June 2026

Built a reward-function debugger for RL. Looking for feedback from people.

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While experimenting with GRPO training, I kept running into a problem that when reward increases, it becomes difficult to tell whether the policy is genuinely improving or simply exploiting the reward function. So I built a small library called rewardspy that wraps an existing reward function and continuously monitors indicators that often precede reward hacking. It currently tracks things like: rolling reward statistics reward variance collapse reward component imbalance response length drift reward slope changes GRPO group collapse, […]

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Build interactive PDF text extraction from Amazon S3

Build interactive PDF text extraction from Amazon S3

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Picture this: a compliance officer needs a specific clause during an audit, an attorney needs contract terms while a client waits on the phone, or a finance analyst needs numbers from last quarter’s report before a meeting that starts in 10 minutes. In each case, waiting for a scheduled job to finish is not practical. You need on-demand access to the text inside your PDFs. In this post, you’ll build a server that extracts text from PDF files […]

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How Cara pioneers domain-specific AI for enterprise insurance brokerages with AWS

How Cara pioneers domain-specific AI for enterprise insurance brokerages with AWS

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Insurance is an $8 trillion global industry burdened by manual workflows and a growing talent shortage. Cara delivers an AI-native solution on AWS that automates back-office processes for insurance brokerages. Insurance agents routinely spend hours on repetitive tasks. These include completing applications, analyzing policy coverages, re-keying data across systems, and relaying information between clients and carriers. As the industry faces a persistent talent shortage, brokerages need to scale revenue without proportional headcount increases. In this post, we explore […]

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Production-grade AI agents for financial compliance: Lessons from Stripe

Production-grade AI agents for financial compliance: Lessons from Stripe

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This post is co-written by Christopher Phillippi and Chrissie Cui from Stripe. Stripe processes $1.4 trillion in annual payment volume across 50 countries, requiring compliance teams to review thousands of transactions daily. This post explores how Stripe built a production-grade AI agent system on AWS using Amazon Bedrock that reduced review handling time by 26 percent while maintaining human oversight. The post covers the technical architecture, infrastructure decisions, and lessons learned from deploying agentic AI that achieved over […]

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Live Continual Learning in Machine Learning [D]

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My question on live continual learning use cases was removed by moderators here because they think i asked basic level question about live continual learning which i thought is a frontier level research. But anyways. Is anyone interested in talking about continual learning (live) and catastrophic forgetting? submitted by /u/fourwheels2512 [link] [comments]

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The Future of Learning

The Future of Learning

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AI will transform how we learn AI will transform the future of learning — (illustration created by author with ChatGPT) Teaching and learning are rapidly evolving powered by an abundance of knowledge and AI-enabled education. Historically education has been a compromise; diverse groups of learners were given the same instruction and content because it was impractical to personalize it to their needs and abilities. Increasingly, knowledge can be sourced broadly, shaped for a specific learner or goal, and delivered through dynamic experiences […]

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SAP aligns commerce data for AI personalisation

SAP aligns commerce data for AI personalisation

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SAP aligns fragmented commerce data structures to enable operational AI personalisation at the execution layer. Enterprise leadership routinely establishes objectives to anticipate customer requirements and deliver relevant interactions across digital touchpoints. However, the actual infrastructure running inside these enterprises fails to support systematic execution at the required volume. Recommendation engines display generic product listings because the underlying behavioural data remains isolated. Marketing departments dispatch email communications based on rigid calendar schedules rather than adapting to individual user habits. […]

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