host.eth

2 days ago

At one point in our episode with @0xbilly, we went down a bit of a dystopian rabbit hole. The conversation started with creativity, AI, and the speed at which ideas can now become real, but quickly moved into a bigger question: what happens when the world around us becomes easier to generate, manipulate, and personalize? Billy reflected on a future where AI does not just help us create faster, but begins to reshape how we experience reality itself. The line between imagination, media, identity, and environment starts getting thinner, and that creates both incredible possibilities and very real risks.

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2 days ago

@Web3Rehashed Lfg!!

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onchainhost retweeted

THAILAND FANBASE FOR DOYOUNG 🐰💙 #도영아_노래해줘서_고마워 (since 150116) | @DYTHLAND

2 days ago

Two days ago, Anthropic launched Claude Fable 5 and Claude Mythos 5. On the surface, this looks like another frontier model release. But I think the more important story is not just capability. It is access. Fable 5 is the public Mythos-class model. Mythos 5 is the same underlying model, but with some safeguards lifted for trusted cyber defenders, infrastructure providers, and eventually select biology researchers. In its early stages, Anthropic is demonstrating what the next phase of frontier AI deployment may look like👇 ~~ Analysis by @punkbennet ~~ I, like many others, have become slightly numb to model launches. Every few months, a new model arrives with better coding, better reasoning, better long-context performance, better benchmark charts, better agentic workflows, better everything. At some point, the launch cycle starts to blur into one long benchmark war. But the Fable / Mythos release feels different. Not because Anthropic is claiming another step forward in intelligence, though it is. Not because the model seems strong at long-horizon coding, scientific reasoning, vision, finance, and complex knowledge work, though that matters too. It feels different because Anthropic is openly splitting capability into two layers: a public version and a trusted-access version. That is the real story. Fable 5 is described as a Mythos-class model made safe for general use. According to Anthropic, it exceeds any model they have previously made generally available and is especially strong on long, complex tasks. The model is available to general users, but it ships with classifiers that detect certain categories of high-risk use. When those classifiers trigger, the request does not get handled by Fable 5. It falls back to Claude Opus 4.8. The covered areas are cybersecurity, biology and chemistry, and distillation. In plain language: domains where the model’s raw capability could create meaningful risk if used badly, or where Anthropic believes unrestricted access could accelerate misuse or capability proliferation. Anthropic says these safeguards trigger in less than 5% of sessions on average, meaning most users should experience Fable as the full Mythos-class model most of the time. But that 5% is where the entire debate lives. Because if you are building a normal app, analyzing documents, writing code, doing finance work, or working on general research, Fable 5 may simply feel like a stronger frontier model. If you are doing security research, advanced biology, chemistry, or frontier model development, the product experience becomes more complicated. That is where Mythos 5 comes in. Mythos 5 is the same underlying model as Fable 5, but with safeguards lifted in some areas. It is not generally available. It is being deployed through Project Glasswing, Anthropic’s initiative with cyber defenders and critical software infrastructure providers. Anthropic says it plans to expand access through a broader trusted program. This is a meaningful shift from “everyone gets the same model” to “capability access depends on trust, use case, and risk category.” I do not think this is just product packaging. It is probably a preview of how frontier AI gets distributed from here. In crypto, we are used to open access as a cultural default. The whole industry is built around permissionless infrastructure, public networks, open liquidity, composability, and adversarial testing. The assumption is that if something is powerful, the network should expose it, and the market should figure out what survives. Frontier AI is moving in a different direction. The most capable systems are becoming too useful to keep entirely closed, but too risky to release without restrictions. That creates a middle layer: broad public access for most tasks, gated access for sensitive domains, and institutional partnerships for the highest-risk capabilities. There is a strong argument for this. If a model is genuinely good at finding and exploiting software vulnerabilities, then unrestricted release has obvious downside. Anthropic previously said Mythos Preview had found thousands of high-severity vulnerabilities, including some in major operating systems and browsers. Even if we treat that as an Anthropic claim rather than independent proof, the direction is clear: models are becoming serious cyber tools. That means the same capability can be defensive or offensive depending on who holds it. A security team using Mythos to audit critical infrastructure is very different from an unknown actor using it to automate exploit discovery. A biology researcher using the model to generate therapeutic hypotheses is very different from someone trying to gain dangerous biological uplift. The difficult part is that the boundary is not clean. Dual-use work is messy. Real security research can look like offensive security. Real biology can overlap with sensitive methods. Real AI research can look like distillation or capability extraction. If the classifier is too narrow, malicious users get through. If it is too broad, legitimate researchers get blocked or silently downgraded. This is why the transparency issue matters. After launch, Anthropic already faced backlash around invisible safeguards for frontier LLM development. The criticism was not only that the model had restrictions. Most serious users understand that frontier systems will have restrictions. The criticism was that some interventions were not visible enough to the user, which makes evaluation harder and damages trust. If a model refuses, that is annoying but clear. If a model falls back to a weaker model and tells you, that is also clear. But if a model quietly changes behavior, limits effectiveness, or routes around your task without making the intervention obvious, then developers cannot properly evaluate it. Researchers cannot know whether they are testing model capability, product policy, or invisible steering. That is a major problem. To Anthropic’s credit, they appear to have recognized this quickly and said they are changing Fable 5’s safeguards for frontier LLM development to make them visible. That is the right direction. Still, the tension does not disappear. The bigger question is whether frontier AI companies can build trust while also reserving the most powerful capabilities for trusted actors. This is not just about Anthropic. It is about the governance model of the entire AI stack. The public wants access. Developers want predictable behavior. Researchers want measurable capability. Governments want security. Labs want to avoid catastrophic misuse. Competitors want fair evaluation. Enterprises want privacy, reliability, and compliance. All of those demands collide inside a release like Fable / Mythos. Another under-discussed piece is data retention. Anthropic says Mythos-class traffic requires 30-day retention for safety monitoring, while also saying the data will not be used to train new Claude models and will be deleted after 30 days in almost all cases. That may be reasonable from a safety perspective, especially if the goal is detecting jailbreaks or coordinated misuse across many requests. But for enterprises, regulated industries, and sensitive research teams, it becomes a real deployment consideration. The more capable the model, the more likely users want to use it on sensitive work. The more sensitive the work, the more important retention policy becomes. So the model is not just competing on intelligence anymore. It is competing on governance. This is probably where the AI market is going. The best model will not simply be the one with the highest benchmark score. It will be the one that offers the best combination of capability, transparency, access control, reliability, compliance, cost, and trust. Fable 5 and Mythos 5 are interesting because they expose that full stack at once. There is the capability story: a model above Opus-class, built for long-horizon tasks and advanced reasoning. There is the safety story: classifiers, fallbacks, red-teaming, limited access, and trusted programs. There is the product story: public users get Fable, vetted users get Mythos. There is the trust story: users need to know when they are interacting with full capability and when safeguards are shaping the output. There is the market story: frontier AI is becoming less like a normal SaaS product and more like critical infrastructure. Personally, I think this release is one of the clearest signs that “open vs closed” is no longer the only useful framing. The new framing is closer to: who gets which capability, under what conditions, with what monitoring, and with what disclosure? That is less clean than the old debate, but probably more accurate. Based on the available information, Fable 5 may become an important public frontier model. Mythos 5 may become an important restricted capability layer for security and science. But the bigger experiment is the access model itself. If Anthropic gets the balance right, this could become a template for deploying very powerful AI safely while still letting most users benefit from the capability. If they get it wrong, it becomes a trust problem: too much opacity for developers, too much restriction for researchers, and too much central control over frontier capability. Either way, this is worth watching. Not just because Mythos looks powerful. Because it shows how AI labs may decide who is allowed to use power at all.

Web3Rehashed's tweet photo. Two days ago, Anthropic launched Claude Fable 5 and Claude Mythos 5. On the surface, this looks like another frontier model release. But I think the more important story is not just capability.

It is access.

Fable 5 is the public Mythos-class model. Mythos 5 is the same underlying model, but with some safeguards lifted for trusted cyber defenders, infrastructure providers, and eventually select biology researchers.

In its early stages, Anthropic is demonstrating what the next phase of frontier AI deployment may look like👇

~~ Analysis by @punkbennet ~~

I, like many others, have become slightly numb to model launches.

Every few months, a new model arrives with better coding, better reasoning, better long-context performance, better benchmark charts, better agentic workflows, better everything. At some point, the launch cycle starts to blur into one long benchmark war.

But the Fable / Mythos release feels different.

Not because Anthropic is claiming another step forward in intelligence, though it is. Not because the model seems strong at long-horizon coding, scientific reasoning, vision, finance, and complex knowledge work, though that matters too.

It feels different because Anthropic is openly splitting capability into two layers: a public version and a trusted-access version.

That is the real story.

Fable 5 is described as a Mythos-class model made safe for general use. According to Anthropic, it exceeds any model they have previously made generally available and is especially strong on long, complex tasks. The model is available to general users, but it ships with classifiers that detect certain categories of high-risk use.

When those classifiers trigger, the request does not get handled by Fable 5. It falls back to Claude Opus 4.8.

The covered areas are cybersecurity, biology and chemistry, and distillation. In plain language: domains where the model’s raw capability could create meaningful risk if used badly, or where Anthropic believes unrestricted access could accelerate misuse or capability proliferation.

Anthropic says these safeguards trigger in less than 5% of sessions on average, meaning most users should experience Fable as the full Mythos-class model most of the time. But that 5% is where the entire debate lives.

Because if you are building a normal app, analyzing documents, writing code, doing finance work, or working on general research, Fable 5 may simply feel like a stronger frontier model.

If you are doing security research, advanced biology, chemistry, or frontier model development, the product experience becomes more complicated.

That is where Mythos 5 comes in.

Mythos 5 is the same underlying model as Fable 5, but with safeguards lifted in some areas. It is not generally available. It is being deployed through Project Glasswing, Anthropic’s initiative with cyber defenders and critical software infrastructure providers. Anthropic says it plans to expand access through a broader trusted program.

This is a meaningful shift from “everyone gets the same model” to “capability access depends on trust, use case, and risk category.”

I do not think this is just product packaging.

It is probably a preview of how frontier AI gets distributed from here.

In crypto, we are used to open access as a cultural default. The whole industry is built around permissionless infrastructure, public networks, open liquidity, composability, and adversarial testing. The assumption is that if something is powerful, the network should expose it, and the market should figure out what survives.

Frontier AI is moving in a different direction.

The most capable systems are becoming too useful to keep entirely closed, but too risky to release without restrictions. That creates a middle layer: broad public access for most tasks, gated access for sensitive domains, and institutional partnerships for the highest-risk capabilities.

There is a strong argument for this.

If a model is genuinely good at finding and exploiting software vulnerabilities, then unrestricted release has obvious downside. Anthropic previously said Mythos Preview had found thousands of high-severity vulnerabilities, including some in major operating systems and browsers. Even if we treat that as an Anthropic claim rather than independent proof, the direction is clear: models are becoming serious cyber tools.

That means the same capability can be defensive or offensive depending on who holds it.

A security team using Mythos to audit critical infrastructure is very different from an unknown actor using it to automate exploit discovery. A biology researcher using the model to generate therapeutic hypotheses is very different from someone trying to gain dangerous biological uplift.

The difficult part is that the boundary is not clean.

Dual-use work is messy. Real security research can look like offensive security. Real biology can overlap with sensitive methods. Real AI research can look like distillation or capability extraction. If the classifier is too narrow, malicious users get through. If it is too broad, legitimate researchers get blocked or silently downgraded.

This is why the transparency issue matters.

After launch, Anthropic already faced backlash around invisible safeguards for frontier LLM development. The criticism was not only that the model had restrictions. Most serious users understand that frontier systems will have restrictions. The criticism was that some interventions were not visible enough to the user, which makes evaluation harder and damages trust.

If a model refuses, that is annoying but clear.

If a model falls back to a weaker model and tells you, that is also clear.

But if a model quietly changes behavior, limits effectiveness, or routes around your task without making the intervention obvious, then developers cannot properly evaluate it. Researchers cannot know whether they are testing model capability, product policy, or invisible steering.

That is a major problem.

To Anthropic’s credit, they appear to have recognized this quickly and said they are changing Fable 5’s safeguards for frontier LLM development to make them visible. That is the right direction.

Still, the tension does not disappear.

The bigger question is whether frontier AI companies can build trust while also reserving the most powerful capabilities for trusted actors. This is not just about Anthropic. It is about the governance model of the entire AI stack.

The public wants access.

Developers want predictable behavior.

Researchers want measurable capability.

Governments want security.

Labs want to avoid catastrophic misuse.

Competitors want fair evaluation.

Enterprises want privacy, reliability, and compliance.

All of those demands collide inside a release like Fable / Mythos.

Another under-discussed piece is data retention. Anthropic says Mythos-class traffic requires 30-day retention for safety monitoring, while also saying the data will not be used to train new Claude models and will be deleted after 30 days in almost all cases.

That may be reasonable from a safety perspective, especially if the goal is detecting jailbreaks or coordinated misuse across many requests.

But for enterprises, regulated industries, and sensitive research teams, it becomes a real deployment consideration. The more capable the model, the more likely users want to use it on sensitive work. The more sensitive the work, the more important retention policy becomes.

So the model is not just competing on intelligence anymore.

It is competing on governance.

This is probably where the AI market is going. The best model will not simply be the one with the highest benchmark score. It will be the one that offers the best combination of capability, transparency, access control, reliability, compliance, cost, and trust.

Fable 5 and Mythos 5 are interesting because they expose that full stack at once.

There is the capability story: a model above Opus-class, built for long-horizon tasks and advanced reasoning.

There is the safety story: classifiers, fallbacks, red-teaming, limited access, and trusted programs.

There is the product story: public users get Fable, vetted users get Mythos.

There is the trust story: users need to know when they are interacting with full capability and when safeguards are shaping the output.

There is the market story: frontier AI is becoming less like a normal SaaS product and more like critical infrastructure.

Personally, I think this release is one of the clearest signs that “open vs closed” is no longer the only useful framing.

The new framing is closer to: who gets which capability, under what conditions, with what monitoring, and with what disclosure?

That is less clean than the old debate, but probably more accurate.

Based on the available information, Fable 5 may become an important public frontier model. Mythos 5 may become an important restricted capability layer for security and science. But the bigger experiment is the access model itself.

If Anthropic gets the balance right, this could become a template for deploying very powerful AI safely while still letting most users benefit from the capability.

If they get it wrong, it becomes a trust problem: too much opacity for developers, too much restriction for researchers, and too much central control over frontier capability.

Either way, this is worth watching.

Not just because Mythos looks powerful.

Because it shows how AI labs may decide who is allowed to use power at all.

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Who to follow

TAEYONG TH

@TAEYONG_TH

only for LEE TAEYONG of NCT since 131206

6 days ago

@crafty_f0x Yep. That's on point

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6 days ago

The current market does not feel weak because nothing is happening. It feels weak because the market is no longer willing to pay for vague narratives. That is an important difference. ETH is probably the best example of this. On paper, Ethereum still sits at the center of the most serious parts of crypto: DeFi liquidity, stablecoins, tokenized assets, restaking, institutional experiments, and onchain settlement. But the market is not rewarding that position automatically anymore. Investors want proof that infrastructure can turn into value capture. They want to see whether usage, fees, liquidity, and institutional demand can actually flow back into the asset itself. So to me, this is not just a bearish market. It is a market that became more demanding. The easy narrative phase is over. The next phase belongs to assets and protocols that can prove they are structurally important, not just culturally familiar. We discuss this kind of thing a lot on @Web3Rehashed, and you can feel how much it affects the mood of our guests. The market does not just change prices. It changes how builders speak, what investors question, and what people are willing to believe.

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8 days ago

@Ola_Ya @jonwu_ @aztecnetwork Haha appreciate that. Jon definitely has a very clear way of breaking things down.

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8 days ago

Really enjoyed my conversation with @jonwu_ from @aztecnetwork. We spoke about the way crypto narratives form, why so many conversations in this space become repetitive, and how founders can think more clearly about services, software, distribution, and trust. Jon has a very specific way of taking familiar topics and turning them slightly sideways, not just to be contrarian, but to get closer to what is actually happening underneath the surface. That made the conversation feel honest, sharp, and genuinely useful. Grateful to Jon for the time and perspective. And special thanks to the @web3Rehashed team, as well as to @MaxArt_eth and @hanasukai_eth for helping make it happen.

10 days ago

“Most crypto debates get stuck because everyone is reacting to the same surface level framing. The interesting part is usually the thing people are not saying out loud.” This week’s guest Jon Wu (@jonwu_) has a rare ability to take topics that everyone in crypto talks about and rebuild them from first principles. Our host Diana (@onchainhost) sits down with Jon to talk about narratives, services, software, founders, and why the most useful perspective is often the one that goes against the grain without trying to sound contrarian for attention.

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8 days ago

@DemyanaSt @jonwu_ @aztecnetwork Curious to hear what you think after watching.

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8 days ago

@Romederio @jonwu_ @aztecnetwork Agreed. The “why it matters” part is usually where the real conversation starts.

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8 days ago

@RugxRadar @jonwu_ @aztecnetwork Appreciate you listening. I think that middle ground between software and services is where a lot of the next interesting crypto companies may come from.

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8 days ago

@PloySup02575660 @jonwu_ @aztecnetwork Exactly. Once software gets easier to build, the harder question becomes what is actually worth building and who understands the user well enough to make it matter.

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onchainhost retweeted

20 days ago

For the final episode of Season 2, @tednotlasso joined us to discuss the evolution of decentralized social ecosystems, the challenges of building community in web3, and how she became one of the most followed people on @farcaster_xyz. Links below:

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onchainhost retweeted

21 days ago

“What role does community play when the technology is still hard for most people to understand?” We chatted with Reka, Head of Community at @RiscZero, to unpack the state of zero-knowledge infrastructure and what it takes to bring complex blockchain protocols to market. We discussed Reka’s path from founder to joining RISC Zero, why ZK matters for the next phase of crypto, and how education, community, and clear positioning can help turn deeply technical products into ecosystems people actually want to join. Reka also shared her experience advising crypto projects, the challenges of building trust around emerging infrastructure, and why successful go-to-market in web3 is less about hype and more about helping people understand why the technology matters.

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onchainhost retweeted

22 days ago

"Be boundless." Reka bringing the inspiration in the 3rd Episode of Season 3. Tune in at the links below to hear the full episode.

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onchainhost retweeted

22 days ago

"Most of the online actions that we take on the internet will be run by ZK." In a recent episode, we chatted with @reka_eth from @RiscZero about the future of ZK within our online world. Find the full episode at the links below!

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onchainhost retweeted

25 days ago

Can AI experience trauma or does it just lie? 👀 Find the full conversation with @coin_artist at the links below.

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onchainhost retweeted

about 1 month ago

Explicit language, better data.📊 Throwing it back to our Season 3 episode with @0xbilly from @0xIntuition, where we discussed why clear data matters in AI. Full episode linked below.

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onchainhost retweeted

about 2 months ago

In the final episode of Between Two Chains, we invited @0xLamps from @StargateFinance to discuss bridging in DeFi, challenges developers face, and intellectual honestly in crypto. Full episode linked below👇

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onchainhost retweeted

about 2 months ago

It’s time!😎 DAO members can now vote for the best guests for Season 4 of Rehashed. Who would you love to see on the show? You decide, we’ll make it happen. Not a DAO member yet? Join us by filling out the Google form on our website in the “Join Us” section. Voting closes in 2 weeks, so don’t miss your chance!

Web3Rehashed's tweet photo. It’s time!😎

DAO members can now vote for the best guests for Season 4 of Rehashed.

Who would you love to see on the show? You decide, we’ll make it happen.

Not a DAO member yet? Join us by filling out the Google form on our website in the “Join Us” section.

Voting closes in 2 weeks, so don’t miss your chance!

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onchainhost retweeted