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[Assistant] I don't mean I understand the physics behind it. Few even among star folk do.


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[Assistant] But you can reach all the controls at once. That's only part of it. I think we tines are more flexibly minded than the poor two legs. Can you imagine what it will be like when we make more radio cloaks? When we make our own flying machines? Woodcarver smiled. A little sadly now. Pilgrim, you dream. This is the slow zone.


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[Assistant] The agrav will wear out in a few years. Whatever we make will be far short of what you play with now. So?


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[Assistant] Look at human history. It took less than two centuries for Nijora to regain spaceflight after their dark age. And we have better records than their archaeologists. We and the humans are a wonderful team. They have freed us to be everything we can be. A century till their own spaceships.


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[Assistant] Perhaps another century to start building sub-light speed starships. And someday, they would get out of the slow zone.


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[Assistant] I wonder if packs can be bigger than eight up in the transcend.


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[Assistant] The younger parts of Woodcarver were up, pacing around the rest. The Queen was intrigued.


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[Assistant] So, you think, like steels seem to, that we are some kind of special race? Something with a happy destiny in the beyond? Interesting.


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[Assistant] Except for one thing. These humans are all we know from out there. How do they compare with other races there? Dataset can't fully answer that.


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[Assistant] Ah, and there, woodcarver, is why green stock is so important.


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[Assistant] We do need experience of more than one other race. Apparently the riders are among the most common throughout the beyond. We need them to talk to. We need to discover if they are as much fun as, as useful as the two legs.


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[Assistant] If we are to be all we can be, we need to know more. We need to take a few risks. She stopped her pacing. All her eyes turned toward Peregrin in a gesture of surprise. Abruptly she laughed. What?


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[Assistant] Something we've thought before, dear Peregrin, but now I see how true it is. You're being a little bit clever and scheming here, a good statesman and planner for the future, but still for a pilgrimly goal, to be sure, and I...


[User] Please transcribe this text

[Assistant] Now I don't care so completely about the planning and the safety. We will visit the stars someday. Her puppies waggled a joyous salute.


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[Assistant] She went down on all her bellies and crept across the floor toward him.


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[Assistant] consciousness slowly dissolved into a haze of loving lust the last thing peregrine remembered her saying was


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[Assistant] How wonderful the luck that I had grown old and had to be new and that you were just the change we need.


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[Assistant] Peregrine's attention drifted back to the present, and Ravna. The human was still grinning at him. She reached a hand across to brush one of his heads. Medieval minds indeed.


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[Assistant] They sat in the fern shade for another couple of hours and watched the tide come in. The sun fell through mid-afternoon.


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[Assistant] Even that it was as high in the sky as any noontime sun could be at Woodcarver's.


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[Assistant] In some ways the quality of the light and the motion of the sun were the strangest things about the scene. The sun was so high


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[Assistant] And came down so straight, with none of the long sliding glide of afternoon in the Arctic.


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[Assistant] He had almost forgotten what it was like in the land of short twilight.


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[Assistant] Now the surf was 30 yards inland of where they had put the rider.


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[Assistant] The crescent moon was following the sun toward the horizon. The water wouldn't rise any further.


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[Assistant] Ravna stood, shaded her eyes against the lowering sun. Time for us to go, I think. You think she'll be safe? Ravna nodded.


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[Assistant] This was long enough for Greenstalk to notice any poisons, and most predators. Besides, she's armed.


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[Assistant] Human and tines picked their way to the crest of the atoll, past the tallest of the ferns. Peregrine kept one pair of eyes on the sea behind them. The surf was well past Greenstalk now.


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[Assistant] Her location was still swept by deep waves, but it was beyond the spume and spray.


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[Assistant] Was broken for an instant by two of her tallest fronds. The tips, gently swaying.


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[Assistant] There was some rain, and no more brush fires. There would even be a harvest, war and drought notwithstanding.


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[Assistant] Each day around the sun hid deeper behind the northern hills, a time of twilight that broadened with the weeks till true night held at midnight. And there were stars.


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[Assistant] Ravna took the kids out skygazing on the fields by Starship Castle.


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[Assistant] No urban haze here nor even near space industry. Nothing to fog the view of heaven except a subtle pinkness in the north that might have been vagrant twilight or aurora.


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[Assistant] The four of them settled on the frosty moss and looked around. Ravana took a deep breath.


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[Assistant] There was no hint of ash left in the air, just a clean chill. A promise of winter.


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[Assistant] The snow will be deep as your shoulders, Ravna, said Jeffrey, enthusiastic about the possibility. You'll love it!


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[Assistant] The pale blotch that was his face seemed to be looking back and forth across the sky.


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[Assistant] It can be bad, said Joanna Olsendott. She hadn't objected to coming up here tonight, but Ravnon knew.


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[Assistant] That she would rather have stayed down on Hidden Island to worry about the doings of tomorrow. Jeffrey picked up on her on ease.


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[Assistant] No, that was Omdi talking now. They would never cure those two of pretending to be each other. Don't worry, Johanna, we'll help you. For a moment no one said anything. Ravna looked down the hill.


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[Assistant] It was too dark to see the 600 meter drop. Too dark to see where fjord and islands lay below.


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[Assistant] But the torchlight on the round parts of Hidden Island marked its location.


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[Assistant] Down there in Steel's old inner court, where Woodcarver now ruled, were all the working cold boxes from the ship.


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[Assistant] 151 children slept there. The last survivors of the Straumer's flight.


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[Assistant] Joanna claimed that most could be revived, with best chance of success if it were done soon. The Queen had been enthusiastic about the idea.


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[Assistant] Large sections of the castle had been set aside, refurbished for human needs.


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[Assistant] Hidden Island was well sheltered, if not from winter snow, at least from the worst winds.


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[Assistant] If they could be revived, the children would have no trouble living there.


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[Assistant] Ravna had come to love Jeffrey and Joanna and Omdi, but could she handle 151 more?


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[Assistant] Woodcarver seemed to have no misgivings. She had plans for a school where tines would learn of humans, and the children would learn of this world. Watching Jeffrey and Amby, Ravna was beginning to see what might become of this.


[User] Please transcribe this text

[Assistant] Those two were closer than any children she had ever known, and in some, more competent.


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[Assistant] And that was not just the puppy's math genius. They were competent in other ways. Humans and packs fit. And old Woodcarver was clever enough to take advantage of it. Ravna liked the queen.


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[Assistant] And liked Pilgrim even more. But in the end, the packs would be the great beneficiaries.


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[Assistant] Woodcarver clearly understood the disabilities of her pack race.


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[Assistant] Tainish records went back at least 10,000 years. For all their recorded history, they had been trapped in cultures not much less advanced than now.


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[Assistant] Erased of sharp intelligence, yet they had a single overwhelming disadvantage. They could not cooperate at close range without losing that intelligence.


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[Assistant] Their civilizations were made of isolated minds, forced introverts who could never progress beyond certain limits.


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[Assistant] The eagerness of Pilgrim and Scrupulo and the others for human contact was evidence of this.


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[Assistant] In the long run, we can move the tines out of this cul de sac.


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[Assistant] Omdi and Jeffrey were giggling about something. The pack sending runners out almost to the limit of consciousness.


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[Assistant] These last weeks Ravna had come to learn that pell-mell activity was the norm for Omni. That his initial slowness had been part of his hurt over steel. How...


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[Assistant] What are you doing? Joanna asked with sisterly belligerence. Watching for meteors, one of the two said. Yes, I watch in all directions and jab Jeffrey. There, where to look when one comes by.


[User] Please transcribe this text

[Assistant] Ravner didn't see anything, but the boy had twisted around abruptly at his friend's signal.


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[Assistant] Neat! Neat! came Jeffrey's voice. That was about forty kilometers up. Speed? The two's voice murmured unintelligibly for a second.


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[Assistant] Even with the pack's wide vision, how could they know how high it was?


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[Assistant] Ravna sat back in the hollow formed by the hummocky moss.


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[Assistant] It was a good parka the locals had made for her. She barely felt the chill in the ground. Overhead the stars. Time to think. Get some peace before all the things that would begin tomorrow.


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[Assistant] Den Mother to one hundred and fifty some kids. And I thought I was a librarian. Back home, she had loved the night sky.


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[Assistant] The evening chill seemed part of a winter that would never go away. Lynn and her folks, and Xander Kay. Her whole life till three years ago. It was all gone now. Don't think on it.


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[Assistant] Somewhere out there was what was left of Aniara Fleet, and what was left of her people.


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[Assistant] Yet, Svensindat, Tyrol and Glimfrell, she had only known them for a few hours, but they were of Siondra K., and they had saved more than they would ever know. They would still live.


[User] Please transcribe this text

[Assistant] S.J.K. Commercial Security had some ram scoops in its fleet. They could find a world, not here, but nearer the battle site.


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[Assistant] Ravna tilted her head back, wondering at the sky. Where? Maybe not even above the horizon now.


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[Assistant] From here the galactic disk was a glow that climbed across the sky almost at right angles to the ecliptic. There was no sense of its true shape or their exact position in it.


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[Assistant] The greater picture was lost to nearby splendors.


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[Assistant] The bright knots of open clusters, frozen jewels against the fainter light.


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[Assistant] But down near the southern horizon, far from the galactic way, there were two splotchy clouds of light. The Magellanics.


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[Assistant] Suddenly the geometry clicked, and the universe above was not completely unknown. Adyara Fleet would be


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[Assistant] I, I wonder if we can see Stromly realm from here, said Joanna. For more than a year she had had to play the adult. Come tomorrow, that role would be forever.


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[Assistant] But her voice just now was wistful, childlike.


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[Assistant] Ravna opened her mouth about to say how unlikely that must be. Maybe we can. Maybe we can't. It was Omni.


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[Assistant] The packet pulled itself together, snuggled companionably among the humans. The warmth was welcome. See?


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[Assistant] I've been reading data set about where things are and trying to figure how it matches what we see.


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[Assistant] A pair of noses were silhouetted against the sky for an instant.


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[Assistant] Like a human waving his hands exuberantly at the heavens.


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[Assistant] The brightest things we see are just kind of local dazzle. They aren't good guideposts.


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[Assistant] He pointed at a couple of open clusters, claimed they matched stuff he'd found in the dataset.


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[Assistant] Omni had also noticed the Magellanic Galaxies, and figured out far more than Ravna. So, anyway, Stromly Realm was... was. You got it, kid. In the high beyond, but...


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[Assistant] Jeffrey came to his knees and stared silently for a second. But so far away is there anything to see? Not the strongly stars.


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[Assistant] But just forty light years from Stroud, there's a blue-white giant. Yeaah! whispered Joanna.


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[Assistant] Storeless. It was so bright you could see shadows at night.


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[Assistant] Well, that's the fourth brightest star up from the corner. See, they almost make a straight line. I can see it, so I know you can.


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[Assistant] Joanna and Jeffrey were silent for a long time, just staring up at that patch of sky. Ravna's lips compressed in anger. These were good kids. They had been through hell, and their parents had fought to prevent that hell. They had escaped the blight with the means of its destruction, but


[User] Please transcribe this text

[Assistant] How many million races had lived in the Beyond, had probed the Transcend, and made bargains with devils? How many more had destroyed themselves there? Ah, but that had not been enough for Strombly Realm. They had gone into the Transcend, and wakened something that could take over a galaxy. Do you think anybody's left there? said Jeffrey. Do you think we're all that's left? His sister put an arm around him. Maybe.


[User] Please transcribe this text

[Assistant] Maybe not stromly realm, but the rest of the universe, look, it's still there." Weak laughter. Daddy and Mom, Ravna and Fam, they stopped the blight. She waved a hand against the sky. They saved most all of it. Yes, said Ravna, we're saved and safe, Jeffrey, to begin again.


[User] Please transcribe this text

[Assistant] And as far as it went, that comfort was probably true. The ship's zone probes were still working.


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[Assistant] Of course, a single measure point is of no use for precise zonography, but she could tell that they were deep in the new volume of the slowness, the volume created by Fam's Revenge.


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[Assistant] And much more significant, the OOB detected no variation in zonal intensity.


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[Assistant] Gone was the continuous trembling of the months before.


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[Assistant] This new status had the feeling of mountain roots to be moved only by the passage of the ages.


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[Assistant] Fifty degrees along the Galactic River was another unremarkable space of sky. She didn't point it out to the kids, but what was of interest there was much nearer, just under thirty light years out. The Blighter Fleet. Flies trapped in amber.


[User] Please transcribe this text

[Assistant] At normal jump rates for the low beyond, they had been just hours away when fam created the great surge. And now?


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[Assistant] If they had been bottom luggers, ships with ram scoops, they could close the gap in less than fifty years. But Anyara fleet had made their sacrifice. They had followed Pham's god-shattered advice. And though they didn't know it, they had broken the Blight fleet.


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[Assistant] There wasn't a single slow-zone capable vessel in the approaching fleet.


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[Assistant] Perhaps they had some in-system capability, a few thousand clicks per second, but no more. Not down here.


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[Assistant] Where new construction was not a matter of waving a magic wand.


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[Assistant] The Blight's extermination force would sweep past Tyne's world in... a few thousand years. Time enough.


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[Assistant] Ravna leaned back against one of Omdi's shoulders. He nestled comfortably around her neck. The puppies had grown these last two months. Apparently steel had kept them on some sort of stunting drugs. Her gaze lost itself in the dark and glow.


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[Assistant] Far upon far that were all the zones above her. And where are the boundaries now? How awesome was Fam's revenge? Maybe she should call it Old One's Revenge. No, it was far more even than that.


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[Assistant] Old one was just a recent victim of the blight. Old one was no more than midwife to this revenge.


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[Assistant] The first cause must be as old as the original blight, and more powerful than the powers.


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[Assistant] But whatever caused it, the Surge had done more than revenge.


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[Assistant] Ravna had studied the ship's measurement of zone intensity.


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[Assistant] It could only be an estimate, but she knew they were trapped between 1,000 and 30,000 light years deep in the new slowness.


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[Assistant] Powers only knew how far the surge had pushed the slowness. And maybe even some of the powers were destroyed by it. This was like some vision of planetary Armageddon.


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[Assistant] The type of thing that primitive civilizations had nightmares about, but blown up to a galactic scale.


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[Assistant] A huge hunk of the Milky Way galaxy had been gobbled up by the slowness, all in a single afternoon.


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[Assistant] Not just the blighter fleet were flies trapped in amber.


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[Assistant] Why the whole vault of heaven, excepting the Magellanic's, faint and far away, might now be a tomb of slowness.


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[Assistant] Many must still be alive out there. But how many millions of starships have been trapped between the stars?


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[Assistant] How many automated systems have failed killing the civilizations that depended on them? Heaven was truly silent now.


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[Assistant] In some ways the revenge was a worse thing than the blight itself. And what of the blight?


[User] Please transcribe this text

[Assistant] Not the fleet that chased the O.O.B., but the Blight itself. That was a creature of the Top and the Transcend.


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[Assistant] At a very far remove, it covered much of the sky they could see this night. Could Fam's revenge have really toppled it?


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[Assistant] If there was a point to all this sacrifice, then surely so. A surge so great.


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[Assistant] That it pushed the slowness up thousands of light years, through the low and mid beyond.


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[Assistant] Past the great civilizations at the top and into the trance end. No wonder it was so eager to stop us. A power immersed in the slowness would be a power no more, would likely be a living thing no more, if, if, if, if am surge could climb so high. That is something I will never know.


[User] Please transcribe this text

[Assistant] Crypto Zero as received by Language Path Optima from Society for Rational Investigation Subject Ping Key Phrases Help Me Summary Has there been a network partition or what? Distribution Threat of the Blight Society for Rational Network Management War Trackers Interest Group Date


[User] Please transcribe this text

[Assistant] 0.412msec since loss of contacts. Text of message, I have still not recovered contact with any network site known to be spinward of me. Apparently, I am right at the very edge of a catastrophe. If you receive this ping, please respond. Am I in danger?


[User] Please transcribe this text

[Assistant] For your information, I have no trouble reaching sites that are anti-spinward.


[User] Please transcribe this text

[Assistant] I understand an effort is being made to hop messages the long way around the galaxy.


[User] Please transcribe this text

[Assistant] At least that would give us an idea how big the loss is. Nothing has come back as yet. Not surprising, I guess, considering the great number of hops and the expense.


[User] Please transcribe this text

[Assistant] In the meantime, I am sending out pings such as this. I am expending enormous resources to do this. Let me tell you, but it is that important. I've beamed direct at all the hub sites that are in range to the spinward of me. No replies.


[User] Please transcribe this text

[Assistant] More ominous. I have tried to transmit over the top, that is by using known sites in the transcend that are above the catastrophe. Most such would not normally respond, powers being what they are, but I received no replies. A silence, like the depths is there. It appears that a portion of the transcend itself has been engulfed. Again, if you receive this message, please respond.


[User] Please transcribe this text

[Assistant] Asked if I wanted to come by, maybe look at a unit, maybe review one.


[User] Please transcribe this text

[Assistant] But it is super interesting. Now, The Rock has been around for a while, like 20 plus years, and this is the first really big update. Now, Patrick did say something that I thought was just fantastic, which is the updates to this maker.


[User] Please transcribe this text

[Assistant] Can be retrofitted as a kit onto the first gen rock espresso makers. So from the beginning, I have to admit, he had me on side. That kind of approach as a manufacturer, I really have a lot of respect for. So I thought that was super cool.


[User] Please transcribe this text

[Assistant] And this is called the GC, which is short I think for glass composite, which refers to the new materials you see in the top.


[User] Please transcribe this text

[Assistant] of the maker here. And that's the part you look to change. They've also changed the piston piece. They've made the actual piston narrower, meaning you drive much higher pressures through that piston. Now, along with that, he did also let me have a look and a play with the new hand grinder that they have coming out.


[User] Please transcribe this text

[Assistant] Which is really quite interesting. This is a hand grinder built for espresso. And there's some things about it that I think are actually very clever when it comes to grinding for espresso. And I'll start here. So what you get here, you get a little.


[User] Please transcribe this text

[Assistant] A little cup for weighing the beans and then for grinding into with a little spout for dispensing into your portafilter. And then underneath here at the bottom here, you can see this is where you adjust the grind. So you would twist as little clicks for grind settings. And again, it is built to go alongside this. So it's built to go really pretty fine.


[User] Please transcribe this text

[Assistant] Now, if you've tried to grind for espresso on a hand grinder, you'll know that grinding like that is incredibly hard work. And the solution is actually just to have a much longer...


[User] Please transcribe this text

[Assistant] Like a lot of travel here. So actually grinding for espresso is physically not that demanding at all.


[User] Please transcribe this text

[Assistant] There's a very simple gear here to allow a vertical drive shaft to drive a metal, uh, conical burr set in here. And I know a lot of people in espresso really have come to prefer flat burr profiles, but conical burrs just allow you a much larger


[User] Please transcribe this text

[Assistant] We'll do that in a second. And then into the little portafilter in here, pour your water in the top.


[User] Please transcribe this text

[Assistant] Lift the arms up, push them down, and then you're making coffee. So as a workflow, not too fiddly. Well, let's go on and make a shot.


[User] Please transcribe this text

[Assistant] Pretty easy. And actually, in the unit, if you look inside, there's a little sort of a metal finger to help kind of feed and push beans down into the burset, acting almost like an auger, just to sort of keep it nice and even and uniform.


[User] Please transcribe this text

[Assistant] The grounds coming out of this thing. You can use it for filter coffee if you want to. It'll obviously grind much faster. And you just need to, to move the burr adjuster, I think, about one and a half full rotations coarser, uh, to get like a filter coffee ground. But I haven't tested it at that. That was just what Patrick told me he'd had some success with. However, at espresso, I'm, uh, I'm pretty impressed. Now onto,


[User] Please transcribe this text

[Assistant] The, the, the Rock Espresso Maker. Now you can get a standard portafilter.


[User] Please transcribe this text

[Assistant] The little plastic portafilter spouts that come with this that fit on the handle, they just clip on very easily, which is good.


[User] Please transcribe this text

[Assistant] Uh, I think they know most people would probably just want to brew a double, so they're not going to use them. And they do feel just a little bit...


[User] Please transcribe this text

[Assistant] As well, if you want that in your life, uh, which is kind of fun. So the challenge of any espresso machine that doesn't have a heating element in it where you're going to add hot water to it is making sure that the machine doesn't steal that heat before the water hits the coffee.


[User] Please transcribe this text

[Assistant] And that's true of just about any one of these kind of units. And this is true here. So you really, really, really want to make sure that you've preheated your portafilter as well as the unit itself. It's probably easiest to do those two things together.


[User] Please transcribe this text

[Assistant] Don't, uh, put water in this and lift the arms up without a portafilter being in place, otherwise you'll just make a mess. So what I would typically do is, is get some hot water into this and kind of pull a blank shot to try and get some heat into the unit before I add my coffee and before I pull a shot.


[User] Please transcribe this text

[Assistant] Now if you're used to using espresso machines a lot, there's one little thing that may throw you. Most espresso machines lock in from left to center. This doesn't. This locks in from right to center. So that'll catch you out the first time if you're not used to it this way. So right now let's just do a little preheat. So boiling water in.


[User] Please transcribe this text

[Assistant] Uh, but it just means it's quite easy for it to be messy. With some grinders like the EK43 in cafes, you'll see them using some sort of dosing tool. And I think actually something like that for this.


[User] Please transcribe this text

[Assistant] Could be, could be quite useful. So they did send me a tamper with this. It is very heavy, actually for its size, which is impressive. It is just for me a little bit small, if I'm honest. I could do with just a little bit more width. So you have to do like a couple of additional sort of sidewall tams just to catch everything.


[User] Please transcribe this text

[Assistant] Now you can obviously filter different levels depending on how much you want. This much water, uh, definitely was more than I would need. So if you raise this up now, you'll begin pre-infusion.


[User] Please transcribe this text

[Assistant] And I had best results with at least 15 seconds of pre-infusion just to help up the extraction a little bit. And then we press.


[User] Please transcribe this text

[Assistant] Messy, right? So if your espresso is the size that you want it, and typically I was brewing 16 grams of coffee to about...


[User] Please transcribe this text

[Assistant] Once you've enjoyed your coffee, either bring the cup back and just give it a final squeeze through, just to make sure you've pushed all liquid out and it just makes knocking out the puck afterwards much, much, much cleaner and easier. And so once you've pushed out all the water,


[User] Please transcribe this text

[Assistant] What you're left with is really a very dry cake, uh, very easy to clean up, knock out. So that's definitely worth making sure we've done completely. So how's the coffee, you ask? Well, the coffees I've had from here have been pretty good.


[User] Please transcribe this text

[Assistant] Is, is definitely my number one priority. And it'll also mean typically the second or third shot out of it will probably be better than the very first. But often you only want to make one first thing in the morning. So, so spend the time getting it as hot.


[User] Please transcribe this text

[Assistant] Now I would recommend brewing on scales if you can. It's how I would brew most espresso in most places, and I feel like getting, uh, an accurate idea of how much liquid you've produced is definitely useful. The shape and design of this make that pretty easy to fit scales underneath here, and that was definitely not a problem for me in using it.


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[Assistant] I think the design is great for an espresso hand grinder. It's definitely grinding fine enough. The grind is really quite even. The long lever arm on here is a good thing and it's good to use. It's pretty easy to use. I have no real complaints about it. It is, in the world of hand grinders,


[User] Please transcribe this text

[Assistant] But it is well built, it is sturdy, and they've thought it through from a usability perspective for grinding very fine. So...


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[Assistant] Things I like, it's compact, it works well. You're definitely hitting high pressures by having a narrower brew chamber than normal. It means that the force exerted per square inch means that actually pushing with only 16 or 17 kilos of force will give you nine bars of pressure inside the brew chamber. So that's a kind of nice, smart thing.


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[Assistant] It does make good tasting coffee on the condition that you manage the heat of this unit. You definitely need to get it as hot as you can before using it, especially if you like slightly lighter roasts in your espresso.


[User] Please transcribe this text

[Assistant] With darker roast, that may be just less of an issue. You can use this to make much larger espressos than I have done, though I don't particularly enjoy those. That's kind of just my thing. I really preferred shots that were about.


[User] Please transcribe this text

[Assistant] One full piston of water, which was usually about just about 40 grams out depending on how long you left it to pre-infuse for. And again, pre-infusion is definitely useful and having total manual control of that.


[User] Please transcribe this text

[Assistant] Maker. I think it's £149. Uh, I think it's well built, start to finish. Uh, it can be a little messy, but actually you can just pick this thing up and run it under the tap if you want to. So, you know, the single unit construction is nice.


[User] Please transcribe this text

[Assistant] But that's it. That's The Rock, that's how I feel about that. Uh, I think it's broadly available about now, I think it's just about released now. Uh, if you have questions, if you have comments, if you've got one of the old ones, if you've got one of the new ones, let me know. Are you using it to make espresso at home still?


[User] Please transcribe this text

[Assistant] How are your shots? What's your best technique? What works for you? I'd be really interested to hear it.


[User] Please transcribe this text

[Assistant] As always, thank you so much for watching, and I hope you have a great day.


[User] Please transcribe this text

[Assistant] He gave me one other nod, compressed the post office exceedingly, gave me one last nod, and went on with his breakfast. Now, said Wemmick, questioning being over, which he emphasized and repeated for my guidance, I come to what I did after hearing what I heard. I went to Garden Court to find you. Not finding you, I went to Claricas to find Mr. Herbert. And him you found, said I with great anxiety, and him I found.


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[Assistant] Tom, Jack, or Richard being about the chambers or about the immediate neighborhood, he had better get Tom, Jack, or Richard out of the way while you were out of the way. He would be greatly puzzled what to do.


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[Assistant] He was puzzled what to do, not the less because I gave him my opinion that it was not safe to try and get Tom, Jack, or Richard too far out of the way at present. Mr. Pip, I'll tell you something.


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[Assistant] Under existing circumstances, there is no place like a great city when you are once in it.


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[Assistant] Don't break cover too soon. Lie close. Wait till things slacken before you try the open, even for foreign air.


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[Assistant] I thanked him for this valuable advice and asked him what Herbert had done. Mr. Herbert, said Wemmick, after being all over heat for half an hour, struck out a plan.


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[Assistant] A mention to me is a secret that he is courting a young lady who has, as no doubt you are aware, a bedridden path.


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[Assistant] Which par, having been in the person line of life, lies abed in a bow window, where he can see the ships sail up and down the river. He were acquainted with the young lady, most probably, not personally, said I.


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[Assistant] The truth was that she had objected to me as an expensive companion who did Herbert no good.


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[Assistant] And when Herbert had first proposed to present me to her, she had received the proposal with such very moderate warmth


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[Assistant] Let Herbert had felt himself obliged to confide the state of the case to me, with a view to the lapse of a little time before I made her acquaintance. When I had begun to advance Herbert's prospects by stealth, I had been able to bear this with cheerful philosophy.


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[Assistant] He and his affianced for their part had naturally not been very anxious to introduce a third person into their interviews.


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[Assistant] And thus, although I was assured that I had risen in Clara's esteem,


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[Assistant] And although the young lady and I had long regularly interchanged messages and remembrances by Herbert, I had never seen her. However, I did not trouble Wemmick with those particulars.


[User] Please transcribe this text

[Assistant] The house with the bow window, oh, said Wemmick, being by the riverside, down the pool there, between Limehouse and Greenwich, and being kept, it seems


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[Assistant] By a very respectable widow who has a furnished upper floor to let.


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[Assistant] Mr. Herbert, put it to me, what did I think of that as a temporary tenement for Tom, Jack, or Richard?


[User] Please transcribe this text

[Assistant] So, I'm Anil Kulkarni. I'm the CEO of a company called Crowdbotics. Uh, we are a company that uses


[User] Please transcribe this text

[Assistant] Uh, combinations of crowds and machine learning to build software faster in ways that we think will be interesting. Um, prior to this, I ran a company for many years called Lead Genius, which was a private, verticalized crowdsourcing platform. Before that, I was at Berkeley.


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[Assistant] Uhm, a little bit about how I got into this space. Uhm, I started a company called Lead Genius when I was in grad school with three other PhD students at the time. Uhm, Lead Genius was a private crowd labor engine. So by crowd labor, uhm, I'm referring to systems that are like Mechanical Turk or Upwork. Uhm,


[User] Please transcribe this text

[Assistant] This was private, meaning that, uh, you couldn't sign up and post any kind of work you wanted to, you could only do one kind of work.


[User] Please transcribe this text

[Assistant] Uhm, it's used by non-specialists. So you didn't know that you were using crowds when you used it. You actually thought you were buying a sales product. But behind the scenes, everything that was happening was crowdsourcing.


[User] Please transcribe this text

[Assistant] Uhm, we were venture backed, we raised about 20 million dollars in venture funding. Uhm, the company is still going strong, employs about 50 people in downtown Berkeley, still close to home. It also employs about 500 people worldwide.


[User] Please transcribe this text

[Assistant] Which was, uh, an interesting goal of the company to try and create employment at large scale and attack poverty through crowdsourcing. Uhm, and we're still successful at doing that. We pay about 4 million dollars out a year to base and middle in income communities around the world. Uhm,


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[Assistant] I started a company called Crowdbotix, uh, late last year, uh, to apply some of the same ideas to software engineering.


[User] Please transcribe this text

[Assistant] I also do research and both these companies are spin outs of the research that I've done in crowdsourcing. Uhm, I'm interested in two questions. The first is how we can build crowd platforms that are


[User] Please transcribe this text

[Assistant] Uh, fairer that, uh, try and create more equitable relationships between people who are doing the work and people who need the work done. How you can use them to address issues around unemployment or, uh, uh, income inequality. Uh, and then also then this question of how you can create more complex, uh, things, uhm, artifacts, outcomes as a result of crowd work. Okay, so uhm, what is crowd computing? Uhm,


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[Assistant] Yeah, so I think that's a terrific definition. Uhm, informally, I sometimes characterize it as how do we get groups of strangers to do stuff that we want them to do over the internet. But I think the remit has actually been transformed recently and it goes into a much more expansive view in the way that you're describing. Uhm, so,


[User] Please transcribe this text

[Assistant] Uh, Rob Miller at MIT used to talk about this definition of crowd computing as how do you get people and computers to work together to achieve things that neither one can do on their own. Uhm, but the modern definition that I like is uhm, I got this from Walter Lisecki at University of Michigan which is computational management science.


[User] Please transcribe this text

[Assistant] Difficult. And that covers a pretty big, uh, spectrum of things. Uhm, uh, so...


[User] Please transcribe this text

[Assistant] You have some examples of this that we may or may not recognize as crowd work at first glance. You have systems that are true virtual labor. Uhm, things like Fiverr, uh, which is a simple platform to pay people a small amount of money to do a thing for you.


[User] Please transcribe this text

[Assistant] Uhm, you have Upwork, which used to be ODesk, which is a system for hiring people over the web, uh, to do freelance work.


[User] Please transcribe this text

[Assistant] So they send you work digitally and response. Then you've got systems that are coordinating groups of people in the real world, uhm, where they're physically executed, but the algorithm is dictating who is going to do what.


[User] Please transcribe this text

[Assistant] The other end of the spectrum that I think is starting to get eaten, uh, starting to be eaten by crowd work is this idea of non-software contract works. You can imagine hiring a freelance writer or a freelance consultant or maybe just getting somebody to, uhm, work for you for a few hours as a favor.


[User] Please transcribe this text

[Assistant] I think that there's a, there is a process we're seeing here where crowd work is getting consumed or what we might think of as, as traditional work is being eaten away. Uhm, as more and more computational processes get involved in our day-to-day jobs, those start to look more like large-scale crowd work. Uhm,


[User] Please transcribe this text

[Assistant] The strong relationship that you have with your employer is usually what differentiates it.


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[Assistant] So uhm, this is the trend that I'm, that I'm alluding to. Uhm, technology mediated crowd labor is starting to eat away at conventional employment in lots of ways.


[User] Please transcribe this text

[Assistant] According to Pew, about a third of Americans, maybe less depending on how you define crowdwork, worked in the gig economy in some form, uhm, last year.


[User] Please transcribe this text

[Assistant] Uhm, so there's a feeling that combined with automation this is going to affect conventional employment in some ways.


[User] Please transcribe this text

[Assistant] In the research community, we've seen an interesting trend in crowd research in the last few years. Uhm, back when we started, uh, ten years ago as a crowd research community, uhm,


[User] Please transcribe this text

[Assistant] Uh, we thought about crowd work as micro work. Stuff you'd do on Amazon Mechanical Turk or Games of the Purpose. Really tiny tasks, right? Things like, uh, ReCAPTCHA, uhm, by Louis Vaughan at Carnegie Mellon, uhm, where you'd do something that was really, really short, you'd have a lot of people do it, and you could


[User] Please transcribe this text

[Assistant] Nowadays, uhm, last couple of years we've seen good work, some of it at Stanford, some of it elsewhere, uhm, looking at how you can think about crowd work as programmatic collaborations for complex tasks. Things like producing a video, uhm, producing a software interface, uhm, using teams in interesting ways.


[User] Please transcribe this text

[Assistant] So this trend of, of increasing complexity is showing, uh, where we're going as a crowd research community. Uhm, but it's also being reflected by the kinds of things we're seeing on the industry side, uhm, which is where I spend most of my time.


[User] Please transcribe this text

[Assistant] So uhm, ten years ago you had a bunch of these platforms that were, uh, basically open-ended, uhm, labor platforms, uhm, that were handling simple micro-tasks. Mechanical Turk.


[User] Please transcribe this text

[Assistant] Uh, ODesk, Elance, Freelancer, Crowdflower, they were all built for simple work back then. Uhm,


[User] Please transcribe this text

[Assistant] Allows you to use Mechanical Turk without knowing you're using Mechanical Turk to digitize lots of documents.


[User] Please transcribe this text

[Assistant] Rainforest QA, which I'll talk about in a little bit, using Mechanical Turk at large scale, to...


[User] Please transcribe this text

[Assistant] To uhm, or that are mediating the relationship between buyers and sellers of work.


[User] Please transcribe this text

[Assistant] In advanced ways, so B12 is a good example.


[User] Please transcribe this text

[Assistant] Which is working on trying to crowdsource the software design process. Uhm, systems like AngelList are modifying the transactions that happen between, uh, groups of, of very specialized crowds, investors and, uh, companies. And then CrowdBotX is thinking about this in software development.


[User] Please transcribe this text

[Assistant] Okay, so today I'm gonna think, uh, talk about three things. And, uh, so we have a little time for discussion. I'll make sure that those, uh, uh, that...


[User] Please transcribe this text

[Assistant] The discussions here are short, uh, on each one of these. So first I'll give you a little retrospective on, uhm,


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[Assistant] Uh, Lead Genius, which was the, uh, large-scale verticalized crowd platform that we built. Tell you a little bit about how we, we, uhm, developed that. Then we'll jump into Crowdbotics, which is our project today to, uh, write software with crowds. And last I'll close with some big open challenges that I think are interesting for, uhm, us to look at as a, uh, research community. Okay, so we'll start with Lead Genius and, uh, its previous form was a system called MobileWorks.


[User] Please transcribe this text

[Assistant] So, uhm, 2012, uhm, along with Bjorn Hartmann and Tapp Parikh at Berkeley, uhm, my co-founders and I started a company called MobileWorks. We were a non-marketplace architecture for accurate human computation.


[User] Please transcribe this text

[Assistant] If you look at that title, you can see the kinds of things that we cared about as crowd researchers back in 2012, which was accuracy and uhm, how you could avoid marketplace dynamics and the negative repercussions of those dynamics. Uhm, if you've used Mechanical Turk,


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[Assistant] You may have, uh, you may remember a time when the biggest challenge was quality.


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[Assistant] And back then we used to think about crowds, uhm, in a way that


[User] Please transcribe this text

[Assistant] Is not very popular today, thanks to the work of many people, uhm, uh, in the crowd research community. We used to think of the crowd as basically, uhm, bandits who are out to trick us, right? Uhm, these, these folks who are showing up, we don't know who they are on a crowd research platform, they're anonymous, they're transient, they show up, they take our money, they disappear.


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[Assistant] Uh, you treated workers like real people who were coming back over and over again, driving engagement over time, uhm, and trying to recognize them as humans. Uhm, which of course is a very, uh, natural approach if, uh, if you, uhm, come from that universe.


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[Assistant] That's a great question. So, in fact, if I had to give a critique looking back on what we were doing in, uh, in Lead Genius and Mobile Works, uhm, I think a lot of the choices that we made, uhm, and the reason we got good results in working with our crowd was that we adopted


[User] Please transcribe this text

[Assistant] Informal mechanisms or social conventions, but uhm, uh, I think we adopted some of the best practices from real world organizations that made us less like a crowdsourcing flat marketplace. It's also why it worked.


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[Assistant] Yeah, so uhm, if I had to expand on this, we talked about this idea as good work, that treating workers fairly would lead to quantifiably better outcomes. I think a, a corollary on that based on your point, Michael, is that, uh, treating workers like they're treated in a real world organization leads crowd workers to, uh, behave better and yield better results.


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[Assistant] So uhm, there were some insights in there that were useful. We had the best workers in the crowd become managers. They would supervise and teach the rest of the crowd.


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[Assistant] We priced automatically. We set a, uhm, a price floor in the system. We set everyone gets a minimum wage. And, uh, that made our workers very happy.


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[Assistant] Uhm, so that management model looks much more like a real world organization. Uhm, the people who were working for the crowd were divided into teams. They had, uh,


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[Assistant] Uhm, teams of two or three at the bottom who rolled up to, uhm, team leads, who rolled up themselves to managers, rolled up to super managers, and at the end of the day everyone reported up to one person in our office, uhm, who was managing a giant crowd of, of lots and lots of people.


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[Assistant] We actually had specialized roles that developed over time as well, uhm, people who were QA, people who were interviewers who were just tasked with evaluating other members of the crowd, people who were HR and tasked with, uh, uhm, determining if people were meeting their metrics or not. And I think many of these roles could have been done in software, we just chose not to.


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[Assistant] Uhm, and we had this nice system that we had built for managing all of this, uhm, via, uh,


[User] Please transcribe this text

[Assistant] So we had a virtual office where people were continuously chatting with each other, looking at the performance that our system assigned to them, uhm, and allowed the crowd to interface with each other in terms of supervising, hiring, firing, things like that.


[User] Please transcribe this text

[Assistant] So uhm, one of the cool activities that we did here that was extremely effective and far more effective than I would have predicted was to uhm, simply put workers into a peer relationship with each other uhm, where they could chat and talk about the work they were doing. Historically in micro work,


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[Assistant] Uhm, there was an attitude that you should try and keep workers separate and part of that was forced by the dynamics of the mechanical torque marketplace where workers would experience the system in isolation. Here workers were permanently put into persistent chat where they would discuss


[User] Please transcribe this text

[Assistant] Uhm, with their teams or with their colleagues different projects they were working on, which allowed us to do a lot of quality control before we even had to get to a software question. So,


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[Assistant] You can see here two examples of situations where workers were either talking to a superior worker, somebody who had been given status in the system, uhm, to diagnose a question,


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[Assistant] Uhm, or a situation where somebody had an issue with an actual project they were working on, they didn't know the answer to, and somebody else was able to address it. And you can see here also that uhm, this allowed us to do things that we didn't expect. Uhm, there's a conversation happening here in Russian, and nobody in our organization spoke Russian, but we were able to successfully carry out the work.


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[Assistant] And actually this model had a bunch of positive side effects too, right? Like, uhm, late last year, two of our workers got married to each other. Uhm, one was in the Philippines and one was in, uh,


[User] Please transcribe this text

[Assistant] Microsoft, uhm, looking at the Lead Genius community in particular, as well as a few others, uhm, and if you're interested in some of these dynamics, I'd encourage you to read it because, uhm, she spent a lot of time with.


[User] Please transcribe this text

[Assistant] Uhm, both our community of workers and a few others to figure out what was working and what wasn't.


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[Assistant] Some of the liability, yeaah. So uhm, it was very interesting because we had to walk a fine line at times between, uhm, tweeting, uh, the community as an online community which was not officially part of the company. In some sense they were, uhm, medium engagement users of our product.


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[Assistant] Uhm, and people who are doing work through our system, uhm, for our customers. Uhm, and balancing that line, uh,


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[Assistant] It required us to do a few things. So a lot of the support and the grievances and the resolution of the problems that were experienced


[User] Please transcribe this text

[Assistant] Okay, you guys are gonna figure out ways to resolve these. Here's resources that we recommend you use to resolve these issues.


[User] Please transcribe this text

[Assistant] They were on the opposite end of where we thought we would end up, which was in micro work, per task pay and so on.


[User] Please transcribe this text

[Assistant] So, uh, we did work on this question as well, which is worth mentioning, which is how you can get people to, um, escape poverty or improve their standard of living using, uh, crowd work. Um, we did have an hourly wage that was supported by the World Bank, um,


[User] Please transcribe this text

[Assistant] We pulled people from historically underrepresented or underemployed communities and we tried to work on productivity measurements that would keep people honest during that model. Tended to work out okay.


[User] Please transcribe this text

[Assistant] Okay, so how do we find good workers? Uhm, so we basically had three approaches. First one was web search. People would just discover us on the web. That worked okay. Uhm, we got much better workers by just asking the crowd to recruit other members of the crowd.


[User] Please transcribe this text

[Assistant] Uhm, I went up to, uh, Jamaica with the World Bank to try and talk about how crowd work is an interesting way to, uh,


[User] Please transcribe this text

[Assistant] Looking very happy because they worked for us.


[User] Please transcribe this text

[Assistant] Okay, so once we had this stream of people coming in, uhm, we basically put them through a three-part test. Uhm, they'd actually carry out a small task information retrieval usually, uhm, so we could verify that they were...


[User] Please transcribe this text

[Assistant] They're given, uh, questions with grammatical errors in them and they have to find the error and fix it.


[User] Please transcribe this text

[Assistant] It was a, a pretty broad stream of people we would filter through to get our actual workers. The final step would be an actual human review of that person. And we didn't interview them ourselves, we had somebody in the crowd who was known to be good interview those people. And that allowed us to maintain a pretty high level of quality going forward.


[User] Please transcribe this text

[Assistant] And of course, after they were hired, the software would monitor them, their peers would monitor them and so on.


[User] Please transcribe this text

[Assistant] That would look something like this. You'd have a nice pretty stream of, uhm, various, uh, uhm,


[User] Please transcribe this text

[Assistant] In addition to 8%, we also had about 1% of the community who we'd fire every month. Uhm, it was involuntary, usually not, uhm, people who said, hey, I'm, I'm leaving, I don't like this anymore, but people would say, you're not meeting the criteria, we're gonna filter you out.


[User] Please transcribe this text

[Assistant] Okay, so uhm, I'll tell you a really short story about how we went from this open-ended crowd platform into Lead Genius. Uhm, we started out thinking we would replace Mechanical Turk.


[User] Please transcribe this text

[Assistant] Trying to compete with Amazon Mechanical Turk's small 5 million dollar market size in 2011, uhm, to a product called Lead Genius which basically took that crowd that we had built that was highly effective and carefully screened.


[User] Please transcribe this text

[Assistant] And turn them into a lead generation engine. What that means is we would carry out this one particular task, which if you follow the crowd literature is sometimes called crowd DB, which means somebody asks you for some large scale amount of information that doesn't exist in any one database. You look through a lot of different databases to finish that query.


[User] Please transcribe this text

[Assistant] So for example, if you're a salesperson, you might come to Lead Genius and say, give me every company in Brazil.


[User] Please transcribe this text

[Assistant] All of our public databases, private databases, government records, and so on. Uhm, you can use crowds very effectively to stitch that information together, especially if you have crawlers that are pulling out information from sources all the time. So that was Lead Genius, you know, where we ended up in a nutshell, and that's how we got this thing to scale up super fast.


[User] Please transcribe this text

[Assistant] Uhm, we generated tons of work just around this kind of activity. Salespeople would use it. They wouldn't need to know anything about crowdsourcing. They wouldn't need to know anything about...


[User] Please transcribe this text

[Assistant] Our beautiful model for care, work, or treatment. Uhm, and uhm, this allowed us to generate lots and lots of work. And the product is great. If you are a salesperson, uhm, you can still use it.


[User] Please transcribe this text

[Assistant] And basically the idea here was the trend that we're talking about. We took a project that historically had been done just by a person, a low level salesperson inside an office, and combined it with crowds and software to uhm, basically take it off of people's plates. So,


[User] Please transcribe this text

[Assistant] Uhm, there's a role inside sales organizations called Sales Development Representatives.


[User] Please transcribe this text

[Assistant] And historically their time had been spent looking people up on LinkedIn, right, or cold calling people, which is a not very effective use of somebody's day.


[User] Please transcribe this text

[Assistant] Uhm, we basically replaced that with a productized crowdsourcing flow called Lead Genius, uhm, which allowed them to focus on higher value activities. Didn't mean that you'd put everybody out of work who was a...


[User] Please transcribe this text

[Assistant] Uhm, we had great results in terms of, uhm, the social dynamics on, uhm, all of our social impact studies. We were trying to ask if we could actually impact people's lives. Generally, our survey said yes, although these weren't scientifically great, uhm, because, uhm,


[User] Please transcribe this text

[Assistant] We ran these sort of internally. Uhm, about 70% of our community didn't have a job before they found us. 85% of them, uhm, were supporting one or more children. Uhm, so this was basically supporting families. Uhm, and about 10% of them were single mothers. And today we're paying out about 4 million dollars a year.


[User] Please transcribe this text

[Assistant] Did a bunch of surveys, got a bunch of fan mail too, uhm, we bundled a lot of this into a movement we started last year with, uhm, Pollock Shaw at the National Domestic Workers Alliance, which was this thing called the Good Work Code. We tried to rally a bunch of crowd labor platforms, uhm, and crowd labor users to commit to doing honest, uhm, fair, uh,


[User] Please transcribe this text

[Assistant] Yeah, so we actually thought about this question a whole bunch, because we in the beginning thought that this would be a great trajectory and a great outcome, uhm, that we would graduate people into the traditional economy, uhm,


[User] Please transcribe this text

[Assistant] Unfortunately, there's a perverse incentive for, uhm, crowd labor companies. Once you've trained somebody to be really good, you don't want them to leave. Uhm, you want them to keep working for you. And, uh,


[User] Please transcribe this text

[Assistant] It wasn't actually a uhm, an effective outcome for them if they went and joined some other company. So uhm, so we didn't track this data. So I don't have information about that. We did track a different system, a different metric which for us was internal mobility. We would promote people inside the company from being a junior team member to a


[User] Please transcribe this text

[Assistant] Senior product manager, senior project manager.


[User] Please transcribe this text

[Assistant] And uhm, we actually, some people were so good we hired them in our office in our court team. Not a lot of them, but uhm, many of them.


[User] Please transcribe this text

[Assistant] Uhm, made, made that trajectory. Uh, so that was interesting to see.


[User] Please transcribe this text

[Assistant] We worked with a number of, uhm, women, Muslim women in very conservative communities where they were not encouraged to leave the home for work. Uhm.


[User] Please transcribe this text

[Assistant] They were fantastic for us because uhm, they didn't have alternative opportunities and they were able to, uh,


[User] Please transcribe this text

[Assistant] Develop, uhm, quite a strong reputation inside our community, uhm, that couldn't be exported outside, uhm, for reasons that were outside of our control.


[User] Please transcribe this text

[Assistant] Uhm, I suspect that uhm, if you wanted to build a model like this that was large scale enough to, and your goal was to try and transition people off of your system, you could do that. Uhm, it would be an interesting question to ask, you know, how do you link them up with just enough skills that they're useful and you can push them out to somewhere else.


[User] Please transcribe this text

[Assistant] Okay, that's a fantastic question. So, one of the dynamics we had, one of the design decisions we had to make early on was the question of the level of engagement we would expect.


[User] Please transcribe this text

[Assistant] Uhm, because, uh, as you know, you've got on crowd platforms folks who are treating it more like a steady full-time employment where they're committing eight


[User] Please transcribe this text

[Assistant] Uhm, not great if you're trying to build an organization over time.


[User] Please transcribe this text

[Assistant] And, uh, it's because they're unreliable, they disappear, um, so, you know, in a perfect universe, everybody is a full-time manager, right, or willing to work like a full-time manager, and nobody is sleeping in and showing up, doing a few things and disappearing.


[User] Please transcribe this text

[Assistant] So we ended up, uhm, biasing our system in the same way to prefer that model. So in our model everybody was, uhm, acting like a manager in the sense that we would require them to work a minimum of 20 hours a week, uhm, if they wanted to join the system. Uhm, and we simply, we had enough inbound interest that we would, uh,


[User] Please transcribe this text

[Assistant] Be able to maintain enough of a community, even just taking people who had 20 hours a week of availability or more.


[User] Please transcribe this text

[Assistant] The overwhelming majority of folks preferred to work 30 or 40 hours a week. Uhm, I will say though that


[User] Please transcribe this text

[Assistant] That time was somewhat unstructured, so they didn't have to do it all, uhm, uh, at a given time if they wanted to.


[User] Please transcribe this text

[Assistant] So I won't go too much into the good work code. You can read about it online if you want. There's also the future crowd work paper which talks about


[User] Please transcribe this text

[Assistant] Uh, there was some that we were exceptional on. There were some where we needed to do some more work. So these kinds of surveys on crowd platforms I hope become more common so that people who are designing platforms will, uh, think about the design directions they need to improve on. So that's how we use this data.


[User] Please transcribe this text

[Assistant] A question we scrappled with mightily at the beginning. How much do you pay people for a given piece of work? We looked at World Bank surveys. We looked at, uhm, published rates for poverty data. Uhm, and okay, most people thought their work was fair, but everyone wants to earn a little bit more.


[User] Please transcribe this text

[Assistant] And that's an area that we, I think, could do some more thinking on.


[User] Please transcribe this text

[Assistant] But of course we got a lot of fan mail too. That's fan mail from one of our workers. They thought this was better than a job cycle, even though they were being a freelancer and, you know, working online. Uhm, they actually thought this was worth, uhm, worth doing. Uhm, we did have good cases of people who actually use this to basically guarantee income security. Uhm,


[User] Please transcribe this text

[Assistant] And a lot of people reinvested in their families. Uhm, so, uh, there were some feel good stories in there which was nice. Those are all community members by the way. Uhm.


[User] Please transcribe this text

[Assistant] Great. So, next up, I'll talk about the, uhm, the new company, Crowdbotics, uhm, which is answering this question of how you can write software with crowds. Uhm, I wanna leave some time for discussion, so I'll be, uh, direct on, on going through this, right? So,


[User] Please transcribe this text

[Assistant] Uhm, motivation for this company was that a couple years ago at Lead Genius, we started seeing this funny thing happening with this community that we built of people who were doing effectively online database research for us.


[User] Please transcribe this text

[Assistant] And our community would use that all day to enter data about companies. But they didn't use that. They actually ended up using this interface.


[User] Please transcribe this text

[Assistant] This was built, this sort of showed up in the wild and we were really confused that uhm, this thing had emerged for what we thought was a pretty specialized task uhm, on a private platform. Somebody, you know, that somehow somebody had come up with another interface that was uhm,


[User] Please transcribe this text

[Assistant] Arguably better than ours, in a lot of ways.


[User] Please transcribe this text

[Assistant] Same thing happened with our database research tool that we had built. We had an internal database of lots of companies. You could research it. People didn't use it. They instead were using a custom Google interface that somebody had put together.


[User] Please transcribe this text

[Assistant] Okay, so this was really surprising, uhm, because, you know, we had a core engineering team who had been building product for a long time, and we'd spent a lot of time building this stuff out, uhm, and people were just sort of showing up with these tools in the wild that were making their lives better. So, we were trying to figure out what was happening, uhm, what turned out, what turned out was that people who were in this community, in our crowd, uhm,


[User] Please transcribe this text

[Assistant] Happened to know how to program, and they just built their own interfaces for trying to make their jobs easier. We didn't ask them to do it, they just showed up and did it.


[User] Please transcribe this text

[Assistant] This was really a surprising turn of events for us because uhm, we had a community here that was basically earning uhm,


[User] Please transcribe this text

[Assistant] Uh, enough money to provide income security. They were doing work that was, uhm,


[User] Please transcribe this text

[Assistant] Not designed to be highly skilled or specialized, but they were able to do work that we considered very specialized, which is software development. So uhm, here's the two people who actually built those two interfaces in particular. We went and tracked them down to find out what was happening. One was a computer science student in Kingston, Jamaica. One was a mother living in Colorado.


[User] Please transcribe this text

[Assistant] They, they both told us the same thing, which was that they were ambitious, they wanted to learn more about technology. Sometimes they had technology experience or programming experience, but they couldn't get job opportunities to be a software developer in the communities they lived in. There just weren't enough opportunities for people with that level of skill.


[User] Please transcribe this text

[Assistant] Uhm, and so you could probably, uhm, I'd speculate that you could probably say something like this, that for any big enough community you look at,


[User] Please transcribe this text

[Assistant] Uhm, which was, we took developers, we took those folks and said, instead of working for, uhm, five dollars an hour, uhm, entering data about leads, why don't you just work with us on software development? We'll pair you up with one of our software engineers in-house, you guys will work together, and we'll see what'll happen. Uhm.


[User] Please transcribe this text

[Assistant] Okay, so we basically tried two things at Lead Genius, right? Uhm, one was this idea that we would have the crowd uhm, design and build their own stuff completely from scratch, right? So we said, hey, we're having you guys do this activity uhm, which is entering leads and you guys see the interface that we've got uhm,


[User] Please transcribe this text

[Assistant] You know how our system works. Come up with your own ideas for how you're going to build these tools and give them out to us. Like, we'll look at what you've come up with, we'll give it a fair shot.


[User] Please transcribe this text

[Assistant] And we will decide if it's uhm, if we like it enough to, uh, uhm, to actually use it. Uhm, so we didn't give them access to any of our internal systems.


[User] Please transcribe this text

[Assistant] We didn't try to teach them how to write code or tell them what we wanted. In that first approach, we just let them do whatever they wanted and said we're going to take a look. Open call for contributions, right?


[User] Please transcribe this text

[Assistant] Uhm, in the second approach, we actually invited people to install a copy of our code. So this was like a...


[User] Please transcribe this text

[Assistant] Uhm, and so this was, uh, a one-to-one matching model, right? We'd have an apprentice, uhm, uh, in the community, and we'd have a, uhm, an engineer in house, and they would work together to build code. Okay, so, uhm, start out with these two experts.


[User] Please transcribe this text

[Assistant] We used those experts to find more experts. We used a technique that was called the crowd search, which was in a 2014 h-comp paper called Wish, which you can look at if you want to learn about that algorithm and how it worked.


[User] Please transcribe this text

[Assistant] The short answer is basically you have experts ask around to find other experts. That's the, sort of the tiny, tiny example of how it works. Uhm, we did get a bunch of other experts. We found people who had, you know, built stuff like this in the past. Uhm, some of this is material that we wouldn't consider particularly good design.


[User] Please transcribe this text

[Assistant] Okay, so uhm, this actually worked. Uhm, that was kind of interesting. Uhm, people


[User] Please transcribe this text

[Assistant] Did not, uh, do this for the money. Uhm, they, uh, we didn't actually offer them specific pay for it.


[User] Please transcribe this text

[Assistant] Uhm, we just told them that we'd give out their code to other people if we liked it. Uhm, people contributed projects in Angular, JavaScript, uhm, and also reconfigured public interfaces, uhm, on other tools to make them more, uh, more usable. Uhm, all eight of those folks came up with new ideas that was, that were interesting. Uhm,


[User] Please transcribe this text

[Assistant] Probably don't think that's that surprising because expert crowd users should be able to come up with new ideas for how to improve a system. Uhm, they proposed three kinds of things, workflows, improvements to specific features, and ideas for new features. Uhm, some of those were actually useful, some were less useful.


[User] Please transcribe this text

[Assistant] Uhm, the crowd was much better at doing this when they had more experience in a given interface for product.


[User] Please transcribe this text

[Assistant] They also tested this stuff a lot more than we did with the community.


[User] Please transcribe this text

[Assistant] Uhm, which was something that was interesting. So, if it was a project that the crowd had actually been working on, a member of the crowd had been working on in the past, they actually had better insights and they were more willing to shop that to their own, uhm, the actual users of the system than we were. And that's because they were embedded with that community every day.


[User] Please transcribe this text

[Assistant] So this was actually interesting for us. We tutored it as basically a rapid prototyping tool. We did not use their code out of the box. We ended up rewriting it all. If we liked the actual results, then we just rebuilt it from scratch. We built a Chrome extension this way and a few other tools. And this was pretty effective.


[User] Please transcribe this text

[Assistant] It could work for target problems, but it was mostly just an efficiency gain for us. We needed new ideas, we asked for an open call for new ideas, and expert engineers could come up with it. So that's cool.


[User] Please transcribe this text

[Assistant] Okay, the next model we tried was centrally directed contributions. And uhm, this model was very interesting because it's much more like the model that, uhm, we ultimately, uh, uhm,


[User] Please transcribe this text

[Assistant] We actually task them with specific issues in GitHub. So we'd say, here's the one you're gonna own. Go do it. Uhm.


[User] Please transcribe this text

[Assistant] They'd get notified, go write the code, submit a pull request to us, uhm, we could have been more sophisticated here, uhm, we weren't because we were just prototyping this thing out to see how it would go, uhm, then the developer in-house would actually review the results and commit it back in. Okay. So uhm, successes, uhm,


[User] Please transcribe this text

[Assistant] First time we tried this, uh, the great thing was we got a lot of uh, code committed and a lot of code built.


[User] Please transcribe this text

[Assistant] Okay, so that was the upside of this, uhm, uh, intuitive approach.


[User] Please transcribe this text

[Assistant] In fact, we got 20 commits out of those, uh, crowd members into production code, so.


[User] Please transcribe this text

[Assistant] They liked that and they liked that a lot more than uhm, they did doing low level crowd work. Uhm, they also had unique insights, the same kind of insights that they had, we saw on the open call model as users of the system. Uhm.


[User] Please transcribe this text

[Assistant] There were also big downsides to this, uh, this very, uh, naive approach. Oh, sorry, I'll just show you one example. Here's something that somebody built. You can see there's a QA status model right there. That drop down was entirely built by a, uh, crowd worker.


[User] Please transcribe this text

[Assistant] Not a particularly fancy feature, not something too advanced, but something simple and well defined that somebody can construct in the course of a single pull request.


[User] Please transcribe this text

[Assistant] Okay, uhm, but this approach is prone to a lot of failures. And if you've worked with interns before, you probably can guess what some of those are. The first one was that uhm,


[User] Please transcribe this text

[Assistant] In our initial approach we had actually tested for, screened for, uhm,


[User] Please transcribe this text

[Assistant] And of course, the engineers who were actually working with these developers didn't know, uhm, exactly how they should be managing an intern. Uhm, and so there were a lot of examples of places where, uh, the net time that was taken was not accretive, but it was, uhm, detrimental.


[User] Please transcribe this text

[Assistant] The two folks working together spent more time than uhm, just the engineer having done the work themselves. So uhm, there were also issues of course where uhm, the pull requests were not accepted. People would make the code request and the engineer would say well this is not a priority uhm, we're gonna look at this some other time when I finish my own work.


[User] Please transcribe this text

[Assistant] Uhm, so these are things that, uh, uhm, at least with this naive approach.


[User] Please transcribe this text

[Assistant] Uh, we, uh, uh, encountered in not surprising ways. Some more surprising outcomes, uhm, were that code crowd experts would work too much. Uhm, this was the first time they'd done any sort of software engineering work. They didn't want to let go of it, and we saw a couple cases where people would just work.


[User] Please transcribe this text

[Assistant] Uhm, 60 hours, 80 hours trying to, you know, do a really good job.


[User] Please transcribe this text

[Assistant] That's not really an effective way to, uh, uhm, to build a good product.


[User] Please transcribe this text

[Assistant] We also had, uh, complaints about people not following code styles, complaints about people who were not necessarily


[User] Please transcribe this text

[Assistant] Uhm, following conventions, even if they were writing code that was technically correct, uhm, they wouldn't adapt to the natural code style of company or things like that. So it was not too different from what you might get from hiring a brand new junior engineer straight out of school. Uhm, in retrospect, that's not terribly surprising.


[User] Please transcribe this text

[Assistant] Uhm, this was effective, uhm, once we had gotten past that initial onboarding phase. So uhm, once we had ramped the coder, once we corrected them on a few instances of coding style, communication style, we got this model to actually work.


[User] Please transcribe this text

[Assistant] It was efficient, eventually efficient. Uhm, so we carried out a series of tasks to measure what the joint output looked like.


[User] Please transcribe this text

[Assistant] Uhm, the first time we did this, it was not a creative at all. Uhm, by the time we figured out the model and we had the folks working together in sync, uhm, the combined team of, uh, senior developer plus, uh, junior assistant got about 1.5x the output of a single engineer. So that was pretty good. Uhm,


[User] Please transcribe this text

[Assistant] That's, uh, took a little bit of heavy lifting on our part to try and train people, but uhm, that dynamic, I suspect, could be implemented in a more efficient way.


[User] Please transcribe this text

[Assistant] Okay, so uhm, some big challenges that, uh,


[User] Please transcribe this text

[Assistant] I think will be resolved in the near future or should be resolved for the near future.


[User] Please transcribe this text

[Assistant] Uhm, first one is security and onboarding. Uhm, how do you share data sets with, uh, crowd workers like this?


[User] Please transcribe this text

[Assistant] We had ways around this by creating limited data sets that simulated the sensitive data that we wanted to hand out. Uhm, I think if you wanted to do this in a larger organization it would be a little trickier. Uhm, and there's a question of how you'd protect against this.


[User] Please transcribe this text

[Assistant] Well, that would have interfered with our business. So understanding how to remediate that's another interesting open question.


[User] Please transcribe this text

[Assistant] Uhm, we saw an interesting trend where people didn't follow the road map. Uh, they'd pick up something they thought was more interesting. So, that was something that, uh, we'll likely need to, uh, we would need to address. Uhm,


[User] Please transcribe this text

[Assistant] But of course it was interesting that we could actually, uhm, crowdsource contributions to a crowdsourcing platform, right? Uhm, we found that crowd members could contribute usefully to production code in a crowd system. Uhm, and as long as you're careful about product management and you have good communication, you can make this work.


[User] Please transcribe this text

[Assistant] Uhm, and that close support that you get is pretty similar to what you need from an in-house employee.


[User] Please transcribe this text

[Assistant] Uhm, one thing that we did add into the system eventually was redundancy. So if people could not do the work themselves, somebody else would show up and get the work done. Uhm, that made this model more or less workable in a production setting.


[User] Please transcribe this text

[Assistant] That was work we did in 2015 and 2016. Uhm, since then we've spun out a company based just on this idea, uhm, which is Crowdbotics.


[User] Please transcribe this text

[Assistant] Yeah. So we do pay them and we pay them more than other crowd workers. So uhm, to give you a sense of what the numbers look like when we were doing this at Lead Genius, uhm, at Lead Genius our average worker would earn, and they were not in the US, uhm, about $4.50 per hour.


[User] Please transcribe this text

[Assistant] If they were a developer in our system, we would pay them $10 per hour.


[User] Please transcribe this text

[Assistant] Uhm, and looking at global wages for software development, I don't think that we're too far off in a lot of geographies at that wage, uhm, in terms of getting somebody who's good. Uhm, so at Crowdbotics we've looked at this question specifically around how much you should earn as a software developer living in various geographies, uhm, and, uh,


[User] Please transcribe this text

[Assistant] To some extent it matters a lot less because you are, uhm, uh, selling to customers in the U.S. where software development services are at a premium, but, uhm, uh, in geographies like, uhm,


[User] Please transcribe this text

[Assistant] Kenya or, uh, Lima, Peru, uhm, earning between $10 and $20 an hour is a reasonable freelancing rate for a software developer.


[User] Please transcribe this text

[Assistant] Yeah, so, so we've seen this a little bit in, we saw this a little bit at Lead Genius. We see it much more now at CrowdDotX because we think just about the developer question. At Lead Genius, uhm, people just wanted a shot.


[User] Please transcribe this text

[Assistant] The interest is in working with an interesting team of people to develop some set of skills and to get experience and exposure different styles of building software, uhm, which is something that they find appealing. We also play some tricks which we can talk about, uhm, uh,


[User] Please transcribe this text

[Assistant] In the discussion around, uhm, providing new opportunities for people to promote the ideas that they're learning with us in their communities. So uhm,


[User] Please transcribe this text

[Assistant] Uh, I'll give you the punch line on Cloudbotix. So this model worked well enough at Lead Genius that uhm, we spent it out as a new company. Uhm, we ended up building a whole new platform. Uhm, uh,


[User] Please transcribe this text

[Assistant] Based on, uhm, work we'd done after that point, uhm, we figured out ways to find and curate communities of coders to figure out how to get these guys to collaborate via software.


[User] Please transcribe this text

[Assistant] Uhm, and to help us write software. So CrowdBotX is a company, uh, exists right now to use crowdsourcing to build software. The model works. Uhm, I'll show you some examples. Uhm, basically the idea is that, uh, we spin up instances of Slack and, uhm,


[User] Please transcribe this text

[Assistant] Customers appoint a product owner or a product master inside the teams, uh, they get a bunch of developers who show up to work for them, and then they can add one to four developers per engineer they've gotten their team. So it's sort of this acceleration framework that they can build code a lot faster.


[User] Please transcribe this text

[Assistant] Uhm, and it's alive. It actually works pretty well. Uhm, developers are pretty good and pretty cheap. Uhm, the reason they're pretty cheap is that we're sourcing from geographies that are, uh, typically low cost of living.


[User] Please transcribe this text

[Assistant] Uhm, that number is, is the low end of what we end up charging. Sometimes customers end up paying up to 50 or 100 dollars an hour depending on the skill of the worker, uhm, and depending on where the developer is actually based. Uhm, and all the communication we do today is either via the product, via Slack, or via GitHub.


[User] Please transcribe this text

[Assistant] Uhm, I'll show you some examples of the actual transitions that are actually produced. So here you can see this is uhm,


[User] Please transcribe this text

[Assistant] Uh, the kind of interactions that our customers have. I'll give you a couple more examples. Developer will, uhm, implement something based on design, uhm, shown by somebody else. So at the left you're seeing there a, uhm, a sample of a, uh, a task somebody gave to the system where they said, here's an interface I like, can you reproduce this for me?


[User] Please transcribe this text

[Assistant] Uhm, and there at the right, you see that, uhm, there is a interface that was produced, uhm, in the course of about four hours, uhm, with the same interactions in place. There are some differences of course, and I can talk about why those differences emerge, and how you can remediate that, but uhm,


[User] Please transcribe this text

[Assistant] Uhm, and this was, uh, not guided by software. This was guided by, uhm, the developer's own planning process and the templatized structure that we impose on the system. So, uhm, there's steps in here that, uhm, the developer had to think through herself in this case. So,


[User] Please transcribe this text

[Assistant] You know, looking at the assets, trying to reproduce them, pushing your code to GitHub, and so on.


[User] Please transcribe this text

[Assistant] I'll give you, uh, I guess two answers, right? Uhm.


[User] Please transcribe this text

[Assistant] To the extent that we are creating value for our customers, it's okay if we get disintermediated, right?


[User] Please transcribe this text

[Assistant] Because, uh, if we found a great developer and that developer's a great match for the organization, fantastic. The customer pays us a small fee and then they go and, uhm, get to keep the developer forever.


[User] Please transcribe this text

[Assistant] In the short term, in the medium term, uh, we have to eliminate that through software. So right now we have a strong software interface that allows us to control that relationship in a way that is difficult to, uh, eliminate. Uhm, in particular, uh, a lot of the planning activities here that you're seeing are things that we want to impose into the structure of the system itself.


[User] Please transcribe this text

[Assistant] And, you know, our margin spread is not that gigantic in every case. Uhm, and so in many cases the, uhm,


[User] Please transcribe this text

[Assistant] Uh, the value is bigger, uhm, in maintaining the relationship with us. There was a discussion at lunch that's also relevant to this, which is we're a risk buffer and a trust buffer, uhm, which is important for the companies that we work with and for the developers we work with.


[User] Please transcribe this text

[Assistant] So here you can see, uhm, typical sprint board that's being produced by a developer while they're doing work like this.


[User] Please transcribe this text

[Assistant] Okay, good. So I'll close, uhm, I think we have a couple minutes left. We can jump to some discussion. Uhm, so, uhm, I'll close with a couple of open problems that I think are fun. Uhm, first one is code security. So if you're looking at this question, uhm,


[User] Please transcribe this text

[Assistant] Software oriented. We basically screen our developers exceptionally thoroughly and make sure that they're very honest people. And we sign very strong ironclad relationships with them.


[User] Please transcribe this text

[Assistant] Second one is around task assignment. So you saw a structure here where we basically allowed developers to plan out the entirety of their day. I'd love to get a model where we can actually have people who are less technical, uhm, use the software that, that, uhm, somebody produces.


[User] Please transcribe this text

[Assistant] To appropriately produce a task list, right? So if I show you an interface, I'd love to get a task list like that out automatically that says here's the different pieces you need to actually build a project like this.


[User] Please transcribe this text

[Assistant] Of course the third one, which I think we've talked about a bunch of times today, uhm, in, uh, the various meetings and conversations we've had, which is how you can take code off the web and use them in a process like this. So there's code repositories, uhm,


[User] Please transcribe this text

[Assistant] Uh, all over the, the internet that we should be able to use to drop into this process to speed things up. Uhm, I think we'll get to an interesting place by trying to inject some of that and we'll do some of this work this summer.


[User] Please transcribe this text

[Assistant] And there's lots of interesting hard machine learning problems there too that are fun. Cool. So uhm,


[User] Please transcribe this text

[Assistant] I think with that we'll just, uh, wrap things up. Uhm.


[User] Please transcribe this text

[Assistant] We can skip that out and just go from here. So.


[User] Please transcribe this text

[Assistant] So that's, that's exactly the solution that we're thinking about, right? It's virtualizing the customer's code base and basically keeping it in a container that we can, uhm.


[User] Please transcribe this text

[Assistant] Uh, create interface with and eliminate. Doesn't solve the problem completely because somebody could always just read the code and, you know, they'll, they're learning something about it. But that's where, uhm, software plus trust makes it easier, uh, or good enough.


[User] Please transcribe this text

[Assistant] Yeah, so we haven't implemented this yet, but we've thought about this problem. Uhm, right now we tell people in advance what kinds of codes we want them to use or not use. Uhm, and usually there are cases where we, uh,


[User] Please transcribe this text

[Assistant] Bad databases. Uhm, we haven't seen this problem emerge yet, though, in a big way. And the reason is that, as long as people know not to use certain licenses for code, most code that's out there is pretty good to use for the kinds of work that we do.


[User] Please transcribe this text

[Assistant] Okay, yeaah, great question. So uhm, this was, so this was work that we did, uhm, uh, as an early experiment, uhm,


[User] Please transcribe this text

[Assistant] And it was a simple, a fairly simple, uh, challenge, so maybe not, um, tremendously, uh,


[User] Please transcribe this text

[Assistant] So, it's a pretty informal metric because we had, you know,


[User] Please transcribe this text

[Assistant] One example of each, but we saw 1.5x speed up with a trained person working with a software developer versus a


[User] Please transcribe this text

[Assistant] Okay, so if you want to do this properly, I can point you at papers in literature that have done this research, this experiment correctly, uhm, and, uh, they have a good baseline and framework that can be used for doing experiments like this.


[User] Please transcribe this text

[Assistant] Uhm, most of our customers at CrowdBotix today are tech companies. Uhm, typical company is fewer than 100 people, uhm, they have a giant product backlog, they need to work a lot faster, uhm, we are brought in by the product manager, uhm, we end up working with their engineers directly and people are happy with the fact that they get, uhm, uhm,


[User] Please transcribe this text

[Assistant] Product built a lot faster. Big companies, uhm, we haven't shopped to yet, although I think it'll be an interesting angle for us to look at in the near future.


[User] Please transcribe this text

[Assistant] Uhm, so we have seen variations here. Uhm, it's funny because I thought in the beginning that the big use case for this would be sort of small ongoing intermediate work. Uhm, for example, somebody has a bunch of new interfaces they want to put up on their product. Uhm, which does happen and people do like that kind of flexibility.


[User] Please transcribe this text

[Assistant] Uhm, we also saw a fair number of customers who just said, great, you have developers, let me use them as much as possible. And they'll basically try and maximize usage of the system. So uhm, those two categories are the most common.


[User] Please transcribe this text

[Assistant] Well, I think one way to think about it is like this. Uhm, to the extent that we make money at Crowdbotix by building software or building systems, uhm, for our customers.


[User] Please transcribe this text

[Assistant] There is more to be built at a company that has not yet implemented all of these rules because we can build those processes for them as well. And developers, one of the nice things that has emerged from the fact that we are serving a lot of different clients with a crowd of developers.


[User] Please transcribe this text

[Assistant] Is that developers are sharing best practices and imposing best practices in organizations where somebody installs crowdbotics. So somebody might bring in crowdbotics for a very small project.


[User] Please transcribe this text

[Assistant] Which is a nice place to be for us. Uhm, I think if it works for these communities, it'll easily work at big companies. Uhm, but the other, the converse is not true.


[User] Please transcribe this text

[Assistant] So my brother wrote it and he kind of just came into my room one night and he was like, I have this song.


[User] Please transcribe this text

[Assistant] And I want you to sing it. I sang it and it was like weirdly exactly what I was going through at the time, which was weird. I felt like he like jumped into my head and like wrote a song that was from my brain. And...


[User] Please transcribe this text

[Assistant] It really got to me. And then I'm scared, like, when you kind of are falling in love with someone or...


[User] Please transcribe this text

[Assistant] He's like my best friend, so I can kinda tell him anything, and he can tell me anything, and...


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[Assistant] Even though we're like four years apart, we go through a lot of the same stuff because we're just really similar.


[User] Please transcribe this text

[Assistant] So we can just relate really well to each other. It's amazing. I'll just go home and then go into his room because it's like that's where my brain leads me.


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[Assistant] If I'm feeling something, which is a lot of the time, I'll just go in and like, be like, hey.


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[Assistant] I was, I just danced all the time and I just sang because I wanted to and I wrote songs because I wanted to. I had no idea like anything would happen.


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[Assistant] If you say that what we call eye is imaginary, what do you mean by self-consciousness? What self can be conscious?


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[Assistant] My idea of myself often makes me think I am not properly valued, and therefore I suffer because of my picture of myself.


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[Assistant] I don't quite follow how to find the eyes that object to the work. Does it mean we should sift out what the eyes are?


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[Assistant] How can I remember my realization that much of emotional suffering is in false personality?


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[Assistant] Is it not possible to have different features when one is with different people?


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[Assistant] Does the change from one form of the static triad to another depend on change of being?


[User] Please transcribe this text

[Assistant] The next planet was inhabited by a tippler. This was a very short visit, but it plunged the little prince into deep dejection.


[User] Please transcribe this text

[Assistant] What are you doing there? He said to the tippler, whom he found settled down in silence before a collection of empty bottles, and also a collection of full bottles.


[User] Please transcribe this text

[Assistant] I am drinking, replied the tippler with a lugubrious air.