Hi all! I m working on an idea that came out of my own frustration:
There are SO many AI tools right now for writing, meetings, code, design, finance but it s getting overwhelming.
Even though I m deep in the AI world, I still find myself Googling things like: Best AI tools for solo founders ; Free AI video editing apps ; Is this tool even legit?
ICYMI: OpenAI posted a cryptic tweet yesterday, announcing a livestream for today at 10am PT. The cryptic part? They swapped the S for a 5 , which, of course, set off a wave of GPT-5 speculation. But this is OpenAI, and at this point, GPT-5 rumors feel like a monthly tradition.
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.
As we were tinkering with Universal Tool Calling Protocol (UTCP) we kept hearing about the same hurdle: Cool spec but where s the list of tools I can actually call?
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We re excited to announce that Claude Opus 4.1, Anthropic s latest and most powerful model, is now fully integrated into Cubent.dev our fast, lightweight AI coding assistant for VS Code.
Lately there s been a growing wave of skepticism around VCs, Y Combinator, and accelerators in general. And to be fair: I get it.
We now live in an age where almost everything you need to learn can be found online. The gatekeepers are fewer, the knowledge is everywhere, and solo builders have never been more empowered.
As someone who's spent a lot of time in both content creation tools and educational content, this is a question I get asked all the time.
It's one of those questions that's neither simple nor complicated.
The trickiest part is that "good" doesn't have a universal answer. "Good" is super subjective. What one person thinks is great, another might find too basic, too difficult, or just plain boring. And honestly, people's judgment about content gets swayed by all sorts of things their environment, social circles, even current trends.
I ve been experimenting a lot with AI tools like V0, Lovable, and Bolt.new to build small products and prototypes.
One pattern keeps showing up: most ideas don t fail because the idea is bad. They fail because the prompt is vague, confusing, or incomplete.
AI isn t a mind reader; it does exactly what you ask. If your prompt is fuzzy, your output will be too.
For example, I recently built PublicWall off a single well-structured prompt. Before that, I wasted hours on iterations that were mostly me not clarifying what I actually wanted the AI to do.
me and my co-founder are building an AI agent because at our last startup we just couldn t keep up with support.
we tried every chatbot out there. they all felt robotic. customers hated it.
hiring more people was too slow + too $$$
so we put together this ai chatbot (think intercom fin but deeper) that trains on your old tickets, learns your tone, doesn t hallucinate, and can actually answer stuff like a real support rep.
It is a question of choosing between two evils for us now. Neither option is completely free of flaws.
Human: Recruiters with "gut feelings" who harbor unconscious bias. they reject excellent candidates who just didn't go to the "right" school or didn't just "click." Inconsistent, unfair, and un-auditable.
AI: Algorithms whose training datasets are themselves replete with historical biases. They increase the scale of discrimination at light speed, becoming so-called black boxes that end up rejecting qualified candidates for reasons that humans cannot even fathom.
We are truly deciding to exchange messy, subjective human prejudice for cold, ruthlessly efficient algorithmic prejudice. Is that really an upgrade?
It will sound like something from prehistoric times, but in my country, the vast majority of employers give women lower salaries than men. Fortunately, I don't feel it that much because I work with foreign clients, but I don't think it's fair.
(But we probably all know why this is the case less is invested in women, because it is expected that they will go on maternity leave one day and therefore have no prospect of staying in the job longer compared to men, where the continuity of work is higher)
1/ The ultra-planners. hey schedule everything down to the minute, know who they re meeting three months from now, and already have their 2027 summer vacation mapped out.
