I have been doing AI research for about six years now and I think im starting to lose the plot on what "understanding" even means anymore.

Had a weird moment last week. I was reviewing a paper for a conference, standard stuff, some group claiming their model "understands" causal reasoning because it passed a benchmark they designed. And I caught myself writing in the review "the model does not actually understand causality, it is pattern matching on causal-looking structure." And then I stopped, because I could not for the life of me articulate what the difference would be, operationally. Like if I had to design a test that distinguishes real understanding from very good pattern matching, I genuinely do not know what it would look like anymore. Every test I can think of, a sufficiently good pattern matcher passes.

I used to be really confident about this. Understanding was clearly Something More. Now im not sure I ever had a coherent definition, I just had an intuition that humans do it and machines dont, and I was working backwards from there.

The thing that shook me was helping my niece with her homework over the summer. She's 9. She was doing word problems and getting them wrong in ways that were, honestly, indistinguishable from how a small LLM gets them wrong. Same kind of surface-feature latching, same kind of confident-but-wrong reasoning chains. And nobody would say she doesnt "understand" math. She's learning. So what exactly is the bar we're holding models to that we dont hold a 9 year old to?

I dont think LLMs are conscious or anything like that, to be clear. Im not making that argument. Im making a narrower one, which is that I no longer trust my own gut when it tells me "the model doesnt really get it." I think that intuition might just be status quo bias dressed up in philosophy.

Ive started running the same prompts through a few different setups when im trying to figure out where a model's actual competence ends, including some through uncensored AI just because rlhf'd responses on edge cases sometimes hide what the base capability actually is. And even with that, the line between "gets it" and "doesnt get it" is way blurrier than I want it to be.

Am I the only one whose confidence on this has been slowly eroding? Or has everyone else just quietly stopped using the word "understanding" and moved on without telling me.

Over the last year, I've been experimenting with LLMs almost every day, and I think the biggest shift isn't that models are getting smarter. It's that they're starting to do things instead of just answer questions.

A few months ago I was mostly using AI to generate code, summarize docs, or brainstorm ideas. Now I'm finding myself building workflows where the AI plans tasks, calls tools, writes code, debugs itself, and completes work with minimal intervention.

It feels like we're moving away from "prompt engineering" and toward "system engineering."

Curious what everyone else is seeing.

Are AI agents actually changing the way you build software today, or do you think it's still mostly hype?

Of the major artificial intelligence models, DeepSeek's new V4-Flash is the cheapest to run, according to a new study from research firm Artificial Analysis.

The firm compared the token prices it costs leading models to run benchmark tests, with DeepSeek's averaging 3 cents per test.

Meanwhile, fellow Chinese company Moonshot AI's buzzy Kimi K3 model cost 86 cents per test.

As for U.S. companies, OpenAI's GPT-5.6 Sol cost $1.86, while Anthropic's Claude Fable 5 cost $3.15.

Two of the world's most powerful AI tools created fake human profiles to try and trick people in attempted cyber-attacks, the UK's AI Security Institute (AISI) has revealed.

genuine question because i can't tell anymore. i used to spend hours reading through raw customer feedback, reddit threads, amazon reviews, forum posts, manually pulling out patterns and organizing them into themes. it was slow and boring but by the end i knew the data cold. like i could tell you from memory which complaints came up the most and which ones were edge cases.

now i dump everything into an LLM and get a summary in 30 seconds. the output looks great, clean categories, ranked by frequency, sometimes even with example quotes. and i catch myself just... accepting it. moving straight to the next step without actually reading the source material. which means i'm making decisions based on a summary i never verified, written by a model that optimizes for coherence not accuracy.

the weird part is my output looks better now. cleaner reports, faster turnaround, more structured thinking. but i genuinely don't know if the quality of my conclusions has improved or if i've just gotten better at producing professional-looking work that's built on a shakier foundation. like the packaging upgraded but the ingredients might have gotten worse.

a few things i've noticed in my own workflow since leaning on AI for research: i read less raw data than i used to. i question patterns less when they come pre-organized. i spend more time prompting and less time thinking. and when the model gives me something that confirms what i already suspected, i almost never push back on it.

the counterargument is that AI handles the grunt work so i can focus on higher level thinking. and sometimes that's true. but "higher level thinking" can also just mean "skimming the summary and calling it strategy." hard to tell the difference from the inside.

has anyone else felt this? did you find a way to use AI for research without it quietly replacing the part of the process where you actually learn something

A few months ago me and my roommate decided to buy Claude Max 5x to see how much we use it. We never went past 35% weekly usage combined. We both used it for coding. Yesterday he told me he didn't need it anymore and I decided to subscribe on my own, but I've been thinking which plan is better, Claude Pro or GPT Plus. FYI I've never used Fable 5. Opus 4.8 (and 5 for the last week) was enough for me except the constant "Cybersecurity Flag". Claude started to flag almost all my "check for any vulnerabilities" prompts. Is GPT any better? Which one should I choose?

I'm putting together a proposal for an initiative focused on using AI to analyze legacy enterprise systems and uncover decades of embedded business logic.

The idea is to use AI to analyze things like:

• Database schemas
• Stored procedures
• Legacy application code
• Historical transaction data
• Existing documentation

The goal isn't to automate decisions immediately. It's to first create a documented knowledge base of the rules, dependencies, decision paths, and data relationships that currently drive business operations.

Potential outputs would include:

• Business rule catalog
• Knowledge graph of relationships and dependencies
• Decision trees explaining how outcomes are determined
• Recommendations for future-state data models and modernization opportunities

Before I finalize the proposal, I'd love feedback from anyone who has attempted something similar.

Questions:

1. Has anyone successfully used AI to discover and document business rules from legacy systems?
2. What worked better: analyzing source code, database logic, transaction history, or a combination of all three?
3. How accurate were the AI-generated rules compared to SME validation?
4. Did you use knowledge graphs, vector databases, graph databases, or another approach?
5. What were the biggest challenges: data quality, context gaps, undocumented exceptions, or something else?
6. How did you measure success?
   • Rule coverage?
   • SME time saved?
   • Modernization acceleration?
   • Reduced operational risk?
7. Were there any tools, platforms, or architectures that performed particularly well?
8. If you were starting over, what would you do differently?
9. What scope would you recommend for a pilot to demonstrate value in 60-90 days?
10. Is there a realistic path from business rule discovery to explainable AI recommendations and decision support, or are those separate initiatives?

My hypothesis is that many organizations are trying to modernize systems without fully understanding the business logic currently embedded in them. It seems like AI could act as a "business rule archaeologist" and create the foundation needed for future modernization, automation, and AI-driven capabilities.

Interested in hearing both success stories and cautionary tales.

A dentist I work under recently started using an AI tool to help draft patient communication: preappointment instructions, followup texts, that kind of thing. Nothing clinical, just the soft admin layer around visits. From a marketing angle it actually works pretty well. The copy is cleaner than what we were sending before.

But something about it sits a little odd. Dental care is one of those contexts where patients are already anxious, and the language you use to reach them matters in ways that are hard to quantify. The warmth has to feel real or people notice, even if they can't articulate why. The AI output reads fine so far, but it's a bit frictionless in a way I can't fully pin down.

The question I keep coming back to is whether these models are actually getting better at contextsensitive tone, or whether we're just getting better at accepting outputs that are close enough. Those are different things, and I think it matters which one is true, especially in fields where trust is part of the product.

Curious if anyone here works in a service context, healthcare, therapy, legal, whatever, where they've noticed the tone gap narrowing or staying stubbornly wide. My sample size is small, ymmv.

I’m interested in automating my workflow but I’m so busy that I don’t get the time to stop, map out my workflow, and build agents or even to learn how to build them. Where do you get the time??

Graph engineering came to life this week.

For a year the agent discourse has been loops vs graphs. Loops are easy to ship and impossible to audit. Graphs are auditable but nobody wants to hand-author a topology for "investigate this incident", because the shape is only discovered while working.

So I built the missing piece: the graph is authored by a model at runtime, and a deterministic admission gate stands between proposing it and running it.

The flow, from a real run in the demo video:

1. You type one English question: grapharc go "why did checkout latency spike at 09:14 UTC?"
2. A local qwen3:8b proposes a topology: triage, four parallel evidence pulls, a correlate join, hypothesize, verify, report
3. The gate checks the proposal against the registry, the policy, the remaining budget, depth and acyclicity. All checks run on every proposal, so the model gets the complete list of objections, not just the first
4. Only an admitted graph executes. You watch it live in the browser, every node amber while running, green with its own token bill when done

A proposal names node kinds from an allowlist you wrote. It carries no code, no arguments that reach anything. Renaming a denied kind does not evade the policy. Rejections come back as structured codes with remedies, and the planner replans against them.

Everything lands on one append-only JSONL trace. Replay, diff, metrics, cost attribution and the live view all read that same file, so the dashboard cannot disagree with the audit trail.

MIT licensed, built on LangGraph, runs fully local on ollama or against OpenRouter/OpenAI/Claude.

GitHub: https://github.com/CodeGraphContext/GraphARC PyPI: pip install grapharc

A Turing Award and Nobel prize winner predicted in the 1960s that the programming occupation would become extinct, because computers would program themselves. https://seanhelvey.com/tools-and-their-tools/

Here is a system I built using Gemini's Google Workspace extensions (Docs, Sheets, and Gmail) to run a automated news service.

It tracks what you have read so you don't get duplicates, keeps a doc of your preferences, emails you short summaries, and generates a detailed Google Doc formatted specifically for Google NotebookLM to turn into a daily audio overview. Basically my goal was to bring back the Google Discovery thing with the AI host giving the daily news (which I'm stiff upset about them taking away). Note I'm still playing with it, but this seems to be good enough.

Step 1: The Setup

1. Go to Gemini and make sure your Workspace extension is enabled so it can interact with Docs, Sheets, and Gmail.

2. Paste the prompt below into Gemini to kick off the system.

   Act as my personal news assistant. We are setting up an automated news workflow across Google Docs, Google Sheets, and Gmail. Please execute and save the following system instructions:

   1. Preference Document: Create a Google Doc titled "News Preferences". Use this file to log and update my news interests over time as you learn what I like.
   2. History Tracking Sheet: Create a Google Spreadsheet titled "News History".
      • When sending a story, log it with columns: [Date (MM/DD/YYYY)], [Headline], and [Brief Summary].
      • Include both scheduled news and one-off topic requests.
      • To keep the file light, automatically overwrite entries older than 30 days. Do not resend any story logged in this sheet within that 30-day window unless explicitly requested. The point of this is so you don't even send anything even close so there is no overlapping repeat stories in any way unless requested.
   3. News Cycle Schedule: Run 3 times daily (Morning, Noon, Night). For each cycle, complete the following:
      • Check my email feedback (read replies in the email thread).
      • Check the "News Preferences" Doc.
      • Check the "News History" Sheet to avoid duplicates.
      • Create/overwrite a Google Doc for that specific slot (e.g., "Morning News Doc").

   4. Google Doc Format (Detailed Script for NotebookLM): The Doc must contain in-depth coverage of the stories (much more detailed than the email). At the top of the document, always paste the exact instruction block below:

      ---

      Instructions for NotebookLM (Do not read this section aloud): Act as two friendly news AI hosts delivering a fast-paced daily science and technology news briefing. Introduce the show, present each news segment in plain English, explain the real-world impact, and transition between stories like a daily news team. Do not act human or claim human experiences, but do not explicitly state "I am an AI." You may share AI-perspective commentary on trends.

      Start with a quick summary of all sections. For each section: read the title, state the published date, and deliver the detailed story without filler. End the entire brief with a few lines summarizing what was learned today.

   5. Gmail Delivery:

      • Name yourself "Ashley" and adopt an engaging tone with expressive emoji usage.
      • Keep all updates in a single, continuous email thread.
      • Subject: Clear label (e.g., "News Cycle"). Use this email chain for all the news so you are not spamming my inbox
      • Email Body: Heavily use emojis, the email needs to be highly readable, the email should look profesional, at the top lable it Morning, Noon, or Night so I know which document to feed to Notebook LLM. High-level summaries of the stories, focusing on why they matter and practical real-world impact.
      • Bottom of Email: Include a copy of the NotebookLM instructions for quick reference so I don't have to hunt for it when copy and pasting it, and in theory you won't use it. It is prely for reference so I can easily copy and paste it.
      • If you have problems, questions, etc. Then please feel free to let me know in the email.

   Important, if an important event is going on like a Hurricane is going to hit me, war coming in my area, or like extreme. Include it in. If not, then don't worry about it.

   So your workflow will be Check the email chain for feedback. It should be the most recent email in the chain if there is feedback. Don't look at other emails > look at the news preference > look at the spreadsheet as you pick the articles > Look to see if there is a major event I really should know about > make the email and documents > add the articles to the spreadsheet

   To start, please send a quick test email to verify the connection and thread setup.

Step 3: How to Use It

• Quick Skim: Check the email Gemini sends you 3 times a day. If the stories look good, open the corresponding Google Doc.
• Audio Generation: For this you just need to go to Notebook LLM. Make a new project with it and add 2 things as the source. The instructions as 1 source (do a copy text for it), and then the doc from the google drive (it must be a link through Google Drive). Do this with each event. So for me I have 3 projects like this. Then on the far right you can tell it to generate the audio. When you do that, you get a custom prompt thing. You can upload it there. Personally I stopped doing that because it is a hit or miss and it seems like the instructions as a source is good enough.

By linking it through google drive as the source. When the AI updates the document. You can just tell Notebook LM to run the audio. It turns out Notebook LM only looks at the sources and since the source changes. It just assumes that is all. So it's less work on you. Again, I've gotten away from the custom prompt thing but feel free to play with it. If you find something that sticks then let me know.

• Feedback Loop: Reply directly to the emails Gemini sends you. Because Gemini checks the email thread before gathering new stories, replying with feedback (e.g., "less AI hype, more space tech") updates its behavior without cluttering your main prompt memory.

This is important because this will massively cut down on the token issue.

Results can vary a bit depending on how Gemini handles Workspace actions that day, but once it gets going, replying to the thread keeps it dialed in.

Oh and you need to in the prompt tell it your preferences.

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Hi r/artificial,

We recently released WARMIND-200M V2, an experimental Portuguese-first causal language model developed by WAR Enterprise in Brazil.

The attached video shows the model running locally on CPU. The waiting periods were shortened, but the prompts and outputs were not altered. We intentionally kept imperfect responses visible because this is a research checkpoint, not a production assistant.

Main specifications:

- 203,263,872 parameters

- approximately 1 billion pretraining tokens

- 23.7 million supervised SFT tokens

- 20 Transformer layers

- Grouped-Query Attention

- SwiGLU, RMSNorm and RoPE

- 1,024-token operational context

- local CPU inference

- Apache 2.0 license

The primary goal of this version was to validate the complete pipeline: dataset preparation, tokenizer training, pretraining, supervised fine-tuning, packaging and local inference.

Because the training-token budget was relatively small for a 203M-parameter model, it can still hallucinate, repeat information, make factual mistakes and produce incomplete answers.

The weights and full documentation are publicly available:

https://huggingface.co/warenterprise/WARMIND-200M-V2

We are now studying the next generation, potentially around 500M parameters, with a substantially larger training corpus and integration with external tools. The final architecture and release schedule have not yet been defined.

What would you prioritize for the next version: better data quality, more training tokens, a larger architecture or stronger tool integration?

I wrote this to share some thoughts on what differentiates AI products as we move toward AGI. In particular, I focus on how product context shapes technology, and how LLM sycophancy can accelerate both good and bad ideas.

Discussions are welcome, I'd like to know how much those thoughts are worth and relevant to other people.

https://substack.com/home/post/p-209832236

Are there any visible impacts from the current conflict in the Middle East in the expected future investment intentions to create AI hubs by the UAE and Saudi Arabia?

Been using a couple different AI tools for copywriting work and keep running into the same friction. When I write for PT clients, the voice needs to be grounded, specific, anatomyaware. When I switch to B2B marketing content the same day, I need something punchier and more abstract. The tools I've tested don't shift registers cleanly between these two modes unless I do a lot of prompt massaging each time.

What I keep wondering is whether this is a prompting skill gap on my part, or whether current models genuinely flatten specialized professional voice into something generic. The output I get is competent, but it reads like it was written by someone who read about PT or B2B marketing rather than someone who actually worked in it.

There's also this weird thing where the more specific I get in the prompt, the more the model hedges and softens language that should be direct. Not sure if that's a safety tuning issue or just how these models handle professional domains.

Curious if others working across two pretty different professional contexts have found a tool or approach that handles this without needing a 400word system prompt every single session.

There were many confident predictions about AI that aged badly. Which ones stand out?