Reliable convergence
Naqvi gates LLM migration work checkpoint by checkpoint: prove behavior, review the pixels, then a human signs.
Working with engineering, product, and the rest of the org.
53 entries
Naqvi gates LLM migration work checkpoint by checkpoint: prove behavior, review the pixels, then a human signs.
Hoang’s case for the design GM: own a P&L, run the roster, stop appeasing the stakeholder and be one.
Merholz maps why AI lands hardest on UX Directors: the mandate is the How, and the How is theirs alone.
NN/g's editor opens the workflow: AI drafts, critiques, compresses. A human expert still signs every claim.
Palma and Martins gate the marketplace: 2,000 skills vetted, 1,600 risks flagged, before one reached a developer.
Kumar’s five layers for agent-built UI: tokens, components, context, discovery, and enforcement wired into CI.
Kaley and Budiu name the custodial era: production got cheaper than evaluation, so UX triages what already exists.
Ramp built Inspect with six years of Linear as its memory. The agent now writes three of every four merged PRs.
King vibe-coded the WebGPU canvas he’d imagined for years, recruited the team it earned, and shipped in two months.
Skovhus turns a 1,000-PR styling migration into a boundaries argument: contracts enforced by tooling, built for agents.
Donald, a career systems hand, suspects we’ve outgrown design systems and sketches the one that absorbs the product.
Turakhia took the editor away from an entire org for two weeks: the old way of working lives there.
Qi’s report from 47,900 workspaces: AI writes half of everything in Linear, PRs are up 111%, and no time came back.
Local components as signals, not violations: designers build in place, and the system team governs what earns promotion.
Merholz borrows Scott’s legibility lens: businesses fund determinate work, and design’s best moves are indeterminate.
Yeo turns murmurations and goose formations into an operating model: rotating leads, domain pods, and a scheduled molt.
Stone runs engineering, product, and design as one system. Her AI-era hiring bar: systems thinkers, fluency for all.
Fernandez turns Butterfield’s 2013 promise into a frame with a gut-check: are we proud to ship this?
Hoang names the discipline forming around agents as users: access, context, tools, and parallel paths for two minds.
The forty-minute scope debate costs more than the diff now. Abuadas ships a constrained probe and argues from evidence.
Every prompt is now a design decision. Alicea’s answer: stop persuading humans and curate context that steers models.
Loop engineering becomes team infrastructure: describe a recurring job in plain language and the agent runs it.
Boom builds a supersonic airliner and its engine at once, on software’s rhythm: short cycles, real parts in hand fast.
Twenty exec interviews and a 458-response pulse check converge: how your org behaves predicts how design feels about AI.
Production stopped being the constraint. Dean maps what leaders manage instead: judgment, cohesion, and when to stop.
Litt argues understanding agent-written code keeps you fluent enough to have the next idea, with four techniques.
Callahan splits product quality in two: the design system sets the floor, and how product teams use it sets the ceiling.
A Linear engineer on how agent-heavy review freed him from parsing lines to judging whether the work earns its place.
Sheta Chatterjee walks the design calls behind Gemini Enterprise: a shared AI inbox and people kept in control.
Martyn Reding argues the old shape of design leadership is gone, and lays out what changed and how to adapt.
Citing a 515-startup field experiment, Nielsen argues AI pays off when teams redesign whole workflows, not single tasks.
An early-career designer keeps reaching for connective work wherever structure is missing, and asks why.
Five lessons from 127 million customers, where hiding complexity is a deliberate architectural and cultural decision.
GitHub’s five-year program turns outward: audit data, design tooling, and AI agents make inclusion operational.
Merholz reads layoffs through org theory: under uncertainty, executives copy each other, then call it AI strategy.
A multi-agent read of 638 Lenny pieces, June 2019 to March 2026, charting what production loses and judgment keeps.
Shakir’s workflow: the AI holds context across Linear, Slack, and research while the designer does the thinking.
Shadi Abd’s positioning move: frame system work as infrastructure with a named cost of inaction, at portfolio level.
GitHub’s accessibility agent reviewed 3,535 PRs at a 68% fix rate. The sharper lesson is what it won’t touch.
Slack’s VP of Product Design guides roughly 70 designers through AI without mandates, keeping the bar high.
AI-readiness is the old spec finally enforced: tokens, layout intent, and governance now have to read to a non-human.
AI code defers cost instead of cutting it: seven debt categories land far from where the velocity gain was booked.
Frontier AI teams optimize what’s measurable, so Microsoft’s researchers want a seat where the metrics get chosen.
McMillin reframes canvas, code, and PRD as one system: an agent translates, and judgment moves through both.
Writing down how you hire forces honesty about what you value. Linear did the work and published it.
Microsoft’s 2026 Work Trend Index, 20,000 workers across ten countries: only 26 percent see leadership aligned on AI.
A Linear engineer scopes agent tasks tight enough that they can’t wander, the same discipline as scoping for a junior.
Appleton reads Yegge’s chaotic agent orchestrator as design fiction: the coding bottleneck fell, the design one stayed.
Agents collapsed the implementation window, and the alignment checkpoints that lived inside it went with it.
Microsoft spent years replacing plastic with paper. Committing was easy; making every millimeter behave was the work.
Linear flew designers and engineers to Athens and shipped a working build by Friday. The part worth lifting is the trust.
A Linear engineer scopes agent tasks tight enough that they can’t wander, the same discipline as scoping for a junior.
Nubank A/B tested whether to scale or shut an internal HR product. The data was unambiguous; the team’s reaction wasn’t.