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Most AI coding tools focus on helping one developer work faster. Reason is taking a different approach. The idea of a multiplayer coding agent is interesting because software development rarely happens in isolation. Reason lets AI agents and humans work together, split tasks, debug issues and verify solutions instead... show more
10,892 次观看 • 4 天前 •via X (Twitter)
34 条评论
@reasonmachines Collaborative AI coding will enhance development efficiency
@reasonmachines There's a good reason to look at this before forming a strong opinion.
@reasonmachines ai is def the future
@reasonmachines A lot depends on how we see things
@reasonmachines Collaborative AI agents could make coding much more efficient.
@reasonmachines Collaborative coding agents will redefine how developers work
@reasonmachines how many people are on the reasonmachines team
@reasonmachines AI tools improve collaboration together.
@reasonmachines same burden in every product means the layer missed
@reasonmachines I like this post because there is something to learn from it
@reasonmachines 👀
@reasonmachines reasonmachines is building multiplayer coding agents where humans + AIs collaborate in parallel with shared context to split, debug and verify work, not just autocomplete faster.
@reasonmachines You’re so right brother
@reasonmachines Parallel collaboration truly speeds up development.
@reasonmachines Different approach makes it batter
@reasonmachines multiplayer agents beat a single agent for debugging issues
@reasonmachines Multiplayer coding could be a big shift if agents can actually share context and verify each other’s work
@reasonmachines Having multiple agents work on different parts of a project could save a lot of time, especially when debugging gets messy. 👀
@reasonmachines reasonmachines shows multiplayer coding could speed up projects dramatically
@reasonmachines Shared context across sessions could still be out of date if a developer changes a dependency after a session ends. In that case, the AI might generate code that compiles locally but fails on the CI server due to the mismatched version.
@reasonmachines multiplayer agents make sense tbh, solo coding is kinda overrated
@reasonmachines team coding with reasonmachines boosts productivity and reduces bugs
@reasonmachines this how teams really move together
@reasonmachines Multiplayer makes way more sense than another solo copilot. The messy part of real projects is the handoff between people, not the typing speed
@reasonmachines but multiple agents could actually slow down debugging
@reasonmachines The real breakthrough is AI agents collaborating, not just coding faster.
@reasonmachines would you trust it on prod code?
@reasonmachines Clear structure supports better understanding
@reasonmachines multiplayer agents are cool, but shared context is the hard part
@reasonmachines This approach could change development teamwork
@reasonmachines love the multiplayer angle, feels way more real-world
@reasonmachines maybe still too dependent on one agent despite multiplayer claim
@reasonmachines this team collaboration vibe feels right. can’t wait to see how it changes the game for devs working together long distance
@reasonmachines multiplayer coding agents splitting tasks beats single agent bottlenecks every time
![We're only year 3 of a decade (if not multi-decades) long transformation of work. 3 years ago we bet on building an horizontal platform for work with agents, a chance to invent a new operating system for companies, from scratch, with AI as a fundamental premise. Many people considered us crazy for going after that, praising verticalized AI products as the winning strategy. But here's the thing: the time horizon of tasks successfully handled by agents has been predictively increasing form minutes to hours and will in all likelihood reach the equivalent of days and weeks of human work equivalent in the coming quarters. This is were verticalized and/or single-player AI falls short. Single-player tools, one person, one agent, confined to your machine is the wrong architecture for what's coming. We're shifting from using AI to produce things, to managing fleets of agents that do the producing. 3 years ago I wrote[1]: "ChatGPT is the Pong of LLMs. [...] Imagine, one day we'll get the DOOM, Civ, Red Alert, and Counter Strike of LLMs. Let alone multiplayer modes." Weeks long tasks in companies are inherently collaborative and mechanically spanning multiple teams. The new bottleneck in harnessing agents within organizations is coordination: multiple humans and multiple agents need to work together, with shared context, shared tools, shared goals. Agents that can hand work off to other agents or surface decisions to the right person at the right time. Humans who can review, steer, and step in without losing the thread. Teams that can run parallel workstreams and actually stay aligned. This is Multiplayer AI, and that's what we've been building at Dust. Across Datadog, Clay, Persona, 1Password, Doctolib and 3,000+ organizations globally, we've watched teams figure out what this looks like in practice. 300,000+ agents deployed. 70% weekly active. 240%+ NRR. Today we're announcing a $40M Series B with Abstract, Sequoia, Snowflake, and Datadog to accelerate our vision. Designing the right interfaces for multiplayer AI is the next frontier. Join us to redefine work by defining multiplayer AI.](https://image.24vids.com/tw-2056658851588399389/amplify_video_thumb/2056658261546262528/img/ma1TE40JmZjHASb3.jpg)
