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AutoBe's Hardcore Function Calling Benchmark Pushes LLM Backend Generation to New Limits

AutoBe's Hardcore Function Calling Benchmark Pushes LLM Backend Generation to New Limits

LLMs as Emacs Elisp Assistants: Reducing the Friction of Infinite Customization

LLMs as Emacs Elisp Assistants: Reducing the Friction of Infinite Customization

Arcee AI launches Trinity Large, a 400B-parameter open-weight model challenging Meta's Llama 4 Maverick

Arcee AI launches Trinity Large, a 400B-parameter open-weight model challenging Meta's Llama 4 Maverick

GNU gettext Reaches Version 1.0 After 30+ Years In Development - Adds LLM Features

GNU gettext Reaches Version 1.0 After 30+ Years In Development - Adds LLM Features

The Double-Edged Sword of AI-Assisted Code Porting: Lessons from Translating TinyEMU to Go

Turns out I was wrong about TDD

Turns out I was wrong about TDD

From 24 to 216: A Systematic Performance Engineering Journey for LLM-based TTS

From 24 to 216: A Systematic Performance Engineering Journey for LLM-based TTS

Small Language Models Narrow the Gap: Efficiency Challenges Scale in Generative AI

Small Language Models Narrow the Gap: Efficiency Challenges Scale in Generative AI

Baidu's ERNIE 5.0: A 2.4 Trillion Parameter MoE Model, But What Does It Actually Do?

Baidu's ERNIE 5.0: A 2.4 Trillion Parameter MoE Model, But What Does It Actually Do?

Why I Don't Have Fun With Claude Code

Why I Don't Have Fun With Claude Code

The best code is no code: composing APIs and CLIs in the era of LLMs

The best code is no code: composing APIs and CLIs in the era of LLMs

The End of One-Size-Fits-All LLM APIs: Why Workload-Specific Inference Is Taking Over

The End of One-Size-Fits-All LLM APIs: Why Workload-Specific Inference Is Taking Over

Conversation: LLMs and the what/how loop

Conversation: LLMs and the what/how loop