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The APM paradox
👁️Observability
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Reliability assessment of multi-performance system incorporating multiple common buses and transformation devices
sciencedirect.com·22h
🏗️Systems Design
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The Advent Of ‘Thinking Tokens’ Causes Unforeseen Inflationary Impact On Generative AI
forbes.com·5h
💬NLP
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NDC Conferences: "Would YOU Survive the Titanic?", with ML and .NET - Simon Painter - NDC Copenhagen 2025
youtube.com·17h·
Discuss: DEV
🔄MLOps
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The AI Village Where Top Chatbots Collaborate–and Compete
time.com·20h·
📱Edge AI
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Most Gen AI Players Remain 'Far Away' from Profiting: Interview with Andy Wu
library.hbs.edu·17h·
Discuss: Hacker News
📱Edge AI
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Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
arxiv.org·1d
📱Edge AI
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Think Smaller: The Counterintuitive Path to AI Adoption
oreilly.com·1h
🧠AI
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100 Techniques for Writing Readable Rust Code
reddit.com·9h·
Discuss: r/rust
🔧Embedded Rust
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Load Balancing 100,000 WebSockets (and Somehow Surviving It)
dev.to·11h·
Discuss: DEV
🚀WebTransport
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DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads
arxiv.org·1d
🏗️Systems Design
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Small Vs. Large Language Models
semiengineering.com·2d·
Discuss: Hacker News, r/LLM
📱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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Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization
arxiv.org·1d
📊Embeddings
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TypeScript Rewrote Itself in Go?! What That “10x Faster” Hype Really Means
dev.to·8h·
Discuss: DEV
🕸️WebAssembly
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BRAINS: A Retrieval-Augmented System for Alzheimer's Detection and Monitoring
arxiv.org·8h
🧠Neural Architecture
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How to Create Your Own AI GPT: A Developer’s Guide
dev.to·1d·
Discuss: DEV
📱Edge AI
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Going From Reactive to Predictive Incident Response with AIOps
hackernoon.com·16h
🔄MLOps
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Writing an LLM from scratch, part 27 – what's left, and what's next?
gilesthomas.com·1d·
Discuss: Hacker News
🔄MLOps
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Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs
arxiv.org·1d
⚙️LLVM
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