Stirling-PDF vs Speakeasy
Side-by-side trajectory, velocity, and editorial themes.
Stirling-PDF deepens real signing and lays MCP groundwork on a fast V2 cadence
Stirling-PDF is in a heavy release cadence on its V2 line, alternating substantive minors with rapid hotfixes. The last month shipped desktop hardware-token signing (PKCS#11/smart-card and OS cert stores), a work-in-progress MCP page, JDK 25 enforcement, and large merge-memory improvements, punctuated by Postgres and desktop-signing hotfixes.
Two threads stand out: deepening real document signing (hardware tokens, shared signing, cert-store integration) and laying agent-facing groundwork via an MCP page and automation-oriented backend work. The team is also steadily reworking the V2 file-management UI that users found clunky. This is a self-hosted PDF tool maturing toward serious signing and automation use.
Expect the MCP page to move from WIP toward a usable agent interface, and the signing feature set to broaden beyond desktop-only.
Speakeasy's Gram is building the governance layer for enterprise AI-coding agents
Speakeasy's platform (Gram, plus the Elements line) governs and observes AI coding agents — Claude Code, Codex, Cursor — across an organization. The recent cadence is fast and dense: prompt-guardrail evaluation, risk policies (including flagging personal versus corporate AI accounts), RBAC scopes for who can read whose agent sessions, shadow-MCP enforcement, per-provider cost and usage breakdowns, and OAuth/CIMD plumbing for strict identity providers. Claude Sonnet 5 is now the default in-app model.
Speakeasy is racing to become the control plane for AI-agent usage in the enterprise: not just connecting agents to tools via MCP, but proving guardrails work before enforcing them, detecting shadow and personal-account usage, attributing cost by provider, and auditing who read which session. The v0.81.0 evaluation workbench — replaying real transcripts through a policy with saved regression sets — signals a shift from static policies to tested, regression-guarded ones. Governance rigor, not raw feature count, is the differentiator being built.
Expect deeper policy tooling (more evaluation, regression, and sensitivity controls), broader provider and account-type visibility, and continued MCP-governance hardening as more coding agents enter the enterprise.
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