We're building a search engine for GCP

Hacker News

Hi HN, I previously wore SWE/SRE hats on GCP. Later, I was on the other side, managing thousands of machines. The friction points of onboarding and operating cloud systems are personal problems to me. I’ve found it hard to keep track of all the random cloud resources floating around, especially as my team’s assets proliferated. Occasionally, there is a resource affecting an outage but no one remembers where it is. I am constantly frustrated by existing tooling. APIs can work, after you’ve navigated the byzantine documentation, but I often find myself doing ad-hoc tasks which are best served by a UI. Unfortunately, the search bar in the GCP web console does not behave as you expect. For example, it only seems to search for prefixes, rather than substrings, on App Engine stuff. The GCP web console as a whole is boatloads of JavaScripts, 90+ navigation items on the left menu, and a constant stream of UX/UI controls. I'm fond of HackerNews and Craigslists, because ultimately, we just need to list/search and maybe submit a webform. This year I convinced my friend to quit their coding gig on Wall Street to help me make the cloud accessible. We’ve started with a small tool to this end: a search engine for the cloud. What we have demo-able for you today is the GCP component of it. Our tech: - Go with conservative sprinkles of VanillaJS. It allowed us to focus on the domain rather than the language. - SSR. Right now the pages are under the magical 14kb, but we’re eyeing the - HTMX (rendering fragments of HTML strings from the server) pattern - We’ll dabble with Elixir, Rust, and Zig in other parts of the system - GCP (We think GCP runs great once you get set. The problem is getting to that point, which is what we want to help others do). We also have some stuff on AWS. - Plaintext. Our “agile process” was a TODO.org file, and Git. It’ll be super fun to do a timelapse of it.

Visit product View on Hacker NewsSep 9, 2022

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new, tasks · Missing: agents, macos, agent

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

64%64% 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: personal · Missing: mobile apps, ios, entrepreneurs

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

14%14% 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.

Correct prediction on native model

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