April Chang

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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WIESNER ON HUMANS VS AI: MIT Media Lab co-founder and science advisor to JFK, Jerome B. Wiesner was a unique figure in the history of MIT at the crossroads of science, engineering, and the arts with a lifelong pursuit of world peace. There's a terrific talk he gave at the founding of the MIT "E15" building designed by I.M. Pei — often nicknamed "the bathroom building" — that's worth a watch, especially during these extraordinary times https://lnkd.in/g2zEJRrk and a piece of the talk he gave is below. Information on the talk I'm giving with Jerome B. Wiesner Professor Hiroshi Ishii is here https://lnkd.in/gJpKkeY8 on November 8th (Fri) from 11 am EST, and you can register online for free here https://lnkd.in/gADUyCs7 --- Jerome B. Wiesner at the founding moment of the MIT Media Lab's E15 building https://lnkd.in/g2zEJRrk > ... Now some people will be offended by what I'm going to say, but I think man-made systems use better components. Electrical signals travel a million times faster than signals on nerve fibers. And semiconductor devices also act billions of times faster than neurons, which are the biological information processing elements. Also, manmade elements appear to be more reliable. But nature has a very large edge, maybe a decisive edge. In design complexity involving a number of individual elements, it can use it in a system effectively. And these are advantages man may never match. Nature may always retain this edge, who knows? > But for the moment, it really doesn't matter. The manmade systems that we are involved with enhance human creative capacities, that's what they can do, but so far they cannot displace them. Cognitive and computer scientists are having an exciting time trying to understand and imitate human reasoning with computing systems. And though they can do a few things that would be regarded as thinking if done by a human, they differ violently among themselves on whether or not machines with human thinking and creative abilities are possible. Even the so-called artificial intelligence field, which prompted Walter Rosenblith to say he preferred natural stupidity, his greatest achievement in this field is capturing knowledge and skills of humans -- of human experts. > For now, it’s clear that thinking machines and true creativity don’t exist. We should focus on how tech can expand human creative capacity.

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🚨🚨 🚨 Unlocking Innovation: The Power of the Adjacent Possible Innovation programs can stall when the team tries to ideate way beyond their collective perspective and frame of reference. Like a software engineer brainstorming genetic modification of mycelia, or a senior executive ideating process improvements which they haven't been participated in for decades. It's challenging, exhausting, and ultimately self-defeating. Enter the concept of the "Adjacent Possible" - a game-changer for innovation. What is it? The Adjacent Possible, coined by Stuart Kauffman, represents the realm of new ideas within reach based on our current knowledge, resources, and capabilities. Why it matters: ➡️ Realistic Innovation: it focuses on achievable advancements, not far-fetched concepts. ➡️ Incremental Progress: each innovation opens doors to new possibilities. How to leverage it: • Constantly explore the boundaries of your adjacent possible. What's in your line of sight is what you know best. • Combine existing ideas in novel ways. Even small perturbations from status quo can generate large innovation leaps. • Stay curious about developments in adjacent industries. Cross-domain patterns create fresh sources of ideas, while also expanding the adjacent possible. Most of all, it takes the 'genius' pressure off the team. No one knows everything, but each one of us knows a lot about something, and we can all drive plenty of innovation potential by focusing on the spaces we know best. Are you exploring your Adjacent Possible? Share your thoughts below! 👇 #Innovation #innovationsystems #AdjacentPossible

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Sure, the opportunity to build AI Agents is there, but existing SaaS companies have a major advantage: their distribution channel. It’s easier to experiment providing agentic solutions (under their tailored business model) if you have an existing customer base and deep domain expertise, otherwise brace for 360° disruptive competition.

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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Happy Monday, Career Karma is hiring for a VP of Engineering to accelerate our AI strategy and you can see the job description here: https://lnkd.in/gWEx6_4W This role is perfect a leader with zero to one experience – someone who has been a founder or part of a founding team and wants to shape the future not only with our current customer base of HR, Education, and Coaching companies, but also in other industries outside of Workforce Development. For context, we've built an AI platform that acts as an orchestration layer, enabling both your engineering and business teams to collaborate on creating, testing, and deploying AI use cases and our solution includes: - A no-code builder for product, operations, and leadership teams - An API for seamless integration by your developers - This allows you to launch AI companions in days, not months. Why Join Career Karma? Cutting-Edge Innovation: We are at the forefront of AI Companion technology, revolutionizing human-AI interaction across voice, text, and video. Joining us means you’ll be working on groundbreaking projects that push the boundaries of what’s possible. Entrepreneurial Spirit: We are looking for someone with the entrepreneurial mindset of a founder. Your hands-on experience in building and scaling startups will be invaluable as we continue to grow and innovate. Financial Stability: With ample runway and financial stability, Career Karma offers a secure environment where you can focus on innovation and long-term projects without the constant worry of funding. Culture of Growth: At Career Karma, we emphasize a culture of innovation, collaboration, and continuous learning. You’ll be part of a team that values creativity, supports professional development, and encourages pushing the envelope. Impactful Work: Your contributions will directly impact the way people interact with AI, making a meaningful difference in the lives of our users. We’re building something special here, and you’ll play a crucial role in that journey. Community and Thought Leadership: As part of Career Karma, you’ll have the opportunity to act as a thought leader within the AI industry. Engage with the community through technical blogs, conferences, and more, sharing your insights and helping shape the future of AI. We’re excited to bring on a leader who shares our vision and is ready to take on the challenge of driving our AI and engineering strategy forward. If you or someone you know fits this description and is ready to join a cutting-edge AI company, please send them the link above to express their interest and apply so we can reach out. Let’s continue to build and innovate together! Onward and upward, Ruben Harris https://lnkd.in/gWEx6_4W cc Amanda Day Timur Meyster

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We are quickly entering the "trough of disillusionment" in the hype cycle for generative AI. While GPTs excel in automating certain tasks (and can even automate away large portions of some jobs) they often fall short in others. Like all new technology, the issue lies in misapplying generative AI for tasks it wasn't designed for. This is a problem compounded by the GPTs themselves suggesting they can handle these tasks (often cheerfully!). This misalignment creates frustration and unmet expectations which we're starting to see more of these days. As the dust settles I firmly believe we'll recognize GPTs as a transformative technology across many industries. However it's crucial to understand their limitations and apply them appropriately. What are your thoughts on the current state of generative AI and its future applications?

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Startups live or die by their ability to execute at speed. For large companies, too, the speed with which an innovation team is able to iterate has a huge impact on its odds of success. Generative AI makes it possible to quickly prototype AI capabilities. AI capabilities that used to take months can sometimes be built in days or hours by simply prompting a large language model. I find this speed exciting and have been thinking about how to help startups and large companies alike go faster. I’ve been obsessed with speedy execution for a long time. When working on a project, I am loath to take two weeks to do something that I could do in one week. The price of moving at that pace is not that we take one week longer (which might be okay) but that we’re 2x slower (which is not)! When building an AI-powered product, there are many steps in designing, building, shipping, and scaling the product that are distinct from building the AI capability, and our ability to execute these other steps has not sped up as much as the AI part. But the speed with which we can prototype AI creates significant pressure to speed up these other steps, too. If it took 6 months to collect data, train a supervised learning algorithm, and deploy the model to the cloud, it might be okay to take 2 months to get user feedback. But if it takes a week to build a prototype, waiting 2 months for feedback seems intolerably slow! I’d like to focus on one key step of building applications: getting user feedback. A core part of the iterative workflow of designing and building a product (popularized by Eric Ries in his book The Lean Startup) is to build a prototype (or MVP, minimum viable product), get user feedback on it, and to use that feedback to drive improvements. The faster you can move through this loop — which may require many iterations — the faster you can design a product that fits the market. This is why AI Fund, a venture studio that I lead, uses many fast, scrappy tactics to get feedback. For B2C (business to consumer) offerings, here is a menu of some options for getting customer feedback: 1. Ask 3 friends or team members to look at the product and let you know what they think (this might take ~0.5 days). 2. Ask 10 friends or team members to take a look (~2 days). 3. Send it to 100 trusted/volunteer alpha testers (~1 week?). 4. Send it to 1,000 users to get qualitative or quantitative feedback (~2 weeks?). 5. Incorporate it into an existing product to get feedback (1 to 2 months?). 6. Roll it out to a large user base of an existing product and do rigorous A/B testing. As we go down this list, we get (probably) more accurate feedback, but the time needed to get that feedback increases significantly. Also, the tactics at the top of the list create basically no risk, and thus it’s safe to repeatedly call on them, even with preliminary ideas and prototypes. [Reached length limit. Full text: https://lnkd.in/gEBBB2UZ ]

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Anthropic's latest Claude AI model is taking human-AI interaction to a whole new level! 🖥️🤖 The new Claude 3.5 Sonnet can now use computers just like a human would - by looking at the screen, moving the cursor, clicking buttons, and typing text. This is a major leap forward in AI capabilities. Key features: Operates based on screenshots, emulating human computer use Can perform complex multi-step tasks across different applications Self-corrects and retries when encountering obstacles2 Accessible via API for developers to build with Why it's game-changing: This opens up incredible possibilities for AI assistance in data entry, form filling, software testing, and automating repetitive computer tasks13. Imagine having an AI assistant that can seamlessly work alongside you, handling tedious computer work while you focus on higher-level tasks. While still in beta, this technology demonstrates how AI is rapidly advancing toward more human-like interaction with digital interfaces. As it develops further, it could revolutionize how we work with computers and boost productivity across industries. The future of human-AI collaboration just got a lot more exciting! What do you think about this new capability? #AIInnovation #FutureOfWork Here is a video about it: https://lnkd.in/gh8aNYEu

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Interesting conversation between Jason Calacanis and Tekedra N. Mawakana Mawakana, CEO of Waymo (Google's self-driving project).... (Paraphrased) Jason Calacanis: Why are you not testing your Waymo self-driving on highways? You seem focused primarily on city environments like San Francisco, yet in my experience being a tesla-owner from the very beginning (and using both auto-pilot and then full self driving) it is better on the highways. You seem to be taking the opposite approach. Is it because speed kills? Tekedra: Our mission is safety and to get to Level 4 driving (the highest level of autonomous driving, no driver behind the wheel, no drivers' license) and so we’ve made a deliberate decision to focus on crowded urban environments because that is the hardest problem. Anything that doesn’t contribute to our getting to Level 4 is not interesting to us (not intended, but could be back-handed compliment to Musk 🙂) You don’t learn anything new self-driving on the same boring monotonous highways. --- Great point! #waymo #allin #selfdriving #google

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PSA: Stop using the original OpenAI CLIP weights. I see a lot of people using CLIP for multi-modal retrieval. What many people may not know is that there are 100’s of CLIP models now since OpenAI’s original release. These newer models offer significantly better performance across many benchmarks. For product search related tasks we recommend the following. + Fastest inference: ViT-B-32 [laion2b_s34b_b79k] + Best balanced: ViT-L-14 [laion2b_s32b_b82k] + Best-all round: xlm-roberta-large-ViT-H-14 [frozen_laion5b_s13b_b90k] You might notice these are not the ones that perform the best on the benchmarks. Why? Because the benchmark only test for very specific tasks which are often quite different from search and recommendations. These are ones we have found to be the best for product related search and retrieval tasks and generally very good first choices if you are unsure where to start (even for non-product related search). Here is a hugging face space with all the data: https://lnkd.in/gxu5UYkt Read more: https://lnkd.in/g5_D4ccs

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I spent five days in the SF Bay Area with B2B SaaS founders, funders, and senior software engineers. The talk is all about adding GenAI and LLM features to software apps, but they were unclear exactly how or when the AI big wave revolution will occur. AI is very cool and will be big, but it's still early to see how this will play out in the next year on the front lines of business. There's a lot of hurrying up to say you have AI features with a healthy skepticism about the user benefit of early GenAI attempts. Coders in big tech say, "Our product leaders told us we need to add GenAI features, but it still doesn't seem that interesting for users. We'll design it, code it, and ship it anyway." Founders in startup SaaS say, "We are running a lot of experiments and keeping up with the big AI platform changes. We're all more productive now but it hasn't made our app much more useful." Smaller SaaS funders say, "It's hard to tell where the winners will be to make our bets. AI tools help software companies grow more efficiently, so they don't need our funding as much. We're being very careful." Big enterprise SaaS buyers say, "We need to start investing, testing, and trying GenAI technology, which means we need to spend less on our existing SaaS apps in our tech budget." Big investors say, "It will be the next big tech wave, so we are making big early bets on chips, infrastructure, foundational models, and platforms. NVIDIA, Microsoft, Google--the big will get bigger." It's all very exciting, but it's still early and moving around quickly. That's a lot different from the last big software tech wave when SaaS apps on the web and mobile took over the last 15 years. It was easier to bet on the massive transition from on-premise Windows software to cloud-based web and mobile apps for businesses everywhere and billions of consumers. Despite that, I'm more excited about GenAI and other ML platforms than I was about VR, blockchain, and other platform opportunities that didn't materialize. It's early, but the practical AI wave will happen in ways we can't imagine or expect. What are you seeing in the current AI wave? #practicalfounders

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One of the best things about being in San Francisco 🌉 is the ability to drop in on great talks. Last week I had a chance to see NVIDIA's CEO Jensen Huang (JH) being interviewed by Stripe's CEO Patrick Collison. Jensen is among the longest-serving founder-CEOs in Silicon Valley at the moment (30+ years). With that comes a special clarity of mind and some hot takes. 🌶👇 🗣 Jensen doesn't believe in 'praise in public and criticism in private'. Instead, he reasons that most information in the organization should not be privy to certain people. By giving feedback in public, you allow everyone to learn. 'What is better than learning from your own mistakes? It's learning from someone else's mistakes!!' 🔥 JH also doesn't like to fire people. He would rather "torture them into greatness". He invests in people and he doesn't like to write off his investments. 👨‍💻 Asked whether he likes his job every day, he answers "no". The perfect job doesn't give you happiness 100% of the time. "No great things were done, that were easy all the way." ... "Even if I come home from a day where I did things I didn't enjoy, it could still be have been a great day." 👩‍👧‍👦 Jensen famously has 60 direct reports. "This allowed us to cut out probably 7 levels of management". His subordinates don't necessarily have such large teams. "It doesn't scale down, less senior people need more guidance". Looking at the numbers, this approach allowed NVidia to reach ~2T market cap with ~28.000 people vs. Microsoft's 3T with ~220.000 people and Apple's 2.6T with 160.000 people. ❔ Jensen answers almost every question in the same way: "Let's break it down, let's reason about it". This is a great response on so many levels. First, it allows him to not be put on the spot. Second, it allows him to teach his workforce how he reasons through a problem so that they can emulate it and create alignment. Third, it gives people a chance to point out flaws in his reasoning. He admits he is not right all the time. ON AI 👨‍🔧 "You will not lose your job to AI, your company will go out of business because another company used AI." "If you're not using AI you're doing it wrong." ⏱ The extent to which AI will enter many processes is largely underestimated. Right now we are focused on language - sounds in a time sequence. "Any time sequence can be tokenized." This will include robotic movement. 🥛 JH also expects that we won't be satisfied with foundation models "You're going to want to tune it into perfection because you care so deeply. Say if it's already at 99% you'll want to get it to say 99.3%" This last piece is also why we created Query Vary (YC W22). AI will never feel truly intelligent until it completely understands your data and context. It needs to be taught, regardless of how powerful the foundation model is. Finally, besides insights, it was remarkable how funny Jensen was, cracking a joke at roughly a 1 per 3 min rate. It was a very entertaining talk. #ai #sf

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Most companies are doing AI all wrong—throwing money at shiny tools that don’t actually transform how work gets done. In my recent conversation with Jasper CTO Melody Meckfessel, we discussed how companies can break this cycle and add real value by deeply embedding AI into business processes (not just adding a superficial wrapper). Melody is a former startup founder and longtime Google engineering leader who’s seen countless companies get stuck in ‘POC purgatory.’ She lays out the rules for true enterprise AI adoption: ✔️Embedding intelligence into core processes ✔️Augmenting human potential within specific workflows ✔️Delivering tangible value by building long-term trust with users Our full conversation in the comments 👇

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AI, Longevity and Love - how are they all connected? They make each other better and are essential to each other’s success Lots of fantastic discussions about how AI will bring out the best in humans including augmenting our time and ability to connect at DOC in Napa and Wisdom 2.0 in SF this past week. Huge thanks to Jordan Shlain, MD, Kevin Ryan and John Battelle for organizing leaders across traditional medicine, longevity, academic research, and biotech and Soren Gordhamer and Mark Hyman, MD for bringing together the best of AI and longevity AI shows incredible promise for healing humans and increasing longevity, here are my key takeaways from these events: We can live to 95 today if we focus on longevity and healthspan, there are biotech solutions using AI to epigenetically reprogram cells to reverse aging and disease that will enable us to extend lifespan by 20+ years but it will take for these to be real Fun to see Mark Hyman, MD interview Eric Verdin, they both discussed how they lowered their biological age by 10+ years and feel the best ever There are so many simple things we can do, walking 30 minutes a day reduces cardiovascular, alzheimer’s and cancer risk by 40% but 80% of the US doesn’t even do this Within 5 years we will have a whole new set of FDA approved psychedelic drugs for depression, anxiety, PTSD, etc. Immunotherapies will also expand so we can avoid chemo and living cancer free will be in reach We need the US to move to a functional medicine, integrative care model and get out of this sick care model, this level of innovation is more likely to come from upstarts, the incumbents have too much revenues to lose in this transition AI will not replace physicians, instead those physicians that don’t use AI will be replaced. 50% of physicians are burned out, AI can help! Fantastic connecting with so many brilliant minds, including Mark Hyman, MD, Eric Verdin Neal Khosla Laura Esserman Abby Levy Sheila Lirio Marcelo Leonard Zon Arjun Desai David Eagleman Lois Quam Vinod Khosla Emi Gal and many others #healthcare #longevity #ai

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And the quasi-monthly books post: 1/ HBR Insights on Generative AI (https://lnkd.in/ezkTNHPj) Interesting and actual, yet it felt a bit like lacking depth and detail. It does hint though at some creative uses of GenAI and extracts some good guidelines on how to think about its applicability in the enterprise. I wanted more. 2/ Voyage en Europe - Francois Reynaert (https://lnkd.in/e_pQ-SPH) I loved this book. I listened to it while driving back and forth from Munich to Fontainebleau. A beautifully written story of our European history, of all the things we have in common, our culture, our roots, our scientific progress, our place and impact on the world stage, our deeply intertwined relationships with other cultures and civilizations, as well as our wars and suffering throughout the centuries. 3/ Petit traité de vie intérieure - Frédéric Lenoir ( https://lnkd.in/enWsFid5) 4/ La puissance de la joie - Frédéric Lenoir (https://lnkd.in/eGPUCw4Q) 5/ Du bonheur: un voyage philosophique - Frédéric Lenoir (https://lnkd.in/eseYfdcD) Three relatively short books on how and why to increase our joy, meaning of life and well-being. A gentle philosophy of happiness, drawing on a wide spectrum of ideas, starting from the Stoics and Epicureans, continuing through the Oriental and Christian beliefs, to the European thought of Spinoza or Montaigne. A take on religion, spirituality, living fully and in alignment with our own selves and, above all, deeply human. The author's personal tales and experiences included. 6/ Stealing The Corner Office - Brendan Reid (https://lnkd.in/e2XCsUMq) A short book on organizational psychology, in simple, non-equivocal terms. A cold shower, yet full of insights.

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"Will LLMs continue to get better or are they plateauing?" This is a question we get asked often and going into 2025, it's clear we're entering a new era of AI scaling. Increasing model size, compute or dataset size doesn't result in much better model performance but new scaling approaches have emerged. Current state-of-the-art pre-trained models operate close to the Compute Efficient Frontier (CEF), the theoretical limit of resource efficiency achievable with current AI architectures. The closer a model gets to the CEF, the smaller the performance gains for every additional unit of compute. But what about the new approaches? 1. Take OpenAI's o1 model. Instead of just making the model bigger, they focused on letting it "think" during inference. The results? Noam Brown from OpenAI pointed out, giving a bot 20 seconds to think in poker matched the performance boost of scaling up the model 100,000x. 2. OpenAI's o3 model has achieved remarkable results on several benchmarks. It scored 87.5% on the ARC-AGI benchmark, a test of general ability, surpassing the typical human score of 85%. Additionally, on the challenging Frontier Math benchmark, o3 achieved a 25% success rate, a significant improvement over the previous state-of-the-art score of 2%. 3. Sparse models like DeepSeek-V3 using a Mixture of Experts approach. They're achieving GPT-4 level performance with just 5% of the training compute by being selective about which parts of the network to use. It's like having a brain that only activates the neurons it actually needs. The next frontier isn't about scaling everything - it's about scaling the right things. Our full analysis on this topic is in comments👇

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CEO Alexandr Wang made waves with his announcement a couple of weeks ago about adopting an #MEI approach at Scale AI. Whether or not you agree with him on this, what rings clear is an unflinching commitment to hiring the best talent possible and some of the strategies used to accomplish this. I use him as an example because even though I am a recruiting and hiring expert, I learn a lot from those out in the trenches building their companies and he has obviously done so with great success! One of the strategies mentioned by Alexandr is "casting a wide net" in recruiting. I have become a firm believer in this strategy and it is one that we employ on behalf of clients. Here are just some of the reasons to cast a wide net in hiring: ✅ Casting a wide net is more likely to attract candidates with diverse thinking styles, problem-solving approaches, and experiences. This type of cognitive and experiential diversity can lead to increased innovation and creativity and can help your organization be more resilient to future changes in the industry or economy. ✅ A wider search can uncover candidates with unique skill combinations or non-traditional backgrounds who might be overlooked in a narrow search. These "hidden gems" can bring fresh perspectives and innovative solutions to your organization. ✅ A larger candidate pool increases the chances of finding individuals who are not just qualified, but are an excellent fit for both the role and the company culture. This can lead to better performance and reduced turnover. ✅ Engaging with a wider range of candidates increases your company's visibility in the job market and naturally expands your company's network. This can also lead to valuable market insights, potential partnerships, or customer connections that extend beyond the hiring process itself. Even candidates who aren't hired can become brand ambassadors if they have a positive experience, potentially leading to future hires. These are many of the reasons we cast a wide net on behalf of clients. Mainly we want them to have the assurance that they are hiring the best candidate possible and not having to settle. Even with all the benefits, I recognize that casting a wide net makes recruiting more labor intensive and time consuming. It also requires a more systematic approach to candidate management and may require advanced skills in assessing candidate fit. Whether for an executive or mid-level hire, we can handle the tedious, time consuming aspects. In addition to our full service model, we offer alternative solutions for non-executive level recruiting and hiring. I'd be glad to hear your needs and discuss ways that we can be of support. I'm also always willing to offer advice or ideas that you can employ on your own. A rising tide lifts all boats. Message me, let's talk.

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