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Engineering Leadership - Page 4
AI decisions through the lens of teams and execution. Curated tldw.news briefings about engineering leadership, with practical engineering takeaways from long-form AI and developer-tool videos.
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GOTO ConferencesAI Finds Vulnerabilities Faster Than Open Source Can Fix Them
AI can now find vulnerabilities at industrial scale, but fixes still depend on burned-out maintainers. Dependency hygiene just became a compliance and…
InfoQContext engineering is the real bottleneck for coding agents at scale
LinkedIn encoded tribal knowledge into MCP playbooks so coding agents actually work across 1,000 repos. 8,000 daily users, but the maintenance burden is real.
AI EngineerCode Quality Has a Simple Answer: It Depends on Disposability
A practical framework for deciding when code quality matters: disposable software needs none, durable software needs all. The real work is making rigor cheap.
The Pragmatic EngineerThe CTO path now runs through shipping code, not managing process
Will Larson on why middle managers must reskill into technical work, and why line managers may be the better future CTOs.
IBM TechnologyAI finds the bugs; the patching gap still decides the damage.
Hacked supermarket fridges, AI ransomware and Bank of America's 1,000-contractor patch team share one problem: nobody owns the update loop.
AI EngineerAI war rooms can crack legacy code, GEICO's $1,000 day shows
GEICO's $1,000 AI war room mapped legacy claims domains in a day. The deciding factor was human approval gates, not model choice.
AI EngineerMake legacy code AI-queryable: Vanguard's 7-day research loop became 15 minutes.
Vanguard cut legacy research from 7 days to 15 minutes by making its mainframe code queryable through MCP. The real lesson is governance.
GOTO ConferencesThe next AI bottleneck is governance, not individual developer speed
Don Syme wants agents running continuous maintenance inside GitHub Actions. The promise is a self-tending repo. The catch: cost, guardrails, and who approves…
Latent SpacePaying engineers for output exposes AI's real bottleneck: process
Tenex runs an engineering team paid by output, not hours. The bigger lesson: enterprise AI stalls because processes aren't standardized enough to automate.
AI EngineerStop Buying GPU Hours: The Operator Decides Your Real AI Cost
GPU-hour pricing hides the variable that determines your AI cost: operator quality. Same B200, up to 50% output spread. A useful framework, but verify.
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