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AI Workflows - Page 32
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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InfoQAgents aren't magic—they're a security and cognitive liability
AI agents promise productivity but introduce security gaps, cognitive overload, and testing challenges. Bannon explains why governance matters more than…
All About AIYour ML model's 93% win rate is probably just luck. Here's why.
A 14-trade winning streak on Kalshi looks like a machine learning edge. The creator's own analysis shows it's luck. Expected loss after 1,000 windows: $12.
IBM TechnologyFive patterns for agent-tool connections: security grows, complexity follows
A security-minded ranking of five agent-to-tool connection patterns, from direct API calls to vault-backed short-lived credentials. The security gains are…
AI EngineerThe Hidden Cost Trap in AI Agents: When Renting Context Fails
AI search and CaaS promise plug-and-play context, but repeated queries create a cost trap. For stable knowledge work, building custom scrapers may be cheaper.
AI EngineerWhy RL-Trained Agents Fail in the Real World — and How to
RL agents break when real UIs fight back. Amazon AGI shows why flight simulators and harness guardrails are the difference between demo and product.
AI EngineerBrowser agents aren't failing because models are weak. It's an engineering problem.
Browser agents aren't failing because models are bad—they're failing because the engineering around them is immature. Three things matter: multimodal design…
NeuralNineDeepSeek's radical transparency reveals what agent tooling has been missing
DeepSeek's developer preview prioritizes modularity and full traceability, offering a transparent alternative to opaque agent tooling. The design philosophy…
AI EngineerYour Agent Improvement Strategy Is Incomplete Without Trace Mining
LangChain's research lead argues that agent improvement is a data mining problem. Trace data—tool calls, outputs, errors—is the signal for continuous…
AI EngineerAI memory has converged on profiles—context silos remain the real gap
After three years, ChatGPT and Claude converged on running profiles for memory—but made opposite compute tradeoffs. The real problem is context access, not…
AI EngineerHow to improve coding agents without golden answers or regression
Continual learning for coding agents doesn't need golden answers. Applied Compute's distillation spectrum shows offline traces plus targeted hints improve…
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