Inngest 1.0 – Open-source durable workflows on every platform

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Hi HN! I’m Tony, one of the co-founders of Inngest ( https://inngest.com/ ) Inngest is an open-source durable workflow platform that works on any cloud. Durable workflows are stateful, long running step functions written in code, which automatically retry on failure. It abstracts everything about queues, event streams and state for you, letting you focus on code. Some examples of uses: managing stateful AI chained step functions; managing search/rag indexes and data pipelines; integrations and webhooks; billing and payment flows. Technical details: unlike other solutions, we put lots of effort into designing our SDK’s step.run APIs to make them extremely easy to use — developer experience is the most important thing for us. We had to design and build our own queueing system to work with multi-tenancy, batching, and debouncing, and we’re iterating on this as we move to FoundationDB. It’s largely all Go in the backend, with a bunch of caching, clickhouse, event streams, and coordination on our behalf. Workers are shared nothing, and run based off of the queue and execution state. We did a post last year as we iterated on our TS SDK. The product has changed a lot since then and wanted to show the community what’s changed as we reach 1.0: * Golang, Java, and Python SDKs with cross-language function invocation (across clouds, too) * Multi-tenant aware flow control (concurrency, throttling, debounce) * Batching, grouping many events into a single function call * Much improved dashboard, with tracing and metrics built in * Advanced recovery tools like function replay, temporary pausing, bulk cancellation (with optional expressions). No more dead letter queues! * Branch deploys built in, with staging env support out of the box * Full local testing with production parity There's a ton on the roadmap, with more launching next week. We’re hiring systems & infra engineers, too — it’s a fun job with lots of challenges! Wanted to say thank you to the HN community for feedback so far! Happy Friday :)

Visit product View on Hacker NewsSep 20, 2024

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

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

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

68%68% 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

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: platform · Missing: plus, intuitive, reviews

32%32% 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

20%20% 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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