标签：#Product

Snap one photo, get listing-ready shots from every angle Discussion | Link

Local-first agent trust console with a safe local workspace Discussion | Link

Something changed in software engineering, and I do not think we have fully named it yet. For years, the job was mostly about writing code directly. Then autocomplete got better. Then chat-based coding assistants arrived. Now the workflow is shifting again: we describe goals, hand off chunks of work to agents, inspect their output, tighten the tests, and decide what gets merged. That is not the same job with a faster keyboard. It is a different shape of work. I would call it agentic engineering. The engineer is becoming a director Agentic engineering does not mean the engineer disappears. If anything, it makes the engineer's judgment more visible. A coding agent can read files, make changes, run commands, open pull requests, and iterate through errors. GitHub describes Copilot agent mode as a workflow where the agent can plan, edit, run terminal commands, and keep working until a task is complete. Google describes Jules as an asynchronous coding agent that can take a task, work in a virtual machine, and produce a pull request. Anthropic's Claude Code guidance talks openly about using multiple Claude sessions in parallel, giving agents clear context, and treating them like workers that need direction. That is the shift. The engineer is no longer only the person typing every line. The engineer is also the person deciding what should be built, what constraints matter, how to verify the result, and when the agent is wrong. Prompting is too small a word for this People often describe this work as prompting, but that undersells it. A prompt can be a single instruction. Agentic engineering is more like delegation. You define the task, provide the relevant context, set the boundaries, create checks, review the work, and decide the next move. If the agent goes in the wrong direction, the failure is not always the model's fault. Sometimes the task was too vague. Sometimes the repository had no tests. Sometimes the acceptance criteria lived only in someone's head. This is why

Migrated 4 of 7 Notion automations to an MCP server in one weekend Two workflows stayed in Notion because the database UI beat any tool call MCP scope rule: one tool does one verb, never a Swiss Army function Result: 12 manual steps collapsed into 3 Claude prompts per publish I spent a weekend pulling four automations out of Notion and rebuilding them as MCP tools. Three of them got faster and one got worse before it got better. The biggest lesson was not about code. It was about deciding which jobs should never leave Notion in the first place. Why I Moved Off Notion In The First Place My Notion setup was not broken. It was just slow in a specific way. I had seven automations stitched together with Notion buttons, formula properties, and two third-party connectors. Every blog publish meant clicking through four pages, copying a title here, pasting a tag list there, and triggering a sync that took 90 seconds to confirm. Multiply that by the 18 articles I push in a normal month and the clicking adds up. The breaking point was a Tuesday where I lost 40 minutes to a connector that silently stopped firing. No error, no log, just a row that never updated. I checked the connector dashboard and it told me everything was healthy. It was not healthy. That kind of invisible failure is the worst kind because you trust it until you do not. MCP changed the math for me. An MCP server lets Claude call my own functions directly. Instead of Claude writing text and me ferrying that text into Notion by hand, Claude can call a tool that does the writing into my systems. The model becomes the operator, not just the writer. If you want the deeper context on what MCP actually is and why it matters at scale, MCP: The 97 Million Agentic Foundation goes through the bigger picture. So I made a list. Seven automations, sorted by how much human judgment each one needed. The ones at the top were pure mechanical steps: format this, push that, fetch a status. The ones at the bottom needed me to loo

Claude Fable 5 me permitiu criar um "GTA" em apenas um prompt. Prompt: "Crie um jogo, Tiny GTA 3D." A própria Anthropic afirma que o Fable 5 é seu modelo mais poderoso já lançado ao público, com avanços significativos em engenharia de software, pesquisa científica, visão computacional e execução autônoma de tarefas complexas. Em testes iniciais, empresas relataram que o modelo foi capaz de comprimir meses de trabalho de engenharia em poucos dias. Cidade 3D aberta com 64 quarteirões, prédios, parques e oceano Dirija, roube carros e fuja da polícia Sistema de procurado com 5 estrelas, viaturas e helicóptero te perseguem 42 pedestres vivos que fogem, voam e morrem 16 missões de entrega com histórias de corrupção brasileira Áudio sintetizado: motor, sirene, buzina e cantada de pneu Recorde salvo no navegador Jogue aqui: https://andredarcie.github.io/tiny-gta/

Automate physical work with a powerful robot Discussion | Link

Learn words in context with AI Discussion | Link

Latest audio model for live speech-to-speech translation Discussion | Link

SQL is arguably the most widely used language in software engineering, yet it is often the least carefully written. Most teams enforce strict linting on their application code but leave SQL queries as a free-for-all. This guide covers the formatting rules that separate maintainable, team-friendly SQL from query spaghetti that haunts on-call rotations. Why Poorly Written SQL Is a Real Engineering Problem Unformatted SQL is not just an aesthetic issue - it is a correctness risk. Dense, run-on queries make it nearly impossible to spot accidental Cartesian products, missing GROUP BY clauses, or WHERE conditions that silently bypass indexes. By the time a performance problem surfaces in production, tracing it back to the root cause becomes a painful exercise in reading someone else's stream of consciousness. Rule 1: Keyword Capitalization SQL engines treat select and SELECT identically, but human readers do not. Always uppercase reserved keywords such as SELECT, FROM, WHERE, JOIN, GROUP BY, and ORDER BY. Keep table names, column names, and aliases lowercase. This single habit immediately creates a visual boundary between the logic structure of the query and the underlying data it operates on. Rule 2: Indentation and Clause Alignment Think of SQL clauses as layers in a data pipeline. Each major clause - SELECT, FROM, WHERE, GROUP BY, ORDER BY - should start at the left margin. Columns and filter conditions beneath them should be indented by 4 spaces (or 1 tab, as long as your team is consistent). This structure lets any reviewer skim the query top-to-bottom and understand the data flow at a glance. Rule 3: Trailing vs. Leading Commas This is a genuinely debated topic on data teams. Leading commas (placing the comma at the start of each new line) make version control diffs significantly cleaner when columns are added or removed. Trailing commas look more natural for developers coming from JavaScript or Python. Neither approach is wrong - what is wrong is mixing both styles

Apple has announced the latest version of macOS. It’s all about the reintroduction of Siri, which is now accessible from anywhere on the Mac desktop.

One command to audit and fix your Mac's security Discussion | Link

Visibility Comes Before Optimization in IT Operations is a practical operating principle, not a slogan. The useful version of analytics, automation, and software operations is usually quieter than the marketing version. It is less about collecting everything or automating everything, and more about making the work easier to understand, review, and improve. The practical problem Teams often try to optimize before they can see the system clearly. That creates confident changes based on partial evidence, especially in infrastructure and telecom-adjacent workflows where signals are distributed. This is where many teams lose clarity. They have tools, charts, workflows, and activity, but the connection between evidence and decision is weak. When that connection is weak, software work becomes harder to evaluate. Teams still make decisions, but they rely more on memory, opinion, or urgency than on a reviewable operating picture. A smaller operating model Start with visibility: what is running, which state changed, where the weak signal appeared, and which workflow was affected. Then connect that signal to a decision or operational review. The important detail is restraint. A useful system does not need to track every possible action or automate every possible step. It needs to preserve the signals that help operators understand the situation and act with more confidence. That usually means naming the workflow, keeping the outcome visible, preserving enough context to explain the signal, and making uncertainty explicit instead of hiding it behind a polished interface. What to review Useful analytics separates normal activity from operational risk. It should make the next investigation smaller, not create another dashboard that requires interpretation from scratch. A reviewable system is easier to trust because it can explain its own state. It shows what happened, what changed, what remains uncertain, and which decision should move next. For WebmasterID, this is the practical

The Problem I was using Claude Code, Codex, and Cursor daily but had no idea how much I was spending on tokens. Bills kept surprising me. The Solution I built AIUsage — a local-first, open-source CLI that tracks everything. Key Features Token usage tracking with daily breakdowns Cost estimation with configurable pricing Model usage ranking Multi-device sync via GitHub or S3 Desktop widget How It Works bash npm install -g @juliantanx/aiusage aiusage parse aiusage serve Why Local-First? Your data never leaves your machine. No accounts, no API keys, no cloud servers. Try It [aiusage.jtanx.com](https://aiusage.jtanx.com)

AI in 2026 is no longer best understood as a technology trend. It has become a structural layer...

Build always-on AI agents without managing servers Discussion | Link

Direct cinematic videos through chat Discussion | Link

With a competitive price, winning design, and better performance than the R1, Rivian could be set to break into the big leagues. Just make sure you get the right model with the right tech.

AI Prompt Generator, Optimizer & Library Discussion | Link

These bird feeders come with cameras and connected apps to let you see and learn about the birds in your neighborhood.

The indie marketplace for Blender artists and creators Discussion | Link