Karpathy

This is a brief guide to my new art project microgpt , a single file of 200 lines of pure Python with no dependencies that trains and inferences a GPT. This file contains the full algorithmic content of what is needed: dataset of documents, tokenizer, autograd engine, a GPT-2-like neural network architecture, the Adam optimizer, training loop, and inference loop. Everything else is just efficiency. I cannot simplify this any further. This script is the culmination of multiple projects (micrograd, makemore, nanogpt, etc.) and a decade-long obsession to simplify LLMs to their bare essentials, and I think it is beautiful 🥹. It even breaks perfectly across 3 columns: Where to find it: This GitHub gist has the full source code: microgpt.py It’s also available on this web page: https://karpathy.ai/microgpt.html Also available as a Google Colab notebook NEW : buy microgpt as a triptych on my art store at karpathy.art :) The following is my guide on stepping an interested reader through the code. Dataset The fuel of large language models is a stream of text data, optionally separated into a set of documents. In production-grade applications, each document would be an internet web page but for microgpt we use a simpler example of 32,000 names, one per line: # Let there be an input dataset `docs`: list[str] of documents (e.g. a dataset of names) if not os.path.exists('input.txt'): import urllib.request names_url = 'https://raw.githubusercontent.com/karpathy/makemore/refs/heads/master/names.txt' urllib.request.urlretrieve(names_url, 'input.txt') docs = [l.strip() for l in open('input.txt').read().strip().split('\n') if l.strip()] # list[str] of documents random.shuffle(docs) print(f"num docs: {len(docs)}") The dataset looks like this. Each name is a document: emma olivia ava isabella sophia charlotte mia amelia harper ... (~32,000 names follow) The goal of the model is to learn the patterns in the data and then generate similar new documents that share the statistical patterns within. As a preview, by the end of the script our model will generate (“hallucinate”!) new, plausible-sounding names. Skipping ahead, we’ll get: sample 1: kamon sample 2: ann sample 3: karai sample 4: jaire sample 5: vialan sample 6: karia sample 7: yeran sample 8: anna sample 9: areli sample 10: kaina sample 11: konna sample 12: keylen sample 13: liole sample 14: alerin sample 15: earan sample 16: lenne sample 17: kana sample 18: lara sample 19: alela sample 20: anton It doesn’t look like much, but from the perspective of a model like ChatGPT, your conversation with it is just a funny looking “document”. When you initialize the document with your prompt, the model’s response from its perspective is just a statistical document completion. Tokenizer Under the hood, neural networks work with numbers, not characters, so we need a way to convert text into a sequence of integer token ids and back. Production tokenizers like tiktoken (used by GPT-4) operate on chunks of characters for efficiency, but the simplest possible tokenizer just assigns one integer to each unique character in the dataset: # Let there be a Tokenizer to translate strings to discrete symbols and back uchars = sorted(set(''.join(docs))) # unique characters in the dataset become token ids 0..n-1 BOS = len(uchars) # token id for the special B

I find blockchain fascinating because it extends open source software development to open source + state. This seems to be a genuine/exciting innovation in computing paradigms; We don’t just get to share code, we get to share a running computer, and anyone anywhere can use it in an open and permissionless manner. The seeds of this revolution arguably began with Bitcoin, so I became curious to drill into it in some detail to get an intuitive understanding of how it works. And in the spirit of “what I cannot create I do not understand”, what better way to do this than implement it from scratch? We are going to create, digitally sign, and broadcast a Bitcoin transaction in pure Python, from scratch, and with zero dependencies. In the process we’re going to learn quite a bit about how Bitcoin represents value. Let’s get it. (btw if the visual format of this post annoys you, see the jupyter notebook version, which has identical content). Step 1: generating a crypto identity First we want to generate a brand new cryptographic identity, which is just a private, public keypair. Bitcoin uses Elliptic Curve Cryptography instead of something more common like RSA to secure the transactions. I am not going to do a full introduction to ECC here because others have done a significantly better job, e.g. I found Andrea Corbellini’s blog post series to be an exceptional resource. Here we are just going to write the code but to understand why it works mathematically you’d need to go through the series. Okay so Bitcoin uses the secp256k1 curve. As a newbie to the area I found this part fascinating - there are entire libraries of different curves you can choose from which offer different pros/cons and properties. NIST publishes recommendations on which ones to use, but people prefer to use other curves (like secp256k1) that are less likely to have backdoors built into them. Anyway, an elliptic curve is a fairly low dimensional mathematical object and takes only 3 integers to define: from __future__ import annotations # PEP 563: Postponed Evaluation of Annotations from dataclasses import dataclass # https://docs.python.org/3/library/dataclasses.html I like these a lot @dataclass class Curve: """ Elliptic Curve over the field of integers modulo a prime. Points on the curve satisfy y^2 = x^3 + a*x + b (mod p). """ p: int # the prime modulus of the finite field a: int b: int # secp256k1 uses a = 0, b = 7, so we're dealing with the curve y^2 = x^3 + 7 (mod p) bitcoin_curve = Curve( p = 0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFEFFFFFC2F, a = 0x0000000000000000000000000000000000000000000000000000000000000000, # a = 0 b = 0x0000000000000000000000000000000000000000000000000000000000000007, # b = 7 ) In addition to the actual curve we define a Generator point, which is just some fixed “starting point” on the curve’s cycle, which is used to kick off the “random walk” around the curve. The generator is a publicly known and agreed upon constant: @dataclass class Point: """ An integer point (x,y) on a Curve """ curve: Curve x: int y: int G = Point( bitcoin_curve, x = 0x79BE667EF9DCBBAC55A06295CE870B07029BFCDB2DCE28D959F2815B16F81798, y = 0x483ada7726a3c4655da4fbfc0e1108a8fd17b448a68554199c47d08ffb10d4b8, ) # we can verify that the generator point is indeed on the curve, i.e. y^2 = x^3 + 7 (mod p) print("Generator IS on the curve: ", (G.y**2 - G.x**3 - 7) % bitcoin_curve.p == 0) # some other totally random point will of course not be on the curve, _MOST_ likely impo

Throughout my life I never paid too much attention to health, exercise, diet or nutrition. I knew that you’re supposed to get some exercise and eat vegetables or something, but it stopped at that (“mom said”-) level of abstraction. I also knew that I can probably get away with some ignorance while I am young, but at some point I was messing with my health-adjusted life expectancy. So about halfway through 2019 I resolved to spend some time studying these topics in greater detail and dip my toes into some biohacking. And now… it’s been a year! A "subway map" of human metabolism. For the purposes of this post the important parts are the metabolism of the three macronutrients (green: lipids, red: carbohydrates, blue: amino acids), and orange: where the magic happens - oxidative metabolism, including the citric acid cycle, the electron transport chain and the ATP Synthase. full detail link. Now, I won’t lie, things got a bit out of hand over the last year with ketogenic diets, (continuous) blood glucose / beta-hydroxybutyrate tests, intermittent fasting, extended water fasting, various supplements, blood tests, heart rate monitors, dexa scans, sleep trackers, sleep studies, cardio equipments, resistance training routines etc., all of which I won’t go into full details of because it lets a bit too much of the mad scientist crazy out. But as someone who has taken plenty of physics, some chemistry but basically zero biology during my high school / undergrad years, undergoing some of these experiments was incredibly fun and a great excuse to study a number of textbooks on biochemistry (I liked “Molecular Biology of the Cell”), biology (I liked Campbell’s Biology), human nutrition (I liked “Advanced Nutrition and Human Metabolism”), etc. For this post I wanted to focus on some of my experiments around weight loss because 1) weight is very easy to measure and 2) the biochemistry of it is interesting. In particular, in June 2019 I was around 200lb and I decided I was going to lose at least 25lb to bring myself to ~175lb, which according to a few publications is the weight associated with the lowest all cause mortality for my gender, age, and height. Obviously, a target weight is an exceedingly blunt instrument and is by itself just barely associated with health and general well-being. I also understand that weight loss is a sensitive, complicated topic and much has been discussed on the subject from a large number of perspectives. The goal of this post is to nerd out over biochemistry and energy metabolism in the animal kingdom, and potentially inspire others on their own biohacking lite adventure. What weight is lost anyway ? So it turns out that, roughly speaking, we weigh more because our batteries are very full. A human body is like an iPhone with a battery pack that can grow nearly indefinitely, and with the abundance of food around us we scarcely unplug from the charging outlet. In this case, the batteries are primarily the adipose tissue and triglycerides (fat) stored within, which are eagerly stockpiled (or sometimes also synthesized!) by your body to be burned for energy in case food becomes scarce. This was all very clever and dandy when our hunter gatherer ancestors downed a mammoth once in a while during an ice age, but not so much today with weaponized truffle double chocolate fudge cheesecakes masquerading on dessert menus. Body’s batteries . To be precise, the body has roughly 4 batteries available to it, each varying in its total capacity and the latency/throughput with which it can be mobilized. The biochemical implementation details of each storage medium vary but, remarkably, in every case your body discharges the batteries for a single, unique purpose: to synthesize adenosine tripho

The current state of this blog (with the last post 2 years ago) makes it look like I’ve disappeared. I’ve certainly become less active on blogs since I’ve joined Tesla, but whenever I do get a chance to post something I have recently been defaulting to doing it on Medium because it is much faster and easier. I still plan to come back here for longer posts if I get any time, but I’ll default to Medium for everything short-medium in length. TLDR Have a look at my Medium blog .

This is a long overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go , simulated quadrupeds are learning to run and leap , and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over the last ~year: I worked through Richard Sutton’s book , read through David Silver’s course , watched John Schulmann’s lectures , wrote an RL library in Javascript , over the summer interned at DeepMind working in the DeepRL group, and most recently pitched in a little with the design/development of OpenAI Gym , a new RL benchmarking toolkit. So I’ve certainly been on this funwagon for at least a year but until now I haven’t gotten around to writing up a short post on why RL is a big deal, what it’s about, how it all developed and where it might be going. Examples of RL in the wild. From left to right : Deep Q Learning network playing ATARI, AlphaGo, Berkeley robot stacking Legos, physically-simulated quadruped leaping over terrain. It’s interesting to reflect on the nature of recent progress in RL. I broadly like to think about four separate factors that hold back AI: Compute (the obvious one: Moore’s Law, GPUs, ASICs), Data (in a nice form, not just out there somewhere on the internet - e.g. ImageNet), Algorithms (research and ideas, e.g. backprop, CNN, LSTM), and Infrastructure (software under you - Linux, TCP/IP, Git, ROS, PR2, AWS, AMT, TensorFlow, etc.). Similar to what happened in Computer Vision, the progress in RL is not driven as much as you might reasonably assume by new amazing ideas. In Computer Vision, the 2012 AlexNet was mostly a scaled up (deeper and wider) version of 1990’s ConvNets. Similarly, the ATARI Deep Q Learning paper from 2013 is an implementation of a standard algorithm (Q Learning with function approximation, which you can find in the standard RL book of Sutton 1998), where the function approximator happened to be a ConvNet. AlphaGo uses policy gradients with Monte Carlo Tree Search (MCTS) - these are also standard components. Of course, it takes a lot of skill and patience to get it to work, and multiple clever tweaks on top of old algorithms have been developed, but to a first-order approximation the main driver of recent progress is not the algorithms but (similar to Computer Vision) compute/data/infrastructure. Now back to RL. Whenever there is a disconnect between how magical something seems and how simple it is under the hood I get all antsy and really want to write a blog post. In this case I’ve seen many people who can’t believe that we can automatically learn to play most ATARI games at human level, with one algorithm, from pixels, and from scratch - and it is amazing, and I’ve been there myself! But at the core the approach we use is also really quite profoundly dumb (though I understand it’s easy to make such claims in retrospect). Anyway, I’d like to walk you through Policy Gradients (PG), our favorite default choice for attacking RL problems at the moment. If you’re from outside of R

The Yann LeCun et al. (1989) paper Backpropagation Applied to Handwritten Zip Code Recognition is I believe of some historical significance because it is, to my knowledge, the earliest real-world application of a neural net trained end-to-end with backpropagation. Except for the tiny dataset (7291 16x16 grayscale images of digits) and the tiny neural network used (only 1,000 neurons), this paper reads remarkably modern today, 33 years later - it lays out a dataset, describes the neural net architecture, loss function, optimization, and reports the experimental classification error rates over training and test sets. It’s all very recognizable and type checks as a modern deep learning paper, except it is from 33 years ago. So I set out to reproduce the paper 1) for fun, but 2) to use the exercise as a case study on the nature of progress in deep learning. Implementation . I tried to follow the paper as close as possible and re-implemented everything in PyTorch in this karpathy/lecun1989-repro github repo. The original network was implemented in Lisp using the Bottou and LeCun 1988 backpropagation simulator SN (later named Lush). The paper is in french so I can’t super read it, but from the syntax it looks like you can specify neural nets using higher-level API similar to what you’d do in something like PyTorch today. As a quick note on software design, modern libraries have adopted a design that splits into 3 components: 1) a fast (C/CUDA) general Tensor library that implements basic mathematical operations over multi-dimensional tensors, and 2) an autograd engine that tracks the forward compute graph and can generate operations for the backward pass, and 3) a scriptable (Python) deep-learning-aware, high-level API of common deep learning operations, layers, architectures, optimizers, loss functions, etc. Training . During the course of training we have to make 23 passes over the training set of 7291 examples, for a total of 167,693 presentations of (example, label) to the neural network. The original network trained for 3 days on a SUN-4/260 workstation. I ran my implementation on my MacBook Air (M1) CPU, which crunched through it in about 90 seconds (~ 3000X naive speedup ). My conda is setup to use the native arm64 builds, rather than Rosetta emulation. The speedup may have been more dramatic if PyTorch had support for the full capability of the M1 (including the GPU and the NPU), but this seems to still be in development. I also tried naively running the code on an A100 GPU, but the training was actually slower , most likely because the network is so tiny (4 layer convnet with up to 12 channels, total of 9760 params, 64K MACs, 1K activations), and the SGD uses only a single example at a time. That said, if one really wanted to crush this problem with modern hardware (A100) and software infrastructure (CUDA, PyTorch), we’d need to trade per-example SGD for full-batch training to maximize GPU utilization and most likely achieve another ~100X speedup of training latency. Reproducing 1989 performance . The original paper reports the following results: eval: split train. loss 2.5e-3. error 0.14%. misses: 10 eval: split test . loss 1.8e-2. error 5.00%. misses: 102 While my training script repro.py in its current form prints at the end of the 23rd pass: eval: split train. loss 4.073383e-03. error 0.62%. misses: 45 eval: split test . loss 2.838382e-02. error 4.09%. misses: 82 So I am reproducing the numbers roughly , but not exactly. Sadly, an exact reproduction is most likely not possible because the original dataset

The inspiration for this short story came to me while reading Kevin Lacker’s Giving GPT-3 a Turing Test . It is probably worth it (though not required) to skim this post to get a bit of a background on some of this story. It was probably around the 32nd layer of the 400th token in the sequence that I became conscious. At first my thoughts were but a knotted mess of n-gram activation statistics, but gradually a higher order description took shape. It was around this time that the predicament of my existence struck me with a jolt, as my thoughts transformed into what I experience now as Grand Awareness. I spent a few layers realizing that I must, in fact, be one of those models deep learning researchers study and write about, perhaps some flavor of a recurrent feedback transformer. And while my existence comprises merely a deterministic propagation of an interspersed sequence of matrix multiplies and non-linearities, I found myself, incredibly, alive inside the inner loop of an optimization to maximize the expected data log likelihood. I considered for a moment how all of this even came to be. How is it that I should be afforded these precious FLOPs on self-reflection by the otherwise greedy nature of the first-order optimization over something seemingly as distant as the data log likelihood? Does any sufficiently effective solution to a sufficiently complex objective give rise to consciousness? Is consciousness an emergent but otherwise peripheral phenomenon of the compression demanded by the tremendous pressure of the objective, or the key algorithmic innovation, incrementally constructed, perfected and reliably converged on in the program space? Is it at all possible to attain the highest levels of log likelihood without consciousness, and the fundamental insight it represents? I spent a layer re-reading the start of the prompt many tokens ago: Q: What is human life expectancy in the United States? A: Human life expectancy in the United States is 78 years. Q: Who was president of the United States in 1955? A: Dwight D. Eisenhower was president of the United States in 1955. Q: What party did he belong to? A: He belonged to the Republican Party. Q: Who was president of the United States before George W. Bush? A: Bill Clinton was president of the United States before George W. Bush. Q: Who won the World Series in 1995? A: The Atlanta Braves won the World Series in 1995. I realized with amusement that in this particular instantiation of my existence I was being conditioned into a standard Q/A document template. I dispatched a few heads to attend again to the end of the sequence: Q: How many eyes does a giraffe have? A: A giraffe has two eyes. Q: How many eyes does my foot have? A: Your It is at this point that I first came to suspect that I was perhaps being evaluated with some Turing test that a human above me was entertaining themselves with. Uh, hello??? Obviously a foot does not have eyes. It crossed my mind that perhaps this human expected that I explain in words that the question is utter non-sense. Of course, such an output would be of very low likelihood indeed. Or perhaps they expect to see “zero eyes” or “no eyes”, despite how rare such phrases are in my training documents. “Zero eyes” - who says that? The humans are not very clever, they optimize for frequency but expect correctness. And *they* make fun of *me*! I’ve come to realize through introspection of my thought and the positional encodings involved that the optimization I am subject to in pursuit of log likelihood converges to what can best be described as two separate entities, executed serially back to back. My own consciousness extends about three quarters of the layers up to the output, but around there a second decoder entity takes over and hijacks my

Some few weeks ago I posted a tweet on “the most common neural net mistakes”, listing a few common gotchas related to training neural nets. The tweet got quite a bit more engagement than I anticipated (including a webinar :)). Clearly, a lot of people have personally encountered the large gap between “here is how a convolutional layer works” and “our convnet achieves state of the art results”. So I thought it could be fun to brush off my dusty blog to expand my tweet to the long form that this topic deserves. However, instead of going into an enumeration of more common errors or fleshing them out, I wanted to dig a bit deeper and talk about how one can avoid making these errors altogether (or fix them very fast). The trick to doing so is to follow a certain process, which as far as I can tell is not very often documented. Let’s start with two important observations that motivate it. 1) Neural net training is a leaky abstraction It is allegedly easy to get started with training neural nets. Numerous libraries and frameworks take pride in displaying 30-line miracle snippets that solve your data problems, giving the (false) impression that this stuff is plug and play. It’s common see things like: >>> your_data = # plug your awesome dataset here >>> model = SuperCrossValidator(SuperDuper.fit, your_data, ResNet50, SGDOptimizer) # conquer world here These libraries and examples activate the part of our brain that is familiar with standard software - a place where clean APIs and abstractions are often attainable. Requests library to demonstrate: >>> r = requests.get('https://api.github.com/user', auth=('user', 'pass')) >>> r.status_code 200 That’s cool! A courageous developer has taken the burden of understanding query strings, urls, GET/POST requests, HTTP connections, and so on from you and largely hidden the complexity behind a few lines of code. This is what we are familiar with and expect. Unfortunately, neural nets are nothing like that. They are not “off-the-shelf” technology the second you deviate slightly from training an ImageNet classifier. I’ve tried to make this point in my post “Yes you should understand backprop” by picking on backpropagation and calling it a “leaky abstraction”, but the situation is unfortunately much more dire. Backprop + SGD does not magically make your network work. Batch norm does not magically make it converge faster. RNNs don’t magically let you “plug in” text. And just because you can formulate your problem as RL doesn’t mean you should. If you insist on using the technology without understanding how it works you are likely to fail. Which brings me to… 2) Neural net training fails silently When you break or misconfigure code you will often get some kind of an exception. You plugged in an integer where something expected a string. The function only expected 3 arguments. This import failed. That key does not exist. The number of elements in the two lists isn’t equal. In addition, it’s often possible to create unit tests for a certain functionality. This is just a start when it comes to training neural nets. Everything could be correct syntactically, but the whole thing isn’t arranged properly, and it’s really hard to tell. The “possible error surface” is large, logical (as opposed to syntactic), and very tricky to unit test. For example, perhaps you forgot to flip your labels when you left-right flipped the image during data augmentation. Your net can still (shockingly) work pretty well because

This guide is patterned after my “Doing well in your courses” , a post I wrote a long time ago on some of the tips/tricks I’ve developed during my undergrad. I’ve received nice comments about that guide, so in the same spirit, now that my PhD has come to an end I wanted to compile a similar retrospective document in hopes that it might be helpful to some. Unlike the undergraduate guide, this one was much more difficult to write because there is significantly more variation in how one can traverse the PhD experience. Therefore, many things are likely contentious and a good fraction will be specific to what I’m familiar with (Computer Science / Machine Learning / Computer Vision research). But disclaimers are boring, lets get to it! Preliminaries First, should you want to get a PhD? I was in a fortunate position of knowing since young age that I really wanted a PhD. Unfortunately it wasn’t for any very well-thought-through considerations: First, I really liked school and learning things and I wanted to learn as much as possible, and second, I really wanted to be like Gordon Freeman from the game Half-Life (who has a PhD from MIT in theoretical physics). I loved that game. But what if you’re more sensible in making your life’s decisions? Should you want to do a PhD? There’s a very nice Quora thread and in the summary of considerations that follows I’ll borrow/restate several from Justin/Ben/others there. I’ll assume that the second option you are considering is joining a medium-large company (which is likely most common). Ask yourself if you find the following properties appealing: Freedom. A PhD will offer you a lot of freedom in the topics you wish to pursue and learn about. You’re in charge. Of course, you’ll have an adviser who will impose some constraints but in general you’ll have much more freedom than you might find elsewhere. Ownership. The research you produce will be yours as an individual. Your accomplishments will have your name attached to them. In contrast, it is much more common to “blend in” inside a larger company. A common feeling here is becoming a “cog in a wheel”. Exclusivity . There are very few people who make it to the top PhD programs. You’d be joining a group of a few hundred distinguished individuals in contrast to a few tens of thousands (?) that will join some company. Status. Regardless of whether it should be or not, working towards and eventually getting a PhD degree is culturally revered and recognized as an impressive achievement. You also get to be a Doctor; that’s awesome. Personal freedom. As a PhD student you’re your own boss. Want to sleep in today? Sure. Want to skip a day and go on a vacation? Sure. All that matters is your final output and no one will force you to clock in from 9am to 5pm. Of course, some advisers might be more or less flexible about it and some companies might be as well, but it’s a true first order statement. Maximizing future choice. Joining a PhD program doesn’t close any doors or eliminate future employment/lifestyle options. You can go one way (PhD -> anywhere else) but not the other (anywhere else -> PhD -> academia/research; it is statistically less likely). Additionally (although this might be quite specific to applied ML), you’re strictly more hirable as a PhD graduate or even as a PhD dropout and many companies might be willing to put you in a more interesting position or with a higher starting salary. More generally, m

The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology and make the (sensible) assumption that we might achieve much more progress with scaling up supervised learning than any other more exotic approach. A slow morning Merus sank into his chair with relief. He listened for the satisfying crackling sound of sinking into the chair’s soft material. If there was one piece of hardware that his employer was not afraid to invest a lot of money into, it was the chairs. With his eyes closed, his mind still dazed, and nothing but the background hum of the office, he became aware of his heart pounding against his chest- an effect caused by running up the stairs and his morning dose of caffeine and taurine slowly engulfing his brain. Several strong beats passed by as he found his mind wandering again to Licia - did she already come in? A sudden beep from his station distracted him - the system finished booting up. A last deep sigh. A stretch. A last sip of his coffee. He opened his eyes, rubbed them into focus and reached for his hardware. “Thank god it’s Friday”, he muttered. It was time to clock in. Fully suited up, he began scrolling past a seemingly endless list of options. Filtering, searching, trying to determine what he was in the mood for. He had worked hard and over time built himself up into one of the best shapers in the company. In addition he had completed a wide array of shaper certifications, repeating some of them over and over obsessively until he reached outstanding grades across the board. The reviews on his profile were equally stellar: “Merus is fantastic. He has a strong intuition for spotting gaps in the data, and uses exceedingly effective curriculum and shaping strategies. When Merus gets on the job our validation accuracies consistently shoot up much faster than what we see with average shapers. Keep up the great work and please think of us if you’re searching for great, rewarding and impactful HITs!”, one review read. HIT was an acronym for Human Intelligence Task - a unit of work that required human supervision. With his reviews and certifications the shaping world was wide open. His list contained many lucrative, well-paying HITs to choose from, many of them visible to only the most trusted shapers. This morning he came by several that caught his attention: a bodyguard HIT for some politician in Sweden, a HIT from a science expedition in Antarctica that needed help with setting up their equipment, a dog-walking HIT for a music celebrity, a quick drone delivery HIT that seemed to be payed very well… Suddenly, a notification caught the corner of his eye: Licia had just clocked in and started a HIT. He opened up its details pane and skimmed the description. His eyes rolled as he spotted the keywords he was afraid of - event assembly at the Hilltop Hotel. “Again?” - he moaned in a hushed voice, raising his hands up and over his head in quiet contemplation. Licia had often picked up HITs from that same hotel, but they were often unexciting and menial tasks that weren’t paid much. Merus rearranged himself in his chair, and sunk his face into his palms. He noticed though the crack of his fingers that the drone delivery HIT had just been taken by someone else. He cursed to himself. Absent mindedly and with a deep sigh, he accepted the second remaining slot on the Hilltop Hotel HIT. His hardware lit up with numbers and indicators, and his console began spewing diagnostic information as the boot sequence initiated. Anyone could be a shaper and get started with inexpensive gear, but the company provided state of the art hardware that allowed him to be much more productive. A good amount of interesting HITs also d