This will leave the Korean carrier with only four passenger 747s.

Korean Air Sells 5 Boeing 747s To US Air Force Contractor For New $13 Billion Doomsday Plane Program

Cuisinart’s air fryer toaster oven combines the power of three appliances all in one device. Here are my thoughts after testing for two weeks.

Cuisinart Air Fryer Toaster Oven review: My honest thoughts

Forward Air’s first quarter missed expectations, sending shares 30% lower in pre-market trading Thursday. The post Forward Air books big loss in post-merger Q1; stock drops 30% appeared first on FreightWaves.

Forward Air books big loss in post-merger Q1; stock drops 30%

Eleven people are injured, four of them seriously, as the Air Senegal plane leaves the runway in Dakar.

Boeing 737 skids off runway during take-off in Senegal

Dozens of cabin crew at Air India Express called in sick earlier this week, forcing the budget carrier to cancel 85 flights since Tuesday evening and disrupting the travel plans of thousands of passengers.

Air India Express cancels scores of flights after about 100 cabin crew call in sick | CNN Business

CEO Campbell Wilson says journey to restoring Air India’s reputation is ‘well under way’.

After decades of decline, Air India is betting billions on a comeback | Aviation | Al Jazeera

Climeworks starts operations of its to-date largest direct air capture and storage plant, Mammoth, in Iceland

Climeworks switches on world’s largest direct air capture plant

Climeworks has opened the world’s largest operational direct air capture (DAC) plant to suck carbon dioxide out of the atmosphere, with its Mammoth plant in Iceland almost ten times larger than the current record holder.

Climeworks opens world's largest plant to extract CO2 from air in Iceland | Reuters

Chairman of the Joint Chiefs Gen. Charles Q. Brown Jr. highlighted why the Air Force must develop a E-4 &#x27;Doomsday&#x27; replacement aircraft.

Brown: Aircraft Age and Need for New Tech Driving Need for ‘Doomsday’ Replacement

Mammoth will suck in air using giant fans, separate the carbon and transport it underground where it will transform into stone

‘World’s largest’ plant to suck carbon out of the air and turn it into stone opens in Iceland | CNN

AI.R

Not Even the EPA Can Say What's Polluting the Air

AIR-Bench’s Chat Benchmark: Open-Ended Audio QA with Mixed Audio Complexity

Authors:
(1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group;
(2) Jin Xu, Alibaba Group, Equal contribution;
(3) Wenrui Liu, Zhejiang University;
(4) Yunfei Chu, Alibaba Group;
(5) Xiaohuan Zhou, Alibaba Group;
(6) Yichong Leng, Alibaba Group;
(7) Yuanjun Lv, Alibaba Group;
(8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn);
(9) Yichong Leng, Zhejiang University
(10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com);
(11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction 2 Related Work 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References A Detailed Results of Foundation Benchmark 3.2 Foundation Benchmark Data Source. We collected over 19k data samples for the foundation dimension, encompassing 19 different subtasks. The data source and statistics are provided in Table 1. To ensure a fair and comprehensive evaluation of each capability, we aimed for an even distribution of problems related to different abilities during the data collection process. All audio sources were obtained from the original dev or test subsets to prevent data leakage. Single-choice Query and Reference. The query Q is formed by concatenating a question q and candidate choices C. For the question q, we mainly construct questions through GPT-4 (OpenAI, 2023), except for QA tasks since the datasets inherently contain questions and we can directly re-use them. Specifically, we design the prompt for the distinct task and provide three questions as demonstrations. Subsequently, GPT-4 generates additional diverse questions based on these inputs. The generated questions are manually reviewed, and 50 different questions are selected for each task. The variability in question format aims to evaluate the model’s ability to follow instructions rather than being overly reliant on specific templates. For each question, we further generate candidate choices C from different sources: 1) For tasks with choices in original datasets like AVQA (Yang et al., 2022), we directly re-use it; 2) For classification tasks, we randomly select options from the predetermined set of categories to serve as candidate choices; 3) For other tasks, we prompt GPT-4 to generate candidate choices directly, consisting of one correct option and three incorrect options. We encourage these incorrect options to resemble the correct one, making the single-choice task more challenging. The reference answer is the golden correct choice. To avoid position bias, the candidate choices are randomly shuffled This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Authors: Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 AIR-Bench and 3.1 Overview 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.1 Models 4.2 Main Results 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References 5 Conclusion and References A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark 3.2 Foundation Benchmark 3.2 Foundation Benchmark Data Source. We collected over 19k data samples for the foundation dimension, encompassing 19 different subtasks. The data source and statistics Data Source. are provided in Table 1. To ensure a fair and comprehensive evaluation of each capability, we aimed for an even distribution of problems related to different abilities during the data collection process. All audio sources were obtained from the original dev or test subsets to prevent data leakage. Single-choice Query and Reference. The query Q is formed by concatenating a question q and candidate choices C. For the question q, we mainly construct questions through GPT-4 (OpenAI, 2023), except for QA tasks since the datasets inherently contain questions and we can directly re-use them. Specifically, we design the prompt for the distinct task and provide three questions as demonstrations. Subsequently, GPT-4 generates additional diverse questions based on these inputs. The generated questions are manually reviewed, and 50 different questions are selected for each task. The variability in question format aims to evaluate the model’s ability to follow instructions rather than being overly reliant on specific templates. For each question, we further generate candidate choices C from different sources: 1) For tasks with choices in original datasets like AVQA (Yang et al., 2022), we directly re-use it; 2) For classification tasks, we randomly select options from the predetermined set of categories to serve as candidate choices; 3) For other tasks, we prompt GPT-4 to generate candidate choices directly, consisting of one correct option and three incorrect options. We encourage these incorrect options to resemble the correct one, making the single-choice task more challenging. The reference answer is the golden correct choice. To avoid position bias, the candidate choices are randomly shuffled Single-choice Query and Reference. 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

AIR-Bench Foundation: Over 19k Data Samples for Comprehensive Audio Evaluation

Fundació AIR-Bench: més de 19.000 mostres de dades per a una avaluació completa d'àudio

AIR-Bench Foundation: Více než 19 000 vzorků dat pro komplexní vyhodnocení zvuku

AIR-Bench Foundation: más de 19 000 muestras de datos para una evaluación integral del audio

AIR-Bench Foundation: 包括的なオーディオ評価のための 19,000 を超えるデータ サンプル

មូលនិធិ AIR-Bench៖ គំរូទិន្នន័យជាង 19k សម្រាប់ការវាយតម្លៃសំឡេងដ៏ទូលំទូលាយ

AIR-Bench Foundation: 포괄적인 오디오 평가를 위한 19,000개 이상의 데이터 샘플

AIR-Bench Foundation: Disampole tša Datha tša go feta 19k bakeng sa Tekolo ya Mamelwang ye e Akaretšago

د AIR-Bench بنسټ: د جامع آډیو ارزونې لپاره له 19k څخه ډیر ډیټا نمونې

AIR-Bench Foundation: более 19 тыс. образцов данных для комплексной оценки звука

AIR-Bench Foundation: Mbi 19 mijë mostra të dhënash për vlerësim gjithëpërfshirës audio

AIR-Bench Foundation: ልዕሊ 19k ናሙናታት ዳታ ንኹለመዳያዊ ገምጋም ድምጺ

AIR-Bench Foundation: Mahigit sa 19k Mga Sample ng Data para sa Comprehensive Audio Evaluation

Authors:
(1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group;
(2) Jin Xu, Alibaba Group, Equal contribution;
(3) Wenrui Liu, Zhejiang University;
(4) Yunfei Chu, Alibaba Group;
(5) Xiaohuan Zhou, Alibaba Group;
(6) Yichong Leng, Alibaba Group;
(7) Yuanjun Lv, Alibaba Group;
(8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn);
(9) Yichong Leng, Zhejiang University
(10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com);
(11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction 2 Related Work 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References A Detailed Results of Foundation Benchmark 3 AIR-Bench There exist three unique characteristics that differentiate AIR-Bench from existing benchmarks for audio understanding: i) AIR-Bench is the first work to incorporate task evaluation from all types of audio in a hierarchical taxonomy; ii) AIR-Bench is the first generative evaluation benchmark that handles the free-form output of LALMs; iii) AIRBench adopts GPT-4-based automatic evaluation yielding trustworthy evaluation results with affordable cost. In Sec. 3.1, we present the hierarchical taxonomy of AIR-Bench and discuss the design philosophy behind it. In Sec. 3.2 and Sec. 3.3, we introduce how we collect the audio-central question-answer pairs for foundation and chat tasks. In Sec. 3.4, we present the evaluation framework of AIR-Bench. 3.1 Overview Chat interaction based on audio is a complex task that encompasses a variety of fundamental competencies. For instance, humans are able to respond to sound events due to their capacities for sound perception and common sense reasoning. Similarly, the ability to respond to others’ spoken words is predicated on foundational skills such as speech-totext recognition and emotion recognition. Based on the motivation, we propose the hierarchical benchmark AIR-Bench by dividing it into foundation and chat benchmarks. The fundamental one is designed to assess capabilities across individual subtasks, serving to diagnose weaknesses within the model, while the chat benchmark directly evaluates complicated audio-based open-ended questions. The data sample is denoted as (A, Q, R), where A denotes the audio, Q represents the query and R is the reference answer. • Foundation benchmark: The purpose of the benchmark is to evaluate the individual capabilities of foundational tasks. To reduce the task difficulties and enable the evaluation of various models, we utilize the single-choice question-answering format. Specifically, the query Q is formed by concatenating a question q and candidate choices C, denoted as Q = (q, C). We curate a collection of 19 audio tasks that span multiple audio types, such as speech, music, and sound. These tasks include tasks like emotion recognition, acoustic scene classification, and music QA. [1] • Chat benchmark: The benchmark encompasses any form of question and answer pairs that could arise from audio signals, with the aim of reflecting the model’s ability to genuinely follow user instructions to perform perceiving, reasoning and interacting within realworld applications. According to the type of audio, the benchmark is categorized into four dimensions: speech, sound, music, and mixed audio, where mixed audio refers to audio that is a mixture of multiple types of audio, such as human voice with background music. The overview of AIR-Bench is shown in Fig. 1. This paper is available on arxiv under CC BY 4.0 DEED license. [1] For transcription tasks such as ASR and S2TT, we incorporate them into the chat benchmark since they are not suitable for the single-choice task format. Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Authors: Authors: (1) Qian Yang, Zhejiang University, Equal contribution. This work was conducted during Qian Yang’s internship at Alibaba Group; (2) Jin Xu, Alibaba Group, Equal contribution; (3) Wenrui Liu, Zhejiang University; (4) Yunfei Chu, Alibaba Group; (5) Xiaohuan Zhou, Alibaba Group; (6) Yichong Leng, Alibaba Group; (7) Yuanjun Lv, Alibaba Group; (8) Zhou Zhao, Alibaba Group and Corresponding to Zhou Zhao (zhaozhou@zju.edu.cn); (9) Yichong Leng, Zhejiang University (10) Chang Zhou, Alibaba Group and Corresponding to Chang Zhou (ericzhou.zc@alibaba-inc.com); (11) Jingren Zhou, Alibaba Group. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 AIR-Bench and 3.1 Overview 3 AIR-Bench and 3.1 Overview 3.2 Foundation Benchmark 3.2 Foundation Benchmark 3.3 Chat Benchmark 3.3 Chat Benchmark 3.4 Evaluation Strategy 3.4 Evaluation Strategy 4 Experiments 4.1 Models 4.1 Models 4.2 Main Results 4.2 Main Results 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 4.3 Human Evaluation and 4.4 Ablation Study of Positional Bias 5 Conclusion and References 5 Conclusion and References A Detailed Results of Foundation Benchmark A Detailed Results of Foundation Benchmark 3 AIR-Bench There exist three unique characteristics that differentiate AIR-Bench from existing benchmarks for audio understanding: i) AIR-Bench is the first work to incorporate task evaluation from all types of audio in a hierarchical taxonomy; ii) AIR-Bench is the first generative evaluation benchmark that handles the free-form output of LALMs; iii) AIRBench adopts GPT-4-based automatic evaluation yielding trustworthy evaluation results with affordable cost. In Sec. 3.1, we present the hierarchical taxonomy of AIR-Bench and discuss the design philosophy behind it. In Sec. 3.2 and Sec. 3.3, we introduce how we collect the audio-central question-answer pairs for foundation and chat tasks. In Sec. 3.4, we present the evaluation framework of AIR-Bench. 3.1 Overview Chat interaction based on audio is a complex task that encompasses a variety of fundamental competencies. For instance, humans are able to respond to sound events due to their capacities for sound perception and common sense reasoning. Similarly, the ability to respond to others’ spoken words is predicated on foundational skills such as speech-totext recognition and emotion recognition. Based on the motivation, we propose the hierarchical benchmark AIR-Bench by dividing it into foundation and chat benchmarks. The fundamental one is designed to assess capabilities across individual subtasks, serving to diagnose weaknesses within the model, while the chat benchmark directly evaluates complicated audio-based open-ended questions. The data sample is denoted as (A, Q, R), where A denotes the audio, Q represents the query and R is the reference answer. reference answer. • Foundation benchmark: The purpose of the benchmark is to evaluate the individual capabilities of foundational tasks. To reduce the task difficulties and enable the evaluation of various models, we utilize the single-choice question-answering format. Specifically, the query Q is formed by concatenating a question q and candidate choices C, denoted as Q = (q, C). We curate a collection of 19 audio tasks that span multiple audio types, such as speech, music, and sound. These tasks include tasks like emotion recognition, acoustic scene classification, and music QA. [1] • Foundation benchmark: • Chat benchmark: The benchmark encompasses any form of question and answer pairs that could arise from audio signals, with the aim of reflecting the model’s ability to genuinely follow user instructions to perform perceiving, reasoning and interacting within realworld applications. According to the type of audio, the benchmark is categorized into four dimensions: speech, sound, music, and mixed audio, where mixed audio refers to audio that is a mixture of multiple types of audio, such as human voice with background music. • Chat benchmark: The overview of AIR-Bench is shown in Fig. 1. 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 [1] For transcription tasks such as ASR and S2TT, we incorporate them into the chat benchmark since they are not suitable for the single-choice task format.

What is AIR-Bench?

Can Anything Stop the Death of the Token Airdrop? No, Bring It On.

የማስመሰያ የአየር ጠብታ ሞትን የሚያቆመው ነገር አለ? አይ፣ አምጣው

¿Kunas Token Airdrop jiwataparux jark’aspa? Janiwa, Apanipxam.

Qualsevol cosa pot aturar la mort del token Airdrop? No, porta-ho.

¿Puede algo detener la muerte del airdrop de tokens? No, adelante.

Edozerk gelditu al dezake Token Airdrop-aren heriotza? Ez, Ekarri.

آیا چیزی می تواند مرگ توکن ایردراپ را متوقف کند؟ نه، آن را روشن کن.

Algo pode deter a morte do token Airdrop? Non, trae.

トークンエアドロップの終焉を阻止できるものはあるか？いいえ、やって来い。

Misy afaka manakana ny fahafatesan'ny Token Airdrop ve? Tsia, Ampidiro.

Token howa gämisiniň ölümini duruzyp biljek zat barmy? .Ok, getir.

May Mapapahinto ba ang Kamatayan ng Token Airdrop? Hindi, Dalhin Ito.

Чи може щось зупинити смерть Token Airdrop? Ні, принеси це.

Dynamic Generation of Airflow DAGs for Multiple Environments

Airbnb (NASDAQ:ABNB) has long been recognized as one of the tourism industry’s most disruptive forces, but are customers finally beginning to turn their backs on the company’s convoluted pricing structures? When announcing its second-quarter earnings, Airbnb pointed to weakening demand among US customers as a leading cause behind a sales slowdown on the platform. To compound the homestay marketplace’s troubles, residents in Hungary’s capital city Budapest recently voted to ban firms like Airbnb to stop the impact that short-term rentals are having on local housing shortages. Should more global cities follow Budapest’s lead, Airbnb could lose significant ground on traditional hotels in offering competitively priced stays throughout a number of popular holiday destinations. Moving into September 2024, Airbnb’s year-to-date performance had fallen 14.5%. In an ominous sign, Airbnb CEO and chairman Brian Chesky sold $27.7 million worth of shares in a pre-arranged trade in September. With Airbnb’s challenges mounting, the stock has arrived at a crossroads in determining its longevity. Could we see how the innovative marketplace overcomes its problems? Or is the stock’s status as the tourism industry’s biggest disruptor over? Learning from Q2 2024 Results Airbnb’s challenges were laid bare in the company’s Q2 2024 results, which saw earnings per share (EPS) miss expectations by 5.7%. Weighing in at $0.87, the stock’s EPS was down significantly on the $1.02 recorded in Q2 2023. Despite this, revenue climbed 11% to $2.75 billion, which offers some solace in an industry that’s been heavily impacted by the uneven impact of the pandemic throughout the beginning of the decade. However, Airbnb’s net income of $555 million came in 15% lower than Q2 2023, and its profit margin fell from 26% to 20% year-over-year. Despite forecasts suggesting that Airbnb’s revenue is expected to grow 10% per annum at a rate that outpaces that of the hospitality industry by 0.4% in the United States, there are clear signs of stress for a stock that’s struggled to secure momentum since going public in December 2020. Fees Drive Customers Away While Airbnb was once a major disruptor for the travel and tourism sector, the opportunistic pricing structures among hosts appear to be eating into the appeal of homestays for consumers. One key problem revolves around hidden costs like cleaning fees, which some hosts have implemented to lure customers into checking out their property before seeing the full cost of their stay. According to one recent Business Insider article, hosts have been known to charge up to $400 for cleaning fees, making many homestays more expensive than local hotels. According to one travel website owner, Michael Rozenblit, the paradoxical fees charged among hosts for cleaning services while often instructing guests to take out the trash and strip the bedding are undermining the platform as a whole. The issue of exploitative fees has long been on Airbnb’s radar, and attempts to implement a more transparent pricing structure could help to restore some faith in the platform. However, it appears that the app’s days as a low-cost alternative to hotel stays are over. Can AirBnb Recover? Airbnb’s attempt to overcome the challenges of the more expensive pricing strategies of its hosts is to embrace higher costs by building a fresh focus on experience-oriented homestays. In recent years, we’ve seen Airbnb’s marketing move consciously towards experience, rather than budget-focused homestays. 2023 saw the Barbie Dreamhouse added to Airbnb’s listings with a ballot added to the site for fans to win a free overnight stay. Ahead of the 2024 Olympics, Paris’s Musée d’Orsay was added to the site as a place to stay during the Opening Ceremony. The stock also has many advantages that could support a stock market recovery, especially due to its operating model. “Airbnb has consistently generated positive operating profit since 2021 thanks to its lower-cost asset-based business model that requires minimal capital expenditure,” explained Maxim Manturov, head of investment research at Freedom24. “This has led to sustainable free cash flow (FCF): last quarter, the company produced a free cash flow of $1.9 billion. This financial discipline reduces the need to raise external capital, proving the effectiveness of its business model.” Recent weeks have also shown signs of an Airbnb rebound off the back of the Federal Reserve’s plans for upcoming rate cuts. With the stock climbing almost 15% over two weeks between the 6th and 20th of September, it’s worth remembering that the aforementioned challenges within Airbnb’s US user base have been compounded by a squeeze on consumer spending power. Challenges Remain Airbnb has been proactive in addressing its challenges to date, and its conscious push towards an experience-based model illustrates that the stock could have a future even as hosts push up prices on the platform. Despite this, high fees and backlashes over the impact of hosts on local housing markets mean that the future won’t be smooth sailing for Airbnb. However, with a sustainable business model and its status as a homestay innovator, the platform’s recent turbulence may be short-lived. Airbnb (NASDAQ:ABNB) has long been recognized as one of the tourism industry’s most disruptive forces, but are customers finally beginning to turn their backs on the company’s convoluted pricing structures? When announcing its second-quarter earnings, Airbnb pointed to weakening demand among US customers as a leading cause behind a sales slowdown on the platform. weakening demand weakening demand To compound the homestay marketplace’s troubles, residents in Hungary’s capital city Budapest recently voted to ban firms like Airbnb to stop the impact that short-term rentals are having on local housing shortages. voted to ban voted to ban Should more global cities follow Budapest’s lead, Airbnb could lose significant ground on traditional hotels in offering competitively priced stays throughout a number of popular holiday destinations. Moving into September 2024, Airbnb’s year-to-date performance had fallen 14.5%. In an ominous sign, Airbnb CEO and chairman Brian Chesky sold $27.7 million worth of shares in a pre-arranged trade in September. sold $27.7 million sold $27.7 million With Airbnb’s challenges mounting, the stock has arrived at a crossroads in determining its longevity. Could we see how the innovative marketplace overcomes its problems? Or is the stock’s status as the tourism industry’s biggest disruptor over? Learning from Q2 2024 Results Airbnb’s challenges were laid bare in the company’s Q2 2024 results , which saw earnings per share (EPS) miss expectations by 5.7%. Weighing in at $0.87, the stock’s EPS was down significantly on the $1.02 recorded in Q2 2023. company’s Q2 2024 results company’s Q2 2024 results Despite this, revenue climbed 11% to $2.75 billion, which offers some solace in an industry that’s been heavily impacted by the uneven impact of the pandemic throughout the beginning of the decade. However, Airbnb’s net income of $555 million came in 15% lower than Q2 2023, and its profit margin fell from 26% to 20% year-over-year. Despite forecasts suggesting that Airbnb’s revenue is expected to grow 10% per annum at a rate that outpaces that of the hospitality industry by 0.4% in the United States, there are clear signs of stress for a stock that’s struggled to secure momentum since going public in December 2020. Fees Drive Customers Away While Airbnb was once a major disruptor for the travel and tourism sector, the opportunistic pricing structures among hosts appear to be eating into the appeal of homestays for consumers. One key problem revolves around hidden costs like cleaning fees, which some hosts have implemented to lure customers into checking out their property before seeing the full cost of their stay. According to one recent Business Insider article, hosts have been known to charge up to $400 for cleaning fees, making many homestays more expensive than local hotels. charge up to $400 charge up to $400 According to one travel website owner, Michael Rozenblit, the paradoxical fees charged among hosts for cleaning services while often instructing guests to take out the trash and strip the bedding are undermining the platform as a whole. paradoxical fees paradoxical fees The issue of exploitative fees has long been on Airbnb’s radar, and attempts to implement a more transparent pricing structure could help to restore some faith in the platform. However, it appears that the app’s days as a low-cost alternative to hotel stays are over. transparent pricing structure transparent pricing structure Can AirBnb Recover? Airbnb’s attempt to overcome the challenges of the more expensive pricing strategies of its hosts is to embrace higher costs by building a fresh focus on experience-oriented homestays. In recent years, we’ve seen Airbnb’s marketing move consciously towards experience , rather than budget-focused homestays. consciously towards experience consciously towards experience 2023 saw the Barbie Dreamhouse added to Airbnb’s listings with a ballot added to the site for fans to win a free overnight stay. Ahead of the 2024 Olympics, Paris’s Musée d’Orsay was added to the site as a place to stay during the Opening Ceremony. The stock also has many advantages that could support a stock market recovery, especially due to its operating model. “Airbnb has consistently generated positive operating profit since 2021 thanks to its lower-cost asset-based business model that requires minimal capital expenditure,” explained Maxim Manturov, head of investment research at Freedom24. “This has led to sustainable free cash flow (FCF): last quarter, the company produced a free cash flow of $1.9 billion. This financial discipline reduces the need to raise external capital, proving the effectiveness of its business model.” cash flow cash flow Recent weeks have also shown signs of an Airbnb rebound off the back of the Federal Reserve’s plans for upcoming rate cuts. With the stock climbing almost 15% over two weeks between the 6th and 20th of September, it’s worth remembering that the aforementioned challenges within Airbnb’s US user base have been compounded by a squeeze on consumer spending power. signs of an Airbnb rebound signs of an Airbnb rebound Challenges Remain Airbnb has been proactive in addressing its challenges to date, and its conscious push towards an experience-based model illustrates that the stock could have a future even as hosts push up prices on the platform. Despite this, high fees and backlashes over the impact of hosts on local housing markets mean that the future won’t be smooth sailing for Airbnb. However, with a sustainable business model and its status as a homestay innovator, the platform’s recent turbulence may be short-lived.

Airbnb at a Crossroads: Is the Innovative Travel Stock Running Out of Steam?!

Airbnb by 'n kruispad: raak die innoverende reisvoorraad op?!

Airbnb yol ayrıcında: İnnovativ Səyahət ehtiyatı tükənirmi?!

Airbnb en una encrucijada: ¿se está agotando el impulso del sector de viajes innovadores?

Airbnb bidegurutzean: Bidaietako Stock berritzailea Lurrunik agortzen al da?!

Az Airbnb válaszúton: kifogyott az innovatív utazási készlet?

岐路に立つAirbnb：革新的な旅行株は勢いを失いつつあるのか？

Airbnb په څلور لارې کې: ایا د نوښتګر سفر سټاک له بخار څخه تیریږي؟!

Airbnb la răscruce: stocul inovator de călătorie se epuizează?!

Airbnb ee Isgoysyada: Suuqgeynta Safarka Hal-abuurka leh miyay ka dhamaanaysaa uumiga?!

Airbnb chorrahada: innovatsion sayohat zaxirasi tugayaptimi?!

Airbnb 处在十字路口：创新旅游股是否已失去动力？！

I-Airbnb e-Crossroads: Ingabe Isitoko Sokuhamba Esisha Siphelelwa I-Steam?!

If there’s anything we don’t pay attention to at airports, it's how airplanes move when they’re on the tarmac. We’re talking beyond the wheels, but the vehicles that help steer the planes in and out of their parking spots. While such a thing feels so rudimentary, it's actually quickly evolving to become more sustainable, and reduce carbon emissions. One company in Germany is changing the game when it comes to tug vehicles, the seemingly golf cart-like cars that drag or push airplanes in and around the tarmac. One expert on this niche is Björn Rösch, the Head of Software at TowFLEXX, who walks us through the evolution of tug vehicles. Who knew that the very same vehicles that are responsible for maneuvering and parking aircraft have undergone a significant change over the past 50 years? Björn is partially responsible for ushering in the new age of tug vehicles, which is bringing the industry into the age of autonomy. “Today, the evolution of automated tug vehicles represents the latest chapter in this history,” said Rösch, a specialist in electronically controlled steering systems for commercial and dual-use vehicles. Having worked as a programmer for steering systems at Mobil Elektronik in the 1990s, to founding an electronics development department at a space and aviation firm called PHBTronics, he is no stranger to designing zero-emission shunting vehicles, and has worked with TowFLEXX since 2018. “Companies are leading the charge with electric, remote-controlled, and increasingly autonomous tugs,” he adds. “These modern vehicles address contemporary challenges such as the need for cost efficiency, reduced environmental impact, and enhanced safety in airport operations.” So when did tug vehicles begin? It all started in the 1940s, as tug vehicles were originally adapted from early military and industrial tractors (yes, you read that right, tractors). These early models were pretty simple and manually operated (obviously) machines that were clunky and took up too much space. But they evolved, just as the aviation industry did. In the Postwar Era, the need for more specialized tug vehicles increased. “They needed to maneuver around the tarmac more easily, but also handle larger aircraft,” said Rösch. “But they still needed a driver behind the wheel.” For a time, the industry remained in a holding pattern with slightly more nimble but still cumbersome tractors being the main form of these tug vehicles. It wasn’t until the 1990s that we saw real innovation with tug vehicles, as there was a significant shift away from the older vehicles. “This shift,” Rösch notes, “began with towbarless tugs, which eliminated the need for specific towbars for different aircraft.” This was a massive change and helped to streamline the process. It also helped smooth over the process for all ground operations of aircraft. The next and exciting step in the evolution of tug vehicles came in the 2000s with the move towards autonomous technology in the tow vehicles. “This progression is driven by the growing complexity of modern airport operations and environments, and the industry's push for greener, more efficient technologies, ultimately setting the stage for a future where fully autonomous ground handling operations of aircraft become the norm,” he said. This move towards integrating autonomous technology is the new standard. With Rösch’s dedication to innovation there has been a decided move towards electric, remote-controlled and autonomous tugs. These modern vehicles help to reduce environmental and sound impact, increase maneuverability and increase efficiency at airports globally, so aircraft can get out and up in the air easily and effortlessly. Getting a tug to push an aircraft from the gate to the taxiway – an aircraft that weighs thousands of pounds – is no easy feat, especially in tiny airports, or even worse, on a US Navy seaplane barge. That’s where autonomous tug vehicles come in – to save space. “Instead of turning the nose landing gear of the aircraft by moving the aircraft forwards or backwards with extreme caution to prevent oversteering of the nose landing gear (NLG) and avoid damage, our technology rotates tug vehicles 360° around the NLG.” It’s no surprise that airports are ushering in the next generation of electric vehicles to help assist aircraft, while reducing carbon emissions. They can “ultimately increase to use a hangar space up to 40% more,” said Rösch. Yes, the shift to electric power in tugs is a given. “It aligns with the global trend toward sustainability, offering zero emissions and reduced noise pollution,” he said.” This not only meets increasing environmental regulations but also supports the aviation industry's broader goals of reducing its carbon footprint. Additionally, electric vehicles tend to have lower maintenance costs and increased reliability, ensuring higher uptime and reduced operational disruptions.” Who knew that towbarless tug vehicles have been an integral part of military, aviation schools, the US Navy and more for over a decade? These tug vehicles have been helping to get them (and their planes) up to speed with increasing effeciency. So what’s next for tug vehicles, you ask? “Future generations of tug vehicles are poised to include AI-powered autonomy, ultimately enabling autonomous towing of aircraft, which will mark a significant leap forward in airport ground handling,” said Rösch. “As the aerospace and defense industry seeks to enhance efficiency, safety, and sustainability, these autonomous tugs are set to play a crucial role in the future,” he adds. “The next, more immediate stages of tug vehicles, however, will be stepping stones towards full autonomy, providing semi-autonomous features for ground handling operations of aircraft at first.” If there’s anything we don’t pay attention to at airports, it's how airplanes move when they’re on the tarmac. We’re talking beyond the wheels, but the vehicles that help steer the planes in and out of their parking spots. don’t While such a thing feels so rudimentary, it's actually quickly evolving to become more sustainable, and reduce carbon emissions. One company in Germany is changing the game when it comes to tug vehicles, the seemingly golf cart-like cars that drag or push airplanes in and around the tarmac. One expert on this niche is Björn Rösch, the Head of Software at TowFLEXX, who walks us through the evolution of tug vehicles. Who knew that the very same vehicles that are responsible for maneuvering and parking aircraft have undergone a significant change over the past 50 years? Björn is partially responsible for ushering in the new age of tug vehicles, which is bringing the industry into the age of autonomy. “Today, the evolution of automated tug vehicles represents the latest chapter in this history,” said Rösch, a specialist in electronically controlled steering systems for commercial and dual-use vehicles. Having worked as a programmer for steering systems at Mobil Elektronik in the 1990s, to founding an electronics development department at a space and aviation firm called PHBTronics, he is no stranger to designing zero-emission shunting vehicles, and has worked with TowFLEXX since 2018. “Companies are leading the charge with electric, remote-controlled, and increasingly autonomous tugs,” he adds. “These modern vehicles address contemporary challenges such as the need for cost efficiency, reduced environmental impact, and enhanced safety in airport operations.” So when did tug vehicles begin? It all started in the 1940s, as tug vehicles were originally adapted from early military and industrial tractors (yes, you read that right, tractors). These early models were pretty simple and manually operated (obviously) machines that were clunky and took up too much space. But they evolved, just as the aviation industry did. tractors). In the Postwar Era, the need for more specialized tug vehicles increased. “They needed to maneuver around the tarmac more easily, but also handle larger aircraft,” said Rösch. “But they still needed a driver behind the wheel.” For a time, the industry remained in a holding pattern with slightly more nimble but still cumbersome tractors being the main form of these tug vehicles. It wasn’t until the 1990s that we saw real innovation with tug vehicles, as there was a significant shift away from the older vehicles. “This shift,” Rösch notes, “began with towbarless tugs, which eliminated the need for specific towbars for different aircraft.” This was a massive change and helped to streamline the process. It also helped smooth over the process for all ground operations of aircraft. The next and exciting step in the evolution of tug vehicles came in the 2000s with the move towards autonomous technology in the tow vehicles. “This progression is driven by the growing complexity of modern airport operations and environments, and the industry's push for greener, more efficient technologies, ultimately setting the stage for a future where fully autonomous ground handling operations of aircraft become the norm,” he said. This move towards integrating autonomous technology is the new standard. With Rösch’s dedication to innovation there has been a decided move towards electric, remote-controlled and autonomous tugs. These modern vehicles help to reduce environmental and sound impact, increase maneuverability and increase efficiency at airports globally, so aircraft can get out and up in the air easily and effortlessly. Getting a tug to push an aircraft from the gate to the taxiway – an aircraft that weighs thousands of pounds – is no easy feat, especially in tiny airports, or even worse, on a US Navy seaplane barge. That’s where autonomous tug vehicles come in – to save space. “Instead of turning the nose landing gear of the aircraft by moving the aircraft forwards or backwards with extreme caution to prevent oversteering of the nose landing gear (NLG) and avoid damage, our technology rotates tug vehicles 360° around the NLG.” It’s no surprise that airports are ushering in the next generation of electric vehicles to help assist aircraft, while reducing carbon emissions. They can “ultimately increase to use a hangar space up to 40% more,” said Rösch. Yes, the shift to electric power in tugs is a given. “It aligns with the global trend toward sustainability, offering zero emissions and reduced noise pollution,” he said.” This not only meets increasing environmental regulations but also supports the aviation industry's broader goals of reducing its carbon footprint. Additionally, electric vehicles tend to have lower maintenance costs and increased reliability, ensuring higher uptime and reduced operational disruptions.” Who knew that towbarless tug vehicles have been an integral part of military, aviation schools, the US Navy and more for over a decade? These tug vehicles have been helping to get them (and their planes) up to speed with increasing effeciency. So what’s next for tug vehicles, you ask? “Future generations of tug vehicles are poised to include AI-powered autonomy, ultimately enabling autonomous towing of aircraft, which will mark a significant leap forward in airport ground handling,” said Rösch. “As the aerospace and defense industry seeks to enhance efficiency, safety, and sustainability, these autonomous tugs are set to play a crucial role in the future,” he adds. “The next, more immediate stages of tug vehicles, however, will be stepping stones towards full autonomy, providing semi-autonomous features for ground handling operations of aircraft at first.”

The Evolution of Automated Tug Vehicles At Airports–With Expert Commentary

SINGAPORE, Singapore, August 14th, 2024, Chainwired/--The decentralized derivatives trading platform, ELFi Protocol, officially launched today on the Arbitrum network. ELFi is a decentralized derivatives trading platform dedicated to creating a unique and forward-looking trading experience that rivals centralized exchanges (CEX). ELFi is the first to implement a Portfolio Margin and vAMM hybrid model on-chain. Additionally, the platform has designed an innovative zero-risk stable coin liquidity pool and a mature risk management system for contracts of various risk levels. The platform strives to better meet market and user needs in areas such as risk isolation, asset pricing, and support for LSD assets. Innovative Features Introduced by ELFi: Portfolio Margin Mode: This feature allows users to use various cryptocurrencies as margin assets, enabling more flexible risk hedging within their multi-asset portfolios. It improves capital efficiency, reduces trading costs, and enhances the implementation of trading strategies. Zero-Risk Stablecoin Liquidity Pool: This on-chain yield product offers returns compared to DeFi industry rates. Users can deposit stable coins such as USDC or USDT to enjoy potential returns without the risk of capital loss due to market volatility. High-Yield Coin-Margined Liquidity Pool: This feature offers potential high-yield products pegged to coin prices for on-chain asset holders, including liquidity pools for major cryptocurrencies like BTC and ETETH. Support for LSD Re-Staking: The upcoming LSD re-staking feature will allow synthetic assets like stETH to be re-staked, providing additional yield opportunities. In May of this year, ELFi Protocol announced its funding, successfully completing two strategic funding rounds totaling $5 million. The latest round was led by IDG Capital and KuCoin Ventures. Mainnet Launch Limited-Time Event To celebrate the launch, ELFi is hosting an event with an initial prize pool of $5,000. For every $10 million in trading volume, the platform will add an additional $1,000 to the prize pool, with a maximum prize pool of $100,000. Users holding ELFi's beta test NFTs can earn accelerated rewards, and each valid user referral will also result in accelerated rewards, with the maximum acceleration being 5x. Users who interact with ELFi by staking or trading will be eligible to share the prize pool. The prize pool will be divided based on each user’s share of the total interaction volume at the end of the event. The final prize pool amount will be announced at the end of the event. NFT Double Rewards for Beta Users Users who obtained the official ELFi NFTs during the beta test can receive an additional 1-2x reward during the event. About ELFi Protocol ELFi is a decentralized derivatives trading platform that focuses on delivering top-notch trading functionalities. It’s the pioneer in supporting Portfolio Margin within the P2Pool model, and boasts a sophisticated risk management system for listing contracts of various risk levels. Additionally, ELFi introduces innovative liquidity pool designs, offering industry-first zero-risk stablecoin liquidity pools and LSD re-collateralized liquidity pools. It strives to better meet market and user demands through features like risk isolation, asset pricing, and LST asset support. Twitter Discord Contact Vicky ELFi Protocol business2@elfi.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SINGAPORE, Singapore, August 14th, 2024, Chainwired/--The decentralized derivatives trading platform, ELFi Protocol, officially launched today on the Arbitrum network. ELFi is a decentralized derivatives trading platform dedicated to creating a unique and forward-looking trading experience that rivals centralized exchanges (CEX). ELFi is the first to implement a Portfolio Margin and vAMM hybrid model on-chain. Additionally, the platform has designed an innovative zero-risk stable coin liquidity pool and a mature risk management system for contracts of various risk levels. The platform strives to better meet market and user needs in areas such as risk isolation, asset pricing, and support for LSD assets. Innovative Features Introduced by ELFi: Portfolio Margin Mode: This feature allows users to use various cryptocurrencies as margin assets, enabling more flexible risk hedging within their multi-asset portfolios. It improves capital efficiency, reduces trading costs, and enhances the implementation of trading strategies. Zero-Risk Stablecoin Liquidity Pool: This on-chain yield product offers returns compared to DeFi industry rates. Users can deposit stable coins such as USDC or USDT to enjoy potential returns without the risk of capital loss due to market volatility. High-Yield Coin-Margined Liquidity Pool: This feature offers potential high-yield products pegged to coin prices for on-chain asset holders, including liquidity pools for major cryptocurrencies like BTC and ETETH. Support for LSD Re-Staking: The upcoming LSD re-staking feature will allow synthetic assets like stETH to be re-staked, providing additional yield opportunities. In May of this year, ELFi Protocol announced its funding, successfully completing two strategic funding rounds totaling $5 million. The latest round was led by IDG Capital and KuCoin Ventures. Mainnet Launch Limited-Time Event To celebrate the launch, ELFi is hosting an event with an initial prize pool of $5,000. For every $10 million in trading volume, the platform will add an additional $1,000 to the prize pool, with a maximum prize pool of $100,000. Users holding ELFi's beta test NFTs can earn accelerated rewards, and each valid user referral will also result in accelerated rewards, with the maximum acceleration being 5x. Users who interact with ELFi by staking or trading will be eligible to share the prize pool. The prize pool will be divided based on each user’s share of the total interaction volume at the end of the event. The final prize pool amount will be announced at the end of the event. NFT Double Rewards for Beta Users Users who obtained the official ELFi NFTs during the beta test can receive an additional 1-2x reward during the event. About ELFi Protocol ELFi is a decentralized derivatives trading platform that focuses on delivering top-notch trading functionalities. It’s the pioneer in supporting Portfolio Margin within the P2Pool model, and boasts a sophisticated risk management system for listing contracts of various risk levels. Additionally, ELFi introduces innovative liquidity pool designs, offering industry-first zero-risk stablecoin liquidity pools and LSD re-collateralized liquidity pools. It strives to better meet market and user demands through features like risk isolation, asset pricing, and LST asset support. Twitte r Twitte Discord Discord Contact Vicky ELFi Protocol business2@elfi.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

ELFi Protocol, a DEX Derivatives Platform, Officially Launched With a $100,000 Airdrop Event

ELFI প্রোটোকল, একটি DEX ডেরিভেটিভস প্ল্যাটফর্ম, আনুষ্ঠানিকভাবে $100,000 এয়ারড্রপ ইভেন্টের সাথে চালু হয়েছে

ELFi Protocol, eine DEX-Derivateplattform, wurde offiziell mit einem 100.000-Dollar-Airdrop-Event gestartet

El protocolo ELFi, una plataforma de derivados DEX, se lanzó oficialmente con un evento de airdrop de $100,000

Lancement officiel du protocole ELFi, une plateforme de dérivés DEX, avec un événement Airdrop de 100 000 $

ELFi प्रोटोकॉल, एक DEX डेरिवेटिव्स प्लेटफ़ॉर्म, आधिकारिक तौर पर $100,000 एयरड्रॉप इवेंट के साथ लॉन्च किया गया

DEXデリバティブプラットフォームELFiプロトコルが10万ドルのエアドロップイベントで正式にローンチ

DEX 파생상품 플랫폼인 ELFi Protocol이 10만 달러 에어드랍 이벤트로 공식 출시

Protocolo ELFi, uma plataforma de derivativos DEX, oficialmente lançada com um evento de airdrop de US$ 100.000

Протокол ELFi, платформа деривативов DEX, официально запущен с раздачей 100 000 долларов США

DEX Türev Platformu ELFi Protokolü, 100.000 Dolarlık Airdrop Etkinliğiyle Resmen Başlatıldı

ELFi Protocol, một nền tảng phái sinh DEX, chính thức ra mắt với sự kiện Airdrop trị giá 100.000 đô la

DEX 衍生品平台 ELFi Protocol 正式上线，并举办 10 万美元空投活动

So, let’s dive into the drama: Microsoft is laying the blame squarely on Delta for its epic struggle to bounce back from that global cyber outage that had the airline canceling over 6,000 flights. Yep, you read that right—6,000! The conflicting accounts from Delta Air Lines, cybersecurity firm CrowdStrike, and Microsoft have erupted into a blame game filled with legal threats. This situation highlights one of the consequences of a world increasingly reliant on a few key technology providers. Here’s the lowdown: Last month, CrowdStrike, a global cybersecurity firm, rolled out a software update that caused some serious chaos for Microsoft’s clientele, including a bunch of airlines. While other major US carriers managed to get their act together the next day, Delta was still floundering. Microsoft’s initial findings suggest that Delta, unlike its peers, hadn’t exactly kept up with the times regarding IT infrastructure. In other words, their tech setup might be a bit of a dinosaur compared to others. Delta, not taking this lying down, has roped in the big guns—David Boies of Boies Schiller Flexner—to seek damages from both CrowdStrike and Microsoft. They’re not just asking for a little something; they’re gunning for serious compensation. Delta’s CEO, Ed Bastian, took to CNBC last week to air some grievances, saying that both Microsoft and CrowdStrike had dropped the ball on providing “exceptional service.” He even went as far as to label Microsoft’s platform as the “most fragile.” Bastian declared that the airline, which leads the U.S. in revenue, has “no choice” but to take legal action against the two tech firms. With an estimated $500 million in damages from lost revenue and refunds due to flight delays and cancellations, Bastian emphasized that pursuing legal recourse is the only viable option. Microsoft’s lawyer has accused Delta of presenting a “false picture” of its actions following the July CrowdStrike outage. Mark Cheffo, a Microsoft lawyer, wasn’t having any of it. He fired back, calling Delta’s accusations “incomplete, false, misleading, and damaging to Microsoft and its reputation.” According to Cheffo, Microsoft’s software wasn’t the culprit behind the CrowdStrike mess, but they did offer help—free of charge!—which Delta apparently ignored. Cheffo also claimed Microsoft’s CEO, Satya Nadella, reached out to Bastian directly via email and got nothing but crickets in return. CrowdStrike, which was responsible for initiating the outage on Microsoft Windows systems with a problematic update, pushed back on Sunday. In a letter reviewed by Axios, the company “strongly rejects” any claims of gross negligence or willful misconduct related to the outage.


The letter also warns that if Delta proceeds with its “public threat of litigation,” the airline will need to justify to the public, its shareholders, and possibly a jury why CrowdStrike accepted responsibility for its actions while Delta did not.


Meanwhile, CrowdStrike is already contending with a proposed class action lawsuit from affected air travelers. Microsoft says it repeatedly tried to lend a hand after the outage, but Delta turned them down. Apparently, Delta was using other tech providers for crew-tracking and scheduling, which Microsoft thinks might be why Delta refused their help. Cheffo made it clear that if Delta decides to sue, Microsoft will be ready to “vigorously” defend itself. And CrowdStrike is also pushing back, stating that their CEO personally offered on-site assistance to Delta’s CEO, which, you guessed it, went unanswered. Here's the latest: On Tuesday, Microsoft sent a letter to Delta outlining what it claims are communications demonstrating Delta's refusal of Microsoft's assistance. According to Microsoft, this refusal was likely due to Delta relying on IBM or other tech companies for support with its downed crew-tracking and scheduling systems. So, let’s dive into the drama: Microsoft is laying the blame squarely on Delta for its epic struggle to bounce back from that global cyber outage that had the airline canceling over 6,000 flights. Yep, you read that right—6,000! Delta The conflicting accounts from Delta Air Lines, cybersecurity firm CrowdStrike, and Microsoft have erupted into a blame game filled with legal threats. This situation highlights one of the consequences of a world increasingly reliant on a few key technology providers. Here’s the lowdown: Last month, CrowdStrike , a global cybersecurity firm, rolled out a software update that caused some serious chaos for Microsoft’s clientele, including a bunch of airlines. While other major US carriers managed to get their act together the next day, Delta was still floundering. CrowdStrike other major US Microsoft’s initial findings suggest that Delta, unlike its peers, hadn’t exactly kept up with the times regarding IT infrastructure. In other words, their tech setup might be a bit of a dinosaur compared to others. Delta, not taking this lying down, has roped in the big guns—David Boies of Boies Schiller Flexner—to seek damages from both CrowdStrike and Microsoft. They’re not just asking for a little something; they’re gunning for serious compensation. Delta’s CEO, Ed Bastian, took to CNBC last week to air some grievances, saying that both Microsoft and CrowdStrike had dropped the ball on providing “exceptional service.” He even went as far as to label Microsoft’s platform as the “most fragile.” Bastian declared that the airline, which leads the U.S. in revenue, has “no choice” but to take legal action against the two tech firms. With an estimated $500 million in damages from lost revenue and refunds due to flight delays and cancellations, Bastian emphasized that pursuing legal recourse is the only viable option. legal action Microsoft’s lawyer has accused Delta of presenting a “false picture” of its actions following the July CrowdStrike outage. Mark Cheffo, a Microsoft lawyer, wasn’t having any of it. He fired back, calling Delta’s accusations “incomplete, false, misleading, and damaging to Microsoft and its reputation.” According to Cheffo, Microsoft’s software wasn’t the culprit behind the CrowdStrike mess, but they did offer help—free of charge!—which Delta apparently ignored. Cheffo also claimed Microsoft’s CEO, Satya Nadella, reached out to Bastian directly via email and got nothing but crickets in return. CrowdStrike, which was responsible for initiating the outage on Microsoft Windows systems with a problematic update, pushed back on Sunday. In a letter reviewed by Axios, the company “strongly rejects” any claims of gross negligence or willful misconduct related to the outage. The letter also warns that if Delta proceeds with its “public threat of litigation,” the airline will need to justify to the public, its shareholders, and possibly a jury why CrowdStrike accepted responsibility for its actions while Delta did not. Meanwhile, CrowdStrike is already contending with a proposed class action lawsuit from affected air travelers. CrowdStrike, which was responsible for initiating the outage on Microsoft Windows systems with a problematic update, pushed back on Sunday. In a letter reviewed by Axios, the company “strongly rejects” any claims of gross negligence or willful misconduct related to the outage. CrowdStrike, which was responsible for initiating the outage on Microsoft Windows systems with a problematic update, pushed back on Sunday. In a letter reviewed by Axios, the company “strongly rejects” any claims of gross negligence or willful misconduct related to the outage. The letter also warns that if Delta proceeds with its “public threat of litigation,” the airline will need to justify to the public, its shareholders, and possibly a jury why CrowdStrike accepted responsibility for its actions while Delta did not. The letter also warns that if Delta proceeds with its “public threat of litigation,” the airline will need to justify to the public, its shareholders, and possibly a jury why CrowdStrike accepted responsibility for its actions while Delta did not. Meanwhile, CrowdStrike is already contending with a proposed class action lawsuit from affected air travelers. Meanwhile, CrowdStrike is already contending with a proposed class action lawsuit from affected air travelers. Microsoft says it repeatedly tried to lend a hand after the outage, but Delta turned them down. Apparently, Delta was using other tech providers for crew-tracking and scheduling, which Microsoft thinks might be why Delta refused their help. Cheffo made it clear that if Delta decides to sue, Microsoft will be ready to “vigorously” defend itself. And CrowdStrike is also pushing back, stating that their CEO personally offered on-site assistance to Delta’s CEO, which, you guessed it, went unanswered. Here's the latest: On Tuesday, Microsoft sent a letter to Delta outlining what it claims are communications demonstrating Delta's refusal of Microsoft's assistance. According to Microsoft, this refusal was likely due to Delta relying on IBM or other tech companies for support with its downed crew-tracking and scheduling systems.

Microsoft Rips Into Delta Airlines For Sluggish Recovery After Global CrowdStrike Outage

Stories, News & Mentions about AIR

Air’s Stories on HackerNoon

Air's News & Mentions