☕️ Microsoft launches 7 AI models to cut OpenAI reliance

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In today's Techpresso:

🧠 Microsoft launches 7 AI models to cut OpenAI reliance

🚀 SpaceX targets record $75 billion IPO raise

📞 Android adds fake AI call detection

👀 Google quietly buys code from Android developers to train AI

🤖 Microsoft launches an OpenClaw-inspired personal assistant

Plus: 🎁 16 other news you might like, 🧰 6 tools, and 📚 3 papers.

🧠 Microsoft launches 7 AI models to cut OpenAI reliance LINK

At its Build conference, Microsoft launched seven of its own AI models to lean less on OpenAI, letting it run the technology on its Azure cloud infrastructure and avoid paying third parties for the models behind its products.

The lineup is headlined by MAI-Code-1, a coding model that turns text prompts into source code inside GitHub Copilot and Visual Studio Code, and MAI-Thinking-1, a medium-sized reasoning model in private preview through Microsoft Foundry.

Cutting reliance on OpenAI matters as Microsoft has put $13 billion into the company and $5 billion into Anthropic, both of which are now moving toward IPOs while charging more for their leading proprietary models.

🚀 SpaceX targets record $75 billion IPO raise LINK

SpaceX plans to fix its IPO price at $135 per share to raise a record $75 billion, selling 555.6 million shares and aiming for a $1.75 trillion valuation, according to a source familiar with the matter.

Setting a fixed price before the roadshow is highly unusual, since companies normally publish a price range and adjust based on investor demand during bookbuilding, but no rule bans the take-it-or-leave-it approach.

The all-primary offering means proceeds go to the company for AI computing and Starlink expansion, Musk must hold his shares for 366 days, and SpaceX aims to trade on Nasdaq under "SPCX" on June 12.

📞 Android adds fake AI call detection LINK

Android is rolling out fake call detection this month to fight AI deepfake impersonation scams, arriving globally in Phone by Google on Android 12+ devices and starting with Pixel phones, switched on by default.

The feature works as a "digital handshake between devices," where a contact calling you through Phone by Google sends a silent confirmation signal that verifies the call is actually coming from their phone.

If a scammer spoofs a contact, the missing signal prompts your device to ping the real phone, and if it replies that no call is happening, you get an on-screen warning to hang up.

👀 Google quietly buys code from Android developers to train AI LINK

Google is quietly paying Android developers on the Play Store for access to the code behind their apps, using the material to train the company's AI coding tools, according to emails obtained by 404 Media.

The emails invite developers into a "confidential content offer pilot" covering both active production codebases and archived prototypes, with a non-exclusive license that lets developers keep their intellectual property and sell the same code elsewhere.

Google says the real-world code helps with product development, coding evals, and benchmarks, and the move suggests scraped web content isn't enough, echoing its $60 million Reddit deal for AI training data.

🤖 Microsoft launches an OpenClaw-inspired personal assistant LINK

Microsoft has rolled out Scout, a new always-on agentic AI assistant for the Microsoft 365 ecosystem, unveiled at its Build developer conference and built on the OpenClaw framework that took off earlier this year.

Scout runs in the cloud but works across desktop and web browser, connecting to inboxes and calendars, and ships with prepackaged skills for calendar management and drafting meeting agendas, plus user-built custom skills.

Available through Microsoft's Frontier early-adopter program and requiring a GitHub Copilot subscription, Scout includes a "policy conformance system" that continuously checks whether the agent follows set guidelines, generating an audit trail for each check.

Other news you might like

• Inside Microsoft’s Project Solara: A new platform for devices that run AI agents instead of appsLINK

• Google will allow websites to exclude themselves from AI search resultsLINK

• Microsoft’s next-gen quantum chip cuts timeline to useful quantum computingLINK

• US sanctions Iranian crypto exchange Nobitex, cites links to central bank and IRGCLINK

• ChatGPT hits 1 billion monthly users faster than any app before itLINK

• Uber caps staff use of AI coding tools after blowing its budgetLINK

• Amazon faces class action lawsuit over Ring facial recognition featureLINK

• AI models are having their iPhone moment. What’s Next?LINK

• In a surprise launch, China debuts another big rocket designed for reusabilityLINK

• Intel may be struggling to supply enough of its new chipsLINK

• Why building two data centers a week won’t fix AI’s bottleneckLINK

• Martin Scorsese becomes the latest — and most unlikely — Hollywood voice for AILINK

• Meta will reportedly let employees take 30-minute breaks from its tracking programLINK

• Companies Are Using Reddit to Manipulate ChatGPT and Google AI SearchLINK

🧰 Trending tools

InsForge Backend Branching: lets AI coding agents manage cloud infrastructure end to end, covering compute, deployment, database, auth, and model gateway via CLI.LINK

superlog: autonomously instruments your codebase with OpenTelemetry, groups incidents, and submits fix PRs directly to Slack without manual setup.LINK

Elentaria: an AI-powered B2B marketing operator that scores channels, builds weekly plans, and refines recommendations using actual revenue data.LINK

Franz 6: a desktop app that consolidates WhatsApp, Slack, Telegram, Gmail, and more into one window, reducing context-switching across messaging platforms.LINK

Spectron: a multi-model database combining documents, graphs, vectors, and relations in one ACID engine, eliminating the need for multiple separate data stores.LINK

Replicas: lets you run Claude Code or Codex agents in isolated cloud VMs, triggered from Slack, Linear, or GitHub, returning finished PRs.LINK

📚 Trending papers & reports

AI-powered computer worms can now adapt their behavior autonomously, making them harder to detect and stop than traditional malware that follows fixed, predictable patterns.LINK

AI error patterns reveal that mistakes in large language models follow a structured, predictable architecture rather than appearing randomly, suggesting errors can be systematically studied and addressed.LINK

ChartNet dataset gives AI systems a new high-quality collection of chart images paired with text, helping machines better read and understand visual data like graphs and diagrams.LINK

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