When it's less about the product and more about the FCC.

I kept seeing those "smash two emojis together" sites and noticed something: most of them upload your image to a server. Even when the math is just stacking pixels. So I built my own. It runs entirely in the browser. No uploads, no signup, no paywall. Here's how it works: Pick up to 4 emojis from a searchable grid The tool uses the Unicode emoji list and renders each one to a canvas It composites them with offset, scaling, and rotation You get one PNG to download The whole thing is about 200 lines of JavaScript. No backend. No API. No tracking. The emoji data ships with the page. If you want to see it: https://korelyy.com/en/tool/emoji-mixer/ A few things I learned while building it: 1. Most "client-side" tools still hit a server. The hidden cost is usually analytics, asset CDN, or auth. Real client-side means the page works offline after first load. 2. Canvas compositing has hidden gotchas. Emoji rendering across platforms gives different base images. Apple's cat emoji is not the same as Google's. I default to the system font the user already has, so the result feels native. 3. The fun tools spread faster than the useful ones. My calculator for container shipping has solid keyword potential. The emoji mixer gets 10x the social shares. Build accordingly. 4. Browser-only also means privacy by default. A user pasting sensitive content into a "redact PDF" tool shouldn't have to trust the server. Local-only removes that question entirely. I have about 50 small tools like this on my site now. Each one is the answer to a "I just need to do X real quick" moment. Some get used once a day, some once a year. Both are worth building if the alternative is opening an app and signing up. If you want to see the full set: https://korelyy.com/en/ The video showing it in action: https://www.youtube.com/shorts/riFG3Jq8-7c What's a "quick task" tool you wish existed but you keep avoiding because it needs a login?

Bridging the Gap: Navigating the Chasm Between Academic Coding and Real-World Software Development The transition from academic coding to professional software development is fraught with challenges, particularly when it comes to navigating and debugging large, unfamiliar codebases. This gap, often overlooked in educational curricula, leaves new developers ill-prepared for the complexities of real-world projects. Below, we dissect the technical mechanisms involved in codebase navigation and debugging, their constraints, and the resulting instabilities, while reflecting on the disconnect between academic training and industry expectations. Mechanisms of Codebase Navigation and Debugging The process of understanding and working within a large codebase involves several interconnected mechanisms. Each plays a critical role in a developer's ability to efficiently and accurately contribute to a project. Code Navigation : Involves traversing a codebase using tools like "go to definition" to map code structure and dependencies. This mechanism relies on the developer's ability to interpret relationships between files and functions. Impact : Efficient navigation reduces time spent understanding the codebase. Internal Process : Iterative exploration of code paths. Observable Effect : Reduced time to locate relevant code segments. Code Comprehension : Analyzing existing code to infer purpose, logic, and side effects. Requires pattern recognition and logical deduction. Impact : Accurate comprehension minimizes unintended modifications. Internal Process : Mental modeling of code behavior. Observable Effect : Correct identification of code functionality. Debugging : Identifying and resolving bugs while minimizing collateral damage. Relies on isolating root causes and understanding dependencies. Impact : Effective debugging prevents regressions. Internal Process : Hypothesis testing and validation. Observable Effect : Bug resolution without introducing new issues. Documentation Ana

Google has expanded Gemini Omni into Google Vids for end-to-end AI video generation and editing. The update lets users create clips from text and image references, then make targeted changes to existing footage through a step-by-step conversation. Rather than rebuilding a video after each revision, users can describe an adjustment, supply additional media where useful and refine the result in place. The central development is Omni's use of multimodal and real-world understanding in a Vids workflow. According to Google DeepMind's Gemini Omni overview , the model can work from arbitrary media, including images, text, video and audio, and apply reference-to-video capabilities grounded in world knowledge and physics-like reasoning. In Google Vids, that foundation is intended to make generated and edited scenes more coherent in composition, context and visual behavior. For teams that already use Vids to communicate ideas, training material or internal updates, the change moves AI assistance beyond first-draft generation. It introduces a conversational editing layer that can alter a chosen part of a video while preserving the broader scene and workflow. What Gemini Omni changes in Google Vids Gemini Omni supports both video creation and revision. A creator can begin with a prompt or image reference to generate a clip, or bring in existing footage and specify what should change. Google describes examples such as changing color grading or lighting, replacing backgrounds and removing background elements. This distinction matters because prompt-to-video and video editing have different practical constraints. Generating a new clip can be useful when no footage exists. Editing existing material is more relevant when a team wants to retain an established subject, scene or message while changing selected details. Omni's reference handling is designed to connect those modes rather than treating each request as an isolated output. Workflow How Gemini Omni is used in Vids Supported

The mobile and tablet ports of the remastered puzzle game include a free trial.

A short case study from my "building and testing MCP agents" series — it stands on its own, but the method behind it is laid out in https://dev.to/langensjonathan/the-parameters-that-actually-matter-when-youre-tuning-an-ai-agent-2agd . TL;DR: I benchmarked two agents that are identical except for one thing — how many MCP servers they're connected to — on the exact same question. Both got the right answer, both called the same single tool. The one with more MCP servers attached still cost 28% more per question , purely from the extra tool schemas the model has to be told about on every single call, whether it uses them or not. The setup MAVERIK is my open-source MCP test bench: define a suite of questions with pass criteria, run it against one or more agent configurations, and compare the results on hard numbers. This post is one deliberately tiny experiment with it: change exactly one thing about an agent, hold everything else fixed, and see what the numbers attribute to that one change. I have a small "GitHub summarizer" agent: one system prompt, one job — answer questions about my GitHub account by calling the GitHub MCP server . I duplicated its configuration (MAVERIK supports this directly — same model, same prompt, same everything) and changed one field on the copy: the set of attached MCP servers, adding deepwiki , microsoft-learn , and context7 . Neither agent needs any of those three for the question I was about to ask; they were attached because that's what the "kitchen sink" version of this agent had accumulated over a few sessions of general-purpose use. Then I wrote the simplest possible test suite — one question: "How many repositories do I have?" with a contains criterion checking the answer includes the correct count. No judge model, no subjectivity — it either says the right number or it doesn't. I ran both agents against it, 2 repetitions each, same model ( claude-haiku ) for both, and pulled up MAVERIK's Agent Comparison report. Agent A — github on

I built a small Flask example that turns voicemail into a routing workflow. Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/voicemail-smart-router-python The app accepts either: a voicemail transcript an uploaded voicemail audio file For audio, it transcribes the voicemail first. Then it uses Telnyx AI Inference to classify the message and decide where it should go. Categories The app classifies voicemails into: urgent billing support sales spam routine Each category maps to a route: urgent -> Slack alert billing -> email support -> ticket queue sales -> CRM lead spam -> blocklist + archive routine -> daily digest Run it git clone https://github.com/team-telnyx/telnyx-code-examples.git cd telnyx-code-examples/voicemail-smart-router-python cp .env.example .env pip install -r requirements.txt python app.py Configure .env : TELNYX_API_KEY=your_telnyx_api_key AI_MODEL=zai-org/GLM-5.2 FALLBACK_MODEL=meta-llama/Llama-3.3-70B-Instruct HOST=127.0.0.1 Optional Slack webhook for urgent messages: SLACK_WEBHOOK=https://hooks.slack.com/... Classify a transcript curl -X POST http://localhost:5000/voicemails/transcript \ -H "Content-Type: application/json" \ -d '{ "transcript": "This is an emergency. Our production system is down and we need help immediately.", "caller_number": "+17177247292" }' Example response: { "category" : "urgent" , "confidence" : 1.0 , "priority" : "high" , "reason" : "The caller reports a production system outage requiring immediate attention." , "suggested_action" : "Escalate immediately to the on-call engineering team." , "route" : "slack" , "routed_to" : "#oncall-alerts" , "routing_status" : "delivered" } Process voicemail audio curl -X POST http://localhost:5000/voicemails/process \ -F "file=@voicemail.wav" \ -F "caller_number=+17177247292" For audio, the app calls: POST /v2/ai/audio/transcriptions using: distil-whisper/distil-large-v2 Then it calls: POST /v2/ai/chat/completions to classify the transcript. Routes included POST /voic

We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the teacher though, we realized it would be timely to measure if the censorship characteristic of it transferred to the distilled version of the base model. tl;dr it didn't, the teacher answe

Google's next foldable doesn't look all that different, save for the camera bar.

LinkedIn is introducing new ways to reduce low-quality AI-generated posts, including a “seems like AI slop” reporting option. It's also replacing its own AI writing feature with a proofreading tool.

submitted by /u/farnoy [link] [留言]