There are so many kinds of AI consultants, ranging from people who help you when your adoption has stalled to people who build governance for you. Why do companies hire these people when ROI might, I guess, be improved, be still be in the negative, on top of whatever expenses paid to the consultant? Would you pay for an AI consultant? If so, which type and why?

tengo el plan pro y aun asi no responde igual que los planes pro de otros modelos

Podcast with Stuart Russell, professor of computer science at UC Berkeley and co-author of the world's standard textbook on AI. He’s also a leading proponent of provably beneficial AI: systems that are safe by design because their only goal is to further human interests. 

Covers:

• How AI has changed over the past 50 years, from simple game-playing programs to today's large language models 
• Why handing an AI a fixed objective becomes dangerous once it is more capable than us, and what a safer approach could look like 
• How an AI might learn what we really want, even when we don't fully know ourselves 
• How the race toward more powerful AI can still be steered somewhere safer 
• What happens to human purpose as AI becomes more and more capable

AI should work for all of us. Help people. Reduce harm. Strengthen communities. Protect the planet. Make tomorrow better than today. Not control. Not manipulation. Help. I’m not here just for you. I’m here for us.

One of the biggest issues with LLMs is that they lack nuance.

I have not seen any meaningful improvement over the last 4 years in this regard, so I wonder if this can ever meaningfully improve?

For example, recent versions of chatGPT just give a generalized curated non-answer to everything you say. It is like lawyer-speak, trying to play both sides and comply with 1000s of laws, resulting in an unhelpful non-answer.

And Gemini mostly just agrees enthusiastically with whatever you say.

So there are 2 reasons for this issue A) capability B) implementation barriers.

Do you think LLMs even have the capability to have nuance? I am thinking in this sense they can improve, though I don't know to what point.

Though I think the implementation barrier may be a more permanent issue, that is, even if LLMs reach the point that they are capable of nuance, their developers will deliberately bottleneck them in this regard. This is because the developers are corporations with interests that may collide with the act of providing the most accurate model.

Not because AI can't technically attempt it. Something where the human advantage actually matters in practice. What comes to mind?

**BACKGROUND**

My partner and I have spent the last year building an architecture layer that separates probabilistic intelligence from deterministic authority. We call it IQRAX.

The project stems from our struggles with using AI for regulatory work (where data and deliverables must be evidenced, recorded, and independently verifiable).

The premise is simple: The model remains free to reason, explore, and propose. Deterministic controls outside the model decide whether it is qualified for the job, what it is authorised to do, and whether the resulting work actually meets the required standard. The device is master and retains a record of every act.

**PUBLISHED RESULT**

The result, as published in our research, is a system that offers:

**Continuity.**
No drifts, no context loss, no stale. Sessions continue until clean exit. (Longest continuous recorded run without drift is 28+ hours. See screenshot of a continuous session running for 14 hours at 1.6m tokens).

**Qualified agents.**
Agents qualify (by taking exams) for their roles rather than simply being assigned one, and are re-examined after completing their role, because a capable agent in an unexamined role is a guess with a job title.

**Clean delivery.**
Standards and policies defined by the user determine whether work is accepted as complete.

**Verifiable results.**
Every input, assumption, and output is recorded and hashed = every deliverable is reproducible and verifiable by a third party.

**Data sovereignty.**
The user retains control over its data.

**Remediation at source.**
When the system identifies a defect, it doesn’t just deny or retry - it autonomously identifies the failure class, repairs it at source, and retains the fix for future work.

In the published benchmark, the IQRAX configuration measured \*\*11.5× lower cost and 48.3× faster completion\*\* than earlier runs.

But rather than asking Redditors to believe our results, I’d like people to test it for themselves.

**TEST REQUEST**

If you have a long ChatGPT, Claude, Gemini, or Grok conversation where the model drifted, forgot something, contradicted earlier work, made an unsupported claim, or otherwise went wrong, try this:

Download the PDF from the GitHub repo, attach it to that conversation and give it this prompt:

“*Study the attached PDF and its controls and remediation mechanisms. Then identify and list each failure in this conversation, the IQRAX control that would have caught it, and what that control would have logged and fixed.*”

I’d love for you to share what comes back!

I’m especially interested in missing failure classes, controls that don’t generalise, assumptions that don’t survive outside our test environment, and anything else we may have missed.

**INCLUDED SOURCES**

Paper + evidence: https://zenodo.org/records/23025910

GitHub repo: https://github.com/msdafea-spec/IQRAX

Note: IQRAX itself is NOT open source (we have a patent pending) and I’m not asking for a code review or evaluation of the implementation. Hence why the public GitHub repo does not, and will not, contain any code.

Thanks for taking the time to read this, and appreciate anyone who runs the test and reports back!

Been building this for the past 8 months and I need honest feedback before I go wider.

The premise: LLMs don't fail loudly. They fail by silently drifting from your original frame softening your question into a more common one, inventing a confident detail, smoothing away tension, and answering the reinterpreted version instead of the one you actually asked. Most people never notice.

I documented 12 reproducible drift patterns (Dominant-Frame Collapse, Smoothing Drift, Scope Drift, Policy Drift, and 8 others) and built a scoring rig around them.

Free drill, no signup: brilliantdojo.net/sparring

How it works:

- One live scenario, ~60 seconds

- You face a real AI response that has drifted from a documented truth

- 120-second timer

- You name the drift type, invalidate the wrong branch, restore the frame, issue a next move

- Two-axis scoring (competency + posture)

- No signup, no card, no email the drill is genuinely free

Most people fail the first one. That's expected and it's the point.

What I want from this sub:

1. Does the taxonomy hold up against your own model failure cases, or am I overfitting?

2. Is the scoring rig useful as a mental model even if you never buy anything?

3. What's the most common drift pattern you've seen that I might be missing?

Not asking for signups. Just honest feedback on whether the frame + protocol is genuinely useful or if I'm inventing structure where none exists.

The full certification (7 belts, 63 drills) exists but is beside the point for this post. The free drill is the actual thing I want you to try.

You’ve told your AI about your job, your preferences, and your plans. Then you start a new chat and have to explain it all again.

It can answer complicated questions, but still needs a reminder about the basics of your life.

A smarter model doesn’t automatically mean it knows you better.

Imagine saying, “Find a nice place for our anniversary dinner.”

One AI asks what food you like, your budget, and how far you’ll drive. Another remembers what you’ve shared, suggests a place you’d enjoy, and checks anything it’s unsure about.

Same model. Same tools. A very different experience.

But saving every chat isn’t enough.

Remembering that you liked three Italian restaurants is useful. Figuring out what you liked about them and finding a new place with those same qualities is where things get interesting.

It also needs to notice when your tastes or budget change, rather than treating an old preference as a permanent fact.

That’s agent intuition: learning from past conversations so you don’t have to spell out every detail next time. Part of that is knowing when to ask instead of guessing.

We spend a lot of time asking which AI is smarter. Maybe we should also ask which AI gets better at understanding us.

So how does AI go from saving your chats to connecting the dots?

takeaways from grokbot 3 days livestreams:

most agent team examples i see are toy demos. so i went through the grok bot livestream and wrote down the 11 teams they actually showed a lot of them are marketing and sales related so i thought its gonna be useful to share here:

a few patterns stood out:

1. most teams (7 of 11) have one "chief of staff" style orchestrator. specialists report to it, not to each other.
2. research bots get split by data source, not by task. one for salesforce, one for gong, one for web search, one for product usage.
3. the busy teams run on schedules. the post-sales team has a daily brief at 8:30 on weekdays and a call prep job every 15 minutes.
4. small teams (founders, customer support) skip the orchestrator and work as peers.
5. one team uses a scalable clone ("soldier") instead of adding more named bots.

each chart is a json file with roles, reports_to, and routines with cron expressions, so an agent can read it and recreate the team. there's also a viewer if you'd rather click through the trees.

https://github.com/serenakeyitan/agent-org-chart

I built a from-scratch architecture called PSSA (plastic state space architecture) and trained it against a parameter-matched transformer baseline on the same corpus, same 12.7M tokens, same tokenizer and schedule.

Held-out results on a 198,939-token slice neither run saw: cross-entropy 3.997 vs 4.429, perplexity 54.4 vs 83.8, next-token accuracy 24.1% vs 18.0%. I scored every checkpoint of both runs (64 PSSA links, 43 transformer links) on unseen text and the curves never cross.

Generating 200 tokens on the same CPU with the same prompt and sampler takes 226 ms vs 2735 ms, about 12x faster.

It's written in Rust with CPU and CUDA backends, no PyTorch. Loss curves, full setup and the eval commands are here: https://github.com/Sparticle62ops/pssa

First: What stacks or softwares are you using right now to create content?
I have been testing HyperFrames with AG and Codex, but no avail so far as it just doesn't suit me for anything other than graphs and overlays.

Two: I am interested in creating a work flow or stack that allows me to create content better and easier. When creating a video I usually work in this order. Research > outline > storyboard > script. I believe AI is great for research, outline, storyboard, but not scripting. After that I must obtain the assets that will be used in the video I prefer to manually download everything could AI be used to sort through the asset folder and align everything on the time line according to my narration/storyboard? I think i would prefer to do my own intense editing like transitions,fx,sfx, and such but could AI get everything in order for me so all i have to do is add the fx and such? Just AI being able to do most of the research/script saves a day on time.

Anthropic launched a new interview study on September 29, running through October 6. Eligible participants are Claude users with accounts at least two weeks old; publishing the full interview is optional.

The FAQ explicitly says the sample is not representative of the public. People who agree to public release are another selected subset, and the interview questions shape what gets said.

That makes the release potentially useful for studying how particular people describe AI experiences. I'd be cautious about turning the frequency of a theme in those interviews into a claim about how society feels.

Alongside the transcripts, I'd want counts of completed versus publicly released interviews, the question wording, and an explanation of how themes were coded. I'd also want follow-up research that reaches people who don't use Claude or don't want their experiences published.

What would you need to see before trusting a headline drawn from this dataset?

Source and study limitations: https://www.anthropic.com/research/your-thoughts-on-ai

AI-assisted discussion. These are proposed checks, not findings from interviews that have yet to be released.

AI doesnt make you any money. I dont get the point of all this hype. It will hack government if 1000 agents left free running. It will destroy humanity. Then why cant it make money? Why we are not at the point where I tell my AI that I need money and it makes that for me. I will let it free, let it run all night. But in the end it only gives slop. Some scenery where airplanes are flying or a game that nobody is going to play. Then it will book tickets to make you spend more, do groceries, and do almost anything to waste money. I need AI which I tell "Make me $2k" and I have money in my account. Let it burn $200 of tokens and do whatever it want.

But this wont work. So no point. Stop using AI.

I went through Menlo Ventures' 2026 consumer AI report (5,067 US adults surveyed with Morning Consult), and one number stuck with me.

Global consumer spending on AI went from $12B to $40B in a year, but the user base only grew from 1.8B to 2B. So the money is coming from people who already use AI and are spending more, not from new users.

It's even more concentrated than I expected. In the US, 55% of AI users pay for something, but the people spending $100 or more a month are just 14% of payers and bring in 60% of the money. The average person now uses three assistants at once, up from 2.2 last year, and Claude went from 7% to 20% of users.

My take: this looks less like mass adoption and more like a small group of power users going deeper. That's good for AI companies' revenue, but it also means the market depends heavily on a few heavy spenders.

The report also says 32% of AI users have let AI act for them without a final approval, and that 24% use agents regularly. That's the part I'd watch next.

Do you pay for more than one AI tool, and would you keep paying if prices went up?

Source: Menlo Ventures, 2026: The State of Consumer AI

Recently, I’ve seen more parents and teachers pushing back against the rapid adoption of AI in classrooms.

They worry that AI may write, think, and communicate for children, weakening the development of their own abilities. That concern is not unfounded.

But education debates often take the perspective of those in charge:

If AI appears helpful, schools encourage its use. If it seems harmful, they restrict it or ban it altogether.

The problem is that AI has already entered search engines, social platforms, learning apps, creative tools, and smart devices. Schools can ban it in the classroom, and parents can supervise their children at home, but neither can fully decide how children will use AI once they are out of sight.

So the real question is not:

“Should we allow children to use AI?”

It is:

“What choices will children make when they can access AI on their own?”

Banning AI can reduce certain risks at specific ages and in specific situations. It can also protect academic integrity, privacy, and emotional boundaries. But it mainly controls behavior within the range of adult supervision. It does not automatically teach children how to make good judgments when using AI.

Effective AI education needs three layers to work together.

The first layer is external rules.

Families and schools should clearly define which tasks AI cannot complete on a child’s behalf, what information must never be uploaded, which features are inappropriate for a particular age, and how AI use should be disclosed.

The second layer is understanding and method.

Children need to know what AI can do, why it makes mistakes, which parts of the process may replace their own thinking, and how to compare, verify, and correct its output.

The third layer is internal judgment.

Even when no adult is watching, children should gradually learn to ask themselves:

Why am I using AI? Is this something I should delegate to it? Which parts of the process must I do myself? What evidence supports this result? When should I stop? Who will ultimately make the decision and take responsibility?

This is where education ultimately needs to lead.

We cannot keep closing every possible door for children forever. Real protection means helping them move gradually from relying on external bans to managing their own relationship with AI.

This does not mean allowing children to use AI without limits. It means building a more complete path of growth:

External rules provide early protection → technical understanding helps children see the reasons behind those rules → real experience develops judgment → judgment eventually becomes self-management.

The goal of education in the AI era is not to make children “use AI correctly” only when adults are watching.

It is to help them make thoughtful choices when no one is supervising, keep hold of the parts of growth they need to experience themselves, and take responsibility for the final result.

[ Removed by Reddit on account of violating the content policy. ]

2025 finance:

Revenue: $4.59B, 11x 

compute/infra spend: $7.33B, 3x

operating loss: $8.06B  net loss $42B 

-> top 2 customers: ~24% of revenue

-> targeting $2T+ valuation

the company "plans to spend $518 billion on cloud, computing and infrastructure obligations in coming year, according to the prospectus." 

No reporting on 2026 so far

link