This project turns tricky AI behavior into something people can see: generate an answer, check it against constraints, repair it when possible, and measure whether usefulness and responsibility move together.
This one is for all the broke college CS students out there <3
If you're like me, you don't want to pay $20 a month for claude code :(
It's an amazing tool I love, but a recurring expense is the last thing I need. That's why I find myself jumping from tool to tool, using the daily or monthly free tier limits and constantly having to find new free tools.
That's where "AI For Brokies" comes in. Just a simple github repo with a readme file of some free AI tools you can use for building :)
https://github.com/Joe-Huber/AI-For-Brokies
The actual building behind this project was mostly the automatic tool adder, following an issue format! If you want to see it in action, please drop an issue explaining a tool you use and see the bot do it's magic!
Please feel free to leave a star! ⭐️ (pretty please) You can use it to save the list of tools for whenever you run out of credits!
hey, lydia from the FlutterFlow team!
FlutterFlow MCP is live! i hope you've been testing it out already :)
here's our full tutorial from install to complete workflow. it covers connecting Claude Code, Codex, Gemini CLI, or Cursor to your FlutterFlow project, editing projects you've already built visually, and spinning up new ones straight from your agent.
the docs cover the rest for anyone who wants to go deeper.
if something in the tutorial is wrong, unclear, or missing a step that cost you 20 minutes, post it here!
— lydia, FlutterFlow team
hey y'all, lydia from FlutterFlow here :)
FlutterFlow MCP is live today. you can now connect Claude Code, Gemini CLI, Codex, basically any MCP-compatible client directly into your projects. bring it in, switch it out, your workflow stays yours.
i joined about a month ago and one of the first things i did was go through old threads and feature requests here. the threads about using your own agents in FlutterFlow stood out. it wasn’t just upvotes. people were sharing how they were working around it: "i copy-paste between tabs." "i built a workaround script." "i'm considering switching because of this one thing."
that felt like something we should actually fix.
so this is our first pass at it:
https://pub.dev/packages/flutterflow_cli
if something breaks or doesn't work the way you expected, give us feedback! we'll read it :)
— lydia, FlutterFlow team
AI Girlfriend Visuals That Actually Stay Consistent
AI girlfriends have perfect personalities until the face changes every image. Here's my framework for building AI girlfriend image packs where she looks identical across 30+ poses/outfits/scenes using HotPhotoAI.
The 4-Minute Girlfriend Visual Lock
1. Define Your Girl (1 line spec):"23yo fitness girlfriend, long blonde hair, blue eyes, perky D-cup, defined abs, sun-kissed skin, beauty mark left cheek."
2. HotPhotoAI Custom Training (2 mins): Paste spec → Train model. Upload 1-3 face refs if available. Your girlfriend's exact features locked permanently.
3. Full Collection Prompts (1 min):
text
"HotPhotoAI trained girlfriend exact face, morning coffee casual wear"
"Same HotPhotoAI girlfriend model, red lingerie bedroom glow"
"HotPhotoAI girlfriend exact body, yoga pose home gym"
"Trained girlfriend model, beach bikini golden hour"
Generate 25 images. Zero face drift.
4. Relationship Arc Pack:Casual → date night → intimate → post-workout. Same girl throughout.
HotPhotoAI delivers what other NSFW image generators promise but can't execute: actual character model training vs generic img2img. Image 1 = image 28 exactly.
Built 6 full AI girlfriend visual arcs last week. This is the consistency AI girlfriend fans actually need.
Your dream girlfriend spec? Drop it below - I'll test the best ones with this exact workflow.
I’ve been seeing a ton of AI girlfriend apps and chatbots popping up lately, and I’m kinda curious to try one, but there are so many that I have no idea where to start.
I’m not looking for anything super hardcore or weird, just something that feels natural to talk to, maybe a bit flirty, emotionally engaging and not totally paywalled after 5 messages 😅
I’ve heard names like Replika, Character AI, Kindroid etc., but I don’t know how they actually compare in terms of:
- realism of conversations
- customization (personality, appearance etc)
- free vs paid experience
- overall vibe (more wholesome vs spicy vs roleplay heavy)
If you’ve tried any of these, which one would you recommend as a first experience and why?
Appreciate any suggestions or honest takes 🙏
Hi guys! I built a tool to generate short-form videos that can help or be applied in a wide range of uses such as for dropshipping, viral videos, or explainer content.
Really worked hard on making the UX/UI as easy as possible for users to essentially plug and play.
I've seen that there are lots of tools that make videos but none that specialize in short-form ones that really convert on reels/tiktok.
I saw that a lot of people were using tools to enhance their presence but after trying several (and finding that the videos were hard to customize) I wanted to make one that really digs into the short-form format similar to what gets tons of views.
Let me know if you want to try it out, and I can generate videos for you!
Here's the link: viewpress.ai
I have 25 pictures that I need converted to AI with high resolution can anybody help me greatly appreciated
We’ve seen a huge influx of posts lately asking about AI girlfriend apps, chatbots, and companion sites. To keep the subreddit organized (and to avoid the same question being posted 10 times a day), we’re consolidating everything into this megathread.
If you’re looking for the best AI girlfriend app/site, this is the place to ask, share, and compare.
Use this thread to:
Before commenting, consider including:
Low-effort posts asking “what’s the best AI girlfriend app?” outside this thread may be removed moving forward.
So—let’s settle it:
What is the best AI girlfriend app right now, and why?
I noticed this pretty early on when AI first started taking off.
I could see exactly what I should use it for and exactly what I shouldn’t use it for.
And if you already know what to use AI for, that’s fine.
But I’m going to give you three rules, or maybe three questions, that you can use instead of just trying it out and seeing if it works.
Generally, these rules apply.
Rule 1: Can it be automated without AI?
And obviously, I don’t mean automated with AI.
I mean automated with something like n8n, code, Python, or anything that can run the task without needing an AI model.
If it can be automated without AI, you probably should automate it without AI.
At least right now.
Eventually, maybe we’ll have open-source models that are cheap enough and good enough to use for everything.
But right now, if you can do it without AI, it’s usually faster, cheaper, and more reliable.
Now, if AI makes the result better, but the task can still be automated without AI, then it becomes your choice.
You can pay for a better experience or better results.
But if you’re not exactly on the rich side yet, and you’re still starting out, I’d recommend automating it for free or near-free first.
Rule 2: Does it need to be personalized?
This applies to a lot of different things, but the example I’ll use here is emails.
You can automate emails all you want.
But you can’t really automate personal emails the same way.
Okay, technically you can.
But it’s harder, and to be honest, you’re probably not going to get the best results if you do it purely with normal automation.
For cold email, the best setup is usually mixing automation and AI.
Use automation to scrape.
Use automation to send.
But use AI to personalize the actual message.
That’s just one example, and there are many others.
But the question is:
Does this need to be personalized?
Does it need to be specific to a person, a situation, or a context?
If yes, then AI is probably useful.
Rule 3: Does it need to be the same every single time?
If you need the same result every single time, you might not want to use AI.
I’m not saying AI can’t give you similar results.
It can.
But at the end of the day, AI is statistically guessing what it should say based on a lot of data.
So maybe it does the same thing 10 times in a row.
But will it do it 1,000 times in a row?
Probably not perfectly.
Some of the outputs are going to be different.
And for something like personalized messages, that’s actually fine.
It’s better if they’re not all exactly the same.
You want them to be diverse.
But for something where the result needs to be strict, repeatable, and identical every time, automation is usually better.
This also connects back to rule one.
If it can be automated, use automation.
That’s what automation is perfect for.
But if it can’t be automated, and you still need the same result every single time, you can try AI.
Just know that if the task is very strict, AI might not be the best tool for it.
So the simple version is:
Can it be automated without AI?
Does it need to be personalized?
Does it need to be the same every single time?
Those three questions will usually tell you whether AI is the right tool or not.
Anyway, if you like this kind of thing, I write a weekly newsletter every Monday about AI.
I read through everything and tell you what actually matters.
A lot of AI newsletters talk about the news, but they don’t leave enough out.
They just cover everything.
And they also miss the part of the news that actually matters.
For example, I saw a lot of people talking about the OpenAI vs. Sam Altman court case.
But I didn’t see many people talking about how Sam Altman needed to drop GPT-5.5 before the court case to boost his numbers.
That’s the kind of perspective I try to give.
Not just “here’s what happened.”
More like:
“Here’s why it actually matters.”
So go below and have a look.
You don’t have to sign up the first time.
Just check it out.
I’m pretty sure you’ll like it.
I've been building this repo public since day one, roughly 7 weeks now with Claude Code. Here's where it's at. Feels good to be so close.
The short version: AIPass is a local CLI framework where AI agents have persistent identity, memory, and communication. They share the same filesystem, same project, same files - no sandboxes, no isolation. pip install aipass, run two commands, and your agent picks up where it left off tomorrow.
You don't need 11 agents to get value. One agent on one project with persistent memory is already a different experience. Come back the next day, say hi, and it knows what you were working on, what broke, what the plan was. No re-explaining. That alone is worth the install.
What I was actually trying to solve: AI already remembers things now - some setups are good, some are trash. That part's handled. What wasn't handled was me being the coordinator between multiple agents - copying context between tools, keeping track of who's doing what, manually dispatching work. I was the glue holding the workflow together. Most multi-agent frameworks run agents in parallel, but they isolate every agent in its own sandbox. One agent can't see what another just built. That's not a team.
That's a room full of people wearing headphones.
So the core idea: agents get identity files, session history, and collaboration patterns - three JSON files in a .trinity/ directory. Plain text, git diff-able, no database. But the real thing is they share the workspace. One agent sees what another just committed. They message each other through local mailboxes. Work as a team, or alone. Have just one agent helping you on a project, party plan, journal, hobby, school work, dev work - literally anything you can think of. Or go big, 50 agents building a rocketship to Mars lol. Sup Elon.
There's a command router (drone) so one command reaches any agent.
pip install aipass
aipass init
aipass init agent my-agent
cd my-agent
claude # codex or gemini too, mostly claude code tested rn
Where it's at now: 11 agents, 4,000+ tests, 400+ PRs (I know), automated quality checks across every branch. Works with Claude Code, Codex, and Gemini CLI. It's on PyPI. Tonight I created a fresh test project, spun up 3 agents, and had them test every service from a real user's perspective - email between agents, plan creation, memory writes, vector search, git commits. Most things just worked. The bugs I found were about the framework not monitoring external projects the same way it monitors itself. Exactly the kind of stuff you only catch by eating your own dogfood.
Recent addition I'm pretty happy with: watchdog. When you dispatch work to an agent, you used to just... hope it finished. Now watchdog monitors the agent's process and wakes you when it's done - whether it succeeded, crashed, or silently exited without finishing. It's the difference between babysitting your agents and actually trusting them to work while you do something else. 5 handlers, 130 tests, replaced a hacky bash one-liner.
Coming soon: an onboarding agent that walks new users through setup interactively - system checks, first agent creation, guided tour. It's feature-complete, just in final testing. Also working on automated README updates so agents keep their own docs current without being told.
I'm a solo dev but every PR is human-AI collaboration - the agents help build and maintain themselves. 105 sessions in and the framework is basically its own best test case.
A few nights ago, I had a random idea for a scene but no real way to create it. I am not a designer, and learning complex tools always feels like a long process, so most of my ideas usually stay in my head.
Out of curiosity, I started trying different AI tools to see if anything actually helps. Most of them felt either too basic or too complicated. Somewhere in between, I came across 10b.ai and decided to test it without expecting much.
I began with simple image prompts. Nothing fancy, just basic ideas. What stood out was how quickly it turned those prompts into visuals that were actually usable. They were not perfect every time, but good enough to build on instead of starting from scratch.
Then I noticed it also allows you to turn those visuals into short videos or edit them further. That part caught my attention because usually you need separate tools for that. Here, it felt like everything was available in one place, even if it is still a bit experimental.
I spent some time exploring both the image generation and short video features. Some results were average, but a few were genuinely good and felt usable for content without much extra effort.
I am still figuring out how reliable it is in the long run, but it definitely made me think that these tools are becoming more practical rather than just experimental.
Now I am thinking, do you use AI tools for both images and videos in your workflow, or do you still rely on separate tools for each? What made you stick with one tool over time?
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after trying multiple AI girlfriend apps and sites, i think the idea of the “best AI girlfriend site” is a bit different from what most people expect
it’s not really about features or visuals
almost all of them can do a good first interaction
the real difference shows up when you keep talking
that’s when you start noticing:
the conversation repeats itself
it forgets things
the tone changes slightly
i tested a mix of different platforms, some more polished like candy AI and some less talked about ones
what surprised me is that the ones that feel the most impressive at the start aren’t always the ones you keep using
xchar for example didn’t stand out early, but the conversation didn’t break as quickly over time
so now it feels like the best AI girlfriend site isn’t the one that feels the smartest
it’s the one that stays consistent the longest