July 2026 monthly summary for zama-ai/kms: Implemented security hardening of the KMS Helm chart by adopting Chainguard utility images and updated registry paths, ensuring automatic patching for sidecar containers and aligning with security standards. Commit 621cbc3f80b5259b2448eff1e9cc0e8a8a664f95. Impact: strengthened supply-chain security, reduced maintenance overhead, and improved deployment safety. No critical bugs reported fixed this month.

June 2026: Implemented KMS Connector enhancements with multi-network support and security hardening. Delivered per-network presets for contract addresses and chain IDs, enabling flexible multi-network deployments and improved maintainability. Updated security posture with Polygon testnet ACL address and related access controls. Applied quality and stability improvements including DRY refactor of kms-connector charts, integer rendering for host_chains chainId, and host URL env vars for the kms-connector worker. Documented network presets to facilitate future deployments. Collaborative effort with co-authored contributions.

May 2026 monthly summary for zama-ai/fhevm focusing on feature delivery, code quality, and developer enablement. What was delivered: - SQL Exporter Chart Integration: Migrated the chart to burningalchemist/sql_exporter, ported the queries to its config format, and updated deployment/config workflows to support the new exporter. Committed as c2d1a202aa5fea04c34ba62780b1a1e33a307f00, with co-authorship noted for Fred. - Documentation and local development readiness: Added a local-run README to simplify local data export and testing, and updated the coprocessor-sql-exporter README to align with the new exporter changes. Major bugs fixed: - None reported this month; effort focused on feature migration and documentation to reduce future blockers. Impact and accomplishments: - Strengthened data-export reliability and observability by aligning with the latest sql_exporter tooling, enabling consistent data exports for analytics and debugging. - Reduced onboarding time for new contributors through clearer local setup instructions and up-to-date documentation. - Improved maintainability of the FHEVM chart integration by standardizing configuration formats and README-based guidance. Technologies/skills demonstrated: - SQL Exporter integration, chart migration, and configuration format adaptation - Documentation best practices, local-development optimization, and cross-team collaboration

December 2025 (2025-12) - Monthly developer summary for zama-ai/fhevm. Focused on delivering cross-region reliability, deployment readiness, and observability improvements. Key features delivered include anonymous AWS S3 access for gw-listener, kms-connector chart upgrades with DB migrations alignment, Hardhat mainnet testing configuration, and Coprocessor SQL Exporter chart introduction. Impact: improved cross-region file fetch robustness, smoother deployments, expanded test coverage for mainnet scenarios, and enhanced monitoring via Prometheus.

Month: 2025-11 — Delivered security-focused enhancements in the zama-ai/fhevm project by enabling AWS KMS signing for the Transaction Sender via the kms-connector. This feature adds Helm chart configuration options and required parameters to enable AWS KMS-based signing, improving key management security and enterprise readiness.

October 2025: Delivered Helm chart enhancements for contract deployment in the fhevm repository to improve deployment portability and flexibility. Removed absolute path dependencies when discovering environment files, introduced a configurable contracts persistence mountPath, and updated Chart.yaml version along with related workflow checks to align release processes. These changes reduce deployment fragility, enable environment-agnostic deployments, and set the stage for smoother future updates.

September 2025 monthly summary focused on delivering configurable and reliable helm-chart based features across the kms (zama-ai/kms) and fhevm (zama-ai/fhevm) repositories. The work emphasized improving deployment flexibility, telemetry configuration, and chart consistency, while advancing reliability for deployment pipelines and ArgoCD compatibility. Key outcomes include feature deliveries, chart refactors, and CI/OCI publishing improvements, all aimed at business value through easier deployments, better observability, and reduced operational risk.

August 2025 monthly summary for zama-ai/kms and zama-ai/fhevm focusing on delivering deployment reliability, flexibility, and multi-contract support through Helm-chart enhancements and targeted bug fixes. Key outcomes include improved deployment configurability for KMS (init/gen-key timeout and resource specs) with a core chart version upgrade, safer AWS KMS env handling when AWS KMS is disabled, multi-contract deployment support via environment-file parsing in FHEVM charts, and a new custom kms-connector DB migration command. Additional robustness improvements cover edge-case env-file parsing (single-line files) and contract chart version/configmap updates. These changes collectively reduce deployment risk, enable scalable configurations, and accelerate time-to-value for customers using KMS and FHEVM.

July 2025 monthly summary focusing on feature delivery and operational improvements across multiple repositories. Key work centered on increasing configurability, improving deployment usability, and reducing operational friction for KMS-related components. No explicit major bug fixes were recorded in the provided data for this month.

June 2025 monthly summary for zama-ai/fhevm: Delivered host contract upgrade support within Helm deployments, enabling upgrade of host contracts via Helm charts with a dedicated upgrade script and adjusted deployment values to cover both deployment and upgrade scenarios. This work reduces deployment downtime and improves lifecycle management for host contracts in production environments.

Concise monthly summary for 2025-05 covering work across fhevm and kms repositories. Focused on feature delivery, reliability improvements, and security/operational enhancements that drive deployment confidence, faster testing feedback loops, and clearer configuration management.

April 2025 monthly summary for zama-ai/fhevm focusing on deployment reliability, configurability, and developer tooling. Delivered Helm and Kubernetes improvements across httpz-sc-deploy, fhevm, and coprocessor charts, added persistent storage for artifacts, introduced a dedicated debug environment, and enhanced verification workflows on the development network.

March 2025 focused on stabilizing KMS deployments, simplifying configuration management, and tightening governance for httpz components, all while supporting the kms-core migration path. Delivered three feature clusters in zama-ai/fhevm: (1) KMS deployment port configuration alignment and reliability; (2) KMS Helm charts maintenance and cleanup; (3) httpz deployment versioning and service account enhancements. The changes improve deployment reliability, reduce configuration drift, and enable smoother migrations, with explicit commits aligning to init_enclave.sh, deprecated-chart removal, and enhanced deployments.

February 2025 monthly summary for zama-ai/fhevm focusing on kms-service work across features, configs, and reliability improvements. Delivered configurable enablement of kms-core and kms-connector, env loading from Kubernetes ConfigMaps, a new decryption mode, and encryption for private vaults using AWS KMS. Extended nitro init-containers with a peers list and enhanced enclave environment handling via a .env file and envsubst. Reorganized telemetry/tracing into a dedicated block, improved container stability with stable names, and added readiness/startup probes for kms-core, boosting reliability and observability. Addressed critical fixes around vault/config logic, port management, envFrom handling, and templating resilience to reduce deployment risks and accelerate safe rollouts.

January 2025 (2025-01) highlights for zama-ai/fhevm: Established automated release readiness and KMS deployment capabilities through CI/CD and Helm-based scaffolding, with robust infrastructure for scalable Kubernetes deployments. Delivered KMS deployment infra via Helm charts with configurable peers, and implemented stability fixes across initialization, simulator behavior, and service suffix handling to improve reliability and time-to-value.