# Open Source Projects

## Gradion AI Projects

| Project | Description |
|---------|-------------|
| [freeact](https://github.com/gradion-ai/freeact) | Lightweight, general-purpose agent that acts via code actions combining Python code, shell commands, and programmatic MCP tool calls, generated in a single inference pass. For programmatic MCP tool calling ("code mode"), it generates typed Python APIs from MCP server schemas and discovers task-relevant tools via agentic and semantic search, loading only what's needed into the context window. Saves successful code actions as reusable tools, supports subagent delegation and application-level approval, and runs locally as both a CLI tool and Python SDK. |
| [ipybox](https://github.com/gradion-ai/ipybox) | Unified execution environment for Python code, shell commands, and programmatic MCP tool calls, all combinable within a single code block executed in a stateful IPython kernel. Generates typed Python APIs from MCP server schemas ("code mode") and supports application-level approval of individual tool calls and shell commands during execution. Runs locally with optional lightweight sandboxing via Anthropic's sandbox-runtime, and is available as a Python SDK, MCP server, and Claude Code plugin. |


## Personal Projects

| Project | Description |
|---------|-------------|
| [bayesian-machine-learning](https://github.com/krasserm/bayesian-machine-learning) | A collection of notebooks about Bayesian methods for machine learning, like [Bayesian regression](/2019/02/23/bayesian-linear-regression/), [Gaussian processes](/2018/03/19/gaussian-processes/), [Bayesian optimization](/2018/03/21/bayesian-optimization/), [variational inference in Bayesian neural networks](/2019/03/14/bayesian-neural-networks/), ..., etc. Each notebook covers a single topic and combines an introduction, mathematical basics and a simple implementation. |
| [perceiver-io](https://github.com/krasserm/perceiver-io) | The perceiver-io library is a modular implementation of [Perceiver](https://arxiv.org/abs/2103.03206), [Perceiver IO](https://arxiv.org/abs/2107.14795), and [Perceiver AR](https://arxiv.org/abs/2202.07765) in PyTorch, with a PyTorch Lightning integration for distributed training and a Hugging Face integration for inference. The project provides both ported [official models](https://github.com/krasserm/perceiver-io/blob/main/docs/pretrained-models.md#official-models) and [custom models](https://github.com/krasserm/perceiver-io/blob/main/docs/pretrained-models.md#training-checkpoints) used in [training examples](https://github.com/krasserm/perceiver-io/blob/main/docs/training-examples.md). |
| [super-resolution](https://github.com/krasserm/super-resolution) | This project provides a Tensorflow 2.x based implementation of three popular single image super-resolution models: [EDSR](https://arxiv.org/abs/1707.02921), [WDSR](https://arxiv.org/abs/1808.08718) and [SRGAN](https://arxiv.org/abs/1609.04802). Pre-trained weights, training and inference examples as well as a data loader for the DIV2K dataset are included. |
| [fairseq-image-captioning](https://github.com/krasserm/fairseq-image-captioning) | Implements an *Image Captioning Transformer* with the [fairseq](https://github.com/facebookresearch/fairseq) sequence modelling toolkit by combining ideas from [Self-critical Sequence Training for Image Captioning](https://arxiv.org/abs/1612.00563) and [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://arxiv.org/abs/1707.07998) with the [Transformer](https://arxiv.org/abs/1706.03762) architecture. |
| [face-recognition](https://github.com/krasserm/face-recognition) | Demonstrates how to build a face recognition system with [Keras](https://keras.io/), [Dlib](http://dlib.net/) and [OpenCV](https://opencv.org/). The process involves preprocessing images for face alignment, generating 128-dimensional face embeddings with a convolutional neural network (CNN), training classifiers on labeled embeddings and predicting identities of new inputs. |
| [streamz](https://github.com/krasserm/streamz) | Streamz is a combinator library designed to integrate [Functional Streams for Scala](https://fs2.io) (FS2), [Akka Streams](https://doc.akka.io/docs/akka/current/stream/index.html), and [Apache Camel](https://camel.apache.org/) endpoints, allowing seamless interoperability between these technologies. Camel endpoints can be integrated into FS2 applications with the [Camel DSL for FS2](https://github.com/krasserm/streamz/blob/master/streamz-camel-fs2/README.md) and into Akka Streams applications with the [Camel DSL for Akka Streams](https://github.com/krasserm/streamz/blob/master/streamz-camel-akka/README.md). |
| [akka-stream-eventsourcing](https://github.com/krasserm/akka-stream-eventsourcing) | This project brings to [Akka Streams](https://doc.akka.io/docs/akka/current/stream/index.html) what [Akka Persistence](https://doc.akka.io/docs/akka/current/persistence.html) brings to [Akka Actors](https://doc.akka.io/docs/akka/current/index-actors.html): persistence via event sourcing. In the same way as persistent actors in Akka Persistence, stateful *request processors* in akka-stream-eventsourcing form a consistency boundary around internal state but additionally provide type safety and end-to-end back-pressure for the whole event sourcing message flow. |


## Contributions

| Project | Contributions |
|---------|--------------|
| [sandbox-runtime](https://github.com/anthropic-experimental/sandbox-runtime) | https://github.com/search?q=repo%3Aanthropic-experimental%2Fsandbox-runtime+author%3Akrasserm&type=pullrequests&s=created&o=desc |
| [llama.cpp](https://github.com/ggerganov/llama.cpp) | https://github.com/search?q=repo%3Aggml-org%2Fllama.cpp+author%3Akrasserm&type=pullrequests |
| [eventuate](https://github.com/rbmhtechnology/eventuate) | https://github.com/search?q=repo%3ARBMHTechnology%2Feventuate+author%3Akrasserm&type=commits |
| [akka](https://github.com/akka/akka) | https://github.com/search?q=repo%3Aakka%2Fakka-core+author%3Akrasserm&type=commits |
| [alpakka](https://github.com/akka/alpakka) | https://github.com/search?q=repo%3Aakka%2Falpakka+author%3Akrasserm&type=pullrequests |
| [akka-persistence-cassandra](https://github.com/akka/akka-persistence-cassandra) | https://github.com/search?q=repo%3Aakka%2Fakka-persistence-cassandra+author%3Akrasserm&type=commits |
| [eventsourced](https://github.com/eligosource/eventsourced) | https://github.com/search?q=repo%3Aeligosource%2Feventsourced+author%3Akrasserm&type=commits |
| [ipf](https://github.com/oehf/ipf) | https://github.com/search?q=repo%3Aoehf%2Fipf+author%3Akrasserm&type=commits |
| [camel](https://github.com/apache/camel) | https://github.com/search?q=repo%3Aapache%2Fcamel+krasser&type=commits |