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Concept Subspaces for Targeted Model Editing
gojiberries.io·23h
📊Embeddings
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Stop Paying for 4 AI Models
riskparody.substack.com·12h·
Discuss: Substack
🆕New AI
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Product Quantization
arpitbhayani.me·5h
🎯Vector Quantization
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Running LLM over RAM
reddit.com·12h·
Discuss: r/LocalLLaMA
🏗️LLM Infrastructure
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GitHub - arafkarsh/ms-springboot-ai: Java 23, SpringBoot 3.4.1 Examples using Deep Learning 4 Java & LangChain4J for Generative AI using ChatGPT LLM, RAG and ot...
github.com·6h
🦙Ollama
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https://www.sciencedirect.com/science/article/abs/pii/S0010027725003270
sciencedirect.com·12h
📊Statistical Ranking
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Two GTX 580s in SLI are responsible for the AI we have today — Nvidia's Huang revealed that the invention of deep learning began with two flagship Fermi GPUs in...
tomshardware.com
·16h
⚡Hardware Acceleration
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Rnj-1: Building Instruments of Intelligence
essential.ai·9h·
Discuss: Hacker News
🏗️LLM Infrastructure
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The Müller-Lyer Illusion in Ant Foraging
pmc.ncbi.nlm.nih.gov·5h·
Discuss: Hacker News
📊Embeddings
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Talking With Paul Kedrosky - Paul Krugman
paulkrugman.substack.com·14h·
Discuss: Substack
📊Model Serving Economics
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Code Evolution: Self-Improving Software with LLMs and Python
github.com·15h·
Discuss: Hacker News
🪄Prompt Engineering
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AI Energy Score v2: Refreshed Leaderboard, Now with Reasoning
huggingface.co·12h·
Discuss: Hacker News
🏆LLM Benchmarking
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ok I promised a thread about my current investment/"macro" (blurgh 🤮) equity plays
threadreaderapp.com·7h
🖥GPUs
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Anatomy of a Domain Risk Engine: Regex vs. LLMs
urlert.com·1h·
Discuss: Hacker News
🔍RegEx Engines
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Everybody Codes 2025 week 3
blog.firedrake.org·20h
🔒Borrow Checker
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Ingestion
natemeyvis.com·10h
🪄Prompt Engineering
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Understanding What Really Happens Behind AI Hallucination: Insights into Causes and Mechanisms
pub.towardsai.net
·15h
🎭Claude
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Enterprise Agents Have a Reliability Problem
dbreunig.com·11h
👨‍💻Software development practices
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(32) Implementing Retrieval-Augmented Generation (RAG) in Java Using Spring AI | LinkedIn
linkedin.com·7h
🔄LLM RAG Pipelines
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An idea for getting approximately calibrated 50% subjective probability ranges
statmodeling.stat.columbia.edu·14h
📊Statistical Ranking
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