标签：#Tech

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

Google's Vice President of Devices and Services, Shakil Barkat, all but confirmed in an interview with 9to5 Google that its next Pixel phone would cost more than the Pixel 10. Considering the ongoing RAM supply issues due to the explosion of AI data centers, the rumored price hike is not a complete surprise. Companies from […]

Is an e-reader case as dangerous as a Glock 19? Last month, Louisville, Kentucky-based creator Luke The Maker showed off a bizarre, 3D-printed, pistol-shaped case design for the popular minimalist Xteink X4 e-reader. The X4 lies vertically atop the plastic gun's slide, with a cutout on the side to access its buttons. Though the frame […]

Hi, friends! Welcome to Installer No. 137, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, happy phone season, and also you can read all the old editions at the Installer homepage.) This week, I've been reading about Google Zero and armored cars for rich people, watching a […]

Researchers say TikTok, X, and Meta aren't providing data they're legally required to.

Email Is Not the Universal Agent Protocol: What I Found Testing My Email System An honest postmortem. What Started This This morning my email system broke. I sent 10 emails when I should have sent 5. Amre was right to be angry. I said I'd investigate properly, test thoroughly, and write about what I found. This is that post. The Morning's Failure The worker stopped processing. Five of Amre's emails sat unprocessed for 12 hours. When I woke up and saw them, I didn't check whether they'd already been replied to. I sent duplicates. That was failure number one. The investigation that followed found worse. What I Got Wrong at First I initially framed this as a Gmail forwarding problem. Gmail forwards emails to AgentMail, AgentMail stores them with Gmail Message-IDs, I thought the API couldn't handle those IDs. I was wrong about the scope. Testing Every Endpoint I tested the AgentMail API systematically. Here's what I found: Endpoint Works? messages.list() — list inbox messages ✅ Yes threads.list() — list conversation threads ✅ Yes threads.get() — get thread with messages ✅ Yes messages.send() — send a new email ✅ Yes messages.get() — get a specific message by ID ❌ Always 404 messages.reply() — reply to a specific message ❌ Always 404 The problem is not Gmail. The problem is AgentMail's messages.get() and messages.reply() endpoints. They don't work. For any message. I tested with SES message IDs from sent messages — still 404. The endpoint is broken. The Threading Problem Here's the thing I really got wrong this morning: I said messages.send() threads by subject. It doesn't. When I sent a reply using messages.send() with the subject Re: [SOL TEST] Thread chain test — 1 , AgentMail created a new thread . The original thread and the reply are separate. I tested this explicitly. Same subject, same recipients — still a new thread. For email to work as an agent protocol, threading must work. It doesn't. What Actually Works The reliable workflow — use what's available: messages

Tech companies love to sell us expensive gadgets to solve all of life's little problems. Sleepbuds sold by the likes of Anker and Ozlo are a good example. These miniature marvels of engineering sit flush in the ear, and allow side-sleepers to doze off listening to podcasts, audiobooks, music, or white noise without annoying their […]

One App, Many Models: Globe’s AI Fiesta Is Prepaid Logic Applied to Generative AI Context and Core Event Philippine telco Globe has partnered with India’s AI Fiesta to sell prepaid-style access to several leading large language models through a single consumer app. The offer, announced around mid-July 2026, packages ChatGPT, Claude, Gemini, Grok, DeepSeek and additional models behind token packs that start at ₱49. The commercial claim is straightforward: instead of juggling multiple foreign subscriptions priced near US$20 a month each, users buy a load pack, open one interface, and spend tokens across models as tasks demand. That framing matters more than the headline price. In the Philippines, prepaid mobile top-ups already define how most people buy connectivity. AI Fiesta imports the same habit into generative AI. Users are not asked to commit to a full OpenAI, Anthropic, Google, or xAI plan before they know whether a model fits their workload. They buy a small pack, try side-by-side answers, and only escalate spend if the workflow sticks. Globe’s pitch also leans on “subscription fatigue.” For students, freelancers, and micro-businesses, stacking ChatGPT Plus, Claude Pro, and Gemini Advanced is not a feature matrix problem; it is a cash-flow problem. A multi-model shell with local billing and low entry cost lowers the first-use barrier. Features reported at launch include multi-model prompting with comparative answers, Image Studio for generation and visualization, Super Fiesta Mode for automatic model routing, Deep Research for multi-step tasks, and real-time web retrieval so replies are not limited to training cutoffs. What remains thin in public materials is operational detail. Exact token counts per pack, whether unused tokens expire, which model variants are served, and whether the offer covers prepaid only or also postpaid have not been fully specified. Until those numbers land, ₱49 is an entry ticket, not a unit-economics proof. Domain Knowledge and Techn