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).
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.
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?
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
After a few minutes spent manually checking I decided to build a tool that:
• Downloads all YouTube comments + replies
• Runs sentiment analysis on each
• Detects bot-like behavior using heuristics + LLMs
On this video, over 40% of comments look like bots, and they overwhelmingly argue the video wasn’t AI-generated.
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.
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
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.