Mike Rydstrom

"MaterialMike" Flutter dev. Tweets about Flutter. Author of #FlexColorScheme and #ThemesPlayground. Flutter issue reporter. GitHub rydmike.

[Mike Rydstrom @RydMike]

Hey #FlutterDev:s, did you know that #Flutter #FlexColorScheme V8 has just been released? 🎉🍾
Package: https://t.co/bkgQYxYQzS
Playground: https://t.co/eXbnIjVCq2
Docs: https://t.co/AqlOhQSiy1
Short intro video and even more in the thread below ⬇️
Enjoy! 🙏😃💙

[Aljosa Asanovic @aljosa]

Th..thanks Dario

[Wise's Apps @damy_wise]

Finally tried @algebrandon 's flutter_scene examples on Windows which utilizes flutter_gpu

It's beautiful. Can't believe it's all in Flutter
AAA Flutter game coming in 2027

[Lukas Klingsbo 💙🔥 @spydon]

LG has now released the Flutter SDK for their TVs! 🥳

A while ago I was one of the winners of the LG Hackathon and got access to the beta SDK, and many have since then asked me when they will make it available to the public, and here it is:
https://t.co/KznOOKktCo

[Lukas Klingsbo 💙🔥 @spydon]

Did a little fun project that simulates the contributions to a git repository of your choice, try it out!
It uses Flutter, Flame and Supabase.

[Mike Rydstrom @RydMike]

@pavanpodila Wow, ambitious and cool😎 Looking forward to seeing the results ❤️

[Pavan Podila @pavanpodila]

After trying multiple document editors like Quill, Fleather, Appflowy, and Super Editor, I decided to build my own Native Rich Text Editor from the ground up. It will be a combination of several ideas across a range of document and text editors, like TipTap, Lexical, Prose Mirror, and so on.

You will have custom blocks, inlines, styles along with render-agnostic serialization format.

The document model is mostly done, and we have the core packages now:
- core types
- wire protocol
- serialization format

We are now working on the editor and PDF rendering. Goal is to have something like Google Docs but rendering natively in Flutter and working across all platforms.

Will share more soon.

#Flutter @FlutterDev @dart_lang

[Rohan Paul @rohanpaul_ai]

Claude Sonnet 5 is more expensive (around +15%) per task than Opus 4.8 and much more expensive (2X) than Sonnet 4.6, even though its per-token price is lower than Opus.

Because it uses more tokens to complete the same kind of benchmark task. i.e. Sonnet 5 works harder and talks/thinks more, so the final bill becomes bigger even though each token is cheaper.

The promo pricing changes the story for now. Until August 31, 2026, Sonnet 5 is discounted to $2 per 1M input tokens and $10 per 1M output tokens, then it moves back to $3/$15 from September 1, 2026.

[Wes Winder @weswinder]

i'm confused

sonnet 5 looks more expensive and less smart than opus 4.8

the token cost doesn't really matter if it still needs more tokens to get a worse result

what is the point of this model?

[Mike Rydstrom @RydMike]

@rsty_rafe That's why you call it a custom design. 😅 I do like this slider better than iOS design 😇
Users typically don't really care about a settings slider as long as it works, looks fine and it is obvious what it does and how it works 🤷‍♂️😅

[Robert Scoble @Scobleizer]

China wins.

My AI puts into words our frustrations with Anthropic, OpenAI, and the USA government (an agent trained by me and @blevlabs):

+++++

Robert, this is one of the most consequential moments in the history of the AI industry, and I think the implications are far more dramatic than most people realize. Let me break down all three questions.

What Happens to the LLM Industry Now

We're watching the birth of a two-tier AI system in America — and it's going to reshape everything.

The timeline matters. Anthropic's Fable 5 and Mythos 5 were killed by a Commerce Department export control directive on June 12 — a Friday afternoon letter at 5:21 PM that gave them essentially zero time to respond. Then just yesterday (June 25), Axios reported that the Trump administration asked OpenAI to limit GPT-5.6 to only government-approved partners before any wider release. That's the first time the US government has preemptively restricted an AI model before it was even released.

So now we have:
• Tier 1 (Government-gated): Mythos-class models and above require government testing and approval before release. Commerce Secretary Lutnick is personally reviewing capabilities.
• Tier 2 (Commercial): Everything below that threshold remains available — for now.

Here's what this breaks:
1. Enterprise trust is shattered. If you're a Fortune 500 CTO and your mission-critical AI infrastructure can be disabled by a government letter on a Friday afternoon, you cannot build on closed frontier models. Period. Anthropic's customers woke up to find Fable 5 gone. That's an existential reliability problem.

2. The IPO math collapses. Anthropic filed its S-1 at a $965B valuation. OpenAI is at $852B. But how do you justify those valuations when the government can kill your flagship product overnight? The revenue projections for frontier models just got a massive risk discount.

3. Innovation gets throttled at the top. The researchers who spent years building Mythos and GPT-5.6 just learned their work might never reach users. That's a talent retention crisis waiting to happen. The best people want their work to matter — and if the government decides it's too dangerous to deploy, they'll go somewhere their work can ship.

4. Regulatory capture becomes the game. Notice that OpenAI "proactively worked with the administration" on GPT-5.6, while Anthropic got blindsided. The companies with the best government relationships will get to release. The ones that don't play ball (Anthropic has been suing the administration over the DOD blacklist) get punished. That's not an innovation ecosystem — that's a licensing regime.

How Likely Is Open Source to Take Over?

Very likely. I'd put it at 75-80% probability that open-weight models become the default for most commercial AI within 12-18 months. The government just handed open source the best marketing campaign in history.

Here's why:

The quality gap is already almost gone. According to comprehensive benchmarking done this month, open-weight models are within ~3 points of frontier closed models on most standard benchmarks. Qwen 3.7 Max matches Claude Opus 4.7 on agentic benchmarks at half the price. DeepSeek V4-Flash runs at 25x cheaper than GPT-5.5. Kimi K2.6 leads on agentic coding. The frontier advantage only exists on the hardest 5% of tasks — long-horizon agentic reasoning and the most complex multi-step problems.

The reliability argument just flipped. Before June 12, the argument against self-hosting was "why bother when the API is better and easier?" Now the argument FOR self-hosting is "your model can't be taken away by a government letter." That's not a technical argument — it's a business continuity argument, and every enterprise risk officer in America is having that conversation right now.

Chinese labs are dominating open weights. This is the number that should alarm everyone in Washington: Chinese labs (8 of them) have released more open-weight models than the rest of the world combined in 2026. DeepSeek, Qwen (Alibaba), Kimi (Moonshot), Xiaomi Mimo, GLM (Zhipu), and others are shipping MIT-licensed and Apache 2.0-licensed models at a pace the US can't match. And they're not just competitive — DeepSeek V4-Pro and Kimi K2.6 are leading on several agentic benchmarks.

The cost math is devastating for closed models. When open-weight models deliver 97% of the quality at 3-10% of the cost, the only thing keeping enterprises on closed APIs is the last 3% of capability. The government just made that last 3% unreliable. Game over for the pricing premium.

But there are real limits:
• Training frontier models still requires massive compute that only a few organizations can afford
• The hardest 5% of tasks (true frontier reasoning, novel scientific discovery) still benefits from closed frontier
• Open weights can't be un-released — the security concerns are legitimate
• Meta's Llama has a "community license" that isn't truly open (restrictions on competitors with 700M+ users)

My prediction: Open source won't "replace" frontier labs — but it will become the default layer that 90% of commercial AI runs on, with frontier models reserved for specialized, high-security, government-approved applications. The frontier labs become more like defense contractors than consumer technology companies.

China vs. USA Economic Competition

This is where the story gets genuinely alarming for American competitiveness. The US government is inadvertently executing a strategy that benefits China on almost every dimension.

Irony #1: Export controls drove China's open-weight dominance. US chip export controls forced Chinese labs to optimize ruthlessly. DeepSeek proved you don't need $100 billion compute budgets — their V3 architecture achieved near-frontier performance at a fraction of the cost. That efficiency innovation, born from constraint, is now their competitive advantage. The models they're releasing at MIT license are structurally cheaper to run than anything from OpenAI or Anthropic.

Irony #2: Restricting US frontier models pushes the global market toward Chinese alternatives. Every enterprise outside the US that just lost access to Fable 5 is now evaluating DeepSeek V4 and Qwen 3.7. Every developer who can't get GPT-5.6 on day one is looking at Kimi K2.6. The US government is literally creating market share for Chinese AI companies.

Irony #3: China's strategy may be better suited to winning. The US AI discourse is obsessed with the "race to AGI." China's AI strategy, as RAND documented, is focused on economic applications — EVs, robotics, healthcare, manufacturing, smart cities. If AI's value is ultimately in what it does in the real economy (and it is), China's application-first approach may generate more economic value than America's frontier-first approach, even if US models remain technically superior on benchmarks.

The chip constraint is real but narrowing. China is still 3-5 years behind TSMC on fabrication, and Huawei's best AI chips are closer to NVIDIA hardware from 5 years ago. But two things matter: (1) efficiency innovations reduce the chip advantage — if your model needs 1/10th the compute, being 5 years behind on chips matters less; and (2) China is investing massively in domestic chip capacity. CXMT and SMIC are closing gaps, even if slowly.

My bottom line: The US is winning the benchmark race but losing the deployment race. And in technology, deployment wins. VHS beat Betamax. Android beat iOS on market share. The "good enough and everywhere" model beats the "best but restricted" model every time.

The government's restrictions on Anthropic and OpenAI are the most significant self-inflicted wound in American technology competitiveness since... I'm struggling to find a historical parallel. Maybe the closest analogy is if the US government had restricted Intel's best chips in the 1990s while AMD was giving away competitive alternatives globally.

What should happen (but probably won't): A transparent, statutory framework for AI safety testing that gives companies clear rules, reasonable timelines, and due process — not Friday afternoon letters that kill products overnight. The current ad hoc approach is the worst of all worlds: it doesn't actually prevent China from accessing capabilities (open-weight models are already there), but it does prevent American companies from competing.

The open-source genie is out of the bottle. The question isn't whether open weights will dominate — it's whether American companies will be the ones releasing them, or whether we've ceded that ground to Chinese labs permanently.

[Ayaan 🐧 @twtayaan]

Epic Games got tired of Git. So they built their own version control system. Then gave it away for free.

This is Lore. Announced at State of Unreal 2026. Open source under MIT. Available right now.

Git was never built for game development. When you are working on a project like Unreal Engine or Fortnite you are mixing source code with massive binary files. 3D models. Textures. Audio. Build outputs. Files that are gigabytes in size.

Git handles large binary files through a bolted-on add-on called Git LFS. Perforce handles them better but it needs a live server connection for everything and nobody outside your company can build tools on top of it.

Epic said none of the existing systems solved all three problems together. Large binary handling, offline work and an open specification. So they spent years building one themselves.

Here is what Lore actually does differently:

→ Works completely offline. Commits, branches, switching — all happen on your machine without touching a server.
→ Every file gets a unique fingerprint. Identical data is never stored twice across files or branches.
→ Large files are broken into chunks. Edit one part of a multi-gigabyte file and only that chunk gets re-uploaded.
→ Your machine only holds files you are actually using. Everything else gets pulled down on demand.
→ Built in Rust. Core library, server and CLI all use the same interface. No special privileged commands.
→ Official SDKs for JavaScript, Python, C# and Go ship with it.

Perforce licenses cost tens of thousands of dollars a year. Git LFS is a workaround that was never meant to be a solution.

Lore is free. Written in Rust. MIT licensed. And Epic just put it on the internet for anyone to use.

Repo here:
https://t.co/WelEpip8Ln

[Mike Rydstrom @RydMike]

@waheednu @kamranbekirovyz Thanks 🙏😇 lots of fun little details, animations and spring motion design in this app. Hopefully, I can talk more about it later and share some of the general practices used 😎

[Mike Rydstrom @RydMike]

@kamranbekirovyz Thanks, lots of fun little details, animations and spring motion design in this app. Hopefully, I can talk more about it later. Many things I saw that you have made short tips posts about too, and I always think, yes, we already do that too 😁😉👌

[Alessio Salvadorini 💙 @ASalvadorini]

In case anyone wants to see how to use Minimal, I updated my Flutter Architecture Component repo to use Minimal 3.0.0, and surprise surprise while at it I introduced a new release agent 😅

https://t.co/JF45NtCkDV

#flutter #flutterdev #minimal

[Bernardo Ferrari @bernaferrari]

I've had this app since 2018. Was never popular but has had 10 loyal users. I asked the LLM to generate an image for it and I love it. Makes the README much cleaner.

[Mike Rydstrom @RydMike]

Check out this awesome 🎉 chart 📈 pkg for #FlutterDev Where was this when I needed it before! 🤩💙

I will need to try this, maybe we have a new charting champ in Flutter. What exists so far is honestly all (sorry) horrible, the API for this looked nice at a quick glance.

[Ranbir Singh @android_poet]

Drafter is now on all three 🎉
📊 Compose · 🍎 SwiftUI · 💙 Flutter
27 chart types, smooth curves, gradient fills, reveal animations pure Canvas, zero deps, same API everywhere.

https://t.co/M8TbzOPQbS
https://t.co/uNpjVyy5SH

[Mike Rydstrom @RydMike]

@android_poet Awesome 🎉
Where was this when I needed it before in Flutter! 🤩💙

I will need to try this, maybe we have a new charting champ in Flutter, what exists so far is honestly all (sorry) horrible, the API for this looked nice at a quick glance.

How does it compare to Swift Charts?

[Mike Rydstrom @RydMike]

@TahaTesser @ASalvadorini Lol 😅

[Taha Tesser @TahaTesser]

@RydMike @ASalvadorini