with-agents

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video CodingAI

Andrew Ng: The Biggest Opportunities in AI Aren't Where You Think

Silicon Valley Girl interviews Andrew Ng, who argues that AI might automate 30–40% of tasks in many jobs rather than whole jobs, making the remaining human work more valuable. He describes how AI broadens professional roles, warns that common usage patterns hurt learning, and hires marketers by asking what software they have built. For his own most sensitive work, he avoids AI or uses a local model. His advice to builders: learn AI, build fast, and talk to customers to decide what to build. This is an opinion interview with unnamed study references, not an evaluation.

Silicon Valley Girl · Source dated 28 Aug 2026

video Coding

How to Stop Building Products Nobody Wants

Teresa Torres explains how story-based customer interviews, assumption tests, and opportunity solution trees help teams decide what to build. With Hamel Husain, she connects discovery to AI evaluation through her interview coach and synthesis tools: domain-specific error analysis, traceable tree changes, and corrective agent loops. A practitioner account of product discovery and AI reliability, not a benchmark of model capability.

Hamel Husain · Source dated 2 Sept 2026

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video CodingAI

AI made me doubt everything about programming by Felienne Hermans - DDD Europe 2026

Computer science professor and Hedy creator Felienne Hermans argues that programming culture rewards difficult technical work over accessibility and social purpose. Moving from localized numerals to feminist epistemology, computing history, chess-derived AI benchmarks, and Peter Naur’s theory-building account of programming, she asks developers to treat LLM use as a choice rather than an inevitability and to focus on problems worth solving. This is an argumentative keynote, not an empirical evaluation; its broad claims about programmers, AI research, and machine understanding are illustrative rather than systematically demonstrated.

Domain-Driven Design Europe · Source dated 30 Aug 2026

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video Coding

Stop Writing Prompts. Start Writing Specs.

Roelant Dieben demonstrates GitHub Spec Kit with GitHub Copilot: store non-negotiable rules in a constitution, capture intent in a specification, move technical choices into a plan, generate ordered tasks, then implement an HTTP MCP server. The demo relies on prepared branches for most stages and shows one MCP Inspector result; it offers no prompt-first baseline, test output, or comparative quality, time, or cost evidence.

Microsoft Developer · Source dated 24 Aug 2026

video Coding

Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber

Uber engineers Uday Kiran Medisetty and Adam Huda present the shared building blocks of a managed software factory: model and MCP gateways, pre-provisioned DevPods, a skills marketplace, a context graph, and the Cortana assistant. A staged walkthrough runs from product exploration through agent implementation, self-healing CI, and evidence-rich review. The adoption, token-saving, and quality figures are first-party reports without baselines or an independent security assessment; the speakers say the components are at different stages of rollout.

AI Engineer · Source dated 21 Aug 2026

video Coding

I can't sleep gud anymore - A Practical Guide to Agentic Computering

Mario Zechner demonstrates a control-heavy Claude Code workflow for established codebases: reviewed project docs, pre-compaction task summaries, jq-queried symbol indexes, persisted porting plans, compilation, and human checkpoints. His report that this cut a 68-file Spine runtime port from about two weeks to one day is an anecdote without logs or a baseline. The July 2025 Claude Code internals and pricing are historical, and his permission-bypass flag is not a security recommendation.

Mario Zechner · Source dated 1 Jul 2025

podcast Coding

High Leverage - Ep. #9, The AI Coding Paradigm Shift with Simon Willison

Joe Ruscio interviews Simon Willison about adapting software practice when coding agents make implementation cheap. Willison proposes concentrating design in a stable data model and API beneath vibe-coded interfaces, scaling human review with the consequence of the code, and treating sustained real-world use as stronger trust evidence than generated tests or docs. His reliability, speed, cost, and market claims are practitioner reports, not controlled comparisons.

Heavybit · Source dated 5 May 2026

video Coding

Google's Open Knowledge Format: Just Markdown for AI Agents

Prism Labs explains Google Cloud’s v0.1 Open Knowledge Format proposal: concepts are Markdown files with minimal YAML metadata, ordinary links between them form a graph, and optional index and log files add navigation and history. Google supplies a BigQuery enrichment agent, a graph visualizer, sample bundles, and Knowledge Catalog ingestion as first-party proofs of concept. The recording shows no independent cross-vendor interchange or retrieval-quality evidence, and it misstates which fields are required.

Prism Labs · Source dated 14 Jun 2026

video Coding

How Anthropic uses Claude Code: Agentic Software Engineering at Scale - Daisy Hollman

Claude Code engineer Daisy Hollman explains a context-budgeted approach to agentic engineering beyond one repository: audit external work systems, treat context and prefix-cache costs as architecture constraints, and prefer lazy or event-driven context. She compares MCP tools, skills, subagents, and hooks, then shows her worktree, messaging, and scheduled-wake-up setup. The cache, cost, and throughput claims are presenter or vendor reports without comparative methods; several roadmap details are tentative.

NDC Conferences · Source dated 11 Aug 2026

video Coding

The Last Human Code Review: Building Trust in AI-Generated Code — Itamar Friedman, Qodo

Qodo CEO Itamar Friedman proposes a gradual path from human-reviewed pull requests to automated approval: cite the team’s own rules in each review, watch where humans still intervene, then add approval policies step by step. A proposed context engine attaches rules, service contracts, and incident history to a software graph. This is a vendor proposal, not an evaluation: review accuracy and defect outcomes are unmeasured, and rollback is unspecified.

AI Engineer · Source dated 20 Aug 2026

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podcast Coding

DHH: Future of Programming, AI, Agentic Engineering, Vibe Coding & Linux

Lex Fridman interviews David Heinemeier Hansson about moving from handwritten code to agent-driven development. DHH reports that agents wrote all new functionality in Omarchy Quattro, while a Basecamp experiment showed that individually plausible generated changes can collectively damage a mature architecture. He recommends describing problems rather than prescribing implementations, preserving system shape, and keeping human judgment on product direction and merges. His speedups, costs, model rankings, and labor forecasts are practitioner reports, not controlled comparisons.

Lex Fridman · Source dated 26 Aug 2026

video Coding

Don't Build Agents, Build Environments Instead

Modal’s Adam Azzam argues that the hard part of background coding agents is the environment around the harness. He distinguishes bare sandboxes from task-ready dev boxes, explains why agent sessions need persistence and fast resumption rather than CI-style reruns, and recommends versioned per-repository images, secret-injecting proxies, and a separate control plane. The Ramp scale, pull-request share, and startup-time figures are first-party reports in a Modal product talk.

Hamel Husain · Source dated 25 Aug 2026

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podcast Coding

How AI Is Changing Software Development with Simon Willison

Claire Giordano interviews Simon Willison about delegating implementation to coding agents while keeping understanding and accountability for production software. Willison uses explicit success criteria, red-green TDD, small reviewable changes, and aggressive nitpicking, and he discards off-concept generated features to protect conceptual integrity. His speed, cost, security, and labor observations are practitioner reports and forecasts, not controlled measurements.

Talking Postgres · Source dated 14 Aug 2026

Collection additions Coding

Matt Pocock: Agent Skills and Engineering Workflows

A reviewed seven-video route through Matt Pocock’s agent-skills and engineering-workflow uploads from May 25 to August 19, 2026. Six tutorials cover Grill Me interviews, anti-slop review, repository-aware implementation, throwaway prototypes, Wayfinder decision maps, agent-facing writing, deterministic setup, and collaborator questionnaires. In a conversation hosted by Pocock, Uncle Bob Martin argues for deterministic quality gates, specialized agents, and modular architecture. The demonstrations and productivity claims are practitioner evidence, not controlled comparisons.

Matt Pocock

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