Triggeris in the Application Development Tools Industry

Trigger

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

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