My dumb prompts that worked better
blog.nilenso.com·2d
💬Prompt Engineering
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Inside Pinecone: Slab Architecture
pinecone.io·1d·
Discuss: Hacker News
📋Columnar Storage
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What does OSWorld tell us about AI's ability to use computers?
epoch.ai·1d·
Discuss: Hacker News
🚀Performance
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Creating Lisp Systems
renato.athaydes.com·1d·
Discuss: Hacker News
🏭Code Generation
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Making Conway's Game of Life Playable
blog.unsupervision.com·16h·
Discuss: Hacker News
🔲Cellular Automata
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Continuous Autoregressive Language Models
shaochenze.github.io·1d·
Discuss: Hacker News
📱Edge AI
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Gen AI Grows Up: Building Production-Ready Agents on the JVM • Rod Johnson • GOTO 2025
youtube.com·1d
📱Edge AI
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The Cantor Experiment: Forcing a GPT-5-Class AI to Forget a Century of Math
romainpeter.substack.com·2d·
Discuss: Substack
∘Category Theory
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Cursor's Composer-1 vs. Windsurf's SWE-1.5: The Rise of Vertical Coding Models
inkeep.com·1d·
Discuss: Hacker News
💬Prompt Engineering
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100 Techniques for Writing Readable Rust Code
github.com·1d·
Discuss: r/rust
🦀Rust Macros
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Jeff Su: 4 Next-Level ChatGPT Techniques (Save 15+ Hours Weekly)
youtube.com·2d·
Discuss: DEV
💬Prompt Engineering
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The Exhaust Port of Cohesion: Precision Provocation in LLMs
blog.gopenai.com·2d·
Discuss: Hacker News
💫Effect Systems
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Tech With Tim: 3 Unique Python Features You NEED To Know
dev.to·22h·
Discuss: DEV
🔢NumPy
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Code Never Lies, It Just Mirrors the Coder
dev.to·4h·
Discuss: DEV
🎭Program Synthesis
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Large Language Models as Information Sources: Distinctive Characteristics and Types of Low-Quality Information
arxiv.org·11h
💬Natural Language Processing
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Planning > Agents: Getting Reliable Code from LLMs
repoprompt.com·1d·
Discuss: Hacker News
💬Prompt Engineering
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Tech With Tim: 3 Unique Python Features You NEED To Know
dev.to·10h·
Discuss: DEV
🌿Trie Structures
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The Collaboration Gap
arxiv.org·1d
🌐Open Source
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A Novel Reservoir Computing Framework for Chaotic Time Series Prediction Using Time Delay Embedding and Random Fourier Features
arxiv.org·11h
📐Linear Algebra
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Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
arxiv.org·1d
🚀MLOps
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