I'm building an app with streamlit. Why streamlit? Because I have no frontend experience and streamlit helped me get off the ground pretty quickly. Also, I'm simultaneously deploying to web and desktop, and streamlit lets me do this with just the one codebase (I intend to use something like PyInstaller for distribution) I have different "expanders" in my streamlit application. Each expander has some data/input elements in it (in the case of my most recent problem, it's a data_editor). Sometimes, I need one element to update in response to the user clicking on "Save Changes" in a different part of the application. If they were both in the same fragment, I could just do st.rerun(scope='fragment'). But since they're not, I have no other choice but to do st.rerun(). But if there's incorrect input, I write an error message, which gets subsequently erased due to the rerun. Now I know that I can store this stuff in st.session_state and add additional logic to "recreate" the (prior) error-message state of the app, but that adds a lot of complexity. Since there is no way to st.rerun() a different fragment than the one I'm in, it looks like I have to give up streamlit - about time, I've been writing workarounds/hacks for a lot of streamlit stumbling blocks. So, would anyone be able to recommend an alternative to streamlit? These are the criteria to determine viability of an alternative: ability to control the layout of my elements and programmatically refresh specific elements on demand web and desktop deployments from the same codebase bonus points for being able to handle mobile deployments as well Python API - I can learn another language if the learning curve is fast. That takes Node/React out of the realm of possibility somewhat mature - I started using streamlit back in v0.35 or so. But now I'm using v1.52. While streamlit hasn't been around for as long as React, v1.52 is sufficiently mature. I doubt a flashy new frontend framework (eg: with current version 0.43) would have had enough time to iron out the bugs if it's only been around for a very short period of time (eg: 6 months). ideally something you have experience with and can therefore speak confidently to its stability/reliability I'm currently considering: 1. flet: hasn't been around for very long - anyone know if it's any good? 1. NiceGUI 1. Reflex If anyone has any thoughts or suggestions, I'd love them Thank you submitted by /u/inspectorG4dget [link] [comments]

I fix Python bugs and small scripts. Common issues I handle: - Scripts crashing with errors - Infinite loops - Input validation problems - File handling errors Available today. Payment via PayPal, Cash App, or Venmo. Message me with: 1) Python version 2) Error message or screenshot 3) What the script should do I’ll confirm price once I see it. submitted by /u/Dazzling_Leather3897 [link] [comments]

I work for a small city planning firm, who uses a ChatGPT Plus subscription to assist us in tracking new requests for proposals (RFPs) from a multitude of sources. Since we are a city planning firm, these sources are various federal, state, and local government sources, along with pertinent nonprofits and bid aggregator sites. We use the tool to scan set websites, that we have given it daily for updates if new RFPs pertinent to us (i.e., that include or fit into a set of keywords we have given the chats, and have saved to the chat memory) have surfaced for the sources in each chat. ChatGPT, despite frequent updates and tweaking of prompts on our end, is less than ideal for this task. Our "daily checks" done through ChatGPT consistently miss released RFPs, including those that should be within the parameters we have set for each of the chats we use for this task. To work around these issues, we have split the sources we ask it to check, so that each chat has 25 sources assigned to it in order for ChatGPT to avoid cutting corners (when we've given it larger datasets, despite asking it not to, it often does not run the full source check and print a table showing the results of each source check), and indicate in our instructions that the tracker should also attempt to search for related webpages and documents matching our description in addition to the source. Additionally, every month or so we delete the chats, and re-paste the same original instructions to new chats and remake the related automations to avoid the chats' long memories obstructing ChatGPT from completing the task well/taking too long. The problems we've encountered are as follows: We have automated the task (or attempted to do so) for ten of our chats, and results are very mixed. Often, the tracker returns the results, unprompted, at 11:30 am for the chats that are automated. Frequently, however, the tracker states that it's impossible to run the task without manually prompting a response (despite it, at other times and/or in other chats, returning what we ask for as an automated task). Additionally, in these automated commands, they often miss released RFPs even when run successfully. From what I can gather, this is because the automation, despite one of its instructions being to search the web more broadly, limits itself to checking one particular link, and sometimes the agencies in question do not have a dedicated RFP release page on their website so we have used the site homepage as the link. As automation is only permitted for up to 10 chats/tasks with our Plus subscription, we do a manual prompt (e.g., "run the rfp tracker for [DATE]") daily for the other chats. Still, we are seeing similar issues where the tracker does not follow the "if no links, try to search for the RFPs released by these agencies" prompt included in its saved memory. Additionally (and again, this applies to all the chats automated and manually-prompted alike) many sources block ChatGPT from accessing content--would this be an issue Python could overcome? See my question at the end. From the issues above, ChatGPT is often acting directly against what we have (repeatedly) saved to its memory (such as regarding searching elsewhere if a particular link doesn't have RFP listings). This is of particular importance for smaller cities, who sometimes post their RFPs on different pieces of their municipal websites, or whose "source page" we have given ChatGPT is a static document or a web page that is no longer updated. The point of using ChatGPT rather than manual checks for this is we were hoping that ChatGPT would be able to "go the extra mile" and search the web more generally for RFP updates from the particular agencies, but whether in the automated trackers or when manually prompted it's pretty bad at this. How would you go about correcting these issues in ChatGPT's prompt? We are wondering if Python would be a better tool, given that much of what we'd like to do is essentially web scraping. My one qualm is that one of the big shortcomings of ChatGPT thus far has been if we give it a link that either no longer works, is no longer updated, or is a link to a website's homepage, ChatGPT isn't following our prompts to search for RFPs from that source on the web more generally and (per my limited coding knowledge) Python won't be of much help there either. I would appreciate some insightful guidance on this, thank you! submitted by /u/Leo11235 [link] [comments]

I sometimes wonder if the Python coding community is effectively a guild that one needs to earn your way into by hard knocks. Why do I need an AI to tell me about "stale code" and what to do about it?| Delete PyCache This is critical to solving the "Ghost" attribute error permanently. Please run this command in your backend directory terminal: Bash # Windows del /S /Q __pycache__ rmdir /S /Q __pycache__ # OR simply manually delete the __pycache__ folder in your backend directory. submitted by /u/Natural-Sentence-601 [link] [comments]

Hi r/Python, I recently released v0.3 of my open-source project, ReFlow. It is a desktop GUI that orchestrates local AI models to handle video translation and content filtering. Repo: https://github.com/ananta-sj/ReFlow-Studio 📽️ What My Project Does ReFlow processes video files (MP4) locally using a pipeline of PyTorch models: 1. ASR: Uses OpenAI Whisper to transcribe audio and generate timestamps. 2. TTS: Uses Coqui XTTS v2 to translate text and generate dubbed audio in a target language while preserving the original speaker's tone. 3. CV: Uses NudeNet for object detection to identify and blur specific visual classes frame-by-frame. 4. GUI: Wraps these backend scripts in a multi-threaded CustomTkinter interface with real-time logging. 🎯 Target Audience This project is for developers and privacy enthusiasts who want to run these workflows offline without relying on cloud APIs. It serves as a practical example of integrating heavy machine-learning models into a user-friendly Python application. ⚖️ Comparison vs. Cloud APIs: Unlike cloud-based solutions which require data upload and API keys, ReFlow runs entirely on the user's hardware (GPU recommended). This ensures zero data latency and complete privacy, though performance depends on local hardware specs. vs. CLI Scripts: Many local implementations of XTTS or Whisper are command-line only. This project provides a full GUI (CustomTkinter) to make the pipeline accessible for testing and daily use. 🛠️ Tech Stack Language: Python 3.10 GUI: CustomTkinter Libraries: torch, ffmpeg-python, better_profanity Models: Whisper (Base/Small), XTTS v2, NudeNet I welcome any feedback on the code structure or the UI implementation! submitted by /u/MeanManagement834 [link] [comments]

Hello pythonistas, two weeks ago I shared a blog post about an alternative way of building API integrations, heavily inspired by the developer experience of python API frameworks. What My Project Does Clientele lets you focus on the behaviour you want from an API, and let it handle the rest - networking, hydration, caching, and data validation. It uses strong types and decorators to build a reliable and loveable API integration experience. I have been working on the project day and night - testing, honing, extending, and even getting contributions from other helpful developers. I now have the project in a stable state where I need more feedback on real-life usage and testing. Here are some examples of it in action: Simple API ```python from clientele import api client = api.APIClient(base_url="https://pokeapi.co/api/v2") @client.get("/pokemon/{pokemon_name}") def get_pokemon_info(pokemon_name: str, result: dict) -> dict: return result ``` Simple POST request ```python from clientele import api client = api.APIClient(base_url="https://httpbin.org") @client.post("/post") def post_input_data(data: dict, result: dict) -> dict: return result ``` Streaming responses ```python from typing import AsyncIterator from pydantic import BaseModel from clientele import api client = api.APIClient(base_url="http://localhost:8000") class Event(BaseModel): text: str @client.get("/events", streaming_response=True) async def stream_events(*, result: AsyncIterator[Event]) -> AsyncIterator[Event]: return result ``` New features include: Handle streaming responses for Server Sent Events Handle custom response parsing with callbacks Sensible HTTP caching decorator with extendable backends A Mypy plugin to handle the way the library injects parameters Many many tweaks and updates to handle edge-case OpenAPI schemas Please star ⭐ the project, give it a download and let me know what you think: https://github.com/phalt/clientele submitted by /u/phalt_ [link] [comments]

Hi this CVE shows as a CVSS score of 10 on MS defender which has reached the top of management level, I can't find any details if 3.14.2 is patched against this or needs a manual patch and if so how I install a manual patch, Most detections on defender are on windows PCs where Python is probably installed for light dev work or arduino things, I don't think anyone's has ever grabbed a tarfile and extracted it, though I expect some update or similar scripts perhaps do automatically? Anyway I installed python with the following per a guide: winget install 9NQ7512CXL7T py install py -3.14-64 cd c:\python\ py -3.14 -m venv .venv etc submitted by /u/Trif55 [link] [comments]

Hi everyone, Ever pushed a feature, only to watch your database scream because a missed select_related or prefetch_related caused N+1 queries? Runtime tools like nplusone and Django Debug Toolbar are great, but they catch issues after the fact. I wanted something that flags problems before they hit staging or production. I’m exploring a CLI tool that performs static analysis on Django projects to detect N+1 patterns, even across templates. Early features include: Detect N+1 queries in Python code before you run it Analyze templates to find database queries triggered by loops or object access Works in CI/CD: block PRs that introduce performance issues Runs without affecting your app at runtime Quick CLI output highlights exactly which queries and lines may cause N+1s I am opening a private beta to get feedback from Django developers and understand which cases are most common in the wild. If you are interested, check out a short landing page with examples: http://django-n-1-query-detector.pages.dev/ I would love to hear from fellow Django devs: Any recent N+1 headaches you had to debug? What happened? How do you currently catch these issues in your workflow? Would a tool that warns you before deployment be useful for your team? Stories welcome. The more painful, the better! Thanks for reading! submitted by /u/Ok-Emphasis-3825 [link] [comments]

I would love to know how you guys like to structure your models/services files: Do you usually create a single models.py/service.py file and implement all the router's (in case of a FastAPI project) models/services there, or is it better to have a file-per-model approach, meaning have a models folder and inside it many separate model files? For a big FastAPI project for example, it makes sense to have a models.py file inside each router folder, but I wonder if having a 400+ lines models.py file is a good practice or not. submitted by /u/omry8880 [link] [comments]

I’m planning to get serious about Python development, but while searching for python development classes in Thane, I’ve realized there are tons of options with very different approaches. It’s confusing to decide what’s actually worth investing time in, especially as a beginner. From my experience so far, Python itself makes sense quickly, but applying it to real projects and understanding how things work end-to-end is where most people struggle. I bounced between random videos and tutorials and often ended up more confused than confident. What helped others here was structured learning with clear explanations and real examples instead of jumping between topics. Some learners I spoke with mentioned that studying at Quastech IT Training & Placement Institute, Thane helped them connect fundamentals with actual development practice because basics were taught properly before moving ahead. I’m still figuring out the right pace and focus, but the path looks clearer now. For those who’ve learned Python development—did you benefit more from classes, project practice, or self-study in the beginning? submitted by /u/PristinePlace3079 [link] [comments]

When starting something new, do you default to: venv or poetry? requests vs httpx? pandas vs lighter tools? type checking or not? Not looking for best, just interested in real-world defaults people actually use. submitted by /u/crowpng [link] [comments]

Topic: LangChain in Production, PostHog Max AI Code Walkthrough ​About Event This meeting will be a hands-on discussion where we will go through the actual code implementation of PostHog Max AI and understand how PostHog built it using LangChain. ​We will explore how LangChain works in real production, what components they used, how the workflow is designed, and what best practices we can learn from it. ​After the walkthrough, we will have an open Q&A, and then everyone can share their feedback and experience using LangChain in their own projects. ​This session is for Developers working with LangChain Engineers building AI agents for production. Anyone who wants to learn from a real LangChain production implementation. Registration Link: https://luma.com/5g9nzmxa A small effort in giving back to the community 🙂 submitted by /u/Upset-Pop1136 [link] [comments]

I’m working with a fairly large dataset (CSV) (~3 crore / 30 million rows). Due to memory and compute limits (I’m currently using Google Colab), I can’t load the entire dataset into memory at once. What I’ve done so far: Randomly sampled ~1 lakh (100k) rows Performed EDA on the sample to understand distributions, correlations, and basic patterns However, I’m concerned that sampling may lose important data context, especially: Outliers or rare events Long-tail behavior Rare categories that may not appear in the sample So I’m considering an alternative approach using pandas chunking: Read the data with chunksize=1_000_000 Define separate functions for: preprocessing EDA/statistics feature engineering Apply these functions to each chunk Store the processed chunks in a list Concatenate everything at the end into a final DataFrame My questions: Is this chunk-based approach actually safe and scalable for ~30M rows in pandas? Which types of preprocessing / feature engineering are not safe to do chunk-wise due to missing global context? If sampling can lose data context, what’s the recommended way to analyze and process such large datasets while still capturing outliers and rare patterns? Specifically for Google Colab, what are best practices here? -Multiple passes over data? -Storing intermediate results to disk (Parquet/CSV)? -Using Dask/Polars instead of pandas? I’m trying to balance: -Limited RAM -Correct statistical behavior -Practical workflows (not enterprise Spark clusters) Would love to hear how others handle large datasets like this in Colab or similar constrained environments submitted by /u/insidePassenger0 [link] [comments]

Weekly Thread: Professional Use, Jobs, and Education 🏢 Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment. How it Works: Career Talk: Discuss using Python in your job, or the job market for Python roles. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally. Guidelines: This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar. Keep discussions relevant to Python in the professional and educational context. Example Topics: Career Paths: What kinds of roles are out there for Python developers? Certifications: Are Python certifications worth it? Course Recommendations: Any good advanced Python courses to recommend? Workplace Tools: What Python libraries are indispensable in your professional work? Interview Tips: What types of Python questions are commonly asked in interviews? Let's help each other grow in our careers and education. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]

This is wxpath's first public release, and I'd love feedback on the expression syntax, any use cases this might unlock, or anything else. What My Project Does wxpath is a declarative web crawler where traversal is expressed directly in XPath. Instead of writing imperative crawl loops, wxpath lets you describe what to follow and what to extract in a single expression (it's async under the hood; results are streamed as they’re discovered). By introducing the url(...) operator and the /// syntax, wxpath's engine can perform deep/recursive web crawling and extraction. For example, to build a simple Wikipedia knowledge graph: import wxpath path_expr = """ url('https://en.wikipedia.org/wiki/Expression_language') ///url(//main//a/@href[starts-with(., '/wiki/') and not(contains(., ':'))]) /map{ 'title': (//span[contains(@class, "mw-page-title-main")]/text())[1] ! string(.), 'url': string(base-uri(.)), 'short_description': //div[contains(@class, 'shortdescription')]/text() ! string(.), 'forward_links': //div[@id="mw-content-text"]//a/@href ! string(.) } """ for item in wxpath.wxpath_async_blocking_iter(path_expr, max_depth=1): print(item) Output: map{'title': 'Computer language', 'url': 'https://en.wikipedia.org/wiki/Computer_language', 'short_description': 'Formal language for communicating with a computer', 'forward_links': ['/wiki/Formal_language', '/wiki/Communication', ...]} map{'title': 'Advanced Boolean Expression Language', 'url': 'https://en.wikipedia.org/wiki/Advanced_Boolean_Expression_Language', 'short_description': 'Hardware description language and software', 'forward_links': ['/wiki/File:ABEL_HDL_example_SN74162.png', '/wiki/Hardware_description_language', ...]} map{'title': 'Machine-readable medium and data', 'url': 'https://en.wikipedia.org/wiki/Machine_readable', 'short_description': 'Medium capable of storing data in a format readable by a machine', 'forward_links': ['/wiki/File:EAN-13-ISBN-13.svg', '/wiki/ISBN', ...]} ... Target Audience The target audience is anyone who: wants to quickly prototype and build web scrapers familiar with XPath or data selectors builds datasets (think RAG, data hoarding, etc.) wants to study link structure of the web (quickly) i.e. web network scientists Comparison From Scrapy's official documentation, here is an example of a simple spider that scrapes quotes from a website and writes to a file. Scrapy: import scrapy class QuotesSpider(scrapy.Spider): name = "quotes" start_urls = [ "https://quotes.toscrape.com/tag/humor/", ] def parse(self, response): for quote in response.css("div.quote"): yield { "author": quote.xpath("span/small/text()").get(), "text": quote.css("span.text::text").get(), } next_page = response.css('li.next a::attr("href")').get() if next_page is not None: yield response.follow(next_page, self.parse) Then from the command line, you would run: scrapy runspider quotes_spider.py -o quotes.jsonl wxpath: wxpath gives you two options: write directly from a Python script or from the command line. from wxpath import wxpath_async_blocking_iter from wxpath.hooks import registry, builtin path_expr = """ url('https://quotes.toscrape.com/tag/humor/', follow=//li[@class='next']/a/@href) //div[@class='quote'] /map{ 'author': (./span/small/text())[1], 'text': (./span[@class='text']/text())[1] } registry.register(builtin.JSONLWriter(path='quotes.jsonl')) items = list(wxpath_async_blocking_iter(path_expr, max_depth=3)) or from the command line: wxpath --depth 1 "\ url('https://quotes.toscrape.com/tag/humor/', follow=//li[@class='next']/a/@href) \ //div[@class='quote'] \ /map{ \ 'author': (./span/small/text())[1], \ 'text': (./span[@class='text']/text())[1] \ }" > quotes.jsonl Links GitHub: https://github.com/rodricios/wxpath PyPI: pip install wxpath submitted by /u/fourhoarsemen [link] [comments]

My son (13) is interested in programming. I would like to sign him up for some introductory (and fun for teenagers) online program. Are there any that you’ve seen that you’d be able to recommend. Paid or unpaid are fine. submitted by /u/CodeVirus [link] [comments]

**What My Project Does** - It’s called audio-dsp. It is a comprehensive collection of DSP tools including Synthesizers, Effects, Sequencers, MIDI tools, and Utilities. **Target Audience** - I am a music producer (25 years) and programmer (15 years), so I built this with a focus on high-quality rendering and creative design. If you are a creative coder or audio dev looking to generate sound rather than just analyze it, this is for you. **Comparison** - Most Python audio libraries focus on analysis (like librosa) or pure math (scipy). My library is different because it focuses on musicality and synthesis. It provides the building blocks for creating music and complex sound textures programmatically. Try it out: pip install audio-dsp GitHub: https://github.com/Metallicode/python_audio_dsp I’d love to hear your feedback! submitted by /u/D0m1n1qu36ry5 [link] [comments]

Hey r/Python! 👋 I just released django-smart-ratelimit v1.0.1. I built this because I needed a rate limiter that could handle modern Django (Async views) and wouldn't crash my production apps when the cache backend flickered. What makes it different? 🐍 Full Async Support: Works natively with async views using AsyncRedis. 🛡️ Circuit Breakers: If your Redis backend has high latency or goes down, the library detects it and temporarily bypasses rate limiting so your user traffic isn't dropped. 🧠 Flexible Algorithms: You aren't stuck with just one method. Choose between Token Bucket (for burst traffic), Sliding Window, or Fixed Window. 🔌 Easy Migration: API compatible with the legacy django-ratelimit library. Quick Example: from django_smart_ratelimit import ratelimit @ratelimit(key='ip', rate='5/m', block=True) async def my_async_view(request): return HttpResponse("Fast & Safe! 🚀") I'd love to hear your feedback on the architecture or feature set! GitHub: https://github.com/YasserShkeir/django-smart-ratelimit submitted by /u/TheCodingTutor [link] [comments]

Hi all! I wanted to share a small library I’ve been working on. Feedback is very welcome, especially on UX, edge cases or missing features. https://github.com/jdvanwijk/dc-input What my project does I often end up writing small scripts or internal tools that need structured user input, and I kept re-implementing variations of this: from dataclasses import dataclass @dataclass class User: name: str age: int | None while True: name = input("Name: ").strip() if name: break print("Name is required") while True: age_raw = input("Age (optional): ").strip() if not age_raw: age = None break try: age = int(age_raw) break except ValueError: print("Age must be an integer") user = User(name=name, age=age) This gets tedious (and brittle) once you add nesting, optional sections, repetition, undo-functionality, etc. So I built dc-input, which lets you do this instead: from dataclasses import dataclass from dc_input import get_input @dataclass class User: name: str age: int | None user = get_input(User) The library walks the dataclass schema and derives an interactive input session from it (nested dataclasses, optional fields, repeatable containers, defaults, undo support, etc.). For an interactive session example, see: https://asciinema.org/a/767996 Target Audience This has been mostly been useful for me in internal scripts and small tools where I want structured input without turning the whole thing into a CLI framework. Comparison Compared to prompt libraries like prompt_toolkit and questionary, dc-input is higher-level: you don’t design prompts or control flow by hand — the structure of your data is the control flow. This makes dc-input more opinionated and less flexible than those examples, so it won’t fit every workflow; but in return you get very fast setup, strong guarantees about correctness, and excellent support for traversing nested data-structures. submitted by /u/Emotional-Pipe-335 [link] [comments]

Hey everyone! I built a database migration tool in Python called Jetbase. I was looking for something more Liquibase / Flyway style than Alembic when working with more complex apps and data pipelines but didn’t want to leave the Python ecosystem. So I built Jetbase as a Python-native alternative. Since Alembic is the main database migration tool in Python, here’s a quick comparison: Jetbase has all the main stuff like upgrades, rollbacks, migration history, and dry runs, but also has a few other features that make it different. Migration validation Jetbase validates that previously applied migration files haven’t been modified or removed before running new ones to prevent different environments from ending up with different schemas If a migrated file is changed or deleted, Jetbase fails fast. If you want Alembic-style flexibility you can disable validation via the config SQL-first, not ORM-first Jetbase migrations are written in plain SQL. Alembic supports SQL too, but in practice it’s usually paired with SQLAlchemy. That didn’t match how we were actually working anymore since we switched to always use plain SQL: Complex queries were more efficient and clearer in raw SQL ORMs weren’t helpful for data pipelines (ex. S3 → Snowflake → Postgres) We explored and validated SQL queries directly in tools like DBeaver and Snowflake and didn’t want to rewrite it into SQLAlchemy for our apps Sometimes we queried other teams’ databases without wanting to add additional ORM models Linear, easy-to-follow migrations Jetbase enforces strictly ascending version numbers: 1 → 2 → 3 → 4 Each migration file includes the version in the filename: V1.5__create_users_table.sql This makes it easy to see the order at a glance rather than having random version strings. And jetbase has commands such as jetbase history and jetbase status to see applied versus pending migrations. Linear migrations also leads to handling merge conflicts differently than Alembic In Alembic’s graph-based approach, if 2 developers create a new migration linked to the same down revision, it creates 2 heads. Alembic has to solve this merge conflict (flexible but makes things more complicated) Jetbase keeps migrations fully linear and chronological. There’s always a single latest migration. If two migrations try to use the same version number, Jetbase fails immediately and forces you to resolve it before anything runs. The end result is a migration history that stays predictable, simple, and easy to reason about, especially when working on a team or running migrations in CI or automation. Migration Locking Jetbase has a lock to only allow one migration process to run at a time. It can be useful when you have multiple developers / agents / CI/CD processes running to stop potential migration errors or corruption. Repo: https://github.com/jetbase-hq/jetbase Docs: https://jetbase-hq.github.io/jetbase/ Would love to hear your thoughts / get some feedback! It’s simple to get started: pip install jetbase # Initalize jetbase jetbase init cd jetbase (Add your sqlalchemy_url to jetbase/env.py. Ex. sqlite:///test.db) # Generate new migration file: V1__create_users_table.sql: jetbase new “create users table” -v 1 # Add migration sql statements to file, then run the migration: jetbase upgrade submitted by /u/Parking_Cicada_819 [link] [comments]