@nitayj

Partner @team8group SW/AI Infra. Host @TotrRocks. Founder CTO @ActionIQinc.

New York
Joined April 2009
Pulling a big result set from Snowflake into Tableau isn't network-bound. It's CPU-bound — burning cycles converting columns to rows and straight back to columns. @ianmcook of Columnar on ADBC, and the 10-100x you get by deleting that step. link below
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Traditional databases were "sold on the golf course" — to purchasing departments, not developers. Hannes Mühleisen on how that blind spot created the gap between pandas and Spark that DuckDB walked right into. Episode link below
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Effective messaging seen on a trip. This sign says - Do not pee. We will cut off your testicles. And make fun with pictures online. 😂
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Nitay Joffe retweeted
Ep 28: Building the Open Lakehouse for the AI Era with @cto_datazip from @_olake Hudi vs. Iceberg vs. Delta, sub-10-min CDC for fintech, Arrow-based ingestion, and what Iceberg decoupling from Parquet means for AI. 🎧 techontherocks.show/28
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"What's your moat?" is the wrong question. In AI, there are no permanent moats — only time-bound advantages and what you build on top of them. The right question: how long does your moat last, and what does it buy you time to do next? link below
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Nitay Joffe retweeted
"Never bait and switch developers." @jamwt watched CRDTs promise magic on day one and deliver misery by month six. Convex starts with serializable transactions and typed, reactive code instead - and it turns out that's what AI agents write best too. episode link below
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Hot take: Feature Stores failed because they were essentially Shadow IT. 📉 @hussainsultan from xorq joins to explain why we need "lock files" for data pipelines—making ML workflows reproducible across engines like Snowflake and DuckDB without the glue code. link below
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LLM apps are moving fast, and the risks are moving faster. That’s why we’ve developed a guide for securing AI Applications. In “Building Secure AI Applications,” we break down how the OWASP LLM Top 10 shows up in real systems and map each risk to controls teams can actually implement today. If you’re building or securing LLM features, we include a full vendor-neutral reference architecture. Download the Guide → dryrun.security/resources/ow…
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“A terabyte is not big data anymore.” New episode with the creator of pandas & Apache Arrow @wesmckinn on: Arrow vs Parquet, next-gen file formats, why DuckDB/DataFusion often beat “big data”, and how AI coding agents are changing open source infra. link below
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Big day - announcing @arcjet's Series A + our new local AI security model 🚀 An opt-in AI layer that runs expert security analysis for every request, entirely locally. 🏡 Accurate detection is the hardest part of security. 🎯 Legacy network-edge tools see packets, not users or logic. Real context lives in your code - where better decisions can actually be made. 🤖 That’s why we built Arcjet’s first AI security model. 🛡️ It runs inference locally in milliseconds, right inside your request handlers. Adds an extra layer to your defenses so you can ship faster and safer. 🍰 Arcjet now protects 500+ production apps used by 1,000+ developers - stopping bots, scrapers, spam, and fake accounts. 🕷️ So we’ve raised an $8.3M Series A led by @pluralplatform, bringing total funding to $12M. Also participating: @a16z, @seedcamp, @feross, and @jeffiel 💸 I'm excited to work alongside a small but exceptional team building the security platform that ships with your code. 🚀
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If you care about making AI features shippable, this episode maps the terrain and the trade-offs. 🔗 Link in the profile
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