The September Swarm Community Call highlighted major progress toward the upcoming Bee 2.7 release, an update on incentive system research, and ecosystem presentations, including a demo of the new File Manager and MetaProvide’s introduction of IPSH (Interplanetary Swarm Hive).
Core Development: Final Stretch Toward Bee 2.7 Release
Bee 2.7 is approaching release readiness, reported Niki Papadatou, and includes a strong mix of performance improvements and new features:
- Erasure coding enhancements strengthen data redundancy and recovery, improving resilience and chunk reassembly.
- Legacy payload support has been improved, making compatibility with older formats more explicit.
- Multiple underlay address support enables nodes to announce and connect across multiple interfaces—a critical step for in-browser node development.
- AutoTLS is being finalized to ensure in-browser nodes can establish secure connections automatically.
- Pin eviction bug fix improves the protection of pinned content from garbage collection.
- Refined pull-sync logic is under review, promising more consistent and performant syncing.
- SOC replica support introduces more robust chunk replication, further boosting reliability.
Alongside release preparations, the team continues research into identity management, i.e. handling multiple personas in a decentralized context. Performance testing is also underway to measure the impact of checkbook funding on upload/download speeds.
As Callum Toner explained, this is critical to the ongoing research into Swarm’s incentive systems, with a particular focus on bandwidth incentives. The current system relies on a combination of time-based settlement (free forwarding allowance) and checkbook-based settlement (paid bandwidth). Early findings suggest that enabling checkbook payments may introduce performance overhead due to additional signatures and steps.
The research team is benchmarking various scenarios—file sizes, erasure code levels, and network conditions—to pinpoint optimisation opportunities. The long-term goal is to ensure robust, performant, and competitive incentive systems, possibly including adjustments to staking mechanisms.
Community feedback and anecdotal performance data remain important inputs to this process.