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AI Workflows - Page 6
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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AI EngineerLocal Agents Must Earn Their Place Within 16 Milliseconds
NYT's on-device agents adapt games to player needs in real time, but the hard part is fitting reasoning into a 16ms frame without draining the battery.
AI EngineerVideo AI’s Missing Piece: A Memory Layer, Not Just Another Model
TwelveLabs' video memory layer preserves spatial-temporal relationships, turning video corpora into a queryable knowledge base.
AI EngineerOntologies: The Missing Guardrail for Agentic AI
Agentic AI loops risk drift and runaway costs. Frank Coyle shows how ontologies serve as a validation layer—a missing guardrail.
Latent SpaceThe Real AI Moat Is an Assembly Line, Not an Algorithm
Poolside compressed frontier model training to 8 weeks via a 'model factory'—infrastructure speed is becoming AI's true moat.
Theo - t3․ggYour Code Is Too Important to Read—Generate More Slop Instead
For mission-critical code, the real AI leverage isn't merging slop—it's generating massive cheap verification code around your important logic.
AI EngineerHire Your Agent, Don't Give It Your Credentials
Agents on user credentials are a security bomb. A new protocol gives each agent its own identity and scoped capabilities, enabling audit and revocation.
AI EngineerYour Agent Can Code the Product—Now It Shoots the Launch Video
HeyGen's Hyperframes turns agent HTML into video, slashing build-launch lag, but quality remains raw.
AI EngineerWrite-Enabled Agents Are Here, But Guardrails Lag
Write-enabled agents tripled, guardrails are primitive. Cost is a first-class constraint; non-developers ship customer-facing features—control must evolve.
Hugging FaceAsync Distillation’s Speed Gains Hide a Complexity Trap
Async LLM distillation promises 2× throughput, but the caching fixes needed for reverse KL may be overkill. Simpler off-policy methods often suffice.
IBM TechnologyFine-Tuning Lost to General Models: Here’s the New Customization Stack
Fine-tuning isn't the only path: a stack of RAG, context engineering, and agents often outperforms custom training. See where fine-tuning still fits.
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