I have done a lot of office automation using agentic AI. Rather than using AI to complete the task every time (which is a waste of resources), I have made python scripts that make event folders in google drive based off of templates, make the events flyer, speaker emails, announcement emails, run of show documents, and google calendar invites. It has made my events planning process much easier, as I can make a single update in a spreadsheet and populate it to all relevant documents, reducing the chance for error. Would folks be interested in a workshop on this? Something like "Office Automation for Admins" #OfficeAutomation #AgenticAI

Watching project assets take shape shouldn't require jumping between disconnected browser tabs and isolated tools. SEMI AI links your text prompts, code generation, media pipelines, and research queries directly inside a single responsive console. Open your workspace and turn complex ideas into production-ready output. #SEMIAI #WorkflowOrchestration #DeterministicControl #MultiModel #CoreInfrastructure #DeveloperTools #SystemArchitecture #AIEngineering

Linear Review 2026: The Developer-First Issue Tracker That Got Opinionated Category: Issue tracking, sprint cycles, projects and initiatives, triage and request intake, AI agent workflows, coding agents, code review, analytics, Slack and GitHub integrations Linear combines issue tracking and product planning with a clean, keyboard-driven interface and a built-in AI agent. It is designed around software teams, while some reviewers want more customization and deeper reporting. Read the full review: https://lnkd.in/gAAcfgRM #ArtificialIntelligence #SoftwareReviews

Most teams don't lose information. They lose context. The decision is in an email. The reasoning is in a chat thread. The deadline is on a calendar. And the AI tool someone used last week has no idea any of it happened. NARV is building a private office suite where mail, chat, calls, calendar and planning share one memory layer, designed to carry that context into the AI tools a team already uses. We're early. Over the next few weeks, we'll share how we're approaching the architecture, including the parts that are hardest to get right. #EnterpriseAI #BuildInPublic

Apollo.io put three products on stage at ApolloNEXT on 30 Sep. Builder Studio: describe a GTM workflow in plain English, get working pages, databases and automations. Intelligence Layer: a shared context engine for contacts, accounts and what should happen next. Messaging OS: signal-based execution that picks who to reach, when, and through which channel across sales and marketing. The line most recaps will skip: Builder Studio and Messaging OS will be available soon. The Intelligence Layer is available now. That is not a small footnote if you are rewriting your outbound stack this quarter. What everyone else will say: all-in-one AI GTM. What to check before you rearrange tools around the announcement: which of the three can your team turn on this week, and which is still not generally available? Apollo also said teams using its MCP grew more than 4x from March to August 2026, and daily credit consumption on MCP was up nearly 10x. So agent-style access inside the product is already rising, ahead of the new builders shipping. If you run Clay, Apollo, n8n or a mix: map each step of your current workflow to "live today" vs "announced, not live." Buy or rebuild only against the live column. GTM engineers: which step in your stack are you about to rebuild on a feature that is not generally available yet?

Building automations used to mean hours of dragging boxes and connecting logic. Now with GHL's Workflow AI, you just type what you want "text every lead who doesn't book within 24 hours" and it builds the automation skeleton for you. Automation used to take a developer. Now it takes a sentence. This is the exact tech stack we use to move fast for clients. Want it for your business?

I've been building Fork Algorithm with 20 browser tabs open and 3 AI tools running at once. Walk into my workspace and it looks like a coordinated scramble—tabs flashing, half-written prompts, a few experiments running in parallel. Most people picture automation as a polished, complicated system. My experience says otherwise: automation is organized chaos made consistent. The messy iteration happens up front so the customer-facing outcome is predictable — faster follow-up, fewer dropped opportunities, clean handoffs. If you run client work or operations, prioritize building repeatable processes that tolerate the mess and deliver reliability. Consistency beats polish when you're solving for outcomes. #automation #operations #productivity #smallbusiness

𝐓𝐡𝐞 𝐭𝐨𝐨𝐥 𝐢𝐬𝐧'𝐭 𝐠𝐞𝐧𝐞𝐫𝐢𝐜. 𝐓𝐡𝐞 𝐝𝐞𝐟𝐚𝐮𝐥𝐭 𝐬𝐞𝐭𝐭𝐢𝐧𝐠𝐬 𝐚𝐫𝐞. Companies pay for enterprise Copilot licenses, hand them out, and watch teams get generic corporate soup. The problem is not the model. It is the unconfigured setup.  Five configurations fix the output: 𝟭. 𝗧𝘂𝗿𝗻 𝗼𝗻 𝗦𝗮𝘃𝗲𝗱 𝗠𝗲𝗺𝗼𝗿𝗶𝗲𝘀 Navigate to Settings > Personalization and enable Saved Memories. Copilot retains context over time instead of wiping the slate clean on every new prompt.  𝟮. 𝗠𝗮𝗻𝘂𝗮𝗹𝗹𝘆 𝗦𝗲𝗹𝗲𝗰𝘁 𝗬𝗼𝘂𝗿 𝗠𝗼𝗱𝗲 Move past Auto. Toggle to Quick Response for speed, Think Deeper for complex reasoning, or select specific GPT backend engines directly.  𝟯. 𝗟𝗼𝗰𝗸 𝗜𝗻 𝗖𝘂𝘀𝘁𝗼𝗺 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 Define formatting constraints upfront in Settings. Require concise summaries, bullet points, or specific tone rules so the model never defaults to fluff.  𝟰. 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗙𝗿𝗼𝗺 𝗧𝗵𝗲 𝗣𝗿𝗼𝗺𝗽𝘁 𝗚𝗮𝗹𝗹𝗲𝗿𝘆 Use the built-in Prompt Lab to pull proven prompts for analysis, code, and drafting rather than guessing how to frame queries.  𝟱. 𝗕𝘂𝗶𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 Open the waffle menu with your Copilot license and set up Notebooks. Feed the folder specific reference files to isolate the AI's knowledge base to project data.  Without configuration, every model defaults to the middle. With architecture, it acts like an operator.  #Copilot #EnterpriseAI #WorkflowOptimization #Productivity

Manual task decomposition is usually where engineering momentum slows down. I used to spend 15 to 20 minutes manually breaking down technical audits, writing step-by-step subtasks, assigning owners, and setting priority flags across our backlog. With @Brain Agent inside ClickUp, that manual friction is completely eliminated. In a recent task, I simply tagged @Brain in the thread: "break down this backend API audit into 3 actionable subtasks with clear execution steps and prioritize them." In under 10 seconds, Brain executed the entire workflow: it created three concrete subtasks (inventory baseline, security controls testing, and remediation reporting), mapped the priorities from Urgent to Normal, and assigned them directly to me. • Before: 20 minutes of tedious manual formatting, clicking, and assigning. • After: 10 seconds to a fully structured, assigned, and prioritized execution path. When your workspace AI can take direct operational actions instead of just answering questions, everything moves faster 🤔 What is one routine workflow task you would automate with AI tomorrow if you could? #BrainAgentHack #ClickUp #ClickUpBrain #Productivity #Workflow

Following Sunday evenings announcement of our support for the Personal Agent Protocol (Poppy), our 8 minute explanation video is now available. Target audience is developers of consumer facing AI agents, but understanding what AI tech can do is something everyone in the tourism sector should be keeping up with