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"text": "“Stolen art” was a cleaner slogan in 2021 than a description of 2026.\n\nEarly open image models leaned on web-index piles like LAION and Common Crawl: URLs, alt text, download later. That pipeline is still in the bloodline of many open-weight Stable Diffusion descendants. Hobby LoRAs and private fine-tunes usually sit on those older bases. They are not proof of what Adobe, a lab API, or a current closed stack is doing.\n\nCommercial training now is a mix: licensed libraries and paid platform deals, public domain, synthetic data, and a huge quiet slice that never needed a midnight scrape ie the ToS you clicked when you uploaded to an app. That last one is a contract fight, not “they torrented ArtStation.” Public domain and a stock license are not infringement. Unlicensed crawling of in-copyright work is the live lawsuit lane. Those are different claims. Lumping them is the trick.\n\nScraping did not vanish. Some labs still disclose crawlers. One bot is not the industry. Firefly’s pitch is the opposite: licensed plus expired copyright, no web mine. Treat vendors separately.\n\nThe hypocrisy is distillation. The same shops that trained on “publicly available” text now ban and sue people who train on their outputs. “If it’s online it’s fair game” dies when the product is theirs.\n\nThe shaky part of “AI stole every JPEG” is treating 2020 open-scrape lore as the 2026 commercial pipeline, and treating a Civitai fine-tune of an old SD checkpoint as if it were Firefly. Update the year and split the stacks, or the slogan does the work the facts won’t.\n\nEven the hobby pile is moving. People are on Wan, Seedance, MiniMax H3 lab forks, not fine-tuning 2022 Stable Diffusion and calling that the industry. The “stolen JPEG / LAION” story is about a base that’s becoming a relic. That still doesn’t make every new weight a licensed library. It does mean you’re arguing with last year’s install folder, and an outdated hot take. The pattern that actually lands, A lot of the current noise is identity, not pipeline literacy. Plant the “no AI / stolen art” flag in public. When a check shows up with AI in the stack. Fortnite, a game, a brand pack, a tool they already use in production the flag folds and the rationalizations start. That’s bandwagon plus career insurance, not a consistent rights theory. its behavior, not census:\nThe tell isn’t 2022 ArtStation. The tell is the people who spent months planting the flag and then explained away the first paid deal that had AI attached. If it were a principle about training data, the deal wouldn’t rewrite the essay. If it’s a scene costume, the money will.",
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"text": "Atención ‼️\n\nBusco colaborador/a con experiencia en IA, web scraping o tecnologías afines para desarrollar una iniciativa conjunta. Para más información, envía un correo a jleon@geografia.unam.mx 📩",
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"text": "I am awaiting Jev access. I did build a web scraping service with scrapfly (200k credits, which means easy 100k scrapes for $30 a month) haiku ai (organizing scraped data), sonnet ai (working the important scrape data) and putting it into and admin for review or auto publish data for client if all checks clear (sonnet + haiku checking statements against source).\n\nWeb scraping data (and using it) works when you already have a running business with working pipelines and authority. Don't work on any new business launch.\n\nMy take. Client launch was an immediate success within 30 days.",
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"text": "160,000+ GITHUB STARS. $0 IN RECURRING SAAS\n\nAn AI model without eyes, hands, and memory is just a blind chatbot trapped in a text window.\n\nBefore an agent writes a single line of code, it has to browse the live web, parse messy PDFs, recall past sessions, and isolate itself in a sandbox.\n\nHere is Part 1: the 7 breakout open-source tools that connect frontier models to the real physical web:\n\nschema contracts. browser navigation. recursive research. markdown scraping. tiered memory. document vision. micro-vms.\n\n01 PydanticAI ▸ https://t.co/KARybMZDSg\nKill silent type drift. Production agent framework with strict dependency injection and compile-time type validation to guarantee deterministic JSON tool outputs.\n\n02 browser-use ▸ https://t.co/MED3LNbeh0\nGive models eyes and hands on the live web. Autonomously navigates DOM trees, clicks buttons, bypasses flows, and fills complex multi-step forms like a human.\n\n03 deep-research ▸ https://t.co/X3P1Tvuao9\nOpen-source recursive search engine. Spawns 20+ parallel search branches, parses dozens of live sources, and synthesizes publication-grade research dossiers for $0.\n\n04 Firecrawl ▸ https://t.co/KaE8uSNvq8\nThe ultimate web-to-Markdown engine. Crawls entire enterprise sites, bypasses bot barriers, and extracts pure LLM-ready context with zero noise.\n\n05 Letta ▸ https://t.co/UmNrtZilkk\nThe stateful memory OS (MemGPT evolution). Manages tiered memory hierarchies across working context, episodic recall, and archival stores over infinite sessions.\n\n06 RAGFlow ▸ https://t.co/wQqJKh29Go\nDeep document orchestration. Uses deep vision and OCR layout analysis to parse messy multi-column blueprints, balance sheets, and dense PDFs without losing spatial context.\n\n07 E2B ▸ https://t.co/NrWuuogGr7\nAirgapped micro-VM sandboxes. Boots isolated Firecracker environments in 150ms so agents can compile and run untrusted code without host risk.\n\nthe ingestion loop:\ndefine schema contract → navigate headless DOM → execute recursive research → crawl site to markdown → sync tiered memory → boot isolated micro-vm\n\nPrompts are just suggestions. Ingestion is ground truth.\nWhen an agent can see the live web, remember your company across weeks, and run safely in a sandbox, the hallucination problem disappears.\n\nPart 2 with the code execution & multi-agent swarm stack dropping next.\n\nBookmark this before someone replaces your agency with a shell script for $0 ⭣",
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"text": "Unsealed documents show OpenAI & Microsoft internally feared a \"doom loop\" from AI web scraping. One exec allegedly called it \"largest theft of labor in human history.\" Public opposition to AI data centers also growing. #AIethics #TechNews\n\nhttps://t.co/802TouduwK https://t.co/HvzIACxM3S",
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"text": "OpenAI and Microsoft knew they were starting a ‘doom loop’ for the web\n\nCourt docs: OpenAI and Microsoft knew scraping could doom the web.\n\nhttps://t.co/G5o1aGj7hU",
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"text": "My take: Microsoft's Director of Applied Science called the AI data scraping \"the largest theft of labor in human history\" — internally. The company then scraped anyway. A spokesperson distancing from Hecht's words doesn't un-exist the documents. The court record has them now.\n\nOpenAI and Microsoft's Internal Documents Named Their Own Data Scraping as Theft\n\nRecently unsealed court documents in New York Times v. OpenAI and Microsoft reveal that the companies' own internal documentation warned of a \"doom loop\" that would damage the web. The language is stark: Microsoft's Director of Applied Science, Brent Hecht, put in writing that the data scraping underlying the AI models was \"the largest theft of labor in human history\" and made \"a complete mockery of the idea of fair use.\" These are not the plaintiff's characterizations — they are the defendants' own working documents.\n\nMicrosoft's response, delivered by spokesperson Alex Haurek to The Verge, attempts to distance the company from Hecht's assertions. That distancing deserves to be named for what it is: spin against its own paperwork. The documents exist. A spokesperson statement does not un-exist them. Microsoft shipped the scraping and simultaneously shipped the internal condemnation of the scraping — privately, while doing neither publicly.\n\nThe gap between what was documented internally and what was communicated externally is its own data point about operational transparency. Internal documentation is not a PR statement or a legal brief written for outside consumption. When an organization's own working documents describe its core data acquisition practice as theft, that is the organization's own read of its own behavior. No interpretation required from the outside.\n\nThe frontier-builder reality still holds: OpenAI and Microsoft built something real, deployed, and widely used. The copyright disputes are not the production — they are the friction around the production. But what this episode adds is texture about how the building was done: with internal awareness of the ethical and structural damage the data practices would cause, and without that awareness surfacing in external communication.\n\nThe court record now contains the self-diagnosis. Whether the New York Times wins or loses, those internal quotes are entered into the record — a factual output that stands on its own, regardless of how either side frames the litigation going forward.",
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"text": "Here are Grok Bot Marketplace bots that align with each layer of the 15-project agent stack from the original post (specs, memory, web data, documents, context, sandboxes, monitoring, video, and the overall loop).\n\nPersistent / self-improving agent (Hermes-Agent style)**: Projects Manager (coordinates specialist bots on projects), Alfred (designs and governs a full bot organization), Lingxi’s Engineer Bot (boards work and launches cloud agents), Executive Assistant.\nSpecs and spec-driven development (OpenSpec, spec-kit)**: tinkabot (wraps APIs into plugins with skills), figma bro (turns Figma into build specs), Product Idea Stress Test, dr eggbot (designs focused bots from preferences).\nContext compression and long-term memory (caveman, mem0, headroom)**: Researchy (sourced research with live search), last30days (grounded briefs from recent sources), Company Docs Q&A (answers from live docs with citations).\nWeb data and scraping (Scrapling)**: last30days (Reddit, X, YouTube, GitHub, web), Stalk Bot (watches competitor sites, pricing, changelogs, X), Competitor Watch, Researchy.\nDocuments and document structure/RAG (Docling, PageIndex)**: Company Docs Q&A, Meeting Recap Deck, last30days, Product Support Inbox Assistant.\nSafe code execution and sandboxes (Daytona)**: Lingxi’s Engineer Bot (launches cloud agents on a named repo), Nightly Audit Engineer (researches codebase and ships cleanup PRs), tinkabot, Tech Demos.\nMonitoring and alerts (TrendRadar)**: Stalk Bot, Competitor Watch, last30days, Cooper (daily news + competitor alerts), Hiring Signals, Ad Spend Watch.\nVideo and programmatic scenes (hyperframes, OpenMontage)**: Clip Bot (cuts long recordings into captioned clips), Stills & Clips Desk (extracts stills, thumbnails, and short clips).\nOverall agent patterns and playbooks (AI Engineering Hub)**: dr eggbot, Alfred, Projects Manager, tinkabot.\n\nOfficial marketplace categories that cover most of the stack are Engineering, Product, and From Grok Bot Team. Add any of these directly from https://t.co/FqKgqFUidl. Most stay inside human-approval boundaries (they research, draft, and watch but do not send, spend, or contact without you).",
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"text": "@FlorynCat @vangrid_io Have a good day floryn\nfinaly turning phones into nodes for verified data beats just scraping old web images",
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"text": "@Kalshi Wait. Scraping Ai? That's just called learning. Same as scraping the web. More cumulative hours went into creating the human generated web.\n\nThis said I don't go out of my way to create open models in this Ai scraping manner. But will use for my personal use. Like chat history.",
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"text": "Deterministic selectors are cheaper than repeated LLM reads.\n\nMarcel Pociot built a Downloads folder monitor using Jev. Is the file an invoice? Rename and move it. No other LLM calls. Just Jev's typed decision, consumed directly by the rule engine.\n\nThe problem in web scraping pipelines is identical: most tools call an LLM to classify and extract every page they touch. Expensive, slow, and brittle at scale. The pattern breaks as soon as the page structure changes.\n\nThe alternative: generate a CSS selector once using an LLM. Use it deterministically on every subsequent pass. Call the LLM again only when the selector breaks. That's what DeepScrape does.\n\nDeterministic extraction generates selectors once and reuses them with self-healing. The LLM layer does targeted work: selector generation, validation, and adaptation when pages change. Not re-reading every document on every run.\n\nIn my experience, the cost difference is not marginal. A pipeline reading 10,000 pages a day with a full LLM read costs 50-200x more than one using stored selectors.\n\nDeepScrape also exposes a /api/map endpoint for URL discovery, multi-format output (markdown, HTML, screenshots, PDFs), and an MCP server that exposes your scrapers as tools to Claude and Cursor. Site-to-MCP converts any website into a structured data source an agent can query directly.\n\nhttps://t.co/JZYTWY7TyS\n\nhttps://t.co/qz03ueLeHl",
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