Roee Adler

And now the photo 😁😁

Sometimes there's a man. As we scale, and more engineering teams are adopting @hud_hq to bring AI into their engineering work, we aim to bring the world's best talent into our team. We have been trying to lure Haim Yadid (@lifeyx) to Hud since we started the company. Haim is one of the world's top experts on performance optimization and scalability, particularly in Java, and we've been picking his brain since day 1. And so ... 🥁 ... @maywa1ter and I couldn't be more excited to welcome Haim as our Chief Architect! Because sometimes there's a man. I won't say a hero, 'cause what's a hero? But sometimes there's a man, and I'm talkin' about Haim here, sometimes there's a man, well, he's the man for his time and place. He fits right in there, and that's Haim.

@ReemSherman @TalMorgenstern Devtools are the new black 😁

@TalMorgenstern פינג לחבר @roee.

עוד דוגמה נהדרת למה שהזכרתי במאמר ההוא על רצועת גרירה. מי שמנגיש את התוכנה שלו למפתחים לא אנושיים בצורה חכמה מקבל (לפחות כרגע) רוח גבית חסרת תקדים. חידה מעניינת לכל צוות בתחום. בצורה אירונית, זה קורה אחרי ש devtools הפך לתחום קצת מוקצה בשנים האחרונות.

@ShaharTzafrir Toda sir 🙏

אני huge fan של האנשים והמוצר. ולמרבה הצער לא הצלחתי להשקיע שם :(

@ReemSherman Toda sir!

אחלה אחלה חבר׳ה עם אחלה אחלה מוצר. כיף שיש פתרונות Deep Tech בעולם התוכנה הישראלי באזורים האלה של התעשייה.

9/ So if production issues are hurting your users and your business, and pulling your engineers away from building - try Hud! Contact us or book a demo here: hud.io/book-a-demo/

8/ Observability was built for humans looking at past data. Runtime intelligence was built for coding agents building the future.

1/ A quarter has ended at @hud_hq, and oh boy what a quarter it was. There are so many words I can share, but I’ll let the numbers talk. 2x ARR growth QoQ 55% of the growth from existing customers 4x growth in production sessions using Hud 6x MCP calls from AI coding agents

The hottest company that nobody has ever heard of in the AI coding shift is @hud_hq

The Automations team at Monday made a deliberate choice to build a repeatable playbook for discovering, understanding, and quickly resolving complex performance issues as the platform evolves.

@python_spaces Thank you!

The AI "Hangover": Why We Need to Stop Debugging Like It’s 2015 I’ve been analyzing the disconnect in modern engineering, and I am genuinely blown away by what is happening with Hud (hud.io). We have handed the keyboard to AI agents, letting them generate code at warp speed. But when that code breaks, we are stuck debugging with tools from 2015: dashboards, logs, and sampling. We are driving Ferrari-level code generation with horse-and-cart observability. Here is why Hud’s story shifts the paradigm: 1. The "Investigation Tax" is Killing Velocity Teams at Drata and monday .com realized they were paying a massive "investigation tax"—burning hours just acting as "human bridges" between disconnected tools to guess why an endpoint was slow. Traditional APMs require you to predict what to monitor, but with AI-generated code, you often don't know what you don't know. 2. The Shift: From Dashboards to "Agentic Investigation" Hud isn't just another monitor; it’s a runtime sensor. It captures full execution context (variables, DB queries) and—this is the genius part—feeds it directly back to AI agents via MCP. - Old way: Engineer digs through logs -> guesses the fix. - New way: Engineer asks Cursor: "Why is this slow?" -> Agent reads Hud’s runtime data -> Agent says: "This function is 30% slower since the last deploy, here is the fix." 3. The Metrics Are Staggering I am rarely impressed by efficiency claims, but Drata’s numbers are undeniable: • Manual triage dropped from 3 hours per day to under 10 minutes. • Mean Time to Resolution (MTTR) improved by 70%. • Monday .com went from chasing "voodoo incidents" to solving deep root causes in minutes. My Perspective: We are entering an era where engineers will not know all the code in their production environment. That is the reality of AI scaling. If you are building with AI agents, you cannot use tools that treat your app like a black box. You need production behavior to be obvious to your AI. If your AI writes the code, it must be able to see how it runs to fix it. Dashboards are for humans. Runtime intelligence is for the future.

We’ve been working with the @awscloud team on integrating @hud_hq into @kirodotdev : install Hud in 1m, and have errors, performance degradations and CPU spikes detected with the root cause needed to fix them quickly. Give it a try!