LazyLLM, Easiest and laziest way for building multi-agent LLMs applications
github.com·1d·
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From vibe coding to context engineering: 2025 in software development
technologyreview.com·2d·
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The Rise of the Specialist: Why Small Language Models are the Future of Enterprise AI
dev.to·18h·
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Good abstractions for humans turn out to be good abstractions for LLMs
betweentheprompts.com·3d·
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Tool-Driven Behavioral Directives: How to Scale LLM Agents Without Prompt Spaghetti
dev.to·9h·
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Prompts that work for beginners (small, clear, and testable)
dev.to·2d·
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Think Smaller: The Counterintuitive Path to AI Adoption
oreilly.com·1d
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A brief guide for those who slept (on AI) the last two years
dev.to·1d·
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4 Rules for Successful Vibe Coding
appsoftware.com·1d·
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How AI is affecting my work
writing.natwelch.com·18h
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Developer Case Study: Cursor and Claude Code Suggested Building a Proxy. Shotgun CLI Found LiteLLM Instead.
dev.to·18h·
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Reasoning with Sampling: Your Base Model Is Smarter Than You Think
aakaran.github.io·18h·
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AI Papers to Read in 2025
towardsdatascience.com·1d
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⚡ Rethinking Prompt Engineering: How Agent Lightning’s APO Teaches Agents to Write Better Prompts
dev.to·23h·
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🏗System design
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Meet Aissist - your personal AI command line sidekick
dev.to·51m·
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Gen AI Grows Up: Building Production-Ready Agents on the JVM • Rod Johnson • GOTO 2025
youtube.com·1d
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Building a Production-Ready AI Agent
api.github.com·3d·
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You Should Write An Agent
fly.io·1d·
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