标签：#ci

How much of the content in Reddit is AI generated do you think? submitted by /u/Zealousideal_Can_411 [link] [留言]

Hey everyone, A while ago, we realized a major annoyance: whenever you actually need an AI to summarize a document, write some quick code, or just brainstorm, you're usually on a flight, on the subway, or dealing with terrible cell reception. And bam, ChatGPT won't connect. Plus, there's the growing privacy concern of feeding all your personal data to cloud servers. So, my team and I started tinkering with a question: "What if we just run the AI directly on the phone's hardware?" We've been spending our evenings and weekends for months trying to make this work smoothly, and the result is Cortex AI. The logic is super simple: You download a highly optimized, small-scale local model (from our library) straight to your device. Put your phone in airplane mode, go off the grid—the AI replies entirely locally. Zero data leaves your phone. 100% private. Some real-world use cases we built this for: Coding help or summarizing offline docs while on a long flight. Getting quick answers while traveling abroad without an expensive data roaming plan. Brainstorming private ideas you just don't want OpenAI or Google to scrape. Note: We do have an optional "Online Mode" if you want to connect to massive models like GPT-4 or Claude, but the local offline models are completely free, and that's what we really want to test right now. We're currently trying to gather real user experiences on the local execution side. I'm not here to just spam a link and grab cash; we genuinely want to improve the offline mobile AI space. If anyone frequently travels, camps, or just loves local LLMs, we'd be super grateful if you could test it out. Brutally honest feedback like "runs too slow on my device," "needs X feature," or "this part of the UI makes no sense" is exactly what we need right now :) submitted by /u/Virtual_Ad_6024 [link] [留言]

Hey everyone, A while ago, we realized a major annoyance: whenever you actually need an AI to summarize a document, write some quick code, or just brainstorm, you're usually on a flight, on the subway, or dealing with terrible cell reception. And bam, ChatGPT won't connect. Plus, there's the growing privacy concern of feeding all your personal data to cloud servers. So, my team and I started tinkering with a question: "What if we just run the AI directly on the phone's hardware?" We've been spending our evenings and weekends for months trying to make this work smoothly, and the result is Cortex AI. The logic is super simple: You download a highly optimized, small-scale local model (from our library) straight to your device. Put your phone in airplane mode, go off the grid—the AI replies entirely locally. Zero data leaves your phone. 100% private. Some real-world use cases we built this for: Coding help or summarizing offline docs while on a long flight. Getting quick answers while traveling abroad without an expensive data roaming plan. Brainstorming private ideas you just don't want OpenAI or Google to scrape. Note: We do have an optional "Online Mode" if you want to connect to massive models like GPT-4 or Claude, but the local offline models are completely free, and that's what we really want to test right now. We're currently trying to gather real user experiences on the local execution side. I'm not here to just spam a link and grab cash; we genuinely want to improve the offline mobile AI space. If anyone frequently travels, camps, or just loves local LLMs, we'd be super grateful if you could test it out. Brutally honest feedback like "runs too slow on my device," "needs X feature," or "this part of the UI makes no sense" is exactly what we need right now :) submitted by /u/Virtual_Ad_6024 [link] [留言]

submitted by /u/Doug24 [link] [留言]

uber blew through its entire 2026 AI budget by april, 4 months in. 95% of their engineers use AI, 70% of commits are AI driven, and their COO still said he cant draw a clear line between all that usage and actually shipping more useful features. microsoft and duolingo have pulled back too. at the same time theres a CEO survey going around (oliver wyman) where the share planning to cut junior roles jumped from 17% to 43% in a year, and only 27% said their AI ROI met expectations, down from 38%. what gets me is the combination. companies are trimming entry level headcount because AI can do junior tasks, but juniors are also how you grow seniors. if that pattern holds for a few years the mid and senior pipeline gets thin right when the current seniors age out. cutting the bottom rung while the ROI is still unproven seems like a weird bet. anyone seeing this play out where they work? sauce: https://finance.yahoo.com/sectors/technology/articles/ubers-coo-says-getting-harder-050841491.html submitted by /u/PROfil_Official [link] [留言]

Spoiler: not a single visible cable, not a single piece of furniture moved twice. When I started, I had an apartment and dimensions from the building blueprint. No designer. No clear idea where to go. But there was a desire to make something that would turn a standard apartment in a high-rise into a place of power — a place comfortable to live and work in. Instead of a designer, I took Claude. How it all began The first conversation wasn't about furniture or wallpaper. It was about direction . I didn't know what I wanted. I knew what I didn't want — kitsch, heavy classics, excessive decoration. We worked through options together. Scandinavian minimalism. Japanese wabi-sabi. Loft. Modern classic. The AI broke down each style by character, materials, color logic. Not "this would suit you," but "here's what this means, here's what this requires, here's what you'll get." In the end I arrived at Scandinavian for the bedroom . Warm, light, calm, with one deliberate accent behind the headboard. The living room–kitchen — loft with a red thread running through the whole space, because the furniture there was already concrete-grey with red niches and replacing it wasn't on the table. The hallway and corridor — neutral grey , as a transition between two characters. Three zones, three moods, one logic. The bedroom This was the most detailed conversation. A room with one window, one door, three free walls. Together we came up with: an accent wall behind the headboard with golden geometric lines, the other three walls in cream from the same collection. Tone on tone, different saturation, same texture. The seam between walls reads not as a boundary but as gradation. White matte furniture with black hardware. A wardrobe with a top cabinet almost to the ceiling. Mirrored doors reflect the accent wall — the golden lines are present even where they physically aren't. Then came the centimeters. The AI calculated . Adding up wardrobe depth, gaps, bed width, nightstands, dresser. Checkin

Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.” Magnifica Humanitas (“Magnificent Humanity”) is a clarion call to all people to act with courage and solidarity as we enter an age already being transformed by artificial intelligence, the greatest change in…

While Blue Origin investigates the root cause behind last night's spectacular explosion of its New Glenn rocket, it's already clear that this will be a major setback for NASA's Moon base plans and Amazon's fledgling Leo space internet constellation. The incident occurred at about 9pm at Blue Origin's Florida launch site during a hot-fire test, […]

submitted by /u/esporx [link] [留言]

Hey everyone, I am not trying to sell or self promote mainly just wanted to showcase a big project I've been working on ever since I started studying data science and artificial intelligence and integrating AI into workflows and using it as an augment to create things that were previously out of reach for so many people, because if used right it can become a second brain and not a crutch. I’m the solo dev behind Void Runner , an isometric ARPG/MOBA hybrid built in Python. I recently hit a wall with traditional procedural quest generation. Hand-crafting templates gets repetitive fast, and players quickly learn the patterns to these things whether you like it or not. To solve this, I built the "Void Caller AI" , a system that uses a local, quantized Llama 3.2 model to act as a dynamic Dungeon Master. Instead of just generating random flavor text, the system uses a lightweight RAG (Retrieval-Augmented Generation) pipeline. It reads live server telemetry (who died, what items were looted, which bosses were defeated recently) and weaves those actual server events into the narrative of the quests it generates. Because it runs locally via Ollama on our backend, there are no crazy cloud API costs, and latency is kept completely manageable. Here is a simplified look at how the Python backend bridges the SQLite telemetry with the Llama 3.2 prompt: import json import ollama from sqlalchemy import text from database import SessionLocal def generate_dynamic_quest(difficulty: str, target: str): db = SessionLocal() # 1. Fetch recent server telemetry for context (RAG-lite) lore_context = "" try: # Grab recent server events to weave into the narrative recent_events = db.execute(text( "SELECT username, event_type, dungeon_type FROM ai_events ORDER BY id DESC LIMIT 3" )).fetchall() if recent_events: events_str = "; ".join([f"Runner '{r[0]}' triggered a '{r[1]}' in '{r[2]}'" for r in recent_events]) lore_context = f" Incorporate this recent live server telemetry into the lore: {events_

The same AI failure should not happen twice. But it does. Teams fix it, change something small, and it returns silently. We built Agent Fail Museum to document these patterns permanently. Submit one sentence about a failure you have seen. Get a regression test draft back. Anonymous by default.If you have built any AI project that broke after a change, your failure probably fits one of the 10 known patterns already in the archive. submitted by /u/taimoorkhan10 [link] [留言]

Large corporations are going to use AI regardless of what the public thinks. They have the money, lawyers, infrastructure, and data to do it. AI isn’t going away for them. But who gets hurt most when ordinary people are told not to use AI? The small business owner who can’t afford an artist to create a logo. The startup founder who can’t hire a copywriter to proofread every email. The family business that can’t pay an accountant for every tax question. The entrepreneur who can’t afford a programmer to build a website or a consultant to review a business plan. For the first time in history, a person with a good idea and a laptop can access tools that were previously reserved for companies with large budgets. I’m not saying AI is perfect. It makes mistakes, and there are legitimate concerns about its environmental impacts. But I do wonder: if AI dramatically lowers the cost of expertise, who stands to lose the most from that? The average person—or the organizations that have always had exclusive access to that expertise? Is the anti-AI push really just a push from big corporations to cut out those who stand the most to gain: small business owners? submitted by /u/Outlasttactical [link] [留言]

I've been working on something others might find interesting. It's under heavy development as I learn. Most AI agent setups treat the model like a better autocomplete — paste a prompt, get output, hope it's right. That works for small tasks. It falls apart when you try to use agents for sustained work across sessions: they skim specs, declare victory at 60%, burn context on noise, silently resolve ambiguity without surfacing it, and mark checklist items done without actually doing them. The failures are predictable and nameable — so I named them. This is a white paper and implementation guide for a full-stack agentic system — everything from planning through promotion under structural enforcement. It documents 24 failure modes from months of multi-agent operation and, for each, describes what actually prevents it: some through mechanical gates the agent cannot skip, some through procedural skills, and some through human supervision. The guide covers how to structure specs, plans, and verification so that agent work is evidence-led rather than vibes-led, how to use MCP capability surfaces as structural levers, and how the failure modes apply regardless of which model or vendor you use. The white paper also includes a Related Work section that positions it against the emerging industry consensus — CodeRabbit, Anthropic, Spotify, Cloudflare, OpenAI, Karpathy, Thoughtworks, and academic research all independently arrived at pieces of the same conclusions. The difference here is the integrated stack: a failure taxonomy mapped to prevention mechanisms, a three-layer enforcement architecture, and a concrete reference implementation with an orchestrator, task graphs, step verification, adversarial review, and model stratification. White paper: https://gitlab.com/naive-x/naive-artifact-coding/-/blob/main/white-paper.md Reference implementation: https://gitlab.com/naive-x/naive-artifact-coding/-/blob/main/docs/reference-implementation-guide.md Implementation guide: https://gi

submitted by /u/Hot-Upstairs9603 [link] [留言]

Hoje eu postei sobre o Mark Zuckerberg lançar a notícia mais patética que vai cobrar 19 dólares para desbloquear o Muse Spark Pro kakakakakakaka Quem vai pagar por essa merda? Mas pensando melhor bem... Talvez eu pague Eu usei muito esse modelo como Early adopter, desde quando o motor era o Llama 3.2 e sendo inferior as outras consegui extrair escrita criativa que batia de frente com Claude em personas graças ao seu RAG no ecossistema da Meta, que tinha uma criatividade absurda quando você forçava ela a consultar as redes sociais e ver como pessoas agem e comentam, porém lançou o Muse Spark que era tipo o GPT 5.2 dos Llamas kkkkkk aí só usei para pesquisa e bem... Minha tese sobre o Muse Spark é que pra mim o problema nunca pareceu ser burrice. Parece CONTENÇÃO. Não dá vibe de modelo incapaz ou inferior. Dá vibe de modelo sendo sufocado em tempo real. Porque se você presta atenção, ele: - pesquisa rápido pra cacete (Já que cada agente pesquisa uma coisa) - alucina menos em busca (pois o modelo refina a busca dos agentes, muitas vezes consegui resultados mais confiáveis que o Gemini) - já trabalha com esquema multi-agente herdado da Manus ( o trunfo dessa IA é que diferente das outras ela não comprimi seu input, ela usa agentes para cada um pesquisar cada trecho dele, o resultado é mais completo) - acha informação boa (ela pesquisa tanto na internet quanto em grupos de Facebook ou Threads se você forçar no prompt, ou seja análises de Devs>>> Wikipédia Inclusive acredito que foi por isso que o Mark lançou o "Fórum" o app que cópia o Reddit, ele quer treinar a IA com isso, o Reddit pra mim seria a fonte perfeita pra qualquer IA se aprofundar além do que pesquisar genéricas no Google, o filha da puta do Mark é rico e filantropo e faz uma cópia só para treinar a IA dele) - conecta coisa rápido (os agentes pesquisam rápido, o modelo revisa rápido, a entrega é bem rápida e gasta bem menos tokens) Só que na hora de responder… Parece o GPT free kkkkkkk O raciocínio corta no

Everyone can build it. Almost no one can afford to run it at scale. And the companies selling the picks and shovels are about to get undercut by the same forces they unleashed. by VEKTOR Memory — 20 min read How This Article Started: 20 Forums, 40 Headlines, and a Growing Sense That Everyone Was Confused I woke up to clear skies and the sun finally shining, and I set out to understand this idea, the truth behind it, and the nagging suspicion that the narrative around AI and software costs had become so loud, so uniform, and so confidently confusing that someone needed to sit down and actually go through it. No tweets, or are they now X's? No LinkedIn thought leader infomercials, no Substack hype, just actual research and deep thoughts. So I spent time reading, collating data. Forums, whitepapers, LinkedIn posts, Hacker News threads, VC essays, Reddit arguments. I went looking for the real signal underneath the noise. What I found instead was the full spectrum of human overconfidence, lots of moat real estate. On one end: the hype machine at full throttle. “Software is going to zero.” “A solo dev can now build what a 50-person team built in 2021.” “The era of the $500/month SaaS subscription is over.” “Vibe coding will replace your entire engineering org.” These headlines were everywhere. Breathless. Confident. Shared tens of thousands of times, this angle gets views, of course, the algorithm loves being fed claps, shares, comments, and reposts. Most were written by people who had a very good Tuesday with Codex, Windsurf, Claude and Cursor and decided that instant dev, open source to Github and getting oodles of stars, maybe even roping in a celebrity, was now the permanent condition of software development. “We are now famous on GitHub!" Very hipster, very vibes, see you on the playa.. On the other end: the backlash. Experienced engineer, people with 15 to 25 years in production systems are pushing back hard. “Show me the vibe-coded app that survived its first real

submitted by /u/armend7 [link] [留言]

Most scraping projects start by finding a website to scrape. This one started from the opposite direction: I knew the data existed as official government downloads, and my job was to make it accessible via a clean API. The data source Ofsted (the UK school inspections body) publishes monthly management information as CSV files on GOV.UK. The file covers all 22,000+ state-funded schools in England with their latest inspection grades, local authority, postcode, phase, and size data. It's 16 MB, published under the Open Government Licence v3.0 — explicitly permitting commercial use. No scraping needed. No authentication. Just a CSV download and some parsing logic. The architecture The actor is deliberately simple: Fetch the GOV.UK stats page to find the current month's CSV URL (the URL hash changes with each release) Download the CSV (~16 MB from assets.publishing.service.gov.uk ) Parse it with csv-parse Apply the user's filters (name, local authority, region, postcode prefix) Push matching records to the Apify dataset No Crawlee. No browser. No proxy. Just fetch() and a CSV parser. const match = html . match ( /href=" ( https: \/\/ assets \. publishing \. service \. gov \. uk \/[^ " ] +latest_inspections_as_at [^ " ] + \. csv ) "/ ); That one regex does the URL discovery. The GOV.UK page lists files in reverse chronological order, so the first match is always the latest release. The interesting part: Ofsted changed their grading system mid-build I built this in May 2026. In November 2025, Ofsted scrapped their 20-year-old four-word judgement system (Outstanding / Good / Requires Improvement / Inadequate) and replaced it with a report card format — six separate grade areas, each on a five-point scale: Exceptional Strong Expected standard Needs attention Urgent improvement Plus a standalone Safeguarding verdict (Met / Not met). The April 2026 CSV reflects this change entirely. There's no "Overall effectiveness" column. Schools inspected before November 2025 have null gr

I was dealing with another "output not usable" issue today in our app, user left a comment saying that no matter what he does the agent returns the result in the wrong format. It took me hours to identify the mistake and AI model missed it. Curious to hear your stories about the times you shipped a feature in your AI product and it flopped. How did you figure out what was actually going wrong? What tools if any did you use? What metrics were key? submitted by /u/pauliusuza [link] [留言]

I love all the wild updates from Anthropic, Open AI, Google, etc. And also seeing the creative stuff that mid-market AI shops are rolling out. I sometimes go through phases where I ping-pong between new tools (mostly just curiosity) but sometimes I tend to go deeper into a specific ecosystem. Right now trying to go "all-in" on Claude but I'm like a cat and Open AI is the laser pointer with new Codex updates. What have you all found works best. Go wide and test everything? Different tools for different use cases. Go deep and specialize in one ecosystem? submitted by /u/BeltwayBro [link] [留言]