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This AI Doesn’t Require You to Install Any Apps — Just Send a Text, and It Will Order Food on Meituan and Book Flights for You
This AI agent operates directly on your actual mobile apps, no need to install any new applications. Just send a text message, and it can complete trivial tasks for you including online shopping, booking fitness classes, posting content, and it can even run repetitive tasks automatically on a schedule.
OpenAI Engineer Breaks Down Reliable AI Agent Sandbox Design: More Useful Than a $2,500 Bootcamp
Many people building Agents get stuck on the 'it crashes as soon as you run it' problem: either they spend tens of thousands on API calls with nothing to show for it, or a simple prompt injection can take down their entire server. This 44-minute talk paired with a full engineering roadmap walks through building a stable, run-overnight-without-crashing Agent sandbox from scratch, with plenty of counterintuitive design insights worth any Agent developer's time.
A 27 Billion Parameter Large Model Compressed to 3.8GB Runs Natively Directly in the Browser
The Bonsai 27B large model released by the Prism ML team has been compressed from an original size of 54GB to just 3.8GB via 1-bit quantization, and can run natively in the browser via WebGPU, completely changing how on-device large models are developed and deployed. However, the community has raised quite a few controversies regarding this compression ratio and its performance. Let's dive into the details.
Distilling Claude's Reasoning into a 1B-Parameter Small Model: Runs on Almost Any Device for Code Writing
Community developers have distilled the Fable 5 reasoning traces from Claude Opus into the 1B-parameter MiniCPM5, creating a code and tool-calling agent. The smallest quantized version is under 700MB, runs on almost all major local large language model frameworks, and many developers have already started testing this model.
A New Subtitle Editing Tool for Mac Users, Even Callable Directly by AI Agents
The long-standing pain points of video subtitling have finally been addressed. This locally running Mac tool integrates AI transcription, translation and editing in one workflow, while retaining a manual-adjustable GUI to solve the old problem that AI-generated subtitles are hard to revise afterward.
SGLang Team Achieves 500+ TPS on GLM-5.2 with 8 B300 GPUs in Just Two Weeks
The SGLang team from LMSYS has implemented multi-layer engineering optimizations for Zhipu AI's GLM-5.2 NVFP4, achieving over 500 TPS throughput on 8 Blackwell B300 GPUs. Compared to the initial version, single-user interaction latency is reduced by 18%-34%, and high-concurrency throughput is increased by 6%-11%. The new TopK-V2 sparse selection kernel delivers 10x the speed of the original version on million-token contexts, while keeping interaction speed from dropping significantly as context length increases.
270-Billion Parameter Large Model Can Now Run on Smartphones
PrismML, a startup backed by top-tier VC Khosla, has just released Bonsai 27B, a 27-billion parameter large model that can run on smartphones. All code and model weights have been open-sourced under the Apache 2.0 license.
Chamath Compares AI Computing Power to Crude Oil — The Staggering Price Gap Shocks Everyone
Venture capitalist Chamath Palihapitiya compared the AI computing power required for one million tokens to a barrel of crude oil during an interview with CNBC. He found that prices charged by leading industry players differ by dozens of times, with low-cost providers already pushing the price down to $0.50. This analogy hits on the most awkward reality of today's AI industry: after months of hype, businesses still have not found a stable path to monetization, and high-cost players are likely to face a tough road ahead.
Running LLMs Locally: 90% of People Ignore This Parameter That Matters More Than VRAM
When building a local AI PC, everyone's screaming about grabbing GPUs and piling up VRAM, but almost no one clearly explains what actually core factor impacts user experience. A foreign developer tested 19 commercially available GPUs, and one enthusiast even pulled off a fully GPU-free solution, fitting an entire 405B-parameter large language model into system memory to get it running. This post compiles real test data and pitfalls to avoid, as a reference for anyone planning to build their own local AI machine.
Add Pytest to Google ADK Agents: Finally Pinpoint Exactly Which Step Broke Your AI Agent
Testing AI agents has always been a headache: outputs are non-deterministic, and when something goes wrong, you can't figure out which step caused it. DeepEval just released a native Google ADK integration that enables full end-to-end tracing with just one line of code. It supports both end-to-end and component-level testing, integrates seamlessly with Pytest for CI/CD, and is 100% open source.
