标签：#tools

The agentic IDE for designers and programmers Discussion | Link

Build live analytics with coding agents Discussion | Link

Let non-engineers build secure internal tools Discussion | Link

Google has connected Gemini Live with its Deep Research capability, allowing users to begin a multi-step research task by voice, leave it running in the background, and return for a spoken or transcript-based follow-up when the work is complete. The change turns Deep Research from a primarily prompt-led activity into a more conversational mobile workflow, particularly for people who need to capture a research request without staying in the app. The key distinction is not simply voice input. Gemini Live can initiate a research process that continues while a user switches apps or locks their phone. Google describes the resulting experience as a way to talk through research, with a notification when the task has finished and a seamless path back into conversation. The company's Gemini Deep Research overview for Pixel presents the capability as part of a broader effort to make in-depth research more usable on mobile devices. Deep Research itself is designed to do more than provide a single response. Google has documented a workflow in which Gemini develops a research plan, searches across sources, expands its investigation as needed, and produces a structured report with links to sources. Reports can also be exported to Google Docs. Bringing that process into Gemini Live changes how a request can begin and how a user can resume it, rather than changing the documented purpose of Deep Research. What changes in the Gemini Live research workflow The update combines conversational initiation with asynchronous execution. A user can explain a complex topic aloud, ask Gemini Live to begin Deep Research, and move on to another task while the system works. When the report is ready, the user can be notified and continue through speech or review the transcript. Workflow element Documented Deep Research experience Gemini Live integration Starting a request A research request can lead to a structured plan. A user can initiate Deep Research by speaking with Gemini Live. Research proce

Google is expanding Gemini into a more structured learning environment with study notebooks , a student-focused workspace for diagnostics, personalized lessons, practice quizzes, flashcards and progress tracking. The rollout turns Gemini from a general-purpose assistant into a tool designed to organize source-based study workflows, beginning with web access worldwide and mobile support planned for later in the summer. In Google's official study notebooks announcement , the company describes a workflow that starts by assessing a learner's baseline knowledge. Gemini can then create smaller lessons tailored to a student's goals and reinforce those lessons with quizzes. The company positions the capability as part of a broader education-focused effort across Gemini and NotebookLM, rather than solely as a standalone product called Student Hub. What Gemini study notebooks add The central change is a dedicated notebook space where students can bring together their course materials and ask Gemini to produce learning activities from them. Google says users can upload sources including notes, PDFs and websites, then generate flashcards and quizzes inside a notebook. Study notebooks can also reference uploaded materials and sources while creating lessons. This structure matters because it moves the interaction beyond one-off prompts. A diagnostic quiz establishes a starting point, personalized bite-sized lessons address a learning goal, and practice quizzes provide a way to revisit material. A dashboard tracks progress within that workflow. Google also points to connections with NotebookLM, including the ability to reference past chats and outputs there. Study notebook element Confirmed role in the workflow Availability described by Google Diagnostic quizzes Establish a learner's baseline knowledge Part of the study-notebook experience Personalized lessons Create bite-sized learning content tailored to goals Part of the study-notebook experience Flashcards and practice quizzes

Agent Skills turn repeated prompts into reusable, inspectable workflows. This collection includes five small skills for work that comes up often when building with OpenCode or Claude Code: reviewing public copy, checking text limits, capturing public webpages as PDFs, sending task notifications, and structuring research for later use. The full index is available at Published Agent Skills . For OpenCode, Skills can live in ~/.config/opencode/skills/ or project-level locations supported by your setup. Claude Code can discover Skills from .claude/skills/ . Put a Skill folder in the right location, restart the agent session if needed, and it becomes available when the task matches its description. 1. AI Writing Detector Skill AI Writing Detector Skill reviews English and Brazilian Portuguese copy for patterns that make AI-written text feel generic. It checks common issues such as repeated sentence rhythm, filler phrases, excessive formatting, and em-dash use. It also ships with a CLI and MCP server for text and file linting. Useful prompts: Review this README introduction with the anti-ai-tells Skill. Keep the technical facts, flag generic wording, and suggest direct replacements. Run the writing linter on this release post, then rewrite only the passages that need attention. This is useful before publishing documentation, launch posts, landing pages, and changelogs. 2. Text Counter Skill Text Counter Skill gives exact counts for characters, words, sentences, paragraphs, lines, graphemes, bytes, and phrase occurrences. It helps when "roughly under the limit" is not enough. Useful prompts: Write a 155-character meta description for this package and verify its exact character count. Reduce this GitHub issue title to 80 characters without removing the error code. Count the phrase "OpenCode" in this Markdown file. The Skill makes counting rules explicit. That avoids surprises with spaces, Unicode characters, emoji, or repeated phrases. 3. HTML to PDF Skill HTML to PDF Skill

OpenAI is positioning Zero Data Retention (ZDR) as a scalable privacy control for eligible frontier-model API and enterprise workloads. The policy matters as businesses use more capable models for longer-running and increasingly autonomous work, where prompts, outputs, and related interactions can contain sensitive operational, customer, or proprietary information. On its official API platform page , OpenAI lists "Zero data retention policy by request" alongside access to frontier models and APIs. The company’s enterprise privacy materials and GPT-5.4 release information add important context: ZDR is a configurable option for eligible organizations and endpoints, rather than a universal default across all OpenAI services or customer configurations. The shift is less about a newly invented privacy principle than about applying retention controls more explicitly to frontier-capable deployments. OpenAI’s GPT-5.4 materials describe Zero Data Retention surfaces and safety controls designed for higher-sensitivity contexts. That framing acknowledges a practical tension for enterprise AI: more autonomous systems can create more valuable workflows, but they also require safety systems that assess risks across related interactions. What Zero Data Retention changes for enterprise AI Under ZDR, OpenAI disables logging of customer content for abuse monitoring and model-training purposes. The setting also affects API behavior. For example, the store parameter for chat completions and responses is forced to false in ZDR contexts. That is a meaningful control for teams that need to minimize the persistence of prompt and response content. It should not, however, be interpreted as a blanket statement that no information can ever be retained anywhere in the service. OpenAI documents that some endpoints may retain application state or metadata for operational reasons. It also describes exceptional safety and retention mechanisms, including Eyes Off and Safety Retention , that may apply

Rewire anxiety for good Discussion | Link

Talk through your app, your coding agents get the plan Discussion | Link

A richup like browser monopoly game Discussion | Link

Frontier Intelligence for Long-Running Agents Discussion | Link

Highlight and summarize any page, PDF, or video in Firefox Discussion | Link

Run AI models on any device Discussion | Link

The European Commission’s 2022 procurement for a participatory foresight study on next-generation online platforms placed platform design and consumer behaviour within a wider policy question: how could the platform economy evolve, and what might those changes mean for European Union policymaking? The work was not a narrow experiment on marketplace user experience. Instead, it was a two-year exercise intended to identify long-term trends across online platforms and assess their policy implications. The Commission published the call, reference CNECT/2022/OP/0049 , in August 2022. Its official announcement of the foresight study on the future of online platforms lists a submission deadline of 22 September 2022 at 16:00 CEST . That makes the procurement a completed historical call, rather than a current tender opportunity. The framing remains relevant because interface design, recommendation systems and other platform choices can influence what people notice, compare and select online. But the Commission’s stated objective was broader than any one marketplace design question. It sought a structured view of the platform economy’s possible future trajectories and the public-policy issues those trajectories could raise. What the 2022 study was designed to examine The Commission described the project as a two-year participatory foresight study . Participatory foresight brings relevant groups into a structured exploration of future developments rather than attempting to predict one fixed outcome. In this case, the study was designed to identify ten topics in collaboration with Commission services, then examine long-term trends and their potential policy relevance. Design’s influence on consumer behaviour was part of the broader theme, not the full scope of the procurement. That distinction matters. A study focused solely on a marketplace interface might measure how a particular ranking, default or layout affects a defined consumer decision. The Commission’s foresight work i

Skip the setup and run OpenClaw & Hermes, fully managed Discussion | Link

MCP servers give large language models a route to query data sources, call software tools, and trigger actions in connected systems. That capability also changes the security boundary. n8n argues that production deployments need a dedicated MCP control plane to govern which actions an agent can take, under what identity, with which credentials, and with what record of execution. In its July 1, 2026, official guide to MCP server security , n8n describes the control plane as an orchestration layer for MCP activity. Its role is not to make an LLM inherently trustworthy. Instead, it applies operational controls around the model's requests before those requests reach target tools and systems. For enterprises exploring agentic automation, that distinction is central: capable tool use requires enforceable boundaries. What an MCP control plane changes An MCP server defines a surface through which an LLM can access tools and data. In a production setting, simply exposing that surface is not sufficient governance. A control plane adds an execution layer that can scope tool calls, isolate credentials, and log each action. n8n positions itself between the agent and target systems in this model. That intermediary role is intended to keep credentials out of the agent while allowing authorized workflows to access connected services. It also gives organizations a place to apply authorization and retain an audit trail as tool use expands across teams and systems. The shift is from treating an MCP connection as a direct capability grant to treating it as a governed request path. A control plane can make several production controls explicit: Authentication verifies the caller before access is granted. Authorization and tool-call scoping constrain which tools and actions are available for a given context. Credential isolation separates agent activity from the credentials used to reach target systems. Execution logging records actions for auditing and investigation. Least-privilege expo

One tiny real-life quest a day. Then it tells you to leave. Discussion | Link

WhatsApp Sidekick for busy professionals Discussion | Link

Unified API & Infra for AI Meeting Agents Discussion | Link

Run your parallel coding agents from your phone Discussion | Link