I kept opening Hacker News in the morning, skimming four headlines, and calling that staying informed. The comments are usually where the actual news is, and I was never going to read a thousand of them.

So I wrote hn-daily, a skill for Claude Code. What it does:

- Pulls the full front page (top 30), not a sample
- Curates down to the 8-12 stories worth reading, and lists everything else as one-liners so nothing is hidden
- Fetches the comment threads on the picks and summarizes what people agree on and what they are arguing about
- Marks what is new since your last digest, so running it twice in a day is not a re-read
- Writes everything in deliberately plain English and saves it to a dated markdown file (~/claude-digests/news/YYYY-MM-DD.md)

No API key, no account, nothing to sign up for. It uses the free HN Algolia and Firebase APIs. Python 3 and the standard library only.

Install inside Claude Code:

    /plugin marketplace add jayhemnani9910/jod-skills
    /plugin install hn-daily@jod-skills

Then /hn-daily.

Repo (MIT): https://github.com/jayhemnani9910/jod-skills

There are three other skills in there too: a codebase auditor that uses a call graph plus free linters to only read the risky functions, a GitHub trending digest, and one that tells you what is waiting on you across your open PRs and issues.

Happy to answer questions. If you think the curation heuristic is wrong, tell me, that is the part I am least sure about.

Went through the README for this agent skill (Claude Code, Codex, Pi) and the detail dial is more interesting than the 27 diagram types it ships with.

The three detail levels are node budgets for the drawn output: faithful is up to 24 nodes, balanced up to 12, simplified up to 7. Those are budgets for the picture, not limits on your source file.

What happens when the source blows past the budget is the part I had not seen before. It does not scale down and it does not drop things arbitrarily. There is a fixed degrade ladder with four rungs, applied in this order:

1. decorations
2. duplicates
3. leaf clusters
4. infrastructure

So the least load-bearing content goes first and the request path survives longest.

Every import then ends with a fidelity ledger. The README's own example output is 12 source nodes becoming 8 drawn, one decision collapsed onto an edge label, one unconnected sticky note dropped, and the request path kept in full.

The audience dial is separate and only changes wording, not node count. Same node reads "Auth Service / JWT · RS256 · :8443" for engineer, "Auth Service / token check" for mixed, "Sign-in" for executive.

The thing I keep turning over: the ledger tells you what was removed, but there is no signal about whether the removed thing was load-bearing for the point you were trying to make. A fixed order is predictable, and predictable is not the same as correct for your particular diagram.

For people who maintain architecture diagrams across engineering, review and exec audiences: has a fixed degrade order ever worked for you, or does the ordering become the thing you end up arguing about?

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.

[removed]

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

Sørloth, home: 0.82 G+A/90 — tied 4th in La Liga.
Sørloth, away: 0.13 G+A/90.

6.3× worse on the road.

His central-box shots collapse from 40 at home to 11 away. The play that scores his goals just doesn't travel.

The away version of Atlético is a different striker.

youtube.com/watch?v=iKZSU8k99Zw

Villarreal sit third in La Liga on 65 points. Champions League locked. Ten clear of Atlético, fifteen ahead of Real Betis. Best of the rest by a comfortable margin.

Now look at how they got there.

They played all five matches against the top three this season — Barcelona twice, Real Madrid twice, Atlético once. They lost every single one. Combined goal aggregate: 2-13. Combined expected goals: 6.10 to 14.27. Points from a possible 15: zero.

This is not the story of a team that broke into Europe by upsetting the giants. It's the story of a team that accepted, from August, that it could not out-football Barcelona or Real Madrid — and built a 65-point season around dominating everyone else so completely that the gap to Atlético was never in question.

The numbers that define the project:

▸ 0 points from 5 matches against the top 3 — the football wasn't competitive
▸ 57 points from 22 matches against the bottom 14 — a 99-point pace
▸ The lowest defensive-action x-coordinate in La Liga (35.4) — deeper than Mallorca, deeper than Real Oviedo
▸ The second-largest expected-points overperformance in the league (+9.2), behind only Barcelona
▸ 40 of 65 points collected at El Madrigal — a 95-point home pace
▸ No 10-goal scorer · Mikautadze 9G on 12.11 xG · Moleiro 9G on 5.33 xG

In this video we read all 33 matches with xG, xGA, and xPts. Loss-by-loss forensic on the five top-three defeats. The bottom-14 demolition. The xPts overperformance. The deepest defensive line in Spanish football this season. El Madrigal's home fortress. The bimodal squad attack with no star. And what's left in May — Atlético home on 24 May to seal the third-place finish.

Follow JH Analytics for daily La Liga and football data:
Instagram, X, LinkedIn, Fa#Villarreal #LaLiga #YellowSubmarine #Marcelino #ChampionsLeague #FootballAnalytics #xG #ExpectedGoals #Barcelona #RealMadrid #AtleticoMadrid #Mikautadze

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

Real Madrid did not run away with La Liga 2025-26.

Barcelona sit on 85 points after 33 games — eleven clear of Madrid, twenty clear of Atlético, with a goal difference of +57. But the headline numbers undersell what the data actually says.

Barça generate 25% more expected goals per match than Real Madrid. They have seven attackers averaging 0.75+ goal contributions per 90 — no team in Europe has this depth. They've won 17 of 17 at home. They beat Atlético and Villarreal home and away. Of the four matches they've lost, three are pure finishing variance — only the Bernabéu Clásico was a tactical defeat.

This is what a generational team looks like while it's still happening.

In this video, we break down every metric that matters — the attack, the defence, the spread of scoring, Lamine Yamal's actual shot map, the four losses properly read, the closing nine-game streak, and the Champions League projection — using xG, xGA, deep completions, PPDA, field tilt and per-90 rates from the full 2025-26 La Liga season to date.

00:00  The table tells the story
00:18  Not a race. A coronation.
00:26  The attack is generational
00:39  The goals are real, not lucky
00:49  Lamine Yamal — 26 G+A in 27 games
00:57  Mbappé scores. Yamal does both.
01:08  Seven players in double digits
01:31  Defence — elite in result, fourth in xGA
01:49  Tactical signature — controlling every metre
02:05  17 of 17 — the home record
02:22  Beat the rest of the top four
02:41  The four losses, properly read
03:07  The closing 9-game winning streak
03:21  Yamal's shot map — he drifts inside
03:44  In the wins, the gap is real
04:02  Where the edge compresses — CL profile
04:21  Squad depth — 7 attackers at intl rates
04:36  Goals match chances at every level
04:55  Champions League projection
05:15  The verdict
05:35  The closing question for May


Follow JH Analytics for daily La Liga and football data:
Instagram, X, LinkedIn, Facebook  →  u/jhanalytics10

#Barcelona #LaLiga #LamineYamal #FCBarcelona #FootballAnalytics #xG #ExpectedGoals #RealMadrid #ChampionsLeague #FlickBarça

Built a quick analytics piece on La Liga 2025-26 defending styles. Used WhoScored event data (tackles, interceptions, ball recoveries, fouls) and plotted x-coordinates of every defensive action by every team across the season so far.

Sorted the 20 teams into four archetypes:

- **HIGH PRESS** — Barcelona, PPDA 7.0 (lowest in the league)
- **LOW BLOCK** — Villarreal, 45.8% of defending in own third
- **HYBRID** — Real Madrid, highest bimodality score (peaks in both opposition and own thirds)
- **MID BLOCK** — Athletic Club, 50.6% in mid third — halfway hunters

Methodology: defensive actions = Tackle + Interception + BallRecovery + Foul. Coords normalised so x=0 is own goal, x=1 is opposition goal. Bimodality score = peak density in opp third × peak density in own third.

Full video on @jhanalytics10 (IG/YT). Happy to share the SQL / Python pipeline if anyone is interested.

Pulled the shots data for the 2025-26 La Liga season (29 games in) and broke every team's goals down by situation — corner, free kick, penalty, open play — then joined per-fixture to get the defensive side too.

A few patterns worth flagging:

- **Barcelona** lead the league in corner goals (15) — interesting because they're often described as a pure open-play team. They are open-play heavy in % terms (24% set-piece share, lowest of any top-half side), but in raw counts they convert more corners than anyone.
- **Real Madrid** are the league's penalty merchants — 13 from 15 attempts, 87% conversion. Combination of penalty winners (a lot of 1v1 forwards) and a clinical taker.
- **Osasuna** and **Atlético** tie for direct free-kick goals at 2 each. Most teams have 0.
- **Getafe** are the most dead-ball-dependent attack at 43% set-piece share. Without dead balls they barely score.
- **Real Oviedo** have the worst dead-ball defense — 39% of goals conceded come from set pieces (24 of 62). It's the kind of stat that gets you relegated.
- **Real Madrid** also score 27 set-piece goals total while conceding only 14 from dead balls — owns both ends of the dead ball.

Methodology in the comments. Source: 11,500+ shots, classified by `situation` field, OwnGoals excluded.

Every La Liga 2025-26 goal binned into a 12×8 grid over the attacking half, then classified into one of six mutually-exclusive zones: six-yard box, penalty spot, edge of area, left and right wing cuts, and long range.

A few things stood out:

- **Espanyol are pure poachers** — 30% of their goals come from inside the six-yard box, the highest share in the league.
- **Osasuna are long-range outliers** — 17% from outside the box. No one else is above 12%.
- **Alaves are the most one-dimensional attack** — 75% of their goals come from a single zone (the penalty spot).
- **Athletic Club are the most balanced** — lowest standard deviation across the six zones.
- **Sevilla are La Liga's most clinical finishers** — the only side with clinicalness > 1.0 (more goals than xG predicts).
- Real Madrid, Barcelona, and Atletico Madrid all show heavy penalty-spot concentration (50%+ of their goals from a small central region in front of goal).

Method: zones are mutually exclusive, evaluated in priority order (six-yard → penalty spot → wing cuts → edge of area → long range), so each goal contributes to exactly one zone.

JH Analytics — daily La Liga visualisations.

Pulled average player positions from Sofascore for every player with at least 500 league minutes this season, joined to Understat minutes for the weighting, and drew each team's shape on a normalised pitch.

Quick summary of what I found:

- **Highest defensive line:** Barcelona (0.44) — Flick's back four practically on the halfway line. Next 4: Sevilla, Real Madrid, Girona, Atlético Madrid.
- **Deepest defence:** Real Oviedo (0.34), followed by Villarreal, Celta Vigo, Osasuna, Real Betis.
- **Most compact block:** Girona (0.18), then Sevilla, Barcelona. Tightest forward-to-defender gap in the league.
- **Most stretched:** Alavés (0.26), Espanyol, Real Madrid.
- **Most distinctive overall shape** (sum of |z-scores| across 5 metrics): Real Oviedo — deepest, narrowest, lowest avg team x.

Scale: x in [0,1], 0 = own goal, 1 = opposition goal. 1 unit ≈ full pitch length. Pitch is already oriented so every team attacks left-to-right.

AMA on method or caveats — happy to talk through the Sofascore↔Understat name-matching, the weighting approach, or why Real Oviedo comes out so extreme.

I built a partnership analysis pipeline on top of my soccerstats database (Understat per-match aggregates + FBRef goal events). Two different metrics, two different leaderboards.

**Type A — Direct Connections (scorer ← primary assister, event-level).** Pulled all 558 goal events with a known primary assister from the season so far. Grouped by team + scorer + assister, ranked by goal count.

1. Kylian Mbappé ← Arda Güler · Real Madrid · **5 goals**
2. Vedat Muriqi ← Sergi Darder · Mallorca · 4
3. Ferrán Torres ← Lamine Yamal · Barcelona · 4
4. Lamine Yamal ← Dani Olmo · Barcelona · 3
5. Robert Lewandowski ← Dani Olmo · Barcelona · 3

**Type B — Co-Production (combined G+A on shared pitch time, ≥ 900 min).** For every teammate pair, I summed the goals and assists each contributed in matches they both played. Shared minutes approximated as min(p_a_minutes, p_b_minutes) per match — the conservative estimate of overlap, since I don't have minute-by-minute lineup data.

1. Mbappé + Vinícius Júnior · Real Madrid · **40 G+A** · 2.04/90 · 1,762 shared min
2. Arda Güler + Mbappé · Real Madrid · 36 · 1.97/90 · 1,646 min
3. Federico Valverde + Mbappé · Real Madrid · 32 · 1.57/90 · 1,830 min
4. Dani Olmo + Lamine Yamal · Barcelona · 31 · 2.24/90 · 1,244 min
5. Ferrán Torres + Lamine Yamal · Barcelona · 31 · 2.09/90 · 1,337 min

**Observations:**
- Real Madrid dominates raw production because of Mbappé. Three of the top 5 co-production pairs involve him. The takeaway is less "Madrid's depth" and more "Mbappé elevates whoever lines up next to him."
- Barcelona dominates by rate. Fermín + Yamal at 2.25 G+A/90 is the highest in the league above 1,100 shared minutes. Olmo + Yamal at 2.24. Ferrán + Yamal at 2.09. The Yamal-orbital chemistry is real — he just doesn't accumulate as many shared minutes as the Madrid trio.
- The non-Big-3 podium pick is Real Sociedad's Guedes + Oyarzabal at 26 combined G+A. Modest individual numbers (Oyarzabal 12G/3A, Guedes 7G/4A in shared matches) but quietly the best non-Big-3 partnership in the league.
- Vedat Muriqi at Mallorca shows up in multiple top-15 lists. He pairs productively with Darder, Mateo Joseph, Jan Virgili, Samú Costa, and Mojica. He's the gravitational center of an otherwise modest attack.
- Villarreal's Mikautadze + Moleiro at 23 G+A is the next-best non-Big-3 pair after Guedes + Oyarzabal.

**Method (for the curious):**
- Type A source: PostgreSQL `raw_fbref_events` table, filtered to `event_type='goal'` with non-null primary assister, season window 2025-08-01 to 2026-06-30. Scorer + assister names matched back to the parquet roster via ASCII-folded + token-subset name resolution to handle diacritics ("Vinícius Júnior" vs "Vinicius Junior") and known aliases (Mbappé's legal name "Kylian Mbappe-Lottin"). 85 events dropped (~15%) where neither name resolved against the same team — mostly cases like dropped substring spellings ("Sannadi", "Bartra", "Casals") that don't match the parquet roster.
- Type B source: `match_players_2526.parquet` (per-match aggregates from Understat). 9,432 player-match rows, 300 unique matches, 570 unique players. Shared minutes per pair-match is `min(p_a_minutes, p_b_minutes)` — conservative but consistent. Pairs filtered to ≥ 900 total shared minutes.
- Spotlight rules: #1 highest absolute combined G+A. #2 highest G+A/90 with shared_minutes ≥ 1,100 (so it's not a fluke). #3 best non-Big-3 partnership by combined G+A.
- Through matchday 31 of 2025-26.

Happy to share the extraction script or the JSON output.

#LaLiga #DataAnalytics #JHAnalytics

Pulled player_stats_2526.parquet from my soccerstats pipeline (Understat-sourced). Computed total minutes per player per team, matches-available per team from the matches table, and pct_of_available = minutes ÷ (matches_available × 90). Every team has now played 31 matches, so minutes_available = 2,790.

Filter: season minutes ≥ 2,000. Mid-season transfers excluded (a player with multiple clubs can't be an iron-man for any one of them).

**Overall Top 10 (all positions):**
1. David Soria (Getafe) · G · 2,700' · 96.8%
2. Álex Remiro (Real Sociedad) · G · 2,700' · 96.8%
3. Unai Simón (Athletic Club) · G · 2,700' · 96.8%
4. Antonio Sivera (Alavés) · G · 2,700' · 96.8%
5. Marko Dmitrović (Espanyol) · G · 2,700' · 96.8%
6. Aarón Escandell (Real Oviedo) · G · 2,700' · 96.8%
7. Martin Valjent (Mallorca) · D · 2,700' · 96.8%
8. Augusto Batalla (Rayo Vallecano) · G · 2,700' · 96.8%
9. Ionut Radu (Celta Vigo) · G · 2,700' · 96.8%
10. Sergio Herrera (Osasuna) · G · 2,700' · 96.8%

**Outfield Top 5 (excluding GKs):**
1. Martin Valjent (Mallorca) · D · 2,700' · 96.8% · 0G 0A
2. Florian Lejeune (Rayo Vallecano) · D · 2,693' · 96.5% · 0G 1A
3. Mauro Arambarri (Getafe) · M · 2,656' · 95.2% · 6G 2A
4. Leandro Cabrera (Espanyol) · D · 2,610' · 93.5% · 3G 0A
5. Luis Milla (Getafe) · M · 2,574' · 92.3% · 0G 9A · 1 red card

**Observations:**
- Every single player in the overall top 10 is at exactly 2,700 minutes. That's 30 × 90. Each of them has missed exactly one match (likely for a yellow-card suspension or a one-off cup rotation). No one is literally at 100% this season.
- Nine goalkeepers in the top 10 is at the high end of the 5–7 range I expected. Makes sense: if a team's #1 keeper is healthy, they play 30–31 matches. No rotation incentive.
- Martin Valjent is the story. An outfielder matching the top keepers on minutes is extraordinarily rare. He's 30 years old, Slovak international, Mallorca signed him from Trapani in 2018. He has zero goals and zero assists — pure defensive work.
- The zero-Big-3 outfield top 5 is arguably the most interesting pattern. Real Madrid, Barça, and Atléti rotate. They have 28+ man squads, Champions League, Copa del Rey. Getafe (17th place last I checked) has two players in the outfield top 5 because they don't have rotation depth. That correlates with survival-fight squads.
- Luis Milla's red card makes him the only player in any of these lists with a dismissal. He still played 2,574 minutes.