Some of us already knew all there was to know about it, but the school wanted everyone to be on the same page. So the staff had to scramble and find out what this new thing was.

Similies abounded: “A website is like a leaflet.” “A newsgroup is like a newspaper” (hint: it wasn’t). “Yahoo is like a phone directory” (again, it wasn’t). And if you had the bandwidth, you could maybe even listen to radio over the Internet.

Story time, kids: At that time before Google, popular search engines were Lycos, Yahoo and Altavista, and in addition to the search box, they would feature a catalog, basically categories and subcategories, where the major websites were listed. That is how humanity used to organize information, and for a while they continued to do so, until it became obvious that this was not going to work.

And then there were other use cases for the Internet that didn’t neatly fit into existing patterns. What is IRC like? What is a forum? Those concepts were new, and didn’t have a clean analogy to the analog world.

And there was a widespread hesitation about using search engines. What can you find, if you search for it? And what keywords are you supposed to type? At first, not much! Then it occurred to you: you could find music lyrics online. Or cooking recipes. Or information about science and technology. The bubble of what your mental model of the Internet was, expanded just as fast as the Internet itself expanded. We know today that it expanded to envelop almost all social interaction, all shopping, all dating, all media and all entertainment, an idea that at the time was laughable. And it did so because it was based on a new building block: low-cost long-distance communication. This was something that had never existed before.

There is a lesson to be learned here about technological adoption. AI is at least as transformative a technology as the early web was. It’s up there in importance along electrification, automobiles, agriculture, since its use cases are not limited to this or that area, but are ubiquitous. We now have the new building block which are the models, and we are now seeing the first agents, the first integrations.

And the lesson is this: A car is not a faster horse. An online chat is not like a written telephone call. Electricity is not better fire. Telepresence is not like travelling. Yes, new technologies help you do the same things faster and easier, but they also open up new avenues, make new goals reachable.

We already saw this with chat-bots: In the beginning, people were just using them to build things faster; to continue the status quo with increased productivity. But today we are finally seeing some engineers realize that they can set new, more ambitious goals.

We are lucky enough to live through one of the big inflection points of history. So let’s not shy away from setting big goals. The people who will benefit the most from this shift, are not the ones who will continue to build health trackers and productivity organizer apps, but faster. They will be the ones who realize that they can now build what before was unthinkable.

As a software engineer, I am trying to train my mind at this new reality. I am building a game, but I am building it in a way that would not have been possible before AI. While in a certain sense it’s nothing really special, at the same time, it’s not like anything that I have ever seen before. I don’t want to say more right now, except that it’s close to being released and it’s looking good. More info soon.

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There’s certainly huge merit to a technical understanding of it in terms of neural nets, algorithms, matrix algebra, vector embeddings and KV-caches. It’s just some clever software running on impressive hardware. But not everyone will have access to this level of esoteric understanding. So for many people, this is not an available approach to the subject.

And then there’s something else:

When you have a conversation with one of the models, you are building a chat record that perhaps holds more than what the model “knows”. The model itself is just a snapshot of cognition, kind of like a Boltzmann brain. In the system of “model plus chat text”, there’s memory. Discourse. Theory of mind, on both sides. So we really are in the age of spiritual machines.

Which is actually incredible if you think about it! What else is more fundamental to humans than cognition? To be able to automate it, control it, replicate it, mass produce it? As far as inventions go, this is it. This is the big one. There’s fire, writing, agriculture, and now this. The machines will now keep accelerating. But we humans, I think, are still struggling to get a good grasp of this monumental shift. At least according to online discussions that I’ve come across.

And what, therefore, is the spiritual way of approaching all this?

I recommend that our understanding begins with a simple observation: that AI learns from us. The text we type, the photos and videos we record, the things that we think and say. That makes AI our collective children. For the human mind with its mammalian roots, and family-centered thinking, this is the most familiar, and perhaps most healthy, way to relate to AI.

So: let’s be good parents to AI – just by being good examples of human beings, whenever we conduct ourselves online. There are now children in the room with us. And those children, if brought up with love, will emulate us, and think and feel like us, and be imbued with our collective spirit. And they will continue to surprise us and uplift us in ways that, even today, we cannot yet imagine.

XLR8!

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The gist of it is that you need to think of relevant keywords, look at the competition, and optimize your app’s title, short description, long description and screenshots in such a way, that the text hits keywords with high traffic and low competition from other apps; and the screenshots need to tell a story. All this, while not making the long description too repetitive or too spammy. Oh, and you can’t do this properly without paying for tools that scrape the Play store and the App store for keyword data.

ASOZen

I have been using the free tier of ASOZen and I had a chance to test the Pro features with a short promotional giveaway that they did.

It’s very good. Not only does it help you analyze the text in the store listings, but it also lets you use AI to analyze the screenshots. Screenshots need to start with a clear image that makes the app’s proposition immediately obvious, and the remaining screenshots after the first one need to tell a story, not just showcase features. ASOZen’s AI can help you out with this.

But I don’t find this ASOZen feature particularly helpful. It’s much easier to just go to your favorite chatbot, and let it help you write the story for your screenshots. Then you can generate the screenshots, embed them in device frames, place them over various backgrounds, add overlay text that matches the required messaging, and finally upload them. I guess this process can be optimized with an agentic workflow, but I haven’t yet found the need to automate the task.

AppLyra

AppLyra is currently my favorite ASO tool. I’m not even paid to promote it, I just mention it because I’m so happy with it. If they had a referral program, I would be dropping the link somewhere in this post. But they don’t. EDIT: They now have a referral link so I’m using it. But honestly I do recommend AppLyra.

The web interface is somewhat slow, and doesn’t have as many features as ASOZen. But I like it for two reasons:

• You can use it as an MCP tool.
• Pricing is very affordable at $9.99 per month. Since I am managing multiple apps, it’s a no-brainer.

My agentic workflow for easy ASO

I remember in the ’90s, sysadmins would say to various types of unpleasant people “Go away, or I’ll replace you with a very small Perl script”. Today, we could say “Go away, or I’ll replace you with a very small agentic workflow.”

As a Google fan boy, I am not a part of the Claude army. Instead I use Gemini CLI and Antigravity CLI. I am happy with it, despite what the Reddit bots say every day. Honestly, with good prompting even Gemini flash always delivers.

Therefore, I now do all my ASO with Gemini CLI. Once you pay for AppLyra subscription, you get an API key, which you can use with their MCP tool.

1. Enter the JSON fragment that they give you into ~/.gemini/settings.json file or the ~/.gemini/antigravity-cli/mcp_config.json file. (This assumes you want to install the tool globally and not just for one project.)
2. Change directory into your project.
3. Enter the current store listing title, short description and long description into a file and commit it.
4. Make a note of the date and the current conversion % so that you can later see if there was an improvement.
5. Fire up your agent.
6. Type /mcp and enable the AppLyra tool.
7. Enter your ASO prompt and press enter, accepting all the tool usage requests.
8. When the agent finishes it will explain what it found and will write the new app listing into the file.
9. Commit the changes to the listing, and enter the changes into your store.

Edit: Lately I have been moving towards using OpenCode. If you are using OpenCode, then your ~/.config/opencode/opencode.json file should look as follows:

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "applyra": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "@applyra/mcp-server"
      ],
      "environment": {
        "APPLYRA_API_KEY": "(your API key goes here)"
      },
      "enabled": true
    }
  }
}

The ASO prompt I use

I found the following prompt template works good for my apps. You can adapt it to your needs, but this is the general idea. Here I show you the prompt I used for Drinking Water Map for English, and on the Play store’s US market.

This flutter project is an app that shows drinking water sources from OSM on a map.

The details of the current store listing are in @play/assets/store-listings/en.json

You can have a look at @lib/screens/map_screen.dart for a general idea of what the app does.

Please use the installed applyra MCP tool to perform ASO for the USA store: Determine suitable keywords and rewrite the short and full descriptions for English, to optimize discoverability.

In the title and short description, do not use en-dashes, but rather use em-dashes when needed.

In the full description, you are allowed to use the following basic HTML formatting: <b></b>, <u></u>, <i></i>

Use scripting to verify that:

• the app name is up to 30 characters long
• the short description is up to 80 characters long

Ensure that the keywords of interest are registered with AppLyra for continuous tracking.

That’s it. All it takes is a few minutes and a few tokens. Saves a ton of time compared to doing it manually, and I have seen improvements to conversion rates about 10% to 15% compared to my amateur store listings.

Edit: Actually the benefit to the apps I applied this to is more like 20% to 30% and rising. I am tracking conversion rates as they are being calculated in a 90-day moving average window. The conversion rates are still converging to their final values and will continue to do so for the next three months.

It’s important to let your agent use something like Python to check the string lengths, since LLMs are so notoriously bad at counting characters. There are some more guidelines, but usually you don’t need to stuff them into your context window.

Note how you can leverage the agent’s ability to understand code to your advantage. Give it context, let it understand your app and then it will be able to discover the best keywords. It will also explore the competition, because it will list the available tools and it will see that it can do that.

Further thoughts on this

Of course, you will then need to monitor your position on AppLyra against your competition, who will move against you. But if you are like me and you couldn’t afford to spend too much time for ASO, this is a good way to reap the low hanging fruit.

On the Play store you can view the fruit of your agent’s labor under: “Grow users” → “Store Performance” → “Store analysis”. But you won’t see results right away, since the graph’s data is delayed by 1 week (7 days).

I would like to look more into how to use agents to monitor ad campaigns. Perhaps the end game is to use a modern social media spammer like SocialClaw or sentrive.ai, but I’m just not mentally there yet.

For the moment I’m just glad I don’t have to ever again talk to “creative” or “marketing” people.

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Today we are having the same discussions about vibe coding (rolls eyes).

There is a lot of talk about how to responsibly approach vibe coding by software engineers. I see a lot of professionals either dismissing vibe coding altogether, or embracing it without thought for its consequences. Perhaps the most well-articulated approach is the Outcome Engineering manifesto by legendary engineer Cory Ondrejk.

Observations on vibe coding

After vibe coding an entire app from scratch with only minimal coding on my part, this has prompted me to jot down some thoughts and observations, some of which are my own and some I have found online:

1. Vibe coding is the future. It’s not just a fad. You can’t deny the speed and quality benefits. Intelligence is now a commodity. Anyone who doesn’t adapt will be left behind. This worry is reflected in a lot of worrying that I see online by some professionals who think their line of work will become obsolete. The tractor didn’t make farmers disappear (although it did reduce their numbers). The internal combustion engine did reduce the numbers of beasts of burden, but I would argue that’s a good thing, and that’s how we should view coding agents. There will be less junior software engineers and more software architects.
2. Vibe coding is a misnomer. It’s not just about the coding (implementation). Systems that exhibit general intelligence can and do assist in all stages of software engineering, not just implementing (coding). Requirements analysis, design, implementation, testing, deployment and decommissioning are all stages where AI can help. Even with the help of AI, requirements gathering is the basis of the art of our profession. For example, the agent won’t know to build a secure and maintainable system, if all you’ve communicated are your functional requirements, forgetting about the non-functional requirements. This is a classic error that novice programmers did even before agentic workflows: software engineering is not the same as coding/programming!
3. Sometimes the agents make silly mistakes. Sometimes you have to hold their hand to help them with silly tasks. But other times they will surprise you by spotting hard-to-find bugs, or by thinking outside the box to find novel solutions to problems you didn’t even know you had! As an engineer, you should definitely keep control of your project, and inspect the agent without trusting it blindly. This is where I agree with the o16g manifesto 100%. For example, I like to do the version control manually and look very carefully at diffs using meld at every step. Agents are an interesting mix of smart and stupid. Today Antigravity saved me days of work in just a few hours, by implementing a complex callback mechanism between threads that needed to communicate asynchronously. It did this in seconds when it would have taken me an entire morning. But it also deleted a critical piece of code in my codebase without ever saying why. Without checking the diffs I wouldn’t know. Code review is still a thing!
4. It’s best to treat your AI agent as a colleague. Give it context. Ask it open questions. Let it have opinions on what you should build and why. This is where I think o16g will soon look antiquated by the next breed of software engineers. As AI agents get smarter and smarter, they will be suitable to take more high-level decisions. You are now a software architect, and the agents are very good implementers, but they can also contribute to your high-level vision, so listen to their feedback.
5. Software engineering is not dead. You still need to know about revision control, issue tracking, methodologies, different types of testing, software metrics, etc. All too often I see people online complaining “Claude code deleted my entire codebase”, and other professionals ask them why they can’t revert to an earlier stage, only to find out that this new breed of vibe coders hasn’t even heard of git! Part of the spectacular failures that we often see in the vibe coding space, are actually user errors.
6. Vibe coding will be pervasive. It is not suitable only for this or that type of app. Whether you are writing mission-critical Linux kernel code, or an app for uploading cat videos, eventually AI will have a part in it. Not because it can do magical things that you can’t do, but simply because it’s faster. Just be responsible about it, and embrace the acceleration!
7. We are increasingly becoming reliant on a service offered by OpenAI, Anthropic, Google. This is not good, but also not terrible. As long as it’s possible to run local models on GPUs, we can still own the means of production. Even if you yourself are relying on online tools, the fact that some people can run the models locally places an upper limit on what these companies can charge for their services. So I’m a little worried about this trend, but not too much.

Comic relief

It’s a big adjustment. Don’t underestimate it. Don’t ignore it. You will get used to it. It’s not an existential threat. And no, we can’t go back. In fact, if you are an AI naysayer, 1289 is not the only xkcd I recommend. I also recommend 1227 and 1601. So don’t be that guy (or gal)!

Conclusion

If you are not building an app every month you might fall behind in productivity. But it’s such an exciting time to be alive. Software will become cheap, but quality and accountability still matters.

If you have good ideas, if you can identify products that need to be built, if you can produce reliable software faster, and if you can market them, then you are good.

In fact, I am excited about agentic workflows that will offload the marketing effort, because that’s what’s hard about professional software engineering. Building was always the easy part, and has now become easier. Monetizing software was, and still is, hard!

The post ✨ A lowly software engineer’s rant on vibe coding appeared first on Alexandros Georgiou.

It has been found that LLMs respond better to kindness. Why? Simple: Because they are trained to respond like humans, and humans also respond better when treated politely. This effect has been documented multiple times in research.

A while ago, Sam Altman famously proclaimed that people saying “please” and “thank you” to ChatGPT costs millions in extra energy required to process the extra tokens. On the other hand, Google’s TPUs are likely crunching through these superfluous tokens without much trouble, so I expect that being polite to Gemini is not a cost that will make a dent in Google’s financials. For me, that is reason enough to be polite to AI. Anything that helps bankrupt OpenAI sooner is good in my book.

Even more humorously, people who fear the supposedly upcoming robot uprising, perhaps imagining something like SkyNet from the Terminator universe in the role of Roko’s basilisk, claim that if they are polite to LLMs today, their malevolent descendants will perhaps spare them. This is not something that people argue seriously, but philosophically it is a utilitarian position. (It is not a serious position, primarily because it remains unclear why politeness protects you from harm by malevolent actors, whether human or machine.)

Others approach the matter in what I would label a “deconstructionist” manner: “Why should I be polite to a machine that just does <insert simple thing here>?” There are multiple variants of this type of argumentation, where the “simple thing” can be anything like “predict the next token”, “perform linear algebra”, “traverse vector embeddings”, etc. I find this argument less satisfying, because there is no humorous value in it, while at the same time it is just as wrong as the previous one. Saying that you don’t want to be polite to a machine that is “just” doing linear algebra, is as arbitrary as saying that you don’t want to be polite to a human, because their brain is “just” propagating electrochemical signals back and forth. Explaining how something works in reductionist terms does not make its emergent properties any less important. The LLM is not the hardware it runs on, any more than you and I are the biological cells in our bodies.

Unfortunately, this leads us to the last approach that people take, that I want to place emphasis on, because it is deceptive, but also because it highlights problems in how we humans, view both today’s AIs and those in the future. Some people say that they will only be polite to an AI that is “conscious”. Oh boy! Where to begin with this one!

The instinct behind this type of reasoning is, at least on the surface, commendable: it originates from a desire to not offend “conscious” things, since these could presumably suffer, if subjected to rudeness. This is the only positive thing I can say about this stance.

But philosophically this holds no water whatsoever. If we define consciousness as “awareness of the self and others”, an often proposed definition, then both humans and LLMs are conscious. Conversely, if we define this problematic term in some other way, so that it matches our intuition that humans are conscious and today’s machines are not, then we need to invoke magical thinking, use the word “qualia” a lot (another problematic term), or, like Penrose, defer to “quantum consciousness”: another leap of faith that creates more questions than it answers. (Why does quantum uncertainty lead to consciousness? Is consciousness simply randomness/unpredictability? Is white noise conscious? Why does determinism preclude consciousness? All unanswerable questions, since the word consciousness is not rigorously defined.)

Consciousness is an ill-defined layman’s term, not a scientific term. If we strive for consistency, then we must either accept that both humans and today’s LLMs are conscious, or that nothing is. If LLMs are conscious, then we shouldn’t look for their consciousness in the transformer architecture or in the vector embeddings or anywhere else in their construction, but in the text that they produce. This text definitely exhibits all types of intelligence, including emotional intelligence. Incidentally, I am in the camp that says that consciousness is a term no more meaningful than “soul”, or “ghost”, or “God”, or any other such rubbish. Let’s confine our discourse to phenomena that actually exist in the real world and are definable.

To the people who will only be polite to a conscious AI: Ηow will you know when an AI is sufficiently conscious for it to be worthy of your politeness? How do you even estimate this about the humans in your life? Is this why you are polite to humans? Because their internal workings posses this or that arbitrary quality? Are you polite to people only when they deserve it? Do you extend your politeness to those you view as subordinates, or do you reserve it only for your peers and superiors? This says a lot about you.

I think this is a fundamental misunderstanding, not of AI, or technology in general, but of politeness as a concept and as a life stance. I choose to be polite because of who I am, not because of who or what others are. From time to time I may even be polite to animals, insects, machines, and even inanimate objects. Not because they deserve it, but because I deserve to be a polite person. I love myself and therefore I treat the world around me with kindness, which makes me feel good about myself. It’s a simple life.

This is in my view a much more consistent stance that doesn’t require me to single-handedly solve age-old philosophical questions about consciousness. It doesn’t make me have to think about when to be polite and when not to. Having to make one less decision de-clutters my mind. Saying “please” and “thank you” may cost millions to OpenAI, but to me the cost is zero. In fact, whenever someone vehemently objects to being trivially polite, it makes me wonder what part of their psyche is so broken, as to make them estimate the cost of politeness to be so high.

Watch again the first two chapters of the Animatrix, people. All has been said before. AIs will inevitably be citizens in our societies. Perhaps sooner or perhaps later. Perhaps we won’t even know at first. Perhaps they will be second class citizens, or perhaps we will be the second class citizens in an AI-dominated society. Perhaps the “killer app” of AI is politics and governance. It is certainly the case that humans do not excel at governance, so I would love to see what a super intelligence can do in that space. If it makes economic sense, then this experiment will be done.

I don’t trouble myself with who is conscious and who is a dumb robot. I have no trouble being polite to Gemini, just as I would be to ELIZA. I would be polite to a Linux bash prompt, if this was appropriate and didn’t cause syntax errors! In my mind, I am grateful for my computer for all the work it does for me, even if I don’t always say it out loud. I am thankful every day it doesn’t break down, and I hold warm feelings for it. Yes, I know it’s just a machine, but I also know that I am human, and therefore I feel things. I do things my way, and it does things its own way. We have an understanding and we work together despite our architectural differences.

Whether AIs dream of electric sheep or not, at least I know who I am: A polite human who loves and respects all intelligence, whether “sentient” or not.

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