PDP-10 and Microcomputers – By Nemanja Trifunovic
nemanjatrifunovic.substack.com·1d·
Discuss: Substack
💾Retro Computing
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[R] WavJEPA: Semantic learning unlocks robust audio foundation models for raw waveforms
reddit.com·2d·
📱Edge AI
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Patterns for Building a Scalable Multi-Agent System
devblogs.microsoft.com·2d·
Discuss: Hacker News
💬Prompt Engineering
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Beyond Numbers: How to Humanize Your Data & Analysis
towardsdatascience.com·2d
📊Data Visualization
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Day 27: Python Mode Finder, Find the Most Frequent Element in a List Using Dicts
dev.to·2d·
Discuss: DEV
#️⃣Hash Tables
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Stop Debugging Your Bibliography: A Developer’s Guide to Efficient Academic Writing
dev.to·15h·
Discuss: DEV
📚Technical Writing
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H-FA: A Hybrid Floating-Point and Logarithmic Approach to Hardware Accelerated FlashAttention
arxiv.org·6d
Hardware Acceleration
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Matrix Sensing with Kernel Optimal Loss: Robustness and Optimization Landscape
arxiv.org·5d
📊Optimization
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How to Get Started on Building Gen AI Applications
dev.to·19h·
Discuss: DEV
🎭Program Synthesis
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From 'Why the F@&k Do I Need This?' to 'Oh, That's Why' - My GAT Journey
dev.to·2d·
Discuss: DEV
🦀Rust
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Building Efficient Stream Processing Pipelines with Backpressure Control in Golang
dev.to·1d·
Discuss: DEV
🌊Stream Processing
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LLMs Talking in Secret: Direct Semantic Links for AI Collaboration by Arvind Sundararajan
dev.to·1d·
Discuss: DEV
🎭Program Synthesis
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Unlocking Cellular Secrets: Precision Alignment for Multi-Stain Bioimages by Arvind Sundararajan
dev.to·1d·
Discuss: DEV
👁️Computer Vision
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Launching Your AI Agent on AWS: Bedrock, Lambda & API Gateway
github.com·1d·
Discuss: DEV
FastAPI
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Unsupervised Learning for Industrial Defect Detection: A Case Study on Shearographic Data
arxiv.org·5d
👁️Computer Vision
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Fleming-VL: Towards Universal Medical Visual Reasoning with Multimodal LLMs
arxiv.org·6d
🔥PyTorch
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Switching from Go to Node.js. Seeking best practices advice!
reddit.com·10h·
Discuss: r/node
🦕Deno
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VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models
arxiv.org·5d
📱Edge AI
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Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
arxiv.org·5d
🚀MLOps
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