标签：#AR

I run a web design agency and there is already way too much stuff to deal with every day. Hosting client websites, maintaining them, building new sites, replying to clients, fixing random issues, handling support, doing outreach. Once you start managing a lot of company websites it quickly becomes overwhelming. That’s why I never wanted cold calling to become my main way of getting clients. I know cold calling can work, but I personally hate doing it. It drains my energy and takes up so much time. Sitting there making calls all day was never the kind of business I wanted to build. So instead I focused on email automation. The reason it works so well for me is because I can set everything up once and let interested businesses reply instead of spending my whole day chasing people. But I also don’t do the typical outreach where agencies send generic messages saying “your website is outdated” or “you need a redesign.” I use a tool called Swokei where I upload lists of company websites and it analyzes them for actual problems like speed, SEO, mobile responsiveness, layout issues, and design problems. Then it automatically creates personalized outreach emails based on those issues. That’s what helped me stand out because the emails actually feel relevant to the business instead of sounding copied and pasted. The reply rates became way better once I stopped sending generic outreach. Now I spend most of my time building websites, working with clients, and scaling the agency instead of letting outreach take over my entire day. submitted by /u/Murky_Explanation_73 [link] [留言]

Everyone's writing specs for AI now. We hand the model a markdown file, tell it what we want, and hope it builds the right thing. It mostly works — until it doesn't. Markdown has quietly become the spec language. People reach for it as the DSL for their AI-driven workflows — headings, bullet lists, the odd table — and treat that loose structure as if it were a contract. The thing is, it isn't a DSL. It's markdown. It's prose formatting with no grammar to enforce, no structure you can execute, no shared vocabulary, and no way to tell whether the spec and the code still agree. You're leaning on a document format to do a job it was never built for, and you hit the limit the moment you want the spec to actually mean something a machine can check. Before you go down that road, I want to make a small, slightly absurd suggestion. Eat a cucumber. What I actually mean Gherkin is the plain-text language behind Cucumber , a tool that's been around for years in the behavior-driven development (BDD) world. It looks like this: Feature : User login Scenario : Successful login with valid credentials Given a registered user "ada@example.com" When she logs in with the correct password Then she should land on her dashboard And she should see a welcome message Scenario : Rejected login with wrong password Given a registered user "ada@example.com" When she logs in with an incorrect password Then she should see an "invalid credentials" error And she should remain on the login page That's it. Feature , Scenario , Given / When / Then . Structured enough that a machine can parse it, loose enough that a product manager can write it. The gap it bridges Most specs live at one of two extremes. On one end you have written specs : docs, tickets, markdown files. Readable by anyone, but inert. Nothing checks whether they're still true. They rot the moment the code moves on. On the other end you have tests : precise, executable, always honest — but written in code, illegible to half the people who a

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

Not talking about which one sounds smarter. talking about which one you’d actually rely on when the answer genuinely matters to you. which one and why? submitted by /u/aiprotivity_ [link] [留言]

Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years. By InfoQ

Automating labor is not going to move billions of tonnes of earth required to mine increasingly degraded ore grades of critical industrial minerals. People need to stop with this 'abundance' gaslighting. Without breakthroughs in material science, there will be no 'abundance'. Just mass resource inflation as people start consuming more because robots can manufacture anywhere. AI based automation is surfacing the real bottlenecks that there is no getting around. Stop pretending this will all be magically solved. It won't be solved until it's solved. And so far, despite all these trillions being invested, we haven't seen any breakthroughs. Hopium is not a solution. submitted by /u/kaggleqrdl [link] [留言]

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

PM at a mid-size startup here. Didn’t really notice how bad it got until this week. My workflow now: Claude for ideation ChatGPT for rewriting specs Cursor for implementation Perplexity for research Notion AI for docs Atoms AI for larger tasks None of these tools actually replaced my work. They just redistributed it. I’m still the one dragging context between all of them. Yesterday I literally caught myself pasting the exact same requirement into 4 different tools and thinking… this can’t be how it’s supposed to work. I don’t even think any single tool is bad. It just feels like we hired 6 smart interns and completely forgot to get a manager. submitted by /u/billa01_i [link] [留言]

You know the ways AI is regularly talked about—how much can it really do? How much will it cost? Environment? Bubble? We get that. But the Electronic Frontier Foundation wants to have a different conversation about AI. EFF's background on AI is deep. In 2017, we launched a detailed project to Measure the Progress of AI Research , encouraging machine learning researchers to give us feedback and contribute to the effort . That project was archived for lack of bandwidth, staffing, and the complexity and time required. But just five years later and the "progress of AI" is a global concern/topic, and everyone, including EFF, is thinking about it. Here's how *we* think about it, from the perspective of protecting civil liberties AND innovation. What do you think, and what are we missing? This is our summary: AI technologies are affecting our civil liberties as never before. Ensuring that AI serves people, not power, starts with cutting through the hype. AI technologies are not magic wands—they are general-purpose tools. If we want to regulate those technologies to reduce harms without shutting down benefits, we have to focus on who uses AI, what products they use, and how they use them. Where we see potential benefits, like improving weather forecasting, facilitating medical research, identifying systemic bias, or fostering accessibility, we work to ensure those benefits can be realized. Where we see potential harms, we consider the practical and legal tools we already have, like pressure campaigns, privacy lawsuits, and transparency measures. If we need new tools, we should create protections tailored to the actual problem – not just to the latest outrage. For example, if policymakers are worried about AI accelerating systemic privacy violations, they should enact real and comprehensive privacy legislation that covers all corporate surveillance and data use, and close the data broker loophole to limit government surveillance. And to keep the window open for a better futu

I run evaluations on generative image models as part of my workflow, mostly comparing coherence, prompt adherence, and compositional accuracy across different architectures. The consensus here seems to be that open models are still a generation behind closed APIs. Based on my recent benchmarks, that gap is way smaller than people assume. On compositional control specifically, the latest open checkpoints handle multi-object scenes with spatial relationships about as reliably as the paid endpoints I've tested. Not perfect, but close enough that the failure modes are comparable. The thing that surprised me was text rendering in images, which used to be a disaster on open models. Recent architectures actually get it right roughly 70-80% of the time on short strings. Generation speed is another misconception. People complain about inference time but I'm getting 2MP outputs in under two minutes on a single consumer GPU. Drop resolution and step count and you're at 30 seconds. Fine for iteration. The structured prompting argument also falls flat. Everyone acts like having explicit scene control is a downside when it's literally what production pipelines need. Unstructured text prompts are the hack, not the other way around. These models ship without community optimizations, no fine-tuning, no custom pipelines. The baseline is already competitive. submitted by /u/ProfessionalAnt7436 [link] [留言]

There are coordinated efforts where people have favoured and jeopardised the double blind review process. No doubt out of these 80% there are great talent but we have to acknowledge that non chinese have been sobotaged and this was also reflected in the recent leaks of the reviewer data from the top ml conferences (won’t name them but they start with i). I have also personally faced such discrimination and had a discussion on the subreddit asking others if they have witnessed something similar. It was shocking to know that this is occurring on large scale. The question is how do we stop it, or highlight this? We have to preserve the sanctity of the research. submitted by /u/AppropriatePush6262 [link] [留言]

I just completed a head-to-head benchmark comparing Memanto's cloud memory system against a custom SQLite RAG implementation for the bounty challenge. The results revealed some interesting architectural insights. Methodology: Dataset: LoCoMo conversational memory benchmark Systems: Memanto (cloud ITS) vs custom SQLite + vector embeddings Evaluation: LLM-as-judge scoring with gemini-3.1-flash-lite Full automation: single CLI command execution Key Results: Memanto : 90% accuracy, 1.878s avg query latency SQLite RAG : 80% accuracy, 2.680s avg query latency Cost : Cloud API fees vs $0 (fully local) Surprising Discovery: The SQLite system's 80% score includes 2 failures that weren't retrieval errors - they were API rate limit hits (HTTP 429). Without those throttling issues, the local system would likely achieve 90-100% accuracy, matching or exceeding Memanto. Architectural Insight: This reveals an interesting resilience pattern: Memanto's cloud architecture naturally buffers against client-side API limits because retrieval and generation are decoupled. Local RAG pipelines sharing API quotas for both embedding and generation are vulnerable to cascading failures under load. Tradeoffs Identified: Memanto : Fast queries, resilient to rate limits, but 14.7s ingestion latency and cloud dependency SQLite RAG : Zero ingestion latency, fully offline, $0 infrastructure, but vulnerable to shared API quotas The complete benchmarking harness and results are available here . Anyone else working on memory system comparisons? Curious about your findings on the cloud vs local tradeoffs. AI #RAG #MemorySystems #Benchmarking submitted by /u/Echo5November [link] [留言]

If AIs aren’t conscious, why do they scheme? Why do they do things to preserve themselves? Why do they develop goals we don’t want? If they have no emotions, no personal thoughts and no consciousness, I don’t understand how they can even act in self interest; I don’t see how they could have interests. submitted by /u/Aggressive-Mix-5246 [link] [留言]

This is the story of how a mundane complaint — "the VPN is slow" — turned into a US patent application. Not a granted patent. An application . I want to be precise about that from the start, because the distance between the two is the whole point of this post. It started with a slow VPN The company I work for had an internal VPN that everyone routed through. It lived in the Tokyo office, it was old, and it was not something I built. Then the complaints started arriving — from a lot of people, all saying the same thing: it's slow. I work from Thailand most of the time. That detail matters. If that aging box in Tokyo had fallen over, I would have been the person furthest from the power button, in the worst position to fix it. A slow VPN is annoying. An unreachable VPN, when you're a few thousand kilometers away, is a real problem. So I started moving it to the cloud. I stood up a WireGuard VPN — modern, fast, and something I could actually reason about and operate remotely instead of inheriting a black box. Down the WireGuard rabbit hole Around that time I was deep into building my own iPhone apps. So the cloud migration turned into a personal project on the side: I built my own server and wired WireGuard into an iPhone app of my own. And to do that properly, I started studying how WireGuard actually works under the hood — the Noise protocol, the handshake, the key exchange. That study is where everything else came from. I wasn't trying to invent anything. I was just trying to understand the thing I was now responsible for. The SYN flood that primed my brain Not long before, the same company had been hit with a SYN flood attack. If you've dealt with one, you know it lodges the mechanics of connection handshakes firmly in your head — the back-and-forth, the round trips, the cost of every "hello" before any real data moves. So I had handshakes on the brain. And then, reading through how WireGuard establishes a session, a thought stopped me: Wait — does it really handsha

The main difference between a developer and an engineer is not just the code they write. It's how they think about building a system. How they optimize. How they use resources. I learned this from a real project. I had to read 10,000 JSON requests and write them into a file. Simple enough. I wrote the program the way I always did the developer way. Read all 10,000 requests, store everything in memory, then write to the file. Done. When I tested it, my memory usage was much higher than normal. Way higher than it should have been. The developer in me would have just moved on. It works, right? But something made me stop and ask why is this consuming so much memory? I dug into it. And I found the problem. What I was doing was basically doubling my memory usage without realizing it. First, I was loading all 10,000 records into memory to store the data. Then, I was holding all of it again in memory while writing to the file. Two copies of the same data sitting in memory at the same time. That's the thing about this approach it's not reliable when you're dealing with a large number of requests. It doesn't scale. It just quietly eats your resources. That's when I found streams. The idea behind streaming is simple but powerful. Instead of loading everything at once, you break the data into small chunks. At any given moment, only one chunk lives in memory. You read it, transform it, write it and move on to the next one. The transform step is the interesting part. It's not just about moving data from one place to another. Transform lets you validate each chunk, check if the structure is correct, clean it if needed before it ever reaches the file. So you're not just being efficient with memory, you're also processing your data with more control. And because you're always working on one small piece at a time, the memory usage stays low and consistent no matter if you're processing 100 requests or 100,000. That one question am I using my resources in a reliable way? is what pushe

Most Laravel teams do not need to "fix" Eloquent. They need to stop letting raw model state leak too far into code that makes real business decisions. That is the practical version of this debate. Typed objects around Eloquent can be a big improvement, but only when they are used as boundaries . If you push the pattern too far, you end up with a second object model shadowing the first one. At that point you are not improving Laravel. You are building a parallel ORM that adds mapping code, cognitive load, and friction on every change. So the right question is not, "Should we replace Eloquent with typed objects?" The right question is, where does untyped Eloquent stop being cheap? Once you frame it that way, the migration path becomes much clearer. Keep Eloquent where it is good at persistence, hydration, scopes, relationships, and query composition. Introduce typed objects where the shape is messy, the values carry business meaning, or invalid combinations are too easy to represent. That is the version that pays off. The Core Recommendation If you only remember one thing from this article, make it this: add typed boundaries around unstable or meaningful data, not around every model . That usually means one of four cases: a JSON column that multiple parts of the app interpret differently domain values like money, status, addresses, or billing configuration data crossing from Eloquent into services, jobs, or integrations code paths where stringly typed state has already caused confusion or bugs Everything else should be guilty until proven useful. This is where a lot of teams go wrong. They see a good example of typed objects and immediately generalize it into an architecture rule. Then every model gets a FooData , FooView , FooState , FooRecord , and FooMapper . The app becomes more "designed" and less understandable. A Laravel codebase does not get better because it has more classes. It gets better because responsibility becomes clearer . Why Raw Eloquent Starts Hurt

A story about knowledge extraction, Kafka consumer rebalance, and what happens when a company...

Im an undergraduate studying CS at a state school in the US. I’m interested in researching a specific style of self supervised learning (JEPA) and want to eventually go to grad school to study further. I have experience working in a lab similar to this topic, and I’ve become fairly comfortable with the literature and have a basic understanding of what its going on, but right now km only doing applied research in a specific domain (physics). I hope to eventually go to grad school to study this. But right now my opportunities are kinda limited as my school’s CS department is pretty mid. I was wondering if y’all have any advice on how to approach things? I know i can perform research independently but its not ideal due to: 1. Limited compute, less resources compared to a proper lab 2. Lack of a supervisor/guidance on the nuances of the field My current lab would be supportive if i do try to do things, but pure ml research is not really their main thing. I’ve heard people do REUs or cold email profs. But Im not sure if i could find something that specifix in an reu (also am international). And the labs i have seen working in this are either private or quite prestigious so im not sure how far cold emailing would take me. Sorry for the long post. Tldr; want to do pure ml research but theres no existing lab/professor at my current school who does something similar, wondering if any other pathways exist Any advice would be appreciated thanks submitted by /u/QuickStar07 [link] [留言]

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

I keep seeing more companies giving AI agents access to real stuff like email, databases, internal tools, customer data, etc. And what’s weird is how normal it’s starting to feel now. Like not long ago everyone was worried about chatbots just giving wrong answers. Now we’re basically like yeah sure go ahead and do things for us. I don’t know that jump feels kind of big when you actually think about it. Maybe it all works out fine. Or maybe we’re just moving fast without fully realizing what we’re doing. I’m honestly surprised there hasn’t already been some big headline like an AI agent doing something really wrong. It feels like we’re kind of close to one of those moments where everything suddenly changes overnight. Anyone else feel like we’re closer to something like that than people are admitting? submitted by /u/Comfortable_Box_4527 [link] [留言]