Feedly vs Hex
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.
Hex is remaking its notebook into an agent that both uses and plugs into MCP
Hex is converting its analytics notebook into an AI agent platform. It now runs as an MCP client, is invocable from Codex, and ships generative data apps built from prompts, while keeping its model roster current with Kimi K2.7 and Fable 5 and giving admins default-model and branding controls. Integration and governance work — a Figma connector, AWS IAM-role support, signed embedding — rounds out the core.
The arc points at Hex as connective agent infrastructure: consuming external context and tools via MCP, distributing itself into other agent surfaces like Codex, and letting analysts assemble apps and dashboards from prompts. Expect the agent, rather than the notebook grid, to become the primary interface, with model choice and governance layered on top.
Likely next steps deepen the agent's tool-use over MCP connections and push generative apps further toward production embedding and governance controls.
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