RapidFire AI today announced at Ray Summit 2025 RapidFire AI RAG, an open-source extension of its hyperparallel experimentation framework that brings dynamic control, real-time comparison, and automatic optimization to Retrieval-Augmented Generation (RAG) and context engineering workflows.
Agentic RAG pipelines that combine data retrieval with LLM reasoning and generation are now at the heart of enterprise AI applications. Yet, most teams still explore them sequentially: testing one chunking strategy, one retrieval scheme, or one prompt variant at a time. This leads to slow iteration, expensive token usage, and brittle outcomes.
Hyperparallel RAG Experimentation
RapidFire AI RAG applies the company’s hyperparallel execution engine to the full RAG stack, allowing users to launch and monitor multiple variations of data chunking, retrieval, reranking, prompting, and agentic workflow structure simultaneously, even on a single machine. Users see live performance metrics update shard-by-shard, can stop or clone runs mid-flight, and inject new variations without rebuilding or relaunching entire pipelines. Under the hood, RapidFire AI intelligently apportions token usage limits (for closed model APIs) and/or GPU resources (for self-hosted open models) across these configurations.
Dynamic Control and Automated Optimization
Beyond parallel exploration, RapidFire AI RAG introduces dynamic experiment control, a cockpit-style interface to steer runs in real time, and a forthcoming automation layer that supports AutoML algorithms and customizable automation templates beyond just grid search or random search to optimize holistically based on both time and cost constraints.
Maximal Generality and Open Integration
Unlike closed-system RAG builders tied to specific clouds or APIs, RapidFire AI RAG supports hybrid pipelines that mix self-hosted models and closed model APIs across embedding, retrieval, re-ranking, and generation steps. Users can run with OpenAI or Anthropic models, Hugging Face embedders, self-hosted rerankers, and any vector/SQL/full-text search backend, all within the same experiment workspace.
RapidFire AI’s technology is rooted in award-winning research by its Co-founder, Professor Arun Kumar, a faculty member in both the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego.
Availability
RapidFire AI RAG is available now as part of the company’s open-source release and installable via pip install rapidfireai.
To learn more, visit rapidfire.ai or explore the open-source repository on GitHub and the documentation site.
October 2025: Ransomware Attacks Rising 25%
Posted in Commentary with tags Comparitech on November 4, 2025 by itnerdn a study published this morning, Comparitech found that ransomware attacks increased by 25 percent in October, rising to 684 in comparison to 546 in September. This is a significant increase in attacks and the third-highest monthly figure in 2025 so far.
Manufacturers continue to see the most attacks, accounting for nearly 19 percent of attacks in October, but only rose 9% from September. In contrast, attacks on the healthcare sector rose significantly, jumping 115%. Other sectors that saw high increases were transportation (109%) and retail (104%).
Key findings for October include:
For full details, including more details on the most impacted sectors, most active ransomware gangs, as well as most targeted countries, the full October ransomware roundup can be read here: https://www.comparitech.com/news/ransomware-roundup-october-2025/
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