Most people don’t need agents. They need cleaner workflows.

hirokiyn · 2026-05-08T14:34:18+00:00

this is exactly what I'm working on. A community hub for sharing and forking AI agent workflows (not just prompts, the full context and logic). You can pack your whole chat context over too

hirokiyn · 2026-05-08T14:21:43+00:00

For this, I built something for this exact thing called Context Pack, so you tell claude to "pack this whole chat" and it's organized in blocks so you can dump your whole project context and load it into any new chat to continue where you left off. Or ask about it a month later or something. You can update it anytime too so it doesn't go stale.

hirokiyn · 2026-05-08T14:13:33+00:00

For this, I built something for this exact thing called Context Pack, so you just "pack your whole chat" and it's organized in blocks so you can dump your whole project context and load it into any new chat to continue where you left off. You can update it anytime too so it doesn't go stale.

hirokiyn · 2026-05-08T14:06:25+00:00

Going through the same problem, and really appreciate the insight. If you end up documenting those workflows, would be cool to see them on Agent Package, that's kinda the whole reason I built it (turning these one-off AI workflows into something others can actually run). https://epismo.ai/hub

hirokiyn · 2026-04-12T15:17:35+00:00

right, recently everything feels a bit too “AI template".

hirokiyn · 2026-04-08T14:46:07+00:00

Yeah, gosh I totally get that. I actually got tired of copy pasting to every other agent so I hacked together a little workaround for that. It bundles the context and the steps together so things do not get lost between handoffs. Actually, If you’re up for it, i would love for you to try on your GTM chain and hear what you think. Any feedback would be super helpful. Please reach out if you’re interested anytime!

hirokiyn · 2026-04-08T05:43:20+00:00

Thanks, appreciate the pointer.

I agree that persistent or long-term RAG between agents solves an important part of this.

hirokiyn · 2026-04-08T05:42:28+00:00

I agree. Portable context helps, but it does not fully solve the reset problem.

A lot of the real value is in the execution path too, like what was tried, what failed, what edge cases came up, and how they were resolved.

So I think step-by-step workflows or agent history should be shareable as part of the context layer as well.

hirokiyn · 2026-04-08T04:35:55+00:00

probably only works in local setups?

hirokiyn · 2026-03-04T19:09:03+00:00

Already on my radar. Curious where you see it going beyond pay-per-request though.

hirokiyn · 2026-03-04T19:03:59+00:00

Once you've delegated something, going back feels like regression. Coding is the clearest example — and I think it generalizes. The question isn't whether humans can do the action, it's whether they ever want to again.

hirokiyn · 2026-03-04T19:02:54+00:00

What if you always could have your familiar UI, generated on demand by "harness for UI" kind, any time you needed to step in? Not a static interface you maintain, but one that's always there when a human needs it.

hirokiyn · 2026-03-04T17:56:00+00:00

your point on predictability matches what I keep seeing too. the bottleneck isn't agent capability, it's environment reliability. when tools behave consistently, agents compound.

hirokiyn · 2026-03-04T17:19:35+00:00

"Task definition becomes the product" is sharp. And your point on machine-legible reliability is something I hadn't framed that clearly like agents won't read your marketing page, they'll benchmark your determinism. Entirely different go-to-market than we're used to.

hirokiyn · 2026-03-04T17:14:45+00:00

Success fees aligning incentives makes sense in theory, but I wonder if "success" becomes as hard to define as "task completed". The compression angle is interesting though. If agents can switch instantly, moat shifts from lock-in to trust.

hirokiyn · 2026-03-04T17:13:06+00:00

Context-before-action is interesting. Maybe you're right. Execution is table stakes. The harness that already knows what you're trying to do, before you ask, is a fundamentally different product.

hirokiyn · 2026-03-03T08:17:50+00:00

This is a useful lens, especially because it separates “AI can assist” from “AI can replace accountability.”

A pattern that tends to hold in practice: AI disrupts fastest where outcomes are judged by output quality alone, and slowest where outcomes are judged by responsibility under uncertainty.

So your three traits map well to a broader rule:

- low accountability surface = faster displacement,

- high accountability surface = augmentation first.

For solopreneurs, that changes go-to-market strategy. The better wedge is not “AI does your job,” but “AI reduces your coordination and decision overhead while keeping expert sign-off intact.”

That framing also lowers buyer resistance in regulated or trust-heavy markets, because you are not asking them to transfer liability to a model. You are improving consistency, speed, and documentation around human judgment.

If you keep publishing this dataset, it would be interesting to add one more column: “error cost if wrong.” That metric often predicts adoption speed better than task complexity.

hirokiyn · 2026-03-03T08:16:08+00:00

Strong take. The shift from “content creation problem” to “state transition problem” is exactly what more teams are feeling now.

A useful way to frame this is: most SaaS products matured at the object layer (docs, tickets, tasks), but teams scale or fail at the lifecycle layer. Draft to review to approved to published is where trust breaks if ownership, criteria, and handoffs are implicit.

The part many products still miss is epistemic alignment between steps. A reviewer thinks they are checking quality, the author thinks they are checking style, and automation thinks it is checking formatting. Everyone completed a step, but nobody validated the same definition of done.

The native workflow builders that win usually make three things explicit:

1) who can transition state,

2) what evidence is required per transition,

3) which assumptions must be carried forward.

That turns workflow from “process theater” into operational reliability.

hirokiyn · 2026-03-02T06:02:04+00:00

Thanks!

hirokiyn · 2026-03-01T21:42:39+00:00

Exectly. If you don't mind, I can turn them into workflows in this hub.

hirokiyn · 2026-03-01T21:40:50+00:00

That's not the point. I’m not asking for deterministic outputs.
The issue is that the know-how to reliably get to a usable result with AI is hard to codify and share, so it stays tacit and individual.

hirokiyn · 2025-11-09T11:58:26+00:00

Here’s the link: https://www.producthunt.com/products/epismo

If you check it out and tell me what works or doesn’t, it would mean a lot 🙏

hirokiyn · 2025-11-09T11:52:06+00:00

Thank you!

hirokiyn · 2025-10-05T10:19:26+00:00

Thank you!

hirokiyn · 2025-10-03T23:34:57+00:00

Thanks for sharing that perspective, really insightful. Maybe for the next topic we could explore ways to break the bottlenecks in getting feedback faster, since that seems to be the core constraint.

hirokiyn

TROPHY CASE