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#Tutorials and practice

All collected and published items, ordered by time. Picks are a selected subset.
9/24Thu
  1. GitHub Blog · AI & ML22

    How the GitHub Copilot app renders enormous pull requests

    The GitHub Copilot app rebuilt its pull-request view to render huge PRs smoothly, including one with 2,200 files, more than one million changed lines, and over 400 inline comments. It separates document height into deterministic code geometry and dynamic-block geometry: code line heights are calculated precisely in advance, while dynamic blocks such as comments are anchored by stable keys to a file, line, and side, measured lazily, and adjusted only slightly to avoid scroll jumps.

  2. Simon Willison18

    A guide to shadow roots: understanding Shadow DOM through interactive examples

    An interactive tutorial on shadow roots demonstrates style encapsulation, inheritance, slots, parts, and JavaScript access. It shows how a shadow root creates an isolated DOM tree with its own private stylesheets and elements, and how that tree interacts with the page DOM in specific, controlled ways. The example was generated by Fable 5.1 Medium from a prompt.

9/18Fri
  1. Mike Krieger · X32

    I had trouble picturing the whole Iron Tangle level from Dungeon Crawler Carl, so I made an interactive artifact and I think I finally understand it: https://claude.ai/artifact/NTcG664cCbibbZNUFnCSu9?sk=HtqECKbpVqi8-DDzHAkh-Q

    I had trouble picturing the whole Iron Tangle level from Dungeon Crawler Carl, so I made an interactive artifact and I think I finally understand it: https://claude.ai/artifact/NTcG664cCbibbZNUFnCSu9?sk=HtqECKbpVqi8-DDzHAkh-Q

9/17Thu
  1. GitHub Blog · AI & ML78

    GitHub uses Copilot to rewrite the Copilot runtime in Rust

    Using the GitHub Copilot app and Copilot CLI, GitHub completely rewrote the Copilot agent runtime from TypeScript into more than 800,000 lines of production Rust. AI agents wrote most of the code, merged incrementally into main across 128 pull requests rather than in a single cutover.

    Why it matters: Drawing on firsthand experience, the author reviews the 800,000-line Rust rewrite and shares agent-orchestration and review practices that can be reused.

9/12Sat
9/11Fri
  1. GitHub Blog · AI & ML38

    Getting started with the GitHub Copilot app: using the diff, terminal, and browser panels

    The GitHub Copilot app includes diff, terminal, and browser panels, letting users review, run, and preview AI-agent code changes without leaving the app. The diff panel marks additions in green and deletions in red, and supports accepting changes, commenting, or asking Copilot to revise them. The terminal can run project commands directly and supports switching between multiple windows.

9/10Thu
9/9Wed
  1. Mistral AI58

    How Mistral uses AI agents to migrate 40,000 lines of legacy Fortran

    Mistral helped a European energy operator migrate a 40,000-line Fortran 77 reservoir simulator to C++. The codebase had no test suite or centralized documentation. The team first built a numerical-equivalence testing framework, then used a Skill.md to guide agents in exporting Fortran state snapshots and validating them in C++. A custom parser generated the call tree, and Vibe CLI coordinated hundreds of agents with Mistral OCR to fill documentation gaps.

9/4Fri
  1. GitHub Blog · AI & ML38

    Getting started with the GitHub Copilot app: running multiple agents at once

    The GitHub Copilot app supports multiple agent sessions at once, each in a separate Git worktree with its own context and no interference from others. Users can switch between sessions and resume where they left off. The sessions view shows each session's title and progress. The tutorial uses the tailspin-toys repository to run three tasks at once: a funded sort, an accessibility review, and tests.

9/3Thu
8/26Wed
8/21Fri
8/19Wed
8/18Tue
  1. Hugging Face Blog48

    Hugging Face introduces constraint-aware GPU allocator, improving cluster utilization by up to 33 percentage points

    Hugging Face built a constraint-aware GPU allocator and compared it with a FIFO scheduler across seven benchmark scenarios. With the same hardware and workloads, GPU utilization improved by up to 33 percentage points, while priority-weighted throughput rose in every scenario by up to 105%.

8/14Fri
  1. Hugging Face Blog62

    Closing the recording-to-training-to-deployment loop with Strands Agents, LeRobot, and Hugging Face Storage Buckets

    A Hugging Face blog post presents a streaming-data loop with Strands Robots: the same `Robot()` records a LeRobotDataset and syncs it to a Storage Bucket, then `stream_dataset()` reads directly from the Hub for training without downloading the full dataset first. To deploy a trained checkpoint back to hardware, users only need to switch the mode to `real`.

    Why it matters: The Strands Robots example breaks down a robotics data loop and shows how recording, streaming training, and deployment can share one LeRobot format.

7/22Wed
  1. Andrej Karpathy · X28

    Karpathy shares a 'long voice ramble' technique for working with LLMs

    Andrej Karpathy shares a practical way to work with LLMs: switch to `/voice` and spend about ten minutes stream-of-consciousness talking through ideas, even if they are messy. He says LLMs are good at restructuring long, incoherent rambles; the resulting reflection often makes the thinking clearer than the original input, deepening the “mind meld” and reducing later corrections.

7/16Thu
7/10Fri
7/7Tue
  1. Lilian Weng · X32

    new post on harness engineering for AI self-improvement: https://lilianweng.github.io/posts/2026-07-04-harness/ It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter models keeps harnesses simple. Even when many harness improvement get eventually internalized into core model, the need to specify goals and context will not disappear.

    new post on harness engineering for AI self-improvement: https://lilianweng.github.io/posts/2026-07-04-harness/ It is hard to forecast how much the future of RSI will rely on harnesses. Likely harness engineering will evolve in the direction of self-improvement and enable auto-research, and, in turn, smarter models keeps harnesses simple. Even when many harness improvement get eventually internalized into core model, the need to specify goals and context will not disappear.

7/6Mon
  1. Hugging Face Blog38

    PRX Part 4: our data strategy

    Part four of the PRX series describes its data strategy: pretraining data combines public and internal datasets; a VLM regenerates long descriptions of images, which are then converted into a streamable training corpus. The team switched to Qwen3-VL for on-the-fly text-latent computation during training, losing only about 3–4% throughput at 7B scale (adding roughly one day to a 30-day run). All images are encoded as JPEG at quality 92.

6/26Fri
  1. Lilian Weng · X57

    Lilian Weng explains scaling laws and compute-optimal allocation

    Lilian Weng shares a long-overdue (3+ years?) post on scaling laws. Compute is expensive. Scaling laws help reason about optimal compute allocation between data and model size before committing to a large run. The post covers what scaling laws predict, how compute-optimal allocation works, why Kaplan et al. and Chinchilla disagree, and how data limits and fitting details make extrapolation tricky. https://lilianweng.github.io/posts/2026-06-24-scaling-laws/

3/11Wed
1/22Thu
  1. Mistral AI34

    Heaps do lie: investigating a memory leak in vLLM

    The Mistral AI team investigated a memory leak in vLLM. In a Mistral Medium 3.1 deployment with graph compilation and disaggregated prefill/decode, system memory grew linearly by 400 MB per minute until the process ran out of memory hours later. Heaptrack showed stable heap usage, indicating the leak was outside the heap. The team traced it to KV-cache transfers through NIXL and UCX.

8/1Fri
4/9Wed
3/4Tue