Labor — Lavi Sahu

07Labor — Bautagebuch

Ich baue diese Seite so, wie ich es erwarten würde, dass ein KI-Anbieter ein Planungssystem baut: Zeig den Diff, nicht den Pitch.

Labor — Open Source

Acht Repos, kein Mockup

Öffentlicher, funktionierender Code, keine Portfolio-Folie: Supply-Chain-Planning-Tools, ein KI-Konfigurationssystem, eine Offline-Sprachdiktier-App und das Designsystem, aus dem diese Seite selbst gebaut ist. Klick auf eine Karte, um zu sehen, was sie wirklich tut.

Explore GitHub @LaviSahu

Resilience Radar — Python (stdlib only) — self-contained HTML dashboard

View on GitHub →

Supply chain resilience control tower — risk radar, what-if simulation (time-to-recover / time-to-survive), and mitigation ranking. No dependencies to install; the dashboard is one self-contained HTML file.

git clone https://github.com/LaviSahu/resilience-radar.git

What the code actually does

# risk_score = likelihood(1-5) * impact(1-5), range 1-25.
TIER_THRESHOLDS: list[tuple[int, RiskTier]] = [
    (20, RiskTier.CRITICAL),
    (12, RiskTier.HIGH),
    (6, RiskTier.MEDIUM),
    (1, RiskTier.LOW),
]

def likelihood_score(event: Event, classification: Classification) -> int:
    """1-5, from event confidence (0-1) plus a source-reliability adjustment."""
    base = event.confidence * 5
    adjustment = SOURCE_RELIABILITY_ADJUSTMENT.get(classification.source_category, 0)
    return _clamp(base + adjustment, 1, 5)

src/resilience_radar/scoring.py — classic likelihood × impact risk scoring, plus a per-source reliability adjustment (geopolitical/regulatory signals score noisier than, say, a supplier or logistics event).

Labor — Bautagebuch

Verification ledger — what's above, and how each piece was actually checked

Seen by human eyes

Every animation on the rebuilt pages

The merge-conflict resolutions of 2026-09-19

Read, not run

The twin's system prompt

Each repo description (2 of 8 carry verbatim code excerpts)

Nobody has checked

Everything the twin tells you

Whether anyone has ever used these repos

The third column is the honest one. It empties only when the gap does — not before.

Labor — Bautagebuch

Verlass dich nicht auf mein Wort — beweg die Stellschrauben.

Zwei Live-Tools, die zeigen, wie Planungs-Trade-offs sich wirklich verhalten. Keine Folien, kein Handwaving.

Labor — Bautagebuch

Ein echtes KI-Feature, kein Mockup

Das ist kein eingebetteter Chatbot. Es ist eine Cloudflare Pages Function, die Workers AI direkt aufruft — an diese Domain gebunden, pro IP rate-limitiert, mit einem Systemprompt, der ihr sagt, sie soll „Ich weiß es nicht“ sagen, statt einen Kundennamen oder eine Zahl zu erfinden. Frag sie etwas zu S&OP oder Bestandsplanung.

Frag meinen KI-Zwilling

Beta

Eine Demo: ein Sprachmodell mit einem kurzen Prompt in meiner Stimme. Es kann überzeugend falsch liegen — handle nicht nach dem, was es sagt. Frag es zu S&OP, IBP, o9, Beständen.

Frag alles zur Planung. Der Zwilling antwortet in meiner Stimme.

Twin self-grade — Tone: close. Facts: unverified by anyone, including me. Model: @cf/meta/llama-3.3-70b-instruct-fp8-fast, capped at 512 tokens, prompted to say "I don't know" rather than invent a client name or a number. A guardrail, not a guarantee.

KI-generiert · kein Ersatz für den echten Lavi

Labor — BautagebuchGemerged, nicht geshipped.

Bautagebuch — 2026-09-19

Labor — Bautagebuchrefkit

Refkit — 28 ports, 1 in production

Every card below is a real VengeanceUI port — working code, not a screenshot. Exactly one of them, VeCreepy, is actually load-bearing right now: it's the Connect link in this site's own footer. The other twenty-seven are verified to render, not yet asked to carry a real page's weight. Same rule as the build log above — a component only counts as shipped once it's doing a job, not once it looks good in a gallery.

Lab — essays

Five frameworks, worked through properly

Not case studies from my own client work — original, research-driven essays on real named frameworks and formulas (bullwhip ratio, TTR/TTS resilience, the Kraljic matrix, Theory of Constraints, digital twins), each grounded in a cited real source, not invented. Pick a card for the summary, or read the full essay.

For the fuller story behind the bullwhip math — two 2000s-era factory fires and what they cost — see Buffer Shock, on the Blog →

# risk_score = likelihood(1-5) * impact(1-5), range 1-25. TIER_THRESHOLDS: list[tuple[int, RiskTier]] = [ (20, RiskTier.CRITICAL), (12, RiskTier.HIGH), (6, RiskTier.MEDIUM), (1, RiskTier.LOW), ] def likelihood_score(event: Event, classification: Classification) -> int: """1-5, from event confidence (0-1) plus a source-reliability adjustment.""" base = event.confidence * 5 adjustment = SOURCE_RELIABILITY_ADJUSTMENT.get(classification.source_category, 0) return _clamp(base + adjustment, 1, 5)

Ein echtes KI-Feature, kein Mockup

Refkit — 28 ports, 1 in production

Five frameworks, worked through properly