curious how you could help to build apps that leverage chatbots + data APIs. specifically, how does it handle workflows involving both structured (eg CRM data) and unstructured (eg chat logs) sources? Can it integrate real-time triggers or long-running processes, like dynamically responding to client actions or data thresholds? Excited to see how it simplifies the dev process!
Connecting to a CRMs and building a generative application is supported, think "Chatting with your CRM" or even doing more complex tasks like "Analyzing your CRM by generating dynamic data dashboards".
To interoperate with other systems, every generative app built in Hal9 also comes with an API. So whenever the CRM has new data, it can call Hal9 to process records or do other kind of actions. We've seen some customers using tools like Zapier in combination with Hal9 to do more complex triggers, say, support SMS and the like.
So short answer, yes! But we expect some integration to happen with other systems in the form of APIs and triggers.
Hi there HN! We built Hal9 to make it radically simpler to create, deploy, and share applications powered by LLMs, diffusers, and other AI models. Whether you're working on chatbots, agents, APIs, or generative apps, Hal9 is designed to minimize the engineering overhead so you can focus on the AI itself.
* Why Hal9?
Most generative AI projects end up dedicating the majority of their time to engineering challenges -- building interfaces, integrating tools, and managing infrastructure -- rather than focusing on the core AI work like refining prompts, implementing RAG strategies, or optimizing model performance.
Hal9 shifts that balance by drastically reducing engineering overhead. It offers a simple, lightweight interface built around Unix IO conventions like stdin and stdout, allowing you to focus entirely on AI innovation without the need to learn complex frameworks or deployment workflows.
With Hal9, you can prototype and run locally without extra dependencies, use our free online platform for quick deployments, or scale effortlessly to enterprise-grade solutions. We can also support organizations by enabling cloud deployments in their own environments or providing additional compute resources for enterprise customers.
Hal9 is designed to get out of your way so you can focus on building smarter, faster.
* What is Hal9?
Hal9 is a deployment platform purpose-built for generative AI, enabling you to create and deploy generative (LLMs and diffusers) applications (chatbots, agents, APIs, apps) in seconds. Key features:
Flexible: Use any library, and any model.
Intuitive: No need to learn app frameworks, simply use `input()` and `print()`.
Scalable: Designed to integrate your app with scalable technologies like Docker and Kubernetes.
Powerful: Using an OS process (stdin, stdout, files) as our app contract, enables long-running agents, multiple programming languages, complex system dependencies, and running arbitrary code in secure Kubernetes pods.
Open: The code behind the Hal9 app, is also open source and open for contributions under our repo.
* The Philosophy
We believe the Python ecosystem already provides great libraries for everything from LLM interactions to generative tasks. Instead of reinventing those wheels, Hal9 integrates them into a unified workflow, letting you focus on AI-specific challenges like retrieval-augmented generation (RAG), fine-tuning, alignment, and training.
Hal9 is perfect for developers who want to experiment, iterate, and deploy AI apps quickly without becoming mired in engineering tasks like frontend design or backend integration. It's also ideal for teams looking to collaborate, thanks to the open architecture and straightforward app structure.
* Our Journey
We started Hal9 in 2021 with the goal of simplifying AI development. Initially, we focused on web developers, combining AI with technologies like D3.js and TensorFlow.js. While the low-code interface was popular, users wanted that, but with Python support.
In 2022, we took less-code a step further and embraced LLMs like GPT-3, moving towards automatic code generation and simplifying the UX. After several iterations, Hal9 has evolved into a platform that enables faster, easier AI app development.
* Resources
We are actively publishing posts that demonstrate how to integrate your favorite frameworks with Hal9. Here are some of the technical blog posts already available:
curious how you could help to build apps that leverage chatbots + data APIs. specifically, how does it handle workflows involving both structured (eg CRM data) and unstructured (eg chat logs) sources? Can it integrate real-time triggers or long-running processes, like dynamically responding to client actions or data thresholds? Excited to see how it simplifies the dev process!
Connecting to a CRMs and building a generative application is supported, think "Chatting with your CRM" or even doing more complex tasks like "Analyzing your CRM by generating dynamic data dashboards".
To interoperate with other systems, every generative app built in Hal9 also comes with an API. So whenever the CRM has new data, it can call Hal9 to process records or do other kind of actions. We've seen some customers using tools like Zapier in combination with Hal9 to do more complex triggers, say, support SMS and the like.
So short answer, yes! But we expect some integration to happen with other systems in the form of APIs and triggers.
Hi there HN! We built Hal9 to make it radically simpler to create, deploy, and share applications powered by LLMs, diffusers, and other AI models. Whether you're working on chatbots, agents, APIs, or generative apps, Hal9 is designed to minimize the engineering overhead so you can focus on the AI itself.
* Why Hal9?
Most generative AI projects end up dedicating the majority of their time to engineering challenges -- building interfaces, integrating tools, and managing infrastructure -- rather than focusing on the core AI work like refining prompts, implementing RAG strategies, or optimizing model performance.
Hal9 shifts that balance by drastically reducing engineering overhead. It offers a simple, lightweight interface built around Unix IO conventions like stdin and stdout, allowing you to focus entirely on AI innovation without the need to learn complex frameworks or deployment workflows.
With Hal9, you can prototype and run locally without extra dependencies, use our free online platform for quick deployments, or scale effortlessly to enterprise-grade solutions. We can also support organizations by enabling cloud deployments in their own environments or providing additional compute resources for enterprise customers.
Hal9 is designed to get out of your way so you can focus on building smarter, faster.
* What is Hal9?
Hal9 is a deployment platform purpose-built for generative AI, enabling you to create and deploy generative (LLMs and diffusers) applications (chatbots, agents, APIs, apps) in seconds. Key features:
Flexible: Use any library, and any model.
Intuitive: No need to learn app frameworks, simply use `input()` and `print()`.
Scalable: Designed to integrate your app with scalable technologies like Docker and Kubernetes.
Powerful: Using an OS process (stdin, stdout, files) as our app contract, enables long-running agents, multiple programming languages, complex system dependencies, and running arbitrary code in secure Kubernetes pods.
Open: The code behind the Hal9 app, is also open source and open for contributions under our repo.
* The Philosophy
We believe the Python ecosystem already provides great libraries for everything from LLM interactions to generative tasks. Instead of reinventing those wheels, Hal9 integrates them into a unified workflow, letting you focus on AI-specific challenges like retrieval-augmented generation (RAG), fine-tuning, alignment, and training.
Hal9 is perfect for developers who want to experiment, iterate, and deploy AI apps quickly without becoming mired in engineering tasks like frontend design or backend integration. It's also ideal for teams looking to collaborate, thanks to the open architecture and straightforward app structure.
* Our Journey
We started Hal9 in 2021 with the goal of simplifying AI development. Initially, we focused on web developers, combining AI with technologies like D3.js and TensorFlow.js. While the low-code interface was popular, users wanted that, but with Python support.
In 2022, we took less-code a step further and embraced LLMs like GPT-3, moving towards automatic code generation and simplifying the UX. After several iterations, Hal9 has evolved into a platform that enables faster, easier AI app development.
* Resources
We are actively publishing posts that demonstrate how to integrate your favorite frameworks with Hal9. Here are some of the technical blog posts already available:
Hal9 with OpenAI Swarm: https://hal9.com/docs/blog/swarm-openai
Hal9 with NVIDIA NIM: https://hal9.com/docs/blog/nvidia-nim
Hal9 with Dagworks: https://hal9.com/docs/blog/burr
Hal9 for Text-to-SQL: https://hal9.com/docs/blog/txt-to-sql
Let us know your thoughts, feedback, and ideas -- Hal9 is as much about building apps as it is about creating a community of creators.