Developer Builds Single-Button ESP32 Bluetooth Remote for iPad Screenshots

·2 views

A developer created a compact Bluetooth remote using an ESP32-C3 microcontroller to trigger iPad screenshots without physically handling the device. The remote pairs with the iPad as a keyboard named 'Screenshot Remote' and sends the Command+Shift+3 key combination when a touch button is pressed. The project was motivated by the inconvenience of reaching for an iPad mounted on a stand, which disrupted recording angles and screen framing. Hardware components include an ESP32-C3 dev board, a touch-button module, and an 18650 battery cell housed in a small project box. The firmware, built with MicroPython and the MicroPythonBLEHID library, stores the Bluetooth bond in non-volatile memory so the device stays paired across reboots.

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)

How to scrape YouTube transcripts at scale for RAG pipelines using Python

Developers building retrieval-augmented generation (RAG) pipelines over video content rely on transcripts as the primary data payload, since titles and descriptions lack sufficient detail. YouTube's official Data API v3 cannot download captions for third-party videos, making it a dead end for most RAG use cases. A practical workaround involves accessing the public player surface, which exposes caption track metadata without requiring OAuth or a Google Cloud key. Using a third-party Apify actor, developers can batch-fetch transcripts, chunk them by character length while preserving timestamps, and prepare them for vector embedding. Key failure modes include videos with no captions at all, auto-generated versus human-authored tracks, and language provenance gaps that must be handled explicitly in the pipeline.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

RecallOps Uses AI and Operational Memory to Speed Up Incident Response

A developer has built RecallOps, an AI-powered incident-response tool designed to help on-call engineers recall past production incidents and their resolutions. The tool uses Hindsight as a persistent memory layer to store root causes, fixes, and engineer feedback from previous incidents. When a new incident occurs, RecallOps retrieves historically similar cases — for example, matching a new Payment API timeout to a prior connection-pool issue with 91% similarity. The system is designed to assist rather than act autonomously, providing evidence and recommendations while leaving all final decisions to the engineer. A database fallback ensures the tool remains functional even if the Hindsight memory service is temporarily unavailable.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Hardcoded Thresholds Slowed App Updates; A Simple Data Class Fixed It

A developer at a location-tracking platform found that storing 18 behavioural tuning parameters as hardcoded constants made adjustments costly, requiring a full code review, build, and app store rollout — taking up to a week to change a single number. The friction meant thresholds were rarely updated, turning informed guesses into unexamined assumptions defended by inertia. The fix was straightforward: moving all parameters into a serializable data class with defaults identical to the old constants, then injecting it as a constructor parameter into the location processor. This preserved existing behaviour entirely — all tests passed without modification — while enabling debug or server-driven overrides simply by changing where the object is constructed. The author argues that true constants belong only to fixed facts like unit conversions, while heuristic thresholds are hypotheses that must remain cheap to revise.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How AI-Driven Systems Are Built to Match Crypto's Round-the-Clock Markets

Unlike traditional financial markets with fixed trading hours, cryptocurrency markets operate 24/7, generating continuous data across centralized exchanges, decentralized platforms, derivatives venues, and blockchain networks. This non-stop activity creates an engineering challenge: conventional rule-based systems that react to single thresholds cannot fully capture shifting market regimes. AI-driven systems aim to evaluate a broader set of simultaneous signals — including volatility, liquidity, market depth, on-chain activity, and capital flows — to build a more complete picture of market conditions. Fragmentation across platforms means a critical signal may emerge in one part of the ecosystem before propagating elsewhere, making cross-signal pattern recognition especially valuable. Aonica, an AI-focused financial technology firm, is developing a continuous-intelligence architecture designed to assess these evolving conditions in real time rather than as a series of isolated decisions.

0 comments Read more at DEV Community