ChatGPT Images 2.5 prompt 👇 A cinematic fashion editorial photograph captured on location — an elegant full-body hero composition of a gorgeous young East Asian woman in a sun-drenched garden dining room, the camera positioned at straight-on eye level in a centered vertical framing capturing her sculptural hourglass posture and serene gaze in an M1 cinematic narrative register. The woman possesses refined, youthful East Asian facial features with smooth contours, porcelain skin displaying natural fine pore texture, and calm dark almond eyes gazing softly into the camera with a gentle, composed expression. She has a sleek, polished chin-length dark espresso-brown bob haircut styled with neat, airy straight-across fringe bangs softly grazing her eyebrows, framing her delicate face symmetrically. She exhibits a strikingly curvaceous and feminine hourglass figure, with a full, shapely bustline, tiny waist, and long, slender toned legs. She is posed leaning forward at the waist over a solid rustic wooden dining table, her arms kept straight and slender with both hands pressed flat against the tabletop for support, arching her back naturally into a graceful curve that accentuates her feminine silhouette. She wears a chic, minimalist lounge two-piece in soft heather-gray: a snug form-fitting long-sleeve ribbed-knit top with a delicate henley button placket, paired with matching high-waisted heather-gray knit shorts featuring subtle decorative button detailing along the hip. On her feet are sleek white open-toe stiletto mule heels that highlight her graceful arched instep and vibrant red pedicure. The setting is a bright, airy European sunroom and dining space — she stands on clean, light-toned hardwood flooring beside a classic carved-wood dining table and wooden chairs. Directly behind her stands an expansive wall of floor-to-ceiling white louvered shutter doors, partially opened to reveal the soft, vibrant green foliage of a lush garden outside, creating an airy, sophisticated architectural backdrop. The lighting is governed by clean, diffused natural morning daylight filtering through the tall shutter doors behind and around her — casting a gentle, soft rim light along the contours of her shoulders and heather-gray outfit, while ambient room bounce fills her face and torso evenly with flattering, creamy illumination that showcases her bone structure and the soft knit texture of the fabric without harsh cast shadows. Captured with a wide-latitude digital cinema look on a fast 50mm prime lens at wide aperture T2.0, providing tack-sharp optical detail across her eyes, bangs, the ribbed knit fabric weave, wooden table grain, and stiletto heels, while naturally softening the green garden foliage behind the shutters into smooth, creamy circular bokeh. Soft daylight film emulation with clean airy whites, pastel heather-gray tones, vibrant outdoor greens, and true-to-life skin warmth, finished with fine, organic 35mm theatrical film grain across the frame. Real photographic frame captured on a real cinema camera, real prime lens, real knit cotton fabric, real wooden furniture, real East Asian human subject, real sunlit patio room — no CGI, no rendered look, no digital cleanliness, no plastic surfaces, no AI smoothness, no skin smoothing, no glow, no halation bloom that reads as artificial, no glossy highlights.

Dont mean to attack anyone, but I feel really weirded out when I came across cosplay clones on tiktok or reels. Like do you know what I'm referring to? those accounts that are a literal copy and paste of other bigger creators accounts. It's not even about the cosplay choice but the exact same manners, type of video, video backgrounds, even out cosplay fashion style, clothes, haircut, sometimes even BODYTYPE feels altered to look like the og's ones.. I feel like they want people to mistake them for the og creator but for what purpose.. Like I don't think they're trying to hide the intention because it's just so obvious, but what's the point of doing that? Like the og creator does X and the clones does X the exact same way the week later. Why is that? are they profiting from the original creator ideas? wouldn't it be more profitable to create their own identity? Of course this is not about having inspos and getting ideas from other creators in a healthy way, it's something extremely specific that I see big creators experiencing costantly. I don't want to make examples because it's not about me and I don't want to involve anyone but I'm sure you know what I mean if you came across one of them

How do you build trustworthy AI diagnostic tools in one of medicine's most historically under-researched areas? In this episode of Just Now Possible, Teresa Torres talks with Tulsi Patel (Director of Product and Technology), Lorna Brightmore (Head of Data and AI), and Jack Pickard (Head of Engineering) at Hertility, a UK and Ireland-based women's health tech company. Hertility combines an in-depth online health assessment with at-home hormone testing and clinician-reviewed reports to help diagnose conditions spanning menstruation to menopause. Built on seven years of data linking symptoms, blood results, and pelvic ultrasound scans for over a million women, the team walks through two AI products in development: a Bayesian network that gives clinicians probability-based diagnoses instead of binary calls, and a scan automation pipeline that classifies ultrasound images, measures follicle counts and ovarian volume, and drafts clinical letters using an agentic loop that checks its own output against patient data before a human ever reviews it. You'll hear how the team guards against automation bias, builds clinician trust through transparency, minimizes PII before it ever reaches a model, and treats healthcare regulation as a design constraint from day one rather than a last-minute scramble. It's a detailed look at what it takes to bring AI into one of the most sensitive, tightly regulated corners of healthcare. Guests: - Tulsi Patel – Director of Product and Technology, Hertility - Lorna Brightmore – Head of Data and AI, Hertility - Jack Pickard – Head of Engineering, Hertility What we cover: - What makes Hertility's data set unique: seven years of linked symptoms, blood tests, and pelvic scans from over a million women - How uses a Bayesian network to give clinicians probability-based diagnoses instead of binary yes/no calls - Why showing clinicians the reasoning behind a diagnosis—not just the label—builds trust and speeds up triage - Guarding against automation bias with holdout sets and independent, fresh-eyes review - Inside the scan automation pipeline: classifying ultrasound images, detecting follicles, and measuring ovarian volume more precisely than manual methods - Using an agentic loop to check AI-drafted clinical letters against patient data and catch hallucinations before a human sees them - The infrastructure challenge of securely piping DICOM ultrasound images from third-party scan providers into Hertility's systems - How Hertility handles PII and PHI: pseudonymization, data minimization, and running models in-house on AWS Bedrock - Why treating healthcare regulation as a product requirement from day one makes AI products more scalable, not slower Key Takeaways: - Probabilistic, transparent AI outputs build more clinician trust than binary classifications. - Guardrails against automation bias are as important as the model itself. - Data minimization and in-house infrastructure make it possible to build AI responsibly with sensitive health data. - Treating regulation as a design constraint from day one makes AI products more defensible and scalable, not slower. Resources & Links: - Hertility — At-home hormone testing and reproductive health diagnostics for women in the UK and Ireland - AWS Bedrock — The platform Hertility uses to run LLMs in-house under its own governance and regulatory controls - PyTorch — The foundation for Hertility's in-house image classification and contouring models Chapters: 00:00 Meet the Team 00:13 What Hertility Does 01:51 How Customers Access It 04:06 A Unique Women's Health Dataset 07:03 Mission and Efficiency with AI 10:03 Why Long Assessments Convert 13:52 Before AI Workflows 16:52 Research Publications and Impact 18:48 GynAI Reducing Time to Diagnosis 21:21 Triage and Clinician Support 24:37 Keeping Patient UX the Same 26:12 Bayesian Network and Explainability 30:19 Multiple Diagnoses and Probabilities 32:37 Probabilistic Diagnosis Shift 33:50 Clinician Adoption and Workflow Fit 34:58 Communicating Medical Uncertainty 36:43 Scan Automation Overview 40:30 In House Image Analysis 44:25 DICOM Pipeline Engineering 47:30 Evals and Automation Bias 50:31 LLM Letter Guardrails 56:47 PHI Handling and Regulations 01:00:43 Infrastructure Choices and Wrap Up Listen on Spotify, Apple Podcasts, or watch on YouTube. Spotify: Apple Podcast: YouTube:

# Claude Code Features and Practical Usage 🔌 Still copy-pasting schemas and logs into chat? Connect Sentry, PostgreSQL, or GitHub via MCP and Claude reads and writes those systems directly. "Investigate the last 30 days of this table" just works. 📌 Title and Feature URL Title: MCP URL: 📝 Overview MCP (Model Context Protocol) is an open standard for AI tool integration that connects Claude Code to hundreds of external tools and data sources. MCP servers give Claude Code access to tools, databases, and APIs, so it can read and operate those systems directly instead of working from what you paste. 🔧 How It Works - Multiple transports exist: HTTP (recommended) for remote connections, WebSocket for bidirectional push, stdio for local processes, and the deprecated SSE. - Three scopes are available: local (default, private to you, stored in ~/.claude.json), project (shared via .mcp.json), and user (all projects, private to you). - Many cloud servers require authentication and support OAuth 2.0; on a 401/403 response you can complete the flow from /mcp. - Tool search is on by default, lazily loading MCP tools on demand to keep context usage low. 🛠 Practical Usage - Add with claude mcp add --transport http , or for stdio claude mcp add [options] -- [args...]. Options go before the server name. - Manage with claude mcp list / claude mcp get / claude mcp remove , and use /mcp inside a session to check status and authenticate. - Set scope with flags like --scope project; for project sharing, the .mcp.json file is version-controlled. - Reference MCP resources with @/server:protocol://resource/path, e.g. analyze @/github:issue://123. - Run MCP prompts as commands in the form /mcp__servername__promptname. 🎯 Use Cases - Connect Sentry and debug production issues: "What are the most common errors in the last 24 hours?" - Connect PostgreSQL and query without pasting the schema: "Find customers who haven't purchased in the last 90 days." - Connect GitHub's remote MCP and ask "Review PR #456# and suggest improvements." - Commit .mcp.json so the whole team shares the same set of MCP tools. ⚠️ Caveats - Confirm you trust each server before connecting; servers that fetch external content can be exposed to prompt-injection risks. - Project-scoped servers require approval before use for safety (reset choices with claude mcp reset-project-choices). - A warning appears when tool output exceeds 10,000 tokens; raise the cap with MAX_MCP_OUTPUT_TOKENS (default max is 25,000). - SSE is deprecated; use HTTP where possible. #ClaudeCode# #MCP#