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akashtndn

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  1. Show HN: Explain this – agent skills to create HTML explainer for topics or code (github.com)

    Over the last few months, I have frequently created HTML explainers using coding agents when trying to understand new topics or exploring codebases. Nothing fancu but the approach has been helpful. Some examples are…

    2 points 1
  2. Turning Strava data and gym photos into a training recap with my coding agent (akashtandon.in)
    1 points 0
  3. Debugging in the Age of Agents (akashtandon.in)
    4 points 0
  4. Fine-tune FLUX.2 [Klein] with a LoRA under 60 minutes (huggingface.co)
    1 points 0
  5. Superpowers: The Anatomy of an Agent Skill (akashtandon.in)
    2 points 0
  6. Autotrader – paper trading AI agent for Indian equities (github.com)
    1 points 1
  7. Awesome Neuroscience (github.com)
    20 points 0
  8. Autotrader: An autonomous paper trading agent, two weeks in (akashtandon.in)
    3 points 0
  9. Artemis II: Why Going Back to the Moon Is a Big Deal (akashtandon.in)
    1 points 0
  10. DSPy: Programming – Not Prompting – Language Models (akashtandon.in)
    2 points 0
  11. Firestore Schema Visualizer (github.com)
    1 points 0
  12. Interactive Explainer: Pi and OpenClaw (akashtandon.in)
    1 points 0
  13. Fal-AI-skill: Claude Code plugin for working with fal AI (github.com)
    1 points 0
  14. Four Years a Founder: Time (akashtandon.in)
    3 points 1
  15. A 7-Step Guide for doing UX research for AI features (looppanel.com)
    1 points 0
  16. Lessons from building an AI feature that works – chunking and summarization (akashtandon.in)
    2 points 0
  17. Firestore schema visualizer: Schema visualization using GPT-4 and PlantUML (github.com)
    3 points 0
  18. What's the best explanation for LLMs being almost right when citing sources?

    For example, I asked GPT-4 to identify the source for a quote from a short story by Edgar Allan Poe. It correctly identified the author but not the title. What's the best available explanation for such behavior by LLMs?

    14 points 11
  19. Classifying Slack messages with GPT-4 (github.com)
    1 points 0
  20. Awesome Neuroscience on Github (github.com)
    1 points 0
  21. How to Fine-Tune LLMs in 2024 with Hugging Face (philschmid.de)
    2 points 0
  22. Reflecting on 2023: The Evolution of Looppanel (changelog.looppanel.com)
    1 points 0
  23. Brief lessons from using LLM APIs in production (akashtandon.in)
    2 points 0
  24. Beyond utility – the role of user experience in enterprise software (akashtandon.in)
    2 points 0
  25. PySpark for NLP workshop – Jupyter notebooks and instructions (github.com)
    3 points 0