SShortSingh.
Back to feed

Comparing eight Tiny Desk AI video generators: two types and their price differences

0
·2 views

A DEV Community writer reviewed eight tools that let users place their own faces into NPR Tiny Desk-style videos, a trend that spread on TikTok and Reels after a viral college football clip. The tools fall into two categories: face-swap tools that edit into the real Tiny Desk footage with the original audio, and generators that create new desk-concert scenes with AI-made music. Prices for a single video range from about $8.90 to $9.99, with lengths of 15 to 19 seconds and varying sound options. The reviewer discloses being the creator of one of the tools and notes that some sites mute copyrighted audio, so silent copies are sometimes offered. The article also mentions a free CapCut template route and advises using clear photos and consent from anyone pictured, while noting that none of the tools are affiliated with NPR.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

FINDR AI Open-Source Tool Targets GPS Spoofing in Parcel Delivery Tracking

FINDR AI is an open-source system built to detect suspicious location manipulation in e-commerce parcel delivery, where GPS coordinates can be misleading. It flags GPS spoofing, physically impossible teleportation jumps, and gradual location drift, then assigns risk scores to suspicious telemetry. The system adds investigation workflows, human approval for critical rescue actions, and hash-chain audit logging for traceable decisions. Built in Python with Streamlit, FastAPI, MongoDB Atlas, and SQLite, it was submitted to the Hacktoberfest Open-Source AI Challenge and reports 105 passing regression tests.

0
ProgrammingDEV Community ·

Debugging Malformed JSON From an Email Send API After Payment

The article advises validating the exact request payload against the provider's documented schema before sending, rather than assuming the response is at fault. It recommends checking the HTTP status before parsing the body, since an HTML gateway error or empty response can cause a JSON decoding failure. Retries should use a stable idempotency key and a policy keyed to status codes, to avoid duplicate sends and redundant payloads. The author also proposes splitting retention, keeping operational metadata and a payload hash longer while shortening retention of sensitive receipt content. The overall guidance is to log request bytes, status, content type, and a size-limited raw body separately from parser errors.

0
ProgrammingDEV Community ·

Measuring a Server: Latency Knees, Misleading Percentiles, and Little's Law

The author built a deliberately simple single-worker server to study how latency behaves under load. Ramping request rates revealed a 'knee' where tail latency rises sharply well before the server is fully utilised. At about 70% busy, the 99th percentile latency had already more than doubled. The author also argues that averages hide slow outliers, so percentiles better reflect what users actually experience. A further experiment showed that fan-out amplifies this effect, since a page is only as fast as its slowest dependency.

0
ProgrammingDEV Community ·

DraftMark uses a model to write ship survey code, checked against a printed booklet

A developer built DraftMark for the Meta AI Developer Hackathon to time-average a ship's moving waterline from video and have a model generate per-ship draft survey programs. Rather than trusting the model's output, the author tested the generated program against 22 worked loading conditions printed in a Ro-Ro ship's stability booklet, requiring agreement within 0.1 percent. A verbatim implementation of the booklet's procedure matched only 14 of 22 rows, because it omitted a second trim correction and contained unstated conventions. After two regenerations with feedback on failing rows, the model's program reproduced all 22 conditions. The finished survey was also checked on one in-sample row, landing about 0.01 percent from the printed value.