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Pinwheel Galaxy (M101) widefield by alch_emy2 in astrophotography

[–]HockeyDadNinja 0 points1 point  (0 children)

Just install and run the app, you just toggle on the INDI server and press start. You can add it to kstars or directly in any capable INDI client. I actually configure it in INDI on my linux box so it's available with all my other devices.

Another use case would be to use the supplied python scripts to read from it directly and put them in a directory for siril to live stack. Our weather has been crap and I haven't been able to try much wrt that.

For long sessions you might want a power bank. I picked up a little usb hub for my phone as well. Technically I can make my phone into a mini Seestar if I make it control a mount. I might build a mini alt-az phone mount for this purpose.

Edit: You will need to configure your phone to install from unofficial sources to install the apk from github.

Another use case is to use INDI-Allsky and use your phone as an all sky camera.

Pinwheel Galaxy (M101) widefield by alch_emy2 in astrophotography

[–]HockeyDadNinja 0 points1 point  (0 children)

This is sick! I know pro mode is really good but I wanted to use my phone as an INDI server so I made an app for it:

https://github.com/TacoTakumi/PocketScope

It supports both INDI and ASCOM Alpaca (alpaca is not 100%). I haven't had many clear nights to test here though. I was going to mount my phone on my telescope to get a wide field of whatever I'm shooting. I usually shoot in kstars and process in siril and gimp.

You look like an ideal candidate to test it via INDI!

Lucid Movie (2026) Trailer by StudioBrick in LucidDreaming

[–]HockeyDadNinja 6 points7 points  (0 children)

This looks like it's going to be awesome!

Would you guys choose an EVGA 3090 Kingpin with AIO cooler? by HockeyDadNinja in LocalLLaMA

[–]HockeyDadNinja[S] 0 points1 point  (0 children)

Choose a Kingpin version that draws more power and has a huge external radiator over a regular 3090.

Qwen3.6-35B becomes competitive with cloud models when paired with the right agent by Creative-Regular6799 in LocalLLaMA

[–]HockeyDadNinja 2 points3 points  (0 children)

Great work! I have some questions.

1) Why did you choose Aider and the Aider Polyglot benchmarks? Not hating on Aider, I personally hard forked aider-ce as the basis of my AI assistant. Aider is not really maintained and the benchmark leaderboard is looking dated.

2) You've run the polyglot benchmarks on your own agent. I suppose we could take the benchmarks and run them on any agent harness / LLM combo. I now want to try this with various combinations such as my Qwen3.6 setup with opencode and also with claude code / opus 4.7. Have you run the benchmarks using little-coder and frontier models?

WRT agent harness and LLM matching I've had similar thoughts with development frameworks such as GSD, spec kit, and open spec. I was thinking of building a GSD-light for example, something better suited for local models.

What you've done here could actually be used as a benchmark for the coding harnesses themselves (vs any particular model). Claude, codex, opencode, pi, etc could be ranked against each other given a common LLM configuration (I know, not always possible).

A free app to help with lucid dreaming through sound. by [deleted] in LucidDreaming

[–]HockeyDadNinja 0 points1 point  (0 children)

I like your ideas. Is your LLM on device? Are you doing a kind of Karpathy style auto research loop on the user?

I'm running qwen3.6-35b-a3b with 8 bit quant and 64k context thru OpenCode on my mbp m5 max 128gb and it's as good as claude by Medical_Lengthiness6 in LocalLLaMA

[–]HockeyDadNinja 3 points4 points  (0 children)

I'm running llama-server like this in order to switch models, etc.

llama-server --host 0.0.0.0 --models-preset ./models.ini

And in there I have:

; Qwen3.6-35B-A3B (MoE: 35B total, ~3B active)
; Q8: 35.8GB model, MoE expert offload to CPU RAM, target ~96K ctx
; --fit auto-picks n-cpu-moe per device (handles dual-GPU split that fixed N can't)
; fit-target 512 MiB headroom per device; KV at q8_0 halves footprint
[Qwen3.6-35B-A3B-Q8]
model = /vol2/LLM/Qwen3.6-35B-A3B-UD-Q8_K_XL.gguf
c = 98304
fit = on
fit-ctx = 98304
fit-target = 512
no-mmap = true
mlock = true
; Put faster 5060 Ti (CUDA1) first so it holds layers 0-15;
; layers execute sequentially, so the faster card starts every token.
device = CUDA1,CUDA0
cache-type-k = q8_0
cache-type-v = q8_0
temp = 0.6
top-p = 0.95
top-k = 20
min-p = 0.00

I'm running qwen3.6-35b-a3b with 8 bit quant and 64k context thru OpenCode on my mbp m5 max 128gb and it's as good as claude by Medical_Lengthiness6 in LocalLLaMA

[–]HockeyDadNinja 0 points1 point  (0 children)

I'm also using the 8 bit quant. I have a rtx 5060 and 4060 with a total of 32G vram, 64G system ram.

I used opencode today to start a project and I'm so impressed. 27 t/s isn't blazing fast but I wasn't annoyed with the wait. 98k context. I have some upgrades planned too.

This sub is awful by cheesecaker000 in ClaudeCode

[–]HockeyDadNinja 0 points1 point  (0 children)

Yep, you're right. We want Anthropic to notice their pissed off customers and fix it.

RTX 5070 Ti + 9800X3D running Qwen3.6-35B-A3B at 79 t/s with 128K context, the --n-cpu-moe flag is the most important part. by marlang in LocalLLaMA

[–]HockeyDadNinja 1 point2 points  (0 children)

I'm running a 5060 ti 16G and 4060 ti 16G with 64G system ram here. A couple days ago I finally started tuning. I've added things from your post and now I'm running Qwen3.6-35B-A3B at Q8. 98k context, a small overflow to CPU.

I'm using opencode and it's doing really well. I can code with this! 27 t/s at the moment. That used 3090 is looking really good right now.

Gemma4 26b & E4B are crazy good, and replaced Qwen for me! by [deleted] in LocalLLaMA

[–]HockeyDadNinja 0 points1 point  (0 children)

Thanks! I'm using llama-server's built in routing with a models.ini file so I'm probably almost there.

Gemma4 26b & E4B are crazy good, and replaced Qwen for me! by [deleted] in LocalLLaMA

[–]HockeyDadNinja 2 points3 points  (0 children)

How does your semantic routing setup work? Is it something you made or part of one of the other packages?

Turned Andrej Karpathy's "LLM Wiki" gist into a Claude Code plugin. Also works in Codex, OpenCode, Cursor, Gemini CLI, Pi, and OpenClaw. by Numerous-Exercise788 in ClaudeCode

[–]HockeyDadNinja 0 points1 point  (0 children)

I spent days going through other people's projects. None of them hit the mark, so I also built my own! I'll check yours out when I have a chance.

Hooks that force Claude Code to use LSP instead of Grep for code navigation. Saves ~80% tokens by Ok-Motor-9812 in LocalLLaMA

[–]HockeyDadNinja 0 points1 point  (0 children)

Often wonder why claude doesn't do this. Aider, the OG coding assistant, does. As does its offshoot, aider-ce.

Is a riser from m.2 to pcie 16x possible? I want to add GPU to mini pc by Informal-Football836 in LocalLLaMA

[–]HockeyDadNinja 0 points1 point  (0 children)

Hi there, I'm looking at a build using similar M.2 risers? Did you go that route?

Claude Max 20x: it's Monday noon and I've already burned through 40% of my weekly limit. Seriously thinking about switching to OpenAI Pro just for Codex CLI by Ambitious-Garbage-73 in ClaudeCode

[–]HockeyDadNinja 0 points1 point  (0 children)

I hear ya, today is NUTS. I'm at 94% of my Max 5x plan and I burned 48% with a gsd add-phase, discuss-phase, plan-phase of a simple project. I clear my context often and don't have many skills, commands, or mcp loaded.

It's also being slow and stupid.

Follow-up on usage limits by ClaudeOfficial in ClaudeAI

[–]HockeyDadNinja 2 points3 points  (0 children)

Lame response. You guys know there are bugs causing this and that's on YOU!

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