▲ 144 r/vibecoding
m'lord
Animated the "make the Chinese one do it" wizard meme: one Seedance 2.5 take, ~€22 all-in
Look first. Cropped the two characters out of the meme and used them as references for Nano Banana Pro stills: the hall, the wizard, the skeleton, the well and the leviathan, all as 19th-century cross-hatched engravings. Then made clean turnaround sheets for each character and an empty set for the hall.
Voices. Auditioned about six ElevenLabs voices on Eleven v3 with performance tags like [raspy, wheezing] and [bored, flat]. Picked Rusty Malone for Hermes (a deep, raspy grumble works better than the high-pitched goblin I expected) and Archivist Arkadi for the wizard.
Motion, attempt 1: H3 Max, the cheaper option, animating between start and end keyframes, plus Kling Avatar for lip sync. Spent several iterations on it and wasn't happy: the style drifted toward smooth cartoon and every cut showed.
Motion, attempt 2: Seedance 2.5. Researched how to prompt it and wrote one structured prompt: five reference images, each with its role stated (set, wizard, Hermes, well, leviathan), the three voice lines as audio references, "exactly one of each character", and a timestamped shot list with HARD CUTs and each line at its second. One 30 s take at 720p came back with all five shots, the right characters and the engraving style held throughout. It placed the dialogue almost exactly on my timestamps, so I laid the original voice takes back over it.
Edit in HyperFrames (HTML → video, free and local): our voice takes with Seedance's own voices ducked under them, engraving-style captions, grain, vignette and an end card.
Tracking: everything ran through makefx.app, a tool I vibe-coded: one credit balance across Google, fal, Replicate and ElevenLabs, a price quote before every run, and every output kept with its exact recipe (model, prompt, refs, cost). So the meme crops fed the stills, the stills fed the video, and my agent (Claude) drove it all over MCP while I picked on the canvas. The whole space is open, every still, take and prompt: https://makefx.app/s/aleksei-krasnoperov/m-lord
Cost: ~€22 total. Seedance €12.90, stills €4.75, H3 tries €2.55, Kling tries €1.84, voices €0.24.
u/snejink — 19 days ago
▲ 13 r/MacStudio
Which Mac Studio for local AI?
I wanted to buy a Mac Studio for some time. And it was obvious that Apple has set very long delivery dates and was hoarding memory chips for new devices. And finally M5 family is announced, so it is time to get one. But, which one? I mean there are many different configurations for different types of workload.
I hope you don't mind a little summary from LLM. In the end, this release is all about local LLM. Codex did some research, collected and fact-checked data, Claude did some infographics, I made the final edits.
So start with the boring useful box: M6 mini 12C/12G · 32/512 · $1,499**.** CPU handles Xcode, tests and CI; GPU runs the no-agent workhorse jobs on Metal: embeddings, semantic search, reranking, query expansion. But 32GB is not enough for Junie Local: JetBrains enforces a 64GB minimum for its bundled Qwen3.6-27B 4-bit model.
At 64GB the options open up: Mac mini M5 Pro 18C/20G · 64GB/512GB · $2,899. In two oMLX runs, Qwen3.6-27B writes at 17.5–17.8 tok/s and reads the prompt at 389–472 tok/s: a 4K prompt in ~9 s, a 500-token patch in ~28 s, about 37 seconds end to end.
Then 14-inch MacBook Pro M5 Pro 18C/20G · 64GB/1TB · $3,699. Same chip and 307GB/s memory bandwidth as the mini, so expect the same 17.7 tok/s.
If you want faster inference: Mac Studio M5 Max 18C/40G · 64GB/512GB · $3,499. With 614GB/s of memory bandwidth, Qwen3.6-27B runs around 30–40 tok/s: the same 4K prompt in ~5 s, the same 500-token patch in 13–17 s, about 20 seconds end to end versus ~37 on the mini.
The next price jump can buy one of two things: more memory for a stronger model, or a faster chip for the models that already fit.
One step up: Mac Studio M5 Max 18C/40G · 128GB/512GB · $5,099. The same 614GB/s, twice the memory, more model choice. MLX runs put Qwen3-Coder-Next 80B at 75–90 tok/s and Qwen3.5-122B 4-bit around 60 tok/s.
Or the speed option: Mac Studio M5 Ultra 30C/64G · 96GB/1TB · $5,499 (1TB is the floor). Only 96GB, but scaling Qwen3.6-27B by GPU cores and bandwidth points to 48–80 tok/s: the same 4K prompt in ~3 s, the same patch in 6–10 s, about 10 seconds versus ~20 on the Max.
Mac Studio M5 Ultra 36C/80G · 256GB/2TB · $11,299. No middle step: Ultra memory jumps from 96GB straight to 256GB. The first single Mac that fits DeepSeek V4 Flash in full 4-bit, expected 80–90 tok/s: a 500-token patch in ~6 s.
Artificial Analysis, checked on August 30, clocks GPT-5.6 Sol at 75–79 tok/s from the cloud and Opus 4.8 around 58. DeepSeek V4 Flash running locally at 80–90 tok/s matches the fastest frontier serving and beats Opus.
Brains: close on broad agentic scores, but not on repo coding. The independent Agentic Index puts V4 Flash a whisker under Opus, 48.4 vs 49.4. On DeepSeek’s own repo bench NL2Repo the gap is wide: 54.2 vs Opus’s 69.7.
DeepSeek-V4-Pro-0813 edges Opus on the independent Agentic Index, 49.6 vs 49.4, but 0.2 points is not why you buy four Macs. The real reasons: the full checkpoint at native precision, the whole 1M context, zero quantization loss.
The full checkpoint weighs 893GB, so no single Mac holds it. Four 256GB Ultras pool 1,024GB over Thunderbolt 5: $45,196. Cluster speeds are still unpublished, and quantized runs on today’s Macs sit near 10–14 tok/s. This rig is for teams, privacy and the long context, not for tok/s.
GLM-5.3 scores 59.1 on the independent Agentic Index, but its 467GB 4-bit build needs two Ultras. GLM-5.3-Flash scores 58.2 and fits on one 256GB Studio.
Models change faster than Macs. Buy enough memory for the class of model you expect to run, then choose the chip for the speed you can tolerate.
So which one for you?
u/snejink — 2 months ago
▲ 82 r/vibecoding
We made this tiny capitals app, then I tried a scroll-video idea I saw here
I love how easy it has become to make visually interesting things.
u/ElenRed wanted a simple app to finally learn all the capitals, so she vibe-coded Capitals for herself and kept polishing the UI until it felt right.
For the landing page, I got the idea from this post. I generated the clips with Seedance 2 via fal.ai, tied the video frames to the scroll position, and kept adjusting the scroll curve until it felt smooth.
Here’s what we ended up with:
https://capitals.elen.red/
u/snejink — 2 months ago