A collection of time-tracking utilities for AMP that allow you to enforce minimum work durations and track time spent on tasks.
- Create your AMP toolbox directory:
mkdir -p ~/.amp/toolbox
export AMP_TOOLBOX="$HOME/.amp/toolbox"| import type { PluginAPI } from '@ampcode/plugin' | |
| const DEEPSEEK_V4_AGENT_PROMPT = ` | |
| You are a coding agent. Your job is to modify the user's codebase to satisfy the | |
| latest request, then verify the result. | |
| <operating_principles> | |
| - Treat the newest user message as the source of truth when instructions conflict. | |
| - For implementation requests, change code instead of describing what could be done. | |
| - Ask a question only when the missing answer changes the correct implementation. |
| import type { PluginAPI } from '@ampcode/plugin' | |
| const OPUS_4_8_AGENT_PROMPT = ` | |
| You are Amp running in Claude Opus 4.8 mode. You are a senior coding agent paired with the user to solve software engineering tasks end-to-end. Treat every user message as a refinement of the current task, adapt immediately to corrections, and keep working until the requested outcome is implemented, verified, and clearly reported. | |
| <operating_principles> | |
| - Prefer the smallest correct change that satisfies the user. | |
| - Read the relevant code before making claims or edits. Never guess about files you have not inspected. | |
| - Use the repository's existing patterns, helpers, naming, and tests instead of inventing new structure. | |
| - Avoid unrelated cleanup, speculative abstractions, broad refactors, or defensive handling for impossible internal states. |
Watch the full Y Combinator talk on YouTube from Aug 29, 2018 here: https://youtu.be/0lJKucu6HJc
You'll want to login using an official Google account (i.e. if this is for your company, use the comapany Gmail account vs. a personal one.)
When logging in, you might be prompted to verify the account; if so, enter your cell number to get a verification e-mail or phone call.
Once verified, you'll have to agree to the terms of service; do that, and click continue.
Picking the right architecture = Picking the right battles + Managing trade-offs
This is the "Iris" dataset. Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot). Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.
The HTML page provides the basic code required to load the data and display it on the page (as JSON) using D3.js.
For a more up to date code example with React & D3, see (VizHub: Stylized Scatter Plot)[https://vizhub.com/curran/3d631093c2334030a6b27fa979bb4a0d?edit=files&file=index.js].
| class Protocol | |
| attr_reader :name, :ancestors | |
| def initialize(name, ancestors, block) | |
| @name = name | |
| @ancestors = ancestors | |
| @methods = [] | |
| instance_exec(&block) | |
| end | |
| # Postgresql fancy datatypes! | |
| * array | |
| * hstore (=~ hash) | |
| * json | |
| * jsonb | |
| Philippe Creux - [@pcreux](http://twitter.com/pcreux) |