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Devorando

devorando

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Awards, with compromised reviews, are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series Apparently, South America startups did incredibly well in that regard. With so many, until now, uncrowned jewels, it’s time we present to you... * drumroll* 🥳 The South America Startups of the Year Winners! Santiago, Chile Chile is known for being the most stable country to do business in Latin America, ranking 13th in The Economist’s Business Environment Index. Moreover, according to the Global Innovation Index, Chile ranks first in Latin America for innovation and is recognised as Chilean Silicon Valley with Santiago being the central city. The winner is…. QCObjects It is the 1st world class open-source framework made in Latin-America. São Paulo, Brazil And the winner of Brazil’s vibrant financial center is... Innvo Digital Their mission is to create the best ecosystem for a technology professional to work and undertake while sharing it with our customers and society. São Carlos The absolute leader of Sao Carlos seems to be: Predify Predify is an online software for optimizing pricing proccess and improving profits with AI and Big Data Analytics. Belo Horizonte, Brazil With a more than noble goal, Belo Horizone’s winner is: CM Tecnologia CM Tecnologia is an online scheduling system for healthcare providers in Brazil. Porto Alegre, Brazil With the focus on data protection, Porto Alegre, Brazil’s winner is…. Privacy Tools Privacy compliance platform, based on AI/Blockchain, which helps global companies to keep compliant with the data protection requirements. Brasil, Brazil With the focus on EdTech, Brasil is bringing us: Awari Awari is a YC-W21 company focusing on education for the Tech Industry. The company is growing at a fast pace and has gathered experts from all leading tech companies into a full education experience to prepare the next cohort of tech talent. Quito The capital of Ecuador, and also the closest capital to the actual equator, as the winner brings us: Agrodemanda The most comprehensive agricultural directory in Ecuador. Rio De Janeiro, Brazil Rio de Janeiro has more than 300 startups and is ranked 3rd in Brazil in terms of startup ecosystem development. The city is famous for the startups in the spheres of education, energy & environment and foodtech. Their winner is…. Bybrand Bybrand is the simplest way to save time and avoid frustration when managing your employee's email signatures. Buenos Aires, Argentina Buenos Aires, Argentina is not only the cosmopolitan capital with a vibrant history. It’s also Latin America’s most important port. Which makes their winner actually a very logical choice. Please meet: Liftit Liftit is the leading technology platform to automate and execute last-mile truck deliveries across Latin America. Curitiba, Brazil With a more noble mission and sustainability in mind, Curitiba, Brazil, as the winner brings us... Discrepante is the marketplace for a more human and sustainable economy Discrepante.com Florianópolis, Brazil Would you really be surprised one of the Brazil’s winners is related to football training? We neither. Please meet... Fútbol Fit Club Football FitClub® is a fitness training firm that gives fitness training to improve like a professional football player. Medellín, Colombia And the first Colombian Startup Winner is is a data intelligence solution powered by a high quality team. Meet... LogComex Market intelligence for international trade in Latin America Montevideo, Uruguay The host of inaugural FIFA world cup of 1930 comes through when it comes to innovation even in 2021. I’d like to introduce you to: Drop Technologies First electronic hand antiperspirant device Lima, Peru Lima, Peru, has in 2021 delivered some amazing results when it comes to blockchain technology. Their winner is very well deserved: RSK Labs RSK is the first open-source smart contract platform São Carlos Do Pinhal Well this is a unique case. The absolute unanimous winner of São Carlos Do Pinhal is: brain4care brain4care solution enables monitoring intracranial compliance noninvasively anywhere Blumenau, Brazil We’re sensing a theme here. Another stellar EdTech winner is coming from Blumenau, Brazil. Please meet... Uniasselvi Uniasselvi is a Brazilian distance-learning company that offers more than 60 undergraduate and postgraduate courses, in person and distance. Bogotá, Colombia Startups of Bogotá, Colombia definitely showed they’re not to be underestimated. With a very noble mission, let me introduce you to: Risü Risü is an online platform that links online consumption to donation to social institutions. Campina, Brazil If there’s one typo of business we’d single out as taking the biggest hit out of the pandemic, it would have to be small businesses. Thankfully, the winner of Campina, Brazil, operates with small businesses in mind Superlógica Tecnologias Ltda. Superlogica is a management system company created for small service businesses. Cerqueira César, Brazil Swooping half the points from the entire City, this year’s winners of Cerqueira César are InfoPrice INFOPRICE is a business intelligence company focused on physical retail pricing. Governador Valadares, Brazil The same can be said for the winners here. Let’s hear it for: Grupo KPG Saas, Payment, Manufacture, Pharmaceutical, E-commerce, Marketplace, Logistics Caxias Do Sul Known for wine making and Italian cultural influence, Caxias Do Sul brings as an interesting winner. Studio 360 Studio 360 is a proptech focused on easing the purchase experience for real state companies, brokers, and costumers. Sorocaba From Sorocaba, we have an interesting concept coming. Looking forward to seeing it further develop. Let’s meet: Quinto Quinto is a social network that represents the collective opinion of society—to help democracy evolve through technology Viña Del Mar To be a venue-oriented business and survive through the pandemic definitely deserves some recognition. In the name of that, let’s meet: SonicPollen SonicPollen, based in Chile, is a web-based booking platform that connects bands with venues and organizers. Lages Learn more about high-growth startups in Lages, South America. and the winner is…. Qualiall Qualiall develops technologies for training in operational processes such as work security, equipment maintenance, and more. Córdoba, Argentina Another gem from Argentina brings us unique agricultural solutions. Please meet: Kilimo Kilimo is a Big Data solution for water management in agriculture. São José Dos Campos, Brazil Another great agricultural solution comes this time from Brasil. Meet São José Dos Campos Startup winner: TerraMagna TerraMagna is a fintech company that focuses on risk mitigation for agribusinesses. São Leopoldo, Brazil Event-oriented businesses that have survived in the previous years should definitely bottle their perseverance and start selling it. One of those is definitely this winner: Wedy Wedy offers a one-stop-social marketplace for weddings. Pelotas, Brazil Medical scheduling platforms are definitely something we need to see more of. So glad Pelotos winner comes from this particular niche. Fácil Consulta Fácil Consulta is a booking platform that allows people to schedule an appointment with a Specialist Physician in their area. Santa Maria Could you believe how versatile the startups of South America are when we are only now coming across a game development studio? Let’s meet them Imgnation Studios Imgnation Studios is a game development studio that focuses on virtual reality. Asunción, Paraguay A FinTech solution coming from Paraguay. Meet Asunción Startup of the Year winner Vision Banco Vision Banco is a solid and growing financial institution serving the financial needs of the micro, small and medium enterprises. Caracas Venezualan food delivery app that seemed to have your heart. Meet Caracas Startup of the Year winner Yummy Inc. First Food Delivery App in Venezuela Recife A suitable combo between gaming and education, Recife Startup of the Year winner brings revolutionary solutions. Let’s meet them. Joy Street Joy Street offers develops video games focused on educational needs. Rosario, Argentina The winner of Rosario Startup of the Year award for 2021 is... Gbot Gbot is an autonomous robot that controls weeds without using agrochemicals. We also collect data in fields and generate precision maps. Vila Olímpia, Brazil The winner of Vila Olímpia Startup of the Year award for 2021 is... Seekr Seekr operates in the Information Technology sector, providing tools for managing and monitoring of brands. Barueri The winner of Barueri Startup of the Year award for 2021 is.... Glic Brazilian mhealth (web/app) platform for diabetes management Brasília The winner of Brasília Startup of the Year award for 2021 is... Ticies Ticies is a social network that helps people plan where to go with their friends, relatives and acquaintances, in all cities of the world. Juiz De Fora The winner of Juiz De Fora Startup of the Year award for 2021 is... Qranio Qranio is a gamified mobile learning platform for enterprises that rewards learning and stimulates users to engage with educational content. La Paz The winner of La Paz Startup of the Year award for 2021 is... Jukumari Advice 360 photo based market place for responsible tour operators Temuco The winner of Temuco Startup of the Year award for 2021 is... GoFeels GoFeels is the first independent hospitality network in the world. We promote collaborative work among hotels around the world. Vitacura The winner of Vitacura Startup of the Year award for 2021 is... MeatMe MeatMe delivers meats and accessories to have a culinary experience. Barranquilla The winner of Barranquilla Startup of the Year award for 2021 is... Mandaos Mandaos is a digital platform that connects companies and people with a network of independent messengers Ceará The winner of Ceará Startup of the Year award for 2021 is... Caroá Caroá is a cooperative that sells 34 varieties of vegetables, organic fruits, and vegetables. Chapecó The winner of Chapecó Startup of the Year award for 2021 is... HortiAgro HortiAgro is a seed and fertilizer market. Chile Chico The winner of Chile Chico Startup of the Year award for 2021 is... Gleengo Gleengo connects children with their favorite toys, through a tiny device and an application based on text-to-voice. Goiânia The winner of Goiânia Startup of the Year award for 2021 is... Auvo Auvo is an external teams management software. Itá The winner of Itá Startup of the Year award for 2021 is... TISSUELAB Tissuelab is a research and development center focused on biotechnology, and systems for reconstructive musculoskeletal surgery. Jardim Paulista The winner of Jardim Paulista Startup of the Year award for 2021 is... DiviCom DiviCom is a digital video networking company that designs, integrates, and markets complete systems. João Pessoa The winner of João Pessoa Startup of the Year award for 2021 is... YupiPlay YupiPlay develops and offers an educational gaming platform. La Reina The winner of La Reina Startup of the Year award for 2021 is... INTI-TECH INTI-TECH is the fastest growing renewable energy in Chile and the world's leading unconventional source. Leme The winner of Leme Startup of the Year award for 2021 is... EcoStart EcoStart focuses on manufacturing sustainable electric bicycles, fans, and accessories. Loja The winner of Loja Startup of the Year award for 2021 is... Enerwi Corporation Keeping people connected and secured through wireless charging novel solutions Manizales The winner of Manizales Startup of the Year award for 2021 is... Porter Metrics Porter Metrics has developed an automated solution for generating social media reports in minutes. Nova Lima The winner of Nova Lima Startup of the Year award for 2021 is... Biomimetic Solutions Biomimetic Solutions is an innovative tissue engineering company. Paraná The winner of Paraná Startup of the Year award for 2021 is... Laura Laura is a provider of artificial intelligence and predictive analytics platform for hospitala and clinics to prevent damage & reduce costs. Salvador The winner of Salvador Startup of the Year award for 2021 is... Sanar Sanar is a platform that focuses on being the doctor's guide and support, offering specialized content that helps in their development. Santa Catarina The winner of Santa Catarina Startup of the Year award for 2021 is... DrPOCKET Sunchales The winner of Sunchales Startup of the Year award for 2021 is... Eolo Pharma Eolo Pharma focuses on developing drugs targeting the molecular and cellular basis of inflammation-related diseases Valparaiso The winner of Valparaiso Startup of the Year award for 2021 is... Invitta Invitta transforms plastic waste into a high-performance construction material for manufacturing sustainable urban furniture. Vila Velha The winner of Vila Velha Startup of the Year award for 2021 is... OneGrid OneGrid is a developer of hardware and software solutions focused on energy management. Indaiatuba The winner of Indaiatuba Startup of the Year award for 2021 is... DWUL Energy Renewable Energy Campinas, Brazil The winner of Campinas Startup of the Year award for 2021 is... Ativy Ativy is an IT company specialized in cloud computing, data centers, IT outsourcing, service desk, and IT management. Las Condes, Chile The winner of Las Condes Startup of the Year award for 2021 is... # MuvPass Subscription services that allows it's users unlimited access to fitness classes in the best gyms and studios in Latin America. Pinheiros, Brazil The winner of Pinheiros Startup of the Year award for 2021 is... GuiaBolso GuiaBolso is a personal finance platform that empowers people to take control of financial well-being. Rio, Brazil The winner of Rio Startup of the Year award for 2021 is... EmCasa EmCasa is a vertically-integrated platform that helps customers buy and sell a home in Latin America, starting in Brazil. Guayaquil The winner of Guayaquil Startup of the Year award for 2021 is... Sabroso Online platform for LatAm's enterprises to order low-cost meals Jundiaí The winner of Jundiaí Startup of the Year award for 2021 is... Sintecsys Sintecsys offers a fast and effective detection, reducing losses and costs with fire brigades. Minas Gerais The winner of Minas Gerais Startup of the Year award for 2021 is... Rizoflora Biotechnology Rizoflora Biotechnology is a producer of biological product designed for nematodes control. São Caetano Do Sul The winner of São Caetano Do Sul Startup of the Year award for 2021 is... Wevo 1 iPaaS in Latam - Enabling companies to be truly digital companies with APIs, systems and data integrated into a single platform Canoas The winner of Canoas Startup of the Year award for 2021 is... ContaÁgil ContaÁgil facilitates and reduces bureaucracy in accounting routines of companies. Chile The winner of Chile Startup of the Year award for 2021 is... Unight Unight is the future of nightlife social media. Israelândia The winner of Israelândia Startup of the Year award for 2021 is... Matchpool Matchpool is a platform that creates human connections. La Plata The winner of La Plata Startup of the Year award for 2021 is... Pixivus Pixivus offers a community-based application that enables its users to collect, store, and share pictures of events. Novo Hamburgo The winner of Novo Hamburgo Startup of the Year award for 2021 is... Devorando Devorando offers an online food ordering system and menus available in restaurants based in Brazil. Pernambuco The winner of Pernambuco Startup of the Year award for 2021 is... Redu Redu is a distant learning platform which is built on a social networking structure. Providencia The winner of Providencia Startup of the Year award for 2021 is... # TrainFES TrainFES is a medical Devices for the rehabilitation of people with paralysis. Ribeirão Prêto The winner of Ribeirão Prêto Startup of the Year award for 2021 is... AllyHub Ally is a sales distribution channel technology that connects Schools and Service Providers with Education Agencies. Santa Cruz De La Sierra The winner of Santa Cruz De La Sierra Startup of the Year award for 2021 is... Mobi Latam Mobi Latam is a clean energy startup that focuses on transportation systems based on electric mobility. Santana De Parnaíba The winner of Santana De Parnaíba Startup of the Year award for 2021 is... MercadoLivre.com is an e-commerce technology company providing solutions to individuals and companies buying, selling, and more online. MercandoLivre.com More Information If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written/compiled by Shreya Kundu and Katarina Andrejević! If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in South America

15+ Rocking Trends Web Designers Should Know in 2022🤩

This paper is available on arxiv under CC 4.0 license. Authors: (1) Ulysse Gazin, Universit´e Paris Cit´e and Sorbonne Universit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation, (2) Gilles Blanchard, Universit´e Paris Saclay, Institut Math´ematique d’Orsay, (3) Etienne Roquain, Sorbonne Universit´e and Universit´e Paris Cit´e, CNRS, Laboratoire de Probabilit´es, Statistique et Mod´elisation. Table of Links Abstract & Introduction Main results Application to prediction intervals Application to novelty detection Conclusion, Acknowledgements and References Appendix A: Exact formulas for Pn,m Appendix B: Numerical bounds and templates Appendix C: Proof Appendix D: Explicit control of (16) for α=0 Appendix E: Proof of Corollary 4.1 Appendix F: The Simes inequality Appendix G: Uniform FDP bound for AdaDetect Appendix H:  Additional experiments 5 Conclusion The main takeaway from this work is the characterization of a “universal” joint distribution Pn,m for conformal p-values based on n calibration points and m test points. We derived as a consequence a non-asymptotic concentration inequality for the p-value empirical distribution function; numerical procedures can also be of use for calibration in practice. This entails uniform error bounds on the false coverage/false discovery proportion that hold with high probability, while standard results are only marginal or in expectation and not uniform in the decision. Since the results hold under the score exchangeability assumption only, they are applicable to adaptive score procedures using the calibration and test sets for training. Acknowledgements We would like to thank Anna Ben-Hamou and Claire Boyer for constructive discussions and Ariane Marandon for her support with the code. The authors acknowledge the grants ANR-21-CE23-0035 (ASCAI) and ANR-19-CHIA-0021-01 (BISCOTTE) of the French National Research Agency ANR and the Emergence project MARS. References Balasubramanian, V., Ho, S.-S., and Vovk, V. (2014). Conformal prediction for reliable machine learning: theory, adaptations and applications. Morgan Kaufmann books. Bashari, M., Epstein, A., Romano, Y., and Sesia, M. (2023). Derandomized novelty detection with FDR control via conformal E-values. arXiv preprint 2302.07294. Bates, S., Cand`es, E., Lei, L., Romano, Y., and Sesia, M. (2023). Testing for outliers with conformal p-values. Ann. Statist., 51(1):149–178. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B, 57(1):289–300. Benjamini, Y. and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Ann. Statist., 29(4):1165–1188. Bian, M. and Barber, R. F. (2022). Training-conditional coverage for distribution-free predictive inference. arXiv preprint arXiv:2205.03647. Blanchard, G., Neuvial, P., and Roquain, E. (2020). Post hoc confidence bounds on false positives using reference families. Ann. Statist., 48(3):1281–1303. Boyer, C. and Zaffran, M. (2023). Tutorial on conformal prediction. https://claireboyer. github.io/tutorial-conformal-prediction/. Courty, N., Flamary, R., Habrard, A., and Rakotomamonjy, A. (2017). Joint distribution optimal transportation for domain adaptation. In Advances in neural information processing systems 30 (NIPS 2017), volume 30. Jin, Y. and Cand`es, E. J. (2023). Model-free selective inference under covariate shift via weighted conformal p-values. arXiv preprint arXiv:2307.09291. Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018). Distribution-free predictive inference for regression. J. Amer. Stat. Assoc., 113(523):1094–1111. Li, J., Maathuis, M. H., and Goeman, J. J. (2022). Simultaneous false discovery proportion bounds via knockoffs and closed testing. arXiv preprint arXiv:2212.12822. Marandon, A. (2022). Machine learning meets FDR. https://github.com/arianemarandon/ adadetect#machine-learning-meets-fdr. Marandon, A., Lei, L., Mary, D., and Roquain, E. (2022). Machine learning meets false discovery rate. arXiv preprint 2208.06685. Marques F., P. C. (2023). On the universal distribution of the coverage in split conformal prediction. arXiv preprint 2303.02770. Massart, P. (1990). The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality. Ann. Probab., 18(3):1269–1283. Papadopoulos, H., Proedrou, K., Vovk, V., and Gammerman, A. (2002). Inductive confidence machines for regression. In 13th European Conference on Machine Learning (ECML 2002), pages 345–356. Springer. Romano, J. P. and Wolf, M. (2005). Exact and approximate stepdown methods for multiple hypothesis testing. J. Amer. Statist. Assoc., 100(469):94–108. Romano, Y., Patterson, E., and Candes, E. (2019). Conformalized quantile regression. Advances in neural information processing systems, 32. Sarkar, S. and Kuchibhotla, A. K. (2023). Post-selection inference for conformal prediction: Trading off coverage for precision. arXiv preprint arXiv:2304.06158. Saunders, C., Gammerman, A., and Vovk, V. (1999). Transduction with confidence and credibility. In 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pages 722–726. Simes, R. J. (1986). An improved Bonferroni procedure for multiple tests of significance. Biometrika, 73(3):751–754. Vanschoren, J., van Rijn, J. N., Bischl, B., and Torgo, L. (2013). Openml: networked science in machine learning. SIGKDD Explorations, 15(2):49–60. Vovk, V. (2012). Conditional validity of inductive conformal predictors. In 4th Asian conference on machine learning (ACML 2012), pages 475–490. PMLR. Vovk, V. (2013). Transductive conformal predictors. In Artificial Intelligence Applications and Innovations: 9th IFIP WG 12.5 International Conference (AIAI 2013), pages 348–360. Springer. Vovk, V., Gammerman, A., and Shafer, G. (2005). Algorithmic learning in a random world. Springer. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43–76.

Transductive Conformal Inference With Adaptive Scores: Conclusion, Acknowledgements and References

ALL IN EXPECTATION

On-Line Data-Acquisition Systems in Nuclear Physics, 1969, by H. W. Fulbright et al.  National Research Council is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Chapter 2: A MEDIUM-SIZED ON-LINE COMPUTER SYSTEM 1. Introduction An EMR 6130 computer system has been installed and is being prepared for use with Columbia University's Neutron Velocity Spectrometer data-acquisition and analysis system. The spectrometer is characterized by high data rates and many events per burst. At present, peak arrival rates are approximately 106 events per second, with 40-60 events per burst and a burst rate of 70 Hz. The arrival distribution is random; therefore, 10 percent of the interarrival intervals are 100 nsec long, and 1 percent are 10 nsec long. In the future, peak arrival rates of 107 events/sec and 400-600 events per burst are possible, with a burst rate of 300 Hz. With an appropriate time-of-flight "front end," the 6130 will be able to handle the anticipated faster rates. The EMR 6130 is a 16-bit, 775-nsec computer. The memory has a multibus structure which permits each bus to communicate simultaneously with a separate memory module. Up to four memory buses may be purchased. The Columbia system has two memory buses. If a high-speed buffered data channel is used, block transfer may occur at memory cycle speeds. With two buses, data may be stored in two memory modules at rates up to twice memory speed. Alternatively, one bus, channel, and one or more memory modules may be dedicated to data acquisition, while the central processor and standard peripheral devices, using the second bus, simultaneously operate in the remaining memory modules. 2. Description of the System A block diagram of the Columbia system is given in Figure 5. The system has three 8k core modules. Memory bus 1 is dedicated to a high-speed channel serving the time-of-flight acquisition system. Memory bus 2 serves both the central processor and a second high-speed channel. Low-speed input-output devices, such as the operator's console, teletype, card reader, and plotter communicate directly through the processor. The high-speed input-output devices, namely, a magnetic tape unit, line printer, fixed head disk, and interactive CRT display, communicate through the channel. The box designated as "time-of-flight system" represents special-purpose electronics, including a 50-mHz clock, time-quantizing circuits which "clock" an input event from one of the detectors to the nearest clock pulse following its arrival, a 50-mHz counter, and a 16-word derandomizing buffer capable of storing a new word of data (i.e., arrival time) every 20 nsec. The number of channels, nominally 16,000, is limited not by the front end but by the amount of core available for histogram storage in the 6130 system. (For the high data rates anticipated in the future, the time-of-flight clock speed and derandomizing buffer data acceptance rate will be increased to 100 mHz. At the same time, an accumulating buffer of several hundred words capacity, with a 20-mHz data acceptance rate, will be added to empty the derandomizing buffer and store temporarily the time-of-arrival data prior to its transmission to the 6130 system.) Diagram illustrating mode of utilization of core memory in the Columbia System shown in Figure 5. FIGURE 6 3. How the System is Used During the time-of-flight experiment, memory is utilized as follows (see Figure 6). The channel dedicated to data acquisition writes on alternate bursts, into two buffer regions, of approximately 100 words each, in the top of memory module 3. The remaining parts of memory module 3 and all module 2 will be devoted to histogram storage (i.e., time-of-flight channels). Module 1 will contain a stripped-down monitor program and all data-handling programs, including buffer regions for the external devices other than the time-of-flight front end. Programs will be capable of referring to all module 1 or 2 in full concurrency with data acquisition. Reference to module 3 will also overlap data acquisition, except for a period of high input data rate of 100-to 200-µsec duration per burst. With the type of memory allocation described, the system will permit the use of all standard I/O devices, concurrent with the essential operations of input data buffering and histogram generation. Thus, new data may be stored on, or old data retrieved from, the disk or magnetic tape; either new or old data may be displayed on the CRT; and the same or other data may be output with the plotter or line printer. Control information will be input from the teletype, the operator's console, or from special-purpose switches. The importance to the physicist is that hard copy output is immediately available during data acquisition and may be used to monitor, or modify, the experiment. Subsequent to the input data increase, a high-speed memory incrementing channel will be used to input time-of-flight data directly to the histogram area. With this channel the buffer area in module 3 will no longer be required. Histogram data will be stored in all modules 2 and 3, and no program intervention will be required for histogram generation. Between data-acquisition runs, the system will be used for data analysis. 4. Present Status The computer, with two memory modules and one channel and bus, was delivered in July 1968. The remaining memory module channel and bus were delivered in the fall of 1968, the CRT arrived in June 1969, and the line printer (which was not purchased from EMR) came shortly afterward. The first time-of-flight run with this system was scheduled for December 1969. During the period from delivery to the first run, one full-time programmer and approximately half the time of one physicist were devoted to the debugging of manufacturer-supplied programs and the writing of the on-line programs required for the run. It has been hoped that the system would be used extensively for the analysis of previously acquired data, beginning shortly after delivery; however, very little such use has proved possible, essentially because of the unreliability of the 100-cpm card reader supplied by EMR. The lack of a line printer was also a factor. A more reliable reader has been purchased. The delivery of a line printer should rectify the second need. The development of high-speed, buffered, time-of-flight front ends has been a continuing interest at Columbia. It is therefore difficult to estimate the precise costs of the time-of-flight system developed for use with the 6130. A rough estimate of the design and development time is approximately 3 engineer man years. 5. Lessons from Development and Testing Experience Columbia chose to order the EMR 6130, even though at that time (1966) it was not in production, because it seemed a very powerful machine which matched the needs of the system planners. The alternate possibility open was to order a larger, much more expensive machine of proven capability. As it turned out, difficulties in the development of the 6130 caused a delay of over a year in the delivery of the main frame and of over two years in the delivery of the CRT display. (When these delays became apparent, EMR loaned Columbia a 24-bit computer and also a small display for use during the interim period.) The EMR 6130 is perhaps the most powerful 16-bit computer available today, in spite of one or two changes in the original specifications, but in order to get it Columbia apparently traded time for money. 6. Cost The costs of the Columbia University EMR 6130 system are given in Table 3. Purchased from printer manufacturer with 6130 interface. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. W., Fulbright et al. 2013. On-Line Data-Acquisition Systems in Nuclear Physics, 1969. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/42613/42613-h/42613-h.htm#Page_28 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

On-Line Data-Acquisition Systems in Nuclear Physics, 1969: A MEDIUM-SIZED ON-LINE COMPUTER SYSTEM

On-Line Data-Acquisition Systems in Nuclear Physics, 1969, by H. W. Fulbright et al.  National Research Council is part of the HackerNoon Books Series. You can jump to any chapter in this book here . Chapter 2: A SMALL TIME-SHARED DATA-ACQUISITION SYSTEM BASED ON A PDP-7 COMPUTER B. A SMALL TIME-SHARED DATA-ACQUISITION SYSTEM BASED ON A PDP-7 COMPUTER 1. Introduction In 1966, two identical computer systems based on PDP-7 computers were set up in two different locations at the Lawrence Radiation Laboratory (LRL), to be used by several groups of experimenters (see Figure 2). Assembly of the hardware for the first system was completed 6 months after delivery of the computer. Assembly of the second system required only 3 months. Two years after operation commenced, the first satisfactory time-sharing monitor was completed and put into service. The basic use of these systems is pulse-height analysis. In principle many other types of operation are possible. PDP-7 data-acquisition system at Lawrence Radiation Laboratory FIGURE 2 A switch panel used for data taking and control of CRT display in conjunction with the PDP-7 computer. The switch-setting codes can be read into the PDP-7 accumulator under program control and are used to select branch points in the program. As many as eight of these units can be connected to the system. The lights are used to indicate program status. FIGURE 3 2. Operational Features Data-reduction jobs currently possible in the shared-time operating mode include spectrum stripping, normalization, smoothing, storage and retrieval of data from magnetic tapes, graph plotting, printout, energy calibration, background fitting, peak integration, and transfer of data from a remote analyzer. Remote control of the computer from up to eight experimental locations is possible using inexpensive switch panels (Figure 3). Remote slave CRT display is also provided. Multiparameter pulse-height acquisition and analysis can be done on a time-shared basis but often requires all the computer's time and memory. 3. Hardware The hardware configuration is shown in Figure 2. The PDP-7 computer was supplied by the Digital Equipment Corporation with an 8k memory (18 bits) extended arithmetic hardware, microtape (Dectape), paper tape, teletype, and cathode-ray tube (CRT). The other items were built or interfaced at LRL. Automatic memory increment and memory-protection hardware, together with suitable programming, allow a user to carry out simple data-reduction jobs with a live CRT display while two other users are independently acquiring separate, 2048-channel, pulse-height spectra in part of the computer memory, with computer-controlled-gain stabilization. ADC dead time per pulse is less that 40 µsec. Up To 6144 words of the memory can be used for data (one PHA channel per word), while machine language programs fill the remaining 2048 words of memory. 4. Lessons Learned from Operating Experience The system works well for pulse-height analysis, but for new applications, e.g., nuclear magnetic resonance magnet control, it needs additional hardware and programs. Two groups of experimenters, doing chiefly pulse-height analysis experiments are very satisfied with the system. Another group, with a wider range of interests, has been dissatisfied because of the time lag to implement new experiments. One programmer is now engaged full time preparing more programs. Lack of free computer time has become a limitation for both users and programmers. Provision for programming at the outset was inadequate. One full-time systems programmer should have been assigned to these systems for 18 months. Experimenters need Fortran or similar language capability. A disk, or more core memory, would make this practical. The memory size is totally inadequate for multiple users because of the large amount of data space needed for the high-resolution spectra now obtainable with Ge(Li) detectors. An external 8k memory is being acquired for data acquisition in each system so that more of the computer memory can be used for computing. Memory CRT's are needed to provide independent displays for each user. A separate teletype for each user would be invaluable. A disk memory is needed for rapid overlay of programs and for sorting of multiparameter data. A "czar" should have been appointed for day-to-day assignment of facilities, consultations with users, and routine maintenance and upkeep of the hardware and programs. The "czar" could be a good electronics technician interested both in programming and in physics. 5. Costs System costs for the PDP-7 with time-sharing are given in Table 1. Fabrication time is included as a dollar cost. Engineering and programming times shown are one half those charged against two identical systems. Additional special-purpose experimental equipment commonly used with the system includes gain stabilizers, analog pulse derandomizers, amplifiers, pulse pileup rejection, low-noise preamplifiers, and Ge(Li) detectors. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. W., Fulbright et al. 2013. On-Line Data-Acquisition Systems in Nuclear Physics, 1969. Urbana, Illinois: Project Gutenberg. Retrieved May 2022 from https://www.gutenberg.org/files/42613/42613-h/42613-h.htm#Page_19 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

On-Line Data-Acquisition Systems in Nuclear Physics, 1969: A SMALL TIME-SHARED DATA-ACQUISITION 

This article presents the business context and a technical deep dive of our serverless approach to data pipeline deployment. Our approach is as handoff: framework for serverless data pipeline orchestration. open-sourced Business Context: Helping businesses bring any data anywhere More and more companies have started to leverage cloud applications and their data in order to make their operation lean and effective. For example, D2C (direct to consumer) companies produce niche household products and ship directly to their customers instead of relying on a traditional retail distribution channel. In doing so, a D2C company may use a marketing automation platform like Marketo or Pardot, run ads on Facebook and Google, and manage the supply chain with Flexport: All of these applications produce customer experience data. It is a common practice to extract data from cloud services and load them into a data warehouse (DWH). By combining data sources and analyzing them together, the business continuously improves the quality of the customer experience. We are a technology-leveraged service company offering custom ETL/ELT solutions. ETL stands for Extracting, Transforming, and Loading of data. ELT workflows are becoming popular. Data are extracted and loaded into the data warehouse before transformation is executed by a massively parallel modern DWH engine. The demand for implementing data pipelines is growing at an unprecedented pace. Data replication services such as Fivetran and StitchData provide data connectors for popular cloud applications such as Salesforce and Zendesk. However, those companies cannot support the long-tail of cloud applications in the Software as a Service (SaaS) market. If a cloud application is not supported, a business needs a custom data engineering job, but not every company can afford to developing an internal data engineering team. That is why like ours are becoming the new option: With a fraction of the cost of hiring a data engineer, businesses can install the tailor-made data pipeline service. fully-managed ETL services While we serve our customers with active monitoring and maintenance, we decided to open-source the core framework as a contribution to data engineering community. In the rest of the article, we will share the technical solution that made our service possible. Technical challenges and solution If you are the director of data engineering for your company’s internal operations, it makes sense to set up an Apache Airflow or subscribe to a managed service by Astronomer or Cloud Composer by Google Cloud Platform. Instead, we are a lean team helping others to perform the data integration of cloud applications in the long tail. Simultaneously serving the data pipelines for many different clients change the technical requirements a lot. The most important part is to provide separate CPU, memory, and storage capacities for each client. We wanted to achieve this with maximum flexibility. When we started we only had a few data pipelines to manage, but we expected the number to rapidly grow. AWS Lambda vs. Fargate We went for a serverless approach. Our first prototype used Amazon Web Services (AWS) Lambda. It was quick to implement. The pay-as-you-go pricing was very attractive. We didn’t have to pay for idling the virtual machine or deal with peak-time auto-scaling challenges. But we soon realized that Lambda’s 15 minutes processing time limit is not enough for some tasks. So we switched to Fargate, another pay-as-you-go serverless platform by AWS. It has no time limit and the life-cycle management and scaling of computing resource is fully managed by the platform. Compared to Lambda, Fargate’s deployment process is much more complex. Lambda could be deployed just by writing a script file. Fargate deployment involves Docker image creation, pushing the image to Elastic Container Registry, creating resources such as Virtual Private Cloud and Security Groups, defining the Fargate task, managing the Policy, and so on. Birth of handoff: A serverless data pipeline orchestration framework The painful Fargate deployment process gave us the idea to implement a command-line interface (CLI) that we later named “handoff”. handoff simplifies the deployment steps for AWS Fargate. We may also provide a multi-cloud capability in the future: handoff unifies the commands to deploy to AWS, Microsoft Azure, Google Cloud Platform, and so on. handoff describes the data pipeline logic in a YAML file. The first thing is to describe the installation of the modules required by the pipeline logic: project.yml: version: 0.3 description: Fetch foreign exchange rates installs: - venv: extractor command: pip install tap-exchangeratesapi - venv: loader command: pip install target-bigquery-partition As you can see, handoff is created primarily for Python. You can install modules in separate virtual environments if needed: handoff automatically generates multiple virtual environments. In local testing, you can install modules by running: handoff workspace install --project <project_dir> --workspace <workspace_dir> The pipeline logic is described as groups of tasks. In a task group, each command can be linked as a Unix pipeline: tasks: - name: fetch_exchange_rates description: Fetch exchange rates and load on BigQuery pipeline: - command: tap-exchangeratesapi args: --config files/tap-config.json venv: extractor - command: "target-bigquery --config files/target_config.json" venv: loader The pipeline is particularly useful in deploying memory-efficient tasks such as the ELT task by singer.io framework. The following command runs the task locally: handoff run -p <project_dir> -w <workspace_dir> local Doing more than a simple data replication task handoff also implements simple control flows such as for-each and fork. You can also define a task beyond simple ETL. Look how we automate the task of fetching stock market data, make a GIF animation, and post it on Twitter: Deployment features We estimate authoring pipeline logic is just 20% of the ETL development. To run the pipeline in production, we need to implement management of: Run-time configuration Secret keys Scheduling Logging Monitoring Containerization …and the list goes on. handoff’s project.yml helps to keep those organized and it is intuitive to understand. Here is an example of project.yml: vars: - key: gcp_project_id value: "handoff-demo" - key: dataset_id value: exchangerate envs: - key: GOOGLE_APPLICATION_CREDENTIALS value: files/google_client_secret.json deploy: cloud_provider: aws cloud_platform: fargate resource_group: handoff-etl container_image: tap-rest-api-target-bigquery task: usgs-earthquakes schedules: - target_id: 1 description: Run everyday at 00 :00:00Z envs: - key: __VARS value: 'start_datetime=$(date -Iseconds -d "00:00 yesterday") end_datetime=$(date -Iseconds -d "00:00 today")' cron: '0 0 * * ? *' The commands to execute the containerization and the deployment are also simple and intuitive: handoff project push -p <project_dir> handoff container build -p <project_dir>
handoff container push -p <project_dir> handoff cloud bucket create -p <project_dir>
handoff cloud resources create -p <project_dir>
handoff cloud task create -p <project_dir> handoff cloud schedule create -p <project_dir> # Push project configuration, supporting files, and secrets to AWS # Build a Docker image and push to ECR # Create resource group, bucket, task... # Schedule a task handoff simplifies development and deployment. Without handoff, you would have to author CloudFormation templates, deal with boto3 commands to transfer files to S3, figure out a way to encrypt and store the secrets, configure the CloudWatch for logging and metrics, etc. For a full handoff tutorial, please visit https://dev.handoff.cloud We made handoff so every engineer having a similar challenge can use it. We also welcome collaborators to improve this new framework. a public project on GitHub What’s next? As we mentioned earlier, we may support deployment to other cloud platforms such as Microsoft Azure and Google Cloud Platform. Unifying the deployment commands for multi-cloud would be very useful in quickly switching platforms. We also prototyped a web UI built on top of the CLI: The web UI could be running as a stand-alone application on your local machine. It will spin up a local web server. You can interact with handoff via the web UI and the backend connects to cloud resources. It can also be hosted on a server for enterprise use cases. Conclusion AWS Fargate lets us create a cost-effective and scalable solution for ETL deployment. However, it hasn’t been a popular approach due to the complex deployment process. handoff, a serverless data pipeline orchestration framework simplifies it. handoff also manages the various aspects of the orchestration such as security and monitoring. We made handoff an open-source project so that the data engineering community can benefit from it and collaborate for further innovation. About the author is CEO and data scientist at , a boutique consulting firm focused on data engineering, analytics, and data science. ANELEN’s mission is to help innovative businesses make smarter decisions with data science. ANELEN is the provider of . Daigo Tanaka ANELEN handoff.could ETL/ELT service Also published on: https://blog.handoff.cloud/handoff-Serverless-Data-Pipeline-Orchestration-Framework/

Introducing Handoff: Serverless Data Pipeline Orchestration Framework

Você deve gastar em média 5 minutos para ler esse texto. Seria necessário lê-lo 1440 vezes até o seu produto comprado online chegar, se não houver atrasos, claro. Muni Perez é um dos muitos brasileiros que gostam de comprar pela internet. De férias nos Estados Unidos, ele percebeu a diferença em relação às entregas das compras online que realizou na terra do Tio Sam com as que são feitas no Brasil. Se você é um dos que não resiste ao carrinho de compras virtual, você precisa entender do que estamos falando. “Se você está lendo esta história, provavelmente gosta de computadores e curte compras online.” “No Brasil, as pessoas também amam compras online e o e-commerce está crescendo exponencialmente. No entanto, cada compra feita cria uma dor de cabeça — o frete. Se você vive nos Estados Unidos, provavelmente, está acostumado a pagar 4 ou 5 dólares pelo frete na Amazon e receber seus produtos em poucos dias. Claro, isso depende da disponibilidade do item, mas em geral, a Amazon tem estoque e preparo para te entregar rapidamente. No Brasil é um pouco diferente. Primeiro, você não tem muitas opções de companhias para transportar seus produtos, onde cada uma teria que competir para ganhar seu coração. A parte boa da competição é que geralmente os preços diminuem e os serviços melhoram. Existe apenas uma grande — e não muito competente — companhia estatal e algumas companhias privadas que possuem contratos com grandes sites de e-commerce. Como resultado, quando você compra um produto online no Brasil, seu frete pode custar mais que o próprio produto e pode ou não ser entregue (Sim! Isso acontece). O frete muitas vezes é bastante demorado, seu produto pode levar semanas para chegar, dependendo de onde você mora, e às vezes ele é roubado da grande companhia estatal de transporte (chamada Correios) — sim, isso acontece, de verdade. Eu estou atualmente de férias nos Estados Unidos e há poucos dias precisei de uma nova tela protetora para meu novo iPhone. Entrei no site da Amazon, escolhi entre uma enorme variedade de protetores de tela e consegui uma por 9 dólares. Quando prosegui para os detalhes da compra, fiquei surpreso com as opções de frete — o que me inspirou a escrever este texto. Veja por você mesmo: Sim! Levariam dois dias e seria entregue num domingo — de graça. É importante ressaltar que minha conta é Amazon Prime, mas ainda assim, acho que é totalmente válido pagar $8,25 por mês se você faz muitas compras. Então, para ter uma comparação justa voltei à Amazon com uma conta comum, não uma conta Prime e tentei comprar o mesmo item. O transporte padrão foi quase o preço do produto, porque o produto é realmente barato: Então, 9 dólares pelo produto, 6 dólares pelo frete e demoraria cerca de uma semana para ser entregue. Ainda assim, ótimo. Aí entrei num dos maiores varejistas e portais e-commerce do Brasil, Americanas.com. Primeiro eu procurei apenas por película protetora. Veja o que encontrei como frete: Sim! O produto custa 20 reais, o frete custa R$13,31 e demoraria 9 dias úteis para chegar. Obs: Quando comparando as moedas, você pode se imaginar pagando 20 dólares por uma película protetora, ao invés de pagar 20 reais. Se você realmente converter reais para dólares americanos seria um preço bem baixo, mas a renda média em reais é menor que a em dólares americanos. Portanto, considere que você pagaria 20 dólares por um item e 13 dólares pelo frete. Ok, de volta à história. Até agora os preços são absurdamente mais altos no Brasil, mas fala sério, é apenas um produto barato. Vamos ver o que acontece quando você quer comprar um celular (que é absurdamente mais caro também): Ok, agora eu desisto. O frete é na verdade MUITO mais caro e demoram 13 dias úteis para chegar, ou seja, você tem que esperar cerca de 3 semanas — se for sortudo. Sobre as moedas novamente, se imagine pagando 700 dólares num Moto G 8GB 2Gen. Para ser honesto, voltei na Amazon e simulei um pedido do mesmo Moto G junto com a tela protetora para meu iPhone, SEM a conta Prime. Surpresa: Frete grátis e demoram de 5 a 8 dias úteis, o telefone era muito mais barato mesmo se eu tivesse convertido as moedas. Oh My God! Ainda bem que tem luz no fim do túnel, mesmo que seja fraca, como algumas startups que começam a mostrar uma tentativa de solucionar esse gigante e absurdo problema logístico no meu país. Um exemplo incrível é a , que até mesmo recebeu uma carta da grande companhia estatal, dizendo que eles estavam quebrando a lei porque os Correios possuem o monopólio de correspondência. Ok, agora eu desisti.” Shippify , desiste não! Muni Perez Estamos aqui justamente para resolver esse problema, pois acreditamos que é possível sim proporcionar uma melhor experiência de entrega a todos. :D História original disponível . aqui Curta a nossa página no e nos siga no e no . Facebook Twitter Instagram

Compras online nos EUA vs Brasil

Today, we're diving deep into the world of anchoring, a cognitive bias that influences our choices far more pervasively than we realize. If you’ve heard of Anchors before, you’ve probably heard it in a sales context. I’ll touch on that, but it goes way deeper than this. Understanding Anchoring isn't just about dodging sneaky sales tactics; it's about understanding how our brains construct reality from imperfect information. Anchors Aweigh: What Is This Thing, Anyway? Picture this: You're browsing for a new jacket. The first one you see is beautiful, but it's a whopping $400. Way out of your budget. However, as you wander through the store, every other jacket now seems like a relative bargain. Suddenly, that $150 option looks downright appealing. That initial $400 price tag served as an anchor. It distorted your perception of what constitutes a "reasonable" price for a jacket. This mental glitch, my friends, is the anchoring effect in action. Another (non sales, example), was shown that if people are first asked whether Gandhi died before or after age 9, and are then asked to guess Gandhi's actual age at death, their guesses will be lower than if they had first been asked whether Gandhi lived past age 200. The first anchor of 9 or 200 pulls their final estimate in that direction, despite it having no logical relevance. The anchoring bias describes our mind's tendency to become overly influenced by the first piece of information (the anchor) it receives on a topic. Once that anchor's dropped, subsequent judgments get made relative to it, even if the initial anchor is totally arbitrary or irrelevant. Anchoring in the Wild Anchoring isn't just about shopping sprees and impulse buys. It permeates all sorts of life decisions: The person who throws out a number first (salary, sale price, you name it) often has an advantage because it sets the psychological stage for the rest of the negotiation. Negotiations: Get too fixated on the price you originally paid for a stock? You might miss signs it's time to sell, even if the fundamentals have changed (that's sunk cost fallacy, a close cousin of anchoring). Investing: From job interviews to dating, that initial impression (positive or negative) can color how people perceive everything else you do. First Impressions: Anchors: The Cognitive Comfort Blanket Think of an anchor as a mental shortcut. Our brains crave stability and order – they don't enjoy floating around in a sea of uncertainty. Anchors, even irrelevant ones, give us a point of reference, a starting line from which to judge everything else. The tricky part? Once an anchor is planted in our minds, it has a cascading effect: We start noticing details that confirm the established anchor and downplay information that contradicts it. This is a form of confirmation bias. Selective Attention: An anchor can make something seem outrageously expensive or like an incredible deal, even if the objective value remains unchanged. Our point of reference becomes skewed. Distorted Valuation: Anchors don't just affect isolated decisions. They can have a long-term influence on our spending habits, expectations, and even our beliefs about the world. The Ripple Effect: Why Are Our Brains So Easily Swayed? Researchers have a few theories: Our brains are cognitive misers—they love shortcuts. Instead of thoroughly evaluating every decision from scratch, referencing an anchor is a quick, albeit imperfect, decision-making hack. Effort Reduction: When clueless about a fair price or course of action, any initial data point seems better than none. The anchor gives us some place to start. Uncertainty: The World is Your Anchor Playground Let's be real, anchors are everywhere: That initial "Free" frames future pricing decisions. Suddenly the monthly subscription feels way less appealing, even if the total cost is reasonable. Companies know this. The "Free Trial" Trap: Have you noticed how tip options on those card readers keep getting higher? They're anchoring your perception of what constitutes a "normal" tip amount. The Power of Suggestion: The first opinion you hear on a controversial topic, or even just the initial word choice ("riot" vs. "protest"), can shape how you process subsequent information. Anchoring in the Social Realm: The Achilles Heel of Anchoring The most insidious thing about anchors is that they work even when we KNOW they exist. Studies show that even when people are explicitly warned about anchoring bias, it still influences their estimation and judgment. Why does this happen? Here's where things get interesting: System 1 thinking (fast, intuitive) is easily swayed by anchors. But while System 2 (slow, deliberate reasoning) can recognize the bias, it takes significant effort to fully counteract its effects. Anchors Tap Into Deeper Thinking: Our brains are wired to assess things comparatively. Even if you intellectually recognize that an anchor is arbitrary, your emotional response is primed to the comparison. Relative, Not Absolute: Anchors create a sense of cognitive fluency. Options that feel familiar or easy to process often seem intuitively correct, even if they aren't the most rational choice. The Fluency Factor: Fight Back Against the Invisible Hand So, are we doomed to be cognitive puppets manipulated by random anchors? Not quite. Here's your toolkit: Impulse buys are the best friend of anchoring. Give yourself time. Before reacting to a price, offer, or information, impose a self-mandated waiting period. That pause gives your logical brain space to catch up. The Art of Delayed Gratification: Never rely on a single data point. Actively seek out alternative pricing, multiple opinions, or different perspectives on an issue. This helps break the spell of the initial anchor. Seek Counter-Anchors: Especially in negotiations, sometimes it pays to be the one who avoids stating the first number. Strategic ambiguity shifts the dynamic in your favor. Embrace Strategic Ambiguity: Beyond the Obvious – Anchoring in Unexpected Places Anchoring isn't just a salesperson's trick. It seeps into areas where we least expect it, subtly shaping our choices, opinions, and even our self-perception: Ever catch yourself longing for "the good old days" when things seemed cheaper, simpler, or better? Those nostalgic memories can become anchors, making us resistant to change or less receptive to the advantages of the present. Anchoring to the Past: Our initial impressions of ourselves (in childhood through early adulthood) have incredible staying power. That old "I'm bad at math" label can linger for decades, leading to missed opportunities because we're anchored to a past limitation. The Self-Perception Anchor: Studies suggest that even something as trivial as the severity of a sentencing demand by the prosecution can unconsciously anchor the judge's ultimate sentence, regardless of the actual evidence. Anchoring in the Courtroom: Anchors: A Double-Edged Sword It's important to remember, anchoring isn't inherently good or bad. Our brains evolved this shortcut for a reason. Here's where it can be helpful: Ever heard the advice to "visualize success"? It's partly an anchoring effect. Setting a high initial goal (sales revenue, fitness target, creative output) creates a reference point that can motivate us, even if we don't fully reach the first ambitious mark. Goal Setting as Anchoring: Consciously reframing negative thought patterns with positive affirmations can be a form of beneficial self-anchoring. Over time, it helps shift your internal reference point. Positive Self-Talk: Charities that display suggested donation amounts or non-profits highlighting the impact of even small contributions are leveraging anchoring principles to nudge people towards greater generosity. Anchors for Social Good: The Ultimate Goal: Cognitive Flexibility It's not about eliminating anchors – they're an inevitable part of how our brains work. The key is becoming mindful of when they're operating, knowing when to deliberately set our own anchors, and developing the flexibility to shift our reference points when necessary. Understanding anchoring is a step towards greater objectivity, better decision-making, and a healthy skepticism towards the hidden forces trying to shape our reality. Let's get meta - could this newsletter be one big anchor to get you thinking more critically about decision-making? Absolutely 😉 Scott Also appears . here

Unveiling the Anchors: How Your Brain Makes Sense of the World (And Gets It Wrong)

অ্যাঙ্করগুলি উন্মোচন করা: কীভাবে আপনার মস্তিষ্ক বিশ্বের অনুভূতি তৈরি করে (এবং এটি ভুল হয়ে যায়)

Die Anker enthüllen: Wie Ihr Gehirn die Welt versteht (und es falsch versteht)

Revelando las anclas: cómo su cerebro le da sentido al mundo (y lo entiende mal)

Dévoiler les ancres : comment votre cerveau donne un sens au monde (et se trompe)

एंकरों का अनावरण: आपका मस्तिष्क दुनिया को कैसे समझता है (और इसे गलत बनाता है)

アンカーを明らかにする: あなたの脳は世界をどのように理解するのか (そして間違ってしまうのか)

앵커 공개: 뇌가 세상을 어떻게 이해하고 잘못 이해하는지

Revelando as âncoras: como seu cérebro dá sentido ao mundo (e erra)

Раскрытие якорей: как ваш мозг воспринимает мир (и делает это неправильно)

Çapaların Açıklanması: Beyniniz Dünyayı Nasıl Anlıyor (Ve Yanlış Anlıyor)

Tiết lộ những điểm neo: Bộ não của bạn nhận thức thế giới như thế nào (và hiểu sai)

揭开锚点：你的大脑如何理解世界（以及理解错误）

Smart contracts have become a vital component of blockchain technology, enabling the development of decentralized applications ( ) that offer unparalleled security, transparency, and immutability. However, debugging smart contracts can be challenging due to the complexity of the code, the lack of centralized control, and the high stakes involved. DApps This is where the Solidity compiler comes in handy, providing developers with a range of tools and techniques for identifying and resolving smart contract errors. In this article, we’ll explore some tips and tricks for debugging smart contracts using the Solidity compiler. Common Issues in Smart Contracts Debugging Before we dive into the specifics of Solidity compiler debugging, let’s take a look at some common errors that can occur in smart contracts. These errors fall into two categories: syntax errors and logical errors. are caused by incorrect or incomplete code, such as missing brackets, semicolons, or curly braces. These errors can be easily detected by the Solidity compiler, which will flag them as errors during the compilation process. Syntax errors , on the other hand, are more difficult to detect as they don’t necessarily cause the compiler to fail. Instead, these errors occur when the smart contract behaves in unexpected ways due to incorrect logic, variables, or functions. These errors can be caused by a variety of factors, including incorrect inputs, incorrect outputs, or external factors such as malicious actors or network congestion. Logical errors include , integer overflows, and timestamp manipulation. These errors can lead to significant losses of funds or data, so it’s crucial to detect and fix them as soon as possible. Some common logical errors re-entrancy attacks Tools for Debugging Smart Contracts Debugging smart contracts can be a daunting task, but fortunately, there are many tools available to help developers identify and fix errors. Some of the most popular tools for debugging smart contracts include Remix IDE, Truffle, and Ganache, . Solidity shield is an online integrated development environment (IDE) that allows developers to write, debug, and test smart contracts in Solidity. It provides a range of debugging tools, including the ability to view transaction logs and debug messages, set breakpoints, and examine variables. Remix IDE Truffle is another popular development framework for building, testing, and deploying smart contracts. It provides a suite of tools for testing and debugging, including a built-in Solidity debugger, contract deployment and migration tools, and integration with popular testing frameworks such as Mocha and Chai. is a personal blockchain for Ethereum development that provides a local testing environment for smart contracts. It allows developers to test their smart contracts in a sandbox environment, simulate various network conditions, and track transaction history. Ganache Solidity shield – An AI powered vulnerabilities detection application for smart contracts. Tips and Tricks for Debugging Smart Contracts with Solidity Compiler Now that we’ve explored some of the tools available for debugging smart contracts, let’s take a look at some tips and tricks for using the Solidity compiler to identify and resolve errors. **1.Use Logs and Events
\ Logs and events are an essential tool for debugging smart contracts as they allow developers to track the execution of their code and monitor the state of the contract. By using the Solidity logging functions, developers can create log events that can be viewed in real-time during contract execution. These logs can be used to trace the flow of execution and identify potential issues. **2.Set Breakpoints
\ Setting breakpoints is another useful technique for debugging smart contracts. By setting a breakpoint at a specific line of code, developers can pause the execution of the contract and examine the current state of the contract. **3.Use Assertions
\ Assertions are statements that check if a condition is true and will trigger an error if the condition is false. In Solidity, assertions can be used to verify the correctness of contract state during runtime. By placing assertions at key points in the code, developers can quickly identify and isolate errors. **4.Use Debugging Tools
\ Solidity provides a range of debugging tools that can be used to identify and resolve errors in smart contracts. These tools include the Solidity debugger, which allows developers to step through their code and examine the state of the contract at each step. Additionally, Solidity supports inline assembly, which can be used to access low-level debugging information and perform advanced debugging techniques. **5.Test, Test, Test
\ Testing is an essential part of smart contract development, and it’s crucial to test your contracts thoroughly before deploying them. By testing your contracts in a range of scenarios, you can identify and fix errors before they cause significant problems. It’s also important to use a variety of testing frameworks, such as Mocha and Chai, to ensure that your contracts are functioning correctly. Conclusion Debugging smart contracts can be a challenging task, but with the right tools and techniques, it’s possible to identify and fix errors quickly and effectively. By using the Solidity compiler and following the tips and tricks outlined in this article, you can ensure that your smart contracts are functioning correctly and avoid costly mistakes. Remember to test your contracts thoroughly and use a range of debugging tools to ensure that your code is robust and secure. By doing so, you can build smart contracts that are reliable, secure, and scalable, and help drive the growth of blockchain technology. In summary, Solidity compiler provides developers with a range of tools and techniques for debugging smart contracts, including logs and events, breakpoints, assertions, debugging tools, and thorough testing. By using these tools and following best practices, developers can ensure that their smart contracts are secure, reliable, and scalable, and avoid costly mistakes. With the continued growth of blockchain technology, the need for effective smart contract debugging will only increase, making it crucial for developers to stay up-to-date on the latest techniques and tools. For more such articles follow us.

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