Uthorp

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So, you realized that “equity and capital” fundraising consultants tend to charge WAY less than almost any broker-dealer you could find (the industry average is around 1-4%) but they won’t take a percentage, and they request substantial upfront payment without any guarantee. If you’re thinking, “Why can’t I just give them a piece of the pie? It’s better money for them, and I can pay if we close; no strings attached. Everyone wins!” Then you’re not alone. For the finance nerds that find their way here: Fundraising consultants in equity, debt, real estate, and other securitized spaces can’t do it, ethically, at least for transactions in the United States or transactions involving US-based investors, FINRA member firms, or US-based fiduciaries. For those not immersed in the depths of finance jargon: Simply put any form of contingency-based payment, commission, finder’s fee, success fee, cut, “slice”, referral bonus, or other names for a “piece of the pie” is off the limits for fundraising consultants—save for those with valid FINRA membership and the corresponding FINRA Series License(s). If you are caught taking that piece of the pie by FINRA or the SEC, they will (likely) make an example of you. Securities don’t just mean tokenized assets, equity, or derivatives. Debt-based funding rounds are still considered securities in many cases—debt is MORE LIKELY to be considered a security if the term of the loan or note exceeds five years, further increasing at the ten-year note mark. Who Determines What Makes It a “Piece of the Pie?” Ultimately, the ecurities and xchange ommission ( ) and ancial egulatory uthority ( ) work together to make these determinations. For a simple litmus test, your consultant may be at risk of taking a piece of the pie if: S E C SEC FIN R A FINRA They don’t have a for your efforts other than a percentage of the transaction. stable, realistic cost basis They or closes successfully. only get paid if the funding reaches a certain milestone This is commonly referred to as “ Basis” Contingency They that you raised. only get paid based on the amount of “Attributable Funding” They on “commissions”. only get paid They before any checks are cut. only get paid a small portion of your price You OR offer to pay them “out of the first check” They if the event isn’t successful. offer a refund or guarantee that you will receive a refund For any reason, except something like on their part. gross negligence Expert fundraising consultants know that generally speaking, any terms like the following may put their neck on the line with regulators in the US: “ ” You only get paid if… “I want a or you will offer us a refund…” guarantee that you will get us funded “I can pay you a small percentage upfront, but I want to know that .” you have skin in the game “ the first check is cut.” You get paid after etc. As a result, most (legally operating) equity, securities, and capital , generally based on the amount of time, effort, and specialized knowledge needed to do the job. fundraising consultants normally charge flat hourly fees or a per-project basis Larger raises often mean more moving parts and added complexity. Some SEC registration exemptions actually won’t allow businesses to raise more than a specific dollar amount per year. For example: ulation ( ) Reg A Reg A Tier 1 ($20m limit per 12-month period) or Tier 2 ($75m limit per 12-month period) ulation rowd unding ( ) ($5 million per 12-month period) Reg C F Reg CF A lot of questions ultimately boil down to the question: Are Fundraising Consultants “Unregistered Broker-Dealers”? The answer is: No, not within a normal scope of work. We provide due diligence for the investors. don’t We manage transactions. don’t We meet with investors on your behalf. don't We fiduciaries or escrow providers (though ethical consultants follow fiduciary best practices, generally speaking). aren’t Why Can Broker-Dealers Charge Success Fees While Fundraising Consultants Cannot? Broker-dealers have special licenses, strict protocols, and specific bookkeeping requirements to ensure that no information falls through the cracks. They also have personal information, bonding insurance, and other things at risk when they are managing transactions. That means there are specific standards and rules they have to follow in order to be broker-dealers. In fact, (including registration-exempt transactions, such as a Reg D issuance) from the time an investor sends it and the moment the receiving party claims it will need special licenses. any party that touches the money from securities transactions . Following the “Silver Leaf Decision,” brokers cannot pay out referral fees if they are contingent on a deal closing. These fees will be considered “transaction-based compensation” and subject to regulation under Section 15 of the Securities Act of 1934, with very few exceptions National Adjudicatory Council (NAC) on June 29, 2019 Complaint No. 2014042606902 “ ” The Silver Leaf Decision “The Hearing Panel also found that Silver Leaf paid transaction-based compensation to nonmember brokers, and failed to establish and maintain a supervisory system reasonably designed to prevent those payments.” This basically means that brokers aren’t allowed to share their success fee with anyone who is involved in the transaction. The SEC has since proposed a series of potential exemptions, based on the concept of “Finders” but has not published any concrete guidance yet. Here’s for “Tier 1” and “Tier 2” Finders. more information about proposed exclusions To be clear, Fundraising Consultants aren’t “Finders.” They may participate in investor relationship management via chats or emails but do so with a clear disclosure that they are working on behalf of the company they are engaged with. Finders are expected to be people that are outside of the company in most cases. It’s not uncommon for larger companies (Series B and so on) to hire an interim CEO or CFO to lead fundraisers. These folks are “insiders” and are expected to lead investor meetings and often take a small piece of the pie. They are also limited to raising with ONLY ONE company per calendar year. This is a special exemption used by companies that are looking to raise and can afford to pay a full year’s executive salary PLUS an equity-based compensation bonus. Fundraising consultants that engage in practices such as charging success fees or requesting other “transaction-based” compensation packages are taking actions that risk placing themselves in the category. “Non-member broker” At best, this can trigger cease-and-desist orders from FINRA. In the worst case, the consultant and their client may be at risk of a civil action or criminal action (in the case of fraud or other criminal activity.) Clients that have raised funds, in exchange for success fees that are paid to nonmember brokers may also be forced to rescind their offering. This requires the “issuer” to repay all investments from all investors 100%. In many cases, businesses have been forced to rescind an offering and ALSO pay interest on the funds. Banks (specifically bancorps) often hold “money transmitter” and “Money Service Business (MSB)” licenses on a state-by-state basis—usually alongside other requirements. If you’ve heard about crypto projects getting regulated in the US, it’s mostly been about them getting approved for money transmitters and/or MSB licenses. Broker-dealers have several more stringent restrictions on what they are able to do, as compared to fundraising consultants. For example, a private placement transaction may be exempt from SEC filing requirements ( ) A broker-dealer will still be subject to certain parts of Section 15 of the Securities Act of 1933. you still have to file Form D under Regulation D exemptions. It’s worth noting that only a select few circumstances will allow a fundraising consultant to legally participate in connecting investors to businesses. Part of these circumstances include: In most cases, broker-dealers are bonded to an investment banking job. They normally require a minimum term commitment in order to accept the risk and vouch for Series 7 hopefuls. You (Series 7, etc.) without a FINRA-regulated firm backing you. cannot even for becoming a licensed broker-dealer take the tests Interested parties without having the backing of a FINRA member firm. The SIE is a prerequisite for all Series exams. are able to sit for their Securities Industry Essentials (SIE) exam Series-Licensed Broker-Dealers (BDs) Licensed Broker-Dealers are involved in structuring deals, as well as the range of services provided by fundraising consultants. A few added responsibilities include specialized marketing channels and relationships with investment banks. BDs may also pre-purchase and sell a client’s securities (unless they are restricted securities.) Most of the time, BDs come from an investment banking background, meaning that, commonly, they have a Rolodex of potentially relevant contacts. Most BDs are also limited to selling general securities. The term “General Securities” typically covers corporate finance instruments such as: Equity Corporate bonds (debt) Mezzanine Financing (debt with equity-based collateral) Etc. Other relevant licenses include the licenses. If you’re in doubt about whether or not they have legit licenses, use FINRA’s BrokerCheck site. Series 7 and Series 24 FINRA Number (CRD Number): Central Registration Depository FINRA BrokerCheck If they aren’t listed or don’t have at least one of the following licenses, be aware that they may not know they are working outside the bounds of the law. Series 7 Series 8 (these are no longer issued) Series 9 & 10 (This combination has replaced Series 8), or Series 24 Equity and Capital Fundraising Consultants: Scope of Work Fundraising consultants have a specific limitation on their scope of work. They can assist in creating collateral, reviewing materials, and conducting investor outreach & relationship management activities. Fundraising Consultants can NOT commit to any deals or offers on behalf of their clients UNDER ANY CIRCUMSTANCES. Further, fundraising consultants or investment advisory activities, such as recommending their clients’ offerings to non-accredited investors, outside the scope of any related exemption, such as Reg D 506(b/c). should not be present in due diligence activities Many fundraising consultants prefer to maintain exclusivity by involving accredited investors in their clients’ raises. only Here are a few things that bar fundraising consultants from taking their piece of the pie: Securities Act of 1933 Section 12 (Broker-Dealers, Control Persons, Registered Persons) Section 15 FINRA Rule Section 2000 For example, (Payments to Unregistered Persons) Rule 2040 (Transactions With Customers) Subsection 2100 (Suitability) Rule 2111 (Communications and Disclosures) Subsection 2200 (Communications with the Public) Rule 2210 In short, between the steep pitfalls that come with accepting any form of performance-based bonus, fundraising consultants generally prefer to stick with hourly-based or per-project compensation structures. So Why Would Anyone Hire a Fundraising Consultant? We often come with more deeply specialized skills, tend to communicate more effectively with the C-level, and seldom send associates (that we don’t have) to do the work for us. Beyond the skills or preferences you may otherwise have, fundraising consultants are, generally, MUCH cheaper to collaborate with. You might pay a fundraising consultant $25k-100k per month, but they tend to rely on much faster workflows and lines of communication. The deal is usually done in 30-90 days. If you raise a Series A at a normal $2-4m deal against a $10-$20m valuation, expect to pay a broker-dealer: 4-8% of funds raised (1-4% is normal on deals that exceed $5-$10m raised); AND 1-3% of offered equity, whether outright or as “warrants” at a steep discount. That 4-8% may amount to $160k-$320k in addition to the equity you paid to “sweeten the pot.” Deals can also take up to a year or more to close. Fundraising consultants are very similar to BDs in their scope of work, but generally for a much more modest payoff and in a much shorter timeframe. Fundraising consultants usually step away once the contracts are closed for your raise, so you only pay them for the term of the engagement.

What The F*CK Do You Mean Fundraising Consultants Can't Take a Percentage?

F*CK আপনি কি বলতে চাচ্ছেন তহবিল সংগ্রহের পরামর্শদাতারা শতাংশ নিতে পারে না?

Was zum Teufel meinen Sie damit, dass Fundraising-Berater keinen Prozentsatz annehmen können?

¿Qué diablos quiere decir que los consultores de recaudación de fondos no pueden tomar un porcentaje?

Qu'est-ce que vous voulez dire que les consultants en collecte de fonds ne peuvent pas prendre de pourcentage ?

एफ * सीके क्या आपका मतलब है कि धन उगाहने वाले सलाहकार प्रतिशत नहीं ले सकते?

The F*CK とは、資金調達コンサルタントがパーセンテージを取得できないことを意味しますか?

F*CK 기금 모금 컨설턴트가 일정 비율을 차지할 수 없다는 게 무슨 뜻인가요?

O que diabos você quer dizer com consultores de arrecadação de fundos não podem obter uma porcentagem?

Bağış Toplama Danışmanlarının Yüzde Alamaması Ne Demek İstiyorsunuz?

Ý bạn là cái quái gì Các chuyên gia tư vấn gây quỹ không thể lấy một tỷ lệ phần trăm?

F*CK 你是什么意思筹款顾问不能拿百分比？

Full-stack smart contract development

240 Stories To Learn About Astounding Stories

This paper is available on arxiv under CC 4.0 license. . Authors: Gemini Team, Google Table of Links Abstract and Introduction Model Architecture Training Infrastructure Training Dataset Evaluation Responsible Deployment Discussion and Conclusion, References Contributions and Acknowledgments Appendix 8. Contributions and Acknowledgments Leads Rohan Anil, Co-Lead, Text Sebastian Borgeaud, Co-Lead, Text Yonghui Wu, Co-Lead, Text Jean-Baptiste Alayrac, Co-Lead, MM Vision Jiahui Yu, Co-Lead, MM Vision Radu Soricut*, Co-Lead, MM Vision* Johan Schalkwyk, Lead, MM Audio Andrew M. Dai, Co-Lead, Data Anja Hauth*, Co-Lead, Data* Katie Millican, Co-Lead, Data David Silver, Co-Lead, Fine-Tuning Slav Petrov, Co-Lead, Fine-Tuning Melvin Johnson, Lead, Instruction Tuning Ioannis Antonoglou*, Co-Lead, RL Techniques* Julian Schrittwieser, Co-Lead, RL Techniques Amelia Glaese, Lead, Human Data Jilin Chen, Lead, Safety Emily Pitler, Co-Lead, Tool Use Timothy Lillicrap, Co-Lead, Tool Use Angeliki Lazaridou, Co-Lead, Eval Orhan Firat, Co-Lead, Eval James Molloy, Co-Lead, Infra Michael Isard, Co-Lead, Infra Paul R. Barham, Co-Lead, Infra Tom Hennigan, Co-Lead, Infra Benjamin Lee, Co-Lead, Codebase & Parallelism Fabio Viola, Co-Lead, Codebase & Parallelism Malcolm Reynolds, Co-Lead, Codebase & Parallelism Yuanzhong Xu, Co-Lead, Codebase & Parallelism Ryan Doherty, Lead, Ecosystem Eli Collins, Lead, Product Clemens Meyer, Co-Lead, Operations Eliza Rutherford, Co-Lead, Operations Erica Moreira, Co-Lead, Operations Kareem Ayoub, Co-Lead, Operations Megha Goel, Co-Lead, Operations Core Contributors George Tucker Enrique Piqueras Maxim Krikun Iain Barr Nikolay Savinov Ivo Danihelka Becca Roelofs Core Contributors Anaïs White Anders Andreassen Tamara von Glehn Lakshman Yagati Mehran Kazemi Lucas Gonzalez Misha Khalman Jakub Sygnowski Alexandre Frechette Charlotte Smith Laura Culp Lev Proleev Yi Luan Xi Chen James Lottes Nathan Schucher Federico Lebron Alban Rrustemi Natalie Clay Phil Crone Tomas Kocisky Jeffrey Zhao Bartek Perz Dian Yu Heidi Howard Adam Bloniarz Jack W. Rae Han Lu Laurent Sifre Marcello Maggioni Fred Alcober Dan Garrette Megan Barnes Shantanu Thakoor Jacob Austin Gabriel Barth-Maron William Wong Rishabh Joshi Rahma Chaabouni Deeni Fatiha Arun Ahuja Ruibo Liu Yunxuan Li Sarah Cogan Jeremy Chen Core Contributors Chao Jia Chenjie Gu Qiao Zhang Jordan Grimstad Ale Jakse Hartman Martin Chadwick Gaurav Singh Tomar Xavier Garcia Evan Senter Emanuel Taropa Thanumalayan Sankaranarayana Pillai Jacob Devlin Michael Laskin Diego de Las Casas Dasha Valter Connie Tao Lorenzo Blanco Adrià Puigdomènech Badia David Reitter Mianna Chen Jenny Brennan Clara Rivera Sergey Brin Shariq Iqbal Gabriela Surita Jane Labanowski Abhi Rao Stephanie Winkler Emilio Parisotto Yiming Gu Kate Olszewska Yujing Zhang Ravi Addanki Antoine Miech Annie Louis Laurent El Shafey Denis Teplyashin Geoff Brown Elliot Catt Nithya Attaluri Jan Balaguer Jackie Xiang Pidong Wang Zoe Ashwood Anton Briukhov Albert Webson Sanjay Ganapathy Smit Sanghavi Core Contributors Ajay Kannan Ming-Wei Chang Axel Stjerngren Josip Djolonga Yuting Sun Ankur Bapna Matthew Aitchison Pedram Pejman Henryk Michalewski Tianhe Yu Cindy Wang Juliette Love Junwhan Ahn Dawn Bloxwich Kehang Han Peter Humphreys Thibault Sellam James Bradbury Varun Godbole Sina Samangooei Bogdan Damoc Alex Kaskasoli Sébastien M. R. Arnold Vijay Vasudevan Shubham Agrawal Jason Riesa Dmitry Lepikhin Richard Tanburn Srivatsan Srinivasan Hyeontaek Lim Sarah Hodkinson Pranav Shyam Johan Ferret Steven Hand Ankush Garg Tom Le Paine Jian Li Yujia Li Minh Giang Alexander Neitz Zaheer Abbas Sarah York Machel Reid Elizabeth Cole Aakanksha Chowdhery Dipanjan Das Dominika Rogozińska Vitaly Nikolaev Core Contributors Pablo Sprechmann Zachary Nado Lukas Zilka Flavien Prost Luheng He Marianne Monteiro Gaurav Mishra Chris Welty Josh Newlan Dawei Jia Miltiadis Allamanis Clara Huiyi Hu Raoul de Liedekerke Justin Gilmer Carl Saroufim Shruti Rijhwani Shaobo Hou Disha Shrivastava Anirudh Baddepudi Alex Goldin Adnan Ozturel Albin Cassirer Yunhan Xu Daniel Sohn Devendra Sachan Reinald Kim Amplayo Craig Swanson Dessie Petrova Shashi Narayan Arthur Guez Siddhartha Brahma Jessica Landon Miteyan Patel Ruizhe Zhao Kevin Villela Luyu Wang Wenhao Jia Matthew Rahtz Mai Giménez Legg Yeung Hanzhao Lin James Keeling Petko Georgiev Diana Mincu Boxi Wu Salem Haykal Rachel Saputro Kiran Vodrahalli Core Contributors James Qin Zeynep Cankara Abhanshu Sharma Nick Fernando Will Hawkins Behnam Neyshabur Solomon Kim Adrian Hutter Priyanka Agrawal Alex Castro-Ros George van den Driessche Tao Wang Fan Yang Shuo-yiin Chang Paul Komarek Ross McIlroy Mario Lučić Guodong Zhang Wael Farhan Michael Sharman Paul Natsev Paul Michel Yong Cheng Yamini Bansal Siyuan Qiao Kris Cao Siamak Shakeri Christina Butterfield Justin Chung Paul Kishan Rubenstein Shivani Agrawal Arthur Mensch Kedar Soparkar Karel Lenc Timothy Chung Aedan Pope Loren Maggiore Jackie Kay Priya Jhakra Shibo Wang Joshua Maynez Mary Phuong Taylor Tobin Andrea Tacchetti Maja Trebacz Kevin Robinson Yash Katariya Sebastian Riedel Core Contributors Paige Bailey Kefan Xiao Nimesh Ghelani Lora Aroyo Ambrose Slone Neil Houlsby Xuehan Xiong Zhen Yang Elena Gribovskaya Jonas Adler Mateo Wirth Lisa Lee Music Li Thais Kagohara Jay Pavagadhi Sophie Bridgers Anna Bortsova Sanjay Ghemawat Zafarali Ahmed Tianqi Liu Richard Powell Vijay Bolina Mariko Iinuma Polina Zablotskaia James Besley Da-Woon Chung Timothy Dozat Ramona Comanescu Xiance Si Jeremy Greer Guolong Su Martin Polacek Raphaël Lopez Kaufman Simon Tokumine Hexiang Hu Elena Buchatskaya Yingjie Miao Mohamed Elhawaty Aditya Siddhant Nenad Tomasev Jinwei Xing Christina Greer Helen Miller Shereen Ashraf Aurko Roy Zizhao Zhang Ada Ma Angelos Filos Core Contributors Milos Besta Rory Blevins Ted Klimenko Chih-Kuan Yeh Soravit Changpinyo Jiaqi Mu Oscar Chang Mantas Pajarskas Carrie Muir Vered Cohen Charline Le Lan Krishna Haridasan Amit Marathe Steven Hansen Sholto Douglas Rajkumar Samuel Mingqiu Wang Sophia Austin Chang Lan Jiepu Jiang Justin Chiu Jaime Alonso Lorenzo Lars Lowe Sjösund Sébastien Cevey Zach Gleicher Thi Avrahami Anudhyan Boral Hansa Srinivasan Vittorio Selo Rhys May Konstantinos Aisopos Léonard Hussenot Livio Baldini Soares Kate Baumli Michael B. Chang Adrià Recasens Ben Caine Alexander Pritzel Filip Pavetic Fabio Pardo Anita Gergely Justin Frye Vinay Ramasesh Dan Horgan Kartikeya Badola Nora Kassner Subhrajit Roy Ethan Dyer Core Contributors Víctor Campos Alex Tomala Yunhao Tang Dalia El Badawy Elspeth White Basil Mustafa Oran Lang Abhishek Jindal Sharad Vikram Zhitao Gong Sergi Caelles Ross Hemsley Gregory Thornton Fangxiaoyu Feng Wojciech Stokowiec Ce Zheng Phoebe Thacker Çağlar Ünlü Zhishuai Zhang Mohammad Saleh James Svensson Max Bileschi Piyush Patil Ankesh Anand Roman Ring Katerina Tsihlas Arpi Vezer Marco Selvi Toby Shevlane Mikel Rodriguez Tom Kwiatkowski Samira Daruki Keran Rong Allan Dafoe Nicholas FitzGerald Keren Gu-Lemberg Mina Khan Lisa Anne Hendricks Marie Pellat Vladimir Feinberg James Cobon-Kerr Tara Sainath Maribeth Rauh Sayed Hadi Hashemi Richard Ives Yana Hasson YaGuang Li Eric Noland Core Contributors Yuan Cao Nathan Byrd Le Hou Qingze Wang Thibault Sottiaux Michela Paganini Jean-Baptiste Lespiau Alexandre Moufarek Samer Hassan Kaushik Shivakumar Joost van Amersfoort Amol Mandhane Pratik Joshi Anirudh Goyal Matthew Tung Andrew Brock Hannah Sheahan Vedant Misra Cheng Li Nemanja Rakićević Mostafa Dehghani Fangyu Liu Sid Mittal Junhyuk Oh Seb Noury Eren Sezener Fantine Huot Matthew Lamm Nicola De Cao Charlie Chen Contributors Gamaleldin Elsayed Ed Chi Mahdis Mahdieh Ian Tenney Nan Hua Ivan Petrychenko Patrick Kane Dylan Scandinaro Rishub Jain Jonathan Uesato Romina Datta Adam Sadovsky Oskar Bunyan Dominik Rabiej Shimu Wu John Zhang Contributors Gautam Vasudevan Edouard Leurent Mahmoud Alnahlawi Ionut Georgescu Nan Wei Ivy Zheng Betty Chan Pam G Rabinovitch Piotr Stanczyk Ye Zhang David Steiner Subhajit Naskar Michael Azzam Matthew Johnson Adam Paszke Chung-Cheng Chiu Jaume Sanchez Elias Afroz Mohiuddin Faizan Muhammad Jin Miao Andrew Lee Nino Vieillard Sahitya Potluri Jane Park Elnaz Davoodi Jiageng Zhang Jeff Stanway Drew Garmon Abhijit Karmarkar Zhe Dong Jong Lee Aviral Kumar Luowei Zhou Jonathan Evens William Isaac Zhe Chen Johnson Jia Anselm Levskaya Zhenkai Zhu Chris Gorgolewski Peter Grabowski Yu Mao Alberto Magni Kaisheng Yao Javier Snaider Norman Casagrande Paul Suganthan Evan Palmer Contributors Geoffrey Irving Edward Loper Manaal Faruqui Isha Arkatkar Nanxin Chen Izhak Shafran Michael Fink Alfonso Castaño Irene Giannoumis Wooyeol Kim Mikołaj Rybiński Ashwin Sreevatsa Jennifer Prendki David Soergel Adrian Goedeckemeyer Willi Gierke Mohsen Jafari Meenu Gaba Jeremy Wiesner Diana Gage Wright Yawen Wei Harsha Vashisht Yana Kulizhskaya Jay Hoover Maigo Le Lu Li Chimezie Iwuanyanwu Lu Liu Kevin Ramirez Andrey Khorlin Albert Cui Tian LIN Marin Georgiev Marcus Wu Ricardo Aguilar Keith Pallo Abhishek Chakladar Alena Repina Xihui Wu Tom van der Weide Priya Ponnapalli Caroline Kaplan Jiri Simsa Shuangfeng Li Olivier Dousse Fan Yang Jeff Piper Nathan Ie Contributors Minnie Lui Rama Pasumarthi Nathan Lintz Anitha Vijayakumar Lam Nguyen Thiet Daniel Andor Pedro Valenzuela Cosmin Paduraru Daiyi Peng Katherine Lee Shuyuan Zhang Somer Greene Duc Dung Nguyen Paula Kurylowicz Sarmishta Velury Sebastian Krause Cassidy Hardin Lucas Dixon Lili Janzer Kiam Choo Ziqiang Feng Biao Zhang Achintya Singhal Tejasi Latkar Mingyang Zhang Quoc Le Elena Allica Abellan Dayou Du Dan McKinnon Natasha Antropova Tolga Bolukbasi Orgad Keller David Reid Daniel Finchelstein Maria Abi Raad Remi Crocker Peter Hawkins Robert Dadashi Colin Gaffney Sid Lall Ken Franko Egor Filonov Anna Bulanova Rémi Leblond Vikas Yadav Shirley Chung Harry Askham Luis C. Cobo Contributors Kelvin Xu Felix Fischer Jun Xu Christina Sorokin Chris Alberti Chu-Cheng Lin Colin Evans Hao Zhou Alek Dimitriev Hannah Forbes Dylan Banarse Zora Tung Jeremiah Liu Mark Omernick Colton Bishop Chintu Kumar Rachel Sterneck Ryan Foley Rohan Jain Swaroop Mishra Jiawei Xia Taylor Bos Geoffrey Cideron Ehsan Amid Francesco Piccinno Xingyu Wang Praseem Banzal Petru Gurita Hila Noga Premal Shah Daniel J. Mankowitz Alex Polozov Nate Kushman Victoria Krakovna Sasha Brown MohammadHossein Bateni Dennis Duan Vlad Firoiu Meghana Thotakuri Tom Natan Anhad Mohananey Matthieu Geist Sidharth Mudgal Sertan Girgin Hui Li Jiayu Ye Ofir Roval Reiko Tojo Contributors Michael Kwong James Lee-Thorp Christopher Yew Quan Yuan Sumit Bagri Danila Sinopalnikov Sabela Ramos John Mellor Abhishek Sharma Aliaksei Severyn Jonathan Lai Kathy Wu Heng-Tze Cheng David Miller Nicolas Sonnerat Denis Vnukov Rory Greig Jennifer Beattie Emily Caveness Libin Bai Julian Eisenschlos Alex Korchemniy Tomy Tsai Mimi Jasarevic Weize Kong Phuong Dao Zeyu Zheng Frederick Liu Fan Yang Rui Zhu Mark Geller Tian Huey Teh Jason Sanmiya Evgeny Gladchenko Nejc Trdin Andrei Sozanschi Daniel Toyama Evan Rosen Sasan Tavakkol Linting Xue Chen Elkind Oliver Woodman John Carpenter George Papamakarios Rupert Kemp Sushant Kafle Tanya Grunina Rishika Sinha Contributors Alice Talbert Abhimanyu Goyal Diane Wu Denese Owusu-Afriyie Cosmo Du Chloe Thornton Jordi Pont-Tuset Pradyumna Narayana Jing Li Saaber Fatehi John Wieting Omar Ajmeri Benigno Uria Tao Zhu Yeongil Ko Laura Knight Amélie Héliou Ning Niu Shane Gu Chenxi Pang Dustin Tran Yeqing Li Nir Levine Ariel Stolovich Norbert Kalb Rebeca Santamaria-Fernandez Sonam Goenka Wenny Yustalim Robin Strudel Ali Elqursh Balaji Lakshminarayanan Charlie Deck Shyam Upadhyay Hyo Lee Mike Dusenberry Zonglin Li Xuezhi Wang Kyle Levin Raphael Hoffmann Dan Holtmann-Rice Olivier Bachem Summer Yue Sho Arora Eric Malmi Daniil Mirylenka Qijun Tan Christy Koh Soheil Hassas Yeganeh Contributors Siim Põder Steven Zheng Francesco Pongetti Mukarram Tariq Yanhua Sun Lucian Ionita Mojtaba Seyedhosseini Pouya Tafti Ragha Kotikalapudi Zhiyu Liu Anmol Gulati Jasmine Liu Xinyu Ye Bart Chrzaszcz Lily Wang Nikhil Sethi Tianrun Li Ben Brown Shreya Singh Wei Fan Aaron Parisi Joe Stanton Chenkai Kuang Vinod Koverkathu Christopher A. Choquette-Choo Yunjie Li TJ Lu Abe Ittycheriah Prakash Shroff Pei Sun Mani Varadarajan Sanaz Bahargam Rob Willoughby David Gaddy Ishita Dasgupta Guillaume Desjardins Marco Cornero Brona Robenek Bhavishya Mittal Ben Albrecht Ashish Shenoy Fedor Moiseev Henrik Jacobsson Alireza Ghaffarkhah Morgane Rivière Alanna Walton Clément Crepy Alicia Parrish Contributors Yuan Liu Zongwei Zhou Clement Farabet Carey Radebaugh Praveen Srinivasan Claudia van der Salm Andreas Fidjeland Salvatore Scellato Eri Latorre-Chimoto Hanna Klimczak-Plucińska David Bridson Dario de Cesare Tom Hudson Piermaria Mendolicchio Lexi Walker Alex Morris Ivo Penchev Matthew Mauger Alexey Guseynov Alison Reid Seth Odoom Lucia Loher Victor Cotruta Madhavi Yenugula Dominik Grewe Anastasia Petrushkina Tom Duerig Antonio Sanchez Steve Yadlowsky Amy Shen Amir Globerson Adam Kurzrok Lynette Webb Sahil Dua Dong Li Preethi Lahoti Surya Bhupatiraju Dan Hurt Haroon Qureshi Ananth Agarwal Tomer Shani Matan Eyal Anuj Khare Shreyas Rammohan Belle Lei Wang Chetan Tekur Mihir Sanjay Kale Jinliang Wei Contributors Ruoxin Sang Brennan Saeta Tyler Liechty Yi Sun Yao Zhao Stephan Lee Pandu Nayak Doug Fritz Manish Reddy Vuyyuru John Aslanides Nidhi Vyas Martin Wicke Xiao Ma Taylan Bilal Evgenii Eltyshev Daniel Balle Nina Martin Hardie Cate James Manyika Keyvan Amiri Yelin Kim Xi Xiong Kai Kang Florian Luisier Nilesh Tripuraneni David Madras Mandy Guo Austin Waters Oliver Wang Joshua Ainslie Jason Baldridge Han Zhang Garima Pruthi Jakob Bauer Feng Yang Riham Mansour Jason Gelman Yang Xu George Polovets Ji Liu Honglong Cai Warren Chen XiangHai Sheng Emily Xue Sherjil Ozair Adams Yu Christof Angermueller Contributors Xiaowei Li Weiren Wang Julia Wiesinger Emmanouil Koukoumidis Yuan Tian Anand Iyer Madhu Gurumurthy Mark Goldenson Parashar Shah MK Blake Hongkun Yu Anthony Urbanowicz Jennimaria Palomaki Chrisantha Fernando Kevin Brooks Ken Durden Harsh Mehta Nikola Momchev Elahe Rahimtoroghi Maria Georgaki Amit Raul Sebastian Ruder Morgan Redshaw Jinhyuk Lee Komal Jalan Dinghua Li Ginger Perng Blake Hechtman Parker Schuh Milad Nasr Mia Chen Kieran Milan Vladimir Mikulik Trevor Strohman Juliana Franco Tim Green Program Leads Demis Hassabis Koray Kavukcuoglu Overall Technical Leads (equal contribution) Jeffrey Dean Oriol Vinyals The roles are defined as below: • : Individual(s) responsible for the sub-team throughout the project. Lead Individual that had significant impact throughout the project. • Core Contributor: : Individual that had contributions to the project and was partially involved with the effort. • Contributor Responsible for the organizational aspects of the Gemini effort • Program Lead: Responsible for the technical direction of the overall Gemini effort • Overall Technical Lead: Within each role, contributions are equal, and are listed in a randomized order. Ordering within each role does not indicate ordering of the contributions. Gemini is a cross-Google effort, with members from Google DeepMind (GDM), Google Research (GR), Knowledge and Information (K&I), Core ML, Cloud, Labs, and more. We thank our reviewers and colleagues for their valuable discussions and feedback on the report— Alexandra Belias, Arielle Bier, Eleanor Tomlinson, Emily Hossellman, Gaby Pearl, Helen King, Hollie Dobson, Jaclyn Konzelmann, Jennifer Beroshi, Joel Moss, Jon Small, Jonathan Fildes, Oli Gaymond, Rebecca Bland, Reena Jana, and Tom Lue. Our work is made possible by the dedication and efforts of numerous teams at Google. We would like to acknowledge the support from Abhi Mohan, Adekunle Bello, Aishwarya Nagarajan, Alaa Saade, Alejandro Lince, Alexander Chen, Alexander Kolbasov, Alexander Schiffhauer, Amar Subramanya, Ameya Shringi, Amin Vahdat, Anda Rabatić, Anthonie Gross, Antoine Yang, Anthony Green, Anton Ruddock, Art Khurshudov, Artemis Chen, Arthur Argenson, Avinatan Hassidim, Beiye Liu, Benjamin Schroeder, Bin Ni, Brett Daw, Bryan Chiang, Burak Gokturk, Carl Crous, Carrie Grimes Bostock, Charbel Kaed, Charlotte Banks, Che Diaz, Chris Larkin, Christy Lian, Claire Cui, Clare Bycroft, Corentin Tallec, Daniel Herndon, Dave Burke, David Battle, David Engel, Dipannita Shaw, Donghyun Koo, Doug Ritchie, Dragos Stefanescu, Elissa Wolf, Emre Sargin, Eran Ofek, Eric Herren, Estella King, Fatema Alkhanaizi, Felix Gimeno, Fernando Pereira, Florent Altché, Gabriel Carvajal, Gaurav Gandhi, George Powell, Goran Pavičić, Harry Richardson, Hassan Wassel, Hongji Li, Idan Szpektor, Igor Ivanisevic, Ivan Jambrešić, Ivan Jurin, Jade Fowler, James Assiene, Jay Yagnik, Jeanbastien Grill, Jeff Seibert, Jenna LaPlante, Jessica Austin, Jianxing Lu, Jim O’Keeffe, Jin Huang, Joe Heyward, Johannes Welbl, John Jumper, Jonathan Caton, Josh Woodward, Joshua Foster, Kathryn Tunyasuvunakool, Katrina Wong, Kavya Kopparapu, Kelvin Nguyen, Kira Yin, Konstantin Sharlaimov, Kun Li, Lee Hong, Lilly Taylor, Longfei Shen, Luc Mercier, Maciej Mikuła, Mania Abdi, Manuel Sanchez, Maria Ines Aranguren, Mario Carlos Cortes III, Matthew Tait, Matthias Lochbrunner, Mehdi Ghissassi, Micah Mosley, Michael Bendersky, Michael Figurnov, Michael Harris, Michael Mathieu, Michael O’Neill, Michael Vorburger, Mihir Paradkar, Nandita Dukkipati, Nathan Carter, Nathan Watson, Neil Rabinowitz, Nikhil Dandekar, Nishant Ranka, Obaid Sarvana, Olcan Sercinoglu, Olivier Lacombe, Ottavia Bertolli, Paul Caron, Pranesh Srinivasan, Praveen Kumar, Rahul Sukthankar, Raia Hadsell, Rajagopal Ananthanarayanan, Roberto Lupi, Rosie Zou, Sachin Menezes, Sadegh Jazayeri, Sam Cheung, Sameer Bidichandani, Sania Alex, Sanjiv Kumar, Sara Wiltberger, Sarah Fitzgerald, Saz Basu, Sebastian Nowozin, Shannon Hepburn, Shayne Cardwell, Sissie Hsiao, Srinivasan Venkatachary, Sugato Basu, Sundar Pichai, Sundeep Tirumalareddy, Susannah Young, Swetha Vijayaraghavan, Tania Bedrax-Weiss, Taylor Applebaum, Teiva Harsanyi, Terry Chen, Tim Blyth, Ting Liu, Tom Cobley, Tomas Izo, Trystan Upstill, Varun Singhai, Vedrana Klarić Trupčević, Victor Cai, Vladimir Pudovkin, Vu Dang, Wenbo Zhao, Wesley Crow, Wesley Szeng, Xiaodan Song, Yazhou Zu, Ye Tian, Yicong Wang, Yixing Wang, Yossi Matias, Yunlong Jiao, Zachary Jessup, Zhenchuan Pang, Žiga Avsec, Zimeng Yang, and Zoubin Ghahramani. We’d also like to recognize the AlphaCode team, the Borg Scheduling team, the Facilities team, the Gemini Demo Team, the Global Server Ops (GSO) team, the JAX team, the the Legal team, ML SRE team, the ML Supercomputer (MLSC) team, the PartIR team, the Platforms Infrastructure Engineering (PIE) team, and the XLA Compiler team. We thank everyone at Google not explicitly mentioned above, who have shared excitement, given feedback on early Gemini models or created interesting demo uses of Gemini, and worked with or supported the core Gemini team on many aspects of this project.

Gemini -  A Family of Highly Capable Multimodal Models: Contributions and Acknowledgments

IRL Art presents "Gallery2," an immersive art installation featuring the NFT exhibit "Cartography Of The Soul." Hosted at the Colorado Convention Center during Psychedelic Science 2023, this groundbreaking exhibition showcases unique digital artworks from renowned artists like Alex Grey, Beeple, and Nadya Tolokonnikova. Curated by Annie Phillips and 1of1, Gallery2 offers a visual masterpiece with large-scale projections, interactive experiences, and cutting-edge technology.  The event serves as a fundraiser for MAPS, and bidding for the artworks can be done on Christie's 3.0 platform. Don't miss this unparalleled intersection of art and technology from June 19th to 24th. What Is Gallery2? Denver, CO - IRL Art, a pioneering force in the digital art realm, is thrilled to unveil its groundbreaking installation, "Gallery2" (pronounced Gallery Squared), featuring an immersive NFT exhibit Cartography Of The Soul. This extraordinary display will be showcased on the renowned Christie's auction house NFT platform, Christie's 3.0. The event serves as a fundraiser for the Multidisciplinary Association for Psychedelic Studies (MAPS), a non-profit organization focused on research and education since 1986. Taking place at the Colorado Convention Center, the Gallery2 exhibition will be a prominent feature during the largest-ever psychedelic gathering, Psychedelic Science 2023, from June 19th to 24th. The exhibit, curated by the esteemed collectors club 1of1, anticipates an audience of over 10,000 attendees. Notably, the previous exhibit, Cartography Of The Mind, raised an impressive $1.5 million in support of MAPS. Inspired by virtual architect "untitled, xyz" and their artwork titled "The Primitive" from The Museum Of Crypto Art, IRL Art founder Annie Phillips has conceived a real-life interpretation of the digital masterpiece through Gallery2. Joining forces with physical architect Yultek and the IRL Art lead designer, the second installment of Gallery2 will be double the size of its debut at ETH Denver 2023. Renowned curator Annie Phillips, in collaboration with 1of1, has handpicked an exceptional lineup of artists for the exhibit, including Klara Vollstaedt, SamJ, and Nadya Tolokonnikova. With extensive experience curating NFT exhibitions since 2019 and working with thousands of artists, Annie Phillips consistently brings diversity, excitement, and innovation to her curation projects. To delve deeper into the creative process and the shared goal of self-discovery and evolution through art and psychedelics, an artist spotlight panel discussion will be held on Wednesday, June 21st. Featuring distinguished artists such as Dustin Yellin, Nadya Tolokonnikova, Mad Dog Jones, Lydia Chan from Christie's, and Lukas from 1of1, this panel promises to be an enriching experience. The list of artists included in the Cartography Of The Soul exhibit is extensive and impressive, boasting names such as and many more talented individuals. Alex Grey, Ash Thorp, Beryl Blici, Brendan Dawes, With such a diverse array of artists, the exhibition offers something for everyone, pushing the boundaries of artistic expression in the digital realm. will be the Cartography Of The Soul exhibit venue from June 19th to June 24th, coinciding with the fourth Psychedelic Science conference, The Colorado Convention Center Psychedelic Science 2023. For press inquiries, interviews, or more information about the Cartography Of The Soul exhibit, please contact: Marketing Director Robert Gray or Founder Annie Phillips Phone: | Email: | 720-447-5224 870-213-7131 Rob@IRLArt.com Annie@IRLArt.com About IRL Art: IRL Art is an art collective that brings together artists, art directors, project managers, metaverse builders, and creatives, focusing on high-impact projects at the intersection of digital and physical art. As a leading innovator in the digital art space, IRL Art leverages cutting-edge technology to redefine the boundaries of artistic expression. With a dedication to showcasing and supporting artists pushing the boundaries of the digital art landscape, IRL Art creates immersive experiences that bridge the gap between art and technology. Note: This article contains several backlinks to relevant websites, including the official websites of , , , , , , , , and . Psychedelic Science 2023 1of1 Alex Grey Ash Thorp Beryl Blici Brendan Dawes Colorado Convention Center Christie's 3.0 PsychedelicScience.org

IRL Art Presents Immersive Art Installation "Gallery2": Cartography Of The Soul ✰

The Valor of Cappen Varra

A SLIP UNDER THE MICROSCOPE

Out of the Dreadful Depths

Fuera de las terribles profundidades

Hors des profondeurs terribles

भयानक गहराई से बाहर

恐ろしい深みから

Das Profundezas Terríveis

Vượt ra khỏi độ sâu khủng khiếp

走出可怕的深渊

400+ Thought-Provoking Software Engineering Quotes

Low-code development platforms trace their roots back to Fourth-Generation Programming Language (4GL), a concept developed from the 1970s through the 1990s. These languages refer to non-procedural and high-level specification languages and include support for database management, report generation, mathematical optimization, GUI development, and web development.  As time progressed, and with more abstraction and more power, several 4GL vendors appeared in the software market, including Oracle (Forms), Progress, Clipper, and Informix. Then came 2003 and Eric Evans, a popular programmer and thought leader in software design and domain modeling. He coined the concept of Domain-Driven Design in a . This methodology led to the rapid evolution of hybrid development platforms, which, in 2014, Gartner and Forrester termed “low-code platforms.” These platforms combined the simplicity of graphical interfaces and the power of programming languages to enhance user experience. book of the same title Today’s low-code products cover nearly every domain, from email builders, CRMs, and web designers to workflow and prototyping tools, and even AI.  But the concept itself is not new, in fact, it’s been around for more than 3 decades. So we’ve picked out some of our favorites. Enjoy a short tour down the annuals of low-code. Microsoft Excel 3.0 Multiple Sheets (1990) Excel From its market launch in 1987, Microsoft Excel quickly became the king of low-code tools and was the master of must-have tools for nearly any commercial business since the 1990s. Before Microsoft Excel existed, there was Microsoft Multiplan. Multiplan was created to compete against Visicalc and Lotus 1-2-3, which were the popular spreadsheet apps at the time. Although it was released for a variety of computing platforms, (including the Apple II, CP/M, MS-DOS, Macintosh, Xenix, Commodore 64), it never reached critical mass in the market as many criticized the addition of having an ‘unstructured data pile’ in the organization. They also considered the heavy usage of Excel as a potential security vulnerability as thousands of Excel files containing important business information remained on user devices and in mailboxes—largely unprotected. But for all their concerts, Excel refused to die and in 1993, Microsoft released Excel v5.0 for Windows, including VBA (Visual Basic for Applications), aka Macros. This opened up almost unlimited possibilities in automating repetitive tasks for crunching numbers, process automation, and presenting data for businesses. So after 33 years—36, if you count Multiplan’s lifespan—Excel continues to survive and thrive. From simple processes such as organizing daily jobs to grocery shopping lists to wedding planning to solving complex business problems — Excel is effortlessly easy and an excellent tool. Microsoft Visual Basic 3.0 Visual Basic The original Visual Basic (also called Classic Visual Basic) was Microsoft’s third-generation event-driven programming language known for its Component Object Model (COM) programming model. The first version of visual basic, VB 1.0, was announced in 1991 and was inspired by a beta generator developed by Alan Cooper at Tripod. It was finally declared legacy in 2008. Microsoft and Cooper developed it into a programmable form system for Windows 3.0, under the code name Ruby (no relation to the later Ruby programming language). However, Tripod did not include a programming language at all, and when Microsoft combined Ruby with the Basic language, Visual Basic was born. The Ruby interface generator provided the "visual" part of Visual Basic, and this was combined with the "EB" Embedded BASIC engine designed for Microsoft's abandoned "Omega" database system. Ruby also provided the ability to load dynamic link libraries containing additional controls (then called "gizmos"), which later became the VBX interface. It was finally unveiled at Windows World 1991 in Atlanta. It opened the world of computer programming to developers of all skill levels — from hobbyists to professionals — by enabling them to quickly build and run applications based on Microsoft’s Windows operating system. about it way back when. See what Bill Gates had to say 1991 was also the year when Advanced Micro Devices (AMD) announced its AMD 386 microprocessor, which was developed to offer direct competition to Intel's 386 chips rather than a secondary source of x86 processors. This paved the way for long-term competition and the introduction of the first notebooks by many PC vendors.  It was also the year when 14.4 kbps modems were introduced. They were pretty fast too, taking 9 minutes 15 seconds to download a 1MB file. Did you know? Microsoft FrontPage Another from the Microsoft stable. It wasn't that long ago that the mere mention of the word FrontPage would bring developers to their knees. Still, for six years, Microsoft FrontPage was an important tool that offered non-technical users an easy way to build websites, express creativity, and conquer the internet, all without being forced to learn HTML or CSS or JavaScript. In that regard, FrontPage couldn't be beaten. Vermeer Technologies originally developed MS Frontpage in the mid-1990s. Microsoft then purchased FrontPage from Vermeer in 1996 for $130m and released its software version soon after. However, it didn’t last long as the internet was new back then and hence, it was designed as the editor for Internet Explorer  - which was the only browser that could render pages that used FrontPage Server Extensions correctly. These Server Extensions were used to generate basic interactive content, and without them, the sites couldn’t function as intended, often causing many users to only use hosting companies that would offer that compatibility. The final version of FrontPage was released in 2003. Want to go down memory lane? Here's a video of building in MS Frontpage Yahoo Pipes 2007 was a busy year for tech - Apple launched the iPhone,  Facebook hit 20 million users; Android was released, and Airbnb was founded - so if you missed the launch of Yahoo’s Pipes, you weren’t the only one. Pipes was an easy way to aggregate data from multiple sources, including RSS feeds, web pages, photosystems like Flickr, and other services and at the time, one of the first high-end examples of how powerful a JavaScript interface could be.  Tim O’Reilly called it “ ”. a milestone in the history of the internet Pipes grew to prominence primarily because it crossed a boundary. It was a hosted service that let you remix feeds and create new data mashups in a visual programming environment. The name of the service pays tribute to Unix pipes, which let programmers do astonishingly clever things by making it easy to chain simple utilities together on the command line. It quickly gained the appeal of hardcore nerds, even though it brought a relatively simple interface and made visual, conceptual data stream management accessible to anyone. It was one of the first software solutions to incorporate APIs. The result was an online tool that pulled in data from various sites and web apps, then set up rules and processes tied to that data. While it mostly revolved around data aggregation and manipulation, it served as a template for later automation services. Then, on a sunny Wednesday in September 2015, Yahoo! Pipes was shut down. Delphi 1.0 With the release of Turbo Pascal 1.0 in November 1983, Borland started its journey into the world of development environments and tools.  However, in 1995 Borland revived its version of Pascal when it introduced the rapid application development environment named Delphi - turning Pascal into a visual programming language. Delphi described itself as “the only development tool that provides the Rapid Application Development (RAD) benefits of visual component-based design, the power of an optimising native-code compiler and a scalable client/server solution.” Delphi 1 was released for the 16-bit Windows v3.1, and was originally targeted towards students, amateurs and individual professionals. It used the Delphi programming language, which is a dialect of Object Pascal. Delphi was inherently very dynamic for its time. You could fire up the IDE, load the source of a visual form connected to (e.g. a database) and see and navigate records fetched from the database live in the designer. The development environment was quick and practical to work in, and you had access to the source for debugging.  However, when the web-based application revolution came, Delphi’s competition came in the form of the LAMP stack, and the rest, as they say, was history. Danny Thorpe, one of the original Delphi 1 team members, recounts the effort that went into the research and development behind the creation of Delphi 1. You can read the thread or watch this Bonus reading: here product walkthrough. Fast forward…. The problem with yesterday’s low-code tools was a primary focus on composition and often ignored the other steps in the software development lifecycle, such as planning, debugging, testing, implementation, and deployment. As a result, publishing low-code software (that wasn’t tested properly) could have added complexity and time to the development process more often than not. Fast forward to today, where many low-code tools provide many advantages to the developer. However, the key metric is still speed-to-value, ​​ improving agility and reducing the complexity of the application development process. Examples of today’s tools cover everything from eCommerce (Shopify), website builders (Webflow), payments (stripe) and many more. Some have infused artificial intelligence into their platforms and even offer innovative features such as integration with semi-structured and unstructured data sources, or “next-best action” advice for various business workflow scenarios. This is quite a jump from where it all started way back when. While most modern platforms make working in a specific domain very easy, modern development often requires working with systems and concepts that do not form part of the main use-case the tool was designed for. But not all…. Linx Born in the financial markets in 2003, is part of a new breed of low-code developer tools. Built for backends,  it is a general all-purpose platform with no limitations on the technology it can connect to and uses visual abstractions of programming concepts to make building complex logic easy and maintainable. By sticking to generic programming concepts, Linx has created a unique approach that addresses many of typical low-code platforms’ common pitfalls and limitations. Linx Linx fills the gaps when domain-specific low-code tools hit their limits, allowing you to combine your business logic with the services of your choice. And as per the new age of development, it also includes deployment to a production environment with built-in management and monitoring. What’s next? Well, no one knows for sure, and certainly, no one would have guessed that a global pandemic would accelerate the adaptation to new technologies such as low-code. However, a recent KPMG survey found that since the onset of the COVID-19 crisis, the number of executives naming low- and no-code development platforms as their most important automation investment has nearly tripled, from . 10% to 26% With so many companies, innovators, and creators joining the movement, we can’t forget the past, but we can’t wait to see the future.

Low-code Programming Through the Ages

(image source- ) Nintendo The entertainment sector is enormous and lucrative. Consumers spend vast amounts of money on entertainment such as video games, amusement parks, sporting games, social networks, cinema and music concerts. With thanks to innovative technologies, the entertainment sector has seen an increase in VR based entertainment. Although VR does not have the ability to replace all aspects of entertainment completely, it has made the industry more inclusive and immersive for consumers. VR technology has made its way into our homes thanks to technology giants Samsung. With the release of their new phones, they brought out . The Gear VR works seamlessly with Samsung smartphones and brings the world of virtual reality to a broad consumer market at affordable prices. Samsung Gear VR Over the past few years, using VR in entertainment has been experimental. However, we now have a better understanding of how best to use technology. Because of this, we have successfully managed to incorporated it into commercial settings, creating unique user experiences and boasting profitable VR projects to boost the economy. What is VR? VR is the use of computer-generated imagery to create a virtual environment that immerses the user through the use of a headset. It has the ability to along with tracking technology to track the user's motions. create 3D images Although VR in entertainment as mention above is still a relatively new concept, here's a look at some of the various VR options currently available. This new way of incorporating VR is transforming traditional types of entertainment and taking them into new dimensions. Cinema VR technology has the ability to transform the cinema. The film industry, like the industry, is always looking for new ways of re-inventing the wheel, and VR technology offers it that potential. As it is one of the most significant parts of the entertainment industry, the film industry can be quite influential. smartphone With lots of users and people interested in the watching movies, large production filmmakers like Marvel's Studios and Warner Brothers have the potential to change the cinema platform. The concept reaches out to who are eager to try cutting-edge technology. The use of these headsets also helps to attract an audience as VR uses technology that can not be used at home. VR enthusiasts The popularity in multidimensional cinema indicates that consumers are hungry for a more immersive cinematic experience, almost as if they are a part of the movie. Due to a demand in the market, there will need to be further developed. This may be in the form of a new way of storytelling which involves the viewer more. Amusement parks- Derren Browns VR Ghost Train The great joy of an amusement park is that it is specifically designed to give consumers an unusual experience. The rides are made for entertaining, and exciting, which why many thrill-seekers love them. The combination of rides with VR headsets pulls away from traditional amusement. Famous magician Derren Browns VR Ghost Train at London Thorpe Park does precisely that. The ride Combines and live-action to terrify ride goers. VR holograms The VR mimics the user's surroundings in a virtual world but with a ghostly green glow once they place the VR sets on their heads. Through the VR headset users are introduced to a specific storyline and new characters all designed to tease and scare. Using VR in this setting creates a different experience for users. It also works out financial friendly for organisations as they do not have to spend much money on trying to create an atmosphere with props and design. Gaming- VR Mario Kart VR technology in the gaming sector has always been popular. It has taken gaming to new heights allowing users to feel fully immersed into the game they are playing. Usually, the hard wear consists of a VR headset along with accessories which are needed to play the game. VR does well at replicating older beloved games and allowing users to play those games like never before. An excellent example of this is Japanese gaming company Bandai Namco, who launched a Mario Kart VR experience. Each player gets a VR headset, headphones and a pair of hand trackers while sitting in a dummy go-kart as players play the game, the combination of VR headset and dummy go-kart that shakes beneath players creating a truly immersive experience for the player. Raising their hands with the helps the player pick up a virtual object in the game. trackers

How VR Technology is Changing the Gaming and Entertainment Sectors

Each week for the last 6 years (2012–2018), I was playing the lottery to win. Not just to win — playing with a ‘positive expected value’ (a mathematical expectation to win rather than lose, on average, over time). In June 2018 this particular window of opportunity closed, so I’ve decided to share more about the winning model and reveal some closely guarded secrets from the clandestine world of professional gambling. hoping Oz Lotteries ‘Pools’ website one day after the game was discontinued Winners walking among us We’ve all heard the maxim ‘the house always wins’. This is true. typically But how do we explain the or who took home millions of dollars in the Massachusetts state lottery…many, many times? There were the who reverse engineered random number generators (pRNG) on slot machines. Or and the blackjack teams who raided Las Vegas casinos. And who can forget the handful of who became billionaires (yes, with a ‘ ’) betting on races at the Hong Kong Jockey Club? MIT students Michigan retirees Russian mathematicians Ed Thorp horse bettors b Where do these professional gamblers go to cut their teeth? How do they learn to beat these games of chance? Famous betting syndicates won billions of dollars betting on horse races at the Hong Kong Jockey Club The good news? There’s no college degree for this stuff — almost all of these players are self-taught . But they do their homework. Homework, and intentional practice. Spend enough time dreaming up ways to make money, pondering angles, probing for weak spots…with effort, time, and a dollop of luck, you’ll find a chink in the armour. hustlers The other good news? Most of these games are governed by the laws of statistics. So with a (basic) understanding of math and probability, you too could be on the road to riches. Origins It was 2012, I was a couple years out of college and working as a derivatives trader in Hong Kong. Life was good! On Wednesday nights we would drink beer at the Happy Valley horse races, and on Summer weekends catch a ferry over to Macau (‘Vegas of the East’) to attend pool parties at the Hard Rock. Macau casinos hosted Summer pool parties In my spare time however, I was becoming increasingly fascinated by odds and statistics. I was captivated by games of probability, and the prizes at stake. After reading about Benter and his horse betting syndicate, I invested even more energy into studying and building winning betting models. Later that year I worked with a machine learning expert attempting to mimic these idols, hoping to achieve just a sliver of their success. Field trip One time, after reading Peter Liston’s book , I was inspired to set out with a friend on a multi-week tour of every casino in Macau, searching for particular slot machines known as ‘limit jackpot mystery progressives’. For research, we: Million Dollar Slots Bought spy camera pens off Amazon Spy camera pen used for filming slot machine reels Filmed slot machine reels from our pocket while we played $0.01 spins Shipped footage to a contractor on to have them manually transcribe the information into a .csv file upwork Used the data to model the precise tipping point when a machine offered positive expected value With research sorted, we returned the following weekend hoping for a conquest. The jackpot on each of our targeted slot machines had to grow, so we sat and waited for other players to do the work for us, each of their spins funneling more money into the jackpot and taking it closer to striking point. Eventually, after a grueling few hours, a machine shifted to a ‘ripe’ state. We pounced! And won…$50. Sadly, while this was a ‘successful’ venture, the return on time wasn’t attractive enough to warrant a repeat visit. Scaling up The Hong Kong Jockey Club is an organisation that has a government endorsed monopoly on gaming (much like the state governments in the US). They run a lottery called , with an high commission (the amount the house keeps as their ‘fee’ before the prizes are awarded) of 46%. FORTY-SIX-PERCENT! Yet punters keep coming back to play Mark Six, week in, week out. Mark Six egregiously Witnessing such large-scale irrational behaviour from the public reinforced my existing belief that there must be a way for me to get a piece of the action. But how? I needed to look closer at the lottery. But not just Mark Six… lottery. every Lotteries around the world The lottery project was so intriguing I got straight to work. Using the undefeated combination of Google and Wikipedia, I began compiling a list of large lotteries around the world. I would perform cursory research on each of the games, and rank them based on: Probability of being beatable. All the vanilla ‘draw balls from a barrel’ type lotteries have well known calculations. It’s very quickly apparent which have attractive ticket price vs chance of winning ( ). Exotic or non-random games (games that are not based on pure luck) are more attractive n spoiler: none hugely Ease of access. Even if I did find a lottery that was attractive, would I be able to buy tickets? Did I need to be a citizen? Do I have friends in the country who could execute on my behalf? . That is, some lotteries are probably not truly ‘random’. Call it cynical (or realistic), but I am typically very quick to assume corruption. It’s simply not possible to win a lottery that is pre-selected for the governor’s cousin’s best friend. Likelihood of corruption And then, right there in Australia, the game that caught my eye… Tatts Pools Tatts Pools was based on the classic skill-based British game (‘soccer’ for the unwashed). On the surface it appeared to be a standard lottery with 6 random numbers being selected from 38 choices (38 choose 6). It had a major jackpot that snowballed if there were no winners, and a range of smaller secondary prizes. However there was a . football pools twist The selected numbers were not completely random, rather determined based on results of scheduled European football matches. Pools match list for determining winning numbers Taking a deep dive into the game rules, I emerged with the following important information: Each scheduled match was given an arbitrary match number. For example, in the image above Manchester United vs Arsenal is match #3. The matches were played, and ranked according to their result. The ranking criteria had 3 parts : Draw > Away team win > Home team win 2. If more than one match shared the same result, more goals scored > less goals scored. For example, 2–2 draw > 0–0 draw 3. If results were identical, the match with a higher match number was selected. For example, if two matches were 2–2 draws, match #32 > match #5 Finally, after matches were played and ranked according to the above 3 steps, the match numbers of the top 6 ranked matches were deemed the winning lottery numbers. Tatts Pools lottery result based on highest ranked football matches At this point, if you’re a professional gambler, you may already feel the tingle. Let’s walk through the ranking criteria again, with some insight into the thought process: Draw > Away team win > Home team win Reaction: OK, sure. Are any of these more probable than others, on average? Need to check data. 2. If more than one match shared the same result, more goals scored > less goals scored. For example, 2–2 draw > 0–0 draw Reaction: OK, sure. Again, need high level look at data to observe distribution of match scores. 3. If results were identical, the match with a higher match number was selected. For example, if two matches were 2–2 draws, match #32 > match #5 Reaction: OK, su…wait WHAT!? At this point I am jumping out of my boots. A basic familiarity with football scores tells us that the scores are often very low, and therefore frequently the same. eg common scores are {0:0, 1:0, 0:1, 1:1}. So if 38 matches are played, the chance that many of the results are precisely the same is very high. Which will then trigger this clause, forcing all the highest ranked matches (winning lottery numbers) to simply be the matches with high match numbers With the above logic alone, I probably already had a small positive expectancy (enough to start beating the game). But it was time to dig deeper… A little help from my friends (at Ladbrokes) The next logical step was to transition from high level -> low level data analysis. Specifically, instead of treating all of the football matches according to large sample averages, there was additional information I could use- I knew exactly which teams were playing each other, and could get information about these teams! For example, in the Premier League, Manchester City (strong team) was likely to beat Huddersfield (weak team). This made a draw, a result that ranked highly according to the ranking criteria, unlikely. Conversely, Manchester City vs Liverpool was a close match that increased the chance of a draw. I was now faced with 2 choices: Build a model to predict football scores (some inputs might be statistics about teams’ winning record, goals scored, corner kicks, player injuries etc) (difficult). or 2. Let other people build the model for me . There was already a multi-billion dollar football betting industry in Europe, with hundreds of bookmakers pricing the outcomes of each match every week. With enough financial incentive + sophistication in a market, wisdom of the crowd should dictate that on average, bookmaker odds will be a reasonably accurate estimate of each match result. No need to over complicate things — this information was available and free. (smart) oddschecker.com’s football bookmaker aggregator The model So now I had some parameters with which to construct a model. Exactly how could I estimate how likely matches were to be highest ranked (and therefore winning lottery numbers)? Broadly speaking, mathematical solutions can be derived analytically (exact solution, with a pencil and paper, eg solving an algebraic equation), or estimated numerically (‘guess and check’ type methods, using computing power to arrive close to the true answer). A model is a simplified version of reality, like a street map that shows you how to travel from one part of a city to another — Ed Thorp The Pools problem was sufficiently complex that I determined the easiest approach would be numerical, arriving at an estimate through brute-force aka Monte-Carlo simulation. This involves: Writing a computer program that knows the match ranking criteria. Listing out all matches for the week and using bookmaker data to assign specific probabilities to each outcome (draw, home win, away win). Simulating all the matches being played 500,000 times to arrive at an estimate of how likely each match is to fall within the top 6 ranked matches (and therefore be deemed a winning lottery number). Probability each match is a winning lottery number from 2018-03-26. Note the sum of all columns = 600%, as there are 6 winning lottery numbers selected each week. Note also the positive slope toward the higher match numbers, courtesy of ranking criterion #3 Practical considerations Finally, once I had estimates for which lottery numbers were likely to win, I had to deal with some real world hurdles such as expected value and execution (ticket purchasing). Concretely, how many lottery tickets do I purchase, and when, and how? Here are some relevant considerations: . Was the jackpot large enough to make it attractive to buy tickets this week? If not, need to sit tight, hope nobody wins, and the jackpot keeps snowballing. Prize pool . Once I knew which matches were likely to be the winning numbers, I couldn’t just buy a single ticket with the top 6 choices. This would be unlikely to win. I needed to buy every combination of 6 choices that I calculated to be positive expected value. Luckily Tatts Pools had a native combination ticket called ‘System 20’. This ticket was the equivalent of buying 38,760 individual tickets (20 choose 6) and cost $21,182 each week. Execution exceedingly $21,182 System 20 Tatts Pools lottery tickets . What was the probability other people would also win, and I’d need to split the prize? This would expected value. Using weekly ticket sales, I would look at the growth of the prize pool, back out how many tickets were sold that week, then use a to estimate how many other winners I expected to split with. Splitting damage Poisson distribution Expected value drops off a cliff when a jackpot is split with other players . If anybody else had figured out the same edge and was operating a similar model / system, it would all but guarantee prize splitting and become an ugly proposition for both groups. Thankfully study of historical Pools prizes made it clear that there were no other players exploiting this edge. Other professionals . There were secondary prizes awarded for tickets that correctly selected 5/6 and 4/6 of the winning numbers. I expected to win hundreds of these on any given week, so needed to factor this in as a discount to the ticket cost. Secondary prizes . Any seasoned gambler is intimately familiar with the and importance of bankroll management. On typical Pools weeks the probability of winning a jackpot with the model and a System 20 ticket was 5–7%. Because this is a rare event, I needed to be prepared to lose a substantial amount of money before emerging victorious. Bankroll Kelly criterion . As all bookmaker odds were simply estimates of true probability, it would be unwise to bet on a slim positive expected value. I needed to wait for a jackpot to be sufficiently large that there was a comfortable margin of error, such that even if bookmaker odds were incorrect, I should still be playing the game. Margin of error Results The model described above was in production for 6 years. Jackpots were attractive infrequently, so aggressive plays (System 20) were made <50 times. While I won’t reveal the precise number of jackpots or dollars won, I will say the bankroll draw down prior to a jackpot win was -$300k. This was running below expectation, but bad variance needs to be accepted in any probabilistic gambling system. Conclusion Reflecting on the opening proposition that ‘the house always wins’, I’d like to point out this is still intact. It’s important to realise that the loser in the Tatts Pools game was Tatts, who routinely took their fee from ticket sales. The money funding the jackpots was coming from ticket sales. The losers were those unsophisticated players that contributed to the growing jackpot each week through random and sub-optimal ticket purchases. not This is true for almost all the examples of professional gambling mentioned in this article. If the house weren’t wining, they’d find out quickly (it is their business to know these things after all). So most professional gamblers are typically sharing winnings with the house, extracted from non-professional players. very I’ll conclude by saying that in most standard lotteries, when you buy a ticket, you have an expected return of 50%. That is, for every $1 you spend on a lottery ticket, on average, you expect to receive only $0.50 in return. An innovative way to lighten your pocket. But with a little math, perhaps you can turn the tables and find that pot of gold at the end of the rainbow. negative Want to chat more / have an interesting proposal? Contact me at brynsolomonmedium@gmail.com

Gaming the lottery: How one winner used math to overcome the odds

“I think the history of science has shown that valuable consequences often proliferate from simple curiosity.” - Claude Shannon Recently, the term has taken on many meanings — growth- for instance. When I was at MIT, a was a term used for a prank or practical joke that required great engineering skill — like the firetruck that appeared on top of the great Dome at MIT — no one knew how they did it! hacking hacking hack While the more popular meaning of is one who gains illicit entry into a computer system through their mad skills, I think the spirit of hacking is more about tinkering, following your curiosity using your engineering skills, rather than doing “serious work”. hacker In this respect, one of the original tinkerers, and perhaps one of the most famous, was Professor Claude Shannon, who was at Bell Labs and later at MIT. Of course, Shannon is best known as the “father of information theory” from his famous 77 page paper, “ ”, which Scientific American later called “The Magna Carta of the Information Age”, which laid the foundations of most digital communications we use today. A Mathematical Theory of Information But beyond this famous paper, Shannon was known by his colleagues at Bell Labs and MIT for his curious mind, his spirit of playfulness, and his ability to “hack together” answers to questions that others weren’t even asking. He took the idea of “following your curiosity” to an extreme — using mathematics and engineering skills to actually build things. In their biography on Shannon, , by Jimmi Soni and Rob Goodman, gives lots of examples. A Mind at Play When Shannon became an MIT professor, he and his wife Betty bought a house in Winchester, 8 miles north of MIT, and the basement became his personal workshop for all kinds of curiosities. When fellow MIT professor Ed Thorp visited, he called it “ a gadgeteers paradise” and that he had seen him as the “master gadgeteer”! Here are some of his top playful : hacks Taking Play Seriously: A Chess Playing Machine Some of you will know that I run an accelerator at MIT called Play labs, and my contention is that a lot of what we consider “computer software” today has its origins in “playful technologies” that were used for games and entertainment. I often use the example of one of the first AI’s ever created: A computer which played Chess built by Shannon and shown below. You can see that it was a big device, and it would light up the move that should be made. According to _A Mind at Pla_y, the computer could only play the final 6 moves of a match, but it proved a point. For Shannon, this was play, but he could foresee a whole future of AI. He wrote a paper, “Programming a Computer for Playing Chess”. Many credit him with showing how a computer could be artificially intelligent, and he was one of the first to predict that at some point in the future, a computer would be able to beat a chess champion (which happened 50 years later). Shannon introduced the paper with his characteristic modesty: “Although perhaps of no practical importance, the question is of theoretical interest, and it is hoped that t a satisfactory solution of this problem will act as a wedge in attacking other problems of a similar nature and of greater significance. According to his biography, Shannon “reminded readers that there were many future applications of a chess playing artificial intelligence: routing phone calls, translating text, composing melodies.” How right he was! Theseus the Mouse and the Maze Many people wondered about Shannon’s genius, and his ability to not just explore an idea but to lay foundations for whole areas of exploration, like information theory, Boolean circuits, and AI. For Shannon, many of his pursuits came from asking a question, and not being satisfied until he found an answer to this question. He says that he and others like him have a quality of “motivation … some kind of desire to find out what makes things tick.” Perhaps even more famous than his chess playing computer was Shannon’s motorized mouse, Theseus, which he built while still at Bell Labs, before coming back to MIT as a professor. The story goes that Shannon took 20 minutes to escape from a garden hedge maze in London, and wondered if it could be done quicker. He built a mechanical maze with a mechanical cheese, and a “mechanical mouse” that would navigate around the maze until he found the cheese. This astonished many who saw it, and Bell Labs ended up producing a short video which gained nationwide attention. The actual mouse, according to his biographers, didn’t really do the work of solving the maze — it was propelled by a set of magnets and switches where under the maze. The mouse itself moved based on the logic of the switches and the magnets. When it couldn’t’ move, the switches changed and the magnets moved the mouse in another direction. While he didn’t write a paper on the mechanical mouse looking for cheese, it did earn Shannon and Bell Labs a patent. Other playful pursuits: Juggling and Gambling The hallmark of many of Shannon’s gadgets (and accompanying papers) s were what his biographers called Vintage Shannon: “whimsical, indifferent to practicalities, and originating in an activity that typical professors might have dubbed unserious, but which Shannon, a tenured professor of the MIT faculty, found amusing enough to merit scholarly time and attention!” At one point, he wrote a serious paper on the mathematics of Juggling, an activity that he spent quite a bit of time on his later years. He even created, shown below, a robot which could jugle 3 balls (it looks a bit like W.C. Fields, who it was modeled on!). Anyone who has been at MIT on the weekends will have seen the MIT Juggling Club, consisting mostly of students who like to Juggle on Sundays. It was quite a surprise when they learned that Shannon was a famous tenured professor hanging out with them. As the final gadget in this list, many of you may have seen the movie 21, about a group of MIT students who went to Vegas to make lots of money using their mathematical skills. Not only was this not the first time something like this was tried, I was surprised to learn that Shannon participated in something similar many years earlier. Ed Thorp, who had called Shannon’s basement, a “gadgeteer’s paradise”, had written a paper about the mathematics of blackjack, which Shannon enthusiastically reviewed and encouraged. As they began brainstorming about the mathematics of roulette, they ordered a full, vegas-style roulette wheel and in shannon’s basement, tried to work out the mathematics of predicgting Rather than card counting, they created a device, perhaps one of the first “wearable computers”, it would emit tones into an earpiece to tell them where to bet. They tried to use it once at an actual casino, but someone noticed the earpiece and they decided to abandon the pursuit. At a time when much casinos were run by the mob, the biography concludes, perhaps this wasn’t such a bad y The conclusion: Follow your Curiosity Shannon, who was a serious scientist, often engaged in pursuits that other “serious” professors and scientists didn’t. He naturally followed his curiosity, asking questions and looking for answers in multiple fields. While his most famous example of this was his paper on information theory, he did this in many fields and didn’t let conventional boundaries define what he could or could not be interested in! Shannon saw the potential of digital communications (at a time when telegraph, telephone, radio, and television were all considered separate fields), brought together disparate ideas (boolean logic was taught in his philosophy class; it took someone of Shannon’s insight to combine it with logical circuits to come up with a mathematical way to model circuits), no one really thougth computers could think (AI), or be wearable, or one of the many other areas Shannon pioneered which have become fruitful places of exploration for other minds. For those with playful, curious minds, I encourage you to read . A Mind At Play More importantly,be like Claude Shannon: Whatever you mind is drawn to, follow your curiosity!

The Top Hacks of a Certified Genius: Claude Shannon at Play

The introduced by ES7 is a fantastic improvement in asynchronous programming with JavaScript. It provided an option of using synchronous style code to access resoruces asynchronously, without blocking the main thread. However it is a bit tricky to use it well. In this article we will explore async/await from different perspectives, and will show how to use them correctly and effectively. async/await The good part in async/await The most important benefit brought to us is the synchronous programming style. Let’s see an example. async/await // async/awaitasync getBooksByAuthorWithAwait(authorId) {const books = await bookModel.fetchAll();return books.filter(b => b.authorId === authorId);} // promisegetBooksByAuthorWithPromise(authorId) {return bookModel.fetchAll().then(books => books.filter(b => b.authorId === authorId));} It is obvious that the version is way easier understanding than the promise version. If you ignore the keyword, the code just looks like any other synchronous languages such as Python. async/await await And the sweet spot is not only readability. has native browser support. As of today, all the mainstream browsers have full support to async functions. async/await All mainstream browsers support Async functions. (Source: ) https://caniuse.com/ Native support means you don’t have to the code. More importantly, it facilitates debugging. When you set a breakpoint at the function entry point and step over the line, you will see the debugger halt for a short while while doing its job, then it moves to the next line! This is much easier than the promise case, in which you have to setup another breakpoint on the line. transpile await bookModel.fetchAll() .filter .filter Debugging async function. Debugger will wait at the await line and move to the next on resolved. Another less obvious benefit is the keyword. It declares that the function return value is guaranteed to be a promise, so that callers can call or safely. Think about this case (bad practice!): async getBooksByAuthorWithAwait() getBooksByAuthorWithAwait().then(...) await getBooksByAuthorWithAwait() getBooksByAuthorWithPromise(authorId) {if (!authorId) {return null;}return bookModel.fetchAll().then(books => books.filter(b => b.authorId === authorId));} In above code, may return a promise (normal case) or a value (exceptional case), in which case caller cannot call safely. With declaration, it becomes impossible for this kind of code. getBooksByAuthorWithPromise null .then() async Async/await Could Be Misleading Some articles compare async/await with Promise and claim it is the next generation in the evolution of JavaScript asynchronous programming, which I respectfully disagree. Async/await IS an improvement, but it is no more than a syntactic sugar, which will not change our programming style completely. Essentially, async functions are still promises. You have to understand promises before you can use async functions correctly, and even worse, most of the time you need to use promises along with async functions. Consider the and functions in above example. Note that they are not only identical functionally, they also have exactly the same interface! getBooksByAuthorWithAwait() getBooksByAuthorWithPromises() This means will return a promise if you call it directly. getBooksByAuthorWithAwait() Well, this is not necessarily a bad thing. Only the name gives people a feeling that “Oh great this can convert asynchronous functions to synchronous functions” which is actually wrong. await Async/await Pitfalls So what mistakes may be made when using ? Here are some common ones. async/await Too Sequential Although can make your code look like synchronous, keep in mind that they are still asynchronous and care must be taken to avoid being too sequential. await async getBooksAndAuthor(authorId) {const books = await bookModel.fetchAll();const author = await authorModel.fetch(authorId);return {author,books: books.filter(book => book.authorId === authorId),};} This code looks logically correct. However this is wrong. will wait until returns. await bookModel.fetchAll() fetchAll() Then will be called. await authorModel.fetch(authorId) Notice that does not depend on the result of and in fact they can be called in parallel! However by using here these two calls become sequential and the total execution time will be much longer than the parallel version. authorModel.fetch(authorId) bookModel.fetchAll() await Here is the correct way: async getBooksAndAuthor(authorId) {const bookPromise = bookModel.fetchAll();const authorPromise = authorModel.fetch(authorId);const book = await bookPromise;const author = await authorPromise;return {author,books: books.filter(book => book.authorId === authorId),};} Or even worse, if you want to fetch a list of items one by one, you have to rely on promises: async getAuthors(authorIds) {// WRONG, this will cause sequential calls// const authors = _.map(//   authorIds,//   id => await authorModel.fetch(id)); // CORRECTconst promises = _.map(authorIds, id => authorModel.fetch(id));const authors = await Promise.all(promises);} In short, you still need to think about the workflows asynchronously, then try to write code synchronously with . In complicated workflow it might be easier to use promises directly. await Error Handling With promises, an async function have two possible return values: resolved value, and rejected value. And we can use for normal case and for exceptional case. However with error handling could be tricky. .then() .catch() async/await try…catch The most standard (and my recommended) way is to use statement. When a call, any rejected value will be thrown as an exception. Here is an example: try...catch await class BookModel {fetchAll() {return new Promise((resolve, reject) => {window.setTimeout(() => { reject({'error': 400}) }, 1000);});}} // async/awaitasync getBooksByAuthorWithAwait(authorId) {try {const books = await bookModel.fetchAll();} catch (error) {console.log(error);    // { "error": 400 }} The ed error is exactly the rejected value. After we caught the exception, we have several ways to deal with it: catch Handle the exception, and return a normal value. (Not using any statement in the block is equivalent to using and is a normal value as well.) return catch return undefined; Throw it, if you want the caller to handle it. You can either throw the plain error object directly like , which allows you to use this function in a promise chain (i.e. you can still call it like ); Or you can wrap the error with object, like , which will give the full stack trace when this error is displayed in the console. throw error; async getBooksByAuthorWithAwait() getBooksByAuthorWithAwait().then(...).catch(error => ...) Error throw new Error(error) Reject it, like . This is equivalent to so it is not recommended. return Promise.reject(error) throw error The benefits of using are: try...catch Simple, traditional. As long as you have experience of other languages such as Java or C++, you won’t have any difficulty understanding this. You can still wrap multiple calls in a single block to handle errors in one place, if per-step error handling is not necessary. await try...catch There is also one flaw in this approach. Since will catch every exception in the block, some other exceptions which not usually caught by promises will be caught. Think about this example: try...catch class BookModel {fetchAll() {cb();    // note `cb` is undefined and will result an exceptionreturn fetch('/books');}} try {bookModel.fetchAll();} catch(error) {console.log(error);  // This will print "cb is not defined"} Run this code an you will get an error in the console, in black color. The error was output by but not the JavaScript itself. Sometimes this could be fatal: If is enclosed deeply in a series of function calls and one of the call swallows the error, then it will be extremely hard to find an undefined error like this. ReferenceError: cb is not defined console.log() BookModel Making functions return both value Another way for error handling is inspired by Go language. It allows async function to return both the error and the result. See this blog post for the detail: _ES7 Async/await allows us as developers to write asynchronous JS code that look synchronous. In current JS version we…_blog.grossman.io How to write async await without try-catch blocks in Javascript In short, you can use async function like this: [err, user] = await to(UserModel.findById(1)); Personally I don’t like this approach since it brings Go style into JavaScript which feels unnatural, but in some cases this might be quite useful. Using .catch The final approach we will introduce here is to continue using . .catch() Recall the functionality of : It will wait for a promise to complete its job. Also please recall that will return a promise too! So we can write error handling like this: await promise.catch() // books === undefined if error happens,// since nothing returned in the catch statementlet books = await bookModel.fetchAll().catch((error) => { console.log(error); }); There are two minor issues in this approach: It is a mixture of promises and async functions. You still need to understand how promises work to read it. Error handling comes before normal path, which is not intuitive. Conclusion The keywords introduced by ES7 is definitely an improvement to JavaScript asynchronous programming. It can make code easier to read and debug. However in order to use them correctly, one must completely understand promises, since they are no more than syntactic sugar, and the underlying technique is still promises. async/await Hope this post can give you some ideas about themselves, and can help you prevent some common mistakes. Thanks for your reading, and please clap for me if you like this post. async/await

JavaScript async/await: The Good Part, Pitfalls and How to Use

Zuckerberg’s testimony live

Everything You Want to Know About Job Applications

Map Room envisions a future for the city that is also wise as a result of a shared understanding of lived experience amongst its citizens. — Jer Thorp, Making Mapping more Human The was an experiment in collaborative mapping as a source of community engagement. A vacant school’s gymnasium provided a forum where, over the course of a month, 29 groups came in to make 100 square foot maps of their neighborhoods and communities. They were aided by robots and projection, but primarily drew huge maps by hand that speak to their lived experience of their city. St. Louis Map Room Community groups mapped hand-collected bicycle traffic data, community gardens, churches, magnet schools, and food banks. Groups of students mapped their schools, how they got there, and where it was or wasn’t safe. Before the project opened, we decided to use robotics to add some fun and to save people work. Map Room’s robots would do the precise but less creative work of laying out the basics of a map, which would fade into the background as humans filled in with their own personal stories. There were a number of constraints on our design: The robots needed to like robots, that’s part of the excitement. look The tracking system has to be extremely affordable. The map drawing has to be accurate enough to really represent a map. The robots and tracking system have to communicate wirelessly. We’ll talk through how we addressed those constraints, with a special emphasis on the computer vision we used to make the project a success. Picking a robot Most drawing machines operate like : they have only three degrees of freedom: the x and y axes plus a pen that can be raised and lowered. And since they operate on rails, their stepper motors or rotary encoders can be calibrated to position a pen very accurately — down to a millimeter or smaller. Check out classic or the more recent for examples of machines like these. CNC routers pen plotters AxiDraw But if you want to fit the capital-R Robot aesthetic, you can’t beat a fully mobile robot. There are a few kinds to choose from. You could use a two-wheeled differential-drive robot, if you’re willing to solve the . Alternatively, you could use a if you’re willing to do more math. Either way, you’ve got more factors to worry about. You’ve got another degree of freedom (the rotation of the robot) and you’re not on rails like a pen plotter, so you have to devise some kind of positioning system. parallel parking problem holonomic-drive robot Positioning a robot That’s the biggest challenge you face with a mobile robot: knowing where it is. One way to solve it is : calculating where you are based on where you’ve been and how you’re moving. It’s the equivalent of oceanic navigation . Polynesian navigators, faced with this problem, came up with solutions like to gather more information. Our robot probably can’t be as clever. dead reckoning before clocks could be used at sea tracking bird migrations You could also mount a camera on the robot itself and it could figure out where it is. If you’re building a self-driving car, you’ve got to take the cameras with you. For our robot, however, we have one big advantage: the area that we need to cover is known and we can build specifically for it. In our case, we wanted to cover a 10’x10’ canvas. Unlike a ship, we can track ourselves externally — like if we had a satellite overhead. On a practical level, that means we’ve got some more tools at our disposal. Movie studios have been using for decades. The Microsoft Kinect brought some of that into people’s living rooms, and these days room-scale virtual reality tracking is advancing by leaps and bounds as well. HTC just released the that should be able to do 6-DoF tracking for <$1000. motion capture technology Vive Tracker But what if you’re really on a budget? Let’s see what we can do for $100. A sketch of a tracking system Schematic of the Maproom tracking system We figured the most cost-effective way to track our robot was visually: put a marker on the robot, track it with a camera, and relay that information to the robot. With a Raspberry Pi 3 ($39), power supply ($10), case ($7), 16gb SD card ($13), and Raspberry Pi Camera Module v2 ($30), you’ve got all the hardware you need for almost exactly $100. Setting up a Raspberry Pi with OpenCV can be a pain, but I highly recommend . pyimagesearch’s Raspbian Jesse + OpenCV 3 set up guide In our case, we also needed the tracking system to be wireless — the robots couldn’t have wires, and we had to suspend the camera 14’ in the air in a place we couldn’t easily run Ethernet cables. The Raspberry Pi worked great for this as well. A word on accuracy: the eventual framerate we were able to get was ~15fps at 960 x 720 px. If the full 10 foot by 10 foot robot area was aligned to 720px, then one pixel represents 0.17 inches (= 10 feet / 720 px). In our case, we could get the map to fill about 500 pixels, so a pixel represented 0.25 inches — pretty good, but definitely jarring. The Maproom’s goal is to focus on human stories, and the underlying map should not distract from these. We had to do better. ArUco Aruco markers (picture from ) http://code.opencv.org/projects/opencv/wiki/GSoC_2016 Enter the ArUco marker detection library. It was the fastest way we found to get set up tracking fiducials ( , — there are even ). You can track markers that look like a bit like QR codes in very few lines of code. The “AR” in ArUco stands for “augmented reality”, but we’re not going to use 3D positioning system it provides — more on that later. src docs ports to Javascript We considered a few other options, like , or , but ArUco had features that we really liked: quick setup and tracking multiple unique markers out of the box. In theory, it would let us do better positioning than the 0.25 inches we calculated above by using refinement algorithms to locate the center of each marker to accuracies better than a pixel. tracking custom markers with SIFT/SURF tracking known shapes Calibrate and capture Any camera that uses a lens — including the Raspberry Pi Camera — has some in the image it produces. This is a problem if you want to be able to use pixel values as positions, like in the case of our robot. distortion Fortunately, a lot of work has gone into making it easy to calculate exactly how a camera’s lens distorts the world, and it’s quick do with OpenCV. We to use a to perform the calibration quickly and easily (more on ArUco. You just print out the board, hold it in front of the camera until enough frames are recorded, and OpenCV does the rest (see a ). wrote a script ChArUco board whole example We found from testing that for best results you should use the largest, flattest board you can — a trip to Kinko’s doesn’t hurt to get a large sheet printed. Our was 16” x 23”, printed on matte paper and mounted on posterboard. The errors here will propagate down to the rest of the system, so it’s worth getting your camera calibrated really well! final board Detect some markers After calibration, you can use ArUco to detect markers in your frame. As we mentioned before, ArUco was designed for augmented reality, so it’s designed to give you coordinate systems for each marker relative to the camera. In theory, that means we know where the markers are in 3d space! That’s a good idea, but in practice we had some issues… The jumpy animation above shows the positions and rotations in space for markers that were all lying still on a flat plane. They should have all been (at least) facing the same way, but clearly have a lot of jitter. Some smoothing would have helped, but to control a robot you need fast update times. We can use the fact that we’re operating on a flat surface to avoid this problem — let’s not do the pose estimation at all! The marker detection routine actually gives us the four (pixel) corners of the marker in the image. It was straightforward to just take the those corners and average them to give the center of the marker. The rotation of the marker can also be found by averaging the “top two” corners of the marker, and drawing a vector between the center and the “top”. We found these positions to be stable and accurate to about 1/8” from 14’ up (about 3mm at 4m) — that’s really good given the price point of the hardware we’re working with! Perspective Left: without perspective transform. Right: after perspective transform. Once we’ve figured out where the markers are in the scene, we still have to be able to compare that to what we want to draw. It’d be nice if the camera were mounted perfectly above the center of the canvas, pointed straight down — but it wasn’t in our setup, and probably wouldn’t be in yours either. We can fix this by applying a to the marker positions, which will take the angled view that the camera sees (left above) and make it flat and square (right above) — just like the SVGs we’ll be drawing. This is different than the calibration step above; it’s not the distortion of the lens we’re correcting for, but instead the tilt of the camera relative to the scene. perspective transform That transformation is just a 3x3 matrix. OpenCV makes it easy to calculate the matrix from four pairs of points. We wrote , which let to better precision than clicking on corners on the screen since ArUco positions are accurate below a pixel. a script to build the matrix using ArUco markers At this point, we’ve got an undistorted, perspective transformed, sub-pixel accurate position for a marker! We can send those coordinates back to the robot, and they’ll know exactly where they are. Screenshot of the openFrameworks app that coordinates the robots drawing an SVG Putting it all together Every frame, these steps have to happen: Detect ArUco marker corners using camera calibration Calculate each marker center and up direction Undistort each marker using a camera calibration Transform each marker using a perspective transform Send the marker center and up to the robot coordinator The fact that this can happen 15 times per second on a $30 computer is pretty astounding! If you have a beefier computer, you could probably get way higher framerates out of this — my Macbook Pro was able to hit 30fps, which was the limit of the camera. There are a whole lot of other tricks we had to apply to get the tracking system working the way we needed: to get a good framerate threading the image capture applying to deal with the height of the robot yet another perspective transform to fit our lighting conditions and give a good framerate adjusting the parameters of ArUco to make the tracking software survive Raspberry Pi reboots using systemd Thanks for following along! Want the source code? It’s written in Python, lives and lives on Github as . O-C-R/maproom-robots/skycam <a href="https://medium.com/media/d797d150bc50113878ff6a073b9f5673/href">https://medium.com/media/d797d150bc50113878ff6a073b9f5673/href</a>

Drawing maps with robots, OpenCV, and Raspberry Pi

An irregular round-up of inspirations and disruptions in advertising, social, urban play and human computer interaction. Joe Hellerstein, a computer scientist at the University of California in Berkeley, calls it “the industrial revolution of data”. Data is everywhere. And it is beautiful. is , and influencing pretty much every industry — business, art, entertainment, music, . Data changing our lives re-inventing online storytelling technology Here’s some faves… Information Journalism: An absolutely brilliant and horrific infographic and piece of journalism, , is timeline of drone strikes in Pakistan, from July 2004 to December 2013 Out of sight, Out of mind http://drones.pitchinteractive.com/ No conversation on data on the web would really be complete without mentioning — the amazing javascript library created by , former New York Times data editor. Most data projects you see online today are built with d3.js, or a subset of the library. d3.js Mike Bostok The One Tech Buzzword Every Journalist Should Know: _For newsrooms, creating interactive stories can be painful-but readers are coming to expect them more and more. As…_www.fastcolabs.com The One Tech Buzzword Every Journalist Should Know _Explore the routes through the electoral battleground and plot victory for either side._www.nytimes.com 512 Paths to the White House , who sadly passed away earlier this year, made data cool at his now famous Ted talk where he presented data that debunked several myths about world development: Hans Rosling He also had his own BBC show, The Joy of Stats: A Time-Lapse Map of Every Nuclear Explosion Since 1945. Scary: More great D3 examples:Mapping US gun deaths: http://data.huffingtonpost.com/2013/03/gun-deaths 2012 NBA finals visualised: http://www.nytimes.com/interactive/2012/06/11/sports/basketball/nba-shot-analysis.html How many years do you have left to live: http://flowingdata.com/2015/09/23/years-you-have-left-to-live-probably/ Daily routines of famous creative people: https://podio.com/site/creative-routines Google’s awesome Music Timeline: http://research.google.com/bigpicture/music/ Quantified Self: , one of the lead designers of Facebook’s timeline, spent years tracking everything about himself (initially meticulously recording his life in analogue and later ), and turning them into annual reports, in many ways spearheading the current Lifelogging/Quantified Self movement. Nicolas Feltron by building an app Check out the beautiful reports here: http://feltron.com/ More quantified self: http://ericboam.com/Seven-Months-of-Sleep-1 http://ericboam.com/2014-A-Year-In-New-Music https://jehiah.cz/one-four/ Federico Zannier sold his data for $2 a day using Kickstarter and managed to make $2,733, sharing his keystrokes, mouse movements and even providing a screenshot every 30 seconds. https://www.kickstarter.com/projects/1461902402/a-bit-e-of-me/description Data coup helps you sell your personal data Personal data value calculator: https://datacoup.com/ http://www.ft.com/cms/s/2/927ca86e-d29b-11e2-88ed-00144feab7de.html#axzz2z2agBB6R Data as Activism: I love the simplicity of this work by , a public art project which simply involved putting up small posters containing data about Boston and L.A as well as street surveys to encourage public conversation. Tim Devin http://timdevin.com/broadsides.html I’ve mentioned and the before (because I’m a massive fanboy and sadly the art project has closed down). Infoviz Graffiti helps you to create your own infographic street art: Golan Levin F.A.T Lab http://www.flong.com/blog/2011/on-infographic-street-art/ http://fffff.at/infoviz-graffiti-stencil/ Chris Jordan is an artist who uses large scale photography to present data. The above picture is from his exhibition “ ”, using stacks of prison uniforms to represent the number of people incarcerated in the US. Running the Numbers More public art and activism infographics:Community data visualised as street art: http://visualisingmillroad.com/ Bear 71 is a first-person documentary centred on a grizzly bear tagged and tracked in Canada’s Banff National Park http://bear71.nfb.ca/#/bear71 Ellie Balk’s public art viz: http://elliebalk.com/#/cityofemotion/ GPS and Tracking: Just Landed is a Twitter visualisation of people moving around the world by plane. The visualisation searches for tweets containing the phrases ‘just landed in…’ or ‘just arrived in…’ and then plots the journey based on the location of the tweet and a person’s home location. It was developed in Processing by , a data artist and lecturer at NYU’s ITP program and one of my one of my favourite data people. Jer Thorp http://datavisualization.ch/showcases/just-landed-a-twitter-visualization-in-processing/ Explore the galaxy using a Minority Report-like interface: Into the Okavango before, is also worth checking out if you haven’t seen: More on Jer Thorp: http://www.fastcodesign.com/1669702/explore-the-galaxy-using-the-actual-minority-report-interface http://intotheokavango.org/ Micro-geolocation: Playing with micro-geolocation and tracking conference delegates through wifi, and visualising their movements and connections, a project I did a few years ago. Interesting because a Wifi can actually be used to triangulate people’s location pretty accurately. http://radarboy.com/george/internetix.php A day in the life of a taxi: More GPS goodness: http://nyctaxi.herokuapp.com/ Visualising the MTA in New York: Drawing with GPS: http://urbantick.blogspot.sg/2009/06/gps-data-visualization-variations.html Invisible cities visualises social network activity: https://www.schemadesign.com/invisiblecities/ Reconstructing the city of Savannah using LEGO bricks and visualising movement: http://www.coryimig.com/projects/24vxlnrcuk3zix354lzdds0hzvxw4u Data as Storytelling: Sex education done in a beautiful and engaging way, an oldie but a real goodie, BBC Channel 4’s Sexperience is a filterable encyclopaedia of the UK’s sexual experiences. Unfortunately, they’ve recently taken down the site, but here’s an overview: https://www.youtube.com/watch?v=Yka077KbbH8&ab_channel=core77inc is a simple but brilliantly effective data visualization on time: Here is Today http://hereistoday.com/ In a similar vein, not so pretty, bust still impressive is the scale of the Universe: http://htwins.net/scale2/ More data storytelling: A nation divided: http://zeit.de/feature/german-unification-a-nation-divided NY Times 2013 roundup: http://www.nytimes.com/newsgraphics/2013/12/30/year-in-interactive-storytelling/#dataviz Data As Art: Artist and media hacker and co-founder, Skymall Liberation is an project visualising inflight catalogues. F.A.T Lab Evan Roth’s http://www.evan-roth.com/work/skymall-liberation/ http://ni9e.com/skymall_liberation.php Dillon Marsh uses compositing to visualise the production output of South African mines: http://www.dillonmarsh.com/ Sensible Data, is a machine that playfully interprets and leaks your personal data http://www.creativeapplications.net/arduino-2/sensible-data-a-machine-that-playfully-interprets-and-leaks-your-personal-data/ The Happy Show by Stefan Sagmeister, an exhibition that uses loads of data viz in an attempt to define and control happiness: http://www.thisiscolossal.com/2013/08/the-happy-show-by-stefan-sagmeister/ We Feel Fine, by harvest the expression of feelings from the blogging world. It searches for the word ‘feel’ and extracts the entire sentence, together with details about the blogger such as gender, age and location. Jonathan Harris _An exploration of human emotion, in six movements by Jonathan Harris and Sep Kamvar_www.wefeelfine.org We Feel Fine / by Jonathan Harris and Sep Kamvar And finally, one of my favourite interaction design studios and public artists, , developed The Source at the London Stock Exchange, a massive public installation for visualising movement in the stock market: Greyworld http://greyworld.org/archives/31 Ok then, well that’s a mass (and treasure trove) of stuff to wade through. Hope you enjoyed. Follow me if you please: Instagram: www.instagram.com/radarboy3000/ Website: radarboy.com/george/ Medium: https://medium.com/@radarboy3000/ Twitter: https://twitter.com/radarboy_japan

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