Latest news with #CoFounder
CNA
6 days ago
- Business
- CNA
CNA938 Rewind - Eat, Drink, Singapore - A new Southeast Asian Cuisine chapter for Ce La Vi
The iconic rooftop restaurant Ce La Vi celebrates its 15th year with a revamped space, menu and meaning to 'elevating the Southeast Asian Soul'. What new flavours and dining experiences are in store for diners? Hui Wong speaks with Harry Apostolides, CEO and Co-Founder of Ce La Vi to find out more.
Forbes
31-07-2025
- Business
- Forbes
3 Candidate Traits That Should Be On Your Hiring Checklist
Milos Eric is the General Manager and Co-Founder of OysterLink, a restaurant and hospitality job platform. Throughout my career, I've read thousands of CVs and spoken to hundreds of people looking to get hired. So, I understand that recruiting is a huge responsibility. It's a function that determines who will support a company's operations and, by extension, its success. While around 65% of employers practice skills-based hiring, I've found that focusing solely on technical ability isn't the way to go. With all candidates having their own diverse set of character, talent and skills, hiring should look beyond matching résumés to job descriptions. When I'm assessing applicants, I like to focus on three key traits: mindset, attitude and priorities. 1. The Foundation: Mindset Recruiters usually tend to focus on objective, role-relevant skills and tools proficiency. But what they fail to realize is that those things can be developed on the job. What can't be learned is an employee's mindset. There's significant evidence of the positive impact of having a growth-focused outlook, particularly in academic settings. The same applies to the professional scene. During the hiring process, I'm more impressed by candidates who show interest in learning and adapting to challenges, rather than a list of skills. Why? Because they have boundless potential, they are less likely to fall stagnant and more likely take ownership of their work. People who seek challenges and learn from failures are on a mission to improve, and this enables growth on both ends. Equally important is long-term commitment. Those who have genuine interest and belief in the company's vision will outlast the people who are in it for short-term successes like a pay increase. While compensation definitely motivates one to show up, can it sustain your drive? At the end of the day, employees need a deeper connection. This will help you reduce turnover and build future leaders as well. 2. The Differentiator: Attitude While hard skills drive daily operations, soft skills influence the company's culture, and attitude is what sets good players apart from the best. Having a good working attitude fosters collaboration and empowerment in the workplace. In fact, the most valuable team members are those who stay positive and uplift others, even in challenging situations. Similarly, self-motivated individuals who are hungry to push limits inspire others to do the same. They're the ones who volunteer, explore new approaches and deliver more than required. And having team members who ask questions and bring fresh ideas to the table can fuel continuous development and innovation. When interviewing, ask candidates about how they manage setbacks or a time they went above and beyond their tasks. This will help you see if they've got the right attitude and ambition. 3. The Accelerator: Priorities When conducting interviews, I present applicants with problem-solving scenarios and ask them to rank the possible approaches from most to least important. This allows me to see where their priorities and efficiencies lie. While we'd always like to aim for perfection, overanalyzing and foot-dragging can lead to more misses. In fast-moving industries, even a short delay can cost you clients and big bucks. Employees who execute quickly, even if it's not with 100% confidence off the bat, can set the company way ahead of others. This, in turn, leads to faster growth. So, look for the candidates who can prioritize action, delegate and preempt possible chaos in the workplace. Employees who can streamline operations and find ways to make work easier but more effective are incredibly valuable. Consider people who've automated work processes and removed repetitive or outdated tasks. This showcases technical talent and displays a level of intelligence and initiative. These are the types of people you want on your team. A Bonus Game Changer: Proactive Problem-Solving Beyond those three key traits, there's another that can set the best candidates apart. The most distinct hires are those who anticipate challenges before they arise—then do something about them without being told. This proactive mentality allows teams and organizations as a whole to think big picture instead of applying Band-Aid solutions when it's too late. Ask candidates about past experiences where they were proactive. You can check with their character references to see whether they tend to solve problems successfully without much supervision. These hires, even if they prefer to operate independently, are great team players because they dedicate their efforts to the company. Match Intrinsic Work Styles To Your Workforce Needs Hiring involves a lot of mixing and matching. Both employers and applicants must be aligned in their vision, principles and needs. To me, a person's mindset, attitude and priorities are fundamentals because they're built on experience and potential. They're traits that pave the way for transformation, whether for the individual or the company. I'm not saying technical skills aren't important; they usually provide the baseline of candidate fit. But deeper, more intrinsic traits are better indicators of long-term success. So, take the time to go over applicants' backgrounds and interview answers. Look beyond what's on paper. When they get hired and elevate the company's status and environment, you'll know you've done something right. Forbes Human Resources Council is an invitation-only organization for HR executives across all industries. Do I qualify?
Forbes
21-07-2025
- Business
- Forbes
3 Shifts That Will Shape The Future Of CGI
Sebastian Bondo Petit, Co-Founder at WHY CGI. Computer-generated imagery (CGI) is no longer just the domain of blockbuster films or high-end commercials. Today, it's becoming a core part of how agencies deliver content across digital platforms. Whether it's a surreal TikTok visual, an AR filter on Instagram or a product render that replaces a traditional photo shoot, CGI is becoming an increasingly important part of how brands present themselves online, especially as content gets faster, more modular and more visual. But what's next for CGI—not in theory, but in the daily workflows, tools and formats shaping how content gets made? Between now and 2027, I believe we'll see a new kind of CGI emerge: faster, more adaptive and built for the way people actually consume media today. At WHY CGI, we've built a studio around that shift. We work fast and are fully remote and deeply embedded in internet culture. From that vantage point, here's what I see coming—both in terms of technology and mindset: AI Will Reshape Creative Pipelines I believe that in the next two years, AI will be embedded across every stage of CGI production. It already supports rigging, texture generation, motion simulation and cleanup—and that's just the beginning. What used to take days can now be prototyped in hours. At our agency, we treat AI as a way to widen the field of exploration. It allows us to move faster without losing creative control. The role of the artist shifts from pure execution to high-level curation: selecting, refining and directing outcomes with greater speed and scale. We bring in AI at the start to test concepts and align faster with clients. That early alignment gives us more time for the hand-crafted parts of production, where taste and precision matter most. In a recent campaign for Gisou, we used AI to visualize the idea of turning Manhattan's Vessel into a giant beehive. That draft helped secure the concept quickly, allowing the team to focus on details that made the final video stand out. In our experience, the sooner AI enters the process, the more room it creates for real craft. Distributed Production Will Define The Next Generation Of CGI Studios By 2027, I believe the most effective CGI teams will be built around remote collaboration, rather than centralized offices. This shift is already visible. Tools for asynchronous feedback, real-time asset management and global version control have matured enough to support high-speed, high-consistency work across borders. For example, our team spans multiple countries, yet operates as a tightly aligned unit. What holds it together isn't process but shared creative culture. We hire people who understand the rhythm, references and pace of the environments we design for. That alignment makes coordination simpler and creative output more consistent, regardless of time zone. Remote doesn't mean fragmented. With the right structure and shared direction, it means faster decisions, broader talent access and scalable creativity. CGI Will Be At The Crossroads Of Platforms And Realities Today, content is made and consumed fast. Platforms shape both format and behavior, and CGI follows suit. Short, stylized visuals now outperform long-form. Loops, surreal edits and meme logic reflect how people scroll, pause and share. At our agency, we build modular assets that match this pace. We track trends, move quickly and focus on timing, clarity and cultural fit over overproduction. But a deeper shift is coming: As spatial computing enters the mainstream, content will move off the screen and into real space. With devices like Apple Vision Pro and AR glasses, CGI will show up in the environments we walk through, not just in our feeds. A poster might respond to your presence. A campaign mascot could appear in 3D through a headset. The logic of the feed will meet the logic of space—flat, fast visuals giving way to layered, interactive ones. Both worlds matter. For CGI creators, the task is to move fluently between them, designing for both the world we swipe through today and the one we'll navigate tomorrow. What This Means For Creative Teams, Brands And Audiences CGI is no longer confined to cinema or advertising campaigns. It moves through social feeds, digital platforms and real-time interfaces. Production cycles have compressed. Formats have multiplied. Creative direction now includes an understanding of algorithms, attention patterns and shareability. For brands, this opens new ground. It's possible to speak visually in ways that are faster, more adaptive and more attuned to culture as it happens. For marketing teams, it means rethinking not just what gets made, but how, when and for whom. For creative studios, it requires systems that can deliver relevance at scale. By 2027, the value of CGI will be measured less by complexity and more by clarity—how well it captures a brand's voice, how confidently it moves through digital formats and how it naturally draws people in. This doesn't mean creative standards will drop. If anything, the expectations will rise. CGI will need to work across channels, resonate in seconds and still carry a distinct visual signature. Audiences move fast. Strong ideas, executed with precision, will be what holds their attention. For creative leaders, investing in faster workflows and sharper systems isn't optional—it's how you stay relevant in a reality that keeps shifting. Forbes Agency Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?
Forbes
08-07-2025
- Forbes
Why Single-Turn Testing Falls Short In Evaluating Conversational AI
Tarush Agarwal is the Co-Founder & CEO of Cekura, a Y Combinator backed company pioneering Testing and Monitoring for Conversational Agents. Conversational AI agents, such as chatbots and virtual assistants, are often evaluated on their ability to answer questions or respond to prompts in single-turn interactions. This means assessing one question and one answer at a time. However, real conversations involve multi-turn exchanges, where context builds over time. Evaluating an AI on isolated responses out of context can be misleading and insufficient, similar to judging a movie by a single scene. Modern research emphasizes that accurately measuring a chatbot's performance requires multi-turn simulations that capture how the AI performs throughout an entire conversation. The Limitations Of Single-Turn Evaluation Single-turn evaluation tests a conversational agent on one input and one output at a time. While straightforward, this approach has significant limitations for conversational systems: • No Context Or Memory: Real dialogues build on previous turns. Single-turn tests overlook this continuity, failing to verify whether the AI retains information from previous conversations or utilizes it correctly. An answer that seems good in isolation might repeat information or miss references to earlier parts of the conversation. • Lack Of Coherence And Consistency: A chatbot might give individually plausible answers, but the conversation as a whole could wander or contradict itself. Single-turn evaluation wouldn't catch such contradictions because each turn is scored in isolation. True coherence—a logical flow of ideas across turns—and consistency (not changing facts or personality mid-conversation) can only be judged by looking at a sequence of interactions. • No Long-Term Goal Assessment: Many conversations have an underlying goal (e.g., solving a problem, gathering information). Evaluating turn by turn might miss whether the agent is effectively guiding the conversation toward that goal. A single-turn score won't tell us if the bot gets stuck, goes off on a tangent or needs too many turns to accomplish something. Why Multi-Turn Simulations Are Necessary To truly gauge a conversational agent's performance, we need to evaluate it in simulated multi-turn interactions that resemble real dialogues. This allows us to measure several critical aspects of conversation quality that single-turn tests miss: • Context Awareness And Coherence: Multi-turn evaluation should check if the AI's responses make sense given the conversation history and if the dialogue stays on a logical track. Coherent dialogues flow naturally, which can only be observed across a chain of exchanges. • Consistency: Over a long conversation, the agent should not contradict itself or switch its story. It should maintain consistent information and a consistent persona or tone. Multi-turn tests reveal if the agent remains consistent from start to finish. • Memory Retention: This refers to the agent's ability to remember details provided by the user or itself in previous turns. In a multi-turn simulation, we can actively test this by requiring the AI to use past information correctly. • Long-Term Goal Completion: For goal-oriented dialogues, multi-turn scenarios allow us to see if the AI is making progress toward the goal at each step. We can measure overall success: Did the user's problem get solved or the task get done by the end of the conversation? A single-turn score cannot capture this overall success. Researchers and practitioners use multi-turn dialogue simulations, often having the AI chat with test users or even itself, to go through realistic back-and-forth scenarios. This kind of evaluation is necessary because multi-turn conversations introduce complexities that do not appear in one-shot interactions, such as maintaining nuance and coherence over many exchanges. The Math Of Multi-Turn Accuracy: Compounding Errors Suppose a voice agent has a 99% accuracy per turn. For a 10-turn conversation, the probability that every single turn is handled perfectly is: 0.99¹⁰ = 0.904 (about 90%). So, even at accuracy, 1 in 10 conversations will have an error. Drop accuracy to 95% per turn, and only 60% of 10-turn conversations will be flawless. The result: As complexity increases, even small per-turn errors compound to limit reliability at scale. Conclusion Single-turn evaluations are easy but fall short of capturing what really matters in conversational AI: context, coherence, memory and long-term goal pursuit. True evaluation means testing AIs in full, multi-turn conversations to see if they deliver a seamless, consistent experience from start to finish. As AI systems grow more capable and take on harder tasks, only holistic, dialogue-level testing can reveal their strengths and weaknesses. Ultimately, to measure real progress, we have to judge the conversation, not just the reply—because in AI, it's the quality of the journey, not just the first step, that counts. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
07-07-2025
- Business
- Forbes
Generative AI Is Booming, But Execution Gaps Remain
Gregorio Patiño Zabala, Co-Founder & Head of Business Development - FSI unit at Pragma. The explosive rise of generative AI (GenAI) is fueled by breakthroughs in cloud computing, smarter algorithms and scalable data storage—making the technology both innovative and accessible for businesses. Yet many organizations overlook key technical hurdles that limit their ability to fully harness GenAI's potential. Here are five proven ways to avoid common mistakes and maximize the value of this transformative trend. 1. Identify And Prioritize A Use Case The first step in any GenAI strategy is to identify a well-defined, high-impact business problem that the technology can address. Start by studying relevant pain points that could be solved through automation or advanced data processing. From experience, I recommend holding discovery sessions to align stakeholder needs with feasible technology solutions. Once potential use cases are identified, prioritize them using two main criteria: business value and implementation feasibility. Remember: Not every challenge needs GenAI. In some cases, traditional machine learning or other analytics techniques may be more effective and cost-efficient. 2. Prepare Your Data And Assess Project Feasibility GenAI is only as strong as the data it's built on. Without a solid, evolving knowledge base, any initiative risks producing vague or inaccurate outcomes. Building a dynamic data infrastructure that grows with your business is essential. One example: In a project we led for the insurance division of a financial group in the Dominican Republic, we helped create a conversational AI agent backed by a secure, up-to-date knowledge base. Given the constantly changing nature of insurance offerings, this empowered advisors to deliver real-time, accurate responses. How? By querying data, extracting customer insights, generating personalized documents and ensuring strong data governance. Today, the solution supports more than a dozen core functions for the business. 3. Embrace An Iterative Approach Implementing GenAI isn't a one-time deployment—it's a continuous process. Start with a focused pilot, adjust based on real-world feedback and scale gradually. This approach ensures reduced risk while staying aligned with business needs. In the earlier-mentioned example, what began as a proof of concept evolved into a full solution with eight core features—from real-time data queries to pricing assistants. Each function was added incrementally based on value and feasibility. The takeaway? Start small, iterate fast and scale wisely. 4. Build A Multidisciplinary Team For Scale And Security To build a scalable, secure GenAI solution, companies must assemble a multidisciplinary team with each function contributing to success: • Backend and frontend developers who ensure technical integrity and user experience • QA engineers who test for reliability and performance • Solution architects who align the system with business goals • Data analysts who refine information and boost model accuracy To reduce risk, begin by following security standards like the Open Web Application Security Project (OWASP) Top 10 for large language model applications. While many tools exist to streamline implementation, success comes from combining technical skill, domain knowledge and governance. 5. Adopt New GenAI Advances With Intention GenAI is still an emerging technology and continues to evolve quickly. Vendors regularly announce breakthroughs, making it hard to keep up. That's why it's essential to research, evaluate and test each advancement carefully. Organizations can adopt generative AI advances more effectively if they follow these steps: • Establish a dedicated interdisciplinary team to evaluate new tools. • Assess how each advancement aligns with business goals. • Integrate human insight throughout the development process. The goal is not to chase novelty but to adopt what creates real value—at the right time. Bonus: Preparing For The Next Frontier—MCP And A2A The next leap in GenAI lies in agent-to-agent (A2A) communication. Instead of standalone tools, AI systems are beginning to talk to each other, coordinate tasks and make shared decisions. Imagine this: A virtual assistant helping with a customer return automatically checks inventory and schedules a pickup—all handled through intelligent collaboration between two AI agents. Making this possible are new standards like the Model Context Protocol (MCP), which allow data systems and AI tools to operate together seamlessly. The real opportunity lies in building secure, flexible solutions ready to grow and evolve with your business. When your systems can talk to each other intelligently, that's when AI becomes a true partner—not just another tool. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
