Kjell Tore Guttormsen
885526738c
Del 1 — scaffold-bånd (KTG-beslutning: behold målbåndet, utvid komponentene). Komponentsummen var 960–1 640 mot målbåndet 1 200–1 800: en skjelett-konform draft kunne lande under gulvet og nådde aldri taket. Nye bånd — Context 250–350, Insight 550–850, Implication 250–350 (Hook 110–140 og CTA 50–100 uendret) — summerer til 1 210–1 790, altså INNI 1 200–1 800. Skjelett-konform er nå gate-konform per konstruksjon. Målbåndet 1 200–1 800 er uendret overalt; hooks/prompts/content-quality-gate.md (den kanoniske gaten) er ikke rørt. Alternativet — å heve taket per AuthoredUp D-4 — ville krevd 25 filer / 40 linjer inkl. gate-prompten, begge skills, quality-scorecard, config-malen og brain-fiksturen. Skjelettet fantes i seks kopier, alle rettet på KTG-go: commands/post.md, commands/batch.md, commands/pipeline.md, references/engagement-frameworks.md, agents/content-optimizer.md, skills/linkedin-content-creation/SKILL.md. De tre siste lå utenfor planens scope, men to av dem var internt selvmotsigende på én linje (overskrift 1 200–1 800 over komponenter som summerte til 960–1 640), og engagement-frameworks.md er nettopp fila post.md/pipeline.md instruerer modellen om å LESE for strukturen. Nabofunn tatt med på KTG-go: post.md:95 ga «Personal stories → 1 000–1 400», som motsa post.md:136s egen gate. Kald-review R2a MAJOR — nå 1 200–1 800. D-4 inn i kanonfila: ny «Post length»-seksjon i references/algorithm-signals-reference.md med AuthoredUp-optimumet 1 301–2 500 (372 126 poster, sep 2025–feb 2026), merket single-vendor/ett vindu, med eksplisitt note om at datapunktet gjør det shippede taket konservativt — ikke feil — og at de to AuthoredUp-N-ene i fila (621K vs 372K) er ulike studier. Kilde verifisert mot primærkilden, ikke overført fra planen. Del 2 — ferskhet-rester (begge påstander verifisert mot LinkedIn Help): - D-6 newsletter-strategy-guide.md: «5 000+ følgere» framstilt som terskel er feil — «All LinkedIn members have access to create a newsletter on LinkedIn» (a517914). Omskrevet til redaksjonell modenhetsvurdering (også i Mistakes-tabellen og Bottom Line). E-post er ikke garantert: LinkedIn de-dupliserer på tvers av kanaler — «if you receive an in-app or push notification, you should not expect to also receive an email for the same notification» (a517914). Bringer referansefila i tråd med newsletter.md:2384. Fjernet samtidig den ukildede «Algorithm favors newsletters from established creators» i den omskrevne blokka. - D-7 first-comment-strategy.md: «pinned by default» er uverifisert og feil — pinning er en eksplisitt forfatterhandling (a524166), og standard kommentarsortering er algoritmisk. Lagt til «What this file does not claim»- avsnitt som speiler kanonfilas «contested»/low-confidence-epistemikk. Scope 3 (KTG-go, amend): references/engagement-frameworks.md har FIRE skjeletter, ikke ett. To til brot gulvet i malbandet — Data-Driven Post (1 050-1 400) og Contrarian Post (1 060-1 410) — og er lagt om til samme komponentprofil som Standard, sum 1 210-1 790. Narrative Arc (1 350-1 500) la allerede inni og star urort; alle tre har na en eksplisitt sum-linje. Fila er den post.md:104 sender modellen til for «story structures», sa a sertifisere den som fikset med to odelagte skjeletter igjen ville vaert usant. SUPERSEDED og ikke gjeninnført: gammel B §S6 Del 2 pkt 1 (first-comment −5/−10 %-tall, pods-eskalering, 360Brew-fotnote). Verifisering: bånd-summen ligger inni målbåndet i alle seks kopier av standard-skjelettet og i alle fire skjelettene i engagement-frameworks.md (grep-bevis, 0 gjenværende 200-300/400-800) · D-6/D-7 omformulert (0 treff på «5,000+ followers» / «pinned by default» / «inbox + email») · alle ti suiter grønne, alle floors uendret: test-runner 270/0 (269 assertions >= floor 251) · trends 300/0 · analytics 202/0 · hooks 191/0 · brain 134/0 · editions 72/0 · render 63/0 · specifics-bank 45/0 · tests 35/0 · contract-gate 33/0. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LhF1H7ctT5Fk8KkoCQpe5n
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LinkedIn Algorithm Signals Reference (2026)
Single source of truth for what the 2026 LinkedIn feed-ranking system rewards. Every other file in this plugin cites this one — do not restate magnitudes elsewhere, link here instead.
How to read this file
The 2026 feed is ranked by an LLM-based relevance system (live in 2026; LinkedIn has no publicly verifiable production name or go-live date — see the model note below). Almost every "coefficient" circulating in the creator community is third-party, observational, and moves year-to-year. So this reference encodes ordering + the two officially-named signals + directional magnitudes with a source and a confidence per claim — never hard coefficients to optimize against.
- Confidence: high = officially confirmed by LinkedIn, or convergent across multiple large-N studies.
- Confidence: medium = single credible large-N source, or convergent direction with a contested magnitude.
- Confidence: low / directional = practitioner heuristic, no primary source. Treat as a hypothesis to test on your own account, not a fact.
Rule of thumb: trust the ordering, test the number.
Officially-named ranking signals (the only two LinkedIn confirms by name)
| Signal | Direction | Source | Confidence |
|---|---|---|---|
| Dwell time | Time spent on a post is a ranking input (active vs passive tasks; long-dwell modeled). No public weight. | LinkedIn Eng — "Leveraging Dwell Time" (2024) | high |
| Topic / interest relevance | Content matched to a viewer's interests is distributed — including beyond your network for strong content. | Tim Jurka, Head of Feed AI (2025-08-11) | high |
Everything below this line is direction + sourced estimate, not officially-weighted.
Engagement order (not coefficients)
The defensible spine is the order, not the multiplier:
saves > shares > quality comments > reactions/likes
| Signal | Direction / estimate | Source | Confidence |
|---|---|---|---|
| Saves | Top engagement signal; also a follow-graph signal (saving a post raises the author's next-post feed odds). ≈ 5x a like / ≈ 2x a comment in single-vendor data. | AuthoredUp, Vertebrae, van der Blom (1.8M) | medium |
| Shares (feed + DM) | Strong distribution signal; public endorsement. | van der Blom (1.8M) | medium |
| Quality comments (15+ words) | Substantive comments outweigh short ones; comment ≈ 2x a like (quality-scored, single vendor). The popular "comment = many-x a like" claim is unverified folklore — dropped. | AuthoredUp (NLP-quality-scored) | medium |
| Reactions / likes | Baseline engagement unit (≈ 1x). | van der Blom (1.8M) | medium |
Note on the old "comment = 15x" / "= 5x" framing: there is no primary source for it. The "5x" was the saves figure mis-assigned to comments. Encode the order above; do not quote a comment multiplier.
Content format
| Format | Direction / estimate | Source | Confidence |
|---|---|---|---|
| Documents / carousels | Top organic format (~7%, Socialinsider, company-page per-impression). "Carousel" = PDF document post (LinkedIn removed native carousels Dec 2023). The 7% / 21.8% / 49.5% spread across studies is a denominator artifact, not disagreement about the winner. | Socialinsider (1.3M), Buffer (2M), Metricool (673K) | high (rank) / medium (number) |
| Native video | #2 format and declining; add captions (most watch muted). No hard aspect-ratio gate — 4:5 / 1:1 preferred, captions are the enforceable spec. | Socialinsider; van der Blom | medium |
| Text-only | Most resilient format; generates the best comment quality. | Buffer, van der Blom | medium |
| Multi-image | Strong, slightly below documents. | Socialinsider | medium |
| Polls | Declining effectiveness; audience research only. | van der Blom | low / directional |
| Link posts (link in body) | Underperform — see external links below. | Ordinal (900K) | medium |
The personal-profile per-post baseline (~2.0–2.6%) is a different denominator from a format benchmark — never present an account baseline as a carousel rate.
Post length (text posts)
| Claim | Statement | Source | Confidence |
|---|---|---|---|
| Engagement optimum | Median engagement peaks in the 1,301–2,500 character band (2.61–2.67%, against 2.10% below 400 chars); 2,501–3,000 falls back slightly. Single vendor, personal profiles, one six-month window — the defensible part is the direction (substance beats soundbite), not the boundaries. | AuthoredUp (372,126 posts, Sep 2025–Feb 2026) | medium |
On the plugin's own 1,200–1,800 standard band: it overlaps the measured optimum from 1,301 up, with its bottom ~100 characters below it, and its ceiling 700 short. Read 1,301–2,500 as evidence that the shipped ceiling is conservative, not wrong — one vendor's medians over one window is not enough to move an SSOT that every gate enforces (
hooks/prompts/content-quality-gate.md). Test a longer post against your own baseline before concluding anything.Two AuthoredUp sample sizes appear in this file — 621K (NLP-quality-scored engagement study) and 372K (this length study). Different studies, different windows; not a transcription error.
External links (in post body)
| Claim | Statement | Source | Confidence |
|---|---|---|---|
| Reach effect | Correlational reach reduction (~38% in 2026; contested band ~19–60% across studies). It is a moving number, not a flat tax. | Ordinal (900K, p<0.001); van der Blom; DigitalApplied | medium |
| Intent | LinkedIn denies an intentional penalty (Sr. Director Product, Aug 2025): no penalty "if the post leads with value" — the effect is engagement-driven. | Matt Navarra (relaying LinkedIn) | medium |
| First comment | Neither a magic fix nor a confirmed penalty — contested. Lead with standalone value; native formats are the durable answer. | Ordinal; practitioner blogs (no large-N) | low |
Design rule: value-first matters more than link location. Soften any enforcing hook from a hard "−X% penalty" mechanic to "body links correlate with lower reach — prefer a first comment, but lead with value either way."
Early-engagement window + evergreen resurfacing
| Claim | Statement | Source | Confidence |
|---|---|---|---|
| Golden window | 60–90 min (90 is the 2026 consensus); the first 15–30 min is the highest-leverage sub-window (~70% of reach decided there). | Buffer; Expandi; van der Blom | high |
| First-hour velocity | Strong early engagement unlocks broader distribution. Directional, not a fixed threshold. | van der Blom | medium |
| Evergreen resurfacing | The relevance model can resurface strong-save / high-dwell posts days-to-weeks later on viewer intent (posts now live 2–3 weeks vs days). No confirmed fixed "24–72h reinjection" rule — it is intent-driven and irregular. | AuthoredUp | medium (direction) / low (timing) |
Profile / topic alignment
| Claim | Statement | Source | Confidence |
|---|---|---|---|
| Topic alignment is a ranking input | Real and officially confirmed (qualitatively): topic/interest relevance drives distribution, including beyond your network. | Tim Jurka (2025-08-11) | high |
| Off-topic reach reduction magnitude | No primary source states a discrete off-topic reach-reduction figure. Treat profile/topic alignment as a real input; do not quote a percentage. | — | n/a (figure removed) |
AI-content down-rank (officially confirmed — justifies the de-AI gate)
| Claim | Statement | Source | Confidence |
|---|---|---|---|
| AI-slop suppression | LinkedIn confirmed an active program suppressing (1) generic AI-written posts/comments, (2) automation tools, (3) attention-bait video. ML models distinguish "original thinking" from "posts lacking substance"; flagged posts are reach-suppressed (reportedly to first-degree), not deleted. | VP & Exec Editor Laura Lorenzetti (2026-05-19) | high |
| Correlational engagement gap | Likely-AI posts saw ~45% less engagement (correlational). | Originality.ai (8,795 posts) | medium |
| Engagement-pod crackdown | Auto-comments demoted out of "Most Relevant", scoped to own network; repeat offenders restricted. | VP Product Gyanda Sachdeva (2026-02-16) | high |
Enforce what LinkedIn named — personal substance, original thinking, concrete specifics, genuine voice — not an unverified SEO "tell-list."
Buzzwords
Buzzword avoidance is editorial guidance for clarity, not a measured reach mechanic. No primary source ties specific words to a reach penalty. Keep the buzzword list (it improves writing); do not justify it as "reduces reach."
| Claim | Source | Confidence |
|---|---|---|
| Specific phrasing reads better than corporate generic | Inc. (editorial) | low / directional |
| A semantic ranker may indirectly favor specific over generic phrasing | inferred | low (not confirmed) |
The deployed ranking model — what we can and cannot say
LinkedIn's feed ranking model has an official name: the Generative Recommender (GR). Announced 2026-03-12 on LinkedIn's engineering blog (Hristo Danchev, Engineering the next generation of LinkedIn's feed): a sequential transformer-based ranker that treats member interaction history as a timeline, paired with a unified LLM-embedding retrieval system. Rollout announced in the same post.
| Claim | Statement | Source | Confidence |
|---|---|---|---|
| Production name | Generative Recommender (GR) — official, primary-source. | LinkedIn engineering blog, 2026-03-12 | high |
| LLM-based retrieval | Confirmed: "a unified retrieval system leveraging advances in LLMs to generate a high-quality representation of our members and content." | Same post | high |
| Deployment | Rollout announced 2026-03-12 ("rolling out a new advanced ranking system"). Full-coverage completion date not stated — do not assert one. | Same post | high (announcement), n/a (completion) |
| "360Brew" as the production name | Still not publishable. The arXiv paper (2501.16450) is a Jan-2025 pre-production research model (V1.0, 150B params, offline parity only), withdrawn 2025-08-23; the "360Brew" label is third-party and has no official confirmation. GR is the official name. | arXiv 2501.16450 | high (on the negative claim) |
Correction note (2026-07-17): this section previously said "No public name. No deployment date." and flagged the Generative Recommender / Hristo Danchev engineering-post citation as likely fabricated. That flag was wrong — and was already wrong at "Last updated 2026-05": the official post had been live since 2026-03-12, two months earlier. The fabrication flag rejected a genuine primary source.
Operational heuristics (directional — test per account)
These are creator-community heuristics with no primary-source weights. Use as starting hypotheses, not targets. Confidence: low / directional for every row.
Engagement velocity (first 90 min)
| Time | Rough target | If well below |
|---|---|---|
| 15 min | a few | check timing / hook |
| 30 min | building | engage in comments |
| 60–90 min | momentum | golden window closing |
Posting time windows (CET / European audience)
| Day | Commonly-cited peak |
|---|---|
| Tue | 8–11 AM (often best overall) |
| Wed | 8 AM, 12 PM |
| Thu | 9 AM–1 PM (extended) |
| Fri | before 3 PM |
| Mon | 7–9 AM |
| Weekend | weaker |
For global audiences: post 8–11 AM local to catch multiple zones.
Quick decision rules
| Situation | Decision |
|---|---|
| Linking? | First comment, lead with value either way |
| Multiple ideas? | Split into separate posts |
| Off your usual topic? | Topic alignment is a real input — stay on-domain or accept lower reach |
| Video or text? | Text for authority, video (captioned, 4:5/1:1) for connection |
| Carousel or text? | Documents for frameworks/guides, text for stories/opinions |
| Comment or like first? | Comment (higher in the engagement order) |
Comment strategy (CEA)
- Compliment — a specific point you appreciated
- Expand — your insight or related experience
- Ask — a question to continue dialogue
Minimum quality: 15+ words, genuine perspective. AI-generated / "Great post!" comments are actively suppressed (see AI-slop down-rank).
2026 reach context
Organic reach declined platform-wide in 2026 — focus on relative performance (your posts vs your own baseline), not absolute numbers. Smaller engaged audiences outperform large passive ones. (Direction: high confidence; exact YoY %: directional, varies by source.)
Last updated: 2026-07-31 (post-length section added). Maintained as the single canonical algorithm statement; cite, do not restate.
Sources (per-claim quality/confidence noted inline): LinkedIn Engineering — "Engineering
the next generation of LinkedIn's feed" (Hristo Danchev, 2026-03-12); arXiv 2501.16450 (pre-production
research paper, withdrawn 2025-08-23); LinkedIn Engineering — "Leveraging Dwell Time" (2024); Tim Jurka, Head of Feed
AI (2025-08-11); Laura Lorenzetti, VP & Exec Editor (2026-05-19); Gyanda Sachdeva, VP
Product (2026-02-16); Matt Navarra relaying LinkedIn Sr. Director Product (Aug 2025);
Ordinal link-penalty study (900K, p<0.001); Socialinsider (1.3M); Buffer (2M+); Metricool
(673K); AuthoredUp (621K, NLP-quality-scored); AuthoredUp post-length study (372,126 posts,
Sep 2025–Feb 2026); van der Blom Algorithm Insights 2025 (1.8M);
Originality.ai (8,795 posts); Inc. (buzzword editorial). Full provenance: research brief
docs/remediation/research/01-linkedin-algorithm-signals.md.