标签：#AR

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]

With true plug-and-play support, versatile connectivity, and excellent sound, Universal Audio’s Volt 876 makes recording simple.

Dozens of people have complained to the Federal Trade Commission about Norse Atlantic Airways’ tech-first customer service operation. Some said they lost thousands of dollars.

Michael Stiefel spoke to Sonya Natanzon, about the intersection of technical and social aspects of software architecture. Understanding the business and how a company operates is more important than the specific technologies used. Effective requirements analysis requires focusing on problems to be solved that describe good and bad outcomes, rather than statements of need or solution statements. By Sonya Natanzon

The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows how, to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent. By Amanul Chowdhury, Vinay Gummadavelli

submitted by /u/Dapper_Order7182 [link] [留言]

submitted by /u/InvestigatorSoft5764 [link] [留言]

Solo dev here. Building an AI companion that lives entirely on your device — no cloud, no account, no data ever leaving your phone. Coming soon to Google Play. Would love honest feedback. I genuinely just want opinions. It is not even available yet. I have been working on this for over a year. I am still building it. I started this project because a regular family budget like mine cannot just go out and purchase an AI companion robot. So I started with a cheap robot kit from Amazon — something my 9 year old son and I could build together. Then I thought... "What if I could give it a real brain?" I had an old Samsung Galaxy collecting dust and went to work. Scout is a calm AI companion that transforms an old phone into a friend. He listens, remembers, learns, and provides a warm family-safe presence — designed to feel less like an assistant and more like someone who is simply glad you are there. Everything runs offline. No account. No subscription. No data leaving your phone. Ever. I will need beta testers later — but right now I am just curious: What would make you look at something like this and think "that is actually kind of nice"? Edit: It will what support your own free Gemini key for online conversations if you want them. I call this "more Intelligent conversations" submitted by /u/CapeManCoral [link] [留言]

Modern search engines do more than match keywords. When you search for "cozy mystery set in Paris" and get results for "atmospheric detective novel in France" that's vector search at work: documents and queries are converted into lists of numbers, called embeddings, and the search engine finds the documents whose numbers are closest to the query's. Manticore Search supports this natively. Under the hood, it uses a data structure called HNSW: a graph that connects nearby vectors, so it can find nearest neighbors quickly without scanning every document. That makes vector search fast enough to run on millions of documents in milliseconds. But HNSW has an inefficiency. Early in the traversal, almost every distance computation finds a better candidate than the ones already in the result set. As the search goes on, those improvements become rarer, but the algorithm keeps traversing the graph until it exhausts its exploration budget. By that point, the result set has often already converged, and the remaining work does little or nothing to improve it. Early termination fixes this by detecting that point and stopping early. The effect becomes more noticeable as k grows, where k is the number of nearest neighbors the query asks Manticore to return. Returning more neighbors requires more graph exploration, and much of that extra work happens after the result set has already stabilized. That also makes early termination more valuable, because it has more unnecessary work to cut. This gets more pronounced with vector quantization . Quantization compresses stored vectors to save memory, which slightly lowers search precision. To recover it, Manticore uses oversampling : it fetches 3x more candidates than requested, then rescores them using the original full-precision vectors. With the default 3x oversampling, HNSW explores many more candidates per query. Large k values often come from this kind of candidate expansion: an application may ask the vector index for hundreds or thous

Hey everyone 👋 I'm exploring ideas for an AI-powered developer tool, but before building anything, I want to understand the real problems developers face every day. There are already plenty of tools that generate code. What I'm interested in is everything around coding: Debugging Code reviews Technical debt Documentation Dependency upgrades Testing Deployment Architecture decisions Learning large codebases I'd love to hear from you: A few questions: What's the most frustrating part of your workflow? What task takes more time than it should? What's something you wish AI could do for you today? Have current AI tools (ChatGPT, Claude, Cursor, Copilot, Gemini, etc.) failed you in any important way? If you could eliminate one developer headache forever, what would it be? I've also created a short 2-minute survey: 🔗 https://docs.google.com/forms/d/e/1FAIpQLSf1M5d2y-0RXEIhrbDBtS5gC900YuzWl43cJCxGUrU38MyeDQ/viewform?usp=publish-editor I'll happily share the survey results and key findings with the community once I collect enough responses. Thanks in advance for any feedback!

Thanks to the newly detailed FROST technique, telltale SSD activity can be measured in the browser using simple JavaScript.

One day last October, sitting in the courtyard of his house in China’s Henan province, Dong Hui decided to see if he could hold a pen to write. Dong, 39, had sustained spinal cord injuries in a car accident six years earlier that left him paralyzed from the neck down. Slowly but determinedly, he wrote…

Follow this link to ask your questions during our Ask Me Anything session on the European Parliament's subreddit, 02/06 15.00-16.00 CET. submitted by /u/Marty_ol [link] [留言]

I do analysis of automobile dealership data and prepare reports based on the analysis for management review. I’m getting way better analytics and cleaner reports being built by ChatGPT Plus compared to Claude pro. Claude is consuming too many tokens and sometimes for longer documents it used my 100% of the 5 hour limit which is very annoying. ChatGPT on the other hand feels to me that it has unlimited usage for my requirement. What is the view of you people when using AI for business and financial data analytics? Is anyone else finding ChatGPT nicer too? submitted by /u/TurboChargedV12 [link] [留言]

Hey everyone, We’re building Antitech , a security layer for AI agents and LLM-powered workflows. We’re opening a small number of free early-access assessments for teams/builders working on AI agents. If you give us access to an endpoint of a Dockerized / sandboxed environment of your agent, we’ll test it against common and emerging AI-agent attack vectors, including: Prompt injection Indirect prompt injection Tool abuse Data leakage / exfiltration Fake authority / malicious context Unsafe agent behavior Weak guardrails and policy bypasses In return, you get a free vulnerability report showing what we found, how serious it is, and practical recommendations to harden your agent. This is completely free. No catch. We’re doing this because we want to work closely with real AI-agent builders while shaping the product. Early participants will also get: A big discount once the final product is ready Insider updates while we build Early access to new features The option to become a design partner Priority access to future assessments What we need from you: An endpoint of a sandboxed/Docker environment Permission to test within agreed boundaries A short feedback call after the report We won’t publicly disclose anything without your permission. If you’re building AI agents and want to know how they can be attacked before someone else finds out the hard way, DM me or comment below. submitted by /u/TheAchraf99 [link] [留言]

I've been paying attention to my own workflow lately and noticed a lot of my time goes into moving stuff between AI sessions, not the actual thinking. Like I'll get an output in one session and then manually bring the relevant pieces into another so it has what it needs. What I can't tell is how much of that is necessary vs. me just being sloppy. So I'm curious how others handle it: When you move from one session to another, what do you actually carry over? Just the output, or also the reasoning, the decisions, the constraints, what to avoid? Have you ever handed off too little and the second session went sideways? Or too much and it got lost in the noise? Does anyone have a mental rule for what's "enough context" to pass along? Trying to figure out if there's a clean pattern here or if it's just inherently messy. Curious what people have landed on. submitted by /u/riley_kim [link] [留言]

How to make LLMs deterministic, in plain English. The version I share with founders and product teams before they make decisions worth real money. You use AI tools every day. But can you explain what happens when you hit send? Most people cannot. And that gap is costing them. Bad prompts. Broken products. Decisions made on the wrong assumptions. The Hard Truth Every LLM explainer out there is written for researchers or so basic it tells you nothing useful. Neither helps you build better products or work with AI more effectively. This is the version I share with senior leaders, founders, and product teams before they make decisions worth real money. 1. It Is Not a Search Engine. It Is Not a Database. It Is a Prediction Machine. When you type a prompt and hit send, the LLM is not finding an answer from somewhere. It is predicting the most likely words to follow your input. Based on patterns it learned from billions of documents. That is the whole process. Wrong: "The AI knows the answer." Right: "The AI predicts the most likely answer based on what it has seen." This changes everything about how you use it. When an AI gives you a wrong answer confidently, it is not broken. It is doing exactly what it was built to do. Predict. Not verify. 2. The Autocomplete Comparison (And Why It Only Gets You Halfway) You have probably heard the phrase "autocomplete on steroids." It is not wrong. But it misses something important. Your phone autocomplete learned from your messages. An LLM learned from most of the written internet. Books. Research papers. Code. Billions of examples. At that scale, the patterns start to look a lot like real thinking. Not because the model understands in the way you do. Because it has seen so much that it can predict what a good answer looks like. When I was building AstroNayak I fed Vedic astrology principles into the system prompt. The LLM produced interpretations that genuinely surprised me. It did not know Vedic astrology. It had seen enough of it t

This article was originally published on aicoderscope.com On paper, Windsurf and Cursor are the same product. Both are standalone IDEs forked from VS Code. Both charge $20/month for their entry paid tier. Both ship a tab-completion model and a multi-file agent. Both wire in the same frontier models — GPT-5, Claude Opus, Gemini. Reviews that score them feature-by-feature end up in 47-43 ties because the feature lists genuinely match. That kind of comparison misses the point. The two editors feel different to use, and the difference matters more than the feature checklist. This piece tests both side-by-side across two weeks of normal client work — Python, TypeScript, Go — and lands on a clear verdict at the end about which one fits which kind of developer. Pricing and feature claims here were verified against Windsurf's pricing page and Cursor's pricing page on May 5, 2026. Both vendors change pricing more than most editors — re-verify before subscribing. Pricing: nearly identical The pricing tables converged in 2025 and have stayed mirrored since: Tier Cursor Windsurf Free Hobby (limited Agent + Tab) Free Entry paid Pro $20/mo Pro $20/mo Heavy individual Pro+ $60/mo (3× usage) / Ultra $200/mo (20× usage) Max $200/mo (heavy users, unlimited extra at API pricing) Team Teams $40/user/mo Teams $40/user/mo Enterprise Custom Custom Cursor offers a middle tier (Pro+ at $60) that Windsurf doesn't match exactly. Windsurf has a "Light" plan with unlimited usage on cheaper models that Cursor doesn't have. These are minor — for the typical individual developer choice, both are $20/month for Pro and $200/month for the power-user tier . The entry decision is therefore not a price decision. It's a workflow-fit decision. Both ship a standalone editor A common misconception: "Windsurf is a VS Code extension, Cursor is its own editor." Both are standalone applications. Both fork VS Code. Both can install most VS Code extensions from the Open VSX Registry (with occasional compatibility

This article was originally published on aicoderscope.com On April 28, 2026, Warp open-sourced its terminal client under AGPL-3.0, picked up 60,000 GitHub stars, and declared itself an "agentic development environment." OpenAI signed on as founding sponsor. The announcement looked like a triumph of developer-first idealism. Read the fine print and a different picture emerges: the terminal is free; the product that matters — Oz, Warp's cloud agent orchestration platform — remains fully proprietary. Warp is not becoming an open-source project. It is becoming an enterprise SaaS company with an open-source frontend. None of that is inherently bad. But it is what this review is actually about: does the $20/month Build plan deliver enough AI value to justify adding Warp to a stack that probably already includes Cursor or Claude Code? What Warp is in May 2026 Warp's product now has three layers: Warp Terminal — the terminal client, open-source AGPL-3.0. Rust-based, GPU-accelerated, available on Mac, Linux, and Windows. The core terminal features (blocks, Warp Drive, session sharing, settings file) are free and remain free. Warp Agent — an AI coding agent embedded in the terminal. Runs locally for interactive work. Handles natural language command generation, code review, debugging assistance, codebase Q&A, and voice input. Consumes credits from your plan. Oz — Warp's proprietary cloud orchestration platform. Runs agents in the background, coordinates multi-agent workflows, triggers on events from Slack, Linear, or GitHub Actions, and orchestrates third-party CLI agents including Claude Code and Codex. Oz is where the enterprise pitch lives. Around 1 million developers use Warp as their primary terminal. The pivot to agentic tooling is a bet that those developers will pay to automate their workflows beyond what a local agent session can handle. Pricing breakdown Warp simplified its pricing in December 2025, replacing the old Pro/Turbo/Lightspeed tiers with two paid plans. P

Great to see Odysseus blow up this past day, local AI getting this much attention is genuinely good for everyone building in this space. Figured this is the right crowd to share what we're launching tomorrow (June 1st), since we're playing a pretty different game. A quick framing: Odysseus is a self-hosted workspace that points at engines (Ollama, llama.cpp, vLLM, cloud APIs) and runs through Docker. Conifer is the engine itself, with our own runtime, running natively on Mac, Linux, and Windows. So we're the layer underneath, not a competitor to the workspace. What's actually in it tomorrow: A native inference runtime across Mac, Linux, and Windows, with our own Metal engine for Apple Silicon already matching or beating llama.cpp on a few models on the M3 Max (full benchmarks, including where we're still behind, are at conifer.build/benchmarks) A real coding IDE on top (CodeMirror, integrated terminal, file viewers), so you can code locally with models that never leave your machine Typhoon, a local agent that can read and edit a folder you point it at, kernel-sandboxed rather than just a shell with a warning Install is a signed app you double-click, no Docker, no localhost ports Fully free and open source The honest reason we exist: PewDiePie's wave defined "local AI" in millions of people's heads as Linux + Docker + an NVIDIA rig. If you weren't on that exact setup, the conversation probably felt like it skipped you. Conifer is what local AI should feel like when it's actually native to your machine, whatever your machine is. Launches tomorrow, free and open source like PewDiePie! You can sign up for our waitlist here: conifer.build I'll be around in the comments all day tomorrow, please bring the hard questions. submitted by /u/No_Elephant_7530 [link] [留言]