Conda Integrations

Reo.Dev helps Go-To-Market teams unlock and act on developer intent signals across the full funnel. It deanonymizes your pipeline by revealing the 80% of developers who engage with your product or docs without signing up and surfaces anonymous sign-ups (like Gmail or GitHub IDs) for better clarity. By tapping into third-party signals from GitHub, LinkedIn, StackOverflow, and Reddit, it identifies high-intent leads early in their journey. You can prioritize accounts based on Dev Funnel stage, build hyper-targeted lists at both the company and individual level (with access to 25M developers across 50K skill sets), and improve outreach effectiveness by surfacing key buying signals and personalizing messaging around high-value actions—ensuring your sales team focuses where it matters most.Reo.Dev is an AI-powered revenue intelligence platform designed specifically for developer-focused companies. It tracks developer activity across multiple platforms, identifying high-intent accounts that

Package manager and build system for Fortran. There are already many packages available for use with fpm, providing an easily accessible and rich ecosystem of general-purpose and high-performance code. Fortran Package Manager (fpm) is a package manager and build system for Fortran. Its key goal is to improve the user experience of Fortran programmers. It does so by making it easier to build your Fortran program or library, run the executables, tests, and examples, and distribute it as a dependency to other Fortran projects. Fpm’s user interface is modeled after Rust’s Cargo. Its long-term vision is to nurture and grow the ecosystem of modern Fortran applications and libraries. The Fortran package manager has a plugin system that allows it to easily extend its functionality. The fpm-search project is a plugin to query the package registry. Since it is built with fpm we can easily install it on our system.

garak checks if an LLM can be made to fail in a way we don't want. garak probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. garak's a free tool, we love developing it and are always interested in adding functionality to support applications. garak is a command-line tool, it's developed in Linux and OSX. Just grab it from PyPI and you should be good to go. The standard pip version of garak is updated periodically. garak has its own dependencies, you can to install garak in its own Conda environment. garak needs to know what model to scan, and by default, it'll try all the probes it knows on that model, using the vulnerability detectors recommended by each probe. For each probe loaded, garak will print a progress bar as it generates. Once the generation is complete, a row evaluating that probe's results on each detector is given.

CodeQwen is the code version of Qwen, the large language model series developed by the Qwen team, Alibaba Cloud. It is a transformer-based decoder-only language model pre-trained on a large amount of data of codes. Strong code generation capabilities and competitive performance across a series of benchmarks. Supporting long context understanding and generation with the context length of 64K tokens. CodeQwen supports 92 coding languages and provides excellent performance in text-to-SQL, bug fixes, etc. You can just write several lines of code with transformers to chat with CodeQwen. Essentially, we build the tokenizer and the model from pre-trained methods, and we use the generate method to perform chatting with the help of the chat template provided by the tokenizer. We apply the ChatML template for chat models following our previous practice. The model completes the code snippets according to the given prompts, without any additional formatting.

Experience the power of large language models like never before, unleashing the full potential of Natural Language Processing (NLP) with Spark NLP, the open source library that delivers scalable LLMs. The full code base is open under the Apache 2.0 license, including pre-trained models and pipelines. The only NLP library built natively on Apache Spark. The most widely used NLP library in the enterprise. Spark ML provides a set of machine learning applications that can be built using two main components, estimators and transformers. The estimators have a method that secures and trains a piece of data to such an application. The transformer is generally the result of a fitting process and applies changes to the target dataset. These components have been embedded to be applicable to Spark NLP. Pipelines are a mechanism for combining multiple estimators and transformers in a single workflow. They allow multiple chained transformations along a machine-learning task.

Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. It allows AI engineers and data scientists to quickly visualize their data, evaluate performance, track down issues, and export data to improve. Phoenix is built by Arize AI, the company behind the industry-leading AI observability platform, and a set of core contributors. Phoenix works with OpenTelemetry and OpenInference instrumentation. The main Phoenix package is arize-phoenix. We offer several helper packages for specific use cases. Our semantic layer is to add LLM telemetry to OpenTelemetry. Automatically instrumenting popular packages. Phoenix's open-source library supports tracing for AI applications, via manual instrumentation or through integrations with LlamaIndex, Langchain, OpenAI, and others. LLM tracing records the paths taken by requests as they propagate through multiple steps or components of an LLM application.

The simplest way to test and deploy your projects in the cloud or on-prem. Easily sync your projects with Travis CI and you’ll be testing your code in minutes. Check out our features – now you can sign up for Travis CI using your Assembla, Bitbucket, GitHub or GitLab account to connect your repositories! Testing your open-source projects is always 100% free! Log in with your cloud repository, tell Travis CI to test a project, and then push. Could it be any simpler? Many databases and services are pre-installed and can be enabled in your build configuration. Make sure every Pull Request to your project is tested before it’s merged. Updating staging or production as soon as your tests pass has never been easier! Builds on Travis CI are configured mostly through the build configuration stored in the file .travis.yml in your repository. This allows your configuration to be version controlled and flexible.

Coiled is enterprise-grade Dask made easy. Coiled manages Dask clusters in your AWS or GCP account, making it the easiest and most secure way to run Dask in production. Coiled manages cloud infrastructure for you, deploying on your AWS or Google Cloud account in minutes. Giving you a rock-solid deployment solution with zero effort. Customize cluster node types to fit your analysis needs. Run Dask in Jupyter Notebooks with real-time dashboards and cluster insights. Create software environments easily with customized dependencies for your Dask analysis. Enjoy enterprise-grade security. Reduce costs with SLAs, user-level management, and auto-termination of clusters. Coiled makes it easy to deploy your cluster on AWS or GCP. You can do it in minutes, without a credit card. Launch code from anywhere, including cloud services like AWS SageMaker, open source solutions, like JupyterHub, or even from the comfort of your very own laptop.

Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.

Sonatype Nexus Repository is a robust binary repository manager designed to store, manage, and distribute open-source components, dependencies, and artifacts across the software development lifecycle (SDLC). It supports over 20 formats, including Maven, npm, PyPI, and Docker, allowing for seamless integration with build tools and CI/CD pipelines. With advanced features like high availability, disaster recovery, and scalability across cloud platforms, Nexus Repository ensures secure and efficient management of your software artifacts. The platform enhances collaboration, automates workflows, and improves visibility into your software supply chain, helping teams manage dependencies and improve software quality.

Seeker® is an interactive application security testing (IAST) solution that provides unparalleled visibility into your web application's security posture. It identifies vulnerability trends against compliance standards such as OWASP Top 10, PCI DSS, GDPR, CAPEC, and CWE/SANS Top 25. Seeker enables security teams to track sensitive data, ensuring it is handled securely and not stored in log files or databases without proper encryption. Its seamless integration into DevOps CI/CD workflows allows for continuous application security testing and verification. Unlike other IAST solutions, Seeker not only identifies security vulnerabilities but also verifies their exploitability, providing developers with a prioritized list of confirmed issues to address. By employing patented methods, Seeker processes extensive HTTP(S) requests swiftly, reducing false positives to near zero and enhancing productivity while minimizing business risk.

Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately.

Ultralytics offers a full-stack vision-AI platform built around its flagship YOLO model suite that enables teams to train, validate, and deploy computer-vision models with minimal friction. The platform allows you to drag and drop datasets, select from pre-built templates or fine-tune custom models, then export to a wide variety of formats for cloud, edge or mobile deployment. With support for tasks including object detection, instance segmentation, image classification, pose estimation and oriented bounding-box detection, Ultralytics’ models deliver high accuracy and efficiency and are optimized for both embedded devices and large-scale inference. The product also includes Ultralytics HUB, a web-based tool where users can upload their images/videos, train models online, preview results (even on a phone), collaborate with team members, and deploy via an inference API.

NYALA is an end-to-end blockchain tokenization platform that lets users mint, distribute, manage, and custody digital assets and securities securely using an intuitive web app or API integration NYALA supports efficient protocol layers like Polygon and Peaq for fast, low-cost token minting and uses integrated services to fetch ISIN and related securities data for compliant digital issuance. It streamlines the full lifecycle of digital securities from issuance and investor onboarding to automated distribution and custody, with regulatory compliance (AML, KYC, European securities standards) built in while enabling connection to a broader investor and partner network to unlock liquidity and secondary market opportunities. NYALA’s solution is designed to simplify token creation and distribution in days, automate onboarding and wallet setups, and focus on growth by reducing manual effort and compliance burdens.