A ServiceNow Partner Demonstrates AI-Assisted Code Generation

A short promotional demonstration shows code generated from a natural-language instruction in ServiceNow.

MBMatthew BermanVortrag · Forward Future
Kanal
Matthew Berman
Veröffentlicht
20. Dez. 2023
Video
0:29
Lesezeit
1 Min.
Original
Englisch
Auf YouTube öffnen
Beim Scrollen schrumpft das Video in die rechte untere Ecke und läuft weiter. Untertitel folgen Ihrer Lesesprache.
LesespracheIn 2 Sprachen verfügbar
Diesen Artikel gibt es noch nicht auf Deutsch, daher wird das Original (Englisch) angezeigt.
ZUSAMMENFASSUNG

In 30 Sekunden

In a December 2023 ServiceNow partner video, Matthew Berman demonstrates generating code from a plain-language request. He suggests the feature could help developers and beginners, but the short clip does not test the resulting code or quantify productivity gains.

  1. 1Natural language to codeBerman demonstrates entering a request and receiving code in ServiceNow.
  2. 2The generated exampleThe editor displays generated code, but the clip does not show a test of correctness.
  3. 3Productivity claimBerman suggests the feature can help teams and learners; no measured comparison is provided.

Das Wichtigste

  1. The clip is a ServiceNow partner promotion.
  2. The demonstration turns a natural-language instruction into code in an editor.
  3. Claims about increased developer output and faster learning are promotional, not measured in this video.
VOLLTEXT

Den bearbeiteten Text nach Kapiteln lesen

Nur das Transkript
01

The code-generation demo

Berman shows a natural-language request producing code in the editor. The clip identifies him as a ServiceNow partner.

In this ServiceNow partner demonstration, I show how an engineer can describe what they want in natural language and generate code in the editor. The example displays a script after the request, but the clip does not show it being run or independently verify its correctness.

02

Suggested benefits

He promotes the feature for developer output and learning. The short demonstration does not establish the size of those benefits.

I suggest that ServiceNow’s AI feature can increase developer output and help beginners get up to speed. These are promotional claims; the video does not include a controlled comparison or measurements of productivity or learning outcomes.

Mehr aus diesem Kanal

12 Folien34:21EN
Matthew Berman· 27. Okt. 2026Matthew Berman Examines Anthropic’s IPO Filing, AI Growth and GovernanceMatthew Berman, Sam Altman · Forward FutureThe presenter reviews the S-1’s financial metrics, compute strategy and voting structure while questioning the tension between rapid AI development and safety.AnthropicAI SafetyEnterprise-KI+2
0:26EN
Matthew Berman· 25. Okt. 2026Sam Altman on Human Values and Problem-Solving in an AI WorldSam Altman · OpenAIIn a short interview excerpt, Altman considers the enjoyment of solving hard problems and the human role in interpreting results.Human-AI CollaborationFuture of WorkAI Strategy
2 Folien0:36EN
Matthew Berman· 25. Okt. 2026Kimi K3.1 and a Rubik’s Cube Solver DemonstrationMatthew Berman · Forward FutureMatthew Berman shows a scrambled virtual cube being solved and gives the Kimi K3.1 test a favorable assessment.EvalsCoding-AgentenOpen-weight models
2 Folien0:21EN
Matthew Berman· 24. Okt. 2026An AI-Generated Kimi Benchmark Chart Used Invented ScoresMatthew Berman · Forward FutureMatthew Berman asks for a graph of Kimi K3.1 Pro benchmarks, then discovers that the displayed figures are illustrative placeholders.BenchmarksEvalsGenerative media
6 Folien25:50EN
Matthew Berman· 24. Okt. 2026Kimi K3.1 Pro Tested: Strong 3D Demos, Uneven Results and Competitive PricingMatthew Berman · Forward FutureMatthew Berman tests the open-weight model on coding, 3D rendering and visual explanations, then compares benchmark results and prices.Open-weight modelsBenchmarksAI cost & performance+1
2 Folien32:20EN
Matthew Berman· 23. Okt. 2026Who Is Local AI For? The Costs, Trade-offs and a Hybrid FutureMatthew Berman · Forward FutureMatthew Berman weighs data control and independence against hardware cost, setup complexity and inference speed.Local inferenceOpen-weight modelsAI cost & performance+1