标签：#art

reddit.com/settings/data-request https://gamma.app/docs/Reddit-Brain-qt0g7e5vktlgifm Implementation Blueprint Your questions answered. Three steps to go from zero to a fully operational Reddit Brain. Step 0: Download Your Archive Go to reddit.com/settings/data-request and request your full data export. You'll receive a ZIP file containing comments.csv and posts.csv — everything you've ever posted on Reddit. Step 1: Get the Data Action: Request your export at reddit.com/settings/data-request . Then: Download ZIP, extract comments.csv and posts.csv . Optionally run reddit-user-to-sqlite to build a parallel SQLite archive for richer querying. Step 2: Build the Brain Action: Load into Sheets or a database. Clean, tag, and compute word count and engagement metrics. Then: Add LLM passes for canonical_question , topic, tone, and content type. Push into a vector store; connect via n8n or your preferred orchestrator. Step 3: Exploit the Hell Out of It Action: Generate content backlogs, podcast outlines, FAQs, scripts, and social copy from your corpus. Then: Use agents to draft from your own history, keep messaging on-brand, and refresh the archive with new exports on a schedule. submitted by /u/jdawgindahouse1974 [link] [留言]

Why is it that most organization are in a hurry to integrate AI agents in process that don't really need the advancement, is that they don't want to be left behind or they are just following the hype submitted by /u/Quiet-Brilliant-1455 [link] [留言]

submitted by /u/EUobs [link] [留言]

AI systems are moving from answering questions to taking actions. That changes the risk. A wrong chatbot answer is annoying. A wrong action inside email, CRM, payments, customer support, or internal data can create real damage. So maybe the next big AI challenge is not just better reasoning. It is knowing: what the AI can access what it can do alone what needs approval who is accountable when it fails As AI agents become more common, who do you think should be responsible when they make a bad decision? submitted by /u/Alpertayfur [link] [留言]

https://preview.redd.it/pv22tsg2ib4h1.png?width=1918&format=png&auto=webp&s=dfeda1000090dc99c57c8150e4de46cfe2ba2e29 I just wanted him to give me a prompt, which then i can give to Nano Banana pro and generate me a completely random thumbnail, i wanted to test its capabilities, but instead of a prompt, he gave me this... 😭😭😭😭😭 submitted by /u/ObjectiveOrchid5344 [link] [留言]

GitHub: Prompt-Logic-Gates-PLG Over the past few days, I've shared my research project Prompt Logic Gates (PLG) and received a lot of interesting feedback. Some people loved the idea, some were skeptical, and many raised valid questions. The most common reaction was: > "Natural language is already the abstraction layer. Why add logic gates?" That's a fair question. My goal isn't to replace natural language prompting. In fact, natural language remains at the center of PLG. The idea is to explore what happens when prompts stop being a single request and start becoming systems. The Problem When we write prompts, we're converting our ideas, requirements, constraints, and expectations into text. For simple tasks, this works perfectly. But as prompts grow, they often include: Multiple objectives Business rules Style constraints Context dependencies Exclusions Fallback instructions Tool orchestration At that point, prompts become harder to maintain. Contradictions appear. Priorities become unclear. Context gets mixed together. The prompt is still text, but the complexity starts to resemble a system. What is PLG? Prompt Logic Gates (PLG) is a visual prompt engineering experiment that explores whether prompts can be organized before being sent to an AI model. Instead of writing one giant prompt, users create prompt components and connect them using semantic logic gates. The AI then analyzes the graph and compiles a final structured prompt. How It Works AND Gate When multiple instructions exist, the system evaluates them against the current context and determines which instruction is more foundational. The higher-priority instruction is applied first. OR Gate When multiple options are available, the system selects the most contextually relevant option instead of blindly including everything. NOT Gate Defines exclusions and negative constraints. It explicitly tells the system what should not be done, reducing contradictions and ambiguity. Ask Questions Gate If the system detec

Do you use the "Act as..." segment in your prompts? Do you think it's effective and why? I know it depends on the rest of the prompt, as well as the main goal, but i'm asking if it's working overall. submitted by /u/ObjectiveOrchid5344 [link] [留言]

With AI becoming part of almost everything now—work, business, investing, coding, spreadsheets, content creation, and more—I'm curious about real-world use cases. What's the one thing you use AI for regularly that has genuinely saved you time, made you money, improved your productivity, or solved a problem? Looking for practical examples rather than just "I use ChatGPT." What specific tasks have you automated or improved with AI? submitted by /u/Acrobatic-Shop4602 [link] [留言]

Standard AI 3D generators (like Meshy or Tripo) are limited. They produce solid, monolithic 3D objects that look good but are practically useless, because: - Want to rig or animate it for a game? Can't easily do that, because it’s a dead, monolithic blob instead of a functional, modular asset. - Want to change the arm of a robot you generated? Regenerate the entire asset. - Want to edit something manually? The whole thing collapses because it's not actually structured. Free github project here: https://github.com/RareSense/Nova3D But you'll need to bring your own API Key (BYOK) Under the hood (if you're interested): It uses an LLM as a structured code compiler, instead of an image generator. It writes native Blender Python (bpy) code blocks that target specific nodes in the scene graph. The trick is that everything compiles through Blender's actual scene graph structures instead of pixel or point-cloud diffusion. Final export is a clean multi-part GLB with transform nodes and working pivot axes preserved. submitted by /u/mhb-11 [link] [留言]

A group of 20 Snap alumni has come together to launch a fund called Ghost Angels to back the next generation of social media.

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible.

The legacy book market and use of AI for this are going to be insane. Less than 1% of the US population writes a book. This is what AI is used for: to stop doing tedious stuff and actually do stuff that matters. https://preview.redd.it/fcn6d2t7ta4h1.png?width=2752&format=png&auto=webp&s=5ab6effcafc1e2156903d274f6a4411e53bd9d37 submitted by /u/jdawgindahouse1974 [link] [留言]

Last week a company nobody can name spent $500 million in a single month on Anthropic's Claude API. Not $500K. Not $5M. Half a billion dollars. In one month. Because nobody set a spending limit. Uber burned through its entire 2026 AI coding budget by April . Four months into the year, done. Microsoft quietly cancelled its internal Claude Code licenses and told engineers to go back to GitHub Copilot. All three stories broke within days of each other, and they all point to the same thing. Token-based billing, when given to an ungoverned team, is a financial weapon pointed at your own company. Every prompt, every context window, every agentic loop gets billed. An engineer running Claude Code seriously can rack up $500 to $2,000 a month just by doing their job well. The answer is not stricter policies. The answer is owning the infrastructure and making tokens free. This article breaks down exactly how to do that for a 100-person engineering team for under $1 million, with real 2026 hardware prices and honest tradeoffs. The Root Problem: You Are Renting the Meter When your team uses Claude Code or any external AI API, you do not own anything. You rent compute by the token. The model is not yours. The data leaves your building on every single request. The bill scales with how well your engineers actually use the tool. That last part is the trap. The better your engineers get at using AI, the more it costs you. Uber's Claude Code adoption jumped from 32% to 84% of their 5,000-person engineering org. That is a success story that turned into a budget crisis. Owning the infrastructure flips this completely. The better your engineers get at using AI, the more value you extract from hardware you already paid for. The Solution: Private On-Premise AI The setup is straightforward: Buy GPU server hardware once Download a state-of-the-art open-source model (free) Run an inference server that speaks the OpenAI API format Point Claude Code, Cursor, or any agent at your local endpoint

I have been doing URL redirect work for client sites for some time now. It’s one of those jobs that’s never quite urgent enough to automate, but tedious enough to dread, especially after a migration when you have hundreds of them. Recently tried it. Connected my AI agent with MCP to handle it. I told it to build a set of redirects and it did. No dashboard, no wrestling with CSVs, no clicking through settings. Teaching in plain language. In seconds. And what I was surprised by was not the speed, but the amount of mental overhead such a task involves. You’re not just doing the task you’re context switching into a tool, remembering where things are, making sure nothing breaks. Giving it to an agent removes all of it. What really made me trust it for real client work was the dry-run feature. See exactly what is changing, before it changes. No surprises here. Curious if anyone else has been using MCP for infrastructure tasks, redirects, DNS, workspace management. I think we are at the start of something that is going to quietly gobble up a lot of tedious technical work. submitted by /u/Scary_Bag1157 [link] [留言]

Ive subscribed and operated with the notion of build, fail, grow, and it has always been a humbling process, but recently I have been hearing about a “new” feeling of failure. "I tried my best and it didn't work." -> Move on "I had this super intelligent tool and STILL failed."-> Rinse and repeat Its like AI accelerates idea failure and because it is embedded in a hyper rinse & repeat, the feeling of failure is amplified. Is anyone else feeling or seeing this? submitted by /u/Outrageous-Pop-2853 [link] [留言]

has many of you know Their are a growing number of CEOs who are looking too replace human workers. We need too start Boycotting companies who replace Human workers with ai. People start calling your elected officials and demand they support legislation restricting Ai and how companies can use it. submitted by /u/thegreatdouchebag69 [link] [留言]

I want 1-2 AI/ML related pirated course. If anyone has it please comment. submitted by /u/hassan21018 [link] [留言]

The Ninja Artisan Outdoor Pizza Oven is aimed at people who want delicious pizza nights without having to deal with things like propane or wood pellets, unlike many other pizza ovens.

We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.

Pulling together this week's major AI releases for anyone who didn't have time to track every blog post. Sticking to substantive changes, not hype. Anthropic — Claude Opus 4.8 Released this week. Headline pricing unchanged, but Fast Mode dropped from $30 input / $150 output per million tokens to $10 / $50 — a 3x reduction on the premium tier. Reported improvements in "judgment" and longer autonomous runs. Also shipped 20+ legal MCP connectors and Microsoft 365 add-ins (Excel, PowerPoint, Word) in GA. Alibaba — Qwen 3.7 Max Launched May 20 at Alibaba Cloud Summit. 1M-token context. Reported to top Claude Opus 4.6 Max on Terminal-Bench 2.0, SWE-Bench Pro, and MCP-Atlas. Pricing $2.50 / $7.50 per million tokens — roughly half of Opus 4.7. Alibaba claims autonomous operation up to 35 hours without performance degradation. Alibaba is now ranked #6 lab globally on Arena text leaderboard. OpenAI — GPT-5.5 Instant Now default in ChatGPT. Reports 52.5% fewer hallucinated claims than GPT-5.3 Instant on high-stakes prompts (medicine, law, finance). OpenAI also shipped a ChatGPT sidebar inside Excel and Google Sheets, plus a personal finance dashboard for Pro users (US only). Google — Gemini 3.5 Flash Reported to beat Gemini 3.1 Pro on coding and agentic benchmarks at ~4x faster output token rate. Ultra subscription cut from $250 to $200/month; new $100/month Developer tier introduced. xAI — Grok Build 0.1 Coding agent moved to public API beta May 28. Custom Skills feature added for reusable user-defined tasks. Connectors for SharePoint, OneDrive, Notion, GitHub, Linear, plus bring-your-own MCP support. Mistral Launched Vibe (unified work + code agent, replaces Le Chat). Acquired Emmi AI for physics-based simulation. Targeting €1B revenue in 2026; new 10MW inference DC announced. Hugging Face Launched an app store for the Reachy Mini robot. ~10,000 units shipped. Also reported a malicious repo masquerading as an OpenAI release that accumulated 244K downloads before takedown — r