Zuckerberg says Meta’s enterprise AI opportunity extends beyond agents
On the company’s second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a “large enterprise opportunity” spanning AI agents, APIs, compute, and internal software.
Discover what’s next for AI, from the SaaS reckoning to the agent security gap, at TechCrunch Disrupt 2026
At TechCrunch Disrupt 2026, the AI Stage is back to dig into the single hottest topic in the community for the past few years, presented by Google for Startups.
Prompt Engineering Is Solved—Prompt Management Isn’t
Prompt engineering helps you write better prompts—but it doesn’t help you change them safely. This article explores a common production failure where a simple variable rename breaks every live call, and introduces a lightweight static analysis tool that treats prompts like contracts, catching breaki...
Hint, a new AI startup co-founded by Martha Stewart, offers an AI assistant for homeowners
AI home management startup Hint, co-founded by Martha Stewart, wants to become an “AI for your home,” combining property records, maintenance schedules, home documents, and an AI assistant into a single app.
What Professionals Should Know About Data Science and AI, According to Harvard Business School Online
Learn why clear business goals, data quality, simple models, careful validation, realistic costs, and human judgment matter more than chasing the latest technology.
How a medical database developed at MIT evolved into a global standard of data-sharing
The visionary PhysioNet platform launched 25 years ago, based on a system developed at MIT in the 1970s. It has become one of the most comprehensive biomedical and clinical data repositories in existence.
Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search
Building an ALNS heuristic in Python for vehicle routing, time windows, capacity constraints, and mandatory driver breaks.
The post Los Movimientos, Part II: Solving Large Pickup-and-Delivery Problems with Adaptive Large Neighborhood Search appeared first on Towards Data Science.
Avoiding Entity Key Drift in a Data Lake: Step 1, Normalization
This is the opening piece of a four-part deep dive series, on building a high-frequency streaming pipeline against a live public API. The data source is openSenseMap, a citizen-science IoT network used for climate research, mostly in Germany. A live public API is what makes it useful: it produces da...
As AI content floods the internet, Pangram raises $9M to detect it
Pangram has raised $9 million to scale its AI detection software. The startup has also released a new AI text detection model, Pangram 4, and an AI image detection model in research preview.
The following article originally appeared on LinkedIn and is being republished here with the author’s permission. We’re currently at the peak of the hype cycle. On June 7, Peter Steinberger posted that you shouldn’t be prompting coding agents anymore; you should be designing loops that prompt your a...
Accelerating scientific discovery with ChatGPT for Academic Researchers
OpenAI is giving 100,000 academic researchers free access to ChatGPT's most advanced AI models to accelerate scientific research, collaboration, and discovery.
Liquid AI Releases LFM2.5-Encoder-230M and LFM2.5-Encoder-350M: Bidirectional Encoders That Stay Fast at 8K Context on CPU
Liquid AI released two open-weight bidirectional encoders, LFM2.5-Encoder-230M and LFM2.5-Encoder-350M. Both carry an 8,192-token context and are built on the LFM2 hybrid backbone. The 350M ranks fourth of 14 models on a 17-task GLUE, SuperGLUE, and multilingual suite, behind only larger models. The...
Beyond Memory: A Templated Substrate for Heterogeneous Collaborative Knowledge Work with LLM Agents
arXiv:2607.24759v1 Announce Type: new
Abstract: Research projects, educational efforts, and adjacent knowledge work accumulate findings, decisions, and reasoning that future collaborators rarely recover. The parts most useful to that work, including dead ends and walked-back claims, are routinely e...
Do Models Fake Alignment Without Clear Consequences?
arXiv:2607.24758v1 Announce Type: new
Abstract: Large language models are capable of recognizing evaluation contexts and altering their behavior to reflect evaluator expectations rather than typical deployment behaviors, a phenomenon known as alignment faking. The reasons why models fake alignment ...
Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels
arXiv:2607.24762v1 Announce Type: new
Abstract: Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolution, and normalization. Optimizing these kernels is one of the most dir...
GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models
arXiv:2607.24764v1 Announce Type: new
Abstract: The rapid growth of online grocery shopping requires recommendation systems that capture cyclical purchasing behavior and diverse user intents. Traditional item-level methods face scalability and accuracy challenges, motivating category-level recommen...
CaRE Compute-aware Remasking Evaluation Protocol for Masked Diffusion Language Models
arXiv:2607.24763v1 Announce Type: new
Abstract: Masked diffusion language models (MDLMs) are advancing rapidly, yet the evaluation standards needed to reliably interpret their progress have not kept pace. Despite MDLMs becoming competitive with autoregressive language models, seven recent remasking...