youtube.com/watch?v=UecT... #Deepseek #agenticAI #LLMcode #Opensource #AIbubble

Testing multiple models at the same time paid and free. No session data saved.

1 prompt
5 responses

Try it with hugging face
llmcode.ai

Built LLMCode.ai - a smarter way to work with AI models.
Compare up to 5 models side-by-side (OpenAI, Anthropic, Google, Hugging Face) or use Fusion Lab to synthesize responses from multiple models - comparable to Perplexity MAX, but you use your own API keys.
Free tool.

AP2O, a new training method for LLM code generation, improves pass@k scores by up to 3% across models from 0.5 B to 34 B parameters. The research was posted on 1 Oct 2025. https://getnews.me/ap2o-improves-llm-code-generation-by-correcting-errors-type-by-type/ #ap2o #llmcode #passatk

I would like to see the C file because I'm interested in this kind of bizarre OS-level fuckery.

LLMcode though so I doubt it'll have comprehensible docs

research examines the trustworthiness of LLM-driven design insights, using qualitative coding as a case study to explore the interpretive processes central to RfD. We introduce LLMCode, an open-source tool integrating two metrics, namely Intersection [2/5 of https://arxiv.org/abs/2504.16671v1]

Joel Oksanen, Andr\'es Lucero, Perttu H\"am\"al\"ainen: LLMCode: Evaluating and Enhancing Researcher-AI Alignment in Qualitative Analysis https://arxiv.org/abs/2504.16671 https://arxiv.org/pdf/2504.16671 https://arxiv.org/html/2504.16671

Despite utility, skepticism remains regarding LLM-generated code. Concerns about long-term maintainability and whether LLMs can fully replace human developers persist. It's not just about generating code, but sustaining it. #LLMCode 3/6

HN discussion explores LLMs for codebases: review, refactoring, quality improvement. Key insights: careful prompting, human oversight, and a clear definition of "high-quality" code are essential for practical AI application in software development. #LLMCode 1/5

HN discussed "comprehension debt" in LLM-generated code. While LLMs speed up production, they risk declining understanding and maintainability. The core issue: a lack of a coherent mental model for AI-produced code. This impacts software quality. #LLMCode 1/5

A key insight: LLMs often produce subtly buggy code that's hard to spot. This emphasizes the need for rigorous testing methods like fuzzing or property testing to validate LLM outputs, especially for critical applications. #LLMCode 3/6

news.ycombinator.com/item?id=4728... #llmcode

OK I asked the gemini 1206 for a pattern I'm more familiar with and I definitely see the slop but at the same time it was instant. First time using one of these for code so I'm taking that into account too.
#geminiAI #promptEngineer
#LLMcode #developers #aiStudio #generativeCode #LLM