Experience
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Education
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University of Louisville
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Finished required coursework and partially completed thesis on intrusion detection and resiliency against distributed denial-of-service (DDoS) attacks. Prematurely discontinued unfortunately.
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Pavan Pothnis
I've always found myself switching to Claude models when working through coding problems. There's just something about how Claude handles context and works its way through large code bases and complex logic. With Claude Code, that workflow has become ridiculously smooth. Also been experimenting with OpenAI's Codex (GPT-5 powered) and the results are way better than previous versions. The code generation quality has taken a real jump. Now here's where it gets interesting. You keep hearing stories about developers opening multiple coding agents, feeding them the same task, and manually cross-referencing outputs to find the best solution. Turns out there's an easier way. By integrating Codex as a skill inside Claude Code, you get insights from both agents in a single terminal session when you need. No tab switching, no copy-pasting, no manual comparison. Claude Code orchestrates the workflow while Codex brings its strengths. Best of both worlds in one window. Claude OpenAI
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Pamela Fox
During the "Ambient Agents" meetup last week, Brace Sproul gave us a walkthrough of Open SWE, a coding agent built on Langgraph. Open SWE monitors GitHub issues. When it gets a new one assigned, it kicks off a multi-agent architecture with a Manager, Planner, and Programmer. It can receive messages from users at any time during the process and incorporate them into the state. Check out the slides for diagrams explaining each agent: https://lnkd.in/g3PnNHqG And the code: https://lnkd.in/gY68TKYy I found the takeaways really interesting: * Summarization is hard: Especially with code changes that can get quite long, you literally can't send the entire context history to the LLM, and highly detailed old diffs may distract it. IIRC, the agent sends the last 20 messages, and summarizes the changes before. * Agentic > hard coded: Open SWE tends to use the agentic approach of "here are your tools, pick which one to use" for almost all tasks. Except for one.. * Explicit error handling: When an error happens, Open SWE stops everything and focuses on resolving the error. Otherwise, it could get too far afield and be unable to get back to a good place to solve the error. (Hopefully I didn't mis-represent anything I heard!) #Langchain #Langgraph
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