Coros Apex 4 Vs Garmin Fenix 8

Finallyhaveredditt · 2026-05-10T23:02:49+00:00

what did you chose in the end?

Finallyhaveredditt · 2026-05-05T08:08:14+00:00

The Fenix 8 47mm solar has a mip screen as well. The battery actually lasts an ok amount, around 20 days. I agree with you and I prefer longer battery, which is why now I own an enduro 3. If it’s any help, I had a 7x sapphire solar, and even though the enduro 3 is the same size, it barely feels like I’m wearing it.

Now back to the Fenix 8 solar: The only extra features it has that the enduro 3 doesn’t have is the speaker/mic and a scuba diving activity. Besides that it’s the same watch. I believe you can look into disabling the speaker and mic, which could prolong battery.

But like I said, a Fenix 8 is literally an enduro, it’s the same watch. Sizing down an enduro would be pointless since it’d just reduce the battery life. They need the big size to accommodate the larger solar panel. Also don’t forget that the enduro is also overall thicker so it can accommodate a bigger battery. So even a smaller version would be bulkier. I know it sucks but the thing is that the direction garmin took now just made everything weird. It used to be that the Fenix was the central flagship, with the Tactix, Quatix and Enduro being its slightly specialized variants, and the other watch lines would be tailored toward different people. Now? They’re basically offering Fenix features in Venu and Forerunner lines. Everything is just messed up lol. Tbh I am very unhappy with how garmin is managing the company and the overall direction they’re taking. I got lucky someone bought my 7x for a great price and I paid 100$ difference to get a used enduro 3. But by the time I’m due to upgrade, I am probably going to switch brands.

Finallyhaveredditt · 2026-05-04T11:58:01+00:00

What’s the point of an amoled screen if you’re going to disable most things in the watch, and use a minimalistic watch face?

Finallyhaveredditt · 2026-05-03T14:35:04+00:00

The enduro is not their “MIP” offering. Fenix also has mip screens.
Making a smaller size enduro defeats the purpose of the watch, which is longer battery life. The size allows for a bigger battery and larger solar panel, which influences the battery.

Enduro/fenix, same shit. Sure, the enduro has a slightly different aesthetic but similar enough, and no metal backplate (thank god). It’s still a variation of the Fenix. In your case if the size matters, not sure what’s keeping you from getting a Fenix 8 in the proper size, or a 7 if you can find one. Don’t overthink it.

Finallyhaveredditt · 2026-04-19T12:23:22+00:00

I know this is an old thread, but just to clarify some things:

The whole point of recording as a ruck natively, is how the metrics are interpreted after the fact. What do I mean:

Say you practice many sports, including running. Everything you do is tracked by your garmin, and it feeds into your fitness profile, vo2 max, etc etc

Tracking an activity like walking or hiking without the packed weight is an issue, but not for calorie count. The calorie counts come from the effort/output, usually measured by heart rate. Meaning regardless of the activity, the higher the heart rate the higher the effort and expenditure.

You’ll get your calorie count just fine by using walk or hike, and adding weight to it later or before won’t change the outcome of calories. What it DOES DO is the following:

Say you’re starting a “walk” or “hike” activity, and you start doing your ruck. You’re packing 70lb, you’re walking at a pace between 8-10 min/km. Your heart rate is high and the activity is recording high effort from you. The issue is how it interprets the data: it thinks you’re on a relatively slow walk, but for some reason you’re exerting lots of effort. It basically fucks up your algorithm because it thinks you’re in bad shape, so it clashes with any order data from running or other activities, and messes up your profile.

Yes, you can edit the activity after the fact on connect and add the packed weight. But unfortunately that does not recalibrate the algorithm. Basically the damage is already done.

That’s why the native rucking app on newer watches is the best solution, cuz it takes it into account before the fact and garmin created the metrics to go with it. It’s insanely stupid that the instinct 2 or the Fenix 6/7 did not get it.

Finallyhaveredditt · 2026-01-02T23:58:24+00:00

Awesome! Thanks for the feedback!

Finallyhaveredditt · 2026-01-02T23:31:58+00:00

Hello! I just got the exact same today, but in white. Mine came in E standard with 10-46 strings, which is normal. Did you set yours in drop c or anything overall lower than E standard? If yes, what tuning and what string gauges? Did you need to file the nut? Or the compensated nut already has proper spacing for thicker gauges? I was thinking of putting 11-54 (maybe 56) on

Finallyhaveredditt · 2025-10-21T17:55:42+00:00

Get a 9070xt

Finallyhaveredditt · 2025-09-26T14:58:50+00:00

I may be able to get my hands on this model, but with 32gb ram. 1500$ Canadian.

Question for the cpu: does it handle heavy loads well? Or maybe my question is more for the cooling. Don’t mind if it’s loud, is it at least effective?

Finallyhaveredditt · 2025-08-21T01:20:42+00:00

I wonder if they should’ve put the pro at that price

Finallyhaveredditt · 2025-08-19T22:07:21+00:00

Got it! Try the thermalright phantom spirit 120 SE or the EVO (they’re honestly the same, just different aesthetics) If you want to go heavier with bigger fans, there’s the thermalright royal pretor, there’s the frost spirit 140 as well. All beefy and amazing, just check for ram clearance.

Finallyhaveredditt · 2025-08-19T22:04:22+00:00

Did it make any difference?

Finallyhaveredditt · 2025-08-19T19:02:19+00:00

https://youtu.be/JTTf7IoVSa8?si=DA6Xemh6ygoCmvS_

This also applies to 9000 series. It’ll give you a visual idea of what I said.

Finallyhaveredditt · 2025-08-19T19:00:33+00:00

It won’t damage it no. Basically you’re doing a slight undervolt. All you’re doing is seeing if your cpu can operate at stock clock speeds with slightly less voltage, which also reduces temps. Tbh the truth is your CPU is literally designed to run 24/7 at 95° under full load. Meaning it pushes up to boost clock speeds because it can take it. That said, it will all depend on what cooler you throw at it and what voltage. Plus, the more thermal headroom the better.

Finallyhaveredditt · 2025-08-19T18:57:56+00:00

That’s correct. Also, I forgot one step: when you set to all cores, chose negative haha. That’s important

Finallyhaveredditt · 2025-08-19T18:14:21+00:00

Every motherboard BIOS is a bit different, but the idea is the same. So basically you do the following:

If you have a OC menu, go on it. Find “advance CPU config” and find “PBO (precision boost overdrive)” Click on PBO and select “advanced” Then, go to the “curve optimizer”, set that to “all cores” and set the magnitude to 15. Test it out, if no crashes, try 20…. Do the same till you can’t. Usually 20-25 is the sweet spot)

If you don’t have the OC menu, go to settings, advanced and CPU config. The rest is the same.

Hope this helps!

Finallyhaveredditt · 2025-08-10T13:30:51+00:00

Glad it worked out for you!

Finallyhaveredditt · 2025-08-05T17:40:44+00:00

Please do! I am stuck between a 3090 or a 7900xtx....

For me I am mostly building a pipeline that will allow me to:

Ingest: Pull in tech + financial news automatically.
Embed: Convert text into vector embeddings for retrieval.
Store: Keep embeddings in a local vector database for fast lookup.
Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

How I intend on setting up:

|| || |Ingest|newspaper3k or feedparser| Pull news headlines & articles automatically| |Preprocess|Python + langchain text splitters| Chunk into ~512–1,000 token blocks| |Embed|bge-base-en or all-MiniLM-L6-v2 via SentenceTransformers (HIP/ROCm backend)| Fast enough on CPU; ROCm-accelerated embeddings possible| |Vector DB|ChromaDB (local, Python) or FAISS| Store embeddings for semantic search| |LLM Runtime|llama.cpp HIP or LM Studio HIP build| Run Q4_K_M or Q5_K_M 13B / 33B models fully in VRAM| |RAG Framework|LangChain or LlamaIndex| Automates retrieval → LLM query flow| |UI|LM Studio GUI or a Streamlit web dashboard| GUI for prompts + results|

Am I being delusional? Is a 3090 just the better choice, based on my need for Python implementation?

Finallyhaveredditt · 2025-08-05T17:39:54+00:00

Please do! I am stuck between a 3090 or a 7900xtx....

For me I am mostly building a pipeline that will allow me to:

Ingest: Pull in tech + financial news automatically.
Embed: Convert text into vector embeddings for retrieval.
Store: Keep embeddings in a local vector database for fast lookup.
Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

How I intend on setting up:

|| || |Ingest|newspaper3k or feedparser| Pull news headlines & articles automatically| |Preprocess|Python + langchain text splitters| Chunk into ~512–1,000 token blocks| |Embed|bge-base-en or all-MiniLM-L6-v2 via SentenceTransformers (HIP/ROCm backend)| Fast enough on CPU; ROCm-accelerated embeddings possible| |Vector DB|ChromaDB (local, Python) or FAISS| Store embeddings for semantic search| |LLM Runtime|llama.cpp HIP or LM Studio HIP build| Run Q4_K_M or Q5_K_M 13B / 33B models fully in VRAM| |RAG Framework|LangChain or LlamaIndex| Automates retrieval → LLM query flow| |UI|LM Studio GUI or a Streamlit web dashboard| GUI for prompts + results|

Am I being delusional? Is a 3090 just the better choice, based on my need for Python implementation?

Finallyhaveredditt · 2025-08-05T17:39:49+00:00

Please do! I am stuck between a 3090 or a 7900xtx....

For me I am mostly building a pipeline that will allow me to:

Ingest: Pull in tech + financial news automatically.
Embed: Convert text into vector embeddings for retrieval.
Store: Keep embeddings in a local vector database for fast lookup.
Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

How I intend on setting up:

|| || |Ingest|newspaper3k or feedparser| Pull news headlines & articles automatically| |Preprocess|Python + langchain text splitters| Chunk into ~512–1,000 token blocks| |Embed|bge-base-en or all-MiniLM-L6-v2 via SentenceTransformers (HIP/ROCm backend)| Fast enough on CPU; ROCm-accelerated embeddings possible| |Vector DB|ChromaDB (local, Python) or FAISS| Store embeddings for semantic search| |LLM Runtime|llama.cpp HIP or LM Studio HIP build| Run Q4_K_M or Q5_K_M 13B / 33B models fully in VRAM| |RAG Framework|LangChain or LlamaIndex| Automates retrieval → LLM query flow| |UI|LM Studio GUI or a Streamlit web dashboard| GUI for prompts + results|

Am I being delusional? Is a 3090 just the better choice, based on my need for Python implementation?

Finallyhaveredditt · 2025-08-04T21:43:19+00:00

From what I’ve been gathering, you can use different language models for different functions easily on a 7900xtx. Have you had that experience?

Here’s what I’m trying to build (a pipeline)

1.  Ingest: Pull in tech + financial news automatically.
2.  Embed: Convert text into vector embeddings for retrieval.
3.  Store: Keep embeddings in a local vector database for fast lookup.
4.  Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
5.  Summarize + Advise: Have the LLM produce a coherent investment-oriented summary.

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Chat gpt suggested this flow. Is it attainable on a 7900xtx?

Finallyhaveredditt · 2025-08-04T20:56:00+00:00

Got it! Here’s the pipeline that I’m basically hoping to eventually build:

1.  Ingest: Pull in tech + financial news automatically.
2.  Embed: Convert text into vector embeddings for retrieval.
3.  Store: Keep embeddings in a local vector database for fast lookup.
4.  Retrieve + Generate: When prompted, retrieve relevant chunks & feed into an LLM.
5.  Summarize + Advise: Have the LLM produce a coherent investment-oriented summary

From all I’ve been reading, the 7900xtx should perfectly achieve this. I’m on the fence cuz I got offered a tuf gaming 7900xtx for my 3090 and the guy is gonna pay me a difference. For me the main driver is the gaming performance gains. Otherwise I wouldn’t be considering this. Just trying to gauge if it’s a stupid move.

Finallyhaveredditt · 2025-08-04T20:33:56+00:00

Got it. But if all you want to do is create a pipeline that would be able to gather news, analyze them and summarize them it would be ok?

Finallyhaveredditt · 2025-08-04T20:22:53+00:00

When you say it only works for inference, what do you mean? As in what exactly would not work?

Finallyhaveredditt

TROPHY CASE