It provides a bit of extra context on startup times and install times of dependencies but I wouldn't trust those given the automated nature of the articles creation.
What's not clearly disclosed is when you are being limited and what the bounds are. If you are developing ML kernels for a computational photography use case will the safe guards miss-fire and sabotage or slow down your efforts? What about distributed GPU interconnect work for a nation super computer lab used in weather simulation?
The reason they are doing this shadow ban style technique, is they don't want users to figure out how to jail break their way out. Or the explicit direct bad PR of when it miss-fires.
Related is Beads [0] which is an external memory and task based issue tracker. Also designed to allow agents to collaborate. I have not actually used Beads but since we are share basics in this space it's a cool one to know if you are looking at ways for agents to collaborate on more complex problems.
Your process loses your unique voice. The content was OK, but too verbose, and needed data on other rust ports of similar scope.
The issue is the quippy titles, “something - aside - continue” phrasing, and other constructions are feel like they or actually are wholly LLM written. I find a high correlation to this and low density fluff. The author did not have 10 paragraphs of things to say, but used an LLM to inflate a short outline to that. We would all of been better off with a tighter document - either human written or better prompted.
It’s becoming like the iPhone, once the software has access to “Claude”, everyone in the org wants it. Finance wants it for excel, marketing and design for image generation, compliance for working with documents. Sure it’s not software engineering rates, but it increases the user base beyond software developers.
That data center is running on local power generation because they failed to get power run to the data center properly and essentially exploited a loop whole in planning permission that allowed them to install local power generation: https://www.loudounnow.com/news/sterling-residents-raise-ala...
It’s the only 1 out of 200 in that area, so it’s not representative of what data centers sound like. It does show how you can’t trust the operators to do what best for the local community. It does show how a functioning government works because Loudon county increased oversight and changes the rules to stop another project like that. Setup policies to manage externalities, and don’t make ignorant bans.
A functioning property tax brings in a lot of revenue for the local government. Areas of the US with lots of data centers, like Loudon County, can have 35% of their budgets covered by data centers, and the worst of it is so ugly big box buildings you drive by sometimes.
Put in place sensible rules around noise, locating in pre-planned areas, and covering the cost for electrical upgrades then let the market decide how many to build. Most people appear to be getting their information from TikTok and have developed a very ignorant NIMBY attitude.
To be blunt progress does get made by listening solely to those who get short end of the stick. Japan and China have good rail in large part because the central government can simply make the globally better choice over the objections of those nearby who lose out due to noise and other factors. We don’t need to do that, we simply need to not let ignorance win and instead regulate the externalities properly, and capture value for the public through property taxes.
Where is the network isolation? I want to be able to be able to limit what external resources the agent can access and also inject secrets at request time so the agent does have access to them.
File system isolation is easy now, it’s not worth HN front page space for the n’th version. It’s a solved problem (and now included in Claude clCode).
Why should he put effort into measuring a tool that the author has not? The point is there are so many of these tools an objective measure that the creators of these tools can compare against each other would be better.
So a better question to ask is - Do you have any ideas for an objective way to a measure a performance of agentic coding tools? So we can truly determine what improves performance or not.
I would hope that internal to OpenAI and Anthropic they use something similar to the harness/test cases they use for training their full models to determine if changes to claude code result in better performance.
https://hackernoon.com/myth-vs-reality-real-world-runtime-pe...
It provides a bit of extra context on startup times and install times of dependencies but I wouldn't trust those given the automated nature of the articles creation.