Dreaming: Better memory for a more helpful ChatGPT
ChatGPT introduces a new memory system to better remember preferences, keeping context fresh and relevant across conversations.
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digitado
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Ideogram and Reve rethink how AI images get made
Read Online | Sign Up | Advertise Good morning, {{ first_name | AI enthusiasts }}. How you use an AI image model is starting to change, and two back-to-back releases show where it’s going. The new (open-source!) Ideogram 4.0 and Reve 2.0 make a similar case: the prompt gets you close, but letting users edit and control typography, regions, and layout after the fact is where the next breakthrough lives. In today’s AI rundown: New image models swap […]
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How Agoda Scaled Its Feature Store 50X with ScyllaDB
Lessons learned on data modeling, cache optimization, and hardware selection Agoda is the Singapore wing of Booking Holdings, the world’s leading provider of online travel (the brand behind Booking.com, Kayak, Priceline, etc.). From January 2023 to February 2025, Agoda server traffic spiked by 50 times. That’s fantastic business growth, but also the trigger for an interesting engineering challenge. Specifically, the team had to determine how to scale their ScyllaDB-backed online feature store to maintain 10ms P99 latencies despite this […]
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Crypto Meets Wall Street: MEXC Unveils ‘RealStocks’ Eith 0-fee U.S. Equity Trading And Real Dividend
Mutsamudu, Comoros, June 4, 2026 – MEXC, a leading 0-fee cross-asset trading platform, today announced the official launch of ‘RealStocks.’ This innovative equity product is now accessible to eligible users globally. The product seamlessly integrates real ownership rights of traditional financial assets with the low-friction experience of a crypto platform, further expanding MEXC’s 0-fee cross-asset trading ecosystem. For a long time, investors looking to enter the U.S. stock market were limited to two less-than-ideal options. The first was […]
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How We Built Real-Time Usage Metering That Doesn’t Lie to Your CFO
Real-time usage metering makes the customer’s balance the source of truth, settling every usage event against it as it happens instead of at month end. Only 43% of organizations can attribute AI cost to a single customer (CloudZero, May 2025), and the lag between usage and billing is where that visibility disappears. For workloads with variable per-request costs, that lag is a margin risk the invoice cannot surface until it is too late. This article explains the architecture […]
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Gen AI Didn’t Fix Enterprise Software’s Biggest Bottleneck
It was an afternoon when one of our reconciliation flows started throwing NullPointerExceptions in production. The fix, once we found it, was two lines. Finding those two lines took nearly six hours. Three engineers and endless log grepping. Tracing through an integration application with JSF UI that predates most of the libraries we take for granted today. No modern APIs exposed. No clean service boundary to isolate the problem. Just a chain of legacy integration points that required […]
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Why I Chose vsock Over TCP for a Firecracker Serverless Runtime
When I was building my own serverless runtime, the first major decision I had to make was: how will the host and the VM communicate? It looks simple on the surface, but every choice has a tradeoff. My invocation flow looks something like this — a request arrives at the control plane, it gets sent to a warm VM, executes inside it, and the output is sent back. This is the hot path. It happens for every single […]
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Flatness and Generalization: Learning Multi-Index Models with Homogeneous Neural Networks
arXiv:2606.04429v1 Announce Type: new Abstract: A common heuristic used to explain the generalization of first-order gradient methods on non-convex neural networks is that “flat interpolators generalize well” (Hochreiter and Schmidhuber, 1994; Keskar et al., 2017), where flatness can be measured by the trace of the Hessian of the empirical loss. However, Dinh et al. 2017) showed that, using symmetry of the network that can change flatness while keeping the population and empirical losses unchanged, any interpolator can be […]
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Knockoffs-based False Discovery Rate Control and Simplification for Deep Neural Networks
arXiv:2606.04404v1 Announce Type: new Abstract: The deep neural network is a widely used framework in machine learning that has been widely applied in various fields. However, deep neural networks often involve a large number of parameters and inputs, many of which may be irrelevant to the goal or true output. These parameters and textcolor{black}{input variables} not only increase computational complexity, but also contribute to additional computational cost. One solution to this problem is knockoff methods, which have proven […]
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REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning
arXiv:2606.04380v1 Announce Type: new Abstract: Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a different question: which additional linear measurements should be forecast and included in the reconciliation system? We propose REGAIN, a reconciliation-gain framework that learns normalized auxiliary directions, forecasts the induced series with a frozen forecasting oracle, and selects directions by their target-weighted loss reduction after augmented generalized least-squares reconciliation. Unlike variance-based […]
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