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I made a controversial podcast appearance a few weeks ago and waited until now to post about it. I've even shared clips from it but never actually linked to the full episode until just now 🤦♂️ So here is my official plug to catch me on From A to B with Shiva Manjunath where I was initially trolled for my belief in "spaghetti testing", and within 35 minutes had turned the tables and steered us into a career growth b̶u̶t̶t̶k̶i̶c̶k̶i̶n̶g̶ coaching session. You can listen here: https://lnkd.in/gPYwUE6u Or if you're looking for something shorter, maybe this quick 5-minute appearance on The Lantern from MGMT Boston - if you've ever wondered why I continue to choose Boston as my homebase for my experimentation evangelism mission (and not, say, SF), I'll break it all down for you real quick: https://lnkd.in/g45wetrV And if you want the *full* briefing on Building Self-Serve Experimentation and Decreasing Cost-Per-Experiment, you've gotta invite me to give a 30min talk to your company. I'll do two of these free of charge for the first folks to get it on the calendar. DM me 😎

Just a small bit of news this morning: Eppo has been acquired by Datadog! You can read our official announcement in the blog post below, but I wanted to share a personal take on the news and why I'm thrilled: I've been running experiments for over a decade. Somewhere along the way, I decided that just running experiments wasn't enough for me - I wanted to focus on accelerating the overall adoption of experimentation in business. I first got the chance to do that as a consultant at Cro Metrics, working with 5-10 companies at a time and helping them build experimentation programs. I joined Eppo to scale my impact 10-100x. When I placed this bet, Eppo was the only warehouse-native experimentation tool on the market, and I saw a founding team equally dedicated to statistical rigor and widely-accessible UI. I knew this could enable end users at a scale far beyond what other products on the market were capable of, and I was right: today many Eppo customers successfully run thousands of experiments/year across dozens of teams in their organizations. And now it's time for another 100x leap, because now we can take the mission to 30,000+ existing Datadog customers. This is obviously not just a huge win for us, this is a big moment for the experimentation space too. It's the biggest bet yet from a wildly successful, publicly traded company who sees the exact same vision we do: a *lot* of growth and opportunity ahead for experimentation leaders, programs, and companies. There will be a lot more to come over the next few weeks but for now, suffice to say: our focus is still on building industry-leading experimentation capabilities, and it will the same Eppo team building the product for years to come. The acquisition also unlocks huge improvements in scaling feature flags and canary tests (necessary infra underlying all experimentation use cases) and some pretty exciting forward-looking capabilities for Agentic AI. New digs, new teammates, and a way bigger war chest - but the mission remains the same. Let's get everybody experimenting. https://lnkd.in/eX_Cs8jV

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Most institutions treat agentic AI like a coding assistant, generating scripts that humans review and deploy. That's incrementally faster, not transformational. 𝗚𝗲𝗻𝗲𝘀𝗶𝘀 𝗮𝗴𝗲𝗻𝘁𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗲 𝗺𝗼𝗿𝗲 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆: they ingest data, reconcile discrepancies, maintain pipelines, detect failures, and implement fixes, with human intervention only when necessary for routine operations. At Evident Symposium, hear Matt discuss this operational model alongside executives from BNY, JPMorgan Chase, Rogo, and BMO who are navigating similar transformations. Learn more here 👉 https://bit.ly/48VQY12

🌏 Asia-Pacific Tops Global FDI Rankings—with India in the Lead Asia-Pacific remains the world’s leading destination for foreign direct investment in 2024, driven by a surge of activity across both mature and emerging markets. India stands out, recording 1,017 inbound projects worth $108.6bn—up 28% year-on-year—making it the second-largest global recipient after the US. China attracted 434 projects, followed by Singapore (415), Australia (389), Vietnam (308), Japan (223) and Malaysia (209). 📥 Download The fDi Report 2025 to explore country-by-country insights, sector shifts, and where investment is accelerating across the region: #FDI #AsiaPacific #India #China #Singapore #Australia #Vietnam #Japan #Malaysia #fDiReport2025 #InvestmentTrends #ForeignInvestment #GlobalEconomy #APAC

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🎥 𝗣𝗔𝗥𝗧 𝟭 𝗢𝗙 𝟯: 𝗪𝗛𝗔𝗧 𝗜𝗦 𝗔𝗡 𝗔𝗚𝗘𝗡𝗧 𝗦𝗘𝗥𝗩𝗘𝗥? | Deep Dive with 𝗝𝘂𝘀𝘁𝗶𝗻 𝗟𝗮𝗻𝗴𝘀𝗲𝘁𝗵 In the first installment of our Agent Server series, Justin Langseth, Co-Founder & CTO of Genesis Computing, breaks down one of the most fundamental yet often misunderstood components of production agentic AI: 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗦𝗲𝗿𝘃𝗲𝗿. 𝗪𝗵𝗮𝘁 𝗘𝘅𝗮𝗰𝘁𝗹𝘆 𝗜𝘀 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁 𝗦𝗲𝗿𝘃𝗲𝗿? Think of it as 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. Just like you can’t run applications without an OS, production agents can’t exist as just a set of files somewhere, they need a 𝘀𝗲𝗿𝘃𝗲𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 behind the scenes. 💡 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗝𝘂𝘀𝘁𝗶𝗻: 𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗖𝗲𝗻𝘁𝗲𝗿 An agent server is where agents “live”, handling all inbound requests from Slack, Teams, APIs, or custom interfaces, and managing all outbound tool calls and actions. It runs 𝟮𝟰/𝟳, must be 𝘀𝗲𝗰𝘂𝗿𝗲, and needs to 𝘀𝗰𝗮𝗹𝗲 𝗱𝘆𝗻𝗮𝗺𝗶𝗰𝗮𝗹𝗹𝘆 as request volumes fluctuate. 𝗪𝗵𝗮𝘁 𝗟𝗶𝘃𝗲𝘀 𝗜𝗻𝘀𝗶𝗱𝗲 ✅ 𝗔𝗴𝗲𝗻𝘁 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝘀 - System prompts and capabilities (𝗚𝗲𝗻𝗲𝘀𝗶𝘀 𝗵𝗮𝘀 𝗯𝘂𝗶𝗹𝘁 𝟭𝟬𝟬+ 𝗯𝗲𝘀𝗽𝗼𝗸𝗲 𝘁𝗼𝗼𝗹𝘀 for data engineering) ✅ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 - What each agent can access in enterprise systems and what users can do with each agent ✅ 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴 𝗲𝗻𝗴𝗶𝗻𝗲 - Managing periodic tasks (checking Snowflake jobs every 15 seconds, Jira tickets every 15 minutes, daily reports every morning) ✅ 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 - Complete logging, observability, and compliance management ✅ 𝗟𝗟𝗠 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻𝘀 - Rate limiting and connection management to underlying language models 𝗧𝗵𝗲 𝗛𝘂𝗺𝗮𝗻 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 Just like humans have to-do lists with immediate tasks and recurring responsibilities, agents maintain short-term work queues alongside periodic activities. The agent server 𝗸𝗲𝗲𝗽𝘀 𝘁𝗵𝗲𝗺 𝗼𝗻 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝘃𝗲, 𝗮𝗻𝗱 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗹𝗲. 🎯 𝗧𝗵𝗲 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 𝗬𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗮𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁 𝘀𝗲𝗿𝘃𝗲𝗿. 𝗣𝗲𝗿𝗶𝗼𝗱. It’s not optional, it’s the foundation that makes agents 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝗲, 𝘀𝗲𝗰𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗿𝗲𝗮𝗱𝘆. 👀 𝗪𝗮𝘁𝗰𝗵 𝗣𝗮𝗿𝘁 𝟭 👉 https://lnkd.in/e3qUkwxU Coming Next: 𝗪𝗵𝗲𝗿𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁 𝘀𝗲𝗿𝘃𝗲𝗿 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗿𝘂𝗻? #AgenticAI #AI #EnterpriseAI #DataEngineering #AIInfrastructure #ProductionAI #TechArchitecture #AIAgents #Genesis

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Every manufacturer wants higher quality, but catching defects early is almost impossible without real-time visibility. FreshBI gives your operators and engineers a live picture of the entire process, with automated alerts that pinpoint exactly where quality is slipping. If you’ve struggled with inconsistent output or late detection, FreshBI can give your team the advantage you’ve been missing. Book a call to explore how AI-driven quality monitoring elevates your entire production flow.

If your FPY keeps fluctuating and you’re not sure why, you’re losing money long before defects reach the quality station. FreshBI’s AI dashboards give you full visibility into every step of the production line highlighting where rework originates, predicting failures before they happen, and recommending exactly what your team should adjust. If your plant has been struggling with hidden inefficiencies or rising waste, this is the moment to fix it. Book a call with FreshBI and see how predictive analytics can finally stabilize your FPY.

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High-performance analytics shouldn't come with high-anxiety pricing. 📉 Firebolt’s decoupled storage and compute architecture delivers total multidimensional elasticity—meaning you only pay for what you use, not for idle capacity. Achieve up to 80% better price-performance than legacy warehouses and keep your data budget as predictable as your growth.

Your dev team should be perfecting your core product, not reinventing the pivot table wheel. Join the market leaders who choose to embed, not build. Flexmonster delivers enterprise-grade reporting that integrates in minutes: ✅ Handles massive datasets with ease ✅ Lightning-fast data visualization ✅ Native-fit UI Try the Live Demo!

Cut development time by embedding Flexmonster directly into your app. It’s a full-featured pivot table component designed to look and feel like a native part of your software. ✔️Native feel: Looks and performs exactly like your own app/solution/ ✔️Framework-ready: React, Angular, Vue, and beyond. ✔️Feature-rich: Sorting, filtering, and grouping out of the box. Focus on your core product. Rely on Flexmonster to handle the data visualization.

Here's just some of what we track, in real-time, from Google Maps and all major delivery platforms: → Total Addressable Market per city/region → Market share by platform and category → New restaurant openings → Competitor promotions & discounts → Restaurant supply gaps Before you enter a market, you should know exactly what you're walking into. We're offering Uber Eats a free Proof of Concept - real data, your target markets, zero commitment.

One of the biggest concerns enterprises have around AI isn’t capability. It’s security. A question that came up during the discussion: “Does it integrate with Google Security?” Yes — fully. But the more important question was this: “If AI is being used, is any customer data leaving the environment?” The answer: no. The product was architected so that: • No customer data leaves the firewall • No metadata leaves the firewall • All processing stays within the customer environment • AI capabilities are packaged directly into the software That also means: • No external model dependency • No uncontrolled data exchange • Zero token consumption You get the advantages of AI without exposing enterprise data externally and without unpredictable token costs. A lot of organizations are realizing that “AI-ready” also means: secure, governed, and operationally sustainable. ❓ As AI adoption grows, what concerns your organization more right now: security, governance, or cost control? #EnterpriseAI #DataSecurity #AIGovernance #DataGovernance #AIInfrastructure

𝐀𝐈 𝐬𝐮𝐜𝐜𝐞𝐬𝐬 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐬𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥. 𝐈𝐭 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐝𝐚𝐭𝐚 𝐭𝐫𝐮𝐬𝐭. Enterprises are moving fast with LLMs, copilots, and AI agents. But as AI moves from answering questions to triggering actions, the risk changes. Bad data is no longer just a reporting issue. It can lead to: • wrong recommendations • broken workflows • poor customer decisions • compliance risk • automation failures That’s why trusted data has become the real foundation of AI success. Before AI can analyze, predict, or act, the underlying data needs to be: • complete • consistent • validated • reconciled • traceable • fit for business context This is the trust layer most AI strategies are missing. In our latest blog, we break down why data trust matters, where most organizations fall short, and how platforms like DataBuck help enterprises validate data before it impacts analytics, automation, or AI decisions. Read the full blog here: https://lnkd.in/g9EJT5wM What is the bigger AI challenge in your organization right now — model performance or trusted data? #DataTrust #AIReadiness #EnterpriseAI #DataQuality #AIGovernance #DataBuck

Data is easy; trusted insight is rare. You tap into real experiences from people across the country, filtered into clean, credible findings that stand up in any meeting. With every chart grounded in human stories, you can speak with confidence, defend your strategy, and show you’re not guessing—you’re proving.

Your reputation rides on the decisions you make from the data in front of you. That’s why you need capture that shows up like your most reliable team member—on time, prepared, and ready to perform. With consistent, professional data collection across your infrastructure, you show stakeholders you’re in control, informed, and ready for whatever the day throws at you.

Linear TV reaches 2.2M Philly adults whether you want them or not. YouTube reaches 77% of low-intent seniors in the exact tracts you care about. Here's the channel mix.

The team behind one of the best open web corpora reported something remarkable: the 10% of data they kept was 'actually worse than the 90% we removed.' That's the free corpus. It took 60,000+ GPU-hours just to validate one filtering pipeline, and the conclusion was that filtering barely helps. The implication for anyone building on web data: · Free and usable are different products · Cleaning at corpus scale is a research programme, not a preprocessing step · The differentiating data was never in the free crawl anyway We acquire, structure, and verify the data your model actually needs, from sources the crawlers don't reach. Talk to us about your corpus.

80% to prepare the documents. 20% to build the system. That's how AI teams describe their own projects, and it matches what we see: · A corpus with 27 different PDF types, each breaking the parser a new way · Tables that lose their structure, entities split across line breaks · A quarter of 'AI work' that is actually document plumbing We take the 80%. Extraction, structuring, verification: your corpus arrives AI-ready, in your schema. Your team keeps the 20% they were hired for. Talk to us about your corpus.