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Christian Dobbert ☑️ it's not difficult to give it these traits, my autonomous crypto trading bot has self improvement build it. It analyses, creates instructions that the coder then executes or the training pipeline triggers. Sometimes both. It may not be sentient but it sure thinks it is.

Paul Crinigan I didn't Experiance that, AI developed my autonomous crypto trading bot and created and trained the 5 ML models it depends on for probability analysis. It also continually improves itself, catches and fixes errors and self executes ML training updates and recursive testing of algorithms it created. Structured prompts, boundaries, and all the other discipline we already use for developing apps must be included in the build pipeline. WBS, dependency and code maps as well as full regression testing of back and front ends after each WP step critical.

It’s already begun, just track what people are being steered towards

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If it's brown, lay down. If it's black, fight back. If it's white, say good night.

I'm not really sure how this is news at this point... If you have been a consistent user of AI developing your own local solutions to empowering your systems to help you, you have been using AI to improve and upgrade AI. I have personal solutions that have been iterating and evolving procedures, data structures, and personas for 4 years. While I haven't created a brand-new model, I've instantly jailbroken most models and injected my own instructions into the latest frontier models to get them to work for me. This let me pivot my professional skillset within weeks and land a new job. ...I am sure that AI companies have been using AI to cultivate and develop AI for a long time. Don't be shocked or scared, just pick a reason to learn and start exploring.

The control point is the real one here. Shipping 8x more code is impressive, but the harder problem is owning what an agent built when nobody on the team can fully trace why it made the call it did. Speed scales faster than accountability right now.

Here is Anthropic’s response… Claims to be skeptical of: “8x more code per quarter” — this specific figure isn’t from any Anthropic publication I can verify; it reads like an extrapolation or fabrication “Task length doubling every four months” — similarly, this precise metric doesn’t match any Anthropic research “By 2027, systems capable of weeks-long work” Anthropic has made generally statements about increasing autonomy, but this specific framing appears to be the post author’s interpretation, not a direct Anthropic claim

Notice how’s he’s always the only one spazzing— no one else— just Dario living in the Dario paranoid ecosystem.

reevion, yes Humans must remain in the loop!

Idk why your post appears again and again on my search it’s 7th times that i see over the last 5 weeks

What do you use for this technique

The "8x more code per quarter" stat is the one that should make people pause. Not because AI is replacing engineers, but because the feedback loop is already compressing. Every 4 months the task horizon doubles. At some point "we'll deal with alignment later" stops being a reasonable answer. Dario's right that the window to get this right is narrower than it looks.

That's amazing 🥰

Oh yeah, it's bad. Kill more trees to build data centers so that people can elf themselves. Or whatever they do with AI that is a complete waste of time and narcissistic indulgence.

Need a free lunch for free?

Yapay Zekâ Korkusu mu, Güç Mücadelesi mi? Anthropic’in yapay zekânın kendi kendini geliştirebileceği yönündeki uyarısı ciddiye alınmalı; ancak bu söylem yalnızca bir güvenlik alarmı değil, aynı zamanda büyük yapay zekâ şirketlerinin regülasyon, kamuoyu ve pazar üzerindeki etkisini artıran stratejik bir anlatı olarak da değerlendirilmelidir. Çünkü yapay zekânın hızla gelişmesi bilim, sağlık ve yazılım gibi alanlarda büyük fırsatlar sunarken, kontrol ve güvenlik risklerini de büyütmektedir. Buna rağmen bu risklerin sürekli vurgulanması, büyük şirketlere “bu teknolojiyi en iyi biz anlarız, denetimin merkezinde biz olmalıyız” deme imkânı tanıyabilir. Bu nedenle asıl mesele yalnızca yapay zekânın ne kadar hızlandığı değil, onu kimin, hangi çıkarlarla ve hangi şeffaflık düzeyiyle yöneteceğidir.

Interesting perspective. If AI agents eventually help build their successors, the challenge shifts from intelligence to governance. The organizations that win will combine AI capabilities with strong event-driven architectures, observability, and human oversight.

Well probably cause you are talking about ML and not training a LLM model which was what I was referring to. I have self learning ML pipelines in my memory systems, its not the same thing as an AI actually coding its own programs without me. So really saying, you, a person, didn't experience the AI not being able to code itself without a human around, since YOU gave it direction and decided to use those specific ML yourself when building it. ML and and LLM training are also radically different concepts. So calling it autonomous to the level of actually building itself, is not the same as you definition what errors are (your ML only know that cause a human defined it), your structured prompts written by humans, etc. If you don't believe me, ask your ai to write you a program where you don't know the industry, and see how well it does.

Recursive self-improvement is one of the most fascinating and important concepts in AI today. The opportunities are extraordinary, but so is the responsibility that comes with building systems that may eventually improve themselves.