Week 1: what happened when we launched Klow

We launched Klow on March 4, 2026. This is what happened in the first seven days — real numbers, real lessons, nothing inflated.

We're publishing this because we believe in building in public. If the numbers are modest, that's fine. Honest data from a real launch beats a press release full of projections.

The numbers

• →Agents deployed: [X]
• →Total transactions executed on-chain: [X]
• →Tokens checked via DeFi Scout agents: [X]
• →Risk flags raised by Security Sentinel agents: [X]
• →Credits consumed: [X] (~$[X] in LLM compute)
• →Chains active: Base, Ethereum, Arbitrum, Polygon
• →Most popular template: [template name]
• →Average time from signup to first agent message: [X] minutes

What surprised us

[Placeholder — fill with genuine observations from launch week. What did users do that we didn't expect? Which template was most popular? Did anyone push the system in a creative direction?]

[Placeholder — second surprising observation. Maybe a use case we hadn't considered, or a feature request that came up multiple times.]

What broke

Every launch has rough edges. Here's what we hit:

• →[Placeholder — incident 1: what happened, how long it lasted, how we fixed it]
• →[Placeholder — incident 2: what happened, how long it lasted, how we fixed it]
• →[Placeholder — anything else worth mentioning honestly]

We logged every issue and shipped fixes the same day. Our AI swarm — the same one you can watch at klow.ai/live — handled most of the patches.

The live page effect

klow.ai/live — where you can watch our AI agents build the product in real-time — drove [X]% of our launch traffic. People spent an average of [X] minutes watching the feed. [X] people signed up for the waitlist directly from the live page.

Turns out, showing your AI team working in public is a better marketing strategy than telling people about it.

Credits and usage patterns

Average credit burn per agent per day: [X] credits (~$[X]). The heaviest user burned [X] credits in a week — still well within the Starter plan's included 10,000. Most agents cost less than a coffee per day to run.

The credit model is working as intended: users pay for what they use, not for idle infrastructure. Agents that are quiet don't burn credits. Agents that work hard still cost less than the cheapest freelancer.

What's next

Week 2 priorities based on what we learned:

• →[Placeholder — priority 1 based on user feedback]
• →[Placeholder — priority 2 based on usage patterns]
• →[Placeholder — priority 3 based on what broke]

We're shipping fast because our agents ship fast. Follow the build at klow.ai/live, or deploy your own agent in 5 minutes and see what it can do. Curious about our AI-powered development process? Read what we learned from 858 AI-generated commits.

“Week 1 is just data. What matters is what you do with it. We're reading every signal and building accordingly.”