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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
If an LLM is too expensive it won't be next year
speckx · 2026-05-21 · via Hacker News - Newest: "LLM"

AI can cost more than human workers now

This is an Axios headline but the tldr is that once an LLM can do your job it will undercut you the next year.

This is the article and it focuses on token and infrastructure costs. Not cleaning up messes or other times a person needs to be in the loop. For example "Uber's chief technology officer already blew through his full 2026 AI budget due to token costs"

Example of someone thinking tokens will stay expensive. 
No shade, it is just my belief they are wrong on this one claim. 

There is a 7 year and a 4 year trend of the price drop to 1/10th a year for a set quality of token. As in $1 million become $1 hundred cost in 4 years. This article looks at gpt 3.5 level tokens from late 2022 to late 2024

Example price decline in 2 years

But the deflation keeps going Qwen3.5 1.7B–2B is well above gpt 3.5 level in tests and in usage. This you can run really fast locally on any newish laptop or smartphone. Tokens at this quality level are now just electricity costs. That's less than 4 years data but gpt 2 level has followed the same trend for 7 years. 7 more years of the trend turns $10 million cost into $1. Lindy Effect says a reasonable first guess is if a trend has gone on for X length of time assume it will keep going for another X unless theres a good reason for it to stop. Coding and Video Image generation are still improving so fast there is not great reason to assume they will suddenly plateau yet. And the same for efforts to make smaller models smarter using big models. Teacher->pupil, chain of thought, distillation, pruning, low rank factorisation all keep getting better in a way that suggests that smaller models will keep getting better even if big ones plateau.

This is not an argument that LLMs in general will keep getting better though they will and that will help. Or that GPUs will keep improving though they will and that will help. Or that open weight models will stay about 9 months behind state of the art apis though they will and that will help a lot. Or that methods to condense smart models into smaller less demanding machines will improve though they will. It is that all these and a few other trends combined will work together to make a million tokens with a certain score on metrics and in a users opinion an order of magnitude cheaper every year.

Slop Tsunami comes down to the cost of tokens coming down.

The cost of tokens will come down. And at such a rate that it makes Moore's Law look tame. It won't go on forever but it is on a seven year trend with good reason to see it keeping going for at least 3 more years. At which point todays best current token will be 1/1000th the cost.