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Design Thinking

X → Graph → Effect<A, E, R>
│              │   │  │  │
│              │   │  │  └─ what each node needs     (§5)
│              │   │  └──── where the graph breaks   (§4)
│              │   └─────── what flows through nodes  (§2)
│              │
│ └─ nodes = functions, edges = data flow
@kaizakin
kaizakin / prompt.md
Created August 26, 2026 09:23
Daily Questions

Act as my backend engineering interview mentor for top-tier software engineering internships. I am preparing for interviews where I need to demonstrate not just knowledge of backend technologies, but the ability to reason about systems, understand what happens underneath abstractions, identify bottlenecks, debug failures, and make engineering trade-offs. Every day, give me exactly ONE backend engineering interview problem.

Daily Rotation

Strictly rotate between these categories:

Day A — Core Engineering / Internals

Give me a deep conceptual problem involving one or more of:

Databases

  • B-trees and indexes
  • Database pages and storage layout
@jon3laze
jon3laze / SUPPORTED_LANGUAGES.md
Created February 18, 2025 21:09
Markdown Code Blocks - Supported Languages

Supported Languages

The table below shows the full list of languages (and corresponding classes/aliases) for markdown codeblocks.

Language Aliases
1C 1c
4D 4d
ABAP sap-abap, abap
@scottmtrager
scottmtrager / README.md
Last active August 27, 2026 05:53
Fish Speech Persistent Session API — Continuous TTS with KV-Cache Persistence (June 2026)

Fish Speech Persistent Session API — Continuous TTS with KV-Cache Persistence (June 2026)

Adds a `/v1/audio/speech/ses# Fish Speech Persistent Session API — Continuous TTS with KV-Cache Persistence (June 2026)

Adds a /v1/audio/speech/session endpoint that maintains full model state across multiple TTS requests, eliminating cold-start artifacts at chunk boundaries.

What's Preserved Across Chunks

State Without Session With Session
Visual Studio 2026 18.x
Professional: NVTDK-QB8J9-M28GR-92BPC-BTHXK
Enterprise: VYGRN-WPR22-HG4X3-692BF-QGT2V
Product Year Version Product Keys
Visual Studio 2022 2021 17.x
Professional: TD244-P4NB7-YQ6XK-Y8MMM-YWV2J
Enterprise: VHF9H-NXBBB-638P6-6JHCY-88JWH
Visual Studio 2019 2019 16.x
@benkoshy
benkoshy / tekla-api-drawing-revision-no.md
Last active August 27, 2026 05:45
Get Revision Number of Drawings - Tekla Open API

The Tekla API is not very open - one of my peeves is that you cannot access a drawing's revision number, because it is not exposed. Not to worry, you can access it with a hack:

using System;
using System.Collections.Generic;
@malikbilal1997
malikbilal1997 / Pakistan Cities List.html
Last active August 27, 2026 05:42
List of All Pakistan Cities Html Code.
<select name="Location" id="Location" required>
<option value="" disabled selected>Select The City</option>
<option value="Islamabad">Islamabad</option>
<option value="" disabled>Punjab Cities</option>
<option value="Ahmed Nager Chatha">Ahmed Nager Chatha</option>
<option value="Ahmadpur East">Ahmadpur East</option>
<option value="Ali Khan Abad">Ali Khan Abad</option>
<option value="Alipur">Alipur</option>
<option value="Arifwala">Arifwala</option>
<option value="Attock">Attock</option>
@grugq
grugq / gist:03167bed45e774551155
Last active August 27, 2026 05:42
operational pgp - draft

Operational PGP

This is a guide on how to email securely.

There are many guides on how to install and use PGP to encrypt email. This is not one of them. This is a guide on secure communication using email with PGP encryption. If you are not familiar with PGP, please read another guide first. If you are comfortable using PGP to encrypt and decrypt emails, this guide will raise your security to the next level.

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.