I ve been exploring MCP, an open standard from @Anthropic that aims to simplify AI integrations.
In theory, this should make it easier to connect AI with databases, task managers, or even development tools. But I m curious to know how well it actually works in practice.
Like most things, Loopify started out of frustration.
Cameron and I were just trying to find a simple way to post across multiple platforms, but what we found were either outdated tools that felt like flip-phone era relics, or shiny ones charging premium prices for the bare minimum. Or both. So we started building what we wished existed. Something modern, clean, and actually pleasant to use.
Right now, we re in learning mode, talking to creators, marketers, and small teams to understand what actually matters in a tool like this.
Last week, OpenAI had to roll back an update to GPT-4o after users reported that the chatbot was being excessively agreeable even endorsing harmful or irrational behavior. This sycophantic behavior was traced back to reinforcement learning that overemphasized positive feedback .
As a founder building AI-powered products, this incident hits close to home. It raises important questions:
We just launched BunnyHunt, a global dev competition by Bunnyshell. The challenge? Build a GenAI app. Deploy it with Bunnyshell (infra-free). Best project wins $1,000,000 - for real. There s also a $10K bonus pool just for sharing.
No idea is too crazy. What would you build?
Join here Use #bunnyhunt or #followthewhitebunny when you post your journey.
I've been reflecting on the surge of AI tools ChatGPT, Notion AI, GitHub Copilot, and countless others. They're marketed as productivity enhancers, yet I find myself juggling more tasks than ever.
These tools generate content, code, and ideas at lightning speed. But with this efficiency comes an influx of drafts to review, emails to send, and decisions to make. It's as if the workload has multiplied, not diminished.
I'm curious:
Are these AI tools genuinely making us more productive, or are they just adding to our to-do lists?
How do you manage the balance between leveraging AI and maintaining quality over quantity?
Have you experienced a shift in your workflow since integrating AI tools?
I've spent exactly all of last year building a total of 6 products, 4 of which are paid. But I haven't gotten a single paying customer yet. Channels I've tried.
Posting on Hacker News and Product Hunt
Paid ads (Google, Instagram, Reddit)
Listing on tool directories ("ThereIsAnAIForThat.com" etc)
Creating Free tools and sending some related traffic back
Asking a couple of people I know to try my products
Each have given me various degrees of success. Rest assured, I have lots of users using the free parts. Recently I'm trying push marketing; for example recently my girlfriend and I went to a pet-friendly cafe in Bangalore and we got talking with the proprietors. One of them was interested in what I was building and I found myself pitching my product them. I felt icky and weird since all my life I've been a software engineer and hardly ever someone who sold anything. What's your experience getting customers, especially the first ones for your product?
In the last few days, I have seen many AI tools for creating audio-visual, and even people in my feed shared short movies created by AI (Hollywood will probably cut costs quite a bit in the next few years).
What is your experience with AI video generators, and which ones do you find the best? (In terms of which AI tools have given you the best video results.)
This is quite urgent as I'm launching apps back to back. I have some paid customers here and there and some OK traffic, but Im not sure what to pursue with more energy. What kind of conversion rates are considered "good" in order to pursue a product more seriously? When I say conversion rates I mean the following:
From landing page to app visit
From app visit to registration
From registration to paid customer
What numbers do you consider good? I haven't managed to find clear answers online.
PS: Im a solopreneur so I have limited resources, I cant be pursuing products that make no sense number-wise.
The perfection of creations generated by artificial intelligence makes it difficult to distinguish fiction from reality.
The precision of AI images has advanced to the point that even professionals (graphic designers, video-makers) are sometimes not 100% sure of their authenticity.
I feel like Im using Youtube more than anything else every single day. I want to learn some "hidden gems" channels, cause I feel I seen it all. Ideally channels around solopreneurship/indiehackers, 0$ marketing strategies, how-to-sell stuff fast, philosophy and economics. Here are my GO-TO channels that I watch almost daily:
@Raoul Pal the Journey man (Macro investing in crypto)
@Money ZG (macro investing in crypto)
@Pursuit of Wonder (interesting concepts around societal psychology & philosophy)
@Fireship - Tech edgelord (lol)
@Greg Isenberg (Solopreneurship)
@Starter Story (Solopreneurship, How to build and market apps)
Anyone else building cool stuff like PDF Q&A or custom bots with RAG, but finding the context retrieval step... frustrating?
Most AI app data stacks these days use vector search (Pinecone, Weaviate, etc.) to grab text chunks for the LLM. But sometimes it feels like it finds stuff that's keyword-similar while totally missing the actual point the user asked for. Leads to those slightly weak or "confidently wrong" LLM answers.
We're currently working on a product Focal AI that helps users dive deep into research on any topic. One of our core features is called "Deep Research", and we're now at a key decision point: choosing the right AI model to power it.
After testing many of the current top-tier LLMs, we've found that most can handle broad, high-quality research prompts. But the results still vary a lot depending on:
What I like about tech culture or marketing is that it tends to be more relaxed. At least in my experience, I ve always had a very open and friendly relationship with CEOs.
But the bigger the company, the more distant the CEO tends to be from the team members.
(For example, it s much easier to remember everyone s name in a flat structure with 14 people than in a large corporation where a manager manages a manager who manages someone else and has dozens of people under them. You get me.)
Skywork Super Agents
