ToolJet
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Comparison · Infra & APIs
Cursor vs ddml
A side-by-side editorial comparison of Cursor and ddml — release velocity, themes, recent moves, and the top alternatives to consider.
Cursor vs ddml: at a glance
| Feature | Cursor | ddml |
|---|---|---|
| Sector | Infra & APIs | Infra & APIs |
| Velocity score | 8.8 | 0.0 |
| Sparks · 30d | 3 | 0 |
| Top themes | ai-agents, autonomous-agents, event-driven, cloud-agents | causal-inference, machine-learning, econometrics, stacking |
| Last editorial update | 5h ago | 8h ago |
| Website | — | Visit → |
What is Cursor?
Cursor's agents stop waiting to be asked - they subscribe, and they hold a goal until it's done.
Cursor has spent two months moving agents out of the editor: cloud agents on iPhone and iPad, in Slack, on schedules, a team marketplace, a router picking the model per request, and Origin hosting repos and pull requests inside the product. This release changes how those agents are started. Cloud agents can subscribe to an event source - a PR, a Slack thread, a schedule - and wake when something happens, and /goal gives one a long-lived objective it works toward until complete. Subagents now get their own virtual machines with isolated project copies.
What is ddml?
Double machine learning in R keeps adding estimands and the inference to go with them.
ddml implements double and debiased machine learning estimators, with a stacking layer so the nuisance functions can be fit by an ensemble rather than a single learner. The estimand list has grown from partially linear models to average treatment effects, treatment effects on the treated, and local average treatment effects, and 0.3.0 added one-way clustered inference. The most recent release is maintenance: xgboost syntax, glmnet binomial predictions, weights in the flexible partially linear IV estimator.
Cursor vs ddml: editorial side-by-side
Cursor
8.8INFRA · APIS
Cursor's agents stop waiting to be asked - they subscribe, and they hold a goal until it's done.
◆ Current state
Cursor has spent two months moving agents out of the editor: cloud agents on iPhone and iPad, in Slack, on schedules, a team marketplace, a router picking the model per request, and Origin hosting repos and pull requests inside the product. This release changes how those agents are started. Cloud agents can subscribe to an event source - a PR, a Slack thread, a schedule - and wake when something happens, and /goal gives one a long-lived objective it works toward until complete. Subagents now get their own virtual machines with isolated project copies.
◆ Where it's heading
The through-line has been removing external dependencies and wait states; this release removes the human from the trigger. Agents that Cursor created now subscribe to their own pull requests and drive them to completion, fixing CI and answering bot comments unprompted. Isolated per-subagent VMs are what make that safe to parallelize - swarms can work without colliding - and steering lets a person redirect a running agent at the next tool call rather than interrupting it. Cursor is building the always-on case rather than the faster-autocomplete one.
◆ Prediction
With subscriptions limited to cloud agents for now, the obvious next step is bringing event-triggered runs to local agents, along with the controls an always-on fleet needs - spend limits, approval gates, and a way to review what ran while nobody was watching.
ddml
0.0INFRA · APIS
Double machine learning in R keeps adding estimands and the inference to go with them.
◆ Current state
ddml implements double and debiased machine learning estimators, with a stacking layer so the nuisance functions can be fit by an ensemble rather than a single learner. The estimand list has grown from partially linear models to average treatment effects, treatment effects on the treated, and local average treatment effects, and 0.3.0 added one-way clustered inference. The most recent release is maintenance: xgboost syntax, glmnet binomial predictions, weights in the flexible partially linear IV estimator.
◆ Where it's heading
Two lines of work run in parallel. One extends what can be estimated, the other makes the estimates trustworthy under real data conditions, and the second is where the recent effort has gone: clustered standard errors, propensity score trimming, higher default fold counts, corrected ATE and LATE scores. Raising sample_folds and cv_folds to ten is a small change with a clear intent, trading compute for stability.
◆ Prediction
Clustered inference arrived one-way; two-way and multi-way clustering are the obvious continuation. The stacking layer is also accumulating edge-case handling, so expect more work on degenerate ensemble weights.
Alternatives to Cursor and ddml
Other Infra & APIs products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either Cursor or ddml.
Honeycomb
Canvas agents gain memory, and onboarding moves into the editor
GitHub
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authentik
authentik 2026.8 ships: Actors, domain-joined Agents, and a push past browser-mediated SSO
Rancher
Rancher's public feed is a build-tag stream: three branches bumped the same Go image on one afternoon
Buildkite
Buildkite keeps converting hand-rolled agent workarounds into first-class CI primitives.
Recent activity from Cursor and ddml
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1d agoCursorCloud agents subscribe to events and hold goals on their own ⚡
- 3d agoCursorCursor starts hosting repos and pull requests ⚡
- 7d agoCursorCloud agents boot from prebuilt environment snapshots
- 22d agoCursorCursor for iPad adds full PR review and an agent inbox
- 29d agoCursorAuto mode moves to Cursor Router with cost/intelligence modes ⚡
- 1mo agoCursorCursor in Slack plans first and works across multiple repos
- 8mo agoddmlFixes for weighted FPLIV, binomial glmnet and empty stacking weights
- 1y agoddmlOne-way clustered inference and higher default fold counts
- 2y agoddmlPropensity score trimming added across the treatment effect estimators
- 2y agoddmlFixes permuted residuals returned by crossval
- 2y agoddmlATT and LATE estimators join the supported estimands
Frequently asked questions
What is the difference between Cursor and ddml?
They serve adjacent needs but don't currently overlap on shipped themes. Cursor is currently shipping more aggressively (velocity 8.8 vs 0.0), with 3 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is Cursor better than ddml?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Cursor is currently shipping more aggressively (velocity 8.8 vs 0.0), with 3 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
What are the best alternatives to Cursor?
Top Cursor alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "Cursor alternatives" section above for the current picks, or visit /alternatives/cursor for the full list with editorial commentary on each.
What are the best alternatives to ddml?
Top ddml alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "ddml alternatives" section above for the current picks, or visit /alternatives/ddml for the full list with editorial commentary on each.