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How I Grew Buildside to 130 Users in 2 Weeks by Building in Public

Two weeks after launching Buildside, 130 people had joined. I did not have an ad budget or a huge email list. There was no secret growth trick. I grew Buildside by sharing the journey on X and LinkedIn, speaking with users, and letting their feedback guide the app. The biggest lesson I learned was: You have to market your product long before you launch it. I started marketing before the app was ready Many founders wait until their product feels finished before they talk about it. Sharing early work can feel risky. You may worry that people will judge it, copy it, or ignore it. I chose to share anyway. Before Buildside launched, I posted about the problem I wanted to solve, why I cared about it, and what I was learning. I showed small bits of progress and asked people what they thought. That gave people a reason to follow the journey. On launch day, I was not asking strangers to care about a new app. I was inviting people who had already watched the idea grow. Y Combinator tells founders to release early. Its guide says waiting too long is often driven by fear and the wish to make everything perfect. An early version lets you ask, “What do you think?” That advice matched my experience . I shared the fails as well as the wins It is easy to post a new user count or a kind comment. It is harder to share what went wrong. Yet the honest posts often led to the best talks. When something failed, I said so. When I changed my mind, I explained why. I shared the good days too. The goal was not to make every post look impressive. It was to show the real work. Honesty builds trust. Buffer has shared parts of its business in public for years, through both strong and hard moments. The company says being open builds trust and keeps it accountable. Its open company page shows this in action . People do not need a founder to look perfect. They want to know there is a real person listening and trying to make something useful. I let users shape Buildside Building in public was not only

2026-09-03 原文 →
AI 资讯

Instagram cracks down on AI accounts pretending to be human

Instagram is finally taking steps to address the rise of fake AI-influencer accounts that have gotten harder to spot. It's also renaming the "AI creator" label to "AI-generated profile" to make it clear when a profile features an AI-generated person that's not a real human being. "We've heard that people don't like seeing a profile […]

2026-08-31 原文 →
AI 资讯

OCI Monitoring Alarms: Six Ways an Alarm Can Look Ready and Still Fail

A practical validation view for metric intervals, dimensions, notifications, suppression, and operator action An OCI alarm can be created correctly and still fail as an operating control. The issue may not be the alarm object itself. It may be the metric interval, a wrong dimension, an unconfirmed notification subscription, an absence query that fires for the wrong reason, or a message that reaches the team without telling them what action to take. This article is written from an OCI readiness and validation point of view. It is not a step-by-step Console walkthrough. The aim is to identify checks that should be confirmed before an alarm is treated as Production-ready. 1. The metric interval does not match the metric emission frequency This is one of the easiest alarm issues to miss. OCI documents a troubleshooting case where an alarm fires and then clears at the next interval even though the last data point exceeded the threshold. The cause is that the alarm interval is shorter than the metric's emission frequency. For example, if a metric is emitted hourly, an alarm interval that evaluates every minute may not behave the way the owner expects. The readiness check is to confirm the metric's documented emission frequency and set the alarm interval at the same frequency or longer when needed. Validate: Metric namespace and metric name Emission frequency for the selected metric Alarm interval in the MQL query Statistic used, such as mean, max, sum, or percentile Expected FIRING and OK behavior during testing Evidence can come from a metric-documentation or chart review, the alarm query, and a test record showing expected state behavior. The question for the owner is direct: does the alarm evaluate data at a rhythm that the metric can actually provide? 2. Dimensions include the wrong resource, or exclude the right one A metric query can be technically valid and still point at the wrong scope. In OCI Monitoring, dimensions help narrow metric streams. That is useful, but

2026-08-30 原文 →