17 ms·

Don't build AI products the way everyone else is doing it

• ge96 3y ago
  Man I saw this product recently it was like "Use AI for SEO, everything else sucks". $3K/mrr I feel like people can just make things up, hype, some landing page, people buy it, get burned, that company disappears.
• roguas 3y ago
  a lot of disagreement, pluggable llms is not something most people look in a product - its rare case of enterprises that are afraid of everything atm, also finetuning should be considered for costs not really range (i have not seen examples of finetuning that couldnt be achieved via prompting in llms, if someone has any, would be great to hear) selfdriving cars are actually going for e2e systems now, there is still infra/architecture and perhaps even different models that comm with each other, but regardless system behaves as e2e solution that learns internal hierarchies of all the signals/tokens large supersmart llms is where the game is currently at, they will get smaller, faster and more efficient we already are on this curve - if you're building llms i think you will fall behind outer leafs of your org fueled by ai - while yes, this model is good for already established companies yet its also super important to think "ai first", ai much like any prev technology can be attached to legacy solutions making them better or can be shaped into a completely new unimaginable before solution... i wouldnt give vanilla advice that discourages the latter
• nittanymount 3y ago
  good points ! :+1:
• bob1029 3y ago
  > The solution: create your own toolchain No thanks. I have an actual job & customer needs to tend to. I am about 80% of the way through integrating with the OAI assistant API. The real secret is to already have a viable business that AI can subsequently improve. Making AI the business is a joke of a model to me. You'd have an easier time pitching javascript frameworks in our shop. Our current application of AI is a 1:1 mapping between an OAI assistant thread and the comment chain for a given GitHub issue. In this context of use, latency is absolutely not a problem. We can spend 10 minutes looking for an answer and it would still feel entirely natural from the perspective of our employees and customers.
  • deleted 3y ago
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  • golergka 3y ago
    > I am about 80% of the way through integrating with the OAI assistant API. I've been there. Turns out, the last 20% takes x10 the time and effort compared to these first 80%.
    • tebbers 3y ago
      Sounds like a normal development project then.
      • golergka 3y ago
        Not really, no. In a normal development project last 20% take just as long. But AI applications are a very special beast.
        • johnnyanmac 3y ago
          If there's anything I would not trust an AI on its polish. It's amazing for prototyping, and has some viability at scaling up an existing operation. But the rough edges (literally in some industries) are the exact reason it's such a controversial tech as of now.
          • golergka 3y ago
            That's exactly my point, yes.
      • pvorb 3y ago
        Just like the old joke: "I'm already 90 percent done, now I'm going for the other 90 percent."
        • morkalork 3y ago
          Before we can finish automating this process, we just have to automate this one other little task inside.
• mmoustafa 3y ago
  Great tips. I tried to do this with SVG icons in https://unstock.ai https://unstock.ai before a lot of people started creating text-to-vector solutions. You also have to keep evolving!
  • ShamelessC 3y ago
    Would it not make sense to use a text to image generator, then convert the image to svg using normal methods?
    • mmoustafa 3y ago
      that’s what I do! it’s a fine tuned model and an svg converter
  • esafak 3y ago
    Color SVGs would be nice.
• JSavageOne 3y ago
  > "One way we explored approaching this was using puppeteer to automate opening websites in a web browser, taking a screenshot of the site, and traversing the HTML to find the img tags. > We then used the location of the images as the output data and the screenshot of the webpage as the input data. And now we have exactly what we need — a source image and coordinates of where all the sub-images are to train this AI model." I don't quite understand this part. How does this lead to a model that can generate code from a UI?
  • obmelvin 3y ago
    If I'm understanding correctly, they are talking about how they are solving very specific problems with their models. In this case, if you look two images up you will see e-commerce image with many images composted into one image/layer. How will their system automatically decide whether all those should be separate images/layers or one composted image? To do so they trained a model that examines web pages and <img> tags and see's their location. Basically, they are under the assumption that their data has good decisions and you can learn in which cases people use multiple vs one image. I could be misunderstanding :)
  • mnutt 3y ago
    They have a known system that can go from specified coordinates to images in the form of puppeteer (chromium) and so they can run it on lots of websites to generate [coordinates, output image] pairs to use for training data. In general, if you have a transform and input data, you can use it to train a model to learn the reverse transform.
• BillFranklin 3y ago
  This is a nice post, and I think it will resonate with most new AI startups. My advice would be don't build an AI product at all. To my mind an "x product" is rarely the framing that will lead to value being added for customers. E.g. a web3 product, an observability product, a machine vision product, an AI product. Like all decent startup ideas the obviously crucial thing is to start with a real user need rather than wanting to use an emerging technology and fit it to a problem. Developing a UI for a technology where expectations are inflated is not going to result in a user need being met. Instead, the best startups will naturally start by solving a real problem. Not to hate on LLMs, since they are neat, but I think most people I know offline hate interacting with chat bots as products. This is regardless of quality, bots are rarely as good as speaking with a real human being. For instance, I recently moved house and had to interact with customer support bots for energy / water utilities and an ISP, and they were universally terrible. So starting with "gpt is cool" and building a customized chatbot is to my mind not going to solve a real user need or result in a sustainable business.
  • fillskills 3y ago
    This. Avoid the “If have hammer, everything looks like a nail” strategy. Find a customer pain point and use the right blend of tools
  • fillskills 3y ago
    This. Avoid the “If have hammer, everything looks like a nail” strategy. Find a customer pain point and use the right blend of tools. You can also make a new tool where tools dont exist
    • ryandrake 3y ago
      Being a "AI startup" makes about as much sense as being a "Python startup." What does your company do? We do Python! OK, but what problem are you solving? Lots of them, but what's important is we solve it using Python code!
      • yen223 3y ago
• adriancooney 3y ago
  With the pace of AI, that (large) investment into a custom toolchain could be obsolete in a year. It feels like ChatGPT is going to gobble up all AI applications. Data will be the only differentiator.
  • dontupvoteme 3y ago
    Not unless your toolchain is highly specialized. There's not even a good way to benchmark language models at the moment.
• infixed 3y ago
  I think the prose in the pre-amble is a bit over-flowery and heavy handed (e.g. LLMs really aren't that expensive, I very much doubt the WSJ claim that Copilot is losing money per user, LLMs aren't always "painfully slow", etc.) Having said that, the actual recommendations the article offers are pretty reasonable: - Do as much as you can with code - For the parts you can't do with code, use specialized AI to solve it Which is pretty reasonable? But also not particularly novel. I was hoping the article would go into more depth on how to make an AI product that is actually useful and good. As far as I can tell, there have been a lot of attempts (e.g. the recent humane launch), but not a whole lot of successes yet.
• ravenstine 3y ago
  I appreciate the overall sentiment of the post, but I can't say I would choose anything like the implementation the author is suggesting. My takeaway is to avoid relying too heavily on LLMs both in terms of the scope tasks given to them as well as relying too heavily on any specific LLM. I think this is correct for many reasons. Firstly, you probably don't want to compete directly with ChatGPT, even if you are using OpenAI under the hood, because ChatGPT will likely end up being the better tool for very abstract interaction in the long run. For instance, if you are building an app that uses OpenAI to book hotels and flights by chatting with a bot, chances are someday either ChatGPT or something by Microsoft or Google will do that and make your puny little business totally obsolete. Secondly, relying too heavily on SDKs like the OpenAI one is, in my opinion, a waste of time. You are better off with the flexibility of making direct calls to their REST API. However, should you be adding compilers to your toolchain? IMO, any time you add a compiler, you are not only liable to add a bunch of unnecessary complexity but you're making yourself dependent upon some tool. What's particulry bad about the author's example is that it's arguably completely unnecessary for the task at hand. What's so bad about React or Svelte that you want to use a component cross-compiler? That's a cool compiler, but it sounds like a complete waste of time and another thing to learn for building web apps. I think every tool has its place, but just "add a compiler, bruh" is terrible advice for the target audience of this blog post. IMO, the final message of the article should be to create the most efficient toolchain for what you want to achieve. Throwing tools at a task doesn't necessarily add value, nor does doing what everyone else is doing necessarily add value; and either can be counterproductive in not just working on LLM app integration but software engineering in general. Kudos to the author for sharing their insight, though.
  • reason5531 3y ago
    I agree with this. I do like the general points about AI in the original post but writing your own compiler doesn't seem like the best solution. Sure, it's unique and people can't just copy it but it will also be a massive amount of work to maintain it, considering all the languages it supports. For me this additional layer of abstraction does have a bit of the 'factory-factory-factory' vibe.
    • 3y ago
• danenania 3y ago
  This is a thought-provoking post and I agree with the "avoid using AI as long as possible" point. AI is best used for things that can only be accomplished with AI--if there's any way to build the feature or solve the problem without it, then yeah, do that instead. Since everyone now has more or less equal access to the best models available, the best products will necessarily be defined by everything they do that's not AI--workflows, UIs, UX, performance, and all that other old-fashioned stuff. I'm not so sure about the "train your own model" advice. This sounds like a good way to set your product up for quick obsolescence. It might differentiate you for a short period of time, but within 6-12 months (if that), either OpenAI or one of its competitors with billions in funding is going to release a new model that blows yours out of the water, and your "differentiated model" is now a steaming pile of tech debt. Trying to compete on models as a small startup seems like a huge distraction. It's like building your own database rather than just using Postgres or MySQL. Yes, you need a moat and a product that is difficult to copy in some way, but it should be something you can realistically be the best at given your resources.
  • JamesBarney 3y ago
    100%, worked with a founder who thought the AI hype was overblown 5 years ago so focused on "workflows, UIs, UX, performance, and all that other old-fashioned stuff" while all his competitors focused on building AI models. Then ChatGPT came out and all his competitors work was instantly obsolete, and he could achieve AI feature parity in weeks. He was right about what to build but for the wrong reasons and it's been a huge boon to his business.
    • fest 3y ago
      Why are these reasons wrong? I have a similar attitude in my field (UAVs), where many desperately chase the next whizzbang technology, ignoring the boring, old fashioned stuff (workflows, UX, 3rd party addons).
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  • SomeoneFromCA 3y ago
• dmezzetti 3y ago
  There is so much available in the open model world. Take a look at the Hugging Face Hub - there are 1000s of models that can be used as-is or as a starting point. And those models don't have to be LLMs. It's still a valid approach to use a smaller BERT model as a text classifier.
• cryptoz 3y ago
  > When passing an entire design specification into an LLM and receiving a new representation token by token, generating a response would take several minutes, making it impractical. Woe is me, it takes minutes to go from user-designed mockup to real, high-quality code? Unacceptable, I tell you! But seriously, if there are speed improvements that you can make and are on the multiple-orders-of-magnitude then I do get it, those improvements are game-changing. But also, I think we're racing too quickly with expectations here; where minutes is unacceptable now when it used to take a human days? I mean, minutes is still pretty good! IMO.
  • willsmith72 3y ago
    From experience developing with GPT4, minutes would be too long for me. I use it for exactly this use-case, converting mockups to code, but you need short feedback loops. It will get things wrong. There'll be things it misunderstood, or small tweaks you realise you need after it's done its first job. Or maybe it misunderstood part of your design, or just needs extra prompting (ALL CAPS for emphasis, for example). Even after multiple iterations it will extremely rarely be perfect, which is fine, because once it has a decent readable solution, you can obviously take ownership of it for yourself. Where minutes might be fine would be in a "handoff" workflow, where designers do design and then handoff to devs. 10 minutes in between of AI processing to get something for the dev to start on would be acceptable, and the dev could then take that first attempt and using GPT4 refine it a bit. But I don't really like handoff teams anyway..
• nothrowaways 3y ago
  Please tell it to Google search.
• nothrowaways 3y ago
  Google search dearly needs this advice.
• andix 3y ago
  I think soon AI will be build into a lot of different software. This is when it will really get awesome and scary. One simple example are e-mail clients. Somebody asks for a decision or clarification. The AI could extract those questions and just offer some radio buttons, like: Accept suggested appointment times: [Friday 10:00] [Monday 11:30] [suggest other] George whats to know if you are able to present the draft: [yes] [no] I think Zendesk (ticketing software for customer support) already has some AI available. A lot of support requests are probably already answered (mostly) automatic. Human resources could use AI to screen job applications and let an AI resarch additional information about the applicant on the internet, and then create standardized database entries (which may be very flawed). I think those kind of applications are the interesting ones. Not another ChatGPT extension/plugin.
  • alchemist1e9 3y ago
    I’m trying to build that already personally. My plan is mbsync to Maildir storage, then process all emails using Haystack. Then trigger a pipeline on each new email with the goal of proposing some actions. Still bouncing around various approaches in my head, but all seems very doable already.
    • andix 3y ago
      Personally I would put this functionality into an email client.
      • alchemist1e9 3y ago
        I was leaning towards the opposite and thinking about some way to support many email clients by perhaps leveraging IMAP to store drafts generated by the AI backend. Another idea I had was to output it’s results into a ticketing system and allowing it to attach related documents and information it finds to be reviewed by a human and provide optional pre-configured actions.
  • immibis 3y ago
    Google already has something like this. You get a text message and it pops up on your phone with buttons like "yes", "no", "sounds good thanks", or "hahaha" depending on what you received.
• adrwz 3y ago
  Feels like a little too much engineering for an MVP.
  • thuuuomas 3y ago
    LLM codegen should herald the death of the MVP. It’s time to solve problems well.
    • dumbfounder 3y ago
      This doesn't make any sense. LLMs help you get to an MVP much faster, then if you want to take it further you can go deeper and make the solution more robust. Don't solve problems well you don't know you need to solve well. This is premature optimization.
      • immibis 3y ago
        If anyone can make the MVP in a few minutes, it has no value.
        • esafak 3y ago
          It lets you get user feedback.
• yieldcrv 3y ago
  > They use a simple technique, with a pre-trained model, which anyone can copy in a very short period of time. This article acts like the risk was something the creators cared about all they wanted was some paid subscribers for a couple months, or some salaries paid by VCs for the next 18 months in which case, mission accomplished for everyone
• hubraumhugo 3y ago
  As in every hype cycle: When all you have is a hammer, everything looks like a nail. A little while ago the hammer was blockchain, now it's AI.
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  • DonHopkins 3y ago
    Blockchain isn't anywhere near as useful as a hammer. If you were gullible enough to fall for the promises of the blockchain, then of course you're going to be disappointed that AI isn't a get-rich-quick pyramid scheme that will make you millions of dollars without any investment of time or energy or original thought, because if that's all you're looking for, you're going to be continuously disappointed (and deserve to be). There's a lot more to AI than to the blockchain, and comparing the two as equal shows you don't understand what either of them are.
    • TobyTheDog123 3y ago
      I don't really understand your hostile response nor foaming-at-the-mouth hatred of blockchain technology (I'm guessing it came from a bad interaction with a crypto bro on Twitter or something), however I think OP is just saying that both were popular technological trends that people tries to exploit to solve problems that society didn't really have. One technology is obviously more helpful than the other, but that doesn't mean either are the right choice for the business you're building.
• liuliu 3y ago
  Very similar sentiment when AppStore built. Everyone tries to avoid host their business on someone else's platform. Hence FB tried to do H5 with their app (so it is open-standard (a.k.a. web) based, people launches their own mobile phones etc etc. At the end of the day, having app in AppStore is OK as long as you can accumulate something the platform company cannot access (social network, driver network, etc). OpenAI's thing is too early, but similar thinking might be applicable there too.
• wg0 3y ago
  I've survived "What is your organization's Kubernetes strategy." And then came along "You need to integrate Blockchain in your business processes." And now is the time for "make your products smart with AI" season.
  • toyg 3y ago
    To be fair, LLMs as a tech are actually useful, even just to take human input. Blockchain an K8s though... Eh. Geekery for geekery's sake.
    • elpakal 3y ago
      I think we'll find that, when the dust settles, AI's usefulness wasn't as impactful as we thought. AI is only as good as its data and no meaningful dataset is 100% representative and 100% accurate IMHO. Don't get me wrong-- it's neat and all, and it can be somewhat useful in the right context, but the hype is huge. Much bigger than Kubernetes ever saw and probably bigger than blockchain.
      • brandall10 3y ago
        There's a huge win to be had though with "this is my natural language data, give me natural language insights at a 100 foot view, at a 50,000 foot view, etc". This is one of the big pulls OpenAI is hoping for w/ GPTs/Assistants.
        • krainboltgreene 3y ago
          Too bad this costs a few million (only if you're using insanely subsidized hardware).
          • brandall10 3y ago
            OpenAI just launched their RAG with the Assistants feature that can ingest a fair bit of documents you choose to upload. So while not enterprise grade just yet, there is immediate utility on the horizon and of course other services have provided something like this for months now. To the parent's point, when the dust settles something like this will probably be commoditized at scale and will likely have a sizable impact on society.
• danielmarkbruce 3y ago
  People are overthinking this from a competitive perspective. Create something that isn't easy to replicate - there are several ways to do that, but it's the only rule required from a competitive perspective.
  • aabhay 3y ago
    1000%. If your business case involves technical differentiation then build an AI stack. If your differentiation is something that can’t be replicated by someone else using OAI then you’re in the clear to use OAI. If your only differentiation is that you use OAI… well you’re hosed anyway.
    • danenania 3y ago
      Yeah, though it's also fine to start as a wrapper and iterate your way into differentiation. That's something people seem to often be missing when disparaging these wrapper products. Like yeah, perhaps the initial version of the business will be disrupted and they'll need to pivot, but if they got a million users in the meantime, they are a lot more likely to iterate toward PMF than someone starting from scratch.
  • sebastiennight 3y ago
    Not sure... Look, even ChatGPT is still only used by an overwhelming minority of users. 100% (or close enough) of the entire market is still up for the taking, in pretty much every vertical. Technical differentiation is only a small piece of the pie ; I think the game is about reach first. It's a race to a billion users (or for B2B like us, maybe a race to a million), and a race to the best value (problem solved + UX) ; not a race to the best tech specs.
    • danielmarkbruce 3y ago
      The comment doesn't mention technical anything. There are many ways to build something difficult to replicate. A billion users is hard to replicate. Deep insight into certain workflows is hard to replicate. A sticky product embedded into a lot of users workflow is hard to dislodge. It's a simple question. Have you built something difficult to replicate?
• sanitycheck 3y ago
  The tech is moving incredibly fast, I think at the moment putting minimal effort into some sort of OAI API wrapper is precisely the right thing to do for most companies whose AI business case is 90% "don't be seen to get left behind".
• deleted 3y ago
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• jumploops 3y ago
  To preface, I largely agree with the end state presented here -- we use LLMs within a state machine-esque control flow in our product. It's great. With that said, I disagree with the sentiment of the author. If you're a developer who's only used the ChatGPT web UI, you should 100% play with and create "AI wrapper" tech. It's not until you find the limits of the best models that you start to see how and where LLMs can be used within a traditional software stack. Even the author's company seems to have followed this path, first building an LLM-based prototype that "sort of" worked to convert Figma -> code, and then discovering all the gaps in the process. Therefore, my advice is to try and build your "AI-based trading card grading system" (or w/e your heart desires) with e.g. GPT-4-Vision and then figure out how to make the product actually work as a product (just like builder.io).
• j45 3y ago
  Shortcuts in early product can definitely affect flexibility as new things keep arriving to tryout and further handcuffing things. I love speed and frequency of shipping but sometimes thinking about things just a bit, but not too much doesn't always hurt. Sometimes simple is using a standard to keep the innovation points for the insights to implement. Otherwise innovation points can be burnt on infrastructure and maintaining it instead of building that insight that arrives. Finding a sweetspot between too little, and too much tooling is akin to someone starting with vanilla javascript to learn the value of libraries, and then frameworks, in that order rather than just jump into frameworks.
• own2pwn 3y ago
  github actually denied they losing money on copilot: https://twitter.com/natfriedman/status/1712140497127342404 https://twitter.com/natfriedman/status/1712140497127342404
• aftoprokrustes 3y ago
  > That car driving itself is not one big AI brain. > Instead of a whole toolchain of specialized models, all connected with normal code — such as models for computer vision to find and identify objects, predictive decision-making, anticipating the actions of others, or natural language processing for understanding voice commands — all of these specialized models are combined with tons of just normal code and logic that creates the end result — a car that can drive itself. Or, as I like to say it: what we now call "AI" actually refers to the "dumb" part (which does not mean easy or simple!) of the system. When we speak of an intelligent human driver, we do not mean that they are able to differentiate between a stop sign and a pigeon, or understand when their partner asks them to "please stop by the bakery on the way home" -- we mean that they know what decision to take based on this data in order to have the best trip possible. That is, we refer to the part done with "tons of normal code", as the article puts it. Needless to say, I am not impressed by the predictions of "AI singularity" and whatever other nonsense AI evangelists try to make us believe.
• fullofdev 3y ago
  I think in the end, it comes down to "does it helpful for the customer or not"
• yagami_takayuki 3y ago
  I feel like chat with a pdf is the easiest thing to integrate into various niches -- fitness, nutrition, so many different options
• tqi 3y ago
  This post seems pretty focused on the How of building AI products, but personally I think that whether or not an "AI product" succeeds or fails mostly wont come down to differentiation / cost / speed / model customization, but rather whether it is genuinely useful. Unfortunately, most products I've seen so far feel like solutions in search of problems. I personally think the path companies should be taking right now is to identify the most tedious and repetitive parts of using the product and looking for ways that can be reliably simplified with AI.
• atleastoptimal 3y ago
  This makes sense because the figma -> code conversion is very programmatic. For anything more semantic or more vague in approach, a heavier dependence on LLM's might be needed until the infrastructures mature.
• digitcatphd 3y ago
  IMO the counter argument is to initially rely on commercial models and then make it an objective to swap them out.
• happytiger 3y ago
  The issue here isn’t AI, it’s not shovels and goldrushes, and it’s not about building how others are doing it. It’s fundamental value. It’s who is creating value that cannot be destroyed. Who owns the house is determined by who builds the foundation first, and that means those that control the ecosystems. All others will play, survive, rent, and buy inside of those ecosystems. If you’re not building fundamental value, you are an intermediary, which may be huge companies, but ultimately companies built on others. If you don’t own the API and the customer, you’re a renter. And renters can get evicted. Those opportunities may still be worth chasing, but we shouldn’t get confused or over complicate what’s going on or we risk investing and building straw houses when brick was available. Nothing wrong with that. Respect to success. But let’s keep fundamental value in mind, as it’s the most important thing for first generation technology companies.
  • zemvpferreira 3y ago
    I agree with you, but only to a point. In a healthy market renters can make landlords compete for their business. Plenty of healthy companies are built on other's infrastructure. But your point remains: Where will the linchpin be? Will AI be a commodity like the cloud, or a fundamental asset like search? And how quickly will we find out?
  • w10-1 3y ago
    It’s fundamental value Yes! value that cannot be destroyed Or taken Who owns the house is determined by who builds the foundation first, and that means those that control the ecosystems Maybe. Most platform plays in tech fail or barely make ends meet, while their renters make bank and impact. we risk investing and building straw houses when brick was available There's no magic bias that solves the build-vs-buy question. More importantly, the article is encouraging people to stick with the structure of the problem and solution as you would normally for building products, and use AI at the edges rather than the engine. IMHO that's much controllable for developers than a full-on dependence on black-box LLM's, and it's even better for the AI providers: they're much more likely to help with a narrowly-defined solution. Even openai is emphasizing incrementalism. It fits available tech, and it counters bubble bias.
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• sevensor 3y ago
  > One awesome, massive free resource for generating data is simply the Internet. Isn't that building AI products _exactly_ the way everyone else is doing it? There are things in the world the internet doesn't know much about, like how to interpret sensor data. There are lots of transducers in the world, and the internet knows jack about most of them.
• orliesaurus 3y ago
  I totally agree with this article, it's actually not that complicated to build your own toolchain, you can use one of the many open models, and if you're building it for profit make sure you read the ToS. Build a moat y'all - or be prepared to potentially shut down!
• jongjong 3y ago
  I built a no-code, serverless platform and intend to use AI to compose the HTML components together. ChatGPT seems to be good at this based on initial tests. It was able to build a TODO app with authentication which syncs with back end in the first try using only HTML tags. My platform allows 'logic' to be fully specified declaratively in the HTML so it helps to reduce complexity and the the margin for error. The goal is to reduce app building down to its absolute bare essentials then let the AI work with that.
• FailMore 3y ago
  Thank you, I thought that was great
• YetAnotherNick 3y ago
  While the differentiation aspect is real, for pricing I did some calculation for self hosting and even with small models, you are likely to loose money unless you have very high rps with users could tolerate some random delay. It's very hard to even get 7B model to be cheaper than ChatGPT API. And that was pre price reduction.
• jmtulloss 3y ago
  Counter point: do whatever you want
• m3kw9 3y ago
  The latency is still too slow to build LLM products other than chatbots where people expects a delay. The rate limit is also a non starter. And most app ideas involving LLM only differ in how well the UI is done. That’s the differentiator right now in AI apps
• EMM_386 3y ago
  > When passing an entire design specification into an LLM and receiving a new representation token by token, generating a response would take several minutes, making it impractical. Meanwhile, we used to sit around the office while waiting on compilers, after which we could see if recent changes actually worked. Now? "5 minutes of a spinning cursor for my design specification to result in usable software?! Ridiculous!"
• zmmmmm 3y ago
  Seems like a big tradeoff against speed to ship. So when you've taken 6-12 months to ship and everybody already iterated twice by directly using a hosted model and is building a real customer base you are only at v0.1 with your first customers who are telling you they actually wanted something else and now you have go and not just massage some prompts but recode your compiler and tool chain and everything else up and down the stack. Perhaps if you already know your customers and requirements really really well it can make a lot of sense but I'd be very sceptical about "given how easy it is to do, why are you not validating your concept early with a fully general / expensive / hosted model". Premature optimisation being root of evil type stuff.
  • epolanski 3y ago
    I've seen the video of this article and wasn't convinced by it a bit. All of this talk about the technology and pipeline but none of this had any relevance without a product to build and a problem to solve. It's like debating if soap or rest is best for the user, user doesn't care how it's built.
    • quickthrower2 3y ago
      This is not a rails vs. php thing where it makes no difference at all. OpenAI vs. say Claude will give a massively different user experience. The user cares. They can’t name the tech but know when something isn’t right. Like resistive vs. capacitive touch screens. Eventually it enters the user lexicon like “V8 engine” for cars or for gaming “refresh rate, resolution, etc.” People grab on to the tech details that matter. Since OpenAI I have heard almost every single person in my company and my tennis coach say “chatGPT”. People understand some tech stuff! Because people are good consumers who want to compare things. I bet if you wanted to buy a dog you would quickly learn a lot about breeds, genetics, animal vaccinations and so on. as you do the research. It is natural.
• it 3y ago
  To view this page without the annoying animations, I recommend printing it to PDF or paper. Safari reader mode doesn't work on it.
• deleted 3y ago
  [deleted]
• wslh 3y ago
  I would say something more radical: the same AI product that you have in mind is being built by many companies at the same time, wait for clarity in the space. Use suspense in your favor, sometimes not doing everything is the best option. This can be applied to every hyped field but AI is specially mesmerizing all of us.
  • esafak 3y ago
    You're witnessing a Cambrian explosion of AI-based products. You could wait for the dust to settle but how will that help you as a founder?
    • wslh 3y ago
      If you are a founder that is not pressed by investors then take your time to observe the ecosystem before defining a specific product.
• blackoil 3y ago
  I would give contra advice. * Never build your own model unless you have proven your model, and you have expertise to build it. Generic models will take you long way before cost/quality becomes an issue. Just getting all the data to train an LLM will be pain. 1000s of smartest people are spending n Billions to improve upon it. Don't compete with them. and if downstream you believe open source or your own is better use it then. * Privacy is overrated. Enterprises are happy to use Google Docs, Office 365 exchange and cloud and ChatGPT itself. Unless you are in a domain where you know it will be a concern, trust Azure/OpenAI or Google. * Let it be an AI startup. It should solve some problem but if VC and customer want to hear AI and Generative, that's what you are. Don't try to bring sanity in hand feeding you.
• _pdp_ 3y ago
  Recently, I embarked on a project to create a song as a tribute to my colleagues' exceptional work in a specific domain. My tools? OpenAI for lyric generation, tailored to my specifications, and Suno for vocal and track synthesis. The resulting song was a blend of AI-driven creativity and my vision. However, as I prepared to share this creation on Slack, I pondered the nature of authorship in the AI era. Was I truly the 'creator' when automated processes played a significant role? This led to a broader realization: the song wouldn't exist without my initial concept and the nuanced curation involved in its completion. It's not merely that AI executed 90% of the work; it's that my 10% contribution leveraged these advanced tools to achieve a 90% outcome, a testament to the power of technology in amplifying human creativity. In a world where websites, businesses, and SaaS tools can be launched in mere minutes, it's becoming increasingly clear that ideas and the ability to effectively harness technology will be paramount. This shift raises fascinating questions about the future of creativity and the evolving role of the human in the creative process. My key message is this: "So what if your business heavily relies on OpenAI models?" The unique prompts you craft hold intrinsic value. They don't diminish the time, expertise, and knowledge you invest in shaping the results. Take designing a 3D chair using an AI system, for instance: achieving optimal results hinges on your ability to precisely describe what you need, a skill that itself depends on your understanding and knowledge of design. In this context, delving into classics and broadening your educational horizons is more crucial than ever. It equips you with the nuanced articulation needed to harness AI's potential fully. P.S. An AI model assisted me in crafting this comment, but the experiences and insights I've shared are my own, as is the majority of the words in this text. The advantage I gain from AI is the better articulation of my ideas. This tool is akin to a dictionary, a grammar checking tool, or a system that translates my native tongue into English.
  • dang 3y ago
    > An AI model assisted me in crafting this comment I don't know what you meant by that, but if it meant that you copy-pasted generated language, please don't do that in HN comments. I mention this because the following sort of rhetoric is definitely not what we want here: > In a world where websites, businesses, and SaaS tools can be launched in mere minutes, it's becoming increasingly clear that ideas and the ability to effectively harness technology will be paramount. This shift raises fascinating questions about the future of creativity and the evolving role of the human in the creative process.
• gdiamos 3y ago
  I thinks it’s sad that LLMs have become so hostile to builders. It doesn’t have to be this way.
• mediumsmart 3y ago
  A good start would be to build AI products for everyone else. And since this thread has defined the number one 'mere puny user' problem to solve (see, that wasn't hard was it?) the marching orders are done too. Now get to it me hearties and build something actually useful - money will of course be a collateral side effect so no need to worry about that. You can write history here instead of commentblogging around the interwebs. God speed.
• 0xDEAFBEAD 3y ago
  >..when we started talking to some large and privacy-focused companies as potential early beta customers, one of the most common pieces of feedback was that they were not able to use OpenAI or any products using OpenAI. It's interesting to me that there are apparently companies that won't let OpenAI see their data, but will let a random startup see it. What's going on with that? Does OpenAI have a lax privacy policy or something?
  • rf15 3y ago
    I'd assume that you have a lot more leverage over a random small startup than OpenAI who serves half the world. This leverage can be used to ensure better privacy/etc..
• jillesvangurp 3y ago
  The article mentions the need to differentiate, which is valid. A related concept here is negative differentiation. You can differentiate yourself negatively by not implementing certain things or doing them poorly. You always differentiate (positive or negative) relative to your competitors. If they do a better job than you, you might have a problem. Adding AI and then doing a poor job isn't necessarily creating a lot of value. So, if you follow the author's advice, you might end up spending a lot of money on creating your own models. And they might not even be that good and differentiate you negatively. A lot of companies want to add AI not just because it looks cool but because they see their competitors doing the same and don't want to differentiate negatively.
• scaraffe 3y ago
  Any idea of what kind of 'custom-trained' model does builder.io use? Is it some kind of an rnn? they claim to have 100k context window
• startages 3y ago
  That's a great post. I like the idea and was trying to do something similar myself, but it just takes so much time to write a toolset that can be easily replaced with some LLM API in 30 minutes. Still, many of the point in this post are valid and have their own use cases.