“Brad is a master of technical architecture with an extensive background in big data. He helped establish the Heartbyte infrastructure - helping our real-time stream processing application scale to hundreds of thousands of requests per-second. Without Brad, it's hard to know how we would have solved some of our most difficult system issues. Additionally, Brad helped set up our operations system to allow our team to easily test, manage, and deploy our codebase. I thoroughly enjoyed working and learning from Brad, and consider anyone who he works with in a very fortunate position.”
Activity
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Authentication is solved. Authorization isn't. Today we open-sourced what we built to change that. 20%. Twenty…
Authentication is solved. Authorization isn't. Today we open-sourced what we built to change that. 20%. Twenty…
Liked by Brad Anderson
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Open Source Scanner for Authorization? YEP, yep... got that too!
Open Source Scanner for Authorization? YEP, yep... got that too!
Liked by Brad Anderson
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NVIDIA just got a standing ovation for building half the solution. Everyone at GTC celebrated OpenShell. Sandboxed agent runtimes. Policy-based…
NVIDIA just got a standing ovation for building half the solution. Everyone at GTC celebrated OpenShell. Sandboxed agent runtimes. Policy-based…
Liked by Brad Anderson
Experience
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Druid Golf, Inc.
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Education
Recommendations received
4 people have recommended Brad
Join now to viewMore activity by Brad
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The Mirus Team enjoyed celebrating the holidays at our holiday party🎄Churrascos Memorial City
The Mirus Team enjoyed celebrating the holidays at our holiday party🎄Churrascos Memorial City
Liked by Brad Anderson
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Apple’s gaining of OPA, the commercial leader behind Open Policy Agent (OPA), is a watershed moment in enterprise security. What will this mean for…
Apple’s gaining of OPA, the commercial leader behind Open Policy Agent (OPA), is a watershed moment in enterprise security. What will this mean for…
Liked by Brad Anderson
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This is where this story ends. After careful contemplation, I've made the decision to depart from the IRS after 31 years of service. I've…
This is where this story ends. After careful contemplation, I've made the decision to depart from the IRS after 31 years of service. I've…
Liked by Brad Anderson
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Celebrating 25 years of data management and analysis innovation! https://hubs.la/Q02xLhbn0
Celebrating 25 years of data management and analysis innovation! https://hubs.la/Q02xLhbn0
Liked by Brad Anderson
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I gave a talk at #nanog90 on how to give a Whiteboard Talk, complete with an Actual Whiteboard! This year I added a section on how to present when…
I gave a talk at #nanog90 on how to give a Whiteboard Talk, complete with an Actual Whiteboard! This year I added a section on how to present when…
Liked by Brad Anderson
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Sumant Thakur
If your agentic system only makes sense when you read the code, it’s already fragile. At scale, agent workflows aren’t linear: they branch, retry, call tools, recover, and sometimes contradict earlier decisions. The real problem isn’t orchestration. It’s observability of intent over time. While working with Mastra framework, I started treating workflows as execution traces, not step definitions: decisions are first-class context mutations are visible failures are explainable, not mysterious This mental shift changed how I reason about reliability, debugging, and long-running agent behavior. I’ve shared the post in the comments for anyone responsible for systems that need to work at 2am, not just in demos.
1 Comment -
Mark O'Neill
MACH is in the Trough of Disillusionment. In our latest Hype Cycle for APIs, Mike Lowndes positions MACH sliding down from the peak: https://lnkd.in/ej2ZyYEN [Gartner sub req'd, preview available]. As is inevitable with any technology past the peak of the Hype Cycle, negativity on MACH has been growing in the market. Mariano Gomide de Faria - VTEX posted this back in March: "Let's be brutally honest: The pure best-of-breed MACH approach has created as many problems as it has solved." https://lnkd.in/ezZ9EfW6 . This type of sentiment shows that disillusionment is setting in. Our own Gartner advice on MACH is practical and pragmatic. Keith Holloway, D.B.A. just published a guide to "When Should Applications Use a MACH Architecture Approach?" which explains when MACH is appropriate, and when it is not https://lnkd.in/efjP5KWa [Gartner sub req'd]. Over the next years will be following the trajectory of MACH with interest for our clients, as it emerges from the Trough of Disillusionment.
1 Comment -
Valentin Matresu
If corporate data can’t move to the cloud, LEANN is a practical way to ship semantic search + RAG fully locally. MIT license, open source. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device. https://lnkd.in/daMD4JAZ It’s designed to index and search very large corpora while using far less storage by recomputing embeddings on demand instead of storing everything. Where LEANN fits - Use it when security, compliance, or contracts require data to stay on device / on‑prem. - It’s built for “personal AI” / private RAG across many sources while keeping data local. Practical use cases - Internal policy + SOP Q&A (HR, Legal, Security) without uploading documents. - Engineering knowledge search across codebases and docs (semantic search vs keyword-only workflows). - Private “ask my files” assistant for teams handling regulated data (finance, healthcare, defense suppliers). - Local investigation / eDiscovery-style retrieval on approved archives (docs, chats, exports) with minimal storage overhead. Why teams care - Privacy-first: data stays local; no forced cloud dependency. - Lightweight indexing: targets major storage savings versus traditional vector DB approaches. - Flexible generation: supports local inference engines (Ollama recommended) and OpenAI-compatible endpoints when your environment allows it. Biggest blocker for your org for internal RAG? Data residency, cost, or latency? #AI #RAG #EnterpriseAI #OnPrem #DataPrivacy #DataGovernance #InfoSec
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