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Weave is a React app that provides a multi-track timeline editor to perform basic video edits like trimming, stitching, transitions, audio tracks etc. which maps directly to an FFmpeg command to render the video. I tried my best to have the React "video" preview closely replicate the FFmpeg lavfi filtergraph output, but naturally this is not perfect (especially replicating the `eq` filter using SVG filters is quite inaccurate). I've built this as a prototype for another project I'm working on, s

i am an incoming msc student thinking about a research direction in reinforcement learning. which rl subfields do you think have the most potential right now? i am particularly interested in emboddied ai and bcis.

Timeline with depth.

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . Written with the help of AI (Claude). The bug, the fix, the validation setup, and every claim below are mine, and were verified against the real codebase and a real full disk. The report Someone lost a Magic: The Gathering decklist. They were playing on Cockatrice — the open-source MTG client — with their decks on a drive that had quietly filled up while Oracle pushed an update in the background. They added a card, hit Ctrl+S, and Cockatrice said it saved. The debug log agreed: [2026-05-28 22:31:42.031 I] Saved deck to "G:/cockatrice300/data/decks/edh-b2-gitrog-reanimate.cod" with format 1 - true - true . Success. The file was 0 bytes. The deck was gone. That was issue #6952 , filed by Mekkiss. The steps to reproduce are four lines long and completely damning: Have a full disk. Open a deck on the full disk Add one card to it Save the deck (ctrl+s) Observe that the deck is now a 0 byte file. Three ways to be wrong at once The save path lived in DeckLoader::saveToFile() . Stripped down, it looked like this: QFile file ( fileName ); if ( ! file . open ( QIODevice :: WriteOnly | QIODevice :: Text )) { qCWarning ( DeckLoaderLog ) << "Could not create or open file:" << fileName ; return std :: nullopt ; } bool success = false ; switch ( fmt ) { /* ... saveToFile_Native / saveToFile_Plain ... */ } file . flush (); file . close (); qCInfo ( DeckLoaderLog ) << "Saved deck to " << fileName << "with format" << fmt << "-" << success ; There are three independent failures stacked on top of each other here, and you need all three to lose data: 1. WriteOnly truncates on open. The instant open() succeeds, the existing deck is 0 bytes. Not after a successful write — at open time . The old deck is already destroyed before a single byte of the new one is written. On a full disk, open() still succeeds: truncating a file doesn't need free space. It frees space. 2. The serializers always returned true . sa

How synthetic reality may influence human intuition about motion, gravity, and causality AI video generation has reached a point where a model can create scenes that look physically convincing at first glance: A person jumping impossible distances Objects moving without inertia Water flowing upward Animals performing human-like actions Buildings bending like rubber People interacting with impossible environments For decades, humans learned physics by observing the real world. A ball falls. A glass breaks. A person cannot walk through a wall. Heavy objects require more force to move. These observations create what cognitive scientists call intuitive physics : an internal mental model that predicts how objects should behave. But what happens when the majority of visual experiences become synthetic? Could AI-generated videos slowly change how future generations perceive reality? Humans Do Not See Reality Directly A common misconception is that our brain works like a camera: Reality → Eyes → Brain → Understanding The actual process is closer to: Reality ↓ Sensory input ↓ Brain prediction model ↓ Perception The brain is constantly predicting what should happen next. When you see a ball thrown into the air, your brain automatically predicts: trajectory speed gravity collision point acceleration This happens before conscious reasoning. This capability is known as predictive processing . Your brain is not only asking: "What am I seeing?" It is also asking: "Does this match my internal model of how the world works?" The Brain Learns Physics From Experience Young children do not learn physics from equations. They learn by interaction. A baby discovers: Objects continue to exist when hidden Unsupported objects fall Solid objects cannot overlap Larger objects require more effort to move Researchers call these abilities core knowledge systems . Humans appear to have an innate expectation that the physical world follows consistent rules. For example: A child watching a ball roll

Building Atomic Cross-Border Settlement on Stellar: The AnchorFX Story A technical deep-dive into Soroban escrow contracts, FX oracles, and mainnet deployment — from testnet prototype to production. se The Problem Cross-border payments still take 3-5 days and cost 6.5% on average. Correspondent banking chains are slow, opaque, and expensive. The $800B remittance market has no atomic settlement layer. Stellar was purpose-built for this. 5-second finality. Built-in DEX. Path payments at the protocol level. And now, with Soroban smart contracts, programmable settlement. AnchorFX is an open-source protocol that combines these primitives into trustless, atomic FX settlement between regulated financial anchors. Two Soroban contracts — an Escrow Factory and an FX Rate Oracle — communicate via cross-contract calls to lock, rate, and settle funds in a single atomic flow. Architecture Sender → [Escrow Contract] → Receiver │ [Oracle Contract] │ FX Rate Data Contract 1: Escrow Factory (995 lines, 23 tests) The escrow contract is a multi-escrow factory with per-escrow storage. Each escrow goes through a defined lifecycle: Created — Sender locks tokens with a timeout and settlement conditions CounterpartyApproved — Receiver signs off on the terms Settled — Admin releases funds at the locked FX rate Refunded — Sender reclaims after timeout expires Cancelled — Admin cancels (circuit breaker) pub fn create_escrow ( env : Env , sender : Address , receiver : Address , token : Address , amount : i128 , timeout_blocks : u32 , corridor : u32 , ) -> u64 { sender .require_auth (); // Read oracle rate at creation time — locks the rate let oracle_addr = env .storage () .instance () .get ( & ORACLE_KEY ) .unwrap (); let rate : u64 = env .invoke_contract ( & oracle_addr , & symbol_short! ( "get_rate" ), ... ); // Store escrow with locked rate // ... } Key security decisions: Per-escrow storage — O(1) reads, independent TTL per escrow Checks-effects-interactions — state saved before token trans

I started dabbling with ESP32-based MCUs from Waveshare with Rust, and I'm quite impressed with the state of things (esp-rs, embassy, probe-rs etc). Note that I haven't really done embedded in any other languages, so I don't really have anything to compare it with. One thing I did notice was that creating beautiful, interactive user interfaces on MCUs with displays is a little harder, libs like embedded-graphics don't look that great. This made me wonder, are people using Rust in their professio