If you’re choosing a language for learning, quick experiments, small games, scripting, or even AI demos, this page shows the tradeoffs clearly — with real code.
| Area | jdBasic | Python |
|---|---|---|
| Learning curve | Very gentle; classic BASIC readability | Good, but grows with ecosystem complexity |
| Fast prototyping | Excellent for “idea → working demo” | Excellent, but setup varies (envs, libs) |
| Graphics & sound | Built-in commands; great for games/demos | Possible, but usually via external libs |
| Array programming | Operators work element-wise; broadcast scalars | Typically via NumPy (extra dependency) |
| AI experiments | Tensors + autodiff built in for learning | Best-in-class libraries (PyTorch, JAX) |
| Web APIs | Built-in HTTP server; return Map → JSON | Many frameworks (FastAPI, Flask, Django) |
In jdBasic, common operators work element-wise on arrays, and scalars broadcast naturally.
' Element-wise + scalar broadcast
V = [10, 20, 30, 40]
PRINT "V * 2: "; V * 2
PRINT "V + 100: "; V + 100
' Element-wise array + array
A = [1, 2, 3]
B = [10, 20, 30]
PRINT "A + B: "; A + BPython is great, but array math typically means pulling in NumPy and learning its model.
# Python + NumPy (typical)
import numpy as np
V = np.array([10, 20, 30, 40])
print(V * 2)
print(V + 100)
A = np.array([1, 2, 3])
B = np.array([10, 20, 30])
print(A + B)jdBasic includes a Tensor type that tracks computation and supports automatic differentiation.
' Autodiff example
A = TENSOR.FROM([[1, 2], [3, 4]])
B = TENSOR.FROM([[5, 6], [7, 8]])
C = TENSOR.MATMUL(A, B)
TENSOR.BACKWARD C
PRINT "Gradient of A:"; TENSOR.TOARRAY(A.grad)For real-world ML systems, Python ecosystems are unmatched. jdBasic shines when you want to understand the mechanics quickly and build small experiments.
Best use-cases:
Register handlers and return a Map for automatic JSON responses.
FUNC HandleApi(request)
response_map = {
"server_time": NOW(),
"status": "ok",
"path": request{"path"}
}
RETURN response_map
ENDFUNC
HTTP.SERVER.ON_POST "/api/info", "HandleApi"
HTTP.SERVER.START(8080)# FastAPI (one common approach)
from fastapi import FastAPI
from datetime import datetime
app = FastAPI()
@app.post("/api/info")
def api_info():
return {"server_time": datetime.utcnow().isoformat(), "status": "ok"}This kind of interactive prototype is where jdBasic shines.
SCREEN 800, 600, "Sprite Demo"
SOUND.INIT
SPRITE.LOAD 1, "player.png"
SPRITE.LOAD 2, "enemy.png"
PLAYER_ID = SPRITE.CREATE(1, 400, 500)
ENEMY_ID = SPRITE.CREATE(2, 100, 100)
DO
k$ = INKEY$
IF k$ = CHR$(27) THEN EXITDO
SPRITE.MOVE PLAYER_ID, MOUSEX(), MOUSEY()
SPRITE.UPDATE
CLS 0, 0, 50
SPRITE.DRAW_ALL
SCREENFLIP
SLEEP 16
LOOP UNTIL FALSEPython game dev commonly means using frameworks (e.g., Pygame). That’s totally fine — it’s just a different setup style.
Rule of thumb:
jdBasic supports ASYNC FUNC and
AWAIT for non-blocking workflows.
ASYNC FUNC DOWNLOADFILE(url$, duration)
FOR i = 1 TO duration
' work...
NEXT i
RETURN "Download of " + url$ + " successful."
ENDFUNC
task1 = DOWNLOADFILE("https://example.com/data.zip", 5)
result1 = AWAIT task1
PRINT result1If you want the fastest path to understanding programming concepts and building fun, interactive projects, jdBasic is a great start. If you’re aiming for big ecosystems and industry tooling, Python is the obvious choice. Many people use both.