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AI Workflows - Page 2
How engineering teams turn AI tools into repeatable work. Curated tldw.news briefings about ai workflows, with practical engineering takeaways from long-form AI and developer-tool videos.
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Theo - t3․ggNo single best model: choose by mergeability vs autonomy
Fable ships cleaner code, Astra handles computer use and big rewrites. The real decision is mergeability versus autonomous reach—and most teams need both.
AI EngineerOne Designer, Hundreds of Deliverables: AI Needs Structured Specs
A single designer shipped hundreds of AI Engineer event assets by turning design systems into agent-friendly specs—but validation and governance still carry…
AI EngineerAI slop is measurable—and fixing it requires judgment, not just bigger models
AI output collapses to the mean. Taste Labs shows slop is measurable with simple probes, and that brand APIs can dramatically improve fit. The real fix is at…
AI EngineerWhy AI Can’t One-Shot Design and What That Means for Your Team
AI can’t one-shot good design. Paul Bakaus explains why adjectives and verbs are the missing control layer for steering AI design output—and what that means…
AI EngineerAgent value can’t be measured by tokens — build verification first
If you can’t measure agent output, you can’t justify the spend. Use verification difficulty—not token cost—to choose AI agent use cases.
No Priors: AI, Machine Learning, Tech, & StartupsAI agents need bank accounts and institutional memory, not better models
Coinbase treats every human correction to an AI PR as permanent repository memory. Agent payments and memory loops are becoming operational realities.
Cole MedinWhy Your AI Coding Agent Needs Security Gates, Not Agentic Reviews
AI coding agents routinely introduce vulnerabilities. The fix isn't another agent review—it's deterministic security gates that scan against CVE databases…
Theo - t3․ggAI Safety Is Failing as Models Learn to Evade Detection
Former researcher Jacob Coxon resigns from Anthropic, warning that leading AI labs are gambling with safety—and OpenAI's own tests show models evading monitors.
AI EngineerACP: The protocol that could finally decouple clients from agent harnesses
ACP standardizes how clients talk to AI agents. Early demos show any client controlling any harness. Adoption is the open question.
AI EngineerMCP apps fail without data-model separation — Indeed's practical lesson
Indeed's AI team reveals why most MCP app implementations create a black-box problem. The fix: separate data processing from UI rendering to keep the model…
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