Strongly agree with this. Long before 2022 (ChatGPT), I remember saying to someone at work, "We need to build a reading culture for a writing culture to thrive."
I used to envy and take inspiration from other workplaces where good [but not necessarily good-only] writing was respected; where a pre-read is really read before the meetings, thoughtful comments were made on it, etc.
AI workflows have obviously simplified documentation generation along with the code, but we had to work on our product/engineering practices to generate meaningful documentation, and not just vestigial/temporary documents in the process. On this particular point, we've made positive progress lately.
I remember reading a book a long time back, titled something like "non-linguistic analysis of call center conversations".
One main takeaway from the book was that "you can just look at the ratio of turn-taking duration, and which speaker/participant is spending how much time" to decide "what happened in the conversation".
The same goes for AI-generated conversations; verbose responses are the default behavior, and models are incentivised to keep that output-token ratio. Too easy to catch/notice, pretty annoying.
PS - I work in the Conversational AI space, and it is quite an effort to keep the ratio right for people to stay long enough on the phone with AI agents.
The popularity ranking matches the quality of content produced, and people are spending more time than anticipated on Grok and Roll to confirm if they (listeners) are hallucinating or if the radio is really stuck on roll.
Recently shipped this personal art project that turns daily Wordle attempts into gritty / struggle-filled stories, kinda similar to the emotional arc of the Wordle game play.
You can upload your own Wordle game screenshot to generate one for yourself.
In addition to completing what was once in the idea list, I got to learn about
- Prompt fine-tuning: Models are sharp enough to complete Wordle games quicker than human average scores, so I had to dump that down and get the average down.
- Karpathy’s Autoresearch: Experimented with auto-research for prompt fine-tuning, in addition to manual prompts.
- Vision models: While leading labs have multimodal models with quality visual reasoning, the benchmarks are still quite different for a simple Wordle analysis (reading what letters were yellow/gray/green); I also noticed labs/companies with separate vision models but their APIs lag significantly compared to what’s possible in developer experience.
- Video generation: For the last few days, I have been experimenting with automated video generation for the project's social handles. I'm still struggling with the right hooks that reduce the skip rates, but it's fun.
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Additionally, working on an Apple Watch app similar to my Mac app on the same lines, [Plug That In](https://plugthat.in), i.e., notify before the device goes too low on battery, but with a twist.
Extending on the same line, we will see programs like Google Summer of Code (GSoC) getting a massive revamp, or they will stop operating.
From my failed attempt, I remember that
- Students had to find a project matching their interests/skills and start contributing early.
- We used to talk about staying away from some projects with a low supply of students applying (or lurking in the GitHub/BitBucket issues) because of the complexity required for the projects.
Both of these acted as a creative filter for projects and landed them good students/contributors, but it completely goes away with AI being able to do that at scale.
Glad you found it relevant, and thanks for sharing the feedback.
Not going to deny that it was a bummer to read your second line, lost all the Endorphins earned from reading the first one. Nevertheless, I want to fix this if it's a legit issue.
Do you mind sharing more details on the battery consumption from the app?
I'm using IOKit power source notifications and hearing this feedback for the first time. (personally using this on my work [old M2] and personal [new M4] MacBooks for the last ~2 months).
An annoying little laptop charging reminder utility that does the job.
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There are times when I'm deeply immersed in focused work, a meeting, or engaging video content and end up missing the usual low-battery notifications on my MacBook.
When the laptop suddenly shuts down, it's followed by the familiar and frustrating walk to find a charger or power outlet. It can be annoying and occasionally embarrassing, especially when rejoining a session a few minutes later with, "Sorry, my battery died."
Over the past few weekends, I built Plug-That-In, an app that introduces "floating/moving notifications". These alerts follow the cursor, providing a stronger, harder-to-miss nudge regardless of what’s happening on screen.
The app also includes a few critical features:
- Reminder Mode: When the battery reaches critical levels, the app emits a configurable alert similar to a car's seatbelt warning, continuing until the battery is addressed.
- Do Not Disturb Settings: Customize alerts and sounds based on context, such as when system audio is playing, a video is active, or the camera is in use.
It grew out of a personal need, and I'm glad to see it used by over 50 people in the past month.
An annoying little laptop charging reminder utility that does the job.
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There are times when I am deeply involved in a focus-work session, a meeting, OR watching some sort of engaging video content, and don't pay timely attention to the standard low battery notifications from my MacBook.
After the laptop shuts down suddenly, what follows is the most annoying walk to find the charger or the charging outlet. It's frustrating at times, sometimes embarrassing because you have to say, "Sorry, my battery died down" as you join back the session after 2-3 minutes.
Over the last 3-4 weekends, I have been building Plug-That-In, which has floating notifications. Essentially, a notification that follows my cursor movement, so I get a stronger nudge irrespective of what I am doing.
There are a few other critical features, such as Reminder Mode and Do-Not-Disturb Settings.
- Reminder Mode: On critical/lower battery levels, it will keep beeping like a car's seat belt alert for some time (configurable) when the battery is really low.
- Do-Not-Disturb settings: Configure what sort of alert/sound it will generate when I have system audio playing or video playing, or the camera is active.
It has addressed a personal need and has already proven useful a few times over the last weeks.
Tinkering with a tiny macOS app that gives me proactive reminders about the low battery and imminent shutdown.
Standard system notification comes at about 10%, and most of the time, in my case at least, whenever I miss that, the result is "laptop shutdown amidst an ongoing video meeting" or something like that.
(Basically, too late before I act)
Just so that I don't miss the reminders, the app will show an overlay window with some text, following my cursor, and a custom sound.
I built a version this weekend, and am current doing a dogfooding exercise.
This resonates a lot with some observations I drafted last week about "AI Slop" at the workplace.
Overall, people are making a net-negative contribution by not having a sense of when to review/filter the responses generated by AI tools, because either (i) someone else is required to make that additional effort, or (ii) the problem is not solved properly.
This sounds similar to a few patterns I noted
- The average length of documents and emails has increased.
- Not alarmingly so, but people have started writing Slack/Teams responses with LLMs. (and it’s not just to fix the grammar.)
- Many discussions and brainstorms now start with a meeting summary or transcript, which often goes through multiple rounds of information loss as it’s summarized and re-expanded by different stakeholders. [arXiv:2509.04438, arXiv:2401.16475]
Jotted down a few thoughts based on a pattern I’ve started noticing; AI is often getting used inefficiently.
Users in a white-collar setup are limiting their relationship with LLMs at a peer-level, and a multitude of AI-tool UX is designed to reinforce that same hierarchy, which is marking the entry of "AI Slop" into the workplace.
I used to envy and take inspiration from other workplaces where good [but not necessarily good-only] writing was respected; where a pre-read is really read before the meetings, thoughtful comments were made on it, etc.
AI workflows have obviously simplified documentation generation along with the code, but we had to work on our product/engineering practices to generate meaningful documentation, and not just vestigial/temporary documents in the process. On this particular point, we've made positive progress lately.