Opendoor switched to Linear. Drawing on lessons from Shopify, they replaced Jira with a purpose-built system designed to make work legible and help teams move faster. Here's how they switched: https://lnkd.in/eFqAhvrN
Today we are launching the inaugural version of Linear Diffs, our take on code reviews. Diffs is meant to enable product teams to accelerate shipping by making code reviews fast, focused, and in context. Code review has remained a painful bottleneck while the rest of building software sped up. Growing volumes of code from agents are making it worse. We designed Diffs around what a code review should actually be: instant to open, stripped of noise, and deeply connected to the issue, project, and customer signal behind the change. It brings reviews inside Linear with smart prioritization, guided chapters for large diffs (following the logic of the work), structural highlighting that removes formatting churn, and rich context. Agents already handle most of the line-by-line correctness. This gives reviewers the space to focus on the judgment that actually matters: architecture, fit, and real customer problems. Available on all plans today. More of the workflow to come. Read more: https://lnkd.in/d2YwBC_s
Spotting patterns across customer feedback takes hours, and it's exactly the kind of work Linear Agent now does and writes up as a brief for you.
Linear can read your codebase, so when you hit an unfamiliar area mid-build, you can ask questions instead of grepping through it.
Using Microsoft Teams, you can turn any conversation into a Linear issue with one mention, so bug reports and ideas become actionable.
Linear’s AI features help your team efficiently manage bug reports.
Linear can hand any issue to Cursor, Claude Code, or Codex with the ticket context already pasted in, so you skip the copy step.
The bugs and feature ideas that get raised in Slack but never reach your plans can now become Linear issues with a single mention.
"The transition from Jira to Linear was the smoothest thing ever" Learn how Lightricks replaced a fragmented stack of tools with a unified system to improve visibility and collaboration across teams. → https://lnkd.in/eGu39sTK
Ask Linear Agent to look at every piece of customer feedback on a topic, and you get a full brief and breakdown instead of hours of digging.
In October of last year, Cars24, while preparing for an IPO, renewed its multi-year Jira license. Within a few weeks, the company would treat that contract as a sunk cost and walk away from it. Here is why: https://lnkd.in/e8spVHe5
I think we have lost some sense of judgment and moderation when it comes to product building currently. The moment you turn something into a universally celebrated metric, whether that is token burn, prototype count, or percentage of agent-written code, you start losing sight of what actually matters. I have felt the same way for a long time about overusing data and A/B testing to build products. The moment you reduce product quality or productivity to a metric, you stop shipping value and start shipping numbers. A lot of what people are doing with AI makes directional sense. The missing piece is counterbalance: 1. AI should help engineers build better products. Leaderboards and adoption metrics can be useful as directional signals. They do not tell you what is being built, whether it is good, or whether it should exist at all. 2. Users do not care what percentage of your code was written by agents. They care about the outcome. Faster output is useful. Like usually, faster doesn't seem to add to quality, clarity, or stability of products. Power to build should not become an excuse to lower quality bars. 3. LLM-generated prototypes can feel like late-night whiteboarding sessions. They look exciting in the moment and feel productive very quickly. Then a few days later you realize the idea was shallow, distracting, or simply wrong. The same trap shows up in jumping straight to code and solutions more broadly. You may just be building the wrong thing more efficiently. Prototyping has its place. So do clear thinking, good design, and a real understanding of the user’s problem. In terms of activities or momentum, the main quest and the side quest can both feel productive but only one actually moves the mission forward. 4. Adding more to products is still dangerous as ever even if time or effort to add it has gone down. Every addition creates complexity, maintenance cost, and user confusion. New features should be pushed back unless they clearly show it should exist and how it improves the product. 5. Not everything needs to be an agent shaped. A simple scheduled task does not need a full LLM sandbox. Making something agentic because it feels current or impressive does not make it right-sized, correct, or effective. The core ideas are: - even if you can, maybe you should not. - more power we have to build should not reduce our need to think, it should increase it.
Linear Agent × Code Intelligence Ask Linear how a feature is implemented, why something behaves a certain way, or which technical constraints should shape a project plan – without digging through the codebase or interrupting an engineer.
Introducing coding sessions. Linear Agent can now triage issues, investigate the cause, write the fix, open a PR, and bring the code back for review. All shared with your team in Linear.
Oscar Health switched to Linear. 600+ engineers, product managers, and other teams left one of the world’s most complex Jira instances, and migrated in just over a month. Here’s how they switched: https://lnkd.in/eE9P-g3v
I think we have lost some sense of judgment and moderation when it comes to product building currently. The moment you …
I think we have lost some sense of judgment and moderation when it comes to product building currently.
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