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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

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33,539 views • 4 months ago •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 views • 4 months ago

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

1,324,142 views • 3 months ago

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 views • 6 months ago

Observability on Cloudflare just got better! Create visualizations from natural language – "show me my cache hit rate". Within a chart you can also click a spike and investigate what happened much easier. Also really enjoying our animated quick start dialogs for products :) 5 new things in total: 1. Investigate logs in one place 2. Trace requests through the entire platform 3. New unified observability SQL API 4. New pricing 5. Custom alerts on observability data Blog:

Observability on Cloudflare just got better! Create visualizations from natural language – "show me my cache hit rate". Within a chart you can also click a spike and investigate what happened much easier. Also really enjoying our animated quick start dialogs for products :) 5 new things in total: 1. Investigate logs in one place 2. Trace requests through the entire platform 3. New unified observability SQL API 4. New pricing 5. Custom alerts on observability data Blog:

Brayden

15,983 views • 11 days ago

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

117,900 views • 8 months ago

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 views • 1 year ago

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:

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DeepLearning.AI

18,793 views • 8 months ago

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

37,518 views • 5 months ago

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 views • 4 months ago

.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

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Matt Turck

68,384 views • 5 months ago

Today we're launching Observability Exports in Jitsu. Everything you see in Live Events can now stream into Datadog, Inc., Grafana Cloud, New Relic, Elastic, or your own OpenTelemetry Collector. Here's how it works: 1. Go to your Jitsu account's Settings → Observability exports. Set your OTLP endpoint: 2. Add your auth header. Datadog wants dd-api-key. 3. Send test log, toggle Enabled, then go find it: service:jitsu-live-events Four record types land: function, bulker_batch, bulker_stream, dead-letter. Your raw event traffic is never exported. These are operational logs about your pipelines. Datadog takes OTLP directly, so there's no agent or collector sitting in between. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Observability Exports is on Jitsu Enterprise plan. DM me to enable it for your org. More in comments!

Today we're launching Observability Exports in Jitsu. Everything you see in Live Events can now stream into Datadog, Inc., Grafana Cloud, New Relic, Elastic, or your own OpenTelemetry Collector. Here's how it works: 1. Go to your Jitsu account's Settings → Observability exports. Set your OTLP endpoint: 2. Add your auth header. Datadog wants dd-api-key. 3. Send test log, toggle Enabled, then go find it: service:jitsu-live-events Four record types land: function, bulker_batch, bulker_stream, dead-letter. Your raw event traffic is never exported. These are operational logs about your pipelines. Datadog takes OTLP directly, so there's no agent or collector sitting in between. Jitsu is one of the fastest growing open-source CDPs with 5,000+ GitHub stars. Capturing events is unlimited and free, and delivery is free up to 200k events a month. Observability Exports is on Jitsu Enterprise plan. DM me to enable it for your org. More in comments!

Soham Mehta

209,141 views • 26 days ago

Your team deserves more from your agent observability platform - a single, connected place for instrumentation, investigation, and measurement, enhanced with intelligence. In Braintrust, you can start with an open-ended prompt about agent behavior and carry the investigation into code, evals, automation, and monitoring. New tools like Patterns and Debugger make it easier to find recurring behavior, get to root causes, and act on what you learn. The context you build in one step automatically follows to the next. Work in the platform with an enhanced Loop experience, or move to your coding agent of choice. Read more →

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Braintrust

43,141 views • 1 month ago

“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 JD Gagnon.🎥

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BENQI🔺

21,244 views • 2 years ago

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 views • 5 months ago

Zero Downtime brought builders together in San Francisco for a full day of talks, building, debugging, and shipping projects. Throughout the day, JBaruch 🎩 from Port spoke about Agentic Software Factories, Goutham Karthi from SigNoz covered OpenTelemetry and observability, and the Bright Data team showed how to bring live web data into AI workflows. A huge thank you to @Port_io, Bright Data, and SigNoz for supporting Zero Downtime, sharing their expertise, and helping us bring the developer community together in San Francisco.

Zero Downtime brought builders together in San Francisco for a full day of talks, building, debugging, and shipping projects. Throughout the day, JBaruch 🎩 from Port spoke about Agentic Software Factories, Goutham Karthi from SigNoz covered OpenTelemetry and observability, and the Bright Data team showed how to bring live web data into AI workflows. A huge thank you to @Port_io, Bright Data, and SigNoz for supporting Zero Downtime, sharing their expertise, and helping us bring the developer community together in San Francisco.

WeMakeDevs

25,356 views • 15 days ago

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

499,481 views • 1 year ago

A while ago I was trying to sell CrewAI to a huge bank. They liked the platform. Then they asked how they would do observability, and I had to say they would need to buy something else. Then memory. Same answer. By the third answer they were sitting there thinking, alright, I have to buy five products to use yours. That meeting ended an argument we had been having internally for months. Everyone can tell you the stack is fragmenting. Data, models, orchestration, observability, connection, interface. Every layer pulling in companies who want to own exactly one thing. Sit on the other side of the table and it flips. Nobody wants five contracts. Five security reviews. Five vendors to chase at 2am. They want one number to call. So what do you do when the market pulls apart and the buyer pulls together? You build the boring parts anyway. Deployment, traces, PII, permissions, the governance a bank asks about first. Architecture arguments get settled by procurement. Not by engineers.

A while ago I was trying to sell CrewAI to a huge bank. They liked the platform. Then they asked how they would do observability, and I had to say they would need to buy something else. Then memory. Same answer. By the third answer they were sitting there thinking, alright, I have to buy five products to use yours. That meeting ended an argument we had been having internally for months. Everyone can tell you the stack is fragmenting. Data, models, orchestration, observability, connection, interface. Every layer pulling in companies who want to own exactly one thing. Sit on the other side of the table and it flips. Nobody wants five contracts. Five security reviews. Five vendors to chase at 2am. They want one number to call. So what do you do when the market pulls apart and the buyer pulls together? You build the boring parts anyway. Deployment, traces, PII, permissions, the governance a bank asks about first. Architecture arguments get settled by procurement. Not by engineers.

João Moura

14,289 views • 12 days ago

How do we run evals with LLMs? Interview with Tanishq Singh. This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 11 minutes. 00:00 Question - Reliability with LLMs 01:00 Observability & Evals 02:50 What are evals? 06:41 Who does evals? 08:11 Answer relevancy, groundedness, tool sequence 10:39 Bloopers :p AI engineering program for software engineers:

How do we run evals with LLMs? Interview with Tanishq Singh. This is a real interview question from a big tech company, asked to a candidate in their technical interview round. The video explains the answer in roughly 11 minutes. 00:00 Question - Reliability with LLMs 01:00 Observability & Evals 02:50 What are evals? 06:41 Who does evals? 08:11 Answer relevancy, groundedness, tool sequence 10:39 Bloopers :p AI engineering program for software engineers:

Gaurav Sen

35,790 views • 2 months ago

💥 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

15,676 views • 2 years ago