World Wire

An AI search agent can work in a demo but fail under production workloads when retrieval returns incomplete evidence, retries repeat unsuccessful actions, or execution exceeds time and cost limits. TinyFish , which provides web infrastructure for AI agents, has published two technical guides addressing these challenges. The first defines a production agentic search architecture , while the second shares lessons from building a deep research system . Together, they examine how an autonomous search agent can plan retrieval, evaluate evidence, handle failures, and manage the boundary between model reasoning and deterministic execution.  TL;DR TinyFish introduced its agentic search architecture and key engineering lessons from building deep research systems. Built on four web primitives ( TinySearch, TinyFetch, TinyBrowser, and TinyAgent ), the system operates via a seven-phase control loop around the base model. On OpenBenchmarks, TinyFish achieved a 79.0% completion rate on cod...

You.com has launched Inference Router , a managed API for running open-weight models in production. The You.com Inference Router is an OpenAI-compatible service that routes requests across 12 live models. It bundles a 99.9% minimum uptime SLA and zero data retention (ZDR). It adds 100% US hosting and server-side web search. It is pay-as-you-go, with no platform fee. TL;DR Size: 12 live models, from GPT-OSS 120B (125K context) to Kimi K3 and Qwen3.8 Max (1M context). Kimi K3 is a 2.8T-parameter model; Qwen3.8 Max is a 2.4T MoE. Runs on: Fully managed. Requests hit You.com Inference Direct first, then fail over to US-hosted partners. No GPUs to manage. Performance: On You.com’s LiveNewsBench run, open-weight models plus You.com search matched frontier models at lower cost. Best: GLM-5.3 with You.com search scored 86.53%, at $0.0485 per question versus $0.0920 for Claude Sonnet 5.5 (self-reported). Worst: Per-token prices sit near market. Kimi K3 is $2...

In this tutorial, we implement on Nsight Python , NVIDIA’s open-source Python interface to its kernel profilers. Instead of running a script under the Nsight Compute command line and digging through a report afterward, we mark the code we care about with nsight.annotate, decorate a benchmark function with nsight.analyze.kernel, and get back a pandas DataFrame of hardware metrics per annotated region, per configuration, and per run. Since version 1.0, the library attaches Nsight Compute to the running Python process, making it usable in Jupyter and Colab, and the upcoming 1.1 release adds an experimental CUPTI backend, explicit tool management, and custom information collectors. We run everything on a free Colab T4: we install a current Nsight Compute next to the one Colab ships, profile a first kernel, decide how many runs a measurement needs, measure what cache and clock control do to the same kernel, sweep matrix sizes in fp32 and fp16 with a derived TFLOP/s metric, use hardwar...