标签：#AR

This is the time of year when the number of cold emails I receive about PhD positions tends to ramp up quite a bit. In many countries, this cold emailing is essentially part of the normal recruitment process, so there is nothing inherently wrong with doing this. However, there are a few things you definitely shouldn't be doing: Massive emails. The probability of me reading your email is inversely proportional to its length. Emailing everyone. Find supervisors that work in areas you are actually interested in. I do relatively foundational ML research (i.e., not associated with a specific application domain), but the majority of emails I get from prospective students are essentially "I want to apply ML to domain X". In many cases this does not constitute an ML research direction; you'd be better off finding a supervisor with expertise in domain X, which is where most of the impact will be. Generic research interests. If the most specific research interests you can give are "Machine Learning, LLMs, and AI" then I assume you only have a surface-level familiarity with the field, and are not ready for a PhD. Passing off workshop papers as conference papers. This has become a much more common thing in the last couple of years. It's a big red flag; I am not going to take on someone who is dishonest. Excessive AI use. Using them for fixing up grammar is fine, but if you outsource your thinking to LLMs then your research direction will be the same as everyone else who outsources their thinking to LLMs. This tends to result in something that would be an okay bachelor's thesis project, but nothing more than that. I get a lot of LLM emails, so determining if you are in this cluster is very easy. Summarise my paper. I already know what's in it, I don't need a summary. I care more about how you think you could build on it, or do something related. Don't use LLMs for this; see above point. Ignoring instructions on my website. Check prospective supervisors' websites for how you shou

I'm sharing this new tabular data diagnostic tool (Entropic Scree). It can be used to estimate these properties of your high-d, real-world, dirty dataset: The informational volume of the signal (i.e., helps you assess whether the signal is strong enough to survive the dataset's idiosyncratic volume). The overall signal-to-idiosyncratic volume ratio (SNR). The intrinsic rank. Provides an exploratory map that allows for the identification of decoupled sub-networks of variables. The linear sufficiency (i.e., does the dataset align with the linear assumptions of standard PCA?). Instead of evaluating linear variance, rank order, or Euclidean distance like traditional PCA variants, this new method evaluates a transformed mutual information metric. Relative to these baselines, it is less reliant on strong parametric or distance assumptions, making it appropriate to apply more broadly. It also serves as a practical diagnostic of the theory explored in the From Garbage to Gold framework, which describes when and why uncurated, error-prone data can be used directly to create accurate prediction models. There is a preprint that presents the full technical details, and Python and R packages will be released soon. Right now though, the original function is already available in R (see Quick Start R Function Code below). Entropic Scree Preprint: https://doi.org/10.5281/zenodo.22028087 Entropic Scree GitHub: https://github.com/tjleestjohn/entropic-scree From Garbage to Gold Preprint: https://arxiv.org/abs/2603.12288 Let me know how it goes if you give it a try... or if you have any questions or comments of course. ############ # Quick Start R Function Code. # To load the function, copy and paste the following into your R console, then hit enter. ############ # 1. Define the direct URL to the raw function script on GitHub url <- "https://raw.githubusercontent.com/tjleestjohn/entropic-scree/main/Entropic.Scree.v1.0.0%20-%20ENLI.R" # 2. Define what you want to name the file on your co

This week's Java roundup for August 24th, 2026, features news highlighting: the GA release of Atmosphere 4.0; point releases of GraalVM, Azul Payara and Quarkus; a maintenance release of WildFly 41; milestone releases of Jakarta Data and Eclipse JNoSQL; a beta release of the September 2026 edition of Open Liberty; and the release of Docker images for GlassFish 8.0.4. By Michael Redlich

How we eliminated subtle stylesheet caching glitches during deployments on DEV without a massive rewrite.

While Trump administration cuts have contributed to the epidemic of foodborne illnesses this summer, there are also deeper issues that make the American food supply chain susceptible to parasites and bacteria outbreaks.

In this episode, Scott Jenson, a veteran UX designer known for his work on the Macintosh, Google Maps, and Chrome examines the long-term stagnation of desktop operating systems and the limitations of current mobile and cloud-centric models. By Scott Jenson

Of the Glassdoor reviews from claims adjusters that mentioned AI, a staggering 98 percent were negative. “AI is just a tool,” one person tells WIRED. “It should never be given the keys.”

Microsoft expanded Foundry's model router from two regions to 28 for global standard and 21 for data zone deployments, while adding Claude Opus 4.8 and GPT-5.6 and removing four deprecated models. Default deployments receive pool changes automatically; configured subsets exclude new models until added. The effective context window equals the smallest model in the pool. By Steef-Jan Wiggers

Hi, I'm making posters for ECCV 2026. Does anyone have any ML/CV posters they thought were really well done? Would love to see some cool examples. Thanks submitted by /u/National-Resident244 [link] [留言]

Keep your phone firmly in hand and add some personality with these comfortable, durable, and nifty smartphone grips.

A few weeks ago I wrote about why I built Apiarium after OpenRouter solved one problem for me and I still had four more. The comments on that post ended up shaping a good chunk of what I actually built next, so this is the "here's what changed" follow-up. The interesting part isn't really the features. It's where they came from. Almost everything below started with someone telling me something was annoying, confusing, or missing. So instead of adding things because they looked good on a roadmap, I tried to fix the problems people were actually running into. Multiple API keys, not one shared key for everything The biggest ask came directly from someone using Apiarium in production. They wanted to know which app or feature was actually driving usage, without having to share one API key across everything and lose that signal. So now you can create multiple keys per account: 2 on Free 5 on Starter 10 on Pro You can name them, revoke them individually, and every request is tagged with the key that made it. Credits are still shared across the account, the keys are about visibility, not splitting your balance. // key for your production app fetch ( ' https://api.apiarium.dev/llm ' , { headers : { Authorization : ' Bearer sk-prod-... ' }, ... }) // separate key for a side project // same account, same credits fetch ( ' https://api.apiarium.dev/llm ' , { headers : { Authorization : ' Bearer sk-sideproject-... ' }, ... }) You can also see when each key was last used and filter usage by key in the dashboard. That last part was really the reason I built it. A dashboard that answers "where did my credits go?" The old dashboard was basically a number going down. That wasn't particularly useful. The new dashboard is split into Overview, Usage, API Keys, and Billing. There's a proper date range filter with 7d/30d presets or a custom range, and that same range drives the usage chart, breakdowns, and request logs together. You can break usage down by model and endpoint, so you can ac

I created this article for the purposes of entering the All Things Agentic Hackathon. Shoots is an Android and web photography Companion. A photographer takes ordinary Shots. Shoots reviews them in the background, preserves the Evidence behind its reading, and can offer one optional Experiment when the record supports it. This is the technical version of the project. It is about how a file becomes a durable learning record, how the agents communicate, and where I deliberately refused to let a model make the decision. The design in one sentence The model panel reads a single Shot. The system around it does the work: it creates a durable Run, moves tiny events through independently retryable stages, re-reads state at every boundary, records every outcome, and only settles a Shoot after every member Run is accounted for. That distinction matters. I did not want a chain of agents passing prose to one another until it sounded convincing. I wanted constrained model calls inside a workflow whose state, retries, and outputs could be inspected later. Repository structure The codebase is deliberately split by responsibility rather than by screen or agent name: android/ phone/ # approved Camera media work/ # background upload and retry data/ # cache, API, identity ui/ # Android screens backend/app/ api/ # FastAPI ingress and push endpoints domain/ # pure rules and state transitions imaging/ # EXIF, pixels, visual artifacts agents/ # ADK agents and prompts services/ # workflow orchestration infra/ # storage, Pub/Sub, Drive, secrets frontend/src/ stores/ # API and SSE state pages/ # web audit desk components/ # receipts and visual Evidence infra/ # Google Cloud deployment and Scheduler The important boundary is domain/ . It has no I/O. It owns the rules that must be reproducible: grid-cell conversion, taxonomy validation, panel consensus, Criteria checks, Technique Map projections, and Run state transitions. services/ can call models and storage. domain/ cannot. Two kinds of orc

Text-to-Speech Is Not a speak() Call The challenge 🧪 If you have ever assumed Text-to-Speech on Android is straightforward, this article is for you. But first, let us test your skills. Think you can make this speak on Android? 🙏 अव्यक्तोऽयमचिन्त्योऽयमविकार्योऽयमुच्यते । "Invisible, beyond thought, unchanging." — Krishna describing the nature of the self. Today is World Sanskrit Day, so the timing is fitting. 🕉️ Try playing it on the plain TextToSpeech API that Google provides — but specifically with a Sanskrit voice. Build a minimal Android app, initialize the TTS engine, set the language to Sanskrit, and call speak() on this string. Chances are it will not speak anything. Not even a single letter would be uttered. 🔇 That is the moment when a developer realizes that TTS is not a simple API call. The twist 🔄 Use a Marathi or Hindi voice instead. Same engine. Same text. Same API call. It plays perfectly. 🗣️ Same engine. Same verse. Different voice. Completely different result. The boundary between "speakable" and "not speakable" is not at the engine level. It is at the voice level within the engine. The Sanskrit voice within Google's TTS engine cannot handle this verse. But the Marathi voice — which shares much of the same Devanagari character set — handles it without issue. This changes how you think about TTS integration. What happened in production 🏭 This is not a theoretical exercise. This is what we actually hit. In Bhagavad Gita, the player screen uses TTS to read verses aloud. The experience is designed to feel like playing a media file: continuous, flowing, uninterrupted. But certain words — especially compound words and special conjunct characters — were being silently skipped. Not errored. Not logged. Just... silent. The engine would skip the entire word if it couldn't speak something in it. So a verse that should take 15 seconds to read would finish in 8. The user would hear a flowing recitation with missing pieces and never know what was lost. 😶 The worst

Paste a long document into ChatGPT and hit enter. Nothing happens for a second or two. Then the answer starts appearing, word by word, at a steady pace until it finishes. You have seen this hundreds of times. Most people never think about it. But those are two completely different things happening inside one request, running on two different parts of the same GPU, limited by two different bottlenecks. Once you know what they are, a lot of confusing things about serving AI models stop being confusing. Including why a faster GPU sometimes makes no difference at all. This is part two of a series. Part one covered the split between VRAM and cores. This post covers what each of them does when a request arrives. The example we will follow I am going to trace one question the whole way through: what is the capital of france Simple, short, and it produces a short answer. Perfect for watching the machinery. Before anything happens, the model server has already loaded the model's weights into VRAM. That happened at startup and they stay there. Nothing about your request loads a model. Your words get chopped into tokens, which are just pieces of words turned into numbers, and placed in VRAM alongside the weights. Now the work begins. Phase one: prefill, or "the pause" Before the model can write anything, it has to read everything you gave it. Here is the important part, and it is the opposite of what most people assume: it reads your entire prompt at once. Not word by word. All six words go to the cores together, in a single pass. That is possible because your whole prompt is already there. Nothing has to wait for anything else. So thousands of cores fire simultaneously and chew through all of it in one burst. flowchart LR A["what is the capital of france<br/>all 6 tokens together"] --> C W["16 GB of model weights"] --> C C["GPU CORES<br/>every core busy"] --> O1["First word: 'The'"] C --> O2["Notes saved to VRAM<br/>the KV cache"] Two things come out of that single pass: The

Almost every codebase I've inherited sends email the same way: somewhere inside a POST handler, between the database write and the response, there's an await on the mail provider's SDK. It works, for months. Then one afternoon your signup endpoint starts timing out, and it takes an hour to work out that the cause is your email provider having a bad day three thousand kilometres away. I build Pulsenote , a transactional email API, so I've spent an unreasonable amount of time in the space between "your API call returned 200" and "the message is in the inbox". This post is what lives in that gap, why a try/catch doesn't cover it, and where the line sits between "you need a pipeline" and "you're overengineering a side project". The naive version Here's the code. You've written this. // users.controller.ts @ Post ( ' signup ' ) async signup (@ Body () dto : SignupDto ) { const user = await this . users . create ( dto ); await this . mailer . send ({ to : user . email , subject : ' Confirm your email ' , html : renderConfirmation ( user ), }); return { id : user . id }; } Nine lines, obvious intent, no infrastructure. For a lot of applications this is genuinely the right answer, and I'll come back to that at the end. But let's be precise about what it costs, because "it's fine" and "I haven't measured it" are different statements. It puts a third party in your request path. Your p99 for POST /signup is now your p99 plus the provider's p99. Not their median — their tail. A slow provider becomes a slow endpoint, then no endpoint. This is the failure mode that actually takes services down. If the provider degrades to five seconds per call, every signup request holds a connection and an event-loop continuation for five seconds. Your connection pool fills, your load balancer queues, health checks fail, the pod gets restarted, and now you're down — because of email. The blast radius of a non-critical dependency became the whole endpoint. A provider 5xx loses the mail entirely.

Every year the tools we build on get more capable and, quietly, more layered. A simple web service today can sit on top of a container, inside an orchestrator, behind a service mesh, fronted by a managed gateway, wired through half a dozen cloud services, each one a convenience that saved someone real work. I am not nostalgic for the days of hand-configured servers. But I have come to see that every abstraction we adopt to make the easy things easier also quietly makes the hard things harder to understand when they break. This is the tax nobody quotes you up front. Each layer solves a genuine problem and hides a genuine mechanism. On the good day, you get to ignore what is underneath, and that is the whole point, and it is wonderful. On the bad day, the failure is happening three layers down in something you have never had to think about, and now you must understand all of it at once, under pressure, with no accumulated intuition because the abstraction was doing its job of letting you not build any. The convenience and the opacity are the same feature seen from two different days. I do not think the answer is to reject abstractions and build everything from bare metal out of some purist principle. That path loses too, slowly, to teams who move faster on higher ground. The answer is to adopt each layer deliberately, understanding what it costs as well as what it saves, and to keep at least a working mental model of what sits underneath the thing you depend on. You do not need to be an expert in every layer. You do need to not be helpless when one of them fails. What I tell people entering the field is that the abstractions will keep stacking, and the pressure to treat each new layer as magic will keep growing. The engineers who stay valuable are the ones who can drop through the layers when they have to, who kept enough curiosity about the floor beneath their feet that they are not stranded when it cracks. The future is more abstraction, not less. The durable skill

A modular monolith without enforcement is not an architecture — it is a monolith with good intentions. The Problem Most teams skip the modular monolith and jump straight to microservices. The ones that do attempt a modular monolith rely on convention — "don't cross module boundaries" — which fails the moment deadlines hit. The difference between a well-structured modular monolith and a mess is whether boundaries are maintained by tooling or by convention. The Solution Structure Each module is a pair of .NET projects: src/Modules/ Orders/ YourApp.Orders/ ← internal: domain, application, infrastructure YourApp.Orders.Contracts/ ← public: DTOs, interfaces, events Payments/ YourApp.Payments/ YourApp.Payments.Contracts/ The rule : modules may only reference each other's *.Contracts projects. The compiler enforces this physically — no project reference means no type access. Four Layers of Enforcement Compiler — project references prevent cross-module type access NetArchTest — architecture tests fail the build on namespace-level violations NDepend CQLinq — catches dependency cycles and coupling the compiler can't see Quality Gates — block PRs that introduce new boundary violations Module-Scoped Data Each module owns a dedicated DbContext with a schema prefix ( orders.* , payments.* ). No module queries another module's tables. Cross-Module Communication Modules communicate via MediatR in-process events. Orders publishes OrderPlaced ; Payments subscribes — without Orders knowing Payments exists. This is also the extraction seam: when you eventually extract a module into a service, MediatR becomes a message broker. The event contract stays the same. Parallel CI strategy : matrix : module : [ Orders , Payments , Inventory ] fail-fast : false Each module's tests run in parallel. CI time scales with the slowest module, not the total count. The Extraction Path When a module genuinely needs independence: Add outbox table → publish to real broker Replace MediatR handlers with brok

The follower counter on my phone says 1,946. I check it more often than my homework diary. 54 to go...

When I first started learning HTML, I thought it would be the easiest part of web development. The basic tags seemed straightforward, and creating a simple page didn't take long. But as I kept learning, I realized that HTML is more than just putting elements on a page. Understanding semantic HTML , proper document structure, forms, accessibility, and knowing which element to use in different situations takes practice. For example, it can be tempting to use for almost everything, but learning when to use elements like <section>, <article> , <nav> , or <header> makes a big difference in writing meaningful markup. I'm curious about other developers' experiences: What HTML concept confused you the most when you were starting out, and what helped it finally click? I'd love to hear different perspectives, especially from people who have been working with HTML for a while.

Verified students can earn a free $100 AWS certification voucher through Student Rewards. Here is the exact path, the badge tricks, and the mistakes to avoid.