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Harness Engineering for Self-Improvement, Lil'Log, July 4, 2026; Boye Niu et al. — Flow: Modularized Agentic Workflow Automation, arXiv:2501.07834, 2025; Andy Xu and Yu-Wing Tai — Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems, arXiv:2605.25233, 2026; Ao Li et al. — GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving, arXiv:2605.22566, 2026; Sydney Runkle and Harrison Chase — 3 Years of Graph Engineering with LangGraph, LangChain, July 22, 2026; Nelso

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gave us static extraction and strong type safety. But its API felt more fragmented, and the separation between component and style files did not fit how we like to work. StyleX by Meta gave us a better balance. Styles stay colocated with components, the API is small, and style resolution is deterministic. It also gives us type-safe styling contracts and makes it deliberately harder to reach into a component and restyle it from the outside. Those constraints come with tradeoffs. Migrating from th

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Claude produces a file of a supported type (such as a .png, .jpg, or .svg), it will attach a content credential in the form of a small, cryptographically signed note in the file’s metadata, saying that the file was made or processed with Claude. This is an open industry standard called C2PA —the same used by camera manufacturers and in photo-editing software to record where an image came from. Any C2PA-aware tool can read it; we’ll be providing our own where you can drop a file and check. This m

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GPU waits; stream dataset() reads batches straight from the bucket with nothing on local disk, so the GPU trains from the first batch. Nothing lands on local disk except the small meta/ folder of schema, statistics, and episode index. Camera frames are decoded from the remote MP4 shards as you iterate; state and action come from the Parquet shards. That loop reads one frame at a time, which suits inspecting an episode. To train, pass the reader to a DataLoader and iterate batches instead. The st

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requests require only a small slice of Linear’s product surface, we introduced system skills as a unit of composition for the agent. Each system skill bundles together some basic metadata, a fragment of the system prompt, and a set of tools. Together, they represent an independent set of capabilities that can be progressively disclosed to the agent when needed. (This is separate from user-created skills, which users can author for their own workflows. Those are reusable instructions created on

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er end of the spectrum is mass approving everything, then something’s gotta give. MORE CONTEXT Bug in top AI coding agents shows that Unix-era security headaches never really die; Meta wants to get inside your terminal with its new coding agent; Prompt injection isn't the bug, AI agent frameworks are; Claude Code bypasses safety rule if given too many commands “I think it becomes clear we need to pay more attention to the permission model of these agents, and devs need to be more aware of the tr

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rprise-grade voice agents. 3. Contextual correctness is another challenge Many errors require semantic disambiguation to even define correctness: “read” (present vs past); “lead” (metal vs verb); “Polish” (nationality vs verb); “bass” (fish vs instrument); “live” (verb vs adjective); years like “2026” (twenty-twenty-six vs two thousand twenty-six) I had to wind the old clock before the wind picked up outside. Proper nouns are worse: names of people, places, products, companies, drugs, and fictio

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s kept in registers, the fastest memory on the chip. sm80 : compiled for Ampere (the A100's compute capability 8.0). This is the memory efficient attention kernel that grew out of Meta's xformers library and was upstreamed into PyTorch. When people say "the xformers backend," this fmha cutlassF kernel is what they mean. Flash backend uv run 04 c sdpa attention.py --backend flash uvx trace-util -f traces -b /traces Figure 15: The flash backend trace, one fused pytorch flash kernel per forward The

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NSFW content and personal information, we build on that work instead of repeating it at scale. Sources arrive in different shapes: some come with the image data itself, others as metadata plus baseline captions that we bring into a common form. We took a pragmatic approach: rather than build a corpus entirely from scratch, we leaned on existing datasets and our own tooling to assemble one quickly. In hindsight it is not necessarily the absolute best dataset one could build, but it was a solid a

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agent does not freely modify the whole SGLang repository: Workloads come from real SGLang diffusion models. The process first runs 20 diffusion models and summarizes actual kernel metadata; The baseline is copied from upstream SGLang main, with source lineage recorded; Baseline and candidate must use the same local ABI and the same build/export path; Benchmarks use fixed production rows, A/B interleaving, and CUDA event or wall timing; Correctness covers production rows, a canonical regression g

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a app to download models directly via the GUI: Otherwise, this can be done on the command line as well via By the way, the above-mentioned qwen3.6:35b-mlx is a model using Apple’s Metal performance shaders, i.e., optimized for Macs with Apple silicon chips. I highly recommend using -mlx versions of models working on Macs (if available). On a Linux machine, use the non-MLX version: Then, to make sure that it works, you can either use the GUI again or launch Ollama from the command line. You can e

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ystem. Every Browser Use Cloud session runs in its own, tiny VM. These VMs run on EC2, Amazon's rented cloud servers. That is the unusual part. Firecracker is normally run on bare-metal servers, where you rent the whole physical machine. To reduce customers' cost, we run it on regular EC2, where AWS has already put your server inside a VM. This should be slow. Nested VMs make memory and CPU operations more expensive, and Chromium takes time to start. This post is about how we made this setup fas

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g, pick a subset based on FPS (Frames Per Second), resize and normalize, then patchify with the temporal dimension baked in. What comes out is a flat patch tensor and a small grid metadata tensor, packed into a gRPC message. The worker just runs the vision encoder directly — no preprocessing on its end. Also, SMG's multimodal pipeline is structured around Rust traits that separate model-specific preprocessing logic from the pipeline plumbing. Adding M3 multimodal support meant implementing these

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for the entire world, that will never be read by most applications. You can skip loading the entire filesystem before the container starts and instead just block start on loading metadata (an index). The metadata is only a few megabytes, so it can be loaded in 100ms or less, along with everything else needed to start a container. The remainder can be loaded concurrently with other work — or not at all! The majority of the files will not be read, as reported in Figure 5 of the Slacker paper from

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different Lambda invocation picks it up. After three failed attempts the message goes to the DLQ. There is no retry counter in our database. The retry state lives entirely in SQS metadata, so if we need to know whether a task is on its second attempt we check the message attributes, not our own tables. The DLQ is a fire alarm. When messages land there it usually means we introduced a regression or there's a deeper issue to investigate. It's rare, and when it happens an engineer looks. We don't

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erate in their own clusters, with zero visibility into adjacent tenants. Together AI’s multi-tenant clusters are a concrete implementation of this pattern, demonstrating what bare-metal performance with cloud-like flexibility looks like for AI-native teams in practices, billed directly per tenant based on actual usage. This is where quota-based allocation becomes essential in any AI-native environment. Administrators set guardrails per team, capping by GPU count, total spend, or reservation wind

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rns emails into scoped issues directly from Intercom, without ever needing to leave the inbox. The agent picks up the full context, including any back-and-forth with the customer, metadata, and attachments. While you can add extra details in Intercom to help define the issue, Alexandra Lapinsky Wilson, who leads Product Operations and CX, says she rarely finds it necessary. The agent typically gets it right. Stage 2: Route it to the right team automatically⁠ Once the agent has scoped the context

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s, returning to works-in-progress, and understanding what each project contains at a glance. User Stories: As a user, I want to: Project Data Model: Each project contains: Project metadata (name, description, created/modified timestamps) Canvas settings (resolution: e.g., 256x224, 320x240, or 160x144) Tile size configuration (8x8, 16x16, or 32x32 pixels) Color palette selection All associated sprites, tilesets, levels, and entity definitions Product updates, how-tos, community spotlights, and mo

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n. Specifically, skills are now standardized using the AgentSkills standard to include files in the following format: The most central part of this is SKILL.md, which contains: 1) metadata with name, description and triggers, 2) markdown with instructions on how to carry out the task, and 3) optional references to scripts and other files. Notice that the only metadata is loaded every time, while the full skill is only loaded to the context when it is triggered. This is called "progressive disclo

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less browser. So we don't try to capture speaker output. Instead, we spy on playback intent. We monkey-patch key Web Audio API and HTMLMediaElement entry points to intercept audio metadata at the source, before it ever reaches a speaker path: When the page plays a sound, we now know: which audio file, when it started, how loud it should be (by walking the GainNode graph), and whether it loops. This approach covers the most common audio paths and is designed to work across Howler.js (Gemini loves

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