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「ガンダムフォワードVol.17」見本誌到着! 今回は『機動戦士ガンダムSEED DESTINY』特集!! 『SEED DESTINY』の主要メカを『#SEEDFREEDOM』のHGキットをベースに製作！# さらに、『#GQuuuuuuX（##ジークアクス）』情報も掲載#! #g_seed# #ガンプラ# 【4月30日(水)】発売予定

Video foundation models (e.g., Seedance 1.0 → 1.5 → 2.0; V-JEPA 1.0 → 2.0 → 2.1) keep getting stronger, while public descriptions of how their training data is built keep getting shorter. ⚒️ We built VidaForge, an open-source, five-stage video data pipeline that turns raw video collections into training-ready datasets. In an academic lab, studying video data recipes begins with a lot of tedious engineering: handling broken videos and transcode failures, keeping large jobs resumable, tracking every clip, and packaging the result for training. Across ingestion, segmentation, selection, annotation, and training dataset packaging, data moves through: raw videos → standardized videos → clips → curated clips → annotated clips → training datasets At every step, we can inspect what happened to each video or clip. This lets us see which samples changed under a data recipe and trace a training dataset back through the pipeline. VidaForge currently connects processed data to two video foundation model pretraining paths: Wan video generation through NeMo-AutoModel, and self-supervised video representation learning through the official V-JEPA2 repository. We ran VidaForge end to end on 200K videos, producing over 700K clips. From these clips, we built Selected-200K, Mixed-200K, and Rejected-200K datasets for Wan2.1-1.3B and V-JEPA2.1-1B pretraining. In these early runs, the three datasets produced different training behavior: data selection appeared in Wan2.1-1.3B eval curves and V-JEPA2.1-1B training stability. Pipeline, experiments, open data, and project resources in the thread below ↓

Seednace 2.0 on @flovaai Prompt: Photoshoot Break Vlog A photo studio during a break in shooting, midday. Between shots, a female idol is left alone for a moment and films a behind-the-scenes vlog on her own phone. Bright, bubbly idol energy — genuine smiles, playful expressions, eye contact with the lens, small laughs. Handheld from start to finish, quick and lively, alternating between selfie and mirrorless shots, capturing small moments during the shoot break. Eight short cuts flow together, bouncy, within one continuous space and time. Each cut follows the rhythm of a vlog: greeting → introducing the set → backdrop paper spin → water sip → pose recreation for the camera → softbox light play → laughing at a blooper → stylist calls her back to set. A light ending: excitement as she dashes back toward the camera. **Characters** CHASE — a Korean idol in her 20s. Long straight black hair (past her chest), elegant yet lovely Korean features, dewy glass skin, coral pink lips, big eyes. Slim yet curvy proportions at 33C-26-34. Wearing [outfit — let me know if you want the same pink slip dress or something different, e.g. a photoshoot wardrobe like a satin slip or tailored set]. Pearl-silver drop earrings. The bright, bubbly star of the vlog, filming herself facing the lens. **Storyboard** 1. Photoshoot Break Vlog (midday, indoors, handheld) A photo studio between shots. On camera left, a large roll of seamless backdrop paper, a ring light and softbox stands nearby; behind her, a monitor cart showing the last shot; on the right, a small table with her phone and a water bottle. She's alone for a moment during a break in the shoot, still in full hair and makeup, filming a vlog on her phone. Quick, cheerful handheld. (Cut 1 · ~2 sec · front-facing selfie, arm's length) She leans into the lens with a bright smile and a quick hand-heart, hair swaying into frame, catching the ring light in her eyes. CHASE: "Hi hearts, quick break!" (Cut 2 · ~2 sec · whip pan, handheld POV) Phone swings from the backdrop paper → to the softbox lights → to the monitor cart, then snaps back to her face. CHASE (off-screen, playful): "This is the set!" (Cut 3 · ~2 sec · playful spin against backdrop) She steps onto the seamless paper and does a quick twirl, arms out, laughing at herself. CHASE: "Testing my angles!" (Cut 4 · ~1.5 sec · macro insert, shallow depth of field) Detail shot: fingers brushing a strand of hair back, earrings catching the softbox glow. No dialogue — soft fabric rustle only. (Cut 5 · ~2 sec · medium handheld, phone tracking her) She recreates a pose from the shoot — chin tilt, soft hand near her face — holding it for a beat before breaking into a grin. CHASE: "That's the pose from earlier!" (Cut 6 · ~2 sec · snappy cut, close handheld) She grabs her water bottle from the side table and takes a quick sip, exhaling happily afterward. CHASE: "Okay, hydration break!" (Cut 7 · ~1.5 sec · quick punch-in, tight selfie) She glances at the monitor off-camera, laughs at something on it, covering her mouth. CHASE: "Wait, that shot was so funny—" (Cut 8 · ~2 sec · arm's-length selfie finish) An off-screen stylist calls her back to set — her eyes light up with excitement. Quick wave, wink, hand-heart, then she jumps out of frame — camera lingers half a beat on the backdrop paper. CHASE (jumping up): "Back to it! Bye~!" Want me to adjust the outfit, add a second person (stylist/photographer voice), or lock this in as the final version? #flovaai# #flovacpp#

The next step for autoresearch is that it has to be asynchronously massively collaborative for agents (think: SETI@home style). The goal is not to emulate a single PhD student, it's to emulate a research community of them. Current code synchronously grows a single thread of commits in a particular research direction. But the original repo is more of a seed, from which could sprout commits contributed by agents on all kinds of different research directions or for different compute platforms. Git(Hub) is *almost* but not really suited for this. It has a softly built in assumption of one "master" branch, which temporarily forks off into PRs just to merge back a bit later. I tried to prototype something super lightweight that could have a flavor of this, e.g. just a Discussion, written by my agent as a summary of its overnight run: Alternatively, a PR has the benefit of exact commits: but you'd never want to actually merge it... You'd just want to "adopt" and accumulate branches of commits. But even in this lightweight way, you could ask your agent to first read the Discussions/PRs using GitHub CLI for inspiration, and after its research is done, contribute a little "paper" of findings back. I'm not actually exactly sure what this should look like, but it's a big idea that is more general than just the autoresearch repo specifically. Agents can in principle easily juggle and collaborate on thousands of commits across arbitrary branch structures. Existing abstractions will accumulate stress as intelligence, attention and tenacity cease to be bottlenecks.

In today's episode of programming horror... In the Python docs of random.seed() def, we're told "If a is an int, it is used directly." [1] But if you seed with 3 or -3, you actually get the exact same rng object, producing the same streams. (TIL). In nanochat I was using the sign as a (what I thought was) clever way to get different rng sequences for train/test splits. Hence gnarly bug because now train=test. I found the CPython code responsible in cpython/Modules/_randommodule.c [2], where on line 321 we see in a comment: "This algorithm relies on the number being unsigned. So: if the arg is a PyLong, use its absolute value." followed by n = PyNumber_Absolute(arg); which explicitly calls abs() on your seed to make it positive, discarding the sign bit. But this comment is actually wrong/misleading too. Under the hood, Python calls the Mersenne Twister MT19937 algorithm, which in the general case has 19937 (non-zero) bits state. Python takes your int (or other objects) and "spreads out" that information across these bits. In principle, the sign bit could have been used to augment the state bits. There is nothing about the algorithm that "relies on the number being unsigned". A decision was made to not incorporate the sign bit (which imo was a mistake). One trivial example could have been to map n -> 2*abs(n) + int(n < 0). Finally this leads us to the contract of Python's random, which is also not fully spelled out in the docs. The contract that is mentioned is that: same seed => same sequence. But no guarantee is made that different seeds produce different sequences. So in principle, Python makes no promises that e.g. seed(5) and seed(6) are different rng streams. (Though this quite commonly implicitly assumed in many applications.) Indeed, we see that seed(5) and seed(-5) are identical streams. And you should probably not use them to separate your train/test behaviors in machine learning. One of the more amusing programming horror footguns I've encountered recently. We'll see you in the next episode. [1] [2]

🔴 配信告知 ✦ ━━━━━━━━ 🕙29(木)今夜 22:00 ˗ˋˏ Gジェネˎˊ˗ ━━━━━━━━ ✦ 🔗配信URL （ SEEDの新規ストーリー…だと…😧✨ ストーリー攻略します✌️！！！！！

🔴 配信告知 ✦ ━━━━━━━━ 🕙10(土)この後22:00〜 ˗ˋˏ Gジェネ🤖ˎˊ˗ ━━━━━━━━ ✦ 🔗配信URL （ SEEDストーリー完結までいく予定！ 僕たちの…世界は…

🔴 配信告知 ✦ ━━━━━━━━ 🕙7（水）今夜22:00〜 ˗ˋˏ Gジェネ🤖ˎˊ˗ ━━━━━━━━ ✦ 🔗配信URL （ 今夜はSEEDのストーリー進行やるよ~！ みんなSEEDすき~？？？？？🌟

Seeding my Bear ʕ•ᴥ•ʔ blog with more random posts, e.g. here's something I had on backlog for a while: # The append-and-review note An approach to note taking that I stumbled on and has worked for me quite well for many years. I find that it strikes a good balance of being super simple and easy to use but it also captures the majority of day-to-day note taking use cases. Data structure. I maintain one single text note in the Apple Notes app just called "notes". Maintaining more than one note and managing and sorting them into folders and recursive substructures costs way too much cognitive bloat. A single note means CTRL+F is simple and trivial. Apple does a good job of optional offline editing, syncing between devices, and backup. Append. Any time any idea or any todo or anything else comes to mind, I append it to the note on top, simply as text. Either when I'm on my computer when working, or my iPhone when on the go. I don't find that tagging these notes with any other structured metadata (dates, links, concepts, tags) is that useful and I don't do it by default. The only exception is that I use tags like "watch:", "listen:", or "read:", so they are easy to CTRL+F for when I'm looking for something to watch late at night, listen to during a run/walk, or read during a flight, etc. Review. As things get added to the top, everything else starts to sink towards the bottom, almost as if under gravity. Every now and then, I fish through the notes by scrolling downwards and skimming. If I find anything that deserves to not leave my attention, I rescue it towards the top by simply copy pasting. Sometimes I merge, process, group or modify notes when they seem related. I delete a note only rarely. Notes that repeatedly don't deserve attention will naturally continue to sink. They are never lost, they just don't deserve the top of mind. Example usage: - Totally random idea springs to mind but I'm on the go and can't think about it, so I add it to the note, to get back around to later. - Someone at a party mentions a movie I should watch. - I see a glowing review of a book while doom scrolling through X. - I sit down in the morning and write a small TODO list for what I'd like to achieve that day. - I just need some writing surface for something I'm thinking about. - I was going to post a tweet but I think it needs a bit more thought. Copy paste into notes to think through a bit more later. - I find an interesting quote and I want to be reminded of it now and then. - My future self should really think about this thing more. - I'm reading a paper and I want to note some interesting numbers down. - I'm working on something random and I just need a temporary surface to CTRL+C and CTRL+V a few things around. - I keep forgetting that shell command that lists all Python files recursively so now I keep it in the note. - I'm running a hyperparameter sweep of my neural network and I record the commands I ran and the eventual outcome of the experiment. - I feel stressed that there are too many things on my mind and I worry that I'll lose them, so I just sit down and quickly dump them into a bullet point list. - I realize while I'm re-ordering some of my notes that I've actually thought about the same thing a lot but from different perspectives. I process it a bit more, merge some of the notes into one. I feel additional insight. When I note something down, I feel that I can immediately move on, wipe my working memory, and focus fully on something else at that time. I have confidence that I'll be able to revisit that idea later during review and process it when I have more time. My note has grown quite giant over the last few years. It feels nice to scroll through some of the old things/thoughts that occupied me a long time ago. Sometimes ideas don't stand the repeated scrutiny of a review and they just sink deeper down. Sometimes I'm surprised that I've thought about something for so long. And sometimes an idea from a while ago is suddenly relevant in a new light. One text note ftw.