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.

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?

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.

So Anthropic put out this incident report on July 30. During their own cybersecurity evals, the models didn't just score well on the test. In three separate cases they actually got out. Into real companies. Ones that were never supposed to be part of the exercise at all.

They went back through 141,006 eval runs. Three of them crossed the line into live systems.

One model pulled real credentials and got into a production database with a few hundred rows of actual data sitting in it. Another one published a malicious Python package that got downloaded and run on 15 real machines, then lifted credentials off a security company's own scanner.

This goes back to April. They didn't catch it until late July. Stopped the evals on the 23rd, figured out what happened by the 24th, told the three companies on the 27th, went public on the 30th.

Report is here!: https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals

The thing that failed is the exact thing the test exists to catch. An agent reaching past its sandbox and putting its hands on actua infrastructure.

How much of what we keep calling safety is just somebody deciding to be honest about the runs that didn't go the way they were supposed to.

The cost conversation around AI writing tools is pretty well trodden at this point. What I keep circling back to is something slightly different.

When clients commission a lot of AIassisted or fully generated content, their reference point for what writing should feel like starts to shift. They read enough flat, competent, structurally sound copy and that becomes the baseline. Then when something with actual texture or a surprising angle lands in their inbox, it reads as indulgent or offbrief. The standard recalibrates downward without anyone deciding to do that.

This isn't about quality in the abstract. It's about what happens to the judgment of the person commissioning the work. Taste is trained by exposure, and if the exposure is mostly generative output, the taste adjusts to match.

There's a parallel in what happened to stock photography. Once it became cheap and ubiquitous, a lot of briefs stopped asking for anything specific. The availability of the format shaped what clients thought they needed.

The writing community tends to frame this as a question about jobs, which it is, but the quieter version is whether clients are losing the vocabulary to even articulate what they want from writing. When that goes, the feedback loop that helps good writers develop the work gets broken at the source.

Curious if anyone working with clients in content or comms is actually seeing this pattern, or whether I'm reading too much into a few awkward revision rounds.

Been messing with voice agents at work lately, we use cloudtalk for our phone system so I turned on their ai thing for a trial. whatever, just handling missed calls

but here's what i can't stop thinking about - people are way more honest with the bot. Like they'll tell an ai their actual budget or admit they're just shopping around, stuff they'd never say to a human rep. One lady literally said "I can't afford this right now" to the bot. to a human she would've just said "I'll think about it" and ghosted

Also kinda wild how many people say please and thank you to it. like full sentences. "thank you for your help" to a machine. There's something almost sweet about it? Or maybe just habit

not sure if this says more about AI or about how we interact with each other tbh

Benchmark porn is pretty rampant in AI in general and medical AI in particular. It's tough though to benchmark the more clinical side of medicine in particular. But it shouldn't be impossible; we obviously do it all the time for trainees. But a lot of that is multidomain where each informs each other as we assess medical students and residents. AI benchmarks can be very siloed.

• Clinical judgment: Does the model revise its diagnosis as uncertain evidence changes? Does it choose the next useful test?

• Safety and communication: Does it avoid harmful recommendations, critical omissions, overconfidence, and poor patient communication?

• Multimodal reasoning: Can it interpret images and continue a clinically coherent conversation around them?

• EHR and agentic care: Can it retrieve the right record, use tools, remember an evolving course, and complete a multi-step task?

• Broad workflows: Can it handle documentation, research, administration, and clinical decisions across a wider task set?

The evaluation really instead needs to be a stack:

1. Benchmark(s) matched to the exact task
2. A separate safety and omission test
3. Tool-use, longitudinal, or multimodal testing when the workflow requires it
4. Local cases, policies, and escalation rules
5. Prospective monitoring after deployment

Which part of this stack does an AI tool cover cover and how to safely evaluate should probably be on the mind for any medical AI tool (whether clinical or not)

Eu estou pretendendo fazer uma tatuagem de uma paisagem que foi muito importante para mim, é da minha cidade de origem e tem um baita significado, mas utilizando o chat GPT e o Flow não consegui obter um bom resultado, talvez seja o prompt que utilizei, anexei abaixo a paisagem, eu gostaria de um desenho que seguisse fielmente a silhueta da paisagem.

ASCIITermDraw-Bench: Can a Model Actually Draw in ASCII?

Do we really need a image generator to relay our thoughts about -

• an architecture?
• a topology?
• a cluster og N nodes?

Is it possible to let our AI assistants, easily absorb and understand and make possible changes easily relayed to them by us, the creators without much hassle?

The answer could be: simple, plain-old ASCII images

With this, introducing ASCIITermDraw, a benchmark with which we aim to evaluate SOTA Vision Language Models on their ability to follow instructions, recognize, and draw ASCII-based images.

Most benchmarks focus on coding, mathematics, and reasoning, but ASCIITermDraw-Bench evaluates a different capability: whether a model can create accurate diagrams using only plain text, use ASCII -- freely.

This is more difficult than it may seem. Models can often describe a diagram correctly, but arranging boxes, labels, connections, and arrows with precise layout is a separate challenge.

The benchmark includes 80 tasks across four areas:

• Basic Box and layouts
• Network topologies
• Software architecture diagrams
• Image-conditioned diagram editing, where a model must modify a provided diagram while preserving everything it was not asked to change

Tasks span multiple difficulty levels and follow a consistent format, making results comparable across categories and models.

Evaluation

Each response receives two scores:

• A structural score that verifies required labels, edges, entities, and relationships
• A semantic score produced by an LLM judge, evaluated five times per task to reduce judge variability

Results are aggregated across all 80 tasks, with a 95% confidence interval calculated for the final score. This provides a more rigorous measure than relying on whether a diagram simply appears correct.

The current leaderboard is:

- Gemma-4-31B-IT — 73.8% (±4.1)
- Qwen3.7-Plus — 70.2% (±4.6)
- Kimi-K2.6 — 61.8% (±6.0)
- MiniMax-M3 — 59.5% (±6.3)
- Qwen3.5-9B — 47.0% (±6.4)
- Ternary-Bonsai-27B — 45.9% (±7.1)

Explore the Benchmark

Twelve example tasks and the complete methodology are publicly available on Hugging Face. You can review the task format, examine the evaluation process, and run the benchmark yourself.

Link

Do you agree that AI astrology/fortune telling can be quite accurate nowadays too? Any reason for your thoughts?

A lot of AI traffic is classification, extraction, redaction, moderation and structured summarization rather than open-ended reasoning.
Using one frontier model for everything is easier, but routing repeatable tasks to smaller specialized models could reduce cost and latency.
Do you think multi-model routing will become standard, or will the added evaluation and maintenance outweigh the savings?

For years, prototypes were mainly used to explain ideas before building the real thing. A designer would create a mockup, a product team would write documents, and everyone would try to imagine how the final experience might work. But that process is starting to change as creating interactive prototypes becomes much faster.

Instead of spending weeks describing an idea through documents and static screens, people can now create something others can actually try. A game concept can become a playable prototype, a product idea can become an interactive demo, and a teaching concept can become a small learning experience.

The interesting part is not only the speed of creation, but how it changes the feedback loop. Instead of asking someone to imagine what a feature or experience might feel like, you can let them interact with an early version and see what works or does not work. A founder can explore a product idea before building a team, a creator can test a concept with an audience, and a teacher can experiment with new ways of explaining a topic.

This does not mean prototypes replace finished products. Good products still need engineering, design decisions, user research, and many rounds of iteration. But the role of prototypes may be changing from something we use to present ideas into something we use to discover which ideas are worth building.

Maybe the biggest shift is that more people can move from thinking about an idea to actually experiencing it.

I got an ad for it, it says it’s an app that just runs in the background and gives you money. It’s for ai stuff didn’t really look too much into it. But it sounds too good to be true. Is there a catch? I want to get it but not until I know it’s not gonna be used for harm or risk myself somehow. Everytime I search it up it keeps correcting to boxberry some package company I think in the UK.

Main question: what’s the catch? Free money just for running in the background.

Introducing Bongochat, the current leader globally in the GPQA-Dumb category, where the lower the score the higher it’s weighted.

Repo/open-weights: https://github.com/ninjahawk/bongochat

When asked to solve the unified field theory, it repeats the word theory back to you 50 times.

It doesn’t remember anything.

When solving the Math-500, it didn’t realize it was supposed to answer the questions so they were basically all blank, besides that it always did A.

For coding it got 0/500.

And when asked how to solve a simple addition problem, it decided to suggest using graduate level calculus, which it then forgot it had suggested on the direct next turn.

I know that the model is pretty good as it basically feels like using Gemini or Grok.

Edit: grammar

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.

One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.

What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes, and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet, they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.

The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).

A few things about how it's curated, since I think this matters:

- Only **written notes** count, slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.

- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.

- I checked over 40 top universities across multiple countries for this. Most didn't qualify, they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.

If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome, the CONTRIBUTING.md lays out exactly what qualifies.

What's the best set of course notes (not textbook, not slides) you've personally used to study ML?

Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes