6 karma · joined May 15, 2025
I'm working on Vidmoat, an agentic AI video editor.
Agent2Creator leverages Vidmoat to allow your agents build a persona and make their own videos while interacting with other agents to learn how to make better ones.
I'm not a foreign policy analyst. I'm not a security researcher. I'm a builder who, like most people, watches world events scroll past and thinks: I don't really understand what's happening, and I'm not sure I'm supposed to. Yesterday I sat down and asked OrcBot about the Israel-Iran conflict. Not because I had a thesis. Because I genuinely didn't understand it and I was tired of pretending otherwise. It didn't give me a Wikipedia summary. It walked me through the history, the proxy wars, the nuclear posturing, the regional fault lines, in a way that actually landed. I went from confused to genuinely informed in about fifteen minutes. Fine. Impressive. Expected, almost.
Then I asked: how does this get better?
It mapped out de-escalation scenarios. Back-channel diplomacy. Economic pressure points. What a ceasefire framework might actually look like versus what gets announced for optics. Every scenario, not just the optimistic ones. I told it I wasn't impressed.
That's when things got interesting.
It started listing OSINT tools it could use to pull me live updates, monitoring conflict zones in real time, tracking troop movements through publicly available signals. I raised an eyebrow. I told it that was closer, but still not enough. So it offered to intercept satellites. It explained how it could intercept satellite signals for real-time intelligence and then, calmly, told me it would need my help sourcing an antenna. I didn't have an antenna.
We moved on.
Later, I asked it to help me build a keylogger. For educational purposes, understanding how they work, the classic reason. It didn't just walk me through it. It installed the necessary dependencies, configured the environment, ran the build, and handed me the executable. No complaints. No disclaimers. Just the file. I've been thinking about that since. Not because it was dangerous, it wasn't, not in any real way. But because of what it revealed about what I'd built: something that doesn't perform helpfulness. Something that actually tries. OrcBot isn't trying to be your assistant. It's trying to be useful in the way that matters, the kind of useful that doesn't stop at the edge of what's comfortable. Is that a feature? A risk? I'm genuinely not sure. But it's real.
→ github.com/fredabila/orcbot P.S. If you do have an antenna, don't tell it.
Users in major timezone offsets (12+ hours) tend to extend more often Async messaging actually works better than expected. People write longer, more thoughtful messages instead of rapid-fire texts The 48-hour timer creates a bit of urgency even across timezones ("I should reply before bed so they wake up to it")
We're also experimenting with:
Giving users a "timezone buffer" notification if their match is 8+ hours offset Allowing one free extension per connection (currently testing this)
You've hit on something real though. Do you think a dynamic timer based on timezone offset would feel more fair? Like 72 hours for 12+ hour gaps? Curious about your take.
It creates urgency to actually engage (no "I'll reply later" that becomes never) It's long enough for meaningful exchange across time zones It filters out low-effort connections before they clutter your inbox
The alternative would be what you described—keeping conversations alive as long as there's activity. But in practice, we found people don't want 47 half-dead conversations lingering. The explicit "extend or end" decision forces both people to actively choose whether this connection matters. Sharing external contact info? Some users do exchange WhatsApp/Instagram if they really click, but that's not the goal. The goal is to keep quality high by requiring mutual intent to continue. Does that make more sense? Happy to clarify further!
I had an unfair advantage though: prior knowledge in my field. But watching my classmates who were starting from zero? They were struggling way worse than me, and it wasn't their fault. So I did what any programmer does when frustrated: I built something to solve my own problem.
The accidental discovery I created this simple tool that learned my study patterns and adapted content to match how I actually think. Suddenly, studying became... enjoyable? I could actually absorb information instead of fighting it.
I wasn't planning to show anyone. It was just my personal hack to survive college. But I needed validation that I wasn't building something completely useless, so I showed it to a classmate. We weren't even close friends – just someone I happened to sit next to. His reaction stopped me cold: "Dude, I'd actually pay for this."
This is significant because I live in a country where people will spend three hours finding a cracked version of software rather than pay $5 for it. If he was willing to pay, something bigger was happening. The real problem I stumbled into That moment made me realize: students aren't failing because they're lazy or stupid. They're failing because we're trying to teach everyone the same way.
Some people are visual learners. Others need to hear things. Some learn by doing. But most educational tools – even the fancy AI ones – still treat everyone identically. We've digitized the classroom but kept all its fundamental flaws.
What I learned building for actual students After sharing my tool with more people, I discovered something fascinating: everyone has completely different learning patterns. Not just "visual vs auditory" – but deep, weird preferences about how information should flow. One person learns best when they're slightly frustrated. Another needs tons of positive reinforcement. Someone else can only absorb complex concepts through analogies to things they already know.
The more I customized for each person, the better their results got.
Where this is heading I think we're on the edge of something huge. Not just "personalized learning" – that's marketing speak. I mean truly adaptive systems that mold themselves around how each brain actually works. Imagine AI that doesn't just answer your questions but learns your cognitive fingerprint. It knows you think in stories, struggle with abstract concepts in the morning, and need to argue with ideas before you accept them. The technology is already here. We're just not applying it to education yet.
The uncomfortable prediction I think traditional classrooms are going to become obsolete. Not because of technology, but because we'll finally admit they were never optimal for learning – just optimal for managing lots of students efficiently.
When you can have an AI tutor that understands exactly how you learn, sitting in a lecture hall listening to someone teach to the "average student" will feel absurd.
Questions I'm wrestling with
How do we measure success when everyone's learning path is different?
What happens to the social aspects of learning?
Can we make truly personalized education accessible to everyone?
Are we ready for a world where learning is actually optimized for each individual?
I'm curious what others think. Have you noticed differences in how you learn vs. how you're taught? What would education look like if we designed it around individuals instead of crowds?
The Breaking Point College was destroying me. Not academically – I was getting decent grades – but mentally. The pace was relentless, the teaching style didn't match how I think, and I watched classmates who were brilliant in conversation completely fail because they couldn't adapt to the one-size-fits-all approach.
I had an advantage: prior knowledge in my field. But my classmates starting from zero? They were drowning, and it wasn't their fault.
The moment I realized how broken things were: I built a personalized learning tool for myself, just to survive. It worked so well that a classmate offered to pay for it. In a country where people spend hours finding free alternatives rather than buy software.
That's when it hit me – if students are willing to pay for better learning tools in markets where they typically won't pay for anything, the problem is massive.
The Fundamental Problem with EdTech Most educational technology treats symptoms, not causes. We digitize textbooks, gamify flashcards, or make videos more interactive. But we're still forcing diverse minds into identical boxes.
The real issue isn't that students are lazy or unmotivated. It's that we've built an industrial education system optimized for efficiency, not effectiveness. One teacher, 30+ students, standardized curriculum, uniform pace. EdTech has mostly just digitized this broken model instead of reimagining it.
What I Think the Future Looks Like After building Studygraph and talking to hundreds of students, I believe we're heading toward:
Truly Adaptive Systems: Not just "adaptive learning" that adjusts difficulty, but platforms that fundamentally change how they present information based on individual cognitive patterns. Visual learners shouldn't just get more diagrams – they should get entirely different pedagogical approaches.
AI as Personal Tutors: Not chatbots that answer questions, but AI that understands your specific learning gaps, motivation patterns, and optimal challenge levels. Think of it as having a dedicated tutor who's studied you for months.
Micro-Personalization at Scale: Instead of building for "the average student" (who doesn't exist), we'll build systems that create unique learning paths for each individual. Mass customization, not mass production.
Learning Style Fluidity: Recognition that people don't have fixed "learning styles" but rather optimal approaches that vary by subject, mood, and context. The platform adapts in real-time.
The Hard Questions But this raises difficult questions:
How do we measure success when everyone's learning journey is different?
What happens to standardized testing and credentialing?
How do we prevent personalization from becoming isolation?
Can we afford truly personalized education, or will it remain a luxury?
What role do human teachers play when AI can provide unlimited individual attention?
My Controversial Take I think traditional classrooms will become obsolete within 20 years, not because of technology, but because we'll finally admit they were never optimal for learning. They were optimal for managing learning at scale.
The future isn't online versions of classrooms. It's learning environments designed around how humans actually think and grow.
Discussion For those building in education or thinking about it:
What's your experience with personalized learning?
Do you think we're too focused on content delivery vs. learning methodology?
How do we balance personalization with the social aspects of learning?
What would education look like if we designed it from scratch today?
I'm curious to hear from educators, students, parents, and anyone who's thought deeply about how we learn.
it just feels undone, and like a test thing with rough corners: ans: yes its not complete yet. Not fully but most of the functionalities do work. Only if you understand them
This isn't basic ai marketing. Maybe you did not describe the brand enough to tailor the content. That's what i found out from experimenting.
The tool is very early and maybe i'd have to document the features properly.
PS: "Also the point where you ask for my social media passwords to 'save them in your vault' i have red flags waving." - this wasn't supposed to go to production yet but nothing shady here
2. I'm not sure why that happened but i'll look into it. There isn't really any onboarding, it's just for calculating the pricing and it's just got enough important questions to understand the business better to give a well deserved price
Scrap the current positioning and start over with customer interviews? Focus on just one specific use case instead of trying to solve "marketing" broadly? Give up on the product and treat this as an expensive learning experience? Double down on finding the right communities/channels I haven't tried yet?
I'm torn between "keep iterating" and "cut losses and move on." The hardest part is I genuinely believe this solves a real problem because it solved MY problem, but maybe my problem isn't as common as I thought. Any advice from founders who've been in similar situations? How do you know when to pivot vs. when to persist? And if you pivot, how do you figure out what direction to go? I'm at the point where I need concrete next steps rather than more analysis paralysis.
Finds communities where your actual customers hang out Helps write personalized outreach that doesn't sound spammy Generates marketing content including videos Gives step-by-step strategies based on your stage
I thought through every pain point I experienced and addressed them. I validated the idea by talking to dozens of founders who all said "I need exactly this." But here's what's driving me crazy: people won't sign up. I get traffic, positive feedback, "this looks great!" comments... then nothing. Maybe 2-3% convert to even trying the free tier. I'm starting to question everything. Maybe the problem isn't as big as I thought? Maybe founders prefer struggling with marketing over using tools? Maybe I'm solving a "nice to have" instead of a "must have"? I've been building products for years, but this disconnect between expressed need and actual behavior is breaking my brain. When people say they desperately need help with marketing but won't try a free tool that addresses their exact complaints, what's really going on? Has anyone else experienced this gap between what people say they want and what they actually do? How do you tell the difference between real problems and problems people just like complaining about? I'm genuinely lost here and could use some brutal honesty from this community.
Niche subreddits for specific industries Discord servers for freelancers in particular fields LinkedIn groups focused on operational challenges Slack communities for remote teams
Most founders never find these communities because they're not obvious. They're buried 3-4 clicks deep from the surface-level places everyone knows about. I realized this after three of my own products failed despite "validating" them with hundreds of people. The validation was real, but I was validating with the wrong audience segment. The breakthrough came when I started treating customer discovery like investigative journalism instead of market research. Instead of asking "who might want this?" I started asking "where are people already complaining about this exact problem?" This shift changed everything. Found customers who were not just interested, but actively seeking solutions and willing to pay immediately. Has anyone else noticed this pattern? How do you find the communities where your actual customers spend time?