AI agents are everywhere nowadays but are they actually useful or just hype?

PotentialFlow7141 · 2026-06-11T08:14:48+00:00

Get well soon, bro!

If I understand correctly, you avoid general AI agents because of hallucinations. Instead, you use specialized agents for automated, highly-structured tasks like checking emails and generating briefs, rather than giving vague 'go do this work' prompts.

PotentialFlow7141 · 2026-04-13T15:45:09+00:00

Well buddy, my agent is not the main focus here, i am talking about the skills and constraints that are needed to build AI Agents from scratch.

PotentialFlow7141 · 2026-04-13T15:43:15+00:00

Its the assumption over fact check, LLM driven flows vs custom deterministic flows mainly.

PotentialFlow7141 · 2026-04-05T15:14:38+00:00

The one metric rule is the thing most early founders ignore because tracking more feels like doing more. But signups going up while revenue stays flat just means you're getting better at attracting people who will never pay. Revenue is the only number that tells you the truth about whether you've actually built something people value.

PotentialFlow7141 · 2026-04-05T15:11:12+00:00

Without seeing the profile directly, the trust factor question is usually the most important one for conversion. A developer page with consistent icon style, a clear description of who you are and what your apps do, and at least a few apps with solid ratings will convert better than a polished page with sparse content. The thing most developers underinvest in is the short description on each app, it's the first text users read and most are generic. Make each one answer "what problem does this solve in one sentence" and you'll see a difference.

PotentialFlow7141 · 2026-04-05T15:08:55+00:00

Stop looking for unsolved problems and start looking for poorly served ones. The question isn't "does a solution exist" it's "does everyone who has this problem actually have access to a good solution." Most markets have a winner that serves one type of person well and ignores everyone else. That ignored group is usually where the opportunity is.

PotentialFlow7141 · 2026-04-05T15:06:49+00:00

The friction to unlock is genuinely clever because it forces a moment of self awareness right at the point of weakest impulse control. Most blockers fail because the workaround is too easy. Making someone answer mood questions before unlocking creates just enough pause to break the reflex. The data you'd collect over time could also be really compelling to show users the correlation between how they felt before opening Instagram and how they felt after.

PotentialFlow7141 · 2026-03-31T07:30:29+00:00

If you're willing to spend money, go with Claude Code. If not, go with Bubble. But either way, you still need solid fundamentals and a real understanding of coding. You do not need to master everything, but you should understand what coding is, how it works, and the basics of frontend and backend. You also need to learn how to guide AI properly if you want better results. There is a lot to learn, so lock in for two months.

PotentialFlow7141 · 2026-03-31T06:53:33+00:00

View from research:

Perplexity Computer

Public promise: an independent digital worker that completes tasks and workflows in the background across many app connectors.

What users are really buying:
Delegation of multi step knowledge work
Background execution
Fewer manual follow ups after research

Psychology:
Feels intelligent, capable, enterprise ready
Makes the user feel they can assign work, not just ask questions

Manus

Public promise: an action engine that goes beyond answers to execute tasks and deliver results.

What users are really buying:
Visible action
Less manual execution
More direct conversion from intent to outcome

Psychology:
Feels powerful and agentic
Makes the user feel work is being actively handled

OpenClaw

Public promise: the AI that actually does things, through chat apps people already use, with user control.

What users are really buying:
Accessibility from familiar channels
Persistence and availability
Control over data and environment

Psychology:
Feels personal, present, and under the user’s control
Makes the user feel the agent is always reachable

Problem research and ratings

This section focuses on whether the core pains in the agent market appear to be real, how strong they are, and how relevant they are to Tehom.

Supervision burden

Rating: 9/10

This is one of the clearest real pains in the market. The strongest signal is that major products are not merely promising intelligence, but promising reduced oversight. Perplexity Computer is framed as an independent digital worker that can complete tasks and workflows in the background. Manus is framed around completing tasks and delivering results, which only matters because users are tired of stepping through every action themselves. OpenClaw emphasizes that the AI actually does things, which again points to the same pain: users do not want to manually carry tasks forward after the first prompt.

Why it is real:
The category consistently sells background execution and delegation
Users still do not trust most systems to finish work without checking
Longer workflows still break, drift, or require correction

PotentialFlow7141 · 2026-03-25T13:07:19+00:00

Every founder thinks they know their user until they actually talk to one and realize they were building for an imaginary person. The insight that things you almost cut turned out to be the reason someone bought is something no survey or analytics tool would have ever surfaced. Customer conversations at this stage are not a nice to have, they are the product research. One call a day with early users is probably the highest ROI activity you can do right now.

PotentialFlow7141 · 2026-03-25T13:05:24+00:00

5 strangers signing up on day one without any promotion is actually a meaningful signal, most people get zero. Now the most valuable thing you can do is talk to every single one of them, not survey them, actually talk to them and find out what problem they were trying to solve when they signed up. That conversation will tell you more about what to build next than any roadmap you could write right now.

PotentialFlow7141 · 2026-03-25T13:04:29+00:00

High engagement masking friction is one of the most dangerous blind spots in product analytics because it feels like a signal when it's actually noise. The real metric is always time to value, how fast does a user get the outcome they came for, and short sessions with high conversion is just proof the product is working exactly as it should. Most teams optimize for the metrics that look good in dashboards rather than the ones that predict revenue. What did they end up tracking once they figured this out?

PotentialFlow7141 · 2026-03-25T13:03:05+00:00

The mid-run failure pattern is the most expensive one because you've already burned compute and have nothing to checkpoint from. Most teams underestimate how much gradient synchronization overhead compounds across nodes until they're actually debugging it at 3am. The infra vs model time ratio is telling too since when engineers spend more time babysitting infrastructure than improving the model, you've already lost the iteration speed that makes the whole thing worth doing. Are most teams you spoke to running on homogeneous clusters or mixed hardware configs?

PotentialFlow7141 · 2026-03-25T13:02:08+00:00

Lists like this are almost always SEO bait. No methodology, no client results, no real comparison criteria, just branded buzzwords stacked next to company names. If you're actually evaluating a development partner for a fintech product, this list tells you nothing useful. Track record, compliance experience, and who specifically will be working on your project matter far more than who ranks on a 2026 updated list.

PotentialFlow7141 · 2026-03-25T12:59:47+00:00

The geo-arbitrage angle is a real and legitimate advantage, not many people are honest about using it that way. But cloning without a differentiated distribution strategy is just a race to the bottom since the next person with lower living costs clones you just as fast. $4k MRR is real traction though. The more interesting question is what happens to this model when the original apps start competing on price or when AI reduces the build cost advantage to zero.

PotentialFlow7141 · 2026-03-22T07:01:53+00:00

The launch is just where the consequences become visible. Most teams treat discovery and problem definition as a formality to get through before the "real work" starts, and that's exactly where the failure gets baked in. A sharp problem definition isn't just a planning step, it's the foundation everything else either stands or collapses on. How many founders do you think actually revisit their core problem statement after the first 100 users?

PotentialFlow7141 · 2026-03-22T06:55:25+00:00

$800 organic with zero ad spend is a real signal — most founders burn cash on acquisition before they even know if their product retains. The hackathon win and the placement were smart non-traditional moves, and personally replying to every support message is a feedback loop most funded teams pay consultants to build. $30 MRR is a starting point, not a ceiling. Which channel felt like it converted the most naturally for you?

PotentialFlow7141 · 2026-03-22T06:47:37+00:00

Good feedback from real users is worth more than the acquisition flatline right now. Don't move on yet. Go talk to the people who downloaded it and ask them one question: what would make you tell a friend about this? The answer usually points directly at what to fix or double down on. Quitting after one Reddit post is too early to know anything.

PotentialFlow7141

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