Jeevan Suresh’s Post

Alternatives to GPT-4 If GPT-4 isn’t right for you, consider open-source LLMs like LLAMA 3.1. These models offer the advantage of creating proprietary technology but come with high engineering and computing costs. For small startups, this can be a barrier. However, an open-source model can increase your company's valuation and provide unique benefits. In particular, LLAMA models are designed to be versatile and highly performant for a variety of tasks, often competing with other leading LLMs in terms of accuracy and fluency. Most importantly, LLAMA models are optimized to achieve high performance with relatively lower computational resources compared to some larger models. This makes them more accessible for deployment in environments with limited hardware. Follow me to learn about startups! 🔔 I am the founder of an EdTech AI startup, building an AI-powered higher education and career- building tool. #AIAlternatives #OpenSource #LLAMA #TechStartups #LanguageModels #MachineLearning #ArtificialIntelligence #Innovation #TechTrends #StartupLife

𝗔𝗜 𝗶𝘀 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝘀𝗲𝗲𝗱-𝘀𝘁𝗮𝗴𝗲 𝗳𝘂𝗻𝗱𝗶𝗻𝗴 𝗶𝗻 𝗘𝘂𝗿𝗼𝗽𝗲 New data shows that one in every two euros invested goes to AI startups. Is this a sign of a booming tech industry or a potential AI bubble? Read our latest blog post to explore the factors driving this trend, the concerns some investors have, and the ethical considerations we need to address. 🔗 https://lnkd.in/ebyhJhTb #innovation #technology #future #markets

Such a great post. What conclusions can be drawn from the length of time it took the companies below to get product-market fit? #ProductMarketFit #StartupJourney #BusinessGrowth #MarketInsights #Entrepreneurship #ScalingSuccess #StartupLessons #BusinessStrategy #InnovationMatters #MarketValidation #StartupSuccess #BuildingBetterBusinesses #TimeToMarket

it's a marketing game. The product must provide value to your users and industry to succeed and get adoption. The most important thing is that your idea is worth sharing.

42% of Startups fail to get to product market fit and die. Still most companies take over 1,5 year to get to PMF, so is it a game of perseverance? How some of the fastest growing startups found product market fit: - Perplexity AI: https://lnkd.in/dzRXWYw3 - Linktree (from 0 to $37M ARR): https://lnkd.in/dET-g-Gd - Wiz ($100M ARR in 18 months): https://lnkd.in/dyHk2Pda - HeyGen, Lemlist & Canny: https://lnkd.in/dYNayi59 Visuals by Lenny Rachitsky

🧐 I’m interested in learning from those in the tech industry who took several years to find product-market fit (PMF). 📝 The journey to PMF is often challenging and complex, involving continuous iterations and deep market understanding. What made your process so lengthy, and did shifts in the market landscape help in achieving PMF? Additionally, what strategies or insights proved crucial during your journey, and how did customer feedback influence your product development? Understanding these aspects can provide valuable lessons for those of us navigating similar paths. #productmarketing #saas #startups #techindustry #productlifecycles

The sooner you develop empathy for your customers, the more likely you are to find the Product Market Fit. Too many startups fail to uncover the Customer’s Problem before they set out to offer a solution. The Customer’s Problem is the Independent Variable. Your startup’s solution can only exist in an environment where you have sufficiently used Customer Discovery to learn, directly from the Customer, what it is they are trying to accomplish and how well they are succeeding in that effort. In the absence of this critical evidence, you are throwing darts in the wind with no real target in sight. Your likelihood of success goes up when you can explain the problem that the Customer prioritizes as well as the solution’s value proposition.

A very important post for founders to see. Product market fit is critical for companies to establish. While perseverance is important, measuring market fit with objectivity is critical as startup often ignore signs and insist they are right. The ultimate test for market fit is when customers are ready to pay / adopt and use the product enthusiastically leading to sustainable revenue growth.

Product market fit is like developing a product for people needs rather than developing a product and finding people who need it. The later one is a disaster most of the time.

Definitely, an interesting read! As much as I firmly believe in this, I still doubt whether investors have ever had or if they have patience for startups to find PMF post funding. In fact, we never recommend getting funded pre-PMF. What are your thoughts on pre-PMF funding?