HN user
Actually HN handles it almost perfectly. The automatic line breaks are really good.
Only complain would be that its sometimes difficult to see the parent comment(s) when you scroll down.
HN could fix this quite easily by making the current parents sticky while you scroll.
"You can lead a horse to water, but you can't make him drink"
Reader view makes the text too narrow: https://imgur.com/a/yQqzxco
Sure i could take extra steps to make it more readable, but at that point I rather not read it as im not that interested nor invested.
Totally fine, no complains from my side. But If the author wants to increase the reach of their posts they just might use agentic coding to have the llm optimize the website for readability.
There is a reasone we use left and right margin/padding.
This blog is quite unreadable for 27/32" monitors.
We should be greatful for this. This is the one public case study on how large-scale llm-driven code generation actually works out.
With node and deno there are reasonable alternatives for everyone who don't want to use bun anymore.
You could also use the LLM create a program to do the conversion and then review and use the program to deterministicly perform the actual conversion.
Have the best of both worlds.
GitLab? We use gitlab for work. Its way worse in comparison.
Last week I encountered a bug where my merge request simply didn't show that I deleted a file. Apparently it's because my MR included the creation of a folder with the same name as the basename of the deleted file. Unacceptable for a code hosting platform.
Other than that I miss GH Actions, a clear ui (gitlab has way too many sub-menus), a responsive ui (gitlab feels very sluggish). And while we don't have the Gitlab duo activated, it still pops out regularly eventhough I can't use it besides closing it. ...and I don't even want to start with their issue buard.
It strongly reminds me of Jira in terms of quality, which is no compliment.
US models align with our "average" (western) values. If we outsource thinking by using LLMs, why would we outsource it to an LLM that doesn't have our values encoded in it?
Couldn't be more clear violation from a legal standpoint.
Though its quite sad that the community had more creativity (and engineering talent) to develop classic(+) wow.
Everything Blizzard now touches is bland, lacks soul, or is straight up bad.
Sure, a poweruser can bring their own ram/ssd. But again they pay almost as much and have a worse system performance wise.
Normal users don't profit from anything you listed. They do have to buy a notebook with all components, and thus currently have to pay more for linux/windows hardware compared to Apple.
Also, RAM isn't backwards compatiple. Literally had this problem with my old ddr4 not fitting in the newer ddr5 slots when my ddr5 acted up.
Framework Laptop is more expensive than a Macbook Air with all around worse hardware. For a framework 13 I'd have to pay 1900€ with a 16GB setup. For 1450 I get a MBA with 24GB ram. Similar with a dell or lenovo who get smoked in performance comparisons.
It might still be worth it for those who hugely value open source and repairability but as for value I think its save to say that Apple is currently in a league of their own. Even if the altest os update is a flop.
Also, the Macbook has improved repairability. While its still not great its better than a few years ago.
Thats just one of the interpretations of a skill.
A skill can also act as an abstraction layer over many tools (implemented as an mcp server) to save context tokens.
Skills offer a short description of their use and thus occupy only a few hundled tokens in the context compared to thousends of tokens if all tools would be in the context.
When the LLM decides that the skill is usefull we can dynamically load the skills tools into the context (using a `load_skill` meta-tool).
Gemini 3 was:
1. unreliable in GH copilot. Lots of 500 and 4XX errors. Unusable in the first 2 months
2. not available in vertex ai (europe). We have requirements regarding data residency. Funny enough anthropic is on point with releasing their models to vertex ai. We already use opus and sonnet 4.6.
I hope google gets their stuff together and understands that not everyone wants/can use their global endpoint. We'd like to try their models.
Thats like being proud of not using google or stackoverflow and only reading manuals, or using notepad instead of an IDE (or editor with language server support).
A 10$ GitHub Copilot or 20$ ChatGPT/Claude subscription get you a long way.
And if the employer isn't willing to spend this little money to improve their workers productivity they're pretty dumb.
There are valid concerns like privacy and oss licences. But lack of value or gain in productivity isn't one of them.
Nah. Plenty people struggles with the use of tailwind or at least were interested in shortcuts. Thats the whole what tailwind plus offers. In some ways tailwind is like matplotlib/pandas/numpy. Increadibly powerfull but some methods/classes are difficult to remember to you keep googleing the same things.
Doesn't matter anyways wether their customers are people who search for shortcuts or people who search for "the best designs".
Their problem was and is that tailwind is used by many of the most profitable companies in the world for free.
Thats so unbelievable stupid. You have corporations paying millions for MS 365 subscriptions, confluence, and other software and basically nothing for a totally optional ui library. If the use of tailwind saves 10 engineering hours per month then it's worth it to pay a few hundred $ for a licence.
Given that their team isn't big they don't even need that many customers. Add a bit consulting for a decent hourly rate and they should be golden.
The more I think about it the more I blame the CEO for poor decisions.
gemini 2.0 flash is and was a godsend for many small tasks and ocr.
There needs to be a greater distinction between models used for human chat, programming agents, and software-integration - where at least we benefitted from gemini flash models.
Seems like their whole business model was based on the fact that tailwind was difficult to use, and now with llm we have a simple way to use it in a good-enough way.
They, and other companies, should rather depend on corporate users. Don't let multi-billion revenue companies use your tech for free.
Seems like many companies leaned it a bit late, we always have the same news every fewe years (docker, mongodb, terraform, elastic).
I assume its still x86-64?
What actually makes it an AI platform? Some tight integration of an intel ARC GPU, similar to the Apple M series processors?
They claim 2-5x performance for soem AI workloads. But aren't they still limited by memory? The same limitation as always in consumer hardware?
I don't think it matters much if you're limited by a nvidia gpu with ~max 16gb or some new intel processor with similar memory.
Nice to have more options though. Kinda wish the intel arc gpu would be developed into an alternative for self hosted LLMs. 70b models can be quite good but still difficult / slow to use self-hosted.
Thats the whole problem. No consistency. Some configurations work, others not - eventhough they should be way more capable.
That's not even limited to linux or gaming. A few weeks ago i tried to apply the latest Windows update to my 2018 lenovo thinkpad. It complained about insufficient space (had 20GB free). I then used a usb as swap (required by windows) and tried to install the update. Gave up after 1 hour without progress...
Hardware+OS really seems unfixable in some cases. I'm 100% getting a macbook next time. At least with Apple I can schedule a support appointment.
No one is claiming that it's a bad move.
It's just an anti-competitive move that could be very bad for the consumer as it makes the inference market less competitive.
For me it was about 8 years ago. Back then TF was already bloated but had two weaknesses. Their bet on static compute graphs made writing code verbose and debugging difficult.
The few people I know back then used keras instead. I switched to PyTorch for my next project which was more "batteries included".
Same as always?
Cheap labor. It doesn't take that much to train someone to be somewhat useful, in mmany cases. The main educators are universities and trade schools. Not companies.
And if they want more loyalty the can always provide more incentives for juniors to stay longer.
At least in my bubble it's astonishing how it's almost never worth it to stay at a company. You'd likely get overlooked for promotions and salary rises are almost insultingly low.
It would be nice if this model would be good enough to update their typscript sdk (+agents library) to use, or at least support, zod v4 - they still use v3.
Had to spend quite a long time to figure out a dependency error...
Kudos to bun for investing in a promising technology.
Does the Zig Foundation have a policy against corporate sponsors?
Otherwise the lack of sponsoring from the "big players" seems rather shocking. You'd think that zig has a decent chance in helping MS/Meta/Google/etc. somewhere along the way.
Yes, thats the problem. Prosecuting crimes on the internet is near impossible due to the restrictions and often anonymity. Thats why we rely on platform providers to help us, the public.
Facebook, Youtube and others put in effort to take down illegal content.
4chan only does the bare minimum such that they don't gain too much relevancy in the public eye.
UK or other countries may decide that 4chan doesn't to enough and ban it because of the help of 4chan in faciliating the spreading of illegal content.
So again, where is the difference between 4chan which hosts/spread illegal content and other sites where we're fine with banning them?
Do you think absolutely all content should be allowed to be accessible?
If you wouldn't allow child porn (which 4chan deletes/doesn't allow), where exactly do you draw the line between blocking sites with cp, and allowing sites like 4chan which host porn without consent (voyeur/spy/revenge)?
Good luck to anyone using it. We used it for embedding about 6k documents.
The API constantly gives you quota errors when you reach about 150 requests/min eventhough the quota should allow about 50_000 requests/min.
We’d like to use the Batch API, but the model isn’t available yet.
Quite a nice model though. Being able to get embeddings for a specific task type [1] is very interesting. We used classification specific embeddings and noticed a meaningful improvment when we used the embeddings as input for a classifier.
1: https://ai.google.dev/gemini-api/docs/embeddings#supported-t...
Very interesting. I wish they had gone into a little more detail about the other technologies involved.
Was the Java Service in Spring (boot)?
What other technologies were considerd?
I'd assume Go was among them. Was it just the fact that Go's type system is to simplistic or what were the other factors?
It's a contender for a "best idea and worst execution" award for programming languages in the last 20 years.
I'd like to think that if things went just a little bit better it would have beed the ideal programming language for anything cloud / backend related.
A 3090 still costs 1800€. Thats not mid-range by a long shot
The 5070 or 5070ti are mid range. They cost 650/900€.
Surprisingly, they didn't provide a comparison to Sonnet 3.7 or Gemini Pro 2.5—probably because, while both are impressive, they're only slightly better by comparison.
Lets see what the pricing looks like.