标签：#mac

If ICML conference paper is rejected and no one opts-in or opts-out to keep the reviews visible, will the reviews be visible to everyone? There was clear instruction that only papers with at-least 1 opt-in AND zero opt-out options will be visible. None of the authors selected any option, But it in my openreview profile, it shows visible to everyone. please clarify. (Just above paper decision, there is a block with "filter by type", "filter by author" etc options. in that block there is eye symbol and everyone is written.) submitted by /u/Curious-Monitor497 [link] [留言]

With more software engineers entering into data science and AI, I feel it's equally important for a person with data and AI background to dive into software development to survive, thrive in industry. I Know it's a very broad question, so suggestions with broad subjects, topics are welcome , like I often wonder how DSA is relevant. I totally understand the needs of the skills are deeply coupled with domain, industry and specific problems but unfortunately the industry doesn't understand this, it judges you, rewards you based on what you already know or pretend rather than your ability to learn or adapt. submitted by /u/Dapper_Chance_2484 [link] [留言]

All tests run on an 8-year-old MacBook Air. All results from shipping 7 Mac apps as a solo developer. No sponsored opinion. You plug in your Android device. A few seconds later, you unplug the cable. The connection stays alive — wirelessly, automatically, without touching a single setting. That's Seamless Link. The Problem ADB over USB is reliable. ADB over Wi-Fi is convenient. But switching between them manually is friction: Plug in USB Run adb tcpip 5555 Find the device IP Run adb connect <IP>:5555 Unplug cable Every. Single. Time. If you're working with multiple Android devices, or doing this across multiple sessions per day, it adds up fast. What Seamless Link Does The moment you plug in a USB cable, Seamless Link runs that entire flow automatically in the background: Detects the USB connection Runs adb tcpip 5555 Grabs the device IP Establishes a Wi-Fi ADB connection By the time you've sat back down, the device is already connected wirelessly. Pull the cable out — everything keeps working. No manual steps. No IP hunting. No re-running commands every session. Working with Multiple Devices This gets more useful the more devices you have. Plug in Device A → Seamless Link connects it wirelessly. Plug in Device B → same thing, simultaneously. Each device goes through the full handover flow independently, in parallel. The more devices on your desk, the more time this saves. Android 16 Compatibility Android 16 changed how wireless debugging ports are assigned — random ports instead of the fixed 5555. Seamless Link handles this automatically. You don't need to know which port the device is using. If you're on an older Android version, it works the same way it always has. Why This Matters for Daily Workflows If you're an Android developer on Mac, you probably already have a USB cable on your desk. Seamless Link just makes that cable optional after the first few seconds. It's one of those features that's hard to go back from once you've used it. The cable becomes a "char

Hi folks, Deciding between these two Mac options has been a challenge for me, so pls help. I know mac is not even necessary for this but just help me to decide between these two options. For the reference, Im a swe student and looking forward to go deep into ml and data science in the near future… EDIT: mac book pro m5 ( base chip) that I’m referring here. submitted by /u/Both-Hovercraft3161 [link] [留言]

Hi everyone, I'm an undergraduate student and ML researcher at UC Berkeley. My colleagues and I are working on a project that hopes to fix some of the problems users face with Colab. What are the features you wish it had as an ML professional, researcher, or enthusiast? What're the biggest problems you've faced while using it? Some of the issues that everyone feels (including us) is environment management and kernel persistence. But we would love to hear more from the community. submitted by /u/myplstn [link] [留言]

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

In the previous article, we explored concepts such as total loss and epochs. Now, we will continue...

I read and collected Arxiv whitepapers starting after the launch of ChatGPT. I copied and pasted excerpts into Word to track them. Then migrated to Obsidian. That vault of some 1700 papers is now online. I figured it was time to see if others would find the collection useful. My whitepapers were organized into some 90 categories, all of which emerged from paper topics. New categories became necessary with the discussion of new methods, techniques, models etc. If I wanted to write about a topic, I'd upload an md file containing research excerpts on that topic to ChatGPT. This worked to a degree but maxxed out context pretty quickly. And I always had related research in multiple categories, according to how the research was framed. (Personas research in Aligment, Psychology, HCI, etc). So I used a plugin to create topic notes that built in and outbound wikilinks across the papers centered on shared concepts. When I ported this all online I added another layer of synthesis: Inquiring Lines as I call them. These cover cross-cutting, tension-surfacing, synthesizing, and frontier-opening research frames. There's 6,000 of them in my collection. Each is a page to itself that's a useful description of a research line of inquiry. These now also have prompts you can run yourself that will find related (and more recent) research - (I can't adequately maintain each topic with new research). It's all at https://inquiringlines.com/inquiring-lines/ if you want to poke around. As is everything in the age of AI, it's a work in progress. But there's a lot of rich material in there. Have a look. submitted by /u/Barton5877 [link] [留言]

Hi, I am and PhD student and trying to run a ML reading group focused on interpretability and robustness every weekend. Its always nice to hear different takes and opinions on a paper and this discussion group could serve the purpose. If you are a fellow PhD student or a ML researcher interested in reading recent papers in depth then please fill this google form to be added in the group for receiving further updates on when we can meet and discuss: https://docs.google.com/forms/d/e/1FAIpQLSdNg4x60lUHV7YW_kKPFlpPR3Rom_rOovbryD8YtOGQR8x0Kw/viewform submitted by /u/Ok_Access_9159 [link] [留言]

I installed Hermes Desktop on macOS but it was different from what I expected lol (I thought it would work as a client application for my Hermes agent I set up). Here are 4 steps to uninstall Hermes Desktop from macOS. 1 Stop Hermes gateway hermes gateway stop 2 Kill the process pkill -f "hermes" \n 3 Remove files rm -f ~/.local/bin/hermes \n rm -rf ~/.hermes \n 4 Clean up system ctl launchctl unload ~/Library/LaunchAgents/ai.hermes.gateway.plist launchctl remove ai.hermes.gateway rm -f ~/Library/LaunchAgents/ai.hermes.gateway.plist

Hi everyone, I’m an amateur student who has been experimenting with neural networks mostly out of curiosity. Over the past few weeks, I ended up going fairly deep into a specific architecture I designed, which I call a Directional Neural Network (DirNN) . This isn’t meant as a polished or formal contribution — it’s something I’ve been tinkering with, iterating on, and testing in my spare time. That said, the architecture does impose real structural constraints and uses a custom backward pass. In my own experiments on simple tasks (including some using GloVe embeddings), the DirNN has repeatedly performed better than standard MLP baselines. This result has been consistent enough that I don’t think it’s pure luck — but I’m very aware that I might be fooling myself. What I’m unsure about is whether I’ve been unfair in my comparisons . I don’t know if: the DirNN is effectively a special or degenerate case of an MLP my training procedure, initialization, or optimizer choices favor it in subtle ways the tasks or datasets I’m using make the comparison misleading I’ve put together a small repository with a README describing the architecture, the custom backward pass, and a minimal script to reproduce what I’m seeing. I’m posting here because I could really use a sanity check from people more experienced than me . If this is obviously flawed, I’d much rather learn that now. Blunt technical criticism, references, or “you’re missing X” comments are all very welcome. Repository: DirNNs Thanks for reading — I’m genuinely here to learn. submitted by /u/jos_lucas73 [link] [留言]

I need a break from social media and all the bots.. Aside from Arxiv are there any sources that do a good job of aggregating the good stuff and filtering out all the junk? submitted by /u/Tiny_Arugula_5648 [link] [留言]

Aerospace completely revolutionized my workflow after 15 years of using macOS the way Apple intended. I no longer hunt for apps and windows in Mission Control or drag them around spaces to organize. I can open as many windows as I need and have them all under my fingertips. And instead of swiping around to find one, I instantly teleport to where they are. This incredible software is technically aimed at advanced users. It’s installed from the command line and offers extensive configuration options. For basic use though, you don’t need to configure it at all, and if you have opened the Terminal application before and know what running a command means, you should be good to go. Rest assured, I will not show you how to configure Aerospace with Vim, or show you how to create an elaborate but useless dashboard! Just the essentials to get you started. How to set up Aerospace Aerospace is a menu bar application, but you can’t download it from an App Store or get it as a DMG file. You need a package manager. Go to the Homebrew website and follow the installation guide. Make sure to accurately follow the on-screen instructions. This may include any of the following: A prompt to enter your password. When you type passwords in Terminal, you will not see stars or anything. Just make sure you’re typing the correct one and hit Enter. A prompt to install XCode Command Line Tools . Somewhere around the end of the installation process, you may get a prompt to run some extra commands, which depend on your system. Make sure you run them as instructed. To test if you have correctly installed Homebrew, run which brew in Terminal. If you see a path printed out, like /opt/homebrew/bin/brew , you’re good to go. If not, something has gone wrong. Try searching for other, more focused guides on installing Homebrew. With Homebrew, you can install applications from the Terminal app using the brew command. For Aerospace, you would run the following command: brew install --cask nikitabobko/tap/ae

Hi all, I have been working on this method based on a hunch along with many llm for quite some time. Though first it was being engineered by me but I was learning in supervised ml area but this hunch took to semi-supervised ml and that to too deep. I then became llm orchestrator of sort while 4 llm's tried to figure it out. I put up a live demo on Hugging Face Spaces where you can try it yourself — set the number of labels, click run, see the accuracy. No installation, no code required. Brief about method Optimus — Graph SSL under Extreme Label Scarcity Key Results (PathMNIST, N=2000, 9 classes) Labels Total Optimus GCN 9(1 per class) 73.9 60.6 27(3 per class) 77.3 68.5 45(5 per class) 79.8 77.1 https://huggingface.co/spaces/Keshu007/optimus-graph-ssl Edit : You can can even run the code on your own dataset submitted by /u/Loner_Indian [link] [留言]

From TF's website: Quantization aware training emulates inference-time quantization, creating a model that downstream tools will use to produce actually quantized models. So is it designed to work with a very specific quantization method (for Gemma-4, presumably, Google's own)? Or would it make sense to use alternative quantization methods? According to the benchmarks unsloth released, its (alternative) quantizations of Gemma-4-QAT are closer to the QAT fine-tunes, but is it a good thing, or does it defeat the purpose of QAT? submitted by /u/we_are_mammals [link] [留言]

I'm planning to submit a paper to either NMI, but this will be my first paper to a nature-like venue. Would love a quick chat with anyone that has experience. My paper's specifically more geared towards signal processing with ML for a specific subfield of engineering. But can be interdisciplinary. submitted by /u/PlateLive8645 [link] [留言]

maybe this should be asked in the Fc26 game subreddit but not sure. Anyway I just saw a video of someone predicting the winner of the world cup using the simulate match feature in the game but he only did it once. Would running this feature 100-1000 times give a significant result ? or is that feature only based on luck ? submitted by /u/Stillane [link] [留言]

Hi all, Lately I have been working on creating a package for Multi Agent RL based drone environments with different objectives, all bundled into a single GitHub repository: tau-intelligence/MuJoCo-drones-gym. I am currently trying to organize things for RL community people, with a couple more tools coming soon. But right now, I want to make it useful for the community and hence would love some feedback from different people, about how I could improve it, incorporate more things into it or fix some broken implementation. Also everyone is welcome to raise issues on the repo. Thank you for the support. PS: I have some research publications at RL and ML venues regarding work on RL, though I still want to consider myself as a student of the field and hence would love your help here. submitted by /u/MT1699 [link] [留言]

The first trillionaire in history won't make their money from banking, oil, or real estate. They'll make it from rockets and algorithms — and the implications of that distinction are genuinely unsettling. The Problem It's Solving (Or Creating) SpaceX is preparing for its IPO. Analysts tracking the raise estimate it will push Elon Musk's net worth past the trillion-dollar threshold, making him not just the richest person on Earth by a wide margin, but something qualitatively different from every billionaire before him. The standard framing treats this as a wealth story. It isn't. A billionaire is powerful because they have money. A trillionaire is powerful because, at that scale, they stop needing permission from anyone — governments, investors, boards, markets. The constraints that keep institutional power in check simply don't apply anymore. How Trillionaire-Scale Power Actually Works There's a clean way to understand the difference. A billionaire can fund political candidates, buy media, lobby aggressively. Another billionaire can fund the opposition. It's expensive, but the system has a counter. A trillionaire doesn't have a counter. They are the counter. They can simultaneously build the communications infrastructure (Starlink), the transportation layer (SpaceX), the compute stack (through xAI), and the political attention economy (via platform ownership). No single democratic institution was designed to regulate someone who owns the pipes that the institution runs on. Arnab Ray's piece in today's Times of India puts it directly: a trillionaire's thoughts and algorithms will shape planetary outcomes. That's not hyperbole. When Musk eventually lands people on Mars, the governance frameworks, the property rights, the social contracts of that colony — those will be engineered by him and his companies, not negotiated through any existing democratic process. What Societies Are Actually Unprepared For Most of the policy debate around billionaires focuses on tax rates

What Ollama actually is Ollama is an open-source runtime for large language models that runs on your own computer — Mac, Windows, or Linux. Think of it as the “Docker for LLMs”: instead of wrestling with Python environments, model weights, and GPU drivers, you type one command and a model is running. The pitch is simple: keep your data on your machine, pay nothing per token, and work offline. When you run ollama run gemma4, Ollama downloads the model, loads it into your GPU’s memory (or system RAM if you don’t have a GPU), and drops you into a chat prompt. That’s it. Behind that simplicity, Ollama is doing a lot of work for you: Model management — pulling, versioning, and storing models from its registry, the way a package manager handles software. Quantization — automatically using compressed (GGUF) versions of models so a 27-billion-parameter model fits in consumer memory. GPU layer allocation — deciding how much of the model lives on your GPU versus CPU, based on the VRAM you have. Context and KV-cache management — handling the memory that grows as a conversation gets longer. A REST API — exposing everything on http://localhost:11434 so your own apps can talk to it. How it works under the hood Ollama is not itself an inference engine. It’s an experience layer wrapped around one. Under the hood it uses llama.cpp, the C++ engine that does the actual math of running a quantized model efficiently on CPUs and GPUs. As of v0.19 (March 2026), Ollama also uses Apple’s MLX backend on Apple Silicon — a change that delivered enormous speedups (on an M5 Max running Qwen 3.5, decode throughput nearly doubled). The workflow looks like this: You run a command — ollama run qwen3 from the terminal, or a request to the API. Ollama resolves the model — if it isn’t already downloaded, it pulls the GGUF weights from the registry. It loads the model into memory — splitting layers between GPU and CPU based on available VRAM. It serves responses — either interactively in your terminal o