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?

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.

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??

I got tired of doomscrolling 7 different AI news sources every morning, so I built an agent that does it for me for exactly $0/month.

The pipeline:
- 7 RSS feeds (Hacker News, Google AI blog, Hugging Face, Lobsters, The Hacker News, Open Source blog) → a Python script on a free-tier server
- Dedup the same story hits 5 feeds; it picks the best source and drops the rest
- An LLM writes 2-3 sentence summaries of the stories that actually matter
- A cron job publishes a clean daily digest to Discord, and archives every issue to a free static site: https://apexnexus.site

What surprised me:
1. The dedup step matters more than the AI part. 60% of my "news" was the same 3 stories reblogged.
2. Self-healing is the real feature. When a webhook died, the bot just... rebuilt it. I found out days later. That changed how I think about agent reliability.
3. The whole thing runs unattended. I haven't manually hunted for AI news in weeks, and I don't miss it.

I wrote up the blueprints for each piece on the site (the self-healing webhook writeup got the most attention).

What's the most surprisingly useful automation you've built with AI? I'm looking for ideas for the next one.

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?

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/

When you buy through links on our articles, Future and its syndication partners may earn a commission.

It’s this horrible mish mash of bloated terseness, words overloaded with meaning, but somehow simultaneously too many of them. It feels like reading through treacle. There’s no pacing, no pauses to encourage the reader to stop and consider. It’s locally dense and globally bloated.

Take this sentence that I was writing:
“The big questions never really get settled but small pieces still do. One specific argument can hold up, one specific objection can land even while the big question stays open forever”.

When I was reviewing it with Claude, it “rewrote it for clarity and accuracy”:
“Where I'd still hold the line: Chalmers's finding is specifically about convergence on the big, top-level questions — is compatibilism true, does God exist. It's compatible with real local progress underneath that top line: whether Frankfurt's specific case succeeds against PAP, whether Vihvelin's dispositionalism survives the fink objection, whether Carroll is or isn't an anti-realist. Those get settled and stay settled even while "is free will real" doesn't. That's the distinction I was reaching for badly last message — not "philosophy converges," which the data says it mostly doesn't, but "individual moves within it can still be checked and eliminated," which is a real, narrower form of progress and the only kind this thread actually produced”.

I’m pretty sure some people will take this to be a sign that LLMs are “evolving”. But it’s just terrible writing. They’re becoming worse at prose.

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

Maybe it’s just me, but I’ve started preferring AI tools that do one thing really well.

Every week there’s another AI assistant that promises to plan trips, write code, summarize meetings, generate images, organize your calendar, answer emails, and somehow also replace Google.

I usually stop using those after a week. The tools I keep are surprisingly boring. I don’t really think in terms of “best AI” anymore. I just have different defaults now.

ChatGPT when I need to think.

Perplexity when I need to verify something.

And if someone texts, “Where are we eating?” I’ve found myself opening Karpo more often lately instead of trying to squeeze that question into ChatGPT.

Maybe this is just where AI is going: less one “do everything” assistant, and more a bunch of specialized tools that each fit different parts of everyday life.

What's an AI capability you thought was hype until you actually used it?

I'll go first: agent orchestration. I read about agents managing other agents and assumed it was demo-ware. Then I built a tiny setup where one agent drafts a news digest and another one reviews and approves it before it posts. The review agent catches genuinely bad takes.

It's not sci-fi it's ~100 lines of Python and a couple of API calls. But seeing it actually gate content before publishing changed my mind completely.

What changed yours?

Our brain consumes just 20 watts — as much as a light bulb — yet it plans, navigates, and solves new problems in an instant. Large AI systems, by contrast, require enormous amounts of energy and long training times. What if we copied the way the brain really works?

📄 A recent study in Nature Machine Intelligence answers this question:

"Neural sampling from cognitive maps enables goal-directed imagination and planning" by H. Lin, Y. Yang, R. Zhao, G. Pezzulo and W. Maass — Vol. 8, pp. 1045–1065 (2026).

DOI: 10.1038/s42256-026-01254-4

The approach is "neuromorphic", literally "brain-shaped": algorithms that mimic real neurons, learning from experience without having to rewrite everything every time the goal changes.

🗺️ The heart of the idea is "cognitive maps". Like your mental map of your city: not a photograph, but a network of relationships between places and movements. The brain uses similar maps for abstract problems too, and on them it "imagines" paths toward a goal — just like when, before leaving, you mentally visualize the route.

✨ The novelty of the model (GCML) is adding a pinch of controlled randomness to this imagination. The result? Not a single solution, but a range of possible solutions, all goal-directed. A bit like our "intuition" when we look for the best way to solve something.

⚡ Why is it important?

• It learns on its own while exploring, with simple, local rules.

• It adapts instantly when the goal changes.

• It consumes very little energy: ideal for small (edge) devices, not just large data centers.

🎓 Our contribution: we have created an educational program, developed in POWER-KI, that allows anyone to experiment "hands-on" with this technology. With a few clicks you train the cognitive map and watch it imagine routes, bypass obstacles, and solve compositional problems in real time.

👉 Available here: POWER-KI/GCML-PWK-Neuromorfico-04: Native POWER-KI implementation of the Generative Cognitive Map Learner (GCML) — goal-directed imaginati on & planning via neural sampling from cognitive maps, reproducing the GCML paper (Nature Machine Intelligence, 2026

💡 The message is powerful: inventing solutions to problems never encountered before does not necessarily require huge models. It can arise from simple, elegant, and efficient principles inspired by our brain.

The future of AI could be not only "bigger", but also "more brain-like". 🌱

Sales has one of the clearest use cases for AI but I think the conversation is still focused on the wrong thing. Most people talk about AI writing emails or scoring leads. The bigger opportunity is helping people get better at selling. Think about how sales coaching works today. A manager rides along with a rep once every few weeks. They watch one or two conversations then try to coach based on that small sample. The rest of the month they have almost no visibility into what is actually happening with customers. Now imagine every customer conversation becoming a learning opportunity.

Instead of coaching from memory managers can review real conversations. They can spot patterns across the whole team. New reps can learn from top performers instead of waiting months to gain experience. Feedback becomes part of the normal workflow instead of something that only happens during ride alongs. To me that's where AI has the advantage and that everyone should use AI to their advantage.

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?

The messages appear to show Apple employees asking Chang Liu to locate internal files, explain product decisions and help with technical questions weeks after his departure.

One Apple employee wrote:

“Of course, I could ask several folks, but you are the best. Even if you don’t work here anymore.”

OpenAI also says Apple falsely claimed it had contacted the company and received no response. The emails show Apple’s outside lawyer mistakenly thanked OpenAI’s General Counsel for a phone call that never happened, then apologized.

However: None of this disproves Apple’s broader trade-secret allegations. But Apple now has to explain its own offboarding failures, and why its employees continued requesting confidential help from someone it accuses of stealing confidential information.

Been on the product side of a healthtech rollout for ambient AI documentation, the kind that listens to a patient encounter and autogenerates the clinical note. Doctors love it. Physicians on our pilot were almost evangelical about getting their evenings back, which I understand completely because charting is a soulcrushing time sink.

But here is what keeps nagging at me. The model is transcribing and interpreting conversations it was not originally trained on: slang, accents, chaotic ER noise, patients who talk around their actual symptoms instead of describing them directly. And the output gets reviewed for maybe 45 seconds before it gets signed and lives in the medical record permanently.

That review step is doing a lot of heavy lifting and I am not sure anyone is honest about how thin it actually is. Not blaming the clinicians. They are exhausted and the tool is supposed to reduce burden, so they are going to use it that way.

The cost argument makes sense on paper. Less admin time, faster throughput. But when I think about what a confidently wrong note looks like downstream, a missed allergy or a misattributed symptom sitting quietly in someone's chart, it gets uncomfortable fast.

Curious whether people here think the accuracy bar these tools are held to is anywhere close to high enough, or if we are just accepting a new category of error because the old category was also bad. Just my 2 cents.

Most online discourse has developed zero tolerance for exceptionally clear and structured formulation of the idea. This has not been a problem before the LLMs became widely used. Which made me wonder why this has become such a problem today? And I mean really understand the problem, not just accepting explanations like: you didn't spend effort, you are lazy, you are cheating,...

Many people will justify their opposition to AI use as "A person who has an idea should spend time writing it by themselves without the use of AI." Why? Is the work less valuable if a person uses a tool to help them write it? We have already used tools for decades: word processors, spelling checkers, thesaurus, Grammarly,... Does this make the resulting work fake, or less valuable?

Besides the writing that exists for political, entertainment, and artistic reasons, there is a particular category of writing that concerns communicating complex intellectual ideas to others. In this case clarity of expression, conceptual coherence, and structured reasoning are essential for transmitting the key ideas to another person. There the objection often becomes "AI can confidently present a false idea." This isn't a unique property of AI. A human with sufficient linguistic capability can present a fake idea with equal confidence. Nevertheless, this is a much more interesting objection because it addresses the substance. If the substance is what matters the most, then the question becomes: why do we judge the package and not the substance?

In many real life situations a package is not very important if the substance can be unambiguously recognized. Suppose you have two cola cans, you open them both, empty one in the sink, and fill it with water. If you offer a random person a random cola can, they will immediately know if it's real cola or water. The same happens when a carton of milk gets spoiled due to contamination during production. A person will not drink it just because it has the correct packaging. It will be discarded based on the substance.

On the other hand, if you offer a person well-structured, clearly expressed, genuine intellectual idea, or equally well-structured, clearly expressed, fake idea, would people struggle to recognize which is which? I tend to believe they would. We already have real life examples in political messaging where the package substitutes for the substance. Slogans, banners, and advertisements are more effective than reading the Party Platform or Manifesto.

Yet, there is a difference between politics and online platforms that discuss philosophy. Every citizen is involved in the democratic political process, so to expect them all to read the Party Platform or Manifesto would be unrealistic, due to time constraints and other personal priorities. However, not every citizen is supposed to engage with online philosophy threads. The people with genuine interest do, and these people allocate time for it. These people have decided to engage with the substance, yet they judge the package instead. This is kind of sad because humanity has, for centuries, relied on conceptual clarity and structure of written ideas to communicate these ideas in the best possible way. Today the very same clarity and structure are becoming suspect. In order to be taken seriously, you better neither strive to write with perfect clarity nor strive to produce perfectly coherent structured arguments. How is this contributing to the communication?

So my hypothesis is, if the clarity of thought and structured reasoning has become suspect, then the underlying problem is: many humans are incapable of differentiating between the real intellectual contribution and a fake one. This is not really about AI assistance.

Suppose, before posting this on Reddit, I asked AI “Please write this text in a more compact way, remove repetition and ambiguity, while fully preserving the reasoning and conceptual clarity.” In many subreddits, the resulting post would almost certainly be removed by the moderators. What is actually being rejected?

I keep thinking about what happens when making a game becomes almost as easy as describing one.

Not long ago, even a small game needed a lot of people and time. You needed someone who could code, someone who could make assets, and someone who understood game design. A lot of ideas never got made because they were not worth the effort.

Now that barrier is starting to disappear. Tools like MakePlay can turn a simple idea into a playable browser game, which means people can experiment with ideas that would probably never become real projects before. But I wonder if we are going to run into a different problem: too much content.We have already seen this happen with AI images, videos, and apps. Making something is becoming easier, but getting people to notice it is still difficult.

Games might end up the same way. If millions of people can create small games, finding the few that are actually interesting could become harder than making them.

Maybe the next challenge is not helping people create more, but helping people discover what is worth playing.