标签：#ci

Been experimenting with AI-generated UGC. This whole clip — the face, the voice, the walk — is generated (I used omnigems.ai). No camera, no actor. What surprised me is the "tells" are mostly gone now if you keep the lighting candid (no studio polish), add real skin texture, and let there be natural micro-motion. Studio-perfect is what reads as fake; messy/handheld reads as real. Posting because I'm curious where this community draws the line: is AI UGC fair game for ads, or does \ undisclosed** AI cross into sketchy territory? Happy to share the exact workflow if it's useful to anyone. submitted by /u/New_Measurement_6962 [link] [留言]

I've been spending a lot of time experimenting with AI filmmaking tools lately, and I've noticed something that feels a bit odd. Most AI video tools seem to be built around generating clips: Text → Video Image → Video Start Frame → End Frame But when I think about how films are actually made, the process usually starts with: Screenplay → Characters → Locations → Storyboard → Shots → Film It feels like there's a gap between how filmmakers think about projects and how AI video tools are currently designed. For example, while working on my ai video, I don't really think in terms of generating isolated clips. I'm thinking about scenes, character continuity, locations, visual references, storyboards, and how everything fits together. Maybe I'm wrong, but it sometimes feels like AI tools are optimizing for clip generation while filmmakers are optimizing for story development and visual planning. Do others here feel the same way? How are you currently bridging the gap between screenplay and AI-generated video? submitted by /u/data-gig [link] [留言]

I too have a different personality when you're paying for me submitted by /u/Complete-Sea6655 [link] [留言]

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

For enterprise AI/security teams, what would make AI SPM useful rather than just another category label? Is the important part AI asset inventory data access mapping, agent permissions, prompt/data monitoring, policy enforcement or something else? submitted by /u/Efficient_Western609 [link] [留言]

it turns into some unfinished project lying in some GitHub private repo lol submitted by /u/Complete-Sea6655 [link] [留言]

When I started learning Excel as part of my Data Science & Analytics course, I assumed it was just a tool for creating tables and performing basic calculations. After spending a week exploring its features, I quickly realized that Excel is much more powerful than I thought. Almost every organization generates data. Businesses track sales, schools monitor student performance, hospitals manage patient records, and marketers analyze campaign results. Before data can be analyzed, it needs to be organized, cleaned, and summarized—and that's where Excel comes in. In this article, I'll share some of the Excel concepts I've learned so far and how they're used in real-world data analysis. Understanding the Excel Workspace Before working with data, it's important to understand the basic structure of Excel. When you open Excel, you're working inside a workbook . A workbook can contain multiple worksheets (often called sheets), which help organize different sets of data. At the top of the screen is the Ribbon , which contains tabs such as Home, Insert, Page Layout, Formulas, Data, and View. The Ribbon acts like a control center where you can access Excel's tools and features. Rows run horizontally and are identified by numbers, while columns run vertically and are identified by letters. The intersection of a row and column is called a cell , where data is entered. At first, all these parts seemed overwhelming, but after using Excel regularly, navigating through them has become much easier. The Different Types of Data in Excel One of the first things I learned is that not all data is the same. Excel commonly works with: Text data (names, product categories, locations) Numeric data (sales figures, quantities, prices) Date and time data (order dates, deadlines) Logical data (TRUE or FALSE values) Understanding data types is important because Excel treats each type differently when performing calculations and analysis. Number Formats Matter More Than I Expected Another concept that

If you ask it to create a recipe, you can click plus and minus buttons to change the amount of portions. You can connect it to other apps like canva. It hallucinates WAY less, and it explains ilvery clearly. submitted by /u/OkComputer_13 [link] [留言]

I have been experimenting with LLMs to see how well they navigate highly cross-referenced texts like the Bible. Standard models often hallucinate verses or lose historical context. To try and fix this, I built a free app called Biblians (no ads, no paywalls). I built it specifically for people who have questions they might hesitate to ask in person, or who simply want a 1-click way to explain a verse. While testing it, I discovered a fascinating denominational bias that is still lingering and changes depending entirely on the language you use: In English: It is Protestant-leaning. It praises Luther, saying things like, "Martin Luther sought to return the Church to the truth of God's Word." In Spanish, French, or Portuguese: It is Catholic-leaning. It condemns Luther's actions, stating: "...trajo confusión..." (...brought confusion...). Has anyone else noticed how drastically the training data changes the core bias based on the language prompted? I would love for this community to test the app, look for other linguistic biases, or just try to break the AI's logic. You can experiment with it here: https://play.google.com/store/apps/details?id=com.biblians.app Let me know what weird outputs you get! submitted by /u/Snorlax_lax [link] [留言]

OK.. so I've had this reasonable PC sitting under my desk for ages.. NOT working because of some reason or other. But it was my baby as is housed in a lovely Soprano DX silver brushed case. SO, I swapped out the old HDD for a couple of SSDs (a couple of mirrored OS disks and a large 2TB storage disk) I swapped out the Nvidia 780ti graphics card for a couple of OG Nvidia 1080ti's. I pulled the whole thing to bits.. repasted the northbridge chip, southbridge chip and central CPU. Upgraded the fans to push pull the CPU heatsink. Wrapped ALL cables in mesh and it's so lovely now. Installed Windows 10 Pro. Installed the Nvidia App. Installed CrystalDiskInfo and all is sweet 😄 EXCEPT... I'd like to use this old bangin box for an HG AI server... now I have read that ALL LLMs need this thing called AVX (Advanced Vector Extensions) I didn't even know that was a THING! So even though I have 22Gb worth of GPU sitting there that I was going to point everything to, because I have a lame ass QX6700 CPU sitting on a kickass D975XBX2 (BadAxe2) main board I CAN NOT fulfill my wish for this OG box to be a headless source of awesomeness sitting in it's home under my desk supplying me with a home grown AI. IS THERE ANYTHING I CAN DO?!?!?! Surely after all this time of parts getting munched by AI farms a plenty people have been using what's around to do what they will... Does anyone know of anything I can do apart from just look at it running at 25 degrees aircooled humming along so lovely... it NEEDS purpose!!! 😄 Cheers and thanks all NB submitted by /u/Independent-Sound196 [link] [留言]

10 year senior dev, vibe coded this in 4 weeks and counting. Something like this would have taken me a year+ before and ive always been a 10x dev. I built this along side my day job (gov contractor dev). Feel free to check it out! https://imgur.com/a/F6OINKR⁠ Game Title: Roguelite MMO Playable Link: https://roguelite-mmo.com/⁠ Platform: PC / Web Description: Roguelite MMO is a browser-based RPG/MMO project built around dungeon runs, exploration, gear progression, PvP, quests, loot, and character building. The game is still in beta and active development, with the latest update adding new side activities and progression options. Latest update: The new Casino is now live, giving players more ways to spend gold, take risks, and chase rewards between dungeon runs and exploration. Horse racing and horse taming have also been added. Players can race horses, bet on races, and work toward collecting better horses over time. Fishing is now available too, adding a more relaxed activity with its own rewards while exploring the world. The core loop is still being refined, but the current focus is making sure players understand what they earned, where important items come from, what to do next, and whether the early gameplay loop feels worth continuing after the first few minutes. Free to play submitted by /u/HeadHunterX223 [link] [留言]

I had a work version of GPT do a very simple spreadsheet summary task for me yesterday. It took it 5 minutes to do it. I could probably have done it myself in 30 or so minutes. The heavily subsidised token cost of that task? 10 dollars. That's with a 10x subsidy. The actual compute cost was about 100 dollars. There's something seriously wrong there. It's going to crash and crash HARD. if people think i'm lying or are just interested. The spreadsheet had 45 sheets. Each sheet had roughly 500 x 50 populated cells. Formatting was not exactly standard across all sheets. The prompt was something like "there is labelled column in each sheet, give me a simple list of all the items from all the sheets in that column and ignore duplicates." We can chose which model to use. The model I chose was one of the newer ones, I honestly can't remember which one, possibly GPT 5.5. It took 5 minutes or more to so and the stated cost for the task was 10 dollars, possibly even more. I can't recall the token amount. submitted by /u/Complete-Sea6655 [link] [留言]

so i kept using chatgpt to ask questions about my pdfs and notes, and half the time i couldn't tell if it actually read the doc or just made something up that sounded right. that bugged me enough to build my own thing over the last few weeks. you upload a pdf (or word, csv, image, or just paste a link), ask whatever you want, and it answers using only what's in your file - and it shows the exact page it pulled the answer from, so you can check. if the answer isn't in the doc, it just tells you instead of guessing. stuff i actually end up using: flip on web search when i want it to look something up online instead one click to turn a doc into a summary / key points / flashcards (this is clutch for studying) resume review + cover letter help you can talk to it and it reads the answer back it's completely free, i'm not selling anything. honestly just want people to break it and tell me what's missing. link: https://athena-wisdom.vercel.app (there's a short guide on the site too if you get stuck) solo project so be gentle lol - but real feedback is what i'm after, especially what you'd want it to do next. submitted by /u/Independent_Diver352 [link] [留言]

Before this week, I thought Excel was just a fancy calculator with boxes. But after three days of my Data Science & Analytics course, I realise I was wrong. Really wrong. Excel is a spreadsheet tool used by millions of people from small business owners to data analysts at giant companies. And the best part? You don’t need to be a programmer to use it. You just need to know a few tricks. Here’s how Excel helps solve real-world problems using exactly what I learned in Week 1. 3 Real-World Ways Excel Is Used Business decisions with logic Managers use IF() statements to answer yes/no questions. Example: =IF(Sales>1000, "Bonus", "Needs Improvement"). One cell can decide who gets paid more. Cleaning messy data Real data is never clean. Marketing teams use Remove Duplicates, Find & Replace, and Text to Columns to fix hundreds of messy rows in seconds. No manual typing. Tracking deadlines and ages HR teams use DATEDIF() to calculate employee ages or years of service. TODAY() and NOW() keep reports automatically updated. No more “oh, I forgot to update the date.” 3 Excel Features I Learned This Week Remove Duplicates – One click, and Excel deletes repeated rows. Saved me from sending the same customer email twice. IFERROR() – Hides ugly errors like #DIV/0! and shows something friendly instead (e.g., “Check data”). Your boss will thank you. Sort & Filter – With AutoFilter, I can find all sales above $500 in one second. Then Custom Sort lets me sort by date and region together. My personal reflection Honestly? Learning Excel has changed how I see data. I used to look at a messy spreadsheet and feel lost. Now I see Remove Duplicates, Text to Columns, and TRIM() as tiny tools that bring order to chaos. Data isn’t scary anymore. It’s just a puzzle and Excel gives me the pieces. I’m only one week in. But I already feel like a junior data analyst in training.

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on AI confusion, follow Robert Hart. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started At first, AI influencers were relatively easy to identify - and to […]

🚀 SpaceX signed a massive $920 million monthly deal with Google for 110,000 Nvidia chips — this is a huge infrastructure play ahead of their monster $1.7 trillion IPO. 🏛️ The Trump administration is discussing taking equity stakes in top AI firms — this would make the public official partners in the upside of AI-driven economic growth. 🔓 Meta's automated AI support was hacked to take over high-profile accounts — it proves that offloading critical security tasks to AI can create dangerous, easily exploited vulnerabilities. 🧠 Tech workers are trading hours of manual labor for high-level strategy thanks to AI — while tasks now take minutes, humans are still needed for crucial, complex decision-making. submitted by /u/Ok_Muffin_7347 [link] [留言]

Just shipped this project and wanted to share the full technical breakdown because hotel/hospitality AI doesn't get much attention compared to the usual chatbot and SaaS use cases. The client manages 500 hotel properties. Their support team was manually handling around 15,000 guest emails per day. Same questions over and over across hundreds of hotels but each one still needed a human to read it, understand it, find the answer, and reply. Here's how the system works end to end: Layer 1: Email ingestion and question extraction This was the hardest part. Guest emails are messy. A typical one looks like: "Hi there, we're coming for our anniversary on the 20th and I was wondering if you have any room upgrades available. Also is the spa open to guests or do we need to book separately? We're driving so need to know about parking too. Last time we stayed the wifi was a bit slow in our room, has that been fixed? Thanks!" That's four separate questions plus a complaint wrapped in one email. If you just embed the whole thing and search the FAQ database you get a blended result that partially answers one or two questions and misses the rest. So I built an extraction layer that reads the full email and breaks it into individual questions. It handles directly stated questions ("is the spa open?"), implied questions ("we're driving" implies they need parking info), complaints that need acknowledgment but aren't FAQ-searchable ("wifi was slow"), and informational context that shouldn't be treated as a question at all ("coming on the 20th"). Getting this extraction reliable was probably 40% of the total development time. Layer 2: FAQ knowledge base with vector search All hotel FAQs get embedded and stored in a vector database. Different properties have different amenities, policies, and details so the search is scoped per hotel. When a guest emails the Berlin property asking about breakfast, it searches the Berlin FAQ, not the Munich one. Each extracted question from Layer 1 gets s

Data analysis is at the heart of how we spot patterns and improve systems today. Tools like Python, SQL, Power BI, and Tableau are everywhere in the data world, but Excel has held its ground as the starting point for anyone getting into data work, and there is a reason for that. What is Excel? Excel is a spreadsheet built on a grid of rows and columns. You use it to organize, format, and calculate data. For analysts it is where messy raw data gets sorted out, numbers get worked through, and everything gets turned into something that actually makes sense to look at. Ways Excel is Used in Real-World Data Analysis 1. Data Cleaning Raw data is almost never clean. Names are misspelled, IDs get duplicated, spacing is off, values go missing. None of that is unusual, it is just the reality of working with real data. Before any analysis happens the data has to be honest, because if the data is wrong the results will be too. Functions like PROPER() and TRIM() are some of the basic tools that help get data into a state where you can actually work with it. 2. Financial Reporting Every business, big or small, needs to know where the money is going. Excel makes that straightforward. SUM() adds up a range of numbers, AVERAGE() finds the mean, and once the calculations are done the data can be turned into charts and dashboards that tell the story of the business clearly. Not everyone in the room is an analyst, but everyone can read a chart. 3. Business Decision Making Clean data presented well becomes a decision making tool. What do customers want? What is working? What needs to change? Sorting figures from highest to lowest or filtering by region can take thousands of rows and turn them into something focused and answerable. That is really what data is for, helping people make better calls. Excel Features I Have Learned and How They Apply Three features that have stood out to me are conditional formatting, data validation, and cell referencing. Conditional formatting highlights ce

submitted by /u/BhaswatiGuha19 [link] [留言]

Most recommendations I’m seeing are for generating presentations from a topic, but I already HAVE the content. Problem is it’s usually: messy notes meeting transcripts random docs giant walls of text Main thing I want is help turning all of that into slides that are actually readable. Does anything handle that well right now? submitted by /u/ragsyme [link] [留言]