标签：#c

Artificial Intelligence has changed the way we write software, but one thing has become clear: AI is a collaborator, not a replacement. After using coding assistants for months, I've realized they're best at handling repetitive tasks: Generating boilerplate code Explaining unfamiliar APIs Refactoring existing functions Writing documentation Creating unit tests Finding bugs faster Where AI still struggles is understanding the bigger picture. It doesn't know your product vision, business requirements, or why one architectural decision is better than another. Those are still human problems. The most productive workflow isn't asking AI to build an entire application from scratch. It's treating AI like an experienced teammate that can help with implementation while you stay responsible for the design and direction. The developers who will thrive over the next few years won't necessarily be the ones writing the most code—they'll be the ones asking better questions, validating AI-generated solutions, and combining technical knowledge with critical thinking. AI is changing software development, but it's also raising the value of good engineering judgment. How has AI changed your development workflow? What's one task you now almost always delegate to an AI assistant?

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

When you fire a fetch() and the component that triggered it unmounts, the request keeps going. The server still processes it. When the response arrives, it calls back into whatever JavaScript it finds — a stale closure, a dead state setter, a global store that has already moved on. React's "Can't perform a state update on an unmounted component" warning is the polite version of this. The silent version is worse: results from an old query overwriting the current UI. These aren't mysterious race conditions. They're the predictable result of starting async work and never telling it to stop. The race condition hiding in every search box The search input is the clearest example. The user types "reac", your debounce fires a request. Before it lands, they finish typing "react" and you fire another. Two requests, in flight at the same time, and no guarantee about which one finishes first. If the "reac" request happens to be slower — network jitter, a cache miss, a heavier result set — it will land after "react" and overwrite the correct results with the wrong ones. The bug reproduces maybe one time in twenty on a local dev server, and consistently in production on a slow connection. The fix isn't smarter debouncing. It's cancelling the previous request when a new one starts. AbortController in plain terms AbortController is a browser-native API for cancelling async work. You create a controller, pass its signal to fetch() , and call controller.abort() to cancel. If the response hasn't arrived yet, the fetch promise rejects with an AbortError . const controller = new AbortController (); fetch ( ' /api/search?q=react ' , { signal : controller . signal }) . then ( res => res . json ()) . then ( data => setResults ( data )) . catch ( err => { if ( err . name === ' AbortError ' ) return ; // expected — not a real error setError ( err ); }); // Somewhere else, when we no longer need this request: controller . abort (); Two things to internalize: signal is how the controller knows

You can minimize the risk of films and shows being spoiled for you by muting comments, conversations, and keywords on various platforms.

AWS recently announced Amazon S3 Annotations, a feature that lets teams attach rich, searchable context such as summaries, classifications, compliance data, or AI-generated insights directly to S3 objects. Annotations can be updated independently of the object and queried across datasets, reducing the need for separate metadata systems. By Renato Losio

Has anyone gotten a response from the eccv travel support program listed on their website? https://eccv.ecva.net/Conferences/2026/DEI Edit: also have anyone applied for this program as an accepted author? I have an independent research paper accepted and am currently looking for funds for paying for the registration fees submitted by /u/tedd235 [link] [留言]

Claude models reached GA on Microsoft Foundry with Azure-native billing and governance, but no European data zone exists. Anthropic's own documentation confirms data residency guarantees apply to Bedrock and Vertex AI but not Foundry. European practitioners from banking and healthcare report the offering is unapproved for production. By Steef-Jan Wiggers

I am delighted to announce that a user reported dysfunction so that I could go down the rabbit hole...

I made a 10MB LoRA adapter for Qwen3.5-4B plus a small orchestration layer. It decides, per query, whether to answer directly, search the web, or retrieve from your own local documents and it refuses to make things up when it can't verify an answer. It runs locally (Apple Silicon / MLX, with a GGUF build for llama.cpp/Ollama). Basically small instruct models are poor at telling users how confident they really are. They can't verbalise it and tend to say they are confident for everyhting. In my past research I tested seven 3-9b models and they all hit a confidence ceiling. But the information is there in the internal activations. The adapter reads the internal signal directly and gates tool use on it. The main elements are that: - it catches its own errors better than the base model's tool calling (d′ improvement of 0.46 (95% CI [0.01, 0.89])). Of the cases the gate flagged that the base model didn't, 87% were genuinely wrong answers. - it is less likely to leak your private queries to public search. A two-signal version routes personal information related questions such as "what did my discharge summary say" to a local retriever instead of a websearch. It cut the rate of private questions sent to public search from 22% to 10% (reduction 0.12, 95% CI [0.02, 0.22]). This is useful for those who are using the LLM for confidential docs. - every answer is traceable. When it retrieves, it cites the specific passage ( report.md ¶2 ), verifies the answer is actually in that passage, and shows a confidence band. Worst case, it says "I couldn't verify that". It is built to say "I don't know," instead of lie. limitations: - Privacy result is n=60; the retrieval/competence dissociation is n=126 hand-authored items. Screened and CI'd, but small. - GGUF reproduces the MLX gate's decisions at --lora-scaled ...:8 (found by sweep — scale 1 does nothing; effective scale ≈ the training scale). Agreement 0.83 on a 24-item probe; disagreements are all conservative-direction (GGUF answer