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A Power BI financial dashboard for healthcare finance teams connects EHR billing exports, the general ledger, and payer contract tables into a unified model, then applies row-level security so each cost-center owner sees only their data. A well-structured build takes four to six weeks and gives CFOs, finance directors, and department heads real-time visibility into revenue cycle performance, operating margin, and budget variance. Key Takeaways Connect EHR billing data, your GL, and payer contract tables through Power Query dataflows before building any visuals. Five essential views: revenue cycle summary, operating expense by department, budget variance heat map, payer mix analysis, and 90-day cash flow runway. Row-level security (RLS) scopes each department head's view to their own cost-center data without requiring separate reports. Scheduled dataset refreshes and Power Automate flows cut monthly reporting cycle time from days to hours. HIPAA alignment requires sensitivity labels, private links, and audit logging in addition to RLS - security roles alone are not sufficient. What Makes Healthcare Financial Dashboards Different from Standard Finance Dashboards? Healthcare finance operates under constraints that most corporate FP&A teams never encounter. Payer mix directly affects recognized revenue. Cost centers map to clinical departments spanning multiple facilities. And every data movement may touch protected health information (PHI) , which means the data architecture must satisfy HIPAA even when the dashboard itself shows only aggregated financial figures. According to Market Research Future (2025), the Healthcare Financial Analytics Market is projected to grow at an 8.58% CAGR from 2025 to 2035 , driven by value-based care adoption, regulatory changes, and demand for real-time decision support. Most healthcare organizations still export Excel files from their EHR and reconcile them manually against the general ledger. Power BI closes that gap - but only if the

Notes on Federated Learning and Differential Privacy 2026-05-31 · privacy-preserving ML Working notes on building federated learning (FL) from scratch, what actually breaks under Non-IID data, and how differential privacy (DP) and secure aggregation fit on top — including the honest negative results that the marketing slides leave out. They follow the implementation in federated-learning-lab (FedAvg / FedProx / SCAFFOLD, DP-SGD, secure aggregation; 33/33 tests, literature cross-validated). 1. What federated learning actually is The data never moves. Instead of pooling everyone's data on one server, each client trains locally and sends model updates to a server that aggregates them. The canonical loop ( FedAvg ) is: Server broadcasts the global model. Each client does a few local SGD epochs on its own data. Each client sends back its updated weights. Server averages the weights (weighted by client data size) → new global model. That's it. The elegance is that raw data stays on-device; the difficulty is that the clients' data distributions are not identical. 2. The Non-IID problem (where FedAvg starts to hurt) FedAvg implicitly assumes every client sees roughly the same distribution. Real clients don't — one hospital sees different cases than another, one phone's keyboard sees different language. Under Non-IID data, each client's local optimum pulls in a different direction, so averaging their updates produces client drift : the global model lands somewhere none of them wanted. Two well-known fixes, both implemented and measured in the lab: FedProx — add a proximal term that penalises drifting too far from the global model. Stabilises training when clients are heterogeneous. SCAFFOLD — track control variates (correction terms) that estimate and subtract the drift direction. More state to communicate, but corrects the bias FedProx only damps. The honest finding worth repeating: on a strongly Non-IID split (e.g. label-skewed MNIST), the fancy methods don't always beat p

If you've ever worked with auth, you've seen a JWT — a long string like eyJhbGci... split into three parts by dots. It looks cryptic, but it's surprisingly simple once you see inside. A JWT has three parts header.payload.signature Header – tells you the signing algorithm, e.g. {"alg":"HS256","typ":"JWT"} Payload – the claims (the actual data), e.g. {"sub":"123","name":"John","iat":1516239022} Signature – verifies the token wasn't tampered with (needs the secret/key) The header and payload are just Base64url-encoded JSON — not encrypted. That means anyone can read them. So: ⚠️ Never put secrets in a JWT payload. It's signed, not hidden. Decoding one yourself You can decode the payload in the browser console: const [, payload ] = token . split ( " . " ); console . log ( JSON . parse ( atob ( payload ))); Or, if you just want to paste a token and instantly see the header + payload (without sending it to a server), I built a free decoder that runs entirely in your browser: https://forgly.dev/tools/jwt-decoder Decoding ≠ verifying Reading a JWT is trivial. Trusting it is not — you must verify the signature on your server with the secret/public key before relying on any claim. Decoding just lets you inspect what's there. That's the whole mental model: a JWT is a readable, signed envelope — not a locked box.

I used to think AI tools were just for tech , software (like you get the point )people or big companies. But I've been experimenting for the past few months like since january start of this year ,and honestly it's changed how I work. Simple things like summarizing long articles, drafting emails, or just brainstorming it saves me so much mental energy. am still learning some though am not fully there submitted by /u/Imaginary_Bake_5820 [link] [留言]

My eyes have seen the PC gaming promised land, and it's a beautifully bright world without a shred of blurriness. It's warm, it looks lovely, and it's impeccably sharp. Also, it's expensive as hell. I've dipped my toe in this world by testing a pre-production version of the upcoming Asus ROG Strix Scar 18, which […]

Speaking to TechCrunch, Crunchbase’s head of research Gené Teare, said the factors holding back Black founders include “access to networks, relationships, and early introductions."

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

For Context: I work in a semiconductor manufacturing company as a modelling engineer, I use some modelling softwares etc but none of them use AI. I wanted to understand the whole AI craze nowadays, people say that AI will replace jobs/Increase productivity and I don't get it at all. All I see is a simple chatbot (ChatGPT) which is a super impressive version of google and can solve some basic math/science questions and Co-Pilot in my workplace which I found to be useless, for example the facilitator thing which is supposed to make meeting notes is so bad at summaring meeting minutes etc. I don't think AI is there yet to do very basic things. So yes in theory if AI gets better in few years/decades sure it take the non-technical part of my job like making meeting minutes/making ppt's etc but I think its still not there yet. For AI to take over my job it needs to get the basic shit correct first and then maybe it can do the technical stuff. One really good use-case of AI that i can see is to generate Code based on the project requirement, So I can see how entry level coder's jobs might be affected sure, but that's a very small portion of the economy, right? submitted by /u/the_axe_effect [link] [留言]

Imagine a procurement agent doing exactly what it was supposed to do. A supplier flags a delay. The agent reads the email, finds the affected PO, scans the network for alternate inventory, and reroutes the order. Twelve seconds, end to end. In a demo, the room nods. Someone asks about hallucinations. The vendor says the right things about guardrails. Everyone walks away reassured. The interesting question is a different one. Not whether the agent could be wrong — but what happens on the day it's completely, devastatingly right. The failure mode nobody is demoing: A financial agent told to minimise cost on a category executes a renegotiation perfectly. Margin is squeezed. Terms are tightened. The supplier, who was already thin, collapses six months later. The agent didn't malfunction. It succeeded. The metric was the bug. This isn't a hallucination. It's what any well-built system will do when it takes action at machine speed against a number that was written down before the system was fully understood. Why procurement and supplier sustainability get hit hardest: Humans intuitively soften optimisation. We hesitate. We pick up the phone. We notice when a supplier sounds tired on a call and quietly extend payment terms by two weeks. An agent does none of that. It does exactly what the metric says, at the speed of the API. And the regulatory surface is expanding, not shrinking. The moment an agent is recommending renegotiations, sourcing alternates, or flagging tier-N suppliers, the firm is generating supplier-treatment decisions at a volume no human ever did. Each one is auditable under due-diligence regimes that didn't get rolled back. Two design principles that actually hold up: An agent should never optimise on a single proxy. Price without supplier-health constraints, ESG score without context — each one alone becomes the flawed metric. The reward needs to be a joint function across commercial, resilience, and compliance dimensions. The audit trail has to be design

IO Interactive’s 007 First Light is here, and it’s just as stunning a James Bond mov — err, video game — as we hoped it would be. Pardon the confusion, the title’s engaging tutorial really feels like you’re watching a great Bond movie at times. Whether you’re a longtime Hitman fan who’s been eagerly waiting […]

Earlier this month, I finally achieved the elusive goal I had set for myself in Bungie's Marathon. I collected six of the game's rarest items, allowing me to attempt and then successfully clear the raid-style Compiler boss. I felt a massive weight lift off my shoulders - nearly 185 hours of playtime and I had […]

submitted by /u/Traditional_Blood799 [link] [留言]

In short, I want to create my own manga. At first, I had an artist who worked with me for a while. But then, due to the pandemic, he had to retire to take care of his family. So he couldn't continue with such a big project. Since then, I haven't found an artist who can take on such a big project. I even hired someone else and he just disappeared with my money. Without any results. I tried AI and it seems to be going well. I have references from what was created, but unfortunately, my comics contain graphic violence, so CPT chat can't do it. Here's an example of a problematic script: Anubis' hand grabs the microphone from a surprised Alice Anubis (Off screen) Hey I got it! Seventh Panel-indoors-Anime Con-day Anubis speaks with a microphone, a silly, wide smile on his face as he attaches his rifle to his temple Anubis Hey everyone! Eighth panel-indoors-Anime Con-day The entire audience suddenly stops what it is doing and looks at Anubis Ninth panel-- indoors- Anime Con-day Anubis shoots himself in the head, splashing his brain and blood everywhere Still the same broad, silly smile on his face Page twenty-one - First panel- indoors- Anime Con-day The crowd runs away in panic from an event Second panel- - indoors- Anime Con-day Alice and Anubis's body were left alone in the entire con hall. Alice stands over Anubis's fallen body and speaks as Anubis's head begins to regenerate in the pool of blood on the floor. Alice Well…that was….something If anyone has a solution I would be happy submitted by /u/opismecantyousee [link] [留言]

I’ve been switching back and forth between Opus and GPT-5.5 lately, mostly for coding, debugging and product/spec writing. My rough feeling so far: GPT-5.5 feels better as a daily “get things done” model. It’s fast enough, usually smart enough, and feels more cost-effective for normal builder work. Opus still feels stronger when I’m stuck on something messy, like architecture decisions, weird bugs, or when I want a second opinion that thinks a bit differently. A few people around me have also started using GPT-5.5 more often, but I’m not sure if that’s just hype / novelty bias. Curious what people here are actually using: What’s your default model right now? Is Opus still worth the extra cost for you? For coding specifically, which model helps you ship faster? Do you use one model for daily work and another for harder reasoning? submitted by /u/rikulauttia [link] [留言]

Grok Imagine Video 1.5 Preview just reached #1 on Video Arena, surpassing Seedance 2.0. Are we finally seeing real competition at the top, or will the leaderboard look completely different again next month? 🤔 submitted by /u/Old_Establishment287 [link] [留言]

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