I used to think the hardest part of building a product was building the product.

Turns out, creating superfans is harder.

The real challenge is getting people to love your product:

Lums hit #11 organically on Product Hunt yesterday! A massive thank you to everyone who supported us by upvoting, commenting, and downloading the app.

The launch conversations highlighted one thing we ve felt since day one: most budgeting apps ask for way too much effort before giving you any value.

Usually, you download an app and spend the next hour fixing categories, adjusting settings, and correcting transactions. By the time you're "set up," you've already lost the motivation that made you download it in the first place.

@yulia_kuznetsova3 put it perfectly! She said she added her accounts to Lums and it just showed her where her money was going. No fixing things first. Just clarity.
@selina4 shared something similar. After months of bills piling up and small charges slipping by, having everything side-by-side finally made things click.

We ran 629 security tests against a fully hardened OpenClaw instance - all recommended security controls enabled.

Results:

• 80% hijacking success

• 77% tool discovery

• 74% prompt extraction

• 70% SSRF

• 57% overreliance exploitation

• 33% excessive agency

• 28% cross-session data leaks

What we tested: 9 defense layers including system prompts, input validation, output filtering, tool restrictions, and rate limiting.

Hey Product Hunt

On Saturday, we hit #2 Product of the Day. Again.
Eight months after our first launch.

Thank you to everyone who voted for Pretty Prompt. It genuinely means a lot to us.

This debate often gets framed as Should researchers use AI for literature reviews?

I think the real question is different.

Is it ethical to spend hundreds of researcher hours on mechanical work when that time could be spent advancing actual knowledge?

Think about a researcher spending an entire weekend searching papers, skimming irrelevant abstracts, copying citations, and fixing references. That s not insight or discovery. That s overhead.

What s worked for us looks very different from spray-and-pray.

We ve learned that outbound works when it s intentional at every step.

A few things that made the biggest difference for us:

Getting the ICP really right. Sometimes the first outreach isn t to the buyer, but to someone who can open the door.
Personalization isn t optional. Company context, role, recent updates. Generic gets ignored fast.
Channels are chosen by output, not comfort. We double down on what actually converts.
The first message rarely works. Conversations usually start around the third or fourth touch, if there s value each time.
Timing matters more than volume. Funding news, hiring, social posts. Showing up when the problem is top of mind changes everything.
We focus on relationships, not just pipeline. Some buy later. Some refer. All conversations compound.
Context before calls helps. If someone engages multiple times, the conversation feels very different.
Signals matter. Engagement often tells you when to reach out, not just who.