Latest news with #Jellyfish
The Herald Scotland
5 days ago
- Entertainment
- The Herald Scotland
True crime tale of Glasgow poisoning turned into city play
Written by Tom Cooper and Jen McGregor, the musical is inspired by the scandalous tale of 19th century femme fatale Madeleine Smith, who was accused of poisoning her lover but given a rare Not Proven verdict. The truth as to what happened is still questioned to this day. Also this season at the West End venue, award-winning poet and playwright Kevin P Gilday makes his PPP debut with Gravity, a drama about a man who refuses to vacate his flat in a condemned Glasgow high-rise, and the unconventional social worker determined to help him before tragedy unfolds. Brian James O'Sullivan (Image: Tommy Ga-Ken Wan) Katy Nixon, writer of the David MacLennan Award-winning Jellyfish, returns with new drama Cheapo about two teenagers who play chess in their local KFC, while a fading starlet holding onto the hope of playing the role of a lifetime is the subject of Maybe Tomorrow by PPP favourite Brian James O'Sullivan and performer Hannah Jarrett-Scott in her playwriting debut. The season also includes two spooky productions - Righ Isagair: The Fisher King, a folk thriller from writer and performer Kenny Boyle, about two best friends who discover they are not alone on the dark Outer Hebridean moors; and Hauns Aff Ma Haunted Bin! by Éimi Quinn, about an auntie and niece attempting to hide a murder. Éimi Quinn (Image: Tommy Ga-Ken Wan) The autumn line-up, which runs from September 1 to November 22, also includes a contemporary hip hop musical exploring the legend of William Wallace. Co-produced with Raw Material, Wallace is an exciting collaboration between playwright Rob Drummond and acclaimed rapper-songwriter Dave Hook, that explores Wallace's contested place on the spectrum between myth and history. Brian Logan (Image: Calum O'Brien) Brian Logan, PPP's artistic director, said: 'The nights may be drawing in but the lunchtimes will be brighter than ever at Òran Mór this autumn, with a scintillating line-up of pastry-assisted theatre. 'As usual at PPP, this season balances broad comedy with big-hitting drama, rookies with well-loved talents, shows about the past with shows thrillingly about the here and now. 'We couldn't be more excited to kick off with Wallace, the hip hop musical they're already calling (well, I am…) Scotland's Hamilton. 'Throw in some spine-chilling Gaelic folklore, a cracking Glasgow drama in a doomed high-rise, and a generational set-to between TikTok and the stage, and you've got twelve weeks of brand new theatre that you really wouldn't want to miss.' Tickets are on sale now for all performances.
Perth Now
09-07-2025
- Entertainment
- Perth Now
Sigrid: I'm trying to not take myself too seriously
Sigrid is trying to not take herself "too seriously". The 28-year-old star is "having fun again" on her new single Jellyfish, confessing that she "really wanted to be taken seriously" earlier in her career. She told The Independent: "I'm so thankful for all of it, obviously, but it's kind of going through a washing machine. And I think on the second album [How To Let Go] , I really wanted to be taken seriously and I put a lot of heavy pressure on myself to write serious songs." Sigrid admits that she adopted a much more relaxed attitude while making Jellyfish. She said: "I think you can hear it in the song. I'm not trying to sing perfectly." The music star - who released her debut album, Sucker Punch, back in 2019 - is actually happy to laugh at herself at this stage of her career. She explained: "I take the p*** out of myself a bit with the new music as well. I'm trying to not take myself too seriously and allow myself to not always be the hero in a situation." Sigrid actually considers herself to be "a bit of a word-of-mouth artist". But the singer guarantees that fans will have a "good time" whenever she performs live. She said: "I'm a bit of a word of mouth artist but when you go to a show of mine, you know you're going to have a good time." Meanwhile, Sigrid previously admitted to suffering a crisis of confidence during the COVID-19 pandemic. The music star's meteoric rise was suddenly halted by the global health crisis, and Sigrid admits that she struggled to cope at the time. She told the Guardian newspaper: "With the success, I had that feeling that maybe I was cool. Then … boom! Isolation. Back home with my parents, in my childhood bedroom, remembering cringe moments of being 14. "I got a bit scared of how quickly I adapted to this completely alternative life, where I was waking up in the morning, having breakfast with my parents, going for a hike and skiing. "Like, the whole day was about getting to the peak of a mountain, skiing down and then coming home to talk about how the snow was while having dinner. There were no emails. There was no stress. I had this serene, alternative life, but there was this really scary thing going on at the same time."
Business Insider
08-07-2025
- Business
- Business Insider
These are the most popular AI coding tools among engineers
AI coding tools are gaining traction across the tech industry. A recent survey sheds light on which services are most popular among engineers. In May, Jellyfish, which helps companies manage developer teams, surveyed 645 full-time professionals in various engineering roles, including individual contributors, managers, and executives. Respondents came from companies ranging from small teams with fewer than 10 people to enterprises with more than 500 engineers. The survey findings shed new light on the explosive growth and impact of AI coding tools in software development. Jellyfish found that 90% of engineering teams are now using AI in their workflows, up from 61% just one year ago. Almost a third have formally supported and widely adopted AI tools, while another 39% are actively experimenting with them. Only 3% of respondents reported no AI usage and no plans to change that. Crucially, 48% of respondents reported using two or more AI coding tools, suggesting teams are taking a diversified, exploratory approach by evaluating multiple solutions simultaneously rather than standardizing on a single platform. The leaders The leader among AI coding tools was GitHub Copilot from Microsoft, with 42% of surveyed engineers naming it their tool of choice, according to the survey. Google 's Gemini Code Assist was second, while Amazon Q (formerly CodeWhisperer) and Cursor were tied at third. These four tools formed the dominant tier of AI-powered code assistance platforms, but there were several other services in the mix, too, according to the report. The study explicitly excluded general-purpose generative AI tools like ChatGPT to focus on products designed specifically for software engineering. This distinction highlights the growing specialization of AI solutions tailored to the needs of development teams. According to the report, 62% of engineers said they've achieved at least a 25% boost in velocity and productivity thanks to AI coding tools, and 8% reported a doubling of their output. Less than 1% believe AI is slowing them down. Human-AI hybrid workflows Looking ahead, 81% of respondents believe at least a quarter of today's engineering work will be automated by AI within the next five years. Yet, the trend isn't toward full automation; it's toward collaboration. "While AI can help creatives, AI itself is not creative," one engineering leader put it in their survey response. "If you have smart people using AI who also understand the topic/issue they are going after, magic happens," the person added. "Otherwise, you have people that desperately just want to look like they have done something amazing, but don't really understand the issues they have just created with the help of AI." With productivity gains already measurable and adoption rising, the current crop of AI coding tools, led by GitHub Copilot, Gemini, Amazon Q, and Cursor, appears to be setting the foundation for a hybrid future where software engineers and AI systems co-create the next generation of digital products.
Yahoo
11-06-2025
- Business
- Yahoo
Other World Computing (OWC) Announces Powerful New Updates to Jellyfish, Network-Attached Storage (NAS) Solutions
Jellyfish Turns Impossible Deadlines Into Achievable Wins, and Makes Teams Wonder How They Ever Worked Without It WOODSTOCK, Ill., June 11, 2025--(BUSINESS WIRE)--Other World Computing (OWC®), a trusted leader in high-performance storage, memory, connectivity, software, and accessories that empower creative and business professionals to maximize performance, enhance reliability, and streamline workflows, today announced the latest software release for its acclaimed OWC Jellyfish network-attached storage (NAS) solution. The new updates, designed specifically for video production teams, introduce a redesigned Jellyfish Manager and an all-new Jellyfish Media Manager, delivering smarter media workflows, deeper integrations, and unprecedented visibility into team activity and storage performance. Jellyfish, built by creators for creators, remains the most intuitive and powerful NAS solution on the market for video teams. From 4K and 8K video editing to remote collaboration and archival, Jellyfish powers the world's top creative workflows, without IT headaches or lagging performance. Now shipping with all new Jellyfish systems and available to current users, the latest update includes: Enhanced Jellyfish Manager Redesigned UI for an intuitive user experience Active Directory (AD) integration for seamless team access control Automated cloud backups to Backblaze, AWS S3, Google Cloud, and Azure Native Archiware P5 integration for enterprise-class backup and archiving Native Resilio Connect integration for accelerated remote file transfers Live Activity Monitor for real-time insight into users and network performance SMB Multichannel support for faster data throughput on compatible devices Warranty and Service Contract Tracking to stay ahead of renewals Dynamic Disk Profiles with self-managed storage configuration Upgraded security to safeguard media assets Notifications module with Slack and email support for proactive alerts New Jellyfish Media Manager View, organize, manage, and download your media from anywhere in the world Quickly search your media library for the right content Add custom metadata to improve organization and asset tracking "Creative teams today are facing tougher demands than ever: faster turnarounds, massive files, remote collaboration, and the need to keep everything secure and accessible. The last thing they need is a storage system that slows them down," said Larry O'Connor, Founder and CEO of Other World Computing (OWC). "Jellyfish has always been known for making the impossible possible. That is, helping teams meet deadlines that once felt out of reach. This latest update takes that power to the next level. We hear it all the time: 'Why didn't we know about Jellyfish sooner?' Now more than ever, we're making sure the people who need it most can finally discover it, tap into its full potential, and stop wasting time and money on solutions that just can't keep up." To learn more, please visit: About Other World Computing (OWC) Founded in 1988, Other World Computing (OWC®) is a trusted leader in high-performance storage, docks, and memory card solutions that empower professionals in video and audio production, photography, and business with the tools to seamlessly maximize the performance and reliability of their workflows. OWC's professional-grade storage, expansion, media cards, docks, and connectivity, as well as its apps and accessories, are built to last by the people who use them: creatives, businesses, and consumers. For further information, please visit OWC can also be found on LinkedIn and X. ©2025 Other World Computing (OWC). All other brand and product names contained in this announcement may be trademarks or registered trademarks of their respective holders. View source version on Contacts PR Contact: Nicole GormanGorman Communications, for Other World Computing (OWC)M: Sign in to access your portfolio
Harvard Business Review
04-06-2025
- Business
- Harvard Business Review
Forget What You Know about SEO—Here's How to Optimize Your Brand for LLMs
Over the past year, consumers have migrated en masse from traditional search engines to Gen AI platforms including ChatGPT, Gemini, DeepSeek, and Perplexity. In a survey of 12,000 consumers, 58% (vs. only 25% in 2023) reported having turned to Gen AI tools for product/service recommendations. Another study reported a 1,300% surge in AI search referrals to U.S. retail sites during the 2024 holiday season. Consumers who use Large Language Models (LLMs) to discover, plan and buy are on average younger, wealthier, and more educated. Their customer journey no longer begins with a search query or a visit to your website—it starts with a dialogue. Consumers are asking AI assistants questions like 'What's the best coffee machine under $200?' or 'Plan me a weekend getaway that won't break the bank.' For brand leaders, the implications cannot be overstated. Your digital strategy must now include optimizing for AI recommendation engines, not just search algorithms. In short, you must boost LLMs' awareness of your brand. The Rise of 'Share of Model' To date, measuring awareness meant assessing consumers' attention—either offline through recall surveys (e.g., 'Which brands come to mind when you think of running shoes?') or online, through search or social media volumes, manifesting private intent or popularity. But the growing role of LLMs as an intermediary between consumers and brands demands that marketers consider another kind of awareness: how often, how prominently, and how favorably a brand is surfaced by LLMs to consumers. We call this awareness Share of Model (SOM). Think of it as the AI-era's offshoot of share of search ('How much do people search for my brand via-a-vis competitors?') and share of voice ('How much do people talk about my brand vis-a-vis competitors?'). SOM uniquely emulates LLMs' perceptions and recommendations given a prompt, rather than reflecting human intent (SOS) or available content (SOV). Two of the coauthors' marketing agency, Jellyfish, has pioneered a methodology to measure SOM through prompting at scale. Building on this approach, we offer a new three-prong lens to unpack what and how AI 'think' about brands: mention rate, which tracks how often a brand is mentioned by a specific LLM; human-AI awareness gap, which measures the disparity in brand awareness when surveying people vs surveying LLMs; and brand and category sentiment, which breaks down LLMs rationale for recommendations into associated strengths and weaknesses. Take for example the laundry detergent market in Italy. We analyzed the top brands' mention rate among six LLMs using Jellyfish's proprietary Share of Model platform. Two observations stand out. First, brands' SOM varies significantly across the models, reflecting differences in how LLMs process brand information. For instance, Ariel's SOM ranges from almost 24% on Llama to less than 1% on Gemini. Second, some brands are totally absent from at least one model. For instance, while Chanteclair enjoys a 19% SOM on Perplexity, it is missing from Meta. Clearly, LLMs either feature brands or not, unlike search engines or social media where brands that don't excite the algorithm are still represented, albeit less prominently. Failure to register on an LLM means a brand doesn't appear at all before consumers. On ChatGPT, unlike Google, there is no 'page two.' Probing the human-AI brand awareness gap Importantly, a brand's visibility on LLMs can differ significantly from its market share or other awareness metrics. Therefore, brand managers' first task is to probe the link between human awareness (e.g. through SOS or SOM) and LLM awareness of their brands. Quick note: Although a brand's SOM often varies across LLMs as we show above, the next examples in this article focus on brands' SOM across LLMs for ease of discussion. We'll outline the implications of SOM variability across LLMs later. Consider our analysis of U.S. automobile brands' visibility in general and on LLMs during the first half of 2024. We constructed a Human-AI Awareness Matrix (Figure 2) that reflects brand awareness on LLMs, assessed through Jellyfish's tool, and in general, assessed by YouGov market research. Brands fall into 4 distinct categories: See more HBR charts in Data & Visuals Cyborgs: These brands have top awareness in both traditional measures (e.g., surveys, search ranking, share of voice) as well as among LLMs. Take Tesla's position in this chart, for example. Elon Musk's ubiquity helps make consumers highly aware of the brand. Tesla also scores well among LLMs because of the brand's emphasis on its specific features. Its new digital advertising strategy attempts to rise the company's scores even higher among both people and large language models. AI Pioneers: These brands are well-represented on LLMs but lack marketplace awareness. Often, they are AI-native brands or emerging digital players that are niche in broader digital spaces. Rivian's spot in this quadrant likely stems from its resolution-focused (which we'll touch on later) content strategy, which aligns with its positioning as a solution creator. High-Street Heroes: These are established brands with high marketplace awareness but underrepresented or missing in AI-generated content. Case in point: Lincoln, which Frank Lloyd Wright famously said makes 'the most beautiful car in the world.' This is likely due to the brand's focus on intangible attributes such as elegance or heritage, which are less prized by LLMs. Emergent: These brands struggle with low awareness in both the marketplace and among LLMs. They risk falling into digital irrelevance as AI-driven search becomes the norm. Despite its premium positioning, Polestar struggles in our analysis to achieve visibility across the spectrum, reflecting a lack of scaled digital footprint or lack of appeal for LLMs' processing style. The main takeaway? Marketers need to come up with strategies designed to push their brands up the 'consciousness' of LLMs. These strategies are likely to be very different from those designed to appeal to humans. For what we know about LLMs is this: LLMs are not optimizing for attention; they are optimizing for resolution. Identifying the ' job to be done ' thus becomes the number one priority for brand leaders if they want to score big on SOM. How to increase brand awareness on LLMs Our analyses across product categories reveals how models' perceptions of different categories presents specific opportunities to brands in those industries. This has implications for not only what content to produce (across text, image, and video), but also where brands may seek to distribute their messages (website, media, expert, or community contexts). LLMs are looking beyond keywords, focusing on concepts and relationships which create new ways to build brand awareness for LLMs. Brands should create content that explains not just what the product is, but how it relates to broader contexts, use cases and user needs. For example, instead of proclaiming 'we sell superb running shoes', go for 'our carbon-plated midsole design improves performance for long-distance runners.' Brands should also highlight proof of expertise. A skincare brand that references dermatologist-backed studies or links to PubMed research is likely to outshine competitors that don't. Brands that 'narrowcast' about pain points—needs, questions and tasks—are more likely to be surfaced. Brands that simply broadcast may be left out. This could explain why traditional car brands like Lincoln, which push aspirational and marketing-heavy content, are less salient to LLMs compared to Tesla or Rivian, which emphasize functions and features including battery life, tech stack, and software. Similarly, although they dominate SOV, fast-fashion brands such as Shein lags in AI awareness due to an overwhelming volume of undifferentiated content and lack of trust signals such as reviews and certifications. In contrast, the Ordinary brand of skincare products offers highly structured product pages with ingredient explanations, transparent science-backed content (explains the 'how' and 'why' of why a face cream works). Nike and its customer-generated content (runners' blogs, Reddit, Strava), detailed product pages with clear use cases (e.g., 'best shoes for marathon training') and integrated app ecosystems (Nike Run Club, Nike Training Club). Both brands topped their respective category in our analyses. Notably, legacy brands can also thrive in the age of AI—if they invest strategically in relevance, representation, and structured digital storytelling. Case in point: Cadillac. The century-old automobile brand scores highly in both human and AI brand awareness. Campaigns like 'Audacity' and 'The Daring 25' as well as international partnerships helped increase its AI visibility. Gauging LLM sentiment Beyond looking into AI brand awareness and how it relates to other awareness metrics—marketers can also explore brand and category sentiment through sentiment (positivity) and semantics (associated terms). This helps them answer questions such as: What are my brand's perceived strengths and weaknesses? How can I change how LLMs perceive my brand? For example, our analysis of the travel industry in the U.S. shows that LLMs value characteristics such as convenience, variety, and space, with Booking taking the overall top spot across models. We also surfaced brands' strengths and weaknesses relative to their competitors. Vrbo, for instance, scores much higher than Booking on privacy and uniqueness—strengths it could exploit to optimize AI awareness See more HBR charts in Data & Visuals See more HBR charts in Data & Visuals How to Market to LLMs Armed with insights on LLM sentiment, marketers may deploy several approaches to optimize their brand's AI visibility. First, adopt a multi-pronged media strategy that covers text, images, videos, and structured data (e.g., tables, lists, reviews). Content that clearly links brands' offerings to broader contexts, use cases or consumer needs (e.g., 'best EVs for winter driving' rather than just 'electric SUV'), generates strong conceptual associations in LLMs. Brands should also lead semantic niches—specific clusters of meaning where their products naturally fit (e.g., like the Ordinary brands with skincare science). Importantly, just as each social media platform has its own 'rules of engagement'—what works on TikTok probably won't fly on LinkedIn—each LLM applies its unique algorithmic lens to content. Take the U.S. travel industry again, focusing on LLM's perception of Airbnb. While Llama focuses on the uniqueness of a brand's offerings, ChatGPT focuses on the extent to which brands offer local options, whereas Perplexity seems to value flexibility most. This ties in with our point earlier about brands' varying visibility across LLMs. We recommend that marketers tailor content to the LLMs whose processing style best amplifies their brand's content and narrative strengths, even as they apply overarching rules (e.g. solution-oriented messaging) across models. It is a fine balance: Tailoring content to the nuances of a dominant model can drive visibility but spreading efforts too thinly across all LLMs risks diluting impact. The shift away from traditional search engines is not just a technological evolution. It's a fundamental change in consumer behavior that demands corresponding shifts in marketing: from persuasion to precision, from keyword to advice, from market share to problem-share. Do it right, and brands can establish themselves as essential participants in the algorithmic conversations that increasingly shape consumer decisions.
