Bernard – a link checker for your website

Hacker News

Introducing Bernard, the project I have been working on solo since November 2022. After months of planning, coding and talking with a small number of potential users, I feel it is time to launch and get some real feedback. GOAL : Bernard is an automated links checker that crawls your website daily, identifying broken links (internal and external), and missing redirects, such as after a redesign or URL change. TARGET AUDIENCE : Anyone with a website—be it is a personal blog or company portal—looking to prevent link rot, to keep old URLs reachable and to avoid the dreaded 404 Page Not Found error. STACK : Elixir, with Phoenix Live View for the interactive dashboard. It runs on PostgreSQL, hosted as Podman containers on a single dedicated Hetzner server. PLAN : I am releasing this as a free, open beta to address the last few issues, and hope to introduce paid plans in February 2024. While there is a backlog of improvements and bug fixes, my primary focus now is making sure the product is aligned with what users need. The pricing model is yet to be defined, but I'm considering a free tier for small website owners, and a usage-based plan starting from $10/mo for X,000 links checked per month. This would pair well with the upcoming REST API to provide a links-checker-as-a-service product to embed in third-party systems. (Feel free to contact me if you might be interested in using this API) I also wrote about my journey to get this off the ground, and the challenges I faced at https://combo.cc/posts/bernard-devlog-3-pre-launch-reflectio... Looking forward to your criticism and suggestions.

Visit product View on Hacker NewsJan 23, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, single · Missing: mac, agents, macos

85%85% 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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, month, users · Missing: mobile apps, ios, entrepreneurs

59%59% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid, introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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