You unlock your phone to reply to a message. A few swipes later, you’re still there scrolling, watching, switching apps without really knowing why. Most of us don’t intend to spend hours on our phones. It just happens. The apps are fast, colorful, and always ready to pull us in during moments of boredom, stress, or habit. But quitting your phone altogether isn’t realistic, right? Modern apps are designed to be sticky & trying to resist them head-on rarely works. What does work is changing how the phone behaves. In this list, I’ve picked five apps that help you slow down, pause, and take back control without blocking everything or making you feel restricted. Some are open-source, some aren’t, but all of them are genuinely effective and built around healthier use.

A research team from Texas A&M and YouTube quietly dropped SparkVSR on GitHub. No big announcement or hype cycle. Just a repo and a paper. Everyone right now is chasing text to video. Sora, Kling, Wan, the list keeps growing. But nobody is talking about the much harder problem sitting right underneath all of it. What happens when your existing footage, your old clips, your AI generated videos, just do not look good enough? You upscale them, the AI guesses, and you get flickering textures and smeared faces with zero way to fix it. SparkVSR is the first tool I have seen that actually lets you step in and correct that.

Anthropic has been explicit about why Mythos isn't public. The model is too good at finding security flaws repeatedly, in production systems that some of the best engineers in the world have been maintaining for years. So instead of a public release, Anthropic built Project Glasswing. Around 40 organizations get controlled access. Anthropic committed $100 million in usage credits to support the effort. The list includes Apple, Google and Microsoft, companies that aren't exactly short on security talent themselves. One of those organizations is Calif, a Palo Alto cybersecurity firm. In April their researchers used techniques derived from Mythos to find two previously undocumented vulnerabilities in macOS. They chained them together into a privilege escalation exploit capable of bypassing Apple's memory integrity enforcement, the part of the system that's supposed to be completely off-limits to normal processes. Then they flew to Cupertino and handed Apple a 55-page report in person. Apple is reviewing it. Patches are expected. And Mythos just added macOS to a list that already includes a 27-year-old OpenBSD bug and multiple Linux vulnerabilities nobody had caught before.

The assumption has always been that serious AI runs on serious hardware. Your phone gets the watered-down version, good enough for a demo but not for real work. MiniCPM-V 4.6 is a direct challenge to that assumption. 1.3 billion parameters. Runs on iOS, Android, and HarmonyOS. Needs 4GB of GPU memory or 2GB on CPU via GGUF. And on the Artificial Analysis Intelligence Index it scores 13 against Qwen3.5-0.8B's score of 10 at 19x lower token cost, and against Qwen3.5-0.8B-Thinking's score of 11 at 43x lower token cost. These parts matter when it comes to a model which runs on your phone.

Mistral built Leanstral to do something most AI models don't attempt, write formal mathematical proofs that a compiler can verify as correct. Not "pretty sure this is right" correct. Mechanically, provably, no-exceptions correct. That's a narrow use case, and the audience for it is small. What nobody expected was that a model trained on IMO-level math problems and abstract algebra benchmarks would end up running against open-source codebases and finding bugs that testing and fuzzing had both missed. Five of them previously unreported on GitHub.

Meta launched a new standalone app called Forum this week, and the easiest way to describe it is: Facebook Groups trying to become Reddit. The app revolves around discussions instead of algorithmic feeds. Users can post with nicknames, follow conversations across communities, and use an AI-powered “Ask” feature that pulls answers from discussions happening in different groups. Meta says the goal is helping people see “what real people are saying, not just what’s trending.” A few years ago, this probably would have looked like another random Meta side project destined for the company’s graveyard of abandoned apps. Right now though, the timing feels more interesting. Social platforms are running into a weird problem in the AI era. Feeds are getting flooded with synthetic content, engagement bait, AI generated replies, and recommendation systems that increasingly feel detached from actual human conversation. At the same time, places built around real discussions, Reddit, Discord communities, niche forums, even group chats, suddenly feel more valuable again. And now Meta, the company that spent years optimizing social media around scale and algorithmic feeds, is building a product around smaller communities and conversation quality instead.

$0.19 vs $1.76. That's the per-task cost of running Step 3.7 Flash with Advisor Mode enabled versus Claude Opus 4.6 on SWE-Bench Verified. The Flash model scores 76.3% to Opus 4.6's 78.7%. Two percentage points of difference. Nine times cheaper to get there. For anyone building agentic coding workflows at scale that math changes the decision about which model actually belongs in production. Frontier performance has been getting cheaper for a while but this is a specific, benchmarked claim with a specific cost figure attached.