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SolidPing — open-source uptime monitoring, agents inside your network

Hacker News

SolidPing — open-source uptime monitoring, agents inside your network

SolidPing is a single Go binary, AGPL-3.0 — 40 check types, status pages, incidents, on-call escalation, and sub-minute intervals that aren't paywalled. I loved Better Stack, and then the free plan kept getting thinner. What finally pushed me over: incidents used to get one Slack thread each, so an outage lived in one conversation instead of a stream of disconnected messages. That went away. I wanted it back, so I built my own. The part I'd most like torn apart is the agents. You run one inside the network you want watched and it dials out over a WebSocket, so there's no inbound port and no firewall hole. On first start it generates its own keypairs and keeps the private halves — Ed25519 for identity, an X25519 (age) recipient that the server seals check credentials to. So the credentials for a private check are encrypted for that agent, and the server storing them can't read them. All of it is open source: the agents, WhatsApp alerts, SSO, every check type. Nothing held back for a hosted edition. Self-hosting is genuinely easy, and I depend on it, https://status.solidping.io is a second SolidPing running on fly.io that watches the cluster hosting the first one. A monitor that lives inside what it monitors isn't a monitor. I've maintained a small Go FTP server library for 10 years (github.com/fclairamb/ftpserverlib), so open source isn't a marketing decision here. Demo, no signup: https://solidping.io/demo Source: https://github.com/fclairamb/solidping What I want is UX feedback and missing features — where the interface gets in your way, what you went looking for and couldn't find, what would have to exist before you'd run this instead of what you run now. Please be blunt. "Nice project" teaches me nothing.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, slack · Missing: mac, macos, cursor
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, ide · Missing: https docs, excited, just released
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive
19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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