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Shams Charania’s phone is a big part of his livelihood as an NBA insider, so when he had his device literally snatched away Thursday afternoon, he seemed gobsmacked. While appearing on FanDuel TV’s “Up and Adams” with host Kay Adams,

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Just recently, Caltrans has the funding to improve eastbound Highway 58. Caltrans is creating a truck lane. It seems a shame to me that while they’re spending this money on a truck lane, there is no mention of a left-hand turn lane or easy access road ...

Letter to the editor: Create safe way to get on 58 from cemetery

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This week we take a look at visual generative modeling. The goal is to generate a complete scene in high-resolution, rather than a single face image or object. This process is similar to StyleGAN, but it uses the GAN in a traditional generative and discriminative way, with convolutional neural networks. Watch the Video: Chapters: - Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise. ​ - Text-To-Image translation -​ Examples ​ - Conclusion 0:00​ 0:24 0:51 5:50 References : ​ : ​ : Drew A. Hudson and C. Lawrence Zitnick, Generative Adversarial Transformers, (2021), Published on Arxiv., abstract: Paper //arxiv.org/pdf/2103.01209.pdf Code //github.com/dorarad/gansformer Complete reference "We introduce the GANsformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. It iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network. We demonstrate the model's strength and robustness through a careful evaluation over a range of datasets, from simulated multi-object environments to rich real-world indoor and outdoor scenes, showing it achieves state-of-the-art results in terms of image quality and diversity, while enjoying fast learning and better data efficiency. Further qualitative and quantitative experiments offer us an insight into the model's inner workings, revealing improved interpretability and stronger disentanglement, and illustrating the benefits and efficacy of our approach. An implementation of the model is available at //github.com/dorarad/gansformer ​." Follow me for more AI content: ►Instagram: ​ ►LinkedIn: ​ ►Twitter: ​ ►Facebook: ​ //www.instagram.com/whats_ai/ //www.linkedin.com/in/whats-ai/ //twitter.com/Whats_AI //www.facebook.com/whats.artifi... Join Our Discord channel, Learn AI Together: ► ​ //discord.gg/learnaitogether The best courses in AI & Guide+Repository on how to start: ► ​ ► ​ //www.omologapps.com/whats-ai //github.com/louisfb01/start-ma... Become a member of the YouTube community and support my work: ​ //www.youtube.com/channel/UCUzG... Video Transcript Note: This transcript is auto-generated by Youtube and may not be entirely accurate. 00:00 the basically leveraged transformers 00:02 attention mechanism in the powerful stat 00:04 gun 2 architecture to make it even more 00:06 powerful 00:10 [Music] 00:14 this is what's ai and i share artificial 00:16 intelligence news every week 00:18 if you are new to the channel and would 00:19 like to stay up to date please consider 00:21 subscribing to not miss any further news 00:24 last week we looked at dali openai's 00:27 most recent paper 00:28 it uses a similar architecture as gpt3 00:31 involving transformers to generate an 00:33 image from text 00:35 this is a super interesting and complex 00:37 task called 00:38 text to image translation as you can see 00:41 again here the results were surprisingly 00:43 good compared to previous 00:45 state-of-the-art techniques this is 00:47 mainly due to the use of transformers 00:49 and a large amount of data this week we 00:52 will look at a very similar task 00:54 called visual generative modelling where 00:56 the goal is to generate a 00:58 complete scene in high resolution such 01:00 as a road or a room 01:02 rather than a single face or a specific 01:04 object this is different from delhi 01:06 since we are not generating the scene 01:08 from a text but from a trained model 01:10 on a specific style of scenes which is a 01:13 bedroom in this case 01:14 rather it is just like style gun that is 01:17 able to generate unique and non-existing 01:19 human faces 01:20 being trained on a data set of real 01:22 faces 01:24 the difference is that it uses this gan 01:26 architecture in a traditional generative 01:28 and discriminative way 01:29 with convolutional neural networks a 01:32 classic gun architecture will have a 01:34 generator 01:35 trained to generate the image and a 01:36 discriminator 01:38 used to measure the quality of the 01:40 generated images 01:41 by guessing if it's a real image coming 01:43 from the data set 01:44 or a fake image generated by the 01:46 generator 01:48 both networks are typically composed of 01:50 convolutional neural networks where the 01:52 generator 01:53 looks like this mainly composed of down 01:56 sampling the image using convolutions to 01:58 encode it 01:59 and then it up samples the image again 02:02 using convolutions to generate a new 02:04 version 02:05 of the image with the same style based 02:07 on the encoding 02:08 which is why it is called style gun then 02:12 the discriminator takes the generated 02:14 image or 02:15 an image from your data set and tries to 02:17 figure out whether it is real or 02:18 generated 02:19 called fake instead they leverage 02:22 transformers attention mechanism 02:24 inside the powerful stargane 2 02:26 architecture to make it 02:27 even more powerful attention is an 02:30 essential feature of this network 02:32 allowing the network to draw global 02:34 dependencies between 02:36 input and output in this case it's 02:39 between the input at the current step of 02:41 the architecture 02:42 and the latent code previously encoded 02:44 as we will see in a minute 02:46 before diving into it if you are not 02:48 familiar with transformers or attention 02:50 i suggest you watch the video i made 02:52 about transformers 02:54 for more details and a better 02:55 understanding of attention 02:57 you should definitely have a look at the 02:58 video attention is all you need 03:01 from a fellow youtuber and inspiration 03:03 of mine janik 03:04 kilter covering this amazing paper 03:07 alright 03:07 so we know that they use transformers 03:09 and guns together to generate better and 03:12 more realistic scenes 03:13 explaining the name of this paper 03:15 transformers 03:17 but why and how did they do that exactly 03:20 as for the y they did that to generate 03:22 complex and realistic scenes 03:24 like this one automatically this could 03:26 be a powerful application for many 03:28 industries like movies or video games 03:30 requiring a lot less time and effort 03:33 than having an 03:34 artist create them on a computer or even 03:36 make them 03:37 in real life to take a picture of it 03:40 also 03:40 imagine how useful it could be for 03:42 designers when coupled with text to 03:44 image translation generating many 03:46 different scenes from a single text 03:48 input 03:48 and pressing a random button they use a 03:51 state-of-the-art style gun architecture 03:53 because guns are powerful generators 03:55 when we talk about the general image 03:58 because guns work using convolutional 04:00 neural networks 04:01 they are by nature using local 04:03 information of the pixels 04:05 merging them to end up with the general 04:07 information regarding the image 04:09 missing out on the long range 04:11 interaction of the faraway pixel 04:13 for the same reason this causes guns to 04:15 be powerful generators for the overall 04:18 style of the image 04:19 still they are a lot less powerful 04:21 regarding the quality of the small 04:23 details in the generated image 04:25 for the same reason being unable to 04:27 control the style of localized regions 04:30 within the generated image itself this 04:33 is why they had the idea to combine 04:34 transformers and gans in one 04:36 architecture they called 04:38 bipartite transformer as gpt3 and many 04:41 other papers already proved transformers 04:44 are powerful for long-range interactions 04:46 drawing dependencies between them and 04:48 understanding the context of text 04:50 or images we can see that this simply 04:53 added attention layers 04:54 which is the base of the transformer's 04:56 network in between the convolutional 04:58 layers of both the generator and 05:00 discriminator 05:01 thus rather than focusing on using 05:03 global information and controlling 05:05 all features globally as convolutions do 05:07 by nature 05:08 they use this attention to propagate 05:10 information from the local pixels to the 05:12 global high level representation 05:14 and vice versa like other transformers 05:17 applied to images 05:18 this attention layer takes the pixel's 05:20 position and the style gun to latent 05:23 spaces w 05:24 and z the latent space w is an encoding 05:27 of the input into an intermediate latent 05:30 space 05:30 done at the beginning of the network 05:32 denoted here 05:34 as a while the encoding z is just the 05:37 resulting features of the input at the 05:39 current step of the network 05:40 this makes the generation much more 05:42 expressive over the whole image 05:44 especially in generating images 05:46 depicting multi-object 05:48 scenes which is the goal of this paper 05:51 of course this was just an overview of 05:53 this new paper by facebook ai research 05:55 and stanford university 05:57 i strongly recommend reading the paper 05:59 to have a better understanding of this 06:00 approach it's the first link in the 06:02 description below 06:03 the code is also available and linked in 06:05 the description as well 06:07 if you went this far in the video please 06:08 consider leaving a like 06:10 and commenting your thoughts i will 06:12 definitely read them and answer you 06:14 and since there's still over 80 percent 06:16 of you guys that are not subscribed yet 06:18 please consider clicking this free 06:20 subscribe button 06:21 to not miss any further news clearly 06:23 explained 06:24 thank you for watching 06:33 [Music]

Visual Generative Modeling: Using GANsformers to Generate Scenes

DeOldify is a technique to colorize and restore old black and white images or even film footage. It was developed and is still getting updated by only one person, Jason Antic. It is now the state-of-the-art way to colorize black and white images. Best of all, everything is open-sourced! Watch the video References What are GANs: ​ //youtu.be/ZnpZsiy_p2M GitHub with full code, in-depth explanation, and Colabs: ​ //github.com/jantic/DeOldify Free DeepAI image colorization API using DeOldify: ​ //deepai.org/machine-learning-m... MyHeritage colorization tool (the best version of DeOldify available, paid): ​ //www.myheritage.com/incolor Follow me for more AI content: Instagram: ​ //www.instagram.com/whats_ai/ LinkedIn: ​ //www.linkedin.com/in/whats-ai/ Twitter: ​ //twitter.com/Whats_AI Facebook: ​ //www.facebook.com/whats.artifi... The best courses in AI: ​ //www.omologapps.com/whats-ai Join Our Discord channel, Learn AI Together: ​ //discord.gg/learnaitogether Chapters 0:00​ - Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise. 0:40​ - DeOldify explanation 3:22​ - How to use DeOldify Yourself 4:24​ - Old Movie Examples 5:06​ - Conclusion Video transcript 00:00 this ai can colorize and restore old 00:03 black and white images and even film 00:05 footage 00:06 this method is called de-aldefy and 00:09 works on pretty much 00:10 any picture if you don't believe me you 00:13 can even try it yourself for free as i 00:15 will show in the video 00:17 but first let's see how it works and 00:19 more amazing results to convince you to 00:22 try it 00:25 [Music] 00:29 this is what's ai and i share artificial 00:31 intelligence news every week 00:33 if you are new to the channel and want 00:34 to stay up to date please consider 00:36 subscribing to not miss any further news 00:40 the aldephi is a technique to colorize 00:42 and restore 00:43 old black and white images or even film 00:45 footage 00:46 it was developed and is still getting 00:48 updated by only one person 00:50 jason antique it is now the 00:53 state-of-the-art way to colorize black 00:55 and white images 00:56 and everything is open sourced but we 00:59 will get back to this in a bit 01:01 first let's see how he achieved that it 01:04 uses a new type of gun training method 01:07 called nogan that he developed himself 01:09 to solve the main problems that appeared 01:11 when training using a normal 01:13 adversarial network architecture 01:15 composed of a discriminator and a 01:17 generator 01:18 typically gun training works by both 01:21 training the discriminator and generator 01:23 at the same time 01:24 where the generator starts by being 01:26 completely random 01:27 and improves over time to fool the 01:29 discriminator 01:30 which tries to tell if the image is 01:32 generated or real 01:34 if this was just completely abstract to 01:36 you i invite you to watch the video i 01:38 made about cans 01:39 in the upper right corner right now and 01:41 linked in the description 01:43 before continuing this video his new 01:46 method 01:46 which he calls the nogan provides the 01:49 same benefits of this usual gun training 01:52 while having to spend way less time 01:54 training the gan architecture 01:56 which is typically pretty heavy in 01:58 computation time 01:59 instead he pre-trains the generator to 02:02 make it already more powerful 02:04 fast and reliable using a regular loss 02:07 function 02:08 this is done by training the generator 02:10 like a regular deep networks 02:12 architecture 02:13 such as resnet that way the model is 02:16 already pretty good at colorizing an 02:18 image 02:18 before training the complete gan 02:20 architecture 02:22 then it only needs a short amount of 02:24 this typical generator discriminator 02:26 gan training to optimize the realism of 02:29 the generated pictures 02:32 gaussian noise is also randomly applied 02:34 to images to generate 02:36 fake noise during training this is a 02:39 type of data augmentation that can be 02:41 performed on the training images to 02:43 improve the results and resistance 02:45 to noisy inputs using the same technique 02:48 as style transfer where the noise will 02:50 be the style of the image we want to 02:52 copy 02:53 and can be applied more or less to the 02:55 transformation 02:57 the whole architecture uses a basic 02:59 resnet backbone 03:00 on a u-net where the generator network 03:02 in the gun training 03:04 is the unet architecture right now 03:07 there is no complete explanation of how 03:09 this works 03:10 but the author is currently working on a 03:13 paper about 03:14 the aldephi where he will further 03:16 investigate 03:17 why and how his technique previously 03:20 found only by trials and errors 03:22 work you can find three things in the 03:25 description of the video 03:27 at first there's the github link with a 03:30 complete detailed explanation of the 03:31 technique and even google collab 03:33 tutorials 03:34 to use it yourself just look at how 03:37 simple this is 03:38 you can just run the few sections enter 03:41 the link of your image 03:43 and run it 04:04 then you can find a free api on deep ai 04:08 using dl defy where you can simply click 04:11 and try yourself 04:13 finally the third link is the most 04:15 advanced version of the aldify if you 04:17 are looking for the best results 04:20 it is on myheritage's website and is 04:23 paid to use 04:24 let's just take a minute to see how it 04:26 works on old movies before ending this 04:40 video 04:50 you better get on the job some of the 04:52 kids may be up this afternoon 04:53 oh jack we can get along without 04:55 dragging those young kids up here oh why 04:57 don't you button up your lip 04:59 you're always squawking about something 05:00 you got more static on the radio 05:07 please leave a like if you went this far 05:09 in the video 05:10 and since there are over 90 of you guys 05:13 watching that are not subscribed yet 05:15 consider subscribing to the channel to 05:17 not miss any further news clearly 05:19 explained 05:20 thank you for watching 05:23 [Music]

DeOldify can Colorize your Black & White Photos with Full Photorealistic Renders

Complicated code bases. Bare-bones specifications. Tight deadlines. If these sound familiar, you’re not alone. Software development is a difficult field to work in, despite being in the United States. Developers burn out quickly and often. In fact, one survey shows tech workers. one of the fastest growing burnout rates of nearly 60% among In this post, we’ll look at six important reasons why you might burn out, and what you can do to make your work a little easier. #6: Managing Multiple Projects and Expectations Developers often juggle multiple projects and tasks simultaneously. At times it can seem like everyone needs your time—even other developers. Knowing where and how to allocate your time is essential for meeting deadlines and avoiding last-minute crunches. Unfortunately, estimating effort is one of the hardest skills for developers to learn. Projects often look easy on paper, but fall apart during development. Quick fixes can turn into long nights. Countless variables such as design problems, integrating with other systems, red tape, and unexpected bugs can hinder progress. Meanwhile, managers and stakeholders want deliverables as soon as possible. Move too slowly, and you’ll get pushback. Move too quickly, and you risk overexerting yourself, burning out, and sacrificing quality. Learning to compromise is important, but requires you to be honest with yourself and aware of your abilities. What Can You Do? Start by tracking the amount of time you spend on tasks. Use tools like , , or , and review them during your next Sprint Retrospective. Once you understand where your time is going, see what tasks you can cut out or delegate to others. Toggl Clockify Kimai If the scope of work seems too large for the current iteration, or threatens a hard deadline, push non-essential changes to the next sprint. This is especially true for minimum viable products (MVPs), where lengthy development cycles can put the final product at risk. With practice, you'll be able to manage projects and tasks more effectively. #5: Not Having the Right Resources or Requirements Developers need resources, and unfortunately, we often don't get what we need. Systems, IDEs, access to environments, specification documents, shared knowledge bases, a decent workstation—without these, you’ll be stuck before you can even write a line of code. Specifications and requirements are particularly important and often missing. Writing code without requirements is like building a house without a blueprint, and almost always results in wasted effort and . Requirements provide a clear direction and establish consensus for everyone involved—as long as everyone sticks to them. technical debt What Can You Do? Reach out to your team for any resources essential to your current work or overall role. Send written requests (email, Slack, and so on) to your team lead, and schedule reminders to follow up if they take too long to get back to you. If you still don't get the resources you need, ask if the feature requiring that resource is truly necessary, or if it can wait until the next iteration. And always be sure to mention your blockers during stand-ups. Keep your team informed and aware of all problems. When it comes to requirements, take the extra time needed for proper review. If there aren't documented project requirements, insist on getting some! Interview the project owner and key stakeholders to understand the purpose of the project. Don't commit to a task unless you have a complete understanding of the work. #4: Understanding Complex Systems An organization’s codebase can have decades of combined work from dozens or hundreds of different developers, each one bringing his or her own coding style, decisions, and level of (or lack of) comments. You’ll likely inherit code that is confusing or downright incomprehensible, and requires a serious time commitment to understand and unravel. Hopefully, your code is under source control, perhaps the single-largest advance our industry has made in the last decade on understanding the history and rationale of a codebase. If it's not, your confusion will be magnified. Even with a good understanding of your code, using third-party products often means learning entirely new APIs. Commercial products make your job even more challenging by masking their inner workings, which makes debugging unexpected behaviors even more difficult. What Can You Do? If this is a new project, make sure you're familiar with the team's coding practices and standards. If you're stuck on a particular module, set aside some distraction-free time to step through it and write down any unanswerable questions that arise. If possible, pair up with a teammate who's more familiar with the code base, ideally the original developer or maintainer. For third-party products, don't hesitate to use documentation, FAQs, customer support, chat rooms, and community forums. When all else fails, Google (or DuckDuckGo) it! #3: Keeping Up With Technology Change is the only constant in software. There are always new tools, programming languages, and best practices to stay aware of, regardless of your level. Even just maintaining code means staying on top of product updates and security bulletins. Of course, nobody can be an expert in everything, but developers are expected to know which tools are suited for which tasks. Developers need at least cursory knowledge of multiple technologies and tools to better evaluate different solutions and avoid choosing the wrong tool for the job. The amount of new skills and technologies you need to understand can be overwhelming. What Can You Do? Stay on top of your technical game by constantly learning new technologies and practices. If you can, schedule "discovery" days where you don't work on your current project, but rather spend your time researching and learning about new technologies. When learning a new service, take advantage of and educational materials. Consider to practice your skills outside of work. And always remember that new is not always the answer—don't get distracted here when looking at a project. Choose the right tools for the task, which may or may not be the shiny, new ones. free trials contributing to open-source projects #2: Balancing Communication and Interruptions Constant communication between developers, team leads, and other departments is essential for keeping everyone on the same page. However, too much communication can have a detrimental effect on productivity. Developers already spend in communication tools, costing companies nearly $30k per person per year. All of these interruptions cause context switching, which makes it even harder to focus on your work. after an interruption. 21% of their working time It's estimated that it takes up to 20 minutes to get back on task Why interruptions are catastrophic to developers. © 2018 Monkey User (//www.monkeyuser.com/2018/focus/) All rights reserved. What Can You Do? When coding, eliminate distractions and dedicate yourself to the task. If you find yourself constantly dealing with interruptions, then signal to coworkers when you are in "focus mode". on your calendar just for coding, wear headphones, and change your status in communication tools. Once you are focused, , evaluate it from different angles, research multiple options, and plan out your implementation strategy in advance. Document your decisions (as well as your code), and be prepared to justify them. Schedule "no meetings" or "untouchable" days think critically about the problem #1: Feeling Like You Don’t Belong Despite their education, expertise, and experience, of tech workers suffer from imposter syndrome. When a developer faces a task that pushes their abilities or challenges their self-confidence, he or she might question their ability to do the job. This could lead to a negative feedback loop that eventually ends with the developer becoming disengaged at work, or even quitting their job. as many as 58% Not belonging is especially difficult for underrepresented groups. In the United States, as many as , , and , unfortunately, is still common. These biases—deliberate or otherwise—can discourage skilled developers and deter new developers. 80% of developers are male 58% are white ageism What Can You Do? Don't isolate yourself! Engage with the development community: network with other developers, follow industry leaders, and . Use the opportunity to share your knowledge, collaborate, and learn something new. Remember, everyone feels this way at times. attend events Embrace Your Strengths The most important thing you can do is be honest with and trust yourself. and branch out of your comfort zone. The more you try new things, the better you will become at determining what your strengths are and where you can improve, which in turn will build confidence. Trust yourself to make the right decisions, but don’t be afraid to reach out for help as well. It’s tough being a software developer, but with the right attitude and effort, it can be an incredibly rewarding experience. Overcome your fear of failure

Why is Software Development So Hard?

Quartz an interesting think piece on whether or not the fintech revolution is overhyped, or understated. I was overjoyed - at last someone was brave enough to question the genuine ‘greatness’ of the fintech revolution, and whether it really is worth all the buzz, investment and coverage in the media. recently published So what if technology is changing yet another aspect of our lives - aren’t we used to it by now, what with the advent of Twitter, Tinder and Alexa having upended everything we know about society and social order? Disruption has occurred in virtually every industry over the past two decades, mostly thanks to technology. From education to healthcare, travel , the internet - and more recently the advent of artificial intelligence, chatbots and blockchain, among other newfangled technologies - has completely transformed the way we live, eat, exercise, spend, travel, date and work. and retail Apps are now capable of connecting lovers, arranging student schedules and hiring new employees, while voice assistants, augmented reality and are no longer phenomenons that are limited to science fiction. regenerative medicine Technology has debatably been the greatest driver of change this century, having brought us the third industrial revolution through new power technologies such as steam power and electricity, and bringing us to where we are now at the dawn of the third millennium and fourth industrial revolution thanks to the emergence of the Internet. So too has technology completely changed the way we bank. It has brought us the ‘Fintech Revolution’. Fintech, which refers to the intersection of finance and technology that “improves and automates the delivery and use of financial services” has been hailed the single largest disruptor of the decade. In the first three quarters of 2019, fintech startups received $24.6 billion in funding, while the sector’s leading 58 companies (including China’s Ant Financial, US-based Stripe, and Germany’s N26) are currently valued at a combined $213.5 billion. It is all the mainstream media can talk about, and it is no wonder. A mere decade ago the concept hadn’t even materialised, or if it had, no one knew about it. But today it is rapidly transforming the financial services sector as we know it. Mobile banking, robo-advisors, peer-to-peer lending services, crypto and crowdfunding - they are all brought to us by fintech. Is it because fintech is dominating the startup market that we are hearing so much about its potential? Is it because millennials are the innovators invested in fintech, as well as the authors and readers wanting to read about fintech-related among other things? complex trading strategy Why does fintech deserve more attention than, say, the disruption of the travel industry, which is , or the transformation of the retail sector thanks to augmented reality? seeking to become more sustainable Whatever the case, whether it be we are overexaggerating fintech’s potential or undermining it, fintech is a very, very good thing for us. It has enabled financial services to become cheaper, more convenient, and safer for all - something that desperately needed to happen. Impact investors have hailed fintech on a global scale, capable of connect off-grid villages in remote corners of the world to capital and banking opportunities for the first time ever. the potential solution to financial inclusion and equality Under-banked communities traditionally suffer from poverty and exclusion from essential services, but with something as simple as an internet connection or an SMS from a mobile phone, farmers can now be included in the financial system, access banking services and receive payment easily. Fintech could very well lead to the next revolution in the developing world, connecting it to the developed world and enabling remote and under-banked communities to benefit from the same banking support as every other town, thereby boosting economic growth in such places. Interestingly, fintech is most prominent in countries with less banking regulations in place, meaning sub-Saharan Africa and parts of Asia stand the chance to gain the most. But history is marked with society’s failure to anticipate the full extent of certain influences, ie. that of technological change. Will fintech be yet another example of this? Are we so focused on leveraging fintech as a means of profiting that we have failed to fully appreciate what it could mean for the global economy? No one would argue with the fact that . Poor customer service, complicated processes, very little in the way of trust and ridiculous fees for inadequate services have led us to “hate on” banking institutions. Let’s not even get started on the slow adoption rate of technology by banks and their Elizabethan-era style communication approach, or the fact that - - banking security seems to be getting worse year on year. banks are the bane of our lives despite constantly rising fees Traditional banks are, in short, incredibly inefficient for the end user, often having clunky or non-existent user interfaces. It is about time we saw a second contender on the scene. However much we choose to acknowledge the power and potential of fintech, there is one thing we cannot deny. That fintech will contribute to the betterment of society, in that it will help developing nations reach “under-banked” communities using technology and will make banking easier and more efficient for all.

Is Fintech Overrated - Or Are We Underestimating It?

My BT2 balance in BitFinex I just spent about $85.000 on buying B2X futures. This just means that once SegWit2x goes live, I’ll already have 90 coins. But why did I spent nearly a $1000/coin for coins that everyone will get for “free”? Simply because I think that the SegWit2x coin will become “ ” and the current Bitcoin will become Bitcoin Classic. Let me explain… Bitcoin Currently 85% of the mining power of bitcoin is signaling they will support a block increase to 2MB around the 16th of November. Once this block is generated, bitcoin will fork into Bitcoin Segwit2X (B2X) and Bitcoin Classic (BTCC). At this point either of them could become Bitcoin “BTC”. So you won’t really get “free” coins. More accurarely, your Bitcoins will be splitting in two… one BTCC and one B2X. One of these will be worth a lot, the other not so much. Together they will be worth around the same as one BTC before the fork, maybe a little more. Let’s looks at what could happen to BTCC. If it really only gets 15% of the hashing power, blocks will take over an hour. Since the BTC is currently already congested, things will get almost 7 times worse, so transaction fees might go up a lot, just to get your transactions into the BTCC chain. This could lead to many people pulling out of BTCC, thereby lowering the price and that would cause a death spiral for BTCC. However, BTC has been proven to be quite resilient, so the price might stay as is and if the price stays high long enough, miners will shift mining power back to BTCC and B2X will crash into a death spiral instead. blockchain Keep in mind that this fork is very different from Bitcoin Cash, Bitcoin Gold and the myriad of newly announced forks coming up. Segwit2X was by: agreed upon back in May 58 companies located in 22 countries 83.28% of hashing power 5.1 billion USD monthly on chain transaction volume 20.5 million bitcoin wallets And more have joined afterwards and some have left. This is still very different from the small amount of support that Bitcoin Cash and Gold had. Another major difference is the lack of strong replay protection. While this sounds desirable, the reason why the SegWit2X team doesn’t want to implement this is clear from what Mike from BitGo had to say about it: ”Replay protection”, as you call it, splits the chain. It simply doesn’tmake sense- you’d suddenly be breaking 10+million SPV clients thatotherwise work just fine. It is a goal of segwit2x to help avoid this. Today, we’re on course to deploy segwit2x with a vast majority of minersstill signaling for it. On top of that, 99.94% of nodes & SPV clients willautomatically follow that longest chain (segwit2x). So in the end it will come down to which of the chains will receive the most support. With the huge support for B2X in the ecosystem, there is a good chance that B2X will be recognized as the “real” Bitcoin and take the price of BTC, around $6000… and BTCC will become an altcoin worth a few hundred dollars. So if SegWit2X becomes the needed upgrade to Bitcoin it’s trying to be, I walk away with a cool half mil. If not I take maybe a $40k hit. I think my odds are pretty good here, but time will tell. I’ve bought about 84 BT2 coins (that’s the name of the B2X future)at and 6 or so at . Bitfinex HitBTC UPDATE 9-Nov: So today I woke up to the news of the fork being cancelled. BT2 trading at $200 and B2X trading at $320. Obviously not the outcome I was hoping for, but I always knew this was a gamble. I still think it was a good gamble for the odds and that it had positive expected value. I’d do it again. The moral of the story is, don’t risk what you aren’t happy to lose. (It looks like there’s a rumor 30% of miners are going ahead anyway… not holding my breath, but I’m holding my BT2) UPDATE 13-Jul-2018: I sold my BT2 at a big loss. I made plenty on other trades and arbitrage. Cashed out most of my crypto holdings around Christmas last year. Trading was fun, but I got frustrated with the lack of quality/support in exchanges, so decided to use my profits . Recently I launched the . to start a different kind of exchange XS2 Exchange

Why I just bought 90 B2X (Bitcoin SegWit2X futures)

There are two* ways to create a Docker image: Write a Dockerfile and run docker build on it Run a container, make changes and run docker commit to create a new image. The commit approach feels like a nice way of saving your work as you go along. Realise you need another package? Run apt-get install inside the container and then docker commit outside, and you’ve got a new image ready to go with that package ready-installed. Don’t do it. No, really, I know it’s tempting, but if you’re every going to use this container image again, don’t use commit. There are significant downsides to the commit approach: You can’t reproduce the image You can’t change the base image Creating a Dockerfile might feel a bit like work, but if you’re ever going to want this image again, it’s the approach you need to take. Dockerfiles for reproducible(-ish) images With the Dockerfile approach you have a written-down list of what went into the image. Re-run the docker build and you’ll get the same image again. Well, near enough. It may well not be bit-for-bit identical. Both the base image and any code you’re installing from elsewhere could have changed since the last time you ran the build. It’s up to you to define your dependencies with whatever level of specificity you need — for example, you could to be completely sure of the version you’re getting. specify the base image by SHA If you use the Dockerfile approach, you’ll add a for each package you install — or, better, concatenate them together into a single RUN, reducing the number of layers in the image. RUN directive If you’re using apt, you can easily see what has been installed by looking /var/log/apt/history.log, which is full of entries like this: Tip: Start-Date: 2017-03-17  17:00:58
Commandline: apt-get install libcgroup-dev
Install: libcgroup1:amd64 (0.41-6, automatic), libcgroup-dev:amd64 (0.41-6)
End-Date: 2017-03-17  17:01:00 This is easier to use for constructing your Dockerfile than apt list --installed which doesn’t differentiate between installed packages and their dependencies. That’s only going to help you with packages you installed with apt, of course. Don’t forget to add the installation of any language- or app-specific requirements you might have. Changing the base image Sometimes it’s a very good idea to upgrade the base image you’re using. An obvious case is when a and patched in a newer version of the base image. You’d like to take that patch in your container, right? vulnerability has been found If you’ve got an image that you’ve saved with a docker commit, well, good luck with that. <thinking out loud>If an image is made up of layers, and the layers are basically tarballs, can’t you manually start with the new base image layers, and then apply the other (committed) layers on top? Or does the layer “diff” only work if the layer beneath is bit-identical to what it was created from? If anyone has tried this let me know, otherwise I feel an experiment coming on…</thinking out loud> Dockerfile as documentation As a side effect, the Dockerfile describes what’s inside your image in a fairly human-readable way. Reading a Dockerfile can be a lot quicker than pulling and running the image to see what’s installed. Of course this suggests you have kept the Dockerfile somewhere, and you can easily find the Dockerfile that corresponds to the image and vice versa. At the moment the tooling that ties images and their source code together is all still pretty Heath-Robinson in my opinion. No-one’s going to make you check your Dockerfile in anywhere, and even if you do, can you always remember which project you put it in? Was it in or Bitbucket? Maybe it was just in a gist? Do all your source code projects match the names of the images in your image registry? GitHub A couple of options for things you can do here: Copy the Dockerfile into the image as you build it. Use so that it’s easy to see where the source code came from. conventional labels When not OK is OK, really There are situations where it’s tolerable to use commit: you’re running an experiment, that you think might break something quite badly — maybe you’re running a script with some scaryrm -rf's in, or you’re mounting a pseudo filesystem where you’re not 100% sure what effect it’s going to have. Commit the image before you run the experiment by all means, and then you can easily come back to where you were before you ran the experiment. Another situation where you could conceivably not ruin yourself with commit is for a temporary container image that you’re using to run something as a one-off. There’s an example of this in ’s excellent tutorial on . If your goal, as here, is to run a binary, and the container image is merely a temporary tool to help you do that, then a commit approach makes sense. Jérôme Petazzoni compiling Go code using Docker But, if you’re going to want the image in the future, take the time to create something reproducible and write a Dockerfile. *You could also create an image from a tarball with but I doubt many people are doing that. Or are you? docker import 💚_. Thanks!_ Help me help people with their commitment issues by hitting the recommend button Picture credit: m.a.r.c

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