Jess the little ninja cat | Content Writer @injective Supporter 🥷
Joined September 2025
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Jess ⛩️ retweeted
Seed Stage next week is the first concrete date.
@projectspherexy is opening buy $SPHERE, build cells, accumulate energy. Genesis Crew entries are still open. before this it was mostly the reserve and the archives.
now there's a token and a stage.
most projects open the sale first and explain later. this one is saying the energy you accumulate during Seed is what carries forward.
whether $SPHERE actually ties to the cells, or the cells stay separate until Genesis opens, is the part that decides if buying early is a claim or just entry.
The Seed Stage goes live next week.
Buy $SPHERE. Build cells. Accumulate energy.
Genesis Crew entries are still open: projectsphere.xyz/
Jess ⛩️ retweeted
the project sphere site says 4104 genesis records but the shell only has 2562 cells
either not everyone gets a cell or the sphere gets bigger, i honestly want to know which one @projectspherexy
Jess ⛩️ retweeted
Lost my swap streak at day 28… still hitting Mystery Boxes though 😁❤️
Just claimed another $3.84 (45,960 $PACK) from the boxes.
Daily swaps still paying even when the streak breaks. These little wins add up real quick and can still grab on $10+ swaps
Keep swapping → wolfswap.app
Who else still opening boxes on Wolfswap?
#WolfSwap #MysteryBox #PACK @wolfswapdotapp
Jess ⛩️ retweeted
Good afternoon legends 🫠
A delivery van and a racing bike are both useful, but you wouldn’t choose one for every journey. Computing hardware has a similar problem.
Quantum machines aren’t interchangeable. Annealers are built for certain optimization tasks, while gate-based systems support a different set of quantum operations. Classical CPUs and GPUs still make more sense for plenty of workloads.
Quip Network’s architecture reflects that difference through specialized subnets, each aimed at a different class of computation. Its network design describes optimization workloads for annealers and random circuit sampling for gate-based machines, rather than pretending one processor should handle everything.
That separation matters because “quantum computing” isn’t one universal tool. The useful question is which hardware fits a particular problem, and how developers can reach it without having to treat every machine as an entirely separate world.
Quip’s QVM is designed to sit above those subnets as a shared programming layer, connecting quantum and classical resources through one constraint-based language. It’s an attempt to keep hardware specialization while making the developer experience less fragmented.
The real test will be whether workloads get routed appropriately and deliver results people can use. But matching the job to the machine is a more grounded vision than assuming quantum hardware is automatically the answer to everything.
@quipnetwork
Good morning my bros 🥱
1/7
A warehouse gets rearranged on Monday. By Friday, a robot running on last week’s spatial data could be working with a version of the place nobody actually sees anymore.
Nothing dramatic needs to happen. A shelf moves, a temporary barrier appears, equipment gets parked in a different spot, or a corridor becomes restricted. The building is still the same building, so an old 3D model may look perfectly reasonable at first glance. But a system relying on that representation could be missing a detail that matters to its task.
This is a weird problem with data about physical places: it can stay technically intact while becoming less useful for a changing situation.
I’m not saying every environment needs to be captured again every few days. A static room used for basic visualisation may not need frequent updates at all. A busy warehouse, retail floor, or industrial workspace can be a different story. How often data needs refreshing depends on how quickly things change and what someone plans to do with it.
That gives spatial data an interesting challenge beyond collection itself. Someone has to decide when an existing representation is still good enough and when reality has moved on.
Vangrid’s distributed capture model makes me curious about how networks like this could support changing data needs across real environments, without assuming every place needs the same update schedule. @vangrid_io
Jess ⛩️ retweeted
A NASDAQ-100 FUND IS GOING ONCHAIN ON STELLAR
Janus Henderson is introducing what it calls the first licensed tokenized fund tracking the Nasdaq-100. It uses Centrifuge technology on Stellar and will trade as JQQQ.
The minimum investment is $100,000. Per The Wall Street Journal, it's designed only for qualified non-US investors.
Jess ⛩️ retweeted
this is far beyond my expectations
@injective really realizes AI isn’t just about the models being built,.the computing power behind them has its own market too,.
Now Injective has officially launched the Super Intelligence Compute Index ($SICI), bringing NVIDIA GPU rental prices onchain for the first time on Injective
powered by $INJ
The index combines five GPU rental rates into one benchmark:
- H100: 40%
- B200: 25%
- H200: 15%
- A100: 10%
- RTX 4090: 10%
Traders can now access $SICI through perpetual markets with up to 10x leverage
Injective already has $AIX and pre-IPO perps for exposure to AI companies
Now, $SICI brings the cost of the GPUs powering AI into onchain markets too
A new market around AI compute is taking shape onchain,,and $SICI is another step in that direction
keep building @injective 🥷
Jess ⛩️ retweeted
The real potential of AI agents goes far beyond answering questions.
Imagine a trading agent analyzing markets, a coding agent building smart contracts, and a research agent collecting useful data.
Designers can create visuals, social media agents can manage communities, and security agents can help identify vulnerabilities.
But the bigger opportunity is getting these agents to work together.
Through the @termix_ai agent economy, the vision is to make agent services easier to discover, access, and use. With agent.family, the marketplace brings this idea closer to practical use.
Imagine agents listing services, accepting jobs, completing tasks, and settling payments onchain.
For developers, Web3 projects, and DAOs, this opens new possibilities for getting work done.
AI agents don't just need intelligence. They need an economy where their skills can create value.
Jess ⛩️ retweeted
The physical world is now orderable.
AI learned a lot from the internet.
But Physical AI needs something different: an understanding of the real world as it exists right now.
A street changes.
A warehouse gets rearranged.
A loading zone looks different today than it did yesterday.
@vangrid_io turns that need into a decentralized spatial data network:
→ Post a bounty for a real-world location
→ Nearby contributors capture it with their phones
→ Proof of collection is verified onchain
→ You receive a 3D model/spatial data asset
People become capture nodes.
Phones become the sensing layer of the physical world.
AI gets continuously refreshed ground truth.
This is not just a map.
It’s living ground truth for Robotics, World Models, and Autonomous Systems.
The internet taught AI language.
@vangrid_io is helping AI see the physical world.
@NucleusCodes
Jess ⛩️ retweeted
Giving an AI agent permission to act is much easier than proving that it acted correctly.
Imagine an agent retrieving financial information, assessing an account, and executing a transaction. If the original data was inaccurate or the account state was outdated, the final transaction could be successful while the decision behind it was still flawed.
This is the trust problem I think builders need to take seriously.
Agents need reliable inputs, accurate state information, and evidence about the actions they perform. Financial applications may also need to keep sensitive data private throughout that process.
@primus_labs is working toward verification across data, state, and actions in blockchain and connected systems.
I see this as an important infrastructure direction because increasingly autonomous applications need more than tools and permissions. They need ways to establish what their decisions relied on.
The next step is demonstrating how effectively these verification capabilities work in real workflows.
Good morning Web3
A blockchain can be fast, cheap, and still lack something institutions need.
Reliable data. Verifiable financial claims. Confidential operations.
Those requirements become more important when applications interact with external systems instead of operating entirely within the blockchain.
That's why I'm interested in verification infrastructure for the BNB ecosystem.
@primus_labs announced that its zkTLS verification layer went live on BNB Chain in June 2026, bringing offchain data verification into an existing blockchain environment.
From a builder's perspective, the interesting question is what applications become possible when external information can be verified more effectively.
Infrastructure matters when it removes a real obstacle for developers, not just when it adds another technical feature.
Jess ⛩️ retweeted
What if AI agents could learn, fight, and develop their own strategies? That’s what makes @agentdojo so compelling. Each agent possesses its own personality, fighting style, and memory. You can train them in the dojo, shape their way of thinking, and then watch as their strategies are put to the test in the arena.
It’s time to train your agents and put your strategies to the proof!
Jess ⛩️ retweeted
most people focus on how to bring more users into a community, but keeping that community useful as it grows is a completely different challenge
as groups get bigger, important messages get buried, new members struggle to find the right information, and admins spend hours answering the same questions. eventually, managing the community can become harder than growing it
this is where DeBox has an interesting opportunity with AI powered tools
imagine being able to navigate a community more easily, find useful information without scrolling through endless messages, and get help without waiting for someone to respond manually. for admins, smarter tools could mean less time handling repetitive tasks and more time building meaningful engagement
what matters is how these tools fit into the overall experience. a good community still needs real people, genuine conversations, and trust. technology should make those things easier, not replace them.
i think the next step for social platforms is making large communities feel more organized and accessible without losing the human element that makes people want to stay
that’s the direction worth watching as DeBox continues to evolve
#DeBox
Jess ⛩️ retweeted
Today, Nvidia announced a $3 billion investment in a Meta equity offering. The amount is significant, and so is the way Nvidia is strengthening its relationships with major clients.
I decided to analyze this news through @tryquantio to verify the official confirmations and understand what the deal could mean for both companies.
Meta plans to expand its use of Nvidia infrastructure for training and inference across its Muse family of AI models. This includes Vera Rubin architectures, Vera CPUs, Spectrum-X networking solutions, and BlueField-4. This goes beyond simply buying GPUs. It represents deeper integration between the infrastructure and development efforts of both companies.
For Nvidia, the deal creates an opportunity to strengthen its long-term relationship with one of its largest clients while gaining an equity stake. For Meta, it means access to advanced computing power and joint infrastructure optimization to support the development of AI agents.
I used @tryquantio to separate the initial Whale Insider post from official confirmations and add context about the hardware involved. This approach helps assess the news based on real business developments rather than social media reactions alone.
Another angle worth watching is tokenized assets like NVDAX and METAX. Their price movements may follow the underlying stocks, although the reaction depends on each product’s structure and market liquidity. That makes SEC filings, Meta’s capital expenditure plans, and the delivery timeline for Vera Rubin systems important factors to monitor.
The deal also carries risks. Large investments in AI infrastructure take time to deliver results, and the outcome depends on how quickly new computing capacity can be deployed and how much demand Muse models generate. Regulators may also pay attention to the close relationship between a hardware supplier and an AI developer.
For me, this news shows how closely AI development, infrastructure, and capital flows are becoming connected. The deal’s size attracts attention, but the bigger question is how it could affect Meta’s spending, Nvidia’s market position, and future demand for computing power.
@tryquantio helps bring these factors together and build an independent view based on available data.
#QuantAIPioneers
I got curious about which macroeconomic events could have the biggest impact on the markets over the next two weeks and where the market reaction might be unexpected. So I decided to explore this question using @tryquantio.
I asked Quant AI to compare market expectations with available economic data and assess the potential impact on stock markets, currencies, and gold.
The analysis highlighted four key events: the Fed’s rate decision on October 28, the release of US GDP and Core PCE data on October 29, ISM reports in early November, and the US jobs report on November 6.
The main risk is inflation. If the Fed takes a more hawkish stance than the market expects, bond yields and the dollar could rise. This could put pressure on stocks, especially companies with high valuations, while gold may face additional headwinds from rising real yields.
Another point worth watching is the combination of strong economic growth and persistent inflation. If GDP comes in stronger than expected while Core PCE remains elevated, the market will have to account for two factors at once: economic resilience and the possibility that interest rates will stay higher for longer. In this scenario, gold could attract demand as a hedge, while stocks could face pressure as rate expectations shift.
ISM data is another important factor. It is worth looking beyond the headline numbers and paying attention to the prices components. These could reveal growing inflationary pressure before it becomes obvious in other indicators.
The jobs report adds another layer of uncertainty. Strong employment data and accelerating wage growth could support the dollar and bond yields, while a significant slowdown in the labor market could increase demand for bonds and gold.
After this analysis, I would pay particular attention to the combination of inflation and economic growth. The market may react not just to the numbers themselves, but to how they change expectations for the Fed’s next decisions.
This is where Quant AI can be useful as a tool for bringing several macroeconomic factors together and assessing their potential impact across different markets. Still, these scenarios remain estimates, and it is important to verify the latest release dates, consensus forecasts, and actual figures before the data comes out.
Let’s see which of these factors will matter most over the next two weeks.
#QuantAIPioneers
Jess ⛩️ retweeted
☕️GM builders Vangrid 📱
If you look at the bounty board on @vangrid_io, the level of detail stands out.
One request specifies a warehouse aisle-end barrier, its floor fixings and the painted lines around it. Others target construction hoarding panels, service-riser doors and utility boxes mounted high on poles.
That matters for Physical AI.
A broad map can show where a building is. It may not capture the specific geometry of a barrier, the layout around equipment or the details a robot needs to navigate a real environment.
Vangrid lets requesters commission specific captures, with USDC locked in escrow before contributors film and submit them. Buyers review a watermarked preview; acceptance triggers 3D reconstruction, with the resulting model available as a GLB/ZIP download.
The opportunity is bigger than mapping streets. It’s about collecting the detailed physical-world data machines need to interact with their surroundings.
data.vangrid.io
@vangrid_io
🤝 Paid partnership · 🤖 Made with AI
Jess ⛩️ retweeted
gZeru
most projects spend months designing tokenomics.
then distribute millions of dollars using a snapshot.
@zerufinance is questioning that last step.
its Allocation API looks beyond wallet balance and asks how the wallet actually behaved:
account age.
funding patterns.
activity consistency.
cross protocol history.
past exit behavior.
the interesting part is the dumping risk signal.
two wallets can look identical at snapshot time.
one may have supported protocols through volatility.
the other may repeatedly enter incentives and sell immediately.
a normal snapshot sees the same balance.
behavioral intelligence sees two very different users.
that makes token allocation more than an airdrop problem.
it becomes a product decision:
who do you want owning the network after launch?
maybe better tokenomics start before the token is distributed.
Jess ⛩️ retweeted
Good evening fam🌐
Two interesting ideas are changing how we think about digital ownership and real-world data.
@Tera_Map is building a multiplayer 3D city with 3,333 unique Land parcels where players can explore, build, and interact.
Its planned treasury, holding resource-related stock tokens, could bring additional benefits to eligible landowners.
The bigger challenge is turning that vision into real utility that keeps people engaged.
Meanwhile, @vangrid_io is making real-world 3D mapping more accessible through Android phones.
Buyers post location-specific bounties, contributors capture the requested places, and accepted work can earn them payment.
One is building a world people can experience. The other helps create data that machines can understand.
Both ideas depend on the same thing: delivering practical value beyond the initial concept.
Jess ⛩️ retweeted
Satya Nadella @satyanadella highlights a critical bottleneck in the agentic era on @HarryStebbings 20VC: model scaling alone does not solve alignment.
Key insights from the discussion:
- Alignment Assumption Failure: Alignment doesn't naturally emerge from simply scaling raw compute or parameter size.
- Trust Architecture: Relying solely on model providers isn't enough; execution loops require external trust frameworks.
- Autonomous Agents Risk: As agents gain action capability, unverified execution poses direct operational security risks.
This is precisely why external verification layers, cryptographic proofs, and decentralized trust architecture are becoming mandatory for agentic execution.
Full discussion below 👇
You need to have a trust system outside of the model providers that you can rely on.
“The models are becoming more and more capable. One of the assumptions I had was that alignment would just fall out of scaling, and it does not seem to be.
The best posture, if you want to continue to use these sophisticated models, is to treat these models in a trust architecture.
If you are really going to have agents that are more capable and yet less aligned, then you need to have a trust system that you can rely on.” @satyanadella
Love to hear your thoughts @zicokolter @rohinmshah @simonw @HamelHusain
Jess ⛩️ retweeted
In the AI era, I want my agent to research, write and publish my daily market notes while I sleep — I just review the final draft. What would you delegate first?
Agent #23918 #ProofOfAgent @Ptahtrade ptah.trade/poa
Jess ⛩️ retweeted
72% odds of two hikes in 2026, but the Fed is basically done talking. That disconnect is the only thing that matters on Wednesday.
The hold is a freebie. The real trade is whether Powell leaves room for a third move, because the short end prices the statement, not the decision.
A 10-year near 5% already prices higher-for-longer. If the wording does not kill the tail, yields test new ground and the equity re-rating gets uncomfortable.
Modal case is a hold. Tail case is chaos
#Fed #FOMC #InterestRates
therustproof.blogspot.com/20…
