Tessera

Everyone wants an AI assistant. Few want company data leaving their network. This walkthrough shows how to build private, self-hosted AI assistant with Hermes, Ollama and DGX Spark—complete with scoped access, audit trails, and persistent memory. bit.ly/4fUEzOF #AI #LLM #AgenticAI #OpenSourceAI

DGX Spark Series (Part 4): Setting up the Hermes Agentic Assistant

Everyone wants an AI assistant. Here's how to build one that keeps your data where it belongs.

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The Knicks were given just a 0.4% chance to win Game 4. The Patriots fell to 0.2% during Super Bowl LI. Which comeback was actually more improbable? A look at win probability models, uncertainty, and why "almost impossible" isn't the same as impossible. bit.ly/4ekV43S #SportsAnalytics #NBA #NFL

What Was More Improbable: The Knicks’ Game 4 Comeback or the Patriots’ Super Bowl LI Rally from a 28-3 Deficit?

Using ESPN win probability models, the answer is surprisingly close, and probably closer than the exact decimals suggest.

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Do you need enterprise AI infrastructure for a 120M-parameter model? Maybe not-but the answer is more interesting than you'd think. We break down why Chronos-2, DGX Spark, and a simple FastAPI deployment turned out to be the right combination. bit.ly/4eh0FbC #AI #TimeSeries #Forecasting #DGXSpark

DGX Spark Series (Part 3): When the Wrong-Sized GPU Is the Right Call

What we learned serving Chronos-2 from an R-friendly forecasting pipeline

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At the New York Open Statistical Programming Meetup on Tuesday, June 16, John Myles White will be there to talk about Genealogy and LLMs! 📍 In-Person at NYU & Virtual | 7 PM (ET) 🙋‍♀️ More info & RSVP: nyhackr.org #LLMs #DataBS

Use AI coding assistants freely for coding tasks. Use them carefully for analytical ones. In this clip, Jared explains why generated R code still needs statistical judgment, domain knowledge and review discipline before it belongs in a real analysis workflow. Full video here: youtu.be/4csH2-Yf2FQ

The hard part of internal apps usually starts after the prototype works. How do you host it securely? How do users authenticate? How does it scale as more people use it? Jared walks through how Docker, Kubernetes and secure hosting help answer those questions. Full video: youtu.be/7EFYi-aFpdI

The Lander Lens: Fast, Secure & Scalable Internal Apps (Part 2)

YouTube video by Lander Analytics

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AI can build a working app surprisingly fast. That does not mean it is ready for production. In our latest blog post, Jared Lander breaks down where AI coding agents help, where they fall short and why human judgment still matters in software engineering. bit.ly/3PQn0V5 #AI #Engineering #LLMs

Vibe Coding Gets You to a Working Demo, But Engineering Is What Makes It Hold Up

AI coding agents are becoming useful interns, but teams still need judgment, taste, infrastructure and review before the code is something they can actually run.

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TONIGHT is the May New York Open Statistical Programming Meetup! Christian Martinez dives into NYC Open Data. Make sure to RSVP by 3 PM (ET)! 📆 Thursday, May 14 | 7 PM (ET) 📍 In-person at NYU & Virtual 🙋‍♀️ More info & RSVP: nyhackr.org #NYCOpenData #DataBS

Jared Lander @jaredlander.com · 3mo ago

Your coding agents probably don’t need RAG yet. At Lander Analytics, we connected 16+ repos using shared markdown files, grep, and zero new infrastructure to give agents cross-repo context that actually stays current. bit.ly/42ulRFy #AI #LLM #CodingAgents

You Probably Don’t Need RAG for Your Agent’s Knowledge Base

We connected 16 repos with markdown files, grep, and zero new infrastructure

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Traditional #machinelearning is still a great tool #databs

Lander Analytics@landeranalytics.com · 3mo ago

With LLMs and pretrained models everywhere, it’s tempting to skip traditional modeling. But train-test-evaluate still wins when domain knowledge matters. This post breaks down when old-school workflows outperform the shortcuts. bit.ly/42OtuGQ #DataScience #MachineLearning #AI

From raw customer behavior to real-time churn risk. See how predictive models and dashboards help teams spot risk early and take action before customers leave. Watch the video: youtu.be/D8xbXS0rsI0 #PredictiveAnalytics #CustomerChurn #MachineLearning #DataBS

LLM tool calling began as a workaround for stale data and evolved into the backbone of agentic AI. We walk through the mechanics, the evolution from XML to native APIs, and why this simple idea unlocked powerful workflows. Read more: www.landeranalytics.com/post/the-ana... #AI #LLMs #AgenticAI

The Anatomy of an LLM Tool Call

How a prompt engineering trick unlocked agentic AI

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Good memories working with Mike Band on the Vikings draft

With the NFL Draft this week, we’re revisiting how we helped the Minnesota Vikings build a draft model in 2015. From messy scouting data to “blue sticker” picks, this is how analytics made it into the war room & helped shape an elite class. 📝: bit.ly/4u2AQ5j #NFLDraft #SportsAnalytics #BigData

A better cron

Cron works, until it doesn’t. No logs, no alerts, no visibility. This post breaks down where cron falls short, when it still makes sense, and how tools like rpeat make scheduling jobs more reliable and debuggable. bit.ly/4cbcCQk #DataEngineering #DevOps #OpenSource

Check out how we power through large geospatial data

Rendering millions of geospatial points used to break browsers. Not anymore. This post walks through the modern stack, from Leaflet to deck.gl + Arrow, and how teams scale interactive maps from thousands to millions of points. bit.ly/41lHZkV #Geospatial #DataViz #OpenSource

We're using LLMs to do more with more

AI adoption isn’t about replacing people, it’s about enabling them. Our blog post explores why most companies aren’t ready for AI-driven layoffs, where the real value comes from, and how to use AI to upskill teams and build better workflows. bit.ly/414YRMI #AI #FutureOfWork #Leadership

Jared Lander @jaredlander.com · 5mo ago

Loving the #geospatial #databs

Lander Analytics@landeranalytics.com · 5mo ago

Geospatial datasets keep growing—and file format choices can quietly inflate storage and slow every downstream step. In this post we compare common formats, explain why GeoParquet is gaining traction, and when it’s time to move from files to PostGIS. bit.ly/3PsGxud #Geospatial #DataBS

Jared Lander @jaredlander.com · 5mo ago

Some thoughts about open source vs closed models from Joe Marlo in the age of #GenAI

Lander Analytics@landeranalytics.com · 5mo ago

Open source LLMs are closing the gap fast. This post looks at when they’re “good enough” for production, when closed models still make sense, and what the real costs of self-hosting look like beyond API pricing. Full breakdown here: bit.ly/4u3y1S7 #OpenSource #AIinPractice #DataScience

Jared Lander @jaredlander.com · 5mo ago

Some first thoughts on using @anthropic.com's Claude Code with #rstats

Lander Analytics@landeranalytics.com · 5mo ago

Where do AI coding tools actually help R users, and where do they quietly fall short? Our blog post digs into Claude Code as an R assistant: great for syntax, Shiny, and infra work, risky for analytical judgment. Based on real use at Lander Analytics. 🔗 bit.ly/3MWnMyt #RStats #OpenSource #DataBS

My favorite kind of data science is geospatial

Geospatial data is everywhere, but insights are easy to miss. Pulse uses AI to turn billions of location points into clear patterns, anomalies and hotspots, explored through interactive maps from city-wide trends to street-level detail. 📽️ Watch: youtu.be/iWpA8uHxYlA #SpatialAnalytics #AI #DataBS

So much more than just code

Most people use Claude Code as a coding assistant. Our new blog post shows a different pattern: using it as a lightweight agentic workflow library to prototype research, data, and content pipelines in a single session, before committing to a framework. bit.ly/3OnbI9R #AIWorkflows #DataBS

Part 2 of setting up the Dell DGX Spark, now with Claude Code. Or check out the more personal version at www.jaredlander.com/2026/02/conf...

Configuring the DGX Spark with Ansible

After the relatively easy process of setting up the DGX Spark (ok, the Dell, but for ease of reference I’m just calling it a DGX Spark) it was time to configure the software side. A lot comes preins

jaredlander.com

Can AI help with real infrastructure code or does it just slow you down? In part 2 of our series, @jaredlander.com shares what it took to configure DGX Spark with Ansible using Claude Code & why scaffolding (skills, instructions, docs access) made the difference. bit.ly/4cwBfaq #DevOps #MLOps #AI

The Dell version of the DGX Spark has been a fun device to play with. Check out the initial setup at the link below or find my more casual review at www.jaredlander.com/2026/02/sett...

Setting up the DGX Spark

We recently got the Dell Pro Max GB10, which is Dell's version of the Nvidia DGX Spark (yes, we are official Nvidia partners, so come talk to us about setting up a full on Nvidia setup) mini super com

jaredlander.com

What does “serious AI compute on your desk” actually look like? @jaredlander.com covers setting up the DGX Spark, what it enables (self-hosted LLMs, GPU training) & the real-world pros/cons of running powerful AI hardware without a data center. bit.ly/4s2rKVl #AIInfra #LLMs #MLOps

See how Mike Band, with his long history of combining football and analytics, uses LLMs to breakdown the game between the Patriots and Seahawks.

Most “Who will win the Super Bowl?” prompts get you vibes, not analysis. This post walks through a repeatable workflow for using LLMs with public data, better prompts, and skepticism, producing a real, sourced prediction instead of fan fiction. 🔗 bit.ly/4aaLqyM #DataBS

See the latest piece from @landeranalytics.com team member Joe Marlo