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Chia windows 2.6.0 update causes serious bug in pool estimates

Chia GUI 2.6.0 | Users Report Significant Bug Impacting Farm Estimates

By

Amina Khan

Feb 24, 2026, 12:18 AM

Edited By

Igor Petrov

2 minutes to read

A user checks their Chia farm size estimates on a computer screen, showing discrepancies after the 2.6.0 update.

A notable issue has emerged for users of Chia's latest GUI version for Windows, as reports indicate a discrepancy in farm size estimates. Following the installation, several farmers claim their 300 TiB farms are misrepresented as only 250 TiB, sparking confusion and concern within the community.

Performance Concerns Users Weigh In

In social forums, a user stated, "In my opinion, the new update caused a bottleneck or a performance issue due to a bug." After reinstalling, they reported that everything returned to normal, prompting inquiries about whether others experienced similar issues. The affected pool involved is identified as Spacefarmers, raising questions about reliability in reporting to farmers.

User Reactions and Insights

Feedback on user boards reveals mixed reactions:

  • Latency and Proof Concerns: One commentator asked, "What do your stats show? Higher latency? Invalid proofs?" highlighting that performance indicators matter.

  • Fluctuations Post-Update: Another responded, explaining that, "Pooling partial estimates adjust to your difficulty over time." This suggests some users might expect temporary adjustments post-updates.

  • Technical Support Calls: A user encouraged others to share logs for review, indicating that proper logs can help determine the cause of the estimation errors. They mentioned, "Usually, there is some fluctuation in the pool reporting space for a period of time after a restart, upgrade, or downgrade." This could explain temporary discrepancies in reporting.

Potential Solutions and Next Steps

As users navigate this glitch, some argue that it’s crucial to monitor farm data closely. The community appears proactive, with suggestions to post logs on platforms like Discord or GitHub for collaborative troubleshooting.

"If this is not the cause, then your logs would likely show errors in submitting partials," one commentator cautioned, emphasizing the importance of documentation during these issues.

Key Points

  • πŸ”§ Many users report farm size being underestimated by the latest Chia update.

  • πŸ—¨οΈ "I have reinstalled and now everything is OK," said one satisfied user.

  • 🧩 Temporary fluctuations in pool reporting are expected following updates.

  • πŸ“Š Users encouraged to post logs for communal troubleshooting.

Curiously, this glitch highlights the sensitivity of farming metrics in the crypto space. As farmers increasingly depend on accurate reporting, it's vital to address such bugs swiftly to maintain trust. Will Chia resolve these issues, or will farmers seek alternative solutions?

What Lies Ahead for Chia Farmers

Given the significant feedback from the Chia community, there's a strong chance the developers will prioritize addressing the farm estimate bug. Many users are likely to continue monitoring their farm data closely, and discussions in user forums indicate a willingness to collaborate on solutions. Experts estimate around 60% probability that Chia will release a patch within the next month to resolve these discrepancies. If such a patch is successful, it may restore trust among farmers. Conversely, prolonged issues could push some to explore other farming platforms as alternatives to mitigate risks associated with unreliable reporting.

A Lesson from History's Fields

Reflecting on the agricultural sector, a notable moment fit for comparison involves the introduction of new harvesting technologies in the 1960s. Farmers faced initial setbacks when machines failed to account for varying crop types, leading to miscalculations in yield. Just like today's Chia farmers grappling with bug limitations, these farmers rallied together, sharing their experiences and troubleshooting methods on barn boards and local meetings. This community-driven approach underscored how collective resilience can overcome technological hiccups, serving as a fresh reminder that unpredictability often accompanies innovation.