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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
Ask HN: What kind of computer language will LLM use?
folderquesti · 2026-05-09 · via Hacker News - Newest: "LLM"
Ask HN: What kind of computer language will LLM use?
3 points by folderquestion 28 days ago | hide | past | favorite | 4 comments

I think in little time LLM will have the capability to translate easily code from one computer language to another, for example the LLM could "think" in prolog + lisp instead python. LLM will be able to write thousands of lines of APL or J in just a minute, we will have to accept that python is not the language for future LLMs. Perhaps the power of macros will not be defeated by its complexity because for an LLM a thousand line macro is just another simple code, prolog unification could mean building slots to construct new powerful artifacts that we are not able to have in our minds at the moment. LLM will no longer learn natural language or our most used computer languages, the next step is designing languages for a system that can keep in his head millions lines of code.



I think that in the near future a super object oriented language will emerge that will let us create computer apps instead of programming them directly. Systems analysts, not programmers, will be able to define a computer app with the objects at a very high level and LLMs will create a JAVA like blob that can be run on multiple systems. The advantage is that the objects will be completely bug free and the app size is only limited by the systems resources. Bottom line, most app development will be done by systems analyst. The AI object programmers, a specialty, will help tweak the underlying function of each object.


what llms reason best in might not be a language at all. more like a graph. graphs say more in less space than prose, and they stop the model from wandering off.


Because a library like cuda allows you to make code shorter?

Thinking twice, such a LLM would aim to create code that maximizes the power of the features it provides (rich api) and minimizes lines of code. So the LLM would aim to develop a language for reaching Kolmogorov complexity.