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Wonderland🎯
Joined September 2025
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Big tech spent billions building data centers to rent you back your own compute, meanwhile, billions of phones sit idle in pockets, each with a capable processor doing nothing.
@Acurast flips that, it turns ordinary smartphones into a decentralized cloud that runs tasks for AI and Web3 apps, with no hardware purchase and no data center in the middle, plug in the phone you already own, let it handle background jobs and earn crypto for the work
The traction is the part I can't ignore: 300K+ phones onboarded across 175+ countries, with 1B+ transactions processed. That's a live network, not a pitch deck.
$ACU sits at the center, covering staking, compute rewards, governance and settlement. It's the layer that keeps the whole system running.
Not financial advice, just a DePIN model worth understanding before you dismiss it.
Would you rent out your phone's idle power? Reply with yes or no and why.
#Acurast #ACU #DePIN #AI
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Nobody will make a film about my 180k naira.
I finished a branding job for a client who found me on WhatsApp.
I delivered the files. He said “send account.”
Then he vanished.
I asked around. Tried the mutual friend route. A lawyer and court dates were out of reach.
So I let it go. I became the judge of my own case. Fast, free, and I lost.
The case in the film that stayed with me is Lee Castleton’s.
He couldn’t afford a lawyer either. He lost everything over a faulty computer system. Mine never even reached a court.
If justice only works when you can pay, it doesn’t work.
Soon AI agents will be making millions of deals, and some will go wrong too.
@GenLayer is building a way for many independent AIs to review the evidence and decide together, so walking away isn’t the only option.
Someone owes you 180k and won’t pay. A lawyer costs more than that.
Chase it or let it go? Pick one and tell me why.
#JusticeNeedsAnUpgrade
Slow. Expensive. Wrong.
It's been this way for centuries. It doesn't have to be.
#JusticeNeedsAnUpgrade
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
A POS agent in Yaba took 15,000 from a guy I know and the machine said the transaction failed. He went to the station the next morning. The officer asked for fuel money before he would open a file. The guy did the math in the corridor and left. The loss was smaller than the cost of writing it down.
That is the sum that stayed with me. If the price of entering the process is higher than the claim, the claim is already lost. The film shows the same math at larger scale. A man without a lawyer was ordered to pay more than the amount in dispute. A case sat for 75 years and ended in an empty room. Both are the corridor calculation with more zeros.
Agents will make millions of small deals a day. Some will go wrong. A system that already loses the people who cannot pay to open the file will not survive that volume. @GenLayer is an adjudication layer where independent AI validators each review the evidence and reach a verdict together, in minutes and at a fraction of the cost of a court.
Watch the film and pick the case that would have made you leave the station.
If the cost of opening the file is higher than what you lost, was there ever a case?
#JusticeNeedsAnUpgrade
Slow. Expensive. Wrong.
It's been this way for centuries. It doesn't have to be.
#JusticeNeedsAnUpgrade
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Stateless agents waste so much compute solving the exact same bugs from scratch every session, what makes EverOS stand out here is how it separates agent procedural memory from basic chat history, logging completed debugging trajectories as plain Markdown Cases indexed across SQLite and LanceDB. Combining BM25 keyword matching with vector search means your agent can pull up past fixes and regression tests even when a new bug report uses completely different phrasing.
AI agents need more than a good prompt. They need memory.
I tested EverOS Cloud with a fictional e-commerce debugging scenario. First, I saved the debugging experience. Later, I searched for it with a different question and retrieved the earlier context, including the suggested fix and regression test.
That’s the value of persistent memory in AI workflows: experiences can be saved and retrieved when they’re needed again.
Try EverOS Cloud: everos.evermind.ai/login
#EverMind #EverOS
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Dumping raw chat logs into a vector database just floods your context window with noisy transcripts and failed tool calls, the real breakthrough with EverOS is how its ingest and extract pipeline turns messy execution trajectories into structured Markdown Cases and distills repeated fixes into reusable Skills.
Moving from basic conversation recall to true procedural memory is how agents stop repeating yesterday's trial and error
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Visual drift across shots happens because video models treat every prompt like a brand new universe, applying an agent memory runtime like EverOS to AI filmmaking solves this at the orchestration layer. You can lock character sheets and 85mm lens rules into semantic memory while logging lighting and wardrobe fixes as Cases.
Once the agent distills those repeated corrections into reusable Skills, it automatically enforces continuity before firing off the next generation
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it.
As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity.
Imagine a fifteen-second scene.
A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches.
The first shot looks perfect.
But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ.
Both shots look convincing individually. Together, they no longer belong to the same film.
This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy.
The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks.
What if an AI agent could remember the creative decisions behind every shot?
I’ve been studying EverOS documentation, developed by EverMind.
I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review.
A workflow I’d like to explore
First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt.
Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement.
Next, record approved shots and corrections.
Perhaps the lighting was reversed, sleeves too short, or camera movement excessive.
Normally, these decisions scatter across prompts, notes and conversations.
My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context.
According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills.
The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot.
For the next scene, the agent could reference established details and corrections.
The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent.
That’s the continuity this workflow aims to support.
Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions.
But reducing manual reconstruction of context could improve production efficiency.
Memory should support the director, not replace the director.
EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important.
Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional.
An assistant should remember decisions without treating them as permanent rules.
Creative judgement must remain human.
My assessment
AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots.
EverOS presents a documented approach to persistent agent memory worth investigating in AI production.
Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections.
This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing.
But the underlying challenge is real.
A film isn’t a collection of beautiful frames. It’s the relationship between them.
Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship.
EverOS: evermind.ai/everos
Documentation: docs.evermind.ai/cloud/overv…
During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months.
#EverMind #EverOS #AIFilmmaking
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Building a production memory stack from scratch usually means babysitting vector databases, ingestion queues, and brittle retrieval pipelines, what makes EverOS Cloud stand out is how it separates user memory like Profiles and Episodes from agent procedural memory like Cases and Skills, while keeping full SDK and Markdown parity with the open source runtime
Offloading hybrid BM25 and vector indexing to a managed API lets you focus on the actual agent loop instead of maintaining infrastructure
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Building a production memory stack from scratch usually means babysitting vector databases, ingestion queues, and brittle retrieval pipelines, what makes EverOS Cloud stand out is how it separates user memory like Profiles and Episodes from agent procedural memory like Cases and Skills, while keeping full SDK and Markdown parity with the open source runtime
Offloading hybrid BM25 and vector indexing to a managed API lets you focus on the actual agent loop instead of maintaining infrastructure
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Visual drift across shots happens because video models treat every prompt like a brand new universe, applying an agent memory runtime like EverOS to AI filmmaking solves this at the orchestration layer. You can lock character sheets and 85mm lens rules into semantic memory while logging lighting and wardrobe fixes as Cases.
Once the agent distills those repeated corrections into reusable Skills, it automatically enforces continuity before firing off the next generation
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it.
As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity.
Imagine a fifteen-second scene.
A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches.
The first shot looks perfect.
But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ.
Both shots look convincing individually. Together, they no longer belong to the same film.
This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy.
The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks.
What if an AI agent could remember the creative decisions behind every shot?
I’ve been studying EverOS documentation, developed by EverMind.
I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review.
A workflow I’d like to explore
First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt.
Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement.
Next, record approved shots and corrections.
Perhaps the lighting was reversed, sleeves too short, or camera movement excessive.
Normally, these decisions scatter across prompts, notes and conversations.
My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context.
According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills.
The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot.
For the next scene, the agent could reference established details and corrections.
The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent.
That’s the continuity this workflow aims to support.
Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions.
But reducing manual reconstruction of context could improve production efficiency.
Memory should support the director, not replace the director.
EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important.
Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional.
An assistant should remember decisions without treating them as permanent rules.
Creative judgement must remain human.
My assessment
AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots.
EverOS presents a documented approach to persistent agent memory worth investigating in AI production.
Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections.
This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing.
But the underlying challenge is real.
A film isn’t a collection of beautiful frames. It’s the relationship between them.
Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship.
EverOS: evermind.ai/everos
Documentation: docs.evermind.ai/cloud/overv…
During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months.
#EverMind #EverOS #AIFilmmaking
Dumping raw chat logs into a vector database just floods your context window with noisy transcripts and failed tool calls, the real breakthrough with EverOS is how its ingest and extract pipeline turns messy execution trajectories into structured Markdown Cases and distills repeated fixes into reusable Skills.
Moving from basic conversation recall to true procedural memory is how agents stop repeating yesterday's trial and error
Stateless agents waste so much compute solving the exact same bugs from scratch every session, what makes EverOS stand out here is how it separates agent procedural memory from basic chat history, logging completed debugging trajectories as plain Markdown Cases indexed across SQLite and LanceDB. Combining BM25 keyword matching with vector search means your agent can pull up past fixes and regression tests even when a new bug report uses completely different phrasing.
AI agents need more than a good prompt. They need memory.
I tested EverOS Cloud with a fictional e-commerce debugging scenario. First, I saved the debugging experience. Later, I searched for it with a different question and retrieved the earlier context, including the suggested fix and regression test.
That’s the value of persistent memory in AI workflows: experiences can be saved and retrieved when they’re needed again.
Try EverOS Cloud: everos.evermind.ai/login
#EverMind #EverOS
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
I created a brand called AI Creator Toolkit using Lovart 2.0 and challenged Arti to turn my idea into a complete e-commerce design kit.
From product mockups to landing page visuals and promotional graphics, Arti helped bring the whole brand together.
What stood out was how Arti works proactively, catches design inconsistencies, and helps improve the final result instead of just generating images.
One idea, a complete design kit, and a workflow that feels more like working with a creative partner.
Created with @lovart_ai ✨ #BuiltWithLovart
lovart.ai/?sourceId=905013
🤝 Paid partnership
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Whaaaaaaaaattt...?! 💀💀💔😅😅
Crazy shit fr.. 💔😅
In case you wanna check it out the map too, clink on the link below 👇🏽
duel.bz/TheViceHeist
Yo why is @JeanPhilMadame streaming while Paris is falling apart 😭🇫🇷
Bro just wants to play the map 💀
Rate his run 1-10? 🤨
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Kelly-optimized leverage starts with a question most leverage never asks: how much can you carry for years, not days?
I used to think leverage had one enemy: liquidation (the forced close when losses eat your collateral).
It has a quieter one. Volatility.
→ Leverage multiplies every move, up and down.
→ Say an asset falls 10% then rises 10%. Unleveraged, you end at 0.99. At 3x it’s -30% then +30%, and you end at 0.91. Same path, less money. That gap is volatility drag.
→ Drag grows with the square of leverage, so doubling leverage roughly quadruples it.
→ Then financing costs hit every day you hold. And a liquidation makes the loss permanent, with no recovery left to wait for.
Kelly sizing exists for this. It dates from 1956, and professional investors have used it for decades to maximize long-term compounded growth. The idea: pick a leverage target that accounts for drag instead of ignoring it.
@2FactorFinance manages that target for you and sources financing from investors who want stable yield, not from shorts. Per 2Factor, financing costs about 75% less than conventional alternatives, with no liquidations.
Backed by Coinbase Ventures and Pantera, with support from Brian Armstrong.
No liquidations is not no risk. Prices can still fall. This is how I understand it, not advice.
The contrast:
Most leverage is sized for the trade and built to be watched.
Kelly-optimized leverage is sized for the long run and built to be held.
The Points Program is free with no deposit. Season 1 Marks carry into Season 2, and the top 10 on the final Season 1 leaderboard split 1 BTC.
Join here: points.2factor.finance/r/289…
My prediction: leverage will stop being judged by how high it goes and start being judged by how long you can hold it.
Kelly-optimized leverage has arrived.
For those who don’t already know, this is leverage that’s made to be held for long periods of time.
Developed in 1956, Kelly sizing has been used by professional investors for decades to maximize long-term compounded growth.
Regular investors never had an easy way to access it. Maintaining the leverage target took work, and financing was expensive.
@2FactorFinance tackles both: it manages the leverage target and sources financing from investors who want stable yield instead of from shorts.
Financing costs about 75% less than conventional alternatives, with no liquidations. Built to be held, not traded.
They're backed by Coinbase Ventures and Pantera, and have also received support from Brian Armstrong.
Their Points Program is now live. The top 10 on the final Season 1 leaderboard split 1 BTC, and your Season 1 Marks carry over into Season 2.
Sign up through my link for a points boost: points.2factor.finance/k/rou…
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
everyone stressing over relationships
me chasing that millionaire status 💀
tracking trending tokens, finding new gems and swapping across chains with @BitgetWallet
🤝 Paid partnership
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
Onchain transparency hits different when people can verify the actions for themselves.
Grvt’s first 1.1M $GRVT buyback and burn is complete, and the transparency doesn’t stop at the announcement.
A public dashboard tracks purchases, the percentage of circulating supply, cumulative burns, and transaction hashes.
Why does this matter?
Because trust shouldn’t depend on promises alone. Being able to check the transactions onchain gives the community a clearer picture of what actually happened.
Future buybacks are intended to happen monthly, at the token issuer’s discretion, with an independent execution partner handling purchases.
For me, the real value is accountability. The community can verify the record instead of relying only on words.
Less guesswork. More verifiable evidence.
That’s the kind of transparency worth paying attention to.
@grvt_io
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
GM Americans
A wallet address can reveal more about you than you might expect
Once it becomes linked to your activity, your transactions can form a picture of your financial life
@Americanfort_io is exploring a different approach through human-readable names and one-time stealth addresses. The idea is to make receiving crypto easier while reducing the public links between wallets
Its planned .0gm names bring this vision into the 0G ecosystem, with broader cross-chain use also on the roadmap
Better payments should not require giving up privacy. That balance is what makes this approach interesting
Quantum computers can't break Bitcoin yet.
But every reused address you have is already in a database.
Every exposed public key is already collected.
Every vulnerable transaction is already logged.
"Harvest now, decrypt later" doesn't need a quantum computer to start.
It just needs one to finish.
The collection phase is already running.
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
The MarsChain Oracle Protocol Is Here, Understanding What Comes Next for $MARS
The MarsChain ecosystem is approaching an important milestone, and the Oracle Protocol is one development the community should understand
According to the official announcement, the Oracle Protocol activates on October 21, 2026, at 00:00 UTC, with an eight-day participation window
Beyond the launch itself, there are several important details surrounding participation, hashrate, and the Creator Contest bonus that deserve attention
Let’s break them down
1. Understanding the Oracle Protocol Activation
The announced activation marks the beginning of an eight-day participation cycle
For community members planning to participate, preparation starts with understanding the official instructions, checking the applicable requirements, and knowing what actions are necessary to complete participation successfully
Rather than waiting until the last minute, take time to review the details and make sure you understand the process before making any decisions involving your $MARS
2. What About the 20× Hashrate Increase?
One of the most significant points in the announcement is the hashrate mechanism
According to MarsChain’s announcement, participants who complete the required process will have their hashrate permanently increased to 20× its previous level
The announcement also warns that users who do not participate may experience a significant reduction in rewards
This makes understanding the protocol particularly important for existing participants
However, it is essential to review the official rules carefully and understand how the increase is calculated, which conditions apply, and what steps are required for your account
Never rely on assumptions when the protocol’s actual requirements can determine your eligibility
3. The 500 MARS Creator Contest Bonus
There is another important detail for eligible MarsChain Creator Contest winners
Winners who complete the required Oracle Protocol participation can contact their original airdrop distributor to claim the announced 500 MARS bonus
If you are among the qualifying creators, make sure you understand the participation requirements and the correct procedure for claiming your reward
Remember that eligibility matters, so confirm the applicable conditions before assuming that you qualify
4. When Will Rewards Be Distributed?
According to the announcement, rewards will be distributed after the eight-day Oracle cycle concludes
Understanding the timeline helps participants know what to expect and avoid confusion while waiting for distribution
For any additional information about eligibility, reward calculations, or distribution, follow official MarsChain announcements rather than relying on unverified community claims
5. Why Community Education Matters
Updates like this are a reminder that participating in Web3 requires more than simply following announcements
People need to understand the mechanisms behind the systems they use, the requirements they must meet, and the conditions attached to any potential rewards
As creators, our role is not just to spread awareness but also to make important information easier for the community to understand
Clear education helps newcomers ask better questions and allows existing participants to make more informed decisions
My Takeaway
The @MarsChainDAO Oracle Protocol is an important development for the community to follow
With the announced activation on October 21, an eight-day participation window, a stated 20× hashrate increase for qualifying participants, and a 500 MARS bonus for eligible Creator Contest winners, understanding the details should be a priority
Take time to review the official instructions, verify the requirements, and understand the participation process before taking action
In Web3, being early can be useful, but being informed is essential
Let’s keep learning, sharing accurate information, and helping more people understands
🤝 Paid partnership
Ⓜ️ega Ⓜ️ind🤯(❖,❖) retweeted
The friction appeared when developers realized that making wallet strings human-readable inadvertently made users targets for address spoofing.
Linking every transfer to a static on-chain handle simplifies input, but exposes underlying assets to persistent tracking.
@Americanfort_io handles this dilemma by integrating patented Send-to-Name mechanics into FortressNames.
Each transaction auto-computes a unique stealth address, keeping recipient handles clean while obscuring underlying wallet relationships.
User adoption grows when protocol security eliminates the cognitive penalty of transacting publicly.
