Antirez returns to Redis! (Interview)

36:58So there is an ongoing discussion inside the company right now. I'm not sure what will happen, but we are focused on the problem. So you've probably heard that Elastic went there and back again. They are fully open source with Elasticsearch specifically once again. And we had Shea Bannon on the show last year talking about that decision. And he says that he thinks nowadays in today's culture, the AGPL license, which is accepted from the OSI as an open source license, gives entities enough of a foothold or legal precedence or holding that you can safely, in his opinion, use that and not be too worried about the rehosting scenario.

37:42Now, he detailed some of that in that show. I don't know all of his reasoning why he thinks that's the case, but it's possible that because SSPL and AGPL are very similar, are they not? Exactly. I believe so as well. There was, I believe some, I was not part of the company when this happened, but I believe that this was a consideration. But then, you know, sometimes also the legal advice that you get, you know, I will say something a bit maybe unpopular, but I think that the legal offices in companies sometimes create the worst possible setups for companies because the legal offices don't really try to usually balance the risk, the legal risk with the company potential.

38:30but they are mostly going away to err on the safe part in order to be right later. You know, if you are super conservative, you never do anything wrong from your point of view. However, you are paralyzing the company. You know, many times when I had to do contracts, I skipped lawyers that were in the between. We could not understand each other and said, okay, I will write the contract. This is the contract. If you want, you can fix the form. And immediately the thing started to work the conversation. And I believe that sometimes it can happen that choices are... But if you read the IGPL license very, very well, you actually discover that also the form is not extremely clear.

39:27You know, it's not very well written. So I believe that it's very hard that somebody will authorize to use it in a software as a service setup without any kind of signet paper, but the folks that have the copyright. And this could be a good... I think that they did a good thing. Switching back. It was, you know, basically in this kind of changing setup, you try. and you believe that this is very similar to a GPL, people will accept it. Instead, you find the cultural assistance and then you step back. It makes sense. I can understand that.

40:25Well, friends, I'm here with a good friend of mine, David Hsu, the founder and CEO of Retool. So David, I know so many developers who use Retool to solve problems, but I'm curious, help me to understand the specific user, the particular developer who is just loving Retool. Who's your ideal user? Yeah, so for us, the ideal user of Retool is someone whose goal first and foremost is to either deliver value to the business or to be effective. Where we candidly have a little bit less success is with people that are extremely opinionated about their tools. If, for example, you're like, hey, I need to go use WebAssembly, and if I'm not using WebAssembly, I'm quitting my job, you're probably not the best Retool user, honestly.

41:11However, if you're like, hey, I see problems in the business, and I want to have an impact, and I want to solve those problems, Retool is right up your alley. And the reason for that is Retool allows you to have an impact so quickly. You could go from an idea, you go from a meeting, like, hey, you know, this is an app that we need to literally having the app built in 30 minutes, which is super, super impactful on the business. So I think that's the kind of partnership or that's the kind of impact that we'd like to see with our customers. You know, from my perspective, my thought is that, well, Retool is well known.

41:41Retool is somewhat even saturated. I know a lot of people who know Retool, but you've said this before. What makes you think that Retool is not that well known? Retool today is really quite well known amongst a certain crowd. Like I think if you had to pull like engineers in San Francisco or engineers in Silicon Valley, even, I think you'd probably get like a 50, 60, 70 % recognition of Retool. I think where you're less likely to have heard of Retool is if you're a random developer at a random company in a random location, like the Midwest, for example, or like a developer in Argentina, for example, you're probably less likely.

42:16And the reason is, I think we have a lot of really strong word of mouth from a lot of Silicon Valley companies like the Brex's, Coinbase's, DoorDash's, Stripes, et cetera, of the world. There's a lot of chat. Airbnb is another customer. NVIDIA is another customer. There's a lot of chatter about Retool in the Valley. But I think outside of the Valley, I think we're not as well known. And that's one goal of ours to go change that. Well, friends, now you know what Retool is. You know who they are. You're aware that Retool exists. And if you're trying to solve problems for your company, you're in a meeting, as David mentioned, and someone mentions something where a problem exists and you can easily go and solve that problem in 30 minutes, an hour or some margin of time that is basically a nominal amount of time.

43:01And you go and use Retool to solve that problem. That's amazing. Go to Retool.com and get started for free or book a demo. It is too easy to use Retool. And now you know. So go and try it. Once again, retool.com.

43:22So you said that you guys are considering licensing updates, let's just say, to paraphrase it. Is AGPL back on the possible table since the SSPL and the AGPL is so similar? do you think like to be culturally fit again, let's just say, that's a wise consideration? There is, I believe, a part of the company that wants to do some change in this direction. However, there is to basically to find an agreement about that. But I am personally, for myself, I am positive about doing this switch. And I will keep the conversation going about this. Yeah, I think it'd be a win. I mean, we started off with, you know, basically 15 years ago and your journey to create it and the process, you know, how it prospered in the community.

44:18Obviously, BSD license, open source thrived, beloved to this day. And many mourned its license change because it limited their ability to use it as it was prior to be open source. And I get it. You got the Goliaths and the behemoths and you got the challenges and license changes are sometimes necessary to protect all of your work and the company you built and the customers you've gained that pay you. and then we kind of forget in a way this open source land, this thriving free ecosystem that's not just free in dollars but also free in freedoms. I can feel that tension there, obviously, but I think it'd be super cool if you guys went back to open source.

45:05Could you imagine, Jared? That'd be awesome. Redis, open source again. There you go. To be honest, I will invest my time. I'm investing my time both in creating the cultural conditions inside the company, but I don't want to stop there. Also, with the vector sets, with a coding act, I want to, in some way, show the company also back the Redis way from the point of view of simplicity. Also, one important thing about the vector set data, the API compared to the other systems that do vector similarity is the trust to the user using them. Instead of exposing a system that is an index, we expose the data structure because Redis users have shown us in the past that they are very smart, they are very capable, and they know how to use building blocks in order to shape them exactly for the kind of problem they want to fix.

46:13So this kind of thing is also part of... So pressing about the license, pressing about the different vibe in the community. In general, all this change, this is what I want to do to be some way the flag of this new revolution inside the Resis company. And I see that I have many, many people that are aligned with this vision. For example, today I received the second pull request from the CEO of Redis Labs that hacked the C code in vector sets in order to fix a bug. So in this moment, Rowan, which is the new CEO, is hacking to the code that I'm showing to my colleagues and sending pull requests.

47:03And since I see he's really a community person that had this kind of basically background, I believe that the company can be aligned towards this vision. Was this one of the reasons for your return? Or did you find this once you got back that you wanted to make this cultural change inside? Or did you see it as a reason to come back to Redis and Redis Labs? Honestly, I started to think that my comeback could be useful from the point of view of recovering what was created in so many years. And I didn't want, you know, after I left Redis, I didn't watch again the comments, whatever, zero. Because I said, okay, during my term, I tried to take the direction straight.

47:54But then now it's too simple to look at the comments, at the commits and saying, you know, this is wrong. If other people are working to the system, you have to let them work. But then when I saw the community in some way, the conflict inside the community, I thought that since I know a lot of people inside the company and I was sure that many of the technical staff, for example, it's not what they wanted happening, I said maybe I can return and I can return also without feeling too much stress. If I, from the point of the code, I focus on only a subsystem each time. And from the point of the other work, do I focus on the community?

48:44That was the idea. Let's paint a picture because you keep saying the company. And I imagine you got Salvatore here. you enter as the person we've known since the beginning and then you say the company. Can you quantify what that means, the company? How many people who's involved? Is there a lot of open source lovers or purists there that really appreciated or loved the days when Redis was fully open source? Help us understand what you mean when you say the company. Okay, that's a very cool question because I believe that Redis is one of the worst communicated companies in the world. Okay. Because it's much better in the inside than it looks in the outside.

49:34Basically, we are 1 ,000. 1 ,000 people. Yeah. That's a lot. Holy moly, that's a lot of people. Okay, the company's big. That's big. A lot of people. It's not Google big, but it's big. That's a lot of people. It's a lot of people and the development part is almost completely in Israel. A lot of remote workers inside Israel, in many places of Israel, in the north, in the small cities, small villages. And there are truly talented people there. Incredible persons, also very loyal persons to the company that understand the project and understand it is very important. Also, there are people that kind of fight for the AGPL thing because they thought it was a good thing.

50:24So people are interested in the project. And I believe it is a very strong pool of talented people in Tel Aviv. Also, another thing that happened that I believe is extremely cool is that we are acquiring new people that are like that. What happened is that one of my colleagues, Oran, which is truly a genius of programming, together with Yossi, they are two really superstars that nobody knows. For example, Yossi was the one that wrote the abstraction layer in order to make Redis SSL as a module. So you load a module and it is SSL, otherwise you don't have the complexity and stuff like that. For example, this Chinese person started starting to do pull requests only on comments.

51:16This comment is... And another guy, which is Portuguese, for example, also doing initially simple pull requests. But he recognized that they were smart and started to help them to create more complex pull requests. and since Oran works a lot, since he's responsible of all the developments of Redis on Flash, which is the way that we have to offload part of the data from run to Flash. It's a complicated fork of Redis. You know, he had to do a true investment and now these two persons are very strong core contributors of Redis making complicated stuff. Also, another guy, which is the one that implemented, expires in the hash type elements, single elements, is a very talented that optimizes this thing to a so low level.

52:15And, for example, one thing that I still love about Redis, and I think that we have an edge here compared to, for example, Valky, is the design idea. in Valky there are a bunch of people and they do a lot of you know agreement design which is a way of working that I don't believe to be optimal and why I hope that you know Valky is going to be great because in some way this was you know the thing about the BSD is that whatever happens to the company to myself the codes in some way can go forward because it takes other streets like Amazon or Google forking and putting people. However, from the point of view of the softer idea, I don't think that Valky can do a better job than us.

53:12And one thing that I would love is to go back to a GPL to be also OSI approved again and then compete on the quality of the ideas and the quality of the developments. And, you know, because Redis is going to diverge a lot. For example, one of the leaders of the project said to me, after some in my blog post, the vector sets thing, we will never do something like that. Because they don't trust in this line of, you know, AI is the hype that is this terrible hype. For example, this is one way to approach the problem. I am, for example, an AI enthusiast. And this already creates a different take on the project.

54:00So I believe they will diverge a lot. That's really interesting. And you're happy about that because now you have basically, like, Redis gets pushed forward, but also Valky gets pushed forward in different directions. And, you know, different things work for different people in different circumstances. So it's kind of spurring some innovation. Did you see Redka? Is that one that crossed your radar? Because there's a bunch of forks that came out back when Redis was first announced that the license changed. Redka is not a fork. It was like a re-implementation. Are you familiar with this one, Salvatore?

54:32No, no. I heard about it, but I didn't want to check what they have done. You didn't check it out? This was just a re-implementation with a SQLite backend trying to just get the Redis API up and running with a different backend. And it was kind of a greenfield thing that was inspired by the re-license. But I thought it was pretty cool how many, the explosion of new projects and ideas that happened, which is kind of like open source working in a sense, right? Like this is the spirit of hackers hacking and starting new things. And it's kind of cool that all of this has inspired you to come back to Redis and like push it forward in new directions.

55:08I think it's pretty cool. Yeah, it's like if we, all the teams together optimize to search the space of potential possibilities, basically. well said yeah that's cool so where do you take it from here ai it sounds like huh i think that uh vectors i mean learned embed embeddings because vectors okay the way i am implementing vector sets is one way that allows the user to also use vectors outside the ai so So, for example, I implemented binary quantization. And if you want, you can put binary vectors where just each bit is a product that a user got in an e-commerce system and you still can do cosine similarity and takes similar products to this user.

56:05But, of course, the main thing is learned vectors created by models that compress something, some object, Bingit, text, image, whatever, in a embedding that captures the semantic. value of the initial object. I believe this is going to be useful in the future, even if I'm not a fan of RAG, because I believe that RAG created a lot of issues. For example, one of the reasons I believe Claude Sonnet is often in the real world so strong for programmers is that when you attach a file there, the model sees all the file, they don't use RAG. instead when you attach a file in the open ai systems it uses rag so you don't see it but the model is just seeing fragments of the code so in some time some in some way cloud looks magical because of that so do you know what technique cloud sonnet is using to get that information that's not rag they just just put everything in the prompt oh they put all i mean does that do they have a huge prompt i guess big context exactly that's the reason why claude sonnet after you fiddle with it a little bit says returning five hours because uh my experience with claude so far has been just that like you they almost they start to yell at you essentially um when you have too many back and forths i'm like that's the whole point is to have the back and forths essentially like the the prompt the response the prompt response and you iterate and massage and form whatever it is you're forming and it's like well that's the whole point of this chat system and we've can we can debate the usefulness of chat if we'd like to but like they discourage long chats essentially i agree but i believe this is a feature and the way why so many programmers speak at the cloud.

58:06Why I'm saying this? Basically, like Redis, they are putting you face-to-face with the limitation of the system. Instead, what OpenAI does is to implement two things. One is memory, which is after the context is too long, the model will summarize it in some way or use lag itself in the history. And so you believe that the model is still aware of all your chats, but the model is no longer seeing the full details, and it starts to act erratic. They do. That's true. So I believe... I experienced that as well. I am more happy with the limitations, but they can understand very well what's happening.

58:45And also DeepSeq v3 and R1 does the same, that when you attach a file, it is put... The prompt is completely... The file is completely inside the prompt. And since attention is quadratic, it burns a lot of GPU compared to not using RAG. How much are you studying this? Because this is something that I'm still sort of catching up to how the GPU interacts with a model. And you've got, let's just say you've got an off-the-shelf one, 24 gigs of VRAM. How does that translate to the model being in the VRAM to consuming the GPU's ability to compute? How are they different? Like memory and compute on a GPU is not one of the same.

59:29Because you may say, well, I've got one or two GPUs and I can consume, you know, 24 to 48 gigs of VRAM, but you still have compute on that GPU. Can you explain? Do you know much about that? Are you becoming more familiar with that? Basically, what is happening right now is that all the Frontier models are MOE, mixture of experts. They are no longer like, for example, LAMA 3.3 or the initial models, dense models. Starting with GPT-4, OpenAI started with the idea of using a mixture of experts. Basically, the model is partitioned inside, like a cluster. and then there is a layer which is the routing layer which selects at each layer for each token which part of the model to activate.

1:00:25In this way, a subset of the model becomes expert in the English grammar, another subset in Python and so forth. However, this division is not so clear. The model learns how to split the knowledge and it's very mixed and under-leveled inside. So basically, with Moes, you still need the VRAM to contain all the model in your RAM. So for example, if you have, let's talk about DeepSeq version 3, which is more or less as big as Cloud, and we have the actual data public for DeepSeq. It's 6 ,600 billion parameters model. Oof, that's big. Yeah, it's very big. and since it is 8-bit for parameter, you need 600 gigabytes just for the model.

1:01:25And then you have the cache for the key vCache for the attention. And for very long context, you need more RAM. So basically, with a mixture of experts, you need less GPU and you need more VRAM. so this is changing the GPU is becoming the GPU power is becoming in some way for inference at least less important than VRAM to the point that I can I have a MacBook M3 Max with 128 gigabyte of memory and I can run I can run DeepSeq in my computer not the distillated versions, the actual thing extremely quantized to 1.5 bits per but still because I have enough RAM even if GPU inference it's able to run this beast so basically what will be the dream for all us will be computers with like one terabyte of RAM and even if the GPU is not so powerful we could run Frontier models inside our machine let's let's uh clarify runs uh tell me tokens per second four tokens per second in my computer right now which is so slow it's very slow but it runs out of it runs yes i know i'm just i want to be clear though because like 100 gigabytes he said yes no i get it but i mean like you got people who are dabbling more and more into this and runs and is usable as potentially two different things because it can run, and I do agree with that.

1:03:09Your hypothesis that more RAM and less GPU makes sense because the Mac, the M1 systems or the M systems, allow you to essentially allocate this RAM as VRAM, as what would normally be VRAM for a GPU. So you can utilize that 128GB that you have available and maybe a less powerful GPU and still run the inference. However, you won't have super fast tokens. And if you want to program a Python program with that, you're going to spend a couple of days. A serious amount of patience and potentially a ton of errors. Who knows? Also, a lot of people don't realize that if you are talking in English with the model, often a single word is one token.

1:04:00However, because of how the tokenization work of large language models, if you talk with them in Italian, for example, since the Italian was very underrepresented in the dataset, or even if it's writing Python, and often for each parenthesis and you know new line is a token, this means basically that you have to generate a lot more tokens for each sentence, and it becomes incredible slow because every two characters are one token, not one single word. It really makes you weigh your words that you put into the prompt too, right? Because like the weight and the structure, it almost kind of brings back prompt engineering in a way because you want to, not so much engineer, but you want to be particular with how you prompt the LLM in this scenario.

1:04:55When you have resource constrained systems, whereas if you've got clod you just realize that oh you can't have long chat histories or if you're on open apis chat gpt 4.0 or whatever model you can go to infinity basically but it's it's not it's not like i had said before it's not real it's faking the scenario it's ragging it later on which is just like retrieving stuff and fragments so you're saying adam is don't waste precious tokens by thanking your models when you're done precisely i'm no longer that's a that's a that's a deep cut precious time because i am the person that says thank you or is generous with my words let's just say i you know can we please you know might be like or let's do this versus generate which is me too but this is nothing the trick the trick is when you start to have a mid mid longer chat to start a new one because since attention is quadratic and once the god okay there is the key vcache but still you are accumulating a too much longer chat and instead you start new fresh also sometimes the model can focus much better on the latest version of the code when you start fresh it becomes more selective and it gets less confusing so my trick is this one Yeah, I like that.

1:06:22So you pull out the latest, you may have like a 10 minute session, let's just say a couple back and forth, but then you take the final artifact you're liking from that session, sort of whole brand new session and bring that latest version of whatever you're liking to that new session and allow it to begin again to have that new finalized context versus all the different bifurcations you could have taken along. Exactly. Okay. That's a pro tip right there. There you go. Prompt engineering is back. It's back. until we have sufficient VRAM that we don't need these things. So, Salvatore, when do you think this new Redis vector set stuff you're working on will be baked?

1:07:02When will people begin using it? Is it out there? It's somewhat open source? What's the status? I think it will be released in one month or one or two months, something like that. And what are some use cases or some people that are currently using Redis that they would be, what would this unlock for certain folks? Okay. An example. Sometimes when I take weight, I want to lose this weight. Normally I do calorie tracking with my fitness polo, all the applications, stuff like that. However, it's so boring. I do that for 10 years at this point, but still it's boring. So I'm writing to a Telegram bot where I just say, I'm eating 10 grams of honey and one slice of bread of 40 grams and it will do it for me.

1:07:52However, you cannot trust the model to have accurate informations about the calories of each kind of food. So basically what I did was to create a vector set inside Redis where it has each line of a very big database where there is each food with the macronutrients, the calories and whatever. So what the bot does is that based on my query, it computes Redis for similar items. Then I take the similar items of foods that are similar, and it takes that in the context of the LLM when asking the question. The user reported eating this thing. Based on this table, don't invent macronutrients. Please tell me what are the total calories and proteins and carbohydrates and fats.

1:08:45This is one use case that works very well. Key phrase there, or key word there, Joe, is please. He did say please, yes. He did say please. the debate is still out okay it's only one token in english you know i don't know about in italian but it's only one token in english okay that's cool that's very cool i'm sure there'll be countless other people with ideas on how they can leverage this to do cool stuff whether it's commercially or you know healthily in their own time to track their calories i think that's pretty sweet

1:09:32Well, friends, I'm here with Samar Abbas, co-founder and CEO of Temporal. Temporal is the platform developers use to build invincible applications. So, Samar, I want you to tell me the story of Snapchat. I know they're one of your big customers, well-known, obviously operating at scale. But how did they find you? Did they start with open source? Then moved to cloud? What's their story? Yes, Snapchat has a very interesting story. So first of all, the thing which attracted them to the platform was the awesome developer experience it brings in for building reliable applications. One of the use cases for Snap was Snap Discover Team, where every time you post a Snap story, there is a lot of background processing that needs to happen before that story starts showing up in other people's timelines.

1:10:19And all of that architecture was built, composing, using queues, databases, timers, and all sorts of other glue that people kind of deal with while building these large scale asynchronous applications. And with Temporal, the developer model, the programming model is what attracted them to the technology. So they start using our open source first, but then eventually start running into issues because you can imagine how many snap stories are being posted every second, especially, let's say, on a New Year's Eve. So this is where Temporal Cloud was a differentiated place for them to power those core mission critical workloads, which has very, very high scalability needs.

1:11:01Although they started with open source, but then very quickly moved to Temporal Cloud and then start leveraging our cloud platform. And they've been running on top of Temporal Cloud for the last two, three years and then a pretty happy customer. OK, so maybe your application doesn't require the scale and resilience that Snapchat requires, but there are certain things that your application may need. And that's where Temporal can come in. So if you're ready to leave the 90s and develop like it's 2025, and you're ready to learn why companies like Netflix, DoorDash, and Stripe trust Temporal as their secure, scalable way to build invincible applications, go to Temporal.io.

1:11:38Once again, Temporal.io. You can try their cloud for free or get started with open source. It all starts at Temporal.io.

1:11:51you know i don't know if this maps to this but there's a particular problem i'm trying to solve for me so i often so as you may know salvatore we are a sponsored podcast and so we have brands like redis but you've never sponsored us that we would happily partner and share with the world the developer world, what you're doing, let's just say. And our ability to win these relationships is really predicated on my ability to be fast with accurately quoting people, what we can do for them. For the most part, we have packages, but sometimes people are asking for this or for that. And so it's not always just here's package one and here's package two.

1:12:38And so what I've done recently is I've gone into opening eyes, chat GPT, and I've created my own GPT. And it sounds like what you're talking about with this vector embeddings is like, give me truths, particulars, like this ad spot costs this much. And when you multiply it by 12, it costs this much. That will never change because it's like a row in a database. The value is X. And so quantify it by Y, for example however in my experience with this even though i've made this gpt and i've told it how i want to format the response and i just say generate it it's driving me crazy because it's doing the math wrong like like i so bad i just want to spend my own ruby app just to like generate like my like this basic text is all i really need but i want to ask i want to prompt and just say give me 12 weeks of the change law give me 12 weeks of this and then generate this thing i would normally handwrite, which I already did.

1:13:37I formatted it, but just do all the word math for me, do the word calculator stuff for me because I'm lazy and I'm, I can be slow and I want you to be fast and I want it to be accurate every single time. So I feel like what you're talking about with this vector embeddings is like, okay, in that world, building my own GPT or my own thing like that is possible because you've got these embeddings in there. And whenever I prompt the LLM, it's going back to those embeddings and saying okay i know this costs y and we're multiplying it and the word calculators might generate my prompt essentially say give me this and it gives me all my words back but all the math is done is that does that map onto what you're talking about with is that an application of vector embeddings yeah yeah in generally if you want to retrieve anything for For example, another example that for your use cases is particularly one of those systems that have a very large context window like Cloud Sonnet and that don't use RUG and see all the files in the context could be the best spot for you.

1:14:46If you want to try, you can take some document, put all the information needed, and then you do few shot learning. So you make examples. Okay. And then if you upload this file to Claude and you ask a question, it should be able to reply in a perfect way. I want that. I want that. I want that so bad. The math being off is just like driving me crazy. I just did it today. I was like, it's accurate 95 % of the time. And then one time it's like, it does terrible math. I'm like, I could never charge our customer that or our partner that. Cause like, that's not the real number. And so I've got to check it every single time.

1:15:30I'm like, I just want the work calculator part of it. Another thing that you can do is to specify to the model that you want to alter calculations performed using a program. This also is a trick that works. And Cloud, unfortunately, cannot run Python, but it writes JavaScript and executes it inside your window of your browser and then takes the calculation back. So, but to make another example of vector sets, imagine that you want to do something like Face ID. So basically, the iPhone Face ID works with an embedding. The dot matrix in your face is provided to a given model that outputs a fixed vector.

1:16:14And imagine an evil government that wants to track you in every second, and it has a database of all the faces in a vector set. And then when you scan yourself, it locates that it's you, for example, or you are similar. Or if you have doors, automatic doors that look at your face and open the door. So it's not just text embeddings. You can use it for every application when you can turn an object into a set of features and you want similar items. So it's possible to use a lot. And as I said, also, it works very well. For example, when you don't have, even outside the AI, a few years ago, there was one guy that did this in Hacker News.

1:17:07It downloaded all the data, all the comments of all the users and created vectors with the most used 10 ,000 words, setting the number of occurrences for each word. So in a given vector, there were many zeros, but other things valorized. And using this and cosine similarity, he was able to extract all the fake accounts that were actually cloned. You know, when you write with a throwaway account, because the cosine similarity is very similar. So also you can use this in order to spot similar texts and other stuff like that. There are many, many use cases. And basically, I have a very active YouTube channel right now talking about AI, mostly.

1:18:01And when Vector Sets will be released, I will write a lot of toy applications showing use cases. Cool. What's your YouTube channel called? Salvador Sanfilippo. Keep it simple. And the videos are, the coding videos are in English. A lot of talking videos are in Italian. But what I do is to take the transcript, pass it to Claude Sonnet, and it cleans up the Italian. And then the auto-translate to English works very well. So there are other people that don't talk Italian following me on the channel. So it sounds like you're a Claude Sonnet fan over OpenAI's models. Like you're hanging out in there.

1:18:48Okay. And how do you get around this limitation of, you know, what we talked about before when you volley back and forth? I don't know. You have a long chat history. How do you get around that? You just restart it? That's your way? Yeah, in general, I use AI a lot for coding, but really big time. But in a strange way. Most of the time I am in my terminal writing my code myself. When I got a problem, I go there and often my prompt is like, don't write any code. Let's discuss about this because I use it like an improved duck in order to create more ideas or explore mathematical concepts that are at the edge of my mathematical skills or stuff like that.

1:19:36for example, or if I have to do some complicated optimization and there are tricks that I don't know or stuff like that. So basically, I'm very conservative. I avoid basically the brute force way of coding, of continuing trying to refine, because I discovered that this often leads to losing too much time to get short, not great results. So either I spend a lot of time writing the code, the prompt in a very subtle way in order to influence the way the design ideas of the model, giving a lot of hints, but positive hints. And also don't use that because I already understand it's not going to work very well.

1:20:19So my chats are usually not super long. And otherwise I start from scratch. So are you using this outside of your editor or inside of your editor? Not integrated, because for me, this is a way to avoid becoming lazy, basically. I want to be sure that 100 % of the code that I use, even if some code is written by the LLM, I understand what's happening. But I use a lot more to track bugs. Like I have a bug. oftentimes if I post the code what's happening it can debug it immediately so good, yeah incredible, like 5 hours of debugging 5 minutes because it will say you did when you like after refactoring a few days ago I forgot a statement, I removed a statement but the statement removal didn't cause any error in the compilation but just created a memory error.

1:21:25And what I do in this case, which is I see that many people don't do that. You have to send the file and the commit diff so that it can see the difference. And then it becomes extremely smart and understanding what you messed up. Well, I'm going to have to try Cloud Sonnet now. I've never tried it. I've been happily doing the other things. I don't know if happily is the right word, but I've been doing other things. And that's one that I've just happily ignored so far, but you've convinced me. I need to give this one a shot. Maybe a few shots. I can toot it a little bit, the horn of Claude, a little bit.

1:22:02So I saw somewhere, I think it was on YouTube or something like that, someone was saying it's really great for building documents. And so I needed to create an agreement. And I knew what I wanted to say, but the way Claude works is it will have a file created, and it's open on the side. let's say like you have your chat thread and then on the side the document is open and it would just update parts of the document and so from a user experience standpoint you see not a regeneration of the whole document that you now have to be like well what changed what did not change and you've got to scan it you waste your time but you literally see Claude pull the document out that you're iterating towards you see the little by little change in a clause or a heading or they're very specifics and as a user i'm so much more comfortable with the process because i'm like i'm i'm working it through and it's it's generating what i want to and it's not like generating this legalese kind of stuff but it's only updating the parts that i want like let's focus on this clause i want to cover this this this and this and here's what i want it to say and it goes and just updates that one part so from a user standpoint you see it removing the lines like pressing the cancel key because it uses the artifact as a tool yeah so i can remove edit change parts and also between three cloud 3.5 at 3 and 3.7 the generation output length was increased like 10 times so now it can can generate like one thousand line of code in one shot or even more, which is pretty impressive.

1:23:43That is impressive. It's a very great system. Also, when Dario Amodei started the company, they were also extremely focused on the ethical part, you know, the safety part, which is also a plus. Even if I believe that what they released a couple of days ago, CloudCode, which is a gen decoding, I believe this will start to To ignite, it's the right word. Ignite, yeah. A lot of firing or missing the hiring of programmers. Because it's like, it's not. Yeah, it's, it's, Cloud Code is the first thing I see, which is not conceived to empower the programmer that wants to do more, but to replace. Ah. Ah, so much for ethics.

1:24:41I'm just kidding. I understand it is inevitable that sooner or later it's going to happen, but what is my interest is that if we have to go for a world like that, that governments in all the world understand that they have to find ways for the people to pay their bills. And then it's okay if machines want to code. As soon as people are safe, I am okay with it. Well, on that demure note, anything that we haven't talked about that doesn't include the career extinction of us software developers. Anything else you want to mention, Salvatore, before we let you go? Just that I see that in some way, even if we say that our field in some way in these 15 years changed.

1:25:36However, imagine something as big as AI in the hands of other industries. For example, pharmaceutical industry or finance. It will never be like that, that we have most of these tools for free. We have front-end models under the open source. A lot of other models and all the information available. So I believe that still for IT, there is hope. because we are in the environment that created the most incredible revolution is creating after the industrial revolution. And still there is this idea of sharing, of making the tools accessible. Also, if you think at the price of OpenAI and Anthropic right now, $20 per month is very low.

1:26:33is very democratizing this tool. So in some way, I continue to be optimistic. I am more about the Hinton side, which I believe that in the short run, the problem that we will face with AI as a society, we will be able to overcome them. But I will focus on the existential threat because the agentic nature of these models, as they become stronger and stronger, Even if superintelligence can be achieved without consciousness, still an agent that breaks all the computers around. This is the worrying part for me, that we will survive social change, but we may not survive some extreme event due to AI.

1:27:24Well, we're excited that you're back at Redis. we're excited for the subtle changes the incremental changes that you're making to help redis be more useful to more people thank you once again for creating such an awesome system and for open sourcing it so that the world can take advantage and benefit i know i've benefited greatly from it in my career even though i only used it for a relatively short time i think the api and the way it works and the way you designed it has influenced my thinking as a programmer so uh you know you can't go back from that that's like fundamental change so i appreciate you for that reason thanks thank you salvatore thank you it was very great to talk with you and thanks for the hospitality oh yes glad to have you back yeah

1:28:16so will redis be open source once again salvatore is back so that means the chances of that being true just increased. I don't know about you, but this conversation, this episode kind of dated me a bit, you know, dated me and Jared a bit because, wow, it's been a while since we've had Salvatore on the show. 15 years of doing this podcast is a long time. And that means we've had the chance to track so much ups, downs, trends, fads, bubbles, et cetera. That's kind of cool. I don't know about you, but I'm kind of excited about the reddest to come, Salvatore being back, the opportunity to rethink the licensing to consider AGPL and what that might mean for the future of Redis.

1:29:01Only time will tell. But I do want to say that I'm very excited about what Augment Code is doing. Scott Dietzen, so cool. Talking to him, these conversations I've had with him behind the scenes have been so much fun and I'm so happy to share some of those with you via the ad spots we produce. And I do plan to get Scott on a full length show sometime soon. Also our friends over at Retool David Hsu and team Love Retool They're doing some cool stuff Just pay attention If you're not already using them to build out your internal tools What's stopping you? Number one, retool.com Check it out And to our friends over at Temporal Samar and team Scaling Snapchat was a fun conversation to have with Samar And just glad to share Those little snippets here In the ad spots of the show That's good stuff.

1:29:52Check them out, Temporal.io. Of course, the Beat Freak in residence, Breakmaster Cylinder, bringing the beats, always banging, always banging. We do have a bonus on this episode, so if you're not a Plus Plus subscriber, changelog.com slash Plus Plus. It's better. That never gets old. It is better, okay? It's better because you get closer to the metal, that cool changelog metal. number one number two you directly support us which is we just love that you know you've earned yourself a virtual hug if not a physical hug in IRL sometime soon and of course you also get a sticker pack in your inbox not your virtual inbox your literal mailbox we will ship you some stickers and you get bonus content like today that's four awesome great points that make it better so changelog.com slash plus plus 10 bucks a month 100 bucks a year and the joy of supporting us and this indie media company we're running and we thank you okay that's it the show's done it's over we'll see you on friday

1:31:24Thank you.

1:31:52so you recently wrote a post called we are destroying software which you know that but i'm telling you that as a setup uh which was almost a poetic piece of maybe a little bit of a rant with like 19 things that we're doing roughly i didn't count one by one but ways that we are destroying software, for instance, with complex build systems, with absurd chains of dependencies, with rewrites of things that already work. Curious, what were you doing the half hour before you decided to write that thing? What happened in your life? It's like, you know what? Because you probably thought this way for a while, but finally you're like, I'm writing it.

1:32:32What inspired this? In general, everybody I look, I see that the complexity is increasing without a proportional utility, usefulness of the systems. For example, one of the most important things that we have right now, which are LLMs, are like 3 ,000 lines of code for the inference. And even if you look at DeepSeek today, released all the training code of distributed training of mixed of experts models. And it's a simple code. So why the Facebook Android application has one million lines of code? They are rewriting it from Java to Kotlin and it's one million lines. And everything is like that.

1:33:23We have the same forms, the same buttons, the same likes of 10 years ago. However, if I check every tab, now recent Chromium tabs show you the memory usage. And the cloud tab, the chat, is 419 megabytes. Just a single tab. And everything is like that. And then also software developers. Plot plot! It's better!