u/Repulsive-Passage627

Came across airllm again on GitHub trending (25.9k stars, +819 today). The approach is layer-by-layer streaming: load one layer into memory, run it, discard it, load the next. So peak memory is the size of the largest layer rather than the full model.

What I find interesting is that this is explicitly not quantization, distillation or pruning. The weights are untouched. That is a genuinely different trade from what most low-VRAM setups make, which is quality for memory. Here it is speed for memory.

The README claims 70B on a single 4GB card, 405B Llama 3.1 on 8GB, DeepSeek-V3 (671B) on ~12GB, and Kimi K3 at 2.8T on under 4GB, with sparse MoE models streaming a single expert rather than a whole layer.

Those are the project's numbers, not mine. What I have not seen anywhere is throughput. If you have run this on consumer hardware, what were you actually getting in tokens per second, and at what context length? I am trying to work out whether the disk read dominates to the point where it is only useful for batch work, or whether it is tolerable interactively.

The project is maderix/ANE. It trains neural networks on Apple's Neural Engine using
reverse-engineered private APIs, which is notable because Apple exposes that chip for
running models and not for training on it.

What made me read the whole thing is that the author argues against the coverage of his own
project. He states that utilization sits at 5 to 9 percent of the chip, that many operations
still fall back to the CPU, and that this is not a replacement for GPU training for anything
beyond small research models.

His framing is that the barrier to NPU training has been software support rather than
hardware capability.

For anyone who has worked on NPU or accelerator access outside the vendor's own toolchain:
is low utilization like this normally a scheduling and memory problem you can chase down, or
does it usually mean the missing operations have to exist in the vendor's compiler before
anything improves?

Found this going through GitHub trending. Impeccable is a design ruleset aimed at AI coding tools, at 51,792 stars.

The premise is that every model trained on the same SaaS templates, so they all produce the same page: purple-to-blue gradient, Inter everywhere, cards nested in cards, a rounded square icon tile above every heading.

What I found more interesting than the commands is that 60 of its checks are deterministic. They run in a CLI and a browser extension with no model and no API key. It is plain code detecting nested cards and font monotony, not an LLM being asked whether something looks good.

It started from Anthropic's frontend-design skill and expanded on it.

Genuine question for people who do design work: how far can a deterministic ruleset actually get before it starts flagging deliberate choices as mistakes?

Made a 10-min breakdown on how Pellegrini built a team whose floor never lets a bad month cost a European place. Sharing the cold-open + thesis here. Full video on YouTube if interested: https://www.youtube.com/watch?v=1B73_quWrVA

Real Madrid 2025-26 — when the headline numbers undersell what the football actually looked like.

Sitting on 74 points after 33 games, eleven behind Barcelona, this season is being framed as "Madrid weren't good enough." The underlying metrics tell a different story:

→ 1.18 expected goals against per match — the lowest in La Liga
→ 28.8% field tilt share — the highest in Spain
→ 31 goals conceded — fewest of any side except Barcelona
→ Bernabéu Clásico won 2-1 with a 3.41 to 1.12 xG margin — the most lopsided 90 minutes against Barcelona produced by any La Liga side this year
→ Mbappé scored 23 goals on 23.11 xG — perfectly clinical across the entire 33-match sample
→ 5W-1D-1L in seven matches Mbappé did not start

Across all 33 matches, Madrid's expected points total is 68.2 vs 74 actual — a +5.8 overperformance. Their losses, however, sit at 7.66 expected points to zero actual. That distribution is unusual: very lucky in many wins, brutally unlucky in five specific matches.

Read against expected points, the title race is 3.5 points wide, not eleven. Both clubs got luck. Barcelona got more of it.

Of the five losses: one is an honest defeat at Atlético. One (Osasuna) is a coin-flip even match. Three (Celta home, Getafe home, Mallorca away) are matches where Madrid generated more xG than the opponent and didn't score. The Getafe defeat — 2.02 xG to 0.48, a single-shot lottery loss without Mbappé — is the most dissonant single-match line of any Madrid season this decade.

The squad rebuild is part of the texture. Trent Alexander-Arnold played 760 minutes. Bellingham was injury-shortened to 1,408. Vinicius dropped to a complementary role. Three planned core pieces underperformed and the defence still gave up only 31 goals.

In any other La Liga season, this Madrid lifts the trophy. In 2025-26, they ran into a finishing bug that kept costing them the matches they should have won — and a Barcelona team riding their own positive variance to 85 points from 71.7 xPts.

Full 21-scene breakdown — 7 minutes — in the video.

#FootballAnalytics #DataStorytelling #RealMadrid #LaLiga #SportsAnalytics #ExpectedGoals #VarianceAnalysis #ChampionsLeague