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Data Science Dojo lists SambaNova's Kwasi Ankomah for Nov 18 coding-agent evaluation session

According to a post on X by @DataScienceDojo, Data Science Dojo has named Kwasi Ankomah, lead AI architect at SambaNova Systems, for a free virtual session on Wednesday, November 18 at 11 AM PT. The session is described as building a full evaluation harness for coding agents that treats passing tests as ground truth. Topics listed include measuring pass@k across a task set, evaluating trajectories, adding full tracing, and using SambaEval to pick a model. The post says the event is already listed on the events page.

Source: Scobleizer News Pulse (X) · x.com Found via Robert Scoble’s Scobleizer News Pulse · a post on X, not independently verified Published 2026-11-09T02:45:00+00:00 Detected 2026-11-09T18:20:13+00:00
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According to a post on X by @DataScienceDojo, Data Science Dojo has named Kwasi Ankomah, lead AI architect at SambaNova Systems, for a free virtual session on Wednesday, November 18 at 11 AM PT. The session is described as building a full evaluation harness for coding agents that treats passing tests as ground truth. Topics listed include measuring pass@k across a task set, evaluating trajectories, adding full tracing, and using SambaEval to pick a model. The post says the event is already listed on the events page.

AI-assisted summary based on the listed source.

The free virtual session runs Wed, Nov 18 at 11 AM PT. Kwasi Ankomah, lead AI architect at SambaNova Systems, will build a full evaluation harness that treats passing tests as ground truth, measuring pass@k across a task set, evaluating trajectories, adding full tracing and using SambaEval to pick a model. It's...

Developers and teams evaluating AI coding agents may find the session useful for seeing how test-based measurement and tracing are applied in practice. Attendees would get a concrete method for comparing models rather than relying on general impressions. Watch whether the session shares details of SambaEval or its results, since the post itself gives no performance data.

Signal Strength 81% Technical label WATCH Public Interest 29 Category CREATOR TOOLS Reader Depth TECHNICAL

Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.

Public Interest components
Recognizable Entity Score 0 Practical Impact Score 26 Novelty Interest Score 70 Consequence Score 22 Curiosity Score 16 Shareability Score 46

VQV surfaced this signal because it is recent, relevant to AI Coding Tools, connected to Scobleizer News Pulse (X).