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Oracle's Richmond Alake proposes an intelligence plane for agent knowledge, memory, data access and learning.

Cole Medin examines a study of AI-influenced expert decisions and suggests ways to keep judgment in the planning process.

Bumble's Supriya Subudhi shares five lessons from testing AI photo feedback: adoption, bad outputs, costs, model choice and regional rollout.

imec benchmarks coding agents across models, harnesses and hardware, finding that utilization and concurrency can change the economics of self-hosting.

Upstart and Coval describe human review, AI judges and voice simulations for a regulated loan-verification agent.

Oracle's Wojciech Pluta demonstrates a memory-promotion gate that retains an operator's preferences while rejecting an unsafe blanket lending rule.

Matt Wolfe reviews Anthropic's proposed IPO, model releases, a hands-on Genie 4 demo, and an AI-generated complexity-theory claim.

Cloudflare security reviews, Claude SEO, design skills, T3 Code, and a shared hosting layer for open-source apps.

Killian Carlsen-Phelan shows Sonar Vortex supplying project context before an agent writes code, then rejecting a temp-file permission fix that still fails the rule.

A timestamped deep dive through a three-hour course: Gmail intake, typed model decisions, Python policy, evals, Docker deployment, and the failure cases still to test.

IBM's Mixture of Experts connects AI-assisted security research, Block's speech-only Dessa preview, culturally specific evaluation and the data infrastructure agents need.

Jack Woodruff argues that faster research raises the baseline for investment work, while auditability and deciding what deserves attention remain human bottlenecks.

Rayan Krishnan shows how task-specific tests can reverse model rankings, expose hidden token costs, and turn a firm's past work into a buying guide.

Sonar’s Gitar workshop checks three ticketing-app changes: a refund bug outside the diff, an all-in pricing rule, and a failing check-in test.

Sonar CEO Tariq Shaukat argues for explicit intent, guidance, independent verification, and evidence as coding agents outpace human review.

IBM’s Grace Ableidinger explains lower-precision model storage, the memory arithmetic, and the tradeoffs between post-training quantization and quantization-aware training.

David Abitbol reports Revolut’s experience serving 2,000 engineers with GLM models: lower token bills, faster delivery, and substantial GPU-serving work.

Temporal’s Shy Ruparel separates agent adoption from production reliability, then shows how recorded workflow history can preserve work across worker crashes.

Qodo’s Cole Gannaway explains cross-repository review, independent review agents, and turning recurring review comments into shared rules.

Mark Hay explains incremental data analysis, shared metric definitions, and customer-led testing. A contested live dashboard shows why review still matters.

Cole Medin audits context, connections, capabilities, and cadence: small core files, restricted tools, reusable skills, regression tasks, and trusted schedules.

Addy Osmani and Manish Kapur discuss first-pass agent review, browser evidence, layered checks, and the human ownership behind larger code-change volumes.

Vik Paruchuri explains how document benchmarks reward formatting, how to test the scorer, and how to build verifiable checks around your own documents.

Peter Yang's early-access demo generates five scenes with sound and live edits. Failed actions, disappearing objects, and weak gameplay keep the limits visible.

Nate Herk turns reference videos into editing skills, stages a simulated advisory debate, and automates Instagram production while retaining human feedback.

Sonar's Hunter Agent maps a repository and challenges logic-bug findings. A prepared Python demo shows 46 issues, without a detection-accuracy benchmark.

IBM's Security Intelligence panel connects AI-assisted vulnerability discovery with patch readiness and debates outside access to frontier-model evaluations.

Chris Vela demonstrates BenchPod, a vLLM benchmarking dashboard, and compares serving configurations against concurrency and response-speed needs.

Kamil Potrec explains agent identities, delegated permissions and credential gateways, including the network controls and operational costs they require.

Patrick Smyth separates malicious packages, known vulnerabilities, zero days and compromised build tools, then maps a different defense to each.

Fuad Ali explains Alex's production design: harness-owned plans, explicit completion, file-backed results, small auditable memory and regression tests drawn from real sessions.

Mikyo King explains Phoenix's code-mode design: discover tools, compose them in code, and return smaller results, while handling secrets, permissions and failed executions.

Andrew Warner rounds up tools for scheduling, voice, video editing and recording, with practical limits on hosting and automated edits.

John Feminella connects Citibank’s mistaken Revlon payment to five agent controls: ownership records, risk-based review, scoped tools, shared tests and selective approvals.

Jeff Crume explains why AI-assisted vulnerability discovery needs maintainer review, tested backports, coordinated disclosure and a safe deployment path.

Tim Tyler's Agent Accountability framework separates agent identity, human ownership, task scope, permissions, outcome checks and intervention.

Runlayer's Aiden Sochowski connects agent session logs to narrower permissions, prompt-injection defenses, broken-tool detection and spending controls.

Optiver's Noyan Tokgozoglu explains internal model tests, portable agent tooling, context-cost controls and the limits of replaying live workflows.

Read traces, group recurring failures, save regression cases and compare a proposed fix against them. Fuad Ali demonstrates the loop with Arize Signal and keeps humans responsible for judging issues, tests and releases.

Put realistic data in fresh sandboxes, check what the agent changes and compare runs task by task. Xander Song shows how Phoenix tests its agents with Harbor, including a SQL-tool optimization.

Blair Metcalf demonstrates Reddit Proto: guided AI prototyping with real data, component-aware edits and a reviewed Figma-to-code handoff.

Cedric Clyburn explains prompts, context, agent runtimes, feedback loops and software factories—and why solo developers can start much smaller.

Target real incident patterns, test against historical pull requests and supply codebase context. Greptile's Daksh Gupta explains model diversity, executable sandboxes and the cost of noisy automated reviews.

Separate conversation history from a shared cloud workspace, then hand files between specialized agents. Jack Wotherspoon demonstrates Gemini Managed Agents, credential proxies and scheduled workflows.

Measure the silence callers hear, keep listening while tools run and test the complete conversation. Jesse Hall shows how LiveKit evaluates voice agents against interruptions and real back-end outcomes.

Start with real traces, define one concrete pass/fail criterion, build a model-based judge and compare its decisions with human labels. SallyAnn DeLucia walks through the process in Arize.

Arize's Aparna Dhinakaran explains the work behind always-on agents: trace-driven triggers, disposable sandboxes, durable state and evaluators checked against human judgment.

Harnesses make one agent more reliable, loops start and repeat the work, and graphs coordinate it. Maddy Zhang explains when each layer earns its cost.

Bridgewater and turbopuffer explain why document permissions must shape the search itself, balancing relevant results with fast access checks.

Jake Moran demonstrates an AI editing workflow built around accurate timestamps, storyboards, brand references and feedback drawn directly on video frames.