We see developers build their own setup over SSH with success, so in that sense I agree with you.
However, once you want to trigger tasks from Slack, Linear, or GitHub issues or onboard teammates who aren't comfortable wiring up LXC + tmux + agent forwarding, a managed layer is needed.
I think we're at a moment where builders with great setups like yours and products like ours are feeding each other good ideas. The patterns you figure out in your zshrc inform what we productize, and the workflows we ship give you new things to try. It's a virtuous circle. Everyone should use the right-sized solution for their situation.
Definitely the same category of product. The main difference with cursor is that we reuse raw harnesses from ai-labs (claude code, codex). Cursor rebuilds its own harness. We believe nothing will beat the "natural" harness of each model because of RL.
You are also free to swap/combine these harnesses as you please, which is something Anthropic can't do. For instance claude code implements and codex reviews.
This definitely feels like the end state. We need to improve models, agent/human UX and make the transition from cloud work to local work seemless to fully get there
There is room for plan adaptation but the agent has to justify and highlight it in the PR.
Defining the plan/acceptance criteria for long running task is the hard part.
We recently added a Ralph loop mode in that spirit. The implementation won't start until the human and agent align on verifiable criteria and a different agent judges if criteria are met at the end of each run.
Overall I think this problem is not yet completely solved and improvement on both the UX and model judgement are needed
This is very convenient but has limitations. GitHub actions are not built to resume state (conversations in our case) and handle multi player experiences.
However reusing the GitHub workflows out of the box feels really nice
We already support computer use out of the box (linux sandboxes). Self hosted runners are not available yet, but Twill is built on a runtime agnostic layer (see https://github.com/TwillAI/agentbox-sdk) so it is feasible!
I believe there is a difference between an open source framework and a product. You would still have to manage and scale your infra, build the integration layer around it to make it accessible where your teams are, fix bugs etc...
I am not saying that build is always the bad choice, but the tradeoff did not disappear imo
Cowork does not seem to be focused on engineering, but we are fully expecting Anthropic to catch up in this category.
What Anthropic can't offer is to let you use Codex or combine it with Claude Code. That is why we think non ai-labs players have a say in this market.
To your last point, as always there is a buy vs build tradeoff which ultimately comes down to focusing on your core business which we think still remains important in the ai era
I agree and that is why I think monorepos are making a comeback.
That said, there are workarounds, like cloning all repos and enabling LSP (coding CLIs added that feature) or using a dedicated solution for codebase indexing and add a skill/mcp.
Super fast models spamming grep commands are also fun to watch!
Yes, the difference is that Twill launches dedicated infra on each sandbox for each task. This means you can work on multiple tasks requiring a DB migration for instance.
Also you can fire and forget tasks (my favorite) and don't have to keep your laptop running at night.