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Practical prompts, workflows, tool usage, and lessons from implementation.

30 picksPast 30 days: 21 itemsTotal: 438 items

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Tutorials & how-to top picks

TodayOct 8ThuItems 1–20
  1. Xiaomi MiMo63

    Xiaomi releases MiMo-V2.5-TTS series of speech synthesis models

    Xiaomi released the MiMo-V2.5-TTS Series, three speech synthesis models for stock voices, voice design, and voice cloning. The models accept natural-language style instructions and inline audio tags, and the source says the three models are free of charge for a limited time on the Xiaomi MiMo API platform. Xiaomi also open-sourced integration Skills for agent applications on GitHub.

    Why it matters: The release shows how a TTS family adds style instructions, inline audio tags, and voice design or cloning to speech synthesis, which matters for agent and creative workflows.

  2. Anthropic Research62

    Anthropic researcher builds first complete UV sky map with Claude Science

    Johns Hopkins astrophysicist Brice Ménard, working as an Anthropic researcher, used Claude Science to produce the first complete map of the sky in ultraviolet light. Claude orchestrated agents to merge GALEX, Swift, and FIMS/SPEAR data, then predicted roughly a third of the sky that no UV telescope had observed, using relationships to visible, infrared, and radio data. Hidden test regions were reconstructed to within about 10% of real measurements, and each pixel is labeled measured or predicted with uncertainty estimates.

    Why it matters: The post shows how an astrophysicist used Claude Science agents to merge UV surveys and predict missing sky regions, with a validation step that makes the method reusable.

  3. LangChain Blog67

    LangChain's Restock agent shows how to build a payment-capable AI agent

    LangChain built Restock, a sample office-supply agent that runs in Slack on Managed Deep Agents and pays through Stripe's Link wallet. The agent searches products, builds a cart, and pays over the Machine Payments Protocol, with the user approving the purchase in Slack and the payment in Link. The post uses a pens order at $22.18 to show the flow from request to confirmed order.

    Why it matters: The post walks through how an agent handles search, budget limits, Slack review, and Link approval, showing where each control sits outside the model.

Oct 7Wed
  1. Google Developers Blog62

    Google's AQuA agent diagnoses production failures in a multi-agent travel concierge

    Google Developers Blog introduces AQuA, an ambient quality agent that runs in a customer's Google Cloud project and samples production sessions to find recurring agent failures. In a 32-session travel-concierge sweep, it verified six issues and traced two of them to specific prompt lines, and a replay after the fixes raised full-session passes from 5/32 to 13/32. The post notes that verification and diagnosis are model-based, and that the tool proposes edits without applying them.

    Why it matters: The post walks through a concrete production workflow, from sweep and verification to a code-anchored fix and replay, that shows how to diagnose silent agent failures.

  2. Hugging Face Blog66

    How one developer built six custom models with ML-Intern for about USD 103

    A Hugging Face blog author used the ML-Intern agent in HuggingChat to build six small models by writing detailed prompts that specify datasets, base models, baselines, smoke tests, and spending limits. The projects include a citrus disease vision-language model, a Huggy character LoRA, a camera-angle LoRA, a doodle-to-object LoRA, a 0.8B prompt rewriter, and a 4-step distilled Agate model, with total compute cost of about USD 103. Each project's prompts and public models are linked from the post.

    Why it matters: The author shows how prompt structure, baselines, smoke tests, and budget caps shape an agent-driven training workflow, with per-project costs given.

  3. Claude Blog66

    Claude skill commands build evals and hillclimb them against overfitting

    Anthropic added build-eval and hillclimb commands to its claude-api skill for designing evaluations and iteratively improving applications against them. The article covers eval design principles, including production-representative tasks, headroom and low variance, and guards against overfitting through train/test splits. Two examples report results: a customer support benchmark where cost fell to under half while accuracy rose, and a claude-api skill eval that rose from 66% to 88%.

    Why it matters: The article gives a concrete workflow for designing evals and hillclimbing without overfitting, with two worked cost and performance examples that show the tradeoffs.

Oct 6Tue
  1. OpenRouter Blog62

    ElevenLabs text-to-speech and speech-to-text models now available on OpenRouter

    ElevenLabs now offers nine Text to Speech models and two Speech to Text models through OpenRouter, callable with an OpenRouter API key and no separate ElevenLabs plan. All ElevenLabs models are 50% off OpenRouter's list price through October 19, 8am PT, and Eleven v4, v4 Turbo, and Scribe v2 are recommended as starting points for narration, voice agents, and transcription.

    Why it matters: The source gives a concrete three-step build path and model selection guidance, showing how speech models plug into an existing text API for voice agents and transcription.

  2. vLLM Blog62

    vLLM Speeds Up DeepSeek-V4.1-Flash Agentic Serving Through Kernel and Replay Optimizations

    Inferact and the vLLM community reported a 1.9× low-concurrency speedup and about 5.3× throughput under a 150 TPS constraint for DeepSeek-V4.1-Flash over three weeks. Gains came from SWA bounded replay with CUDA graphs, which cut TTFT by about 30%, and from integrated DeepSeek kernels such as MegaAttention, Mega-mHC, Mega-Gate, and DeepSelect. The post measures these results on the SemiAnalysis AgentX benchmark.

    Why it matters: The post breaks down how SWA bounded replay and fused kernels cut prefill and decode costs, a reusable engineering pattern for long-context agentic serving.

  3. Claude Blog62

    Comcast and Booz Allen use Claude Mythos to find exploit chains in codebases

    Comcast and Booz Allen used Claude Mythos Preview to find vulnerabilities that arise from interactions across code, configuration, and deployment rather than single-file bugs. Comcast identified a critical authentication flaw across 258 systems and about 170 million lines of code before any exploitation was observed. Booz Allen reported that one analyst reviewed eight production systems across 138 repositories in twelve days, a review its team estimated would have taken several months without the model.

    Why it matters: The case studies show how security teams validate and remediate model-found exploit chains, a workflow relevant to anyone managing large codebases.

Oct 5Mon
  1. Google Developers Blog62

    EmbeddingGemma 2 releases multimodal embeddings with modular encoder loading

    Google released EmbeddingGemma 2, an open embedding model under the Apache 2.0 license that maps text, code, images, video, and audio into a shared 768-dimensional space. Developers can load a 270M-parameter text and code setup, or add vision and audio encoders up to a 740M-parameter full multimodal model. Matryoshka truncation to 256 or 128 dimensions reduces vector storage, with the guide noting quality losses on image, video, and speech retrieval at lower dimensions.

    Why it matters: The guide gives concrete encoder sizes and dimension-storage tradeoffs, showing how to choose a configuration for text, code, image, video, and audio retrieval.

Oct 2Fri
  1. Baseten Blog70

    Baseten's agent-built VibeQwen engine beats vLLM on Qwen-3.6 decode speed

    Baseten tested the MetaInfer skills-only approach by having Claude Code build an inference engine, VibeQwen, for Qwen-3.6-35B-A3B in NVFP4 on a single B200. On single-stream text, VibeQwen decoded 90% faster than a tuned vLLM 0.25.1 deployment (1,792 vs. 943 TPS) and cut time to first token from 28 ms to 12 ms, with a 71% throughput gain at concurrency 32. The author notes this was an outcome-focused run that allowed some numerically different outputs as long as accuracy stayed at or above the BF16 baseline.

    Why it matters: The post tests a skills-only inference engine method on a real model and states the speed and accuracy constraints used, helping readers judge how far such automated optimization can be trusted.

Sep 27Sun
  1. Xiaomi MiMo62

    Xiaomi MiMo Explains Fixing Tool-Call Repetition in MiMo-V2.6 Models

    Xiaomi 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.

Sep 24Thu
  1. GitHub Blog · AI & ML66

    GitHub Security Lab shows an LLM agent running AI-driven fuzzing for C/C++ projects

    GitHub 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.

  2. Lovable Blog80

    How Lovable's Chats connect conversations to agent work on projects

    Lovable 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.

Sep 23Wed
  1. eric zakariasson67

    Cursor shares a prompt for reducing token cost in agent harnesses

    Cursor'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.

  2. Microsoft Research60

    Microsoft Research shows offloading robot AI inference improves performance and battery life

    Microsoft 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.

Sep 22Tue
  1. Google Developers Blog62

    Antigravity SDK adds local Gemma 4 26B agent support via LiteRT

    Google announced that the Antigravity SDK supports local agent workflows, with initial support for Gemma 4 26B A4B through Google AI Edge's LiteRT. The post includes Python setup steps and says a recommended machine has more than 24GB VRAM or unified memory. It also describes a hybrid pattern in which a cloud Gemini 3.8 Flash planner hands work to local Gemma 4 26B models, with 97.2% of tokens in one recorded run staying local.

    Why it matters: The source shows how to run an agent with a local Gemma 4 26B model using LiteRT, plus a hybrid cloud-planner pattern that keeps most tokens on-device.

Sep 21Mon
  1. Tencent Hunyuan67

    Tencent Hy4 preview compressed to 214 GiB with mixed-precision quantization

    Tencent Hunyuan says it shrank the 770B-parameter Hy4 preview from roughly 1.5TB to 214 GiB while keeping the parameter count unchanged. The quoted Zhihu post by a Tencent Hunyuan quantization team member describes the method: a 1.25-bit sparse ternary encoding, mixed precision across expert layers, and STQ1_0 CUDA kernels in llama.cpp. The author reports nearly unchanged MRCR retrieval and a small decline in math.

    Why it matters: The quoted Zhihu post explains how Hy4 preview's weights were quantized and kept usable at inference, a concrete engineering case for compressing large MoE models.

Sep 14Mon
  1. Google Developers Blog60

    Build zero-trust AI agents that judge intent, not just syntax

    Part 2 of the zero-trust agents series moves security checks from agent code to the Gemini Enterprise Agent Platform runtime. Model Armor screens prompts and responses, Semantic Governance Policies judge proposed tool calls against intent and business rules, and Agent Anomaly Detection flags multi-turn drainage that single-turn checks miss. The same Customer Support and Returns Agent from Part 1 is used, with the companion demo open-sourced on GitHub.

    Why it matters: The post walks through a concrete refund agent under four attacks, showing how screening, intent judgment, and anomaly detection each catch what the others miss.

  2. vLLM Blog62

    How vLLM Speculators trained a DSpark draft model for Kimi K3 on GB300 NVL72

    The vLLM team trained a DSpark speculative decoding draft model for Kimi K3, a 2.8T-parameter model, using the Speculators library on GB300 NVL72 hardware. They added a MooncakeHiddenStatesConnector to stream hidden states from disaggregated vLLM inference nodes to training nodes across multiple machines. The released speculator raises single-stream interactivity from about 110 to about 435 tokens per second per user on math reasoning, with up to about 3.5x higher output throughput under concurrent load.

    Why it matters: The post shows how hidden-state extraction and Mooncake transfers let a 2.8T-parameter model's speculator be trained across multiple nodes, a reusable pattern for similar setups.