imranq

Okay so the OP is saying that since Macbook Neo has the same hardware as iphone, but not locked down, so why is iphone locked down. They say its because of the app store profits.

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

Amazing write up and i wish more people showed the process for discovery which is often even more interesting than the result itself

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 think it might be 2020 when the M1 was released since I remember i had bought a mac book in 2019 and it was still intel

I really like Clawdbots safety gloves off approach - no handholding or just saying yes to every permission.

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

Some great life lessons here, but also some I don't agree with:

- 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

Just because it is in C, doesn't mean you will get C like performance. Just look at the benchmarks, it is 8x slower than just using PyTorch... while I get its cool to use LLMs to generate code at this level, getting super high performing optimized code is very much out of the domain of current frontier LLMs

I really liked the approach of getting new topics to research via embeddings, trails, and claude code, but often what will this give you outside of novelty?

Hey HN! I built an explorer for all NeurIPS 2025 Main Conference and workshop papers with reviews, scores, and code links.

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

The claims in this paper don't make sense. There is no proof that anything has been decompressed

There is one: https://pricepertoken.com/

Turning it off and then on again works in a lot of surprising places

People basically want a life coach, someone by their side who can tell them what the best next thing to do is at any given moment. Everything else are just approximation of that ideal.

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.

While I agree with the author's vision for a more human-centric AI, I think we're closer to that than the article suggests. The core issue is that the default behavior is what's being criticized. The instruction-following capabilities of modern models mean we can already build these Socratic, guiding systems by creating specific system prompts and tools (like MCP servers). The real challenge isn't technical feasibility, but rather shifting the product design philosophy away from 'magic button' solutions toward these more collaborative, and ultimately more effective, workflows

My two takeaways is you build 1) Having a precise vision of what you want to achieve 2) Being able to control / steer AI towards that vision

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

This was a bit hard to read. It would be good to have a narrative structure and more clear explanation of concepts.

I'd love to host my own LLMs but I keep getting held back from the quality and affordability of Cloud LLMs. Why go local unless there's private data involved?

I like the idea of removing quadratic scaling for attention, this paper has thin experimental support. No real tasks tested beyond perplexity. Nothing on reasoning, retrieval QA, or summarization quality. Even in perplexity the gains are marginal.

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

The powers of kerning are great indeed

Nice idea!

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

Could you pop your code into an LLM and ask it to write comments for you? I'm not sure how accurate it is though

You can describe a diagram with markdown like mermaid, so you can at least understand state changes and processes which are core to engineering.

This just seems like a fancy way of describing LoRA? At the end of the day you are still learning weights based on a described set of outputs and then applying them to inference

There are already some resources like that like:

leetgpu.com

https://github.com/srush/GPU-Puzzles

For me its about a sense of progress, like in chess you can have an ELO score. Or in Duolingo theres a roadmap. If there were levels to this you could get more confident in your abilities.

Right now the levels are basically bachelors, masters, and PhD. Coarse and expensive

If someone could turn these into an adaptive Khan Academy style app, that would be incredible

I wish every paper had a succinct image like that!

Dang only forward passes. The real secret was in the backward pass! I was also curious to learn how they implemented the dualpipe scheduler

Amazing, this is why I visit HN :D

Combining the two ideas, "hand drawn" animation flip books could be neat. Start with a video, convert frames to a drawable vector, draw each frame with a pen plotter, assemble the flip book

Love it - I wonder if you could create a animation book this way cheaply, see a moving image as you flip through it

I think its amazing that posts like this exist, and more should definitely be written so that people don't feel powerless after a layoff. Too often we tie our identity to institutions and it isn't doing anyone any good (well maybe it helps the shareholders).

Based on the chart, the Kaggle SOTA model is far more impressive. These O3 models are more expensive to run than just hiring a mechanical turk worker. It's nice we are proving out the scaling hypothesis further, it's just grossly inelegant.

The Kaggle SOTA performs 2x as well as o1 high at a fraction of the cost