Typora will give you a seamless experience as both a reader and a writer. It removes the preview window, mode switcher, syntax symbols of markdown source code, and all other unnecessary distractions. Replace them with a real live preview feature to help you concentrate on the content itself.
Seamless Live Preview
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Simple, yet Powerful
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When handling relative path of a local image file, you could set its base path towards the root folder of your static blog.
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Use your own css code to change font size, alignement, or even make some magics possible, such as Auto Numbering Headers.
Type `[TOC]` to insert table of contents, all headings will be listed here.
Set the href to headers, which will create a bookmark that allow you to jump to that section after clicking.
Arrange nested lists like a rich editor, by tab and shift tab key.
GFM task list supported. Able to manage simple todos in a markdown file.
Use shortcut keys, context menu or touch bar to change list type from one to another.
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Typora supports around 100 languages for syntax highlighting, covers all common programming languages.
Preview while you are typing.
Auto numbering math equations (enable in preference panel).
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Generation of flowchart, sequence, gantt and more by mermaid engine.
Draws simple SVG sequence diagrams.
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Set your the link targets towards a header, a markdown file, or an URL.
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All styles incldue Strong and emphasis can be correctly rendered in CJK charsets.
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Outline structure of your documents will be extracted in outline panel, which allows you to quickly go through the document and jump to any section with one click.
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See how large your document is in unit of words, characters, lines, or reading minutes.
Focus Mode & TypeWriter Mode
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Auto complete pair of brackets and quotes like a code editor. Also, a option is provided to auto pair markdown symbols, like * or _.
Cover image: Detail from Patroclus by Jacques Louis David, 1780. Courtesy Wikipedia
It’s tempting to think of the mind as a layer that sits on top of more primitive cognitive structures. We experience ourselves as conscious beings, after all, in a way that feels different to the rhythm of our heartbeat or the rumblings of our stomach. If the operations of the brain can be separated out and stratified, then perhaps we can construct something akin to just the top layer, and achieve human-like artificial intelligence (AI) while bypassing the messy flesh that characterises organic life.
I understand the appeal of this view, because I co-founded SwiftKey, a predictive-language software company that was bought by Microsoft. Our goal is to emulate the remarkable processes by which human beings can understand and manipulate language. We’ve made some decent progress: I was pretty proud of the elegant new communication system we built for the physicist Stephen Hawking between 2012 and 2014. But despite encouraging results, most of the time I’m reminded that we’re nowhere near achieving human-like AI. Why? Because the layered model of cognition is wrong. Most AI researchers are currently missing a central piece of the puzzle: embodiment.
Things took a wrong turn at the beginning of modern AI, back in the 1950s. Computer scientists decided to try to imitate conscious reasoning by building logical systems based on symbols. The method involves associating real-world entities with digital codes to create virtual models of the environment, which could then be projected back onto the world itself. For instance, using symbolic logic, you could instruct a machine to ‘learn’ that a cat is an animal by encoding a specific piece of knowledge using a mathematical formula such as ‘cat > is > animal’. Such formulae can be rolled up into more complex statements that allow the system to manipulate and test propositions – such as whether your average cat is as big as a horse, or likely to chase a mouse.
This method found some early success in simple contrived environments: in ‘SHRDLU’, a virtual world created by the computer scientist Terry Winograd at MIT between 1968-1970, users could talk to the computer in order to move around simple block shapes such as cones and balls. But symbolic logic proved hopelessly inadequate when faced with real-world problems, where fine-tuned symbols broke down in the face of ambiguous definitions and myriad shades of interpretation.
In later decades, as computing power grew, researchers switched to using statistics to extract patterns from massive quantities of data. These methods are often referred to as ‘machine learning’. Rather than trying to encode high-level knowledge and logical reasoning, machine learning employs a bottom-up approach in which algorithms discern relationships by repeating tasks, such as classifying the visual objects in images or transcribing recorded speech into text. Such a system might learn to identify images of cats, for example, by looking at millions of cat photos, or to make a connection between cats and mice based on the way they are referred to throughout large bodies of text.
Machine learning has produced many tremendous practical applications in recent years. We’ve built systems that surpass us at speech recognition, image processing and lip reading; that can beat us at chess, Jeopardy! and Go; and that are learning to create visual art, compose pop music and write their own software programs. To a degree, these self-teaching algorithms mimic what we know about the subconscious processes of organic brains. Machine-learning algorithms start with simple ‘features’ (individual letters or pixels, for instance) and combine them into more complex ‘categories’, taking into account the inherent uncertainty and ambiguity in real-world data. This is somewhat analogous to the visual cortex, which receives electrical signals from the eye and interprets them as identifiable patterns and objects.
But algorithms are a long way from being able to think like us. The biggest distinction lies in our evolved biology, and how that biology processes information. Humans are made up of trillions of eukaryotic cells, which first appeared in the fossil record around 2.5 billion years ago. A human cell is a remarkable piece of networked machinery that has about the same number of components as a modern jumbo jet – all of which arose out of a longstanding, embedded encounter with the natural world. In Basin and Range (1981), the writer John McPhee observed that, if you stand with your arms outstretched to represent the whole history of the Earth, complex organisms began evolving only at the far wrist, while ‘in a single stroke with a medium-grained nail file you could eradicate human history’.
The traditional view of evolution suggests that our cellular complexity evolved from early eukaryotes via random genetic mutation and selection. But in 2005 the biologist James Shapiro at the University of Chicago outlined a radical new narrative. He argued that eukaryotic cells work ‘intelligently’ to adapt a host organism to its environment by manipulating their own DNA in response to environmental stimuli. Recent microbiological findings lend weight to this idea. For example, mammals’ immune systems have the tendency to duplicate sequences of DNA in order to generate effective antibodies to attack disease, and we now know that at least 43 per cent of the human genome is made up of DNA that can be moved from one location to another, through a process of natural ‘genetic engineering’.
Now, it’s a bit of a leap to go from smart, self-organising cells to the brainy sort of intelligence that concerns us here. But the point is that long before we were conscious, thinking beings, our cells were reading data from the environment and working together to mould us into robust, self-sustaining agents. What we take as intelligence, then, is not simply about using symbols to represent the world as it objectively is. Rather, we only have the world as it is revealed to us, which is rooted in our evolved, embodied needs as an organism. Nature ‘has built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it’, wrote the neuroscientist Antonio Damasio in Descartes’ Error (1994), his seminal book on cognition. In other words, we think with our whole body, not just with the brain.
I suspect that this basic imperative of bodily survival in an uncertain world is the basis of the flexibility and power of human intelligence. But few AI researchers have really embraced the implications of these insights. The motivating drive of most AI algorithms is to infer patterns from vast sets of training data – so it might require millions or even billions of individual cat photos to gain a high degree of accuracy in recognising cats. By contrast, thanks to our needs as an organism, human beings carry with them extraordinarily rich models of the body in its broader environment. We draw on experiences and expectations to predict likely outcomes from a relatively small number of observed samples. So when a human thinks about a cat, she can probably picture the way it moves, hear the sound of purring, feel the impending scratch from an unsheathed claw. She has a rich store of sensory information at her disposal to understand the idea of a ‘cat’, and other related concepts that might help her interact with such a creature.
This means that when a human approaches a new problem, most of the hard work has already been done. In ways that we’re only just beginning to understand, our body and brain, from the cellular level upwards, have already built a model of the world that we can apply almost instantly to a wide array of challenges. But for an AI algorithm, the process begins from scratch each time. There is an active and important line of research, known as ‘inductive transfer’, focused on using prior machine-learned knowledge to inform new solutions. However, as things stand, it’s questionable whether this approach will be able to capture anything like the richness of our own bodily models.
On the same day that SwiftKey unveiled Hawking’s new communications system in 2014, he gave an interview to the BBC in which he warned that intelligent machines could end mankind. You can imagine which story ended up dominating the headlines. I agree with Hawking that we should take the risks of rogue AI seriously. But I believe we’re still very far from needing to worry about anything approaching human intelligence – and we have little hope of achieving this goal unless we think carefully about how to give algorithms some kind of long-term, embodied relationship with their environment.
This article was originally published at Aeon and has been republished under Creative Commons.
What a word. Freelancer! So lithe and nimble and untethered. Strap a rocket to your back, aim for the stars, and whoosh! Any client, any project, any day now.
Good on you. It takes guts to sail the seas of self-employment. There are no guarantees, but you looked uncertainty in the eye and chose to bet on yourself. That’s admirable.
Rewards and Reality
Quickly, you’ll find yourself collecting new skills, experiences, and a whole helluva lot of confidence. You’ll also take your lumps. You’ll be low-balled, nitpicked, and no-showed. There will be big-timers, down-talkers, and dozens of well-meaning folks who want to hire you for any job but the one you’re good at.
Don’t misunderstand. Freelancing is incredibly rewarding. Wanna work on a big time project? Master a new discipline? Go on an extended vacation? Get paid more? It’s all on you, and when you come through, it’s euphoric.
But it takes a while to get rolling. The first year is disorienting, the obstacles abundant. If you’re a fan of finding things, you’ll have a blast. Find a niche. Find clients. Find work that actually pays.
You’ll also have to sell. A lot. Skills and experience are ingredients, but if you want the whole cake, you’ll need to build trust, identify unspoken needs, and close deals. Is selling ever awkward? Of course, but awkwardness is irrelevant. Just remember three things:
Ask informed questions.
Listen carefully. Listen actively. Most of all, listen.
Don’t be stingy. If you see how you can solve a problem during a sales call, share the vision.
With selling comes rejection, a freelancer’s constant companion. There are thousands of ways to say no. You’ll face most, and just beneath your outer layer of composure and determination, there will be a persistent ache. That’s disappointment.
It’d be dishonest not to tell you upfront: success and struggle made a prehistoric pact that one could not be had without the other. Freelancing, like most worthwhile endeavors, has been fictionalized. Don’t be fooled by the social media mirage of tricked-out travel vans and pool-side conference calls. Even freelancers get flat tires and sunburns.
Time, Money, and Triumph
There are events in life that take on new meaning once you’ve experienced them. Switching jobs. Ending a relationship. Buying a home. Raising kids. Freelancing is like that. With time, you’ll forge a path that’s all your own, but it also helps to know what lies ahead.
Like irregular financial rhythms. Bid adieu to that bi-weekly bankroll you’ve grown accustomed to. Steady paychecks are a thing of the past. Early on, money comes sporadically. No work in the pipeline? No pay on the horizon. Plan ahead and stay on budget.
Then there’s the element of time, which touches every facet of freelancing. You’ll track time, bide time, waste time, and wonder where the time went. Use time wisely, and the myriad problems of work-life balance, blown deadlines, and burnout will solve themselves.
There’s other stuff, too. How to manage difficult clients. How to combat isolation. How to overcome irrational self-doubt and leap to the next level of your freelancing career. Each could be its own book.
Just know that you’re not inferior, cursed, or powerless when difficulty comes knocking. You’ve simply arrived at the door that exists to be karate kicked into oblivion. On the other side? A more confident and capable version of you.
Finding Work and Getting Better
The first few months of freelancing are oozing with minutiae. Every day will bring a hundred time-consuming details—new licenses, new bank accounts, new user profiles.
With so much to do, you might decide to do nothing at all. A better option? Make a daily to-do list. So simple. So effective. If you’re really ambitious, organize your days into time blocks. Just make sure to spend more time doing your to-do’s than arranging them. Speaking of which, keep your admin tools minimal. An email account and a calendar will take you a long way.
When it comes to finding your first few jobs, don’t hesitate to mine your personal network. Call aunts and uncles. Email old bosses. Text childhood friends. If it’s been a while, don’t pretend that you’re “reconnecting” for the sake of the relationship. Be honest—you’re freelancing and looking for projects.
And when you quote those first few jobs, don’t forget to do the math. If you have to take on 50 projects per year to make the equivalent of a junior-level employee, reconsider your prices.
Most importantly, carve out time every day to hone your skills. No clients? No worries. Create your own projects, and push yourself to do the caliber of work you want to be hired to do. No matter what, don’t stop sharing what you create, and don’t restrict yourself to social media. You can build a solid client base and make a good living without becoming an Instagram all-star.
Secrets, Success, and the Only Way Forward
If you haven’t noticed, you soon will. There’s a horde of online experts hoping you’ll pay big bucks for their secret freelancing formulas. Here’s a secret: you don’t need career coaching, motivational gurus, or expensive seminars to succeed as a freelancer. If you want to grow and work to grow, you will. If you treat clients well and solve their problems, you’ll stay busy.
If there’s a freelancing secret worth sharing, it’s this. When you fly solo, the world is malleable. Mold and shape it to your advantage. The road ahead is paved by you.
Well, we’ve reached the point where a letter like the one you’re reading comes to a crescendo. Normally, there’d be a rousing call to perseverance, but you don’t need that either. You’ve made the leap and landed two feet firmly planted. The only option now is forward—one foot, then the other.
As a freelance designer, you have a number of ways to promote yourself: social media, in-person networking events, and securing referrals are common strategies. It’s important to know who your ideal client is and what kind of work you specialize in, otherwise, you risk wasting promotional efforts and resources.
How do freelance artists succeed?
The success of creative freelance careers depends on a few variables. If you’re a freelance artist or designer, you must be highly skilled and capable of world-class work. It’s also important to be able to sell your services, manage projects, and maintain client relationships.
How can I become a freelance designer?
There are a variety of ways to become a freelance designer, but like any career, it’s best to have a plan before starting. To land freelance clients, you’ll need a portfolio that showcases your problem-solving skills. It’s also a good idea to have some savings, as finding freelance design jobs can take some time.
How can I be a successful freelancer?
Whether you work at an agency, a corporation, or as a freelancer, forging a successful design career takes knowledge, skill, persistence, and a willingness to continually improve. At times, it can be hard to find freelance work, but a commitment to great design and customer satisfaction will take you a long way.