@virtualandy

I'm virtual, see. Yeah, see, yeah. Software, dad jokes, public speaking, and other such business. Lead Software Engineer @focused_dot_io

 Denver, CO
nomatter.press
Joined August 2008
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347Media
Great article. Something we did recently was to wrap every individual workflow in otel instrumentation going to @honeycombio, allowing us to better track workflow performance over time.
Depot
@depotdevSep 29

CI is changing

Article · @depotdev
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Over the summer, @sh_reya and I hosted 13 sessions on AI Engineering topics like retrieval, post-training, inference, and evals. I've summarized all the sessions, organized by theme, with links to the source materials. Warning: I've tried to pull the most important ideas from each talk, so some notes are short (9.5 hours of sessions comes out to about 20 minutes of reading). Enjoy! hamel.dev/notes/llm/ai-produ…
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introducing tau τ — an educational agent harness that teaches you how to build agent harnesses i will be publishing tutorials and demos on how to use it to create your own TUIs, harnesses, extensions, etc. Happy Tau Day!! 🤓 👉 twotimespi.dev/
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. @Rackspace you have been trying to charge me $0.06 for months on a long dormant account. Why bother? This is a bad look to randomly charge $0.06 after literal years of 0 activity. Also, please stop and close my account. Ticket you created is 260625-ord-0001169
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Corey Quinn
@QuinnyPigMay 22
This slide transition from my #o11ycon keynote just might be the greatest slide transition I’ve ever pulled off as a speaker.
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LangChain
@LangChainApr 29
1️⃣ Get traces 2️⃣ Enrich them 3️⃣ Improve from them 4️⃣ Repeat Head of Product @samecrowder on how to do this in practice: langchain.com/blog/traces-st…

The Agent Improvement Loop Starts with a Trace

Tracing is the foundational primitive for understanding and improving agents. Learn how traces power the AI data flywheel — from automated evaluators and human review to offline evals and regression...

langchain.com
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Hiring: Agent Engineer, Denver. LangChain. LangGraph. TDD. Pairing. Real clients, real production systems. $120–175K xitter.cf/i/jobs/193154631097035…

Agent Engineer (Langchain)

@ · Denver, United States
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Spotted: The Glizzilla 😈
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Yes! This!!! Observability is the sense mechanism that creates a feedback loop. No observability? No loop. You're just injecting chaos into the system and walking away. If you observe, and learn, and iterate based on what you learned... That's how you build great software 💜
Jamie Mallers@JamieMallersMar 9
Replying to @mipsytipsy
The teams that get this right treat o11y as product infrastructure, not ops tooling. When devs self-serve production answers, the deploy-learn-iterate cycle compresses. That flywheel compounds faster than anyone expects.
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vicki@vboykisMar 4
If you’re writing opinions on whether to do code reviews, write tests, or any part of production lifecycles these days, you should have to disclose if you personally are part of a PagerDuty rotation
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You don’t know what your agent will do until it’s in production

Article · @hwchase17
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Viv
@Vtrivedy10Feb 17

Improving Deep Agents with Harness Engineering

Article · @Vtrivedy10
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"Everything is spiritual." @MattForte22 on the latest TUES MORN RB Sports Spectrum podcast.
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My son's 12U baseball team is headed to Arizona in March and 10ish tournaments this summer in CO. They're doing a Player Assist Fundraiser to raise money. I would be thrilled if you could support him! event.operationplayerassist.…
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In software, the code documents the app. In AI, the traces do.

Article · @hwchase17
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It's the time of year for reflection. I jotted down my thoughts on why books are still essential in the age of instant information. Link below. Oh, and there's a 40% off coupon on all books from pragprog.com
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boris
@boristane21 Dec 2025
I'm trying something new with my blog, making it interactive first article is about something I care deeply about: logs logging sucks so much loggingsucks.com

Logging Sucks - Your Logs Are Lying To You

Why traditional logging fails and how wide events can fix your observability

loggingsucks.com
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😍Really loved this framing from @eugeneyan on how to build product evals (for ai applications) in three simple steps! We built LangSmith to make it easier to build evals - so I recorded a quick video showing how you can do each of these three steps: youtu.be/mz7mAo4zIC8

Product Evals (for AI Applications) in Three Simple Steps

Eugene Yan wrote a GREAT blogpost on how to build product evals (fo...

youtube.com
Eugene Yan
@eugeneyan26 Nov 2025
After repeating myself for the nth time on how to build product evals, I figured I should write it down. It's just three basic steps(i) labeling a small dataset, (ii) aligning LLM evaluators, and (iii) running the eval harness with each config change. eugeneyan.com/writing/produc…

Product Evals in Three Simple Steps

Label some data, align LLM-evaluators, and run the eval harness with each change.

eugeneyan.com
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🤖Agent Framework vs Runtime vs Harness A term I've heard recently is agent "harness". We're building DeepAgents to be that I wrote a blog on my interpretation of that term and how it differs from "framework" (LangChain) and "runtime" (LangGraph) blog.langchain.com/agent-fra…
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Waiver wire deadlines should always be Wednesday: -Tuesday is a day of research and rest. -Waiver wire Wednesday. The alliteration is perfect. It’s right there. -Also get new injury info on Wednesday. Just a complete letdown by society to have Tuesday as the usual day.
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