I was in Hong Kong this week for FCM Wallet Launch Event. Huge thanks to @fcmoffice , @NonsyncA and @CoinRank_io for giving me the opportunity to speak at this amazing event.
@fcmoffice wallet is currently in the internal testing phase and will soon be availableΒ onΒ appΒ stores.
What makes the $ZIG model interesting is how the different parts connect.
Capital flows into vaults and earn products, ZIG Markets generates revenue, ZIG Chain adds another revenue layer, and a portion goes back into open-market buybacks.
If adoption grows, the whole flywheel has room to scale. Thatβs the part worth watching.
Every onchain product needs capital to become a market.
ZIG Chain provides the infrastructure. ZIG Markets earns where capital reaches it, from origination, structuring and distribution.
A portion of that revenue can fund open-market $ZIG acquisitions, linking activity to our token.
What really got me is @nectarsocialai being able to pick up brand mentions directly from videos by analyzing the audio and text on screen, even when nobody tags you.
If you worked as outreacher or growther, you know how many opportunities get missed simply because you never see them in the first place. This is the kind of tool that makes a lot of sense once you understand the problem it's solving. So this is amazing product especially for the people like me
Engineers stopped writing every line of code and started directing agents.
Growth and marketing teams are in that moment now.
Meet the next chapter of Nectar: the agentic growth stack. One platform replacing 5+ legacy tools. Agents that work every message, creator outreach and creative research about your brand, and tie it all back to revenue π
One thing thatβs always bothered me with AI coding tools is having to choose between model capability, speed, cost and privacy. @Recursive_SIβs Flint feels like a step toward solving that problem. A 40B coding model built to run locally, with impressive inference speeds and open weights.
If this keeps improving, the way we build software could look very different. Worth keeping an eye on what @RichardSocher and the team build next.
Introducing Flint, @Recursive_SIβs first model: a fast local coding model built with our automated research system, the Eureka Machine.
β’ Best-in-class fast local coding
β’ Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
β’ Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
As Elon has said, until we give AI products a physical form, theyβll remain products trapped behind a screen.
Thatβs why weβre seeing more progress toward making AI tangible. I shared one example of this on my profile yesterday named @roboticscorp.
People who use AI agents for coding know how frustrating it can be when you're waiting on model responses instead of getting things done.
I think one of the more overlooked parts of inference is that generating tokens isn't just about compute. moving model data fast enough matters just as much.
that's why i like what @general_compute and @positron_ai are doing here. Atlas first, then Titan, with inference capacity available through General Compute from Q1 2027.
Using different chips for different stages of inference makes a lot of sense, especially as coding agents handle longer, more complex tasks with more model calls along the way.
Our heterogeneous cloud just got better. We're announcing a cluster scale deployment of @positron_ai as our next partner.
We believe demand for fast inference will outstrip supply for the next decade. Our bet on Positron is another step towards getting fast inference for opensource models into builders' hands.
Hardware goes live Q1 2027.
Thanks to @mitesh711, @trsohmers and the whole Positron team! We're excited for this partnership.
LFG!
AI made creating visuals easier, but somehow made everything look the same.
@anurahk and @cocoailabs are tackling the actual problem: designs built around your brand, with real assets and editable layers.
AI should make design more original, not more generic that's what i want and will definitely use it for our designs:
I launched the first image and video generation models at Google, and kept asking why all AI designs look the same.
AI has brought us the WORST era of design.
It made content creation effortless, but it also made 90% of it look painfully generic.
My co-founder and I spent 2 years building in stealth to change that.
Meet Vyb, the first AI design model that understands your taste like a designer would.
RT + comment your website URL and weβll make your first design.
I didn't think a robot's personality would matter this much to me, but here we are.
@make_robot_go and @roboticscorp are building Voxel, a droid butler designed to actually feel at home in your home. not just what it can do, but how it feels to live with it.
The little gentleman energy is a pretty good touch. finally, a robot i'd actually want around not a cassandra type :)..
When i ask the devs on our team about the biggest challenges they face, one thing that comes up often is how a design can look right in 3d but still not be buildable in the real world. complex products aren't just 3d shapes. every component needs to fit together, follow mechanical constraints, and be built in the right sequence.
that's why @skyfallai caught my attention. SKY-001 breaks complex objects down into individual components and builds them back up into a complete CAD model. the reported results are pretty wild too: 100x faster and 150x cheaper than GPT Astra on CAD and BIM tasks.
if world models can eventually help automate the process from design to production, that's a much bigger deal than just generating 3d models.
Introducing SKY-001, the first Engineering World Model.
On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested:
-> 51-125x faster
- > 150-210x cheaper
-> Higher IOU than all frontier models
SKY-001 lets you dream a physical object before you build it.
18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch).
The first βAI General Engineerβ will be the backbone for Autonomous Manufacturing.
The strongest tokenomics are backed by real business, not just hype.
@ZIGFinance has now acquired 5.37M $ZIG through revenue-driven buybacks, while expanding real-world finance partnerships and distribution:
The fourth monthly ethereum:0xb2617246d0c6c0087f18703d576831899ca94f01 acquisition cycle is complete.
A portion of ZIG Markets revenue from October funded daily open-market acquisitions totalling 1,350,000 ethereum:0xb2617246d0c6c0087f18703d576831899ca94f01, worth $68,175 at todayβs price.
Half goes to the Ecosystem Pool. The other half goes to validators, who vote on whether to retire it.
Every cycle, onchain: https://t.co/HYGegdPKRg
If you've been sitting on an idea because you didn't want to spend time or money figuring out how to build it, this is a good chance to give it a shot.
@boltdotnew is free this weekend, and there's $10K in prizes for builders too. Might as well stop overthinking it and see what you can actually achieve in 2 days;
Bolt is free for EVERYONE, all weekend long.
And you could win $10k!
Turn your idea into a product.
Submit it by Oct 20 for your shot at the prize.
Bolt Builders Week starts now π
Details here π https://t.co/vzI8Nngl7G