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Just in case you were wondering, Nvidia's RTX Spark isn't supposed to be a one-off. The company is not just flirting with becoming the fifth high-profile vendor of consumer laptop chips to see if people bite. At Computex 2026 in Taipei, Nvidia CEO Jensen Huang confirmed at least two additional generations of RTX Spark are […]

At Microsoft's annual Build conference on Tuesday, the company announced a slew of new or expanded AI initiatives, including a super app, in-house reasoning models, a cybersecurity tool, and OpenClaw-esque AI agents. All this news added up to a clear message: Microsoft is positioned to be one of the biggest players in AI, and it's […]

Microsoft has two new Surface devices arriving later this year, both powered by Nvidia's RTX Spark chips. I got a chance to take a closer look at both the Surface Laptop Ultra and Surface RTX Spark Dev Box at Microsoft's Build conference this week, and while both have the same chip inside, they're utilizing Nvidia's […]

Every detection vendor ships a MITRE ATT&CK heatmap, and every one of them is mostly green. Broad coverage, techniques lit up across the board, a reassuring wall of color in the sales deck and the board slide. It's the universal flex. We cover the matrix. Then you parse the actual rules – the real YAML in the public repo, not the marketing layer on top of it – and the green collapses into three tactics. Everyone covers execution and persistence. Almost nobody covers discovery, lateral movement, or collection. The heatmap wasn't measuring coverage. It was counting rules, and counting them in a way designed to look complete. What a green cell actually means A green cell in a Navigator layer means one thing: at least one rule somewhere references that technique tag. That's it. Not "we detect this reliably." Not "this fires on real attacks and stays quiet otherwise." Not "this survives an attacker who knows the rule exists." One rule that names the technique in its metadata turns the cell green, and forty rules turn it the same shade. Unless the layer is scored by rule count – and most published heatmaps aren't – one and forty are indistinguishable. I've written before that an untested detection isn't a detection, it's a query that runs on a schedule. The heatmap is the same lie one level up. A green matrix isn't coverage. It's a wall of queries that run, rendered in a color that means "present," dressed up as a color that means "protected." The vendor knows the difference. The buyer staring at the green doesn't. You can measure the real shape yourself Here's the part the heatmap marketing depends on you not doing: the rules are public, and you can count them. SigmaHQ, Elastic's detection-rules, Splunk ESCU, Panther, Sublime – all on GitHub, all tagged with attack.TXXXX technique IDs and tactic tags in the rule metadata. The method is boring on purpose. Walk the rule directories. Pull the ATT&CK tags out of each rule. Aggregate by technique, roll the technique counts up

Microsoft launched seven new in-house AI models at Build 2026 on June 2, 2026, marking the company's most significant push yet to build its own frontier AI stack independent of OpenAI. The centerpiece is MAI-Thinking-1, Microsoft's first large-scale reasoning model, built from scratch on clean commercially licensed data using a sparse Mixture of Experts architecture. Alongside it: MAI-Code-1-Flash, a 5-billion-parameter coding model that outperforms Claude Haiku 4.5 by 16 percentage points on SWE-Bench Pro while using 60% fewer tokens on complex tasks. This is the complete developer guide to all seven MAI models, their specs, benchmarks, deployment paths, and what they mean for the AI development ecosystem. Why Seven Models at Once? The strategic context matters. For three years, Microsoft's AI product surface — GitHub Copilot, Azure AI, Bing Chat, Microsoft 365 Copilot — ran almost entirely on OpenAI models. The Build 2026 announcement is Microsoft's public declaration that it is building a parallel, proprietary model stack. Every new MAI model is trained from scratch using "clean and appropriately licensed data, without distillation from third-party models" — language that directly addresses the intellectual property concerns that have accompanied third-party model licensing. The distribution strategy is equally deliberate. Microsoft is not routing MAI models exclusively through Azure. MAI-Thinking-1 and MAI-Code-1-Flash are available via Fireworks AI, Baseten, and OpenRouter — three infrastructure providers that collectively reach developers who explicitly do not want cloud vendor lock-in. This signals a platform-first posture: Microsoft wants MAI to become a model ecosystem, not just an Azure feature. MAI-Thinking-1: The Reasoning Flagship MAI-Thinking-1 is Microsoft's answer to Claude Opus 4.x and GPT-5.5 on the reasoning side of the model spectrum. The architecture is a 35-billion-parameter active / approximately 1-trillion-parameter total sparse Mixture of Ex

A user picks a video from Photos. The app turns that video into a file the server can accept. Then it uploads the file. On the surface, this sounds like a simple upload flow. In practice, iOS makes you answer a much more specific question: How do you reliably turn a video from the user's Photo Library into an uploadable MP4 file? At first, I used AVAssetExportPresetPassthrough . It looked like the right default. It preserves the original media as much as possible, avoids unnecessary re-encoding, and is fast when it works. No quality loss, less CPU usage, less battery cost. But some videos failed during export. The error was usually in the AVFoundationErrorDomain Code=-11838 family. Apple describes this error as operationNotSupportedForAsset : an operation was attempted that is not supported for the asset. At first, I suspected an iCloud download issue, a Photos permission edge case, or something retryable inside PHImageManager . That was not the real problem. The actual problem was more fundamental: I was trying to write an iPhone MOV asset into an MP4 file using a passthrough export. The Original Flow The original code was roughly shaped like this: PHImageManager . default () . requestExportSession ( forVideo : asset , options : options , exportPreset : AVAssetExportPresetPassthrough ) { exportSession , info in exportSession ? . outputURL = outputURL exportSession ? . outputFileType = . mp4 exportSession ? . exportAsynchronously { // upload } } The intention was reasonable: Ask Photos for an export session for the PHAsset . Use AVAssetExportPresetPassthrough to preserve the original quality. Write the result as an .mp4 file for upload. Many videos exported successfully this way. That is what made the bug confusing. Some videos worked. Some did not. The hidden assumption was: Any iPhone video can become an MP4 file through a passthrough export. That assumption is not always true. iPhone Videos Are Usually MOV Files To a user, it is just "a video." Internally, an iPh

One hardware announcement and several software highlights from Microsoft Build.

Privacy-First Personal Finance for iOS Your finances. On your phone. Nowhere else. A privacy-first personal finance app that connects your banks, brokerage, and investment accounts into one unified dashboard — entirely on-device, no backend, no account required, no subscription. View on GitHub At a Glance 🏦 Multi-source 🔒 100% On-device 📊 Full picture Banks, Revolut, Trading 212 and more No server. No account. Your data stays in your Keychain. Net worth, spending, investments — all in one place Screenshots Log Insights Budget Transaction Entry Settings About Escudo Built out of two frustrations: every decent finance app costs a monthly subscription, and none of them support Trading 212. Escudo connects your banks, brokerage, and investment accounts and gives you a single view of your net worth, spending, and investments — without your data ever leaving your phone. Key Features Net worth dashboard — aggregated balance across all accounts and investment portfolio P&L Unified transaction log — every account in one feed, auto-categorised Spending insights — breakdown by category and trends over time Budget tracking — per-category budgets with visual progress dials Multi-currency — EUR, GBP, USD with stored exchange rates Recurring transactions — template-based recurring transaction engine Shortcuts support — deep linking via escudo:// URL scheme Integrations Source Method Trading 212 REST API — portfolio, orders, dividends Revolut Enable Banking OAuth 2.0 Bankinter PT Enable Banking OAuth 2.0 SIBS SIBS Open Banking (PT market) CSV import Revolut & Bankinter statements All credentials live in the iOS Keychain — never in UserDefaults, never in iCloud, never on a server. Known Limitations Enable Banking does not expose credit card accounts — only bank accounts and transactions are available through the PSD2 API; credit card balances and transactions are not accessible No token auto-refresh for Enable Banking — manual re-auth when tokens expire Categorisation rules are hard

The LedgerSync pipeline was inserting 1.5 million rows into PostgreSQL using pandas.to_sql() . It took four minutes per run. I switched to psycopg2's COPY command and it dropped to 18 seconds. Same data, same schema, same machine. That is not an optimization tip. It is the difference between a pipeline that fits in an Airflow schedule and one that does not. This article is about patterns like that: the ones that matter when you are building pipelines that run on a schedule, not when you are writing ad-hoc queries. Loading Data: to_sql vs execute_values vs COPY There are three ways to write rows from Python into PostgreSQL, and the performance gap between them is significant. pandas to_sql issues one INSERT statement per row by default, or a multi-row INSERT with method="multi" . It is the easiest to write and the slowest for any serious volume. psycopg2 execute_values batches many rows into a single multi-row INSERT VALUES statement. About 5x faster than to_sql for medium-sized loads. psycopg2 COPY streams rows directly to PostgreSQL using its native bulk-load protocol. No statement parsing, no row-by-row overhead. For LedgerSync at 1.5M rows, this was the one that mattered. import psycopg2 import io import pandas as pd conn = psycopg2 . connect ( " host=localhost dbname=proj_db user=proj_user password=proj_pass " ) def bulk_copy ( df : pd . DataFrame , table : str , columns : list [ str ]): buf = io . StringIO () df [ columns ]. to_csv ( buf , index = False , header = False ) buf . seek ( 0 ) with conn . cursor () as cur : cur . copy_from ( buf , table , sep = " , " , columns = columns ) conn . commit () print ( f " Loaded { len ( df ) } rows into { table } " ) Use COPY for initial loads and large backfills. For incremental daily writes of a few thousand rows, execute_values is fine and gives you more control over conflict handling: from psycopg2.extras import execute_values def bulk_insert ( rows : list [ dict ], table : str ): if not rows : return columns = list

Intel couldn't catch a break. Layoffs. Shakedowns. Crashing CPUs torpedoing its reputation, sending desktop gamers fleeing to AMD. Apple and Qualcomm pushing Intel out of multiple flagship laptops. A gaming graphics card going MIA. But its Panther Lake laptop chip, the first on its all-important 18A process, turned out excellent - and a handheld version […]

Microsoft just kicked off Build 2026 with a keynote from CEO Satya Nadella and other company leaders. As expected, it was filled with announcements, ranging from new Surface hardware to an always-on personal assistant and updates across Microsoft's in-house AI models. If you didn't watch the event live, you can catch up on all the […]

Microsoft on Tuesday took the wraps off Adaptive Spec-driven Scoring for Evaluation and Regression Testing, an open source framework for spinning up AI evaluations.

Hi everybody. We have created this app around two months and this is third version with fixed bugs. Now it is amazing app that synchronization your tasks throughs iPhone iPad Mac via iCloud with no Sign In! And in pocket you will already have a useful notes. It was a huge code work. Hours and hours. Such a pleasure. What I want to note here for you fellas, we were going from these scheme: What's really happening The self-overwrite loop User edits subtask title ↓ onChange fires → scheduleSave() → debounce 1s ↓ ...debounce fires → DataManager.save() writes todos.json to iCloud ↓ NSMetadataQuery detects file change on disk ↓ ↑ └── todosChanged() ───────┘ ↓ taskManager.loadTodos() ↓ self.todos = loadedTodos ← 💥 replaces entire array mid-edit The core problem is that NSMetadataQuery watches the file at the OS level. It has no concept of who made the change — your own app writing the file looks identical to another device syncing a change over iCloud. So every save you make triggers a reload that cancels whatever the user is currently doing. Why subtask titles are worst affected A TextField bound to $subtask.title is live — it reflects the array value character by character. The moment self.todos = loadedTodos runs, SwiftUI throws away the in-memory array and rebuilds from the freshly decoded JSON. If the save hasn't happened yet (debounce still counting down), the loaded file has the old title, and the field visually snaps back. Progress sliders have the same issue but it's less noticeable because a slider value is a Double — the snap-back is a jump rather than disappearing characters. Why it only shows up on real devices The simulator runs everything on the same Mac so iCloud writes are near-instant and the race window is tiny. On a real device the file system is slower and iCloud sync adds latency, making the timing gap between "user is editing" and "reload fires" much more visible. The three fixes needed Fix 1 — Ignore self-triggered reloads in DataManager Track a isS

Microsoft claimed last year that it had made a key breakthrough in quantum computing with Majorana 1, the company's first quantum processor. While physicists were immediately skeptical of Microsoft's claims, the software giant is announcing Majorana 2 today, the next generation of its topological quantum chip. Majorana 2 contains qubits, a unit of information in […]

Microsoft announced a bunch of new in-house AI models at Build 2026, including a new "flagship" model: MAI-Thinking-1. It's an ambitious step into model development for Microsoft, which introduced its initial in-house models last year - before then, it had relied on OpenAI's models. The two companies recently renegotiated their deal to loosen ties. According […]

Launched at Build, Microsoft Scout is a new AI assistant meant to bring the power and flexibility of OpenClaw into the Microsoft 365 system.

Microsoft’s OpenClaw-style agent appears in Teams, just like a human colleague, and automates your dull office tasks.

The specification lets developer, compliance, and security teams define their own policies for agents to follow in portable policy files.

Much like Google, Microsoft is launching its own version of OpenClaw. Microsoft Scout is an always-on assistant that integrates into Microsoft 365 apps like Outlook, OneDrive, and Microsoft Teams, allowing businesses to assign a virtual assistant to employees to help with organizing calendars, expense reporting, email drafts, and much more. Unlike Copilot that lives inside […]

The company demoed Solara on an Echo Show-style smart display and a smart key badge.