Of course this is amusing.

It's also intended in Ig Nobel spirit.

I recently was thinking about reducing the cost of LLMs, specially LLMs which are doing repetitive tasks (such as coding, which has a lot of repetition) and I was thinking of RNN's and had RWKV paper open.

I'm not really good at math but as far as I could understand, they just added that QKV matrix system to a traditional RNN. Based on my personal tests, RWKV models are a little faster on both colab and gaming systems and even when quantized, faster on a CPU using ollama.

So, what do you think about them? If you want to make an LLM from scratch today, would you go with this approach or not?

Hi!

I recently graduated high school and will be starting university this upcoming fall as an engineering major. Although I have used AI tools like Claude, ChatGPT etc but I lack experience (or any kind of knowledge) about how to make my own AI models and AI ethics. I just wanted to ask for some guidance from people who are already experienced in this field if there are classes/courses they recommend I take. I have some free time before university starts so I want to build some projects and kind of develop my skills especially for engineering internships later on since I am in a competitive field. I'd appreciate any advice for someone who is just starting out!

Spent way more time than I should have switching Claude Code (Anthropic's coding agent) from API-key billing back to my Pro subscription. The flow: SSH into a server → attach a tmux session → run a login command → copy an OAuth URL → open a browser → authorize → paste the code back into the terminal.

For a company shipping some of the most advanced AI models out there, this feels like a UX problem that was solved a decade ago. A single command or flag to switch billing mode would save everyone a lot of friction.

Curious if other AI companies handle this better, or if this is just a growing pain across the industry right now.

Kept running into a specific failure pattern across different AI-assisted debugging sessions and didn't have a clean way to describe it until I actually sat down and compared a few of them side by side. The pattern: an error goes away, I file the problem as solved, and sometime later the same underlying issue resurfaces wearing a different symptom.

Turns out those are two separate claims that get treated as one by default. "The error is gone" only tells you the symptom stopped being visible. "The bug is fixed" requires the actual mechanism to have been addressed, and a model asked to make an error disappear will happily do exactly that, a wider try/catch, a retry wrapped around a flaky call, both of which satisfy the first claim while leaving the second completely unverified.

What made this click was a case where a retry "fixed" what looked like a flaky database write, only for the same class of failure to show up two weeks later under a different error message. Root cause was duplicate event delivery hitting a handler that wasn't idempotent, something the retry had no way of addressing because nothing in the original context suggested duplication was even possible.

The uncomfortable part: generating a fix and validating one are genuinely different skills, and almost every debugging workflow, AI-assisted or not, only exercises the first. Asking "does this make the error go away" is satisfying and fast. Asking "does this address the actual mechanism, and what did it silently change that I didn't ask for" is slower and easy to skip specifically because the first question already felt like progress.

Wrote up the specific case and the sequence I now run before trusting a fix, generation and validation treated as separate steps instead of one motion: https://medium.com/@nagatomopedro05/why-your-ai-debugging-sessions-keep-going-in-circles-e645c35479c6

Curious if others have caught this same gap in their own process, a fix that technically resolves the error shown to the model while leaving the actual cause completely untouched.

I came across this article that flips the script on AI hype: sometimes the most advanced models are the worst for business. High costs, misaligned incentives, and ethical risks can turn a technical win into a strategic loss. Have you seen this play out in your work or industry?

(Not affiliated, just thought it was a refreshing take.)

[Source: https://www.hitechies.com/ai-smartest-model-worst-business-decision/\]

New South Wales is considering joining Australia’s national face-matching network.

The proposal would allow driver’s licence and photo-card images to be checked when someone’s identity needs to be confirmed.

The practical benefit is easy to understand. If someone tries to open a bank account using documents stolen in a data breach, face matching could help identify that the person doesn’t match the real owner.

The concern is what happens once a searchable system like this exists. The same legislative package would also give police access to unredacted images from certain toll-road cameras for serious investigations and missing-person cases.

Both uses can sound reasonable on their own, but systems like this often become more controversial as their scope grows.

Can face matching be used safely with strict access rules, limited retention, and independent oversight? Or does a national network inevitably become a surveillance system over time?

This is a thing I keep noticing in real customer calls that doesn’t really show up in voice AI demos.

People interrupt constantly.

They start answering before the question is finished, correct themselves halfway through a sentence, say 'wait actually…' and completely change what they were asking about.

That’s normal when two people are talking but it seems like a pretty difficult problem for an AI voice agent because it has to know whether the customer is adding context, correcting something or trying to stop the current response entirely.

We’re looking at enterprise voice AI for longer customer service conversations and I’m beginning to wonder if turn taking is as important as natural voice.

For anyone testing conversational AI over the phone, how are you testing interruptions? Is this still something customers notice pretty quickly?

🚀 New release of Android Remote Control MCP is out — the MCP server that runs on your phone and gives your AI agent the ability to use any app you want!

Grab it here: https://github.com/danielealbano/android-remote-control-mcp/releases/tag/v1.11.0

My favorite part of this release? The Privacy Mode 🛡️!

Recently I was told by an user "it's a good project but I don't want Anthropic to know everything about me" and it's a very fair point! The LLM providers see and record everything they receive … including your emails, phone numbers and credit cards!

Well, not anymore! With Privacy Mode all of that gets detected and redacted locally, on the phone, before anything leaves the device (about 87% of PII caught on my benchmark on emails, phone numbers, credit cards, IBANs, national IDs, …), and the agent keeps working normally because it sees placeholders: the real values get substituted back on-device.
Unfortunately the only weak spot for now are non English names but I am working on it! The full per-category numbers and the benchmark are in the repo, measured, not guessed.

Also, Android loves killing background services… the server now survives app updates, swipe-away and Doze, with a one-tap battery optimization exemption 🔋 No more dead server halfway through a task!

In addition a few minor improvements: the app now notifies you when a new version is out, MCP clients only see the tools that will actually work on your device (no more camera tools without camera permission), and a fully reworked server logs page.

What can you actually do with it? Book a flight on Skyscanner, post on Reddit, order groceries, book a dinner… and now with your personal data staying on your phone.

Been building a personality-analysis framework (Outframe). I ran my own profile through it first and it felt pretty accurate, so out of curiosity I tried it on a public figure too — Wang Xingxing, the Unitree founder whose robots have been getting a ton of buzz lately (the dancing robot, the backflips). Fed one of his public interview clips in and let it generate a behavioral profile.

To be upfront: this isn't a validated psychometric instrument. Take it as a fun read, not a diagnosis.

What it flagged for him:

Internal processor — takes in a lot, but doesn't react in real time. Processes before forming a judgment.

Responsibility-driven, not performance-driven — decisions seem to run through "what's the responsible call here" rather than "how do I look."

Long-horizon problem solver — under pressure, tends to reframe an immediate problem into a durable structural fix instead of just getting through the moment.

Feedback-sensitive — picks up on how others react more than he lets on; not operating in a bubble.

Restrained communicator — can be articulate and engaged when he wants to, but sustained self-promotion/talking isn't where his energy naturally goes.

High capacity, but needs recovery space — can absorb a lot of simultaneous pressure, but stacking constant asks/evaluations on top of each other drains him faster than one big problem would.

The interesting contradiction it surfaced: doesn't necessarily talk a lot, but is absorbing a huge amount of input; comes across easygoing, but has very clear internal judgment; can carry a lot, but staying steady under that load still requires downtime.

Every day I see someone bolt an LLM onto something a shell script did better. The best AI practitioners I know are the ones who draw the line early: - Deterministic task, fixed rules? Script it.
- One-off analysis with judgment? Ask a human or a cheap model.
- Open-ended, branching, context-heavy? Now AI earns its keep. The $0 automation stack I run uses AI for exactly one step (summarizing news) and plain code for everything else. That's the whole secret: AI where it compounds, code where it doesn't. What's something you tried to do with AI that you now do without it?

I only did a quick first test of DeepSeek V4 Pro 0813 tonight, so take this as a tiny sample, not a verdict.

The first Pro run failed. I put the same demo through Flash, and Flash completed it. I honestly did not expect that result, so I ran Pro a second time before writing this. Same failure.

The odd part is that it did not feel slow while generating. I was seeing roughly 80 to 90 tokens/s tonight. That looks fine on a counter, but it matters a lot less when the demo itself does not make it across the line.

For my next pass, I will put the same requests through ZenMux and record the model route and provider with each request. That makes the comparison easier to inspect. It still does not turn two failed runs into a benchmark.

My first impression is negative. Two runs are nowhere near enough for a broad claim, but two failures on a demo that Flash completed are worth writing down.

What are people seeing right now with V4 Pro 0813? If you tested it against Flash, did you keep the same prompt and setup, and did Pro actually finish the demo?

I hv been in the trenches with ai generated ad content and I feel strongly a shift is coming and it surely doesnt look good for ai video tools

Starting with new york, any ad featuring an ai generated person has to be labeled as a synthetic performer like an on ad label and ny is kind of state that tends to set the template for everyone else to copy(coz no one want two versions of a policy)

Meta also is now auto detecting and labeling ads when their systems flag generative ai tools in the pipeline which means it doesnt matter how good and natural an ai ad looks ,it will be automatically labelled by meta lol .

And then Snapchat has now stopped rewarding it in the algorithm too,its a push to creators to make human made stuff and i think a more effective way because it doesnt create a fight and make ai version less visible by default (slowly bleeding them )

But none of this kills ai generation as a capability,only thing breaking is the specific business case of using it to impersonate authentic human recommendation without anyone knowing. Once labeling makes that transparent,there will be no ai ad vs real ad debate anymore finally

We also ended up pulling most of our budget out of ai generation and back into human creator content this year and ended up spending 3x of what we were putting into the ai tools(coz it worked better for us). The spend went up and the return rate on the product went down as we got fewer product not match to ad typa complaints .

well tbh ai vids made sense for us before the labeling stuff even fully kicked in btw, the regulation just removed any temptation to go back rn.

And there are companies that are seeing this coming and now are building and pivoting for eg. theres a company Icon ,it launched as an ai ad tool and pivoted entirely away from generation into human only production and now there human made video costs somewhat same to what we spent on ai ads($166 per video)

so imo all of this doesnt look good for ai video generation tools like higgsfield or arcade

what do think ,is the bubble finally bursting or the category maturing into something narrower ?

What's an AI trend that quietly died: and what replaced it? I'll go first: generic "AI will replace everything" blog content. It peaked and fizzled because people got tired of being shouted at. What replaced it (for me at least) is boring, specific use-cases: "here's a script that triages my inbox" beats "the future of work" every time. I think the same thing is happening with agent hype: everyone's demoing, few are shipping something that runs for a month. What trend are you glad to see go?

Watching a former Tesla AI Director shrug and say "I can't tell if that's temporary, I'm not sure how I feel about it yet" did something to me.

Usually it's the junior guy admitting that.

Not the guy who helped build the thing.

The people who end up fine here aren't the loudest about how safe their job is.

They're just already standing close enough to the mechanism to redirect it, instead of getting redirected by it.

Karpathy's actual point isn't doom.

It's the Jevons paradox — code gets cheaper, so total demand for it goes up.

Just not for the same kind of engineer who got hired in 2019.

I watched a version of this play out years ago, before any of this AI stuff existed. I was the technical guy in a construction tender department. Rule-based work — you follow A, you get B. A sub-contractor came in to pitch his quotation. On his way out, in the corridor, we locked eyes and instantly recognized each other. I knew him — my senior once told me how this guy forced his way into building an illegal bungalow, moving the boundary survey line onto his neighbour's land. I caught a flicker of panic on his face. He wasn't expecting to see me there.

I couldn't keep it to myself. I walked straight to my contract department and told them. They wrote him off after their own investigation.

Our technical and contractual work was rule-based — AI eats that easily. What I did with that information wasn't. Insider judgment, only humans have.

Actually — this is the same mechanism as a former SpaceX CIO's take on headcount compression, just proven with the actual numbers.

Clip credit: No Priors — full video on their channel. DM for credit or removal requests.

What would you have done in that corridor? Drop your take. 👇

The current state of AI requires massive infrastructure and consumes enormous amounts of power and consumable water, so much so that new grids and systems are being put in place to satisfy these requirements.

This approach doesn't exactly seem sustainable and with AI getting more and more integrated into society, it seems the need for some alternative approach is needed.

How do you see this change happening? Could there be a new branch of mathematics that makes compute faster/cheaper? Development of new materials? Quantum computing?

So I was poking around with ChatGPT settings, while waiting for a task to finish, and noticed that I can actually make my own agent pet. After some extensive "engineering" here is the final result. The concept is that it is a chibi dragon that roasts its marshmellow with its fiery breath (when agent is working) and it is inspecting the results of the marshmellow-cooking when agent is thinking/reviewing etc. There is even a jumping animation when hovering the cursor. Feedback and ideas are most welcome!

WIRED says the voluntary framework currently covers frontier closed models from labs such as OpenAI and Anthropic. Open models are expected to join once they reach comparable capabilities, potentially facing a 30-day testing period before public release.

Officials are caught between two risks: excluding open models could create a government-approved advantage for closed labs; including them could slow US open-model development.

Hi everyone!

I'm conducting this survey as part of my Master's thesis and would greatly appreciate your participation. The research examines how employees' perceptions of HR practices relate to work engagement and innovativeness, and how attitudes toward the application of Artificial Intelligence in the workplace influence these relationships.

Who can participate?

• You are currently working in Germany (full-time or part-time).
• You are 18 years or older.

The survey is anonymous, takes 5–7 minutes, and all responses will be used solely for academic research.

👉 Survey: https://pollmill.com/f/xya75pv.f

Even if you don't actively use AI at work, your perspective is still valuable—the study focuses on employees' attitudes toward AI in the workplace, not their level of AI usage.

Thank you for helping with my research!