ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ @DanielMiessler

if alcohol can shorten your memory imagine what alcohol can do

Think of them as articulations of your ideal state. Here are things I want to always be true. Sure things I want to never be true. Here are things that I want to happen if this happens. This is life and work infrastructure, and it’s much bigger than code.

The best way to think of loops is as a precursor to PROACTIVE AI Assistants. This combines with you providing your DA your goals in life and work. So then your assistant goes through and sets up tons of constant checks (loops) to proactively make sure your current state is as close as possible to your ideal state.

NATE SCHNELL @nate_schnell_

19 hours ago

What are the best use cases for loops? I’ve heard a ton of people like @bcherny and @steipete talking about how everything’s a loop, but I haven’t found a good use case for it beyond upgrading dependencies.

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@LLMJunky Ha

I’m going make a list of core, semi-unsolvable questions that we want better and better answers to, and then provide the best AIs answers to them as a running log. What should we call the project and what should some of the first questions be?

@navalismHQ @naval Still television.

1. as a mental model it is more correct to think of fable+ class models as english -> code interpreters - converts your idea into code into "correct" code regardless of problem complexity and output complexity (diff size). Fable 5 will be the worst of this new class of models 2. diff size/complexity is to be managed purely for review: small diffs - in high risk areas of code (auth/identity/data access/network access/money movement) large diffs for code that can be empirically verified (frontend/backend plumbing/code without network or db access/performance code that can be empirically verified) 3. time it takes to ship software is completely disconnected from time to produce the PR - how long the work takes depends fully on ability to review/merge code while managing risk at scale 4. solving the bottlenecks for above matter enormously- linters/testing/CI/shadow mode verification/empirical verification 5. agency matters enormously- what are the biggest bottlenecks to speeding up the loop and eliminating them? what are the problems that need solving and when do they need solving? what does it take to the solution to all of them today? 6. deep understanding of the full stack matters enormously- what problems are worth pursuing? is there a higher level of problem abstraction to address first? should I give it the sub-sub task, the sub task, or the task itself. what are the major risks with this PR (order of importance: security holes/correctness holes/performance holes). is there a higher speed way of producing data that allows me to merge this? should this be run in shadow or in a sandbox or a flag. understanding every line of logic may not be needed but understanding and managing risk matters enormously. 7. the cost of complexity itself is changing. it might be now worth "maintaining" 50% more code to get a 5% performance win. getting the right abstractions matter less because larger refactors are less tedious. code quality nits become huge drag. very likely, a much smarter model will be maintaining your code so worth taking on more technical debt now. taking the time to hand architect and rebuild systems comes with an enormous cost of velocity 8. if it quacks like a duck and walks like a duck, it's a duck. For low risk cases, it might be more sane to treat code chunks (services / functions) as a black box, like we do for neural networks: do full empirical verification only: has code produced correct outputs for the last 10,100,1000,10k inputs ? can we quarantine this large piece of code - no outbound access to network / database ? what happens when this code is wrong? do we get hacked/or crash(memory/cpu)/is an inconvenience? is it internal facing or external? what can we do to address these risks? 9. eventually, logical verification (line by line review) will come at an enormous cost- save it for where it matters and build systems that are tolerant to empirical verification. is there a decorator that prevents db / network access? correctness bugs are significantly easier to rectify than access bugs 10. what are the rails that allow for even faster iteration? code permissions can be opt in - db writes, db reads, network egress (to where?), PII access. how long does it take to get shadow mode data? how many PRs can be tested? What are the categories of diffs

@DavidWaigh66890 We describe ourselves doing most of those things. And so do LLMs. And we get very similar results. So what makes ours real.

Basic world model question. I know it’s agreed that LLMs don’t have real world models, but the assumption seems to be that humans do. What evidence is there that we do? Aren’t we also a black box built on some sort of neural net substrate? We can’t seem to inspect our thinking processes much better than those of LLMs. And we widely believe things that are demonstrably false. So, non-rhetorically, why do we think we have them and LLMs don’t?

@daniel_mac8 Yes absolutely. I mean ultimately it’s how do we create the government, society, and tech that gets everyone into thriving. So we don’t have this nasty bifurcation anymore.

A Unified Theory on AI and Jobs. - Yes, many jobs will go away - Yes, many more will be created - But the question is who gets those new jobs

The Essay: danielmiessler.com/blog/unified-t…

I’ve been saying for a couple of years that I think the final form of AI is continuously transitioning things from current state to ideal state. This is the entire purpose of the PAI system, from the algorithm to the ISA document. It’s open source. /goal and loops and such are all sneaking up on it, but the endgame is what I wrote in 2016. We articulate our goals as people, governments, countries, etc., and AI helps us pursue them. danielmiessler.com/blog/ai-state-…

Theo - t3.gg @theo

a day ago

I hate to admit it but the loop people were right

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What I don’t think people are understanding is that ALL AI will soon be Mythos-level. Mythos wasn’t some special spooky cyber model. It was just the next thing in the factory track. Gpt-5.5 was immediately pretty close. This is just natural progression. And within a few weeks or months the open models will catch up. We need to be thinking about what’s going to happen to cyber, bio, and tons of other threat domains when every new model is this good. And perhaps even more importantly, what happens to the economy when the government pulls the lever saying it’s all now illegal. POOF

github.com/danielmiessler…

@Kenvsryu24 They didn’t release mythos they released fable. They are not the same thing.

Everyone keeps saying this, but they’re not giving a better answer for what they should have done. I guess one option would have been to just not say anything publicly and just release Fable later with controls. But that doesn’t work if you think this level of progress is imminent, and that you need to raise the alarm and do something like Glasswing to get people ready.

Jake Williams @MalwareJake

3 days ago

Anthropic having Fable be export controlled is a self-inflicted wound. If you spend a bunch of time telling people how dangerous your technology is, don't be surprised when some of them agree with you.

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It’s a smarter model that’s specifically smarter at connecting dots across a large plot and over a long period of time. As someone who’s done offensive cyber for decades, I can tell you that’s one of the main things you need to be good at cyber. So it’s a general capability upgrade that specifically but inadvertently benefits cyber.

@henokcrypto What’s AGI?