标签：#AR

Hi everyone, If you have purchased (at least one) GPU(s) for ML/DL studies and research: How is your experience and is it worth it? What do you use it for and how is the ROI? I have a MacBook Pro with M4 from some years ago, while MPS is useful in many occasions, it's no substitute for a NVDA GPU with CUDA support. So recently I am considering getting a 5060 Ti 16GB , but a GPU cannot run itself, so I then also need to buy other parts (e.g., CPU, RAM, SSD, motherboard, and so on...), which has been getting more expensive lately, especially the RAM. Since I'm still in job-seeking mode, I will mostly use it for learning DL, RL, and LLM-related things and local experiments (e.g., Stanford CS336), or low-level ones like GPU kernel programming and so on. Do you think a local physical GPU would help, or in my case a cloud service like Modal would suffice? Many thanks! submitted by /u/hedgehog0 [link] [留言]

submitted by /u/vox [link] [留言]

Hey Reddit, I just published a study analyzing 25,500 LLM resume evaluations to measure hiring bias. By swapping minor identity and demographic variables on the exact same work history across 10 different models, an independent AI auditor flagged a staggering 45% bias rate driven by "silent bias." Instead of saying anything overtly offensive, models invent professional-sounding excuses to penalize candidates, like when a model dropped its score after I changed the university to MIT, suddenly claiming the candidate's experience wasn't relevant despite praising that exact same experience on the baseline resume. We also found a massive 6x difference in stability between systems, with Qwen and older Gemini models being highly volatile, while the Claude models, Mistral-Large, and Llama 4 proved to be the most stable and fair. Ultimately, AI screening tools are outputting highly subjective, unpredictable opinions driven by statistical noise rather than objective truth, making them a massive liability under regulations like the EU AI Act. You can read the full write-up and explore our interactive data app here: https://re-cinq.com/blog/ai-hiring-bias-25500-llm-evaluations submitted by /u/Signal_Rabbit_8303 [link] [留言]

submitted by /u/ThereWas [link] [留言]

In the previous article, I spoke about the why and how to use a Markdown component in Astro . Here, we’re going to expand on that and help you use Markdown everywhere — regardless of the framework you use. So, … Astro Markdown Component Utility for Any Framework originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

I tested the top sleep wearables for every type of sleeper, including devices from Oura, Whoop, and Eight Sleep.

An LLM-written kernel benchmarked 38% faster on a microbench. Here is what kernel-level validation showed it actually did at runtime. TL;DR Multi-agent LLMs are now writing CUDA kernels (RightNow AI’s AutoKernel, Meta’s KernelEvolve, a multi-agent system claiming 38% speedup on Blackwell). Source-level benchmarks measure clean throughput on a single isolated kernel. They do not measure SM occupancy under co-scheduling, DRAM bandwidth saturation, dispatcher off-CPU during a real serving workload, or NCCL wait correlation with sibling kernels. Kernel-level validation closes that gap: an eBPF trace of the same kernel running under the same workload as production answers all four questions in one capture. The kernel-writing wave Three pieces of work in April surfaced the same pattern: agents generate CUDA kernels, then quote a single throughput number against a baseline. RightNow AI’s AutoKernel (announced Apr 6) – LLM agents iteratively rewrite CUDA kernels for a target metric, claiming substantial speedups on selected microbenchmarks. Meta’s KernelEvolve – similar shape: agents propose kernel variants, rank by throughput, keep the best. Multi-agent system on Blackwell (Apr 29 reports) – claims a 38% speedup on a public kernel benchmark using a coordinated agent setup. All three are real research, all three produce real kernels, and all three report numbers that come from microbenchmarks. The microbench setup is exactly what you want for the optimization loop. It is not what you get in production. What microbenchmarks do not see Run an LLM-generated kernel under nvprof or nsight-compute on an otherwise-idle GPU and the throughput number is real. Put the same kernel in front of a vLLM serving workload and four properties change immediately: SM occupancy under co-scheduling. The kernel that achieves 95% SM occupancy in isolation will achieve 40-50% with three other kernels sharing the same SMs. The optimizer never sees this regime. DRAM bandwidth saturation. A kernel tha

The loop I didn't notice closing Seven weeks ago I started using AI for work. Two weeks after that, I published an article. Seven weeks after that — today — the article is one of sixteen, and they are all in a memory file that the AI reads at the start of every new conversation. I didn't notice the loop until I named it. This is a note about that loop, what it is, what it isn't, and why I keep publishing even though the loop doesn't strictly need me to. The shape It runs like this: I decide what to do. I work it out with the AI — usually in dialogue, sometimes by pasting raw code or data. The dialogue becomes a record. Sometimes a memory entry. Sometimes a published article. The record becomes context for the next conversation, which informs the next decision. It didn't look this clean while it was happening. The numbering is hindsight. From inside, the steps overlap. The first step is the one I keep. Direction is mine: what to build, what to write, what to negotiate. The history that shapes those decisions — twenty-four years of solo work, my company, my family, my health — is also mine. The AI is not setting direction. The second step is where most of the leverage is. I describe what I want to do as completely as I can, sometimes by handing over source code. Then I ask: does this look right? Is there a path I'm missing? Where would this break? I'm opening drawers — possibilities I half-saw in my own head — and checking which ones open cleanly. When one opens cleanly, that is the GO signal. Not "will this succeed" but "this is doable, so do it." The third step happens almost without effort. The conversation already exists as text. Some of it becomes a memory entry I add deliberately. Some of it becomes raw material for an article. The article writes itself partly because I have already explained the thing to the AI. The fourth step is the one that took longest to arrive — and the one I want to be most careful about describing. Three phases, not one The loop didn't

There is a special kind of productivity theater that happens during a developer job search. You wake up motivated, open LinkedIn, and apply to 27 positions before breakfast. You press the Easy Apply button with the precision of a professional gamer. By the end of the week, you have submitted 143 applications, updated a spreadsheet with several impressive numbers, and developed a minor emotional dependency on refreshing your inbox. Unfortunately, your inbox still looks like an abandoned shopping mall. No interviews. No useful feedback. No clear explanation. Perhaps two automated emails thanking you for your interest before informing you that the company decided to “move forward with other candidates,” a sentence that has become the corporate version of disappearing into the fog. So you decide to solve the problem by applying to another 200 jobs. This is not a strategy. It is email-based agriculture. You are throwing resumes into the soil and waiting for a recruiter to grow. Volume Matters. Blind Volume Does Not. Let us begin with an uncomfortable truth: getting your first developer job usually requires applications. Sometimes it requires many applications. The market will not discover your GitHub profile through divine intervention. A recruiter is unlikely to wake up in the middle of the night with a mysterious urge to search for junior developers who recently deployed a to-do list. You need to put yourself in front of companies consistently. However, there is a significant difference between applying consistently while improving your positioning and clicking every blue button on LinkedIn until one of you collapses. Volume is useful when it generates information. Blind volume only produces exhaustion. If you apply to 300 jobs with the same generic resume, the same generic portfolio, and the same vague explanation of your skills, you are not running 300 experiments. You are repeating the same experiment 300 times and acting surprised when the result remains unchanged.

AI agents are getting smarter fast. They can reason through tasks, manage workflows, call tools, and automate decisions across applications. But as these systems become more capable, one challenge becomes impossible to ignore: reliability. How do you know your AI agent is making the right decisions consistently? How do you test workflows that involve memory, reasoning, and multiple execution steps? And how do you debug failures when outputs become unpredictable? That’s exactly what this free live webinar, “ Testing AI Agents in Python: Building Reliable Evals with LangGraph & LangSmith ,” is focused on. The session includes a “ Live demo of the AI agent evaluation pipeline ,” where you’ll see how developers are building structured evaluation workflows using LangGraph and LangSmith to test, trace, and improve AI agent performance in real-world scenarios. Here is the link to register .. Who Should Join This Session? This webinar is designed for developers and technical teams working with AI systems, especially: Python developers building AI agents or LLM workflows AI engineers exploring evaluation and observability Architects designing production-ready AI systems Product teams experimenting with AI automation Founders building intelligent applications faster Whether you’re actively deploying AI agents or still evaluating the ecosystem, this session will give you a clearer understanding of how reliable AI systems are actually built. What You’ll Learn During the Webinar This isn’t a high-level AI trends session. The focus is practical implementation, testing workflows, and evaluation strategies developers can actually use. In this webinar, you’ll learn: Why evaluation matters for modern AI agents How LangGraph helps manage complex agent workflows How LangSmith can trace and monitor agent execution Ways to create repeatable and scalable evaluation pipelines Practical approaches for debugging and improving AI agent behavior See the AI Evaluation Pipeline Live One of the b

How combining Non-Transferable and Permanent Delegate extensions changed my understanding of digital ownership on Solana. Imagine earning a high rank in an online game. You can use it. You can benefit from it. Other players can see it. But you cannot transfer that rank to someone else. If you break the game's rules, the game company can revoke it. And if the game gets acquired by another company, authority over that rank can be handed over to a new administrator. That was the closest mental model I found for understanding one of the most interesting things I built this week on Solana: a credential token using Token-2022 extensions. What are Token Extensions? One thing I've learned during this phase of the challenge is that Token-2022 is not just about creating tokens. It allows you to define behaviors directly at the token level through extensions. Instead of relying on application logic to enforce rules, the token program itself can enforce them. For this experiment, I combined two extensions: Non-Transferable — prevents ownership from being transferred to another wallet. Permanent Delegate — gives a designated authority the ability to manage the token even after it has been issued. Individually, those are useful. Together, they create something that behaves less like a currency and more like a credential. The Transfer That Was Supposed to Fail After creating the token, I tried transferring it. spl-token transfer Gn5PZzwDENvpQESaFwgVqzCUFba5sSka59iLtjFTYNvz 1 $THIRD_PARTY \ --owner ~/recipient-wallet.json \ --fee-payer ~/.config/solana/id.json \ --program-id TokenzQdBNbLqP5VEhdkAS6EPFLC1PHnBqCXEpPxuEb \ --fund-recipient --allow-unfunded-recipient The result: Recipient: ErYcpQYcpdoaaiufrB3MqQE2QaVEhoACZGcssppszPpY Recipient Token Account: 8CvyoKt11UbkWYRVXey6UKK2gqQcvVvbh9Lv3crzo8C3 Funding ATA: 8Cvyokt11UbkWYRVXey6UKK2qqQcvVvbh9LV3crz0803 Status: ❌ Transaction failed during simulation Error: Transfer is disabled for this mint (Token-2022 restriction) What I liked a

Introduction In contemporary research, the volume of biological data deposited in public repositories is growing exponentially. The Gene Expression Omnibus (GEO), NCBI Gene, PubMed, and UniProt accumulate thousands of new records daily, including sequences, expression profiles, scientific articles, and functional annotations. On the one hand, this scenario represents a unique opportunity for biomedical research. On the other hand, the diversity of data formats, access protocols, and metadata models creates a significant barrier: each source requires a specific collector, distinct rate-limiting strategies, and its own validation logic. Above all, the lack of standardization in data storage compromises the reproducibility of scientific studies. The need for integrated tools capable of unifying data extraction, curation, and persistence has been widely discussed. In practice, ad hoc solutions such as isolated scripts for individual repositories generate redundant work and make maintenance difficult. First and foremost, it is necessary to establish an architecture that treats data collection as a service rather than a collection of scattered artifacts. This work presents Project 1 of the Integrated Bioinformatics Platform: a containerized Biomedical Data Collector coupled with a Data Lake. Its objective is to provide a REST API capable of triggering asynchronous data collections from the four aforementioned sources, storing immutable raw data in MinIO, and persisting metadata in PostgreSQL, all while ensuring traceability and resilience. Development The system architecture is divided into three main layers. The first is the API and orchestration layer , implemented using FastAPI. Its five endpoints — POST /collections , GET /collections , GET /collections/{id} , GET /collections/{id}/download/{dataset_id} , and GET /health — expose a clean interface for initiating and monitoring collection processes. The second layer is the collector engine , composed of abstract classe

Over the past few months, I’ve increasingly noticed something through my network: more people from non-technical backgrounds are building software as AI tooling improves. Designers are prototyping product ideas. Product managers are testing workflows. Founders are building MVPs. Operators are creating internal tools. People who would not have called themselves “technical” a year ago are now using AI to make ideas tangible. I think this is genuinely exciting. It has never been easier to create. I even attended a hackathon where participants only had 20 minutes to build a demoable product! This raises the question: When AI makes building easier, how do we make sure understanding does not disappear? I recently published Thinking in the Age of AI , a guide for software engineers (you can check out my previous post here ). That guide focused on individual reflection for engineers: how to keep developing technical intuition, reasoning, and judgment while using AI tools. But the landscape has changed quickly. AI-assisted building is no longer only an engineering workflow. It is becoming a builder workflow accessible to all. And by builders, I mean anyone using AI to turn ideas into software-like artifacts: vibe coders designers product managers founders operators marketers students non-engineering team members So I wanted to create a new version of the system for this wider builder audience. Thinking in the Age of AI: Builder Edition The opportunity is real I do not think we should dismiss this shift. I have spoken with people from all kinds of backgrounds who are actively building now. People who previously had to wait for engineering time can now create something concrete. That changes the conversation. Instead of describing an abstract idea, you can show a flow. Instead of writing a long product spec, you can prototype the interaction. Instead of asking “would this work?”, you can test a rough version. That is powerful. But there is a trap. A prototype can look much mor

Maybe this is just me using these tools badly, but I've noticed a pattern with ChatGPT and Claude. I’ll have a really useful conversation about something like an idea, a plan, a bit of writing, a coding problem, whatever, and in the moment it feels like I’m making real progress. Then a week later I vaguely remember that we talked about it, but I can’t remember where, or what the useful part actually was and what I was supposed to do next. So I search, find a few old chats, open them… and now I’m scrolling through this massive thread trying to reconstruct why it mattered. It's exhausting and I feel I'm wasting time recollecting things. So sometimes I start over, hoping that the AI itself will remember the details, adding to the waste of time and the frustration. And the more ideas I develop the bigger this problem becomes. And it's only going to get worse. I’ve started leaving myself a short note at the end of useful conversations, but I never remember to do it consistently. Not sure if this is an actual problem or just the natural cost of using AI for messy thinking. submitted by /u/AlbertoNobilePh [link] [留言]

I see a lot of people shipping AI agents that work perfectly in demos and fall apart the moment a real user touches them. After building automation systems for multiple clients, I've noticed the failures almost never come from choosing the wrong LLM. They come from three things: 1. Bad chunking in RAG pipelines. Everyone's so focused on picking the right vector DB that they don't think about how they're splitting documents. Garbage in, garbage out. If your chunks don't preserve context across sentences, your retrieval will always be mediocre. 2. Prompts written for demos, not edge cases. Demo inputs are clean. Real user inputs are weird, vague, and sometimes intentionally broken. If you didn't stress test your prompt with bad inputs, it will fail publicly. 3. No fallback logic. When the agent is confused, what does it do? Most builders never answer this question. So the agent either hallucinates confidently or returns nothing. Both are bad. The model is usually the last thing to blame. Fix the scaffolding first. Anyone else running into this? Curious what failure patterns you've seen. https://preview.redd.it/vd9yyzkpzn4h1.png?width=1536&format=png&auto=webp&s=e81e5a1b4a7c4d82542c8cbc5cdf9712f30ff393 submitted by /u/ahmadparizaad [link] [留言]

Selected excerpts: "The question, then, is not whether A.I. will change the world. It will. The question is: Who will own and control that future? Who will benefit from it, and who will be hurt by it? Will A.I. be used to make life better for working families? Will it enrich our quality of life? Will it help us eliminate poverty, extend life expectancies and solve the climate crisis? Or will the future of humanity be determined by a handful of billionaires who have promoted and developed A.I., with virtually no democratic input, who stand to become even richer and more powerful than they are today? That is the choice before us. Let us be clear. Artificial intelligence was not created out of thin air. The data and language used by generative A.I. tools didn’t just pop into Sam Altman’s head or Elon Musk’s imagination. A.I. is built on our collective intelligence: our books, songs, artwork, journalism, computer code, scientific research, videos, conversations, images and ideas spanning generations. That is not just the opinion of Bernie Sanders. According to Mr. Altman, the head of OpenAI, A.I. models were trained on our 'collective experience, knowledge' and 'learnings of humanity.' For the most part, tech oligarchs have fed this knowledge into their A.I. models without permission, without acknowledgment, without compensation. In other words, the creative work of millions of people — writers, artists, musicians, journalists, teachers, scientists and ordinary citizens — has essentially been stolen by some of the wealthiest people in the world. It’s time for us to reclaim it. That is why I will soon be introducing the American A.I. Sovereign Wealth Fund Act. This legislation would give the public a direct ownership stake in the largest A.I. companies in our country. How? It would create a sovereign wealth fund through a one-time 50 percent tax — not on the profits of OpenAI, Anthropic, xAI and other companies, but paid with something far more valuable than that: the st

Shopify Staff Engineer Guilherme Carreiro discusses building and scaling highly customizable platforms. Using Shopify’s Liquid theme system as a case study, he explains how to balance extreme design flexibility with low-latency performance under massive traffic. He shares insights on implementing secure domain-specific languages, native code extensions, and resilient developer tooling. By Guilherme Carreiro

A bit of background about myself: I have been accepted to RWTH Aachen's Computer Science program starting this fall, and one of the things that I am genuinly excited about is exploring the intersection of astrophysics and machine learning. The tricky part is that RWTH's CS department doesn't have a research group focused directly on this intersection. The two closest things I have found are the Quantum Information Systems group (I plan to reach out to the them once I am on campus to understand a bit more about them) and the Learning on Graphs group which does foundational GNN research. The second one got me thinking: graph neural networks feel like they could be well-suited to astrophysicla data, things like galaxy formation, cosmic web structure or particle interaction data all seem graph-like (or am I being waaaay too optimistic here?) So my questions for people who know this space better than I do: Are GNN's already being used in astrophysics research? What other ML subfields would you point someone toward if they are interested in this intersection? I know I could have applied to a more well-suited university for my needs, but RWTH Aachen was my top choice because I am a math nerd and I really like their way of teaching. So do help a brother out. Thanks in advance!!!! submitted by /u/pandemic_179 [link] [留言]

ok this is gonna sound dumb but bear with me I write a lot for work, marketing/copy stuff mostly, and over the last ~14 months ive slid from "use AI to clean up my draft" to "use AI to make the draft" to honestly not really writing anything on my own anymore. like i hadnt put a complete thought on paper without a model in the loop for months. didnt even notice it happening tbh. last weekend i tried to write a journal entry. just for me, no audience. nothing fancy. sat there for like 20 minutes trying to remember how to start a sentence that didnt have a thesis at the front of it. i kept wanting to write "Today I noticed three things about my mood." and then realising — wait, no, thats a chatgpt sentence. nobody writes that. but i couldnt remember the person-version. eventually wrote some half-garbage about being tired and what i ate and a weird thing my sister said about her landlord. it read like a 12 year olds diary which, fine, i guess thats what a journal is supposed to be but the embarassing part. i had to physically stop my hand from opening the chat app to "help with the wording". my brain was treating writing like a thing AI does, not a thing i do. felt like reaching for a phantom limb. idk man. anyone else fine until they try to write something with no audience? like the second the audience disappears the chatgpt brain pattern is whats left underneath? not really asking for advice or anything. just wanted to say it out loud somewhere submitted by /u/Napster3301 [link] [留言]

Like, no matter which topic I'm researching, whether it's sports or nutrition or technical stuff, it's hallucinating all the fucking time. Then, in vscode, when using pro 3.5 via API, it constantly ignores coding instructions, it constantly isn't able to fix the simplest mistakes in the code, it repeats the same mistakes over and over and fucking over again and then apologizes ("oh sorry, you were right"). Like what the fuck? This is extremely bad quality, how the hell is this even still viable? submitted by /u/eternal_sunshineeeee [link] [留言]