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Aperture – Flow Control for Cloud Apps

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Aperture – Flow Control for Cloud Apps

Hello HN, Over the past year, we have been building Aperture - an open-source flow control and reliability platform for cloud applications. Over the past few years, companies like LinkedIn[1], Google [2], Netflix [3], Stripe [4] have built cutting-edge flow control technologies to keep their applications reliable. Flow control is powerful because it enables graceful degradation- the ability to preserve key user experience pathways, even in the face of application failures. With Aperture project, we hope to democratize building reliable applications with effective flow control. Aperture enables flow control through Aperture Agents and an Aperture Controller. Aperture Agents live next to your service instances as a sidecar and provide flow control components such as a weighted fair queuing scheduler for prioritized load-shedding and a distributed rate-limiter for abuse prevention. A flow is the fundamental unit of work from the perspective of an Aperture Agent. It could be an API call, a feature, or even a database query. The Aperture Controller is powered by always-on, dataflow-driven policies that continuously track deviations from SLOs and calculate recovery or escalation actions. The policies running in the Controller are expressed as circuits, similar to circuit networks in the game Factorio[5]. You can deploy Aperture into your service instances through Service Mesh (using Envoy) or Aperture SDKs. We are excited to release Aperture as an open source project under the AGPL v3 license, and invite SREs, DevOps & enthusiasts to give the project a try. We would love to hear your feedback on Aperture and how we can improve! GitHub: https://github.com/fluxninja/aperture Docs: https://docs.fluxninja.com/ Announcement: https://techcrunch.com/sponsor/fluxninja/fluxninja-announces... Explainer Video: https://www.youtube.com/watch?v=sEl4SMo3KNo [1]: https://engineering.linkedin.com/blog/2022/hodor--detecting-... [2]: https://sre.google/sre-book/handling-overload/ [3]: https://netflixtechblog.com/keeping-netflix-reliable-using-p... [4]: https://stripe.com/blog/rate-limiters [5]: https://wiki.factorio.com/Circuit_network

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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: agents, agent, google · Missing: mac, macos, cursor
84%84% 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, open source, ide · 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.
TrustMRRFits verified-revenue profile · Strong signals: apps, video, google · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
49%49% 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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