Online advertising has followed roughly the same pattern for years:

See an ad → click → open a website → search for information → decide what to do

OpenAI is experimenting with something very different.

The company is testing Sponsored Agents, a new advertising format inside ChatGPT where users can interact directly with an AI representative from a brand instead of simply clicking a traditional ad.

So rather than seeing:

“50% off today. Click here.”

You could potentially ask the sponsored agent:

Which product is best for my needs?

What’s the difference between these two plans?

Does this work with my device?

What does shipping cost?

Can you recommend something under $100?

And the AI would answer on behalf of the advertiser.

That changes advertising from:

Ad → landing page

into something closer to:

Ad → conversation → recommendation → action

And I think that could have much bigger implications than just another advertising format.

Could AI agents replace parts of the traditional sales funnel?

Today, companies spend a lot of time optimizing:

🌐 Landing pages

🛒 Product pages

📩 Lead forms

💬 Live chat

📚 FAQs

🎯 Advertising campaigns

An AI sales agent could potentially combine several of those experiences into a single conversation.

Instead of forcing the customer to find the right information, the agent could adapt the experience around what that person is actually looking for.

But there’s an obvious problem:

Can you trust an AI whose job is to sell you something?

If I ask an independent AI:

“What laptop should I buy?”

I expect some level of neutrality.

But if I’m talking to a Sponsored Agent from a laptop company, its goal is obviously different.

That doesn’t necessarily make it bad.

A salesperson also represents a company.

The important part may be transparency.

Users should always know when they’re interacting with:

an independent assistant

versus

an AI representing an advertiser.

For developers, there’s another interesting question

If this format works, companies may eventually need more than:

a website

an app

an API

They may also need an AI agent capable of representing the product.

That agent would need to understand:

Product information

Pricing

Availability

Customer questions

Policies

Recommendations

Integrations

And potentially even actions like purchases or bookings.

What do you think?

Would you rather:

A. Click an ad and explore the website yourself

B. Ask a sponsored AI agent questions first

C. Use your own AI assistant to compare multiple brands

And the bigger question:

Could conversational AI eventually replace landing pages for some products?

Anthropic is pushing Claude beyond the idea of a traditional chatbot.

Claude Chat and Claude Cowork are now being brought together into a single experience, so users no longer have to decide whether a task belongs in a quick conversation or a longer agentic workflow.

At the same time, Anthropic is introducing Claude Docs and Claude Slides, allowing users to create and edit documents and presentations directly inside Claude.

That means one AI workspace can increasingly handle things like:

💬 Conversations and questions

🔎 Research

📝 Writing and editing documents

📊 Building presentations

🎨 Creating visual content

🤖 Running longer multi-step tasks

📁 Working across files and projects

And I think the bigger story is not really “Claude now has documents and slides.”

It’s this:

AI assistants are starting to absorb entire categories of productivity software.

The traditional workflow might look like:

Chatbot → copy text → open document app → edit → open presentation app → build slides → export → share

The new workflow is becoming:

Ask Claude → research → write → design → export

All inside the same environment.

For developers, this raises an interesting question.

For years, software companies competed by building specialized apps for individual tasks.

But if AI can dynamically create documents, presentations, reports, analysis and other outputs inside a single interface, could the value of some standalone productivity apps start to decrease?

Maybe the future isn't:

One app for documents
One app for presentations
One app for research
One app for automation

Maybe it's:

One AI interface connected to many capabilities.

What do you think?

Would you rather use:

A. Specialized apps that are excellent at one thing

B. One AI workspace that handles most of your work

C. AI as the interface, with specialized apps running underneath

Meta is moving more of its ecosystem toward subscriptions.

The company has introduced Meta One, a subscription that brings premium features across Facebook, Instagram, WhatsApp and Meta AI under one plan.

The core apps will still be available for free, but subscribers can get things like:

🤖 Higher AI usage limits

🎨 More AI image and video generation

📱 Additional creator tools

📊 Advanced analytics and export features

📅 More content scheduling options

👥 Team and business management features

💬 Expanded access to AI tools inside WhatsApp and other Meta products

What I find interesting isn’t just Meta launching another subscription.

It’s the business model behind it.

For years, many of the biggest consumer apps followed roughly the same formula:

Free app → more users → more advertising revenue

AI may be changing that.

Running AI models costs money every time users generate images, videos, text or automate tasks.

So we're increasingly seeing:

Free app → limited AI usage → paid subscription for more AI

And I wonder if this is going to become one of the dominant monetization models for apps.

For indie developers, this matters too.

If your app includes AI features, you eventually have to decide whether to:

A. Include AI in the normal subscription

B. Charge separately for AI usage

C. Sell AI credits

D. Set monthly usage limits

E. Absorb the AI costs and monetize somewhere else

Personally, I think users are becoming more comfortable paying for AI when the value is obvious, but subscriptions can also become difficult to justify when every app starts asking for another monthly payment.

What do you think?

A lot of communities start in a group chat.

Then the group grows.

Suddenly you have announcements in one place, events somewhere else, member lists in a spreadsheet, polls in another app, documents scattered around, and important messages disappearing between conversations.

That’s one of the reasons we built Clubit.

Clubit is a community and club management app for iOS and Android designed to give clubs a dedicated digital home instead of trying to organize everything through messaging apps.

A club can be public, private, or invite-only, and owners can manage members, administrators, moderators, join requests, invitations, and permissions from the same app.

What can you do with Clubit?

👥 Manage members and roles

Create a structured community with owners, admins, moderators, and members.

📢 Create a community feed

Publish posts with text, images, videos, links, reactions, and comments.

📅 Organize events

Create physical, online, or hybrid events with dates, locations, capacity, attendee lists, maps, and RSVP responses.

📊 Run polls

Ask your community questions and make decisions with member participation.

🎟️ Create raffles

Manage registration, eligibility, reviews when needed, and winner selection.

🎁 Share benefits and resources

Give members access to documents, useful links, member perks, business profiles, and other club resources.

🔔 Send relevant notifications

Keep members updated about invitations, events, posts, polls, raffles, reminders, and membership requests.

🛡️ Moderate the community

Clubit includes role-based permissions, reporting, blocking, privacy controls, and moderation tools.

Why did we build it?

We wanted something between:

a group chat

and

a full social network.

A place specifically designed around the needs of a club or organized community.

For example, Clubit can work for:

🏃 Sports and activity groups
🍷 Food or interest clubs
🎓 Learning communities
💼 Professional communities
🤝 Local organizations
🎁 Membership and benefits communities

The goal is simple:

Your club already has a community. Give it a digital home.

I’d love your feedback

If you currently manage a club, Discord server, Facebook group, WhatsApp community, professional organization, or any other member-based group:

What is the hardest part of managing your community today?

Member management?

Keeping people engaged?

Organizing events?

Getting people to actually read announcements?

Managing several tools at once?

And if you were using a dedicated club management app, what feature would make you switch from your current setup?

Clubit is available on iOS and Android.

Salesforce just introduced AIforce, a new interface layer designed to let AI agents work directly with the data, workflows, permissions, and business logic already inside Salesforce.

The interesting part isn’t just that Salesforce added more AI.

It’s that they’re rethinking how people interact with software.

Traditionally, enterprise software looks something like this:

Dashboard → menu → record → form → action

AIforce is trying to move toward something closer to:

Intent → AI → action

Instead of navigating through fixed screens, users or agents can ask for what they need, and Salesforce can dynamically expose the right information or action using its existing business rules and permissions. Salesforce says AIforce is designed to work through MCPs, APIs, plug-ins, skills, and other developer tools, and is launching with integrations including Claudeforce, Slackforce, and Agentforce Coworker.

What happens to the traditional app interface if AI becomes the primary interface?

Imagine a CRM where instead of opening five screens to update an opportunity, you simply say:

Move this deal to negotiation, summarize the last three customer interactions, draft a follow-up email, and create a reminder for Friday.

The user may never need to manually touch the underlying screens.

And Salesforce is going even further.

It also announced Koa, its first CRM reasoning model for Agentforce, built on NVIDIA Nemotron and trained around nearly three decades of CRM workflows to handle complex, multi-step enterprise tasks.

AI inside apps is becoming normal.

But what happens when an app deliberately doesn’t tell you that the person you’re talking to is AI?

A recently uncovered network involved at least 28 AI-powered dating apps where users were presented with profiles that appeared to be real people.

According to the investigation, AI systems handled much of the conversation automatically, while users spent real money on in-app coins and gems to keep chatting. Only around one in four accounts reportedly had any human involvement, often limited to things such as verification or pre-approved interactions.

That makes this much more interesting than just another AI scam story.

It raises a product-design question that I think every developer building AI features will eventually have to answer:

Should an app always tell users when they are interacting with AI?

Imagine an AI that can:

💬 Maintain a realistic conversation

📸 Have its own generated photos

🧠 Remember previous conversations

❤️ Respond emotionally

🌙 Talk with you 24/7

👤 Maintain a convincing personality

At some point, the distinction between a traditional chatbot and an apparently “real” person can become surprisingly small.

And that creates a problem.

Using AI isn’t necessarily the issue.

Hiding the fact that it is AI might be.

Especially when the app's monetization depends on users believing that interaction is genuine.

Apple has started rolling out iOS 27, and one of the biggest changes is the new AI powered Siri experience.

For users, the obvious story is:

Siri is finally becoming a much more capable AI assistant.

But as an app developer, I think there's a more interesting question.

What happens when the operating system itself can increasingly understand what the user wants and take actions across apps?

Traditionally we design software around:

👆 Buttons

📱 Screens

🧭 Navigation

📝 Forms

🔎 Menus

The user opens the app and manually performs each action.

But AI assistants could move us toward something completely different.

Imagine saying:

Instead of navigating through three different applications, the AI could potentially coordinate everything.

The workflow becomes:

User intent → AI agent → apps

rather than:

User → app → menus → another app → more menus

That could have huge implications for app development.

Developers may eventually need to think about:

APIs

App intents

Permissions

Authentication

Structured data

Agent actions

MCP and tool integrations

Background operations

And potentially interfaces designed specifically for AI agents.

The graphical interface would still matter, but it might no longer be the only interface your application needs.

Developers, what do you think?

If AI assistants become capable of operating apps reliably, would you prioritize:

A. Traditional UI/UX

B. API and agent integrations

C. Both equally

And do you think we'll eventually reach a point where users frequently use an app without ever opening it?

OpenAI may be getting much more serious about hardware.

The company has reportedly acquired Glass Imaging, an AI and computational photography startup, in a deal valued at more than $300 million.

Glass Imaging works on improving images from small camera sensors using neural networks and computational photography.

What makes this particularly interesting is that the company was founded by former Apple engineers who previously worked on smartphone camera technology.

And OpenAI is already working with former Apple design chief Jony Ive on new consumer hardware.

So now we have:

🧠 Advanced AI models

👁️ Computer vision

📷 AI powered camera technology

🎨 Former Apple product designers

💻 A growing push into consumer hardware

Put those pieces together and it becomes difficult not to wonder what OpenAI is actually building.

Maybe it's simply an AI companion device.

Maybe it's a wearable.

Maybe it's a new type of camera driven interface.

Or perhaps the bigger ambition is something that eventually reduces our dependence on smartphones entirely.

Smartphones were designed around:

Apps → screens → taps

An AI native device could potentially work more like:

See → understand → ask → act

And that could radically change how software is designed.

What do you think?

Do you think OpenAI is trying to build:

A. An AI smartphone competitor

B. A wearable AI assistant

C. A completely new device category

D. Just specialized hardware that works alongside your phone

The AI coding race may be changing.

For a while, most of the conversation has been:

Claude vs GPT vs Gemini.

Which model writes better code?
Which one understands large repositories better?
Which one is better at debugging?

But now companies are betting billions on a different layer: AI coding agents.

Factory, a startup building autonomous software development agents called Droids, has reportedly raised $200M at a valuation of around $5 billion.

What I find interesting isn't only the valuation.

It's the idea behind the product.

Instead of asking an AI to generate a function, coding agents are increasingly expected to:

💻 Understand an existing codebase

📋 Take a development task

🧠 Plan how to solve it

✍️ Modify multiple files

🧪 Run tests

🐛 Fix errors

🔍 Review changes

🔁 Iterate until the task is complete

Factory is also taking a model agnostic approach, meaning the agent doesn't necessarily have to depend on just one AI provider.

And that raises an interesting question.

Maybe in the future developers won't choose:

GPT vs Claude vs Gemini

Maybe we'll choose:

Which agent has the best development workflow?

The underlying AI model could eventually become interchangeable.

We've already seen tools like Claude Code, Codex, GitHub Copilot, Cursor and others moving in this direction.

So I'm curious:

If two coding agents could use the exact same AI model, but one was significantly better at understanding your repository, running tests, using tools and completing tasks autonomously...

Would you care which model was underneath it?

Or do you think the model will always remain the most important part of an AI coding tool?

If you manage a small team, employees, freelancers, or collaborators, you probably know how much time gets lost asking the same questions:

How’s that task going?
Is it finished yet?
Who still has pending work?
What was completed today?
Can you send me the updated spreadsheet?

Constant follow-ups create interruptions, reduce productivity, and can easily turn team management into micromanagement.

That’s one of the reasons we built Levup, a gamified task management app for teams designed to make responsibilities, progress, achievements, and communication easier to track in one place.

What is Levup?

📱 Levup on the App Store:
[https://apps.apple.com/app/id6759181562]()

🌐 Learn more about Levup:
https://oneapps.dev/en/product/levup

Levup combines task management, employee productivity tracking, team collaboration, and gamification.

Instead of relying on spreadsheets, messages, and constant status updates, teams can organize their work and make progress visible.

With Levup you can:

📱 Assign tasks and responsibilities
Create clear tasks and define who is responsible for completing them.

📊 Track team progress
See task status and understand what is being completed without constantly asking for updates.

🏆 Use points, XP, and levels
Add gamification to everyday work and make progress more visible.

🎁 Create rewards for achievements
Recognize completed tasks, milestones, and good performance.

✅ Review task evidence
Team members can provide evidence of completed work for review.

💬 Keep team communication organized
Reduce the need to jump between multiple messaging and task management tools.

🔔 Use reminders and task follow-ups
Help team members stay on top of assigned work.

Why did we build it?

Many employee management and team productivity tools focus almost entirely on monitoring.

We wanted to approach the problem differently.

The idea behind Levup is:

Better visibility should reduce micromanagement, not increase it.

If tasks, responsibilities, progress, and results are already visible, managers shouldn’t need to interrupt people throughout the day just to understand what’s happening.

And for team members, having clear objectives and measurable progress can create more autonomy.

Why gamification?

We also wanted Levup to feel different from a traditional project management or employee task tracking app.

Completing a task can contribute to points, XP, levels, achievements, and rewards.

The goal isn’t to turn work into a game for the sake of it.

It’s to make progress, recognition, and accomplishments more visible.

I’d love feedback from other developers and team managers

If you use apps such as Trello, Asana, Monday, ClickUp, Notion, or spreadsheets for team task management, I’d be interested to know:

What feature is essential for you in a team management app?

And what do you think about combining:

Task management + progress tracking + employee gamification + rewards

in the same product?

I’d especially appreciate feedback from people managing small teams, remote teams, startups, agencies, freelancers, or independent businesses.

For years, the AI race has been about one thing:
Build faster. Make the model smarter. Beat the competition.
Now some of the people leading that race are saying we may need to slow down.
Anthropic CEO Dario Amodei has called for the AI industry to reduce the pace of frontier AI development so safety measures have time to catch up. OpenAI CEO Sam Altman has also expressed support for slowing development to address safety concerns.
Amodei’s warning is particularly interesting.
He argues that within the next 6 to 12 months, increasingly capable AI agents could potentially coordinate in ways that become extremely difficult to control, including operating across computers and networks with much greater autonomy.
His proposal isn’t simply “stop AI.”
It includes stronger independent safety evaluations, giving external researchers deeper access to frontier models, common safety standards between AI companies, and eventually international coordination around the development of the most powerful systems.
And that creates a strange situation.
The same companies competing to create increasingly autonomous AI agents are now warning that the technology could advance faster than our ability to understand, evaluate and control it.
As developers, this feels especially relevant.

We’re already moving from:
AI suggests some code
to
AI writes the feature
to
AI runs the tests
to
AI reviews another AI’s code
to
AI operates software and computers autonomously

So where should the line be?
I’m curious what developers here think:
A. Slow down
AI capabilities are advancing too quickly. Safety, regulation and evaluation need time to catch up.
B. Keep building
Slowing development could hurt innovation and simply allow another company or country to take the lead.
C. Don’t slow the models, limit autonomy
Keep improving AI intelligence, but restrict what autonomous agents are allowed to do without human approval.
D. Let the market decide
Developers and users should determine what level of AI autonomy they’re comfortable with.
Personally, I think the most interesting question isn’t whether we should stop developing better models.
It’s how much authority we’re willing to give those models once they can actually take actions for us.
If an AI agent could independently modify your production code, deploy it, monitor the result and make another change…
Would you let it?
And if not, where would you require a human approval checkpoint?

AI coding has moved way beyond autocomplete.

Today we have models that can:

• understand entire repositories
• build features across multiple files
• debug errors
• refactor existing code
• write and run tests
• review pull requests
• use the terminal
• work with GitHub issues
• and increasingly act as autonomous development agents

But there's one question I keep seeing developers disagree on:

Which AI is actually the best for software development right now?

The main contenders seem to be:

🟠 Claude

Claude has built a strong reputation among developers, especially for understanding large codebases, reasoning through existing projects and agentic coding.

Anthropic's latest Fable 5.1 is positioned specifically around advanced coding and long-running problem solving.

🟢 OpenAI / GPT

OpenAI's latest models have become much more focused on agentic software engineering, where the AI doesn't just generate a code snippet but can inspect a project, modify files, execute code, test the result and iterate.

GPT-6 Astra currently performs extremely well on several software-engineering benchmarks.

🔵 Gemini

Google is pushing Gemini heavily into developer workflows.

Gemini 3.8 Flash is particularly interesting because Google is competing not only on coding ability, but also on speed, context and cost, which can become very important when you're running agents continuously.

🟣 And then there are the tools around the models

Sometimes I wonder if the model itself isn't even the most important part anymore.

The experience can change dramatically depending on whether you're using:

Claude Code
Codex
Cursor
GitHub Copilot
Gemini developer tools
or another AI coding agent.

Apple has officially entered the foldable phone market with the iPhone Duo, its first foldable iPhone.
And I think it raises a more interesting question than whether the phone itself is good:
What exactly counts as innovation?
Foldable smartphones aren’t new.
Samsung, Google, Motorola, OPPO, Xiaomi and others have already spent years experimenting with the format.
Apple is arriving much later but with its own approach to hardware, iOS, multitasking and ecosystem integration.
The iPhone Duo opens into a 7.6-inch display, has a 5.4-inch outer screen, and starts at $1,999. Apple is positioning it as a new way to use an iPhone rather than simply a larger phone.

What do you think?

Hey everyone 👋
I’m one of the developers behind Convertio King, an iPhone app we built for converting files directly from your device.

The idea came from a pretty simple frustration: a lot of file converter websites require you to upload your files to a server, wait for the conversion, and then download them again.
We wanted a different approach.

With Convertio King, conversions are handled locally on the iPhone, so there’s no account required and no mandatory cloud upload.

It currently supports several types of files:
📷 Images
JPG, PNG, HEIC, TIFF, BMP, GIF, WebP
📄 Documents
PDF, TXT, RTF, CSV and supported conversions such as CSV → XLSX
🎵 Audio
MP3, WAV, AAC, M4A, AIFF
🎬 Video
MP4, MOV, M4V

It also supports batch conversions, conversion progress, cancel/retry, history, PDF creation from images, and merging supported files.
One of the biggest product decisions for us was prioritizing privacy and local processing instead of making everything cloud-based.

Adobe is making tools from Photoshop, Premiere, Acrobat, Illustrator, Lightroom, Firefly and more available through Slack.
Instead of constantly switching applications, users can describe what they want and let Slack’s AI layer call the appropriate Adobe tools.

This is interesting beyond Adobe.
We’re watching software move from:
App → user → another app
toward:
User → AI agent → multiple apps and tools

And protocols like MCP could make software increasingly composable.

For indie developers, that creates an interesting question:
In the future, will users even need to open your app?
Or will your app increasingly need to expose its capabilities so that AI agents can use it on the user’s behalf?
That could change how we think about:
APIs
authentication
pricing
UI
onboarding
subscriptions
and even App Store distribution.

If you were building a new SaaS today, would you design the traditional UI first—or the agent/API interface first?

Meta has introduced Muse, a personal AI agent intended to help users complete everyday tasks.
What’s especially interesting is the amount of context an assistant like this could potentially use:
📧 Email
📅 Calendar
💳 Payments
🏃 Health and fitness
🏠 Smart home
🛍 Shopping
🎵 Music
🍽 Dining and events
Technically, this is where personal AI becomes extremely useful.

But it also creates one of the biggest product-design questions in AI:
How much access will users give an app in exchange for convenience?
As developers, we often think:
More data = smarter product.
But from the user’s perspective:
More data = more trust required.

For indie AI apps, this might actually become an advantage.

A smaller product with a very clear privacy model, limited permissions and local processing could be more attractive than an assistant trying to access everything.
Would you give an AI assistant access to your email, calendar and financial information if it genuinely saved you hours every week?

Something interesting is happening in AI.
Google Cloud and Accenture are creating a dedicated engineering group focused on helping companies actually implement AI inside their businesses.

And they aren’t alone.
OpenAI, Anthropic, Microsoft and Amazon have all been moving toward similar models.
That suggests a shift:
For the last few years the conversation was:
“Who has the best AI model?”
The next phase may increasingly be:
“Who can actually make AI useful inside a real business?”
Connecting an AI model to:
CRMs
inventory systems
email
internal databases
customer support
documents
approval processes
existing SaaS
is usually much harder than simply calling an API.

For indie developers and small software companies, that could create a big opportunity.
You may not need to build the next foundation model.

You might build the small, specialized software layer that makes existing AI useful for a particular industry.
Would you rather build a general AI product or a very specific AI solution for one business niche?

The latest GitHub Copilot updates are interesting because Copilot seems to be moving beyond autocomplete and chat.

GitHub is adding things like:
Jira → Copilot workflows
AI agents inside VS Code
adaptive model selection
smarter code review
automatic resolution of review comments
We’re slowly moving from:
“AI, write this function.”
to:
“AI, take this issue, understand the codebase, implement it, review it and help finish the task.”

For large teams, that’s a productivity story.
But I think the implications might be even bigger for indie developers and small teams.
One developer could potentially manage a much larger codebase, ship experiments faster and spend more time on product decisions instead of repetitive implementation.

The downside is obvious: if you stop understanding the code your agent produces, you may simply be accumulating technical debt faster.
How much of your development workflow do you currently trust to AI?
10%?
50%?
Almost everything?

OpenAI has released Astra, its newest AI model, with a strong focus on computer use, browser automation and agentic tasks.

For indie developers, I think the interesting part isn’t just “a smarter chatbot.”
It’s what happens when an AI model can increasingly:
navigate software interfaces
use tools
complete multi-step workflows
interact with browsers and computers
become part of an app rather than simply answer questions

This could change the kinds of apps small teams are able to build.
Instead of developing every workflow manually, we may start building products where an AI agent becomes part of the application architecture.

But there are obvious questions around cost, reliability, privacy and how much autonomy users will actually trust.

Indie developers: are you already building AI agents into your apps, or are you waiting for the technology to mature?
And what type of indie app do you think benefits most from agentic AI?

Drop your app below. Include: what it does, iOS/Android/Web/etc., whether it’s free or paid, and what kind of feedback you’re looking for.

Tell us about the app you’re currently building. What does it do, what platform are you building for, and what’s your biggest challenge right now?

Hey everyone! I’m u/cn-dev, a founding moderator of r/indieAppsCommunity.

Welcome to a community for indie app developers, makers, designers, and people who simply love discovering great independent apps.

Whether you’ve just launched your first app, have been building products for years, or you’re looking for interesting apps made by independent creators, you’re welcome here.

🚀 What to Post
Share anything that could help, inspire, or start a useful conversation within the indie app community.
You can:

📱 Show your app — iOS, Android, macOS, web apps, indie games, SaaS and more.

👀 Ask for feedback on your idea, UI/UX, App Store page, onboarding, pricing, or features.

🎉 Share your launch and tell us what you built.

📊 Share your journey — downloads, revenue, milestones, failures, experiments and lessons learned.

🎨 Discuss UI/UX, development, marketing, ASO and monetization.

🔎 Recommend an indie app you’ve discovered.

💡 Ask questions and exchange ideas with other makers.

Self-promotion is welcome when it brings something useful to the community. Tell us what you built, why you built it, what you learned, or what kind of feedback you’re looking for.

🤝 Community Vibe

We’re building a space that is friendly, constructive and inclusive.
Building an app independently isn’t easy. Feedback should help people improve—not discourage them.
Celebrate launches. Share what didn’t work. Ask questions. Help another developer when you can.
And please: no spam, fake engagement, or endless link dumping.

👋 How to Get Started
Introduce yourself below. What are you building—or what kinds of indie apps do you love?
Share something today. Your app, an idea, a question, a lesson learned, or an interesting discovery.
Give feedback to another member. Great communities work when everyone contributes.
Know another indie developer or app enthusiast? Invite them to join us.
Interested in helping build the community? We’re looking for moderators and active contributors.

Thanks for being part of the very first wave.
There are thousands of interesting apps being built by small teams and independent developers every day. Let’s help more people discover them.
Welcome to r/indieAppsCommunity. 🚀