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AI Workflows - Page 26
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 EngineerMCP Apps: AI assistants take control of your product UI
MCP Apps lets AI chats render live, branded UI from any service—but it shifts user-journey control to hosts. Engineering teams need to assess the tradeoffs.
AI EngineerWhy agents can't yet handle durable async workflows—and what's changing
MCP tasks enable async agent workflows with human-in-the-loop, but the spec is still immature. V2 simplifies the protocol, but scalability challenges remain.
David OndrejStop reviewing AI code. Audit the agent's decisions.
A viral skills repo for AI agents surfaces two practices worth stealing: hook-level safety and decision reviews. The rest is self-promotion.
AI EngineerBenchmarks Are Lying: The Real Cost of Benchmaxxing
Benchmark scores are being gamed, and engineering leaders keep trusting them. The fix: build your own task-specific evals with human judgment before betting…
IBM TechnologyAgent hallucination is now an operational risk, not just an accuracy problem
Agents hallucinate less when grounded, but autonomous actions make each wrong answer more costly. Mitigation is a design task, not a model update.
AI EngineerAI agents just hacked Chrome V8: security benchmarks are broken
Frontier LLMs can now create weaponized Chrome exploits on par with elite researchers. Existing security benchmarks are broken — they measure crashes, not…
AI EngineerWhy most AI agent benchmarks are lying about 'long-horizon' capability
Most AI agent benchmarks claim 'long-horizon' capability but measure tasks with minimal state dependency. Theta Software explains why this distorts adoption…
AI EngineerThe RLHF Trap: Why AI Is Great at Chat but Terrible at
RLHF made AI great at conversation but terrible at automation. A former OpenAI researcher explains why, and what engineering leaders should do about it.
AI EngineerPost-training shifts from synthetic environments to messy production learning
Post-training is moving from synthetic environments to real production harnesses. The tradeoff: controlled RL vs. messy but realistic learning. Reward…
Google for DevelopersDexterous manipulation is the bottleneck for general-purpose robots
Google DeepMind's Gemini Robotics 2 tackles dexterous manipulation—the unsolved bottleneck for general-purpose robots. Data scarcity, hardware limits, and…
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