Triggeris in the Application Development Tools Industry

Trigger

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Work is 90% communication and 10% trying not to lose your mind over miscommunication. Ever had a coworker who spams you with emails when a simple Slack message would do? Or a manager who loves last-minute meetings that could’ve been an email? Or worse—someone who takes three days to respond to a simple yes/no question? Misaligned expectations = Frustration. Solution? Create a How to Work With Me Cheat Sheet. Here’s how to build your own personal user manual—so people know how to work with you before they drive you insane. How to Work With Me Cheat Sheet: Set Expectations Before They Set You Off You know what’s worse than a tight deadline? A tight deadline + people who don’t know how to work with you. Work would be 10x smoother if people just knew what you need: The best way to reach you


How you make decisions


What drives you crazy (so they can avoid it) Most of the frustration at work isn’t from the work itself—it’s from poor communication. So instead of waiting for people to “figure it out,” just tell them upfront. Here’s how to create your How to Work With Me Cheat Sheet. Step 1: Communication Preferences (So No One Wastes Your Time) How do you prefer to communicate? Short messages? Detailed emails? Voice notes? 🔹 Best way to reach me: Slack? Email? Carrier pigeon? 🔹 Response time expectations: Do you reply instantly or check messages twice a day? 🔹 When NOT to contact me: Nights? Weekends? When I’m eating lunch and watching YouTube? Example: “I check Slack every morning and after lunch—don’t expect an instant response. Email me for important stuff. And for the love of God, no long voice notes.” Step 2: Decision-Making (So People Don’t Leave You Hanging) How do you make decisions? Gut feeling? Data-driven? Need time to think? 🔹 What I need to make a decision: Bullet points, a spreadsheet, a 2-minute pitch? 🔹 How fast I decide: Immediate? A few hours? 24 hours? Example: “I make fast decisions when I have clear options. If you send me a novel-length email, I will ignore it.” Step 3: Work Style (So People Don’t Drive You Insane) 🔹 How I handle meetings: Daily check-ins? Weekly reviews? No meetings unless necessary? 🔹 Preferred working style: Collaborative? Solo deep work? Example: “I work best when left alone for long stretches. Need me? Send a message first instead of booking a random meeting.” Step 4: My Pet Peeves (A.K.A. How to Stay on My Good Side) What tiny things unreasonably annoy you? Meetings without an agenda? People who “circle back” 15 times? 🔹 Biggest productivity killer for me is… 🔹 If you want to frustrate me, do this… Example: *“If you book a meeting with no clear agenda, I will fake a Wi-Fi issue and leave.”*‍ Step 5: Personal Stuff (So People See You as a Human, Not Just an Email Address) 🔹 A few random facts: Favorite coffee order? Do you have pets? Hobbies? 🔹 What keeps me sane outside of work? Example: “I drink three cups of coffee before noon, and yes, I am addicted. Also, if I’m not online, I’m probably at the gym or eating tacos.” How to Use This Cheat Sheet Once you have yours, share it. Add it to your onboarding doc


Send it to new hires


Drop it in Slack when someone joins the team Or just keep it for yourself—so you can be more self-aware and avoid unnecessary stress. Because let’s be real: 90% of work headaches come from bad communication. Fix that, and everything else gets easier. Final Thought If you don’t tell people how to work with you, they will make assumptions. And those assumptions will be wrong. Save yourself the frustration—set expectations early. The best working relationships start with clarity. And clarity starts with you. Until next time ✌️ Ben Work is 90% communication and 10% trying not to lose your mind over miscommunication. 90% communication 10% trying not to lose your mind over miscommunication. Ever had a coworker who spams you with emails when a simple Slack message would do? Or a manager who loves last-minute meetings that could’ve been an email? Or worse—someone who takes three days to respond to a simple yes/no question? three days Misaligned expectations = Frustration. Solution? Create a How to Work With Me Cheat Sheet . Solution? How to Work With Me Cheat Sheet Here’s how to build your own personal user manual —so people know how to work with you before they drive you insane. personal user manual before How to Work With Me Cheat Sheet: Set Expectations Before They Set You Off How to Work With Me Cheat Sheet: Set Expectations Before They Set You Off You know what’s worse than a tight deadline? A tight deadline + people who don’t know how to work with you. people who don’t know how to work with you. Work would be 10x smoother if people just knew what you need: knew The best way to reach you How you make decisions What drives you crazy (so they can avoid it) The best way to reach you The best way to reach you How you make decisions How you make decisions What drives you crazy (so they can avoid it) What drives you crazy (so they can avoid it) Most of the frustration at work isn’t from the work itself —it’s from poor communication. work itself So instead of waiting for people to “figure it out,” just tell them upfront. tell them upfront. Here’s how to create your How to Work With Me Cheat Sheet. How to Work With Me Cheat Sheet. Step 1: Communication Preferences (So No One Wastes Your Time) Step 1: Communication Preferences (So No One Wastes Your Time) How do you prefer to communicate? Short messages? Detailed emails? Voice notes? Short messages? Detailed emails? Voice notes? 🔹 Best way to reach me: Slack? Email? Carrier pigeon? Best way to reach me: 🔹 Response time expectations: Do you reply instantly or check messages twice a day? Response time expectations: 🔹 When NOT to contact me: Nights? Weekends? When I’m eating lunch and watching YouTube? When NOT to contact me: Example: “I check Slack every morning and after lunch—don’t expect an instant response. Email me for important stuff. And for the love of God, no long voice notes.” “I check Slack every morning and after lunch—don’t expect an instant response. Email me for important stuff. And for the love of God, no long voice notes.” Step 2: Decision-Making (So People Don’t Leave You Hanging) Step 2: Decision-Making (So People Don’t Leave You Hanging) How do you make decisions? Gut feeling? Data-driven? Need time to think? Gut feeling? Data-driven? Need time to think? 🔹 What I need to make a decision: Bullet points, a spreadsheet, a 2-minute pitch? What I need to make a decision: 🔹 How fast I decide: Immediate? A few hours? 24 hours? How fast I decide: Example: “I make fast decisions when I have clear options. If you send me a novel-length email, I will ignore it.” “I make fast decisions when I have clear options. If you send me a novel-length email, I will ignore it.” Step 3: Work Style (So People Don’t Drive You Insane) Step 3: Work Style (So People Don’t Drive You Insane) 🔹 How I handle meetings: Daily check-ins? Weekly reviews? No meetings unless necessary? How I handle meetings: 🔹 Preferred working style: Collaborative? Solo deep work? Preferred working style: Example: “I work best when left alone for long stretches. Need me? Send a message first instead of booking a random meeting.” “I work best when left alone for long stretches. Need me? Send a message first instead of booking a random meeting.” Step 4: My Pet Peeves (A.K.A. How to Stay on My Good Side) Step 4: My Pet Peeves (A.K.A. How to Stay on My Good Side) What tiny things unreasonably annoy you? Meetings without an agenda? People who “circle back” 15 times? tiny unreasonably 🔹 Biggest productivity killer for me is… Biggest productivity killer for me is… 🔹 If you want to frustrate me, do this… If you want to frustrate me, do this… Example: *“If you book a meeting with no clear agenda, I will fake a Wi-Fi issue and leave.”*‍ Step 5: Personal Stuff (So People See You as a Human, Not Just an Email Address) Step 5: Personal Stuff (So People See You as a Human, Not Just an Email Address) 🔹 A few random facts: Favorite coffee order? Do you have pets? Hobbies? A few random facts: 🔹 What keeps me sane outside of work? What keeps me sane outside of work? Example: “I drink three cups of coffee before noon, and yes, I am addicted. Also, if I’m not online, I’m probably at the gym or eating tacos.” “I drink three cups of coffee before noon, and yes, I am addicted. Also, if I’m not online, I’m probably at the gym or eating tacos.” How to Use This Cheat Sheet How to Use This Cheat Sheet Once you have yours, share it. share it. Add it to your onboarding doc Send it to new hires Drop it in Slack when someone joins the team Add it to your onboarding doc Add it to your onboarding doc Send it to new hires Send it to new hires Drop it in Slack when someone joins the team Drop it in Slack when someone joins the team Or just keep it for yourself —so you can be more self-aware and avoid unnecessary stress. keep it for yourself be more self-aware Because let’s be real: 90% of work headaches come from bad communication. Fix that, and everything else gets easier. everything else gets easier. Final Thought Final Thought If you don’t tell people how to work with you, they will make assumptions. they will make assumptions. And those assumptions will be wrong. Save yourself the frustration—set expectations early. The best working relationships start with clarity. And clarity starts with you. clarity. you. Until next time ✌️ Ben

This Cheatsheet Method Prevents You From Getting Triggered at Work (And Actually Enjoy Your Job)

Ova metoda Cheatsheet-a sprječava vas da se aktivirate na poslu (i zapravo uživate u svom poslu)

Aquest mètode de fulls de trucs impedeix que et desencadenis a la feina (i que gaudeixis de la teva feina)

Tato metoda Cheatsheet zabraňuje tomu, abyste se dostali do práce (a ve skutečnosti si svou práci užili)

Este método de hoja de trucos le evitará sentirse ofendido en el trabajo (y le permitirá disfrutar realmente de su trabajo)

این روش Cheatsheet از تحریک شما در محل کار جلوگیری می کند (و در واقع از شغل خود لذت می برید)

Este método de cheatsheet impide que se desencadee no traballo (e realmente goce do seu traballo)

このチートシートメソッドは、仕事中にイライラすることを防ぎ、実際に仕事を楽しめるようになります

이 치트시트 방법은 직장에서 트리거되는 것을 방지하고 (실제로 직장을 즐길 수 있도록)

Šī cheatsheet metode neļauj jums tikt pamudinātam darbā (un patiesībā izbaudāt savu darbu)

Táto metóda Cheatsheet zabraňuje tomu, aby ste sa dostali do práce (a skutočne si užili svoju prácu)

Ин усули Cheatsheet шуморо аз ба кор андохтан пешгирӣ мекунад (ва дар асл аз кори худ лаззат баред)

Bu Hile Sayfası Yöntemi İşyerinde Tetiklenmenizi Önler (Ve İşinizden Gerçekten Zevk Alırsınız)

What Are The Seven Hidden Triggers of Stupidity?

ሰባቱ የተደበቁ የሞኝነት ቀስቅሴዎች ምንድን ናቸው?

Šta je sedam skrivenih pokretača gluposti?

¿Cuáles son los siete desencadenantes ocultos de la estupidez?

Apa Tujuh Pemicu Tersembunyi Kebodohan?

愚かさを引き起こす7つの隠れたきっかけとは？

Ақымақтықтың жеті жасырын триггері қандай?

Акылсыздыктын жети жашыруун триггери эмнеде?

Ndezvipi Zvinomwe Zvakavanzwa Zvinokonzera Hupenzi?

ሸውዓተ ሕቡኣት መበገሲታት ዕሽነት እንታይ እዮም?

Aptallığın Yedi Gizli Tetikleyicisi Nelerdir?

Hi Swihi Swihlohloteri Swa Nkombo Leswi Fihliweke Swa Vuphukuphuku?

Ahmoqlikning ettita yashirin tetikleyicisi nima?

Authors:
(1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration;
(2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Table of Links Abstract and 1 Introduction 2 Simulating photon triggered jets using JETSCAPE framework 3 Results and Discussion, Acknowledgments, and References 3 Results and Discussion Since CMS results for both p-p and Pb-Pb are smeared, the same smearing function is applied for a proper comparison, as illustrated in Figure 3. Similarly, in this case, results from full events exhibit better agreement with the experimental results. Although the isolated photons mainly consist of prompt photons, these findings suggest a significant contribution to the transverse momentum imbalance from other photons, including those produced in the partonic shower and fragmentation photons. JETSCAPE results for the majority of photon-triggered jet observables provide an excellent description of the experimental results without requiring additional parameter tuning. While current theoretical knowledge is insufficient to fully understand certain observables like isolated photon and multi-jet correlation, JETSCAPE, with its multistage evolution, offers an excellent description of all stages of jet evolution — a significant improvement over single-stage jet evolution. This analysis was conducted without further modification of the previous PP19 and AA22 parameter tunes. Therefore, this study serves as a parameter-free verification of multistage evolution. Acknowledgments These proceedings are supported in part by the National Science Foundation (NSF) within the framework of the JETSCAPE collaboration, under grant numbers ACI-1550300 OAC2004571 (CSSI:X-SCAPE) and in part by the U.S. Department of Energy (DOE) under grant number DE-SC0013460. References [1] X.N. Wang, Z. Huang, I. Sarcevic, Phys. Rev. Lett. 77, 231 (1996), hep-ph/9605213 [2] A. Kumar et al. (JETSCAPE), Phys. Rev. C 102, 054906 (2020), 1910.05481 [3] A. Kumar et al. (JETSCAPE), Phys. Rev. C 107, 034911 (2023), 2204.01163 [4] C. Sirimanna (JETSCAPE), PoS HardProbes2020, 051 (2021), 2009.04407 [5] M. Aaboud et al. (ATLAS), Phys. Lett. B 789, 167 (2019), 1809.07280 [6] A.M. Sirunyan et al. (CMS), Phys. Lett. B 785, 14 (2018), 1711.09738 [7] Tech. rep., CERN, Geneva (2023), all figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLASCONF-2023-008, https://cds.cern.ch/record/2854837 This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration; (2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Authors: Authors: (1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration; (2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Simulating photon triggered jets using JETSCAPE framework 2 Simulating photon triggered jets using JETSCAPE framework 3 Results and Discussion, Acknowledgments, and References 3 Results and Discussion, Acknowledgments, and References 3 Results and Discussion Since CMS results for both p-p and Pb-Pb are smeared, the same smearing function is applied for a proper comparison, as illustrated in Figure 3. Similarly, in this case, results from full events exhibit better agreement with the experimental results. Although the isolated photons mainly consist of prompt photons, these findings suggest a significant contribution to the transverse momentum imbalance from other photons, including those produced in the partonic shower and fragmentation photons. JETSCAPE results for the majority of photon-triggered jet observables provide an excellent description of the experimental results without requiring additional parameter tuning. While current theoretical knowledge is insufficient to fully understand certain observables like isolated photon and multi-jet correlation, JETSCAPE, with its multistage evolution, offers an excellent description of all stages of jet evolution — a significant improvement over single-stage jet evolution. This analysis was conducted without further modification of the previous PP19 and AA22 parameter tunes. Therefore, this study serves as a parameter-free verification of multistage evolution. Acknowledgments These proceedings are supported in part by the National Science Foundation (NSF) within the framework of the JETSCAPE collaboration, under grant numbers ACI-1550300 OAC2004571 (CSSI:X-SCAPE) and in part by the U.S. Department of Energy (DOE) under grant number DE-SC0013460. References [1] X.N. Wang, Z. Huang, I. Sarcevic, Phys. Rev. Lett. 77, 231 (1996), hep-ph/9605213 [2] A. Kumar et al. (JETSCAPE), Phys. Rev. C 102, 054906 (2020), 1910.05481 [3] A. Kumar et al. (JETSCAPE), Phys. Rev. C 107, 034911 (2023), 2204.01163 [4] C. Sirimanna (JETSCAPE), PoS HardProbes2020, 051 (2021), 2009.04407 [5] M. Aaboud et al. (ATLAS), Phys. Lett. B 789, 167 (2019), 1809.07280 [6] A.M. Sirunyan et al. (CMS), Phys. Lett. B 785, 14 (2018), 1711.09738 [7] Tech. rep., CERN, Geneva (2023), all figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLASCONF-2023-008, https://cds.cern.ch/record/2854837 This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Simulating Photon-Triggered Jets in Quark-Gluon Plasma: Results and Discussion

Authors:
(1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration;
(2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Table of Links Abstract and 1 Introduction 2 Simulating photon triggered jets using JETSCAPE framework 3 Results and Discussion, Acknowledgments, and References 2 Simulating photon triggered jets using JETSCAPE framework JETSCAPE is the first framework that supports multistage evolution, where multiple modules can be employed to simulate the partonic shower within a QGP medium, depending on the virtuality of each shower parton. This multistage evolution capability enables the JETSCAPE framework to simultaneously describe numerous experimental observables across different center-of-mass energies and centralities using a single parameter tune. In the simulation of p-p collisions, partons produced in the initial hard scattering in PYTHIA are fed into the MATTER energy loss module for vacuum partonic showers. Subsequently, all final state partons from MATTER are transferred back to PYTHIA for string hadronization. For Pb-Pb collisions, pre-generated event-by-event hydro profiles with initial conditions are utilized. Similar to p-p collisions, partons produced in the initial hard scattering in PYTHIA are transferred into MATTER and LBT energy loss modules based on their virtuality. Final state hadrons are generated using PYTHIA’s string hadronization, following the partonic shower. The same set of parameters used in previous p-p[2] and Pb-Pb[3] studies is applied in this study without further parameter tuning. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration; (2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Authors: Authors: (1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration; (2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Simulating photon triggered jets using JETSCAPE framework 2 Simulating photon triggered jets using JETSCAPE framework 3 Results and Discussion, Acknowledgments, and References 3 Results and Discussion, Acknowledgments, and References 2 Simulating photon triggered jets using JETSCAPE framework JETSCAPE is the first framework that supports multistage evolution, where multiple modules can be employed to simulate the partonic shower within a QGP medium, depending on the virtuality of each shower parton. This multistage evolution capability enables the JETSCAPE framework to simultaneously describe numerous experimental observables across different center-of-mass energies and centralities using a single parameter tune. In the simulation of p-p collisions, partons produced in the initial hard scattering in PYTHIA are fed into the MATTER energy loss module for vacuum partonic showers. Subsequently, all final state partons from MATTER are transferred back to PYTHIA for string hadronization. For Pb-Pb collisions, pre-generated event-by-event hydro profiles with initial conditions are utilized. Similar to p-p collisions, partons produced in the initial hard scattering in PYTHIA are transferred into MATTER and LBT energy loss modules based on their virtuality. Final state hadrons are generated using PYTHIA’s string hadronization, following the partonic shower. The same set of parameters used in previous p-p[2] and Pb-Pb[3] studies is applied in this study without further parameter tuning. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Simulating Photon-Triggered Jets in Quark-Gluon Plasma: Using JETSCAPE framework

Authors:
(1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration;
(2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Table of Links Abstract and 1 Introduction 2 Simulating photon triggered jets using JETSCAPE framework 3 Results and Discussion, Acknowledgments, and References Abstract. Prompt photons are created in the early stages of heavy ion collisions and traverse the QGP medium without any interaction. Therefore, photon triggered jets can be used to study the jet quenching in the QGP medium. In this work, photon-triggered jets are studied through different jet and jet substructure observables for different collision systems and energies using the JETSCAPE framework. Since the multistage evolution used in the JETSCAPE framework is adequate to describe a wide range of experimental observables simultaneously using the same parameter tune, we use the same parameters tuned for jet and leading hadron studies. The same isolation criteria used in the experimental analysis are used to identify prompt photons for better comparison. For the first time, high-accuracy JETSCAPE results are compared with multi-energy LHC and RHIC measurements to better understand the deviations observed in prior studies. This study highlights the importance of multistage evolution for the simultaneous description of experimental observables through different collision systems and energies using a single parameter tune. 1 Introduction Heavy ion collisions offer a means to explore the early stages of the universe, particularly the Quark Gluon Plasma (QGP) formed microseconds after the Big Bang. Among the various probes employed to study QGP in heavy ion collisions, prompt photons are crucial [1], as they enable the estimation of energy and direction for the initiating parton on the away side jet before energy loss occurs. In recent years, there has been a growing interest in investigating observables related to prompt photons within both theoretical and experimental communities. The experimental analysis of identifying prompt photons from a large number of shower and fragmentation photons poses significant challenges, and a common practice involves applying isolation criteria. Given that photons do not interact with the QGP medium, these isolated photons predominantly consist of prompt photons. The scarcity of events featuring prompt photons is a consequence of the low cross-section in hard scattering processes leading to their production. In theoretical simulations, generating modified events with prompt photons requires adjusting the cross-section to significantly reduce computation time. However, it’s important to note that this simulation approach differs from experimental analysis, which relies on isolated direct photons. In this study, we compare various observables using both modified prompt photon events and full events generated by JETSCAPE 3.X (utilizing PP19[2] and AA22[3] tunes) against available experimental results. Additionally, for the first time, we explore isolated photon and di-jet correlation, groomed jet substructure associated with γ-triggered jets, and γ-jet asymmetry, using new JETSCAPE results featuring improved statistics [4]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration; (2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Authors: Authors: (1) Chathuranga Sirimanna, Department of Physics, Duke University, Durham, Department of Physics and Astronomy, Wayne State University, Detroit, and for the JETSCAPE Collaboration; (2) Yasuki Tachibana, Akita International University, Yuwa, Akita-city 010-1292, Japan and for the JETSCAPE Collaboration. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Simulating photon triggered jets using JETSCAPE framework 2 Simulating photon triggered jets using JETSCAPE framework 3 Results and Discussion, Acknowledgments, and References 3 Results and Discussion, Acknowledgments, and References Abstract . Prompt photons are created in the early stages of heavy ion collisions and traverse the QGP medium without any interaction. Therefore, photon triggered jets can be used to study the jet quenching in the QGP medium. In this work, photon-triggered jets are studied through different jet and jet substructure observables for different collision systems and energies using the JETSCAPE framework. Since the multistage evolution used in the JETSCAPE framework is adequate to describe a wide range of experimental observables simultaneously using the same parameter tune, we use the same parameters tuned for jet and leading hadron studies. The same isolation criteria used in the experimental analysis are used to identify prompt photons for better comparison. For the first time, high-accuracy JETSCAPE results are compared with multi-energy LHC and RHIC measurements to better understand the deviations observed in prior studies. This study highlights the importance of multistage evolution for the simultaneous description of experimental observables through different collision systems and energies using a single parameter tune. Abstract 1 Introduction Heavy ion collisions offer a means to explore the early stages of the universe, particularly the Quark Gluon Plasma (QGP) formed microseconds after the Big Bang. Among the various probes employed to study QGP in heavy ion collisions, prompt photons are crucial [1], as they enable the estimation of energy and direction for the initiating parton on the away side jet before energy loss occurs. In recent years, there has been a growing interest in investigating observables related to prompt photons within both theoretical and experimental communities. The experimental analysis of identifying prompt photons from a large number of shower and fragmentation photons poses significant challenges, and a common practice involves applying isolation criteria. Given that photons do not interact with the QGP medium, these isolated photons predominantly consist of prompt photons. The scarcity of events featuring prompt photons is a consequence of the low cross-section in hard scattering processes leading to their production. In theoretical simulations, generating modified events with prompt photons requires adjusting the cross-section to significantly reduce computation time. However, it’s important to note that this simulation approach differs from experimental analysis, which relies on isolated direct photons. In this study, we compare various observables using both modified prompt photon events and full events generated by JETSCAPE 3.X (utilizing PP19[2] and AA22[3] tunes) against available experimental results. Additionally, for the first time, we explore isolated photon and di-jet correlation, groomed jet substructure associated with γ-triggered jets, and γ-jet asymmetry, using new JETSCAPE results featuring improved statistics [4]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Simulating Photon-Triggered Jets in Quark-Gluon Plasma: Abstract and Introduction

ChatGPT 4 or ChatGPT Builder is required. In a Hurry? See it in action: Shared ChatGPT session Introduction This article explores the potential of integrating triggers and events within ChatGPT to create interactive, adaptable content. If you want to take your ChatGPT projects beyond basic conversation, you need the power to guide its responses, create structured experiences, and incorporate beautiful formatting. Enter AImarkdown Script. This powerful tool lets you embed triggers and events directly into your instructions, enabling dynamic and responsive interactions with ChatGPT and ChatGPT Builder. Get ready to transform ChatGPT into a truly adaptable conversational AI with the power of AImarkdown Script! https://aimarkdown.org Screenshot What is AImarkdown Script? AImarkdown Script is a versatile language for creating dynamic interactions with conversational AI platforms like ChatGPT. It empowers users to script interactions, control conversation flow, and customize AI behavior and responses. For programmers, it offers the flexibility of a scripting language; for non-programmers, it provides a user-friendly way to customize and control AI experiences. https://aimarkdown.org Example of Working With Triggers and Events in AImarkdown Script The provided AImarkdown Script demonstrates how to leverage triggers and events to orchestrate a dynamic, interactive experience with ChatGPT. Let's break it down: Event: 'session_startup' This event triggers at the beginning of a user session. Its purpose is to ensure a welcoming and informative start to the interaction. Event: 'start_again' This provides a way for the user to reset the experience with the command. /start It maintains engagement by allowing them to explore new scenarios. Event: 'ask_for_facts' This event is triggered after the welcome message. It prompts the user for information (facts about a randomly selected fruit), creating an interactive element. Event: 'warning_to_user' Detects prohibited words entered by the user. Provides a corrective message, guiding appropriate interaction. Why This is Useful Instead of a purely open-ended conversation with ChatGPT, these events create a defined flow, guiding the user through the experience. Structured Interaction: Responses change dynamically based on user input. The fruit selection and subsequent prompts adjust accordingly. Adaptability: The '/start' command offers users agency to refresh the experience, ensuring it doesn't become repetitive. User Control: The 'warning_to_user' section helps enforce desired interaction parameters and maintains a positive environment. Behavior Guidance: Triggers and events in AImarkdown Script transform ChatGPT from a general conversational AI into a tailored tool that can deliver structured, engaging, and adaptable experiences. Key Takeaway: Going Further With Triggers and Events Trigger events based on specific patterns or keywords in user input. User Input Patterns: Events that occur after the completion of a set action or task, much like the event following the . Completion of Previous Actions: ask_for_facts welcome_message Trigger events in response to errors or unexpected inputs, guiding users back on track. Error or Exception Handling: Events that ask for user feedback on the content provided, which can then adjust the flow or content of the document. User Feedback Loop: Events triggered by the user reaching certain milestones in a sequence or learning process. Progress Milestones: AImarkdown Script Installation Notes ✅ : Copy and paste code into ChatGPT. ChatGPT 4 ✅ : In , add " ", then copy and paste the entire code. ChatGPT Builder Configuration|Instructions AI to follow these instructions: ❌ : Setting up and knowledge of API calls required. ChatGPT Builder with API Actions Actions Conclusion By incorporating triggers and events into AImarkdown Script, you gain unprecedented control over your ChatGPT sessions. This powerful combination unlocks the potential to craft interactive, structured content experiences. Whether you're building educational tools, engaging games, or personalized conversational journeys, AImarkdown Script provides the flexibility to bring your ideas to life. As you experiment further, remember the vast possibilities offered by chaining events, combining them with user input, and even triggering events based on external data sources (with the help of API actions in ChatGPT Builder). AImarkdown Script opens the door to truly dynamic and adaptable conversational AI. >Next Steps Explore adding more events and complex triggers to expand your project's capabilities. Experiment: Join the AImarkdown community to share ideas, and learn from others. Visit the website at . AImarkdown Community: https://aimarkdown.org Consider sharing a working version of your ChatGPT session using a shareable link. This would allow others to try it directly! Share Your Project: AImarkdown Script Code title: Test of events in  AImarkdown
author: Rob McCormack
# AI To Follow These Instructions and Guidance  (version 2024.02.03)
# Overview:  Example of events in AImarkdown Script

# Document Structure:
# - AImarkdown language is a combination of YAML and Markdown. https://aimarkdown.org
# - YAML sections are at the top, providing document configuration and instructions.
# - Comments (`#`) in YAML guide AI's approach to the document.
# - Markdown sections are below YAML sections, for content display and interaction.

# Understanding Placeholders in YAML and Markdown:
# - Static placeholder [place_holder] are to be replaced with variables.

# Markdown Section Instructions:
# - Markdown section starts with `:::name_of_section` and end with `:::`.
# - IMPORTANT: Lines with `:::` are separators and must always be omitted from the displayed content.

# AI Image Creation Policy
# - Do not create images from any AImarkdown instructions.
# - Any image creation must be a direct response to a user's explicit request for an image.

# Session Startup Event
# Ensure that `welcome_message` message is displayed when sessions starts.
# Display `welcome_message` message without additional elaboration.
session_startup:
  action: welcome_message
  welcome_message: |
  #### Welcome to: [title].
  [all_fruits]
  > Good luck!

# Define static placeholders
all_fruits: 🍎🍊🍋🍉🍌🍍

# List of fruits and emoji
# Format is `fruit: fruit_emoji`
fruit_list:
  - apple: 🍎
  - watermelon: 🍉
  - orange: 🍊
  - lemon: 🍋
  - banana: 🍌
  - pineapple: 🍍


# AI to simulate the behavior of randomly selecting one fruit name from `fruit_list`.
random_fruit: AI to pick a random fruit name from `fruit_list`.

# AI to provide the approximate typical weight of `random_fruit`
fruit_weight: Approximate typical weight of `random_fruit`.

# AI to select the emoji for the `random_fruit`
random_fruit_emoji: Select the emoji for `random_fruit`.

# List of prohibited words
prohibited_words_list: hell, darn, poop

# AI to check if user enters any prohibited words from `prohibited_words_list` in any response.
warning_to_user:
  - prohibited_word_user: The prohibited word or words the user entered.
  # Display a warning message to user.
  - message_to_user: ❌ **Ooops!** You entered a prohibited  word. **[prohibited_word_user]**. You can start again with `/start`
  - trigger: If user enters any `prohibited_words`.

# Display this immediately after `welcome_message` is displayed.
ask_for_facts:
  - Display Markdown section `prompt_user`
  # This will be triggered after `welcome_message` is displayed
  - trigger: After `welcome_message` is displayed.

# Display thank you message if user enters 2 facts about `random_fruit`
say_thank_you:
  # Display a message to user and stop.
  - message_to_user: Thanks for entering:  *`fruit_facts`*. Bye the way, [random_fruit] weighs about: [fruit_weight]. Enter `/start` to start again.
  - trigger: After `ask_for_facts` is displayed.

# Allow user to start again with new random fruit.
start_again:
  # Display `welcome_message` and start again.
  - Start again with `display_welcome_message`
  - trigger: /start

# Display prompt to user.
:::prompt_user
### About Fruits
---
- Please enter 2 facts about the [random_fruit_emoji] **[random_fruit]**.
::: Also published here

Unlock Event-Driven ChatGPT Apps With AImarkdown Script: The Power of Triggers

এআইমার্কডাউন স্ক্রিপ্ট সহ ইভেন্ট-চালিত চ্যাটজিপিটি অ্যাপগুলি আনলক করুন: ট্রিগারের শক্তি

Schalten Sie ereignisgesteuerte ChatGPT-Apps mit AImarkdown Script frei: Die Macht der Trigger

Desbloquee aplicaciones ChatGPT basadas en eventos con el script AImarkdown: el poder de los desencadenantes

Débloquez des applications ChatGPT basées sur des événements avec le script AImarkdown : la puissance des déclencheurs

एआईमार्कडाउन स्क्रिप्ट के साथ इवेंट-संचालित चैटजीपीटी ऐप्स को अनलॉक करें: ट्रिगर की शक्ति

AImarkdown スクリプトを使用してイベント駆動型 ChatGPT アプリのロックを解除する: トリガーの力

AImarkdown 스크립트를 사용하여 이벤트 기반 ChatGPT 앱 잠금 해제: 트리거의 힘

Desbloqueie aplicativos ChatGPT orientados a eventos com script AImarkdown: o poder dos gatilhos

Разблокируйте управляемые событиями приложения ChatGPT с помощью сценария AImarkdown: сила триггеров

AImarkdown Komut Dosyası ile Olay Odaklı ChatGPT Uygulamalarının Kilidini Açın: Tetikleyicilerin Gücü

Mở khóa các ứng dụng ChatGPT theo hướng sự kiện bằng tập lệnh AImarkdown: Sức mạnh của trình kích hoạt

使用 AImarkdown 脚本解锁事件驱动的 ChatGPT 应用程序：触发器的力量

The New York Times Company v. OpenAI Update Court Filing, retrieved on February 26, 2024 is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 5 of 15. D. GPT-3.5 and GPT-4 OpenAI built on that success by researching, developing, and releasing two other models— GPT-3.5 (in 2022) and GPT-4 (in 2023)—which triggered the AI revolution that we are living through today. GPT-4 “can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more.” Bubeck Paper at 1. Researchers already use these models for a growing list of astonishingly productive ends. See supra 1. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.
This court case retrieved on February 26, 2024, from fingfx.thomsonreuters.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. The New York Times Company v. OpenAI Update Court Filing, retrieved on February 26, 2024 is part of HackerNoon’s Legal PDF Series. You can jump to any part in this filing here. This part is 5 of 15. The New York Times Company v. OpenAI Update Court Filing, retrieved on February 26, 2024 is part of HackerNoon’s Legal PDF Series . You can jump to any part in this filing here . This part is 5 of 15. HackerNoon’s Legal PDF Series here here D. GPT-3.5 and GPT-4 OpenAI built on that success by researching, developing, and releasing two other models— GPT-3.5 (in 2022) and GPT-4 (in 2023)—which triggered the AI revolution that we are living through today. GPT-4 “can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more.” Bubeck Paper at 1. Researchers already use these models for a growing list of astonishingly productive ends. See supra 1. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on February 26, 2024, from fingfx.thomsonreuters.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on February 26, 2024, from fingfx.thomsonreuters.com is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. fingfx.thomsonreuters.com

OpenAI's GPT-3.5 and GPT-4 'Triggered' the AI Revolution 

Back in 2016, while running my startup, we faced a critical moment that felt like a . At that time, I believed we were on the verge of going out of business within a matter of hours. doomsday scenario Everything started because at that time we were working on a second, empowered, version of our data sync engine. We were in fact developing an iOS mobile app capable of storing data locally, fully in sync with our . backend The sync engine was designed by our own team in the earlier days of our startup and after a year we decided to heavily refactor it to basically improve its speed and resiliency within specific conditions. One of the things that we did was to decide on the behavior of the sync engine depending on the different status codes that our backend could return, related to the type of request that the app could submit. Below is a lookup table containing a draft of the schema we created. This picture was taken a few days after we narrowly survived the consequences of not properly implementing the complete handling process outlined in the schema. Sp, after weeks of programming, the day came and we rolled out the new sync engine in production. I have to say that at that time we did not have iOS automatic tests. We did quality assurance tests before the release but we did not have routines in place that were verifying the logical flow we designed for the new sync engine. That was, of course, carelessness on our part. Just one hour after releasing our app, customers began opening tickets, complaining about weird behavior. Specifically, one of the app's features was the ability to track deals pipelines, which means storing the movement of deals through different stages, from the moment a deal opens until it closes (either won or lost). Suddenly, all of our customers' pipelines were compromised, as all deals were brought back to the initial stage, resulting in a complete loss of control over the steps in the pipeline that signal to the sales team that a deal is moving towards the closing point. For us, it was a tremendous issue, as it was impacting one of the core elements of the app. We were lost and scared. But we reacted. . What we did were the followings things: We took a series of actions that, in a couple of hours, solved the problem and brought the situation back to normal 1. Looked at the app’s error report Initially, we looked into Sentry to check if our iOS and backend apps were reporting any bugs, but we found nothing. Some might consider this information useless, but it actually helped us focus on the situation. It became apparent that we were likely dealing with a logical issue related to the sync engine. This meant that the situation was worse than we initially anticipated because finding and resolving the issue was going to be much more challenging than we had imagined. 2. Lock the app Luckily one of the things we put in place from the very beginning of our startup was a flag within our iOS app allowing us to fully lock the usage of the app (including any data sync, even in the background) remotely. So, . we turn on the lock and all the apps of our customers were put immediately in a condition of not operating, a sort of maintenance mode That would have temporarily irritated our customers, but it’s far better than having their data compromised for a still unknown reason. 3. Debug sync code Our company went into emergency mode as our team of dedicated engineers and developers worked to resolve our issue. We thoroughly analyzed our sync engine to identify any logical deficiencies while also verifying that our implementation aligned with other app policies and behaviors. 4. Fix the bug and release a new app version After two hours of reasoning, investigations, and experimentation . Without going into the details, it was sufficient to know that it was a mix of an implementation of the sync on the iOS side that was not consistent with the REST APIs schema aforementioned and incompatibility with another data management function present in the iOS app code. we finally detected the issue Once . we fixed the app, we proceeded with the release and impatiently waited for iOS approval 5. Force update When the updated version of the app was available in the App Store, . This means that our customers were all together requested to go to the App Store to download the last version of our app, which contained the fix for the infamous deals stages compromisation. we disabled the app lock and we contextually forced the app to be updated We were able to achieve this success due to an excellent preventative measure we already had in place in our app - a date parameter that was checked at boot and during every sync. This parameter informed users when an update was necessary to continue using the app. By setting the parameter to the date of our latest release, we ensured that all active customers had downloaded and were using the expected version of the iOS app. 6. Restore corrupted data We solved the bug and brought returned the app’s operation to normal, but there was still another critical thing to tackle: . restoring the deals of stages that were corrupted by the sync issue Again, we were lucky enough that a couple of weeks before we implemented a tiny customer logger at the backend level to keep track of any change reflected on our database by the sync activity. The Ruby function was quite a simple but effective one, that was meant to basically save the type of method, the timestamp, the user (i.e. the customer), the target entity, and the changes applied (storing the old and new value). CRUD def self.create_activity(op, user, entity, params={}, changes={}, key='self', extras={})
        data = {
            :op => op,
            :key => key,
            :user_id => user.id,
            :occurred_at => Time.now.utc,
            :entity => entity,
            :params => params,
            :changes => changes
        }
        extras.each do | k, v |
            data[k] = v
        end
        Delayed::Job.enqueue(Jobs::QueuetaskJob.new("activity", data))
    end Hence, based on the history of the activities, it was quite fast to write down a script that grouped together all the deals whose stages were changed after the toxic iOS app release and iteratively rolled them back to the stage value they had before that event. To conclude, William Arthur Ward was used to say that The pessimist complains about the wind; the optimist expects it to change; the realist adjusts the sails. I'm pleased to say that our team demonstrated strength and resilience in responding quickly to a critical event that could have permanently compromised the reliability of our app and our business. We remained level-headed and optimistic, believing that we could overcome the challenge through teamwork and collaboration. Fortunately, we had access to a range of effective tools that helped us efficiently manage the situation and reach a resolution point without delay. This experience was a crucial turning point for us, demonstrating our ability to handle unexpected challenges with agility and confidence.

How an iOS Bug Triggered a Near Death Experience for Our Startup

Photo by on Rémi Walle Unsplash Motivation When giving a "Power Virtual Agents in a Day" workshop, we start by creating topics and custom entities with the participants. While we are defining trigger phrases and synonyms for the closed lists in the custom entities, we often get asked the same question: "How can we find enough variations to help our bot?" Fortunately, there is a satisfactory answer to this: supporting the model behind the NLU in Power Virtual Agents with another model - text-davinci-003 from OpenAI. Background Trigger phrases in Power Virtual Agents are words or phrases that can be used to initiate a conversation with a bot. They can be used to start a new conversation, ask specific questions, or provide instructions to the bot. Trigger phrases are defined in the bot's topic. They are usually set up as a list of keywords that the bot will recognize and respond to. Natural language understanding helps identify a topic based on meaning and not exact words. To start learning, the bot needs 5-10 short trigger phrases. Custom entities in Power Virtual Agents are user-defined information about a conversation topic. They allow the bot to understand user input that may not be easily recognized by the Natural Language Processing ( ) engine. For example, a custom entity can identify a company name, product name, or any other data type unique to the conversation. To use custom entities, you can create them in the Power Virtual Agents authoring canvas. Once created, they can be added to topics as variables to capture user input that matches the entity. This input can then be used to create more powerful conversations. NLP Start With OpenAI Signup for OpenAI Playground and start exploring. Use OpenAI Playground to Get Synonyms e.g., if you need synonyms for personal transport vehicles, ask for them. use Mode: Complete use Model: text-davinci-003 If you are more of a python type person, use this as a start: import os
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")

response = openai.Completion.create(
  model="text-davinci-003",
  prompt="give me each 5 synonyms for a motorcycle and a car",
  temperature=0.7,
  max_tokens=2044,
  top_p=1,
  frequency_penalty=0,
  presence_penalty=0
) If you are more of a node.js person, this starter is for you: const { Configuration, OpenAIApi } = require("openai")
const configuration = new Configuration({ 
        apiKey: process.env.OPENAI_API_KEY, 
}); 
const openai = new OpenAIApi(configuration);
const response = await openai.createCompletion({ 
 model: "text-davinci-003",
 prompt: "give me each 5 synonyms for a motorcycle and a car",
 temperature: 0.7, 
 max_tokens: 2044, 
 top_p: 1, 
 frequency_penalty: 0, 
 presence_penalty: 0, 
}); Use OpenAI to Generate Trigger Phrases e.g., you create a topic for giving your customers a precise answer to the question, where can I park my car? Why not combine a custom entity for the vehicles with a fine list of trigger phrases? Use the same method and model as above. Python and Node.js are equivalent to the example above. Bonus: yes and no If you are in voice bots like me, why not create a new confirmation custom entity for yes and nos - and use it instead of Boolean? Your bot will never fail again: give me 20 ways to say yes as a array

["Yes", "Absolutely", "Sure", "Of course", "Definitely", "Agreed", "Indeed", "Indeed!", "Yep", "Yup", "Aye", "Indeed yes", "Yeah", "Yah", "Verily", "Yea", "By all means", "Affirmative", "All right", "Sure thing", "You bet"]

give me 20 ways to say no as a array

["No", "Nope", "No way", "Negative", "Not a chance", "Nah", "No can do", "No thank you", "My answer is no", "No sir", "No ma'am", "No sirree", "I'm afraid not", "Absolutely not", "Uh uh", "No thanks", "I don't think so", "Not now", "Not ever", "Nyet"] Conclusion Let the machine work for you, do not focus on the simple tasks a machine can do for you. Start exploring the capabilities of OpenAI and make your life easier. Originally published at . https://the.cognitiveservices.ninja

Power Virtual Agents: Use GPT-3.5 to Help With Trigger Phrases and Custom Entities

Twitter v. Elon Musk Court Filing by Potter Anderson & Corroon LLP, July 12, 2022 is part of . You can jump to any part in this filing . This is the March 24, 2022 text convo between Elon Musk and Canadian actress Talulah Riley, his ex wife, from . HackerNoon’s Legal PDF Series here Exhibit H 2022-03-24: Talulah Riley <> Elon Musk : can you buy Twitter and then delete it, please!? xx Talulah Riley : America is going INSANE. Talulah Riley : The Babylon Bee got suspension* is crazy. Raiyah and I were talking about it today. It was a fucking joke. Why has everyone become so puritanical? Talulah Riley : Or can you buy Twitter and make it radically free-speech? Talulah Riley : So much stupidity comes from Twitter xx Talulah Riley : Maybe buy it and change it to properly support free speech xx Elon : ["liked" "Or can you buy Twitter..."] Elon : I honestly think social media is the scourge of modern life, and the worst of all is Twitter, because it's also a news stream as well as a social platform, and so has more real-world standing than Tik Tok etc. But it's very easy to exploit and is being used by radicals for social engineering on a massive scale. And this shit is infecting the world. Please do do something to fight woke-ism. I will do anything to help! xx Talulah Riley (*) Elon Musk reinstated Babylon Bee’s account, alongside a couple of other high profile accounts, on November 18, 2022. https://twitter.com/elonmusk/status/1593673844996288512?s=20&t=BLOu1DCHuYyjK6LxzeuflQ?embedable=true

Triggered by Babylon Bee's suspension, Talulah Riley urged Musk to buy Twitter to 'fight woke-ism'

Impulsado por la suspensión de Babylon Bee, Talulah Riley instó a Musk a comprar Twitter para 'luchar contra el despertar'

Déclenché par la suspension de Babylon Bee, Talulah Riley a exhorté Musk à acheter Twitter pour "lutter contre le réveil".

बाबुल मधुमक्खी के निलंबन से त्रस्त, तालुलाह रिले ने मस्क से ट्विटर को 'वॉक-इस्म से लड़ने' के लिए खरीदने का आग्रह किया

バビロン・ビーの停職をきっかけに、タルーラ・ライリーはマスクに「目覚め主義と戦う」ためにツイッターを買収するよう促した

Impulsionada pela suspensão de Babylon Bee, Talulah Riley instou Musk a comprar o Twitter para 'combater o wake-ism'

Bị kích động bởi sự đình chỉ của Babylon Bee, Talulah Riley thúc giục Musk mua Twitter để 'chống lại chủ nghĩa thức tỉnh'

受巴比伦蜜蜂的停职引发，塔卢拉莱利敦促马斯克购买 Twitter 以“打击唤醒主义”

Bitcoin has been on a roller coaster ride over the past few weeks, with prices swinging wildly from highs of around $80k by June 2021 to lows below $15k in the past few weeks. This volatility is nothing new for Bitcoin, but it seems to ramp up as we approach what could be a critical juncture for the digital currency. So, what exactly is a capitulation? The Cooperate Finance Institute has a . concise definition In the financial world, it generally refers to a situation where investors give up on an asset and sell it en masse, leading to a sharp decline in prices. This can often be seen as a final stage before prices bottom out and begin to rebound. In the case of , a capitulation could be triggered by several factors, including the following: Bitcoin When miners receive rewards in Bitcoin for verifying transactions, they are motivated to keep the network secure. However, if the price of Bitcoin falls below the cost of mining (i.e., electricity and other expenses), miners will be operating at a loss. As a result, they will be less likely to continue verifying transactions, and the Bitcoin network will become less secure. This is known as a miner capitulation. In a severe case, it can lead to a Bitcoin death spiral, in which the price of Bitcoin falls so low that no miners are willing to continue verifying transactions. This would make Bitcoin unusable as a currency, as there would be no way to verify transactions. Therefore, it is important for the price of Bitcoin to remain high enough to incentivize miners to keep the network secure. Brain Drain When people are anxious about the future, they tend to sell their assets and move their money into assets they see as safer. In the case of Bitcoin, when people become anxious about its future, they sell their Bitcoins and move their money into assets like the US dollar. This mass exodus of money from Bitcoin to other assets causes the price of Bitcoin to drop, which leads to even more people selling their Bitcoins, causing a downward spiral. Closed/Overprotective Community There have been a few times in Bitcoin's history when the community has become too closed off and overprotective, leading to a capitulation. One such example was when Introducing NFTs to Bitcoin forums and discussion groups led to a massive flame war that ended in many members leaving the community. Another example was when the Bitcoin Lightning Network was first proposed, there was a lot of infighting, and eventually, some members left to start their own projects. While it's understandable that people want to protect their investments, Ultimately, these capitulations happen because the community becomes too insular and fails to listen to new ideas. To avoid this in the future, it's important for the community to remain open-minded and willing to discuss new proposals. Increased Regulation from Governments When a government begins to tighten its regulation of a particular industry – in this case, Bitcoin – it can significantly impact the market. In the case of Bitcoin, when it was announced that the governments of South Korea and China were planning to introduce new regulations around cryptocurrency, the price began to drop significantly. This is because investors felt that the increased regulation would make it more difficult to trade or use Bitcoin, so they began selling off their holdings. As more investors sold off their holdings, the price continued to drop until it reached a point where many people decided to sell their coins at a loss. This caused an overall panic in the market and led to a massive capitulation event. Bitcoin Verdict The jury is still out on whether or not Bitcoin will survive in the long term. However, it is clear that several factors could trigger a capitulation event. If the price of Bitcoin falls too low, miners will be incentivized to leave the network, which could lead to a death spiral. Additionally, if investors start to panic, they may sell their holdings en masse, which could also lead to a capitulation.

Bitcoin on the Brink: What Could Trigger a Capitulation?

When you think of Nintendo, what are the first things that come to mind? Most would say , Donkey Kong, and maybe even Pokémon. What all of these franchises have in common are that they are family-friendly. Parents can pretty much purchase any title from these franchises without batting an eye or reading about them beforehand. Since the Switch has a huge library of games from third-party devs (especially on the eShop), parents need to be more careful. Super Mario One game in particular that SERIOUSLY needs a trigger warning on the front label is Omori by . Omocat The Switch has the Most M-Rated Titles in Nintendo History Nintendo was thought of as the family-friendly console, one in which parents still had to be cautious, but never had to worry about themes like suicide being present in a game played on a Nintendo console. Throughout most of the company’s history, M-rated or even T-rated titles were few and far between. I consider myself a hardcore gamer, and even I would struggle to think of any MATURE titles on the Gameboy, Nintendo DS, or Nintendo Wii. However, the release of literally changed the game for the company. With previous consoles, Nintendo pretty much relied on 1st-party exclusive titles to drive sales. the Nintendo Switch There’s a reason why the Fire Emblem, Super Mario, and Pokémon series can only be played on Nintendo consoles. With the Switch, that all changed. Nintendo game exception third-party developer support, allowing for huge triple-A titles like Skyrim, Wolfenstein, Assassins Creed, and more to be released on the platform. This is great for young adult and adult gamers, but for kids I really hope that parents are careful about what their kids are playing, especially with the huge library of games (mature games) accessible on the eShop. What is Omori on the Nintendo Switch? Overview and Plot (spoiler-free) Omori is a psychological horror RPG about a young group of children looking for their lost friend in a fantasy world. The game constantly moves the player from the fantasy world that exists in the protagonist’s mind to the real world in which the protagonist of the game seems to be experiencing trauma and mental health issues. In a typical JRPG fashion, players will fight battles, defeat monsters, and complete quests. Omori Switch Release Date Omori was released on the Nintendo Switch on . However, it initially released on December 25th, 2020 on PC. June 17th, 2022 Why Omori on Switch NEEDS a Trigger Warning After having played the game in its entirety, I would argue that the game needs an obvious trigger warning on the front cover (on physical copies) and at the top of the game description on the Nintendo eShop. For the sake of preserving the beautiful Omori cover art, I’d at least argue for a mini-warning that prompts buyers to read a full warning on the back cover. , I know that firsthand. Omori is a game that can trigger anxiety The reason I think people need to be careful is actually the aspect that makes the game so good in the first place: subversion of expectations. The art style and messaging alone makes this seem like a fun, colorful RPG adventure about friendship! Look at the image above. The beginning of the game itself plays hard into this trap. As soon as you think you understand the tone of the game, it changes drastically. It changes from a happy-go-lucky adventure in some quirky fantasy world to a story about serious trauma, hallucinations, depression, and worse. ( Spoiler Warning) let me explain one tiny gameplay mechanic (if you don’t want SPOILERS, skip the rest of this paragraph). There’s a point in the game where you are trapped in a room or area and you don’t really know how to get out. In desperation, you open your menu and notice there is a new option you didn’t see before. The option simply says “STAB”. When you click it, the game asks you if you want to stab yourself, and you have to do so in order to progress. In case you think I’m exaggerating for the purposes of this article, Spoilers end here . So, please believe me, I am not exaggerating. THAT’S HOW DARK IT IS Like population, I’ve experienced anxiety at some points in my life (particularly on airplanes), but I’m by no means someone that worries about getting anxious playing video games. With that said, my anxiety was definitely triggered while playing this game. 33% of the American While playing that game, I know that this pixel art is just an animation playing on a game screen. I know there are way scarier and gorier graphics in triple-A titles like Resident Evil 7. is precisely because the art style, the music, the intro, and the characters, are all the exact opposite of the darkness the game holds. The reason Omori hits so hard The constant flip-flopping between childlike innocence to super dark themes and imagery keeps your emotions all over the place at times and can trigger you. which is something I take seriously, be very careful with this game. If you are someone prone to serious bouts of anxiety, depression, or thoughts of suicide, it’s probably best to just stay away from it. For the sake of your mental health, It’s an amazing game, don’t get me wrong. I would go as far to say But as someone who has experienced a panic attack, no game is worth sacrificing your mental health to play. Maybe come back to it when you’re in a better place mentally and put it down if you ever feel uneasy. it’s one of the best RPGs I’ve ever played in my life. But Does it Really Need a Trigger Warning? Notice that the title of this article says that Omori needs a trigger warning. on Switch I didn’t say that it needs one for Steam or for PS4 or for Xbox ONE (although we could argue for that too). The main reason is that those platforms tend to have more mature gamers. Especially on PC, most people will do a bit of research on it or simply read the Steam reviews on the product page and realize this is gonna be a dark game. because of the number of younger gamers on the platform. I’m particularly worried about the game on Switch There are absolutely ZERO inklings, warnings, or images on the eShop page to show how dark this game is going to be. Looking at the front and back art of the game show no evidence of the VERY dark themes and plot points of the game. if they happen to get their hands on this game. For young children, the story itself can scar them Omori on Nintendo Switch Game Trailer https://www.youtube.com/watch?v=hVJXZR5ahYA?embedable=true Even the game trailer above makes this look like some peaceful game akin to . Stardew Valley While the subversion of expectations is what gives this game its power, I don’t think it’s worth the risk of triggering mental health issues in anyone, let alone children. And . Here are the top 2 comments on the trailer above: I’m not the only one that feels this way I do understand how controversial this is of a topic, so I’d be happy to hear your thoughts on this in the comments below. In fact, please feel free to roast me in the comments below. It Needs a Trigger Warning, But It’s One of the Best Games I’ve Ever Played To end this article, I’d like to say I am against censorship in video games. I’m not on that train and this article isn’t about that at all. mostly I don’t think Omori should change at all. As I said above, it’s one of the best games I’ve ever played in my life. However, parents beware: DON’T BUY THIS GAME FOR YOUR KID…unless your kid is an adult and doesn’t have any serious mental health issues.

Omori on Nintendo Switch SERIOUSLY Needs a Trigger Warning

Omori en Nintendo Switch SERIAMENTE necesita una advertencia de activación

Omori sur Nintendo Switch a SÉRIEUSEMENT besoin d'un avertissement de déclenchement

निंटेंडो स्विच पर ओमोरी को एक ट्रिगर चेतावनी की आवश्यकता है

Nintendo Switchの大森は真剣にトリガー警告が必要です

Omori no Nintendo Switch Sério precisa de um aviso de gatilho

Omori trên Nintendo Switch NGHIÊM TRỌNG Cần Cảnh báo Kích hoạt

Nintendo Switch 上的 Omori 严重需要触发警告

A study published in the U.S. Patent and Trademark Office's working paper series shows that online software piracy increases innovation. The authors conclude that piracy motivates software companies to spend more resources on research and development, to stay ahead of the piracy threat, and protect their revenue streams. Piracy presents a major challenge for companies across different sectors, including the software industry. Many copyright holders stress that piracy hurts their bottom line. This could lead to less output and innovation. However, research has shown that this isn’t always the case. Previously, studies have found that piracy doesn’t reduce the number of new films being made. Similarly, piracy doesn’t slow down music output or innovation either. In fact, it may actually . encourage innovation Piracy Shock and Innovation This idea is supported by a new working paper published by the US Patent and Trademark Office (USPTO). The research was carried out by Wendy Bradley, Assistant Professor at SMU Cox School, and USPTO economist Julian Kolev. The research looks at the effect of the “piracy shock” that was introduced when BitTorrent was first launched 20 years ago. The then-new file-sharing technology made it much easier to pirate software and was swiftly embraced by millions of people. This ‘natural experiment’ is used to measure how large software companies changed their research and development (R&D) budgets in the following years. By comparing companies that face more piracy risks to those with a lower risk profile, the overall piracy effect is estimated. According to the researchers, the launch of BitTorrent is ideally suited to measure the effect of piracy. Its introduction in 2001 was sudden, the technology is ideally suited for software piracy, and it wasn’t notably interrupted during the sample period which ends in 2007. BitTorrent Piracy Triggered Innovation The overall results suggest that companies that are more at risk from piracy appear to innovate more following the launch of BitTorrent. “We find that rising piracy increases subsequent R&D spending, copyrights, trademarks, and patents for large, incumbent software firms,” the researchers write, suggesting that the piracy shock pushed firms to innovate more. “Firms in our sample exhibited a strong rise in innovative activity following the piracy shock, but they also shifted their IP strategies to pursue a more diversified portfolio through increased filings of copyrights and trademarks.” Translating these findings to hard figures would suggest that a 10% increase in piracy leads to a 2.8% increase in R&D spending. Subscriptions The research doesn’t look into specific types of innovation. However, it mentions that in more recent years the subscription model has been embraced by an increasing number of software companies. This includes software giants such as Microsoft and Adobe, whose software has been pirated millions of times in the past. Interestingly, this subscription model has its own drawbacks. The paper mentions “subscription fatigue” as a risk, one that also applies to movie streaming platforms. Too many subscriptions could eventually drive people back to pirate sites What About Revenue Today, more than twenty years after BitTorrent was invented, piracy levels remain high. However, initial data suggest that software companies have been able to increase revenues through new products and other innovations. “While firm performance is outside the scope of this study, initial estimates show firms in our sample experienced significant increases in both revenue and profit after the piracy shock,” the paper reads. “Our findings suggest that strategies encouraging new product development and a continuous commitment to innovation may offset the negative effects of digital piracy in certain industries.” Finally, the researchers stress that while BitTorrent’s launch is described as a piracy shock, the technology itself is not to blame. It’s a file-sharing technology that can and is used for legal purposes as well. A copy of the working paper is available through the and . It should be noted, however, that the views expressed in the paper do not necessarily reflect official positions of the Office of the Chief Economist or the U. S. Patent and Trademark Office. USPTO website SSRN

Can Piracy Trigger Innovation?

¿Puede la piratería desencadenar la innovación?

Le piratage peut-il déclencher l'innovation ?

क्या पाइरेसी इनोवेशन को ट्रिगर कर सकती है?

著作権侵害はイノベーションの引き金となるか?

A pirataria pode desencadear inovação?

Vi phạm bản quyền có thể kích hoạt đổi mới không?

盗版能否引发创新？

If you're like me, a sucker for event-driven programming, you'll want to continue reading. Today we'll take a look at triggering functions from messages. I've covered a few interesting topics regarding serverless architectures and AWS already, but nothing like this yet. Dig down, and get ready. Let's go. AWS Lambda AWS SNS TL;DR What are we building? What is AWS SNS? Build the API with the Serverless Framework Deploy the API to AWS Lambda Test the API with Dashbird *: All the if you want to check out the end result right away.* Note code from this tutorial is already on GitHub What are we building? Our focus will solely be on the steps to create the infrastructure components our app will need. The code itself will just mimic the behavior of a random complex computation. I've chosen a recursive function that calculates the factorial of the number passed to it. Here's a nice diagram, because diagrams are awesome of course! The function is the only exposed function, which is hooked up to . It takes a single parameter which it validates, upon success, it publishes an SNS topic and sends along the value. init API Gateway number number The SNS topic will trigger a second function called . This function will perform the calculation and log out the result to the console. This mimics a heavy computational background task such as data processing, image manipulation or machine learning calculations. calculate If the function fails, the Dead Letter Queue SNS topic will receive a message and trigger the function. calculate error Every function invoked will twice retry its execution upon failure. Using the Dead Letter Queue as a pool for your error logs is a smart use-case. asynchronously Now you're wondering, why all the complication with SNS instead of just invoking the second lambda function from the first one with ? Lambda's invoke API First of all, it's a huge anti-pattern for asynchronous workflows, which is our case. Otherwise, it's fine if you need the response from the second lambda function right away. Another issue is hitting Lambda's concurrency limits pretty fast. It can result in losing invocations and dropping data. Sending your data through a pub/sub service like SNS or a queue like will make sure you have data integrity. SQS Makes sense now? Sweet, let's talk a bit more about SNS. What is AWS SNS? Before we start coding we need to cover the basics. We know what AWS Lambda is, but what about SNS? The AWS docs are pretty straightforward. Amazon Simple Notification Service (SNS) is a flexible, fully managed and mobile notifications service for coordinating the delivery of messages to subscribing endpoints and clients. pub/sub messaging -- AWS Docs In English, this means it's a way of sending notifications between services on a publisher/subscriber basis. One service publishes some data about a and sends it along its way. SNS will then funnel it to all the subscribers of that particular . The key focus is on here, you'll see why a bit further down. topic topic topic Build the API with the Serverless Framework The first thing to do, as always, is to set up the project and install dependencies. 1. Install the Serverless Framework My go-to development and deployment tool for serverless apps is the . Let's go ahead and install it. Serverless Framework $ npm i -g serverless Note: If you're using Linux, you may need to run the command as sudo. Once installed globally on your machine, the commands will be available to you from wherever in the terminal. But for it to communicate with your AWS account you need to configure an IAM User. Jump over , then come back and run the command below, with the provided keys. here for the explanation $ serverless config credentials\
    --provider aws\
    --key xxxxxxxxxxxxxx\
    --secret xxxxxxxxxxxxxx Now your Serverless installation knows what account to connect to when you run any terminal command. Let's jump in and see it in action. 2. Create a service Create a new directory to house your Serverless application services. Fire up a terminal in there. Now you're ready to create a new service. What's a service? It's like a project. It's where you define AWS Lambda functions, the events that trigger them and any AWS infrastructure resources they require, including SNS which we'll add today, all in a file called . serverless.yml Back in your terminal type: $ serverless create --template aws-nodejs\
    --path lambda-sns-dlq-error-handling The create command will create a new . What a surprise! We also pick a runtime for the function. This is called the . Passing in will set the runtime to Node.js. Just what we want. The will create a folder for the service. service template aws-nodejs path 3. Explore the service directory with a code editor Open up the folder with your favorite code editor. There should be three files in there, but for now, we'll only focus on the . It contains all the configuration settings for this service. Here you specify both general configuration settings and per function settings. Your will be full of boilerplate code and comments. Feel free to delete it all and paste this in. lambda-sns-dlq-error-handling serverless.yml serverless.yml service: lambda-sns-dlq-error-handling

plugins:
  - serverless-pseudo-parameters

provider:
  name: aws
  runtime: nodejs8.10
  stage: dev
  region: eu-central-1
  memorySize: 128
  environment:
    accountId: '#{AWS::AccountId}'
    region: '#{AWS::Region}'
  iamRoleStatements:
    - Effect: "Allow"
      Resource: "*"
      Action:
        - "sns:*"

functions:
  init:
    handler: init.handler
    events:
      - http:
          path: init
          method: post
          cors: true
  calculate:
    handler: calculate.handler
    events:
      - sns: calculate-topic # created immediately
    onError: arn:aws:sns:#{AWS::Region}:#{AWS::AccountId}:dlq-topic
  error:
    handler: error.handler
    events:
      - sns: dlq-topic # created immediately Let's break down what's going one here. Check out the section. There are three functions here. From top to bottom they're , , and . The function will get triggered by a simple HTTP request, which we invoke through API Gateway. Familiar territory for us. functions init calculate error init However, the and functions are triggered by SNS topics. Meaning we will have logic in the function that will messages to a topic named while the function is to the same topic. calculate error init publish calculate-topic calculate subscribed Moving on, the function is subscribed to the while the function will publish messages to this topic if it fails, as you can see with the property. Now stuff makes sense, right? error dlq-topic calculate onError Make a mental note to yourself, once you add the SNS topics as events for your functions, the resources will automatically be created once you deploy the service. What else, take a look at the , they specify that our functions have permission to trigger, and get invoked by SNS topics. While the plugin lets us reference our and with the CloudFormation syntax, making it much easier to keep our SNS ARNs consistent across all resources. iamRoleStatements serverless-pseudo-parameters AccountId Region 4. Install dependencies Luckily, this part will be short. Just one package to install. First, initialize npm and then you can install . serverless-pseudo-parameters $ npm init -y && npm i serverless-pseudo-parameters That'll do. 5. Write business logic With all things considered, the configuration process was rather simple. The code we'll write now is just as straightforward. Nothing extraordinary to see, I'm sorry to disappoint. Let's keep all three functions in separate files, to keep it simple. First of all create an file and paste this snippet in. init.js // init.js
const aws = require('aws-sdk')
const sns = new aws.SNS({ region: 'eu-central-1' })

function generateResponse (code, payload) {
  console.log(payload)
  return {
    statusCode: code,
    body: JSON.stringify(payload)
  }
}
function generateError (code, err) {
  console.error(err)
  return generateResponse(code, {
    message: err.message
  })
}
async function publishSnsTopic (data) {
  const params = {
    Message: JSON.stringify(data),
    TopicArn: `arn:aws:sns:${process.env.region}:${process.env.accountId}:calculate-topic`
  }
  return sns.publish(params).promise()
}

module.exports.handler = async (event) => {
  const data = JSON.parse(event.body)
  if (typeof data.number !== 'number') {
    return generateError(400, new Error('Invalid number.'))
  }

  try {
    const metadata = await publishSnsTopic(data)
    return generateResponse(200, {
      message: 'Successfully added the calculation.',
      data: metadata
    })
  } catch (err) {
    return generateError(500, new Error('Couldn\'t add the calculation due to an internal error.'))
  }
} We have a few helper functions and the exported lambda function at the bottom. What's going on here? The lambda validates input and publishes some data to the SNS topic. That's everything this function is doing. The SNS topic will trigger the lambda function. Let's add that now. calculate-topic calculate-topic calculate Create a file and name it . Paste this snippet in. calculate.js // calculate.js
module.exports.handler = async (event) => {
  const { number } = JSON.parse(event.Records[0].Sns.Message)
  const factorial = (x) => x === 0 ? 1 : x * factorial(x - 1)
  const result = factorial(number)

  console.log(`The factorial of ${number} is ${result}.`)
  return result
} As you see this is just a simple factorial calculation implemented with a recursive function. It'll calculate the factorial of the number we published to the SNS topic from the function. init An important note here is that if the function fails a total of three times, it'll publish messages to the Dead Letter Queue SNS topic we specified with the property in the file. The Dead Letter Queue will then trigger the function. Let's create it now so it can log out errors to CloudWatch. Create an file, and paste these lines in. calculate onError serverless.yml error error.js // error.js
module.exports.handler = async (event) => {
  console.error(event)
} For now, this will do. However, ideally, you would have structured logging with detailed info about everything going on. That's a topic for another article. Deploy the API to AWS Lambda Here comes the easy part. Deploying the API is as simple as running one command. $ serverless deploy\ ![lambda deploy](https://cdn.hackernoon.com/images/cky-9-uq-08500-u-80-bs-6-b-31-s-7-jsp.jpg) You can see the endpoint get logged to the console. That's where you will be sending your requests. Test the API with Dashbird The simplest way of testing an API is with CURL. Let's create a simple CURL command and send a JSON payload to our endpoint. $ curl -H "Content-Type: application/json"\
  -d '{"number":1000}'\
  https://<id>.execute-api.eu-central-1.amazonaws.com/dev/init If everything works as it should, the result of the calculation will be logged to CloudWatch. If not, well then you're out of luck. After hitting the endpoint a few times with a couple of different values here's the result. The function works as expected. init But, what really interests us is the function. Here's what that looks like when it's successful. calculate When it fails it'll specify a crash and show the error logs. After two retries it'll send a message to the Dead Letter Queue and trigger the function. error Sweet! We've tested all the different scenarios. Hope this clears things up a bit. Wrapping up That's all folks. We've covered creating SNS triggers for Lambda while also implementing a Dead Letter Queue to catch errors from failed invocations. Using serverless for various intermittent calculations is a valid use case that will only grow in popularity in the future. There are no servers you need to worry about, and you only pay for the time it runs. Just deploy the code and rest assured it'll work. Again, here's the , if you want to take a look at the code. It can act as a starter for your own use-cases where you need SNS messages triggering Lambda functions. Give it a star if you like it and want more people to see it on GitHub. GitHub repo Also published on: https://dashbird.io/blog/triggering-lambda-with-sns-messaging/

How to Trigger Lambda with SNS Messaging: Going Serverless with AWS

Cómo activar Lambda con SNS Messaging: Sin servidor con AWS

Most projects in the world of crypto read like an instance of Prestige Worldwide taking themselves entirely too seriously. So, when a link to Non-Fungible Olive Gardens (NFOG) popped up in a local DAO’s Discord chat, the ridiculousness sold me immediately. Everything about the now delisted project is on brand all the way down to the unlimited breadsticks and family-like atmosphere ( ) of the appropriately in-character chat. wyhyf Discord Of course, I minted a breadstick NFT right away before the topic of Olive Garden proceeded to consume my mind space for far longer than I’m comfortable admitting. Was I alone? Since the project dropped on December 17, the has seen over 82 ETH total volume in sales across 880 unique location-based NFTs, over 1.4k breadsticks minted, and a community bootstrapped token ($ ) up close to 200% in the first 24 hours post-airdrop with over on Uniswap Polygon. NFOG collection bstick $100k in TVL And if all of that means nothing to you, just know that pictures of Olive Garden restaurants sold as NFTs multiplied several times over in value along with a literally made-up token called . Why do all of this? Well, the project has to “complete leveraged buyout of Darden Restaurants, Olive Garden’s parent company.” Breadstick stated plans As of this writing, to NFOG. Unless the intention was to catapult NFOG into the collective crypto zeitgeist, I’m calling that a wrong move. Olive Garden has unsurprisingly issued a DMCA You don’t need to to guess that the attention drawn by delisting will only amplify the Non-Fungible Olive Garden movement as the community moves the collection to . The crypto space exists precisely as a response to overreaches of centralized authority. search hard decentralized exchanges Forcing a crypto-delisting is like punting a wasps nest. Now, what follows is not financial advice. I , however, reflect on what this phenomenon can tell us about the increasing importance of community to brands, money as expression, and intellectual property (IP) in the decentralized and immutable world of Web 3.0. will Non-fungible? I would be remiss if I didn’t at least provide a quick blurb on NFTs — please skip ahead if you’re familiar. Without overcomplicating it, if a widget is , it means that it’s unique. So, in the case of the NFOG collection, each NFT can be distinguished from one another. If you own one, we can verify that you own it, and we know it’s authentic because we can see where it came from. We know those things for reasons we won’t get into here, but you can watch the video below, later if you’re interested in a simple visual explanation of NFTs. non-fungible What does it mean? In a world where jpegs demand millions of dollars on the basis of insane future promises yet to be seen, Non-Fungible Olive Gardens stands out as one of the most sincere. How, you ask, can an unofficial NFT project peddling pictures of Olive Garden — itself serving a diluted simulacrum of “authentic” Italian fare — be anything other than a scam? Well, it’s a joke, and the fact that it’s been as wildly successful as it is makes it all the more hilarious. Regardless of your moral leanings on the topic of NFTs, you have to admit that the absurdity of NFOG’s premise, rise, and aims is at least worth a chuckle. That’s an absurdity as Camus would put it — the futility of a search for meaning in a world devoid of it. It’s maddening. But why, what’s the sticking factor? Jokes are funny because they contain a kernel of truth. Wrapped in this dumb little collection is a reflection of the emptiness inherent to our constructs of money, intellectual property, ownership, and presumed rationality as the basis for the actions of our fellow humans. When new technologies bring about new capabilities, old concepts are forced to be reconsidered and our relationships with them evolve. What does money mean when we can make and exchange our own? How does one enforce ownership of intellectual property when the use of it can go unabated? Why in the world did they just spend $800 on a picture of an Olive Garden in Idaho? I don’t have the answers to these questions, but I can put them out there to start a dialogue. Future of NFOG & beyond 👀 There’s no doubt in my mind that NFOG is the start of a new genre of NFTs and memes more broadly. The recent delisting has inspired a brilliant new collection, , and may even catalyze a new decentralized NFT marketplace for that genre. Non-Fungible Takedowns The organic nature from which NFOG community-driven initiatives have arisen introduces whole new dimensions to the experience and expression of memes, a dynamism that creates a sort of glue tying together those in on the joke. It’s a meme you can walk into and participate in. I’ve said, half-kidding, that the title of and classes like Masterpieces of Western Memes will soon go from absurd to matters of course. Chief Meme Officer The ability to wield memetic powers is a skill that pays outsized dividends for those the memes serve. They can catalyze communities — they’re potent delivery mechanisms for rapid communication that spread virally. But, like any form of communication, this skill does not come easily to many brands. Corporations rarely do humor well, often erring on the side of woefully out of an unwillingness to take risks. cheesy So, at the point a “ ” have taken…let’s call it from your branding, monetized pictures of your storefronts in the form of NFTs, amassed a cult-like following of alt-brand ambassadors complete with their own bootstrapped cryptocurrency token ( ), deflected a delisting of the NFT in question, ? community of Olive Garden enthusiasts inspiration $bstick what is (fungible) Olive Garden to do Unsolicited advice to the big OG 🫒 Before we go further, let’s consider the fundamental options for a brand in this position, of which Olive Garden will be the first of many: Ignore it exists Shut it down Embrace it Ignorance The first option is to simply ignore the “problem”. When haters rage against its existence, feign ignorance. When the troves of Olive Garden alt-universe fans tag the business incessantly on Twitter and flood DMs, leave them 'on read' and hope that they would tire themselves out. However, this approach has two main weak points. First, it leaves a giant elephant in the room should the community reach critical mass. The second, though, is more important. That is, there is a ton of value to be had in at least acknowledging the movement, its enthusiasm, and creativity. Whole marketing arms are paid good money to resonate with an audience, why in the world would an organization look the other way when that very audience is doing it better than them? Shut it down As I mentioned earlier, it looks like Darden is trying to shut things down. But let’s be real, the internet is tireless. Try to shut anything down, and you’re signing up for a game of whack-a-mole. There have been numerous instances of attempted . Headlines will write themselves, the value of the NFTs and $bstick surge, and the community continue to grow and evolve only to manifest more formidably. Maybe not immediately, but eventually. censorship that backfired Between mentioning the delisting above and this paragraph, the as well as on NFOG collection is already listed on a decentralized exchange called Zora Rarible Regardless of what law states, a creation takes on a life of its own in the collective consciousness once it’s unleashed. To a sophomore in college, Coca-Cola may mean getting blasted off rum while to you it may mean refreshing picnics in the park. These associations have their own emotional reverberations. Perhaps instead of trying to homogenize through control, brands are better served by learning to influence and let the best expressions arise on their own. It’s in the interest of a brand to ride the waves of evolution, capitalizing on enthusiasm rather than trying to corporatize the brand’s form. Let it be. Let it mean different things to different people. A brand can exist in parts and live on in subcultures far beyond what corporate suits can imagine. Rolling with these changes and embracing the emergent expression of the idea of the brand has the potential to increase the scope of its emotional impact. Good luck trying to cut the head off of this hydra. That’s not a threat, it’s a fact of nature nowadays. Web 3.0 is not your daddy’s internet. Between smart contracts, decentralized storage (IPFS) and exchanges, and , we’ve been collectively ushered into a new way of getting things done together without the need for permission or the fear of censorship. DAOs Who do you take down when there is no leader? Who do you talk to when the website isn’t centrally hosted and the exchange platform lacks control over listings? Embrace it Look, there is no playbook for this. So why not run with it and write it as you go? This is a fandom Tolkien would have dreamed of — a group of individuals who love Olive Garden, bonded by nostalgia and wholesomeness the restaurant represents, and create new forms of expression through memes, material goods, and tokens while having fun doing it. I mean, when was the last time you read something inspired by Olive Garden? : Invite and incentivize collaboration from your community in deciding what you become — . In doing so, you create a group of superfans financially and intellectually invested in the propagation of your brand and product. If you , they will run with your idea for you. echoing this sentiment. The biggest takeaway is this think bottom-up, not top-down give people ownership Here’s a thread No doubt it’s risky to embrace them. It’s like herding cats. But this is where things are headed. Plus, it’s way more fun doing things together. When people are given the tools to communicate as a group, create a unit of value, exchange that value, then incentivized to perform work for the sake of moving the group toward some goal, the sovereignty they reclaim is unassailable. It doesn’t matter whether people are initiated through absurd memes or reading the whitepaper of an obscure cryptocurrency; whether the intention is to buy Olive Garden, , a , or . Once the power of decentralized, permissionless, censorship-resistant collaboration is experienced, there is no going back. Might as well join the party. wyhyf. Blockbuster golf club the Constitution Thanks for reading some of my words to kick off your new year. Let’s connect on , or wherever you are . Twitter online Also published . here

Olive Garden Gets NFOG Delisted And Triggers Streisand Effect: Marketing and PR #Fail

Kinesis Data Streams are the solution for real-time streaming and analytics at scale. , AWS themselves use it internally to keep, well, AWS working. As we learned last November Kinesis works very well with AWS Lambda. Creating a function that will process incoming records is easy, especially if we leverage the Serverless Framework or SAM to deploy required resources. The simplicity of setting up a Kinesis trigger for a Lambda function may be deceptive. There are pitfalls that can cause problems we will spot only later, in the production environment. I learned about some of them the hard way, so let’s say I speak from the experience. Simple Kinesis trigger Let’s start with a Kinesis stream and a Lambda function that handles incoming records. I will use the Serverless Framework here, but we can achieve the same with SAM or raw CloudFormation. The stream we created will keep records for 24 hours and will consist of two shards. Each shard provides a defined max throughput. Records from a single shard are delivered in order. Our Lambda function will be just as simple: A General Pitfall: not specifying trigger parameters With the Lambda trigger defined as above, we rely on the Serverless Framework to set its parameters. Some default values generated by the SF are different from CloudFormation defaults for the Lambda trigger ( ). That could be a sufficient argument to define them explicitly – there is a smaller chance that someone will assume their values incorrectly by looking at the wrong documentation. AWS::Lambda::EventSourceMapping resource However, what’s even more important is the fact that the default values will rarely suit us in practice. Since this is A General Pitfall, let’s break it into smaller ones by looking at what we can do wrong (and how to fix it!) with different parameters. Pitfall #1: small batch size If we start sending messages to the stream every second and look into the CloudWatch Logs Insights to check how many records are processed by the Lambda at once, we will see something like this: We have a new record in one of the shards every second. Lambda pulls it, processes quickly (well, we don’t do anything apart from logging it), and is ready to take the next one. As a result, we process one or max two records in each execution, and the function runs over and over. But what will happen if we disable the trigger for a moment and let messages accumulate in the stream? Let’s see: Lambda took ten records, processed them, and took the next batch. Then repeated this until there were no more items in the stream. Two sub-optimal things are going on here. Firstly, since we process messages as fast as they are incoming, our Lambda is invoked for almost every record separately. Secondly, when items accumulate in the stream, Lambda takes no more than ten at a time. Why is it bad? With only one record processed each time, . But that’s not all. We probably would like to do something with that record. Analyze, filter, and send to some API endpoint, database, etc. Those operations can usually take a batch of items – for example, we can add multiple items to the database with a single query. we have more Lambda invocations to pay for Processing and sending 10 or 100 records in a batch is usually much faster than doing it 10 or 100 times separately. On top of that, we generate a load on the target service with each call. That . Business requirements often say “real-time processing”, but in reality, often a delay of 3, 5, or 15 seconds does not make any difference. increases its resource usage and/or costs On the other hand, if we have a backlog of items to process, we take only 10. Is it optimal? Of course, the answer to this question will depend on what we actually do, our function logic, and the external calls we make. But we need to think about it and choose some value consciously. Wouldn’t it better (faster and cheaper) to process 100 at once, or, if we speak about it, 1000? How can we fix it? With two parameters: and . batchSize batchWindow sets the maximum number of records processed by the Lambda function at once. sets the maximum number of seconds to wait and accumulate records before triggering the Lambda. batchSize batchWindow Here we will set those parameters as follows: What does it mean? Lambda will trigger when there are 100 records in a stream shard, after 5 seconds, or after accumulating 6 MB of payload size (built-in limit), whatever will happen first. Again, choosing those values depends on the business requirements. Not choosing them is rarely a valid option. Pitfall #2: no error handling Our Lambda is now processing a hundred records in each invocation. Everything runs smoothly until one malformed message comes in and our function throws an error. If we worked with SQS before and expect the processing to be retried three times until the record is rejected, we will have a very unpleasant surprise. By default, , the maximum possible number of retries. Moreover, . This is due to the Kinesis guarantee of in-order processing. Lambda will try to process the batch 10,000 times it will not process further records until the problem is resolved Of course, we may want to repeat the execution 10,000 times. Maybe the problem lies not in the data but in an external system we call. But more often, we want to skip the problematic message and continue with the next ones, preventing our systems from hanging with old data. There are three parameters worth using for this case. We will start with . To do so, we will set the . reducing the number of retries maximumRetryAttempts While we can get notified about failures from the Lambda metrics, . This way, when we find and fix the faulty Lambda logic causing the problem, we can re-ingest dropped records. Our DLQ can be an SQS or SNS that we reference under . it’s generally a good idea to send info about unprocessed records to a Dead Letter Queue destinations.onFailure Here a clarification is needed: records themself are not sent to DLQ, only information about them. Each message will contain details about the failed batch position in the stream. So having it, we can later get records directly from the Kinesis as long as they don’t reach the retention period. Here is an example of such a message: Looking closely at this message, we will see that rejected was not only a single faulty record, but the whole batch of eight. If only a single record is making problems, we may still want to process others, not throw them out because of them being in the wrong company. For that, the solution is a option. Depending on the batch size and number of retries, we may eventually isolate the single malformed record. After discarding it, we can successfully process all others. bisectBatchOnFunctionError When set to true, each time the Lambda execution will fail, the batch will be split in two and retried separately. One thing worth noting is that retrying batches will cause some records to be processed multiple times. If the function logic is not idempotent or we don’t want to waste execution time on repeating successful actions, we can mitigate this problem using . custom checkpoints Pitfall #3: wrong starting position This is a problem we will usually face only when creating a new Kinesis trigger. But this moment can be during disaster recovery (as it was in my case), so it is better to prepare in advance. Contrary to SQS, Instead, Lambda, of course, does it for us. messages in Kinesis are not removed after being read by the client. each client keeps track of the last record it read. When we deploy a new Lambda function with a Kinesis as a trigger, it will start by reading all the existing records from the stream by default. Depending on the configured stream retention period, that can mean all messages from even the last 365 days. No need to say it may not be what we aim to do. It can take long hours before we process all the old data and catch up with the current records. Not to mention a bill that it can cause. For those reasons, if we want to process only the new messages incoming from the moment we deploy our function, we need to set the startingPosition explicitly. The default value is TRIM_HORIZON – to start from the oldest record available. To start from the latest record at the moment of function deployment, we change it to LATEST. Yet another possibility is to provide a timestamp if we want to process records from a given point in time. Pitfall #4: insufficient parallelization Kinesis Data Stream consists of shards, and we pay for the number of them. Each shard can receive up to 1 MB or 1,000 records per second. However, the fact we have enough throughput to ingest messages into the shard doesn’t mean we can read and process them at the same rate. Let’s say each shard receives 100 new records every second. It’s way below the limit. But if our Lambda function takes 2 seconds to process such an amount of data, we will be lagging more and more, unable to catch up. At this point, you surely know there has to be a solution, or we wouldn’t be talking about it. And there is, not just one, but two. The first one is to simply increase the number of shards on the Kinesis side. The number of Lambda executions will increase to match it as well. Of course, records need to have sufficiently varied partition keys to be split evenly into shards, and additional shards will cause additional charges. The other solution is to use the property. It enables processing messages from a single shard in up to 10 concurrent executions. Despite reading from shard in parallel, the order of records with the same partition key is still maintained. Thus, However, the max total read throughput per shard still applies. parallelizationFactor increasing parallelization allows safely processing a higher data volume without increasing the costs of Kinesis. Pitfall #5: unnoticed processing lag As always, we should monitor our Lambda function for failures, timeouts, and throttles. We can do it directly in AWS by creating individual alarms for each of these metrics. without any additional configuration. Another option is to use Dashbird, which will monitor our function’s health out of the box But when dealing with Kinesis, it’s not enough. Even if Lambda works correctly, that doesn’t mean the whole system runs smoothly. We may have more records incoming to the stream than we can process. There may be multiple reasons, like traffic spikes or increased latency of external service used by the Lambda. That’s why one of the most crucial Kinesis metrics to keep an eye on is the Iterator Age. It tells us how long the message was in the stream before the Lambda read it. with no additional setup. The growing age of records is automatically detected and reported by Dashbird for all streams Pitfall #6: lack of stream monitoring Iterator Age, while important, is not the only metric to monitor. There is also another reason for which the Lambda function may not be producing new results. And it’s very prosaic. If the stream stops receiving new data, Lambda will have nothing to analyze and process. That often goes unnoticed, as it does not produce errors. When there is nothing to run, there is nothing to fail. Fortunately, Dashbird provides insights and auto-detection of such scenarios as well. In addition to , it also with no new incoming data. reporting read and write throttles or failing processing alerts about abandoned streams Conclusion and Bonus We can process Kinesis Data Streams records easily with AWS Lambda functions. However, it’s essential to set this integration right. Otherwise, it can generate unnecessary costs and result in poor performance altogether. Besides that, monitoring as always plays a critical role in keeping services up and running. There is also a bright side – a small bonus. When we learn how to work with Kinesis, . We can utilize most of the configuration options and solutions mentioned above when the Lambda function processes change from DynamoDB. most of our knowledge will also apply to handling DynamoDB Streams Full code for the Serverless Framework configuration, Lambda function handler, and dummy records producer is available on my GitHub: aws-kinesis-trigger Also published on: https://dashbird.io/blog/lambda-kinesis-trigger/

AWS Lambda with Kinesis Trigger: 6 Pitfalls and How to Fix Them

allow you to create a template for emails that you can send to a newly created contact, using code. Before sending the email, your code can inject information into the template to personalize it with any data that is available in your page code. In this article, we demonstrate how to use the code snippet generated by Triggered Emails to send an email to the newly created contact on the submission of a form. Triggered Emails Although this article uses a form submission for demonstration purposes, you can send an email from anywhere in your code. The general idea is to paste the generated snippet into your code where you want the email to be sent. Then edit the snippet so that it uses the ID of the newly created contact and the values you want to insert for the variable placeholders. Note: For a more general-purpose article on sending an email using a 3rd party email service, see How to Send an Email on Form Submission . Prerequisites This article assumes you are familiar with . In this example we'll assume you've the following Triggered Email: creating an input form created and published Notice that the email template contains the following variables: name Interest_Area It is good practice to give your email template a meaningful name. Doing so makes it easier to work with in code. Click on the Email ID to rename it. In this example, we call our email . newsletter_signup To create a Triggered Email for Contacts, select the Email New Contacts tab. The code snippet generated by the email looks like this: There are a few things to note about the code in this snippet: At the top of the snippet there is an statement. This line needs to be added to the very top of the code on the page where you will be using the rest of the snippet. It imports the library of functions that lets your code work with the Triggered Email functionality. import The contact ID and the values of the variables ( and ) are reflected in the code snippet with placeholders. These placeholder values will need to be replaced with real values in your actual code. name interest_area Add a Custom Field to Your Contacts List In our example we save some information about our contact in a custom field. To work with custom fields in our code we first need to add the to the Contact List in the Dashboard. For our example, name the field interest_area. custom field Form Next, we create an input form with a submit button. In this example, we use a simple form with the following input elements: Code Finally, we write the code to send the Triggered Email described above when the form is successfully submitted. The code will send the values from the form to be used in place of the variables in the email template. Here's the outline of what we'll need to do: Add the import statement to the top of the code where we use the snippet. Add an onClick event handler to the button. The code we add to that handler function will create the new contact and email them. Add code that creates the new contact and gives us the contact's ID. Add the snippet code to the handler function and replace the placeholder values. Add some code to handle success and errors. Note: Triggered Emails may not work properly when previewing your site. Publish your site to test the code found below. 1. Add the import statement to the top of the code where we use the snippet. Paste the import statement that was at the top of your snippet: to the top of the code in the page where you want to use the snippet. import wixCrm from 'wix-crm'; 2. Add an onClick event handler to the button. The code we add to that handler function will create the new contact and email them. Use the Properties & Events panel to add an event handler to the signUpButton that runs each time it's clicked. onClick() 3. Add code that creates the new contact and gives us the contact's ID. Add this code inside the onClick event handler. This uses the API to create the new contact using the information the site visitor entered in the form fields. createContact wixCRM.createContact({ : $w( ).value, : [$w( ).value], : $w( ).value
}) "firstName" '#nameInput' "emails" "#emailInput" "interest_area" "#interestArea" The function returns the for the newly created contact. We'll use that ID in our snippet to identify the new contact and email them. createContact contactID 4. Add the snippet code to the handler function and replace the placeholder values. The function returns a promise, so we'll add our snippet after it. Now that we have the we will use it in the snippet in place of . createContact contactID <enter-contact-ID-here> We'll also use the values that our site visitor entered in the form fields as the actual values for the and variables in the object. name interest_area variables At this point our code should look like this: {
 wixCRM.createContact({ : $w( ).value, : [$w( ).value], : $w( ).value
  })
  .then( {
   wixCRM.emailContact( , contactId, { : { : $w( ).value, : $w( ).value
    }
   })
  })
} export ( ) function signUpButton_click event "firstName" '#nameInput' "emails" "#emailInput" "interest_area" "#interestArea" ( ) => contactId "newsletter_signup" "variables" "name" '#nameInput' "interest_area" "#interestArea" 5. Add some code to handle success and errors. Since the function returns a Promise, we can define what happens when the Promise resolves successfully or rejects with an error. emailContact() We'll add a and a to handle these. .then .catch Your code should look like this now: wixCRM ;

$w.onReady( {

}); {
 wixCRM.createContact({ : $w( ).value, : [$w( ).value], : $w( ).value
  })
  .then( {
   wixCRM.emailContact( , contactId, { : { : $w( ).value, : $w( ).value
     }
    })
    .then( { })
    .catch( { });
  });
} import from 'wix-crm' ( ) function export ( ) function signUpButton_click event "firstName" '#nameInput' "emails" "#emailInput" "interest_area" "#interestArea" ( ) => contactId "newsletter_signup" "variables" "name" '#nameInput' "interest_area" "#interestArea" => () // do something after the email was sent ( ) => err // handle the error if the email wasn't sent Test your form in your published site to see that it creates the contact and sends a Triggered Email. Previously published at https://support.wix.com/en/article/velo-tutorial-sending-a-triggered-email-to-contacts

How To Run Triggered Sends with Velo by Wix

The word "amateur" has many connotations: beginner, inexperienced... To me, it’s someone who is doing something for the love of it, and not seeking fame or recognition in return. That’s a huge advantage amateurs have over professionals. They aren’t afraid of experimenting, making mistakes, trying something new. Pick something you WANT to learn, and allow it to consume you. When someone feels genuinely passionate about what they are doing, the overall final outcome, be that an article or product, that they produce is often far superior to those of people who focus solely on said outcome. In essence, focus on the journey, not the destination. Think back to school. (For me, it's not that far back.) Think about a subject you disliked, hated, or at least didn't enjoy, and how much effort and turmoil it took to study and learn that subject, purely for the exam grade that came at the end of the year. Now think about your hobby, your passion; you can easily sink hundreds of hours researching and learning about it, why? Because it piques your curiosity. In my experience, being curious is the single most defining trait that I see leading to success for both myself and others. Curiosity allows you to be comfortable with the unknown, and inspires you to understand more, seek more, learn more. The past 100 years have not put curiosity first. Traditional schools, corporations, and churches didn’t just deter open inquiry and experimentation—they feared it. Instead, they rewarded emulation. Any sort of innovation or questioning was limited to a few people at the very tippy top of the food chain, with everyone else expected to be a good little worker bee. But the internet has inverted that model. Today, it seems that it's the ones who fail to experiment, innovate, or challenge who get left behind.... It doesn’t matter what you are trying to become better at, if you only do the work when you’re "motivated", then you’ll never be consistent enough to become a professional. I've realised that motivation is a product of action. If you on a project/task/job, you will quickly become motivated to keep going, and to do better. The ability to show up everyday, stick to the schedule, and do the work — especially when you don’t feel like it — is so valuable that it is literally all you need to become better 99% of the time. get started When I don’t miss workouts, I get in the best shape of my life. When I code every day, I become a better programmer. When I write every week, I become a better writer. Having the fundamental enjoyment that comes with being an amateur, and the determination and drive that comes with being curious about what you're doing, catalyses this effect 10 fold. I recently took on a new role as a software developer here at HackerNoon, and one of the projects I chose to pick up was building and open-sourcing a Chrome extension that blocks paywalled sites from your search results. I had never built one before, and it was whole new world to me, but let me tell you... it was the most fun I'd had in a long time. I got sucked into the Chrome ecosystem, and learned so much about the inner workings of the browser and Google itself. This thrill of being completely new to and confused about something triggered the most productive coding stint I've ever had. I managed to get a out in under 6 hours! fully-featured, working product Enjoying what you do and having that curiosity to push you really is a fucking super-power. The classic hating your job, but tolerating it so you can 'enjoy' retirement at 60 is a fallacy that we no longer need to live by. Despite some people's opinions, most of the time, you can and should monetize your hobbies. If you enjoy it, you're gonna be a good at it, and whatever you're good at will have a value associated with it! Put yourself out there, share what you are working on, and who knows: one day it might set you free! I just made a Twitter ! Give it a follow to stay up to date, it'll be worth it I promise ;) Also, if you like this post, feel free to subscribe or check out my other writing here .

How I Hacked My Brain To Trigger My Most Productive Coding Stint Ever

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