submitted by /u/fagnerbrack [link] [留言]

If you refresh a page quickly, sometimes the new AudioContext will be faulty, it won't pick up any audio and throws no errors. Fix is to create and immediately close a throwaway context before creating your real one: await new AudioContext().close(); const ctx = new AudioContext(); submitted by /u/GramDanD [link] [留言]

If you’ve been doing Android development for a while, you know the drill. You start a new project,...

In your opinion what would you say is the quintessential CRUD app? If you were to make a sample demo app utilizing and comparing the different tech stack (eg Laravel vs Django vs Spring Boot etc), which CRUD app would be the best? Thanks submitted by /u/Temporary_Practice_2 [link] [留言]

Making a personal website and trying to showcase projects I've worked on and trying to make it filterable. But I can not for the life of me figure out how the javascript part works? W3schools has been amazing and digestible up until this point. Using this as my base but what do I replace in the java part to make it filterable???? https://preview.redd.it/a35guno7yt3h1.png?width=1990&format=png&auto=webp&s=169b5610856995b84e1cee0881dac68dbc8be32d submitted by /u/ToxicGhostJuice [link] [留言]

Samsung has teamed up with diabetes experts at Massachusetts General Hospital to see how Galaxy Watch 8 can help prevent muscle loss in GLP-1 patients.

The AC email says the rebuttal is between 28 to 4th. The June 4th on website is the deadline. So I created this post for the discussion. I know it's a MM conference and less about ML but I think many people here are still submitting there. submitted by /u/Striking-Warning9533 [link] [留言]

The beleaguered luxury phone maker is pushing the AlphaFold, which has decent specs and comes with Vertu’s new Hermes Agent on board, to wealthy would-be buyers.

Built on top of the open-source Hermes project, Vertu's new foldable combines AI-agent workflows, enterprise integrations, and ultra-premium luxury finishes.

AI wrote the code in 30 seconds Three lines A simple function I prompted it generated I copied It...

The conversation about cognitive debt in AI-assisted development has been framed as a tradeoff: you can go fast, or you can understand your system, but not both. The proposed mitigations — pair programming, code reviews, requiring a human to understand each change — are braking mechanisms. They trade speed for comprehension. TRIZ (Theory of Inventive Problem Solving) says braking is a compromise, not a resolution. A resolved contradiction eliminates the conflict. You don't choose between speed and understanding. You restructure the system so they don't conflict. There are six root causes of cognitive debt in AI-augmented development. Each one is a contradiction. Each one has a TRIZ resolution that doesn't involve slowing down. Root Cause 1: The Velocity-Comprehension Gap AI generates complex logic in seconds that would take a human hours to write. The human never spends the time typing the code during creation. The theory of the program is never fully formed. The Contradiction Technical contradiction: Improving development speed (AI generates code faster) worsens depth of understanding (human doesn't internalize the logic). Physical contradiction: The development process must be simultaneously FAST (to capture AI's productivity gains) and SLOW (to allow human assimilation of the system's behavior). Resolution: Separation in Space (Principle 2 — Extraction + Principle 1 — Segmentation) The contradiction assumes that the thing being understood IS the code. Extract the understanding target from the code and put it somewhere else — a smaller, slower-moving, human-readable artifact that captures what the code must satisfy, not how it works. Segment the system's theory into independent, composable units. Each unit is one property: "this service must never accept unauthenticated requests," "this data pipeline must preserve ordering," "this retry loop must terminate within 30 seconds." Each property is 1-3 sentences in natural language or 3-10 lines in a predicate language.

I’m a big fan of PostgreSQL and recently started thinking about building a new database system as a side project. I know creating a database is a huge challenge, and I’m still learning, but I really like the idea. If a few passionate people team up together, I believe we can build something interesting. I’d love to know: What struggles do you face with current databases? What features are missing in existing systems? What problems do you wish databases solved better? Performance? Scaling? RAM usage? Replication? Developer experience? Your feedback and ideas would really help. Or shorter and more Reddit-style: Title What problems do you face with existing databases? Content I’m thinking about creating a new database project because I really enjoy working with PostgreSQL. I know it’s a huge challenge, but I like the idea and want to learn deeply by building something. If a few passionate developers team up, maybe we can create something interesting. What problems or frustrations do you currently face with databases? What would you improve if you could design your own database? I’m deeply interested in backend architecture, distributed systems, and database engineering. Previously, I built a backend framework in Go called KVolt, which gave me strong experience in scalability, API architecture, performance optimization, and system design. Recently, I started exploring a bigger challenge: I’m a big fan of PostgreSQL, but I believe there’s still room for innovation in areas like: Performance Scalability Distributed systems Replication Memory optimization Developer experience Modern architecture design I know database engineering is one of the hardest areas in software development, and that’s exactly why it interests me. I’m also looking for a few passionate developers, backend engineers, and system-level programmers who genuinely enjoy solving hard engineering problems and would like to team up on this open-source journey. I’d love to hear from the community: What struggl

I was playing around with antigravity/claude last weekend and put together a 2026 rendition of a project I did almost 15 years ago. Basically it's an app that lets you watch videos with friends. Back in the day it was a great way to share and discover music, especially stuff you normally wouldn't listen to. I'm not quite sure it would work in today's age but maybe there's something there. Either way it's pretty awesome that you can prototype something so quick by just chatting with an LLM. submitted by /u/Zenpher [link] [留言]

give some suggestion in that my company two partner and one partner has no knowledge of development and he will do the busineess related things submitted by /u/Party-Tension-2053 [link] [留言]

In part 1 , the single-document case was easy. In part 2 , two documents brought Write Skew, and we saw that even a native ACID transaction — snapshot isolation — lets it through. So teams reach for the reflex fix: a distributed lock — Redis-based, often a Redlock-style implementation. Acquire a lock on a key, do your Read → Modify → Write, release. On paper, you've finally serialized the critical section — operationally, at least. In practice, you've stepped on three mines. 1. Network latency Every guarded transaction now makes extra round-trips to Redis — before and after hitting your NoSQL store. You've doubled your coordination surface and taken a hard dependency on a second system being up, reachable, and fast on the hot path of every write. The "fast" database is now gated by the lock service. And the coupling bites harder than the average latency suggests: every Redis tail-latency spike becomes your write-latency spike — your p99 inherits Redis's p99 — and if Redis fails over mid-transaction, the lock you think you're holding can effectively vanish on the new primary, dropping you straight into the corruption case below. 2. Deadlock You can dodge deadlock entirely with a single coarse lock — but then every writer serializes on it, and you've thrown away the very concurrency you reached for NoSQL to get. So to keep throughput you go fine-grained, one lock per resource — and the moment an invariant touches more than one key (across this series, it always does), deadlock is back on the table: Transaction A locks key X, then needs Y. Transaction B locks Y, then needs X. Both block until timeout or intervention. The textbook cure — real deadlock detection, maintaining a wait-for graph across every lock holder and breaking cycles as they form — is a distributed-systems project in its own right: not something you bolt onto a cache you reached for precisely to save engineering time. So nobody builds it. Instead teams impose a standing discipline: always acquire locks