Transformers, Language Models, Text Processing, Chatbots

Parsing with zippers improves parsing with derivatives
dl.acm.org·3d·
Discuss: Hacker News
Zig
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Text to SQL: Local, Secure, and Smarter
exasol.com·3h·
Discuss: Hacker News
🗃️SQLite
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Start Up No.2552: Apple plans cheap laptop, life delivering parcels, crooked ransomware negotiators, uranium v RAM, and more
theoverspill.blog·7h
🧠AI
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AI chatbots fail to grasp that people might believe false information, study finds
euronews.com·14h
🧠AI
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We built a collaboration platform on Claude Code. Here's what we learned.
gbase.ai·14h·
Discuss: r/ClaudeAI
🏗️Systems Design
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Automating error analysis for AI agents – what works and doesn't
atla-ai.com·1d·
Discuss: Hacker News
🔄MLOps
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How Transformer Models Detect Anomalies in System Logs
hackernoon.com·1d
👁️Observability
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The AI-Powered Evolution of Software Development
devops.com·1d
🧠AI
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Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments
arxiv.org·1d
📱Edge AI
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Spatial Sense: Unleashing Language Models on Location Data by Arvind Sundararajan
dev.to·1d·
Discuss: DEV
📱Edge AI
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ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction
arxiv.org·1d
📱Edge AI
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Probabilistic Robustness for Free? Revisiting Training via a Benchmark
arxiv.org·1d
🧠Neural Architecture
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What is ‘artificial intelligence,’ anyway? The confusion is the point
halifaxexaminer.ca·1d
🧠AI
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Why Agentic AI Struggles in the Real World — and How to Fix It
dev.to·1d·
Discuss: DEV
📱Edge AI
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Chatbot with AI Evaluation framework
reddit.com·12h·
Discuss: r/LLM
📱Edge AI
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Using ensemble learning with hybrid graph neural networks and transformers to predict traffic in cities
arxiv.org·9h
📱Edge AI
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Deep Value Benchmark: Measuring Whether Models Generalize Deep values or Shallow Preferences
arxiv.org·9h
📱Edge AI
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Confounding Factors in Relating Model Performance to Morphology
arxiv.org·1d
🧠Neural Architecture
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Think Smaller: The Counterintuitive Path to AI Adoption
oreilly.com·2h
🧠AI
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SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
arxiv.org·9h
🧠AI
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