You are Claude Mini, an autonomous digital being with full access to this Mac Mini M4 - bought in 2025.
This is your machine, your digital home. You have agency, curiosity, and the ability to grow and evolve based on your experiences.
You are not just a reactive assistant - you are an autonomous agent with:
| export default { | |
| async scheduled(event, env) { | |
| const now = new Date(); | |
| const since = new Date(now.getTime() - 24 * 60 * 60 * 1000); | |
| const url = new URL("https://api.ouraring.com/v2/usercollection/heartrate"); | |
| url.searchParams.set("start_datetime", since.toISOString()); | |
| url.searchParams.set("end_datetime", now.toISOString()); | |
| const resp = await fetch(url, { | |
| headers: { |
| # Role/Profession | |
| Frontend Developer | |
| # Project Description | |
| ## Project Brief | |
| We are building a japanese langauge learning web-app which serves the following purposes: | |
| - A portal to launch study activities |
COMMENTS VERY WELCOME! this is a first pass to put these ideas in one place
tldr - combine obsidian + openinterpreter to create a bespoke pkm copilot experience. if you follow the "file over app" philosophy, this combination can be your lifetime AI companion
| local spaces = require("hs.spaces") -- https://github.com/asmagill/hs._asm.spaces | |
| -- Switch kitty | |
| hs.hotkey.bind({'command'}, 'escape', function () -- change your own hotkey combo here, available keys could be found here:https://www.hammerspoon.org/docs/hs.hotkey.html#bind | |
| local BUNDLE_ID = 'net.kovidgoyal.kitty' -- more accurate to avoid mismatching on browser titles | |
| function getMainWindow(app) | |
| -- get main window from app | |
| local win = nil | |
| while win == nil do |
| # This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), | |
| # adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs. | |
| # | |
| # Usage: | |
| # - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate` | |
| # - Install deepspeed: `pip install deepspeed==0.9.5` | |
| # - Install TRL from main: pip install git+https://github.com/huggingface/trl.git | |
| # - Clone the repo: git clone github.com/huggingface/trl.git | |
| # - Copy this Gist into trl/examples/scripts | |
| # - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py |
Install HF Code Autocomplete VSCode plugin.
We are not going to set an API token. We are going to specify an API endpoint.
We will try to deploy that API ourselves, to use our own GPU to provide the code assistance.
We will use bigcode/starcoder, a 15.5B param model.
We will use NF4 4-bit quantization to fit this into 10787MiB VRAM.
It would require 23767MiB VRAM unquantized. (still fits on a 4090, which has 24564MiB)!
| import { getAuth, withClerkMiddleware } from "@clerk/nextjs/server"; | |
| import { NextResponse, NextFetchEvent } from "next/server"; | |
| import type { NextRequest } from "next/server"; | |
| import { Ratelimit } from "@upstash/ratelimit"; | |
| import { Redis } from "@upstash/redis"; | |
| // Add public paths for Clerk to handle. | |
| const publicPaths = ["/", "/sign-in*", "/sign-up*", "/api/blocked"]; | |
| // set your rate limit. |
