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Hackers compromised the Injective Labs SDK project's GitHub repository and used it to publish a malicious package on the Node Package Manager (npm) that stole cryptocurrency wallet private keys and mnemonic seed phrases. [...]
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Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings... Fine-tuning LLMs for financial natural language processing (NLP) is constrained by limited, imbalanced data. Real-world financial news overrepresents earnings and stock movements, while rarer events such as credit-rating changes, product approvals, and labor issues are harder to capture at scale. Synthetic generation can help fill those
If you're handling them like a service account or API token, consider yourself behind. AI agents need a fundamentally different approach.
Judge reluctantly approves $1.5M settlement with SEC over Twitter stock violation.
Production Weaviate in minutes, managed by DigitalOcean. Starting at $20/month. Vector databases have become a core piece of the AI application stack. Whether you’re building retrieval-augmented generation (RAG), semantic search, agentic workflows and memory, or similarity-based recommendations, you need a vector store that’s reliable, fast, and doesn’t require a dedicated ops engineer to keep running. Weaviate has become a critical part of that stack — its open-source AI-native vector database
Disaster recovery is often discussed as if it were mainly a technical discipline. Build the standby environment, configure replication, document failover, test the process, and the job is largely done. If the primary system fails, the recovery target takes over. The topic is framed as one of topology, tooling, replication, and automation. All of those things matter. None of them answers the hardest operational question: when should recovery actually be invoked?
OpenAI may be sanctioned for hiding, deleting ChatGPT logs in NYT copyright fight.
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