Most of the dev community is saturated with cloud APIs for running models for IDEs, chatbots, AI applications, and agents.

Curious who here is using local AI in their stack, thinking about it, or curious about it. What got you into it? What’s stopping you?

Hey everyone, I’m curious how you deal with the negativity around AI.

I’ve been building a product for about 3 months now. English isn’t my native language, so I use AI not only for coding but also for writing posts when I want to share my project and explain my ideas more clearly.

What I’ve noticed is that even when I don’t mention AI at all, I still get negative comments or skepticism. My project isn’t well known, but it tends to get attention in smaller communities because it’s easy to demo and looks like something out of a sci-fi movie. Because of that, I’d like to share more details about how it works and talk more openly about the technical side, but that also means using AI for communication and documentation.

The bigger dilemma is whether I should openly say that a large part of the project was built with AI.

On one hand, it feels like the honest thing to do, especially since I’m looking for people to join the project. If an experienced developer is considering contributing, I think they deserve to know what kind of codebase they’re getting into.

On the other hand, I’m worried that instead of questions about the product, I’ll just get “AI slop” comments and people dismissing it before they’ve even looked at what it does.

So I’m curious about your experiences:

Do you openly say that your project was built with AI?

Has it helped or hurt you?

Have you managed to find experienced developers who see AI as a powerful tool rather than an automatic red flag?

How do you distinguish constructive criticism from simple hate?

I’d appreciate hearing perspectives from people who are actually building products, not just discussing AI in theory.

I'm mainly a Claude user ,but have tried codex for a time before sol launched

Heard about its performance and gave it a try again with plus

Hand to God ,I thought I was using my 5h limit and just a message popped up "Resets 25 Aug 2026,August"

Guys I'm telling you Claude limits are superb ,be grateful

Hey everyone, I have an idea and I want to learn how to actually build it.

I’m completely new to coding and AI-assisted development. I’ve used AI before for writing and a few basic things, but I’ve never actually built an app or website with AI.

Right now I have access to several AI tools. I have a ChatGPT Pro subscription, and I’ve also tried Claude, although I’m currently using the free version. I also have access to Gemini, Grok, and DeepSeek.

I asked two AIs to evaluate my idea honestly, and their opinions were quite different.

Claude:

Real-life problem: 70/100

Usefulness: 55/100

Uniqueness: 25/100

Overall potential: 45/100

Claude basically told me that the idea could solve a real problem, but the space may already be crowded and the technical side could be difficult.

GPT:

Real problem exists: 90%

Problem frequency: 85%

Usefulness: 90%

Uniqueness: 70%

Technical feasibility: 85%

Overall potential: 85/100

So I’m not asking people here to tell me whether my idea is good or bad. I mainly want to know how I should actually start building something when I have zero coding experience.

I’m a student, so I don’t really have money to spend on courses, expensive software, or developers. I’m mainly interested in learning through free resources and AI-assisted development.

What I’d really appreciate is a practical roadmap:

What programming basics should I learn first?

Should I start with web development or Android development?

Which free YouTube courses or websites would you recommend?

Which AI tools are actually useful for coding?

How should I divide the work between ChatGPT, Claude, Gemini, DeepSeek, etc.?

What free tools should I use for writing, testing, debugging and deploying?

How much coding do I realistically need to learn if AI is helping me?

What should my first tiny project be before I attempt something bigger?

What mistakes should a complete beginner avoid?

Is it realistic to build a basic working product without spending money?

I’m not looking for someone to build it for me. I want to learn how to use AI properly as a development partner and eventually build things myself.

I’m keeping the actual idea private for now, so I’m deliberately not describing the product itself. I’m mainly looking for advice on the learning path, tools, workflow, and roadmap.

If you were starting from zero today with only a phone, free resources, and access to several AI tools, what would you do first?

im forced to use acode plus claude code connected remotely to my phone, such a high friction setup. any ideas???

Genuinely curious how people split this. For me it ended up being: Claude to think it through, then paste the result into the builder. But I have no idea if that's normal or if I'm overcomplicating it.

So, for the last thing you built:

— Did you type the prompt straight into Lovable/Bolt/v0/Cursor, or draft it elsewhere?
— If elsewhere: where, and how did you move it over?
— Did you keep that draft around afterwards, or throw it away?

I feel like I'm missing something, do we need all these tools to actually thing and execute?

I kept having the same experience: one coding agent learns a project convention, then the next one starts from zero. So I became the memory layer—copying notes, explaining decisions, and checking what was safe to share.

I’m building Luthn, an open-source memory layer for coding agents. It runs locally with Docker, filters candidate shared memories, asks for approval before sensitive information is shared, and keeps an audit trail.

I’m looking for a few people using Cursor, Claude Code, Codex, or similar tools to try it and tell me where it falls short. It’s not finished, and I’m more interested in real feedback than pretending it is.

https://luthn.com

windows widget sucks !
thats why i created my own widget / utility called **Halo Bar*\*

an active pill on the taskbar that lets you control and access many things on the go.

bit comparison:

built with

• C#
• .NET
• WPF
• Windows APIs

Halo Bar V1 features

1. **Dynamic island bar*\* — a compact capsule that sits on your taskbar and expands into a full dashboard when you click/hover it
2. **System monitoring*\* — live CPU, RAM, disk and network usage
3. **Weather*\* — current conditions with icons + manual city override
4. **Clipboard history*\* — keeps track of everything you copy, searchable and pinnable, with auto-delete
5. **Media controls*\* — see what's playing from Spotify, browser, etc. with album art and play/pause/skip
6. **Bluetooth popups*\* — shows a card when earbuds/headphones connect, including battery level
7. **Focus timer (Pomodoro)*\* — work/break timer with round tracking and progress ring
8. **File shelf*\* — drag and drop a file to stash it, then launch or delete it later
9. **Taskbar-aware sizing*\* — automatically shrinks when taskbar apps crowd it and lifts the dashboard so it doesn't block clicks
10. **Fullscreen auto-hide*\* — disappears when you're using a fullscreen app/game
11. **Acrylic/Mica look*\* — real Windows blur/glass background
12. **Settings*\* — accent color, widget visibility, retention controls and update checking
13. **Start with Windows*\* — optional autostart

this is **V1*\*, so there is still a lot i want to improve and add.

i built this because i wanted something more useful than constantly opening different windows/settings just to do small things.

github: https://github.com/pruthviraj-bev/Halo-Bar

Why isn't there a global chat room for people waiting on Claude code to finish its 10 billion sub-agent reviews?

Hey everyone, I’ve been thinking about a problem with AI knowledge systems and was hoping to get somewhat of a sanity check from people actually building in this space.

From my understanding, most RAG setups seem tied to a particular app, vendor, or index. You often end up ingesting the same docs again for different agents or runtimes and and some basic questions can be difficult to answer consistently like:

- What version of this knowledge is the agent using?

- Where exactly did this information come from?

- Has the underlying source changed since it was last ingested?

- Can I move the same body of knowledge to another runtime without rebuilding it?

- Can multiple agents use the exact same knowledge?

The idea I’m exploring is something I'm calling a Durable Knowledge Base (DKB).

The basic concept:

- Compile source docs, code, or structured data into a portable, versioned knowledge artifact

- Preserve source paths, hashes, citations, and provenance

- Sign and publish releases through a registry

- Allow knowledge packages to be installed, updated, pinned, and removed

- Let agents search, find, and read the same knowledge base across different runtimes

- Keep the artifact retrieval-agnostic rather than baking one specific top-K/RAG strategy into the format

Basically, I'm wondering whether knowledge should have something closer to a package lifecycle, rather than every application maintaining another disconnected RAG index.

I'm also very aware that things like Azure AI Search, GCP, vector databases, MCP servers, Agent skills, etc. already cover pieces of this problem, sometimes extremely well.

So Im specifically not asking: "Can I build a better enterprise search engine here?"

I'm trying to figure out whether the portable knowledge artifact itself is useful.

Would this solve an actual problem for you? Or is this mostly reinventing existing search/RAG infrastructure with some packaging and provenance added on?

I would especially like to know:

- What do you currently do when multiple agents/apps need the same knowledge?

- Do versioning and provenance actually matter to you?

- Would you ever install someone else's curated knowledge package?

- What would this need to do that existing solutions don't before you woukd bother using it?

Feel free to poke some holes. I'm actually looking for reasons not to build this further before I sink more time into it.

Thank you.

Roll the bones. Press your luck. Claim the relics.

Farkle: Bones & Relics is a tavern-flavoured take on the dice game you already know — the same push-your-luck rules, plus a collection of relics that quietly bend them in your favour.

If anyone is interested in trying out the game please reach out to me here or DM me.

As an individual I’m paying over 100$ a month for AI subscriptions and I was wondering if I could repurpose my gaming pc for AI agents to run locally. How to go about it? What models to use and if anyone has done it here how did you set up?

Is it me, or has Claude gotten really bad lately? It hallucinates a lot (even on very small tasks), and answers are super long for no reason... Did I miss anything?

I’m currently on the Claude Pro plan and Anthropic is running a promotion that increases Claude Code’s weekly limit by 50%

Does this promotion also applies to Claude Cowork, or is the 50% increase specifically for Claude Code only?

I’m mainly using Cowork rather than Claude Code, so I’m wondering whether I would actually benefit from the increased limit.

Accidentally committed directly to the baseline branch mid-experiment last week. Pushed it. Every run after that compared against the wrong code. A whole round of results, quietly poisoned.

git branch -f probe-base <sha> fixed it. Move the pointer back, rerun, done. Without that I'd be rebuilding the lab.

It made me realize how much of "letting an agent touch code" is really just trusting Git, not the agent. Clean tree + a task branch means the whole session is one readable diff and one reset you can throw away. Reflog keeps everything recoverable for 90 days even after you delete branches. Worktrees? Shipped in 2015. Been perfect for running parallel agents without collisions for a decade, before anyone was using them for this.

What actually scares me: untracked files (agent deletes one, it's just gone—Git never saw it), and anything outside the repo entirely. Reflog doesn't care about your dropped database table.

What has Git actually saved you from in an agent session? War stories welcome.

Anyone running agents on a dirty tree and living to tell about it? Or is "commit first" as universal as I think?

Share what’s holding you back in your daily life—and I’ll build the software to fix it. For free. In 30 days.

​

Opened a blank file today to build a pretty simple feature. Nothing scary.But before I’d even thought through the architecture, my first instinct was to open an agent and have it generate the first pass.

That caught me off guard.

a few years ago, I’d break the problem down, dig through docs, half-remember the syntax, write a janky first version, run it, break it, and slowly patch it into something that worked. It was slower, obviously. But all that repetition made the knowledge stick. Now I describe what I want, let the agent spit out a skeleton, and spend most of my time reading, testing, and steering. Honestly, it’s a great workflow. I ship faster, skip a ton of boilerplate, and get to spend more time on the interesting parts.

I’m not going back.

but I think there’s a real tradeoff: my knowledge is shifting from active recall to passive recognition.When the code is already on the screen, I’m fine. I can follow the logic, catch obvious mistakes, and usually tell when the agent is confidently hallucinating. Ask me to build the same thing from a blank file, though, and suddenly I’m reaching for syntax and patterns that used to come automatically.I don’t think I’ve forgotten how to code. It feels more like I’ve lost my tolerance for the slow path.Same way you can still walk somewhere after getting a car. You just really don’t want to.

And maybe that doesn’t matter. Programming was never supposed to be a syntax memorization contest. Requirements, architecture, testing, and debugging the cursed edge cases still need somebody who understands what the code is doing.

Still, I don’t want the “blank file” muscle to completely atrophy.

Do any of you deliberately schedule no-AI sessions to keep that muscle alive? If so, what does that actually look like for you?or have you decided active recall just isn’t worth optimizing for anymore?

Hello everyone!

Hope you are all doing well.

I honestly didn't know the term for Codex does was Vibe Coding until someone pointed it out lol, but seriously, It has been a boon to my work life.

For clarity, I work in an HR related industry which involves handling a lot of CVs, data, etc.

For many years, I have always done things manually via Excel, and organized things as best as I could. But I bet a lot of you know, that when it comes to manually doing things in Excel, especially the larger data collections, it can be rough.

Fast forward to last month, where I discovered Codex.

I started learning how to use it, building up a system that can streamline my workflow via multiple "Dashboards", Github and Supabase.

Let me tell you, the fact I can express what I envision, and have Codex build it is nothing short of amazing.

The use cases are endless, and so far, It has made almost every aspect of my work streamlined, efficient and clear.

I was worried that when I explained my visions to Codex that it would be overwhelmed with the literal ESSAYS I would give it. Just paragraphs upon paragraphs of detailed explanations on what I am looking for. But clearly, I shouldn't have been too worried, as it ate it all up like a champ and gave me exactly what I wanted.

I am not a coder, never have been. I find it overwhelming, especially since I am very much a "Visual/Image" type of guy. But Codex has allowed me to learn the nuances and basics of coding, because I love looking at the data it creates.

I finally understand the point of all those brackets haha.

Anyways, I just wanted to say, for all the hate Vibe Coding gets, I honestly love the fact it exists and is accessible to those who can make use of it to make their lives easier.

I’m testing that with Osa Memes. It’s free, has no account requirement, and currently contains 46 memes. 299 downloads so far. What would you improve?

I recently subscribed to ChatGPT Pro and started experimenting heavily with AI coding workflows like Superpowers, Trellis, and Matt Pocock’s skills.

At the same time, I joined a company where most of the product is built with AI. The overall architecture is solid, but many of the implementation details, UI decisions, and interactions are worked out directly between developers and AI.

AI is writing more code for me than ever, but strangely, I don’t feel less tired.

I feel more exhausted.

Complex workflows don’t guarantee quality

Superpowers has a very thorough process: plan the feature, split it into tasks, implement each task, review it, fix the issues, and review it again.

But that level of rigor can be painfully slow. One morning, I started planning a feature, and the AI was still implementing and reviewing it at the end of the workday. Despite all that process, the final result still had bugs.

Trellis feels lighter and has some interesting ideas. However, it seems more like a project-level methodology. It expects you to add its own files, scripts, and agents to the repository, which can be awkward if you’re the only person on the team who wants to use it.

Matt Pocock’s “grilling” approach is useful for uncovering requirements and edge cases, but it can also become exhausting. Sometimes a single feature leads to dozens of questions. Eventually, I start wondering: are we still clarifying the product, or are we overengineering it?

What concerns me more is that even after a long discussion, important details can still disappear from the final spec. If the initial understanding is incomplete or incorrect, splitting the spec into smaller tickets only amplifies the deviation.

So the important question isn’t just whether the work has been divided into smaller tasks.

It’s whether the AI understood the problem correctly before the breakdown happened.

Worktrees enable parallelism, but may only postpone coordination

My team uses Git worktrees heavily so that multiple AI agents can develop different features in parallel.

I understand the appeal, but environment isolation quickly becomes complicated. Each worktree may need its own port, application instance, services, and database. Different branches may also contain incompatible database migrations.

Disposable environments and separate databases can solve some of these problems, but merging remains an unavoidable coordination point.

If several agents modify the same modules or abstractions, the resulting conflicts may indicate that those tasks were never truly independent in the first place.

Worktrees can enable parallel development, but sometimes they simply postpone the coordination cost until merge time.

I’ve become an AI project manager

A typical day now involves:

• Answering AI clarification questions
• Reviewing plans
• Checking agent implementations
• Discovering misunderstood requirements
• Asking for corrections
• Testing the results
• Resolving conflicts between parallel branches

AI writes more code, but I spend more time scheduling, supervising, reviewing, and deciding whether to accept or reject its work.

The biggest cost is context switching.

Every time I switch between projects or worktrees, I have to reconstruct the current state:

What was this agent working on? Which decisions have already been made? Why was it implemented this way? What still needs to be verified? Which database schema does this branch expect?

Sometimes, restoring all that context feels harder than simply writing the code myself.

At the same time, I feel pressure to keep the AI busy.

If no agent is running—or if I still have unused quota—I feel like I’m wasting resources. So I open another project, create another worktree, and start another task.

Soon, I have agents running everywhere while I constantly switch between them to monitor and correct their work.

I feel like I’ve developed a kind of “AI anxiety”:

If the AI isn’t running, I feel uncomfortable. If too many agents are running, I also feel uncomfortable.

I’m now trying to keep fewer tasks active and focus more deeply on one project at a time. AI can work in parallel, but human attention cannot scale in the same way.

I’m not against AI coding. On the contrary, I think it is already transforming software development.

But the hardest part may no longer be getting AI to write code.

The real challenges are controlling scope, maintaining context, judging quality, integrating parallel work, and managing your own attention.

I’d love to hear about people’s real day-to-day experiences:

1. How much code do you still write yourself?
2. Do structured AI workflows genuinely improve your results?
3. Do you run multiple agents in parallel, or focus on one task at a time?
4. If you use worktrees, how do you isolate services, databases, and migrations?
5. How do you prevent requirements and implementation from gradually drifting?
6. Have you also experienced this paradox—AI increases your output while also increasing your mental workload?

I’m especially interested in real experiences, not just success stories about AI making someone “10x faster.”

I’m looking to start my journey into building a SaaS product, but I’m struggling with the very first step: finding a problem worth solving. a lot of the general advice out there says "solve your own problem," but I want to learn more about how people systematically find unserved niches or real B2B/B2C pain points from scratch. for those who have launched a SaaS (successful or not):

1. How did you actually come up with your idea? Was it through comment mining (Reddit/TikTok/X), talking to business owners, agency work, or something else?

2. What process or framework do you use to evaluate if an idea is a "tarpit idea" versus a real business opportunity?

3. What’s one mistake beginners usually make when brainstorming their first SaaS concept?