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Introduction Serverless computing has become a buzzword in cloud architecture. But like any tool, it has sweet spots and sharp edges. After building and maintaining several serverless applications, I've learned where it shines and where it creates headaches. This article shares those lessons. When Serverless Helps 1. Event-Driven Workloads Serverless excels when your code runs in response to events: file uploads, database changes, HTTP requests. The pay-per-execution model means you don't pay for idle time. // AWS Lambda handler for image resizing exports . handler = async ( event ) => { const bucket = event . Records [ 0 ]. s3 . bucket . name ; const key = event . Records [ 0 ]. s3 . object . key ; // resize and save return { statusCode : 200 }; }; 2. Variable or Unpredictable Traffic If your app has occasional spikes (e.g., a marketing campaign), serverless auto-scales instantly. No need to provision for peak load. 3. Rapid Prototyping and MVPs You can deploy a fully functional API in minutes without managing servers. This accelerates feedback loops. 4. Microservices and Glue Code Serverless functions are perfect for small, single-purpose services that connect other services (e.g., processing webhooks, data transformation). When Serverless Hurts 1. Long-Running Processes Most providers have a maximum execution timeout (e.g., 15 minutes for AWS Lambda). Batch processing or video transcoding may hit this limit. # This will timeout if processing takes > 15 minutes def handler ( event , context ): process_large_file ( event [ ' file ' ]) return { ' done ' : True } 2. Cold Starts After a period of inactivity, the first request may have a delay of several seconds. This is detrimental for latency-sensitive applications like synchronous APIs. 3. Stateful Applications Serverless is stateless by design. If you need persistent connections (e.g., WebSockets) or local state, you'll need additional services like Redis or DynamoDB, adding complexity. 4. High, Steady Load If your

Health in ChatGPT now lets eligible U.S. users securely connect medical records and Apple Health to get more personalized insights and better understand their health.

Full-Stack Platform for Training Small Language Models Discussion | Link

After IBM's stock crashed last week on warnings of poor mainframe sales, the CEO explained that AI wrecked corporate hardware budget, temporarily.

Article URL: https://withdocket.com/blog/why-im-building-a-note-taking-app-without-ai Comments URL: https://news.ycombinator.com/item?id=49014798 Points: 7 # Comments: 4

Vivace uses only Qt (v6.11.1 or newer) itself — no Qt Widgets, no external player processes. I built it because I wanted SMPlayer's UI conventions and features without depending on the external player processes(mpv/mplayer). It's a ground-up rewrite, not a fork. - DVD playback with interactive menus, from a from-scratch IFO/PCI parser (no libdvdnav) - a hybrid WSOLA + phase-vocoder approach to speed-adjusted audio, since neither algorithm alone sounds good in both directions - secure credential

For quite a while now, I've had the feeling that AI agents are surrounded by this mystical aura....

We spent the last months consolidating seven separate sequence classifiers into one multi-head model, our apex model, so to speak, and since the weights are now public, I wanted to share what worked and what surprised us. Setup: a shared mmBERT-small encoder with seven task heads, binary injection (BCE), document class (7-way), tool type (14-way), tool operation (6-way), tool data-flow tags (3× BCE, multi-label), intent routing (5-way), and threat type (7-way). The part that needed care: our training rows only carry labels for a subset of tasks, so absent tasks are masked out of the loss entirely. We ended up writing a self-test that asserts absent-task gradients are exactly zero, which caught two subtle bugs, and I'd recommend it to anyone doing similar masking. About 5k synthetic/real multi-task rows help the heads co-train; the test sets stay 100 % real data. Held-out results per head: injection F1 0.962, documents 0.980, tool type 0.957, tool operation 0.945, tool tags 0.958, routing 0.916, threat 0.952. Quantization: both the unified model and the dedicated single-task variants ship quantized -edge builds (ONNX INT8 + INT4 embeddings, from 96 MB) with measured parity benchmarks in the repos, the worst head loses 0.012 against FP32. Was it worth it vs. seven dedicated models? We released both variants, so you can judge for yourself, the dedicated models score marginally higher on most tasks, but the unified one does one encoder pass instead of up to seven. Our weak spot: routing, at 0.916. The intent classes overlap semantically ("write code that analyzes my data" is that code or analytics?), and I suspect the ambiguity is genuinely in the data. If you have ideas beyond relabeling, let me know :) Weights and per-head metrics: https://huggingface.co/patronus-studio submitted by /u/PatronusProtect [link] [留言]

Hey, I would like to show you my project, but it's difficult because it only works with a registration, so I explain the concept to you. The Idea: Everyone who registers has to do an onboarding and answer meaningful questions that help you to find a right match. The matching happens by the system. Once you're done with the onboarding, you enter the pool. When a match is found, you go into a 1on1 14 question-set with that person. All answers are revealed immediately. At the end both decide if the

Less than a week before Meta's lawyers were set to return to a Los Angeles courtroom, the plaintiff accusing the platform of inflicting harm dropped the case. Brought by 15-year-old Florida plaintiff going by initials R.K.C., the case was set to be the second in a set of bellwether trials meant to test legal arguments […]

Google's cloud business is thriving, as companies adopting its AI and AI infrastructure services help the tech giant to report record profits.

Hello. I really liked stackoverflow >5 years ago. There were many people asking about easy-to-medium problems to be solved, and it was a great way for me to learn C/C++/Bash/awk/sed/cmake/Linux/whatever by solving real-life(!) mediocre problems and also helping people in the process and also being criticized and corrected at the same time, from which I learned triple as much. Now stackoverflow is dead. My almost 150k reputation means nothing. Finally they added a "advice" type of questions which is way way too late. Now I lurk over reddit for typic-specific type of questions, but reddit is more a social network then help-me-with-programming-problem site, and the "help me" part is anyway so easy to solve with an AI. It was great back then - the feeling of learning something new and at the same actually helping and actually feeling like an expert in something. I learned the C programming standard by heart by answering really niche questions about C program behaviors. This was really fun for me. I enjoyed the specificity of stackoverflow - the idea of being "exact", answering only the question asked. There are just no questions nowadays on stackoverflow. There is a void now. And AI. There are topic-specific Discord chats, as a modern replacement of IRC, but I always assumed they are used by developers, not for noobs. I know the times without AI will never come again, but the internet, the thing connecting people across the whole globe, just feels more empty, more robotic and like an advertisement nowadays. submitted by /u/kolorcuk [link] [留言]

Undetectable AI copilot for live meetings Discussion | Link

Tired of squinting at VS Code’s stacked merge editor? MergeForge brings a JetBrains-style three-pane conflict resolver to VS Code and Cursor — and pairs it with an AI assistant that actually reads your repository before it suggests a fix. The problem We’ve all been there. You’re halfway through a rebase. Git stops. Twelve files are conflicted. You open one in VS Code… and get that familiar stacked layout: Incoming, Current, and a result pane that somehow still feels like a puzzle with half the pieces missing. If you ever used WebStorm or IntelliJ, you know how good merge tools can feel: Your side on the left Their side on the right The result in the middle Gutter arrows that just… work In VS Code land, that flow never quite arrived. You click Accept Current, Accept Incoming, Accept Both, and hope nothing important got flattened. Word-level diffs? Authorship? A clear “who wrote this chunk?” signal? Often missing when you need them most. And when AI entered the chat, a lot of tools treated conflicts like isolated text blobs: “Here are the <<<<<<< markers. Good luck.” But real merges need context. What was the branch trying to do? What does the surrounding file look like? Who touched this last? Without that, “AI resolve” is just confident guessing. I wanted the JetBrains merge experience — inside VS Code and Cursor — with an assistant that behaves more like a careful teammate than a slot machine. So I built it. The solution: MergeForge MergeForge is an open-source VS Code / Cursor extension that turns conflicted files into a proper three-pane visual merge. Layout: Left Center Right Yours (local) Result (editable, seeded from the merge base) Theirs (incoming) Panes scroll together. Chunks connect with bands. Gutter controls let you accept, ignore, or blend sides without fighting the UI. When you’re done, Apply writes the result and stages it with git. If you prefer Cursor, you’re covered too. The editor works the same; for AI features you plug in your own provider key (

Most SQL performance tooling requires either a $400/month monitoring agent or asking models and hoping the advice applies to your database. The interesting architecture decisions: Heuristic engine runs first (<200ms), LLM is optional and additive — if LLM fails, you still get structured findings All dialect logic lives in one dialect_config.py — DDL templates, optimizer syntax, LLM system prompts, maintenance commands for all 5 DBs Schema-aware mode: paste DDL, get confirmed recommendations with real table names instead of placeholders Every analysis gets a permanent shareable URL Source: https://github.com/AutoShiftOps/querytuner Live: https://querytuner.com submitted by /u/sajjasudhakararao [link] [留言]