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Live AI face swap: become anyone on Google Meet, Zoom and Discord
AI music studio for creators, turn any idea into song, beat & soundtrack
Give your beats a name people know. Use our free producer tag generator to write a short line and ch
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AWS's Strands Agents team released Strands Decider 2B, an open-source (Apache-2.0) 1.9B 'decision model' built from Qwen3.5-2B-Base by removing the language-modelling head and replacing it with a ~1M-parameter pointer head. It never generates text; given a state plus typed questions, it returns one of three calibrated outputs: choice (pick 1 of N options), noul (yes/no probability), or score (rubric level). One forward pass, no decoding loop, with a rank-16 LoRA on the torso. Benchmarks (v19 checkpoint): JevBench v1 public set 0.723 accuracy (167/231), Brier 0.342, ECE 0.052; 115 ms median per question on an RTX 3090 and 153 ms warm median on M3 Pro. Answers at 0.9+ confidence were right ~95% of the time on unseen short tasks. Caveats: weak on long multi-step documents, calibration fitted on short classification tasks, and the bundled HTTP server binds to localhost with no auth. It ranks 3rd of 33 in the 2B class on JevBench v1.4.2, behind Mapika's decider-2b v11 (175/231). Intended uses include model routing, tool selection, tool-argument checking, triage, guardrails, cheap evals, and hybrid agents. Links to the full write-up, blog, GitHub repo, and Hugging Face weights are included.
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Browser-Use, Stagehand, and the lab assistants have quietly become genuinely useful for repetitive web tasks. Here is what they can really do in September 2026, where they break, and the approval-gate pattern that makes them production-safe.
Three ways AI generates API tests now: from your OpenAPI spec (Schemathesis), from recorded traffic (Keploy), or via workspace agents (Postman Agent Mode, Kiro). A worked endpoint with three planted bugs shows what each approach catches and what each one structurally misses, plus how to layer all three in CI.
AI summaries of lectures are accurate and misleading at the same time: they keep the numbers and quietly drop the caveats. Here is the transcript-first workflow, Gemini Notebook (formerly NotebookLM), one re-tested summarizer, and a two-minute check that catches what compression deletes.
Learn to run AI locally on your own PC or Mac. Compare LM Studio, Ollama, and llama.cpp, master GGUF quantization, and match local AI models to your RAM and VRAM for private, offline AI in about ten minutes.
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