Tinylytics
Tinylytics adds account-wide IP ignores and monitoring-only sites
A side-by-side editorial comparison of Count and DebugBear — release velocity, themes, recent moves, and the top alternatives to consider.
Count's agent now shows the chain of cells and assumptions behind every answer it gives.
Count is a data-canvas analytics tool organized around an AI agent. Its late-October release added Reasoning Chains, which link each claim and chart in an agent answer back to the cells, sources and assumptions behind it. Over the summer it rebuilt the agent with web data and auto layout, moved Python to a server sandbox with any PyPI package, and added longer agent conversations, a faster empty-canvas start and catalog views that stay populated while they refresh.
DebugBear is growing from performance testing into uptime, browser support and ad-impact data.
Seven monthly digests show a tool widening on several fronts. August added uptime monitoring, Server Timing in lab and RUM, and URL-based conversion triggers. September added a Browsers tab showing which Baseline web features visitors' browsers support, CrUX Ad Insights metrics, and more custom dashboard work. Earlier months brought a bottleneck finder, HAR exports, custom dashboards and an MCP server for Claude, ChatGPT and Cursor.
Count is a data-canvas analytics tool organized around an AI agent. Its late-October release added Reasoning Chains, which link each claim and chart in an agent answer back to the cells, sources and assumptions behind it. Over the summer it rebuilt the agent with web data and auto layout, moved Python to a server sandbox with any PyPI package, and added longer agent conversations, a faster empty-canvas start and catalog views that stay populated while they refresh.
Count is making the agent the main way to work in the canvas, then adding the checks that let people trust its output. Reasoning Chains and server-side Python with managed secrets point at analysis that can be audited and run on real data. Steady fix lists for agent edits show the agent is still being hardened.
Expect more ways to inspect and correct agent work, building on Reasoning Chains. The entries don't show whether chains will extend to Slack answers.
Seven monthly digests show a tool widening on several fronts. August added uptime monitoring, Server Timing in lab and RUM, and URL-based conversion triggers. September added a Browsers tab showing which Baseline web features visitors' browsers support, CrUX Ad Insights metrics, and more custom dashboard work. Earlier months brought a bottleneck finder, HAR exports, custom dashboards and an MCP server for Claude, ChatGPT and Cursor.
DebugBear is moving from 'is my page fast' to a wider view of who visits and what slows them down: availability, browser capabilities, and now the cost of ads. Custom dashboards are the surface that ties those data sets together, and they get work almost every month.
Expect October's digest to add more RUM breakdowns or dashboard widgets built on the new browser and ad data, following the monthly pattern.
Other Analytics 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 Count or DebugBear.
Tinylytics adds account-wide IP ignores and monitoring-only sites
OpenReplay lets self-hosters block the hosts a recorded session can make its asset cacher fetch.
OpenHouse records why table maintenance fails and adds timezone-aware retention.
OpenCTI accepts Filigran licenses and closes a draft-access hole in its TAXII routes.
InfluxDB keeps patching its 3.11 and 3.12 lines, now with a catalog repair tool.
Basedash adds verified dashboards so teams know which numbers to trust.
See all Count alternatives → · See all DebugBear alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Count is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Count is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top Count alternatives in Analytics are ranked by recent ship velocity. Browse the "Count alternatives" section above for the current picks, or visit /alternatives/count for the full list with editorial commentary on each.
Top DebugBear alternatives in Analytics are ranked by recent ship velocity. Browse the "DebugBear alternatives" section above for the current picks, or visit /alternatives/debugbear for the full list with editorial commentary on each.