Starlink's latest V5 residential dish doesn't boost data speeds but offers several improvements.

OpenAI wants its first AI device to feel like the physical manifestation of ChatGPT, Bloomberg says.

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SpaceX's latest residential dish - the Starlink V5 - is now available in "select areas." It's notably smaller and lighter than the V4 dish with improved power efficiency. It'll be available in more places as SpaceX ramps up production to meet global demand. The company notes that Starlink V5 is not intended for in-motion use […]

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On 9 July 2026 the head of Windows published a post about AI-powered vulnerability discovery. One line in it was a warning to customers: "As AI helps defenders discover more issues, customers will see a higher volume of security updates included in each security release." It does not say how much higher. The post runs about 1400 words and contains no numbers at all. Five days later Microsoft shipped the July package: 1150 CVEs. The number Microsoft would not put in the blog post is sitting in Microsoft's own API. The Security Update Guide publishes every monthly package as machine-readable CVRF, acknowledgments included, no key required. So I pulled twelve months of it and did the arithmetic. What the data says I sampled eight months before the ramp and four after it. Month CVEs Month CVEs 2024-07 454 2026-04 737 2025-01 343 2026-05 991 2025-04 374 2026-06 1281 2025-07 527 2026-07 1150 2025-10 427 2026-01 310 2026-02 169 2026-03 460 The eight pre-ramp months average 383 CVEs. July 2026 is 1150, so the package is 3,0 times the old normal. The baseline broke in April and peaked in June at 1281. April to July inclusive is 4159 CVEs. At the old rate that is 10,9 months of output, delivered in four. The number I am not going to use February 2026 had 169 CVEs. It is the lowest month in two years, less than half the baseline. Divide July by February and you get 6,8 times, which is a much better number for a headline. I am not using it, because choosing your denominator is how honest people produce dishonest numbers. February is an outlier, and the only reason to anchor to it is that it flatters the story. The real multiplier is 3,0. It does not need help. It is not noise The obvious objection is that volume without quality is just a bigger pile. If AI were generating low-value findings that got patched anyway, the severity distribution would sag. It did the opposite. Measure 2025-07 2026-07 CVEs 527 1150 CVSS median 6,5 7,5 CVSS mean 6,47 7,26 CVSS 7,0 and above 48,0 % 71,

Build Firebase AI Logic with Antigravity CLI Note: Google Cloud credits are provided for this project. In this blog post, I demonstrate how to use the Antigravity CLI (an agentic AI assistant integrating directly with development workflows via skills and servers) to build an image analysis demo using Angular, the Firebase Hybrid & On-device Inference Web SDK, and Gemini models. Users upload an image and use a Gemini model to analyze it to generate a few alternative texts, tags, recommendations, and CSS tips to enhance the image quality. When the demo is running in Chrome 148+, the Hybrid & On-device SDK leverages the Prompt API of the on-device Gemini Nano model to perform the image-to-text tasks, and the token usage is 0. When other browsers, such as Safari or Firefox, execute the same tasks, the SDK falls back to Cloud AI (Gemini 3.5 Flash model), which consumes tokens. Next, I describe how to install the skills in my Angular project and register the Angular and Stitch MCP servers in the Antigravity CLI to develop the infrastructure, services, and UI design of my demo. 1. Workflow This is my entire workflow from implementing features, generating UI screens, and mapping the screens to Angular components. 2. Skills I installed the grill-with-docs , angular , and firebase skills in my project for the following reasons: grill-with-docs: Conduct a rigid Q&A session to generate a specification for a feature, refactor, or critical fix. AI is responsible for performing thorough analysis, and putting in more efforts to generate code to achieve the task. domain-modeling: The skill is referenced in the SKILL.md of the grill-with-docs skill, so a copy of it is required. code-review: Spawn two sub-agents to review changes to detect code smells and verify that the changes align with the specification. angular: Provide the best practices of modern Angular architecture, such as using signals and signal forms. firebase: Provide the skills for Firebase AI Logic, Firebase Remote, et

Twelve pricing tabs open. Neon, Hetzner, Supabase, Prisma, Scaleway, OVH. My database is half a gigabyte. I was comparing ten-terabyte price curves. At some point this week I typed the words "I am super lost here" about my own infrastructure. I advise companies on this exact class of decision. That sentence still came out of my hands. If you have ever spent an evening deep in provider pricing pages for a workload that fits on a USB stick from 2009, this one is for you. All numbers below come from the live pricing pages as of July 2026. Rates move, so verify before you commit. Three fears, all pointed at the wrong layers I went in worried about getting attacked, running out of space, and being locked in. All three dissolved under ten minutes of honest reading. DDoS lands on the website edge, not the database. My site already sits behind Cloudflare and Vercel, and a database is never publicly exposed. Only the app talks to it. Whichever provider I picked, that attack surface stayed identical. Here is the shape of the stack, and where each fear lives. MANAGED (what I run today) visitors ──> Cloudflare edge ──> Vercel app ──> managed Postgres [DDoS absorbed] [stateless] [never public, app-only access, provider patches, provider backups, provider on-call] SELF-HOSTED (the alternative I priced) visitors ──> Cloudflare edge ──> Vercel app ──> Hetzner CAX11 [DDoS absorbed] [stateless] [Postgres :5432 firewalled to app, SSH hardened, fail2ban + auto- patching = MINE] │ pg_dump every 6h ▼ encrypted ────> Cloudflare R2 [off-site copies] Same edge, same app, same attack surface. Everything in the right-hand box is what changes owners. Storage was a rounding error. My data is 0.5 GB. Even the cheapest self-hosted box includes 40 GB, eighty times headroom before the first extra cent. Lock-in was a phantom too. Managed Postgres is still stock Postgres. Exiting means a dump, a restore, and one connection string change in the deployment environment. Minutes of cutover, no rewrite an

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Body: ​Hi everyone! 👋 ​I am excited to share my latest project, LingoBridge-AI, which I have been building to solve a critical problem in rural healthcare. ​The Problem 🩺 ​In many rural areas, patients receive medical reports that are complex and filled with technical jargon. Due to this, they often struggle to understand their own health conditions, which leads to confusion and delayed medical care. ​The Solution: LingoBridge-AI 💡 ​I developed LingoBridge-AI, an AI-powered tool designed to: ​Simplify complex medical reports into easy-to-understand language. ​Translate information into local languages to ensure better accessibility for patients. ​Bridge the gap between healthcare providers and patients who have limited medical literacy. ​Tech Stack 🛠️ ​Built using Python and AI frameworks. ​Focuses on accuracy, simplicity, and user-friendly output. ​Check it out! 💻 ​You can view the source code and documentation here: 👉 [ https://github.com/cherukuriLakshmi/LingoBridge-AI ] ​I am still working on improving this, and I would love to get some feedback from this amazing community! If you have any suggestions on how to improve the AI or the user experience, please let me know in the comments below. ​Thanks for your support! ​Tags (Add these at the bottom): ai #healthtech #opensource #python #beginners

The first sign something was wrong wasn't a crash. It was a pattern. blockly-platform was the first real thing I built with Claude Code end to end — a Blockly-based platform for university programming exercises, driven entirely through Claude Code's Telegram channel. No editor open, no repo checked out on my machine, just a chat thread. I'd describe what I wanted, Claude Code would build it on a box I never looked at directly, and I'd judge the result by clicking around the deployed app. On March 22nd, the home page came up empty. GET /api/exercises/published was returning 403. I said so in the chat; a few messages later, Claude Code said it was fixed — the endpoint hadn't been added to Spring Security's permitAll() list. I moved on, tried the category filter. Also empty, also 403, also missing from the same permitAll() list — same file, same class of fix, different line. Then the exercise detail page. Same story, third time, same day. Three days later, the like button stopped working — root cause, again: POST /api/exercises/*/like had never been whitelisted either. Four times, one file, one recurring gap. None of these were hard bugs. Each one, in isolation, is a one-line fix a competent engineer makes without thinking twice. What bothered me, once I noticed the pattern, was that I hadn't noticed it as it happened. I had no diff to scroll through, no file to glance at and think "wait, didn't we just fix this exact class of thing twice already?" I had a chat log and a live app to poke at. The fourth fix looked, from where I sat, exactly like the first: a message telling me it was resolved. That was the moment I started to suspect the problem wasn't the model. It was that nobody — not the model, not me — had anything to look at. Why chat-only vibe coding breaks down Here's what makes that pattern more interesting than "the AI made a mistake": every one of those four fixes was correct. Claude Code read the error, found the missing permitAll() entry, added it, and move