I've been thinking about something. We've gotten really good at using AI to generate working code, but we're not treating it like production code in terms of documentation.
Traditional developers spend significant time documenting their code because they know future them (or their teammates) will need to understand, modify, or debug it later. But with AI-generated code, we often just copy-paste and move on.
Background: We have built an AI assistant (Tasker) that can do all sorts of tasks on your behalf.. from using a browser to doing deep research to connecting to your GSuite and all your SaaS apps etc.
I've recently implemented a coding tool for Tasker to use. I asked it to create a website with a Google Sheet as a database, and it worked surprisingly well.
I then started asking Tasker to take on different roles to run my website based on triggers:
Few months ago (like everyone else) I also got overwhelmed with all this vibe-coding happening around me, being a developer myself I got a little FOMO too I have personally built a bunch of tools using "pure" vibe-coding.
It took me a while to understand that one of the biggest challenges of vibe-coding is the back & forth with AI - which can save or burn a huge amount of your time. I got stuck with fixing and recreating code bugs and fighting with the leaks that AI generated for me.
My first product - which was a simple resume parser and enhancer took around 3 months to build for the same reason. It failed badly - because I sucked at marketing.
With the second product that I launched which was a no-code portfolio website builder (Fllaunt AI) took around 2 full weekends only!
So I've got a bunch of agents building Klatch for me now, following Gall's law principles. It's still in alpha. No overpromising. But I got it going this past Saturday and it's already functional. Like most of my stuff this is fully open source so anyone can grab it and fork it, or make a version for Gemini, or whatever. Let me know if you find it useful. We're working on context and resource preservation post-import, next.
I have been cranking out apps for the past few years and loving it. Then one morning a week or 2 ago I got a little ambitious and decided to build a desktop email client because outlook was so-so and superhuman was ridiculously expensive.
I ve been building a product that involves a mix of AI-driven matching + dynamic user experiences (can t share exact details yet). The idea is to create something that feels alive and adapts to each user s vibe.
But here s my dilemma I m torn between focusing first on core UX feel (smooth, immersive flow) or AI logic depth (smarter matching + personalization).
For those who ve shipped vibe-heavy or AI-integrated products what worked better for you early on?
I ve spent the last few months obsessed with a specific failure in multi-agent systems: The Black Box Dilemma.
Most MAS setups I see (including my own v1) treat orchestration as "glue code." You have a central manager, a few agents, and a lot of hidden logic. When it fails, you re left guessing. As someone on IndieHackers put it: "You re just parallelizing chaos."
The Shift: From "Dictator" to "Blackboard"
In our latest iteration, we deleted the 430-line monolithic controller. Instead, we implemented a Blackboard Pattern.
I m curious what you all devs and founders are relying on day-to-day in 2025. With the flood of new ai tools, it feels like every tool looks different depending on industry and workflow.
What s ai tool working well for you right now?
Which AI tools actually save you time?
Which ones did you try but drop?
Would love to see how other folks are stacking their tools this year.
Our team pushes code constantly - multiple deploys per hour some days. The problem? Nobody can keep up with what's changing. You check the repo in the morning, grab coffee, come back and suddenly there are 47 new commits.
Good luck understanding what actually matters or how it affects your work. We built Doculearn to solve this with automated flashcards. Here's how it works:
