标签：#t

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, how AI is playing a part.

From the opening moments of bitknot, it's obvious that Feeble Little Horse has found an entirely new gear. Where on Girl with Fish the blown-out textures were more '90s indie rock and shoegaze, on their latest LP, there's a more modern edge to the distortion and the riffs cut cleaner. Similarly, where the digital glitchiness […]

About this series. I'm going to take a fresh paper - Self-Distilled Agentic Reinforcement Learning (SDAR, arXiv:2605.15155 ) - and architect it end to end on AWS: the system design, the actual gate code, the evaluation plan, and a brutally honest cost model. What I'm not going to do is wave a benchmark number around. Reproducing a paper like this costs thousands in GPU time, and I'd rather show you the machinery than a screenshot you can't audit. The design is the deliverable. This is Part 1. A small, infuriating problem Picture an LLM agent working a web-shopping task. It reads the goal, searches, clicks a category, filters, opens a product, compares, adds to cart - twelve steps in all. At the end, it bought the wrong thing. So you do what reinforcement learning tells you to do: you score the trajectory. Reward = 0. Bad agent. Now answer this: which of the twelve steps was actually wrong? Maybe step 3, the search query, was fine and step 9, a filter choice, doomed everything. Maybe steps 1–11 were brilliant and step 12 fat-fingered the wrong button. Your single scalar reward has no idea. It punishes all twelve equally, including the eight that were correct. That's the supervision problem in agentic RL, and it's the thing this whole series is about. Why "just use RL" isn't enough for agents RL has become the default way to post-train LLM agents. The catch is that the reward usually lands at the trajectory level - one number for the entire multi-step episode. For a single-turn task ("answer this question"), that's tolerable; the action and the outcome are close together. For a long-horizon agent - ten, twenty, fifty turns of searching, calling tools, and reacting to an environment - it's a disaster of credit assignment . The signal is too coarse to tell the model which decisions earned the reward and which torpedoed it. You can throw more episodes at it and let statistics sort the credit out eventually. But "eventually" on a 30-turn task burns a lot of expensive comp

United flight 236 from Newark to Palma de Mallorca on Saturday night was forced to turn around just an hour after takeoff due to security concerns around a Bluetooth signal. Multiple Redditors claimed to be on the flight and reported that the crew repeatedly requested passengers to turn off their Bluetooth. According to one poster, […]

Token Budgeting: Optimizing Generative AI Costs and Performance Modern generative AI applications offer unprecedented capabilities, yet their operational costs can quickly escalate. The primary driver of these costs, alongside computational resources, is token consumption . Understanding and implementing effective token budgeting strategies is not merely an optimization; it is fundamental to building scalable, efficient, and economically viable AI systems. The Economics of Tokens Tokens are the atomic units of text that large language models (LLMs) process. Whether you're sending a prompt (input tokens) or receiving a response (output tokens), each token incurs a cost. This cost varies by model, but the principle remains: more tokens mean higher expenses and often, increased latency due to longer processing times. Efficient token management directly impacts your application's bottom line and user experience. Strategic Pillars of Token Efficiency Optimizing token usage requires a multi-faceted approach, focusing on both input and output, as well as the underlying model choices. 1. Input Optimization: Crafting Smarter Prompts The most direct way to save tokens is to be judicious with the information sent to the model. Every word in your prompt counts. Concise Prompt Engineering : Avoid verbose instructions or unnecessary conversational filler. Get straight to the point. Instead of: "Hey AI, I was wondering if you could please help me summarize this really long article I have here. It's about quantum computing. Could you make it brief, maybe just a few sentences?" Opt for: "Summarize the following article about quantum computing in three sentences: [Article Text]" This significantly reduces input tokens without sacrificing clarity. Context Window Management : LLMs have a finite context window , the maximum number of tokens they can process at once. Sending an entire document when only a specific section is relevant is wasteful. Employ techniques like: Summarization : P

There is a fascinating psychological phenomenon in modern software engineering: the relentless pursuit of the upgrade. As frontend developers, we are conditioned to believe that speed and efficiency come from adopting the newest technologies. We migrate from Webpack to Vite to shave seconds off our build times. We transition between UI libraries in search of better reconciliation algorithms. We constantly audit our CI/CD pipelines. We treat performance as a destination we must reach by continuously adding or swapping out the moving parts of our toolchain. Yet, amidst this endless cycle of optimization, we consistently overlook the most sophisticated, highly optimized piece of software in our entire stack. It is the software you are using to read this article right now: the web browser. The Underappreciated Engine The modern browser is an absolute marvel of engineering. Over the past decade, teams of the world's most talented systems engineers have engaged in a fierce arms race to optimize browser engines like V8, SpiderMonkey, and JavaScriptCore. Today’s browsers feature Just-In-Time (JIT) compilation, sophisticated garbage collection, and massively parallelized rendering pipelines. They are capable of executing highly complex, interactive applications with a level of fluidity that was unimaginable a few years ago. However, when we evaluate developer tools—specifically the online IDE or the browser-based code editor—there is a stark contrast. The environments we use to write and test our code rarely reflect the speed of the engine they run inside. Why the Standard Web IDE Misses the Mark If you want to quickly prototype a component or isolate a bug, you will likely reach for a frontend playground or a popular Replit alternative. What happens next is often a masterclass in friction. The environment feels heavy. The interface is cluttered with features you didn't ask for. As you type, the live code editor experiences micro-stutters. The instant live preview isn't actu

How a Product Sync Automation Project Transformed Customer Onboarding When people think about impactful engineering work, they often imagine distributed systems, high-scale infrastructure, or complex algorithms. One of the most impactful projects I worked on wasn't any of those. It was solving a seemingly simple problem: Keeping product data in sync across multiple retail systems. Years later, our CEO still remembers how much smoother customer onboarding became after this project. The Context: What is Commerce Connect? At Casa Retail AI, we have an internal platform called Commerce Connect (CC) . Commerce Connect acts as the central Product Information Management (PIM) system and serves as the source of truth for product information. Under the hood, it is built on top of a customized version of the open-source e-commerce platform Spree Commerce , extended with multi-vendor and multi-tenant capabilities. Its primary responsibility is simple: Collect product information from multiple retail ecosystems and distribute it to every Casa product that needs it. Once product data enters Commerce Connect, it is synchronized to multiple downstream systems. Why Product Data Matters Many applications inside Casa depend on product information. Product Consumers Once product data enters Commerce Connect, it is distributed to multiple systems across the Casa ecosystem. Customer-Facing Applications Several products rely on product information to provide context and improve customer experience: Lead management applications use product information during customer interactions. Ticket management systems link customer issues to specific products. Digital receipts display product names, images, and related details. Analytics & Reporting Product data powers business dashboards and reports, helping retailers answer questions such as: Which categories perform best? Which products attract the most attention? Which products generate the most complaints? It is also used for filtering and segme

On the latest episode of Equity, we debate whether tech CEOs are "uniquely prone to AI psychosis."

🏦 Day 6 of 7: Building a Mini Uniswap in 80 Lines of Solidity Imagine a vending machine. It has 1,000 coffee beans and 1,000 coins. No menu, no cashier — just one iron rule: the product of the two numbers inside must never decrease. That's it! This is how Uniswap works — and this is what I built on Day 6, coming from .NET. Here's how, why it's elegant, and where you can step on a rake. Why an Order Book Doesn't Work on a Blockchain Traditional exchanges — Binance, NYSE, any CEX — run on an order book . Market makers post bids and asks. A matching engine pairs them. Millions of updates per second, all in a centralised database. In a blockchain, this is impossible. Transactions take 12 seconds. Every state change costs gas. Storing millions of constantly changing orders would eat all the profit before a single trade completes. Uniswap's solution: replace the order book with a liquidity pool — a smart contract holding two tokens — and replace the matching engine with pure math. Just a formula — below. x · y = k — The Formula That Broke Finance The Constant Product Invariant : x · y = k Where x is the reserve of Token0, y is the reserve of Token1, and k is a constant that must never decrease during swaps. When a trader sells Token0 into the pool, x increases. To keep k constant, y must decrease — the contract sends out Token1. The price is determined automatically by the ratio of reserves. Live example with numbers: Pool: 1,000 Token0, 1,000 Token1. k = 1,000,000. Trader sells 100 Token0: amountOut = (reserveOut × amountIn) / (reserveIn + amountIn) amountOut = (1000 × 100) / (1000 + 100) amountOut = 100,000 / 1,100 amountOut ≈ 90.9 Token1 The trader gets ~90.9, not 100. That gap is slippage — and it's not a bug. It's the formula protecting the pool. The more you buy relative to pool size, the worse your price gets. Naturally. Mathematically. After the swap: pool has 1,100 Token0 and ~909.1 Token1. k ≈ 1,000,000. Invariant holds. The Contract: SimpleAMM Three functions.

A year ago, I wrote about our first year with micro-frontends . It was a frank retrospective: the win was framework autonomy and independent deploys, the cost was 20 GB of RAM during local dev, 15–20 minute deploy cycles, and a monorepo that became its own coordination tax. The closing line was the honest one — if I could choose again, I would think carefully about using Microfrontend or not. This article is the follow-up the previous one didn't write: what we actually built around Single-SPA to make it work. Not the architecture diagram. The workarounds. The dead code that's not really dead. The regexes we wish we didn't need. The .NET app no Single-SPA tutorial mentions. The browser-side singletons we use as message buses. The dev experience we documented and the parts we didn't. If you're evaluating Single-SPA in 2026, this is the article I wish I'd had a year ago. Where we left off The platform is a Single-SPA monorepo with seven in-repo apps and five shared packages. On top of that sit roughly ten standalone repositories for newer features, loaded at runtime via SystemJS import maps. Shared dependencies — React 18.2, ReactDOM, axios, react-bootstrap, single-spa 5.9.5 — are served from CDN and externalized in every webpack config. The architecture is textbook. The reality, a year in, is that the textbook stops where the workarounds start. The shell isn't where you think it is If you read our frontend repo you'd conclude the shell is whatever directory has single-spa in its package.json . That's where Single-SPA gets configured. That's where start() is called. There's even a webpack entry that, until recently, was producing an index.ejs HTML template. That HTML template is dead code. The real shell — the HTML the browser actually receives, in every environment — is rendered by an ASP.NET MVC application, in a Razor view. The frontend repo ships JavaScript modules. The .NET host ships the page that loads them. That's the unlock. Once you see this, every other work

The plan was simple Take the specs we'd written, turn them into Jira epics, stories and subtasks, and start sprinting. It took longer than expected. Here's what actually happened — and what I learned. Why automate Jira setup at all? HandyFEM has 8 epics, 37 stories and ~160 subtasks. Creating that manually would take a full day and be error-prone. More importantly: the specs were already written in a structured format. Translating structured data into Jira issues is exactly the kind of repetitive task that should be automated. So I wrote a Node.js script to do it via the Jira REST API. Problem 1 — Jira Spaces ≠ Jira Classic My account uses Jira Spaces — Atlassian's newer interface. The classic Jira has CSV import built in. Jira Spaces doesn't. This isn't documented anywhere obviously. You discover it by looking for the import option and not finding it. Lesson: always check which version of Jira you have before planning your workflow. The API still works, but some endpoints behave differently. Problem 2 — The API token wasn't the issue (until it was) First attempt: connection error. I assumed it was the token. It wasn't — it was an expired token from a previous session. Regenerating it fixed the connection. The real lesson: curl -u email:token https://your-domain.atlassian.net/rest/api/3/myself is the fastest way to verify auth before running any script. Problem 3 — customfield_10014 doesn't exist in team-managed projects In classic Jira, linking a story to an epic uses a field called customfield_10014 (Epic Link). In team-managed projects (Jira Spaces), this field doesn't exist. You use parent instead. The error was clear once I saw it: "customfield_10014" : "Field cannot be set. It is not on the appropriate screen, or unknown." Fix: remove customfield_10014 , keep only parent: { id: epicId } . Problem 4 — Board search doesn't work for team-managed projects The Agile API endpoint /rest/agile/1.0/board?projectKeyOrId=HFM returns empty for team-managed projects, even

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