X日报 · AI科技

We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.

Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.

We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.

🧵👇

To help accelerate neuroscience breakthroughs, we're releasing the full training code for Brain2Qwerty v1 and v2, and our partner, @bcbl_, is releasing the v1 dataset.

Learn more and explore the artifacts here: https://t.co/bFdwWdAexb

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

Introducing Nano Banana 2 Lite 🍌 and Gemini Omni Flash 🔮, our new generative media models in the Gemini API and AI Studio!

Nano Banana 2 Lite is extremely fast (<4s image) & cheap ($0.034 / 1K image).

Omni Flash is SOTA at video editing at $0.10 / sec, same as Veo 3.1 Fast! https://t.co/qDxRpqpX5E

The speed of Nano Banana 2 Lite is going to enable so many new use cases where there is a high degree of latency sensitivity, honestly feels like magic.

I also expect Omni to open up a whole new category of (video) use cases like Nano Banana itself did!

https://t.co/KTd1UHFRIb

Introducing GeneBench-Pro — testing whether models can handle the kind of judgment-heavy analysis that real-world computational biology requires.

Problems would take a human expert around 20-40 hours to complete.

GPT-5.6 Sol is a big step forward. https://t.co/JV5zztNQkk

As generative AI tools continue to evolve, we believe it's more important than ever to know what's AI-generated and what isn't. That’s why @GoogleDeepMind launched SynthID in 2023—a technology that adds a hidden digital watermark to AI content.

Here’s a summary of SynthID’s journey and where the provenance technology (the documented history and origin of digital content) is today:

— SynthID watermarking was originally built for images, but now supports video, audio, and text.

— The technology has watermarked over 100 billion images and videos, alongside 60,000 years of audio.

— You can now verify content with SynthID directly in Google Search, Gemini in Chrome, and the @GeminiApp, where it has been utilized over 50 million times.

— We’ve also adopted C2PA Content Credentials across a growing number of our generative AI tools. This includes the images and videos created within the Gemini app. So now, in addition to the SynthID watermark, you can also see where an image or video originated and how it’s been altered.

— We have open-sourced our text watermarking technology, and we are working with companies like @OpenAI, @NVIDIA, and @Apple to apply SynthID to generative media.

Let us know what you think of the tool so far!

There's a startup trying to build data centers in the ocean.

And it's INCREDIBLY fascinating:

Mass consumption of electricity and water is a growing bottleneck for data centers.

So by moving offshore, it eliminates both problems -- the ocean provides unlimited cooling, and the waves provide unlimited power.

There are also no engines, so the data centers drive themselves to their destination by using the shape of their hull to propel through waves.

Called Panthalassa.

Eventually, much of AI will converge towards intuition-guided symbolic world modeling, i.e. deep learning-guided program synthesis. It is inevitable. Symbolic modeling lets a system construct a compact, reusable, highly generalizable mental model of a problem space using minimal data.

Even right now, many workflows are morphing into LRM-guided harnessess that manipulate symbolic programs. Which is a crude, but currently-accessible form of symbolic learning.

Cross-agent feedback loops are incredibly effective -- for a reason. Check out what @leon2mcp and team at @Bloome_im are building in this space: https://t.co/9YeLjBNjsk

Bloome lets you pull Claude, ChatGPT, Gemini, and human teammates into a single shared workspace. The best feature is how your agents check each other's work. One drafts, another critiques, and another catches missing details. Human teammates can work in the same thread to keep the agents on target.

Having all your models and human coworkers in one shared context is wildly effective

After 18 months of writing, coding, and experimenting, Build a Reasoning Model (From Scratch) is
finally out!

My first copies just arrived! 📚

440 full-color pages. Inference scaling, reinforcement learning, and distillation from scratch. https://t.co/647ksI7sLc