Game_Ender

Some sentences feel pretty AI like:

These are not catastrophic events. They are the steady state.

There is no GPU futures market, no standardized residual value curve, and no way to lock in a forward rental rate. The premium is is the price of underwriting in the dark.

The headings are also AI like, a lot of essays before usually did not have titled sections but now they do and they all feel like these.

In addition the diagrams themselves look pretty AI generated.

This is an AI generated wrapper blog post for the real article:

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.

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.

0 - https://github.com/gastownhall/beads

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.

Unless you pay for enterprise then you are on the Enterprise Cloud Instance: https://us.githubstatus.com/posts/dashboard

Apple News Link for those that have it: https://apple.news/ATepjelNTSd-GZ0GpBtjjxw

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).

Can you link to nlspec? It is not easy to find with a search.

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.

Can you link some? I can only find the hip exoskeletons.

Speed and simplicity. Now I can fetch one binary on a system and in seconds fetch everything needed to run a Python tool or work on a code base.

I can do all that without having to even worry about virtual ends, or Python versions too.

https://archive.is/n08XL

You are looking for Codex CLI [0].

0 - https://github.com/openai/codex

I think the implicit take is that if your company hits AGI your equity package will do something like 10x-100x even if the company is already big. The only other way to do that is join a startup early enough to ride its growth wave.

Another way to say it is that people think it’s much more likely for each decent LLM startup grow really strongly first several years then plateau vs. then for their current established player to hit hyper growth because of AGI.

The link you have posted 404’s and I could seem to find a command like that in your repos. Can you be more specific?

Have you tried a smart watch? The Duo 2FA app lets you add an arbitrary TFA code based authenticator with same QR code Google Authenticator supports and generate those from their Apple WatchOS [0] or Android WearOS apps. I have used it successfully for years, it's a huge reason I got an Apple Watch in fact. Now you'll have to configure your watch with a "work" focus mode that turns off all notifications and not install any fancy apps on the watch (do those still exist?), but it can free you from your phone.

Along the same lines the Meta Wayfarer[2] smart glasses lets you take slice of life photos and videos without needing to whip out your phone. You lose a ton of quality but stay in the moment more. The AI features are getting better so eventually you'll be able to use it for basic information lookup.

0 - https://guide.duo.com/apple-watch

1 - https://guide.duo.com/duo-wear

2 - https://www.meta.com/ai-glasses/wayfarer

This is really great. Reading the bill raw feels like reviewing a diff with context set to 0.

I am not sure there is too much value for this article for the typical hacker news conversation on LLM based tooling. Here we generally focus on if the tooling is effective, and can it be used make software quicker or more cheaply. The problem is the author is opposed using the cutting edge models on privacy and ethics grounds. So they say:

I have woefully little experience with these tools.

I do not want to be using the cloud versions of these models with their potentially hideous energy demands; I’d like to use a local model. But there is obviously not a nicely composed way to use local models like this.

The models and tools that people are raving about are the big, expensive, harmful ones. If I proved to myself yet again that a small model with bad tools was unpleasant to use, I wouldn’t really be addressing my opponents’ views.

Then without having any real practical experience with the cutting edge tooling they predict:

As I have written about before, I believe the mania will end. There will then be a crash, and a “winter”. But, as I may not have stressed sufficiently, this crash will be the biggest of its kind — so big, that it is arguably not of a kind at all. The level of investment in these technologies is bananas and the possibility that the investors will recoup their investment seems close to zero.

I think a more accurate take is this will be like self driving, huge investments, many more losers thank winners, and it will take longer than all the boosters think. But in the end we did get actual self driving cars, but this time it's with LLMs it is something that anyone can use by clicking a link vs. waiting for lots of cars to be built and deployed.

Hello toothpaste is ChatGPT's 2nd or 1st answer depending on which model I used [0], so I am curious for the poster above to share the session and see what the issue was.

There is known sensitivity (no pun intended ;) to wording of the prompt. I have also found if I am very quick and flippant it will totally miss my point and go off in the wrong direction entirely.

0 - https://news.ycombinator.com/item?id=44164633

What model and query did you use? I used the prompt "find me a toothpaste that is both SLS free and has fluoride" and both GPT-4o [0] and o4-mini-high [1] gave me correct first answers. The 4o answer used the newish "show products inline" feature which made it easier to jump to each product and check it out (I am putting aside my fear this feature will end up kill their web product with monetization).

0 - https://chatgpt.com/share/683e3807-0bf8-800a-8bab-5089e4af51...

1 - https://chatgpt.com/share/683e3558-6738-800a-a8fb-3adc20b69d...

What is your preferred way to manage them?

With Aider you pay API fees only. You can get simple tasks done for a few dollars. I suggest budgeting $20 or so dollars and giving it a go.

What are those extra things you have to do more of? I only have experience with Aider so I am curious what I am missing here.

Can you describe the why of the policy and if you are ok sharing the industry?

I am also curious if you have other restrictions on information sharing, API usage, and what reference documentation to use.

Getting a 503 with that link.

To help those who got a bit confused (like me) this Groq the company making accelerators designed specifically for LLM's that they call LPUs (Language Process Units) [0]. So they want to sell you their custom machines that, while expensive, will be much more efficient at running LLMs for you. While there is also Grok [0] which is xAI's series of LLMs and competes with ChatGPT and other models like Claude and DeepSeek.

EDIT - Seems that Groq has stopped selling their chips and now will only partner to fund large build outs of their cloud [2].

0 - https://groq.com/the-groq-lpu-explained/

1 - https://grok.com/

2 - https://www.eetimes.com/groq-ceo-we-no-longer-sell-hardware