Fine-tune and Deploy Open-weight Models in TypeScript
Start a real LLM training in 10 minutes. Tell Claude Code or Codex what the model is for; they prepare the datasets and create the training project. Click "Run Training" in the local studio. Arkor runs the training, and deploys the trained model as an OpenAI-compatible API. Think Next.js and Vercel for fine-tuning: code you can review, infrastructure you do not have to manage, and a model your app can call. No GPU setup. No ML expertise required. No Python training code to write.
We built Arkor because we kept running into the same gap: adding an AI feature to an application is easy, but adapting a model to a specific task still feels like joining an ML infrastructure team.
The moment you want to fine-tune, you often end up maintaining a separate Python project, converting datasets into unfamiliar formats, provisioning GPUs, and moving between scripts, notebooks, and dashboards.
We wanted model development to feel more like ordinary software development.
With Arkor, the workflow starts inside your existing repository:
Tell Claude Code or Codex what behavior you want from the model.
Your coding agent can find or prepare a dataset, write conversion scripts, create the TypeScript trainer, and add evaluation.
Review the generated code and changes.
The agent runs pnpm dev, and Arkor Studio opens locally at localhost:4000.
Click Run Training, monitor the loss and checkpoints, test the trained adapter, and deploy the result.
For example, you can give your coding agent a prompt like:
Use Arkor to fine-tune a model that rewrites rough drafts as tweets in my style: https://github.com/arkorlab/arkor Find or prepare a suitable dataset, create the TypeScript training workflow, and tell me when it is ready to review in Arkor Studio.
Arkor is not intended to be a magical prompt-to-model black box. It is a developer-controlled workflow where coding agents can handle much of the setup, while the training code, data transformations, evaluation, and final decisions remain visible and reviewable.
We’re especially interested in feedback on:
Whether the coding-agent workflow feels intuitive
Which parts of fine-tuning still feel unclear or intimidating
What you would need before using Arkor for a production model
Which models, datasets, and deployment workflows we should support next
Thanks for checking out Arkor. We’ll be here throughout the launch and would love to hear what you try building. 🙏
About Arkor on Product Hunt
“Fine-tune and Deploy Open-weight Models in TypeScript”
Arkor launched on Product Hunt on July 22nd, 2026 and earned 143 upvotes and 22 comments, placing #11 on the daily leaderboard. Start a real LLM training in 10 minutes. Tell Claude Code or Codex what the model is for; they prepare the datasets and create the training project. Click "Run Training" in the local studio. Arkor runs the training, and deploys the trained model as an OpenAI-compatible API. Think Next.js and Vercel for fine-tuning: code you can review, infrastructure you do not have to manage, and a model your app can call. No GPU setup. No ML expertise required. No Python training code to write.
On the analytics side, Arkor competes within Open Source, Developer Tools, GitHub and Development — topics that collectively have 632.2k followers on Product Hunt. The dashboard above tracks how Arkor performed against the three products that launched closest to it on the same day.
Who hunted Arkor?
Arkor was hunted by Hina. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Arkor including community comment highlights and product details, visit the product overview.
Hina
Hi Product Hunt! 👋
I’m Hina, one of the makers of Arkor.
We built Arkor because we kept running into the same gap: adding an AI feature to an application is easy, but adapting a model to a specific task still feels like joining an ML infrastructure team.
The moment you want to fine-tune, you often end up maintaining a separate Python project, converting datasets into unfamiliar formats, provisioning GPUs, and moving between scripts, notebooks, and dashboards.
We wanted model development to feel more like ordinary software development.
With Arkor, the workflow starts inside your existing repository:
Tell Claude Code or Codex what behavior you want from the model.
Your coding agent can find or prepare a dataset, write conversion scripts, create the TypeScript trainer, and add evaluation.
Review the generated code and changes.
The agent runs pnpm dev, and Arkor Studio opens locally at localhost:4000.
Click Run Training, monitor the loss and checkpoints, test the trained adapter, and deploy the result.
For example, you can give your coding agent a prompt like:
Arkor is not intended to be a magical prompt-to-model black box. It is a developer-controlled workflow where coding agents can handle much of the setup, while the training code, data transformations, evaluation, and final decisions remain visible and reviewable.
We’re especially interested in feedback on:
Whether the coding-agent workflow feels intuitive
Which parts of fine-tuning still feel unclear or intimidating
What you would need before using Arkor for a production model
Which models, datasets, and deployment workflows we should support next
Thanks for checking out Arkor. We’ll be here throughout the launch and would love to hear what you try building. 🙏