Simple AI

The first step of any analytical system setup and integration is to follow Donald Knuth's order of prioritization 1) Get it working 2) Get it right (Make sure your data matches the ground truth and please verify) 3) Optimise the system for performance #startupanalytics

@RaghavT10944 * double the profit margin with 30 inventory days!

@RaghavT10944 It's a function of margin (%) and inventory days. If Zudio makes more than double with profit margin with 30 days, it's still should be a better business than V2 retail

ZUDIO is slowing down and here’s the real reason : Zudio follows a mass-production model. The same T-shirt is produced and sold across 1,000+ stores. Their core target audience? College-going youths But Zudio’s biggest strength slowly turned into its biggest problem. As the brand exploded in popularity, everyone started wearing Zudio. And for Gen Z, common = uncool. Once customers began seeing the same designs everywhere, they pulled back. The numbers reflect this: 44 stores opened in Q2 40 stores shut in the same quarter SSSG (Same Store Sales Growth) is weak Now compare this with V2 Retail : V2 follows a completely different playbook. They source from 70+ different vendors High design churn Faster inventory rotation If a T-shirt at V2 sells in 15 days, the same category product at Zudio may take 30 days or more. Why? Because variety + freshness > scale alone in value fashion. I knew people will say ki zudio ka scale toh dekho 1000+ stores hain par I visited zudio and v2 retail stores and crowd at V2 RETAIL >> ZUDIO.

When I left science and academia for tech I still wanted to utilize my *analytical* and scientific reasoning skills to the largest extent possible. Unfortunately pretty much every tech job that I came across just pushed you hard into becoming another SWE. Your worth and your output were consistently based on the number of the lines of code you wrote, even for data science projects where code was just a convenient representation of your work, and not the core "product". I am so glad that this age of SWE supremacist inanity is finally coming to the end.

If you want Claude to give you a better looking frontend, copy and paste this prompt: "This frontend looks terrible. Fix in accordance to the following: Instead of emojis, use icons. Fix the padding so every component is spaced perfectly - not too close to other components but not too dispersed to waste space. The goal of the site is to look sleek, premium, and minimalist, like a spa in Switzerland. Design this in a way that matches what a working professional would reasonably pay thousands of dollars a month for, in a way that would make Steve Jobs smile. Avoid using colors unnecessarily, instead pick from a palette that is cohesive and stick to it. Ensure the site is responsive and elegant on both desktop and mobile." It actually works.

WHY RETENTION IS SO HARD FOR NEW TECH PRODUCTS I’ve been staring at retention curve data for 15-plus years now. I was a founder, a product manager, and now a VC. And at Andreessen Horowitz, I end up meeting hundreds of startups each year, many of them through our a16z speedrun program, where we invest up to $1M in brand new startups. NOTE: And yes, we just announced the 2026 program and you can apply now: speedrun006.a16z.com/x But back to retention — I’ve seen thousands of curves, and it’s among the first things I ask for when evaluating a new startups. I've looked through thousands of data rooms, analyzed retention curves sliced across many segments and denominators. I've also seen it from the other side, as someone building products. I’ve run hundreds of A/B tests and drafted countless variations of onboarding and notification emails in attempts to bend the curve. There are patterns. Just as there’s the laws of physics, weirdly there are some constant patterns that keep cropping up over time. Here are a few that I’ll share: - You can’t fix bad retention. No, adding more notifications will not fix your retention curve. You can’t A/B test your way to good retention - Retention goes down, it doesn’t go up. And weirdly, it decays (oh, does it decay) at a predictable half life. Early retention predicts later retention. - Revenue retention expands, while usage retention shrinks. Good news: You lose people over over time, but the ones that remain sometimes spend more more money! - Retention is relative to your product category. There’s nature, and there’s nurture. Sorry, you’ll never make a hotel booking app a daily use product - Retention gets worse as users expand and grow. The best users are early and organic. The worst users come after that - Churn is asymmetric. It’s far easier to lose a user forever than to re-win them back - Retention is weirdly hard to measure. Seasonality is a real thing. New tests throw things off. Bugs happen. D365 is a real metric but you can’t wait - Crazy viral growth with shitty retention fails. We’ve run this experiment many many times already, across multiple platforms and categories - Great retention is magic. When you see it out in the wild, it’s amazing. We’ll dive into each of these. You can’t fix bad retention. You've seen this happen before: You spend months developing a new product, and then you launch it. Bad news hits. The initial retention stats come in, and it’s terrible. You're already months into the product development, and it's hard to turn back now. How do we improve retention? I know, let’s add notifications and remind people to come back. Let's add a bunch of new features. Maybe we can A/B test the landing page and increase conversion. I think we know how this ends. Unfortunately, it seems to be the case that when you have poorer retention, it's very, very hard to fix - nearly impossible. Yes, you might be able to make a marginal improvement. Let's say that your D1 retention is 40% and you'd like it to be 50%. This is great and potentially workable. If the D1 is 10% on the other hand, well, you probably just have built something that nobody wants, and all the local optimizations around A/B tests and notifications aren't actually enough to bend the curve enough for it to work. When there's been months of development and sunk cost, it's hard to not just give it a college try. But I think in many cases new products are better off pivoting right away. The type of pivot that fixes retention involves a complete new redesign of the app's home screen. If it looks like a feed, maybe it needs to be a structured step-by-step flow. If the product is about sharing, maybe it needs to be mostly about creating and saving. You might need to describe the product in a totally different way and position it against a different product. It needs to be a big pivot in many ways, the bigger the better, in order to have a chance at changing retention. Retention goes down, it doesn’t go up. Retention curves often follow very geometric curves that you see. For instance many curves I see resemble the following: Whatever the D1, it drops by 50% on D7. Whatever the D7, it drops by another 50% at D30. Months out you might end up at roughly zero, or if you’re lucky you might retain 10% overall. There’s just a predictable decay. What you never see is a curve that starts high, then goes low, then becomes high again. That’s not possible. In other words, if your early retention isn't incredibly good, then it means that your late retention also probably isn't any good. You need to start strong in order to end strong. There are some interesting exceptions to this rule that are worth calling out: - Some products are extremely hardcore (e.g. online poker). You might have relatively low retention, but those who stay are extremely sticky and spend a ton of money. It turns out that this can work. - A product that has network effects where new users might start out strong, then drift for a bit. But if the product (which might be a social network or a collaboration tool or something else that has network effects) is able to use more and more users to reactivate older users, you often see a small curve where retention comes up. This is a very rare type of situation but amazing when possible. Revenue retention expands, while usage retention shrinks. One of the best and most important dynamics with retention curves is that you can apply them to users, but then also revenue. Thus far, we've been talking about user retention, and unfortunately, it has the undesirable dynamic of always going down. Revenue retention on the other hand is really interesting because people often end up spending more money over time with you, at least the ones that remain. This is one of the biggest strengths of B2B SaaS products. Take a product like Slack. If you look at the user cohorts, what you'd likely find is that the retention curves go down just like any other product. Some people take to it, and some people don't. However, for the companies where people spend time adopting Slack, what happens is it will start to organically grow, and the amount of revenue you earn from that company starts to increase dramatically over time. The revenue retention curves start to grow rather than shrink. This is amazing, and unfortunately doesn’t apply to most consumer products. It’s one of the biggest ways in which B2B products have easier business model dynamics than consumer. The consumer version of this looks more like Amazon where you might have started by buying books and music and over time as the product grows in its capabilities you start to use it to buy more and more things. Because of that your LTV in the product is essentially unbounded. We also saw this at Uber as well where user cohorts would decay over time but the amount of money that people would spend initially on Uber rides to the airport would grow into rides to restaurants or for commuting purposes to work. So the user retention curves go down but the revenue retention curves go up. Retention is relative to your product category. I've written about this in the past on the concept of nature vs. nurture for retention. The reality is that there's just a natural use case for many products - for example with collaboration tools or coding apps, you might use them every day at work, capping your usage to 5 active days out of 7. Contrast that to something like a bug alert system - hopefully you don’t use it often! Same with consumer products, where people check news, messaging, and social apps daily, but generally don’t use medical reference guides frequently. Some apps have great retention but infrequent usage, like weather or banking apps. And some categories like gaming are highly addictive and frequent, but people usually quit after a few weeks of use once the content is played out. Nature vs. nurture is important because it tells you that many new products simply don't have a chance. If you're developing a travel app, but it's meant to be social, the reality is people don't travel that much. It'll be hard to create a product that sole mission is interaction with friends. Instead, it would be better to accept its infrequent nature and figure out how to monetize it better by owning part of the transaction or to have a more frequent use case like a restaurant and nightlife app like Yelp but also be able to use travel features as well. It's just hard to fight nature. You can only do so much. It's also for this reason that if you want to build a very, very high retention, high frequency app, you have to probably build within some of the categories that people are already identifying as core daily products. This means that most likely if your app is successful, it takes away from some other daily product. It's no wonder that my constant use of ChatGPT has dramatically diminished the number of Google searches that I do. Or that when I began to use Substack for reading and writing blogs that I stopped using many other kinds of social news software. Retention gets worse as users expand and grow. If you are lucky enough to build a product that experiences great retention, one of the natural things is simply to extrapolate all the behavior, monetization, and usage to a much broader market and assume that naturally you end up with a very, very big good number because you're multiplying a bunch of small good numbers together with a big one. The reality is that as you start to scale your user base, bad things start to happen. Let’s say you begin adding Android and international users and you acquire more customers using paid marketing and other channels — you'll quickly find that all of your metrics get worse. The reason is that the best users show up early. The ones that are the most highly monetizable, that have the highest intent, that are the most digital and plugged in, well, these guys tend to find your product early and start using it due to a recommendation from a friend. Later on, as you bring in new users from other sources, it's likely that your product just isn't as good for them. It could be as simple as building an iPhone app for college students in Western countries and simply getting worse metrics as you bring in Android users from emerging countries where the feature sets just don't quite work. Of course you can work to improve this over time, but I assure you it will never be the same. Instead, the question is: As you grow your users and they get worse and worse, are they still valuable and can you still operate your product profitably? And more importantly, are you able to hold on to that core highly valuable user base that came in early? No wonder these early users are often called The Golden Cohort. Churn is asymmetric. It's incredibly easy to churn users. In fact, most products churn 90% or more in the first 30 days. Simultaneously, it's incredibly hard to win back a user that's already quit. This is the core asymmetry around churn. In fact, it's so bad that it's often easier to simply try to acquire a new user rather than to try to get someone back. It's for this reason that life cycle marketing that involves trying to resurrect dormant users by sending them discounts or offers tends to be extremely painful and expensive. The version of this that often works is to get existing engaged users to resurrect somebody through the natural usage of the product. For example, if somebody at work tries a new project management tool and it doesn't stick, then you probably won't get them back by bombarding their emails with reminders of features. Instead, you try to get one of their coworkers to invite them back into the tool to work on a new project. That's what works. But again, insanely difficult and complex and is really only available to products that have network effects (i.e., sharing and collaboration). Retention is weirdly hard to measure. When people talk about retention, they tend to try to measure what happens in the first day, first week, and first month. But they'll rarely talk about what happens two years ahead. The reason is that when you're working on a product, you need a short enough time frame and a thing that's easy enough to measure that teams can make decisions about what is happening. As a result, although annual churn or long-term monetization is incredibly important, you tend not to measure it, instead focusing on what's right in front of you and what's easy. However, this approach has many problems. Unfortunately, many categories of products experience huge amounts of seasonality. Anything involving commerce, travel, wellness, or online dating are obvious examples. But there are cycles even to the way that companies use business software as well. Seasonality throws things off because you might be down month over month or quarter over quarter, but is that because of features that you launched? Or is it because user behavior is simply different in this quarter? It's just hard to measure retention when it's super laggy. Same with bugs or new tests that you're running or new market launches. These are all things that muck up the data, and you end up finding yourself reviewing reports where retention curves went up or down. But there's an asterisk on every number because they're trying to validate that the new Android launch didn't create an apples-to-oranges comparison. Crazy viral growth with shitty retention fails. Many folks working on new products find themselves very focused on signing up new users and not on retention at all. After all, if you just want to see a graph that goes up and to the right, why not simply ramp your top of funnel and show that you're growing quickly, raise a bunch of venture capital money, and then you can figure out the retention issue later. We're seeing this all the time right now in the industry when products have a crazy TikTok ramp because a creator pushed their app to millions of followers or because a launch video caused a bunch of revenue growth. Even though the usage and churn are not in a good place. The tech industry has already run this experiment many, many times. And the conclusion is the same: Highly viral products with shitty retention do not last because it's so hard to fix retention. Eventually, the user acquisition fades as the novelty factor goes away, and eventually you're left with shitty user acquisition and shitty retention, and what goes up must come down. We've seen this across many contexts. During the early social network phase, there were many products that used email address books to spam their way to growth, but drove users to bad products. Sometimes, if you could get them to sign up to some shitty ringtone annual subscription, you could try to monetize them and make some money along the way. It wasn't until Facebook, of course, with their UX innovations like The Feed and Real Names, that eventually created a product that was both highly viral and had very high retention. The same thing has happened in mobile apps as well, where you see big hits pop up sometimes caused by forced invitations via SMS, but again, if the products aren't sticky, the whole thing collapses quickly. Great retention is magic. You might read this whole essay and feel a little bit depressed. I know that sometimes it's hard to get things going. However, it's amazing when something really works. When you see a product out in the wild with a 50% D30 (I do once every couple years), it's just amazing. I've come to believe that these lightning-in-a-bottle products happen not because the builders had some incredibly systematic way to A/B test their way to great metrics or that they employed some kind of high-velocity iteration process that got them there, but simply that there's a little bit of magic that's required. This magic comes from a fresh insight about the market or customer needs, and while it might seem obvious in retrospect, it drives super high retention because this product is the first to figure it out. We can say this now about video conferencing software or disappearing photos or a magic AI that replies back to you about any topic. There's just a magic here that no amount of iteration and metrics-driven testing can get you to. The Big Question You might read all of this and still have a big question: So wait, how do you get to great retention? (If I knew the answer in a deterministic way, my job as a startup investor would be so much easier, wouldn’t it?) But let’s try our best. In my points above, there’s a few clues: The idea really matters. - If you want a high retention product, you need to pick a category that is high retention already. - You need to pick a product category where you already use an existing product every day. - You're going to build something that directly competes against that. - If you win, then you'll stop using that other product and use your product instead. - That's a high bar, but I think it's a good start. Of course, if you build something that's quite head-to-head with something that already exists, you might suitably object: "that's going to be really hard to switch somebody over." It is. So then this is where you need to decide to take enough market risk, but just the appropriate amount, where you do something new and different that reinvents that core interaction. But you're probably talking more about a 20% remix rather than an 80%. Ideally, you need to be able to describe this to your users in a way that they can understand quickly and viscerally within the first 60 seconds of usage. This is where the dreaded investor question "why now?" starts to matter quite a bit. Because what you're saying here is that ideally there's some kind of new development in the industry, whether that's a general-purpose technology like LLMs or a societal difference like the oversaturation of social media, that allows you to make this twist happen at exactly the right moment. This gets you into an existing market quickly, and you're more likely to have great retention numbers early on. Timing matters a lot. If you get the timing off, and it's a low-interest category, and your differentiation isn't different enough, then what you'll find is you've traded a retention problem for a user acquisition problem. Here's the difficulty with building a new kind of internet browser: if you win, it's incredibly sticky. But people are so happy with their existing browsers that it's very expensive and complex to get them to try yours in the first place. This is why I don’t blame folks who have a “Cursor for X” idea, or “Figma for X,” just like the “Uber for X” ideas of the past generation. They’re trying to piggy off some existing markets and behavior so that they don’t have to take crazy market risk. And if you get the differentiation right, the timing right, and there’s a ton of user demand, and you’ve nailed the right base product category, then I think it can really work. But what about new markets? The natural counterpoint is that new markets are often more exciting than existing ones. Isn't tech about building brand new things rather than innovating 20% on old stuff? Of course this is true, but I think this is the tiny tiny minority of products. My counterpoint to this counterpoint is that most products actually have some kind of prior lineage, even if those prior products are quickly forgotten. Before Instagram there was Hipstamatic, which had become the #1 paid photo app in the early App Store. It demonstrated the success of photo filters. Of course Google was not the first search engine, it was actually #10 or whatever, after Lycos, Excite, Infoseek, etc., which demonstrated consumers wanted search but that it was impossible to monetize. Tesla was not the first electric car, nor iPhone the first smartphone. Sometimes it’s the 10th iteration that matters. Some call this “last mover advantage” rather than first mover. I think an important point. Yet sometimes new things do happen. Uber was created to turn an existing offline action — calling a cab — into an app, not because there was already a hugely successful ridehailing app. (And no, not Lyft — it was a weird bus booking thing at the time). Of course a lot of ChatGPT, with OpenAI’s 5 year journey between inception and v3 which really took off, and without any real blueprints for what it might replace. These types of journeys are remarkable, and the tech industry is better off for it, because they involve real risk as part of new category creation.

Anyone seen any credible successs stories stories for fine-tuning LLMs to solve real-world business challenges? I want stories where a company solved something using a fine-tuned model where previous attempts at solving it had failed (I feel like I ask this about once a month)

@MattsTechWorld @n_sri_laasya What constitutes as an agent, reads unstructured data, process, does a bunch of actions and shuts down?

@n_sri_laasya Genuine question and not just engagement farming.. how are most people building AI agents today? Is there a platform that makes it easier? (Surely there's got to be a few of these) Or are people building these from scratch with just python and whatever else?

Building AI agents is 5% AI and 100% software engineering

I spoke to 100+ mobile devs about app monitoring. They all say finding the root cause means jumping between too many tools and dashboards. So, we built Measure—an open-source, self-hosted tool to find patterns and pinpoint the root cause. Check it out: git.new/x-measure-sh

So, where do we go from here? 1) Many VCs will likely shut down. This sucks. 2) I think the top VC funds will be fine. There will always be generational businesses founded every year. Top VCs will get the pick of the litter. They will downsize AUM. 3) Overall LP allocation to VC will likely go down. 4) There will be many 'orphaned' VC backed software companies that don't have a path forward in the new environment. IF you have >$10mm in ARR and want out, DM me. I'll help you find a solution. Don't work the rest of your life for an outcome that's unlikely.

@themkmaker There are still lots of variables to be understood to figure out the direction for foundational models, by building products in parallel, you also build a feedback loop for foundational model on what's working, not and what direction or areas to focus on!

I don’t understand two things: Why is OpenAI focusing on the application layer (like Tasks, Operators etc) instead of developing better foundational models and providing a better API for developers? Why are OpenAI wrappers seen as a bad thing? Historically, companies that built operating systems (e.g., Windows, Mac, iOS, Android) didn’t build the application layer. It was done by developers.

@hyderabaddoctor How do you read these graphs?

✅Heart Block detected on Apple watch in an asymptomatic person ➡️One of my friends, aged about 60 years, noticed features of Mobitz type 2 atrio-ventricular block (a type of heart block) yesterday. At that time, he was exercising, however, he had no symptoms. He was immediately evaluated by his Cardiologist, and underwent pacemaker implantation. He is doing well. ➡️In this case, technology proved to be efficient and aided in timely detection of an illness, which could be life-threatening (in some cases), if appropriate & timely treatment is not provided on time. Request expert comments from Senior Cardiologist, @DrDeepakKrishn1 Sir (on pros and cons, as well as precautions to be taken while using apple watch for detection of atrial fibrillation and other cardiac arrythmias).

@StartuAnalytics @ahrefs SEO Tools do not need to be that expensive; we will change that! 🤩💪

@data_e_academy Absolutely, if looking for a structured way to pick it up use a dedicated platform and stick to one instead of jumping from one resource to another:startupanalytics.in/learn-sql-basi…

Learning SQL doesn’t have to be daunting. At its core, it’s like English—simple and logical. Dedicate some time, and you'll be surprised at how quickly you can pick it up.

@zutrixcom seems to be killing it with their pricing model, way more affordable vs @ahrefs ! Does the community feel the same?

@probnstat 1/3 aka 2 in 6 combinations

Three friends go to a pub to drink. Before entering the pub, they leave their umbrellas outside. While leaving drunk, they grab the umbrellas randomly. What is the probability that no one gets their own umbrella?

If you want to grow profitably, you need to attack a small niche and grow from their with differential offering or superior specific qualities.

Conversely, some early-stage niche companies maintain low burn by growing profitably on the outset. You can either disrupt an existing mass market with price or capability. But a large market disruption needs pricing edge.

If you are new entrant looking to disrupt the large market, then pricing is key and you will need to play by offering very key essentials products at competitive prices.