标签：#tech

Perplexity has expanded its agentic Personal Computer tool to Windows, allowing computers running the world's most popular OS to be used as a locally run AI system. Like the Mac version that Perplexity launched in April, Personal Computer for Windows operates like a "general-purpose digital worker" that can access local files and apps to perform […]

Apple has officially introduced "Apple Upgrade," a new leasing program that aims to make it easier to get your hands on the latest iPhone, Mac, iPad, and Apple Watch models. The service is launching today in the US, and works like a car lease - allowing users to keep a device at the end of […]

Over the last few months, I've spent a lot of time talking to my computer. One underrated feature of the LLM revolution has been a remarkable leap in all kinds of dictation technology - even the fastest, cheapest models are getting very good at understanding and processing speech. I've tested lots of these apps, from […]

Anthropic and OpenAI employees are expected to give generously after their companies go public. “It’s going to be a wild ride,” says one nonprofit leader.

Hugging Face is being used to make nonconsensual deepfakes, and the popular open-source AI model repository is doing very little to prevent it. That's according to a new report published by the European nonprofit AI Forensics, which found that seven out of the top nine image editing models hosted by Hugging Face readily complied with […]

X Money, a core part of Elon Musk's mission to turn X into an "everything app," is rolling out starting today, 9to5Mac reports. The payment platform offers a digital wallet and peer-to-peer payments similar to Venmo, along with a metal Visa card users can emblazon with their X username. Other perks include Apple Wallet support […]

Only Premium and Premium X subscribers get the privilege of banking with Elon for now.

These are likely the last updates of note before Apple's bigger fall updates.

Nanoleaf’s Blocks Combo XL Smarter Kit is a fun back-to-school buy that can add pops of customizable light and storage to your wall. It combines colorful smart lighting panels with a low-profile shelf and pegboard, and right now you can buy the kit for half off at $99.99 from Nanoleaf, which marks a new low […]

YouTube's ad-free Premium subscription is getting another perk: access to Peacock. In an announcement on Monday, NBCUniversal says the multi-year agreement will allow Premium subscribers to stream Peacock's ad-supported shows, movies, and live sports directly through the YouTube app in 2027. YouTube will bundle Peacock in its standard subscription, which increased to $15.99 / month […]

The government says destroying his own data during an airport interrogation was illegal.

At TechCrunch Disrupt 2026, the Smart Systems Stage will be where energy, infrastructure, and technology collide, covering everything from fusion breakthroughs to the grid strain AI is putting on the entire economy.

Framework's latest modular laptop trades one set of problems for another.

Today, I’m talking with Evan Smith, who is cofounder and CEO of Altana, a company that develops software tools to manage big, messy supply chain networks around the world. We last had Evan on in early 2025 to talk about how Trump’s first few waves of tariffs were starting to affect global trade and what […]

The China-based memory firm ChangXin Memory Technologies (CXMT) had a massive first day on the Shanghai stock exchange after its shares surged 466 percent, as reported earlier by The Wall Street Journal and CNBC. The stock market frenzy boosted CXMT's valuation to $484 billion, making it the most valuable Chinese company listed on the exchange. […]

Nvidia on Monday said it is joining forces with Microsoft, SpaceX, IBM, and other tech companies to build and share open-source AI security tools. The new Open Secure AI Alliance said open tools are required to effectively defend against attacks from frontier models. The initiative is a direct response to mounting concerns over the safety […]

After processing thousands of bank statements, invoices, and receipts through Claude Vision API, I've learned that financial document OCR is harder than it looks. Here's what actually works in production. The Problem: Why Traditional OCR Fails on Financial Documents Traditional OCR tools like Tesseract or AWS Textract struggle with financial documents for three reasons: Table structure is implicit — Banks don't use HTML tables. Columns are separated by whitespace, making it hard to know where one column ends and another begins. Numbers must be perfect — Confusing 1 with l or 0 with O creates accounting errors. A single misread digit can break double-entry bookkeeping. Format chaos — Every bank uses different layouts. Chase statements look nothing like Wells Fargo statements. Traditional OCR gives you raw text. You still need to write hundreds of lines of regex to parse it into structured data. Why Claude Vision API Changes the Game Claude Vision doesn't just extract text — it understands document structure . You give it an image and a prompt like: "Extract this bank statement into JSON with transaction date, description, debit, credit, and balance columns." Claude returns structured JSON directly. No regex. No manual column detection. Real Example Input: Bank statement PDF (converted to PNG) Prompt: Extract all transactions from this bank statement. Return JSON with: - header: {accountNumber, statementPeriod, bankName} - transactions: [{date, description, debit, credit, balance}] Rules: - Dates in YYYY-MM-DD format - All amounts as numbers (no currency symbols) - If a field is unclear, use null (never guess) Output: { "header" : { "accountNumber" : "****1234" , "statementPeriod" : "2024-01-01 to 2024-01-31" , "bankName" : "Chase Bank" }, "transactions" : [ { "date" : "2024-01-03" , "description" : "Amazon.com" , "debit" : 49.99 , "credit" : null , "balance" : 1450.01 }, { "date" : "2024-01-05" , "description" : "Salary Deposit" , "debit" : null , "credit" : 3500.00

The government is prosecuting US citizen Sam Tunick for allegedly providing authorities with a "duress password" that wiped his phone when they tried to seize it at Atlanta's Hartsfield-Jackson airport on January 24th, 2025. Federal agents detained Tunick at the airport, allegedly questioning him about child exploitation images. However, a motion filed by Tunick's lawyers […]

Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.

Most tutorials on AI agents assume the agent should get more autonomous over time — more tools, more scope, less human intervention. That's the wrong architecture for regulated professional-services work, and if you're building for accounting or legal clients, it's worth understanding why before you write a line of code. The constraint that shapes everything In accounting and legal workflows, there's a hard line between preparing work and exercising professional judgment. A first-pass extraction of numbers from a bank statement is preparation. Deciding how to characterize a transaction for tax purposes is judgment. An agent that drafts a client letter from a template is preparation. An agent that decides what legal advice goes in that letter is not — and building one that does is a liability, not a product. This isn't a hypothetical concern. The 2026 legal industry data shows the gap plainly: 69% of individual lawyers now use generative AI at work, according to the 8am 2026 Legal Industry Report, but firm-wide adoption of legal-specific AI sits at only 34%, and 54% of firms report no training or governance plan for responsible AI use at all. The technology is ahead of the guardrails, and that's exactly the gap this architecture is meant to close. So the architecture I use treats "human approval" as a first-class step in the pipeline, not an afterthought bolted on for compliance theater. Concretely, that looks like four layers. 1. Ingestion layer Documents come in from wherever the firm already receives them — email, upload, integration with QuickBooks, Xero, Clio, iManage — get classified, and get normalized into structured data. 2. Extraction / drafting layer The agent does the actual work: pulling line items, matching transactions, assembling a first-draft letter, flagging clauses in a contract that need a human eye. 3. Approval gate Nothing produced in step 2 moves forward without an explicit human action. This isn't a suggestion in a UI that can be ignored; it's