`rkdebian` turns an $80 RK3562 Android tablet into a Debian workstation

// related

• #0001

  #0001Otherr/MachineLearningyesterday

  Hugging Face revives `PapersWithCode` with AI-parsed leaderboards

  50radar

  PapersWithCodeAI paper tracker — links code and benchmarks

  The rebuilt site tracks trending papers, methods, citations, repos, artifacts, and benchmark results. Useful for model scouting, but still manually verified and early-stage.

  • —Default ranking uses GitHub star velocity, so it surfaces research projects gaining developer attention, not just citation-heavy papers.
  • —Coverage starts with high-impact items like Qwen 3.5, RF-DETR, DINOv3, MTEB, Open ASR Leaderboard, and coding-agent benchmarks.
  • —Paper pages auto-link GitHub repos, project URLs, artifacts, PDFs, and external non-Arxiv papers; multiple repos per paper are supported.
  • —Leaderboards exist by benchmark and domain, including MMTEB, COCO val 2017, and Terminal Bench; handy for fast model/vendor filtering.
  • —Result extraction uses AI agents, but verification is still manual. Treat it as a shortlist generator, not a source of record yet.

  Source: www.reddit.com/r/MachineLearning/comments/1tgmwqr/reviviRead original →
• #0002

  #0002OtherGeekNews2 days ago

  Stay Native Until Text Forces Your Hand

  40radar

  SwiftUI can handle Markdown chat UI until document-wide selection enters scope. Jumping to NSTextView brings TextKit 2 complexity and streaming CPU spikes, so delay it.

  • —SwiftUI gives acceptable baseline performance for Markdown chat, but full-document text selection is hard to support cleanly.
  • —Moving to NSTextView and TextKit 2 trades native UI simplicity for lower-level text control and more performance work.
  • —Streaming input can trigger CPU spikes in the text stack. Chat apps should benchmark incremental rendering before committing.

  Source: news.hada.io/topic?id=29602Read original →
• #0003

  #0003Otherr/ClaudeAI2 days ago

  5 repeatable `Claude` prompting patterns for cleaner outputs

  50radar

  Prompt quality improves most when context becomes explicit: plan first, provide examples, ban bad phrases, and constrain answers to sources. Good enough to turn into reusable product and support workflows.

  • —Ask Claude to list success criteria before writing. The same request gets better structure when the model plans first.
  • —Examples beat tone adjectives. Two or three real paragraphs give the model a stronger voice target than words like “friendly.”
  • —Negative constraints matter: banning phrases like “unlock” or “revolutionize” cuts generic SaaS copy fast.
  • —Persistent context via Projects, AGENTS.md, or CLAUDE.md saves repeated setup and raises baseline output quality.
  • —Source-bound prompts reduce hallucinated citations. Paste the material and ask for answers only from that source.

  Source: www.reddit.com/r/ClaudeAI/comments/1tg52af/5_claude_pattRead original →