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Daggyr DAG Work Orchestration Engine

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Daggyr DAG Work Orchestration Engine

Hello all, I've been seeing a lot of interest in work orchestration frameworks, and thought I'd show off my own take. It's the project I've used to learn rust (entire project is async rust using tokio and actix), but it's been really useful for my main work, too. Daggyr is an engine for executing tasks as quickly as possible. It can run tasks on flexible back-ends (slurm, local threads, etc), and report updates / events to trackers (currently only in-memory, working on postgres and mongodb). There's no UI, just a REST interface. It doesn't do scheduling (there are tons of tools for that). It doesn't currently have multiple users / auth, but I'm working on that. It does support pipelines defined in JSON, task parameterization, a very simple and fast REST interface for job submission and tracking, and resuming failed runs from where they left off. Why another one of these things? 1. I couldn't get behind Airflow's weird scheduling opinions. 2. Dagster, Airflow, and Prefect rely on tight integration with python. A lot of my work flows are calling custom binaries, so python provides no value, and it's also a barrier to defining jobs. 3. Daggyr can scale to pipelines of hundreds of thousands of tasks. 4. Daggyr is stupidly simple to run: no external services or dependencies (unless you want them). Simple run the binary and send some work. Long story short, it does what it says on the tin, but there's more work to be done. I'd really appreciate any feedback. No idea if I'm going to try and commercialize it, but if you'd like to see a feature ASAP, I'm always open to sponsored feature development! edit: formatting

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, tasks, using · Missing: mac, agents, macos
87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
51%51% 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: users, way, para · 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: interface, users · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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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