Oct 08, 2026

How Cline Embraces an Open-Weights Model Ecosystem for AI Agents

Cline's CEO explains why open-weight models are rapidly taking over coding-agent workloads, and why the agent harness, not raw model capability, determines reliability. The talk covers model-specific tuning and failure handling, context and token optimization, self-improving harness loops, an SDK-first and UI-agnostic architecture, interactive evaluation built from real-world usage data, and production inference challenges such as KV cache hit rates.

Oct 07, 2026

AI Hackers Are Faster Than Your Pen Test — Eli Cohen, Snyk

Eli Cohen of Snyk argues that AI-generated code and AI-powered attackers have broken the traditional security model. He calls for continuous offensive security built on AI pentesting on every code change, agent red teaming, and a multi-agent system that validates exploits. He compares SAST, DAST, and human pentesting, and closes with four questions to ask any AI pentesting vendor.

Oct 06, 2026

Harness Engineering: How to Build a Software Factory — Dru Knox, Tessl

Dru Knox of Tessl discusses harness engineering, the discipline for building software factories with AI agents. He outlines a factory's three pillars—autonomy, automation, quality—and introduces inner, outer, and meta loops. Knox explains why this field is challenging and proposes a three-layer architecture: control plane, agent-ready stack, and improvement loops, concluding with key metrics to track progress towards an agent-driven software development future.

Oct 05, 2026

Agentic Search vs Vector Search for Coding Agents: We Ran the Eval — Braintrust

This talk by Jess Wang from Braintrust delves into the critical importance of evaluations (evals) in AI development, moving beyond subjective "vibes" to data-driven decision-making. She breaks down the four core components of an eval—dataset, task, scoring, and experiments—and illustrates the continuous improvement "eval flywheel" integrating observability. The central experiment compares agentic search against vector search for a coding agent tasked with finding buggy code in Microsoft's TypeScript Go repo. While both methods achieved identical accuracy, vector search proved four times more expensive due to its inability to capture surrounding context within code chunks, leading to excessive, inefficient searches. Wang concludes with practical caveats and best practices for conducting effective LLM evals.

Oct 02, 2026

The 5 Levels of Self-Driving Production — Eric Schwartz, Traversal

Eric Schwartz, a product manager at Traversal, argues that AI-generated code has moved engineering time from building to troubleshooting, and that observability dashboards show what broke but not why. He frames root cause analysis as a causal problem, lays out five levels of self-driving production, walks through alert intelligence at PepsiCo and incident response at American Express, and offers five questions for evaluating an AI SRE.

Oct 02, 2026

Your Coding Agent Is 6 Months Out of Date — Jakub Hojsan, Exa

Jakub Hojsan, a forward deployed engineer at Exa, explains how search built for coding and code-review agents closes the gap left by model knowledge cutoffs. He covers why agents need explicit rules for when to search, how query-dependent highlights cut a 100,000-character page to about 500 characters without added latency, and how Exa compares with native web search on transparency, cost, and model-provider independence, with a preview of Exa Agent.