While my TL is full of mixed opinions on this, I wanted to pen down my thoughts- - This current 'breakthrough' is not at all unexpected- AI correlated stuff and found something 'novel' based on existing discovered systems (CRISPR & Reverse Transcriptases). This is not like decoding CRISPR for the 1st time. The discovery of CRISPR in 2012 was a leap from known biological knowledge. This is not. It is finding patterns that others haven't seen. Frankly, I think this is doable even with Opus 4.8 level capability + access to the right bio tools. -I'm pretty sure a lot of other biologists might've also discovered similar systems over the last couple of months, because we've seen firsthand how easy this has become. However, they might be busy right now getting the imp data on what the systems even do, rather than just hyping up the fact that they found another system that is interesting and naming it something cool. Hopefully, that work comes out soon and we actually learn some cool things about new biology. And that will be really exciting. -The fact that they couldn't do the Mol Bio assays even though they set up a lab is just one sign of how they must've felt once they learnt about the long, grueling process that it is lol -Lastly, to all the people saying 'gene editing is disrupted now', gene editing has never been just about discoveries. It's about having the right solution to a specific problem. CRISPR was discovered in 2012 for gene editing, along with exactly how it works, and even after more than a decade and a half, we just have a few therapies that cost in the range of millions of dollars per treatment per patient. Because there are so many other things that need to be figured out before you're actually able to put that new system inside the body of a patient and actually cure them without killing them. That's where all the value lies, and that's what's gonna be actually hard. That said, we're in exciting times! We'll soon be seeing a lot more important discoveries coming up (hopefully) that actually move the needle on some really hard problems which aren't solvable now. AND I'm an AI optimist, and I'd always prefer 'discovery' news over 'AI is gonna take your job' hype.

We have released the biggest protein data collection on Hugging Face, guys! We have been working on this for more than 3 weeks now, starting from curating the raw data, doing a lot of filtering, splitting the datasets, sharding them, and doing a lot of analysis. Everything is summarized in our recent blog post.

Today, 18 months after launch, our annualized revenue crossed $1 billion. The platform now powers organizations across the Fortune 500. Enterprise adoption has grown 10x since June. More than 30 million people worldwide now use Higgsfield. Thank you to the creators and teams building with us.

first thing I had @Muse do is turn 5 years of saved recipes from IG reels into a cookbook

Jev's popularity is Jevons paradox playing out in AI ecosystem. Jev model is an interesting signal for where agentic AI architecture is heading for both consumer and enterprise use cases. A surprising amount of work inside AI agents and solutions like @atomicworkhq AI Coworkers is low entropy tasks like classify, route, score, pick a tool and check a condition. Yet we often use autoregressive LLMs built to generate language for tasks that only need a decision. Jev's architecture flips that model. For the many workloads, one-pass typed decisions can deliver incredibly lower cost and latency, with some multi-step AI workflows like browser-use showing 100x+ improvements. However, the bigger insight is Jevons paradox. Make intelligence 100x cheaper and faster, and we probably won't use 100x less of it. We'll use it 1,000x more. The future of AI agents and AI workforce will not use use just frontier LLM models for doing everything. Cheap System 1 models like Jev for low-entropy decisions. Frontier LLM models for high-entropy reasoning. Incredible launch from @typesafeai and congrats @CompleteSkeptic for reigniting AI build energy on X.💜🔥

Glad to the MDR finally come to life, a re-birth to the industry. :) I think we've struck a fine balance as a country, keeping digital payments a public utility for the vast majority of consumers, while engineering a commercial model that can sustainably fund the next phase of UPI's growth. Keeping P2P free, protecting small merchants, and introducing MDR only on select high-value merchant transactions feels like a pragmatic middle path. It preserves inclusion while creating incentives to invest in resilience, cybersecurity, innovation, and merchant acceptance. I won't be surprised if, 10 years from now, UPI is doing 10 billion transactions a day, up from about 1 billion today. The real story won't just be scale—it will be the millions of new merchants, use cases, and businesses that become part of India's digital economy because the rails remained both inclusive and sustainable.

Frequent video gamers performed on cognitive tests like non-gamers that were ~13.7 years younger

When leadership at any startup or company just keeps talking about ideas, and not of execution or outcomes - the business will eventually fail.

Love both products, but I’m starting to think @Muse is materially better than Instinct. Like how it pops up a mini browser when it hits something it can’t do. Gives the user back control instead of stalling. Thoughtful, human-centric AI design.