Superlog (YC P26) – Observability that installs itself and fixes bugs

Hey HN, we’re Nico and Arseniy, co-founders of Superlog (https://superlog.sh). We're building a self-installing, self healing observability tool meant not to be opened. It has a wizard that daily sets up proper logging and an agent that investigates errors and opens PRs.Super short demo: https://www.youtube.com/watch?v=xFhU9Mk247M.In our earlier startups, we tried Sentry, Datadog, Grafana, Dash0, and nothing was good enough. Proper telemetry and alerting still requires a ton of manual setup. We struggled with adding good logs, so debugging was tough, especially as codebases grow at a faster pace. Meanwhile, the Datadog/Dash0 bill kept climbing, and we still spent engineering hours to learn, configure, and maintain our observability tooling.With Sentry, we found ourselves flooded by a stream of alerts into our Slack channel, most were duplicates or lacked context, so alert fatigue/constant interrupts were a real pain. The #ops notification is consistently the worst feeling on a Saturday morningWe’ve seen too many times servers run out of memory and disk, and three AWS metrics giving us three different values. Half of the graphs on dashboards are normally empty or outdated, and manually clicking through UIs, especially when the team is small, seems like a huge waste of time.At some point we realized that solving this problem would be more valuable than the things we had been working on, and we had the expertise to do it, since Arseniy had spent years at Datadog, getting paged during the night to debug production incidents. So we decided to build a platform that would just work: agent-first, MCP-native, zero-setup.Here’s how Superlog works: we have a wizard that scans your repo, and automatically instruments it with well-structured logs, traces and metrics via OpenTelemetry. We make sure to highlight main failure modes, endpoint performance, usage per tenant, and LLM/upstream cost (by callsite, tenant and model).Errors get

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BUILD

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Auto-instrumenting observability platform that eliminates setup friction and alert fatigue by intelligently logging, detecting, and fixing bugs without human intervention.

The pain

Engineering teams waste hundreds of hours annually on observability setup, maintenance, and alert triage. Existing tools (Datadog, Sentry, Grafana) require extensive manual configuration, generate alert fatigue, produce incomplete insights, and scale poorly with team size. Production incidents remain hard to debug despite expensive tooling.

The gap

No observability platform today combines: (1) zero-setup auto-instrumentation, (2) intelligent error context aggregation, (3) automated PR generation for fixes, and (4) cost-aware monitoring. Teams choose between powerful-but-complex (Datadog) or simple-but-limited (Sentry).

Build angle

Agent-first, MCP-native architecture that treats observability as a solved problem rather than a dashboard to manage. The wizard handles instrumentation; the agent handles investigation and remediation. Positioning as 'observability you never open' directly inverts the category's UX paradigm.

Strengths

  • Founder expertise: Arseniy's Datadog pedigree provides deep domain knowledge and credibility
  • YC P26 validation: Already accepted to top accelerator, reducing execution risk
  • Acute pain point: Alert fatigue and setup burden are universally acknowledged problems
  • Defensible moat: Auto-instrumentation + PR generation creates switching costs and network effects
  • Timing: LLM agents + MCP make autonomous debugging feasible for first time
  • TAM expansion: Can upsell into incident response, cost optimization, and compliance

Risks

  • Execution complexity: Auto-instrumentation quality varies by language/framework; poor suggestions damage trust
  • Competitive response: Datadog/New Relic will rapidly copy auto-instrumentation features
  • Enterprise sales friction: Autonomous PR generation may face security/governance objections
  • False positive rate: If agent opens bad PRs, product becomes liability rather than helper
  • Integration breadth: Requires deep support for multiple languages, frameworks, and deployment models
  • Pricing model unclear: How to monetize when positioning as 'set and forget'? Risk of commoditization

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