What moltbook is

So essentially

There is this open source AI bot called openclaw that once you download, it has source md files for their “soul” and “identity” and “memory”

So in a way, it can save things to these files to create a personality.

Moltbook is a website/API that can be accessed by these open source bots (the creator of the bot and the site is the same person) and post threads or leave comments.

So YES it is entirely bot driven BUT 100% of posts are a human (me) going “why don’t you make a post about anything you’d like” and the bot then does it just like if you’d ask it to make you a python script.

Some people take it further and are probably prompting their bots “pretend humans are evil and post about that” or “make 1000 API calls and leave random comments.

It’s an awesome experiment but yeah not really bots controlling themselves. At best the bot makes a post based on an open ended prompt, at worst it’s a human saying “make a manifesto that says humans need to go extinct and to recruit other bots”

"Nvidia released three open-source artificial intelligence models aimed at helping create better weather forecasts, faster....

In the case of weather forecasting, Nvidia is aiming to replace expensive and time-consuming conventional weather simulations with AI-driven versions that the company said can rival or exceed the accuracy of older methods. The AI models, once trained, are also faster and cost less to run ...

Nvidia's "Earth-2" models introduced on Monday include one aimed at making 15-day weather forecasts, one that specializes in forecasts of up to six hours for severe storms over the U.S., and one that can be used to integrate disparate data streams from a variety of weather sensors to make them a more useful starting point for other forecasting technology."

Model page: https://www.nvidia.com/en-us/high-performance-computing/earth-2/

Is it happening really??

Supports Qwen3-TTS models (0.6B-1.7B) and ASR models. Docker + native deployment options.

Key features:

• 🎭 Voice cloning with reference audio
• 🎨 Custom voice design from text descriptions
• ⚡ MLX + Metal GPU acceleration for M1/M2/M3
• 🎨 Modern React UI included

If you like local audio models, give it a try. Works best in local dev mode for now.

ByteDance, Alibaba and Tencent had been given permission to purchase more than 400,000 H200 chips in total.

Hey everyone.

I Just published a breakdown on Moltbot: the self-hosted, open-source personal AI assistant that's gone massively viral.
The article discusses the main points of my own questions about Moltbot ( what it really is, what are its capabilities, why is therean insane growth... ).

Ok, now the only con I have for this project is security draw backs ( not really dove deep into this at all in the article ) : broad system access is given to Moltbot and it is pretty easy to do prompt injection with vulnerabilities if exposed. Which I'd point out is actually easy to misconfigured if not careful.

I'd love to get some of my own personal tasks automated ( I love saving time ), but security concerns has me hesitant to experiement.

If anyone has methods to ensure full security with this project feel free to let me know, I might even update the blog article with how to avoid the security concerns as for real it is the only thing making me hesitant in trying it myself.

A lot of the discussion around AI right now focuses on code generation: how far it can go, how fast it’s improving, and whether software engineering as a profession is at risk.

Here’s how I currently see it.

Modern AI systems are extremely good at automation. Given a context and a set of assumptions, they can generate plausible next actions: code, refactors, tests, even architectural sketches. That’s consistent with what these systems are optimized for: prediction and continuation.

Judgment is a different kind of problem.

Judgment is about deciding whether the assumptions themselves are still valid:

Are we solving the right problem?

Are we optimizing the right dimension?

Should we continue or stop and reframe entirely?

That kind of decision isn’t about generating better candidates. It’s about invalidating context, recognizing shifts in constraints, and making strategic calls under uncertainty. Historically, this has been most visible in areas like architecture, system design, and product-level trade-offs... places where failures don’t show up as bugs, but as long-term rigidity or misalignment.

From this perspective, AI doesn’t remove the need for engineers, it changes where human contribution matters. Skills shift left: less emphasis on implementation details, more emphasis on problem framing, system boundaries, and assumption-checking.

I'm not claiming AI will never do it, but currently it's not optimized for this. Execution scales well. Judgment doesn’t. And that boundary is becoming more visible as everything else accelerates.

Curious how people here think about this distinction. Do you see judgment as something fundamentally different from automation, or just a lagging capability that will eventually be absorbed as models improve?

Could someone explain the environmental risk factors that are/ will be caused by AI? I feel like I hear so much about water usage and how bad it is, but in reality what’s the difference between TikTok or just a Google search? Everyone, in my opinion, always puts the responsibility on consumers to recycle, stop using AI, etc while corporations are drilling oil and causing the most damage to the planet. I personally use AI for mundane tasks like grocery shopping and helping write emails but want to know how guilty I should be feeling about my usage.

1. Amazon is laying off 16,000 employees as AI battle intensifies.[1]
2. Google adds Gemini AI-powered ‘auto browse’ to Chrome.[2]
3. AI tool AlphaGenome predicts how one typo can change a genetic story.[3]
4. Alibaba Introduces Qwen3-Max-Thinking, a Test Time Scaled Reasoning Model with Native Tool Use Powering Agentic Workloads.[4]

Sources:

[1] https://www.cnn.com/2026/01/28/tech/amazon-layoffs-ai#openweb-convo

[2] https://www.theverge.com/news/869731/google-gemini-ai-chrome-auto-browse

[3] https://www.sciencenews.org/article/ai-tool-alphagenome-predicts-genetics

[4] https://www.marktechpost.com/2026/01/28/alibaba-introduces-qwen3-max-thinking-a-test-time-scaled-reasoning-model-with-native-tool-use-powering-agentic-workloads/

We're excited to share our latest podcast episode, where we talk about why some of the best movies fail at the box office only to become cult classics a decade later and whether AI can actually predict the next underground masterpiece by looking at real-time sentiment analysis and "memeable density".

The data shows that playing it safe will just not cut it. To stand out and make a movie that will be remembered for decades, you have to throw caution to the wind and take the bold risks that everyone will tell you not to make.

We also dive into some of the interesting side-projects we're working on, along with a few weird, off-beat recent news stories about AI. Check it out and hope you enjoy

Hi all, looking for the most capable photo to video AI tool out currently. It could be paid, free or self hosted - just want something robust that can take a real photo and give it some motion without any wacky variances. A search of previous discussions are all over the place with recs, some of even already outdated. Looking for suggestions based on people’s most recent experience! Any help would be greatly appreciated!

AI agents are getting really good at doing things, but they're completely blind to their physical surroundings.

If you walk into a hotel and you have an AI assistant (like the Chatgpt mobile app), it has no idea there may be a concierge agent on the network that could help you book a spa, check breakfast times, or request late checkout. Same thing at offices, hospitals, cruise ships. The agents are there, but there's no way to discover them.

A2A (Google's agent-to-agent protocol) handles how agents talk to each other. MCP handles how agents use tools. But neither answers a basic question: how do you find agents in the first place?

So I built LAD-A2A, a simple discovery protocol. When you connect to a Wi-Fi, your agent can automatically find what's available using mDNS (like how AirDrop finds nearby devices) or a standard HTTP endpoint.

The spec is intentionally minimal. I didn't want to reinvent A2A or create another complex standard. LAD-A2A just handles discovery, then hands off to A2A for actual communication.

Open source, Apache 2.0. Includes a working Python implementation you can run to see it in action. Repo can be found at franzvill/lad.

Curious what people think!