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[CI] Handle GitHub API error when getting the list of changed files (…
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github![[llama.cpp] 3.1x Q8_0 speedup on Intel Arc GPUs - reorder optimization fix (PR submitted)](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-neural-network-P6fqXULWLNUwjuxqUZnB3T.webp)
[llama.cpp] 3.1x Q8_0 speedup on Intel Arc GPUs - reorder optimization fix (PR submitted)
TL;DR : Q8_0 quantization on Intel Xe2 (Battlemage/Arc B-series) GPUs was achieving only 21% of theoretical memory bandwidth. My AI Agent and I found the root cause and submitted a fix that brings it to 66% - a 3.1x speedup in token generation. The problem : On Intel Arc Pro B70, Q8_0 models ran at 4.88 t/s while Q4_K_M ran at 20.56 t/s; a 4x gap that shouldn't exist since Q8_0 only has 1.7x more data. After ruling out VRAM pressure, drivers, and backend issues, we traced it to the SYCL kernel dispatch path. Root cause : llama.cpp's SYCL backend has a "reorder" optimization that separates quantization scale factors from weight data for coalesced GPU memory access. This was implemented for Q4_0, Q4_K, and Q6_K - but Q8_0 was never added. Q8_0's 34-byte blocks (not power-of-2) make the non-r
I vibecoded a skill that makes LLMs stop making mistakes
i noticed everyone around me was manually typing "make no mistakes" towards the end of their cursor prompts. to fix this un-optimized workflow, i built "make-no-mistakes" its 2026, ditch manual, adopt automation https://github.com/thesysdev/make-no-mistakes submitted by /u/Mr_BETADINE [link] [comments]
How to Create a Pipeline with Dotflow in Python
In this tutorial, you'll learn how to build a complete data pipeline using Dotflow — a lightweight Python library that requires zero infrastructure. No Redis. No RabbitMQ. No Postgres. No Docker. Just pip install dotflow . What we'll build A pipeline that: Extracts user data from a source Transforms it by filtering active users and calculating stats Loads the results into storage Along the way, we'll add retry with backoff, parallel execution, checkpoint/resume, and cron scheduling. Step 1 — Install Dotflow pip install dotflow Step 2 — Create your first pipeline Create a file called pipeline.py : from dotflow import DotFlow , action @action def extract (): """ Simulate extracting data from a database or API. """ return { " users " : [ { " name " : " Alice " , " age " : 30 , " active " : Tr
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d318 is almost always suppressive in Qwen-2.5-3B emotional vectors, built an emotion vector steering pipeline, positive steering collapses to a single 'preschool teacher' register regardless of emotion
It appears that on lower weight models, behavior converges to either be highly sycophantic or neutral with no real in between, however existentialism did seem to be somewhat present. Using some heatmaps and visualizations, the cosine similarities between emotions appears coherent with what'd be expected, and there's really interesting dimensional dominances. In Qwen-2.5-3B, d318 is almost always the greatest in magnitude and almost always suppressive. Could be interesting for interpretability research. Vector merging also appears to lead to model incoherence if you merge a lot of vectors without normalizing their influences to some maximum. Built an automated emotion vector pipeline on top of Anthropic's emotional vector research . It makes the detection and correction of unwanted behavior
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A bacterium arrives at the surface of a squid's light organ. It is one of a million bacteria in the surrounding seawater. It has never been here before. The squid has never met it. Neither has any reason to trust the other. Within hours, one of them will be living inside the other's body. The Hawaiian bobtail squid hunts at night in shallow water. To avoid being silhouetted against the moonlit surface, it uses counter-illumination: a light organ on its underside produces a glow that matches the ambient light from above. Predators looking up see no shadow. The squid cannot produce this light itself. It outsources the job to Vibrio fischeri , a bioluminescent bacterium. But V. fischeri is less than one in ten thousand of the bacteria in Hawaiian seawater. The squid hatches with a sterile lig
5 CLAUDE.md Rules That Made My AI Stop Asking and Start Doing
After months of running Claude Code autonomously, I've learned that most of the interruptions aren't the AI's fault. They're the CLAUDE.md's fault. Here are 5 rules that eliminated most of my "should I do X?" questions. 1. The irreversibility rule (not the uncertainty rule) What most CLAUDE.mds say: "Ask for clarification when uncertain." What actually works: "Ask only for: irreversible actions, external credentials, external visibility (publishing, sending emails), costs beyond the subscription." The difference is significant. Uncertainty is constant — every decision has unknowns. Irreversibility is rare — most code changes can be reverted with git reset . When I switched from "ask when uncertain" to "ask only when irreversible," the question count dropped by about 80%. 2. Explicit decisi
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