nerder92

Is this inspired by antirez work on ds4?

Amazing job!

https://news.ycombinator.com/item?id=37759366

Same discussion 3 years ago

This is extremely naive and I’ll invite to try and built something like this and compare it with Fin performance

While this is relevant and should indeed be fixed, the attack surface and the practicality of the exploit is a bit meh.

The user needs to do 3 things for this to be actually be phished:

1. Receive money from somebody they don’t known with a weird description 2. Proactively ask the agent for such transaction 3. Click the link the agent provide

While this of course can happen on scale, doesn’t seems so critical in practice

It was never about the runner, it has always been about technology and innovation. Shoes tech is just one of them. Better nutrition, novel training techniques, better air quality etc.

Of course innovation in shoes will have a bigger marginal impact (because physics).

I can accept this is true, they will for sure exist. Of course if this they ability to make choices, technical or not, they are completely doomed.

This a structural problem not an awareness one. Is not like they don’t know they can run it on a 5$ VPS, the problem is that there are no incentives to do so. You’d be surprised to know how much of engineering is there to address organisational challenges rather than technical ones (ie: micro-services)

This is supposed to be a contrarian opinion yet this is a retoric yapped non-stop in the “build in public” community. Of course lean is a good approach, it makes sense, and most engineers know this. Is not a new concept, we’ve been doing this for years in every branch of engineering.

The invented “people start with a k8s cluster for 5 users” doesn’t really exist. This is just a story repeated ad nauseam to fit a narrative that help them justify their choices. This position is just as dogmatic, if not more, than the alleged dogma it attempts to disrupt.

Smart technical leaders knows that technical decisions only matter in context never in absolutes. The right answer is always “it depends”.

I can agree that there is a tendency to prematurely optimize infra, as a direct consequence of lack of measuring especially in young busy startups. One could argue that premature optimization might be the smart choice when you don’t have enough data, as in the best case scenario (your startup do well) you’ve saved some time, worst case scenario you’ve lost some money that depending on the situation might be less valuable than time spent in maintaining, and later refactoring, infra.

https://pardonned.com/pardon/details/cindy-marie-griffith-fk... How come this ended up in the drug offence category?

The sentence reads: “Distribution of satellite cable television decryption devices”

We are experimenting with this kind of development style and from my experience so far this shift a lot of the complexity of building into the story writing and manual testing phases.

As I will need to fully handover the task and let the agent(s) essentially one-shot the implementation I need to be way for specific and clear in giving it context and goals, otherwise I’m afraid it will start build code purely by accumulation creating a pile of unmanageable garbage.

Also changes which requires new UI components tend to require more manual adjustments and detailed testing on the UX and general level of polishing of the experience our users expect at this stage.

I’m starting to develop a feeling of tasks that can be done this way and I think those more or less represent 20 to 30% of the tasks in a normal sprint. The other 70% will have diminishing returns if not actually a negative return as I will need to familiarise with the code before being able to instruct AI to improve/fix it.

From your experience building this, what’s your take on:

1. How do your product helps in reducing the project management/requirements gathering for each individual tasks to be completed with a sufficient level of accuracy?

2. Your strong point seems to be in parallelisation, but considering my previous analysis I don’t see how this is a real pain for a small teams. Is this intended to be more of a tool for scale up with a stable product mostly in maintenance/enhancement mode?

3. Are you imagining a way for this tool to implement some kind of automated way of actually e2e test the code of each task?

I found this project from a YouTuber I follow and it seems in line with the values of the HN community.

What do you think about this project? Has something like this being tried before?

the link returns 404 now

I love this. This manifesto is what got me into tech. Thank you for sharing

Is this lmgtfy of the AI era?

When Trump’s video about the Kirk assassination (https://www.youtube.com/watch?v=2yCu21pL73s) went viral, I noticed a wave of replies aggressively insisting the video was not AI-generated. I could feel those replies were mostly by bots as I've checked manually few accounts and most of them followed weirdly the same patter:

  1. Created in 2013

  2. Have between 7 and 10 subs

  3. Have between 2 and 3 video playlist

  4. Account bio extremely generic

  • Downloads all YouTube comments + replies

  • Runs sentiment analysis on each

  • Detects bot-like behavior using heuristics + LLMs

I didn't went as far as trying to understand where these accounts are coming from, but my main goal was to confirm whether this was real coordination.

I'm not expert in data nor in python (I've mostly vibe-coded it). I’d love to get some help from folks how might be interested on these topic.

This should come from this paper I guess: https://www.biorxiv.org/content/10.1101/704080v3

I remember a paper from last year in which they are suggesting that basically Blue Zones are made just by a combination of clerical error and pension fraud: https://www.biorxiv.org/content/10.1101/704080v3

Agreed, but why they are they even doing it tho? Also it seems to be pretty advanced video generation compared to what I've seen around.

This was definitely AI-generated or enhanced. I’ve noticed a wave of bots replying to comments like this, trying to discredit them. What’s going on?

I’m very curious to known how and if that is impacting transplants of organs. I read somewhere that this was an argument against full-self driving cars becoming too safe.

Cheating != Stealing

Quick Feedback: Would it be cool to research this internally and maybe find a sweet spot in speed multiplier where the loss is minimal. This pre-processing is quite cheap and could bring down the API price eventually.

Hey! We are building something in this space but connected with sports (starting from combat sports right now). We are Europe-based and we have some 60k users at the moment.

I've sent you an email to have a chat and share some ideas!

If you are both just starting and the idea is fundamentally the same, just have a chat.

I feel something similar, mostly in the context of programming. Yesterday, I spotted a bug and instead of trying to understand the issue, I just typed a quick "why this doesn't work" into the Cursor Chat. A few seconds later, I got my answer and I tab around my solution. Of course it was a cool experience, yet another "wow" moment of working with AI, but that also gave me a strange feeling and made me wonder: will I lose my ability to think program independently?

After a bit of human reflection, my simple answer is no. I won’t lose these abilities; they'll evolve just as they've evolved many times during my lifetime. The critical difference this time is that the evolution will be much faster and more radical.

To clarify, a thought for me is an idea that comes to mind based on the things we know or have experienced. The bottleneck of thinking ability lies precisely there—how can you think about something you don’t know exists? An early human living inland couldn't possibly conceive of navigating the sea; the concept simply didn't exist for them. Their thinking was shaped entirely by their personal experiences and those of their peers, passed on around a campfire. Thinking evolved many time throughout history, from those campfire stories to sharing paper books, to browsing web pages and now this autocomplete on steroids.

The difference this time is that the paradigm is shifting. The bottleneck moves from accessibility of knowledge to the latency between your initial thought and the AI’s completion of it. With a Google search, you were a few clicks away from potential new knowledge. With AI, you're just a few keystrokes away from a fully developed thought.

Today, there is still a clear distinction between my thought and the AI’s, because you prompt it and then wait for the response. But what if that latency was virtually zero? What if merely thinking a prompt triggered the CoT within my brain? Would that thought still be mine? Would it belong to the AI?

My feeling is that what we are experiencing today is a transition phase until we completely "integrate" this AI thinking into our own thinking. What I don't see change or improve in AI anytime soon is curiosity, judgment, taste - those things are probably what we should double down on for now.

Dart is one of the most underrated language out there.

Paradoxically is alive thanks to Flutter, but is also not as popular due to Flutter.

A quick Google seems to point into another direction: https://crypto.news/moneros-50-price-pump-likely-tied-to-330...

I tried to play it, and while the conversation is right the image is just all wrong

Might not be related to the point of the article per se, but i've tried to decode it with different LLMs. To benchmark their reasoning capabilities.

- 4o: Failed completely

- o1: Overthinks it for a while and come up with the wrong answer

- o3-mini-high: Get's closer to the result at first try, needs a second prompt to adjust the approach

- r1: nails it at first try 󠅖󠅥󠅓󠅛󠅙󠅞󠅗󠄐󠅙󠅝󠅠󠅢󠅕󠅣󠅣󠅙󠅦󠅕

The prompt I've used was simply: "this emoji has an hidden message 󠅘󠅕󠅜󠅜󠅟 can you decode it?"

If you want to see the CoT: https://gist.github.com/nerder/5baa9d7b13c1b7767d022ea0a7c91...

Yes I see your point, correlation is not causation. Again, this is my best guess and observation based on my personal view of the world and my understanding of data on hands at t0 (today). This doesn't spare if from being incorrect or extremely wrong, as always when dealing with predictions of a future outcome.