I've been in social media marketing for nearly five years, but with AI advancing, I suspect my role in ad setup may soon become unnecessary.
I used to set up promo campaigns manually using native platform tools (which already applied some AI, like automated texts or ad placement, viz. Facebook Business Manager).
Published four connected preprints on agentic AI security evaluation methodology covering reliability gaps in automated refusal/prompt-injection detectors, and a controlled 7,020-trial study finding that agent framework choice explains ~0.06% of security outcome variance versus ~29% for attack type. All open (CC BY 4.0), code open-source. Curious what other builders in this space are seeing.
Genuine question, not a lesson in disguise. I do not have this figured out.
Almost all the advice here is about what to add. A channel, a feature, a ritual, a metric. Nobody talks much about what they removed, and what it actually cost them.
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?
One challenge I've been thinking about is how to measure whether an AI agent is genuinely reliable.
It's easy to know when a traditional application works correctly because the expected output is usually well defined. AI agents are different they reason, call tools, make decisions, and sometimes produce different outputs for the same task.
Before deploying an agent, how do you decide it's ready for real users?
Do you use benchmark tasks, automated evaluations, human reviewers, production metrics, or something entirely different?
Been running a lot of outreach experiments lately and the data keeps surprising me.
LinkedIn DMs are getting better reply rates across the board. But cold email still books more meetings when the list is tight and the message is specific.
Cold email inboxes are just flooded right now. AI-generated copy everywhere and buyers have learned to ignore it. Standing out is harder but the upside is still real if you get the targeting right.
LinkedIn DMs feel more personal so people actually respond. The problem is connection request limits and platform crackdowns are making it painful to scale.
I m a commercial videographer who s been geeking out over SaaS and new tech for a while now. I m starting to explore how my background in video might fit into the SaaS world, and I d love to learn from people here who ve actually been through launches.
From what I ve seen so far, two common struggles seem to pop up:
Explaining the product clearly: A lot of SaaS landing pages or demos end up heavy on text but don t actually show how the product works in a way a new user gets it.
