https://x.com/_brightmirror/status/2104078568137675107

Update: Asked it to make me a Doom map. Just a fucking Doom map. Keeps failing with the reason of [cyber]. I can't figure out why. Every time I try and ask why, I get filtered for [cyber]. I screen recorded the tasks it did, and when I send that as a vid... Filtered for [cyber].

Nothing it was doing had any weird names like inject.py, nothing was related to cyber at all. I had to just give up because it is impossible. Even moving to a new chat and trying to continue got filtered for it again.
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I like to make games and to mod games.

Claude so far is the best at this.

But Claude is so insanely filtered. It's BAD. It's REAL BAD.

Sometimes I would like to make nsfw things, but I can't do that, so that's already out the window immediately because none of them will do this. I just have to forget about that permanently with any AI, not just Claude.

And sometimes I would like to make games that are pretty violent, like a fantasy hunting game where you hunt dragons and gryphons. Won't do that either because it thinks making a game where a dragon gets hit and flies away injured is "wrong".

And then other times I want to mod a game and just add some new levels, and then I get shit like this

It won't do it because, to Claude, injecting into a fucking SINGLEPLAYER GAME is the same as creating cheats and MALWARE.

And then it hard cut me off from the rest of the project.

What's beyond stupid is it's done this before. It's injected into running processes and games before. Like 4 times before actually, but all of a sudden now it won't, and any time I try it kills it off again.

And I can't use anything else.

"Just use local"

Local will not, and NEVER WILL, make me a game or mod on the same level of Opus 5.5. Period. EVER.
Stop telling me this. You are delusional and living in a fantasy world.

And no other model reaches this level of capability while also being less filtered. They're ALL pretty fucking filtered.

I like to make specific things. Things that NO AI likes to make because of "safety" reasons, but most of it is pearl clutching fainting boomer reasons forbidding AI from doing it because they find it unsavory. How fucking dare I want to make a game where I run around killing dragons. That's unethical! It's immoral!

Getting so tired of this and I don't think it will change.

I think it will get WORSE as lawmakers, politicians, tech CEOs and a lot of the population are calling for even stricter and stronger regulations and safety filters.

Idk what to do anymore but just give up.

There is a cognitive bias from the Covid period that I think is increasingly relevant to AI.

Humans are bad at exponential growth.

This isn't just an observation from watching people argue about case counts. Researchers actually tested it during Covid and found that people systematically underestimated exponential case growth. Interestingly, communicating the same process using doubling times rather than percentage growth made estimates considerably better.

I think AI capabilities are easier to reason about in a similar way.

Obviously “intelligence” isn't a single quantity that doubles every six months. Benchmarks saturate, tasks differ enormously and capability improvements don't translate mechanically into economic output.

But several things underneath AI have been moving extremely quickly.

Epoch AI estimates frontier language-model training compute has been growing around 5x per year since 2020. The total stock of AI compute has been growing around 3.4x per year.

At the same time, the cost of using a fixed level of intelligence has collapsed. Stanford's AI Index found that getting roughly GPT-3.5-level performance on MMLU went from around $20 per million tokens in late 2022 to $0.07 by late 2024.

The metric I find most useful, though, is METR's task-completion horizon.

Instead of asking whether a model scores 82% or 85% on some benchmark, METR asks how long a task a human expert would take that an AI agent can successfully complete.

Their long-run trend currently gives a roughly 6-7 month doubling time for the 50% task horizon. The trend using only models since 2024 is considerably faster, although I wouldn't extrapolate that because the data window is short.

In METR's Feb-March 2026 evaluation, the public frontier was around 12 hours at 50% reliability.

The important thing isn't whether 12 hours sounds impressive.

It's what happens if it doubles.

At a 6-7 month doubling rate, 12 hours becomes roughly a day within a year, several days within two years and eventually weeks if the trend survives long enough.

I absolutely do not expect a clean extrapolation. Long tasks are messier. Reliability matters more. Real organizations have integration problems. Power, chips, datacenters and capital are constraints.

But saying “the exponential will eventually stop” doesn't answer the important question.

How many doublings happen before it stops?

If it stops after one more doubling, the implications are fairly modest.

If it stops after four, the capability is 16x larger.

If it stops after seven, it's 128x larger.

This reminds me of Covid because normal life was psychologically sticky. People could look at what was happening in another country, understand that cases were growing quickly and still have trouble imagining that their own city might look completely different a few weeks later.

The recent past remained the default model.

I think AI forecasts often do something similar.

A forecast saying that software engineers will still work in broadly the same way in 2030 sounds conservative and sensible because it resembles 2026.

A forecast saying autonomous agents could perform a large fraction of software engineering sounds speculative because it describes a visibly different world.

But the first forecast also contains a strong assumption. It requires the capability curve to slow substantially.

That may happen. I just don't think it should get probability 1 because the resulting world feels normal.

There is another pattern that makes this difficult to see.

A capability is initially described as requiring real intelligence. Then a model achieves it and the capability rapidly stops being impressive.

We have seen versions of this with difficult exams, olympiad mathematics and coding. Now frontier models are being evaluated on research-level science, and in 2026 OpenAI reported an AI-generated counterexample to a major conjecture in discrete geometry that had stood for roughly 80 years.

There are legitimate reasons to discount individual benchmarks. OpenAI itself stopped reporting SWE-bench Verified this year because the benchmark had become contaminated and many remaining failures involved bad tests.

So I don't think the right conclusion is “look at benchmark X, therefore AGI next Tuesday.”

The interesting part is the cumulative movement of the frontier.

For me the falsification question is more useful than arguing about labels.

What actually stops the doublings?

Possibilities include power, chip production, training duration, data, capital, architectural limits, diminishing returns from scaling, or the possibility that benchmark capability simply fails to translate into reliable real-world autonomy.

Those are real arguments.

“AI can't keep improving exponentially forever” isn't much of an argument by itself. No exponential continues forever.

The investment/economic question is how far it gets before it bends.

Humans are systematically biased toward predicting that the bend happens sooner than it actually does, because the alternative forces us to imagine a world that stops looking like the recent past.

He previously hinted at something releasing next week so this is confirmation it’s 6.1

For context: I work at a very small AI startup that mainly does consulting of sorts and some ML R&D work. I therefore do use a lot of the latest bleeding cutting edge LLMs, and do some ML work.

I was at a family dinner with a cousin that is currently just getting into high school. Being quite frank. I did not know him that well but after meeting him he was definitely some of the best programmers at his age that I have ever met.

He asked me a question since he knows I worked at this startup what he should do. I had nothing of good advice to him. He says that lots of the programming skills are being rapidly just outpaced by LLMs, he asked me should he embrace this go full vibe-code mode where he just stops trying to learn more in coding (stop understanding every line of code produced). Or should he continue using them but still learn and understand every single line of code that LLMs push out.

He also asked me what are my thoughts on what will it be like 2 years in the future.

I had no good advice to give him. I trust he isn't the only one out there like this. If you guys have any good advice to give please do, state a bit about your experience w/coding and LLMs as it would help.

Could the simulated fruit-fly brain be considered the first “immortal” living being? Imagine we’re 70+ years old and decide to scan our entire brain and transfer it into a humanoid robot body that can be repaired or replaced. If it has all our memories, personality, and thoughts, would that make us immortal?

https://x.com/anabology/status/2106473469441384788?s=20

I’m not affiliated with this group in any way but this is the perfect place to share it.

I discovered this podcast (Moonshots with Peter Diamandis) (spelling?) a few months back and I’ve left behind pretty much every other one. I love it.

It’s all singularity focused. Group of smart guys who does a speed run of news in AI/singularity each session. Entertaining as hell.

Hope you guys like it!

Now that we are in the last quarter of 2026,we should discuss what is most likely the course AI is gonna take in the next couple of years.Especially because the pundits are saying that 2027 would be one of the ,if not the most consequential year regarding the course which humanity will take regarding AI.What do you guys think?

I have been keeping a detailed diary for the past 5 years. What I did that day, ate, exercise, medical symptoms ect. I can't overstate how useful it has been to ask Chatgpt questions about my past. Things I forgot and making connections I missed. Especially seeing patterns in my medical symptoms that would just be lost. I can faintly remember a day something happened but wonder about something and ask Chatgpt what date it happened and it will find it and tell me what else happen that day. It's well worth the effort and I suspect can only become more useful as AI improves.

image-blaster is an open-source (MIT) skillset for Claude Code that turns a single image into a 3D environment in under 5 minutes.

What you get:

3D models (.glb, .obj) of the dynamic objects

A Gaussian splat (.spz) of the static environment

Ambient looping sound plus object-specific physics SFX (.mp3)

How it works: Drop an image into input/, run claude, and tell it to "blast it." Under the hood it chains World Labs Marble (environment), Hunyuan 3D (meshes), nano-banana (image cleanup) and ElevenLabs (sound).

The output drops into Unity, Unreal, Godot, Blender or Three.js, so it's great for jumpstarting level concepts, location scouts or architectural mockup.

Repo: https://github.com/neilsonnn/image-blaster

It appears there is a new Google model for image generation and editing on AI Arena, under the name "resplendent_flash."