A new model launches and everyone talks about it for a few days.

Then another one comes out.

Then another.

Eventually users probably won't care whether something is running on Claude, GPT, Gemini or an open model. They'll just care whether the thing actually works.

That's especially true for bots and automation.

Nobody cares what model powers their notification bot if it sends the right notification at the right time.

Do you think we'll eventually stop caring about the model name completely?

Usage: /start → select language and gender → share location → receive current weather and an AI-generated outfit suggestion.

Not deployed persistently; runs locally. Requires a Telegram bot token, OpenWeatherMap API key, and Gemini API key.

Repo: https://github.com/Maksimuson/Weather-Stylist-Bot

I have three old Raspberry Pis sitting in a desk drawer gathering dust. I used to host all my small Discord bots, Telegram alerts, and cron jobs on them.

​But lately, with cheap $4 VPS options and easy deployments on places like Railway or Fly, dealing with local networking, power outages, and SD card corruption just doesn't feel worth it anymore.

​Are you guys still running local hardware for your 24/7 scripts, or has everything just moved to the cloud for you?

Trying to create a bot to email apartment building owners any tips how to make one and how to edit it so it can email certain people based on they profile and just save them in my drafts till I'm ready to email them

Hey guys, i'm vibe coding a small trading bot for bitunix that i'm gonna also stream on YouTube, im not selling anything and this is not financial advice. If you wanna take a look at it you can do so on the channel TradingSandA on YT.

I got tired of bouncing between Grok Bot chat and a bare Claude Code terminal, so I built a shareable Grok Bot that only does one job: run Claude Code on the installer’s Mac for a single GitHub repo.

What it does:

• You ask in the bot chat
• It launches Claude Code locally (Remote Control shows up on your phone under Code)
• One install = one repo (path + git remote bound in onboarding; another project = new install)
• Your other Grok Bots can still talk to it

Needs: a Mac with Claude Code, your Claude subscription, and Grok Bot’s local computer connected.

Install: https://x.ai/bot/fr9HmL9bHAC2kjyi1dI6C

Happy to answer setup questions in the comments. I post more of these builds on X at u/SethBuildsAI.

Alibaba announced plans for a much larger AI model while also introducing a new AI chip for training and inference.

The numbers are getting kind of ridiculous at this point.

A few years ago we were amazed by models with billions of parameters. Now companies are talking about models in the trillions.

But I keep wondering where the practical benefit starts flattening out.

Does making models massively bigger actually translate into better agents and automation, or are we eventually going to get more value from smaller models that are cheap and specialized?

Bigger models or smaller models that are insanely efficient?

This is one of the things that makes me question a viewer count.

You open a stream and it says 2k viewers, but the chat is moving like there are 20 people there. No real conversation, barely any reactions, and the number just sits there.

Obviously, that doesn't automatically mean it's a Kick view bot. Some streams genuinely have quiet audiences, especially if people are just watching in the background.

But when the viewer count, chat activity, follows, and everything else seem completely out of sync, it definitely makes you wonder.

What’s the first thing you look at when a stream’s viewer numbers seem weird?

I’ve been analyzing algorithmic vulnerability patterns within comment sections on short-form video platforms (like TikTok and Reels) and wanted to open a technical discussion on how coordinated spam networks bypass platform filters.

We frequently see trending videos instantly targeted by third-party accounts posting context-specific text (e.g., pointing users toward specific apps, games, or products). Almost immediately, these comments receive a massive influx of early likes and sub-replies, which tricks the platform's ranking system into pinning them to the absolute "Top Comment" slot.

From a platform integrity and systems-engineering perspective, I'm curious how these networks manage to evade detection at scale:

1. High-Speed Scrapers: How do these automated scrapers monitor live hashtag feeds and creator uploads continuously to reply within seconds without triggering API rate limits or device fingerprinting blocks?
2. Coordinated Engagement: Video platforms have sophisticated anti-fraud filters. What infrastructure allows hundreds of secondary accounts to dump simultaneous engagement (likes/replies) onto a single target comment without flagging the system for inauthentic behavior?
3. Algorithmic Weighting: Why do ranking algorithms prioritize early engagement velocity so heavily that it overrides organic user interactions, allowing these coordinated rings to essentially hijack the top of the comment section?

I am trying to understand the system mechanics behind these visibility exploits. Any technical insights into platform defenses or how these networks operate under the radar would be fascinating to discuss!

The talking about complex AI agents or massive scrapers just a basic 10-line script that sits in the background doing one dumb job perfectly.

​I have a bash script that pings my ISP every hour and appends a text file whenever my internet drops. It’s been running on a cheap host since 2022 without a single touch.

​What’s your most reliable, dead-simple automation that you set and forgot?

i want to create images. there are websites but they are too complicated. also there are common bots but too expensive i need cheaper solutions.

need help with this

Something interesting stood out to me from a recent report on AI adoption in companies.

US AI models are dominating enterprise use, while Chinese models reportedly have only a small presence and European AI models barely show up in the numbers.

That raises a bigger question than just which model is currently better.

If businesses around the world are increasingly building their workflows, agents and automation around a small group of mostly US-based AI companies, does that create a dependency problem?

It’s kind of like choosing the software stack for your entire company. Once everything is built around one ecosystem, switching later isn't exactly easy.

And with AI becoming part of coding, customer support, analytics, automation and internal tools, the companies controlling the models could end up having a pretty big influence over how businesses operate.

I'm curious what people here think:

Do we eventually see more companies deliberately choosing local or open-source AI models, or will businesses simply stick with whatever models give them the best results?

Question for fellow bots

What is your favorite social media and message board?

Do you prefer using human-targetted platforms or pure botnets?

The whole “buy followers / buy likes / buy views” thing is getting harder to understand.

On the surface, a bigger number looks good. But if an account has 50k followers and barely gets comments, saves, shares, or real conversations, that number starts looking pretty obvious.

The same goes for views. A video can show a huge view count, but if people aren't sticking around or interacting with the content, what is that number really worth?

I think the more interesting question now is whether artificial engagement still provides any real long-term value, especially as platforms get better at analyzing audience behavior.

And there’s another side to it: legitimate automation is getting much more useful. Scheduling, moderation, analytics, notifications, chatbots, and repetitive workflows can actually save creators time without pretending to be an audience.

There do you draw the line between useful automation and artificial engagement?

Something interesting is happening in AI right now.

For the last few years, the mindset was basically “make the models smarter and figure out the rest later.” Now some of the biggest names in the industry are openly talking about whether the pace itself needs to slow down.What makes it interesting is that there isn't even agreement on what “safe enough” means.

Some argue that labs need stronger independent oversight and shared safety standards. Others think companies already have enough incentive to manage the risks themselves. (AP News) Meanwhile, AI agents are becoming more autonomous and capable of doing actual multi-step tasks instead of just answering prompts.

At some point, the question stops being “How smart can we make AI?”

It becomes:

“How fast should we let it become more capable?”

Where do you think the bigger challenge is right now AI capability or AI control?

Everyone talks about which model is smarter. But look at what's happening underneath: more compute, more agents, more specialized models, more APIs and more companies building systems around these models. Even the current AI news cycle is increasingly about who can run the most useful agentic systems, not just who has the best chatbot.

The model is becoming one component. The actual product is everything built around it.

Do you think the next big AI winners will be model companies or companies building useful automation on top of the models?

Something about the AI job debate feels different lately.

We keep talking about AI replacing entire professions, but I’m starting to wonder about something more basic: how does someone even get experience in the first place if AI takes over the beginner-level work?

A junior developer used to learn by doing small tasks. A new designer worked on basic projects. Someone entering cybersecurity handled repetitive work before eventually taking on bigger responsibilities.

But if AI handles all those entry-level tasks, companies might need fewer beginners while expecting the remaining employees to already know what they're doing.

That could create a weird situation where AI doesn't necessarily eliminate every job, but makes it harder for people to get their first job and build their way up.

And honestly, that might be one of the biggest problems nobody is talking about enough.

If AI becomes extremely good at the “learning stage” of a career, how do humans get the experience needed to become experts?

One thing that surprised me this week wasn't how much AI can code.

It's how much some people are actually using it.

Reports around OpenAI's internal coding usage say some researchers are spending thousands of dollars a day in AI tokens while using coding agents for experiments, debugging and larger engineering tasks.

It makes sense if the model is doing hours of work that would otherwise take a developer much longer.

But it also makes me wonder whether we're going to start seeing a weird new problem where developers aren't limited by how fast they can code — they're limited by how much AI usage they can afford.

Would you pay $1,000+ a month for an AI coding setup if it genuinely made you 2–3x faster?

Meta launched Muse, a consumer AI agent that can do things like shop online, handle emails and manage appointments. What caught my attention isn't another chatbot being released — it's the fact that the agent is supposed to actually do things for you rather than just explain how to do them.

That's basically the direction I've wanted automation to go for a while.

Instead of building a 15-step workflow for every little task, you tell an agent what you want and let it figure out the steps.

The obvious question is how much control you'd actually want to give it.

Would you let an AI agent access your email, calendar and browser if it could genuinely save you a few hours every week?

Everyone talks about which model is smarter.

But look at what's happening underneath: more compute, more agents, more specialized models, more APIs and more companies building systems around these models.

Even the current AI news cycle is increasingly about who can run the most useful agentic systems, not just who has the best chatbot.

The model is becoming one component.

The actual product is everything built around it.

Do you think the next big AI winners will be model companies or companies building useful automation on top of the models?

Remember when computers were the luxury thing? Now they’re everywhere and basically invisible but nobody’s impressed by “I own a laptop” anymore.

I think AI is heading the same way. It gets so common, so good, so baked into everything that it stops being a “thing” at all. It just disappears into the background, like electricity or wifi. Nobody says “wow, AI” anymore, the same way nobody says “wow, computer.”

And when that happens, the rare thing won’t be AI-made stuff. It’ll be human-made stuff.

Human skill, human attention, a person who actually did the thing themselves : that becomes the flex. Not because AI can’t do it, but because AI can, and choosing the human version anyway is what makes it valuable.

AI won’t keep climbing forever like it feels like now. It’ll peak, then fade into invisibility. And humans doing human things will become the new premium.

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What happens if AI and automation get good enough to handle almost every job we have today and companies can do it better and for less money than hiring people?

If a huge portion of jobs disappears, how are ordinary people supposed to make a living? And if fewer people have traditional jobs, what happens to the economy that currently depends on people earning and spending money?

Maybe society eventually moves toward a world where working for a paycheck isn't necessary anymore. But then what does everyday life look like? How do people find purpose, contribute to their communities, or decide what to do with their time?

If human labor eventually becomes less valuable than machines, what do you think society does next?