I can't imagine who is using something like this for agentic coding, but I see exciting opportunities on the horizon when we can have hundreds of reasonably rational and conversational agents working on local machines to simulate emergent behavior (simulating crowds, markets, ecosystems, game NPCs, etc.)
> We recommend using it for agentic workloads, tool use, data extraction, RAG, and long-context workflows. It is not recommended for agentic coding and knowledge-heavy tasks.
Really curious about people's workflows with these agentic-but-not-for-coding workflows. Are there some interesting people to follow there or just good testbeds/environments to get an idea ?
Yeah. Clearly the target for these models is on-device assistant/analyst tasks where really everything is agentic.
Thinking out loud: any text-based generative AI application is on a continuum between:
- document-to-document: solve this language problem using language skills by outputting only new symbols, and
- document-to-tasks: solve this language problem by only operating these tools.
In a sense, given access to vast compute to train a very large model, agentic coding is one of the easier applications that is somewhere between the two. It is manipulating symbols in a mixture of languages that are biased towards context-free (code), it exploits the embedded knowledge in a vast number of weights, and it calls fairly simple tools. The user-focussed solution is happening inside the LLM.
A small model can't do this job well, even if it has a good understanding of programming languages, because it lacks the world knowledge to understand the problem.
At the other end of the continuum is: these words mean do this thing. That Cactus Needle 2 model mentioned earlier is here. It doesn't even produce a language description of what it did, because it one-shots tasks.
In the middle but near this end is the fantastically hard job: train a model that understands language and reasons well enough to respond to queries about the tools it has access to, operate them and reply in natural language, without being large or slow. That is, understand language without being overburdened by details of scenarios that caused the need for the words in the first place.
This is maybe the great grand challenge of LLMs: make it know how to speak and reason and think and work for us while discarding everything that is just knowledge of unnecessary facts.
Small reasoning models are where most of the really big challenges are.
I am getting to know Hermes agent and in the last couple of days I used it for searching the web for medical specialists or finding the best deal for a product online, manipulate with excel and word documents, gather data from APIs, finding stuff in my computer or keeping track of itself running in another process and keep the computer awake and when detecting the process is finished, put computer to sleep.
I have noticed that these cheaper and faster models are very great for Ops-work. Luna max is beast when you use some stronger model to write detailed instructions/run book what to do and when to stop.
I feed these things my tax transcripts and it found all sorts of refunds and ways to waive penalties that the IRS and my state agreed I was eligible for, because my current CPA doesn’t do this kind of tax matter
The high parameter 2026 models know the latest tax law, revealed deadlines for court rulings to me that are in fact real and new branches of service on the IRS website, or can learn while also agentically search
While many debate the utility in legal matters and 2023’s hallucination issue, the rest of us just do
“High parameter […] models” was mentioned above. It’s still not a good take however, as laws of civilizations getting more convoluted over time is the general behaviour, not specific to LLM-producing countries.
LFM's training/post-training is famously different than other models. They target reliable operation of tiny models in ways other model families don't (they aren't just scaling a larger model to a smaller size). If you're looking for good performance out of tiny models, LFM has the most advanced design.
Note how they're much smaller than all other models in the comparison yet match or exceed them. This is for 2.6B params, but they have models as small as 230M. Nobody else designs models that small.
> Note how they're much smaller than all other models in the comparison yet match or exceed them.
There's a strong incentive to cherry pick in self-reported comparisons. If there is a model that's better, it gets left out. Have you seen Nanbeige4.2-3B or Ling-3.0-tiny?
Always put "Lower is better" or "higher is better" in benchmarks. Not everyone knows what your numbers mean.
I can't imagine who is using something like this for agentic coding, but I see exciting opportunities on the horizon when we can have hundreds of reasonably rational and conversational agents working on local machines to simulate emergent behavior (simulating crowds, markets, ecosystems, game NPCs, etc.)
Agentic behaviour isn't just coding. In fact:
> We recommend using it for agentic workloads, tool use, data extraction, RAG, and long-context workflows. It is not recommended for agentic coding and knowledge-heavy tasks.
Really curious about people's workflows with these agentic-but-not-for-coding workflows. Are there some interesting people to follow there or just good testbeds/environments to get an idea ?
My little home assistant.
It has to query the tool service, invoke tools, synthesize results, or request new tools. Nothing really complex.
New tool requests from it are plain english and go into a separate pipeline using more appropriate models. It doesn’t have to write anything itself.
Yeah. Clearly the target for these models is on-device assistant/analyst tasks where really everything is agentic.
Thinking out loud: any text-based generative AI application is on a continuum between:
- document-to-document: solve this language problem using language skills by outputting only new symbols, and
- document-to-tasks: solve this language problem by only operating these tools.
In a sense, given access to vast compute to train a very large model, agentic coding is one of the easier applications that is somewhere between the two. It is manipulating symbols in a mixture of languages that are biased towards context-free (code), it exploits the embedded knowledge in a vast number of weights, and it calls fairly simple tools. The user-focussed solution is happening inside the LLM.
A small model can't do this job well, even if it has a good understanding of programming languages, because it lacks the world knowledge to understand the problem.
At the other end of the continuum is: these words mean do this thing. That Cactus Needle 2 model mentioned earlier is here. It doesn't even produce a language description of what it did, because it one-shots tasks.
In the middle but near this end is the fantastically hard job: train a model that understands language and reasons well enough to respond to queries about the tools it has access to, operate them and reply in natural language, without being large or slow. That is, understand language without being overburdened by details of scenarios that caused the need for the words in the first place.
This is maybe the great grand challenge of LLMs: make it know how to speak and reason and think and work for us while discarding everything that is just knowledge of unnecessary facts.
Small reasoning models are where most of the really big challenges are.
I use luna to schedule my work day for me and then put it in todoist.
Better question is does a coding model do better on these agentic tasks? And is the only advantage efficiency??
I use them for game AI
I am getting to know Hermes agent and in the last couple of days I used it for searching the web for medical specialists or finding the best deal for a product online, manipulate with excel and word documents, gather data from APIs, finding stuff in my computer or keeping track of itself running in another process and keep the computer awake and when detecting the process is finished, put computer to sleep.
I have noticed that these cheaper and faster models are very great for Ops-work. Luna max is beast when you use some stronger model to write detailed instructions/run book what to do and when to stop.
I feed these things my tax transcripts and it found all sorts of refunds and ways to waive penalties that the IRS and my state agreed I was eligible for, because my current CPA doesn’t do this kind of tax matter
The high parameter 2026 models know the latest tax law, revealed deadlines for court rulings to me that are in fact real and new branches of service on the IRS website, or can learn while also agentically search
While many debate the utility in legal matters and 2023’s hallucination issue, the rest of us just do
So many people have outdated notions of what LLMs can do; and tbh, I think a lot of it comes down to free models being weaker.
Using climate-destroying AI to understand the overly convoluted tax regime in the US is peak capitalism.
Climate destroying? How do they destroy the climate by hosting a few gigabyte model on their computer?
It's the whole point with these. Uses hardware they already own, uses energy they very possibly produce for themselves.
Or is this just me needing to take a joke?
“High parameter […] models” was mentioned above. It’s still not a good take however, as laws of civilizations getting more convoluted over time is the general behaviour, not specific to LLM-producing countries.
These LiquidAI models have never worked well for me in practice.
Care to share any details? I'm about to check the 2.6b lfm on document editing.
I agree. They have serious issues with coherence.
It's not even competitive with 2x sized Qwen 4B.
Why is Qwen3.5 2B not in the table?
LFM's training/post-training is famously different than other models. They target reliable operation of tiny models in ways other model families don't (they aren't just scaling a larger model to a smaller size). If you're looking for good performance out of tiny models, LFM has the most advanced design.
Note how they're much smaller than all other models in the comparison yet match or exceed them. This is for 2.6B params, but they have models as small as 230M. Nobody else designs models that small.
> Note how they're much smaller than all other models in the comparison yet match or exceed them.
There's a strong incentive to cherry pick in self-reported comparisons. If there is a model that's better, it gets left out. Have you seen Nanbeige4.2-3B or Ling-3.0-tiny?
> Nobody else designs models that small.
There are people building even smaller models.
Granite IBM make tiny
The model is cool but I would prefer if people do not editorialize the titles on their HN submissions.
Tbf its in the description on the page "Competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks."
Will this work in i3/i5 laptops?
It should. With good speed even on a 12th gen Intel. But more importantly, what's your use case?