5 ms·

New coding models and integrations

221 points by meetpateltech 1y ago

thot_experiment 1y ago
[flagged]
- colesantiago 1y ago
  So what do we do here? What are some 100% free and open source alternatives that won't grift and won't enshittify that we can use instead?
  - testaccount28 1y ago
    man i don't know, rick. i'm just reading comments on hacker news but maybe the one llama.cpp called out by GP could be a place to look? not sure, rick.
    - mchiang 1y ago
      but that is VC funded
      - CaptainOfCoit 1y ago
        > but that is VC funded The difference between Ollama and llama.cpp boils down to "venture-backed product company" vs "community OSS project; creator’s separate company has angel/VC-style pre-seed". I hope even you could squint and see the difference :) Btw, I feel like it's somewhat poor taste to comment about something that is effectively a competitor to you (even though you base your own product on it) and not disclose that you're working full-time at Ollama Inc. At the very least put the info in your profile.
- mchiang 1y ago
  sorry, I don't use 4chan, so I don't know what's said there. May I ask what system you are using where you are getting memory estimations wrong? This is an area Ollama has been working on and improved quite a bit on. Latest version of Ollama is 0.12.5 and with a pre-release of 0.12.6 0.7.1 is 28 versions behind.
  - thot_experiment 1y ago
    I recently tested every version from 0.7 to 0.11.1 trying to run q5 mistral-3.1 on a system with 48GB of available vram across 2 GPUs. Everything past 0.7.0 gave me OOM or other errors. Now that I've migrated back to llama.cpp I'm not particularly interested in fucking around with ollama again. as for 4chan, they've hated ollama for a long time because they built on top of llama.cpp and then didn't contribute upstream or give credit to the original project
    - mchiang 1y ago
      ah! This must be downloaded from elsewhere and not from Ollama? So sorry about this. To help future optimizations for given quantizations, we have been trying to limit the quantizations to ones that fit for majority of users. In the case of mistral-small3.1, Ollama supports ~4bit (q4_k_m), ~8bit (q8_0) and fp16. https://ollama.com/library/mistral-small3.1/tags https://ollama.com/library/mistral-small3.1/tags I'm hopeful that in the future, more and more model providers will help optimize for given model quantizations - 4 bit (i.e. NVFP4, MXFP4), 8 bit, and a 'full' model.
      - thot_experiment 1y ago
        Yeah, I think the idea that models that don't come from ollama.com are second class citizens was what made me fist start to think about migrating back to llama.cpp and then the memory stuff just broke the camel's back. I don't want to use a project that editorializes about what models and quants I should be using, if I wanted a product I don't have control over I'd just use a commercial provider. For what it's worth I actually did download the full fp16 and quant it using ollama and still had the memory error for completion's sake. I truly don't understand the reasoning behind removing support for all the other quants, it's really baffling to me considering how much more useful running a 70b parameter at q3 is that not being able to run a 70b parameter model at all, etc. Not to mention forcing me to download hundreds of gigabytes of fp16 because compatibility with other quants is apparently broken, and forcing me to quant models myself.
qwe----3 1y ago
Just a paste of llama.cpp without attribution.
- mchiang 1y ago
  https://github.com/ollama/ollama?tab=readme-ov-file#supported-backends https://github.com/ollama/ollama?tab=readme-ov-file#supporte...
- swyx 1y ago
  i mean they have attributed but also it's open source software, i guess the more meaningful question is why didn't ggerganov build Ollama if it was that easy? or what is his company working on now?
  - homarp 1y ago
    >what is gg working on supporting models so ollama can then 'support' them too if you use llama.cpp server, it's quite a nice experience. you can even directly download stuff from Huggingface.
    - homarp 1y ago
      e.g. https://nitter.poast.org/ggerganov/status/1978491194842677742#m https://nitter.poast.org/ggerganov/status/197849119484267774...
  - monkmartinez 1y ago
    I can not answer for GG, but the early days of llama.cpp were crazy and everything was so very hacky. Remember, Textgen-webui was 'the way' to run models at first because it supported so many different quant types and file extensions. At the time, most people were using multiple different quantization methods and it was really hard to figure out which were performing better or worse objectively. GGUF/GGML was like the 4th iteration of file type quantization from llama.cpp and I remember that I had to consciously begin watching the bandwidth usage from my ISP. Up to that point, I had never received an email warning me about reaching limits of my 2TB connection. All for the same models just in different forms. TheBloke was pumping out models like he had unlimited time/effort. I say all that to say, llama.cpp was still trying, dare I say 'inventing', all the things throughout these transitions. Ollama comes in to make the running part easier and less CLI flag dependent building off of llama.cpp. Awesome. GG and company are down in the trenches of the models architecture with CUDA, Vulkan, CPU, ROCm, etc. They are working on perplexity, token processing/generation and just look at the 'bin' folder when you compile the project. There are so many different aspects to make the whole thing work as well at it does. It's amazing that we have llama-server at all with the amount of work that has gone into making llama.cpp. All that to say, Ollama shit the bed on attribution. They were called out on r/localllama very early on for not really giving credit to llama.cpp. They have a soiled reputation with the people that participate in that sub-reddit at least. They were called out for not contributing back if I remember correctly as well, which further stained their reputation among the folks who hang in that sub-reddit. So it's not a matter of "ease" to build what Ollama built... At least from the perspective of someone who has been paying close attention from r/localllama; the problem was/is simply the perception (right or wrong) of the meme; Person 2 to person 1: "You built this?" -> Person 2: takes item/thing -> person 2: Holds up item/thing -> "I built this". A simple act that really pissed off the community in general.
jhancock 1y ago
I've been using GLM-4.6 since its release this month. It's my new fav. Using it via Claude Code and the more simple Octofriend https://github.com/synthetic-lab/octofriend https://github.com/synthetic-lab/octofriend Hosting through z.ai and synthetic.new. Both good experiences. z.ai even answers their support emails!! 5-stars ;)
- mchiang 1y ago
  Z.ai team is awesome and very supportive. I have yet to try synthetic.new. What's the reason for using multiple? Is it mainly to try different models or are you hitting some kind of rate limit / usage limit?
  - jhancock 1y ago
    I tried synthetic.new prior to GLM-4.6...Starting in August...So I already had a subscription. When z.ia launched GLM-4.6, I subscribed to their Coding Pro plan. Although I haven't been coding as heavy this month as the prior two months, I used to hit Claude limits almost daily, often twice a day. That was with both the $20 and $100 plans. I have yet to hit a limit with z.ai and the server response is at least as good as Claude. I mention synthetic.new as it's good to have options and I do appreciate them sponsoring the dev of Octofriend. z.ai is a China company and I think hosts in Singapore. That could be a blocker for some.
    - mchiang 1y ago
      Do you find yourself sticking with GLM 4.6 over Claude for some tasks? Or do you find yourself still wanting to reach for Claude?
      - jhancock 1y ago
        I have been subscribing to both Claude and ChatGPT for over two years. Spent several months on Claude's $100 plan and couple months on ChatGPT's $200 plan but otherwise using their $20/month plans. I cancelled Claude two weeks ago. Pure GLM-4.6 now and a tad of codex with my ChatGPT Pro subscription. I sometimes use ChatGPT for extended research stuff and non-tech.
mike_d 1y ago
> For users with more than 300GB of VRAM, qwen3-coder:480b is also available locally. I haven't really stayed up on all the AI specific GPUs, but are there really cards with 300GB of VRAM?
- bakugo 1y ago
  No, you need multiple GPUs. These models are not intended to be run by the average user.
  - OneDeuxTriSeiGo 1y ago
    Not necessarily. You need either multiple GPUs or unified memory. There are a handful of UM platforms out there nowadays (mainly Macs but AMD has some as well albeit none with 300GB ram)
    - packetslave 1y ago
      Also the just-released DGX Spark from Nvidia (although it "only" has 128gb of unified memory)
      - Computer0 1y ago
        One of its defining features is the ability to link them together at speeds about the same as their ram speed iirc.
- Hamuko 1y ago
  You can buy an M3 Ultra Mac Studio and configure it with 512 GB of memory shared between the CPU and the GPU. Will set you back about $9500.
  - Schlagbohrer 1y ago
    And that'll be two orders of magnitude slower right?
- 1y ago
bigyabai 1y ago
Been disappointed to see Ollama list models that are supported by the cloud product but not the Ollama app. It's becoming increasingly hard to deny that they're only interested in model inference just to turn a quick buck.
- mchiang 1y ago
  Qwen3-coder:30b is in the blog post. This is one that most users will be able to run locally. We are in this together! Hoping for more models to come from the labs in varying sizes that will fit on devices.
  - bigyabai 1y ago
    I'm looking forward to future ollama releases that might attempt parity with the cloud offerings. I've since moved onto the Ollama compatibility API on KoboldCPP since they don't have any such limits with their inference server.
    - mchiang 1y ago
      I am super hopeful! Hardware is improving, inference costs will continue to decrease, models will only improve...
  - Balinares 1y ago
    How does Qwen3-Coder:30B compare to Instruct-2507 as a coding agent backend? I was under the impression that Instruct was intended to supersede Coder?
  - hephaes7us 1y ago
    In this case, it's not about whether it fits on my physical hardware or not. It's about what seems like an arbitrary restriction designed to start pushing users to their cloud offering.
- colesantiago 1y ago
  I know this is disappointing, but what business model would be best here for ollama? 1. Donationware - Let's be real, tokens are expensive and if they ask for everyone to chip in voluntarily people wouldn't do that and Ollama would go bust quickly. 2. Subscriptions (bootstrapped and no VCs) again like 1. people would have to pay for the cloud service as a subscription to be sustainable (would you?) or go bust. 3. Ads - Ollama could put ads in the free version but to remove them the users can pay for a higher tier, a somewhat good compromise, except developers don't like ads and don't like pay for their tools unless their company does it for them. No users = Ollama goes bust. 4. VCs - This is the current model which is why they have a cloud product and it keeps the main product free (for now). Again, if they cannot make money or sell to another company Ollama goes bust. 5. Fully Open Source (and 100% free) with Linux Foundation funding - Ollama could also go this route, but this means they wouldn't be a business anymore for investors and rely on the Linux Foundation's sponsors (Google, IBM, etc) for funding the LF to stay sustainable. The cloud product may stay for enterprises. Ollama has already taken money from investors so they need to produce a return for them so 5. isn't an option in the long term. 6. Acquisition by another company - Ollama could get acquired and the product wouldn't change* (until the acquirer jacks up prices or messes with the product) which ultimately kills it anyway as the community moves on. I don't see any other way that Ollama can not be enshittified without making a quick buck. You just need to avoid VC backed tools and pay for bootstrapped ones without any ties to investors.
zozbot234 1y ago
For those interested in building Ollama locally, note that as of a few hours ago, experimental Vulkan Compute support (will not be in official binary releases as of yet) has been merged on the github main branch and you can test it on your hardware!
- mchiang 1y ago
  this one is exciting. It'll enable and accelerate a lot of devices on Ollama - especially around AMD GPUs not fully supported by ROCm, Intel GPUs, and iGPUs across different hardware vendors.
Schlagbohrer 1y ago
For those running locally with more VRAM than an NVIDIA 4090 or 5090, what are you using to get more than 32GB of VRAM?
deleted 1y ago
[deleted]
qqxufo 1y ago
Interesting to see more people mentioning GLM-4.6 lately — I’ve tried it briefly and it’s surprisingly strong for reasoning tasks. Curious how it compares to Claude 3.5 in coding throughput though?
esafak 1y ago
Has anybody that has tried their cloud product care to comment? How does it compare with Anthropic's and OpenAI's offerings in terms of speed and limits?
skeeter2020 1y ago
Seems appropriate that the top-level image is a ~sheep~ llama wearing a headband that says "coder"...
- lghh 1y ago
  Why? Can you explain?
danans 1y ago
Question for those using local models for coding assistance: how well do the best locally runnable models (running on a laptop with a GPU) work for the easy case: Writing short runs of code and tests after I give an clear description of the expected behavior (because I have done the homework). I want to save the keystrokes and the mental energy spent on bookkeeping code, not have it think about the big problem for me. Think short algorithms/transformations/script, and "smart" auto complete. No writing entire systems/features or creating heavily interpolated things due to underspecified prompts - I'm not interested in those.
- tomck 1y ago
  I have tried a model on my laptop+GPU before, and it is incredibly unusable. Incredibly slow and just bad output for exactly the work you describe If you're looking for a cheap practical tool + don't care if it's not local, deepseek's non-reasoning model via openrouter is the most cost efficient by far for the work you describe. I put 10 dollars in my account about 6 months ago and still haven't gotten through it, after heavy use semi regularly.

221 points by meetpateltech 1y ago

thot_experiment 1y ago

[flagged]

colesantiago 1y ago
So what do we do here? What are some 100% free and open source alternatives that won't grift and won't enshittify that we can use instead?
- testaccount28 1y ago
  man i don't know, rick. i'm just reading comments on hacker news but maybe the one llama.cpp called out by GP could be a place to look? not sure, rick.
  - mchiang 1y ago
    but that is VC funded
    - CaptainOfCoit 1y ago
      > but that is VC funded The difference between Ollama and llama.cpp boils down to "venture-backed product company" vs "community OSS project; creator’s separate company has angel/VC-style pre-seed". I hope even you could squint and see the difference :) Btw, I feel like it's somewhat poor taste to comment about something that is effectively a competitor to you (even though you base your own product on it) and not disclose that you're working full-time at Ollama Inc. At the very least put the info in your profile.
mchiang 1y ago
sorry, I don't use 4chan, so I don't know what's said there. May I ask what system you are using where you are getting memory estimations wrong? This is an area Ollama has been working on and improved quite a bit on. Latest version of Ollama is 0.12.5 and with a pre-release of 0.12.6 0.7.1 is 28 versions behind.
- thot_experiment 1y ago
  I recently tested every version from 0.7 to 0.11.1 trying to run q5 mistral-3.1 on a system with 48GB of available vram across 2 GPUs. Everything past 0.7.0 gave me OOM or other errors. Now that I've migrated back to llama.cpp I'm not particularly interested in fucking around with ollama again. as for 4chan, they've hated ollama for a long time because they built on top of llama.cpp and then didn't contribute upstream or give credit to the original project
  - mchiang 1y ago
    ah! This must be downloaded from elsewhere and not from Ollama? So sorry about this. To help future optimizations for given quantizations, we have been trying to limit the quantizations to ones that fit for majority of users. In the case of mistral-small3.1, Ollama supports ~4bit (q4_k_m), ~8bit (q8_0) and fp16. https://ollama.com/library/mistral-small3.1/tags https://ollama.com/library/mistral-small3.1/tags I'm hopeful that in the future, more and more model providers will help optimize for given model quantizations - 4 bit (i.e. NVFP4, MXFP4), 8 bit, and a 'full' model.
    - thot_experiment 1y ago
      Yeah, I think the idea that models that don't come from ollama.com are second class citizens was what made me fist start to think about migrating back to llama.cpp and then the memory stuff just broke the camel's back. I don't want to use a project that editorializes about what models and quants I should be using, if I wanted a product I don't have control over I'd just use a commercial provider. For what it's worth I actually did download the full fp16 and quant it using ollama and still had the memory error for completion's sake. I truly don't understand the reasoning behind removing support for all the other quants, it's really baffling to me considering how much more useful running a 70b parameter at q3 is that not being able to run a 70b parameter model at all, etc. Not to mention forcing me to download hundreds of gigabytes of fp16 because compatibility with other quants is apparently broken, and forcing me to quant models myself.

qwe----3 1y ago

Just a paste of llama.cpp without attribution.

mchiang 1y ago
https://github.com/ollama/ollama?tab=readme-ov-file#supported-backends https://github.com/ollama/ollama?tab=readme-ov-file#supporte...
swyx 1y ago
i mean they have attributed but also it's open source software, i guess the more meaningful question is why didn't ggerganov build Ollama if it was that easy? or what is his company working on now?
- homarp 1y ago
  >what is gg working on supporting models so ollama can then 'support' them too if you use llama.cpp server, it's quite a nice experience. you can even directly download stuff from Huggingface.
  - homarp 1y ago
    e.g. https://nitter.poast.org/ggerganov/status/1978491194842677742#m https://nitter.poast.org/ggerganov/status/197849119484267774...
- monkmartinez 1y ago
  I can not answer for GG, but the early days of llama.cpp were crazy and everything was so very hacky. Remember, Textgen-webui was 'the way' to run models at first because it supported so many different quant types and file extensions. At the time, most people were using multiple different quantization methods and it was really hard to figure out which were performing better or worse objectively. GGUF/GGML was like the 4th iteration of file type quantization from llama.cpp and I remember that I had to consciously begin watching the bandwidth usage from my ISP. Up to that point, I had never received an email warning me about reaching limits of my 2TB connection. All for the same models just in different forms. TheBloke was pumping out models like he had unlimited time/effort. I say all that to say, llama.cpp was still trying, dare I say 'inventing', all the things throughout these transitions. Ollama comes in to make the running part easier and less CLI flag dependent building off of llama.cpp. Awesome. GG and company are down in the trenches of the models architecture with CUDA, Vulkan, CPU, ROCm, etc. They are working on perplexity, token processing/generation and just look at the 'bin' folder when you compile the project. There are so many different aspects to make the whole thing work as well at it does. It's amazing that we have llama-server at all with the amount of work that has gone into making llama.cpp. All that to say, Ollama shit the bed on attribution. They were called out on r/localllama very early on for not really giving credit to llama.cpp. They have a soiled reputation with the people that participate in that sub-reddit at least. They were called out for not contributing back if I remember correctly as well, which further stained their reputation among the folks who hang in that sub-reddit. So it's not a matter of "ease" to build what Ollama built... At least from the perspective of someone who has been paying close attention from r/localllama; the problem was/is simply the perception (right or wrong) of the meme; Person 2 to person 1: "You built this?" -> Person 2: takes item/thing -> person 2: Holds up item/thing -> "I built this". A simple act that really pissed off the community in general.

jhancock 1y ago

I've been using GLM-4.6 since its release this month. It's my new fav. Using it via Claude Code and the more simple Octofriend https://github.com/synthetic-lab/octofriend https://github.com/synthetic-lab/octofriend Hosting through z.ai and synthetic.new. Both good experiences. z.ai even answers their support emails!! 5-stars ;)

mchiang 1y ago
Z.ai team is awesome and very supportive. I have yet to try synthetic.new. What's the reason for using multiple? Is it mainly to try different models or are you hitting some kind of rate limit / usage limit?
- jhancock 1y ago
  I tried synthetic.new prior to GLM-4.6...Starting in August...So I already had a subscription. When z.ia launched GLM-4.6, I subscribed to their Coding Pro plan. Although I haven't been coding as heavy this month as the prior two months, I used to hit Claude limits almost daily, often twice a day. That was with both the $20 and $100 plans. I have yet to hit a limit with z.ai and the server response is at least as good as Claude. I mention synthetic.new as it's good to have options and I do appreciate them sponsoring the dev of Octofriend. z.ai is a China company and I think hosts in Singapore. That could be a blocker for some.
  - mchiang 1y ago
    Do you find yourself sticking with GLM 4.6 over Claude for some tasks? Or do you find yourself still wanting to reach for Claude?
    - jhancock 1y ago
      I have been subscribing to both Claude and ChatGPT for over two years. Spent several months on Claude's $100 plan and couple months on ChatGPT's $200 plan but otherwise using their $20/month plans. I cancelled Claude two weeks ago. Pure GLM-4.6 now and a tad of codex with my ChatGPT Pro subscription. I sometimes use ChatGPT for extended research stuff and non-tech.

mike_d 1y ago

> For users with more than 300GB of VRAM, qwen3-coder:480b is also available locally. I haven't really stayed up on all the AI specific GPUs, but are there really cards with 300GB of VRAM?

bakugo 1y ago
No, you need multiple GPUs. These models are not intended to be run by the average user.
- OneDeuxTriSeiGo 1y ago
  Not necessarily. You need either multiple GPUs or unified memory. There are a handful of UM platforms out there nowadays (mainly Macs but AMD has some as well albeit none with 300GB ram)
  - packetslave 1y ago
    Also the just-released DGX Spark from Nvidia (although it "only" has 128gb of unified memory)
    - Computer0 1y ago
      One of its defining features is the ability to link them together at speeds about the same as their ram speed iirc.
Hamuko 1y ago
You can buy an M3 Ultra Mac Studio and configure it with 512 GB of memory shared between the CPU and the GPU. Will set you back about $9500.
- Schlagbohrer 1y ago
  And that'll be two orders of magnitude slower right?
1y ago

bigyabai 1y ago

Been disappointed to see Ollama list models that are supported by the cloud product but not the Ollama app. It's becoming increasingly hard to deny that they're only interested in model inference just to turn a quick buck.

mchiang 1y ago
Qwen3-coder:30b is in the blog post. This is one that most users will be able to run locally. We are in this together! Hoping for more models to come from the labs in varying sizes that will fit on devices.
- bigyabai 1y ago
  I'm looking forward to future ollama releases that might attempt parity with the cloud offerings. I've since moved onto the Ollama compatibility API on KoboldCPP since they don't have any such limits with their inference server.
  - mchiang 1y ago
    I am super hopeful! Hardware is improving, inference costs will continue to decrease, models will only improve...
- Balinares 1y ago
  How does Qwen3-Coder:30B compare to Instruct-2507 as a coding agent backend? I was under the impression that Instruct was intended to supersede Coder?
- hephaes7us 1y ago
  In this case, it's not about whether it fits on my physical hardware or not. It's about what seems like an arbitrary restriction designed to start pushing users to their cloud offering.
colesantiago 1y ago
I know this is disappointing, but what business model would be best here for ollama? 1. Donationware - Let's be real, tokens are expensive and if they ask for everyone to chip in voluntarily people wouldn't do that and Ollama would go bust quickly. 2. Subscriptions (bootstrapped and no VCs) again like 1. people would have to pay for the cloud service as a subscription to be sustainable (would you?) or go bust. 3. Ads - Ollama could put ads in the free version but to remove them the users can pay for a higher tier, a somewhat good compromise, except developers don't like ads and don't like pay for their tools unless their company does it for them. No users = Ollama goes bust. 4. VCs - This is the current model which is why they have a cloud product and it keeps the main product free (for now). Again, if they cannot make money or sell to another company Ollama goes bust. 5. Fully Open Source (and 100% free) with Linux Foundation funding - Ollama could also go this route, but this means they wouldn't be a business anymore for investors and rely on the Linux Foundation's sponsors (Google, IBM, etc) for funding the LF to stay sustainable. The cloud product may stay for enterprises. Ollama has already taken money from investors so they need to produce a return for them so 5. isn't an option in the long term. 6. Acquisition by another company - Ollama could get acquired and the product wouldn't change* (until the acquirer jacks up prices or messes with the product) which ultimately kills it anyway as the community moves on. I don't see any other way that Ollama can not be enshittified without making a quick buck. You just need to avoid VC backed tools and pay for bootstrapped ones without any ties to investors.

zozbot234 1y ago

For those interested in building Ollama locally, note that as of a few hours ago, experimental Vulkan Compute support (will not be in official binary releases as of yet) has been merged on the github main branch and you can test it on your hardware!

mchiang 1y ago
this one is exciting. It'll enable and accelerate a lot of devices on Ollama - especially around AMD GPUs not fully supported by ROCm, Intel GPUs, and iGPUs across different hardware vendors.

Schlagbohrer 1y ago

For those running locally with more VRAM than an NVIDIA 4090 or 5090, what are you using to get more than 32GB of VRAM?

deleted 1y ago

[deleted]

qqxufo 1y ago

Interesting to see more people mentioning GLM-4.6 lately — I’ve tried it briefly and it’s surprisingly strong for reasoning tasks. Curious how it compares to Claude 3.5 in coding throughput though?

esafak 1y ago

Has anybody that has tried their cloud product care to comment? How does it compare with Anthropic's and OpenAI's offerings in terms of speed and limits?

skeeter2020 1y ago

Seems appropriate that the top-level image is a ~sheep~ llama wearing a headband that says "coder"...

lghh 1y ago
Why? Can you explain?

danans 1y ago

Question for those using local models for coding assistance: how well do the best locally runnable models (running on a laptop with a GPU) work for the easy case: Writing short runs of code and tests after I give an clear description of the expected behavior (because I have done the homework). I want to save the keystrokes and the mental energy spent on bookkeeping code, not have it think about the big problem for me. Think short algorithms/transformations/script, and "smart" auto complete. No writing entire systems/features or creating heavily interpolated things due to underspecified prompts - I'm not interested in those.

tomck 1y ago
I have tried a model on my laptop+GPU before, and it is incredibly unusable. Incredibly slow and just bad output for exactly the work you describe If you're looking for a cheap practical tool + don't care if it's not local, deepseek's non-reasoning model via openrouter is the most cost efficient by far for the work you describe. I put 10 dollars in my account about 6 months ago and still haven't gotten through it, after heavy use semi regularly.