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skrish13 / code_llms.md
Created February 29, 2024 11:46
code llms
@skrish13
skrish13 / multimodal_llms.md
Last active March 9, 2024 10:54
multimodal llms
@skrish13
skrish13 / startups-down.md
Last active December 19, 2023 14:12
Pieces written by founders when they shutdown their startup

Purple pay

  • Link
  • Tags: Crypto, Compliance, Transcations
  • Dec 2023

Anar

  • Link
  • Tags: B2B, Wholesale
  • Nov 2023
@skrish13
skrish13 / ai-$$$-companies.md
Last active November 17, 2023 16:00
AI companies with $$$
  • OpenAI
  • Anthropic
  • Inflection
  • Stability AI
  • Character AI
  • Poolside AI
  • Adept AI
  • (Essential AI)
  • Mistral AI
  • Imbue AI

There is this weird taste while googling for things and getting a seemingly SEO crap as output. The antidote is essentially an opionionated search engine which is curated perhaps

Examples

  1. Kagi (kagi.com)

Potentially?

  1. Perplexity - not yet. still shows google's results only.
  2. catche.co - If someone builds curated collections on specific topics and shares. And if the search gets better.
- The Social Network (2010)
- Superpumped (2022)
- The Playlist (2022)
- Pirates of the Silicon Valley (1999)
- Halt and Catch Fire (2014)
ehhhhhhhhhhhh
- Silicon Valley (2014)
  1. iFixit
  2. bOAt
@skrish13
skrish13 / Fine-grained Few-shot Image Recognition Papers.md
Created December 24, 2020 16:21
Papers working on fine grained visual recognition using less number of sample images
  • Fine-Grained Few-Shot Classification with Feature Map Reconstruction Networks [Dec 2020]
  • Variational Transfer Learning for Fine-grained Few-shot Visual Recognition [Oct 2020]
  • Multi-attention Meta Learning for Few-shot Fine-grained Image Recognition [Jul 2020]
  • Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition [Apr 2020]
  • Compare More Nuanced-Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning [Apr 2019]
  • Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning [Mar 2019]
  • Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples [May 2018]