Senior eng mgr (AI Team) @honeycombio. Previously @circleci. Word nerd, occasional writer/runner/yogi. @hendersgame.bsky.social she/her
San Francisco, CA
Joined February 2017
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As a Clojure developer named `hannah`, this parens-palindrome fact upsets me on both a visual and a personal level.
Sparks joy in my heart to see Anthropic embracing OpenTelemetry, with @honeycombio right there in the mix.
with the release of le chonk, the distinction between open weights and heavy weights can no longer be ignored
There are sooo many thought pieces flying around re-the future of softare development. I recommend @seldo 's latest: seldo.com/posts/we-are-all-p…
In addition to all the lovely data and references, a few choice quotes 🧵
"For a fair number of the people who think they loved the typing, the part they actually loved was the moment before the typing, when a vague mess of a problem resolved into a precise shape in their head. That moment is the job now. If that's what you loved, you're going..."
Can confirm: it’s fantastic.
Very proud of the Agentic Intelligence team and what they’ve built here.
An incredibly valuable use-case for the @honeycombio MCP: "Look at my data, find telemetry antipatterns and fix them."
LinkedIn feels passionately that I should know about all of my coworkers that play Zip.
I have never played Zip, but LinkedIn is determined to peer pressure me into giving it a go.
I am similarly pumped to discuss org-wide AI-native adoption with @mihail_eric and @NWidal67643! Tragically, “give everyone Claude Code” is not an organizational transformation strategy, and the process of trial and error involves error. We have, however, learned a lot.
Pumped to discuss the challenges and learnings of org-wide AI-native adoption this Thursday.
Too many talks and articles discuss the nitty-gritty of tools, prompts, context engineering. Not enough talk about how coding agents will change the very DNA of engineering teams. That's what this panel is about:
- How adoption differs by scale of company
- Challenges of pushing AI-adoption org-wide
- The first 30 days of an AI-native push including important tooling and policy decisions
Looking forward to diving into all of these topics with Nils Widal, CTO of Cara, and @HendersGame, Senior Eng Manager at @honeycombio. @MavenHQ
30 min. Free. All signal, no noise. maven.com/p/c79c8d/challenge…. .
Love the smalltalk at morning meetings these days.
OH: "making oatmeal as my contribution to the Granola discourse"
Reset my X passcode and apparently vaporized an unread DM in the process. If you messaged me recently and I never replied, please send it again 🙏
Nothing makes me grateful for the word "fortnightly" like a conversation about a "bi-weekly" meeting cadence.
Is it once every two weeks or twice a week?
Love the descriptive shorthand @zapier's AI fluency levels offer:
❌ Unacceptable
😐 Capable
📈 Adoptive
✨Transformative
zapier.com/blog/raising-ai-f…
Nobody at @cursor_ai is going to tangle with Disney IP... but it’s hard not to say this is the way 🤌
Hannah Henderson retweeted
“98% of surveyed developers use AI coding tools several times a week.”
Announcing the 𝟮𝟬𝟮𝟱 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗔𝗜 𝗖𝗼𝗱𝗶𝗻𝗴, a study of AI software tool adoption, proficiency, and transformation among professional software developers globally. Key findings:
𝗔𝗜 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘄 𝗻𝗲𝗮𝗿𝗹𝘆 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗮𝗺𝗼𝗻𝗴 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀: 98% of surveyed developers use AI coding tools several times a week. That being said, there is still some resistance among senior developers with them reporting less adoption.
𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝘄𝗶𝗱𝗲𝗹𝘆 𝘂𝘀𝗲𝗱 𝗳𝗼𝗿 𝗮 𝘃𝗮𝗿𝗶𝗲𝘁𝘆 𝗼𝗳 𝘁𝗮𝘀𝗸 𝘁𝘆𝗽𝗲𝘀: There is strong adoption with “explain this” workflows, debugging, test generation, document generation, UI development, and code review. Security use cases however trail behind.
𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗮𝗻𝗱 𝗖𝘂𝗿𝘀𝗼𝗿 𝗱𝗼𝗺𝗶𝗻𝗮𝘁𝗲 𝗮𝗺𝗼𝗻𝗴 𝘁𝗼𝗼𝗹 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗮 𝗹𝗼𝗻𝗴 𝘁𝗮𝗶𝗹 𝗼𝗳 𝗼𝘁𝗵𝗲𝗿 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝘂𝘀𝗮𝗴𝗲: These two have emerged as the coding platform category winners, though many developers are experimenting with other tools. Dedicated code review platforms still aren’t as widely adopted.
𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 𝗼𝗳 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗮𝗿𝗲 𝘀𝗲𝗲𝗶𝗻𝗴 𝘂𝗻𝗲𝘃𝗲𝗻 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗴𝗮𝗶𝗻𝘀: Frontend use cases are seeing the largest productivity gains. However in other software categories like infrastructure, mobile, and design the gains are not as widely perceived.
𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝘀𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹 𝘀𝗲𝘁𝘂𝗽 𝗶𝘀 𝗾𝘂𝗶𝘁𝗲 𝗵𝗶𝗴𝗵: Developers are mostly happy with their AI coding tool setups though satisfaction levels drop at larger companies.
𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝘀𝘁𝗶𝗹𝗹 𝗵𝗮𝘃𝗲 𝗼𝗽𝗲𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝗲𝗲 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱: Hallucination and lack of customization are reported as some of the biggest AI software failure modes. Of additional note, developers say context switching among tasks has become worse with AI coding.
Full link to report: stateof.themodernsoftware.de…
