Harness hits $5.5B valuation with $240M raise to automate AI’s ‘after-code’ gap
Goldman Sachs has led Harness’s Series E round, with participation from IVP, Menlo Ventures, and Unusual Ventures.
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5 AI Model Architectures Every AI Engineer Should Know
Everyone talks about LLMs—but today’s AI ecosystem is far bigger than just language models. Behind the scenes, a whole family of specialized architectures is quietly transforming how machines see, plan, act, segment, represent concepts, and even run efficiently on small devices. Each of these models solves a different part of the intelligence puzzle, and together they’re shaping the next generation of AI systems. In this article, we’ll explore the five major players: Large Language Models (LLMs), Vision-Language Models […]
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How to Get AI to Deliver Superior ROI, Faster
Reducing total cost of ownership (TCO) is a topic familiar to all enterprise executives and stakeholders. Here, I discuss optimization strategies in the context of AI adoption. Whether you build in-house solutions, or purchase products from AI vendors. The focus is on LLM products, featuring new trends in Enterprise AI to boost ROI. Besides the technological aspects, I also discuss the human aspects. I discussed the topic at a recent webinar. You can watch the recording, here. What is […]
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Advancing science and math with GPT-5.2
GPT-5.2 is OpenAI’s strongest model yet for math and science, setting new state-of-the-art results on benchmarks like GPQA Diamond and FrontierMath. This post shows how those gains translate into real research progress, including solving an open theoretical problem and generating reliable mathematical proofs.
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Google launched its deepest AI research agent yet — on the same day OpenAI dropped GPT-5.2
For the first time, developers can embed Google’s Deep Research tool, based on Gemini 3 Pro, into their own apps.
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GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems
Smarter retrieval strategies that outperform dense graphs — with hybrid pipelines and lower cost The post GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems appeared first on Towards Data Science.
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How to build AI scaling laws for efficient LLM training and budget maximization
When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate the quality and accuracy of a large model’s predictions, practitioners often turn to scaling laws: using smaller, cheaper models to try to approximate […]
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7 Pandas Performance Tricks Every Data Scientist Should Know
What I’ve learned about making Pandas faster after too many slow notebooks and frozen sessions The post 7 Pandas Performance Tricks Every Data Scientist Should Know appeared first on Towards Data Science.
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Fine-Tuning a BERT Model
This article is divided into two parts; they are: • Fine-tuning a BERT Model for GLUE Tasks • Fine-tuning a BERT Model for SQuAD Tasks GLUE is a benchmark for evaluating natural language understanding (NLU) tasks.
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Kernel Principal Component Analysis (PCA): Explained with an Example
Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets such as two moons: PCA flattens the structure and mixes the classes together. Kernel PCA fixes this limitation by mapping the data into a higher-dimensional feature space where nonlinear patterns become linearly separable. In this article, we’ll walk through how Kernel PCA works and use a simple example to visually compare PCA […]
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