theaniketgiri

5 ms·

1. 1.▲
   by theaniketgiri 7mo ago

   Hi HN — I'm Aniket, working on AIP. As AI agents begin executing real-world actions (calling APIs, writing to databases, triggering workflows), there's currently no native identity or authorization layer for them. AIP is an open p
2. 2.▲
   Show HN: AIP – A Cryptographic Identity Protocol for Autonomous AI Agents(github.com)

   1 points by theaniketgiri 7mo ago|1 comments
3. 3.▲
   by theaniketgiri 8mo ago

   Exactly — current platforms authenticate the account, but with agents the account isn’t the decision-maker anymore. Two identical API calls can come from either intended behavior or a manipulated model, and today they look the same to the s
4. 4.▲
   Show HN: AIP – An open protocol for verifying what AI agents are allowed to do(github.com)

   1 points by theaniketgiri 8mo ago|2 comments
5. 5.▲
   Show HN:AIP Protocol–Solving the agent revocation problem in distributed systems(github.com)

   1 points by theaniketgiri 8mo ago|0 comments
6. 6.▲
   by theaniketgiri 11mo ago

   Thanks a lot, really appreciate that Building something new always gets mixed reactions, but messages like yours keep me going.
7. 7.▲
   by theaniketgiri 11mo ago

   Yeah, that’s how I see it too. LLMs are part of the toolkit now what matters is how you use them to actually ship something useful.
8. 8.▲
   by theaniketgiri 11mo ago

   Yeah, I did! I trained a few small ones — mostly the “nano” and “tiny” templates (a few million params) on datasets like Shakespeare and Alpaca. The goal was to make sure the training loop, tokenizer, and evaluation all worked smoothly. Did
9. 9.▲
   by theaniketgiri 11mo ago

   Thanks everyone for the feedback and discussion. For those asking technical questions - happy to help! The tool works on Mac/Linux/Windows, check the README for setup. For those concerned about the architecture - it follows stand
10. 10.▲
    by theaniketgiri 11mo ago

    Fair point I agree embedding code as strings isn’t ideal. I did it mainly to make npx create-llm portable without needing a Python setup during scaffolding. Definitely open to improving that happy to refactor if you have suggestions.
11. 11.▲
    by theaniketgiri 11mo ago

    The Python-in-TS bit made me smile But to clarify, it’s a standard TypeScript CLI — no such hacks involved, just template-based generation.
12. 12.▲
    by theaniketgiri 11mo ago

    Fair points and I get where you’re coming from. I’ve been very open that AI helped with repetitive parts (docs, boilerplate, commit messages). The functional code training logic, model architecture, CLI was written and tested by me. Some de
13. 13.▲
    by theaniketgiri 11mo ago

    Thanks for the support! Appreciate you trying it out. Let me know if you hit any issues or have ideas for improvements.
14. 14.▲
    by theaniketgiri 11mo ago

    Good question! I think you mean nanoGPT (Karpathy's minimal GPT implementation)? Key differences: nanoGPT: - Minimal reference implementation (~300 lines) - Educational code for understanding transformers - Requires manual setup and co
15. 15.▲
    by theaniketgiri 11mo ago

    To clarify the AI question once and for all: What AI did: - Generated README templates (boilerplate markdown) - Suggested commit messages (I didn't always edit them) - Helped with documentation structure What I wrote: - All Python trai
16. 16.▲
    by theaniketgiri 11mo ago

    Thanks for the support! And yeah, the commit history is messy - I was learning and shipping fast. Not perfect, but the tool works and people are using it. Let me know if you have any questions when you try it!
17. 17.▲
    by theaniketgiri 11mo ago

    Thanks! The blog post is just my honest journey - spent way too much time trying to understand LLMs, figured others had the same frustration. If you try create-llm, would love your feedback. Always looking to make it better.
18. 18.▲
    by theaniketgiri 1y ago

    Great question - I should've been clearer. When I started, I wanted to understand LLMs deeply. But I hit a wall: tutorials were either "hello world" toys or "here's 500 lines of setup before you start." What I
19. 19.▲
    by theaniketgiri 1y ago

    Totally fair question! Docs / Markdown: AI handled repetitive stuff like READMEs and summaries. Core logic / Python: fully written by me. Commit messages: some minimal ones just for quick iterations — the real work is in the code.
20. 20.▲
    by theaniketgiri 1y ago

    Mostly the repetitive stuff like README generation and pushing code with meaningful commit messages was handled by AI. The actual work and logic were done by me.
21. 21.▲
    by theaniketgiri 1y ago

    Yep, works fine on Mac. Try the nano or tiny templates if you want quicker training runs
22. 22.▲
    Show HN: Create-LLM – Train your own LLM in 60 seconds(github.com)

    54 points by theaniketgiri 1y ago|44 comments