ggpointless
ggpointless keeps adding the ggplot2 layers nobody else bothered to write.
Comparison · Infra & APIs
Tailscale vs tf
A side-by-side editorial comparison of Tailscale and tf — release velocity, themes, recent moves, and the top alternatives to consider.
Tailscale vs tf: at a glance
| Feature | Tailscale | tf |
|---|---|---|
| Sector | Infra & APIs | Infra & APIs |
| Velocity score | 6.3 | 0.0 |
| Sparks · 30d | 0 | 0 |
| Top themes | networking, scale, api, kubernetes | functional-data-analysis, vctrs, multivariate, r-packages |
| Last editorial update | 4h ago | 50m ago |
| Website | — | Visit → |
What is Tailscale?
Tailscale is paying down scale in two dimensions: nodes per tailnet, tailnets per org.
Three threads run through this window. The tailnet management API is the newest: creation landed in alpha in late July, and the list endpoint now paginates at 100 results with limit and cursor parameters. The client releases are patch-grade but weighted toward scale — v1.102.1 made node additions and removals constant-time, and v1.102.3 fixes Tailnet Lock startup failures on large tailnets while cutting memory use on iOS and tvOS. The Kubernetes operator runs on its own track, adding in-cluster PeerRelays, workload identity federation and IPv6 egress.
What is tf?
tf gave functional data a second dimension: curves whose values are vectors.
tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.
Tailscale vs tf: editorial side-by-side
Tailscale
6.3INFRA · APIS
Tailscale is paying down scale in two dimensions: nodes per tailnet, tailnets per org.
◆ Current state
Three threads run through this window. The tailnet management API is the newest: creation landed in alpha in late July, and the list endpoint now paginates at 100 results with limit and cursor parameters. The client releases are patch-grade but weighted toward scale — v1.102.1 made node additions and removals constant-time, and v1.102.3 fixes Tailnet Lock startup failures on large tailnets while cutting memory use on iOS and tvOS. The Kubernetes operator runs on its own track, adding in-cluster PeerRelays, workload identity federation and IPv6 egress.
◆ Where it's heading
The qualifier that keeps recurring is “large”: tailnets big enough to break Tailnet Lock at startup, node churn that pinned CPU, mobile clients running short of memory, and organizations holding more than a hundred tailnets. Tailscale is absorbing the cost of customers who outgrew the shape the product originally assumed, in two directions at once — nodes inside a tailnet, and tailnets inside an organization. The second is the more consequential, because allocating a tailnet per customer or per environment is a different product than a company network. Security work stays continuous alongside it, with TS-2026-011 closed here and a run of SSH and Serve advisories backported the month before.
◆ Prediction
The tailnet creation API should leave alpha carrying the same limit-and-cursor contract just applied to the list endpoint, with further startup and memory work aimed at large tailnets on the client side.
tf
0.0INFRA · APIS
tf gave functional data a second dimension: curves whose values are vectors.
◆ Current state
tf supplies the vector classes underneath the tidyfun stack — tfd for raw functional observations, tfb for basis-represented ones, both built on vctrs so curves sit in a data frame column and behave like any other vector. Until July that codomain was scalar. The 0.5.0 release adds tfd_mv and tfb_mv, classes for functions whose values are vectors in R^d, and rebuilds the analysis verbs to match.
◆ Where it's heading
The package is widening what a functional observation can be, then porting the toolkit onto it. Registration arrived first in 0.4.0 for univariate curves and immediately gained an srvf_mv method for aligning components jointly, and tfb_mfpc() ports principal component analysis to the multivariate case with a single set of scores shared across components. Alongside that runs steady dependency shedding — mvtnorm and pracma both replaced by inlined samplers that reproduce prior draws bit-for-bit, glue dropped for cli in the previous release — and an unusually long tail of NA-handling and edge-case fixes, several caught in pre-release review of the new classes.
◆ Prediction
The new classes ship with FPCA, registration and shape alignment but the release notes describe tidyfun::tf_unnest() as the consumer of one new export, so the visible next step is the rest of the tidyfun stack catching up to vector-valued columns. Expect follow-up patches on the vctrs casting paths, which is where most of this release's late fixes clustered.
Alternatives to Tailscale and tf
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 Tailscale or tf.
mpactr
mpactr spent two spring releases normalizing case in metadata after users kept tripping on it.
surveytidy
surveytidy taught every dplyr verb to operate on a whole collection of surveys at once.
surveycore
surveycore declared its API stable with every survey design type covered.
PEIMAN2
PEIMAN2 cut its annotation database loose from its release cycle without breaking CRAN.
prospectr
prospectr spent its biggest release in years fixing spectra it had been quietly mangling.
See all Tailscale alternatives → · See all tf alternatives →
Recent activity from Tailscale and tf
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1d agoTailscalev1.102.3 patches a 4via6 routing flaw and large-tailnet startups
- 2d agoTailscaleTailnet list API pagination
- 9d agoTailscaleOperator adds in-cluster PeerRelays and workload identity federation
- 13d agoTailscaleContainer image v1.102.2: library updates only
- 16d agoTailscalev1.102.2 fixes a Funnel incoming-connection regression
- 17d agoTailscalev1.102.1 adds Services CLI and constant-time node churn
- 1mo agotfVector-valued functional data becomes a first-class type ⚡
- 5mo agotfCurve registration, five depth measures and sub-domain splitting
- 2y agotfFix: tf_crosscov normalization
Frequently asked questions
What is the difference between Tailscale and tf?
They serve adjacent needs but don't currently overlap on shipped themes. Tailscale is currently shipping more aggressively (velocity 6.3 vs 0.0), with 0 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 Tailscale better than tf?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Tailscale is currently shipping more aggressively (velocity 6.3 vs 0.0), with 0 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 Tailscale?
Top Tailscale alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "Tailscale alternatives" section above for the current picks, or visit /alternatives/tailscale for the full list with editorial commentary on each.
What are the best alternatives to tf?
Top tf alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "tf alternatives" section above for the current picks, or visit /alternatives/tf for the full list with editorial commentary on each.