Principal Technologist at MongoDB | Adjunct Professor At Tulane University

New Orleans, LA
Joined April 2022
Devin Dickerson retweeted
Most teams scaling in the cloud pay for more than their workload needs. ☁️ 🚇 Principal Technologist, @Dev_TheAnalyst, on why Atlas Infinite, with independent storage and compute scaling, deserves your attention.
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I was unfamiliar with the traffic in Atlanta. Dont take ubers to and from the airport in Atlanta if you can help it! Lesson learned and banked!
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Pro tip, go to AI Studio and look at the lower left corner and check to see if you have credits. I had $400 bucks in credits unclaimed.
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Last week @MongoDB hosted an event at The Battery in SF with friends from @AnthropicAI and @salesforce. The topic: AI Agents in the enterprise. Key takeaway: Automation can only scale with trust. You can delegate anything in this world except for accountability.
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Devin Dickerson retweeted
I Love You Coach. Thank you.
A statement from Head Coach Jay Johnson on Skip Bertman's passing.
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Devin Dickerson retweeted
We're celebrating 10 years of MongoDB Atlas! 🎉 Meet Chris Shum, Director of Product Management at MongoDB, who joined MongoDB as a software engineering intern back in 2016, right as Atlas was taking shape. Hear how Atlas has transformed over the past 10 years and what's next: mongodb.social/6019BEFNZx
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Has anyone figured out a prompt to push back on Gemini 3.5 flash's desire to be more concise? My use case is to clean some text without truncating it.
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Temporarily giving up on loop based spec-driven engineering with local models. It works with a frontier grade model as orchestrator but the compute is very expensive and not beneficial (better off just using the frontier at that point). Swapping to me being the orchestrator.
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I tried to have claude code orchestrate my local models via ssh. I used 66 percent of my work provided anthropic credits in 12 hours. New plan: trying @goose_oss next.
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My first attempts to integrate local models into my coding workflow has gone terribly. Reminds me a bit of the very early adopter days of coding with GPT. Hallucinations, overwriting files, and unhinged rabbit holes. Surely this is my fault
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Getting much better output now. No model swap...agent skills and better prompting.
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Claude Code told me it couldnt be done. I fact checked with a Gemini Deep Research run. Turns out there are pathways to solve my problem. I cant consolidate my AI subscriptions because I still find so much discrete utility from different models...
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I love Haaland but I have to go for England today. 1. I used to live in London and 2. I love me some Jude Bellingham
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This was in my MongoDB onboarding reading. Re-read it this weekend and it still holds up. @sogrady called the developer experience gap in 2020. It moved down the stack, and agents are the ones fighting the duct tape now. redmonk.com/sogrady/2020/10/…
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Not that I measure success in lines of code, but my loop created 12000 lines of code across 13 enhancements to my tabletop rpg app today, tested it, captured screenshots, pushed to feature branch (I approved the pushes)...all while I hung with my family. Dope.
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To all of us grilling in 100 degree+ heat indexes, Happy Fourth and dont forget to hydrate!
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Loop engineering has made a believer out of me. Automating giant feature blocks and staying in the loop on evals.
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I've done a lot of building with Gemini CLI and now antigravity. The dev ex with MongoDB MCP and agent skills is quite slick I must say.
Using the @GeminiCLI to build with MongoDB? Install the official extension to get the best experience. The MongoDB Gemini Extension comes with two crucial features: ✔️ The MCP server that lets your agents interact directly with your MongoDB database ✔️ Built-in agent skills for data modeling, query optimization, MongoDB Search, Vector Search, and more This means agents that actually understand your data layer, so you can build right from your CLI, without the context-switching. Get started today: mongodb.social/6014BDapGr
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Devin Dickerson retweeted
Vector Search On-Prem for the people!
Today at MongoDB.local Bengaluru, we announced new capabilities designed to help teams move AI into production with increased accuracy and deployment flexibility. Here’s what shipped: ✅ voyage-context-4 ✅ Public Preview: Native Reranking ✅ Search and Vector Search in MongoDB Enterprise Advanced and Community Edition ✅ Hybrid Search ✅ Apache Iceberg Support in Atlas Stream Processing ✅ Gen2 Atlas M30+ Dedicated Clusters on AWS See these and more: mongodb.social/6012BDakGp #MongoDBlocal
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