Browse Comments — Clean (de-noised)

In terms of data security, using 7z and setting passwords of 20 characters or more keeps our data safe; VeraCrypt remains secure.

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

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!

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.

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.

Anyone who thinks that at least part of their experience interacting with any online service or application that hasn’t been at least partially coded by “an AI” is kidding theirself. It’s that pervasive. I’m open to being proven wrong. But I don’t think that I am.

Anyone who thinks that at least part of their experience interacting with any online service or application that hasn’t been at least partially coded by “an AI” is kidding theirself. It’s that pervasive. I’m open to being proven wrong. But I don’t think that I am.

Potential compounding effect? Bruno Larvol

Like GPT-3 and Mythos were to dangerous for the world and in the end they were not that special? I no longer buy that. I think that they have reached a peak in the current LLM approach and need an excuse to focus on research without the pressure to release a new model every x months to show progress for financial funding.

To me, the challenge is not that AI is advancing too quickly, but that our ability to absorb, commercialize, and responsibly consume these advances is not keeping pace. The technology is evolving at an extraordinary rate, while the mechanisms required to translate breakthroughs into sustainable products, business value, and widespread adoption inevitably move more slowly. That is why I find some of the recent “slow down” narratives difficult to separate from commercial and strategic interests. The issue may be less about controlling AI itself and more about giving markets, institutions, and companies enough time to adapt and capture value from what is already being created. More importantly, the real bottleneck may not be AI at all, it may be people. The success of AI will ultimately depend on whether users, organizations, and societies can develop responsible consumption habits, effective usage patterns, and the skills required to integrate these tools into everyday decision-making and work. In that sense, the conversation remains fundamentally human, not technological. AI may be accelerating, but adoption, trust, behavior change, and value creation still depend on us. Not totally AI. Still very much about humans. 😉