Build predictive models with no prior ML experience
TLDR: Product developers need tools designed for them to build ML models. We’d love for you to try a demo of Mage without needing to sign up: https://www.mage.ai/onboarding My name is Tommy DANGerous (or Tommy Dang) and I’m the CEO and co-founder at Mage. I worked at Airbnb for over 5 years as a product developer building features for guests. Mage is a web-based tool for building, training, and deploying ML models that make predictions based off your data. Training and using ML models in production typically requires working knowledge of building data pipelines, algorithms,…
In plain words
Mage is a web-based platform that enables product developers to build, train, and deploy machine learning models without specialized data science knowledge. The tool handles data pipelines, algorithms, and deployment infrastructure, allowing developers to create predictive models directly from their data. It targets product developers and teams who need ML capabilities but lack background in machine learning or data engineering.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
TLDR: Product developers need tools designed for them to build ML models. We’d love for you to try a demo of Mage without needing to sign up: https://www.mage.ai/onboarding My name is Tommy DANGerous (or Tommy Dang) and I’m the CEO and co-founder at Mage. I worked at Airbnb for over 5 years as a product developer building features for guests. Mage is a web-based tool for building, training, and deploying ML models that make predictions based off your data. Training and using ML models in production typically requires working knowledge of building data pipelines, algorithms, infrastructure for deployment and inference, and more. Because of this highly specialized skillset, mostly data scientists and ML engineers are the only ones able to build and use ML models. Existing ML tools cater to this audience. Over my 5+ years at Airbnb, I helped build and launch the Airbnb Experiences product, created ML models before and after we had in-house tooling, and built a devtool platform called Omni. I worked with 100s of product developers across the company and saw that they knew how ML is being used and had ideas on how they would apply ML to their specific feature. However, they relied on data science resource to help them implement their ideas even though we had ML tools built in-house for data scientists. Mage is a low-code tool that you can access via your web browser. You can build ML models through our user interface. How it works: 1. First, you add data by uploading a file or connecting to data source like Amplitude, AWS Redshift, S3, Snowflake, GCP BigQuery, etc. Once you add your data, we store it on AWS S3 for fast retrieval and transformations. 2. Next step is you are given suggestions on how to enhance your dataset. You can perform functions like filtering, aggregating, adding columns, etc. We provide a GUI for you to perform these transformations. Behind the scenes, we’re translating your input into code using the Pandas API. 3. Once you’re done preparing and cleaning your data, we’ll train your model by launching a few data pipelines in Airflow, use Spark to build your training data, and then run our proprietary ML pipeline to train your model. 4. Finally, when you’re ready to use the model, we’ll deploy your model to an online API endpoint that is running on AWS ECS. You can get your model’s predictions via a POST request. Existing ML tools are designed and built for data scientists and ML engineers. Mage is designed and built for product developers by product developers. This means we designed our tool to be usable by someone with no ML experience and we provide guided suggestions throughout the process to help educate users and help them become an ML expert. We’d love for you to try a demo of Mage without needing to sign up: https://www.mage.ai/onboarding. If you love it and want to use the entire set of features, you can sign up and use it for free (the best developers tools are free!). Thank you so much, your support is ultra appreciated!
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, March 2022
the whole month →- IM
Hi HN! I made many small web games in the past with Unity or Phaser. But this time I wanted to make something completely on my own. So I coded a basic game framework in plain JavaScript, and used it to make Almost Pong. This was a really fun project that taught me a lot about JavaScript, and I plan to make more minimalist games with this framework. Interesting fact: Almost Pong doesn't load any assets, all sprites and sounds are generated with code. Happy to answer questions, and please let me know if you have any feedback on the game. Thanks!
Dev tools · 2022 · lessmilk.com
- IM
Life & fun · 2022 · networksfromscratch.com
- HA
Paste the following into the console of any HN page - for annotated avatars on all HN comments. (self contained code) for(u of document.querySelectorAll('.hnuser'))for(u.prepend(c=document.createElement('canvas')),x=c.getContext('2d'),c.width=18,c.height=14,s=u.innerText,r=1,i=28+s.length;i--;i>>29>X*X/3+Y/2&&x.fillRect(6+2*X,2*Y,2,2)&x.fillRect(6-2*X,2*Y,2,2):r+=s.charCodeAt(i-28,x.fillStyle='#'+(r>>8&0xFFFFFF).toString(16)))r^=r>>17,r^=r>2
Dev tools · 2022
- AG
Life & fun · 2022 · backofyourhand.com