OpenAI

OpenAI AI

OpenAI AI

OpenAI AI

OpenAI AI

OpenAI AI

OpenAI AI

OpenAI AI

OpenAI AI

OpenAI AI

OpenAI AI

ChatGPT seems to be pretty bad at deskewing and cropping images. Why?

I've been doing this manually forever after scanning magazines to archive online. Sooo many people have suggested I try AI to deskew and crop the images as it would save sooo much time. So I signed up for GPT yesterday and spent all yesterday and today "discussing" with it what it was doing right and wrong and it's still giving me mostly garbage. I've uploaded before/after examples of things I've done via NAPS2, and GPT recognizes and tells me what I've done there, but it can't seem to replicate it. Out of about 100 attempts/batches, so far it's given me maybe a dozen acceptable batches back, and those were only acceptable because those particular magazines were old and crappy and I didn't care too much about it being super precise. Is there something else I should be trying, or is ChatGPT just not good at this sort of task? submitted by /u/human-in-a-can [link] [comments]

why cant i delete my acc😭

submitted by /u/DlCKH34D [link] [comments]

Open-sourced an MCP server that catches the security mistakes Claude / Cursor / Copilot actually make

AI coding tools like Claude, Cursor, and Copilot sometimes write code that looks fine but quietly leaves your app wide open like turning off security checks to make an error go away, or telling you to install a software package that doesn't actually exist (which means a bad actor can create that name later and take over anything that installs it). Made a free tool that scans your project or any GitHub repo and tells you what's broken, ranked by how bad, with the exact commands to fix it. https://github.com/ExecutiveKoder/sureguard-code-scanner submitted by /u/sks8100 [link] [comments]

Large built, muscular young Ohio dude enjoys a quiet, disciplined afternoon at home. Realistic images.

submitted by /u/Automatic-Algae443 [link] [comments]

Former Google CEO Eric Schmidt, Big Machine Records CEO Scott Borchetta & Tavistock VP Gloria Caulfield were all booed at commencement speeches, as AI backlash is now hitting campus stages🇺🇸

submitted by /u/Democrat_maui [link] [comments]

OpenAI Pro Plan Pencil Gift Confirmed

Just got my email confirmation for the OpenAI pencil gift, and it includes a tracking number. Pretty excited to see what actually shows up. Has anyone else received their confirmation or shipping email yet? I’ll update once it arrives submitted by /u/SodaAnSumWii [link] [comments]

chatGPT assesses itself after multiple tests - utter failure

I have spent a little time testing the reliability of Open I'd ChatGPT on a wide variety of tasks. I was genuinely curious what it could and could not do. There was so much conflicting information and I was hoping I could perhaps use it in my work as a tool. So I designed seven very different tests requiring different kinds of "thinking". I just completed the last test. I asked ChatGPT to self assess. I've never seen a product throw it's own marketing team under the bus before. The response is hilarious and a little disturbing. submitted by /u/YakStunning7755 [link] [comments]

What is currently the best AI model for my situation?

I've only been using the free versions so far, mostly for brain storming ideas and assisting with interview prep and work related tasks, however, I know I'm missing out on a lot more functionality and potential for either developing myself, my skills, or actually creating some form of income with it. Content creation is the obvious one, however I'm not aware of how to utilise it for streamlining anything in terms of video editing, apart from learning the skill faster than watching tutorials for days upon days. As everyone else - own business or freelancing would be ideal, but I am not sure what sort of business I can start myself at my current stage in life (medium level finance and accounting career, 5 years in, but mostly on the transactional side with a recent move into analysis and reporting). I know my post is all over the place, but to summarise it briefly - What use cases and functionalities am I not aware of that could help me with the above mentioned issues, or in general would be worth knowing to stay ahead of the game/everyone else? How do I go about discovering more? Which AI model should I go for? submitted by /u/ADK-KND [link] [comments]

Wow so analog clocks are their kryptonite.

I heard several AI engines have issues with reading analog clocks, so I tried. And here we are. submitted by /u/Ejay222 [link] [comments]

What’s your experience using ChatGPT as a psychologist/coach?

I’d like to try using ChatGPT as a psychologist/coach, but I’m worried about whether it will reliably forget our discussions if I ask it to. I do notice that it remembers things between different chats and tries to enhance its responses with references to past chats/problems. That’s okay if we’re talking about coding or designing things, but it would not be okay if I told it personal stuff. I’m wondering if anyone has experience with this, and whether ChatGPT can be trusted in this regard yet. I guess I can always delete the chat, but that feels like a waste. If I already decide to commit and make the effort to discuss personal things, I don’t want to delete the chat unless I feel like the issue is fully resolved. That said, making another account just for that also seems like a waste of money, and the free version is dumb as fuck, so that wouldn’t be helpful. submitted by /u/kaljakin [link] [comments]

GitHub’s Fake Engagement Problem Is Hiding in Plain Sight

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars. What I built phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers): Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes Pulls star and fork events from the last 24 hours per repo Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history) Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%) Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window Files an issue directly on the targeted repo so the maintainer knows what's happening Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended. What the pattern actually looks like It's remarkably consistent. A fake engagement campaign in the raw data: 40-200 accounts, all created within the same 1-2 week window Zero original repositories, or only forks they never touched No bio, no location, no followers, no following All of them starring the same repo within a 90-minute window The target repo usually has a name implying it's a tool, hack, executor, or generator Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast. Notifying the affected repo When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first. Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently. Why I built this Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected. It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/ The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users. All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq. Repo: https://github.com/tg12/phantomstars Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process. Questions welcome on the detection approach, GraphQL batching, or campaign ID stability. submitted by /u/SyntaxOfTheDamned [link] [comments]

Built an invoice-scanning service for our accounting team in one afternoon with Claude — sharing the architecture in case it helps someone else

Our AR team was hand-keying ~25 invoices a week into a spreadsheet. I had Claude build us a Python service that watches a network folder, extracts invoice data from any PDF dropped in (vendor, dates, totals, line items, addresses), and appends a row to a shared Excel register. Total chat-to-deployed time: about half a day, including all the deploy headaches. The architecture, for anyone who wants to replicate this: Python service on our Windows file server, registered with NSSM. Auto-starts with the host. watchdog library polls the SMB share for new PDFs. Each new file goes through a pipeline. Two-tier extraction: per-vendor regex templates first (free, instant, deterministic), then Azure AI Document Intelligence "prebuilt-invoice" model as a universal fallback. Azure handles OCR for scanned PDFs natively, so the same flow works whether AR drops a digital PDF or our MFP scans one from paper. SQLite on the local disk is the source of truth. The shared .xlsx is a curated view that gets appended to on each batch. Delete the .xlsx and it'll repopulate fresh from the next batch — handy for resetting. Failed extractions go to a Failed\ folder with a sibling .error.txt explaining why. Cost reality check: Azure DI free tier covers 500 pages/month. At our volume (~25 invoices/week, mostly 1-2 pages) that's well under the cap. Paid tier is roughly $0.01–$0.05 per page. Cheap enough that I don't think about it. Gotchas I ran into so others don't have to: Azure returns addresses as structured objects, not strings. If you naively str() them you get the raw Python dict repr in your spreadsheet. Format them manually from street_address / city / state / postal_code. On Windows Server, PowerShell 7's Restart-Service can throw "Cannot open service" against NSSM-wrapped services for no good reason. Use nssm restart instead. Python 3.14 is so new that some package wheels aren't published for it yet. Stick with 3.12 for production. Tracking "what's new this batch" is way simpler than maintaining a watermark in DB. Just snapshot MAX(invoice_id) before and after the batch, and only project that range to the spreadsheet. Things I'd add if/when I have time: vendor templates for our top 5 recurring vendors (cuts Azure cost to zero for those), a daily canary PDF for monitoring, swap the LocalSystem service account for a dedicated low-privilege one. Happy to answer questions about any specific piece. The whole thing is ~1,500 lines of Python plus a deploy script. submitted by /u/Blake_Olson [link] [comments]

An OpenAI model has disproved a central conjecture in discrete geometry

submitted by /u/simulated-souls [link] [comments]

The Hybrid Method: how I split tasks between the chat (Claude.ai) and a background agent (Claude Code)

After a month of running this daily, I've settled on what I call the Hybrid Method: keep Claude.ai (the chat) as my only surface, and delegate engineering work in the background to Claude Code. The chat writes the engineering prompt, launches the executor, supervises through the filesystem and git log, and reports back without me ever opening a terminal. The piece I find most useful to share is the **allocation matrix** — which kind of work goes to which engine. Took weeks of measurement to stabilize. **Background agent (Claude Code) handles:** Large refactors across many files Tedious mechanical work (renaming patterns, applying fixes from a list) Anything that needs filesystem + git access without back-and-forth Tasks that take more than ~2 minutes of pure execution **Chat (Claude.ai) handles:** Architecture decisions and tradeoffs Reviewing the agent's diff and discussing the output Sprint planning while the agent runs the current sprint Quick edits where the round-trip to a background process is wasted Anything where the answer needs human reading anyway **The hand-off:** The chat writes a detailed prompt for the background agent (including a fail-fast spec and what to commit at the end). It launches `claude --headless --instruction "..."` as a subprocess via a small MCP bash bridge (~200 lines of Python using Anthropic's MCP SDK; community implementations exist too). Then it polls the git log and a status file every 30–60 seconds while I plan the next thing. When the agent finishes, the chat reads the diff and reports. **Why "hybrid":** The analogy is the hybrid car. Two engines with different load profiles. The chat is electric — instant startup, smooth low-load, great for transitions and decisions. The background agent is combustion — cold-start cost (5–15 seconds while it loads the project's memory file and explores the repo), but sustained throughput once running. They specialize, they hand off, the user never feels the seam. **What changes from running Claude Code alone:** Context-switching cost drops to near-zero — I never leave the chat session Strategic and execution work happen in parallel (the chat plans the next sprint while the current one runs) The chat acts as supervisor — better wired for high-level reasoning than the executor agent which is wired for action **Caveats:** This is the operator pattern Anthropic has documented elsewhere; the specific assembly (Claude.ai web as the chat + an MCP bash bridge + Claude Code as the executor) is what I haven't found written up specifically No sandboxing on personal hardware; if any of this ever runs on someone else's machine, careful sandboxing is non-negotiable The chat saturates beyond ~2 parallel background tasks — past that, the supervision quality drops Curious whether anyone else has converged on something similar, or what variations work for you. submitted by /u/Krycekk [link] [comments]

Auroch

I’ve been working on Auroch. Hard to describe cleanly, but the closest version is: An AI operating layer. Not a chatbot. Not another dashboard. Not another productivity wrapper. Auroch is built around the idea that AI should feel native to the machine — like memory, context, creation, automation, and intelligence are part of the system itself. The pieces are starting to connect: AVN turns wire-source news into personalized interpretation. Winnie is the assistant layer. Prospect mines signal from the open web. Forum is AI-native media/social creation. Prometheion is the visual/world-generation branch. The design language is white-gold-blue, Art Deco, Apple-native, machine-age. Calm power instead of tech clutter. The phrase guiding the whole thing right now is: Organic intelligence. Not AI bolted onto software. AI growing through the system. It’s still early, but it’s live: aurochthryx.com Curious what people think. submitted by /u/CarterBirchll [link] [comments]