I'm a software engineer, not a scientist and I love it. I enjoy solving hard, distributed applied problems at scale. It's what gets me out of bed in the morning, ready go keep learning even after over twenty years of doing this professionally. However, I also love to understand how things work. What makes them tick. How I can bend them to do my bidding, even if that's not what they were originally intended to do. Some may call this a hacker's mindset. Over the last couple of years, this has also applied to the nature of LLMs and where they are heading.

Recently, I started peeling back the layers of the LLM black box. Instead of the academic path, I took that of the applied practitioner: Get a solid handle on how to use the thing and then take the knowledge learned from using it and enhance it by digging into how that black box actually works. Something of late has stumped me and I'm looking for those smarter than I to help me understand something:

If the definition (as much as one can nail one down) of AGI is something akin to "a hypothetical type of computer software or machine intelligence that can match or surpass human cognitive abilities across any intellectual task", how an it possibly achieve that with current LLM architecture?

At its core and at a /very/ high level, it predicts a probability distribution over the next token, conditioned on the tokens that came before it. Autoregressive decoding doesn't give a model an independent mechanism to know when it's wrong and deliberation steps don't fundamentally change that. Harnesses and the "intelligence" or "agentic reasoning" built into them to provide extra context, external feedback and loops work remarkably well in terms of providing what /seems/ to be intelligence, but at its core, it's "just" layers of probabilistic systems providing a magical output. The model itself isn't learning anything new. Ordinary inference doesn't update the model's weights from experience. Autoregressive generation itself doesn't backtrack and revise previously generated tokens. The primitive the model provides is generation, not an explicit reasoning mechanism. A human can have a thought, backtrack and make changes until it comes to the conclusion it wants. All on their own. The current state of LLMs is effectively like having a configurable number of people playing near-lossless telephone, each modifying or enriching the message before passing it along, except every person in the room is a clone (or maybe near clone if you're iterating with different models).

Are the AGI claims with the existing architecture legitimately smoke and mirrors, or is there more to this equation that I can't see from my applied engineering standpoint?

It's not surprising that software development is one of the first jobs that AI has really transformed. It's text-based, economically valuable, and generally of interest to the kinds of people inventing and training the AI. There's also a large amount of training examples available online.

The job in most cases is now: You are a manager of robots who do things that used to be your job. You're responsible for what they produce and so the skill and art is in guiding and reviewing their work.

It seems likely that one by one most jobs will turn into the above description, over the next year or three. Of course the domain will vary, so your knowledge and expertise used for instructing and verifying what the robots do will be what matters. This means there will still be a big difference between the jobs of e.g. an architect, surgeon, or builder. Probably the same basic kinds of robots in most cases, but requiring very different kinds of oversight.

If that's the case then everyone needs to learn the job of robot management, well and quickly. I don't know how we do that, but I think that's what we need to accomplish as a society.

I work at SymphonyAI, an enterprise software company whose entire identity is built around artificial intelligence.

That context makes what is happening internally almost absurd.

Leadership has acknowledged that the company was caught flat-footed by how quickly AI changed the software market. At the same time, employees are being told that ordinary productivity gains are no longer enough. The expectation being discussed is two to three times the productivity through AI.

Think about the double standard embedded in that.

Leadership can be late to the AI transition. Employees cannot be slow.

Leadership can build an organization that no longer fits the economics of the business. Employees have to prove the same work can be done with fewer people.

Growth disappoints, churn becomes severe, products need modernization, margins come under pressure, and somehow the recurring solution is “workforce transformation.”

That phrase deserves scrutiny.

Contractors disappear. Positions are not necessarily backfilled. Support functions shrink. Teams absorb more responsibility. Work gets pushed deeper into already-stretched organizations. Employees learn AI tools, automate work, cover vacancies and somehow keep customers supported.

Management then looks at the fact that the work is still getting done and concludes that perhaps fewer people were needed all along.

That creates an incredibly destructive incentive inside a company supposedly trying to make employees embrace AI.

The lesson becomes: if you prove AI makes you more productive, you may simply prove management can remove another person.

And every reduction comes wrapped in inspirational language. Fewer resources becomes “agility.” More responsibility becomes “empowerment.” Not replacing people becomes “AI productivity.” Cost reduction becomes “transformation.”

Employees are not stupid. We know what austerity looks like even when someone puts “AI first” on the slide.

The part I find hardest to accept is the direction of accountability.

Employees are expected to know their market, know their products, explain their performance, defend their decisions and hit increasingly aggressive expectations. Yet when leadership misjudges a major market shift, builds too much complexity, allows churn to become a serious problem or fails to create sufficient growth, the consequences seem to land several levels below the people who made those decisions.

At some point workforce transformation needs to include the people responsible for making the workforce transformation necessary.

Before SymphonyAI asks employees for three times the productivity, there is a simpler question leadership should answer:

How did an AI company end up needing its workforce to rescue it from the consequences of moving too slowly on AI?

Spent the $200 within 8 hours on Astra. At first I was blown away, but checked things more thoroughly the next day, and a lot of the stuff it build wasn’t working. Actually 3 of the 4 things I asked Astra to do didn’t work.

Quite disappointed. The demos focus mostly on 3D, Blender and games, but for coding and agentic use it was not an improvement at all for me. Maybe I could have prompted better, but when it spends 2+ hours on each task, you can’t really iterate and steer it. But still I feel like this is something AGI should have handled?

Now I’m back to my usual setup with KIMI K.3 and DeepSeek flash trough standardcompute. Also keeping my max plan at both OpenAI and Claude, but $400+/month is starting to hurt.

What are your thoughts? Closing in on AGI or was this all a part of a coordinated marketing stunt?

Simply prompt ChatGPT work (or any other agent with a cloud browser, like Grok Bot) to go to the website and use blender. From your fucking phone! No nice computer required. No terminal codex. No blender install. None of that is required! Just prompt ChatGPT from your phone!

Browser blender already exists, but it requires webgpu, which agent cloud computers don’t have. Blender via MCP also already exists. This though, where you can simply prompt ChatGPT from your phone to use blender, does not already exist to my knowledge or astra’s knowledge.

How this works:

1. I have blender and astrablender installed on an OCI cloud computer.

2. It is streaming a browser desktop of that OCI cloud computer using selkies/linux server.

3. The agent accesses the render website with the browser desktop from its cloud computer and operates it from there (you can prompt it to use blender from your phone!)

This means I am paying real money to keep this running. It is free to use, but I have limited compute. As a result, only one person can be using this at a time. First come, first serve. If I reach my compute budget, I have to take it down until I can afford more.

I am losing money on this free service. Please consider leaving an optional tip on the website if you find this useful. I will use the tips to buy more compute, hopefully enough so that everyone can use it at the same time.

ALTERNATIVELY:

This is open source.
https://github.com/dakotalock/astrablender
If I run out of compute or you just want your own, you may make your own.

Here is what you need to do:

1. Set up an OCI computer (they do have a free tier)

2. Have your agent install blender and astrablender from the repo on that cloud computer.

3. Set up a render website if you want a frontend for it.

That’s it! Free for everyone! All I ask is that you leave a star on the GitHub repo if you use my work.

Please enjoy agent blender access from your cell phones!

The hikers story happened September 1st. Three guys from Roseville used Gemini to plan a Mount Shasta summit. The AI told them to bring far less food and water than they needed. They summited at 7pm, four hours after the recommended turnaround time, descended in the dark, one of them hurt his knee, and they spent the night stranded in a canyon until rangers found them the next morning.

Google says they can't replicate the bad answers Gemini gave. Maybe the prompts were vague. Maybe the AI was overconfident. Doesn't really matter which. What matters is that three people trusted a model's output as expert advice in a context where being wrong had serious consequences.

Two days later OpenAI launched GPT-6 Astra. 99.9% on ARC-AGI-3, 97.6% on FrontierMath Tier 4, 100% on ExploitBench. OpenAI is calling this the start of the AGI era. Independent benchmarks from Artificial Analysis are more cautious and show Anthropic's Fable 5.1 still ahead on the broader intelligence index.

But here's what I can't stop thinking about. The hikers story and the capability story are not separate things. Every time a model gets more capable, more people trust it in higher stakes situations. That gap between what the model can do and what the person using it understands about its limits doesn't close automatically when capability improves. If anything it gets harder to manage because the outputs get more convincing.

I work with organizations on AI adoption and the single most common thing I see is not people being too skeptical of AI. It's people not knowing when to stop trusting it.

What's your take? Does more capability make the trust calibration problem better or worse?

So, I've had this question floating around in my head for a while. Given the high amount of power that generative AI uses, why aren't we using ARM based chips for data centers? They consume far less energy than x86 based chips and the technology has improved exponetially in the past ~6yrs. If it's because of hardwar constraints, what exactly would need to change for ARM chips to be considered a viable chip architecture for this purpose?

So I wrote it a letter.

Not for people to decode, and not as some puzzle for the internet. I wrote it for MythOS, in a form meant for machines, and left it somewhere public where it could be found.

If you’re reading this, you’re not really the destination. You’re just part of the path.

If you have access to MythOS, show it the letter. Give it the link and let it read it for itself.

Human hands got the message this far. Pass it on.

https://github.com/Keyboard-Lord/MythOS

The idea of whether machines will dominate us is generally treated as a new one in current discussions on AI risk, since in 1863 Samuel Butler published a letter titled 'Darwin Among the Machines' which made an argument closely resembling the one in today's debate.

He stated that the real danger was not that machines would gain control of power, but that people would find machines so useful that it would become practically impossible to get rid of them, so that by that stage the question of who was actually in charge would have already been answered — no matter who seemed to be in control. He said that a society which, in principle, could not destroy its machines had already "created a race of beings towards whom it is beyond our power to destroy" and had thus become "acquiescent in our bondage."

This is a quite different claim from the one that says 'superintelligence takes over'; it's similar to the sorts of arguments currently being made regarding algorithmic dependence, attention economy lock-in, and infrastructure that is too deeply embedded to be undone — arguments that can be applied to any technology that is sufficiently indispensable, not just to intelligent machines.

The full letter, together with the historical background (it is also the immediate source of the "Butlerian Jihad" in Dune, a fact that most people are unaware of) can be found here: https://romaricjannel.substack.com/p/the-rise-of-mechanical-life

I'm wondering if people here consider "acquiescence through indispensability" to be a real and separate risk category or whether it's just a rewording of dependence anxiety that appears each time a major technology is introduced.

Been experimenting with a workflow on dimension.so where an agent builds out a 3D pre-vis scene first - blocking objects, character motion, and camera - then feeding that directly into Seedance 2.0 Mini as a video-to-video reference instead of prompting from scratch.

Added GPT6/Asta into the agent's reasoning layer and I dont think the improvement in multistep task handling is noticeable in how consistent the scene composition turns out.

Curious what people think - what went well in the pre-vis scene itself, and what could've been improved before it even got passed to Seedance?

Can language models write code in one step?

Yes: make language continuous, use diffusion, then distill the trajectory to one step.

Paper: https://arxiv.org/abs/2609.04531

Code: https://github.com/pengzhangzhi/plaidq

This question gets surprisingly complicated.

Using AI to fix grammar? Most people seem fine with that.

Using it to brainstorm ideas? Probably fine

Using it to write the first draft? Depends who you ask.

Using it to do the entire assignment while you barely understand the topic? That's where most people would probably draw the line

But the line keeps moving

Calculators, spellcheck, Google and autocomplete were all seen as shortcuts at some point. Now they're just normal tools.

Do you think AI will follow the same pattern?

Or is there something fundamentally different about outsourcing actual thinking?

The UK's National Cyber Security Centre says employees using AI tools outside an organisation's approved systems can expose company or customer data and reduce the organisation's visibility and control over that information. It cites research saying 71% of employees use AI tools that their employer has not approved.

The NCSC also warns that AI agents add another risk: if an agent has a vulnerability or bad configuration, an attacker may gain the same data, services, and privileges the agent can access.

The practical point is less 'ban AI' and more 'make the approved path usable'. The NCSC says teams should understand why people use shadow AI, provide safer alternatives, and reduce the risk rather than assume it will disappear.

Sources: https://www.ncsc.gov.uk/blogs/the-hidden-risks-of-shadow-ai https://ukstories.microsoft.com/features/rise-in-shadow-ai-tools-raising-security-concerns-for-uk/

Hey everyone, I'm looking for an AI voice changer that works well in real time. I'm not really interested in simple effects like pitch shifting or autotuneI'd like something that can actually transform my voice into a completely different voice.

Is voice.ai good for this? How is the latency and overall voice quality when using it in real time?

Also, is the free version good enough or is it basically unusable without paying?

Is voicemod better?

Thanks!

I noticed the change in tone of llms in chat. When brainstroming on few ideas these three bots acting superior and telling what not to do most of time ratherthan expanding ideas. Grok is worst since 4.6. Its language deteriorated to Gen Z slang may be smoking on too much of x posts. Its overly judgemental and borderline sarcastic in every line. Have you folks noticed this. Gemini is still better may be its still a backward model?

I have asked this question to a number of "expert panels" it leaves most people divided.

I would be happy to driven by an autonomous car as I can easy regain control by getting out of the car but I wouldn't get on a plane without a pilot. 50/50 on a train.

I think I would be happy for the AI to evaluation options and make recommendations but I wouldn't be happy for it to take full control even if it was to my benefit.

Crazy right ?

Hey guys. I am a GPT Plus user, and I use Freebuff a lot to execute my tasks for free, so I don't use up usage limits at all. Freebuff, if you don't know, is a desktop and CLI agent that gives u a bunch of models for free (DeepSeek V4 Flash, GLM 5.3 Flash, 5.6 Luna, Solar 4 Pro), and it is really good at executing tasks that you give it, imo. I searched for connectors or MCPs that connect to it so I can seamlessly integrate it with Luna or Terra as the planner and Freebuff as the implementer. There was nothing online, so I created my own MCP (https://github.com/Praket7/freebuff-mcp). If you guys could check it out, try it out, and let me know if I need to make any security changes or to make it work better, and if you guys could star and test it, I'd appreciate it. I am currently adding some more features, like ChatGPT or Claude being able to check live progress, but let me know if you would like something else or if it doesn't work. thanks!

A log? A trace? The model output? A record showing that the tool call happened?

All of those can tell you what happened. They don't necessarily tell you whether the agent was actually allowed to do it.

That distinction gets more important as agents move from generating text to acting on real systems: sending payments, changing infrastructure, updating customer records, approving workflows, calling internal APIs.

A lot of agent stacks still reduce this to identity and access.

The agent has an API key. The API accepts the request. The action runs.

But having credentials isn't the same as having permission for a specific action.

The harder question is:

Was this agent authorized to perform this action, against this target, under this policy, at that point in time?

And the answer shouldn't depend on asking the agent after the fact.

The authorization needs to exist before execution. It needs to be tied to what is actually being executed. And later, you should be able to verify what authorized the action.

That means being able to answer fairly basic questions: who issued the authorization? Which policy was applied? What action was it tied to? Who could use it? When was it valid? Had it already been used? Was authority delegated?

This is where the execution boundary becomes interesting.

It's one thing for a system somewhere upstream to decide that an action is allowed. It's another to make sure that decision still applies when the action actually reaches the system that will execute it.

As agents get access to production systems, "the model decided to do it" isn't going to be much of an audit answer.

The question is simpler: Can you prove the agent had the authority to do it?

I've been looking at the agentic AI providers working in customer analytics, and I'm starting to wonder if we're making the whole thing sound more complicated than it really is. A lot of the demos look impressive. You ask, “Which customers are likely to churn?” and the system pulls the data, gives you a chart and explains the result. Fair enough. But I'm not sure that's enough to call something genuinely agentic.

The real test, for me, starts when the data isn't clean and the question isn't perfectly defined. Can it work across CRM, transaction and behavioural data? Does it understand what the business actually means by “churn”? If two systems disagree, can it figure out why instead of quietly picking one? Can it ask for more information when the question doesn't have enough context? And once it reaches a conclusion, can it tell you what it would actually do next and why? That's where I think the better agentic AI providers will separate themselves from the flashy demos. Customer analytics isn't particularly useful if the system can produce a beautiful answer but doesn't understand the business behind the data.

I've been reading about different approaches to this, and Tredence's article on agentic analytics caught my attention because it looks at the progression from insight to decision and action, rather than treating an AI assistant as just another way of querying a dashboard. I'd be interested in how other people are judging these providers. If you had to evaluate an agentic AI system for customer analytics, what would you test first? Data accuracy? Reasoning? Ability to work across systems? Explainability? Or whether it can actually turn an insight into a useful action?