Great to have builder insights here. What's the biggest surprise you've seen with agents on decentralized nodes? And what type of AI workload works best in this setup? Looking forward to real stories.
Great tool for early trend-spotting and lead gen.
Potential filters: tech stack, geo/IP, registrant history.
Useful for VCs, journalists, competitive intel.
Nostalgic + practical. Good launch.
Definitely not alone. Many seniors are in the same boat. On skills focus on durable, not trendy. System design, modernization, or deep domain expertise in stable sectors.On savings - diversify beyond stocks. I-bonds, hard assets, and geographic spread if possible.
Old playbooks are outdated. Resilience is the new goal.
That stall happens when tests stop being a design tool and become just a correctness check. Switching the prompt from "Does this work?" to "What would break if this core assumption changed?" has helped me break through it.
That shift from “does this work?” to
“what breaks if this assumption is wrong?”
is very close to what I’ve been circling around.
For me, the stall seems to happen when green tests stop reducing doubt
and start just confirming structure.
Integration-level tests sometimes help me reintroduce that pressure.
The main gap is state sync, not sending alerts. Small businesses usually adopt reminders as a checkbox feature within a primary tool (like a scheduler or vertical CRM). The robust solutions I've seen tie a single calendar to an SMS service via automation (Zapier). The real problem is managing cancellations and reschedules, not the initial notification.
RoboKiller blocks known spam numbers, but new scam numbers slip through daily. The idea here is detecting scam tactics during the call — urgency, pressure, "act now" language — not just the number. But I hear you on the creepy factor.
Fair point — false sense of security is a real risk. The idea isn't to replace awareness, but to add a safety net. Even trained people slip up when caught off guard. But you're right that it shouldn't be install and forget.
I prune some, but less than I probably should, and less than most other serious SR people do. I'm more interested in techniques for (i) raising the quality of even my lower-value cards and (ii) figuring out how to actually learn the stuff that people think of as "leeches."
Love the on-device approach. The fact that it never phones home is a huge differentiator — most "utility" apps these days are just data collection with a feature attached.
The regex filtering is clever. Have you thought about adding ML-based classification for notifications that are harder to catch with patterns? Something lightweight like a small on-device model could detect promotional vs. transactional notifications without needing manual rules.
Also curious about battery impact — how often does it process the notification stream?
> Have you thought about adding ML-based classification for notifications that are harder to catch with patterns?
Honestly that's a little out my league. The idea did occur to me, but I'm discouraged by the amount of compute required for most ML.
> Also curious about battery impact — how often does it process the notification stream?
The OS sends any new notification to the app (it is a push based approach) automatically. On my own phone, this app currently shows at the bottom of the list in battery usage (<1%).
This should be required reading for anyone building government or emergency sites.
During any crisis, people have spotty connections, dying phones, and zero patience for loading spinners. A plain HTML page with bullet points would save lives over a fancy React app that needs 5MB to render.
The irony is we have better tools than ever to build fast sites, yet the average webpage keeps getting heavier. Somewhere we forgot that the web worked fine before JavaScript frameworks.
That bulleted newsletter list being the most useful thing says everything.
