Accurately counting calories requires more data than a smartwatch can easily gather.

I'm a software engineer who mainly builds softwaes/applications, and I'm starting to work on machine learning projects. Since ML workloads often require GPUs, I know services like Google Colab and Kaggle exist. but, I'm looking for something a bit different. Is there a platform where I can use AI coding agents (such as Codex, Claude Code, or OpenCode) while running the actual ML code on a cloud GPU? Ideally, I'd like to: Work locally with my preferred editor and AI coding agent. Have the code execute on a remote GPU machine. Be able to build, debug, and iterate on ML projects as if the GPU were attached to my local development environment. Does a setup like this exist? If so, what tools or platforms do you recommend? submitted by /u/Fickle_Degree_2728 [link] [留言]

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

Apple Silicon delivers enough performance without overheating.

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.

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Timeline with depth.

Part 1 documented the recurring snapback in practice. This note asks a narrower question: what does the observed pattern support, what remains a working hypothesis, and what changes should follow in the project? Status: Bounded project conclusion. This note separates observed behaviour, working hypothesis, and practical consequence. It is based on current project documents and interactions; it is not external validation or a universal claim about AI systems. What the evidence supports 1. The project already contains a stable relational model The working model is not generic “AI assistance.” It separates reasoning surfaces, uses bounded comparisons, permits two-way cognitive pressure, and keeps final acceptance authority with the human. Reciprocal cognitive contribution, asymmetrical governing authority. 2. Concrete project work preserves the structure better than public abstraction At the concrete level, instructions such as: Review this proposal against that architecture. preserve the distinction between the object being reviewed, the surface applying pressure, the evidence, and the authority that may accept a change. When the same structure was compressed into general prose, generated explanations repeatedly returned to a simpler one-way model of either human control or transferred AI authority. That is an observed pattern in this development process. 3. Public explanation is a separate reasoning surface A README, article, summary, or portfolio page is a projection of the model, not the model itself. It cannot be assumed to reproduce the internal structure faithfully merely because that structure is present in context. The explanation must be reviewed against the model it represents: Does this explanation preserve the actual authority, review, evidence, and state-transition structure? 4. Annoyance was useful boundary data The irritation indicated that the generic rendering was no longer merely an imperfect exploration. It was colliding with an internal frame that

The moment that started it A colleague of mine was cleaning up some old records in a staging environment. Same query he'd run a dozen times before, except this time he was connected to production. He hit F5. No WHERE clause. 47,000 rows gone in milliseconds . We recovered from a backup, lost about two hours, and nobody got fired. But it stuck with me:** SSMS will let you delete an entire production table with the same amount of friction as running a SELECT 1**. No pause, no confirmation, nothing. The tool that DBAs and backend developers spend all day in has zero built-in protection against the single most common way people destroy data. So I built one. What SQL Guard does SQL Guard is a free SSMS extension (18 through 22) that inspects your query the moment you press F5, and pauses execution if it matches a known destructive pattern: DELETE without WHERE UPDATE without WHERE TRUNCATE TABLE DROP TABLE / DROP DATABASE / DROP PROCEDURE ALTER DATABASE Dangerous EXEC calls MERGE without a filter When one of these fires, you get a dialog showing exactly what object would be affected, with three options: cancel, run anyway, or run and ignore for the rest of the session. The point isn't to block you — it's to make the action conscious. Most accidental damage happens because muscle memory took over, not because someone genuinely meant to wipe a table. How the detection works Nothing exotic here, and honestly that's by design. SQL Guard runs a lightweight pattern-matching pass over the query text before it hits the connection. No parsing tree, no round-trip to the server, no measurable latency — it runs in under a millisecond, so you never notice it on normal queries. The core idea is simple: certain statements are only safe when scoped by a WHERE clause, and the extension checks for that clause's absence rather than trying to understand the full semantics of the query. That keeps false positives low and means it works the same way whether you're on SQL Server 2016 or the la

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