lower

I follow back 💯💯💯💯💯

[Kuria | AI @I_amShiti]

Inworld’s docs are clearly built for coding agents, not just humans.

Point Claude Code or Cursor at the URL and you can go from docs to a working TTS-2 prototype without having to read through everything yourself.

This is what developer documentation should look like in the agent era.

More vendors should be doing this.

https://t.co/UIztrzIpnT

[Chioma Chukwurah ⚛️🥷⚔️ @ChiomaChukwura2]

One thing I value most in an app is simplicity.

With @CronosApp, the experience feels intuitive from the moment you open it. No long tutorials, no unnecessary complexity,
just a clean interface that lets you explore and understand things as you go.

Good UX should feel effortless.

Curious to see what’s coming? Join the waitlist at

@CronosApp

[Mercyjoy @Mercyjoy674091]

Web3’s future belongs to those who build, contribute, and create real value. Social Mining can turn participation into skills, opportunities, and a pathway for the next generation of builders. 🚀 https://t.co/WYLpDQ3yRq

[Deborah @n__deborah]

A 44 on an aggregate index is useful context.

The more revealing split is @Apodex_AI at 1,348 Elo on GDPval-AA v2 and 70% on Terminal-Bench v2.1.

That reads less like a general-purpose leaderboard flex and more like a model tuned for work that actually has to get done.

[Dunamis @duna_mis1]

https://t.co/1gsjiEi7Ex

[BheeCrush | Web3 Content @bheecrush]

If you’re looking to use a decentralized exchange without lags and slippage, @AlphaX_DEX is your best bet.

You also get to enjoy strong on-chain security, zero fees, and there is no KYC needed.

Learn more about AlphaX in the video below 👇

#AlphaX

[Crypto DeFi 𝕏 @Crypt0_DeFi]

The crypto market has gone through a serious reset.

BTC ETF outflows lasted four straight months, DeFi took major hits from exploits, and the meme sector dropped 82%.

But the picture is starting to change.

Over the past 30 days:

BTC: +23.9%
ETH: +30.4%
SOL: +44.3%

ETF flows have reversed, DeFi is showing signs of stabilization, and capital is gradually rotating back into higher-risk assets.

That looks more like a structural recovery than just a short-term bounce.

Still, I’m not calling it a full bull market yet.

We need to see stronger confirmation, sustained inflows and broader market participation before getting too confident.

For now, the recovery is worth watching closely.

[M @mala_sarkady]

My man noticed something by my door and now he’s acting weird 😭

[FashionHive @Fashion_hivee]

Hislon 1928😍

Sapphire crystal, 316L stainless steel, butterfly clasp.

Luxury without the huge price tag ₦225,000.⌚

HislonWatch LuxuryWatches WatchesForMen MensFashion DesignerWatches Uber Enzo Monaco #Goka Bolt Chelsea Lagride Akara Adingra Balogun Kayda #TelexZikoraMovie

[OVA TV MEDIA📡 @ovatvofficial]

Nigerian Man causes scene at a Car Dealership in China After Dealer Tries to Increase Price of Car He Already Paid For.

[Winter Johnson @Wagmi100xx]

The gap between renting forever and owning a home usually comes down to one thing whether your money is moving toward something or just moving. @MegPrimePay puts your rent on the right side of that equation. Stack base:0x6e02f4a1631379a49e8b7e222cfa6bf913b05e89 with every payment up to $800/month at a Max Rewards Property, or up to $100/month on the rent you're already paying wherever you live. It's a small shift with a real destination every rent payment becomes one more step toward homeownership instead of another expense that disappears, Start stacking your rewards today.

[Chioma Chukwurah ⚛️🥷⚔️ @ChiomaChukwura2]

One feature I’m particularly interested in on @CronosApp is the Feed.

Instead of scrolling through endless market updates and random opportunities, you can shape your Feed around the assets and trading activity you actually care about.

Think of it like having a personalized market dashboard rather than opening a platform and being hit with everything at once.

The more relevant the information, the easier it becomes to focus on what matters.

For me, that’s a small UX decision that can make a big difference.

Choose your interests. Build your Feed. Discover what matters.

👉 Join the waitlist: https://t.co/I6QmVjV59Y

[𝙹𝚘𝚜𝚊𝚒𝚑 ∀𝚔𝚒𝚗𝚕𝚘𝚢𝚎 𝙹𝚗𝚛 @Akinloyejosiah]

Physics has a long history of making the complex tractable. When a system has too many interacting parts to solve exactly — a gas, a magnet, the early universe — physicists reach for mean-field theory: replace all the individual interactions with an average, and work from there. It's an approximation, but often a powerful one.

A research group at Princeton just applied that framework to something considerably more complicated: networks of biological neurons in the brain.

The connection is more direct than it might seem. At any given moment, each neuron in a network is either firing or not — an action potential either travels along the axon or it doesn't.

That binary state maps naturally onto the Ising model, one of the most studied frameworks in statistical physics, where each particle carries a spin that is either up or down. A network of thousands of neurons is, in the language of physics, an Ising model.

The problem is that standard mean-field theory — which works by assuming each neuron interacts only with the average activity of the whole network — fails to accurately describe what real neural populations actually do. Fluctuations matter. The distribution of activity patterns across the network carries information that the simple average throws away.

Luca Di Carlo and colleagues, publishing in Physical Review Letters, found that an extended version of mean-field theory could succeed where the simpler version couldn't. The approach is built around finding the maximum-entropy distribution of neural activity states — the probability distribution that is as unstructured as possible while still matching what's actually observed in the data.

This leads to a Boltzmann distribution, where the probability of any particular network state depends on its total energy. From there, predictions can be made and tested without introducing additional assumptions.

The team validated the model against recordings from more than 1,000 networked mouse brain neurons. Where standard mean-field models failed to reproduce the observed variations in collective activity, the extended model matched the statistics.

What makes this approach particularly interesting is that it doesn't depend on knowing the specific interactions between individual neurons — only on the distribution of activity patterns at any given time. That's a meaningful practical advantage when working with biological data, where the wiring diagram of a real neural circuit is rarely fully known.

The current model captures time-averaged statistics. Extending it to describe how activity patterns evolve dynamically during behaviour is the natural next step — and likely where the most interesting neuroscience questions live.

Physics has always been good at finding structure in apparent complexity. Turns out the brain is no exception.

#Neuroscience #NeuralNetworks

[Daily Spill Naija ☕️ @DailySpillNaija]

Osasu dey underrated for this content thing 😂

[Silver @Una_onchain]

https://t.co/w1waIEaN8P

[Bounteah 🧬 @Jhoesaya]

September is stacked and if you're trading anything right now this is the month to actually pay attention.

NFP and unemployment data land early, then CPI and PPI follow with the freshest inflation readings we'll have had in a while.

The big one is September 16 when the Fed announces its rate decision alongside a Dot Plot, which means we get actual projections from the whole committee rather than just a yes or no on rates.

On the corporate side Apple's fall launch, Tesla Cybercab, Meta Connect and Alibaba Apsara are all happening before the month is out.

BingX TradFi covers gold, oil, stocks, indices and forex if you want to trade the reaction as things land.

[Aakash Gupta @aakashgupta]

Wonderful just raised $550M at a $5B valuation, 20 months after launch. The number that matters more is 35.

That's how many countries they run local deployment teams in. Most enterprise AI companies sell from one office and ship a platform. Wonderful puts forward deployed engineering pods on the ground in each market, co-building with the customer's own team until the customer can build on the platform themselves.

The reason that model exists is the gap everyone in enterprise AI keeps hitting. Models are far ahead of what companies have actually operationalized, and the bottleneck was never model quality. It's the last mile inside a bank in Frankfurt or a telco in Manila, where the workflows, the systems, and the regulators are all local.

Their thesis is that AI should run horizontally across whole workflows rather than department by department. Hyperlocal is how you earn the right to do that. You can't rewire an entire enterprise's workflow from a sales deck.

Insight leading and Salesforce joining reads like a bet that this is a platform with a services on-ramp, not a services company with a platform.

Hundreds of agents in production across a dozen verticals in 20 months is the proof point. Whether the transfer part holds at 35 markets is what the $550M gets tested on.

The enterprise AI race was supposed to be won by whoever had the best model. It's starting to look like it goes to whoever is actually in the building.

[Rex σ @Rexacp]

🤩🇪🇸 Time to step out of the shadow!

Real Sociedad mixed results. Celta cautious and defense-first. Who finds their rhythm and takes the 3 points?

Make your prediction on 1xBet!
Code: RXC1XB
👉 https://t.co/Hop1sVw8me

#RealSociedad #Celta #LaLiga #1xBet

[Wesley @Ambani_Wessley]

https://t.co/4ON7vopj5I

[Sammy @Sammyt1r]

Moments besties never forgets🥶🎥👇