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easyaligner: Forced alignment with GPU acceleration and flexible text normalization (compatible with all w2v2 models on HF Hub) [P]

easyaligner: Forced alignment with GPU acceleration and flexible text normalization (compatible with all w2v2 models on HF Hub) [P]

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https://preview.redd.it/f4d5krhkjyvg1.png?width=1020&format=png&auto=webp&s=11310f377b22abbe3dd110cc7d362ba8aae35f8d I have built easyaligner, a forced alignment library designed to be performant and easy to use. Having worked with preprocessing hundreds of thousands of hours of audio and text for training speech-to-text models, I found that the available open source forced alignment libraries often missed some convenience features. For our purposes it was, in particular, important for the tooling to be able to: Handle cases where the transcript does not cover all of the spoken content in […]

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If AI is Writing the Code, What’s Left for Us?

If AI is Writing the Code, What’s Left for Us?

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A real look at where developers, CS students, and the job market are heading Let me say the thing everyone is thinking but nobody wants to say: it feels scary right now. If you are a CS student, a junior developer, or someone who just started learning to code, the world is moving faster than you can keep up with. One day you are learning React, the next an AI is building full React apps in 30 seconds. So […]

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SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models

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Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models by leveraging rule-based reward signals. However, RL training typically tends to improve single-sample success rates (i.e., Pass@1) while offering limited exploration of diverse reasoning trajectories, which is crucial for multi-sample performance (i.e., Pass@k). Our preliminary analysis reveals that this limitation stems from a fundamental squeezing effect, whereby probability mass is excessively concentrated on a narrow subset of high-reward trajectories, restricting genuine exploration and constraining attainable […]

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In-Context Learning Under Regime Change

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Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models must detect the change, discard or downweight obsolete evidence, and adapt to new dynamics on the fly. Transformer-based foundation models increasingly rely on in-context learning for time series forecasting, tabular prediction, and continuous control. As these models are deployed in non-stationary environments, understanding their ability to detect and adapt to regime shifts is important. We formalize this as an in-context […]

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Claude system prompts as a git timeline

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Research: Claude system prompts as a git timeline Anthropic publish the system prompts for Claude chat and make that page available as Markdown. I had Claude Code turn that page into separate files for each model and model family with fake git commit dates to enable browsing the changes via the GitHub commit view. I used this to write my own detailed notes on the changes between Opus 4.6 and 4.7. Tags: system-prompts, anthropic, claude, generative-ai, ai, llms

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How to Add AI Features Without Rebuilding Your App

How to Add AI Features Without Rebuilding Your App

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Scaling intelligence in existing mobile platforms without a total rewrite Developers explore seamless AI integration into applications with holographic projections of interactive features, framed by a vibrant city skyline. Integrating artificial intelligence into your product is no longer a “nice to have” luxury; it is a competitive necessity. Many product leaders hesitate to pursue this because they assume adding AI features without rebuilding your app from scratch is impossible. Fortunately, the 2026 ecosystem of modular APIs, microservices, and edge computing […]

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Great white sharks are overheating

Great white sharks are overheating

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The evolutionary edge that fueled great white shark dominance for millions of years could soon become its greatest downfall. The ocean’s most iconic predators maintain warmer body temperatures than the surrounding seawater and are paying an increasingly steep price for it. As the oceans warm due to climate change, they now face the risk of potentially fatal overheating, according to a new report in Science. Several large tuna species and sharks, known as “mesothermic” species for the way […]

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Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering

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Incomplete Multi-View Clustering (IMVC) faces the challenge of learning discriminative representations from fragmentary observations while maintaining robustness against missing views. However, prevalent Euclidean-based methods suffer from a geometric mismatch when modeling real-world data with intrinsic hierarchies, leading to semantic blurring where representations drift towards spatially proximal but semantically distinct neighbors. To bridge this gap, we propose HERL, a Hyperbolic Enhanced Representation Learning framework for IMVC. Operating within the Poincaré ball, HERL constructs a structure-aware latent space to enhance […]

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Using Salesforce Agentforce for Enterprise Solutioning, Case Intake, and Assignment

Using Salesforce Agentforce for Enterprise Solutioning, Case Intake, and Assignment

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An enterprise architecture perspective on how service organizations can use Prompt Builder to transform unstructured requests into governed triage and routing decisions. Most enterprise service organizations do not primarily suffer from a case volume problem. They suffer from a context and decision-quality problem. Most organizations already have intake channels, queues, routing rules, knowledge articles, and escalation paths. What they often lack is a reliable way to convert messy, incomplete, multi-channel requests into structured work that can be triaged […]

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The Data Engineering

The Data Engineering

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From pipelines to platforms — inside the $105 billion ecosystem where Databricks and Snowflake wage war, open table formats reshape storage, and every AI breakthrough depends on the engineers building the foundation beneath it. Last week, we published a comprehensive deep dive into the state of agentic AI in 2026 — the autonomous systems, the big players, the frameworks, the standards. We ended with a promise: a two-part series on data engineering. Because here’s the thing that most AI hype cycles conveniently ignore — every […]

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