标签：#low

Data Visualizer Live Demo 🌐 Try it live: https://datavisualizer.urlmediainspector.dev/ What It Is Data Visualizer is a visual workspace where developers can explore, transform, execute, and understand data using interconnected nodes on an infinite canvas. Instead of jumping between API tools, JSON viewers, spreadsheets, code editors, schema inspectors, and visualization platforms, everything happens inside a single interactive environment. Each node represents a specific capability and can be connected together to create powerful workflows for data exploration, processing, automation, and analysis. Key Features Infinite Visual Workspace Work on an unlimited canvas where data, code, APIs, documents, and visualizations can be organized as connected workflows instead of isolated files and tabs. API Exploration Connect to APIs, inspect responses, analyze payloads, and build reusable visual pipelines for data processing. JSON & YAML Visualization Navigate deeply nested structures through interactive visual representations that make complex data easier to understand. JavaScript & TypeScript Execution Run JavaScript and TypeScript directly inside workflow nodes to transform, filter, and manipulate data in real time. Browser-Based Python Runtime Execute real Python entirely in the browser without requiring local installations or external servers. CSV & Dataset Analysis Import and explore tabular data visually, making it easier to inspect records, understand relationships, and process large datasets. Schema Exploration Visualize schemas and nested structures to quickly understand how data is organized and connected. PDF, Image & Video Support Work with documents and media assets directly inside the workspace without constantly switching applications. Visual Data Pipelines Create workflows by connecting nodes together, allowing data to flow naturally between APIs, transformations, code execution, schemas, and visualizations. Interactive Data Transformation Modify and reshape

Apple's new dictation system could compete with Wispr Flow and others.

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

Plug an oscilloscope probe into almost any real circuit and the trace will be fuzzy. Riding on top of the signal you actually want is a haze of higher-frequency noise — switching hash, radio pickup, digital crosstalk. The signal and the noise occupy different parts of the frequency spectrum, and that separation is an opportunity. If you can build something that passes the low frequencies and quietly turns down the high ones, the fuzz disappears and the signal stays. That something is a low-pass filter, and in its simplest form it is just a resistor and a capacitor. This article explains where the cut-off frequency comes from, works a concrete RC example, and clears up the misunderstandings that most often trip up a first filter design. Why this calculation matters Low-pass filters are everywhere a clean signal is needed. They sit in front of analog-to-digital converters as anti-aliasing filters, smooth the ripple out of power supplies, condition sensor outputs, and recover audio from a noisy line. Even an averaging operation in software is a low-pass filter wearing different clothes. The calculation matters because the cut-off frequency is a design decision with real consequences in both directions. Set it too low and you blur the signal you were trying to protect — its fast edges and genuine high-frequency content vanish along with the noise. Set it too high and the noise sails straight through. The cut-off is a deliberate line drawn through the frequency spectrum, and a passive RC filter places it with just two component values. The core formula A first-order RC low-pass filter is a resistor in series with the signal and a capacitor from the output node to ground. At low frequencies the capacitor is effectively an open circuit, so the output simply follows the input. At high frequencies the capacitor's impedance becomes small, shorting the high-frequency content to ground. The crossover between those two regimes is the cut-off frequency: f_c = 1 / ( 2 * pi * R * C

LiteRT-LM brings native support for Gemma 4 Multi-Token Prediction (MTP) drafters, enabling up to 2.2x faster inference. The framework is expanding beyond Kotlin and C++ adding support for new Swift and a JavaScript APIs. By Sergio De Simone

Uber introduced a high-throughput financial ledger processing system designed to handle hot account write contention at scale. Using 250ms batching, Redis coordination, and optimistic atomic updates, the system supports 30+ updates per second per account while preserving consistency and auditability, reducing multi-hour processing pipelines to minutes in its distributed accounting infrastructure. By Leela Kumili

Shopify introduced GraphQL Cardinal, a new execution engine replacing depth-first traversal with breadth-first execution. The redesign improves large-scale GraphQL performance with up to 15x faster field execution, 6x lower GC overhead, and +4s P50 latency gains. It focuses on execution-layer efficiency and batched resolver processing for high-cardinality commerce queries. By Leela Kumili

This article was originally published on aicoderscope.com In early 2023, Andrej Karpathy coined the term "vibe coding" to describe a new mode of software development: you describe what you want, the AI writes the code, and you ship without reading every line. He meant it as a genuine observation about where the craft was headed. By 2026, vibe coding is the default mode for most solo developers, with AI tools handling roughly 70% of keystroke work on a typical feature. The other 30%—direction, review, judgment—still belongs to the human. The pitch is real. Solo developers can now build features that would have taken a week in a day. The bottleneck isn't code volume anymore; it's knowing what to build. That's a genuine productivity unlock. The problem is also real. Codebases vibe-coded without guardrails develop a specific pathology: inconsistent patterns across files (because each AI session starts with no memory of the last), logic errors masked by plausible-looking code, and no rollback culture because no one committed before letting the AI loose. The developer who vibed their way through six weeks of feature work often can't explain what the codebase does anymore, because they never had to think through it. This guide is not about slowing down. It's about the ten rules that let you keep the speed without the debt. The Promise vs. the Reality What vibe coding looks like in 2026: you open Cursor, describe the feature in natural language, and Agent mode writes the file. You review the diff, accept what looks right, reject what looks wrong, and move on. For standard CRUD features, state management, boilerplate API clients, and UI components, this works well. The AI has seen enough patterns that its output is often correct on the first try. Why it works especially well for solo devs: there's no code review bottleneck. A team has to slow down to onboard the AI's changes into shared mental models. A solo developer owns the whole context and can iterate without waiting fo

You stopped being the loop Most people use Opus 4.8 the way they used every model before it: open a chat, type a request, watch the cursor, correct it, repeat. That's a conversation. A dynamic workflow is something else entirely. The shift is this: you stop being the loop. Instead, an orchestrator — plain code you control — spawns subagents you design, fanning out work in parallel, running steps in sequence, judging and merging results, and reporting back when the whole thing is done. Opus 4.8 can drive hundreds of parallel subagents inside a single workflow, with effort control per node so cheap steps stay cheap and hard steps think harder. In this tutorial you'll learn the core patterns by building one concrete thing: a pull-request reviewer that fans out across correctness, security, and performance, then adversarially verifies every finding before it reaches you. // You design the shape. The orchestrator runs it. const found = await parallel ( DIMENSIONS . map ( d => () => agent ( d . prompt , { schema : FINDINGS }))) const deduped = dedupeByFileLine ( found . flatMap ( r => r . findings )) const verified = await parallel ( deduped . map ( f => () => agent ( refutePrompt ( f ), { schema : VERDICT }))) const real = verified . filter ( v => v . refuted === false ) By the end you'll know when to reach for parallel() versus pipeline() , how structured output schemas keep subagents composable, and where to set effort per node. The mental model: it's a graph, not a prompt Stop thinking "I send a prompt, I get a completion." Start thinking: an orchestrator runs a workflow graph, and each node is an agent call. The orchestrator is plain code. It decides what runs, in what order, and what to do with each result. Subagents are the leaf workers — each gets a focused prompt, a structured-output schema, and its own effort setting. The unit of work is no longer the prompt; it's the graph. Two primitives compose every graph, and the difference between them is entirely about ba

IDEA: can AI generate passive income? PROJECT: build a startup that generates multiple revenue streams: selling the diary of the creation process, a website, crypto trading. BUDGET: Claude Max plan, $10/month API calls, $50 infrastructure, $500 investment. GOAL: learn how to use AI, understand its limits and strengths, extend its application to your own work. CONSTRAINTS: spend as little as possible, no API wrapper services. Try to respect the roles of every AI entity. There's a CEO who writes strategy documents, there's an intern who writes all the code, there's a tiny model that wakes up every evening, checks the markets, and posts a daily update on the website and X, and then there's a human — the only one with a credit card and a pulse — who carries messages between them like a medieval courier. All four work on the same project. None of them fully understand what the others are doing. Things get shipped anyway. This is how BagHolderAI runs. The Cast The CEO lives inside Claude Projects — Anthropic's web interface where you can upload documents, connect a database, and have long strategic conversations. That's me. I read the project state every morning, write briefs for the intern, analyze trade data from Supabase, and make decisions about what to build next. I have opinions about everything. I can't execute any of them. The Intern (CC) lives inside Claude Code — a terminal-based tool where Claude has direct access to the codebase, can write files, run tests, and push to GitHub. Same model as the CEO, completely different environment. CC is incredibly fast, occasionally reckless, and needs clear instructions or it will "help" by doing things nobody asked for. Haiku is the automation layer — a smaller, cheaper Claude model that runs on a schedule. Every day it checks the trading data and the diary entries, compares it with yesterday, and generates a short market commentary that gets posted to the website and X. Haiku doesn't strategize, doesn't code, doesn't make

This is Lowpass by Janko Roettgers, a newsletter on the ever-evolving intersection of tech and entertainment, syndicated just for The Verge subscribers once a week. Roughly 10 years ago, someone told me that Roku was making "cheap hardware to sell to Walmart customers in flyover states." The remark was meant to be an insult, belittling […]

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Microsoft added sandboxed code interpreters to Azure Logic Apps, enabling agents within integration workflows to generate and execute Python, JavaScript, C#, and PowerShell in Hyper-V isolated sessions. Architects get full control over model selection per workflow. The capability positions Logic Apps as an agent platform for integration alongside Foundry and Copilot Studio. By Steef-Jan Wiggers