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2ndorderthought
I test drove it yesterday. It's pretty impressive at 8b. Runs on commodity hardware quickly.
Qwen3.6 35b a3b is still my local champion but I may use this for auto complete and small tasks. Granite has recent training data which is nice. If the other small models got fine tuned on recent data I don't know if I would use this at all, but that alone makes it pretty decent.
The 4b they released was not good for my needs but could probably handle tool calls or something
3abiton
> Qwen3.6 35b a3b is still my local champion but I may use this for auto complete and small tasks.
I second this! Using the Unsloth Q6 (I forgot the exact name). Currently using it with forgecode (with zsh), on my Strix Halo, and it's suprisingly really good. I would say slightly Similar to Haiku 4.5, plus additional privacy, minus speed. It's surprisingly really fast for the hardware, given the speculative decoding, still PP is on the slow side.
bpye
Out of interest, what are you seeing for token generation - especially as the context fills?
lostmsu
If you use it for agentic coding and often hit PP, there's something wrong with your harness IMO
vessenes
Have you tried the Gemma 4 series, out of curiosity? I haven’t run a local model in a while, but the benchmarks look good. I’d take a free local tool-use model if it was relatively consistent.
v3ss0n
Qwen 3.6 burns it to the ground. it was not even a challenge. Gemma4 seriously fails at toolcalls and agentic works. It got all messed up after 2-3 turns of Vibecoding.
xrd
How do you run it? vllm? llama.cpp?
Can you share some parameters you enable tool calling and agentic usage?
Or, higher level, some philosophies on what approaches you are using for tuning to get better tool calling and/or agentic usage?
I'm having surprisingly good success with unsloth/Qwen3.6-27B-GGUF:Q4_K_M (love unsloth guys) on my RTX3090/24GB using opencode as the orchestrator.
It concocts some misleading paths, but the code often compiles, and I consider that a victory.
You have to watch it like you would watch a 14 year old boy who says he is doing his homework but you hear the sound effects of explosions.
thot_experiment
naw, i mean i prefer Qwen 3.6 to Gemma 90% of the time, especially the MoE with a light tune to make it's tone more claude-like, but Gemma 4 is definitely better in some cases and I think they're pretty close in general.
The difference basically boils down to Gemma 4 making more assumptions and Qwen 3.6 sticking closer to the prompt, if your prompt is bad or leaves things up to the imagination, Gemma will do a better job, if you need strict prompt adherence Qwen is better. Since local models are "dumb" i think it makes sense to prefer prompt adherence, but there are complex tasks that Gemma will complete much much faster than Qwen because it makes the right assumptions the first time and as a result even with slower inference requires way fewer turns.
My speculation is that this comes from google having a much better strategy for filtering their training data, I think this also shows up in the shape of the world knowledge of the models. Gemma's world knowledge seems deeper even though the models are of roughly equivalent size to the Qwen counterparts so it's mostly likely just concentrated in places that are more relevant to my queries.
Most notably in my testing, Gemma 4 31b is the ONLY local model that will tell me the significance of 1738 correctly. Even most flagship/cloud models answer with some hallucinatory nonsense.
59nadir
Counter-point: I built an agent that can only interface with Kakoune, a much less common and more challenging situation for an LLM to find itself in, and Gemma4-A4B 8bit quantized does remarkably better in actually figuring out how to get text in buffers than Qwen3.6-35B-A3B in a similar class as Gemma4 A4B.
Now, is this the usual use case? No, it's a benchmark I created specifically in order to put LLMs in situations where they can't just blast out their bash commands without having to interface with something else and adapt.
lambda
Gemma 4 31b was working ok for me; but it was consuming tons of memory on SWA checkpoints, I had to turn them way down, and as a 31b dense model is fairly slow on a Strix Halo. I did have a lot of tool calling issues on 26b-a4b, though.
The Qwen models are quite solid though.
2ndorderthought
Gemma4 is definitely not used for vibe/agentic coding. Not even worth trying. But its a different weight class.
blurbleblurble
I agree but would add that gemma 4 is really nice at vibing though in ways qwen 3.6 could never.
Maybe it could be fun to hook them up via a2a protocol as left and right brain agents operating in tandem.
zkmon
I have tested Gemma4-26B against Qwen3.6-35B. Gemma beats Qwen on structured data extraction and instruction following. Gemma is far more precise than Qwen in these tasks, while Qwen gets a bit more creative, verbose, and imprecise. However Qwen has far more general smartness, high token throughput. Qwen could precisely pinpoint the issues in data quality and code, while Gemma had no clue. On the coding skills, Qwen appears to have edge over Gemma, but this could depend on the agent you use. For direct chat (llama_cpp UI), bot models show same skills for coding.
seemaze
That's interesting. I've been using Qwen3.5-35B for (poorly) structured table extraction based largely on the reports that Qwen had a much better vision implementation.
I have not benchmarked Qwen3.5 vs. Qwen3.6 for the same task, nor trialed Gemma4-26B. Guess it's time for some testing!
2ndorderthought
I tried the Gemma 4 I think 2 and 4b. The 2b was not useful for me at all. A little too weak for my use cases
The 4b was okay. It didn't get all of my small math questions right, it didn't know about some of the libraries I use, but it was able to do some basic auto complete type stuff. For microscopic models I like the llama 3.2 3b more right now for what I do, it's a little faster and seems a little stronger for what I do. But everyone is different and I don't think I'll use it anymore this past month has been crazy for local model releases.
throwaw12
can you share your use cases for 2b and 4b models?
curious how people are leveraging these models
UncleOxidant
> I may use this for auto complete
Using an 8B LLM for auto complete seems kind of like overkill. Couldn't a much smaller model handle that? IIRC there's a Qwen 1B model.
steveharing1
Yea, No doubt Qwen 3.6 open weights are far more strong
rnadomvirlabe
Why no doubt?
captainbland
No comparison with competitor models other than the previous granite version strongly implies that it does not compete well with other comparable models. At least this is the most reasonable assumption until data comes out to the contrary
2ndorderthought
Qwen 36 is effectively a pocket sized frontier model. It's really surprising for me anyway
steveharing1
Because Qwen 3.6 pushes way above its weight. Granite 8B is impressive, but Qwen still wins on raw capability, especially for coding.
cyanydeez
Qwen3-Coder-Next seems to be perfect sized for coding. I tried the new and just found the verbosity not really useful for coding. But probably for more analytical tasks or writing docs.
UncleOxidant
Qwen3-coder-next is still my favorite local model. Qwen3.6-27b is probably a bit better, but it also runs much slower on my Strix Halo box. Hoping we see a Qwen3.6-coder soon!
m3at
https://research.ibm.com/blog/granite-4-1-ai-foundation-mode...
Original article on IBM research
Hugging face weights: https://huggingface.co/collections/ibm-granite/granite-41-la...
cbg0
The real "sleeper" might be https://huggingface.co/ibm-granite/granite-vision-4.1-4b if the benchmarks hold up for such a small model against frontier models for table & semantic k:v extraction.
adrian_b
There is also its companion:
https://huggingface.co/ibm-granite/granite-speech-4.1-2b
designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST) for English, French, German, Spanish, Portuguese and Japanese.
uf00lme
Woah, is this part of the future of models? Basically little models you can use as tools.
2ndorderthought
It's looking like running your own mini ecosystem is the way of the future to me. No data centers, just a decent GPU 16-24gb of VRAM, CPU, and 32gb of RAM.
Lalabadie
This is Apple's bet, among others.
Training purpose-specific miniature models lets you have a lot of tasks you can run with high confidence on consumer hardware.
tonyarkles
I don’t know how many difference little models this uses under the hood, but I was shocked at how good it was at the couple document extraction tasks I threw it at.
SecretDreams
Eventually we'll have models small enough to do a single thing really well and we'll call them functions.
hathym
True if you can write a function that summerize an article for example
cyanydeez
I'm pretty sure there's someone somewhere who'll create a proper harness that's equivalent to one giant model. The difficulty is mostly local hardware has lot of memory constraints. Targeting 128GB would seem to be the current sweet spot. If we could get out of the corporate market movers of buying up all the memory, we could maybe have more.
Regardless, the people in the 80s capable of pruning programs to fit on small devices is likely happening now. I'd bet most of the Chinese firms are doing it because of the US's silly GPU games among other constraints.
nickpsecurity
What needs to happen is for companies (or individuals) tired of that to pool money together to build new, memory products. Then, sell them to consumers first and for non-AI use. If not that, then round-robin scheduling of quantities so the units are spread around more.
If costs are high, they might reserve a certain percentage for big business at market prices (or just under) to cover the chip's mask costs.
After DDR5+ RAM, then GDDR5-6 RAM for use with AI accelerators. They might try to jump right in on a HBM alternative. That could be the percentage for AI buyers I just mentioned. Especially if they could put 40-80GB on accelerators like Intel ARC's.
If successful enough, they license MIPS' gaming GPU's to combine with this stuff with full, open-source stack and RTOS support for military sales.
smj-edison
On the topic of local models, is there a good equivalent to something like Claude's chat interface? I've recently started transitioning to open models after getting fed up with Claude's usage limits (I'm not in a position to drop $200/month), and for coding tasks Kimi 2.6 has been about the same as Sonnet in my experience. The only thing I've found myself missing is a nice interface to ask it questions and have it help me with my math assignments.
0xbadcafebee
Yes but not exactly.
- A lot of people suggesting llama-server's web ui, but that requires you use local AI (llama.cpp), it's persisting content into your browser rather than the server (so you can lose your chats), and it doesn't support much functionality.
- There are some pure-browser chat interfaces that are like llama-server but you can use remote LLMs. This is closer to what you want, but everything is stored in the browser, so backup is harder.
- There's LocalAI, which is like the llama-server option, but more stuff is built in and it persists data to disk. It's flashy and very easy if all you want to do is local AI.
- There's LM Studio, which is another thing like LocalAI, but a desktop app.
- There's OpenWebUI, where it's like LocalAI, except you don't do local inference, you use remote LLMs. It sucks to be honest, just stops working a lot of the time, UX is terrible, lots of weird bugs.
- There's OpenHands, which is more like Codex/Claude Code web UI. You run it locally and connect to remote LLMs. Kinda clunky, limited, poor design. Like most coding agents, it doesn't support all the features you would want, like LocalAI/OpenWebUI do.
- There's OpenCode's web UI, which is like OpenHands, but less crappy.
- There's Jan, which is probably what you want. It's a desktop app rather than a web UI.
lostmsu
I started using https://github.com/milisp/codexia/ (which is a desktop app or a web server) that wraps your regular codex-cli or Claude Code CLI. So you can see Codex/Claude threads in your web UI and access it remotely. I love it because you can do Web UI or terminal and all conversations are preserved.
Unfortunately it is pretty buggy, so I am maintaining a fork matching my personal needs with bugfixes and a few extra features.
SwellJoe
Most of the common ways to run local LLMs include a chat interface. llama.cpp's `llama-server` stands up a chat interface on 8080, as well as an OpenAI compatible API. LM Studio is a desktop app with a chat interface and API, as well. unsloth Studio, too.
LM Studio is nice in that it makes it easy to add tools, like search. Qwen 3.6 is such a small model that it lacks a lot of knowledge of the world (so it can hallucinate at an uncomfortable rate, which is a common failure mode of very small models), but it can use tools, so being able to search lets it research before answering. It has pretty good reasoning and tool calling, so it's actually pretty effective. I've been comparing Gemma 4 (31B at 8-bits, also very good with tools and reasoning for its size), Qwen 3.6 (27B at 8-bits), against Claude Opus and Gemini Pro lately. And, obviously the frontiers are better, but most of the time, I find the tiny models are fine. I'm still not quite at the point where I'd be willing to code with local models, as the time wasted on hallucinations and logic bugs and sloppy coding practices are much higher, as is the cost of security bugs that make it past review.
rglullis
Open WebUI or Jan (https://www.jan.ai/). Work well with Ollama.
mudkipdev
I re-created Claude's interface closely here, feel free to fork https://github.com/mudkipdev/chat
camdv
Ollama does this, as does llama-server from llama.cpp
steveharing1
You can try Open WebUI. Its genuinely useful when it comes to running open models locally with a clean interface
RationPhantoms
Yep, couple Open WebUI for general chats and OpenCode for software-specific tasks and it feels close to Claude Desktop and Claude Code.
simonw
I've been mostly using LM Studio for this recently. Ollama has an OK chat UI now too. 'brew install llama.cpp' gets you 'llama-server' which provides quite a good web UI.
Svoka
With Ollama* you can use Claude Code with `ollama launch claude`
Havoc
Interesting to see a pivot away from MoE by both IBM and mistral while the larger classes of SOTA of models all seem to be sticking to it.
Quick vibe check of it- 8B @ Q6 - seems promising. Bit of a clinical tone, but can see that being useful for data processing and similar. You don't really want a LLM that spams you with emojis sometimes...
embedding-shape
Makes sense, dense for small models, dense or MoE for larger ones, end up fitting various hardware setups pretty neatly, no need for MoE at smaller scale and dense too heavy at large scale.
npodbielski
I never want LLM to span me with emojis. What is the use case for that? I find it highly annoying.
2ndorderthought
Shh people are paying for each token. Don't get them asking too many questions
Havoc
Think it can be a plus in moderation. eg in openclaw it can add some character
But yea dislike that style where each heading and bullet point gets an emoji
0xbadcafebee
People complain a lot about LLM-written articles, but the human comments here on HN are far worse. Mostly a bunch of people extremely proud of themselves for not reading an LLM-written article, and then a bunch of people who take it at face value and make the model seem almost useful, and one comment that actually looked at other benchmarks. Good 'ol humanity, good at.. being emotional... and not doing analysis.....
The article makes some good points about model design (how different size models within a family can get similar results, how to filter out hallucination, math result reinforcement), so that's worth understanding. It's analyzing a paper, which only discussed 3 sizes of the same model family. But what the article doesn't say is, compared to other model families, Granite 4.1 8B sucks. The only benchmark it does well at compared to other models is non-hallucination and instruction following. Qwen 3.5 4B (among other models) easily outclass it on every other metric.
This article teaches a valuable lesson about reading articles in general. You can take useful information away from them (yes, despite being written by LLM). But you should also use critical thinking skills and be proactive to see if the article missed anything you might find relevant.
sureMan6
The pro LLM rant is weird, LLMs "hallucinate" in creating detailed elaborate lies, the frontier models still do this egregiously, an LLM written article by default has 0 value since every single line could be true or it could be a convincingly crafted lie, every line has to be fact checked
I'm using Gemini 3.1 pro to help me research my thesis, it still with search enabled and on pro mode, invents entire papers that don't exist, and lies about the contents of existing papers to relate them to the context or to appease me, if I submitted an LLM written article based on the results its given me 80% of the article would be lies
Commenting to complain that the article is LLM written is helpful too since some people aren't able to distinguish
0xbadcafebee
> an LLM written article by default has 0 value since every single line could be true or it could be a convincingly crafted lie, every line has to be fact checked
The exact same thing is true of Human speech. You have no idea if anything a human says is true until you fact check it. But you don't fact check everything every person says, do you?
So what do you do instead? You use heuristics. Simple - and quite flawed - subconscious rules to stop worrying about things. You find a person you like, and you classify them "trustworthy", and believe almost all of what they say, not considering if any of it might be false. But of course, humans are fallible, and many of them receive "poisoned" input, and even hallucinate (making up information). They then spread that false information around. Yes, even the people you trust.
And when you're faced with something untrue, said by someone you trust, you rationalize it. "Oh, they just made a mistake." And you completely ignore that the person you trust told you a falsehood. Life is hard enough without having to question if everything we hear is false. So we just accept falsehoods from some people, and not others.
LLMs are likely more factual and knowledgeable today than humans are, thanks to their constant improvements via reinforcement. They're going to keep getting better too. But they'll never be perfect. Rather than rejecting anything they produce, my suggestion would be to do what you do with humans: trust them a little, verify big things, let the little things go, accept that there will be errors, and move on with life.
WarmWash
If you are asking an LLM to cite it's sources you are wasting your time and degrading the quality of the response. LLMs have no inherent mechanism for "knowledge source tracking", because that isn't at all how they work. We're trying to get there with agentic stacks, but it's still too new.
For sparse knowledge tasks, where you know that the model can't possibly have much training because even humans themselves don't have much knowledge there, use it as a brainstorming partner, not as a source. Or put relevant papers in it's context to help you eval those papers in relation to your work. But it's just going to hurt itself in confusion trying to tie fuzzy ideas to sparse sources embedded in pages upon pages of mildly related google search results.
kevin42
If they can't distinguish LLM text, then why should they care?
Anti-AI people like to bring up hallucination as if everything AI generates is false.
I can write pages of text, with my own content, and then use AI to improve my writing and clarity. Then I review and edit. It might have some LLM markers in there, which I remove sometimes because it's distracting. But the final, AI assisted writing is easier to read and better organized. But all the ideas are mine. Hallucinations are not remotely a problem in this case.
Forgeties79
If you can’t distinguish between fake images and real ones why should you care?
halJordan
No, you're being weird (why are you calling people weird anyway, not helpful).
You're complaining about facts that have been true since words have been written on paper. If you read the article with the same criticality you read any other article you wont have the problem you complain about.
The reality is, you're only complaining because you hate ai. Cool, but dont dress it up and resort to name calling to browbeat the other guy
lelanthran
If I read something and cannot tell that it is AI generated, then there's no problem.
If it has AI tells then I wont bother to continue reading because it was either written by an AI or it was written by someone who can't tell the difference.
Either way it's probably a poor piece of writing.
phkahler
>> The only benchmark it does well at compared to other models is non-hallucination and instruction following.
I think instruction following is going to be the most useful thing these models do. Add a voice interface and access to a bunch of simple, straight-forward devices or APIs and you have a mildly useful assistant. If that can be done in 8B parameters it will soon run on edge devices. That's solid usefulness.
encrux
Anything that beats alexa-level intelligence on an edge-device is what I'd call useful as well, which shouldn't be too hard.
It's mind-boggling how bad current voice assistants sometimes are when you prompt them some fairly easy questions.
haolez
The problem is the signal/noise ratio in these articles. If the AI has written the article, then this same info could have been generated by my own AI, but tailored to my needs. So what, exactly, is the new info that this article is generating that I can use to consult with my AI? That's what I want to get out of this interaction.
Maybe my point is something on the lines of "Just send me the prompt"[0]
danielbln
prompt + all other bits of information the context has been seeded with before the output was created (documents, web searches, other sources) in which case it might be more efficient to just consume the final deliverable (yourself or via LLM).
lelanthran
> people complain a lot about LLM-written articles, but the human comments here on HN are far worse.
No, they aren't.
You are comparing writing produced with little to no effort to writing produced with the minimal effort required to communicate.
It's reasonable for people to complain that they are presented material that not even the author thought was worth the effort.
simonw
"The article makes some good points about model design"
But how can I tell if those are good points or not?
I don't want to invest time in reading something if the presence of those "good points" depends on a roll of the dice.
steveharing1
even calling it roll of the dice is an assumption. Can you point anything you find as mistake?
lelanthran
You expect people to read every single excretion, which can be generated faster than I can read,just to find the rare gem that might exist?
The problem is that in the past it took multiple times more effort and hours to write something than it took to read. That served two purposes:
1. Lazy people just looking for an audience were effectively gatekept from drowning the world with their every vapid thought.
2. Because supply was many times slower than consumption it was viable to give most articles a chance: the author could not drown me in a deluge even if they wanted to.
Having the criteria now that the author should spend at least as much effort creating the piece as they expect the reader expend reading it is a damn useful bar: instead of reading 1000 AI articles just to find the one good one, I can simply read 10 human authored articles and be certain that 9 of them have something worthwhile.
simonw
No, because I'm not going to spend a bunch of my time fact-checking obvious AI slop.
geraneum
> the human comments here on HN are far worse
I already assume some comments here are LLM written.
mkovach
I just wait until I'm hallucinating, then I comment. Keeps the classifiers honest.
elxr
I mean, obviously.
I assume some people here have never programmed a single useful thing even once in their lives.
drob518
> But what the article doesn't say is, compared to other model families, Granite 4.1 8B sucks.
Right. This just says that Granite 4.1 8B is better than a previous version, Granite 4.0-H-Small, which has 32B, 9B active.
So, they made a less bad model than before. But that doesn't tell you anything about how it compares with other models.
DetroitThrow
>Mostly a bunch of people extremely proud of themselves for not reading an LLM-written article
I'm not sure it's proud as much as people voicing displeasure with the uncertainty about what went into the LLM prompt. This may have been a 1 sentence prompt, or it may have been some well researched background that simply reformatted it. Why waste minutes-hours on verifying it if it's possible someone could have spent 10 second on it? It's very easy to see their point.
People seem to indicate people they disagree with voicing their opinion about anything lately is some auto-fellatio, I wonder what causes them to think this way.
simonw
The Granite 4.1 3B model is only 2GB from Unsloth: https://huggingface.co/unsloth/granite-4.1-3b-GGUF
I ran it in LM Studio and got a pleasingly abstract pelican on a bicycle (genuinely not bad for a tiny 3B model - it can at least output valid SVG): https://gist.github.com/simonw/5f2df6093885a04c9573cf5756d34...
tredre3
Do you have any reasons to believe that granite is more immune to the effects of quantization than other tiny models? Otherwise it seems odd to judge a tiny model true capabilities by using its 4bit quant.
simonw
This model is small enough that it might be sensible to try the same prompts against all of the quant sizes to try and spot any differences.
simonw
This inspired me to give that a go: https://simonw.github.io/granite-4.1-3b-gguf-pelicans/
100ms
> Full stop.
Why people don't edit out obvious sloppification and expect to still have readers left
wewewedxfgdf
Third line in to the article: "But there’s one result in the benchmarks I keep coming back to."
I hear this sort of thing all the time now on YouTube from media/news personalities:
“And that’s the part nobody seems to be talking about.”
"And here's what keeps me up at night."
“This is where the story gets complicated.”
“Here’s the piece that doesn’t quite fit.”
“And this is where the usual explanation starts to break down.”
“Here’s what I can’t stop thinking about.”
“The part that should worry us is not the obvious one.”
“And that’s where the real problem begins.”
“But the more interesting question is the one no one is asking.”
“And this is where things stop being simple.”
It doesn't really worry me but I think its interesting that LLM speak sounds so distinctive, and how willing these media personalities are to be so obvious in reading out on TV what the LLM spat out.
I've never studied what LLMs say in depth is it is interesting that my brain recognises the speech pattern so easily.
frereubu
I think this kind of language predates widespread LLM use, and has been picked up from that kind of writing. It's a "and here's where it gets interesting" pattern that people like Malcolm Gladwell and Freakonomics have used, even if the same thing could be said in a way that makes it sound much less intriguing.
cwillu
There's even a word for it: “cliché”
helsinkiandrew
Isn't this the format of "hook-driven media" a constant stream of "second-act pivots" - where some new twist is added to a story to re-engage the reader and keep them reading.
BuzzFeed and Upworthy etc pioneered this for web 'news stories', then it got used in linkedin, twitter, and everywhere where views are more important than the content.
jmbwell
The language of drama and import without meaningful substance. Words statistically likely to be used in a segue, regardless of the preceding or subsequent point. Particularly effective when it seems like you’re getting let in on a secret. Really fatiguing to read
A writing teacher once excoriated me for saying that something was important. “Don’t tell me it’s important, show me, and let me decide, and if you do your job I’ll agree”
I don’t know how a completion can tell when it needs to do this. Mostly so far it doesn’t seem capable
MarsIronPI
Maybe the solution is to cull the bad, cliché writing from the training data.
MarsIronPI
Ugh, you're making me remember the last time I listened to NPR. It's so bad.
stuff4ben
I listen to NPR daily and I don't think I've ever heard any of them use that phrasing.
bambax
I notice this very often in LinkedIn posts, and it's annoying, but I had not realized it was LLM-speak? Isn't it possible that people write like this naturally?
wewewedxfgdf
I think LLM's have that sort of "summarise, wrap it in a bow tie, give a little dramatic punch as a preview to the next few points".
spicyusername
Arguably it's exactly because it was used naturally so often that the LLMs parrot it so frequently.
trvz
Yes. Some people are very trigger happy in attributing human slop to LLMs.
steveharing1
[dead]
nwatson
Nate B Jones videos ... YouTube channel "AI News and Strategy Daily" channel uses all of these. Every video.
bityard
I listened to a lot of NPR podcasts before LLM were around, and most of them are full of these kinds of filler phrases.
riknos314
The general concept of a hook with delayed payoff is far from new, and generally one of the better ways at keeping attention.
It's also exactly the Mr beast playbook, and got him to the largest channel on YouTube.
Any system attempting to capture human attention will use these techniques, nothing LLM-specific here at all.
cbg0
So are we saying it's fine that the article is written by an LLM as long as it doesn't have the tell-tale signs of LLMs?
ramon156
It's more about curating the things you're publishing. Why would I bother reading what you couldn't bother to read?
alienbaby
They could easily have read it, and thought , that communicates the information that it needs to.
No point creating busywork for yourself just shuffling words around when the information is there, no?
I guess it depends on what you want out of the article. Substance, or style?
100ms
I don't really see reason to complain about tool use, so long as the result is cohesive, accurate and that ultimately means a human has at least read their own output before publishing. It's a bit like receiving a supposedly personal letter that starts "Dear [INSERT_FIRST_NAME_FIELD]," are you really going to read such a thing?
HighGoldstein
An article without telltale signs of an LLM is indistinguishable from an article written by a human, so yes.
spicyusername
My opinion is that literature and art will continue pushing the envelope in the places they always pushed the envelope. LLMs will not change this, humans love making art, and they love doing it in new ways.
Corporate announcements were never the places that literature and art were pushing the envelope. They were slop before, and they're slop now.
crunis
Are you referring to the literal use of the expression "full stop"? I don't see it anymore in the article, maybe they edited it out?
pjmalandrino
Very impressive series of SLM by IBM here.
I have been using it with their Chunkless RAG concept and it is fitting very well! (for curious https://github.com/scub-france/Docling-Studio)
I convinced that SLM are a real parto of solution for true integrated AI in process...
nielsbot
Very much an aside, but I'm struck by IBM's consistent iconic design language. For me it harkens all the way back to the futuristic design in 2001: A Space Odyssey from 1968. But you can also see it in their old mainframe hardware designs and other places.
dash2
Nah, I ain't reading that. If they can't be bothered to get a human to write it, it can't be that important. I'm glad for them though. Or sorry that happened.
osener
This is the official announcement: https://research.ibm.com/blog/granite-4-1-ai-foundation-mode...
It is not the researchers' fault that some slop got posted here instead.
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altmanaltman
[dead]
tosh
IBM announcement: https://research.ibm.com/blog/granite-4-1-ai-foundation-mode...
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https://research.ibm.com/blog/granite-4-1-ai-foundation-mode...