271 summarised stories about Agentic Workflows, each linking back to the original source. Browse all topics →
Product Hunt 5 days ago 38
ToDo.is launched its second version of an always-on AI agent that turns written to-dos into completed work across the web and apps. The offer is $1 to try it. After signing up, users can schedule tasks like daily briefings and price alerts while the agent runs in a sandbox and asks before it pays, sends, or deletes anything.
The New Stack 6 days ago 39
BrowseComp-Plus evaluations showed that agentic systems using BM25 to retrieve evidence fell sharply in accuracy compared with a setting where GPT-4.1 was given the supporting documents directly. The score dropped from 93.49% to 14.58% when GPT-4.1 switched from oracle evidence to BM25 search on the benchmark. The article concludes that retrieval quality, query interface, and retrieval-layer capacity/latency become the key design focus, because better models cannot compensate for missing or stale evidence.
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TLDRocket
21 October 2026
The day’s biggest throughline wasn’t new model announcements—it was the messy, expensive reality of getting AI to run at scale, profitably, and reliably. Google Cloud shipped llm-d 1.0, a Kubernetes-native distributed inference layer that routes requests to pooled vLLM or SGLang servers, while Google also unveiled TorchTPU, a PyTorch-focused path for developers trying to move off Nvidia GPUs (notably, the pitch leans on PyTorch’s dominance, cited as 85% of AI apps). Off the hardware side, Dutch startup Photon Bridge raised €10 million to build laser tech for optical links in large AI clusters, targeting first samples in 2027 and volume manufacturing in 2028, and framing the bottleneck as a manufacturing-scale problem: millions of lasers per year, delivered via multiplexing designed for higher yield.
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