This guide offers 15 sci-fi prompts for AI video generation across alien worlds, spacecraft, and practical effects, each built on a five-part structure of subject, sequence, setting, shot, and style. It argues that generation is only the start, with refinement, consistency, and delivery determining whether clips are usable in professional production.
a big advantage of doing planning like this is that it’s a lot more token efficient compared to raw HTML- the model doesn’t need to remake the components or logic to do common things like state machines, diagrams, code snippets, etc.
this looks a lot like the "outline" skill we bundle with @humanlayer_dev that mixes in /show-me with a planning language. I like the mix-in of HTML for questions and interactive diagrams @trq212 the questions -> clipboard pipeline is cool
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.
AIWhy 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.
Vercel's COO Jeanne DeWitt Grosser described how the company built an AI agent that runs the top of its sales funnel, starting from a roughly 125-line prompt written by its best SDR. The team moved the agent from supervised drafting to autonomous operation by August, then split the prompt into 14 deterministic rules and a model-handled judgment layer. Grosser said the system runs inbound for about $1,000 per year in inference and infrastructure.
Dex Horthy advises writing all decisions and context into documents in the artifacts, such as design or research files, so sessions can resume after compaction or be handed to another person. He suggests loading them in a new session with a skill like `/rpi:iterate-design-discussion`, or simply @-mentioning the relevant artifacts. His core principle is that nothing important should live only in the context window.
A classifier assigns an input to a fixed label set, covering binary, multiclass, and multilabel variants, such as spam versus not spam or movie genres. It encodes the input into a vector using hand-built features like logistic regression or a learned encoder such as a CNN or BERT. A linear layer then projects that vector into K logit scores, which softmax or sigmoid turns into probabilities.
To experiment with dynamic game audio, @mwjoffe paired @Antigravity with Lyria RealTime to build a living, interactive 3D meadow that generates its own adaptive soundtrack live in your browser:
Q: How often should I run my evals? A: Weigh the cost and how saturated the eval is against the business value of catching the error. Read more: https://hamel.dev/blog/posts/evals-faq/how-often-should-i-run-my-evals.html Read all the FAQs here: https://hamel.dev/blog/posts/evals-faq/
Read more about memory and dreaming at: https://devin.ai/blog/memory-and-dreaming
You can use Hermes Agent for a lot more than flight booking. Here are 326 use cases from real users: https://hermes-agent.nousresearch.com/docs/user-stories
create motion graphics with Krea agent
Bruce Hopkins shows how to wrap an existing stock-data REST API, the Twelve Data API, in a Model Context Protocol (MCP) server so an MCP client can discover and call it. The demo uses Python with FastMCP, exposing current and historical stock-price functions as tools and resources with descriptive prompts. Developers can add an MCP interface around existing capabilities without replacing their underlying application logic.
The post, by 卡尔的AI沃茨, publicly shares an AI workflow previously presented internally at 影视飓风, covering finding suitable Skills, packaging experience into Skills, combining Skills into workflows, and batching and scheduling them. The author says this lets readers build a local batch video-editing Skill and a full-chain content pipeline spanning copy, posters, video, and web pages.
Great work from @fractalyze_io optimizing Qwen3-Omni on vLLM-Omni for a single RTX 5090. Their AWQ-4bit, batch-1 text-prompt tests cut time to first audio from 213ms to 23ms vs. stock vLLM-Omni. We’d love to see these optimizations contributed upstream to vLLM-Omni so more users can benefit!😄
The PyTorch Accelerator Integration Working Group released updates on its H1 2026 progress toward standardizing how new hardware connects to the framework. Key workstreams include the Cross-Repository CI Relay (CRCR), which automatically reports downstream backend test results to a shared dashboard, and refactored test suites that decouple PyTorch's 600,000-plus tests from specific accelerators.
A native word-level transcription model outputs structured, timestamped arrays of word, spacing, and audio_event tokens directly from audio input, without a secondary forced-alignment pass. Audio events such as laughter or applause are tagged separately, which the source says helps with captioning, searchable archives, and highlight identification. The source notes Scribe's word-level transcription supports up to 5 independently transcribed channels.
Reliable AI agent systems need deterministic policy checks, not just better prompts or stronger models, because a model's proposed action can succeed at the API level while still updating the wrong account. The article recommends separating the model's proposal from a policy service that checks actions before execution and records an audit trail. It also advises treating agent context as untrusted input, using narrow capabilities instead of broad tokens, and building in stopping rules and idempotent recovery.
Brian's Grok Bot method treats each bot like a new hire: define the role, test it on text first, run three trials, escalate based on evidence, and add a second agent only after a bottleneck appears. Each bot's role is defined by five fields: a real name with a short label, a one-line job tied to an outcome, what it owns, its inputs, and what it may do freely versus what it must ask before doing. The post frames an Agent Team as the final result of this process, starting with one coordinator and three specialists.
The /break-ui Skill, added to the Skills For Designers and Engineers repo with 43K stars and 1.9M installs, plays the most annoying real user to stress UI components with worst-case but realistic data. It targets bugs manual testing misses, such as "1 members" pluralization errors, zero-value "0 seconds ago" rendering, cross-timezone date shifts, and emoji or CJK names breaking initials logic. The skill reports issues before fixing them, and only changes the data, never the component.
Uber uses a control plane and data plane gateway to automatically convert its internal APIs into MCP tools, with 800+ MCP servers and 5,000+ tools hosted. The design includes an AutoCrawler that generates tool descriptions with an LLM, a default-disabled discover-not-expose security model, and techniques such as Omni MCP, Response Projection, and Code Mode to limit context bloat.
The article argues that improving an AI setup can sometimes mean giving the AI fewer rules to follow, based on the author's experience across a million words of failed drafts. The source text provided is mostly paywall and subscription material, so no further specific figures, products, or benchmarks can be verified.
OpenRouter's shell and bash tools, along with those from OpenAI and Anthropic, run an agent's commands in provider-managed sandboxes during the same API request, so developers don't provision or patch containers. OpenRouter's tools are in beta, with sandbox time billed at $0.0001 per second and a 30-second minimum for a new or sleeping container. The article compares the four providers and notes that self-run sandboxes remain better for custom base images, GPU work, or multi-hour sessions.
Take this course to learn to design hierarchical agent trees and deterministic workflow agents while identifying the ideal hosting environment to ensure your AI agents are secure, scalable, and production-ready → https://goo.gle/4hudimz
Dots love nothing more than getting to reducing your backlogs / lists of tasks!
The author spent over half an hour using Claude to produce a company logo animation that is now roughly usable. The post reflects surprise that motion graphics and video work can now be generated directly through code by AI.
Kling AI used its Kling 4.0 model to produce "The Beat," a short film built around a 30-second continuous shot and surpassing 5 million impressions across social platforms. The model's native 30-second generation, Omni Reference supporting up to 15 multi-modal references, Multi-Keyframe control for up to 10 keyframes, and 10-bit HDR output shaped the film's continuity, consistency, and color. The post walks through these features shot by shot.
LangChain says its coding agent costs fell significantly for a second straight month after adopting three steps. The steps are cost visibility through LangSmith tracing, per-user cost caps via its LLM gateway, and harness optimization such as model routing in its open-source OpenSWE cloud agent harness.
Setting up your Hermes with Discord should now be a little less confusing or painful
As for data synthesis and refinement, we used a two-pronged approach: Best-per-Language Forward Translation and Post-Edit Driven Preference Distillation. Hear what they are, and why we used them: (4/6)
Sebastian Raschka released round six of his Reasoning from Scratch series, introducing Reinforcement Learning with Verifiable Rewards (RLVR) and Group Relative Policy Optimization (GRPO) with an implementation. The video covers accuracy and format rewards, DeepSeek-R1 training, and GRPO versus PPO, then walks through a training loop and evaluates checkpoints on MATH-500.
继续随手做视频：100 秒搞懂 homebrew Translation: Continuing with casual videos: understand Homebrew in 100 seconds.
Learn about confidence here: https://docs.typesafe.ai/confidence#how-confidence-is-calculated
Hamel Husain announced a final open lesson of the year on how to crack AI evals interviews, covering common traps and what companies look for. The session, on Tuesday, Oct 6 at 8:30am PT, also covers structuring a take-home or report, and assumes basic evals knowledge.
How to get started prime inference chat 'z-ai/glm-5.3' "Write a haiku about KV caches.” Or point any OpenAI SDK at https://api.pinference.ai/api/v1
KV transfer: reducing copy overhead on NVLink At first, KV transfer on NVLink is slower than InfiniBand due to KV layout fragementation: one 200K-token request turned into 32K tiny copies. vLLM's block-major BLHNC layout cut transfer descriptors ~10x and cut mean transfer time by half.
Prefill: time to first token DEP8 gave us ~5x the prefix-cache capacity of TEP8 on the same GPUs. Fast KV retrieval doesn’t guarantee a fast first token. We also need to reduce the time requests spend waiting to run after their KV is ready. Cached KV was often ready before the request could join a batch. Halving the prefill budget cut median queue wait time and TTFT.
Long-context agent serving depends on retaining history, scheduling new work, and moving cached state efficiently. We optimized these paths separately: 1. Prefill topology and scheduling 2. Compressed KV and a fused attention kernel 3. Transfer-friendly cache layout
Wonderful writeup on Jev + Python
A few of you asked what mods are, here's a quick walkthrough! They’re really just plugins with a few special functions that let your code run inside Claude Code, like middleware. (don't worry, you can just ask Claude to write them for you 😛)