AlphaSignal AI

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/5 MinerU parses ugly documents into clean Markdown and JSON for LLM workflows. It supports PDFs, DOCX, PPTX, XLSX, images, and web pages through a VLM + OCR dual engine. It can handle scanned docs, handwriting, formulas to LaTeX, tables to HTML, multi-column layouts, and cross-page table merging. The output follows human reading order, removes headers and footers, and integrates with MCP, LangChain, Dify, and FastGPT. Repo: github.com/opendatalab/mi…

Top AI Repos of the Week (June 19 - 26) 1. Hermes Creative-Ideation skill: 22 methods to break your agent's creative rut 2. Loop engineering: design the system that prompts agents for you 3. Headroom: cut LLM token costs 60-95% without losing answers 4. Hyperextract: turn messy documents into structured knowledge in one command 5. MinerU: high-accuracy document parsing for LLM and agent workflows More details in the thread.

Full breakdown with benchmark tables and serving recommendations on AlphaSignal: alphasignal.ai/news/z-lab-s-d…

/7 Drop-in for SGLang, vLLM, and TensorRT-LLM. No code refactoring. SGLang: --speculative-algorithm DFLASH --speculative-draft-model-path z-lab/Qwen3-8B-DFlash-b16 vLLM: via the Speculators library (docs.vllm.ai/projects/specu…, algorithm "dflash") MIT license. ICML 2026 accepted. NVIDIA-endorsed. Hugging Face: huggingface.co/collections/z-… GitHub: github.com/z-lab/dflash Paper: arxiv.org/abs/2602.06036

Diffusion LLMs have been losing to autoregressive models for two years. UC San Diego just found the one thing they're actually better at: not generating, drafting. DFlash hits 6x lossless acceleration on Qwen3-8B. 2.5x faster than EAGLE-3. Check out the detailed breakdown below!

Blog & Paper: research.google/blog/thinking-…

@noahzweben Thanks for reading! Follow @AlphaSignalAI for more content like this. Check out alphasignal.ai/newsletter to get a daily summary of the latest breakthrough news, models, papers, and repos. Read by 300,000+

/5 Loop engineering has been everywhere lately. This 14-step guide shows what it actually means to move from prompting coding agents by hand to designing systems that prompt, verify, remember state, and keep running without you babysitting every turn. It covers the basics of loops, when they are actually worth building, the core pieces like automations, skills, worktrees, connectors, sub-agents, and state files, plus the failure modes that turn “agentic workflows” into expensive chaos. x.com/0xCodez/status…

Top AI Educational Pieces of the Week (June 18 - 25) 1. Autoresearch for GitHub repos 2. How to configure agent identity in Claude Tag 3. Codex's SSD bug and how to safely check disk usage 4. Andrew Ng: Prompting could be gone in 3-6 months 5. The 14-step guide to loop engineering More details in the thread.