Sure App store is not to be understated, but I'd add our phones include way way more personal information than a laptop like NFC for credit cards, personal photos, and all biometric and contact information. Not to mention cellular network connection and generally forms as a soft form of identity. None of these apply to a laptop. So form factor does matter.

BUT even if we unlocked the iPhone, the desire for 'MacOS on iPhone' is actually the wrong thing to ask for. Pete Steinberger had in this interview (https://www.youtube.com/watch?v=AcwK1Uuwc0U&t=1182) that UI is basically the wrong paradigm in a world where agents should do tasks for us in milliseconds. We should be able run any local services from our phone like grabbing

Good news is we already have this via terminal apps in Android. Now what's left is the ability for agents to run on your device and basically accomplish tasks for you

Still the result is really interesting being able to stack abstract reasoning and get better performance and the heat maps to show the prob results

The academic literature seems to be catching up:

- *[SOLAR / DUS (Kim et al., 2023)](https://arxiv.org/abs/2312.15166)* — duplicated transformer layers to build a 10.7B model that outperformed 30B parameter baselines.

- *[The Curse of Depth (2025)](https://arxiv.org/abs/2502.05795)* — explains why this works: Pre-LN causes deep transformer layers to converge toward identity functions, meaning middle layers are where real computation happens, and duplicating them concentrates that capacity.

- *[Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach (Geiping et al., NeurIPS 2025)](https://arxiv.org/abs/2502.05171)* — takes the idea to its logical conclusion: a model trained with a single recurrent block repeated at inference time, scaling reasoning depth without adding parameters.

I set it up on a old macbook pro I had that had a broken screen and it works great. Now I just message my server using telegram and it does research for me, organizes my notes, and builds small apps on the fly to help with learning.

However security is a real concern. I need to understand how to create a comprehensive set of allowlists before expanding into anything more serious like bill payments or messaging people / etc

- The lazy person works twice as hard. Often I found you can save a lot of time just trying to the minimal possible and gain a lot of insights of why something is minimal vs not

-The opinion of the person who rarely offers it is listened to more closely. I found the opposite to be true, those who don't offer their thoughts frequently are often dismissed when they do want to share something

Anyway, many of the points are great.. I would also add to keep a journal and write down what was meaningful throughout the day.. you will find time passing by with more quality since you know what the take and what to avoid

But the unique feature: AI-generated "explainers" that break down complex papers with interactive visualizations. Example: https://neurips2025.pages.dev/explainers/linear_attention/

It explains why attention is hard to optimize, shows the math with interactive demos, and includes critical analysis of limitations.

The explainers are generated using Gemini3 to parse papers and create:

- Interactive visualizations

- Step-by-step mathematical walkthroughs

- Critical analysis sections

- "What would convince me?" sections

Tech stack: OpenReview API, Gemini API for explainer generation, static hosting on Cloudflare Pages for speed.

I'm planning to generate explainers for more papers based on what people find interesting, so any feedback would be amazing

Comments URL: https://news.ycombinator.com/item?id=46065572

Points: 2

# Comments: 1

The author's .txt file works because its simplicity forces a daily ritual of self-coaching. The tool demands that the user manually review, prioritize, and decide what matters. There are no features to hide behind, only the discipline of the process itself.

The impulse to use complex apps or build custom scripts is the attempt to engineer a better coach. We try to automate the prioritization and reminders, hoping the system can do the coaching for us.

The great trap, of course, is when we fall in love with engineering the system instead of doing the work. This turns productivity into a sophisticated form of procrastination.

Ultimately, the best system is the one that removes the most friction between decision and action. For the author, that meant stripping away everything but the list itself.

Teams that can do both of these things, especially #1 will move much faster. Even if they are wrong its better than vague ideas that get applause but not customers

However it removes attention so I think its worth watching that space of non-attention models

I think it would be better if it was just context and not connected to any model. Think of one place where you can hook in your drive folder, GitHub, etc. and have it produce the best context for the task you want to achieve. Then users can copy that to their model or workflow of choice