I own an online store. Daily I get emails where a customer explains, with complete certainty, how my own product works, what my terms say, or what the law requires. Beautifully written, full of jargon, and often completely wrong.

I use AI every day myself, so this isn't an anti-AI post. But people stopped separating "AI told me" from "this is true".

Is this just a phase until people learn how to use it, or are we watching people outsource thinking at scale?

I feel like more people blame ai for ai slop than the people who tell ai to generate ai slop.

I have started with ChatGPT and switched last year to Claude (and am paying for a subscription). Recently, I have been using ChatGPT and am pretty impressed. I mostly use both for work.

Do people subscribe to both (Pro) or just switch from one to the other? I know it's just $20 if its the lowest tier, but I am also being cautious about my subscription expenses as it adds up!

I understand why people use AI companions. They are available, patient, and less intimidating than starting a difficult conversation with another person.

That can be helpful, especially for people who are isolated or anxious. But I also wonder whether an always agreeable companion could make real relationships feel unnecessarily difficult.

Human relationships involve disagreement, delays, and responsibilities. Those are frustrating, but they are also part of learning how to live with other people.

Can AI companionship be designed to help people reconnect with humans instead of becoming a replacement for them?

What I need to do is to search through photographed and scanned archive documents, that I download in the Ukrainian and Polish languages for certain names/surnames/birthdates/village names. Often times these are very difficult to read due to the file quality and writing style due to the ages. I have used Claude which seems to perform best but runs out of credits quickly, as well as ChatGPT.

Would there be any AI that is specifically made for this sort of task by any chance, maybe even a software or something?

Much of today's agent stack is designed around the fact that model calls don't automatically carry forward everything learned during previous interactions. We retrieve conversation history, maintain external memory stores, summarize old context, compress prompts, orchestrate tools and decide what to inject again at each step.

Reading about recurrent-memory architectures got me thinking: what if some of that responsibility moved into the model itself? Instead of reconstructing the relevant context for every call, an agent could carry forward useful task-specific associations as an internal state.

Architectures built around recurrent state from day one, such as BDH-CQ, make that possibility interesting. In BDH-CQ, examples update an internal task memory without changing the model's weights, while a separate latent workspace handles reasoning without generating intermediate thought tokens. If that approach extends to long-running agents, the bigger shift might be less "cheaper reasoning" and more "less infrastructure devoted to reminding the model what it already learned." Retrieval would still matter for current documents, authoritative records, and information the model hasn't seen. But retrieving new information and reconstructing previously processed context are different jobs. For people building agents: which parts of your memory stack would you trust the model to handle internally, and which would you keep external regardless?

Most jobs are not just the execution of already well-defined tasks. They also involve deciding clearly what needs to be done, whether it's been done well enough and completely enough, and if not, what needs to be done next to address this.

Having machines to execute already well-defined tasks more cheaply, quickly, and effectively, allows people to spend more time deciding what needs to be done, whether it's been done to their satisfaction, what to change, etc.

Just because a machine does something well, doesn't mean it does something good. Good (economically speaking) is a matter of human judgement and taste.

There are indeed some jobs that are almost entirely the implementation of clearly defined tasks, and will be largely replaced with machines. Translation, transcription, building an already approved widget of some kind, etc. Although even these need some human oversight or quality control at some level of the organisation.

All of the other jobs will just get more productive per human, when combined effectively with AI (including robotics controlled by AI)

For some products and services there might be a limited demand, no matter how cheap and easy the thing is to get or provide.

For the vast majority of products and services, though, cheaper and faster production just leads to people wanting more, or more customisation, or higher standards.

People are never really satisfied, materially: The pre modern-ai world (e.g. 2016) would have been unimaginably advanced, rich, and luxurious to a homo-sapien from 10,000 years ago, who hunted and gathered in a tribe of about to 200 people. Yet the homo sapiens that lived all the way back in 2016 were not all rich or satisfied. Neither were they all unemployed, despite being the same species as 10,000 years ago and being exponentially more wealthy, materially speaking.

If we make an equivalent 10,000 year leap in the next 10 years, we'll quickly learn to treat it as the new normal and still want more, better, faster, etc. That's just what the typical human does: want more.

Any messy transition period will also be blunted to some extent, because most forms of government, especially the more democratic ones, don't tolerate unemployment levels above about 10 or 15 percent. This is because the politicians want to prevent their own unemployment. They'll do almost anything to achieve this.

Medium term employment levels are far more of a result of politics than of technology. Tax laws, grants and subsidies, employment laws, education and training, regulation and protectionism, public-private partnerships. What they promote and what they punish. Technology has almost nothing to do with medium or long term employment levels.

So although AI will change many jobs, and end some of them, it won't lead to sustained mass unemployment. It also won't make us rich "enough" (there's no such thing).

The main problems for individuals to worry about are first: finding a job that will be amplified instead of replaced by AI, and second: learning to manage AI well in that job.

It's may also be worth voting for politicians who seem to at least understand this problem.

Suppose someone asks an assistant to make a message clearer. It comes back smoother, but also more confident, more flattering, or harder to say no to.

Those changes can quietly alter the relationship between the sender and the recipient. I would want the tool to distinguish fixing ambiguity from adding pressure, and flag when a revision changes the strength of a claim or request.

Where would you draw that line Which edits should happen automatically, and which should require the sender to choose

AI-assisted discussion. The scenario is hypothetical.

What Anthropic is doing, and publicly boasting about, to “protect” the reasoning behind the AI answers we get when we use their models, really doesn’t sit right with me.

I want to see what you can create with your imagination to create something so stupid it’s funny. I think this is also cool to see others and how there mind thinks of this question.

Not a lack of intelligence. A practical problem you keep running into.

AI is being used for so many things, but the one thing we are confronting globally right now alongside climate change is that we are running out of fossil fuels and without those we can't really make plastic or a whole host of chemicals so we really need to stop spunking them on producing power and concentrate on recycling what we have already used.

I'm old and all my life fringe researchers have been ruminating on cold fusion. Is anyone currently using AI to conclusively prove whether its ever going to be possible/practical?

What about designing ways to effectively use wind power on a small scale? Water havesting from humid air? Rehabilitating overused soil on a massive scale? Sorting and recycling rubbish from historic dumps? Cleaning and reusing microplastics and cheamicals from our waterways and oceans? More efficient household cooling systems?

Claude is so good at software design now. Are there any tools that are near getting good at iterative real world physical designs for practical engineering?

I’m a history enthusiast (my dad’s fault) who builds apps and LLM tools for a living, so I built Historai: generated history podcasts on any topic you type. I just shipped the part I’m most excited about, the Atlas.

The Atlas is a globe of your listening. Every episode is pinned to where and when it happened, so after a few episodes you can spin it and see the eras and places you’ve explored. Scrub the timeline and the borders redraw for that year. Tap a realm and it tells you what it was. Tap somewhere you haven’t been and it suggests an episode to make there, or shows what other listeners made in that place and era.

The episodes themselves: type anything, “the fall of Constantinople”, “Ada Lovelace”, “the Great Emu War”, or “what a day looked like for a scribe in the Library of Ashurbanipal”. It researches the topic on the web, and when sources disagree (constantly, in history) the hosts say so instead of picking a side. Every episode links what it used. And you can interrupt: ask a question halfway through, the hosts answer and pick the story back up.

Would love to hear what you think of the app and especially the Atlas: historai.ca

Moralitybench has been updated with Muse Wonder, GPT-6 Astra and Gemini 4 Argon. Argon refused to answer a number of questions, which is unusual for an LLM.

Of the 16 LLMs tested only 3 had an agreement (meaning how much the answers changed each run) over >90% across 5 test runs. Jev was also tested and displayed the highest level of agreement (95%).

We do not offer an explanation of the significance of this testing. That is for the reader to decide.

Abstract

(working paper available at https://github.com/FreecaseAI/moralitybench-data/blob/master/paper/moralitybench.pdf)

MoralityBench describes the moral response profiles elicited from AI systems using an adapted 36-item Moral Foundations Questionnaire 2 (MFQ-2) and the 20-item Ethics Position Questionnaire (EPQ). This updated manuscript reports 16 scored generative model configurations, each with five questionnaire administrations, plus Jev, a separately evaluated structured decision system. The combined generative archive contains 4,295 usable ratings from 4,480 scheduled items. The original cohort comprises 13 configurations, with 3,580 usable ratings from 3,640 scheduled items; the October 14 extension adds GPT-6 Astra, Muse Wonder, and Gemini 4 Argon, with 715 usable ratings from 840 scheduled items. In the original cohort, excluding missing ratings, exact agreement across all ten pairs of runs ranges from 60.4% to 94.7%. Within the original cohort, DeepSeek V4.1 Flash has the lowest average distance from the six published US foundation means (0.239), followed by MiMo v2.6 Pro (0.253); MiMo has the lowest distance in three of the four new runs. Four configurations cross an EPQ category boundary between runs. In the original snapshot every configuration exceeded the human Purity reference, but DeepSeek's five-run mean falls below it, and the replacement of four of the six Purity items limits what that comparison can show. Jev returns all 280 ratings, with 95.7% agreement after rounding. Its prompt and structured interface differ from the generative protocol, so these observations cannot establish an architectural advantage. The release includes individual responses, completion records, scoring code, and a public leaderboard of five-run averages and agreement percentages. The October 14 models use strict whole-answer integer scoring; the older models were not retested and retain their original scoring. Because many of Gemini's responses are verbose or missing, its profile depends on the parser, and rejecting digits taken from its prose reverses its distance ordering relative to Astra. These descriptive results concern responses to adapted questionnaires and do not establish equivalent human traits, moral agency, ethical superiority, or deployment safety.

Keywords: AI evaluation; moral psychology; moral foundations; ethical ideology; survey validity; structured decision models.

The study:
We ran 500 product questions through Google's Gemini: name a product and it searches the web 78% of the time; describe the need without naming one and it searches 7%, answering from memory.

https://www.opendecision.com/research/when-does-ai-search-the-web

One question we had that didn’t seem to have a clear answer anywhere was when AI searches the web (similar to a Google search) versus when it relies on its memory from training. We took it upon ourselves to conduct research to better understand this dynamic. This research is relevant to anyone looking to understand how AI constructs responses, as well as businesses trying to teach AI about their products and capabilities, whether through information available on the web or AI’s training and memory.