Quaro

quaro

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Quartz is an open-source Java job scheduling framework that provides powerful capabilities for scheduling tasks. In DolphinScheduler, Quartz is used to implement task scheduling and management. DolphinScheduler integrates with Quartz through the QuartzExecutorImpl class, combining workflow and schedule management operations with Quartz's scheduling framework to achieve task execution. This article provides a detailed analysis of Quartz's principles and implementation within DolphinScheduler. Quartz Entity-Relationship Diagram QRTZ_JOB_DETAILS and QRTZ_TRIGGERS are the central tables, defining the relationship between jobs and triggers.


The QRTZ_TRIGGERS table links with multiple trigger-type tables, such as QRTZ_SIMPLE_TRIGGERS and QRTZ_CRON_TRIGGERS, to enable different trigger mechanisms.


QRTZ_FIRED_TRIGGERS records execution history, associating with both the job and trigger tables.


QRTZ_CALENDARS defines calendar exclusion rules for triggers, while QRTZ_PAUSED_TRIGGER_GRPS manages the pause state of trigger groups.


QRTZ_SCHEDULER_STATE and QRTZ_LOCKS are used for scheduling coordination in clustered environments, ensuring high availability. Quartz in DolphinScheduler We will focus on the principle analysis of the use of quartz in DolphinScheduler, so the steps for using quartz in DolphinScheduler will be introduced briefly. Generally speaking, there are 4 steps: Creating a New SHELL Task
Defining and Configuring Workflow Scheduling
Scheduling Activation
Workflow Instance Execution Principle Analysis Creating a Schedule org.apache.dolphinscheduler.api.controller.SchedulerController#createSchedule  
--org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#insertSchedule  
...  
Schedule scheduleObj = new Schedule();  
Date now = new Date();  
scheduleObj.setTenantCode(tenantCode);  
scheduleObj.setProjectName(project.getName());  
scheduleObj.setProcessDefinitionCode(processDefineCode);  
scheduleObj.setProcessDefinitionName(processDefinition.getName());  
ScheduleParam scheduleParam = JSONUtils.parseObject(schedule, ScheduleParam.class);  
scheduleObj.setCrontab(scheduleParam.getCrontab());  
scheduleObj.setTimezoneId(scheduleParam.getTimezoneId());  
scheduleObj.setWarningType(warningType);  
scheduleObj.setWarningGroupId(warningGroupId);  
scheduleObj.setFailureStrategy(failureStrategy);  
scheduleObj.setCreateTime(now);  
scheduleObj.setUpdateTime(now);  
scheduleObj.setUserId(loginUser.getId());  
scheduleObj.setUserName(loginUser.getUserName());  
scheduleObj.setReleaseState(ReleaseState.OFFLINE);  
scheduleObj.setProcessInstancePriority(processInstancePriority);  
scheduleObj.setWorkerGroup(workerGroup);  
scheduleObj.setEnvironmentCode(environmentCode);  
scheduleMapper.insert(scheduleObj);  
... At its core, this operation inserts a new entry into the schedule table, as shown below: Activating a Schedule org.apache.dolphinscheduler.api.controller.SchedulerController#publishScheduleOnline  
--org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#onlineScheduler  
----org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#doOnlineScheduler  
------org.apache.dolphinscheduler.scheduler.quartz.QuartzScheduler#insertOrUpdateScheduleTask  

// Simplified code:  
JobKey jobKey = QuartzTaskUtils.getJobKey(schedule.getId(), projectId);  
Map<String, Object> jobDataMap = QuartzTaskUtils.buildDataMap(projectId, schedule);  
String cronExpression = schedule.getCrontab();  
String timezoneId = schedule.getTimezoneId();  

Date startDate = DateUtils.transformTimezoneDate(schedule.getStartTime(), timezoneId);  
Date endDate = DateUtils.transformTimezoneDate(schedule.getEndTime(), timezoneId);  
JobDetail jobDetail = newJob(ProcessScheduleTask.class).withIdentity(jobKey).build();  
jobDetail.getJobDataMap().putAll(jobDataMap);  
scheduler.addJob(jobDetail, false, true);  

TriggerKey triggerKey = new TriggerKey(jobKey.getName(), jobKey.getGroup());  
CronTrigger cronTrigger = newTrigger()  
                    .withIdentity(triggerKey)  
                    .startAt(startDate)  
                    .endAt(endDate)  
                    .withSchedule(  
                            cronSchedule(cronExpression)  
                                    .withMisfireHandlingInstructionIgnoreMisfires()  
                                    .inTimeZone(DateUtils.getTimezone(timezoneId)))  
                    .forJob(jobDetail).build();  

scheduler.scheduleJob(cronTrigger); Related Tables Job Details Table: Stores detailed information about each task. Trigger Base Table: Stores basic information for all trigger types. Cron Trigger Table: Stores information about Cron expression triggers. Schedule Execution org.apache.dolphinscheduler.scheduler.quartz.ProcessScheduleTask

protected void executeInternal(JobExecutionContext context) {  
    JobDataMap dataMap = context.getJobDetail().getJobDataMap();  
    int projectId = dataMap.getInt(QuartzTaskUtils.PROJECT_ID);  
    int scheduleId = dataMap.getInt(QuartzTaskUtils.SCHEDULE_ID);  

    Date scheduledFireTime = context.getScheduledFireTime();  
    Date fireTime = context.getFireTime();  

    Command command = new Command();  
    command.setCommandType(CommandType.SCHEDULER);  
    command.setExecutorId(schedule.getUserId());  
    command.setFailureStrategy(schedule.getFailureStrategy());  
    command.setProcessDefinitionCode(schedule.getProcessDefinitionCode());  
    command.setScheduleTime(scheduledFireTime);  
    command.setStartTime(fireTime);  
    command.setWarningGroupId(schedule.getWarningGroupId());  
    String workerGroup = StringUtils.isEmpty(schedule.getWorkerGroup()) ? Constants.DEFAULT_WORKER_GROUP  
            : schedule.getWorkerGroup();  
    command.setWorkerGroup(workerGroup);  
    command.setTenantCode(schedule.getTenantCode());  
    command.setEnvironmentCode(schedule.getEnvironmentCode());  
    command.setWarningType(schedule.getWarningType());  
    command.setProcessInstancePriority(schedule.getProcessInstancePriority());  
    command.setProcessDefinitionVersion(processDefinition.getVersion());  

    commandService.createCommand(command);  
} Essentially, this is a callback function in Quartz that ultimately generates a Command. Quartz is an open-source Java job scheduling framework that provides powerful capabilities for scheduling tasks. In DolphinScheduler, Quartz is used to implement task scheduling and management. DolphinScheduler integrates with Quartz through the QuartzExecutorImpl class, combining workflow and schedule management operations with Quartz's scheduling framework to achieve task execution. QuartzExecutorImpl This article provides a detailed analysis of Quartz's principles and implementation within DolphinScheduler. Quartz Entity-Relationship Diagram QRTZ_JOB_DETAILS and QRTZ_TRIGGERS are the central tables, defining the relationship between jobs and triggers. The QRTZ_TRIGGERS table links with multiple trigger-type tables, such as QRTZ_SIMPLE_TRIGGERS and QRTZ_CRON_TRIGGERS, to enable different trigger mechanisms. QRTZ_FIRED_TRIGGERS records execution history, associating with both the job and trigger tables. QRTZ_CALENDARS defines calendar exclusion rules for triggers, while QRTZ_PAUSED_TRIGGER_GRPS manages the pause state of trigger groups. QRTZ_SCHEDULER_STATE and QRTZ_LOCKS are used for scheduling coordination in clustered environments, ensuring high availability. QRTZ_JOB_DETAILS and QRTZ_TRIGGERS are the central tables, defining the relationship between jobs and triggers. QRTZ_JOB_DETAILS and QRTZ_TRIGGERS are the central tables, defining the relationship between jobs and triggers. QRTZ_JOB_DETAILS QRTZ_TRIGGERS The QRTZ_TRIGGERS table links with multiple trigger-type tables, such as QRTZ_SIMPLE_TRIGGERS and QRTZ_CRON_TRIGGERS, to enable different trigger mechanisms. The QRTZ_TRIGGERS table links with multiple trigger-type tables, such as QRTZ_SIMPLE_TRIGGERS and QRTZ_CRON_TRIGGERS , to enable different trigger mechanisms. QRTZ_TRIGGERS QRTZ_SIMPLE_TRIGGERS QRTZ_CRON_TRIGGERS QRTZ_FIRED_TRIGGERS records execution history, associating with both the job and trigger tables. QRTZ_FIRED_TRIGGERS records execution history, associating with both the job and trigger tables. QRTZ_FIRED_TRIGGERS QRTZ_CALENDARS defines calendar exclusion rules for triggers, while QRTZ_PAUSED_TRIGGER_GRPS manages the pause state of trigger groups. QRTZ_CALENDARS defines calendar exclusion rules for triggers, while QRTZ_PAUSED_TRIGGER_GRPS manages the pause state of trigger groups. QRTZ_CALENDARS QRTZ_PAUSED_TRIGGER_GRPS QRTZ_SCHEDULER_STATE and QRTZ_LOCKS are used for scheduling coordination in clustered environments, ensuring high availability. QRTZ_SCHEDULER_STATE and QRTZ_LOCKS are used for scheduling coordination in clustered environments, ensuring high availability. QRTZ_SCHEDULER_STATE QRTZ_LOCKS Quartz in DolphinScheduler We will focus on the principle analysis of the use of quartz in DolphinScheduler, so the steps for using quartz in DolphinScheduler will be introduced briefly. Generally speaking, there are 4 steps: Creating a New SHELL Task Defining and Configuring Workflow Scheduling Scheduling Activation Workflow Instance Execution Creating a New SHELL Task Defining and Configuring Workflow Scheduling Scheduling Activation Workflow Instance Execution Principle Analysis Creating a Schedule org.apache.dolphinscheduler.api.controller.SchedulerController#createSchedule  
--org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#insertSchedule  
...  
Schedule scheduleObj = new Schedule();  
Date now = new Date();  
scheduleObj.setTenantCode(tenantCode);  
scheduleObj.setProjectName(project.getName());  
scheduleObj.setProcessDefinitionCode(processDefineCode);  
scheduleObj.setProcessDefinitionName(processDefinition.getName());  
ScheduleParam scheduleParam = JSONUtils.parseObject(schedule, ScheduleParam.class);  
scheduleObj.setCrontab(scheduleParam.getCrontab());  
scheduleObj.setTimezoneId(scheduleParam.getTimezoneId());  
scheduleObj.setWarningType(warningType);  
scheduleObj.setWarningGroupId(warningGroupId);  
scheduleObj.setFailureStrategy(failureStrategy);  
scheduleObj.setCreateTime(now);  
scheduleObj.setUpdateTime(now);  
scheduleObj.setUserId(loginUser.getId());  
scheduleObj.setUserName(loginUser.getUserName());  
scheduleObj.setReleaseState(ReleaseState.OFFLINE);  
scheduleObj.setProcessInstancePriority(processInstancePriority);  
scheduleObj.setWorkerGroup(workerGroup);  
scheduleObj.setEnvironmentCode(environmentCode);  
scheduleMapper.insert(scheduleObj);  
... org.apache.dolphinscheduler.api.controller.SchedulerController#createSchedule  
--org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#insertSchedule  
...  
Schedule scheduleObj = new Schedule();  
Date now = new Date();  
scheduleObj.setTenantCode(tenantCode);  
scheduleObj.setProjectName(project.getName());  
scheduleObj.setProcessDefinitionCode(processDefineCode);  
scheduleObj.setProcessDefinitionName(processDefinition.getName());  
ScheduleParam scheduleParam = JSONUtils.parseObject(schedule, ScheduleParam.class);  
scheduleObj.setCrontab(scheduleParam.getCrontab());  
scheduleObj.setTimezoneId(scheduleParam.getTimezoneId());  
scheduleObj.setWarningType(warningType);  
scheduleObj.setWarningGroupId(warningGroupId);  
scheduleObj.setFailureStrategy(failureStrategy);  
scheduleObj.setCreateTime(now);  
scheduleObj.setUpdateTime(now);  
scheduleObj.setUserId(loginUser.getId());  
scheduleObj.setUserName(loginUser.getUserName());  
scheduleObj.setReleaseState(ReleaseState.OFFLINE);  
scheduleObj.setProcessInstancePriority(processInstancePriority);  
scheduleObj.setWorkerGroup(workerGroup);  
scheduleObj.setEnvironmentCode(environmentCode);  
scheduleMapper.insert(scheduleObj);  
... At its core, this operation inserts a new entry into the schedule table, as shown below: schedule Activating a Schedule org.apache.dolphinscheduler.api.controller.SchedulerController#publishScheduleOnline  
--org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#onlineScheduler  
----org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#doOnlineScheduler  
------org.apache.dolphinscheduler.scheduler.quartz.QuartzScheduler#insertOrUpdateScheduleTask  

// Simplified code:  
JobKey jobKey = QuartzTaskUtils.getJobKey(schedule.getId(), projectId);  
Map<String, Object> jobDataMap = QuartzTaskUtils.buildDataMap(projectId, schedule);  
String cronExpression = schedule.getCrontab();  
String timezoneId = schedule.getTimezoneId();  

Date startDate = DateUtils.transformTimezoneDate(schedule.getStartTime(), timezoneId);  
Date endDate = DateUtils.transformTimezoneDate(schedule.getEndTime(), timezoneId);  
JobDetail jobDetail = newJob(ProcessScheduleTask.class).withIdentity(jobKey).build();  
jobDetail.getJobDataMap().putAll(jobDataMap);  
scheduler.addJob(jobDetail, false, true);  

TriggerKey triggerKey = new TriggerKey(jobKey.getName(), jobKey.getGroup());  
CronTrigger cronTrigger = newTrigger()  
                    .withIdentity(triggerKey)  
                    .startAt(startDate)  
                    .endAt(endDate)  
                    .withSchedule(  
                            cronSchedule(cronExpression)  
                                    .withMisfireHandlingInstructionIgnoreMisfires()  
                                    .inTimeZone(DateUtils.getTimezone(timezoneId)))  
                    .forJob(jobDetail).build();  

scheduler.scheduleJob(cronTrigger); org.apache.dolphinscheduler.api.controller.SchedulerController#publishScheduleOnline  
--org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#onlineScheduler  
----org.apache.dolphinscheduler.api.service.impl.SchedulerServiceImpl#doOnlineScheduler  
------org.apache.dolphinscheduler.scheduler.quartz.QuartzScheduler#insertOrUpdateScheduleTask  

// Simplified code:  
JobKey jobKey = QuartzTaskUtils.getJobKey(schedule.getId(), projectId);  
Map<String, Object> jobDataMap = QuartzTaskUtils.buildDataMap(projectId, schedule);  
String cronExpression = schedule.getCrontab();  
String timezoneId = schedule.getTimezoneId();  

Date startDate = DateUtils.transformTimezoneDate(schedule.getStartTime(), timezoneId);  
Date endDate = DateUtils.transformTimezoneDate(schedule.getEndTime(), timezoneId);  
JobDetail jobDetail = newJob(ProcessScheduleTask.class).withIdentity(jobKey).build();  
jobDetail.getJobDataMap().putAll(jobDataMap);  
scheduler.addJob(jobDetail, false, true);  

TriggerKey triggerKey = new TriggerKey(jobKey.getName(), jobKey.getGroup());  
CronTrigger cronTrigger = newTrigger()  
                    .withIdentity(triggerKey)  
                    .startAt(startDate)  
                    .endAt(endDate)  
                    .withSchedule(  
                            cronSchedule(cronExpression)  
                                    .withMisfireHandlingInstructionIgnoreMisfires()  
                                    .inTimeZone(DateUtils.getTimezone(timezoneId)))  
                    .forJob(jobDetail).build();  

scheduler.scheduleJob(cronTrigger); Related Tables Job Details Table: Stores detailed information about each task. Job Details Table: Trigger Base Table: Stores basic information for all trigger types. Trigger Base Table: Cron Trigger Table: Stores information about Cron expression triggers. Cron Trigger Table: Schedule Execution org.apache.dolphinscheduler.scheduler.quartz.ProcessScheduleTask

protected void executeInternal(JobExecutionContext context) {  
    JobDataMap dataMap = context.getJobDetail().getJobDataMap();  
    int projectId = dataMap.getInt(QuartzTaskUtils.PROJECT_ID);  
    int scheduleId = dataMap.getInt(QuartzTaskUtils.SCHEDULE_ID);  

    Date scheduledFireTime = context.getScheduledFireTime();  
    Date fireTime = context.getFireTime();  

    Command command = new Command();  
    command.setCommandType(CommandType.SCHEDULER);  
    command.setExecutorId(schedule.getUserId());  
    command.setFailureStrategy(schedule.getFailureStrategy());  
    command.setProcessDefinitionCode(schedule.getProcessDefinitionCode());  
    command.setScheduleTime(scheduledFireTime);  
    command.setStartTime(fireTime);  
    command.setWarningGroupId(schedule.getWarningGroupId());  
    String workerGroup = StringUtils.isEmpty(schedule.getWorkerGroup()) ? Constants.DEFAULT_WORKER_GROUP  
            : schedule.getWorkerGroup();  
    command.setWorkerGroup(workerGroup);  
    command.setTenantCode(schedule.getTenantCode());  
    command.setEnvironmentCode(schedule.getEnvironmentCode());  
    command.setWarningType(schedule.getWarningType());  
    command.setProcessInstancePriority(schedule.getProcessInstancePriority());  
    command.setProcessDefinitionVersion(processDefinition.getVersion());  

    commandService.createCommand(command);  
} org.apache.dolphinscheduler.scheduler.quartz.ProcessScheduleTask

protected void executeInternal(JobExecutionContext context) {  
    JobDataMap dataMap = context.getJobDetail().getJobDataMap();  
    int projectId = dataMap.getInt(QuartzTaskUtils.PROJECT_ID);  
    int scheduleId = dataMap.getInt(QuartzTaskUtils.SCHEDULE_ID);  

    Date scheduledFireTime = context.getScheduledFireTime();  
    Date fireTime = context.getFireTime();  

    Command command = new Command();  
    command.setCommandType(CommandType.SCHEDULER);  
    command.setExecutorId(schedule.getUserId());  
    command.setFailureStrategy(schedule.getFailureStrategy());  
    command.setProcessDefinitionCode(schedule.getProcessDefinitionCode());  
    command.setScheduleTime(scheduledFireTime);  
    command.setStartTime(fireTime);  
    command.setWarningGroupId(schedule.getWarningGroupId());  
    String workerGroup = StringUtils.isEmpty(schedule.getWorkerGroup()) ? Constants.DEFAULT_WORKER_GROUP  
            : schedule.getWorkerGroup();  
    command.setWorkerGroup(workerGroup);  
    command.setTenantCode(schedule.getTenantCode());  
    command.setEnvironmentCode(schedule.getEnvironmentCode());  
    command.setWarningType(schedule.getWarningType());  
    command.setProcessInstancePriority(schedule.getProcessInstancePriority());  
    command.setProcessDefinitionVersion(processDefinition.getVersion());  

    commandService.createCommand(command);  
} Essentially, this is a callback function in Quartz that ultimately generates a Command . Command

The Heart of DolphinScheduler: In-Depth Analysis of the Quartz Scheduling Framework

The HackerNoon Newsletter: Teslas Good Quarter (10/28/2024)

Tesla's back on the menu, baby! The world's most valuable car company became even more valuable this past week after it posted its quarterly results, and boy, they were something to marvel at. The company's profits were up — by 17% from last year — while sales grew 8% when compared to the same period in 2023. More importantly, not only did Elon Musk's company say that it was producing cars at their cheapest levels yet, but that it was producing them at record volumes. On top of that, Musk said it expected Tesla's vehicle sales to grow 20% to 30% next year as it introduces more affordable models in the first half of 2025. Naturally, investors went crazy, and the company's stock recorded their biggest single-day gain in over a decade on Thursday — a day after the results were released — adding nearly $150 billion to the company's market value. This couldn't have come at a better time for Musk as Wall Street was becoming increasingly concerned that Tesla was focusing a little too much on new projects like the robotaxi, away from its core business of selling electric cars. In fact, when Tesla announced the robotaxi and the so-called Cybercab at its "We, Robot" event just a few weeks prior, its stock dropped around 9% — indicating investors' dislike of Musk's plan to transform Tesla into an artificial intelligence and robotics company. Never the one to shy away from proving his naysayers wrong, Musk isn't giving up on the promise of offering autonomous transport at a "cost per mile below rideshare, personal car ownership and even public transit." Some would argue that is a bold goal, while others would point to the long list of Musk's broken promises. I'm just wondering if Musk is trying to reinvent public transportation 😂 I'm not alone, since Tesla investors are also wondering how Musk plans to pull off his grand vision, with Reuters reporting that Musk's optimism on having driverless ride-hailing services in California and Texas next year is likely going to face an uphill battle due to regulatory and technical challenges. One element that is not being discussed at all is of course Musk's relationship with Donald Trump. It's still too early to call who will win the elections, since both Trump and Kamala Harris are neck to neck, but if Trump were to become the next US president, Musk will benefit from it one way or the other. Trump has, in recent days, praised Musk's SpaceX on at least two occasions: once at an event in Chicago hosted by Bloomberg, and the second during his appearance on the Joe Rogan podcast. Musk has also been a part of Trump's campaign trail and has made numerous appearances during the Republican candidate's rallies, including the most recent one at Madison Square Garden. In fact, Trump has gone as far as to indicate he will appoint Musk as the head of a new government efficiency commission if he gets elected to office. But there might be more at play here. Trump has been very vocal about imposing tariffs on goods coming from foreign countries, including cars, due to a belief that it would generate jobs in America and boost the economy. Such a move would directly benefit Musk since Tesla can't compete against Chinese electric car makers, meaning any policy that makes them more expensive in the US would be a net positive for one of the world's richest men. For now, Musk can bask in the glory of his company's quarterly win. As things heat up, I'll be around to report on it. Tesla ranked #9 on HackerNoon's Tech Company Rankings this week. GET PUBLISHED ON HACKERNOON.
Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. In Other News.. 📰 Bitcoin Has a Lot Going for It, Except the Persistent Slide in Copper-Gold Ratio — via CoinDesk
Read AI raises $50M to capitalize on strong demand for its AI summary bot — via TechCrunch
X changed its terms of service to let its AI train on everyone’s posts. Now users are up in arms — via CNN
Microsoft's AI demand under scrutiny as investors seek payday — via Reuters
Meta strikes multi-year AI deal with Reuters — via Axios
How I turned ChatGPT into my tour guide in Italy — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tesla 's back on the menu, baby! Tesla Tesla The world's most valuable car company became even more valuable this past week after it posted its quarterly results, and boy, they were something to marvel at. The company's profits were up — by 17% from last year — while sales grew 8% when compared to the same period in 2023. More importantly, not only did Elon Musk's company say that it was producing cars at their cheapest levels yet, but that it was producing them at record volumes. On top of that, Musk said it expected Tesla's vehicle sales to grow 20% to 30% next year as it introduces more affordable models in the first half of 2025. producing producing Naturally, investors went crazy, and the company's stock recorded their biggest single-day gain in over a decade on Thursday — a day after the results were released — adding nearly $150 billion to the company's market value. recorded recorded This couldn't have come at a better time for Musk as Wall Street was becoming increasingly concerned that Tesla was focusing a little too much on new projects like the robotaxi, away from its core business of selling electric cars. In fact, when Tesla announced the robotaxi and the so-called Cybercab at its "We, Robot" event just a few weeks prior, its stock dropped around 9% — indicating investors' dislike of Musk's plan to transform Tesla into an artificial intelligence and robotics company. dropped dropped Never the one to shy away from proving his naysayers wrong, Musk isn't giving up on the promise of offering autonomous transport at a "cost per mile below rideshare, personal car ownership and even public transit." Some would argue that is a bold goal, while others would point to the long list of Musk's broken promises. I'm just wondering if Musk is trying to reinvent public transportation 😂 I'm not alone, since Tesla investors are also wondering how Musk plans to pull off his grand vision, with Reuters reporting that Musk's optimism on having driverless ride-hailing services in California and Texas next year is likely going to face an uphill battle due to regulatory and technical challenges. how reporting reporting One element that is not being discussed at all is of course Musk's relationship with Donald Trump. It's still too early to call who will win the elections, since both Trump and Kamala Harris are neck to neck, but if Trump were to become the next US president, Musk will benefit from it one way or the other. Trump has, in recent days, praised Musk's SpaceX on at least two occasions: once at an event in Chicago hosted by Bloomberg, and the second during his appearance on the Joe Rogan podcast. hosted hosted appearance appearance Musk has also been a part of Trump's campaign trail and has made numerous appearances during the Republican candidate's rallies, including the most recent one at Madison Square Garden. In fact, Trump has gone as far as to indicate he will appoint Musk as the head of a new government efficiency commission if he gets elected to office. recent recent head head But there might be more at play here. Trump has been very vocal about imposing tariffs on goods coming from foreign countries, including cars, due to a belief that it would generate jobs in America and boost the economy. Such a move would directly benefit Musk since Tesla can't compete against Chinese electric car makers, meaning any policy that makes them more expensive in the US would be a net positive for one of the world's richest men. For now, Musk can bask in the glory of his company's quarterly win. As things heat up, I'll be around to report on it. Tesla ranked #9 on HackerNoon's Tech Company Rankings this week. GET PUBLISHED ON HACKERNOON. Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. GET PUBLISHED ON HACKERNOON. GET PUBLISHED ON HACKERNOON . GET PUBLISHED ON HACKERNOON Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. Join a global network of   45,000+ published devs, builders,  founders, makers, VCs, hodlrs, and   hackers. Start submitting your tech  stories and tutorials to get   published FREE on HackerNoon — no pop-ups,  no paywalls. In Other News.. 📰 In Other News.. 📰 Bitcoin Has a Lot Going for It, Except the Persistent Slide in Copper-Gold Ratio — via CoinDesk Read AI raises $50M to capitalize on strong demand for its AI summary bot — via TechCrunch X changed its terms of service to let its AI train on everyone’s posts. Now users are up in arms — via CNN Microsoft's AI demand under scrutiny as investors seek payday — via Reuters Meta strikes multi-year AI deal with Reuters — via Axios How I turned ChatGPT into my tour guide in Italy — via CNBC Bitcoin Has a Lot Going for It, Except the Persistent Slide in Copper-Gold Ratio — via CoinDesk CoinDesk CoinDesk Read AI raises $50M to capitalize on strong demand for its AI summary bot — via TechCrunch TechCrunch TechCrunch X changed its terms of service to let its AI train on everyone’s posts. Now users are up in arms — via CNN CNN CNN Microsoft's AI demand under scrutiny as investors seek payday — via Reuters Reuters Reuters Meta strikes multi-year AI deal with Reuters — via Axios Axios Axios How I turned ChatGPT into my tour guide in Italy — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

Tesla's Good Quarter

Teslova dobrá čtvrť

Teslas gutes Quartal

El buen trimestre de Tesla

テスラの好調な四半期

테슬라의 좋은 분기

Тесланын жакшы кварталы

Quartier Malamu ya Tesla

Quarter-ka Wanaagsan ee Tesla

Семоҳаи хуби Tesla

Ang Magandang Quarter ng Tesla

Teslaning yaxshi kvartal

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

The Noonification: The Good Quarter (8/25/2024)

The Noonification: The Good Quarter (8/14/2024)

The past two weeks, I've focused on some of the bigger news coming out of this earnings season, so I thought it was only fair to recap all the results I did not get a chance to cover. Here's a breakdown of what the magnificent 7 reported recently, minus Nvidia, which is yet to report its results. You can find my coverage of Google here. If you’re new to HackerNoon or a repeat reader, I would highly encourage you to start writing! HackerNoon is an independent publishing platform that gives technologists an opportunity to showcase their expertise and thought leadership with fellow like-minded individuals on the internet. We host a range of diverse views, and yours could be next! Start writing today! Amazon The ecommerce giant had a good quarter. Net sales rose 10% year over year to $148 billion while profit more than doubled to $13.5 billion from a year earlier. Despite the spectacular results, Amazon warned that its online sales weren't growing as much because consumers were looking for deals, alluding to platforms like Temu and Shein that have severely undercut the company's prices by shipping products directly from mainland China. Not one to be bested, Amazon has already started onboarding Chinese sellers on its website and is looking to attract small and medium-size sellers that could utilize its logistics network to export overseas. Amazon landed #30 on HackerNoon's Tech Company Rankings page. Apple The maker of iPhones also had a good quarter. Revenue was up 5% year over year while profit rose close to 8% to $21.4 billion from a year ago. Despite facing weakness in China, the company believes it can woo customers with its next iPhone launch which will come with deep AI integrations. Perhaps anticipating this launch, Google went ahead and announced that its next Pixel lineup will be powered using AI processors, specifically the Tensor G4, to help the phone understand text, images and audio. Would Apple's next smartphone top that? It might. Apple actually has a leg up on Google on AI processors, having invested in developing AI-integrated processors — such as the A-series and M-series — as part of a shift to edge computing i.e. have users' phones do the heavy lifting of processing instead of on the cloud or the back end. That experience makes Apple uniquely positioned to compete against Google's hardware, especially considering that Google's proliferation in the smartphone market is negligible. Meta The OG social media company reported an increase in financial metrics across the board. Profit was up 73% to $13.5 billion after revenue rose 22% year over year to $39.1 billion. "We had a strong quarter, and Meta AI is on track to be the most used AI assistant in the world by the end of the year," Mark Zuckerberg said of the results. That latter part of his statement is interesting. Unlike ChatGPT, which requires a separate app or browser tab, Meta AI is actually integrated in ALL of Meta's offerings, chiefly Instagram, but also WhatsApp (which is used by over a billion people around the globe), and Facebook Messenger. That means users utilizing any of Meta's apps don't need to leave it for their search queries, the way they would for ChatGPT or any other AI assistant. Exactly how being the most used AI assistant in the world positions Meta AI against ChatGPT is yet to be seen, but all that data Meta is probably gathering could give it leverage in the long run. Instagram ranked #1 on HackerNoon's Tech Company Rankings page. Microsoft ChatGPT-backer Microsoft also reported a strong quarter, with revenue rising 15% year over year to $64.7 billion while profit up 10% to $22 billion from a year ago. Despite the results, investors couldn't help but get worried about a slowdown in the company's cloud business and the cost associated with building the company's AI infrastructure, which essentially leaves less in profit for investors or other business ventures. Wall Street has been getting jittery on AI recently, raising questions about how soon it can make a buck on the technology. At Microsoft, the company says it could be monetized over 15 years and beyond. I wonder where that would leave the rest. Microsoft ranked #2 on HackerNoon's Tech Company Rankings page. Tesla Elon Musk's electric car maker did not fare as well as the rest, reporting a drastic drop in its profit, down 42% year over year to $1.81 billion. Tesla's achilles heel appears to be its costs, and the company said as much when it attributed an increase in expenses on AI projects — which could include perfecting the company's autopilot mode for cars. One would assume that Musk is laser-focused on realizing the autopilot technology but recently redirected 12,000 of Nvidia's (NVDA) widely-in-demand H100 GPUs that were slated for Tesla over to his social media company X instead. This did not slip under the radar of an outspoken Democrat lawmaker, who recently wrote to the Tesla board suggesting that she may call on US regulators to discuss whether an intervention at the company was necessary given Musk's apparent conflicts of interest. In her letter, Elizabeth Warren cited Musk's role in SpaceX, Neuralink, The Boring Company, and X Corp. as examples of the billionaire juggling various enterprises and said the Tesla CEO "may not be adequately addressing his competing priorities." While Musk's other ventures are privately held, Tesla is not, and thus subject to securities law. Warren believes the Tesla board is doing a disservice to other shareholders by letting Musk run amok. The letter comes as the United States heads into elections, with Musk publicly coming out in support of Donald Trump — even going as far as to host the former US President on his social media platform X to boost the  Republican Presidential nominee's popularity and reach. Is Warren's letter politically motivated? One would hope not, but the intrigues of Washington and the frustration of the American public with the establishment is perhaps why Trump was able to become President in the first place. Tesla ranked #9 on HackerNoon's Tech Company Rankings page. In Other News.. 📰 Goldman Sachs Holds Over $400M in Bitcoin ETFs — via CoinDesk
Apple finally allows Spotify to show pricing info to EU users on iOS — via TechCrunch
How Elon Musk has turned X into a pro-Trump machine — via CNN
Decentralized AI network Sahara raises fresh capital in Samsung-backed round — via Reuters
The chatbot wars get personal — via Axios
Texas judge who owned Tesla stock recuses himself in Elon Musk’s X case against advertisers — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. The past two weeks, I've focused on some of the bigger news coming out of this earnings season , so I thought it was only fair to recap all the results I did not get a chance to cover. bigger news bigger news this earnings season this earnings season Here's a breakdown of what the magnificent 7 reported recently, minus Nvidia, which is yet to report its results. You can find my coverage of Google here . here here If you’re new to HackerNoon or a repeat reader, I would highly encourage you to start writing! HackerNoon is an independent publishing platform that gives technologists an opportunity to showcase their expertise and thought leadership with fellow like-minded individuals on the internet. We host a range of diverse views, and yours could be next! Start writing today! If you’re new to HackerNoon or a repeat reader, I would highly encourage you to start writing! HackerNoon is an independent publishing platform that gives technologists an opportunity to showcase their expertise and thought leadership with fellow like-minded individuals on the internet. We host a range of diverse views, and yours could be next! Start writing today! If you’re new to HackerNoon or a repeat reader, I would highly encourage you to start writing! HackerNoon is an independent publishing platform that gives technologists an opportunity to showcase their expertise and thought leadership with fellow like-minded individuals on the internet. We host a range of diverse views, and yours could be next! Start writing today! If you’re new to HackerNoon or a repeat reader, I would highly encourage you to start writing ! HackerNoon is an independent publishing platform that gives technologists an opportunity to showcase their expertise and thought leadership with fellow like-minded individuals on the internet. We host a range of diverse views, and yours could be next! Start writing today ! start writing Start writing today Amazon Amazon The ecommerce giant had a good quarter . Net sales rose 10% year over year to $148 billion while profit more than doubled to $13.5 billion from a year earlier. ecommerce giant ecommerce giant good quarter good quarter Despite the spectacular results, Amazon warned that its online sales weren't growing as much because consumers were looking for deals, alluding to platforms like Temu and Shein that have severely undercut the company's prices by shipping products directly from mainland China. Amazon Amazon warned warned severely undercut severely undercut Not one to be bested, Amazon has already started onboarding Chinese sellers on its website and is looking to attract small and medium-size sellers that could utilize its logistics network to export overseas. started onboarding started onboarding Amazon landed #30 on HackerNoon's Tech Company Rankings page. Amazon Amazon Tech Company Rankings Tech Company Rankings Apple Apple The maker of iPhones also had a good quarter . Revenue was up 5% year over year while profit rose close to 8% to $21.4 billion from a year ago. maker of iPhones maker of iPhones good quarter good quarter Despite facing weakness in China, the company believes it can woo customers with its next iPhone launch which will come with deep AI integrations. believes believes Perhaps anticipating this launch, Google went ahead and announced that its next Pixel lineup will be powered using AI processors, specifically the Tensor G4, to help the phone understand text, images and audio. Google Google announced announced Would Apple's next smartphone top that? It might. Apple actually has a leg up on Google on AI processors, having invested in developing AI-integrated processors — such as the A-series and M-series — as part of a shift to edge computing i.e. have users' phones do the heavy lifting of processing instead of on the cloud or the back end. That experience makes Apple uniquely positioned to compete against Google's hardware, especially considering that Google's proliferation in the smartphone market is negligible . Apple Apple negligible negligible Meta Meta The OG social media company reported an increase in financial metrics across the board. Profit was up 73% to $13.5 billion after revenue rose 22% year over year to $39.1 billion. OG social media company OG social media company increase increase "We had a strong quarter, and Meta AI is on track to be the most used AI assistant in the world by the end of the year," Mark Zuckerberg said of the results. That latter part of his statement is interesting. Unlike ChatGPT , which requires a separate app or browser tab, Meta AI is actually integrated in ALL of Meta's offerings, chiefly Instagram , but also WhatsApp (which is used by over a billion people around the globe), and Facebook Messenger. ChatGPT ChatGPT Instagram Instagram WhatsApp WhatsApp Facebook Facebook That means users utilizing any of Meta's apps don't need to leave it for their search queries, the way they would for ChatGPT or any other AI assistant. Exactly how being the most used AI assistant in the world positions Meta AI against ChatGPT is yet to be seen, but all that data Meta is probably gathering could give it leverage in the long run. Meta Meta Instagram ranked #1 on HackerNoon's Tech Company Rankings page. Instagram Instagram Tech Company Rankings Tech Company Rankings Microsoft Microsoft ChatGPT-backer Microsoft also reported a strong quarter, with revenue rising 15% year over year to $64.7 billion while profit up 10% to $22 billion from a year ago. Microsoft Microsoft Despite the results, investors couldn't help but get worried about a slowdown in the company's cloud business and the cost associated with building the company's AI infrastructure, which essentially leaves less in profit for investors or other business ventures. get worried get worried Wall Street has been getting jittery on AI recently, raising questions about how soon it can make a buck on the technology. At Microsoft, the company says it could be monetized over 15 years and beyond. make a buck make a buck I wonder where that would leave the rest. Microsoft ranked #2 on HackerNoon's Tech Company Rankings page. Microsoft Microsoft Tech Company Rankings Tech Company Rankings Tesla Tesla Elon Musk's electric car maker did not fare as well as the rest, reporting a drastic drop in its profit, down 42% year over year to $1.81 billion. electric car maker electric car maker drastic drop drastic drop Tesla 's achilles heel appears to be its costs, and the company said as much when it attributed an increase in expenses on AI projects — which could include perfecting the company's autopilot mode for cars. Tesla Tesla One would assume that Musk is laser-focused on realizing the autopilot technology but recently redirected 12,000 of Nvidia 's (NVDA) widely-in-demand H100 GPUs that were slated for Tesla over to his social media company X instead. Nvidia Nvidia X X This did not slip under the radar of an outspoken Democrat lawmaker, who recently wrote to the Tesla board suggesting that she may call on US regulators to discuss whether an intervention at the company was necessary given Musk's apparent conflicts of interest. wrote wrote In her letter , Elizabeth Warren cited Musk's role in SpaceX, Neuralink, The Boring Company, and X Corp. as examples of the billionaire juggling various enterprises and said the Tesla CEO "may not be adequately addressing his competing priorities." letter letter While Musk's other ventures are privately held, Tesla is not, and thus subject to securities law. Warren believes the Tesla board is doing a disservice to other shareholders by letting Musk run amok. The letter comes as the United States heads into elections, with Musk publicly coming out in support of Donald Trump — even going as far as to host the former US President on his social media platform X to boost the  Republican Presidential nominee's popularity and reach. coming out coming out Is Warren's letter politically motivated? One would hope not, but the intrigues of Washington and the frustration of the American public with the establishment is perhaps why Trump was able to become President in the first place. Tesla ranked #9 on HackerNoon's Tech Company Rankings page. Tesla Tesla Tech Company Rankings Tech Company Rankings In Other News.. 📰 In Other News.. 📰 Goldman Sachs Holds Over $400M in Bitcoin ETFs — via CoinDesk Apple finally allows Spotify to show pricing info to EU users on iOS — via TechCrunch How Elon Musk has turned X into a pro-Trump machine — via CNN Decentralized AI network Sahara raises fresh capital in Samsung-backed round — via Reuters The chatbot wars get personal — via Axios Texas judge who owned Tesla stock recuses himself in Elon Musk’s X case against advertisers — via CNBC Goldman Sachs Holds Over $400M in Bitcoin ETFs — via CoinDesk CoinDesk CoinDesk Apple finally allows Spotify to show pricing info to EU users on iOS — via TechCrunch TechCrunch TechCrunch How Elon Musk has turned X into a pro-Trump machine — via CNN CNN CNN Decentralized AI network Sahara raises fresh capital in Samsung-backed round — via Reuters Reuters Reuters The chatbot wars get personal — via Axios Axios Axios Texas judge who owned Tesla stock recuses himself in Elon Musk’s X case against advertisers — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan , Editor, Business Tech @ HackerNoon Sheharyar Khan Sheharyar Khan *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here. Tech, What the Heck!? is a once-weekly newsletter written by HackerNoon editors that combine HackerNoon's proprietary data with news-worthy tech stories from around the internet. Humorous and insightful, the newsletter recaps trending events that are shaping the world of tech. Subscribe here . here here

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