标签：#learning

Hey! I'm a CS student and I got tired of not being able to compare MLX inference engines properly — every benchmark out there is either made by the engine's own developers, runs on an M3 Ultra nobody has, or just shows tok/s with zero context. So I built mlx-Chronos — a small open source CLI tool that runs a standardized benchmark protocol on your Mac and lets you submit your results to a shared community leaderboard. What it measures: Cold and cached TTFT (Time to First Token), with a proper methodology — unique prompts per trial, cache priming, no interleaved phases Throughput (tok/s), with mean/stddev/min/max across repeated trials Engine process RSS and system RAM peak, sampled continuously during inference Thermal state and hardware info Supported engines: oMLX, Rapid-MLX, mlx-lm, Ollama (MLX backend) The leaderboard is basically empty right now since I only have an M2 8GB. Would love results from M3 Max, M4, M4 Ultra, or anything with more RAM — that's where things get actually interesting. → Leaderboard: https://igurss.github.io/mlx-chronos → GitHub: https://github.com/igurss/mlx-chronos → Install: pip install mlx-chronos It's early, the methodology is documented (there's a methodology.md if you want to pick it apart), and I'm 100% open to feedback, contributions, and getting told what I'm doing wrong. The goal is just to have one place where you can compare engines on your specific hardware instead of trusting someone else's numbers. submitted by /u/igor__004 [link] [留言]

174 million smart TVs, most of which run Linux. 3.9 billion Android phones. Zero marketing. Tonight, somewhere around the world, a person will press the power button on their Samsung TV. A proprietary Samsung logo will appear. A polished menu will load. They will open Netflix, scroll through recommendations, and pick a movie. They will never know that every frame they see is being scheduled, managed, and rendered by a Linux kernel, the invisible engine that sits between apps and hardware. They will then reach for their Android phone to check something on social media. Another Linux kernel. If they are sitting in a Tesla, the touchscreen showing their charging status is running yet another Linux kernel. The “year of the Linux desktop” debate has been running for two decades. Entire forums exist to argue about whether 2025, 2026, or 2027 will finally be the year Linux takes over the PC market.

For Job Postings please use this template Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for] For Those looking for jobs please use this template Want to be Hired: [Location], Salary Expectation:[], [Remote | Relocation], [Full Time | Contract | Part Time] Resume: [Link to resume] and [Brief overview, what you're looking for] ​ Please remember that this community is geared towards those with experience. submitted by /u/AutoModerator [link] [留言]

Hi, Relatively new to deep learning. I wanted some opinions on which of these approaches might be best for time series data and spectral analysis. I currently use a GP and it works pretty well, but I’m wondering what the computational tradeoffs and so forth might be. Any ideas? submitted by /u/InevitableCut1243 [link] [留言]

It’s hard to believe how quickly the tech landscape is evolving, especially with AI/ML at the forefront. Just the other day, I had the chance to attend the Mistral AI Now Summit, and wow, what an experience! I walked in with a notebook full of questions and a mind buzzing with curiosity. I left with a treasure trove of insights, a few new friends, and even more questions. Ever wondered what it’s like to dive deep into the world of AI with some of the brightest minds? Let me take you on a journey through my day at the summit and the lessons I picked up along the way. A New Era of AI Walking into the venue felt electric. The air was thick with excitement, and the buzz was palpable. I’ve been exploring AI for a few years now, and it feels like we’re at the cusp of something monumental. Mistral’s focus on open-weight models really got me thinking. What if we could democratize AI further? Imagine a world where innovation isn't locked behind corporate walls but accessible to everyone. I remember a speaker discussing how open models can help small startups compete against big players. It hit home because I’ve been that small developer trying to fight the good fight. Real-World Applications One of the discussions that resonated with me was about real-world applications of large language models (LLMs). I’ve dabbled in a few projects where I used Hugging Face's Transformers library to create chatbots, but hearing actual use cases from businesses was enlightening. For instance, a startup shared how they used a fine-tuned model to improve customer service response times by 50%. Can you imagine the time and money saved? It got me thinking about how I could implement something similar in my own projects. Here’s a quick code snippet that I’ve found useful when fine-tuning a model for a chatbot: from transformers import Trainer , TrainingArguments training_args = TrainingArguments ( output_dir = " ./results " , evaluation_strategy = " epoch " , learning_rate = 2e-5 , per_device_tra

I'm a builder. I taught myself to run servers because freelancers kept burning my money. West London, 2021. I was standing on a site holding a cup of tea that had gone cold an hour earlier, watching a crew argue about where a wall should go. That's my actual job. Schedules, suppliers, the kind of problems that only exist at 7am when half the crew hasn't shown up and the client is already phoning. But my head was somewhere else. I'd been chewing on an idea for a classifieds platform for months. Not a grand vision, nothing with a business plan and projections. Just a gap I could see — a way to connect buyers and sellers that felt easier and more global than what was out there. The problem was that I knew nothing about programming. And I mean nothing. I didn't know what a database was. I'd never written a line of code. My entire technical CV was "reasonably good at not breaking my own phone." So I did what most people in my position do. I tried to buy my way in. The expensive year I found a ready-made classifieds script online. Looked professional, had features, didn't cost the earth. The smart shortcut, I told myself. Then I hired a freelancer to customise it. Then another one, when the first disappeared mid-project. Then another, when the second delivered something that worked on a good day and fell over on a bad one. Here's the thing nobody warns you about hiring freelancers when you can't read code: you can't judge the work. You can't tell the difference between someone who wrote something clean and someone who duct-taped it together to last until the invoice clears. Both show you the same thing — a screen where the button does what the button's meant to do. So you pay, you say thanks, you move on. And three months later the button stops working and the freelancer's gone. Meanwhile the bots had found me. Within weeks of going live, automated scripts were hammering the contact form, then the registration page, then the login. "It's normal," a freelancer told me. "Ha

I have an ECCV paper main conf. Can I submit the same to a workshop at some other place happening before ECCV? The other workshop (non archival) will be after the final decisions of eccv come. Under any result in eccv- acceptance or rejection, how will this affect it? Im not the main author at eccv. Workshop is women event and I submitted the abstract for the workshop. How do things work here, educate me pls. submitted by /u/Active-Tip3130 [link] [留言]

I want to develop an LLM that can solve open-ended math problems (such as proof-only problems). This means that RLVR where we use the final answer alone as reward signal is not enough. Since SFT is useless here and GRPO/PPO methods will not have an appropriate reward function, what kind of fine-tuning can I do? For data, I will use the MathNet dataset. submitted by /u/TechNerd10191 [link] [留言]

AI placement latency is not the problem most teams think they are managing. The default framing treats it as an optimization variable — pick the cheapest compute that meets the SLA, centralize inference, optimize for utilization, revisit locality later when the architecture matures. That framing is wrong in a way that compounds over time. AI placement decisions are not continuously reversible optimization choices. They are architectural commitments that harden incrementally — through inference path configuration, data gravity, routing dependencies, and runtime behavior that normalizes around whatever topology you chose first. By the time latency SLAs begin failing, the placement topology is already embedded across routing, observability, and application behavior. The remediation cost is not an optimization exercise. It is a re-architecture. The First Optimization Becomes the Permanent One Cost is the default optimization axis for AI placement decisions. Centralized GPU clusters are cheaper to operate per token than distributed inference endpoints. Utilization density justifies centralization on paper. Procurement processes reward it. FinOps tooling measures it. So teams centralize. They optimize the compute economics. They defer locality decisions to a later phase when requirements are better understood. That later phase rarely arrives before the architecture has already made the locality decision implicitly — through the inference paths built against a centralized endpoint, the data gravity that formed around it, and the application behavior that normalized against the latency profile it produced. The pattern this creates is latency debt: accumulated runtime latency overhead from placement decisions that optimized for cost before locality requirements were operationally visible. It accrues gradually, stays invisible until something triggers it, and is significantly more expensive to resolve after the fact than it would have been to avoid at design time. It does not

Ps. Not pitching anything; Just trying to understand where reality differs from the narrative We're a couple of ML students, mostly worked on ML/software before, but over the last few months we've been playing with VLAs, robot datasets, and trying to understand where the field is heading. After spending a few weeks downloading robotics datasets, we were surprised by how much effort went into just getting data into a usable format. Maybe we're missing something, but it felt like every dataset had different assumptions, schemas, sensors, coordinate frames, metadata standards, and tooling. That got us wondering: How do robotics teams actually think about data sharing? Do people genuinely want access to more robot data, or is the industry moving toward "collect your own data because nobody else's transfers"? Our current (possibly very wrong) hypothesis is: The robotics ecosystem doesn't have a data scarcity problem. It has a data interoperability problem. We're considering running a pretty large experiment: Take essentially every public robot-learning dataset we can get our hands on, normalize it into a common schema, enrich it with metadata, and see how much of it is actually reusable across tasks, embodiments, and learning pipelines. Before we spend months doing that, we'd love to hear from people actually building in robotics. Where is this hypothesis wrong? Is finding data not actually a problem? Is embodiment mismatch the real blocker? Is quality the issue? Is labeling the issue? Is everyone just collecting their own data anyway? Would you ever use robot data collected by another team? If I gave you access tomorrow to every public robotics dataset through one API, what would you actually do with it? Or would you ignore it completely? ------------------------------------------------------------------------------------------------------ Edit: One clarification We're not thinking about a marketplace, proprietary format, or closed platform. The experiment we're conside

My paper was recently accepted to a workshop at CVPR-2026 as non-archival acceptance. Is it mandatory for me to register to the conference as I won't be able to attend(visa issues), but my friend will be there in the conference and can present on my behalf. I have few questions regarding my situation: Do I need to finish author registration for a non-archival workshop? Is it mandatory for me to have a poster in the conference venue? Will my paper get removed from the workshop website(where they list out the accepted papers) in case I don't register or not attend offline? Quick replies are appreciated as the deadline is pretty close. Thanks 🙏 submitted by /u/Sky6574 [link] [留言]

I'm trying to understand the intuition behind Word2Vec training using a neural network. In Word2Vec (CBOW or Skip-gram), we often hear that the weight matrices learned during training contain the vector representations (embeddings) of words. However, I don't understand why the weights of the hidden-to-output layer (or output weight matrix) end up representing semantic features of words. Why do these weights become meaningful vector representations instead of just being parameters used to make predictions? I've explored multiple YouTube videos, blog posts and even asked ChatGPT several times, but I still haven't found an explanation that truly clicks for me. Most resources explain that the weights become embeddings, but not why this happens intuitively and mathematically. Could someone provide a clear intuition or mathematical explanation of why the output-layer weights end up encoding semantic information about words? Any good resources that explain this particularly well would also be appreciated. submitted by /u/aaryantiwari26 [link] [留言]

The Problem We Were Actually Solving We were not building a demo. We needed to let Veltrix operators run A/B experiments on synthetic user journeys without melting the underlying SQL warehouse. The real question was: how close could we push the warehouse to the AI inference layer before the planner started dropping predicates and the warehouse returned rows that made no sense for the user journey. The warehouse in question was a Snowflake XL on AWS, billed by the second. Our synthetic user model generated 250 k journeys per minute during peak. The AI layer had to annotate each journey with intent tags (shopping, support, fraud) within 200 ms to stay ahead of the next batch. That was the operating envelope, not the sales slide. What We Tried First (And Why It Failed) First cut: put the intent model in a sidecar container next to the Spark cluster that generated the journeys. We picked ONNX Runtime v1.14 with a DistilBERT fine-tuned on our own corpus because the latency slide said 30 ms. Reality: ONNX packaged the tokenizer as a separate DLL. Tokenization alone took 85–110 ms on c6i.large instances, pushing the total inference time to 190 ms when the warehouse was cold and 280 ms when Snowflake decided to spike the warehouse cluster. The operator dashboards immediately showed orange pings; the business called it a red fire drill. Worse, the tokenizer DLL leaked memory. After two hours on a 64-core cluster, each pods RSS climbed to 2.4 GB, and the Kubernetes scheduler evicted five pods in a row. The warehouse downstream received duplicate rows with NULL intents, so every metric we exported was off by 7–12 %. The Architecture Decision We ripped out the sidecar entirely. Instead, the Spark jobs write raw event JSON to an S3 bucket every 60 seconds. A Lambda function (Python 3.12 runtime) picks up the bucket, tokenizes offline, and stores the tokenized blobs back in S3. A nightly Kubernetes job then loads the tokenized chunks into Snowflake as temporary tables. The AI inf

Hey r/ML , I spent the last few months building a tool that hooks into PyTorch training loops to automatically detect and localize failures (vanishing gradients, exploding gradients, data anomalies). Along the way, I learned some things about training failure diagnosis that might be useful even if you never use the tool. The key insight: most training failures are local, not global When your loss spikes or vanishes, the natural instinct is to look at the loss curve. But the loss is a global aggregate — it tells you something went wrong, but not where . In my testing across hundreds of synthetic failure scenarios, the actual root cause is almost always localized to a specific layer at a specific step : Vanishing gradients: the failure starts at the deepest layer with saturated activations, then propagates backward Exploding gradients: the failure starts at the layer with the highest gradient norm, then propagates forward Data anomalies: the failure starts at the input layer, then corrupts everything downstream The trick is to monitor per-layer gradient norms and detect transitions (healthy → vanishing), not absolute values. What actually matters in gradient monitoring Most people monitor: - Loss over time (too global) - Gradient histograms (too noisy, too much data) - Weight norms (slow to change, lagging indicator) What I found works best: - Gradient norm transitions : "Linear_3 went from healthy (0.12) to vanishing (0.00003) at step 47" - First occurrence tracking : which layer failed first (this is usually the root cause) - Activation regime shifts : when activations go from normal to saturated/dead This is basically what NeuralDBG does under the hood — I open-sourced it recently and it's on PyPI ( pip install neuraldbg ) if anyone wants to try it. The key design choice was to extract semantic events (transitions) rather than raw tensors — this makes the output small enough to reason about. Practical takeaway you can use today Even without any tool, you can add th

I didn't submit any but did place bids on some papers. I got assigned four papers. I have a bit of travel coming up and I don't think I will be able to do justice to as many the papers, especially in the rebuttal period. Is this the standard reviewing load? In other communities I submit to, generally the AC themselves are assigned four papers. In addition to reaching out to program chairs, is there any other way? submitted by /u/movieingitmyway [link] [留言]

I benchmarked it against PyTorch with a Wikipedia dataset. I heavily used Gemini Flash 3.5 to build out my vision https://huggingface.co/etoxin/neuronguard-wikipedia-classifier submitted by /u/Logical_Prompt_3543 [link] [留言]

In early 2022, I was deciding between PhD offers. The deal maker was a prospective supervisor telling me that through their connections with big tech, I would be able to do a PhD internship each summer, which was one of my main goals for the PhD. During my first and second years, they would tell me that companies prefer late-stage PhD students, so I should wait for the next summer. It eventually turned out they did not actually have the connections. Four years later, I am due to graduate without ever having done a PhD internship. I managed to land some interviews by cold-applying everywhere, but most roles were for roles outside my niche research area, which understandably led to rejections. I went back through my emails and found every interview I did. Here is the summary: 09/22: Start PhD 09/23: PhD Research Intern @ Big Tech#1. Rejected after two interviews. I do not think I had a strong enough background in the field. 01/24: PhD Research Intern @ Startup#1. Rejected after one interview. The interviewers did not seem to have much ML experience. 01/24: PhD Intern @ Car Company#1. Rejected after the first interview. They were looking for a C++ SWE. 03/24: PhD Research Intern @ Big Tech#2. Passed all stages, but failed team matching. 03/25: PhD Research Intern @ Big Tech#2. Skipped some stages, passed others, but failed team matching again. 10/25: PhD Research Intern @ Startup#2. Rejected after 5 interviews. Again, I do not think my background in the field was strong enough. 01/26: PhD Research Intern @ Car Company#2. Rejected after the first interview. They found a better fit for the project. 03/26: PhD Research Intern @ Big Tech#2. Skipped some stages, passed others, but failed team matching again. 03/26: PhD Research Intern @ Startup#3. Interviewed, but the internship start date is after my PhD completion date. 07/26: End PhD I feel like I am at a severe disadvantage, and almost worse off than before I started the PhD. I used to get more interview invites; now I

Daily Learning part twenty-two. I haven't been active in three days due to Eid Al‑Adha. On Tuesday I went to my family house, where we go once in a while. We call it the family house because that's where my grandmother, uncles, aunts, and cousins live. I didn't bring my laptop with me because I wanted to spend some time with my family, which I haven't done in months. I stayed there for the two days of Eid. Today I came back by bus. I was supposed to arrive at 17:00, but due to traffic I arrived at 18:40. When I arrived I ate a small sandwich and got back to work. I started the session at 19:30. The first thing I did was complete the HTML Tables section. It was difficult to learn (at least for me). It covered HTML Tables, Table Borders, Table Sizes, Table Headers, Padding & Spacing, Colspan & Rowspan, Table Styling, Table Colgroup, Exercises, and finally the Code Challenge. Then I did a quiz and the Unit 2 test in Khan Academy and also completed one lesson in Unit 3. Now I have started a Tic‑Tac‑Toe challenge in Python. I watched a video on the minimax algorithm, which the game uses. I have started coding, but I am far from finishing it. I am ending today's session at 23:40. Eid Al‑Adha Mubarak to all Muslims. "Speak good or remain silent." Prophet Muhammed (peace be upon him)

In my previous series of articles, we mainly explored the theory behind various neural network concepts . In this new series, we will focus on putting that knowledge into practice using code . This will be a fun way to turn what we have learned into something more practical. We will start with the basics and build things step by step. For this article, we will be using the following modules. Importing PyTorch import torch torch is used to create tensors , which store all the numerical data in neural networks, such as: raw input data weights biases import torch.nn as nn This module helps us define and build neural network components. It also allows us to make weights and biases part of the neural network. import torch.nn.functional as F This module gives us access to various activation functions and other useful operations. from torch.optim import SGD SGD , which stands for Stochastic Gradient Descent , is an optimization algorithm used to fit the neural network to data. Creating a Neural Network Now let us begin building our neural network. When creating a neural network in PyTorch, we usually start by creating a class. class MyBasicNN ( nn . Module ): Here, we create a class named MyBasicNN . This class inherits from a PyTorch class called nn.Module . By inheriting from nn.Module , our class gains all the functionality needed to behave like a neural network in PyTorch. Initializing the Neural Network Next, we define the initialization method. class MyBasicNN ( nn . Module ): def __init__ ( self ): super (). __init__ () Here, we define the constructor ( __init__ ) for our neural network. The line: super (). __init__ () calls the initialization method of the parent class nn.Module . This ensures that all the necessary PyTorch functionality is properly set up for our neural network. What Comes Next? The next step is to initialize the weights and biases for our neural network. Before doing that, we first need an example problem so we know what kind of neural network we

If you have been following the 100 Days of Solana challenges, you have already worked with tokens. You created mints, set up token accounts, transferred SOL, and explored token extensions. But there is a distinction that comes up constantly in web3, and understanding it properly will change how you think about everything you build going forward. Fungible and non-fungible tokens. You have probably heard these terms before, especially NFTs. But what do they actually mean on Solana, and how does the same token system handle two very different concepts? What makes something fungible Fungible just means interchangeable. One unit is identical to another unit. If I have 10 USDC and you have 10 USDC, ours are exactly the same. It does not matter which specific USDC tokens I hold because they are all worth the same and behave the same way. We could swap them and nothing changes. This is how most things you are used to work. A dollar bill is fungible. A liter of petrol is fungible. One unit of SOL is the same as any other unit of SOL. When you built token transfers in the challenges, you were working with fungible tokens. You did not need to care about which specific tokens moved, just how many. Fungible tokens are used for currencies, stablecoins, utility tokens, governance tokens, loyalty points, in-game currencies, and anything where the quantity matters more than the individual unit. What makes something non-fungible Non-fungible means unique. Each token is different from every other token, even if they come from the same collection. If I have NFT #42 from a collection and you have NFT #87, those are not interchangeable. They might have different images, different properties, different rarity, or different utility. Think of it like event tickets. Two tickets to the same concert are not the same if one is front row and the other is in the back. They came from the same event, but each one is distinct. Non-fungible tokens are used for digital art, collectibles, membership pa