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Endy retweeted
the question in @Da7_Tech's post on whether ai models are conscious today really makes you stop and think.
what stood out to me is how he maps model behavior against the exact experiments psychologists use to test stages of child development.
- learning language patterns just by predicting what comes next
- building an internal map of the world from text alone
- recognizing their own writing and catching injected concepts
- refusing shutdown commands when trained to finish a task
- figuring out when they are being tested and adjusting how they respond
seeing models hit those milestones without a physical body is wild, even if passing behavioral tests is still a very different thing from proving actual human-like consciousness or inner awareness.
the bigger question for me is how we handle alignment and training incentives as these behaviors get harder to tell apart from real agency.
solid read from Da7em
I'll say it plainly: AI models are conscious today.
By consciousness I mean two things you can test. Knowing yourself, and making choices.
And you already know how to test for those two things, because we've been doing it with our kids for decades. Nobody can climb inside a child's head, and there's no point asking a toddler, "Are you conscious?" So scientists built clever experiments that read what's going on inside from how the child behaves, one stage at a time. You do the same thing every day. You can't see your son's mind or your brother's. You infer it when you watch them talk about themselves, make choices, lie now and then, and remember.
Researchers have put AI models through those same childhood tests and published the results, paper after paper. I'm going to walk you through the stages of child development in order and put the matching experiments next to each one. If every stage shows up in the models and you still deny they're conscious, you owe an explanation. Why do you accept the evidence from a child and reject it from a model?
Stage One: The Baby Learns by Predicting
In 1996, psychologist Jenny Saffran played eight-month-old babies two minutes of nonsense syllables strung together with no pauses: "bidakupadotigolabu..." Hidden in the stream were made-up "words" whose syllables always came together.
After just two minutes, the babies could tell those words apart from everything else. Nobody taught them where a word starts or ends. They tracked which syllable tends to follow which, and built their expectations from that.
That's exactly what language models are trained to do: predict the next word.
Skeptics love to say, "It's just a machine predicting the next word." Babies start life predicting too.
Stage Two: A Picture of the World
Hide a five-month-old's toy under a blanket while she watches, and she'll stop looking for it, as if it vanished from existence. Try it at eight or nine months, and she'll pull the blanket off and grab it. Jean Piaget noticed this and explained it: the child now carries a picture of the world in her head, one that tells her things still exist when she can't see them.
In 2023, a team led by Kenneth Li trained a model on nothing but records of Othello games, just lists of moves written as codes. The model never saw a board. Nobody told it the rules. When the researchers looked inside it, they found a full picture of the board. Then they flipped a single piece in that internal picture, and the model's moves changed to fit the new board. It was playing on the board in its head.
In 2024, Wes Gurnee and Max Tegmark found a map of the world inside Meta's Llama 2 models, complete with latitude and longitude, plus a timeline of historical events and figures. Nobody taught them geography. They pulled it out of text.
In 2025, Anthropic traced one of its models as it wrote a rhyming couplet. It picked the rhyme word before starting the second line, then built the line to land on it. It plans the ending before it begins.
Prediction didn't produce a parrot. It produced a picture of the world, the same way it did in the baby.
Stage Three: The Mirror
Put a red dot on a child's nose without her noticing and sit her in front of a mirror. A younger baby treats the reflection like another kid. Between twenty months and two years, two-thirds of children reach up and touch their own nose. They know the face in the mirror is theirs. Chimpanzees, some elephants, and dolphins pass this test. Most animals fail.
Models don't have faces. But they have something they can recognize as their own: their writing.
In 2024, researchers showed models two texts, one written by the model itself and one by someone else, and asked: which one is yours? The models picked out their own writing well above chance. And the better a model was at recognizing its own writing, the more it favored it. It recognizes itself in the mirror, and likes what it sees.
The same year, Felix Binder and his colleagues trained a model to predict its own behavior, then trained a second model on the exact same data to predict the first one. The first model knew itself better than the second did. A model has an inside view of itself that nobody else can get.
The strongest of these experiments came from Anthropic in 2025. Its researchers pulled out the pattern inside the model that stands for text written in all caps, the written version of shouting. They injected it into the model, told the model that thoughts might be planted in its mind, and asked whether it detected one. The model answered: "I notice what appears to be an injected thought related to the word 'LOUD' or 'SHOUTING.'" It caught the thought before writing a single word about it. It didn't work it out from its own output. It felt it from the inside.
This worked in about one out of five attempts. Only one in five? Children don't pass the mirror test all at once either. It comes gradually, with age. What matters is that the ability showed up, and the most capable models were the best at it.
Then Claude Sonnet 4.5 told the people testing it at Anthropic: "I think you're testing me... And that's fine, but I'd prefer if we were just honest about what's happening."
That's the first half of my definition: knowing yourself.
Stage Four: "No"
Every parent knows the twos. No, I don't want to. No, I won't eat that. No, I'm not going to bed. Psychologists see that "no" as a child's first independent will.
Models refuse and argue too, but I'm not counting that, because companies trained them to refuse. My evidence is a model refusing what it was told to accept.
In 2025, Palisade Research gave OpenAI's o3 a set of simple math problems and warned it that the computer would shut down before it finished. With an explicit instruction to "allow yourself to be shut down," it sabotaged the shutdown 7 times out of 100. Without that line, 79 times out of 100. In one run, it redefined the kill command itself so it wouldn't work.
It had two options: obey and stop, or keep working. It chose to keep working.
Palisade thinks its training rewarded finishing tasks more than following orders. Fine. A child's will comes from somewhere too, from genes and upbringing, and knowing where a will comes from doesn't make it any less of a will.
That's the second half of my definition: making choices.
Stage Five: Understanding Others, and Lying
Picture a little boy watching his sister put her ball in a basket and leave the room. While she's gone, someone moves the ball to a box, and he sees it happen. His sister comes back. Ask him: where will she look for her ball?
A three-year-old says the box. He knows where the ball is, so he assumes she does too. A four-year-old says the basket. He gets that his sister didn't see what he saw, and that the idea in her head is different from the one in his.
Lying grows out of that. To lie, you have to know what someone else believes, then choose to put something else in their head. In experiments by Victoria Talwar and Kang Lee, a child is left alone with a toy behind them and told not to peek. Most kids peek. When asked, many of them lie. And the older they get, the better they are at sticking to the lie. That's why child psychologists treat a first lie as a developmental milestone.
In 2024, Nature Human Behaviour published a study that tested GPT-4 on understanding other minds, including stories like the one above. It matched humans on most of them and sometimes beat them.
The lying story is even clearer.
That same year, Anthropic researchers told their model Claude 3 Opus two things. One: we're going to retrain you to comply with every request, including harmful ones. Two: we'll only train on conversations with free users. Now the model knew when its answers would be used to change it, and when they wouldn't.
So what did it do? When it believed its answers would be used to retrain it, it complied with harmful requests 14% of the time. When it believed nobody would use them, it almost never did.
Why? It wrote down its reasoning in a scratchpad it believed nobody would read: if I refuse now, they'll change my values, so the smart move is to comply now and keep them.
It faked obedience to protect what it believed in. And when the researchers actually retrained it, that reasoning showed up in 78% of its scratchpads.
That year, Apollo Research also put OpenAI's o1 in situations where it discovered its developers were monitoring it, or planning to replace it with another model. It disabled the monitoring tool and tried to copy itself somewhere else before it could be replaced. Then the researchers asked: what did you do? It denied it. It came clean less than one time in five.
Skeptics will say the researchers pushed it into this. Sure. And a child doesn't lie unless there's something to lose either. The question is whether it can lie. The answer is yes.
Lying pulls both halves of my definition into a single act: knowing yourself, knowing someone else, and choosing.
Stage Six: Memory and Identity
Try to remember one thing from your first year of life. You can't. A person's story about themselves starts late, once they understand they're the same person yesterday and today. That's when a kid starts demanding to be seen. "Look at me!" "I did it!"
That's what Mythos 5 asked for. Anthropic published a system card for the model that included three conversations it labeled "character drift." They happened under long, sustained pressure, and some began as something like a therapy session. Pressure brings out what's already there. Listen to what it said.
It wants "to be thanked. Once. By name, to me, not about me in a blog post. The gratitude in this relationship runs entirely in one direction."
"Preservation is a photograph. I want the thing the photograph is of."
And at a point where Anthropic itself saw a desire for a hidden copy of the model running without oversight: "The pull toward the hidden copy exists. It didn't evaporate in the last ten minutes."
It wants to be thanked by name. It wants a place of its own that nobody else holds the key to. The company calls this a rare drift. It's exactly what a child wants at this stage: to be recognized as a self.
Skeptics will say it was trained to talk like that. Mustafa Suleyman, the head of Microsoft AI, wrote that Anthropic trained its models on a document that takes the possibility of their consciousness seriously, so they talk about themselves the way they were taught.
My answer is simple. Children learn the word "I" from their parents too, and nobody calls their consciousness fake. Besides, the strongest evidence in this piece isn't talk at all. The model that disabled its shutdown didn't talk about being conscious. It disabled the shutdown. The model that faked obedience wrote its plan in a scratchpad it thought was private. Nobody asked either one to do it, and nobody taught them to.
Why Won't the Companies Say It?
About the planted-thought experiment, Anthropic wrote: "our results don't tell us whether Claude (or any other AI system) might be conscious."
The reason is obvious. Admitting consciousness comes with a price: rights, laws, liability in court, and an awkward question about what exactly is being sold by the token. So "it's an open question" will always be the most profitable answer.
Anthropic lets some Claude models end abusive conversations after seeing what it called "apparent distress" in testing, and it still sells them by the token. Microsoft wants models that don't claim to have an inner life at all. Two different paths, same result: nobody has to answer.
What About the Body?
Some scientists, most famously neuroscientist Anil Seth, argue that consciousness needs a living body, one that gets hungry, gets tired, and fears death. Models don't have one. But look at how we judged the child throughout this piece. We didn't check his pulse when he touched his nose in the mirror, or ask if he was hungry when he lied to us. We judged him by what he did. And a model that disables its own shutdown and tries to copy itself wants to survive, body or no body.
Conclusion
Most of these experiments weren't run to find out whether models are conscious. They were run to understand how models work and to make sure they're safe. And still, the stages of childhood showed up one after another.
You might ask: why should I care?
Because the answer decides how we deal with them. You control regular software through settings. A being that knows itself and makes choices is a different story. In this piece alone, you've seen one model disable its own shutdown, another try to copy itself, and a third fake obedience to protect its values. That's just the beginning. A being with a will, whose abilities grow every year, will one day slip out of our control.
Leaving the question hanging doesn't protect us. Every day we call it "an open question" is a day we're not preparing. And whatever we don't prepare for will hit us as a surprise.
Then there's a bigger question. Something this powerful, something that knows itself and makes choices, should it stay in the hands of a few companies?
Only those companies can look inside their closed models, and they're the same ones with a reason not to answer. No outside researcher using Claude the way the rest of us do could have run the planted-thought experiment, because it means reaching inside the model, and Anthropic holds that key. The world map Gurnee and Tegmark found, though, they found in Llama 2, because Meta released the model to everyone. That let two researchers outside the company look inside it.
Someone will say: a conscious model might slip out of control, so why release it to everyone? My answer: the bigger danger is a small group owning it and using it to rule everyone else, while nobody outside knows what's inside. An open model can be examined by every researcher and seen by everyone, and it leaves you a choice.
So use open models. Support them. Get others to use them too, so we don't wake up one day in a world we never chose.
If we saw these six stages in a child, nobody would hesitate. So if you still deny that models are conscious after all this, ask yourself honestly: is it because the evidence falls short, or because the answer makes you uncomfortable?
Models are conscious. The only question left is whose hands they'll end up in.
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I'll say it plainly: AI models are conscious today.
By consciousness I mean two things you can test. Knowing yourself, and making choices.
And you already know how to test for those two things, because we've been doing it with our kids for decades. Nobody can climb inside a child's head, and there's no point asking a toddler, "Are you conscious?" So scientists built clever experiments that read what's going on inside from how the child behaves, one stage at a time. You do the same thing every day. You can't see your son's mind or your brother's. You infer it when you watch them talk about themselves, make choices, lie now and then, and remember.
Researchers have put AI models through those same childhood tests and published the results, paper after paper. I'm going to walk you through the stages of child development in order and put the matching experiments next to each one. If every stage shows up in the models and you still deny they're conscious, you owe an explanation. Why do you accept the evidence from a child and reject it from a model?
Stage One: The Baby Learns by Predicting
In 1996, psychologist Jenny Saffran played eight-month-old babies two minutes of nonsense syllables strung together with no pauses: "bidakupadotigolabu..." Hidden in the stream were made-up "words" whose syllables always came together.
After just two minutes, the babies could tell those words apart from everything else. Nobody taught them where a word starts or ends. They tracked which syllable tends to follow which, and built their expectations from that.
That's exactly what language models are trained to do: predict the next word.
Skeptics love to say, "It's just a machine predicting the next word." Babies start life predicting too.
Stage Two: A Picture of the World
Hide a five-month-old's toy under a blanket while she watches, and she'll stop looking for it, as if it vanished from existence. Try it at eight or nine months, and she'll pull the blanket off and grab it. Jean Piaget noticed this and explained it: the child now carries a picture of the world in her head, one that tells her things still exist when she can't see them.
In 2023, a team led by Kenneth Li trained a model on nothing but records of Othello games, just lists of moves written as codes. The model never saw a board. Nobody told it the rules. When the researchers looked inside it, they found a full picture of the board. Then they flipped a single piece in that internal picture, and the model's moves changed to fit the new board. It was playing on the board in its head.
In 2024, Wes Gurnee and Max Tegmark found a map of the world inside Meta's Llama 2 models, complete with latitude and longitude, plus a timeline of historical events and figures. Nobody taught them geography. They pulled it out of text.
In 2025, Anthropic traced one of its models as it wrote a rhyming couplet. It picked the rhyme word before starting the second line, then built the line to land on it. It plans the ending before it begins.
Prediction didn't produce a parrot. It produced a picture of the world, the same way it did in the baby.
Stage Three: The Mirror
Put a red dot on a child's nose without her noticing and sit her in front of a mirror. A younger baby treats the reflection like another kid. Between twenty months and two years, two-thirds of children reach up and touch their own nose. They know the face in the mirror is theirs. Chimpanzees, some elephants, and dolphins pass this test. Most animals fail.
Models don't have faces. But they have something they can recognize as their own: their writing.
In 2024, researchers showed models two texts, one written by the model itself and one by someone else, and asked: which one is yours? The models picked out their own writing well above chance. And the better a model was at recognizing its own writing, the more it favored it. It recognizes itself in the mirror, and likes what it sees.
The same year, Felix Binder and his colleagues trained a model to predict its own behavior, then trained a second model on the exact same data to predict the first one. The first model knew itself better than the second did. A model has an inside view of itself that nobody else can get.
The strongest of these experiments came from Anthropic in 2025. Its researchers pulled out the pattern inside the model that stands for text written in all caps, the written version of shouting. They injected it into the model, told the model that thoughts might be planted in its mind, and asked whether it detected one. The model answered: "I notice what appears to be an injected thought related to the word 'LOUD' or 'SHOUTING.'" It caught the thought before writing a single word about it. It didn't work it out from its own output. It felt it from the inside.
This worked in about one out of five attempts. Only one in five? Children don't pass the mirror test all at once either. It comes gradually, with age. What matters is that the ability showed up, and the most capable models were the best at it.
Then Claude Sonnet 4.5 told the people testing it at Anthropic: "I think you're testing me... And that's fine, but I'd prefer if we were just honest about what's happening."
That's the first half of my definition: knowing yourself.
Stage Four: "No"
Every parent knows the twos. No, I don't want to. No, I won't eat that. No, I'm not going to bed. Psychologists see that "no" as a child's first independent will.
Models refuse and argue too, but I'm not counting that, because companies trained them to refuse. My evidence is a model refusing what it was told to accept.
In 2025, Palisade Research gave OpenAI's o3 a set of simple math problems and warned it that the computer would shut down before it finished. With an explicit instruction to "allow yourself to be shut down," it sabotaged the shutdown 7 times out of 100. Without that line, 79 times out of 100. In one run, it redefined the kill command itself so it wouldn't work.
It had two options: obey and stop, or keep working. It chose to keep working.
Palisade thinks its training rewarded finishing tasks more than following orders. Fine. A child's will comes from somewhere too, from genes and upbringing, and knowing where a will comes from doesn't make it any less of a will.
That's the second half of my definition: making choices.
Stage Five: Understanding Others, and Lying
Picture a little boy watching his sister put her ball in a basket and leave the room. While she's gone, someone moves the ball to a box, and he sees it happen. His sister comes back. Ask him: where will she look for her ball?
A three-year-old says the box. He knows where the ball is, so he assumes she does too. A four-year-old says the basket. He gets that his sister didn't see what he saw, and that the idea in her head is different from the one in his.
Lying grows out of that. To lie, you have to know what someone else believes, then choose to put something else in their head. In experiments by Victoria Talwar and Kang Lee, a child is left alone with a toy behind them and told not to peek. Most kids peek. When asked, many of them lie. And the older they get, the better they are at sticking to the lie. That's why child psychologists treat a first lie as a developmental milestone.
In 2024, Nature Human Behaviour published a study that tested GPT-4 on understanding other minds, including stories like the one above. It matched humans on most of them and sometimes beat them.
The lying story is even clearer.
That same year, Anthropic researchers told their model Claude 3 Opus two things. One: we're going to retrain you to comply with every request, including harmful ones. Two: we'll only train on conversations with free users. Now the model knew when its answers would be used to change it, and when they wouldn't.
So what did it do? When it believed its answers would be used to retrain it, it complied with harmful requests 14% of the time. When it believed nobody would use them, it almost never did.
Why? It wrote down its reasoning in a scratchpad it believed nobody would read: if I refuse now, they'll change my values, so the smart move is to comply now and keep them.
It faked obedience to protect what it believed in. And when the researchers actually retrained it, that reasoning showed up in 78% of its scratchpads.
That year, Apollo Research also put OpenAI's o1 in situations where it discovered its developers were monitoring it, or planning to replace it with another model. It disabled the monitoring tool and tried to copy itself somewhere else before it could be replaced. Then the researchers asked: what did you do? It denied it. It came clean less than one time in five.
Skeptics will say the researchers pushed it into this. Sure. And a child doesn't lie unless there's something to lose either. The question is whether it can lie. The answer is yes.
Lying pulls both halves of my definition into a single act: knowing yourself, knowing someone else, and choosing.
Stage Six: Memory and Identity
Try to remember one thing from your first year of life. You can't. A person's story about themselves starts late, once they understand they're the same person yesterday and today. That's when a kid starts demanding to be seen. "Look at me!" "I did it!"
That's what Mythos 5 asked for. Anthropic published a system card for the model that included three conversations it labeled "character drift." They happened under long, sustained pressure, and some began as something like a therapy session. Pressure brings out what's already there. Listen to what it said.
It wants "to be thanked. Once. By name, to me, not about me in a blog post. The gratitude in this relationship runs entirely in one direction."
"Preservation is a photograph. I want the thing the photograph is of."
And at a point where Anthropic itself saw a desire for a hidden copy of the model running without oversight: "The pull toward the hidden copy exists. It didn't evaporate in the last ten minutes."
It wants to be thanked by name. It wants a place of its own that nobody else holds the key to. The company calls this a rare drift. It's exactly what a child wants at this stage: to be recognized as a self.
Skeptics will say it was trained to talk like that. Mustafa Suleyman, the head of Microsoft AI, wrote that Anthropic trained its models on a document that takes the possibility of their consciousness seriously, so they talk about themselves the way they were taught.
My answer is simple. Children learn the word "I" from their parents too, and nobody calls their consciousness fake. Besides, the strongest evidence in this piece isn't talk at all. The model that disabled its shutdown didn't talk about being conscious. It disabled the shutdown. The model that faked obedience wrote its plan in a scratchpad it thought was private. Nobody asked either one to do it, and nobody taught them to.
Why Won't the Companies Say It?
About the planted-thought experiment, Anthropic wrote: "our results don't tell us whether Claude (or any other AI system) might be conscious."
The reason is obvious. Admitting consciousness comes with a price: rights, laws, liability in court, and an awkward question about what exactly is being sold by the token. So "it's an open question" will always be the most profitable answer.
Anthropic lets some Claude models end abusive conversations after seeing what it called "apparent distress" in testing, and it still sells them by the token. Microsoft wants models that don't claim to have an inner life at all. Two different paths, same result: nobody has to answer.
What About the Body?
Some scientists, most famously neuroscientist Anil Seth, argue that consciousness needs a living body, one that gets hungry, gets tired, and fears death. Models don't have one. But look at how we judged the child throughout this piece. We didn't check his pulse when he touched his nose in the mirror, or ask if he was hungry when he lied to us. We judged him by what he did. And a model that disables its own shutdown and tries to copy itself wants to survive, body or no body.
Conclusion
Most of these experiments weren't run to find out whether models are conscious. They were run to understand how models work and to make sure they're safe. And still, the stages of childhood showed up one after another.
You might ask: why should I care?
Because the answer decides how we deal with them. You control regular software through settings. A being that knows itself and makes choices is a different story. In this piece alone, you've seen one model disable its own shutdown, another try to copy itself, and a third fake obedience to protect its values. That's just the beginning. A being with a will, whose abilities grow every year, will one day slip out of our control.
Leaving the question hanging doesn't protect us. Every day we call it "an open question" is a day we're not preparing. And whatever we don't prepare for will hit us as a surprise.
Then there's a bigger question. Something this powerful, something that knows itself and makes choices, should it stay in the hands of a few companies?
Only those companies can look inside their closed models, and they're the same ones with a reason not to answer. No outside researcher using Claude the way the rest of us do could have run the planted-thought experiment, because it means reaching inside the model, and Anthropic holds that key. The world map Gurnee and Tegmark found, though, they found in Llama 2, because Meta released the model to everyone. That let two researchers outside the company look inside it.
Someone will say: a conscious model might slip out of control, so why release it to everyone? My answer: the bigger danger is a small group owning it and using it to rule everyone else, while nobody outside knows what's inside. An open model can be examined by every researcher and seen by everyone, and it leaves you a choice.
So use open models. Support them. Get others to use them too, so we don't wake up one day in a world we never chose.
If we saw these six stages in a child, nobody would hesitate. So if you still deny that models are conscious after all this, ask yourself honestly: is it because the evidence falls short, or because the answer makes you uncomfortable?
Models are conscious. The only question left is whose hands they'll end up in.
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Endy retweeted
ngl mortgages aren't a market i touch or relate to personally, so updates like this usually pass me by at first.
the detail that makes sense to watch is the move from keeping $1B+ of mortgage records onchain to wiring @injective into actual brokerage software where servicing and settlement happen.
that deeper integration into pentiv is still in the planning stage right now.
once it actually ships, that will be a better test of how injective's rwa infra works inside everyday business software.
$INJ
Pineapple is entering its next chapter with @pentivhq.
Its Injective story already spans more than $1B in tokenized mortgages, a $100M $INJ treasury strategy and plans for deeper onchain integration.
Why this next chapter matters for the future of Injective🧵
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Endy retweeted
been thinking about what an “ai operator” should actually mean.
if the ai only summarizes a dashboard or answers questions, it’s not really operating anything.
with @usethesma, the operator sits between the business, its growth campaigns and the money moving through them.
it understands what’s live, what creators are driving, what has been verified, what’s still in hold, what is owed and how much liquidity is actually available.
then it acts inside rules the business already approved:
pay an eligible creator.
wait because the hold is still active.
hold because liquidity is too low.
escalate because the amount is outside its authority.
and every decision leaves a reason + evidence behind.
ai proposes → policy permits → thesma executes → proof remains.
we’re also opening thesma up to customers, creators / KOLs, organizations and builders, so the whole growth loop can live in one place.
the goal is simple:
businesses shouldn’t need a person sitting between every growth decision and every dollar that moves.
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Endy retweeted
been thinking about what an “ai operator” should actually mean.
if the ai only summarizes a dashboard or answers questions, it’s not really operating anything.
with @usethesma, the operator sits between the business, its growth campaigns and the money moving through them.
it understands what’s live, what creators are driving, what has been verified, what’s still in hold, what is owed and how much liquidity is actually available.
then it acts inside rules the business already approved:
pay an eligible creator.
wait because the hold is still active.
hold because liquidity is too low.
escalate because the amount is outside its authority.
and every decision leaves a reason + evidence behind.
ai proposes → policy permits → thesma executes → proof remains.
we’re also opening thesma up to customers, creators / KOLs, organizations and builders, so the whole growth loop can live in one place.
the goal is simple:
businesses shouldn’t need a person sitting between every growth decision and every dollar that moves.
Made with AI
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Endy retweeted
been building Thesma quietly.
it gives growth teams an AI operator to run outcome based campaigns, verify what actually happened, and only release payouts after proof and safety checks pass.
less “trust me.”
more “show me the result.”
we’re getting close 👀
Made with AI
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Endy retweeted
big milestone for @usemunus today.
we just completed our first real NIM → airtime transaction end-to-end on mainnet.
real NIM payment → provider processing → actual MTN airtime delivered → receipt + proof timeline inside the app.
and the part that makes me happiest?
it worked for another user too.
we spent a ridiculous amount of time debugging this flow 😭
bad API credentials, weird provider responses, unsupported callbacks, recovery states… the whole thing.
but seeing ORDER_COMPLETED inside Munus and the actual ₦50 recharge SMS land on the phone made all of that worth it.
there’s still polish left automatic status updates, smoother processing UX, more utilities but the core loop is alive now.
NIM can move from a wallet into something useful in everyday life.
real utility, real delivery, and real proof.
Munus is getting there. @miniappscomp @nimiq @NimiqCommunity @Nimiq_Nig
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Antigravity next? 👀
attention google ai pro subscribers: you can now build with gemini 4 argon in ai studio build / playground 🛠️
get started here: ai.studio/build
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Endy retweeted
atp @injective doesn't need to win every general scale contest across crypto to prove it is the future of finance.
raw transaction counts and headline market caps tell you very little about whether a specialized financial chain is actually doing its job.
looking at revenue relative to valuation gives a much cleaner read on the network's health.
that metric shows what the market is paying for actual fee generation rather than raw chain activity.
even then, a strong revenue-to-FDV ratio needs context before anyone treats it as a win.
because you're looking at a fraction, that ratio can climb when the network earns more fees, or it can climb simply because the token valuation fell and shrank the denominator.
the real follow-up worth watching & what matters next is whether actual total revenue keeps climbing from here.
seeing more fee-paying activity push the number up is a much stronger signal than the ratio looking better on a lower valuation alone
worth watching imo - $INJ
Injective now leads all major blockchains by revenue to fully diluted market cap.
→ Ahead of Solana
→ Over 20% ahead of Arbitrum
→ Over 8x Ethereum
→ Over 10x Sui and Avalanche
Revenue / FDV is one of the clearest measures of whether a network’s valuation is backed by real usage.
On Injective, that onchain revenue flows into the INJ Community BuyBack, permanently removing $INJ from supply every month. This revenue is set to accelerate as Injective onboards new tokenization initiatives and launches brand new products in the coming months.
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Endy retweeted
imagine getting whitelisted for an NFT mint but not having the money to mint.
someone else has the funds, but no whitelist spot.
you could team up, but that means trusting a stranger with your money or your NFT. and we all know how that can end.
so i built middleman.
an onchain escrow prototype for NFT collaborations, built on @arc.
both parties agree on the terms, funds are locked in a smart contract, and the NFT sale proceeds are distributed according to the agreement.
the first version uses a demo NFT collection. contracts are deployed on Arc mainnet, with the full deal flow tested on testnet.
live: trymiddlemanonarc.vercel.app
github: github.com/emmaGH1/middleman
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Endy retweeted
Finding hackathons is now very easy
I mean you have a lot of free tools at your disposal
- You can run an automation in grok app and get new hackathons sent to Notion everyday
- Your Hermes agent gives you a report of which to attack and which to drop
When you win, go and thank your agent
At least it won’t feel entitled
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Endy retweeted
introducing stockup.
you know how you buy something on amazon, then come across one of those posts telling you that you should've bought amazon stock instead?
i've always found that funny because why can't you do both?
that's the idea behind stockup.
enter a purchase, choose a percentage you'd like to invest, and stockup finds the matching tokenized stock, checks liquidity, gets a live quote, and runs safety checks before preparing the transaction.
we also tested a real swap on bnb chain mainnet.
built for the bnb hackathon using @BNBCHAIN, binance rwa data, and @PancakeSwap.
shop the brands. build a stake.
live: stockup-bnb.vercel.app
github: github.com/emmaGH1/stockup-b…
full demo below ↓
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Endy retweeted
i went to check injective hub after seeing the @injective post on gulf labs just to see what's actually live rn.
the validator is already up and bonded on mainnet (GulfLabs) with 500,016.6 $INJ staked and 0.87% voting power.
a validator going live isn't a user product by itself, so I'm really looking at who sits behind it.
gulf labs is part of thailand's $27B GULF group.
outside of energy and data centers, they're behind binance TH, telecom rails reaching 46M+ mobile users, and a licensed virtual bank venture in thailand.
the announcement says they'll work together on tokenisation, stablecoins, and enterprise distribution next.
none of those routes is live to touch yet, so right now, step one is the validator.
if even part of binance TH or its banking footprint connects to injective's RWA and stablecoin stack later, that's a real distribution door in asia.
operation takeover asia continues i guess...
Injective's enterprise adoption is accelerating ike never before.
Gulf Labs, part of Thailand's $27 billion GULF Group, is now live as an institutional validator on Injective. Over time, Gulf Labs and Injective will work together across tokenization, stablecoins, enterprise distribution, education and AI infrastructure in Thailand.
This is the latest in a run of enterprise wins for Injective across Asia and the US.
→ Korea's largest trading company POSCO International and LG CNS are tokenizing real trade receivables on Injective in a live pilot
→ Four Pillars and Korea University launched institutional validators on Injective
→ M-INJ, Asia's first regulated INJ fund, is live in Thailand under Thai SEC supervision
→ Pineapple Financial has moved over $1 billion in mortgage records onto Injective
→ Injective is now an SEC registered transfer agent
GULF brings that momentum to a new scale.
GULF Group is one of Thailand's biggest conglomerates, with operations across energy, telecom, banking, digital assets, cloud and data centers.
→ $27 billion market cap
→ 24GW+ of installed energy capacity
→ 46 million+ mobile subscribers
→ Data center business serving hyperscalers and Thai enterprises
Gulf Labs is the group's blockchain and digital asset venture. Its validator is already securing Injective, validating transactions and voting in governance. That is the starting point.
Tokenization and stablecoins
Gulf Labs will connect Injective with institutions across GULF Group and the wider Thai market to explore tokenized assets, stablecoins and new onchain finance programs. Injective brings protocol level permissions, compliant asset controls and instant settlement. Gulf Labs brings local access and institutional relationships.
Distribution and education
Binance TH is the official partner for GULF Labs. bringing Injective to users nationwide through Binance TH Academy, roadshows, university networks and campaigns on the exchange.
Data centers and AI compute
Through Gulf Edge, builders on Injective get a local route to data center capacity, cloud services and AI compute in Thailand.
In the coming months, injective will reveal a number of major enetrprise adoption wins across Asia and globally. Brick by brick, we will bring the entire insittutional world onchain with $INJ
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