SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Developer uses Meta Quest app to turn YouTube viewing into amblyopia vision therapy

0
·13 views

A developer with lifelong amblyopia, or lazy eye, describes how a Hacker News discussion on attention as a trainable skill inspired them to try a different approach. They used Amblyotube, a Meta Quest app by Seven Sports, which presents YouTube videos through separate display panels so both eyes must cooperate. The app adjusts the stronger eye's image digitally with a software shader rather than a physical patch, and sharpens figures in the weaker eye's view using frame flicker. After two weeks, the author reported better depth perception when parking and earlier recognition of construction signs. The article stresses that the app is not a medical device and does not replace a doctor's care.

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 ·

Python 3.14 subinterpreters cut a CPU-bound job from 3.6 to 0.9 seconds, but reject concurrent calls

A developer benchmarked a CPU-bound prime-counting task in a Python 3.14 container with four cores. Four subinterpreters from the new concurrent.interpreters module finished in about 0.9 seconds, close to a single-thread baseline and far faster than four classic GIL-bound threads at 3.6 seconds. Subinterpreters performed comparably to multiprocessing in the same tests. However, a single interpreter rejects a second concurrent call with an InterpreterError rather than queuing it. The author concludes that running N tasks in parallel requires N separate interpreters, so thread-pool patterns do not carry over directly.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

SlothSort: Local AI Tool Organizes Downloads Without Deleting Files

A developer built SlothSort, an open-source Python tool that uses the locally run Gemma model via Ollama to sort cluttered Downloads folders. The agent scans text files and PDFs, suggests clearer filenames, and moves items into sensible folders, while image classification currently relies only on filenames and extensions since OCR is not yet implemented. A dry-run preview lets users check proposed changes before anything moves, and every operation is logged in SQLite so it can be undone. Because processing happens on the user's own machine, private documents never need to be uploaded to a cloud AI service. The project was submitted to a Hacktoberfest open-source AI challenge, and its next planned steps include adding OCR and a review step for uncertain classifications.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Shiftary Android shift planner adopts offline-first pricing with trial, lifetime and subscription options

Shiftary, a Flutter-built shift planner for Android that works offline without accounts, has been monetized without relying on a backend server in the purchase flow. Its model includes a 14-day free trial of all features requiring no card, a one-time $19.99 Lifetime purchase, and a $9.99 yearly Cloud subscription adding backup and sync. A discounted $9.99 Lifetime upgrade is offered only to Cloud subscribers. The developer describes storing the trial start date locally and within user data, always keeping the earliest date, and using Google Play as the source of truth for ownership via the in_app_purchase plugin. The article also highlights Play's three-day acknowledgment requirement for purchases and how renewal order IDs can be used to estimate subscription periods.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

AWS proposes layered trust framework to give AI agents more autonomy

Amazon Web Services has introduced a Trust in Depth framework aimed at letting enterprise AI agents act with less human approval while keeping control. The approach argues that autonomy should be granted only where agent actions can be verified, with precise scoping of permitted tools and data and separate identities for each agent. It recommends enforcing limits in the surrounding environment rather than relying on the agent to police itself, along with full observability and automated proofs where feasible. The article notes that the framework is closely tied to AWS's AgentCore product and offers no measured outcomes or independent evaluation to confirm its effectiveness.

0 comments Read more at DEV Community