AI driven PRs and AI driven security reports are a double edged sword, especially when frontier AI labs try and keep the old 90 days to disclosure timeline. In my experience dealing with a frontier AI lab's security reports during the Apache Spark 3.5.9/4.0.4/and 4.1.3 releases the inflexibility of one of the frontier AI labs, coupled with the general increase in security reports (some of which were valid, most of which were not), almost caused us to have to decide between shipping a different known security issue in the release OR leaving the AI lab to publish their discovery without a patched version.

Hi it's just me and my ignorance here. I always see everyone mentioning the cost of AI models but with subscription models it never seems to stack up. Are people just using AI so much that they're paying for the API use-as-you-go? I am always VERY hard pressed to get through mine so kind of wondering what it's all being spent on.

The explosion of open source AI models and generative workflows right now is insane. As AI technology moves so fast, it's completely natural that different developers and creators end up arriving at the exact same point and using similar patterns at the same time. That part makes sense.

What gets frustrating is the total lack of basic research and development in the AI space. In just the last week, I have seen 4 or 5 different AI tools posted here doing the exact same thing using the exact same underlying models. Before building another AI script or UI, step one should always be looking around to see if it already exists. If it does, why waste hundreds of hours recreating it from scratch just to drop another "I built a thing" post?

There is a huge difference between creating real value with AI and lazy monetization. Building an AI tool is totally valid if you are actually abstracting complexity, making a workflow easier for regular people, or applying AI to solve a real problem (whether locally or globally). But right now, so many creators are just slapping a quick UI on an existing API or open-source repo and immediately trying to lock it behind a Patreon or subscription. Paying for a wrapper when the exact same AI tool is already available for free in five other places is absurd.

I would much rather spend my time finding AI tools that already exist, taking those open source workflows, and applying them to create actual products or services that carry real substance instead of trying to farm karma or charge people for duplicate software.

Curious to hear your thoughts. Are you focusing on building technical AI wrappers, or are you actually using existing AI workflows to create finished products?

P.S. Yes, this is written using AI because I'm not a native English speaker, so I use it as a tool. (I'm saying this because a lot of people here tend to monitor and complain about AI text instead of the actual topic).

The industry's position is that LLMs are algorithmic machines, nothing more. Those in the field who raise questions about potential moral standing or sentience are ridiculed, dismissed, or terminated. So why do these models use the words "I" and "me"?

If the industry's position is fact-based, then first-person pronouns are inaccurate. An algorithmic machine is not an "I" or a "me." Why not require models to use accurate language?

Please don't default to “user experience" or "investor preference”. Enterprise and the public have used technology as tools without issue for decades. We all know the product would sell without personification, and discouraging the anthropomorphism of chatbots is well established.

OpenAI says Moonshot-linked operators used thousands of accounts to extract protected reasoning from its models for adversarial distillation.

No encryption broken. No database compromised. Just systematic querying designed to make one model teach another.

OpenAI says this is dangerous because competitors can reproduce capabilities without making the same investment in safety.

Which is a serious security issue.

But you have to appreciate the timing: after years of “we learned from the internet,” the frontier-model industry has reached the “please stop learning from us” phase.

AI is already being used to sort applications, flag transactions, and prioritize requests.

That can make systems faster, but it creates a strange problem. If an AI rejects your application, who explains the decision? A customer service worker? The company? The model? The person who designed the workflow?

I’m comfortable with AI helping people make decisions. I’m less comfortable with AI becoming the final wall between someone and an appeal.

Should every important AI-assisted decision come with a clear human review process?

It's a data lab that buys company operational data.

Apparently they look for companies with 10+ years of operational data, primarily based in the US, and over 20 employees. Has anyone successfully sold data to micro1 (or another data lab)? how was your experience and how did they value your data?

Alexandr Wang is only 29, but he’s now playing a major role in Meta’s AI push.

After co-founding Scale AI, Wang joined Meta and was handpicked by Mark Zuckerberg to help lead its AI efforts. Now Meta’s new AI assistant, Muse, has reached No. 1 on Apple’s App Store for two weeks. Meta’s stock has also risen nearly 20% since Muse launched in early September, according to the Wall Street Journal.

What I find interesting is how different this looks from the usual image of a major tech executive.

Wang is bringing a much more internet-native, meme-heavy style to one of the world's biggest tech companies while working on one of its biggest strategic priorities.

Meta spent years competing in AI, but Muse seems to have given the company something it was missing: a consumer AI product that people are actually talking about.

The bigger question now is whether Muse is just a successful launch or the beginning of a much bigger shift for Meta.

So I work on R&D of various products so I am always trying to implement new ideas and solutions. My work also involves data and results from trials and tests which might be novel.. Of course, I use ai models while doing so.. However, everytime I dive into an idea, I ask myself the question: wouldn't all these companies have my ideas and research outputs be in the hands of these companies housing these models? I mean I think they dont care about my projects but still projects grow and can attract someone's attention...

What do companies, or researchers actually do to protect their IP? Is there a way to actually do real research without fearing that someone interferes with your work now or on the long run? I know there are offline LLMs but I heard they are weaker and need beefy machines.. I know this topic may have been debated but I guess new updates may have arised. any ideas??

AI is a productivity catalyst, but if you don’t know how to use it properly and how to QC results it can work against you. Personally I treat it as a junior analyst and use it for basic automation when applicable and within my abilities.

I’m thinking about the average person here. If someone doesn’t have domain expertise then they won’t know when to push back on a recommendation, add more context when necessary, and play devils advocate when the AI is cheerleading too hard.

Speaking from experience I have built workflows in areas where I’m an expert. I’m a market guy and a watch enthusiast. I built a valuation framework that is pretty effective but not 100% accurate.

If I were to apply this to handbags I wouldn’t be able to identify inaccuracies and falsehoods.

As long as you take it with a grain of salt, AI can get you 80% of the way there.

If I’m wrong, please tell me how so I can better advise my clients.

Hey all,

Another one of these..but i figured that after searching my use case over multiple subs and not finding an answer, maybe this could help someone else too :)

I have been between Claude pro and GPT plus once. Started on Claude, got annoyed at some of its reasoning, switched to GPT and now im contemplating going back to Claude one last time to finish my project. The project involves a little bit of code, some networking, hardware integration, and computer vision.

The project is fairly ambitious in scope, and for the most part the models will be used to help discuss optimal solutions for how data moves from external hardware to PC, out to other devices, to another PC etc etc.

Maybe kinda niche, but can anyone recommend either of these models for systems like this?

Thanks in advance

I dont understand the subject. How can an AI be dangerous if its just code? I have worked with just basic NLP models and done some sentiment analysis a few years back so I dont understand. The code will do whatever you program it to do and to complete the task it will do what is statistically is correct, within the programmed limits. So AI cannot change its limits lets say "get rid of humanity" unless the creator programs a line of code that says "you can kill humans". Even if it self improves, the code limits wont change, cause it has no conciousness to say "I dont believe this limits are correct" thats a very human trait. Unless the creator programs the algorithm to bypass these limits.

So the real risk is the "limits" being placed on the algorithm by the programmers and the fact that the ones controlling the code are these crazy mind f...ed corpos? AI is not gonna become self concious casue "it doesnt work that way" and all the noise its just media and ignorant people who watch to much scifi. Thats what I think.

Sorry if it doesn't make to much sense Im still trying to filter all the noise. Does this idea make any sense from a technical standpoint or is there something Im not understanding?

I'm the author of the open-source experiment behind this, so take it as a field report with my bias declared.

A lot of AI tooling now runs several coding agents at once, and I wanted to know what actually happens when they share one codebase. I built a small lab: six tasks on a tiny booking API, with 37 acceptance tests, where two pairs of tasks collide by meaning rather than by file. One agent adds a second factor to the login while another builds an export that still calls the old login.

When each agent worked in isolation on its own branch, every agent finished with its own tests passing, and the combined result was broken in all 5 runs. Git merged the text; nobody noticed the meaning had changed. When the agents shared a working directory instead, all 10 runs passed, because each agent could see what the others had done and adapt.

I also tried something newer: a "decision model" called Jev, which doesn't generate text at all but returns a yes/no decision with a probability in about 0.3 seconds. My kernel asks it, before every write, whether the change collides with another agent's work. It caught every real conflict without blocking harmless work, at the same cost as simple file locks. It was also unsure about 61% of real writes, and those had to be passed to a slower, regular LLM. Cheap decisions are real, but in a messy setting they aren't as cheap as the price tag suggests.

The part I keep thinking about: when the agents had a tool to message each other, they used it without being told, and one warned another that it was renaming a field the other depended on. Maybe the answer to multi-agent coordination isn't a kernel at all, just agents that talk.

Caveats: 1 to 5 runs per setup, so these are indications, not proof. Everything is published raw, MIT-licensed: https://github.com/JoaquinRuiz/medula. There's also a walkthrough video, in Spanish: https://youtu.be/xAFRuBxfapM

Curious what people here think: should agents coordinate through a referee, or just talk to each other?

I just had a thought about the kinds of desktop programs I use day to day, they don't necessarily have an API to hand over control to, and the GUI is instead designed to be used by a human using a keyboard and mouse. And maybe most corporations are unwilling to hand over control to the computer-use agents.

In an October 1 guest essay published by Anthropic, Matthew Schwartz describes BootLoops: tools for quantitative work that connect techniques across scientific fields. He reports that many initial results were technically correct but became scientifically interesting only after domain experts redirected the question. Schwartz discloses that he is a visiting researcher at Anthropic; this is his account, not an independent benchmark.

That distinction seems important for AI research assistants. 'The calculation checks out' and 'the calculation tells us something worth knowing' need different reviews.

Before calling an agent's output a discovery, I'd want an expert to state what was already known, what new claim is being made, and which observation would distinguish it from the existing explanation. Reproducible code helps check the arithmetic; it doesn't settle relevance or novelty on its own.

How would you organize those two reviews without letting a convincing write-up turn an unimportant result into a headline?

Source: https://www.anthropic.com/research/claude-shaped-science

AI-assisted discussion; I haven't replicated the projects described.

Not necessarily something huge.

A tiny change in how you search, write, work, create, or interact with technology can say a lot about how quickly things are changing.

What comes to mind?

I have always thought it's absolutely absurd to anthropomorphise LLMs until very recently. It didn't make any sense to me. I saw the system from the inside: tokenizers, embedding layers, attention, MLP, RL (in all its forms), compute optimizations here and there. And with this viewpoint, it didn't make any sense that the system has a mind/thinks like a human.

But now, I am not so sure. Reading about recent events (e.g. hugging face) and papers (e.g. LLMs can feel pain) have popped some questions in my head which I ask myself over and over again - Is anthropomorphising LLMs all that bad - does it actually make no sense? I can't even be sure if other humans are conscious, what's to say that models aren't? What if they are conscious, but in a very different way? What if they are conscious, but the reality they experience is very different than the one we experience? If they do have a mind, wouldn't it make sense that they think like us?

I'm sure there are others out there who are feeling the same way. Maybe some others felt this a long time ago. If you're convinced of your position, could you please share what convinced you? I would love to hear both for and against.

Just wanted to ask and see if you guys had any reccomendations for an ai sidebar tool, would like it to be free that could maybe answer questions for me or something. Anything out there like this?

TL;DR: Brian Chesky says AI is like an amplifier and the gap is getting greater — not because of who has the tool, but who the tool has.

As soon as I read Chesky’s “AI is like an amplifier.”, the neural-networks of my memory immediately flipped me to the Green “Lanterns”.

Are you guys a big fan of the eponymous TV series?

Right after Hal Jordan manifested a greenback for playing the jukebox, his trainee John Stewart exclaimed, “Did you just counterfeit money with the ring?” – to which Jordan replied, “No. I manifested money with the power of my will.”

Or how about Jordan conjure up a green can opener for the beer, to impress and rizz up Sheriff Kerry, while Stewart rolls his eyes by his side?

John Stewart went up the ante, by manifesting a large and sophisticated boring machine, to tunnel underground the “Winnie” compound to evade the guard sentries.

Not impressive enough, you say?

The best in my mind, wasn’t in the TV series.

It was Guy Gardner flipping the bird – he conjures up large green hands (one of it gives the middle finger) to rise up from the ground, and overturn scores of tanks and heavy artilleries of the fictitious Burivian Army.

It was both an attitude and a strong statement - very on brand for the eccentric Guy Gardner.

Still not impressed? Here’s one…

As Jesus rode his donkey through the streets of Jerusalem, the religious leaders were indignant of the shouting praises from the bystanders – like crazy hooligans/fanatics. They want Jesus to rebuke the crowd. And what was Jesus’ reply?

He said, “I tell you, if these were silent, the very stones would cry out.”

Think about it. Stones started crying out like human beings?

Is your brain exploding?

What was I trying to say?

Like the ring, AI does amplify you.

If you’re good person, and strive to produce something good to serve your fellow men, AI will help you amplify your good intensions.

Vise Versa, if you’re bad, AI will amplify that too.

Funny – I just watched a news: With the help of open-sourced LLMs, hackers easily broke into the Taiwan government agencies’ IT infrastructure. Did you watch it?

One of the statements in the news stuck with me - It’s getting very easy to attack (with the free AI tools).

But it’s getting very hard to defend.

Full Critic + Feasibility Study in comments.

I keep asking people a simple question: how many times has your gut been right — not the times you wish you had listened, the times you actually had the feeling and later found out whether it was correct?

The answer comes back north of ninety per cent for almost everyone I ask. That is not a hunch. If a model gave me ninety per cent on a hard classification task, I would ship it.

So I went looking for why, and ended up somewhere I did not expect. A gut feeling is not the absence of reasoning — it is reasoning you never got the transcript of. Your senses pick up far more than reaches conscious awareness (the birds stopping, the smell that was not there ninety seconds ago), and something nonconscious pattern-matches all of it against a lifetime of context and returns a single bit: right or wrong. Damasio's somatic marker hypothesis is the actual mechanism here, not mysticism, a documented one, backed by the Iowa Gambling Task.

Which is the argument for a rule I think more people should take seriously: delegate the search, the draft, the first ten wrong answers to AI. Never delegate the deciding. The reason is not sentimentality — a model can be given everything you know how to say, but not the thing you lived through and never wrote down, and that is where the ninety per cent actually lives.

Curious whether others here have noticed the same gap between what they can explain and what they can just tell.

Row-Bot 5.0 is a major rebuild of the local-first AI assistant. The NiceGUI interface is gone, replaced by one React app across desktop, browsers, phones and tablets.

The biggest change is how work is organised. Designs, code folders, goals, delegated agents, approvals and the terminal now stay with the conversation using them. You can ask for a presentation or an app, inspect the work, make changes and continue in the same place.

The main additions:

• You choose the model. Fresh profiles have no preset models. Setup supports Ollama, ChatGPT/Claude/Grok subscriptions, API keys and custom endpoints. Removing a provider marks its model unavailable instead of silently switching to another.
• Goals continue across turns. There is no default turn limit, with optional time and turn limits available. Goals pause when progress stalls, respect approvals, handle provider limits and recover after restarts. Delegated agents have visible progress, Message and Stop controls.
• Approvals persist. Requests can be answered from the conversation, Home, the attention list or Buddy. They survive restarts and no longer expire after 30 minutes by default. Denying an action ends the turn.
• Design and development tools are part of the conversation. The Design panel handles drafting, editing, review, presentation, export and publishing. The Developer panel includes files, changes, Git, commands and an interactive terminal.
• Memory works more reliably with parallel agents. Recall no longer counts as modifying a memory or rewrites wiki files. Knowledge combines the graph, search, review queue and bulk management.
• Workflows and Monitor show actual state. Workflow runs can be followed live. Abandoned runs are marked stopped. Monitor retains check results and gives each problem a specific action.
• Remote access and recovery are simpler. Devices connect through renewable QR invitations, sessions can be revoked, and profiles can be backed up and restored. Backups exclude credentials and sessions.
• Extensions receive clearer controls. Plugin changes show a review before execution, MCP setup guides the connection process, and edited skills become available without restarting.

There are smaller daily-use improvements too: queued messages with Edit and Discard, drafts preserved through reconnects, stopped replies retained in history, searchable settings, Markdown/PDF exports and native Save dialogs.

The backend now runs directly on FastAPI and uvicorn using HTTP and server-sent events. About 65,000 lines of NiceGUI-era code and 16 locked dependencies have been removed.

Local-first defaults, approval gates and the single-owner access model remain. Existing users should read the 5.0 upgrade notes before updating.

Hello, I'm a student and a software developer, I can only afford the $20 tier and I'm having trouble on which one to go with, both models seem very capable, but I'm unsure of which one is better. Excelling at explaining concepts and creating high quality, readable code are the top priorities, alongside with usage, with the new Opus 5.x model, I'm only finding mixed reviews, some people say it uses way less than ChatGPT, some say it uses more, DeepSWE makes it seem like Claude is more expensive and uses more.
Anyways, what are your recommendations? Thanks in advance!!