Staff Software Engineer · Cisco Meraki · San Francisco Bay Area

Avinash Gosavi

15+ years building production systems at enterprise scale — I care most about distributed systems, API design, and reliability, and enjoy the unglamorous work that makes platforms feel solid: data modeling, integration paths, performance, and being useful when things break. These days that means Rails, React, Kafka, and Kubernetes by day; off hours I build privacy-first AI tools and write about the craft of software engineering.

Recently on the site

  • Writing The Attractor post's "context windows" now links to its plain-language definition in the agent memory glossary. →
  • Engineering Each event on the events page is now marked up as an h-event, so IndieWeb readers and calendar tools can pick up its name, dates and place. →
  • Engineering The "On this page" box on long posts is now part of the page itself, so the post no longer jumps down when it appears, and it works with JavaScript off. →

Now

Judging
Judging student projects at HackUMass XIV (UMass Amherst) on Nov 15.
Going to
OSA Con 2026, the open-source analytics & AI conference in San Francisco, on Nov 2.
Reading
Papers on memory for AI agents — plain-language summaries of them are collected on the Paper TL;DR shelf.
Running
A home inference server (Ryzen AI 9 HX 370, Radeon 890M) serving local models through Ollama over Tailscale — it powers most of my side projects.

Hobby Projects

Experience

  • Cisco Meraki Staff Software Engineer
    2021 – Present
  • Handshake Software Engineer II
    2020 – 2021
  • CoStar Group Software Engineer II
    2018 – 2020
  • Dominion Enterprises — ForRent Media Solutions Intern → Senior Software Engineer
    2015 – 2018
  • Old Dominion University Teaching & Research Assistant
    2014 – 2015
  • Freewave Tech Solutions Software Developer
    2013 – 2014
  • Indiavidual (via Neosoft Technologies) Ruby on Rails Developer
    2013
  • Allerin Tech Ruby on Rails Developer
    2011 – 2013

Things I'm curious about

How AI systems should remember — and who gets to read those memories. Brain-inspired retrieval. Running useful models on hardware I own. Keeping big APIs fast. I write up the papers behind my projects in plain language in the Paper TL;DR section — start with six papers that shaped how I think about AI memory. There's also a papershelf and a list of conference talks worth watching.

Recent Writing

  • An Agent Memory Glossary: Terms in Plain Language
    5 min read

    The vocabulary around AI agent memory borrows from neuroscience, databases, search and cryptography all at once, which makes it easy to talk past each other....

  • Doing the Right Thing, Fast
    3 min read

    We recently deprecated the initial version of Cisco Catalyst Cloud Monitoring. That means the code that drove it — onboarding flows, device-specific paths, i...

  • How daylog Keeps Your Diary Private (Even from Itself)
    6 min read

    daylog is a voice diary. You open it, tap the mic, say what’s on your mind, and it saves. You can search your entries semantically — type “frustrated” and it...