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Kraken – Flow-Based Service Orchestration Framework

Hacker News

Kraken – Flow-Based Service Orchestration Framework

Erlang BEAM VM is an excellent choice for organizing communication between different services in distributed systems. With proper formalism, it is possible to express the logic of communication in a simple declarative way. Kraken suggests the flow-based programming approach for organizing the interaction between services in a software system. It means that every interaction with the system is presented as an event. It goes through a predefined chain of components (pipeline), which call underlying services and modify the event. The final state of the event represents the result of the interaction. Kraken provides JSON DSL for the definitions of pipelines and clients to underlying services. The interaction with the Kraken-based orchestrator is also carried out via JSON API. One may think about Kraken as a Kubernetes for the orchestration layer of a system, but instead of defining infrastructure, one defines high-level application logic.

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Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
20%20% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
15%15% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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