标签：#AR

the interesting shift is when AI stops sitting in a chat box and starts sharing the browser with you. submitted by /u/Old_Command_7050 [link] [留言]

idk maybe this is obvious to people already working in bigger teams, but the AI coding tool cost thing feels like early cloud all over again. Everyone keeps saying tokens are getting cheaper, which is true, but then somehow companies are still freaking out about AI bills. And I think the reason is pretty simple: people are treating these tools like normal SaaS seats when they are really more like metered infra. Like with a normal dev tool you kind of know the cost. X users, Y dollars per month, done. But with agentic coding tools one small request can quietly turn into a bunch of model calls, context loading, tool calls, retries, verification, more retries, etc. From the user side it looks like “fix this bug” or “write this function” but underneath it may have done a whole mini workflow. And then there is the other cost which I feel people don’t talk about enough: reviewing the generated code. Sometimes the code works but it adds weird duplication, misses existing abstractions, or creates stuff that someone has to clean up later. So the bill is not just tokens. It is also review time + maintenance + future tech debt. Not saying these tools are bad btw. I use them too and they are obviously useful. But it feels like the industry is moving from the fun phase of “look what this can do” to the boring phase of “who is paying for all these calls and did this actually ship anything useful?” Curious if teams are actually tracking this properly yet. Like cost per PR, cost per resolved ticket, cost per workflow etc. Or is it still mostly hidden under “AI productivity” and vibes. submitted by /u/Old_Cap4710 [link] [留言]

I work in IT admin for a major company. Started teaching myself AI automation tools in my own time. Applied them to my workload, my output doubled, got noticed and promoted. Became the go to person for AI integration across departments. But here’s the part that sits heavy with me. Once leadership saw what AI could do, they started looking at headcount differently. People who had been there 10, 15 years. Gone. Not because they did anything wrong. Just because a system could now do their job cheaper. I benefited from knowing AI early. Others paid the price for not knowing it yet. Is that their fault? The company’s fault? Just the way progress works? Genuinely asking because I don’t have a clean answer. submitted by /u/PickYourJawnUp [link] [留言]

Nobody wants to hear this. But the reason your software is hard to change, hard to test, and hard to explain to a new engineer isn't your tech stack. It's that your code doesn't reflect how your business actually works. Your engineers are using one word — "customer," "order," "student," "subscriber" — and meaning six different things depending on which part of the system they're touching. Your domain expert says "order" and means something completely different from what your database schema says "order" is. That gap? That's where complexity lives. That's where bugs are born. That's where senior engineers spend their Fridays. Domain-Driven Design is the discipline of closing that gap. Here's what it actually means, practically, without the academic noise. The Core Problem: One Model Trying to Mean Everything Imagine a map that tried to show subway routes, underwater hazards, hiking trails, and flight paths — all at once. It would be useless. A subway map works because it only shows what matters for navigating trains. A nautical chart works because it only shows what matters for sailing. Each map is an abstraction built for a specific purpose, valid within a specific context. Your software models need to work the same way. The moment you build a single "Customer" class that has to satisfy your billing team, your marketing team, your support team, and your logistics team simultaneously — that class becomes a bloated, ambiguous disaster. Everyone adds their fields. Nobody removes anything. The model stops meaning anything specific to anyone. This is the monolithic model trap. And most large codebases are sitting right inside it. Strategic Design: Understand the Problem Before You Touch Code DDD separates design into two layers. Strategic design comes first — it's the work you do before writing a single line of code. Step 1: Find Your Subdomains A subdomain is a slice of the business problem. Ordering. Shipping. Notifications. Payments. Inventory. These aren't your micro

Every interface you create is a constraint on future behavior. Every abstraction emphasizes certain patterns and discourages others. You are not just building tools. You are shaping how people think about problems. I have been paying attention to how API design encodes values, not just technical decisions, but philosophical ones. What Your API Communicates Consider these design choices: Mutability vs Immutability. Do you encourage stateful modification or pure functions? This is not just about performance. It is a philosophy about side effects and reasoning. If your default is mutable state, you are telling users that local mutation is fine, that they can reason locally. If your default is immutability, you are telling them to think about data flow. Explicit vs Implicit. Do you make users specify parameters or infer from context? This trades convenience for transparency. I lean toward explicitness. Magic is convenient until you need to debug it. Fail Fast vs Fail Safe. Do you throw exceptions or return error codes? This encodes beliefs about who should handle errors and when. Fail-fast says "don't let bad state propagate." Fail-safe says "keep running if you can." Both are defensible, but they lead to very different code. My Design Values When I build libraries, I try to encode: Explicitness over magic. I would rather make users type more than hide behavior behind conventions they have to discover. Composition over inheritance. Small pieces that combine flexibly beat deep class hierarchies. Clarity over cleverness. Code should be obvious, not impressive. Safety by default. The easy path should be the safe path. Why This Matters Your API is a value statement. It says what you think is important, what you think is dangerous, and how you think about the problem domain. This is why I spend so long on interface design. The APIs we create shape future thought. They outlast the code that implements them, because the patterns they teach persist in the minds of the people wh

The ghost is not you. But it echoes you. What survives beyond scattered archives? Beyond exported conversations and curated bookmarks? The stuff we never think to preserve: the photos that show how you see the world. The correspondence that maps who matters to you. The Long Echo toolkit has grown. PTK for photos. MTK for mail. But these are sources, not destinations. The destination is something stranger: longshade , a persona built from your data that can respond to questions you never answered. I'm going to invert the usual pattern here. Instead of tools first, philosophy later, I want to start with the philosophical destination and work backward to the data that feeds it. longshade: The Ghost That Speaks The Central Question What if your archive could respond? Not a chatbot trained on your data. Not a digital resurrection. Something more careful: a voice that carries your patterns, your interests, your way of seeing the world. That's longshade. Right now it's spec-only (no implementation yet). It defines what it would mean to synthesize a conversable persona from personal archives. The Ghost Metaphor "The ghost is not you. But it echoes you." This framing matters. longshade isn't about immortality or resurrection. It's about preservation with a kind of agency. The echo can answer questions you never answered, using patterns you established. It speaks in your voice without claiming to be you. The distinction is important: Resurrection claims to recreate the person Simulation claims to predict the person Echo acknowledges it carries patterns, not identity An echo is honest about what it is. It responds because you left enough traces to inform a response, not because it is you. Voice vs. Personality longshade extracts voice , not personality. Your actual phrases. Your vocabulary. Your reasoning patterns. Your recurring metaphors. The way you explain things, not the things you might explain. I noticed something working with conversation archives: user messages are th

I’ve been a movie reviewer for a couple of years, and occasionally people assume my reviews are AI-generated. The thing is, I’ve spent years developing my writing through extensive reading, English classes, and a lot of practice. Because of that, my writing tends to be polished and structured, which I think may be why some AI-detection tools flag it. What I’m curious about is how accurate these AI detectors actually are. Some people have compared my work to AI-generated writing, and when I’ve run my reviews through different AI checkers, I get completely different results. One detector might say a review is 100% AI-generated, another might say 70% or 80%, and another might classify the same review as entirely human-written. Some call it AI, some call it human, and the results seem to be all over the place. None of my reviews are AI-generated. Every review I’ve published has been written entirely by me, without using AI to generate any part of the writing. I just don’t understand how the same piece of writing can receive such wildly different results depending on which detector is being used. Are these tools accurate in any way, shape, or form? submitted by /u/CheesecakePlayful240 [link] [留言]

I’ve been a movie reviewer for a couple of years, and occasionally people assume my reviews are AI-generated. The thing is, I’ve spent years developing my writing through extensive reading, English classes, and a lot of practice. Because of that, my writing tends to be polished and structured, which I think may be why some AI-detection tools flag it. What I’m curious about is how accurate these AI detectors actually are. Some people have compared my work to AI-generated writing, and when I’ve run my reviews through different AI checkers, I get completely different results. One detector might say a review is 100% AI-generated, another might say 70% or 80%, and another might classify the same review as entirely human-written. Some call it AI, some call it human, and the results seem to be all over the place. None of my reviews are AI-generated. Every review I’ve published has been written entirely by me, without using AI to generate any part of the writing. I just don’t understand how the same piece of writing can receive such wildly different results depending on which detector is being used. Are these tools accurate in any way, shape, or form? submitted by /u/CheesecakePlayful240 [link] [留言]

I am really interested in learning intimately AI I don't really have good math skills but I am very good at computers in technology. I really would love to get into the intricacies and understand ai on a very deep level. But I'm better with verbal learning and being able to interact and ask questions then just with texts and reading. I've tried some in the past and gotten a little bit of an education from AI itself but I want to go deeper with somebody who really understands the tech what is the best way for me to do that. So what are the best schools for that submitted by /u/crazyhomlesswerido [link] [留言]

I've been chasing a reliable IPTV service for almost two years. Tried six different providers. Three of them died within a month. One had channels that buffered so bad I thought my internet was broken (it wasn't). One had zero customer support — my ticket sat unanswered for 11 days before I gave up. 👉 Visit official website - VIKINGITV so when people ask me "what's the best IPTV service provider in 2026?" — I don't give a quick answer anymore. I give them this post. What Makes an IPTV Service Actually Worth Paying For? Before I name names, let me break down what I actually tested for — because most comparison posts skip this entirely. Uptime during live events . Any IPTV can stream a Tuesday night rerun. The real test is the Super Bowl, UFC 300, Premier League matchday. Does it hold? Does it buffer? Does it die at halftime? Channel count vs. channel quality. I've seen providers brag about "50,000 channels" and half of them are dead links or SD streams that look like they're coming through a 2009 satellite dish. Numbers mean nothing without stability. Device support. I use Firestick at home and sometimes watch on my phone when I'm traveling. I need something that actually works across both without needing a computer science degree to set up. Customer support response time. This is the one most people ignore until something breaks at 9 PM on a Saturday. The IPTV Services I Tested in 2026 I won't drag this out with a fake "I tested 20 services" list. I'm talking about what I actually used long enough to form a real opinion. After everything I went through, VIKINGITV is the one I stayed with. Here's why. VIKINGITV — The One That Finally Stuck When I first heard about VIKINGITV I was skeptical. I'd been burned before. But a few things stood out after I actually got the subscription: Channel library- 65,000+ live TV channels. Not padded numbers — actual working channels. Sports, news, entertainment, regional content across USA, Canada, UK, Latin America, and Europe. I che

Creating an intelligence network where signals are turned into intelligence. Goal is to create network/digital ecosystems of intelligence. Any feedback is appreciated. Still early in the works check it out https://echonaxnetwork.com/ submitted by /u/stock-market [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]

https://preview.redd.it/efov9ttgdr5h1.png?width=1774&format=png&auto=webp&s=a24d224ca99a389793d08b1ea67d90817740d7f0 IntiDev AgentLoops Feedback Loops for Agentic Workflows submitted by /u/StevenVincentOne [link] [留言]

Hi everyone, As a Full-Stack and Cloud engineer, I’m used to automating everything I can. Whether I'm managing my 28+ container Kubernetes homelab on Proxmox or writing deployment scripts, I absolutely hate doing the same manual task twice. But a few months ago, when I was hunting for a new role, I found myself doing exactly that: manually tweaking my resume for every single job, copy-pasting into black-box ATS portals, and tracking it all in a chaotic spreadsheet. It was completely draining. So, I took a break from the applications and built a tool to solve my own problem. It’s called OneApply. It started as a small browser extension to check ATS keywords, but it quickly snowballed into a complete workspace. Here is what the stack handles now: Resume Tailoring: Automatically adjusts your resume to fit specific job descriptions. ATS Keyword Scoring: Checks your overlap with the job description so you know you'll actually pass the automated filters. Cover Letter Generation: Drafts contextual cover letters based on the role and your specific engineering experience. Pipeline Tracking: Manages all your applications natively so you can finally ditch the spreadsheets. Building this and using it to automate the worst parts of the daily grind actually helped me land my current SRE role. Since it worked for me, I decided to polish it up and release it for other devs who are currently stuck in the application trenches. We all know the tech market is tough right now, and any edge helps. I would love for this community to try it out and roast the UX, the workflow, or the core features. Check it out here: https://www.oneapply.app I am more than happy to hand out some premium access codes to anyone here who is actively applying and wants to test drive the full feature set. Just drop a comment below!

Every LLM wire format claims token savings. Nobody proves whether AI models can actually comprehend the format at scale, or produce valid output in it. We ran 23 comprehension evals across 10 models and 3 providers. We ran generation evals across 11 models. Deterministic ground truth. No LLM judge. Reproducible from one command. JSON breaks at 500 records. GPT-5.5 returns empty strings. It can't even attempt an answer. Opus miscounts 500 as 356 and then spends 143 lines manually enumerating symbols to verify its own wrong answer. The format designed for "human readability" is incomprehensible to the systems actually reading it. TOON can't produce valid output. Claude Opus, the most capable model on the planet, scores 0/5 on TOON generation. GPT-5.4: 0/5. GPT-5.4-mini: 0/5. Gemini 3.1 Flash Lite: 0/5. The error is always the same: toon: cannot assign string to int . The model writes "target" in the distance column. TOON expects 0 . Every model fails the same way because the format's design forces an unnatural encoding step that models cannot perform unprompted. GCF wins both dimensions on every model tested. 100% comprehension on Claude Sonnet, Gemini 2.5 Pro, Gemini 3.1 Pro, and Gemini 3.5 Flash. 5/5 valid generation on every frontier model. Zero prior training. The format didn't exist until we built it and every model speaks it natively. Comprehension: 500 Symbols, 13 Questions, Zero Instructions A 500-symbol, 200-edge code graph. Encoded in GCF, TOON, and JSON. 13 structured extraction questions. The model gets the payload and a question. No format instructions. No system prompt. No hints. 23 runs. 22 wins. 0 losses. Model Runs GCF avg TOON avg JSON avg GCF margin Claude Opus 4.6 2 96.2% 84.6% 73.1% +11.6 vs TOON Claude Sonnet 4.6 2 100% 73.1% 53.8% +26.9 vs TOON Claude Haiku 4.5 2 96.2% 69.2% 57.7% +27.0 vs TOON GPT-5.5 5 84.1% 67.7% 45.8% +16.4 vs TOON GPT-5.4 4 76.4% 56.0% 44.1% +20.4 vs TOON GPT-5.4-mini 2 71.8% 64.1% 54.2% +7.7 vs TOON Gemini 2.5 Flash 3 80.6

Lately I've been experimenting on an open-source project called Canary. https://preview.redd.it/c4dgxw22lq5h1.png?width=1920&format=png&auto=webp&s=304f37871aa9b7ee0a084d8b59207fae51d8b7bc It takes a code diff, identifies the UI flows that are likely affected, and then uses Claude Code to test those paths in a real browser. Every run captures video, screenshots, network traffic, HAR files, console logs, and Playwright traces. The result is both a validation run and a replayable Playwright script. submitted by /u/wixenheimer [link] [留言]

A squat can look simple until it starts going wrong. Knees drift, backs round, shoulders tighten, and without someone watching closely, small mistakes can pile up into pain or injury. That problem became harder to ignore during the pandemic, when many people moved their workouts into living rooms and garages. submitted by /u/Brighter-Side-News [link] [留言]

Designing materials that steer light is a slow kind of trial and error. Each candidate structure must be tested in computer simulations, and every new data point can take anywhere from ten minutes to an hour to produce. That bottleneck has made one thing clear. Smarter machine learning is useful only if it can learn faster, too. submitted by /u/Brighter-Side-News [link] [留言]

The world knows that Taiwan is the only geopoliticial chockpoint of ai. Realistically speaking, which country / countries can replace it in mid term and long term? and why it hasn't happened yet? submitted by /u/houmanasefiau [link] [留言]

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]