Roee Adler

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Roee Adler

Roee Adler

@roee

Co-Founder and CEO at @hud_hq. Formerly Envara (aq by Intel), AeroScout (aq by Stanley), Soluto (aq by Asurion), Santa & WeWorkโ€™s OG CPO. Also ๐Ÿ‘ฉโ€๐Ÿ”ฌ๐Ÿ‘ง๐Ÿ‘ง๐Ÿ‘ฆ๐Ÿฅ‹๐Ÿค“โšฝ๏ธ

Tel Aviv Sumali Nisan 2009
368 Sinusundan3.4K Mga Tagasunod
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Michael Eisenberg
Michael Eisenberg@mikeeisenbergยท
The hottest company that nobody has ever heard of in the AI coding shift is @hud_hq
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Hud
Hud@hud_hqยท
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.
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Python Space
Python Space@python_spacesยท
The AI "Hangover": Why We Need to Stop Debugging Like Itโ€™s 2015 Iโ€™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 Hudโ€™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"โ€”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; itโ€™s a runtime sensor. It captures full execution context (variables, DB queries) andโ€”this is the genius partโ€”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 Hudโ€™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 Drataโ€™s numbers are undeniable: โ€ข Manual triage dropped from 3 hours per day to under 10 minutes. โ€ข Mean Time to Resolution (MTTR) improved by 70%. โ€ข 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.
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Hud
Hud@hud_hqยท
Weโ€™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!
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Roee Adler
Roee Adler@roeeยท
โ€œFor over a month, my team hunted for a bug that kept eluding us. We tried everything. Then we started using @hud_hq - and within hours, we pinpointed the root cause with ease.โ€ - thank you @AU10TIXLimited for sharing this great moment!
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Ro Khanna
Ro Khanna@RoKhannaยท
Peter Thiel is leaving California if we pass a 1% tax on billionaires for 5 years to pay for healthcare for the working class facing steep Medicaid cuts. I echo what FDR said with sarcasm of economic royalists when they threatened to leave, "I will miss them very much."
Teddy Schleifer@teddyschleifer

NEWS: Larry Page and Peter Thiel are making moves to leave California by the end of the year to avoid a possible billionaires tax that could hit them where it hurts. With @RMac18 + @hknightsf. nytimes.com/2025/12/26/tecโ€ฆ

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Roee Adler
Roee Adler@roeeยท
Oh boy do we agree with #7 by @ttunguz. Agents must be aware of code execution, security vulnerabilities, and data lineage - all in a ubiquitous way. We at @hud_hq are proud to throw our hat into that ring!
Tomasz Tunguz@ttunguz

11 Predictions for 2026 Every year I make a list of predictions & score last yearโ€™s predictions. 2025 was a good year : I scored 7.85 out of 10. Here are my predictions for 2026 : 1. Businesses pay more for AI agents than people for the first time. This has already happened with consumers. Waymo rides cost 31% more than Uber on average, yet demand keeps growing. 1 Riders prefer the safety & reliability of autonomous vehicles. For rote business tasks, agents will command a similar premium as companies factor in onboarding, recruiting, training, & management costs. 2. 2026 becomes a record year for liquidity. SpaceX, OpenAI, Anthropic, Stripe, & Databricks IPO, with SpaceX & OpenAI ranking among the ten largest offerings ever. The pent-up demand from 4+ years of drought finally breaks. Fear of disruption by fast-growing AI systems drives defensive acquisitions exceeding $25b as incumbents buy rather than build. 3. Vector databases resurge as essential infrastructure in the AI stack. Multimodal models & world/state-space models demand new data architectures. Vector databases grow revenue explosively as they become the connective tissue between foundation models & enterprise data. 4. AI models execute tasks autonomously for longer than a workday. According to METR, AI task duration doubles every 7 months. 2 Current frontier models reliably complete tasks taking people about an hour. Extrapolating this trend, by late 2026, AI agents will autonomously execute 8+ hour workstreams, fundamentally changing how companies staff projects. 5. AI budgets receive scrutiny for the first time. Buying committees & boards push back on AI spend. Small language models & open-source alternatives rise in popularity as research labs determine how to specialize them for particular tasks, achieving state-of-the-art performance at a fraction of the cost. Developers prefer them for 10x cost reductions. 6. Google distances itself from competitors via breadth in AI. No other company achieves breakthroughs across as many domains : frontier models, on-device inference, video generation, open-source weights, & search integration. Google sets the pace, forcing OpenAI, Anthropic, & xAI to specialize in response. The era of every lab competing on every frontier ends. 7. Agent observability becomes the most competitive layer of the inference stack. Engineering observability, security observability, & data observability fuse into a single discipline. Agents require unified visibility across code execution, threat detection, & data lineage. This marks the beginning of the confluence I predicted in 2025 : the three observability spaces finally converge. 8. 30% of international payments are issued via stablecoin by December. The efficiency gains in cross-border settlement are too large to ignore. As regulatory clarity improves in major markets, stablecoins move from the periphery of crypto to the core of global trade finance, displacing traditional SWIFT rails for a significant portion of B2B volume. 9. Agent data access patterns stress & break existing databases. Agents issue at least an order of magnitude more queries to databases & data lakes than people ever did. This surge in concurrency & throughput requirements forces a redesign of the overall architecture for both transactional & analytical databases to handle the relentless demand of autonomous systems. 10. The data center buildout reaches 3.5% of US GDP in 2026. The scale of investment mirrors the historical expansion of the railroads. The only factor that slows overall building is perceived risk within the credit market, particularly in the private credit market. The massive growth in that asset class suddenly shows strains of increasing default rates, creating a potential bottleneck for the most capital-intensive infrastructure projects. 11. The web flips to agent-first design. Most developer documentation & many websites become agent-first rather than people-first. This shift occurs because many purchasing decisions are now informed first through agentic research. Consequently, the front door needs to be designed for robots, while the side door caters to people.

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Roee Adler
Roee Adler@roeeยท
Wishing light and happiness, from our team at @hud_hq to yours โœจ
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Sean Kerner
Sean Kerner@TechJournalistยท
How Hud's runtime sensor cut triage time from 3 hours to 10 minutes venturebeat.com/ai/how-huds-ruโ€ฆ via @VentureBeat "Every software team building at scale faces the same fundamental challenge: building high-quality products that work well in the real world," @roee Adler, CEO and founder of Hud, told @VentureBeat in an exclusive interview.ย "In the new era of AI-accelerated development, not knowing how code behaves in production becomes an even bigger part of that challenge."
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Roee Adler
Roee Adler@roeeยท
Dear hivemind... Got new profile photos ahead of some press. The question is - should I give up my signature "too big of a smile with eyes closed" look in favor of a more serious one? Is it time? Which one should I choose?? Thank you for your attention on this matter...
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All The Right Movies
All The Right Movies@ATRightMoviesยท
What is the greatest movie plot twist?
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Roee Adler
Roee Adler@roeeยท
We absolutely love @ClickHouseDB - it helped us transform the speed and cost with which we ingest, analyze and coalesce data arriving from our sensors. Read more in this joint case study ๐Ÿ‘‡
ClickHouse@ClickHouseDB

โ€œEven though observability is a $100 billion market, it still sucks and production is on fire and no one knows why.โ€ clickhou.se/4qSMcrF @maywa1ter and her team at @hud_hq are using @ClickHouseDB to transform observability with the worldโ€™s first runtime code sensor. See how ๐Ÿ‘‡

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Yonatan Melech
Yonatan Melech@YonatanMelechยท
@_orcaman ืื’ื‘ ืžื•ืฆืจ ืฉื”ื™ื” ืžื˜ื™ืก ืื•ืชื™ ืงื“ื™ืžื”: ืื ื”ื™ื™ืชื™ ื™ื›ื•ืœ ืœื”ื’ื™ื“ ืœืื™ื™ื’ืณื ื˜ ืดื”ื™ื™, ืชืขื™ืฃ ืžื‘ื˜ ืขืœ ื”ืœื•ื’ื™ื ืฉืœ ื”Function App ื”ื–ื”, ืชืขื–ื•ืจ ืœื™ ืœื”ื‘ื™ืŸ ืœืžื” ืงืจืกืด ืื• ืดืชืขื–ื•ืจ ืœื™ ืœื”ื‘ื™ืŸ ืื™ืคื” ื–ื” ื ืชืงืขืด. ื”ื™ื” ื‘ืงืœื•ืช ืคื•ืชืจ ืœื™ ืืชืžื•ืœ ื ื’ื™ื“ ื‘ืื’ ืฉืœ ืฉืขืชื™ื™ื ื•ื—ืฆื™
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Or Hiltch
Or Hiltch@_orcamanยท
ืคื™ืชื•ื— ืชื•ื›ื ื” ืขื AI ืžืชืงื“ื ื‘ืงืฆื‘ ืžืกื—ืจืจ, ื•ื‘ื›ืœ ืฉื‘ื•ืข ืฉืื ื™ ื—ื•ืฉื‘ ืœื›ืชื•ื‘ ืคื” ืื™ื–ื• ืžื’ื™ืœื”, ืื ื™ ืžื•ืฆื ืืช ืขืฆืžื™ ืžืฉื ื” ืงืฆืช ืื• ื”ืจื‘ื” ืืช ื”-flow. ื”ืฆื•ืจืš ื‘ืžืคืชื—ื™ื ืžืงืฆื•ืขื™ื™ื ืœื“ืขืชื™ ืื›ืŸ ื™ืจื“ ื“ืจืืกื˜ื™ืช ื‘ืขืงื‘ื•ืช ื”ื—ื™ื“ื•ืฉื™ื ืฉืœ ื”ืฉื ื” ื”ืื—ืจื•ื ื”. ืื ื™ ืœื ืžื“ื‘ืจ ืขืœ Lovable ื•ื“ื•ืžื™ื”ื - ื”ื ืขื“ื™ื™ืŸ ื‘ืงื˜ื’ื•ืจื™ื” ืฉืœ ืชื•ื›ื ื” ืœื™ืฆื™ืจืช ืžืฆื’ื•ืช ื‘ืฆื•ืจืช ืืชืจ ื•ืœื ื ืจืื” ืฉื–ื” ืžืฉืชืคืจ, ืืœื ืขืœ ื“ื‘ืจื™ื ื›ืžื• Codex ืฉืžืืคืฉืจ ืœื”ืจื™ื ืฆื‘ื ืฉืœ agents ื‘ืžืงื‘ื™ืœ, ื›ืœ ืื—ื“ ืขืœ branch ืžืฉืœื•, ืขื•ืฉื” ืขื‘ื•ื“ื” ืื™ื›ื•ืชื™ืช ื›ืžื• ืกื™ื ื™ื•ืจ (ื•ื’ื ืœื•ืงื— ืœื• ืœื ืžืขื˜ ื–ืžืŸ), ืคื•ืชื— PR, ืขื•ืฉื” Review, ืžืชืงืŸ, ื•ื—ื•ื–ืจ ื—ืœื™ืœื”. ืื‘ืœ ื“ื‘ืจ ืื—ื“ ืฉื ืฉืืจ ืื•ืชื• ื”ื“ื‘ืจ ื›ื‘ืจ ื‘ืขืจืš ืขืฉื•ืจ, ื•ื”ืžื•ื“ืœื™ื ืขื“ื™ื™ืŸ ืžืžืฉ ื’ืจื•ืขื™ื ื‘ื•, ื–ื” ื›ืœ ืขื ื™ื™ืŸ ื”-Deployment/DevOps. ื‘ื’ื“ื•ืœ, ื›ืŸ, ื™ืฉ ื”ื™ื•ื ืืช Vercel ืฉืžืกืคืงื™ื ื—ื•ื•ื™ืช cloud deployment ืžื“ื”ื™ืžื” (ืื’ื‘ ืงื•ื“ืงืก ืฉืžื™ื™ืฆืจ PRs - ืžืื•ื“ ืงืœ ืœื’ืจื•ื ืœื›ืœ ืฉื™ื ื•ื™ ืœื”ื™ืคืจืก ืœ-Vercel ืื•ื˜ื•ืžื˜ื™ืช), ื•ื™ืฉ ื’ื ื“ื‘ืจื™ื ื›ืžื• Cloud Run ืฉืœ ื’ื•ื’ืœ. ืื‘ืœ: 1. ืฉื ื™ื”ื ื’ื™ืœื’ื•ืœื™ื ืžื•ื“ืจื ื™ื™ื ืฉืœ ื˜ื›ื ื•ืœื•ื’ื™ื” ืฉืื™ืคืฉืจื” ื“ื‘ืจื™ื ื“ื•ืžื™ื (ืืžื ื ื‘ื—ื•ื•ื™ืช ืคื™ืชื•ื— ืคื—ื•ืช ื˜ื•ื‘ื”). 2. ืฉื ื™ื”ื ืžืชืื™ืžื™ื ืจืง ืœื™ื•ื– ืงื™ื™ืกื™ื ืžืกื•ื™ื™ืžื™ื. ืื ื™ืฉ ืœื›ื ืžื–ืœ ื•ื–ื” ืžืชืื™ื ืœื›ื, ื”ื›ืœ ื˜ื•ื‘. ืœืจื•ื‘ ื”-production workloads ืขื ืกืงื™ื™ืœ ืกื‘ื™ืจ, ืฆืจื™ืš ื›ื‘ืจ ืœื”ืจื›ื™ื‘ ืœื’ื• ืขืœ GCP/AWS/Azure ื•ืฉื•ืชืณ, ื•ื–ื• ืขื“ื™ื™ืŸ ื›ืœื‘ืช ืจืฆื™ื ื™ืช ืžืื•ื“. ื”-AI agents ืœื ื™ื•ื“ืขื™ื ืœื”ืชืžื•ื“ื“ ืขื ื”ืขื ืŸ - ืงื•ื“ IaC ืฉื’ื ื‘ืœื™ ืงืฉืจ ืœ-AI ื”ื•ื ืœื 100%, ืขื•ื‘ื“ ืžืžืฉ ื’ืจื•ืข. ื›ืคื™ ื”ื ืจืื” ืื™ืŸ ืžืกืคื™ืง ืงื•ื“ ื›ื–ื” ื‘ืขื•ืœื ื›ื“ื™ ืœืืžืŸ ืžื•ื“ืœื™ื ื‘ืฆื•ืจื” ืกื‘ื™ืจื”, ืื– ื ื“ืคืงื ื• ืงืฆืช ื‘ืขื ื™ื™ืŸ ื”ื–ื”. ืื ื™ ืžืจื’ื™ืฉ ืงืฆืช aื›ืžื• ืฉื”ืจื‘ื” Data Scientists ืขื‘ืจื• ืœืขืฉื•ืช Evaluation ืœืžื•ื“ืœื™ ืฉืคื” ื‘ื’ืœืœ ืฉ-Gen AI ืื›ืœ ื”ืจื‘ื” ืžื”ืžืฉื™ืžื•ืช ืฉืœื”ื, ื”ืจื‘ื” ืžื”ืžืคืชื—ื™ื ื›ื‘ืจ ืขื‘ืจื• ืœืขืฉื•ืช ื‘ืคื•ืขืœ DevOps ืื• Cloud Architecture ื”ืจื‘ื” ื™ื•ืชืจ ืžืงื•ื“, ื›ื™ ื–ื” ื”ื—ืœืง ืฉ-AI ืคื—ื•ืช ืžื•ืฆืœื— ื‘ื•.
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