Dropbase is a developer-first platform to build internal web apps with just Python. It lets you easily import your existing Python libraries and scripts so you don’t have to rewrite them to fit our framework.
Build apps by selecting UI components from a list and binding them to data fetcher functions or Python scripts. Use State & Context objects to access and modify the UI state and context directly via Python functions. There's no need to write frontend code.
Dropbase has a highly opinionated app layout that speeds up app development and results in simple apps that effectively solve user problems. All apps consists of a table view and a widget sidebar. By placing table(s) in the table view and UI components in the sidebar widget, you can quickly build anything from admin panels, billing dashboards, and internal engineering tools.
Once you've built your apps, share them with other users via roles, groups, permissions, and granular controls.
MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research.
Some key features of MLX include:
Familiar APIs: MLX has a Python API that closely follows NumPy. MLX also has a fully featured C++ API, which closely mirrors the Python API. MLX has higher-level packages like mlx.nn and mlx.optimizers with APIs that closely follow PyTorch to simplify building more complex models.
Composable function transformations: MLX has composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization.
Lazy computation: Computations in MLX are lazy. Arrays are only materialized when needed.
Dynamic graph construction: Computation graphs in MLX are built dynamically. Changing the shapes of function arguments does not trigger slow compilations, and debugging is simple and intuitive.
Multi-device: Operations can run on any of the supported devices (currently, the CPU and GPU).
- Unified memory: A notable difference from MLX and other frameworks is the unified memory model. Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without moving data.
Onyx compiles solely to WebAssembly. You can use a builtin WebAssembly runtime using onyx run, or compile to WASM and run using a WebAssembly runner, like Wasmer or Wasmtime.
It has tagged unions, optional, and macros
The release notes cover a deluge of exciting new features in detail, but a few highlights are:
- The database-computed default values allow for defining database-computed defaults to model fields.
- Continuing the trend of expanding the Django ORM, the generated model field allows the creation of database generated columns.
- The concept of a field group was added to the templates system to simplify form field rendering.
Congratulations to the team and community
We expect WASI to become a standard set of WIT types that provide access to low-level functionality (like getting the time and reading a file). These low-level types effectively form a “Wasm standard library” across programming languages and operating systems. We’re never had standard and shared functionality that Rust devs and .NET devs, for example, could both use. There isn’t any widely-deployed historical precedent of native code that exposed APIs with OO-ish shape (like interfaces) that could be used across programming languages and operating systems.
.NET 8 includes a new workload called wasi-experimental. It builds on top of the Wasm functionality used by Blazor, extending it to run in wasmtime and invoke WASI interfaces. It is far from done, but already enables useful functionality.