Feedly vs Lightdash
Side-by-side trajectory, velocity, and editorial themes.
Feedly's cyber-threat-intelligence engine grows through steady coverage and enrichment additions.
Feedly has settled firmly into cyber and market threat intelligence, shipping a biweekly changelog aimed at CTI and analyst teams. Recent releases add analyst-usable output (Suricata detection rules pulled straight from Insights Cards), broader vulnerability and exploit coverage (Oracle and Atlassian advisories, exploit-type tracking), and third-party enrichment (GreyNoise, VirusTotal, Analyst1), alongside a smarter Insider Threats AI model and an Ask AI Research Playground for evaluators.
The arc is deepening the intelligence graph and making its output directly operational: more sources and advisories feeding the model, richer IoC context via enrichment integrations, and AI features (Ask AI, Cyberattack Agent, insider-threat models) that sit on top of that data. The feed also carries near-duplicate entries for the same release, a crawl artifact rather than shipping cadence.
Expect continued coverage expansion (more advisory sources, enrichment partners) and incremental AI-research tooling on the biweekly cadence, with no single directional pivot signaled in these entries.
Lightdash is turning the analyst's prompt into the primary way to build BI
Lightdash is pushing hard on AI-native BI. Its data apps now generate reusable chart types from a plain-language prompt, verified content has gone GA and merged with the AI-agent and MCP layer, and AI-written summaries are appearing in scheduled deliveries. Alongside that, steady core work continues on SQL parameters, chart layouts, and enterprise controls like user impersonation.
The clear direction is a prompt-driven analytics surface backed by a trusted-content layer that external agents like Claude and Cursor can query through MCP. Expect the 'describe it and Lightdash builds it' pattern to spread from chart types into more of the modeling and dashboard workflow, with verification as the guardrail that keeps agent answers trustworthy.
The next moves likely push prompt-to-artifact generation deeper into dashboards and the semantic model, and expand what the MCP and verified-content layer exposes to external agents.
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