Readit NewsBut this looks like an expensive toy.
The stuff of nightmares is this being adapted by the DoD. I can almost imagine your website as a scene in the prologue of a terminator like movie.
Nightmare 2 is this becomes a companion of some sort. Detroit Become Human goes into this. You have a theme of the robots basically wanting freedom. Which throws out a moral conundrum, if someone buys an AGI enabled bot just to be mean to it, have they done anything wrong.
I like technology , but this feels like step one to a whole lot of weird stuff.
Most people have proprioception - you know where the parts of your body are without looking. Close your eyes and you intuitively know where your hands and fingers are.
When parking a car, it helps to sort of sit in the drivers seat and look around the car. Turn your neck and look past the back seat where your rear tire would be. sense the edges of the car.
I think if you sort of develop this a bit you might "feel" where your car is intuitively when pulling into a parking space or parallel parking. (car-prioception?)
(but use your mirrors and backup camera anyway)
It's made me realize that objects are much further from the boundaries of my car when backing into a spot parallel parking. I would never think to get so close to another car if I had to only rely on my own senses.
With that said, I realize there's a significant number of people that are even poorer estimators of these distances than myself. I.e. those that won't pass through two cars even though to me it's obvious that they could easily pass.
I have to imagine a big part of this has to do with risk assessment and lack of risk-free practice opportunity IRL. Nobody is seeing how far they can push or train themselves in this regard when the consequences are to scratch up your car and others' cars. With the birdseye view I can actually do that now!
It started as a side project to explore the latest AI trends. Now it’s something we use daily — and others are starting to as well.
Thoughtcatcher is a lightweight, AI-powered notes + reminders app that acts like a memory companion.
It helps you: - Capture raw thoughts and auto-tag them using AI - Set smart reminders triggered by context and meaning - This was a game changer for me personally - Search and chat with your notes like a conversation — not just by keywords, but by intent
Example? You’re walking out of a meeting and think: “We should revisit that pricing model after the new release.” You jot it into ThoughtCatcher — no structure, no stress. A week later, right before the next sprint planning, it reminds you. Just when you would’ve forgotten — it remembers.
What started as a learning project has grown into something useful — not just for individuals, but for teams too.
We’re now exploring B2B use cases like: • Project knowledge management • Shared team notes with smart search and chat • Meeting follow-up insights and reminders • AI-powered team memory for client or product work
Want to try it out? Android users: Download the app iOS users: Use the PWA — just “Add to Home Screen”
Still early. Still learning. But ThoughtCatcher already feels like something I wish I had years ago.
Would love your feedback or thoughts. And if you’re building something similar— let’s connect
Random callout: the copy in your app store preview images would benefit from some proof reading. Example: "WE dont just store thoughts, but makes sense of them" should likely be "ThoughtCatcher doesn't just store thoughts, it makes sense of them". My 2 cents is to also rework "Capture your mind" as it's a little awkward. Maybe "Organize your thoughts", "Supercharge your thoughts", or something along those lines.
Differences in features, personality, output formatting, UI, safety filters… make it nearly impossible to migrate workflows between distinct LLMs. Even models of the same family exhibit strikingly different behaviors in response to the same prompt.
Still, having to find each model’s strengths and weaknesses on my own is certainly much better than not seeing any progress in the field. I just hope that, eventually, LLM providers converge on a similar set of features and behaviors for their models.
The key seems to be in curating your application's evaluation set.
I'll beginning to integrate it into my user-facing application for language learners soon: www.abal.ai
Whether or not a general purpose foundation model for coding is trained on more backend or frontend code is largely irrelevant in this specific context.