VQV Signal

AI AT WORK WATCH TECHNICAL

ODSC AI West day two streams free coding agent sessions online

According to a post on X by @_odsc, ODSC streamed its day two sessions free online, with a focus on coding agents. The post says Marius Hobbhahn gave a keynote on observability and control for coding agents, followed by Cline's Ara Khan on recursive self-improvement, which it describes as an agent that fixed its own harness. Google's Joe Malone also took the keynote stage. The snippet does not include further detail on what was discussed in the talks.

Source: Scobleizer News Pulse (X) · x.com Found via Robert Scoble’s Scobleizer News Pulse · a post on X, not independently verified Published 2026-10-28T18:35:00+00:00 Detected 2026-10-28T21:24:18+00:00
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According to a post on X by @_odsc, ODSC streamed its day two sessions free online, with a focus on coding agents. The post says Marius Hobbhahn gave a keynote on observability and control for coding agents, followed by Cline's Ara Khan on recursive self-improvement, which it describes as an agent that fixed its own harness. Google's Joe Malone also took the keynote stage. The snippet does not include further detail on what was discussed in the talks.

AI-assisted summary based on the listed source.

ODSC streamed Marius Hobbhahn's keynote on observability and control for coding agents free online, followed by Cline's Ara Khan on recursive self-improvement, an agent that fixed its own harness. Google's Joe Malone also took the keynote stage. Also: https://x.com/_odsc/status/2115496316939718700 · <a...

The free streams give builders and teams a way to hear directly about observability and control for coding agents, plus an example of an agent that fixed its own harness. Anyone tracking AI coding tools can use the sessions to compare how these speakers describe agent oversight and self-improvement. The information comes from a conference account's post, so the claims made in the talks have not been checked by VQV.

Teams using AI at work may want to compare this against current productivity and review workflows.

Signal Strength 83% Technical label WATCH Public Interest 39 Category AI AT WORK 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 51 Practical Impact Score 8 Novelty Interest Score 70 Consequence Score 22 Curiosity Score 16 Shareability Score 53

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