with-agents

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video Coding

Nell'era AI il tuo prodotto non è così facile da copiare come credi

Salvatore Sanfilippo uses an AI-assisted CAD prototype to argue that useful products depend on accumulated design choices, domain experience, and refined interactions—not implementation cost alone. He acknowledges that cheaper implementation can also lower the barrier to copying a successful product. An opinion-led discussion based on Italian auto-generated captions, summarized in English; product ideas and competitive claims are not validated market findings.

Salvatore Sanfilippo · Source dated 25 Sept 2026

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video Coding

Rails World 2026 Opening Keynote - DHH

DHH describes 37signals making handwritten code exceptional, early native HEY apps, a Rust backend, and CLIs that let users bring their own agents. He argues that cheaper implementation changes architecture trade-offs while preserving the web's no-install advantage. Project results and efficiency figures are speaker-reported; claims about productivity, programming careers, and AI abundance are opinions and forecasts rather than controlled findings.

Ruby on Rails · Source dated 23 Sept 2026

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podcast AI

Peter Thiel: The AI Crisis, Europe’s Decline & the Battle for America

Peter Thiel and Mathias Döpfner discuss whether AI can restart broad growth, the risks of stagnation and global slowdown proposals, and Europe's ability to build and scale technology companies. Thiel's political judgments, analogies, and forecasts are attributed arguments, not established causal findings; his Palantir adoption example is a first-party account.

MDMEETS - Essential Talks with Mathias Döpfner · Source dated 24 Sept 2026

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podcast Coding

Peter Naur - Programming as Theory Building

Felienne Hermans and Hanna Schraffenberger discuss Naur's view that programming builds the understanding needed to explain and adapt a solution, not just its code. Hanna argues that parts of this understanding can be documented and shared. Based on the official companion article and Hanna's written homework, not an audio transcript; the AI claims are arguments, not evaluation results.

Computer Science Off Course · Source dated 7 Jul 2026

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article AI

Machines of Loving Grace

Dario Amodei explores a favorable future with powerful AI: which work could accelerate, and where experiments, missing evidence, institutions, adoption, or physical limits would still constrain progress. Its sector outcomes and timelines are speculative first-party forecasts conditional on the assumed arrival of powerful AI.

Dario Amodei · Source dated 11 Oct 2024

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article AI

The Adolescence of Technology

Dario Amodei separates risks from autonomous model behavior, destructive misuse, deliberate power concentration, economic disruption, and indirect effects, then proposes technical and policy responses. Anthropic's CEO assumes powerful AI and offers his own risk framework and policy proposals, not a complete list of risks or a consensus view.

Dario Amodei · Source dated 26 Jan 2026

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podcast CodingAI

A Cheeky Pint with Anthropic CEO Dario Amodei

Dario Amodei and John Collison discuss why coding tools are adopted faster than other AI applications, how overlapping model generations affect company economics, and why agent products need manageable review interfaces. The profitability example is hypothetical, not audited company results. Customer outcomes are first-party claims, timelines are forecasts, and the product strategy is Amodei's advocacy.

Cheeky Pint · Source dated 6 Aug 2025

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podcast CodingAI

Dario Amodei: Anthropic CEO on Claude, AGI & the Future of AI & Humanity

Lex Fridman interviews Anthropic's Dario Amodei on scaling, computer use, and safety; Amanda Askell on prompting and constitutional AI; and Chris Olah on how neural networks compute. The episode combines technical explanations with Anthropic leaders' first-party accounts and dated forecasts; it is not an independent evaluation of Claude's capabilities or safety.

Lex Fridman · Source dated 11 Nov 2024

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video Coding

Tobi Lütke: The Skills AI Makes More Valuable

Tobi Lütke describes Shopify's shared Slack agent, agent-assisted coding, and his use of multiple agents to prepare decision material. He argues that people remain responsible for choices and warns that cheap generation can shift lazy work from too little output to unreviewed excess. His adoption figures, workflow outcomes, and forecasts are speaker-reported, not independently verified.

The Knowledge Project Podcast · Source dated 15 Sept 2026

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podcast Coding

ACQ2: How to Live in Everyone Else's Future (with Shopify CEO Tobi Lütke)

Tobi Lütke explains how he retains failed tasks as model evaluations, supplies enough context to make work plausibly solvable, and codifies non-platitudinous product principles. Shopify uses AI critiques to find discussion points and refine those principles rather than as an automated approval gate. The practices and outcomes are Lütke's account.

Acquired · Source dated 18 Sept 2025

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podcast AI

AI and eCommerce with Shopify CEO Tobias Lütke

Tobi Lütke, Richard Sutton, Sendhil Mullainathan, and fellow panelists examine Shopify's SimGym shopper agents, which navigate stores through a vision-model browser loop. They distinguish interface operation, predictive validity, real commercial outcomes, and benchmarks for people working with AI. Product accuracy and benefits are participant accounts, not independent evaluations.

Intrepid Growth Partners · Source dated 18 Dec 2025

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podcast Coding

Tobi Lütke – Building Shopify and the Future of AI | Ep. 50

Tobi Lütke describes Shopify's AI mandate, why a token-usage leaderboard produced bad incentives, and how agent-summarized customer feedback supports smaller teams. He argues that experienced engineers still steer coding agents through problem understanding. Shopify's adoption, cost, and workflow outcomes are speaker-reported, not independently measured.

Uncapped with Jack Altman · Source dated 20 May 2026

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video Coding

AWS's Marc Brooker: Specs, Not Code, Are the Hard Part

Marc Brooker discusses testable specifications, learning from production incidents, enforceable agent policies, and shared team context. He forecasts increasing automation of implementation while emphasizing the human work of defining good outcomes. A practitioner interview with AWS product claims, not an independent evaluation; based on English YouTube captions.

AI Native Dev · Source dated 16 Sept 2026

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video Coding

Agents Without Code: Skills, YAML, and Filesystems Replaced Python — Philipp Schmid, Google DeepMind

Philipp Schmid compares a Python tool loop, an agent framework, and a hosted agent on the same GitHub PR-review task. In the hosted version, the runtime handles the loop and the agent uses the GitHub CLI instead of custom wrappers; developers supply instructions and skills and retain responsibility for evaluations and outcomes. A Google DeepMind product demonstration, not an independent reliability or security assessment.

AI Engineer · Source dated 14 Sept 2026

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video Coding

How I manage 15 AI agents 24/7 as a solo founder | Ryan Carson

Ryan Carson describes prioritizing concurrent Devin threads, using Watchdog to triage customer-account problems, and requiring video approval before his land PR playbook merges. He and the host discuss verification, design handoffs, and why customer learning matters more than code volume. Based on English auto-generated captions; workflow outcomes are speaker-reported, not independently verified. Carson discloses receiving monthly Devin credits from Cognition.

How I AI · Source dated 24 Aug 2026

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video Coding

The Centrifuge Method: Spin the Stupid Out of AI

Lada Kesseler describes refining agent output through focused write-read-revise loops, teaching TDD through reusable skills, and separating agent-facing tests from human-readable behavior checks. A practitioner interview covering review, exploratory prototypes, and her reservations about software factories; improvements are speaker-reported, not benchmark results.

AI Native Dev · Source dated 25 Aug 2026

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video Coding

Developing for the iPhone Duo ⟡ React 19.3 Is Here ⟡ Pi announces Radius ⌁ Syntax Weekly ⌁

Syntax discusses Pi Radius as a managed model gateway, remote agent sessions with Herder, and the trade-off between personal-agent access and prompt-injection risk. A concrete validation anecdote shows an agent accepting malformed metadata instead of rejecting it. This summary selects coding-agent material from a broader weekly roundup; product descriptions and workflow outcomes are host-reported, based on English auto-generated captions.

Syntax · Source dated 14 Sept 2026

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