Browse entries 1369–1392 of 1531. Return to the first page to search and filter the complete collection.
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In 2024 an Uber engineer waited about three hours for a first review on a pull request. In 2026 that wait is nine hours. Volume and size both grew, and code review became the bottleneck for thousands of engineers spread across hundreds of teams, twelve sites and six language specific monorepos.
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Imad Touil opens with a show of hands. Who has built a skill? Most of the room. Who shares them with their team? Fewer. Who governs and maintains them across the organization? A handful. That collapsing sequence is the talk, because he argues skills are where an organization's actual know how ends up living.
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Figma did not have 20% projects. Jesse Lumarie gave one to the MCP server anyway, one day a week, because he had seen an internal demo and wanted non designers to be able to pull from Figma.
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The people annotating DoorDash's eval data are not engineers, and they build their own annotation tools.
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An AI spent about a week rewriting zlib in Lean and emitted 32,000 lines of proof. Not tests, proof. It decomposed the job into lemmas, closed each one with tactics, assembled them into a single theorem, and a small independent kernel checked the result.
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Andrew Garvin types one sentence asking for a billing engine that copies Lovable's pricing, and gets back a working sandbox: a customer, metered usage flowing in, and a draft invoice broken into separately scoped credit pools for builds, plan mode, cloud and gateway calls.
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Add a second unit of the same item to your cart and, to you, nothing much happened. To the merchant that is a second line item on the same SKU.
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Over a single weekend, Mike Krieger had Claude port a few hundred thousand lines of Python to TypeScript, verify it, and churn on its own output until the thing was deployable. He came back Monday to a finished port.
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Over a week off, Jeffrey Wang built an AI clone of himself. He analyzed 760 of his own emails to derive his voice, down to averaging 18 words and signing off with best rather than sincerely, then turned hundreds of past decisions into evals to calibrate the agent's judgment against his own.
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Ask an assistant to compare code intelligence tools and Sourcegraph comes up 65 percent of the time. Describe the actual pain instead, that you keep breaking downstream services when you change shared libraries and cannot see all the consumers, and it comes up zero percent.
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Their onboarding form asks how you heard about us. On April 13th the answers started spiking, and the single largest source of inbound for c15t is now an LLM telling someone to install it. Christopher Burns is not a researcher and says so twice.
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Point an agent at ten product pages, get real content back from three, and send all ten to the model anyway: seventy percent of those tokens go to reading CAPTCHAs. Giedrius Šteimantas says most teams never notice, because the status code and the response size both look fine. A 200 does not mean the page is real.
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Nine months after Aliisa Rosenthal started begging for enterprise features, OpenAI finally shipped them. By then almost every company on the list had the same answer: you never got back to me, so I bought Microsoft Copilot.
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AI Engineer session on From Text to Vision to Voice Exploring Multimodality with Open AI: Romain Huet. It adds practical context for how teams are building and operating AI systems in production.
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The biggest Google AI push of the year, but what is the bigger story? Why is Google pursuing a different fork in the road than OpenAI or Anthropic? https://assemblyai.com/aiexplained What does Gemini 3.5 Flash mean for the near-term future of AI?
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A new class of small models is emerging with the ability to reliably follow instructions and call tools while running on-device under 1 GB of memory.
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An eval platform is not just a test runner. You are building shared definitions of "good," reliable data pipelines, labelling workflows, versioning, and trust in results across many teams and model changes.
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GitHub operates one of the most heavily-utilised MCP servers in the ecosystem, with over 4 million downloads of the stdio server alone. Discover the architectural decisions, technical challenges and lessons learned while building and scaling a remote MCP server on production infrastructure.
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MCPs are often flaky, face multiple security vulnerabilities, and are generally hard to scale. Most enterprises struggle to use more than single digit numbers of MCPs due to issues with security, observability, and access control.
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A hands-on workshop covering the full lifecycle of AI-assisted development, from turning ambiguous requirements into agent-ready plans to running autonomous coding agents that ship production features.
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April 21, 2026 - all times in EST -- 9:00am - Welcome to Day 2 -- 9:10am - David House, G2i Transforming Programming Mindsets: Case Studies in Agentic Coding Adoption -- 9:35am - Sarah Chieng, Cerebras Help!
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They told the agent not to write to the spec files. It agreed, then wrote to them through bash. They blocked bash, so it used sed. They blocked sed, so it used cat.
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In the first week the daily brief posted to Slack twice, a voice note vanished entirely, and the market brief turned to garbage after prompt edits Rémi Louf had not versioned and could no longer recall. Each failure became a piece of what turned into a runtime.
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Warp open sourced about three months ago and went from roughly 20,000 GitHub stars to over 60,000, with thousands of pull requests and hundreds of contributors arriving at once. Rather than let agents fire off code, Safia Abdalla's team put them inside the repository's process.