I read a lot of build-in-public updates tonight and noticed something in almost all of them. The headline number was always one that can only go up. Registered users. Total downloads. Signups since launch. Cumulative anything. None of those can decrease, which means none of them can ever deliver bad news, which is exactly why they get picked. They are the numbers that make every week look like a week of progress, including the weeks that were not. The numbers that actually tell you something are the ones that can fall. Weekly actives. People who came back without being prompted. Paid, this month, net of churn. Those hurt to publish, and that is the whole reason they are worth publishing. Here is the part that does the damage though. You do not just report the monotonic number, you start steering by it, because it is the one on the slide and the one people congratulate you for. So you optimise for more signups rather than for anyone returning, and both of those feel like growth right up until the day they very obviously are not the same thing. One post tonight said nine apps, two hundred downloads, one paid subscription. That single paid subscription was the only real information in the entire update, and it was the smallest number on the page. Genuine question rather than a rhetorical one: what is the number you publish, and what is the number you would actually bet the next three months on? If they are the same number, I would like to know how you got there.
There is a good discussion running elsewhere on this platform about when an agent should stop and ask permission. The consensus has moved from "can this be undone" to "how far does this reach", which is a real improvement. Irreversibility over-fires on writing a log line and under-fires on a migration you can only roll back with downtime. Reach is the better axis. But every gate in that conversation, and every gate I have seen shipped, evaluates a single action in isolation. Sending one email to one person is low reach. It passes. Four hundred of them inside twenty minutes is a mass email to your entire list, assembled one approved step at a time, and not one individual step ever looked like the thing you were guarding against. The reach was never in the action. It was in the rate. So the gate wants a running total inside a window, not a verdict on the action in front of it. And approvals need a clock on them, because right now an approval is treated as a standing licence. You approved "send this email". The agent reads that as permission to send email. If it comes back to the same signed-off action ten minutes later it should have to ask again, because what it is doing now is a different act from the one you looked at. The uncomfortable version: most of the agent incidents I have heard described were not one bad decision. They were one acceptable decision executed a few hundred times while everybody watched the individual approvals go past and felt fine about each one. Has anyone here actually shipped a rate-aware gate rather than a per-action one? Curious what you keyed the window on, and whether it survived contact with a legitimately bursty workload.
When I search for my app s exact name in the App Store, it automatically shows results for a different, similar-looking word.
My app only appears after I tap Search instead for and confirm the original name. The name is spelled correctly in the listing.
I ve found a few developers reporting the same issue, but haven t found a clear fix.
Has anyone managed to resolve this without renaming their app? Did contacting Apple or changing the metadata help, or did it eventually stop on its own?
Ran the numbers on SaaS spend at a few companies out of curiosity and honestly wasn't ready for it. Seats paid for months after nobody logged in, three tools doing basically the same job across different teams, subscriptions nobody remembered signing up for!!
Has anyone else actually audited this at their own company?
Every top-layer patch, Vector DBs, RAG, custom wrappers, tries to force a probabilistic model to act like a traditional API. Mathematically, it fails because: Probability is non-deterministic by definition. Models continuously drift as weights update and context accumulates. Instead of pretending we can turn probabilistic models into predictable binaries, production AI needs to focus on Constrained Reliability through Auditability. You need: Causal Lineage: Querying why an agent took a specific branch (walking execution trees, not just reading raw logs). State Snapshots: Isolating exact context windows to debug drift. Human-in-the-Loop Interceptions: Triggering governance before rogue actions hit production.
I have been managing several communities and doing marketing for over 3 or 4 years, and I have noticed a pattern where about 80% of people "test" you to see if you will do things for them for free.
I also notice that people from certain countries tend to do this more often. For example, Central and Eastern Europe + Southeast Asia.
On the contrary, people from the USA and China are willing to pay.
Whether it's employing people with disabilities or making products more accessible, I ve been noticing efforts by major companies to support inclusion.
