Shreya Shankar Profile picture
Jul 28, 2020 17 tweets 3 min read Read on X
I wrote this while crying in the B40 bathroom at Google because I was going through a depressive episode.

People are talking about bipolar now re: Kanye, but I don’t think there’s enough talk about high-functioning neuroatypicals. Maybe I should speak up, so here goes:
The media portrays us as crazy. People are afraid of us. I can speak to my experience — I have bipolar 2, which means I oscillate between periods of hypomania (weeks of excitement, little sleep, and being on top of life) and depression (weeks of being unable to get out of bed).
Outside perspective: I feel like I have only 50% of my time to be as productive as a normal human, because I know the other 50% of my time goes to the trash can. Maybe that’s why I’m classified as “high-functioning,” even though when amortized, my work output is pretty normal.
Therapy (pt 1): It’s been a year of 3hr/week dialectical behavioral therapy (DBT) for me. I’ve learned skills for distress tolerance, emotion regulation, and interpersonal relationships. I do weekly homework and have a support group. It's not always fun, but it's so useful.
Therapy (pt 2): Before DBT, I struggled so hard to have meaningful relationships — people didn’t want to be close to me. Maybe I had unrealistic expectations or wasn’t like their other friends. I blamed myself and tried to “get better.” Even now I lose relationships. It hurts.
No one gets me (pt 1): It isn't my fault that I was born with a brain that experiences emotions 10x more than the average human. Highs are super high. Lows are super low. No one taught me to work with this range of emotions.
No one gets me (pt 2): Nobody understands why I’m so motivated to crank out 1000s of lines of code for a project. Nobody also understands why I constantly reschedule meetings because I don’t feel like living anymore.
[CW] Self-harm: Yes, I have had suicidal thoughts. But having thoughts is not bad. I now approach my thoughts with curiosity rather than judgement: what does this feel like in my body? What does my brain want to do? I wish neurotypical people were open-minded about such thoughts.
Stuck in a rut: When switching to another episode, it’s so hard to remember how I felt before the episode and how I might feel in the future. When hypomanic, I scoff at the thought of ever being sad. When depressed, I can’t fathom happiness.
Strategies (pt 1): What has worked for me? I try to separate my identity from my brain. My brain might be broken, but that does not mean I am any less of a human being. I used to lament that life sucks, but it’s easier to accept my broken brain and problem-solve around it.
Strategies (pt 2): When I’m in a depressive episode, my manager messages me at 9AM and 5PM for check-in and check-out updates. I forgive myself immediately when I make mistakes — if I sleep in, don’t feel like working out, or skip a meal.
Strategies (pt 3): When I’m hypomanic, I try to stick to a regular sleep schedule. But I have never successfully done so. Also, journaling documents my emotions, so when I'm "stuck in a rut," there are timestamped records of instances where I've felt differently.
Mental illness in others (pt 1): Most bipolar people are undiagnosed until 20s or 30s, or never diagnosed at all. I sometimes wonder what the world’s perception of neuroatypical people would be like if many high-functioning neuroatypical people openly spoke about their illnesses.
Mental illness in others (pt 2): When I was going through my bipolar diagnosis, former managers, mentors, and people I look up to shared their personal experiences with mental illnesses with me. It’s more common than you think.
Maybe we’re all so afraid to talk about our experiences with mental illness because people don’t understand mental illness, and we don’t want to be judged for something we can’t control.
I think we’re just regular human beings with a broken body part. Just because you can’t see the fracture doesn’t mean we’re aliens. We are no less worthy of stability. Great teammates help us when we’re down, and we have so much value to add to a team when we’re feeling good.
Frankly, I don’t see why a bipolar person or anyone with a mental illness can’t be a president.

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More from @sh_reya

Oct 17, 2023
recently been studying prompt engineering through a human-centered (developer-centered) lens. here are some fun tips i’ve learned that don’t involve acronyms or complex words
if you don’t exactly specify the structure you want the response to take on, down to the headers or parentheses or valid attributes, the response structure may vary between LLM calls / it is not amenable to production
play around with the simplest prompt you can think of & run it a bunch of times on different inputs to build intuition for how LLMs “behave” for your task. then start adding instructions to your prompt in the form of rules, e.g., “do not do X”
Read 9 tweets
Sep 12, 2023
thinking about how, in the last year, > 5 ML engineers have told me, unprompted, that they want to do less ML & more software engineering. not because it’s more lucrative to build ML platforms & devtools, but because models can be too unpredictable & make for a stressful job
imo the biggest disconnect between ML-related research & production is that researchers aren’t aware of the human-centric efforts required to sustain ML performance. It feels great to prototype a good model, but on-calls battling unexpected failures chip away at this success
imagine that your career & promos are not about demonstrating good performance for a fixed dataset, but about how quickly on average you are able to respond to every issue some stakeholder has with some prediction. it is just not a sustainable career IMO
Read 8 tweets
Mar 29, 2023
Been working on LLMs in production lately. Here is an initial thoughtdump on LLMOps trends I’ve observed, compared/contrasted with their MLOps counterparts (no, this thread was not written by chat gpt)
1) Experimentation is tangibly more expensive (and slower) in LLMOps. These APIs are not cheap, nor is it really feasible to experiment w/ smaller/cheaper models and expect behaviors to stay consistent when calling bigger models
1.5) we know from MLOps research that high experimentation velocity is crucial for putting and keeping pipelines in prod. A fast way is to collect a few examples, load up a notebook, try out a heck of a lot of different prompts—calling for prompt versioning & management systems
Read 15 tweets
Dec 23, 2022
IMO the chatgpt discourse exposed just about how many people believe writing and communication is only about adhering to some sentence/paragraph structure
I’ve been nervous for some time now, not because I think AI is going to automate away writing-heavy jobs, but because the act of writing has been increasingly commoditized to where I’m not sure whether people know how to tell good writing from bad writing. Useful from useless.
In my field, sometimes it feels like blog posts (that regurgitate useless commentary or make baseless forecasts about the future) are more celebrated/impactful than tooling and thought. Often such articles are written in the vein of PR or branding
Read 5 tweets
Dec 7, 2022
I want to talk about my data validation for ML journey, and where I’m at now. I have been thinking about this for 6 ish years. It starts with me as an intern at FB. The task was to classify FB profiles with some type (e.g., politician, celebrity). I collected training data,
Split it into train/val/test, iterated on the feature set a bit, and eventually got a good test accuracy. Then I “productionized” it, i.e., put it in a dataswarm pipeline (precursor to Airflow afaik). Then I went back to school before the pipeline ran more than once.
Midway through my intro DB course I realized that all the pipeline was doing was generating new training data and model versions every week. No new labels. So the pipeline made no sense. But whatever, I got into ML research and probably would never do ML in industry again.
Read 22 tweets
Sep 20, 2022
Our understanding of MLOps is limited to a fragmented landscape of thought pieces, startup landing pages, & press releases. So we did interview study of ML engineers to understand common practices & challenges across organizations & applications: arxiv.org/abs/2209.09125
The paper is a must-read for anyone trying to do ML in production. Want us to give a talk to your group/org? Email shreyashankar@berkeley.edu. You can read the paper for the war stories & insights, so I’ll do a “behind the scenes” & “fave quotes” in this thread instead.
Behind-the-scenes: another school invited my advisor to contribute to a repo of MLOps resources. We contributed what we could, but felt oddly disappointed by the little evidence we could point to for support.
Read 11 tweets

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