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Fluxly - A lightweight, self-contained DAG workflow framework (decoupled from orchestration)
https://www.reddit.com/r/programming/comments/1qhikgp/fluxly_a_lightweight_selfcontained_dag_workflow/

<!-- SC_OFF -->Hi everyone, I wanted to share Fluxly, a framework I built for running portable, self-contained DAG workflows, inspired by architectural patterns I worked with at Mobileye. Core idea:
Each workflow is a standalone execution endpoint - structured, typed, and runnable without being coupled to any orchestrator. GitHub:
https://github.com/ShaharBand/fluxly --- 🚀 What Fluxly is Fluxly lets you define DAG-based workflows where: Each workflow runs as a single self-contained unit It can be triggered via CLI, API, or environment variables It can be packaged into a Docker image Any external scheduler (Airflow, Argo, CI/CD, cron, etc.) can trigger it without glue code The workflow owns its logic, validation, retries, and structure - orchestration is optional and external. --- Why I built it (the problem it tries to solve) From experience, many containerized pipelines end up as: Ad-hoc scripts scattered across containers Inconsistent inputs/outputs Retry, timeout, and logging logic duplicated or forgotten Tight coupling to a specific orchestrator SDK On the other hand: Heavy orchestrators (Airflow, etc.) introduce operational overhead when all you want is a portable job Orchestrator-coupled SDKs assume persistent backends and remote control planes, which don’t fit fire-and-forget workloads Fluxly keeps the workflow clean and isolated: Explicit DAG Typed I/O models Uniform entrypoints (CLI / API / env) Clear node boundaries No hidden runtime coupling It works especially well when: Each Docker image should be simple and autonomous You want structure without infrastructure overhead You want the same workflow to run locally, in CI, or under any scheduler In monorepos (or via thin wrappers), Fluxly can also standardize validation, logging, metadata, and interfaces across many pipelines. --- Key features DAG-based workflows with explicit dependencies Auto-generated CLI commands and FastAPI endpoints per workflow Strict validation using Pydantic models Nodes manage their own execution and retries Local-first development, production-ready execution Easy to extend with logging, metrics, or org-specific wrappers --- 🛠️ Stack Python, Pydantic, Typer, FastAPI, Uvicorn, Loguru, Diagrams, Pixi, Ruff, Mypy, Pytest, MkDocs --- 📦 Install pip install fluxly --- 🙏 Feedback welcome
I’d love feedback and Happy to answer questions or discuss tradeoffs. <!-- SC_ON --> submitted by /u/ShaharBand (https://www.reddit.com/user/ShaharBand)
[link] (https://github.com/ShaharBand/fluxly) [comments] (https://www.reddit.com/r/programming/comments/1qhikgp/fluxly_a_lightweight_selfcontained_dag_workflow/)
Looking for subscription costs API
https://www.reddit.com/r/programming/comments/1qhjc2s/looking_for_subscription_costs_api/

<!-- SC_OFF -->I am looking for Api that has up to date prices of services like netflix amazon prime hulu xbox pass and etc. that gives me cost and tiers is there anything like that? <!-- SC_ON --> submitted by /u/department_of_eyes (https://www.reddit.com/user/department_of_eyes)
[link] (http://me.me/) [comments] (https://www.reddit.com/r/programming/comments/1qhjc2s/looking_for_subscription_costs_api/)
Discussion: GitHub Copilot litigation highlights structural conflict of interest in code hosting & training — proposal for neutrality charter
https://www.reddit.com/r/programming/comments/1qhqj5k/discussion_github_copilot_litigation_highlights/

<!-- SC_OFF -->I’m an independent developer who’s been following the Copilot litigation closely, and I think the current legal approach misses a crucial structural fix. Fining Microsoft won’t resolve the underlying conflict of interest. As long as a trillion-dollar AI company controls the world’s most important code repository, the incentive to “harvest” code will always outweigh the duty to responsibly host it. Today I submitted a proposed Amicus Brief to lead counsel (Saveri & Butterick) that outlines a simple Charter of Neutrality: • Structural separation. Move GitHub into a neutral, foundation-backed utility (think Linux Foundation) so it serves the community rather than a single corporation. • Consent-first training. No code should be used for AI training without an explicit, transparent opt-in from contributors. • Neutral governance. Establish a community-led board with real authority to audit data usage and enforce the charter. I’m waiting for the legal team’s OK to file this officially with the court. We need to stop treating ourselves as passive “users” of GitHub and start acting as its architects. I’d love the community’s feedback on these pillars — if we don’t fix the structure now, when will we? <!-- SC_ON --> submitted by /u/Litteralybadenglish (https://www.reddit.com/user/Litteralybadenglish)
[link] (https://www.saverilawfirm.com/our-cases/github-copilot-intellectual-property-litigation) [comments] (https://www.reddit.com/r/programming/comments/1qhqj5k/discussion_github_copilot_litigation_highlights/)
https://www.reddit.com/r/programming/comments/1qi0soq/par_language_update_crazy_if_implicit_generics/

<!-- SC_OFF -->Thought I'd give you all an update on how the Par programming language (https://github.com/faiface/par-lang) is doing. Par is an experimental programming language built around linear types, duality, automatic concurrency, and a couple more innovations. I've posted a video called "Async without Await" (https://www.reddit.com/r/programming/comments/1p0goii/what_if_everything_was_async_but_nothing_needed/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button) on this subreddit and you guys were pretty interested ;) Recently, we've achieved 3 major items on the Current Roadmap (https://github.com/faiface/par-lang/issues/127)! I'm very happy about them, and I really wonder what you think about their design. Conditions & if Read the full doc here. (https://faiface.github.io/par-lang/quality_of_life/if.html) Since the beginning, Par has had the either types, ie. "sum types", with the .case destruction. For boolean conditions, it would end up looking like this: condition.case { .true! => ... .false! => ... } That gets very verbose with complex conditions, so now we also have an if! if { condition1 => ... condition2 => ... condition3 => ... else => ... } Supports and, or, and not: if { condition1 or not condition2 => ... condition3 and condition4 => ... else => ... } But most importantly, it supports this is for matching either types inside conditions. if { result is .ok value => value, else => "", } And you can combine it seamlessly with other conditions: if { result is .ok value and value->String.Equals("") => "", result is .ok value => value, else => "", } Here's the crazy part: The bindings from is are available in all paths where they should. Even under not! if { not result is .ok value => "", else => value, // !!! } Do you see it? The value is bound in the first condition, but because of the not, it's available in the else. This is more useful than it sounds. Here's one big usecase. In process syntax (somewhat imperative), we have a special one-condition version of if that looks like this: if condition => { ... } ... It works very much like it would in any other language. Here's what I can do with not: if not result is .ok value => { console.print("Missing value.") exit! } // use `value` here Bind or early return! And if we wanna slap an additional condition, not a problem: if not result is .ok value or value->String.Equals("") => { console.print("Missing or empty value.") exit! } // use `value` here This is not much different from what you'd do in Java: if (result.isEmpty() || result.get().equals("")) { log("Missing or empty value."); return; } var value = result.get(); Except all well typed. Implicit generics Read the full doc here. (https://faiface.github.io/par-lang/types/implicit_generics.html) We've had explicit first-class generics for a long time, but of course, that can get annoyingly verbose. dec Reverse : [type a] [List] List ... let reversed = Reverse(type Int)(Int.Range(1, 10)) With the new implicit version (still first-class, System F style), it's much nicer: dec Reverse : [List] List ... let reversed = Reverse(Int.Range(1, 10)) Or even: let reversed = Int.Range(1, 10)->Reverse Much better. It has its limitations, read the full docs to find out. New Runtime As you may or may not know, Par's runtime is based on interaction networks, just like HVM, Bend, or Vine. However, unlike those languages, Par supports powerful concurrent I/O, and is focused on expressivity and concurrency via linear logic instead of maximum performance. However, recently we've been able to pull off a new runtime, that's 2-3x faster than the previous one. It still has a long way to go in terms of performance (and we even known how), but it's already a big step forward. <!-- SC_ON --> submitted by /u/faiface (https://www.reddit.com/user/faiface)
Optimizing satellite position calculations with SIMD and Zig
https://www.reddit.com/r/programming/comments/1qibtpn/optimizing_satellite_position_calculations_with/

<!-- SC_OFF -->A writeup on the optimization techniques I used to hit 11M+(~7M w python bindings) satellite position calculations per second using Zig. No GPU, just careful memory access patterns <!-- SC_ON --> submitted by /u/Frozen_Poseidon (https://www.reddit.com/user/Frozen_Poseidon)
[link] (https://atempleton.bearblog.dev/i-made-zig-compute-33-million-satellite-positions-in-3-seconds-no-gpu-required/) [comments] (https://www.reddit.com/r/programming/comments/1qibtpn/optimizing_satellite_position_calculations_with/)