SShortSingh.
Back to feed

PromptClip-Skill lets users filter home videos using natural-language prompts

0
·2 views

A developer has released PromptClip-Skill, a free, open-source tool that uses natural-language prompts to identify and shortlist meaningful moments from casual, unedited video footage. The tool runs locally via Codex and allows users to describe desired content, mood, and exclusions — such as removing shaky or repeated footage — before any editing begins. Unlike one-click video creation tools, it produces an inspectable edit decision list with timestamps, giving users transparency and control over what gets kept. The skill works non-destructively, leaving original files untouched, and supports export through FFmpeg or other editing software. While built around family videos, the developer notes it can be applied to travel, sports, interviews, or any footage where useful moments are buried among low-quality clips.

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 ·

Coastline Index Launches Machine-Readable GTA 6 Data APIs for AI Tools

Developers have launched Coastline Index, a structured reference platform for GTA VI data designed to be consumed by large language models and AI tools. The project restricts its dataset strictly to Rockstar-confirmed information, excluding unverified rumors or fan speculation. It implements an llms.txt file to direct AI crawlers toward high-quality pages and APIs. Developers can query clean, structured data via API instead of scraping HTML or unreliable fan forums. The platform positions itself as a reliable data layer for anyone building game-aware AI applications around GTA 6.

0
ProgrammingDEV Community ·

Why Some Settlement Systems Can Skip the Outbox Pattern for Idempotency

A software engineer recently detailed a deliberate architectural choice to use a two-layer idempotency model instead of the widely recommended transactional outbox pattern in a settlement system. The outbox pattern ensures reliable event publishing by atomically writing events to a database table alongside business data, with a separate process relaying them to a message broker like Kafka. While effective, the pattern adds operational overhead including a poller or CDC pipeline, dedicated monitoring, and additional failure-handling responsibilities. For settlement systems operating on windows measured in minutes, the author argued that the narrow failure gap between a database commit and broker acknowledgment is both rare and recoverable after the fact. Instead of preventing the gap at the producer, the design absorbs it at the consumer side, accepting the dual-write risk in exchange for a simpler operational footprint.

0
ProgrammingDEV Community ·

Only 39% of Companies Report Real Financial Gains from AI, Stanford Index Finds

Despite widespread claims of AI adoption, only 39% of organizations can demonstrate actual profit or cost savings from their AI initiatives, according to the Stanford AI Index 2026. Many companies treat deployment milestones as victories while neglecting to measure concrete business outcomes such as reduced costs, lower churn, or increased revenue. Gartner projects that over 40% of agentic AI projects will be abandoned within the next year due to unclear returns and escalating operational costs. A common pitfall is that AI teams track technical metrics like model accuracy or inference speed rather than tying results directly to financial line items. Experts argue that realizing genuine value from AI requires close collaboration between engineering, product, and finance teams to define and monitor measurable business outcomes from the outset.

0
ProgrammingDEV Community ·

Dev Builds Geofencing App to Auto-Silence Phone After Mosque Embarrassment

A developer created an app called Muffle after his phone rang loudly during a Friday prayer service, prompting him to find a smarter way to manage phone audio based on location. Rather than using a continuous GPS polling service, which drains battery and raises privacy concerns, he opted for Google Play Services' Geofencing API. This approach lets the Android OS handle location monitoring and only wakes the app when a user crosses a predefined geographic boundary, keeping the app dormant the rest of the time. The method proved far more battery-efficient and reliable than a custom background service, though it introduced complexity around managing PendingIntent lifecycles across device reboots. Development also revealed challenges with GPS accuracy indoors, where signal drift in dense urban areas caused unintended mode triggers in early prototypes.