Op

Open Fire Serverless CI

Hacker News

Open Fire Serverless CI

Hello, HN community, I'm really excited to show you Open Fire a serverless CI, how does it work? relying on serverless technologies (firecracker) we can spin up a new VM in less than 200 ms running in a more powerful and newest CPU on the market, just change 1 line of code on your GitHub Actions Workflow and you're up and running! You can see our use case, where we implemented Open Fire in the NextJS repo taking down build time from 1 hour 17 minutes to 26 minutes which is a 66 % of improvement without any engineering effort! [1] The value of a CI/CD Pipeline is inversely proportional to how long the pipeline takes to run and is a limiting factor for companies to release quickly and often. For a little bit of background, I have been working in the CI/CD space for the last 9 years in small startups, my own CI/CD startup for mobile games, and big enterprises like PayPal and Binance, and you see the same pattern emerges In today's life developers are pretty good when they're building new features on their local machines, they have top-edge hardware like MacBooks with tons of core and RAM, but when they push and need to run all CI/CD steps building (multi-arch x86, x86_64, ARM), unit testing, e2e they start to feel very frustrated with the state of the art of their CI/CD pipeline because those will be running on some cloud provider crappy VM that has between 2vCPU 4 GiB to 4 vCPU 8 GiB of RAM, is in that place when they see that their local workflow from 2 minutes build time will become something in the range of 30 minutes to 1 hour! And if you want to migrate to self-hosted CI you will get: High cost of idle infrastructure waiting to pick up jobs to run. Big queues for accessing the resource to run your pipelines, because everyone is working at the same time frame the high demand overlaps and you can't scale your self-hosted solution that fast without building a team of ~ 20 people. Spent all day installing and updating all the dependencies of the VM and now have to maintain the software packages installed on that machine Companies tend to have 3 different kinds of CI/CD platforms inside them, legacy systems using Jenkins and for new systems GitHub or GitLab, and Buildkite, so you need to create and maintain new runners for all these CI/CD systems, pre-install software for all the build pipelines that may run in your runner Now you have a new platform to develop, update and support every day for the whole company. And the list goes on [1] - https://open-fire.notion.site/Open-Fire-Serverless-CI-788479... Thanks for reading, if you want to try us, want to say high, or give us some feedback just ping me jean _at_ open-fire.dev - Jean

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
97%97% 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, using · Missing: agents, macos, agent
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, pipe · Missing: https docs, just released, exist
73%73% 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 · 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: platform, host · Missing: plus, intuitive, reviews
25%25% 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.

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