Introducing ML Times: your destination for digestible AI news and insights

Introducing Lambda's ML Times

ML Times is your new go-to for streamlined AI updates. Overwhelmed by the sheer amount of AI + ML news, development, and discussions? Tired of sifting through bloated articles? So were we. That's precisely why ML Times was born—to cut through the noise and deliver AI insights efficiently and clearly, leveraging cutting-edge AI technology. Learn how we built ML Times at Lambda, and join us at to experience a new way to stay informed in the rapidly evolving age of artificial intelligence. 

What is ML Times

ML Times functions as a centralized AI hub, curating and distilling AI news into digestible insights using advanced aggregation and summarization technology. Our platform, designed for professionals and enthusiasts alike, sources content from diverse channels including Hacker News, r/MachineLearning, top-tier blogs, YouTube channels, and Arxiv papers, ensuring comprehensive coverage of AI, ML, foundation models, LLMs, and general ML techniques. Our summarization process keeps you connected to the core of the story without the unnecessary bulk, while each summary is accompanied by a reference link to the source sentence for full transparency and trust. ML Times is hosted at

Why use ML Times

ML Times is the premier resource for machine learning engineers, developed by machine learning engineers. We understand the challenges of keeping up with the latest AI developments, applications, technologies, and techniques, which is why we've created a platform to sift through the clutter and deliver what you need.

Sift through the excess: Pinpoint the essential insights and advancements in the vast sea of AI news and development.

Make every word count: Leveraging LLM summarization, we trim out the fat and provide only the most important insights.

How we made ML Times at Lambda

ML Times was created by the machine learning team at Lambda. It curates only the most relevant and groundbreaking AI news from the most reputable sources, generates summaries and outlines, and cites its sources.

Scan selected content sources

We've handpicked a mix of platforms and channels that are at the forefront of AI and Machine Learning conversations. From engaging community discussions on Hacker News and Reddit's r/MachineLearning to insights from giants like OpenAI and DeepMind, this collection is tailored to keep you in the loop with the latest breakthroughs, trends, and discussions in the AI world.

Filter news

Our LLM prompt (see below) is built to select only the most relevant articles related to Artificial intelligence (AI), Machine Learning (ML), foundation models, LLMs (large language models), GPT, generation models, and general techniques in Machine Learning. We are looking for breakthrough approaches. Currently the big problems in the field are:

  1. Processing long strings of text with better than n^2 scaling
  2. Training and inference with large models that don't fit into GPU memory
  3. Dealing with hallucination from language models
  4. Constraining language models to be more consistent in the output

Any Articles that address these issues in meaningful ways are of interest.

Fun fact: we filter out roughly half the articles we pull from Hacker News.

Prompt snippet

This is the filter we’ve used:

[begin Article]
[end Article]

We are looking for Articles that match the following Filter Articles
related to Artificial intelligence (AI), Machine Learning (ML), foundation models,
LLMs (large language models), GPT, generation models.

Does the above Article match the above Filter? The Answer should be Yes or No:


Generate Summaries

LLMs are good at distilling concepts, but require guidance regarding the style and target audience. In our approach to summarization, we leverage the strengths of LLMs with the following strategies:

  • We begin our prompt with “As an SEO analyst, your goal is to distill key information for busy individuals, emphasizing lucidity and economy of language. [….]", setting the strategy to deliver insights efficiently. 
  • We instruct the model to have each summary feature exactly three bullet points, integrate essential links for further exploration, and use markup to enhance readability. 

The trick to summarize reading general HTML pages using an LLM is to convert the HTML to a (token-wise) much smaller markdown format first, where links are included without all the code responsible for the layout of the page. For instance, click here to read the summary of this page.

Create Outlines

In addition to the content of an article, you might be interested in what others' opinions are. Thus, another style of summarization you can use with our tool is to extract a quick overview of a discussion. The feature called “Outline” extracts the gist of multiple discussions into a tree of opinions and their accompanying sentiments.

Cites Sources

To increase trust in our summaries, each summary on our page is accompanied by a clickable reference, allowing you to access the source sentence directly.

How clickable references work

We break the article into sentences and create vector embedding for each sentence using OpenAI’s “text-embedding-3-small” model. We store the embeddings in Pinecone and retrieve the three nearest neighbors for each summary. To ensure accuracy, we restrict the retrieval to the same article instead of the entire vectorDB.

How highlight feature works

When you click “Highlight Link”, Chrome browsers open the source page and automatically scroll to the relevant quote. This facilitates deeper exploration into the content right from where you left off while reviewing the summary.


ML Times presents an efficient solution for ML engineers to stay up-to-date on the latest advancements and breakthroughs in AI. With its curated content, efficient summarization techniques, and user-friendly interface, ML Times empowers professionals and enthusiasts to navigate the complex landscape of artificial intelligence with ease. Visit for a valuable tool for enhancing your AI knowledge and staying ahead in your ML endeavors.