Geospatial Data at Scale (Part 2): Why Your Spatial Queries Are So Slow From six hours to seconds: speeding up geospatial queries

Spatial queries don’t have to take hours. In our latest blog post, we walk through real benchmarks across SF, PostGIS, and DuckDB, explain projection pitfalls, and show how techniques like indexing and H3 can deliver massive speedups.

bit.ly/4sXjndU

#Geospatial #DataEngineering #DataScience

Clear segments turn customer data into action. See how AI clustering + real-time dashboards reveal motivations and behaviors, so teams personalize better and waste less spend.

📽️ Watch the video: youtu.be/HhASkYGZoM0

#AI #CustomerSegmentation #MarketingAnalytics #DataBS

At the New York Open Statistical Programming Meetup on Thursday, April 16, Muskan Walia will be there to talk about using Gen AI for redacting race information from police reports!

📍 In-Person at NYU & Virtual | 7 PM (ET)

🙋‍♀️ More info & RSVP: nyhackr.org

#GenAI #DataBS

Geospatial Data at Scale (Part 1): Why File Format Matters The wrong format can 10x your storage costs before analysis even begins

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

Customer data is rich, but insights get buried. We use AI clustering to uncover real segments, then generate clear, human-readable personas you can explore in real time with interactive dashboards. More relevant offers, higher conversion, less churn.

📽️: youtu.be/HhASkYGZoM0

#CustomerSegmentation

The Container Orchestration Complexity Trap - Finding the balance between simplicity and scale Finding the balance between simplicity and scale

Kubernetes isn’t always the right next step. This post looks at the real operational costs of container orchestration and how teams can balance simplicity and scale using Docker Compose, k3d, or full Kubernetes depending on their needs.

Read more: bit.ly/4bfOVEx

#DevOps #DataEngineering

Next week at the New York Open Statistical Programming Meetup on Tuesday, March 10, Jason Bryer will be there talking about clustering!

📍 In-Person at NYU & Virtual | 7 PM (ET)

🙋‍♀️ More info & RSVP: nyhackr.org

#DataBS

Do You Really Need an AI Platform or Can Open Source Get You 90% There? When open models are enough

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

At the New York Open Statistical Programming Meetup on Tuesday, March 10, Jason Bryer will be there to talk about clustering!

📍 In-Person at NYU & Virtual | 7 PM (ET)

🙋‍♀️ More info & RSVP: nyhackr.org

#DataBS

Pulse reveals patterns hidden in massive geospatial data, spotting anomalies, hotspots and trends across defined zones with AI-powered maps. Turn location data into smarter decisions.

📽️ Watch the video: youtu.be/iWpA8uHxYlA

#AI #Geospatial #SpatialAnalytics

R Community Rejoice: Coding Assistants for R Users What Claude Code gets right (and wrong) as an R coding assistant What Claude Code gets right (and wrong) as an R coding assistant

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

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

Claude Code as an Agentic Workflow Library From messy ideas to working agents in a single session

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

DGX Spark Series (Part 2): Configuring the server with Ansible DGX Spark Series (Part 2): Configuring the server with Ansible - When AI actually helps write infrastructure code

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

DGX Spark Series (Part 1): Setting up the DGX Spark DGX Spark Series (Part 1): Setting up the DGX Spark - Serious AI compute, no data center required

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

At the New York Open Statistical Programming Meetup on Tuesday, February 17, George Perrett will take a critical look at bold claims about LLMs and why they fall short in statistics.

📍 In-Person at NYU & Virtual | 7 PM (ET)

🙋‍♀️ More info & RSVP: nyhackr.org

#DataBS #LLMs

An LLM predicts the winner of Super Bowl LX How to get a real prediction from public data with better prompts By Mike Band

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

Kubernetes powers resilient systems, self-healing clusters and automated scaling to keep your applications online, even during failures or traffic spikes. No downtime, no lost opportunities.

📽️ Watch here: youtu.be/Cf_vX-0Qrlc

#Kubernetes #DevOps #CloudComputing #HighAvailability #Databs

Why We Don't Just Trust the Model - Lander Analytics Why We Don't Just Trust the Model The case for grounding, retrieval, and verification in enterprise AI

LLMs don’t hallucinate because they’re broken, they hallucinate because of how they’re trained.

This blog post breaks down why that matters for enterprise AI, & how grounding, retrieval (RAG), & system design reduce risk and improve reliability.

🔗 bit.ly/4rweWpL

#AI #LLMs #EnterpriseAI #DataBS

At the New York Open Statistical Programming Meetup on Tuesday, February 17, George Perrett will be there to cast doubt on LLMs!

📍 In-Person at NYU & Virtual | 7 PM (ET)

🙋‍♀️ More info & RSVP: nyhackr.org

#DataBS #LLMs

Apps crashing under load? Servers failing? We use Kubernetes to keep your systems resilient, self-healing clusters, automated scaling, and rolling updates ensure high availability and uninterrupted performance.

📽️ Watch here: youtu.be/Cf_vX-0Qrlc

#Kubernetes #CloudComputing #DevOps #databs

Can Small and Medium-Sized Organizations Compete in AI? How nimble teams create real value with the resources they already have

AI isn’t an arms race for most organizations.

This post explains how small and mid-sized teams create real value with AI by focusing on workflows, context, and domain expertise, not massive budgets or moonshots.

🔗: bit.ly/49YekBY

#AI #DataScience #SMB #databs

Tuesday night (1/27) at the New York Open Statistical Programming Meetup, Bill Gold will share how he vibe coded his recreational schedule. Don’t miss it!

📍 In-person at NYU & virtual | 7 PM ET
🙋 More info & RSVP: nyhackr.org

#DataBS #VibeCoding

Thanks for the reco! Sign up for our monthly emails- www.landeranalytics.com/blog

Welcome back to the Lander Analytics Blog A place where data-driven exploration meets practical LLM solutions

The Lander Analytics blog is back!

We’re writing about what happens when AI meets real systems: legacy stacks, production constraints, and teams that need results, not hype.

Practical lessons. Real tradeoffs. What actually works.

🔗: bit.ly/3NHArVX

#DataScience #AI #Analytics #databs

See how AI embeddings turn unstructured incident reports into deeper insights, helping teams identify risks faster and act smarter.

📽️ Watch the video: youtu.be/QuQqw_AeGLI

#AI #SafetyAnalytics #WorkplaceSafety #databs

NYC slices now far more expensive than subway fare as 'pizza principle' disappears The "pizza principle" no longer holds in New York City due to rapid inflation.

Check out this article, “NYC slices now far more expensive than subway fare as the ‘pizza principle’ disappears”, featuring our Chief Data Scientist @jaredlander.com! 🍕📈

gothamist.com/news/nyc-sli... via @gothamist.com

At the New York Open Statistical Programming Meetup on Tuesday, January 27, Bill Gold will talk about how he vibe coded his recreational schedule.

📍 In-Person at NYU & Virtual | 7 PM (ET)

🙋‍♀️ More info & RSVP: nyhackr.org

#databs

Turn messy incident reports into actionable insights. Our AI categorizes incidents instantly like fall hazards, equipment malfunctions, fire risks, and embeddings give deeper, tailored insights for safer workplaces.

📽️ Watch here: youtu.be/QuQqw_AeGLI

#AI #SafetyTech #databs

Tomorrow night (12/11) at the New York Open Statistical Programming Meetup, Ipek Ensari will be there to discuss how to predict women's chronic disease flares from wearables. Don't miss out!

📍 In-Person at NYU & Virtual | 7 PM (ET)

🙋‍♀️ More info & RSVP: nyhackr.org

#databs