“Linear’s first production workflow turned sales notes and Slack discussions into issues; the team launched it quietly and used observed behavior to guide subsequent workflows.”

Peter Yang recapped five takeaways from @thenanyu and @delashum at Linear for building production agents end to end: map the real workflow, equip agents to retrieve context, start with one frequent job, use the strongest model until the workflow works, and turn real failures into evals or product tasks. Linear’s first production workflow turned sales notes and Slack discussions into issues; the team launched it quietly and used observed behavior to guide subsequent workflows. Linear also created two feedback loops for poor agent behavior and missing-tool gaps.

“Linear Agent dynamically loads task-specific skills in production #1 ▶️ 5 Rules for Building AI Agents That Work in Production | Nan Yu & Jacob Shumway Peter Yang Linear Agent runs an LLM in a tool-calling loop, loading task-specific skills and context from systems such as Slack, Linear, and a codebase to turn a Slack discussion into a Linear issue and a pull request.”

Linear Agent dynamically loads task-specific skills in production #1 ▶️ 5 Rules for Building AI Agents That Work in Production | Nan Yu & Jacob Shumway Peter Yang Linear Agent runs an LLM in a tool-calling loop, loading task-specific skills and context from systems such as Slack, Linear, and a codebase to turn a Slack discussion into a Linear issue and a pull request. Linear’s first prototype called an LLM directly from the frontend, exposed Linear command-menu actions as tools, and was initially released internally through Slack app mentions without an announcement.

“Peter Yang turned Codex into a “heartbeat” by having it check his email, Slack, and Linear at 9 am, 1 pm, and 5 pm to prioritize tasks—and then gradually added preferences like including links, integrating with Linear, and pre-drafting his Slack replies and emails.”

#1 𝕏 Peter Yang turned Codex into a “heartbeat” by having it check his email, Slack, and Linear at 9 am, 1 pm, and 5 pm to prioritize tasks—and then gradually added preferences like including links, integrating with Linear, and pre-drafting his Slack replies and emails.

“How I run autonomous coding agents from my phone with OpenAI Symphony + Linear How I AI Podcast Alessio Fanelli runs OpenAI Symphony on a 32 GB/4-core cloud VPS integrated with Linear as an agent state machine to autonomously manage coding tasks with per-task token tracking (peaking at 221 million tokens) and leverages OpenAI Codex with in-app browser access to scrape PSA certificate numbers and hunt underpriced $10 K–$20 K Pokémon cards on eBay for his Merlin Games shop.”

GenAI PM Daily July 07, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 20 insights for PM Builders, ranked by relevance from Blogs, X, YouTube, and LinkedIn. #6 ▶️ How I run autonomous coding agents from my phone with OpenAI Symphony + Linear How I AI Podcast Alessio Fanelli runs OpenAI Symphony on a 32 GB/4-core cloud VPS integrated with Linear as an agent state machine to autonomously manage coding tasks with per-task token tracking (peaking at 221 million tokens) and leverages OpenAI Codex with in-app browser access to scrape PSA certificate numbers and hunt underpriced $10 K–$20 K Pokémon cards on eBay for his Merlin Games shop. VPS “Zoo” (32 GB RAM, 4 cores) hosts Symphony tied to Linear projects where Linear issues auto-spawn Codex workpads containing implementation plans, acceptance criteria, rework checklists and GitHub PR previews for human review. Symphony’s built-in ledger records token consumption per task in Linear fields, showing tasks from ~15 million tokens up to a 221 million-token rewrite to make the app deployable on Vercel.

“Rohan Varma uses the OpenAI Codex app (agent control plane) to pull context from Slack, Notion, Linear and Google Drive; automate Slack reply triggers; prototype UI variants via the built-in ImageGen skill; and convert Notion documents into live Sites.”

#3 ▶️ OpenAI PM Reveals How He Uses Codex to Do Product Work | Rohan Varma Peter Yang Rohan Varma uses the OpenAI Codex app (agent control plane) to pull context from Slack, Notion, Linear and Google Drive; automate Slack reply triggers; prototype UI variants via the built-in ImageGen skill; and convert Notion documents into live Sites. Codex integrates with Slack, Notion, Linear, email and Google Drive to synthesize thousands of daily feedback messages, enabling onboarding to a new project with full context in 20 minutes. Invoking the "imagegen" skill with a single screenshot in Codex produced four distinct UI mockups for a project-selection interface in under a minute and then generated a live prototype via the Sites feature. Codex was instructed to schedule a daily automation that parses a Slack channel for feedback, creates or updates Linear issues, and posts a summary in Slack to Rohan Varma, deleting the automation after each run.

“The video demonstrates how to set up and run OpenAI's open-source Symphony orchestrator to manage coding agents via Linear tickets using a workflow.md file and isolated workspaces.”

#4 ▶️ New AI coding paradiagm - OpenAI Symphony AI Jason The video demonstrates how to set up and run OpenAI's open-source Symphony orchestrator to manage coding agents via Linear tickets using a workflow.md file and isolated workspaces. Symphony runs as a background scheduler polling a Linear project every 30 seconds, spinning up isolated workspaces per “to-do” ticket and managing session lifecycles with parallel agents configured in workflow.md. workflow.md includes YAML front matter specifying project slug, ticket filters, workspace paths, init hooks, parallel agent limits and CodeX settings, followed by a markdown-based SOP prompt detailing planning, validation, done criteria and human-review triggers.

“#7 𝕏 Peter Yang echoes @karrisaarinen (CEO @Linear) that when you can spin up 10 agents in 10 directions, shared clarity on your target users, the problem you’re solving, and your product vision is critical to keep fast execution focused.”

Today's top 10 insights for PM Builders from X and Blogs. #7 𝕏 Peter Yang echoes @karrisaarinen (CEO @Linear) that when you can spin up 10 agents in 10 directions, shared clarity on your target users, the problem you’re solving, and your product vision is critical to keep fast execution focused.

“#13 𝕏 Kevin Yien reveals that Linear’s 2026 roadmap isn’t just about issue tracking—it includes new capabilities like Linear Agent, Skills, Automations, Code Intelligence, and the Linear Coding Agent.”

#13 𝕏 Kevin Yien reveals that Linear’s 2026 roadmap isn’t just about issue tracking—it includes new capabilities like Linear Agent, Skills, Automations, Code Intelligence, and the Linear Coding Agent. #14 𝕏 clem 🤗 Anthropic revoked OpenAI’s access to its Claude AI models, citing repeated misuse of proprietary system prompts and escalating the rivalry between the two labs.

“Peter Yang explains how @Linear embeds AI agents into every product step—auto-reading customer conversations to create, dedupe, and route issues; generating and splitting specs into tickets (now most of their backlog); then sending small fixes to coding agents and invoking Cl...”

GenAI PM Daily March 06, 2026 GenAI PM Daily 🎧 Listen to this brief 3 min listen Today's top 25 insights for PM Builders, ranked by relevance from Blogs, X, LinkedIn, and YouTube. OpenAI Introduces GPT-5.4 Model #1 📝 OpenAI News Introducing GPT-5.4 - Announcement of GPT-5.4 as a new product release, highlighting improvements and new capabilities over prior models. The post introduces features and potential applications of GPT-5.4. Also covered by: @There's An AI For That , @Kevin Weil 🇺🇸 #9 𝕏 Peter Yang explains how @Linear embeds AI agents into every product step—auto-reading customer conversations to create, dedupe, and route issues; generating and splitting specs into tickets (now most of their backlog); then sending small fixes to coding agents and invoking Cl...

“Peter Yang unveils how three AI-native companies—Linear assigns tasks to AI “team members” via natural language, Ramp drives performance by mandating Claude Code usage, and Factory AI packages product management, UI, and data analysis into reusable AI skills—offering concrete...”

#10 𝕏 Peter Yang unveils how three AI-native companies—Linear assigns tasks to AI “team members” via natural language, Ramp drives performance by mandating Claude Code usage, and Factory AI packages product management, UI, and data analysis into reusable AI skills—offering concrete...