Gyeeta – An Open Source and Free Observability Tool

Hacker News

Hello Everyone, We are excited to announce the public release of Gyeeta - https://gyeeta.io Gyeeta is a free, eBPF based Open Source (GPLv3) Observability tool which provides the following capabilities : - Service Level Statistics such as Queries/sec (Requests/sec), Response Times (Latency) and HTTP Errors (if HTTP based) with no manual inputs or integrations. Monitors binary / proprietary network protocol or non HTTP Service statistics as well. - Service Maps, Process and Host level Network Flows with info on all Services and Processes. - Detection of Host and Process Level CPU starvation, Virtual Memory or IO Bottlenecks. - Monitor all applications without any instrumentation or tapping irrespective of the programming language used. - Self Learning Algorithms that can detect Anomalies, Contention or Degradation without any manual inputs. - Advanced Cluster, Service or Process Level Alerts using a powerful Web UI or REST APIs. - All Data In-House (On Prem). Not a SaaS tool. - All Linux Kernels released since 2016 supported (Linux Kernels v4.4.x or higher). Gyeeta is optimized (C++ based) for minimal CPU and Memory requirements. Website : https://gyeeta.io Github link https://github.com/Gyeeta/gyeeta

Visit product View on Hacker NewsMar 23, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, ide · Missing: https docs, just released, exist

84%84% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntOn track for Day 1 leaderboard · Strong signals: using, apis, open · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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