I was doing some research today and ran into a couple instances where part of one law was copied and pasted verbatim into another law. I suppose this is not uncommon, but I’m not a lawyer, so I don’t have that much experience comparing laws. I do, however, consult for lawyers and have to look up laws from time to time. Here’s an example from California Health and Safety Code § 1385.10 and the California Insurance Code § […]
Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive local data. However, FL often results in lower accuracy than traditional ML algorithms due to statistical heterogeneity across clients. Prior works attempt to address this by using model updates, such as loss and bias, from client models to select participants that can improve the global model’s accuracy. However, these updates neither accurately represent a client’s heterogeneity […]
Unprocessed emotions act like background apps hogging your brain’s limited working memory, slowing you down even in quiet moments. This fresh lens recasts cognitive overload as emotional RAM drain—and introduces introspection as the ultimate cleaner. Some cognitive models suggest that unprocessed emotions can waste 20–30% of our thinking bandwidth. It’s like trying to ship code while a background process silently occupies one-third of your CPU. This “emotional residue”—the stuff we suppress to get through the day—doesn’t go away; […]
I Built ArtemisFlow: A Local-First Job Tracker for Tech Candidates Modern hiring processes often involve multiple companies, stages, and timelines happening in parallel. At some point, keeping track of everything becomes harder than it should be. Like many developers, I initially used spreadsheets. They worked well enough in the beginning. But over time, they became harder to maintain. I kept adding columns, moving things around, and writing notes in places that didn’t really belong anywhere. Some companies replied […]
In this tutorial, we focus on building a transparent and measurable evaluation pipeline for large language model applications using TruLens. Rather than treating LLMs as black boxes, we instrument each stage of an application so that inputs, intermediate steps, and outputs are captured as structured traces. We then attach feedback functions that quantitatively evaluate model behavior along dimensions such as relevance, grounding, and contextual alignment. By running multiple application variants under the same evaluation setup, we show how […]
Building a Retrieval-Augmented Generation (RAG) pipeline is easy; building one that doesn’t hallucinate during a 10-K audit is nearly impossible. For devs in the financial sector, the ‘standard’ vector-based RAG approach—chunking text and hoping for the best—often results in a ‘text soup’ that loses the vital structural context of tables and balance sheets. VectifyAI is attempting to close this gap with the launch of Mafin 2.5, a multimodal financial agent, and PageIndex, an open-source framework that shifts the […]
In the high-stakes world of AI infrastructure, the industry has operated under a singular assumption: flexibility is king. We build general-purpose GPUs because AI models change every week, and we need programmable silicon that can adapt to the next research breakthrough. But Taalas, the Toronto-based startup thinks that flexibility is exactly what’s holding AI back. According to Taalas team, if we want AI to be as common and cheap as plastic, we have to stop ‘simulating’ intelligence on […]
Critical labor shortages are constraining growth across manufacturing, logistics, construction, and agriculture. The problem is particularly acute in construction: nearly 500,000 positions remain unfilled in the United States, with 40% of the current workforce approaching retirement within the decade. These workforce limitations result in delayed projects, escalating costs, and deferred development plans. To address these constraints, organizations are developing autonomous systems that can perform tasks that fill capacity gaps, extend operational capabilities, and offer the added benefit of […]
We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributions drawn from a Wasserstein ambiguity set centered at past observations. While this paradigm is well understood in the offline setting through Wasserstein Distributionally Robust Optimization (DRO), its online extension poses significant challenges in both convergence and computation. In this paper, we address these challenges. First, we formulate the problem as an online saddle-point stochastic game between a decision maker and […]