifted a faint, sharp yelp, followed by a chorus of similar sharp yelps. As the moments passed the yelps grew closer and louder. Again Buck knew them as things heard in that other world which persisted in his memory. He walked to the centre of the open space and listened. It was the call, the many-noted call, sounding more luringly and compellingly than ever before. And as never before, he was ready to obey. John Thornton was dead. The last tie was broken. Man and the claims of man no longer bound him.
Hunting their living meat, as the Yeehats were hunting it, on the flanks of the migrating moose, the wolf pack had at last crossed over from the land of streams and timber and invaded Buck’s valley. Into the clearing where the moonlight streamed, they poured in a silvery flood; and in the centre of the clearing stood Buck, motionless as a statue, waiting their coming. They were awed, so still and large he stood, and a moment’s pause fell, till the boldest one leaped straight for him. Like a flash Buck struck, breaking the neck. Then he stood, without movement, as before, the stricken wolf rolling in agony behind him. Three others tried it in sharp succession; and one after the other they drew back, streaming blood from slashed throats or shoulders.
This was sufficient to fling the whole pack forward, pell-mell, crowded together, blocked and confused by its eagerness to pull down the prey. Buck’s marvellous quickness and agility stood him in good stead. Pivoting on his hind legs, and snapping and gashing, he was everywhere at once, presenting a front which was apparently unbroken so swiftly did he whirl and guard from side to side. But to prevent them from getting behind him, he was forced back, down past the pool and into the creek bed, till he brought up against a high gravel bank. He worked along to a right angle in the bank which the men had made in the course of mining, and in this angle he came to bay, protected on three sides and with nothing to do but face the front.
And so well did he face it, that at the end of half an hour the wolves drew back discomfited. The tongues of all were out and lolling, the white fangs showing cruelly white in the moonlight. Some were lying down with heads raised and ears pricked forward; others stood on their feet, watching him; and still others were lapping water from the pool. One wolf, long and lean and gray, advanced cautiously, in a friendly manner, and Buck recognized the wild brother with whom he had run for a night and a day. He was whining softly, and, as Buck whined, they touched noses.
Then an old wolf, gaunt and battle-scarred, came forward. Buck writhed his lips into the preliminary of a snarl, but sniffed noses with him, Whereupon the old wolf sat down, pointed nose at the moon, and broke out the long wolf howl. The others sat down and howled. And now the call came to Buck in unmistakable accents. He, too, sat down and howled. This over, he came out of his angle and the pack crowded around him, sniffing in half-friendly, half-savage manner. The leaders lifted the yelp of the pack and sprang away into the woods. The wolves swung in behind, yelping in chorus. And Buck ran with them, side by side with the wild brother, yelping as he ran.
And here may well end the story of Buck. The years were not many when the Yeehats noted a change in the breed of timber wolves; for some were seen with splashes of brown on head and muzzle, and with a rift of white centring down the chest. But more remarkable than this, the Yeehats tell of a Ghost Dog that runs at the head of the pack. They are afraid of this Ghost Dog, for it has cunning greater than they, stealing from their camps in fierce winters, robbing their traps, slaying their dogs, and defying their bravest hunters.
Nay, the tale grows worse. Hunters there are who fail to return to the camp, and hunters there have been whom their tribesmen found with throats slashed cruelly open and with wolf prints about them in the snow greater than the prints of any wolf. Each fall, when the Yeehats follow the movement of the moose, there is a certain valley which they never enter. And women there are who become sad when the word goes over the fire of how the Evil Spirit came to select that valley for an abiding-place.
In the summers there is one visitor, however, to that valley, of which the Yeehats do not know. It is a great, gloriously coated wolf, like, and yet unlike, all other wolves. He crosses alone from the smiling timber land and comes down into an open space among the trees. Here a yellow stream flows from rotted moose-hide sacks and sinks into the ground, with long grasses growing through it and vegetable mould overrunning it and hiding its yellow from the sun; and here he muses for a time, howling once, long and mournfully, ere he departs.
But he is not always alone. When the long winter nights come on and the wolves follow their meat into the lower valleys, he may be seen running at the head of the pack through the pale moonlight or glimmering borealis, leaping gigantic above his fellows, his great throat a-bellow as he sings a song of the younger world, which is the song of the pack.
\n
:::info About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books.
This book is part of the public domain. Astounding Stories. (2008). ASTOUNDING STORIES OF SUPER-SCIENCE, JULY 2008. USA. Project Gutenberg. Release date: JULY 2, 2008, from https://www.gutenberg.org/cache/epub/215/pg215-images.html
This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html.
:::
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2026-03-18 15:03:09
Following a joint demonstration at Embedded World (10–12 March), RemotiveLabs and Vayavya Labs announced a system-level ADAS validation setup that brings the full vehicle electronic architecture into software-in-the-loop (SIL). By combining Autoware, CARLA, and a virtual E/E architecture represented by RemotiveTopology, the companies demonstrated how OEMs and Tier 1s can validate autonomous driving behavior together with the vehicle’s electronic architecture - already at the SIL stage, before physical ECUs, HIL benches, or vehicle prototypes are available.
In most ADAS/ADS verification workflows today, simulation tools validate algorithms using scenario generators, sensor models, and vehicle dynamics. What is typically missing is the surrounding vehicle network - signal timing, cross-ECU dependencies, combining CAN/Ethernet/LIN communication, and system-level interactions.
To reproduce that full complexity, teams must move to HIL benches or physical vehicles - both expensive, capacity-constrained, and dependent on specialized integration teams. This often creates early project waiting time and long feedback loops. Instead of validating the ADAS stack in isolation, the demo first shown at Embedded world runs the AD stack operating as part of a realistic vehicle network in a closed-loop simulation.
Vayavya Labs specializes in building production-grade SIL and system validation environments. With the reference integration demonstrated at Embedded World they leverage RemotiveTopology to showcase how teams can establish a realistic system-level setup in a fraction of the traditional time. They presented a closed-loop reference integration where full vehicle behavior becomes visible already in SIL - not reconstructed later on hardware. A closed-loop reference setup:
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| 
| "Testing an ADS ECU within a vehicle EE network is typically effort-intensive, requiring complex HIL or bench setups, physical harnesses, and ADAS environment simulation - often consuming tens of person-months and still resulting in only semi-automated testing. " Nitin Swamy, Director, Automotive Systems, Vayavya Labs |
|----|----|
\
The collaboration between Vayayva Labs and RemotiveLabs is a paradigm shift, reducing system-level testing effort to a fraction of traditional bench-level effort by enabling virtual EE network configuration, integrated ADAS simulation, and scalable cloud-based validation. This allows system-level testing much earlier in the development cycle - without waiting for physical ECUs - saving both time and cost for OEMs. - Nitin Swamy
By combining Vayavya Labs’ validation expertise with RemotiveLabs’ virtual vehicle platform, teams can start integration testing in week one and scale depth as projects evolve.
Unlike traditional hardware-centric validation environments, the architecture is simulator-agnostic and supports integration of tools from multiple vendors. OEMs and Tier 1s are not forced into a single proprietary ecosystem. Existing simulation tools can be integrated into the topology, networks can be reconfigured as architectures evolve, and system-level test cases can be reused across SIL, HIL, and future vehicle platforms.
| 
| "For too long, system-level validation has been gated behind hardware availability. With this integration, we're showing that the full vehicle network can be part of the loop from week one. That's not an incremental improvement - it's a different way of working." Per Sigurdson, CEO and co-founder, RemotiveLabs |
|----|----|
Because the network structure remains available throughout the lifecycle, the same system-level environment continues to deliver value beyond early development which supports regression testing, platform updates, and reuse across future programs.
For ADAS and ADS development, simulation is the only scalable way to explore edge cases and rare scenarios safely. Bringing the full vehicle network into SIL enables:
https://www.youtube.com/watch?v=cE76Qkp2uoU&embedable=true
The result is less waiting at program start and validation environments that continue to deliver value throughout the vehicle lifecycle.
The integration built by Vayavya Labs using RemotiveLabs’ tooling is designed to scale across compute environments - from local GPU workstations to cloud infrastructure. It can also expand to include additional vehicle domains within the same virtual topology. The next planned step is to include Android running in Cuttlefish, bringing the infotainment node into the same environment as the ADAS stack and vehicle E/E architecture.
:::tip Vayavya Labs is a Silicon-to-System engineering services partner specializing in vECU development, SIL, and system-level verification and validation for ADAS and autonomous systems: https://vayavyalabs.com/ \n RemotiveLabs provides a simpler way to prototype, build, and test vehicle software with RemotiveTopology, enabling modular vehicle development across SIL and HIL: https://remotivelabs.com/
:::
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2026-03-18 14:46:35
This article presents a novel methodological framework for utilizing retrieval-augmented generation (RAG) systems specifically Google NotebookLM to address the critical challenge of tonal misalignment in competitive grant applications and institutional support documentation. By establishing a localized, source-grounded AI environment that synthesizes institutional data corpora with funder-specific linguistic patterns, this workflow enables rapid production of contextually precise, tonally calibrated grant narratives. The methodology operates in three phases: (1) source corpus ingestion and institutional knowledge mapping, (2) requirement-data alignment through constraint-based synthesis, and (3) semantic tone extraction and replication from funder exemplars. This approach reduces proposal development time by 60-70% while maintaining scholarly rigor and funder-specific rhetorical conventions. Critically, the source-grounding architecture of NotebookLM mitigates hallucination risks inherent in large language models (LLMs), ensuring factual accuracy in high-stakes regulatory and legal documentation. This framework represents a significant advancement in research administration practice, transforming grant writing from an ad hoc creative process into a systematic, data-driven operation that scales institutional competitiveness without compromising epistemic integrity.
The contemporary grant acquisition landscape is characterized by increasingly sophisticated evaluation criteria that extend beyond traditional metrics of scientific merit. Federal agencies, private foundations, and international funding bodies now employ nuanced assessment frameworks that privilege not only research quality but also narrative coherence, institutional strategic alignment, and communicative resonance with funder priorities (Melin & Danell, 2006; Luukkonen, 2012). This multidimensional evaluation paradigm creates what can be termed "tonal misalignment" a systematic disconnect between the linguistic register, rhetorical structure, and strategic framing employed by applicants versus the implicit stylistic expectations embedded in funder culture.
Tonal misalignment manifests in several critical failure modes:
Traditional approaches to grant writing treat these challenges as matters of individual writing skill, resolved through iterative drafting, peer review, and institutional memory. This artisanal model scales poorly, creates knowledge silos, and generates inconsistent outcomes dependent on individual grant writers' tacit expertise.
The emergence of retrieval-augmented generation (RAG) systems presents a fundamentally different paradigm: knowledge-grounded AI assistants that synthesize institutional data corpora with external stylistic exemplars to produce contextually precise, tonally calibrated outputs. Unlike general-purpose LLMs (ChatGPT, Claude), which rely on parametric knowledge from web-scale training data, RAG systems like Google NotebookLM operate on user-defined source documents, grounding all generated text in verifiable institutional data while extracting and replicating funder-specific linguistic patterns from exemplar documents.
This article presents a systematic methodology for leveraging NotebookLM's RAG architecture to eliminate tonal misalignment in grant applications, demonstrating how localized AI can bridge the semantic gap between institutional capabilities and funder expectations while maintaining the epistemic standards required for regulatory compliance.
Phase 1: Source Corpus Ingestion and Institutional Knowledge Mapping The foundational step involves constructing a private knowledge base within NotebookLM that serves as the authoritative source for all subsequent AI-generated content. This corpus typically includes:
Institutional Data Assets:
Technical Implementation: NotebookLM accepts up to 50 source documents (combined limit of ~500,000 words), each indexed and semantically embedded for retrieval. The platform constructs a vector database representation of the corpus, enabling the AI to perform similarity searches across institutional knowledge when responding to queries.
Example Source Structure:
├── PI_Credentials/
│ ├── CV_Dr_Principal_Investigator.pdf
│ ├── NIH_Biosketch_2024.pdf
│ └── Publication_Record_2019_2024.pdf
├── Institutional_Infrastructure/
│ ├── Core_Facilities_Catalog.pdf
│ ├── Strategic_Plan_2023_2028.pdf
│ └── Letters_of_Support_Archive/
├── Prior_Awards/
│ ├── NSF_CAREER_Successful_2022.pdf
│ ├── NIH_R01_Continuation_2023.pdf
│ └── Foundation_Grant_Archive/
Theoretical Rationale: By constraining the AI's knowledge domain to institutional sources, this approach implements a bounded rationality framework (Simon, 1957) where generated content cannot exceed the evidentiary basis provided by source documents. This architectural constraint is critical for regulatory contexts where unverifiable claims constitute compliance violations.
The second phase operationalizes the AI's retrieval capabilities to map funder requirements onto institutional capabilities through constraint-based query design.
Technical Process:
Prompt Architecture:
"Based solely on sources provided, identify institutional capabilities
that satisfy NSF CAREER requirement for 'integrated education and
research plan demonstrating creative, original concepts.'
Constraints:
- Evidence must come from PI's prior work or institutional programs
- Must align with NSF's definition of 'broader impacts'
- Response must cite specific source documents
- Flag any requirement gaps where institutional data is insufficient"
Methodological Innovation: This phase transforms grant writing from generative creativity to evidence-constrained synthesis. The AI does not invent capabilities; it performs systematic pattern-matching between funder criteria and institutional assets, surfacing alignments that might be overlooked by individual grant writers operating from incomplete institutional knowledge.
The final phase addresses the core challenge of tonal misalignment through exemplar-based style transfer.
Protocol Steps:
3.1 Funder Corpus Acquisition Collect 5-10 successfully funded proposals and support letters from the target funding mechanism. Sources include:
NIH RePORTER abstracts (public)
NSF Award Search database (public)
Foundation annual reports citing grantees
Institutional archives of successful applications
3.2 Linguistic Feature Extraction Upload exemplar documents to NotebookLM alongside institutional sources. Use targeted queries to extract funder-specific stylistic patterns:
Analytical Queries:
"Analyze the rhetorical structure of successful NIH R01 abstracts
in sources. Identify:
- Average sentence length and complexity (Flesch-Kincaid grade level)
- Frequency of first-person vs. passive voice
- Ratio of methodological detail to impact framing
- Use of hedging language vs. assertive claims
- Positioning of innovation narrative (opening vs. conclusion)"
\ 3.3 Tone-Matched Draft Generation With both institutional data and stylistic exemplars loaded, the AI can generate drafts that satisfy dual constraints:
Production Prompt:
"Draft a letter of support for PI's NIH R01 application using:
1. Institutional capabilities from [Strategic_Plan_2023_2028.pdf]
2. PI's track record from [CV_Dr_Principal_Investigator.pdf]
3. Linguistic style matching [NIH_R01_Successful_Letters_Archive/]
Requirements:
- Mirror the rhetorical structure of exemplar letters (2-3 paragraphs)
- Match tone: authoritative, outcome-focused, institutionally grounded
- Cite specific collaborations and resources
- Avoid generic praise; provide quantified evidence of support"
Output Validation: The generated draft includes inline citations to source documents (e.g., "According to [StrategicPlan2023_2028.pdf, p.12]…"), enabling human reviewers to verify factual accuracy while assessing tonal calibration against exemplars.
Computational Linguistics Foundation: This approach operationalizes register theory (Halliday, 1978) and genre analysis (Swales, 1990), treating funder-specific writing as a distinct linguistic register with learnable conventions. By providing the AI with exemplars, we enable computational extraction of implicit stylistic rules that would otherwise require years of tacit experience to internalize.
Scenario: A research institution is preparing a Multi-PI R01 application to NIH requiring:
Traditional Workflow:
RAG-Enhanced Workflow:
Setup (One-Time Investment, ~2 hours):
Production (Per Letter, ~20 minutes):
Outcome:
Quantified Impact:
Ethical and Strategic Implications: Human-in-the-Loop Verification and Source Grounding
The deployment of AI in grant writing raises legitimate concerns about epistemic integrity, regulatory compliance, and ethical boundaries of computational assistance. The RAG architecture of NotebookLM addresses several critical risks:
1. Hallucination Mitigation Through Source Grounding
Problem: General-purpose LLMs are prone to confabulation generating plausible but factually incorrect content (Maynez et al., 2020; Ji et al., 2023). In grant contexts, hallucinated capabilities, false collaboration claims, or invented credentials constitute fraud.
Solution: NotebookLM's retrieval-augmented architecture constrains generation to provably sourced content. Every factual claim includes citation metadata linking to source documents, enabling systematic verification:
Example Output with Source Grounding:
"The university's Center for Advanced Microscopy provides
access to cryo-electron microscopy facilities [Source:
Core_Facilities_Catalog.pdf, p.34], operated by Dr. Sarah
Chen [Source: CV_Dr_Chen.pdf] with 15 years of experience
in structural biology [Source: CV_Dr_Chen.pdf, p.2]."
If a required capability is absent from sources, the AI explicitly flags the gap rather than fabricating content:
Gap Identification:
"WARNING: No evidence of biosafety level 3 (BSL-3) facilities
found in institutional sources. This requirement cannot be
addressed without additional documentation."
Principle: AI-generated content must undergo domain expert validation before submission. The proposed workflow positions AI as draft generator rather than autonomous author.
Implementation:
Theoretical Grounding: This approach implements distributed cognition theory (Hutchins, 1995), treating the AI as a cognitive artifact that augments human expertise rather than replacing human judgment. The division of labor is clear:
Federal funding agencies increasingly require disclosure of AI assistance in grant preparation. The source-grounded workflow provides audit transparency:
Legal Implications: In contexts where false claims carry criminal liability (e.g., NIH fraud investigations), source-grounded AI provides defensive documentation demonstrating due diligence in factual verification.
The methodology presented herein represents more than an incremental improvement in grant writing efficiency it constitutes a paradigm shift in how institutions operationalize competitive research funding acquisition. By transforming grant development from an artisanal, knowledge-siloed process to a systematic, data-driven operation, this RAG-based framework addresses several critical challenges in contemporary research administration:
1. Scaling Institutional Competitiveness Mid-sized research institutions often lack the grant writing infrastructure of R1 universities, where dedicated offices employ specialists in proposal development. The NotebookLM methodology democratizes access to sophisticated grant writing capabilities, enabling smaller institutions to compete on narrative quality without proportional investment in human capital.
2. Preserving Epistemic Integrity Under Efficiency Pressures The tension between rapid proposal turnaround and scholarly rigor has historically forced a choice between thoroughness and timeliness. Source-grounded RAG systems resolve this dilemma by automating synthesis while maintaining verifiable factual grounding achieving both speed and accuracy.
3. Institutionalizing Tacit Expertise Grant writing expertise traditionally resides in individual practitioners who accumulate tacit knowledge of funder preferences through trial and error. By systematically encoding successful exemplars and institutional capabilities in a queryable knowledge base, this approach converts individual expertise into organizational infrastructure that persists beyond staff turnover.
4. Enabling Evidence-Based Proposal Strategy Traditional grant writing relies on subjective judgments about "what funders want." The tone-matching protocol introduces empirical rigor to stylistic decision-making replacing intuition with computational analysis of successful applications.
This work advances professional communications theory and research administration practice in three dimensions:
Theoretical Innovation: Operationalizes register theory and genre analysis through computational methods, demonstrating how RAG systems can learn and replicate domain-specific linguistic conventions.
Methodological Advancement: Presents the first systematic protocol for using source-grounded AI to bridge institutional knowledge bases with external stylistic exemplars in regulatory contexts.
Practical Impact: Provides a replicable framework that has demonstrated 60-70% time savings in grant development while improving narrative quality and compliance accuracy.
The convergence of retrieval-augmented generation, institutional data infrastructure, and systematic tone analysis creates emergent capabilities that exceed the sum of individual components. As research funding becomes increasingly competitive and narratively sophisticated, institutions that adopt evidence-grounded, AI-enhanced proposal development will possess decisive strategic advantages.
Future Directions:
Subsequent research should investigate:
The framework presented here establishes the foundation for a new subdiscipline at the intersection of computational linguistics, research administration, and AI-augmented professional practice one that promises to fundamentally reshape how knowledge institutions compete for resources in the 21st century.
References
Gross, A. G. (2010). The rhetoric of science. Harvard University Press.
Halliday, M. A. K. (1978). Language as social semiotic: The social interpretation of language and meaning. Edward Arnold.
Hutchins, E. (1995). Cognition in the wild. MIT Press.
Ji, Z., et al. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-38.
Latour, B., & Woolgar, S. (1979). Laboratory life: The construction of scientific facts. Princeton University Press.
Luukkonen, T. (2012). Conservatism and risk-taking in peer review: Emerging ERC practices. Research Evaluation, 21(1), 48-60.
Maynez, J., et al. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of ACL, 1906-1919.
Melin, G., & Danell, R. (2006). The top eight percent: Development of approved and rejected applicants for a prestigious grant in Sweden. Science and Public Policy, 33(10), 702-712.
Myers, G. (1985). The social construction of two biologists' proposals. Written Communication, 2(3), 219-245.
Simon, H. A. (1957). Models of man: Social and rational. Wiley.
Swales, J. M. (1990). Genre analysis: English in academic and research settings. Cambridge University Press.
2026-03-18 14:44:26
Every decentralized protocol built so far has shipped the same architectural mistake. Here's what we borrowed from 150,000 years of human behavior to fix it.
There's a pattern in how anthropologists describe information flow in pre-literate communities. News doesn't broadcast — it cascades. A person hears something from someone they trust, judges the claim against what they know of that source, and decides how far to pass it along. The further information travels from its origin, the more social proof it accumulates. Weak signals die locally. Strong ones reach everyone who needs them — and no one needs a server to make this work.
We've known this for decades. Robin Dunbar quantified the cognitive architecture behind it. Mark Granovetter mapped its structural mechanics. Watts and Strogatz showed it produces provably efficient routing through small-world network theory. The human social graph is not a metaphor for distributed systems. It is one — and a remarkably well-engineered one.
So why did every decentralized social protocol ever shipped route around it entirely?
Over the last decade, a generation of decentralized social protocols emerged in direct response to platform capture — Twitter/X banning accounts, Facebook harvesting data, Reddit killing third-party clients. The goal was user sovereignty: your identity, your data, no platform intermediary.
Three protocols represent the serious attempts. Nostr separates identity from infrastructure entirely — your keypair is your account, no domain tied to it, portable across any relay. Mastodon (ActivityPub) federates across independently-run instances, breaking the single-platform monopoly. Bluesky (AT Protocol) takes a similar federated approach but with portable accounts and a centralized relay aggregating the firehose.
Each made real progress. Each reproduced the same structural failure.
In Nostr, a client talks to relay servers. The relays decide which events to keep and for how long. Relay operators can drop your content without notice, and your data disappears with them unless you manually replicate across multiple relays. In Mastodon, your account is bound to an instance operator who can suspend it, shut down the server, or change terms unilaterally — your posts don't survive the instance dying. In Bluesky, a centralized relay aggregates and serves the firehose, recreating a single point of control at the infrastructure layer.
The specific architecture varies. The dependency is the same: servers control what gets stored, served, and censored. The user's social graph lives on infrastructure they do not own or control.
What none of them did — what no decentralized protocol has done — is make the social graph itself the storage layer. In every implementation, the trust relationships you've built are a display layer. They render your feed. They shape your recommendations. They do not touch the infrastructure. Your follow list is a UI concern, not a load-bearing one.
The promise of decentralization is sovereignty. The delivered reality is a different set of platform operators with a better origin story. The server dependency was never eliminated — it was rebranded.
What no protocol has yet done is use the social graph for what it actually is: a load-bearing distributed system that humans have been running reliably since before writing existed.
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Dunbar's research established that human social networks exhibit concentric structure. You maintain roughly 5 intimate contacts, 15 close friends, 50 good friends, 150 casual acquaintances — each layer approximately three times larger than the last, with intimacy and obligation halving at each step.
This isn't arbitrary. It's a solution to a resource allocation problem. Maintaining relationships costs cognitive overhead. The Dunbar layers represent different obligation tiers: the inner circle (5–15) are the people you'd help move at 2am. The sympathy group (15–50) are people whose social updates you track reliably. The affinity group (50–150) are people you recognize and trust provisionally.
What's structurally interesting is that these layers map directly onto information propagation dynamics:
Reciprocity as mechanism. Anthropologists have documented that information flow in communities is heavily shaped by reciprocal relationships. You share news selectively with people who share news with you. One-sided information relationships decay — the same way one-sided friendships do. This isn't altruism. It's a stable equilibrium that emerged because it produces good outcomes for both parties.
Triadic closure as verification. Rapoport formalized what communities already knew: if you trust A and A trusts B, your prior on B is substantially higher than on a stranger. The probability that information from B is relevant to you scales with how many of your mutual contacts also follow B. This is not a heuristic — it's the structural mechanism that makes recommendations reliable.
Proportional redundancy. Important information reaches you through multiple independent paths. Less important information dies locally. The redundancy is not uniform — it's proportional to social relevance. Gossip about your immediate community saturates; gossip about distant events fades. This is a feature, not a bug. It's content-addressable replication, implemented socially.
The weak tie bridge. Granovetter's 1973 paper remains one of the most structurally important results in network science: your strong ties (mutual follows, reciprocal relationships) provide redundancy — you and your close contacts share most of the same information. Your weak ties (one-directional follows, acquaintances) provide reach — they bridge you to communities outside your cluster. Information diversity comes from weak ties; information reliability comes from strong ties.
Every one of these mechanisms is an engineering solution to a distributed systems problem: storage, routing, verification, redundancy, discovery. Evolution ran the experiment for 150,000 years and shipped a working protocol. We just haven't been implementing it.
Gozzip is an open protocol that inherits Nostr's proven primitives — secp256k1 identity, signed events, relay transport — and adds the layer that makes the social graph structural rather than cosmetic.
The core mechanism is a storage pact: a bilateral agreement between two peers in your Web of Trust to hold each other's data. Volume-balanced (within 30% tolerance), cryptographically verified through periodic challenge-response, maintained in the background. Your pact partners receive a copy of your events alongside your relays. When someone reads your content, they check the pact network first.
This is not a clever engineering trick. It is a direct formalization of how human communities actually store information.
The protocol parameters map explicitly to Dunbar's social layers:
| Dunbar Layer | Size | Protocol Role | |----|----|----| | Support clique | ~5 | Full-node pact partners (complete history) | | Sympathy group | ~15 | Active pact partners (20 target) | | Affinity group | ~50 | Direct WoT tier (mutual follows) | | Dunbar number | ~150 | 2-hop WoT boundary | | Acquaintances | ~500+ | Relay-discovered peers (no storage obligation) |
The 20-pact target sits in the sympathy group layer because storage pacts require ongoing reciprocal obligation — a level of commitment that corresponds to the ~15 relationships humans maintain with regular mutual contact. The 2-hop gossip boundary aligns with Dunbar's number because trust assessment beyond ~150 nodes becomes cognitively unreliable in humans — and it becomes structurally unreliable in the protocol for the same reason.
These aren't aesthetic choices. If you required 500 pact partners, you'd exceed the sympathy group's capacity and incentivize defection. If you relied on 5-hop WoT propagation, you'd extend trust beyond the Dunbar boundary into structurally unverifiable territory.
Volume matching in pact formation formalizes what unstable human relationships already demonstrate: asymmetric obligations incentivize defection. A prolific poster paired with a lurker creates an asymmetric storage burden. Asymmetric friendships decay in human communities for the same structural reason — the cost/benefit ratio breaks down.
The protocol prevents this by matching peers on compatible activity levels. This is not an optimization — it is the minimum viable condition for the reciprocal relationship to be stable.
Reliability scoring operates identically to how human reputation actually works: not as a global score, but as a private per-peer assessment using a 30-day rolling window of observed behavior. There is no central reputation authority. Each node computes its own assessment. Dropped pacts lose you a storage advocate — the protocol equivalent of being talked about less.
When humans gossip, they don't broadcast. They share selectively based on trust and relevance. The protocol's WoT-filtered gossip layer works identically:
Pastor-Satorras and Vespignani proved that epidemic spreading on scale-free networks has a vanishing threshold — any nonzero transmission rate produces network-wide propagation. This means gossip is efficient but also means spam and attacks propagate freely if unrestricted.
The 2-hop WoT boundary solves this by creating a finite epidemic threshold. Within the WoT community, gossip spreads with near-scale-free efficiency. Beyond the boundary, propagation requires relay assistance. This produces the same dual regime that human gossip exhibits: saturating within communities, fading across them.
Every community has established members who vouch for newcomers. The protocol formalizes this as guardian pacts: an established user voluntarily stores data for one newcomer outside their WoT, accepting a small storage cost without reciprocal obligation.
The framing is deliberate. Today's newcomer is tomorrow's guardian. The pay-it-forward dynamic isn't enforced by the protocol — it's encouraged by client UX — because in human communities, the same dynamic operates through social norm rather than contractual obligation.
Data requests flow through four tiers, each activated only when the previous fails:
Tier 1 — Local pact storage. You already have it. Zero network traffic.
Tier 2 — Cached endpoint. You've interacted with this author's pact partners before; you have their addresses cached. Direct connection, ~60ms.
Tier 3 — WoT gossip. Blinded request (daily-rotating pubkey hash) broadcast to your WoT peers with TTL=3. With mean degree 20 and a clustering coefficient of 0.25, three hops covers ~4,500 nodes — 90%+ of a 5,000-node network. Gossip reach follows the small-world property: path length scales logarithmically while clustering remains high.
Tier 4 — Relay fallback. Last resort, 30-second timeout, ~200ms. The relay is still there. It's just no longer the load-bearing wall.
A critical emergent property: reads create replicas. When Bob fetches Alice's events from a pact partner, Bob now holds a local copy. When Carol later requests Alice's events via gossip, Bob can respond — without being a formal pact partner. Read load scales O(followers), not O(pact_partners). This is exactly how popular information propagates in human social networks: the more people read it, the more redundantly it's stored.
The protocol is analytically validated and simulated. It is not production-validated. Nostr has ~1,000 relays and real users. This doesn't.
Three open questions that matter:
Will 25% full nodes emerge organically? The data availability guarantee (P(all-pacts-offline) ≈ 10⁻⁹) assumes 25% always-on participants. Whether social incentives alone sustain this ratio is empirical. If it falls significantly, the math breaks.
Cold-start graph sparsity. New users have no WoT. The bootstrap phase requires relay dependence by design — pact formation is impossible without mutual follows. The transition is gradual, not instant. Early adopters in thin networks see less benefit.
Full node economics. Full nodes bear disproportionate storage and bandwidth costs. Social reciprocity may be sufficient incentive; it may not. Micropayments or premium services might be necessary. Unknown until production data exists.
The architectural claims are sound. The deployment parameters require production to validate.
A relay going offline no longer destroys the data it hosted — because that data exists across 20+ pact partners who have bilateral obligations to preserve it. But relays don't disappear. They earn a more honest role:
Discovery layer — finding people outside your trust graph. The WoT doesn't help you find people you don't know yet. Relays do.
Curation layer — editorial judgment, spam filtering, topic organization. This is legitimate value that relay operators can provide.
Performance accelerator — CDN behavior for content beyond the 2-hop WoT boundary.
What changes is that relay operators become curators rather than gatekeepers. They decide what to surface, not what exists.
The structural insight here is not about Nostr or decentralization specifically. It's that human social architecture contains solutions to distributed systems problems that we keep reinventing badly.
Dunbar's layers are a resource allocation protocol. Reciprocity is a Byzantine fault-tolerance mechanism. Triadic closure is distributed trust verification. Proportional redundancy is content-weighted replication. The epidemic threshold is a spam filter. These are not metaphors — they are functional isomorphisms.
We've spent decades building decentralized infrastructure that fails because we modeled data as content to be stored and served, rather than as a social object embedded in a trust network. The social graph isn't the user experience layer sitting on top of the infrastructure. It is the infrastructure. We just designed around it.
The Gozzip protocol whitepaper is at github.com/gozzip-protocol/gozzip.
The author is on Nostr: npub1gxdhmu9swqduwhr6zptjy4ya693zp3ql28nemy4hd97kuufyrqdqwe5zfk
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2026-03-18 14:43:26
Do you have many if/else conditions in your codebase? Or duplicated logic in both backend and UI? Does enabling a new set of similar behaviors mean a code change and a deployment? If so, it may be time to rethink the architecture.
An alternative approach could be: data-driven architecture. Instead of encoding every nuance in code, we let data—configuration, schemas, and metadata—drive behavior. One configuration becomes the single source of truth for both backend and UI, allowing us to adapt to new rules and fields without fragmenting our codebase.
This post walks through what data-driven architecture is, why it’s worth it, how to apply it—and, for production, how to track lineage and debug behavior when config is in the driver’s seat.
In this post:
Data-driven architecture means the structure and behavior of our system are determined by data—configuration, schemas, metadata, rules—rather than by hardcoded logic. The code is generic; the data defines what actually happens.
So “what should happen” lives in data; “how we execute it” lives in code.
The opposite is code-driven (or logic-driven) architecture:
| Data-driven | Code-driven | |----|----| | Behavior from config, schemas, rules | Behavior from code branches | | Change by updating data | Change by editing and deploying code | | One generic engine + many configs | Many special cases in code | | Declarative (“what”) | Imperative (“how”) |
Data-driven doesn’t mean “no code”—it means code is generic and data is authoritative.
The diagram below contrasts the two approaches. In code-driven architecture, the application contains multiple branches (e.g., per document type, tenant, or region); each new case adds a new path in code. In data-driven architecture, the application has a single path and reads from a central config keyed by context; new cases are new config entries, not new code.
Suppose we need approval workflows that differ by document type: expense reports → Manager then Finance; contracts → Legal, Manager, Finance; leave requests → Manager only.
Less suitable (code-driven): A branch in code for each document type; every new type means a new branch and a new deployment.
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if (documentType == "ExpenseReport") {
steps = [ { role: "Manager" }, { role: "Finance" } ];
runApprovalFlow(document, steps);
} else if (documentType == "Contract") {
steps = [ { role: "Legal" }, { role: "Manager" }, { role: "Finance" } ];
runApprovalFlow(document, steps);
} else if (documentType == "LeaveRequest") {
steps = [ { role: "Manager" } ];
runApprovalFlow(document, steps);
}
\ More suitable (data-driven): One engine: load the workflow for this document type, execute the steps in order. Each type has its own config entry (with different steps and roles). New document type = new config entry, no code change.
1. Engine (coded once): A workflow is a list of steps (role, optional conditions). Load by document type, execute in order—no branches on document type.
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// Coded once; never branches on document type
configKey = documentType; // e.g. "ExpenseReport", "Contract"
workflow = configService.getWorkflow(configKey);
for (step in workflow.steps) {
assignToRole(document, step.role);
waitForApproval(document, step);
if (!approved) break;
}
\ 2. Config (data): One entry per document type; each defines its own steps and roles.
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{
"ExpenseReport": {
"steps": [
{ "order": 1, "role": "Manager" },
{ "order": 2, "role": "Finance" }
]
},
"Contract": {
"steps": [
{ "order": 1, "role": "Legal" },
{ "order": 2, "role": "Manager" },
{ "order": 3, "role": "Finance" }
]
},
"LeaveRequest": {
"steps": [
{ "order": 1, "role": "Manager" }
]
}
}
\ 3. New document type = config only. Add an entry (e.g. PurchaseOrder); the engine already runs any list of steps.
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"PurchaseOrder": {
"steps": [
{ "order": 1, "role": "Manager" },
{ "order": 2, "role": "Procurement" }
]
}
The same config can drive the UI (e.g. progress view of steps).
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When config is the source of truth, we have a single place that defines what the backend validates and what the UI shows (fields, order, labels, widget types). Backend and UI share the same contract; no scattered logic per type or segment, no separate “UI config” that drifts from backend rules. Changes are centralized, and there are fewer places to look when debugging or evolving behavior.
We get faster iteration and fewer code paths to maintain.
So data-driven architecture supports consistent behavior and clear ownership of “what is true where.”
A generic engine, combined with data, scales to many entities without a proportional explosion of code. We add data, not code paths.
Data-driven architecture has real benefits—but it also has trade-offs. Acknowledging them keeps the approach realistic and helps us invest in the right places.
None of these are showstoppers—they’re the price of moving behavior into data. It’s worth planning for tooling, observability, and config hygiene from the start.
Data-driven architecture is powerful—but over-engineering and generalizing too early can backfire. It’s tempting to build the “perfect” config-driven system before we've seen real variety. The catch: it's hard to predict what use cases our application will actually see. We might design for dimensions (document type, tenant, product type) or rules that never matter, or miss the ones that do.
A better approach: code a couple first, then generalize by the third.
If we generalize on the first or second case, we risk building a flexible system around the wrong abstractions. Real use cases will then fight the design, and we’ll pay for “flexibility” we don’t need. Let the problem show itself before we build the generic solution. Data-driven architecture shines when it’s informed by real variation—not by speculation.
Sometimes, data-driven is the wrong choice altogether. Skip it when:
Data-driven architecture pays off when we have real, recurring variation and multiple consumers (e.g. backend + UI) that benefit from one source of truth. If we don't have that yet, we stay simple.
We can decide what dimensions matter (e.g. document type, entity type, tenant) and key the config by those dimensions.
Example structure:
Both backend behavior and UI display are configured from the same place, keyed by the same dimensions, so they stay in sync by design.
No if (type == "X") in business logic—only “load config for this context and apply it.”
Data-driven UI means the UI is also driven by config rather than hardcoded layouts. For a given context, the UI reads the fields to show, their order, labels, widget types, and visibility from config, and renders accordingly.
The UI has one generic flow: resolve config for this context, then render from it. Backend and UI each use the parts of the config relevant to them, and because everything lives in the same place under the same key, they naturally stay aligned.
Result: the UI “loads differently” per context because the data is different, not because the code is different. No separate front-end code per type or segment.
Storing config in a single system (e.g., config service, database, or versioned files), keyed by the dimensions that drive behavior, gives us one source of truth. Both the backend and the front end read from it—either directly via the API or with the backend passing the right slice to the UI.
Backend and UI configs don't have to be the same structure—decoupling and denormalizing them by concern (behavior vs. display) is often preferred. What matters is collocating them under the same key in the same system. That's what keeps them aligned without extra coordination: when we add or change a context, both sides are updated in one place.
Treating config as versioned and reviewable—with the same rigour as code but the flexibility of data—is what makes this sustainable in production.
We don’t have to go all-in on day one—and it’s better not to before we’ve seen a few real cases (see When Not to Generalize Too Soon above). Once we’re ready:
Moving behavior into config introduces a question that doesn’t often come up in code-driven systems: why did it do that? The answer depends on which config was loaded, when it changed, and who is affected. Two practices keep this under control: lineage and observability.
Config should be versioned and identified—every read path knows which version it used. If config is layered (base + tenant + region overrides), recording the resolution path (e.g. “base v3 + tenant-A v1 + region EMEA v2”) makes behavior reproducible and auditable. Attaching provenance to each entry—who changed it, when, and why—turns config into an auditable artifact.
Equally important is knowing who reads which config keys. Tracking consumers (backend validation, UI renderer, batch jobs) lets us answer “what breaks if we change this?” before we deploy. It helps us track the consumers who should be notified of the change. If config drives stored data, lineage can also link a record to the config version that wrote it—critical for interpreting historical data when schemas evolve.
Attaching config key and version to every request—in logs, trace spans, or response headers—means any log line or trace answers “which config drove this.” Structured logging at resolution time (configKey, configVersion, cache hit or fallback) and metrics on resolution latency and validation failures give dashboards and alerts that catch regressions quickly.
When a bug is reported, we can look up the config key and version from the trace, re-fetch that exact version, and reproduce the behavior locally. When someone reports “it worked yesterday,” we compare config versions across that window to see exactly what changed.
For high-risk changes, a staged rollout (canary tenant or percentage of traffic) limits the blast radius. Treating config as a dependency—alerting on load failures, validation errors, or unexpected fallback rates—keeps production stable.
With lineage and observability in place, config-driven behavior stays inspectable and reproducible—we can change behavior with confidence even when the logic lives in data.
| \n | What | Why | How | |----|----|----|----| | Core idea | Data (config, schemas, rules) defines behavior; code is generic. | Single source of truth, easier changes, less fragmentation, consistency, scalability. | Keyed config (e.g. by document type, tenant); one engine in backend and UI. | | Backend | No branches per type or segment; load config and apply. | Fewer code paths, faster iteration. | Config-driven validation, persistence, and rules. | | UI | Same config drives layout and behavior (data-driven UI). | Same contract as backend; no separate code per type or segment. | Generic render-from-config; config defines fields, order, labels, and widgets. | | Production | Config is versioned and traceable; behavior is reproducible. | Safe rollouts, impact analysis, and auditability. | Lineage (source, version, consumers); observability (logs, metrics, traces, alerts). |
\ Data-driven architecture puts configuration at the center: it controls how the backend behaves and how the UI works. That reduces fragmentation, makes future changes easier, and keeps one clear place to define “what’s true” for each context (e.g., document type, tenant). In production, lineage and observability make that config auditable and debuggable—so we can change behavior with confidence.
If we take one thing away: Let data define what should happen, and keep code generic so it just interprets that data.
Have you moved from code-driven to data-driven (backend or UI)? What worked best for you? Share your experience in the comments.
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