u/Unique_Reputation568

I worked in my field for years until I got laid off and things went south. I ended up doing whatever I could to pay the bills like flipping burgers at McDonald’s, stocking shelves, and even washing cars. During that time I got back into drawing. It was just an old hobby and even though a former coworker thought I could go pro I knew my skills were not at that level yet. Eventually I saw that AI channels were trending online and decided to give it a shot.

I started with AI music using Suno but that did not go well at all. My taste is a bit niche and people in the comments were really trashing my stuff which was pretty demoralizing. I decided to change my approach and used my own sketches and scripts to make videos. I was using Sora at first but lately it feels like they might be shutting down their servers entirely cuz the videos it generates have started looking very distorted and strange. I have been researching alternative platforms on reddit and noticed that kling and dreamina seedance 2.0 are hot discussed models lately.Considering my specific needs, which one do you think is the best choice for me in terms of both features and price? Or are there other better options that I should consider instead?

Solo founder here. been running a b2b content + ai consulting business for about 14 months. no employees, no contractors, just me and a stack of ai tools.

Read an article recently about a guy in china running his entire company with 16 custom ai skills. writing, editing, cover design, data visualization, project management, client delivery. all automated through custom prompts and workflows. his content output is 30x what it was before.

So i tried building something similar for my own business. not 16 skills but i got to about 9 that actually stuck.

Heres what works:

Content drafting. i write 4 newsletters a week. used to take me a full day each. now i have a custom prompt that knows my voice, my formatting rules, my banned words list. first draft takes 20 minutes. editing still takes an hour but thats fine.

Client proposal generation. i feed in the brief, my pricing tiers, past similar projects. get a solid first draft in 10 minutes. used to spend 2 hours per proposal.

Competitor research. set up a workflow that scans specific sources weekly, pulls out relevant changes, summarizes into a report. saves me maybe 3 hours a week of manual browsing.

What doesnt work as well as people claim:

Client communication. tried automating email responses. clients can tell. stopped doing that immediately.

Strategic decisions. ai can give me options but picking the right one still requires understanding my specific market, my relationships, my risk tolerance. no prompt fixes that.

Complex project planning. for my dev side projects i use verdent to break down features into tasks and it does a decent job with the technical decomposition. but for business planning, the ai suggestions are too generic. "build an email list" yeah thanks.

The revenue numbers. went from about 8k/month to 12k/month over the past 6 months. but i also raised prices and got better clients during that time so hard to attribute it all to ai.

Real talk: the biggest win isnt speed. its that i can take on more clients without burning out. before ai i was maxed at 4 concurrent clients. now i handle 6-7 comfortably. thats where the revenue growth actually comes from.

Total tool spend is around $180/month. roi is clearly positive but its not passive income or anything close to it. i still work 40-45 hours a week. just different hours, less grunt work more thinking work.

The "ambient business" hype where ai runs everything while you sleep on the beach? not there yet. maybe not ever for service businesses. but as a force multiplier for a solo operator, yeah its legit.

Claude Mythos. ten trillion parameters. reportedly cost ten billion to train. scores 94% on the hardest software engineering benchmark that exists.

But the part that got me wasnt the benchmark score. its what it did with real systems.

It found a security vulnerability in software that had been running in production for 27 years. every human engineer, every automated scanner, every audit missed it. mythos found it overnight. then it found another bug that survived five million test runs over 16 years.

Anthropic looked at what this thing could do in cybersecurity and decided the public cant have it. instead they launched Project Glasswing with $100M in compute credits to help secure critical infrastructure. only 12 partners got access. amazon, apple, google, microsoft, nvidia, jpmorgan, crowdstrike, a few others.

This is a weird inflection point. weve gone from "AI might be useful someday" to "this AI is so capable we need to restrict who can use it." thats a fundamentally different conversation.

What strikes me is the gap between what we use daily and what exists behind closed doors. i use ai coding tools every day. cursor, verdent, claude code. theyre good. they catch bugs, suggest fixes, plan out features. but theyre working with models that score maybe 60-70% on the same benchmark mythos hits 94% on. the jump from "helpful assistant" to "finds things humans literally cannot find" happened faster than i expected.

The restricted access model is interesting too. nuclear technology went through a similar phase. manhattan project was classified, then atoms for peace opened civilian use, then nonproliferation treaties tried to control spread. we might be watching the same pattern start with AI. capability exists, access is restricted, eventually some framework emerges for broader use.

But nuclear tech had physical constraints. you need enrichment facilities, rare materials, massive infrastructure. AI models need compute and data. the barriers to replication are lower and shrinking.

The 27 year bug thing keeps nagging at me. not because AI found it but because of what it implies about the limits of human review. we built systems we cant fully understand and now we need AI to audit them. thats a dependency that only deepens from here.