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This week I'm launching a new series: "Observability from Zero to Hero" Over the next 7 posts, we'll cover: • Monitoring vs Observability • Metrics, Logs & Traces • Incident Investigation • Observability Maturity Models • Golden Signals, RED & USE • AI Observability • Designing a Modern Observability Platform

Jaydeep

30,470 subscribers

33,488 次观看 • 1 个月前 •via X (Twitter)

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Day 4 in Observability Zero to Hero We look at How Observability Reduces MTTR(Mean Time to Resolution) I explain in this one 1. Production Incident Investigation Explained 2. Observability Maturity Models

Day 4 in Observability Zero to Hero We look at How Observability Reduces MTTR(Mean Time to Resolution) I explain in this one 1. Production Incident Investigation Explained 2. Observability Maturity Models

Jaydeep

17,644 次观看 • 1 个月前

We raised $8m to build self-healing software. In 2026, software moves fast. But monitoring and observability are still manual and slow. Sazabi is a next-generation observability platform for fast-moving engineering teams. Not another AI SRE. Not another LLM observability tool. A new, general solution to observability that works for any workload (including agents) and leverages AI for maximum speed and automation. Time to suit up.

We raised $8m to build self-healing software. In 2026, software moves fast. But monitoring and observability are still manual and slow. Sazabi is a next-generation observability platform for fast-moving engineering teams. Not another AI SRE. Not another LLM observability tool. A new, general solution to observability that works for any workload (including agents) and leverages AI for maximum speed and automation. Time to suit up.

Sherwood

1,302,912 次观看 • 1 个月前

This week metrics and logs join traces in Mastra Studio. • Logs: every event, searchable with full context • Traces: debug any run, end to end • Metrics: cost, latency, errors, and scores at a glance With all three pillars of observability in one place, you can now see the whole picture.

This week metrics and logs join traces in Mastra Studio. • Logs: every event, searchable with full context • Traces: debug any run, end to end • Metrics: cost, latency, errors, and scores at a glance With all three pillars of observability in one place, you can now see the whole picture.

🏴󠁧󠁢󠁥󠁮󠁧󠁿 Paul

10,509 次观看 • 4 个月前

Introducing: Sazabi (Sazabi). The AI-native observability platform for fast moving engineering teams. Backed by engineering leaders from the world's top AI and dev tool companies: Graphite, Vercel, Browserbase, LangChain, Browserbase, and more. Sazabi is taking a radically different approach to observability, centered on three core principles: 1. LESS IS MORE Product engineers don't need more observability bells and whistles. They need clear, actionable answers. The best UX for observability is chat. 2. LOGS ARE ALL YOU NEED The "Three Pillars of Observability" is an outdated idea. Logs can do everything traces can do, and more. 3. MONITORING IS DEAD Creating static monitors for fast-evolving systems is a fool's errand. The future is something different entirely: agentic anomaly detection. I started Sazabi out of desperation. After years of working on observability systems at Crunchbase, Brex, and 11x, I was all too familiar with the toil and complexity. Dashboards. Monitors. Session recordings. Error tracking. APM. RUM. Instrumentation. OpenTelemetry. Endless screens and configuration. It was too much. And despite everything, my teams still struggled to find and fix production issues. I was fed up. I dreamed about a better way. AI makes it possible. Today, I'm proud to introduce Sazabi to the world and announce our closed alpha program. We're selecting 10 startups to participate. Learn more:

Introducing: Sazabi (Sazabi). The AI-native observability platform for fast moving engineering teams. Backed by engineering leaders from the world's top AI and dev tool companies: Graphite, Vercel, Browserbase, LangChain, Browserbase, and more. Sazabi is taking a radically different approach to observability, centered on three core principles: 1. LESS IS MORE Product engineers don't need more observability bells and whistles. They need clear, actionable answers. The best UX for observability is chat. 2. LOGS ARE ALL YOU NEED The "Three Pillars of Observability" is an outdated idea. Logs can do everything traces can do, and more. 3. MONITORING IS DEAD Creating static monitors for fast-evolving systems is a fool's errand. The future is something different entirely: agentic anomaly detection. I started Sazabi out of desperation. After years of working on observability systems at Crunchbase, Brex, and 11x, I was all too familiar with the toil and complexity. Dashboards. Monitors. Session recordings. Error tracking. APM. RUM. Instrumentation. OpenTelemetry. Endless screens and configuration. It was too much. And despite everything, my teams still struggled to find and fix production issues. I was fed up. I dreamed about a better way. AI makes it possible. Today, I'm proud to introduce Sazabi to the world and announce our closed alpha program. We're selecting 10 startups to participate. Learn more:

Sherwood

117,798 次观看 • 6 个月前

The irony: world-class platform engineering teams spend a majority of their time trying to govern high-cardinality use cases. Otherwise bills go through the roof! This is a major problem with Observability 1.0 tools. From Charity Majors in The Pragmatic Engineer Podcast

The irony: world-class platform engineering teams spend a majority of their time trying to govern high-cardinality use cases. Otherwise bills go through the roof! This is a major problem with Observability 1.0 tools. From Charity Majors in The Pragmatic Engineer Podcast

Gergely Orosz

28,807 次观看 • 1 年前

Production-ready RAG systems need observability. From tracking latency and throughput to evaluating response quality with human feedback or LLM-as-a-judge, robust observability gives you visibility into both system performance and output quality, on both a component and system-wide level. This lesson from our Retrieval Augmented Generation course breaks down the core components of an effective eval system and how to balance cost, automation, and accuracy when choosing your metrics. 📚 Learn more in the full course:

Production-ready RAG systems need observability. From tracking latency and throughput to evaluating response quality with human feedback or LLM-as-a-judge, robust observability gives you visibility into both system performance and output quality, on both a component and system-wide level. This lesson from our Retrieval Augmented Generation course breaks down the core components of an effective eval system and how to balance cost, automation, and accuracy when choosing your metrics. 📚 Learn more in the full course:

DeepLearning.AI

18,793 次观看 • 6 个月前

A production issue shouldn't require a detective Yet that's how most observability works today Most observability tools just dump alerts. Duplicates, no context, and you still fix it yourself Nicolò Magnante (YC P26) and his cofounder Arseniy Shishaev (YC P26) - who spent years building part of Datadog's metrics product - think that's backwards So they built Superlog (YC P26): Observability that's meant never to be opened A wizard scans your repo, installs proper OpenTelemetry, and runs daily to keep up as you ship When something breaks, it groups the errors into one incident, investigates with full context - logs, traces, recent deploys, past Slack threads - and drops a single mergeable PR in Slack. Merge it Ignore it Or open it in Claude Code and tweak it Vendor-neutral, so you keep every log, trace, and metric - even if you leave Not another notification. A fix. Nicolò Magnante (YC P26) CEO & Co-Founder Superlog (YC P26) on Fondo.com START Huge thanks to Eragon for making this episode possible!

A production issue shouldn't require a detective Yet that's how most observability works today Most observability tools just dump alerts. Duplicates, no context, and you still fix it yourself Nicolò Magnante (YC P26) and his cofounder Arseniy Shishaev (YC P26) - who spent years building part of Datadog's metrics product - think that's backwards So they built Superlog (YC P26): Observability that's meant never to be opened A wizard scans your repo, installs proper OpenTelemetry, and runs daily to keep up as you ship When something breaks, it groups the errors into one incident, investigates with full context - logs, traces, recent deploys, past Slack threads - and drops a single mergeable PR in Slack. Merge it Ignore it Or open it in Claude Code and tweak it Vendor-neutral, so you keep every log, trace, and metric - even if you leave Not another notification. A fix. Nicolò Magnante (YC P26) CEO & Co-Founder Superlog (YC P26) on Fondo.com START Huge thanks to Eragon for making this episode possible!

David J Phillips

40,983 次观看 • 2 个月前

Superlog (Superlog (YC P26)) is the observability tool you're not supposed to open: a wizard sets up your logs, traces, alerts, and dashboards daily, and an agent investigates incidents and posts one mergeable PR per issue into Slack. Sign up here: Congrats on the launch, Nicolò Magnante (YC P26) and Arseniy Shishaev (YC P26)!

Superlog (Superlog (YC P26)) is the observability tool you're not supposed to open: a wizard sets up your logs, traces, alerts, and dashboards daily, and an agent investigates incidents and posts one mergeable PR per issue into Slack. Sign up here: Congrats on the launch, Nicolò Magnante (YC P26) and Arseniy Shishaev (YC P26)!

Y Combinator

36,507 次观看 • 2 个月前

.Ramp Labs (the AI unit of Ramp) has been *cooking* with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories

.Ramp Labs (the AI unit of Ramp) has been cooking with agentic innovation Here's Alex L discussing and demo'ing code self-maintaining software and the concept of AI software factories #DataDrivenNYC ______________ 00:04 - Intro 01:11 - The shift from writing code to code maintenance 01:59 - Introducing Ramp Inspect, the background coding agent 03:05 - The first experiment: Nightly AI code automation 04:23 - The limits of stateless monitoring in large observability surfaces 05:47 - Using Datadog monitors to give the AI state and focus 07:23 - Real-world example: AI autonomously fixing an authentication bug 08:14 - How to control noise and implement an AI triage pattern 09:27 - The old vs. new paradigm for continuous code observability 10:21 - Key learnings on building autonomous AI software factories

Matt Turck

67,624 次观看 • 3 个月前

“What Chaos Labs has been able to do really well, is move from a service based offering to this product based offering where you have a full observability stack.” - Head of BD Halcyon Price discusses their protocol’s continued evolution with the introduction of risk oracles- a game changing new approach to identifying risk signals and preventing attacks.🛡️ Watch part 3 of this week’s Safety and Security series with BENQI Co-Founder Diamond Rock.🎥

“What Chaos Labs has been able to do really well, is move from a service based offering to this product based offering where you have a full observability stack.” - Head of BD Halcyon Price discusses their protocol’s continued evolution with the introduction of risk oracles- a game changing new approach to identifying risk signals and preventing attacks.🛡️ Watch part 3 of this week’s Safety and Security series with BENQI Co-Founder Diamond Rock.🎥

BENQI🔺

21,072 次观看 • 2 年前

Sharing an OS tool I have been building for a few weeks: - git for agent traces. With OT, you have a local client that helps you santise, review and securely share traces from your coding sessions, privately or publicly, as a Hugging Face dataset. 0.3 adds experimental session blame which I am very excited about - attributing commit/PR changes back to the traces that produced them (all local) unlocks a new layer in observability and issue tracking. A few short demos of the local TUI client and the upcoming opentraces hub platform. Try it out! keen on your feedback🙏

Sharing an OS tool I have been building for a few weeks: - git for agent traces. With OT, you have a local client that helps you santise, review and securely share traces from your coding sessions, privately or publicly, as a Hugging Face dataset. 0.3 adds experimental session blame which I am very excited about - attributing commit/PR changes back to the traces that produced them (all local) unlocks a new layer in observability and issue tracking. A few short demos of the local TUI client and the upcoming opentraces hub platform. Try it out! keen on your feedback🙏

Gabriele Farei

10,565 次观看 • 3 个月前

Debugging agents shouldn’t feel like detective work. Today, we’re excited to release two new AI agent interfaces that make agent observability & evaluations even more effective. 🔎 Timeline View – See execution flow and bottlenecks at a glance. No more guessing where your agent gets stuck. 💬 Conversation View – Experience exactly what your users see. Debug from the user's perspective, not just the system's. Combined with last week's Graph View, you now have three complementary ways to debug your agents: → Graph: Visualize decision paths and tool usage → Timeline: Spot performance bottlenecks instantly → Conversation: See the user experience end-to-end AI evaluations + observability are crucial to building reliable AI. These interfaces make it simpler to identify blockers and improve your agents faster. See all three views in action, and try it free with the link below 👇

Debugging agents shouldn’t feel like detective work. Today, we’re excited to release two new AI agent interfaces that make agent observability & evaluations even more effective. 🔎 Timeline View – See execution flow and bottlenecks at a glance. No more guessing where your agent gets stuck. 💬 Conversation View – Experience exactly what your users see. Debug from the user's perspective, not just the system's. Combined with last week's Graph View, you now have three complementary ways to debug your agents: → Graph: Visualize decision paths and tool usage → Timeline: Spot performance bottlenecks instantly → Conversation: See the user experience end-to-end AI evaluations + observability are crucial to building reliable AI. These interfaces make it simpler to identify blockers and improve your agents faster. See all three views in action, and try it free with the link below 👇

Galileo, now part of Cisco

499,475 次观看 • 1 年前

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

Applied Compute

64,309 次观看 • 5 个月前

💥 OSS product analytics for LLM apps. Launching PostHog x langfuse.com integration 🤝 🫳 Day 1 drop of Langfuse Launch Week: We’re marrying product analytics and LLM observability. This integration pipes Langfuse events/metrics/evals into your PostHog project. This allows you to access all of your LLM metrics straight from your Posthog dashboards. I've used it to reality-check our evaluations by correlating them with user retention and ingestion metrics that we already measure in PostHog. This integration is now in public-beta and you can enable it within your Langfuse project settings. Thank you james hawkins, timgl and Lior Neu-ner for working on this over the past couple of weeks and to everyone who joined the private beta. More details: Quick tour of the integration 👇

💥 OSS product analytics for LLM apps. Launching PostHog x langfuse.com integration 🤝 🫳 Day 1 drop of Langfuse Launch Week: We’re marrying product analytics and LLM observability. This integration pipes Langfuse events/metrics/evals into your PostHog project. This allows you to access all of your LLM metrics straight from your Posthog dashboards. I've used it to reality-check our evaluations by correlating them with user retention and ingestion metrics that we already measure in PostHog. This integration is now in public-beta and you can enable it within your Langfuse project settings. Thank you james hawkins, timgl and Lior Neu-ner for working on this over the past couple of weeks and to everyone who joined the private beta. More details: Quick tour of the integration 👇

Marc Klingen

15,658 次观看 • 2 年前

Our infra team breaks down the concept of “context engineering”: martin_casado - “If you are going to call a model, you have to know what to put in the context… At some point you’re probably going to use traditional computer science." Jennifer Li - “What’s the new form factor of infra that needs to become part of this context engineering?... How do you have agents use tools, or infrastructure that will provide discovery and guarantees of observability of these systems as well?” “New infrastructure pieces create new patterns and methods of software and building systems. This is a great example of it emerging before our eyes.”

Our infra team breaks down the concept of “context engineering”: martin_casado - “If you are going to call a model, you have to know what to put in the context… At some point you’re probably going to use traditional computer science." Jennifer Li - “What’s the new form factor of infra that needs to become part of this context engineering?... How do you have agents use tools, or infrastructure that will provide discovery and guarantees of observability of these systems as well?” “New infrastructure pieces create new patterns and methods of software and building systems. This is a great example of it emerging before our eyes.”

a16z

21,801 次观看 • 1 年前

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

Andrew Ng

63,773 次观看 • 7 个月前

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️

LangChain

41,016 次观看 • 7 个月前

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

LangChain

50,475 次观看 • 9 个月前

Today, we're making Error Tracking by Better Stack generally available. Sentry-compatible. AI-native. At 1/6th the price. Here's why we built it, and how to get the most out of it. What's wrong with error tracking today? Most teams use Sentry. It's solid! But at scale, the bills get brutal. Just 100M exceptions with 90 day lookback? ~$30,000 on Sentry. We charge ~$5,000 for the exact same thing. The math isn't subtle. And so most teams still end up sampling. Which means missing the exact exception that caused the outage. The bigger problem: errors are orphaned data. Your exception lands in Sentry. Your logs are in Datadog. Your traces are somewhere else. Root cause analysis becomes a multi-tab archaeology project at 3 am. We built error tracking natively inside Better Stack: the same platform where your logs, traces, metrics, uptime checks, and on-call schedules already live. Errors are just another signal. They belong together. The part that changes how your team works: Our AI SRE doesn't just surface errors. It fixes them. See a new exception? One click. The AI SRE analyzes the full context, from stack traces, environment variables, browser sessions, related logs and recent deploys, and opens a pull request. Not a ticket. Not a summary. A pull request with the fix. This is what happens when error tracking is fully integrated with the rest of your observability stack instead of bolted on separately. The AI has everything it needs to actually act. The migration is trivial: 1. Keep your existing Sentry SDK. Don't touch a single line of instrumentation code. 2. Point the DSN at Better Stack. 3. Done. Errors flow in. Your dashboards work. Your alerts work. 4. New exception appears. Click "Fix with AI SRE." Pull request lands in your repo. 5. Review, merge, close. That's the whole workflow. The AI angle is real, not a marketing badge. LLMs are genuinely good at fixing bugs if they have full context. The reason AI coding assistants sometimes frustrate engineers is incomplete information, not the model. We solve that by giving the AI SRE your entire telemetry stack as context. Stack traces, logs, traces, service maps, previous incidents and much more. All of it, in one place, at the moment it matters. Observability tools are only useful if you actually ingest all your data. At current prices of other tools, most teams can't afford to. Now you can, and your AI SRE can actually do something about it.

Today, we're making Error Tracking by Better Stack generally available. Sentry-compatible. AI-native. At 1/6th the price. Here's why we built it, and how to get the most out of it. What's wrong with error tracking today? Most teams use Sentry. It's solid! But at scale, the bills get brutal. Just 100M exceptions with 90 day lookback? ~$30,000 on Sentry. We charge ~$5,000 for the exact same thing. The math isn't subtle. And so most teams still end up sampling. Which means missing the exact exception that caused the outage. The bigger problem: errors are orphaned data. Your exception lands in Sentry. Your logs are in Datadog. Your traces are somewhere else. Root cause analysis becomes a multi-tab archaeology project at 3 am. We built error tracking natively inside Better Stack: the same platform where your logs, traces, metrics, uptime checks, and on-call schedules already live. Errors are just another signal. They belong together. The part that changes how your team works: Our AI SRE doesn't just surface errors. It fixes them. See a new exception? One click. The AI SRE analyzes the full context, from stack traces, environment variables, browser sessions, related logs and recent deploys, and opens a pull request. Not a ticket. Not a summary. A pull request with the fix. This is what happens when error tracking is fully integrated with the rest of your observability stack instead of bolted on separately. The AI has everything it needs to actually act. The migration is trivial: 1. Keep your existing Sentry SDK. Don't touch a single line of instrumentation code. 2. Point the DSN at Better Stack. 3. Done. Errors flow in. Your dashboards work. Your alerts work. 4. New exception appears. Click "Fix with AI SRE." Pull request lands in your repo. 5. Review, merge, close. That's the whole workflow. The AI angle is real, not a marketing badge. LLMs are genuinely good at fixing bugs if they have full context. The reason AI coding assistants sometimes frustrate engineers is incomplete information, not the model. We solve that by giving the AI SRE your entire telemetry stack as context. Stack traces, logs, traces, service maps, previous incidents and much more. All of it, in one place, at the moment it matters. Observability tools are only useful if you actually ingest all your data. At current prices of other tools, most teams can't afford to. Now you can, and your AI SRE can actually do something about it.

Juraj Masar

14,920 次观看 • 4 个月前