DEV Community: heather

Grokking with ChatGPT - The "Triple Sum Close to Target" algorithm, explained.

heather — Fri, 15 Aug 2025 18:52:58 +0000

This morning I was going crazy trying to complete "Triplet Sum Close to Target", a medium-level two-pointer algorithm. Basically,

I could not "grok" how a number's difference could be used to evaluate its size relative to another number. I was also trying to keep track of too many numbers in my head. Here's a convo with ChatGPT that helped a lot.

Real-World example of differences - Shot Put

me: Can you help me understand why using the diff is key to determining if two numbers are closer than two other numbers?

gpt: Yep — this is the heart of why “the diff” is the comparison tool in these problems.

Let’s slow down and strip away the “triplet” part — we’ll just compare two sums and see which is closer to the target.

What is diff?

We define:

diff = target - sum

If diff is positive, the sum is too small (we’re under target by diff units).
If diff is negative, the sum is too big (we’re over target by |diff| units).
If diff is zero, the sum exactly matches the target.

Why not compare sums directly?

If you compare sums directly, you have to handle two cases:
Sum is less than target (how much less?)
Sum is greater than target (how much more?)
The diff unifies these into one number that encodes both direction (sign) and distance (magnitude).

So the key insight is:

diff = target - sum shifts the problem so that “closeness to target” becomes “closeness to zero,” which is way easier to reason about and compare.

me: ok, I see. I'm picturing a shot put competition and how far someone is throwing something. That helps.

gpt:
Got it — let’s put your triple sum closest loop into shot put competition mode so every diff update feels like a throw getting measured.

Vocabulary swap

target → the mark on the field where you want to land.
sum → the throw’s landing spot.
diff = target - sum → how far you missed the mark and in which direction:
Positive diff → short throw (didn’t reach the mark).
Negative diff → long throw (overshot the mark).
Zero diff → bullseye.

Algorithm in “coach-speak”

int mark = targetSum;  // the distance you want to throw
Integer bestMiss = null; // best miss distance so far (diff)

for (int p = 0; p < arr.length - 2; p++) {
    int l = p + 1;
    int r = arr.length - 1;

    while (l < r) {
        int throwSpot = arr[p] + arr[l] + arr[r];
        int miss = mark - throwSpot; // how far you missed the mark

        if (miss == 0) {
            // Landed exactly on the mark, competition over
            return throwSpot;
        }

        if (bestMiss == null || isCloserToMark(miss, bestMiss)) {
            // New best throw
            bestMiss = miss;
        }

        // Adjust next throw attempt:
        if (throwSpot < mark) {
            // Short throw — try to throw further
            l++;
        } else {
            // Long throw — pull back a little
            r--;
        }
    }
}

private boolean isCloserToMark(int newMiss, int bestMiss) {
    // Case 1: both throws land short
    if (newMiss >= 0 && bestMiss >= 0) {
        return newMiss < bestMiss; // smaller miss is closer
    }
    // Case 2: both throws land long
    if (newMiss < 0 && bestMiss < 0) {
        return newMiss > bestMiss; // less negative miss is closer
    }
    // Case 3: one short, one long — choose the one closer to zero
    return Math.abs(newMiss) < Math.abs(bestMiss); // <-- here you could replace abs with sign logic if you want to go full no-abs
}

me: I get it. The one with the bigger sign in the end "wins". It was far enough away from zero to preserve its sign! Thus the other one was closer to zero.

gpt: Exactly — you nailed it.

When the signs differ, the one with the bigger magnitude will “pull” the total (diff + bestDiff) over to its side of zero.
So if adding them doesn’t pull the sum away from bestDiff’s sign, it means the new one (diff) must have had the smaller magnitude — it was closer to zero.

That’s the little mental judo move that lets you compare distances without ever taking an absolute value.

It’s like two kids in a tug-of-war:

If the rope ends up on the same side it started, the other kid wasn’t strong enough to pull it across — meaning they were “weaker” (smaller magnitude).

If the rope flips sides, the new kid overpowered the old one.

Want me to diagram that tug-of-war analogy with number lines so you can see it visually?

How to Simplify when Thinking about Algorithms

me: I was trying to relate the diffs to the sums instead of just thinking about how the diffs relate and that was too many variables to keep in my head.

gpt:

Yep — that’s a super common trap.

When you keep flipping back and forth between sum space (“how far is this sum from target?”) and diff space (“how far is this number from zero?”), your brain ends up juggling two number lines at the same time.
That’s cognitive overload — especially when the signs flip.

The beauty of using diff consistently is:

You stay in one number line (zero is the goalpost).
Every comparison is about one thing: “Which is closer to zero?”
You don’t have to re-map to the original sums until the very end, once you know the winning diff.

It’s like translating everything into one currency before comparing prices — instead of trying to mentally compare yen to dollars to euros all at once.

Then, because we all know chatGPT likes to offer things :-) it made me this checklist to avoid overthinking in algorithm problems. I thought I'd share

gpt: here’s a pattern list you can keep in mind for avoiding overload in algorithm problems.
Think of it as your “mental clutter cleanup” checklist.

1. Distance / Target Problems → Switch to Diff Space

Example: Closest sum, nearest point, minimal adjustment
Work with diff = target - value instead of raw values.
Stay in “distance from zero” land until done.
Translate back at the very end. (You just did this with Triple Sum Closest.)

2. Geometry / Coordinates → Shift or Rotate Origin

Example: Distance between points, closest pair
Translate the system so one point is (0,0).
For angles/slopes, rotate so a key line is horizontal.
You avoid carrying extra terms in all your calculations.

3. Intervals / Ranges → Normalize to Start=0

Example: Meeting rooms, merging intervals, timeline problems
Shift all intervals so the earliest start is 0.
Makes overlap and gap checks easier.
Often turns into simple array index math.

4. Percent / Ratio Problems → Use a Single Ratio Space

Example: Speed/distance/time, growth rate comparisons
Convert to one ratio type (miles/hour, profit/unit cost).
Keep all comparisons in that space until final answer.

5. Modular Arithmetic → Work Modulo Early

Example: Rotations, circular arrays, cyclic patterns
Reduce numbers modulo n immediately.
Avoid juggling big numbers and wrap-around logic later.

6. Graph Problems → Fix a Single Perspective

Example: BFS shortest path, Dijkstra’s
Store only the current cost-to-reach node instead of storing both “steps” and “path length” in separate forms.
Add metadata back if needed at the end.

7. Counting / Frequency → Use One Aggregation Metric

Example: Anagrams, inventory matching
Convert items to one comparable form (sorted string, hash, vector of counts).
Compare those forms instead of juggling raw items.

💡 Meta-rule: Always look for a transformation that removes at least one dimension of thinking.
That’s the lever that turns a problem from “too many moving parts” to “just compare this one thing.”

Variable Naming in Algorithms for Coding Interviews

heather — Wed, 13 Aug 2025 14:20:51 +0000

Variable naming in algorithms is a silent skill in coding interviews. Instead of naming a variable "i", if you give it a meaningful name like "lastKnownDuplicate", it will help you keep track of the meaning of that variable, like labeling storage boxes.

Here are some charts and tips to help you with naming variables as you prepare (with some help from ai and Grokking the Coding Interview).

🗂 Grokking Patterns + Variable Names (Quick Glance)

Pattern	Go-To Variable Names
Sliding Window	`windowStart`, `windowEnd`, `windowSum`, `charFrequency`
Two Pointers	`left`, `right`, `targetSum`, `lastNonDuplicate`
Fast & Slow Pointers	`slowPointer`, `fastPointer`, `cycleStart`
Merge Intervals	`currentInterval`, `mergedIntervals`, `intervalStart`, `intervalEnd`
Cyclic Sort	`currentIndex`, `correctIndex`
In-place Linked List Reversal	`currentNode`, `previousNode`, `nextNode`
Tree BFS	`queue`, `nodeQueue`, `currentNode`, `levelSize`, `levels`
Tree DFS	`currentPath`, `allPaths`, `remainingSum`
Two Heaps	`maxHeap`, `minHeap`, `median`
Subsets / Backtracking	`currentSubset`, `subsets`, `startIndex`
Modified Binary Search	`start`, `end`, `mid`, `target`
Top K Elements	`minHeap`, `maxHeap`, `topKElements`, `distance`

🗂 Grokking Patterns + Variable Names (In-Depth)

Sliding Window

Role	Good Name	Notes
Start index of window	`windowStart`	First element in current range
End index of window	`windowEnd`	Last element in current range
Frequency counter	`charFrequency`, `numFrequency`	Use type-specific name
Current sum/length	`windowSum`, `windowLength`	Describes what’s being aggregated

Two Pointers

Role	Good Name	Notes
Left pointer	`left` or `start`	Works for arrays and strings
Right pointer	`right` or `end`
Target sum	`targetSum`	Use when looking for sum
Last valid position	`lastNonDuplicate`, `lastSorted`	Clarifies purpose

Fast & Slow Pointers

Role	Good Name	Notes
Slow pointer	`slowPointer`	Advances 1 step at a time
Fast pointer	`fastPointer`	Advances 2 steps at a time
Cycle start	`cycleStart`	For cycle detection problems

Merge Intervals

Role	Good Name	Notes
Current interval	`currentInterval`	Often a tuple/array `[start, end]`
Merged result list	`mergedIntervals`	Plural for clarity
Interval start/end	`intervalStart`, `intervalEnd`	Explicit meaning

Cyclic Sort

Role	Good Name	Notes
Current index	`currentIndex`	Avoid `i`
Correct index for value	`correctIndex`	Makes swap logic obvious

In-place Linked List Reversal

Role	Good Name	Notes
Current node	`currentNode`	Where we are now
Previous node	`previousNode`	Node before current
Next node	`nextNode`	Node after current

Tree BFS

Role	Good Name	Notes
Processing queue	`queue` or `nodeQueue`	BFS uses queues
Current node	`currentNode`
Current level size	`levelSize`	Used to group levels
Result list	`result`, `levels`	Use plural for collection

Tree DFS

Role	Good Name	Notes
Current path	`currentPath`	For backtracking
All paths	`allPaths`	For collecting results
Remaining sum	`remainingSum`	For path sum problems

Two Heaps

Role	Good Name	Notes
Small half	`maxHeap`	Stores smaller numbers
Large half	`minHeap`	Stores larger numbers
Median	`median`	Optional if caching

Subsets / Backtracking

Role	Good Name	Notes
Current subset	`currentSubset`	Or `subset` if short-lived
Result list	`subsets` or `result`	Plural for clarity
Start index	`startIndex`	For recursive calls

Modified Binary Search

Role	Good Name	Notes
Search start	`start`	Lower bound index
Search end	`end`	Upper bound index
Midpoint	`mid`	Middle index
Target value	`target`	What we’re searching for

Top K Elements

Role	Good Name	Notes
Heap	`minHeap` or `maxHeap`	Match name to type used
Result list	`topKElements`	Describes final goal
Distance function	`distance`	For closest point problems

🗂 Grokking Patterns + Variable Names (Quick Tips)

Role + Context
Instead of i, say what it is and why it’s there.

❌ i → ✔ windowStart
❌ j → ✔ windowEnd
❌ count → ✔ charFrequency
Relationship to Other Variables
If there’s a pair, name them so they “talk to each other.”

fastPointer / slowPointer
minHeap / maxHeap
left / right
Avoid Mental Gymnastics
If a name makes you stop and translate it in your head, it’s too short or too vague.

❌ ptr → ✔ currentNode
❌ n1, n2 → ✔ intervalStart, intervalEnd

Deploying a fastapi/vite/React Router application with Docker in AWS ec2

heather — Mon, 11 Aug 2025 16:16:56 +0000

I wanted to deploy a modern python + react application in the easiest possible way. My application uses:

Frontend:

    "@tailwindcss/vite": "^4.1.11",
    "react": "^19.1.0",
    "react-dom": "^19.1.0",
    "react-router": "^7.7.1",
    "react-router-dom": "^7.7.1",
    "tailwindcss": "^4.1.11"
 "vite": "^7.0.6"

Backend:
fastapi
uvicorn[standard]
sqlalchemy
pydantic
chromadb

I had been developing in pycharm using a venv. That is stable and predictable, but does not allow for CD (continuous deployment) which is what I wanted.

So it was time to deploy, I switched to:
docker
docker-compose
caddy

Here's how I did it.

My application layout:

├── backend
│   ├── auth_providers
│   ├── config
│   ├── data
│   ├── db
│   ├── db.py
│   ├── Dockerfile
│   ├── main.py
│   ├── models
│   ├── __pycache__
│   ├── requirements.txt
│   ├── routers
│   ├── server.js
│   ├── services
│   ├── tests
│   └── utils
├── Caddyfile.dev
├── Caddyfile.prod
├── docker-compose.override.yml
├── docker-compose.prod.yml
├── docker-compose.yml
├── frontend
│   ├── dist
│   ├── Dockerfile
│   ├── index.html
│   ├── node_modules
│   ├── package.json
│   ├── package-lock.json
│   ├── public
│   ├── src
│   └── vite.config.js
├── package.json
├── package-lock.json
└── README.md

I decided to use caddy because, as long as you have a domain name mapped to your aws instance it provides an ssl certificate for that domain using Let's Encrypt. It all truly "just works". For someone who spent years configuring certificates in Apache, this is amazing!

PREPARING FOR DEPLOYMENT

Transitioning from my .venv environment to a docker container on my localhost was challenging.

In my .venv environment, I had written my app with this straightforward structure:

myapp
├── backend
├── frontend

But with Docker I changed the architecture to three different containers governed by a docker-compose.yml file:

backend container:

myapp
├── backend
├── frontend_dist
├── requirements.txt

frontend container (builds asssets served by caddy. Not needed in prod):

myapp
├── src
├── dist
├── vite.config.js

caddy (web server) container (serves assets in prod):

srv
├── assets
├── index.html
├── manifest.json

So a lot of work just changing paths and ports. I could have prevented this by containerizing with Docker from the get-go. Oh well, 20-20 hindsight!

TROUBLESHOOTING:

Here are some issues and solutions I encountered.

ISSUE: docker compose up returns this error:

api       | ModuleNotFoundError: No module named 'backend'

SOLUTION: Because I was using /app for the docker environment as opposed to /myapp in my .venv environment, i had to add "app" to my PYTHONPATH in backend/Dockerfile:

WORKDIR /app
ENV PYTHONPATH=/app
COPY backend /app/backend

then when I mounted /backend to /app/backend in container from docker-compose.yml, the paths in main.py made sense.

ISSUE: Anything other than an exact url match resulted in a 404 error.

'http://mysite/login' worked
'http://mysite/bla' gave 404 error

SOLUTION: I needed redirect, so used a react catch-all route, the path="*" route ie:

 <Routes>
      <Route path="/login" element={<Login />} />
      <Route
        path="/"
        ...
      />
      <Route path="*" element={<Navigate to="/" replace />} />
</Routes>

Also, configure Caddyfile like this with try_files before file_server, inside handle {} block:

handle {
    try_files {path} /index.html
    file_server
}

ISSUE: npm run dev gives this on startup:

TypeError: crypto.hash is not a function

SOLUTION: Be sure to work with node 20 (or 18+) which has upgraded Crypto API. So instead of FROM node:18-bullseye, use FROM node:20 in your frontend Dockerfile.

ISSUE: "RuntimeError: Directory '../frontend/dist/assets' does not exist"
SOLUTION: in /app/main.py I had: app.mount("/static/assets", StaticFiles(directory="../frontend/dist/assets"), name="static")

I had to define an env variable in docker-compose.yml and access it in main.py to determine whether I was in local architecture or docker container. I then used it to create a prefix which I used to build paths.
docker-compose.yml:

    environment:
      - APP_ENV=docker

main.py:

ENV = os.getenv("APP_ENV", "local")

if ENV == "docker":
    path_to_dist = Path('/app/frontend_dist')
else:
    path_to_dist = Path(__file__).resolve().parent.parent / 'frontend' / 'dist'

path_to_assets = path_to_dist / 'assets'
path_to_index = path_to_dist / 'index.html'

HINT:* digging around in my running containers helped a lot when trying to see where things were mounted.

docker exec -it <mycontainer> sh
docker exec -it <mycontainer> bash #bash is better, install it when you can

HINT: my .dockerignore contained /frontend_dist, causing it not to be seen. Always check your .dockerignore. Here are the dockerignore entries I did need:

.venv/
__pycache__
*.pyc
node_modules

ISSUE: docker ran, but I got a blank screen and this error:

Loading module from “http://localhost:5000/static/assets/index-CaU4slfD.js” was blocked because of a disallowed MIME type (“”). localhost:5000

SOLUTION: In docker, I was trying to mount assets like this: /app/frontend_dist/assets/

but main.py had: app.mount("/static", StaticFiles(directory=FRONTEND_DIST), name="static") causing assets to be mounted from wrong place.

So I changed main.py to: app.mount("/assets", StaticFiles(directory=path_to_dist), name="assets")

and in vite.config.js I switched to this "base" value:

export default defineConfig({
 base: "/",

ISSUE: 405 error (Method Not Allowed)
SOLUTION:
The problem was that my ports were not aligned, between backend/Dockerfile, frontend/Dockerfile, docker-compose.yml, and Caddyfile.

This can be challenging when trying to handle the transition from .venv to Docker, while maintaining different environments for dev and production.

These tips are not bulletproof security but may help you get unstuck from 405 errors.

This is what I needed:

*caddy serving on port 80 in the container, and 443 in production.
*frontend hot-reloading on 5173 for testing.
*api Caddy reverse-proxies from port 5000 (host) to api:8000 (container), for dev.

Caddy (dev): host :5000 → Caddy :80 (in container).
*Caddy (prod): :443 with TLS.
*Vite (dev): :5173 with hot reload.
*Reverse proxy (dev): Caddy proxies /api → api:8000.

vite.config.js:

server: {
  proxy: {
    '/api1': 'http://localhost:5000', // adjust to your backend
    '/api2': 'http://localhost:5000',

Caddyfile (dev environment only):

handle /api1/* {
        reverse_proxy api:8000
    }
handle /api2/* {
        reverse_proxy api:8000
    }

HINT: If you include the path such as "api1" in your route, use handle. If you want it stripped, use handle_path.

docker-compose (dev environment):

services:
  api:    
    ports:
      - "8000:8000"

  caddy:
    ports:
      - "5000:80"

  frontend:
    build:
      context: .
      dockerfile: frontend/Dockerfile
    container_name: frontend
    ports:
      - "5173:5173"   # default Vite dev server port for testing and hot-reloading

Here is a simple dev/prod diagram for ports:


Dev:
Browser → Vite :5173
  └─ proxy /api1,/api2 → Caddy :5000 → reverse_proxy → api:8000

Prod:
Browser → Caddy :443
  ├─ / (static Vite build)
  └─ /api → reverse_proxy → api:8000

ALSO REMEMBER: These issues are usually not caused by CORS, so look elsewhere.

ISSUE: Could not access my api2.
SOLUTION: All apis needed to be added as proxy in vite.config.js, ie:

server: {
  proxy: {
    '/api': 'http://localhost:5000',
    '/api2': 'http://localhost:5000' #the one that I could not access.
  }
}

ISSUE: Needed different ports and values for dev/prod.
SOLUTION: I wound up splitting up my docker-compose and Caddyfiles. Here's an example of how I called them:

docker-compose -f docker-compose.yml -f docker-compose.prod.yml up --build

FINAL TIPS

It may be no surprise that I used AI extensively for this project. Here are some final tips from GPT-4 that I thought were particularly useful:

✅ Final Tips

Action	Recommended?
Run FastAPI directly from .venv	❌ Not when testing Docker
Keep .venv in project root	✅ (but add it to .dockerignore)
Use Docker for full-stack testing	✅ Definitely
Use uvicorn outside Docker	Only for quick local debugging

DOCKER COMMANDS that I used:

docker compose build --no-cache
docker compose up

docker compose up --build

Setting up config variables in a Flask app

heather — Sat, 28 Jun 2025 20:57:49 +0000

Here is how I set up configuration files in my Flask app.

app.config object

my_app/config.py:

import os
basedir = os.path.abspath(os.path.dirname(__file__))

class Config(object):
    SQLALCHEMY_DATABASE_URI = 'sqlite:///test_whatever.db'
    SQLALCHEMY_TRACK_MODIFICATIONS = False

class ProductionConfig(Config):
    SQLALCHEMY_DATABASE_URI = 'sqlite:///prod_whatever.db'
    SQLALCHEMY_TRACK_MODIFICATIONS = False

class DevelopmentConfig(Config):
    SQLALCHEMY_DATABASE_URI = 'sqlite:///test_whatever.db'
    SQLALCHEMY_TRACK_MODIFICATIONS = False

my_app/app/app.py:

from flask import Flask, send_from_directory
from config import ProductionConfig, Config, DevelopmentConfig
from app.models import db
import os

def create_app():
    app = Flask(__name__)

    # Here, you refer to the DevelopmentConfig as a string
    app.config.from_object('config.DevelopmentConfig')

    # more init stuff here, blueprints, etc

    db.init_app(app)
    return app

environment variables

Sometimes you might want to load environment variables instead. For example to avoid the whole circular dependency issue when initializing blueprints within your app.

Here's how I initialized and read environment variables:

in venv pip install python-dotenv
include .env file at application root. Format should be MY_VAR='whatever'

in my_app/app/routes.py (or whatever .py file):

from dotenv import load_dotenv

you can initialize and refer explicitly to the environment vars:

load_dotenv()  # take environment variables
api_key=os.getenv('MY_API_KEY') #refer to environment variable
print('api key: ', api_key)

openai is a special case, they don't want it passed in explicitly. This worked for me:

load_dotenv()  # take environment variables
openai = OpenAI()

You can find a more exhaustive guide to configuration here. Hopefully this will get you started.