#Tutorial/How-to

AIMastra's guide explains how teams can run agents under GDPR, with self-hosted deployments in any EU region or a platform environment created with --region eu. It covers PIIDetector redaction before data reaches the model, SensitiveDataFilter for trace fields, and retention and deletion handled in the team's own database. Mastra says it offers a DPA with EU Standard Contractual Clauses, a SOC 2 Type II audit, and no training on personal data.

AIMastra now offers classifiers that use evaluation models to answer questions defined as choice, score, or boolean, returning criteria keys, ordered positions, or true probabilities. Classifiers are registered on the Mastra instance and can drive workflow branching, such as routing a request to one of several agents. The feature requires @mastra/core 1.69.0 or later.

AIXiaomi MiMo reports that tool-call repetition in MiMo-V2.6 reached over 0.05% of responses across agent harnesses, causing stalled agents and wasted context. The team traced the cause to an RL flooding penalty set at 32 calls per turn, which missed smaller excess behavior, and replaced the approach with a specialized teacher distilled via MOPD. Repetition rates for both Pro and Flash dropped substantially, at roughly $90,000 versus an estimated $2.31 million for the alternative fix.

Why it matters: The post traces an agent failure to a reward blind spot and compares the costs of two fixes, offering a transferable debugging method for RL-trained tool-calling models.

AIXiaomi has released MiMo-V2.6-Pro-MOPD, an upgrade of the MiMo-V2.6-Pro-RL checkpoint that fuses several domain-specialized teachers into one model via MOPD2 and targets tool-call repetition. The sparse MoE model has 1.02T total and 42B activated parameters, a 1M-token context length, and accepts text, image, video, and audio inputs. Weights are available on Hugging Face and ModelScope, with deployment recipes for SGLang and vLLM.

AICanvases in the GitHub Copilot app are customizable interfaces that you and the agent share, such as kanban boards, dashboards, or checklists. You create one by running /create-canvas and describing the workflow, what you can do in the interface, and what the agent can do. Changes made by either you or the agent appear immediately in the shared canvas, and completed canvases can be saved as reusable extensions.

AIGoogle Cloud has launched a managed reinforcement learning fine-tuning service (RLFT) that lets customers adapt Gemini models using a reward function they define instead of labeled answers. Users supply prompts and a reward function, while Google handles the RL infrastructure and proprietary model internals. The guide advises exhausting prompting and supervised fine-tuning first, and notes that RLFT suits tasks that are easy to score but hard to demonstrate.

AIUsing the Neuron Kernel Interface, Reactor and Amazon's Neuron Science team built a kernel-centric path to real-time autoregressive diffusion video generation on Trainium. They addressed dynamic shapes, memory access patterns, and cache management, which are hard for generic compilers, and developed techniques intended to generalize across models.

AIAmazon Neuron Science and Reactor collaborated to optimize Rolling Forcing, an autoregressive diffusion video model, for real-time generation on AWS Trainium. The model generates 16 frames per second, the standard video playback rate, meeting latency requirements for streaming video generation.

AIvLLM has published a blog post explaining how its integration with TileRT works, alongside a public benchmark configuration in the InferenceX repository. The benchmark script covers GLM-5.3 FP8 on MI355X hardware and is linked on GitHub. The post itself provides the integration details.

AILangChain launched LangSmith Fine-Tuning, which lets users fine-tune open models on their LangSmith agent traces using the open-source smithtune CLI. Training runs on Baseten Loops in the user's own workspace, and smithtune deploy places the evaluated checkpoint on a Baseten Dedicated Inference deployment. Loops is in early access, so users may need to request access for their workspace.

AIGitHub Security Lab describes the Fuzzing Taskflow, an LLM agent pipeline that identifies entrypoints, writes harnesses, runs AFL++, reads coverage reports, and triages crashes for C/C++ repositories. The agent makes decisions while MCP tools handle execution, and state is stored in a SQLite database. The post also warns that the taskflow runs AFL and build commands directly on the host, so it should be used only in disposable environments without elevated privileges.

Why it matters: The post explains how an LLM agent automates fuzzing steps like harness writing, coverage gap chasing, and crash triage, with a runnable workflow and design tradeoffs.

AIMicrosoft's Foundry Agent Service preview lets developers attach a named, ordered egress policy to a hosted agent, allowing only approved destination hostnames. The walkthrough uses an invoice agent, an Audit-mode RAI policy with a Deny default, and Allow rules for two finance and vendor hosts, configured outside the agent code. Network egress controls are preview features, not GA, with no preview SLA, and are not intended for production use.

AIGemini 3.8 TTS lets users replicate their own voice or design a custom voice from a text prompt. The workflow is to record about 20 seconds of speech with a consent sentence, create the voice through an API call, then use it in any request with styles set in speech_metadata. The author also points readers to a guide for setting up and testing the process with an agent.

AIGemini 3.8 Flash TTS and Flash-Lite TTS are now available in the Gemini API and AI Studio, with a new option to replicate a user's own voice from two recordings or design one from a sentence. The guide says the reusable voice ID can be passed in later requests, or an encrypted voicekey that expires after 7 days can be used if nothing is stored server-side. Prompting changed from gemini-3.1-flash-tts-preview: input text is spoken word for word, delivery goes in speech_metadata.style, and non-streaming responses are now real WAV.

AILovable describes how its Chats feature lets a workspace-level chat agent hand work to project builder agents and receive progress back. The design records each agent's history as an append-only, forkable trajectory, and passes messages through durable inboxes that activations wake. Agents can suspend at iteration boundaries and resume on freshly deployed nodes without killing long-running runs.

Why it matters: The post details how trajectories, inboxes, and activations let agents share work and resume after deploys, useful for designing comparable agent systems.

AILangChain launched LangSmith Fine-Tuning and smithtune, a CLI that turns LangSmith agent trajectories into fine-tuned models through dataset creation, training with Fireworks or Baseten, and evaluation in LangSmith. smithtune currently supports supervised fine-tuning, training models on recorded examples of good agent behavior by updating model weights. The tool lets teams train specialized models without building the data pipeline by hand.

AIAfter a restart, OpenClaw can return unfinished conversations with their saved history, progress, and tool results. The agent checks earlier actions before continuing interrupted helpers, while chats the user stopped remain stopped.

AISimon Willison had a voice conversation with his blog's Datasette backup through datasette-mcp, which is now accessible from the ChatGPT iPhone app. The quoted post says ChatGPT Voice can now use plugins and run on GPT-6 Astra, Sol and Luna in ChatGPT Work on web and mobile.

AIGemini 3.8 Flash TTS and Flash-Lite TTS are now available in the Gemini API and AI Studio, with a new feature to replicate your own voice or create one from a sentence. The guide shows recording two clips, one of 15-20 seconds of natural speech and one reading a required consent sentence, then creating a reusable voice ID. It also explains that input text is now spoken word for word, so delivery belongs in speech_metadata.style and short sounds inline.

AIvLLM now supports Gumbel-max watermarking, which embeds a keyed signal into generated text without changing the expected token distribution. Detection requires the secret key and tokenizer, and the signal accumulates over longer outputs. Benchmarks on Qwen3.5-27B with MTP-3 show throughput changes between -1.1% and +2.0% across batch sizes, with no consistent slowdown.

AIClaude builds a model of a program's most complex parts, such as state machines or race-prone code, and searches that model for counterexamples that signal suspected bugs. It then reproduces those bugs and fixes them in the code. The post clarifies that the whole codebase is not formally verified, only the riskiest sections are modeled and checked.

AIGoogle's new Gemini 3.8 TTS models generate conversations between multiple voices, with over 2,000 preset voices available and voice cloning supported. Simon Willison says the models are super-cheap and built a small UI to try them, with Claude writing a script in which two pelicans debate moving to Pacifica Pier.

AIAnthropic says it made claude.ai 3x faster in two weeks, and the same speedup applies to the Desktop app. A linked blog post explains how the team used Claude to measure, debug, and improve performance, and includes prompts and methods.

AICursor's Eric Zakariasson shared a prompt for improving an LLM agent harness to lower token cost per completed task without losing quality. The prompt covers the system prompt, tool definitions, cache layout, tool results, compaction, and subagents, and reports that one team's round of these changes cut overall token cost about 7%.

Why it matters: The prompt gives a concrete checklist for cutting agent token cost per completed task, with tested figures on cache layout, tool offloading, and compaction.

AIAnthropic published a write-up on how it made the Claude frontend faster using agentic optimization techniques. The post coincides with Max Woolf's separate blog post showing that prompting agents can make code faster than current state-of-the-art libraries, with prompts and benchmark results included.

AIInferact says vLLM maintainers used megakernel optimization to reach 700 tokens per second per user on TPUv7 running Kimi K3. SemiAnalysis, which shared the work, reports this is 56% better performance than Nvidia's GB200 NVL72. Inferact links a full technical breakdown of the TPU megakernel work on its blog.

AIGitHub rebuilt the pull request view in the GitHub Copilot app to keep review fast on very large diffs, testing it on an open source pull request with 2,200 files, over a million changed lines, and more than 400 inline review comments. The core difficulty is that review comment heights can only be measured at render time, which breaks the fixed-geometry virtualization used for code-only diffs. GitHub split the document height into a deterministic code domain and a separately measured domain for comment blocks.

AIWrite-up and megakernel: 2/2

AIMicrosoft Research reports that running physical AI inference on onboard GPUs can limit robot performance and battery life, while offloading inference to edge or cloud GPUs improved results in mobile manipulation tests. In its evaluation, smaller onboard GPUs slowed mapping and planning by up to 383% compared with an A100, and large onboard GPUs such as Jetson Thor drained robot batteries by up to 160%.

Why it matters: The study measures how offloading robot inference to edge or cloud GPUs changes task success, battery life, and model size, offering evidence for infrastructure design.

AICursor announced improvements to the token efficiency of its agent harness, linking to a blog post with the details. The post itself offers no figures or specifics beyond that headline claim.