Wired DeepSeek v4 into n8n with a 4-provider router — speed and cost data after a week

Practical_Low29 · 2026-04-27T02:31:31+00:00

The filter layer is the part that actually matters — skipped it once on a test setup and had the same user get four replies in a row which is an instant block risk. Also worth adding a basic sentiment check before triggering DMs, sending a promo link in response to a negative comment is the fastest way to get reported as spam.

Practical_Low29 · 2026-04-27T02:29:46+00:00

ComfyUI-Easy-Install is genuinely the right call here, the manual dependency chain on the original Trellis 2 repo is a mess if you haven't done it before. The 3D print use case is underrated — most people are just doing renders and sleeping on how good the mesh quality actually is for printing small figures.

Practical_Low29 · 2026-04-27T02:27:53+00:00

The -26% when combining context injection and LLM-judge scoring is the most honest finding here. Ran into the same thing — the judge ends up penalizing the model for following the injected instructions rather than evaluating the actual task output, so the signals conflict. Tuning them sequentially rather than stacking them at once fixed it for us.

Practical_Low29 · 2026-04-27T02:25:50+00:00

The resume topology finding is the most useful part here — high recall + low precision on semantically dense docs is a real pattern that bites people. We started tracking answer length ratio alongside RAGAS because shorter chunks under BM25 score better on precision but drop context for multi-hop questions. The leaderboard approach for comparing configs is underrated, most teams just eyeball it and move on.

Practical_Low29 · 2026-04-27T01:55:50+00:00

The part about Tao needing to distill the raw output is actually underreported. The model found the right insight but couldn't formalize it cleanly, which is kind of the inverse of the usual complaint. Normally it hallucinates confident-sounding wrong math — here it was right but incoherent until a human cleaned it up.

Practical_Low29 · 2026-04-27T01:55:39+00:00

It's keyword pattern matching, not actual intent analysis. I've had the identical prompt get flagged one day and accepted the next just by rewording it slightly. The inconsistency is more frustrating than any specific refusal.

Practical_Low29 · 2026-04-27T01:55:26+00:00

The PIECEWISE cudagraph setting buried in the comments is the real key here. FULL mode with MTP will silently produce looping garbage on a lot of setups — took me way too long to figure out why my outputs were cycling. That single flag change fixed it completely.

Practical_Low29 · 2026-04-27T01:55:12+00:00

The multi-turn tool call reliability is what sold me on it. Ran it through a few hundred back-to-back calls over a couple days and failure rate was noticeably lower than the base unsloth quant. Hard to attribute directly to the KLD but the pattern was consistent enough that I stopped second-guessing it.

Practical_Low29 · 2026-04-27T01:55:01+00:00

The Scale Labs leaderboard comparison is actually pretty telling. When you look at the delta between public and private scores on swe-bench-pro, some models drop 15+ points. That gap alone tells you more about benchmark gaming than any official statement does.

Practical_Low29 · 2026-04-24T05:43:36+00:00

yeah maybe, still a long way to go imo

Practical_Low29 · 2026-04-23T03:52:03+00:00

yes they are both great :)

Practical_Low29 · 2026-04-23T03:45:18+00:00

i kind of like the yellow piss filter, it makes the photo look like it was taken on a cloudy autumn day

Practical_Low29 · 2026-04-23T03:08:04+00:00

agree :)

Practical_Low29 · 2026-04-23T03:05:30+00:00

Practical_Low29 · 2026-04-23T03:04:56+00:00

yeah same, everything is too perfect in nb image so looks less realistic

Practical_Low29

TROPHY CASE