⚠️CKenJa⚠️ @CKenJaC

マインクラフター。コマンド勢 / #CreateMod / 生成AI CKenJaは「しーケンじゃ」と読みます。JaはJapanの略。Cは我らが炭素です。 Mod鯖 #のんカフェのサーバー広報 (@NonbiriCreate ) kenhimagame.blogspot.jp 歌がうまい配信者が好き Joined October 2018

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Chris @ChrissGPT

2 days ago

This is a 2.5 minute coherent video from Seedance 2.5. Wow.

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Unlimited-OCR is now on ModelScope! A 3.3B multilingual OCR model for one-shot parsing across single images, multi-page documents, and PDFs. License: MIT🚀 🤖 modelscope.ai/models/PaddleP… 📄 Full-document parsing instead of cropped-region OCR 📏 32K output length for long OCR sequences 🧩 Base and gundam image modes for different document layouts ⚙️ Transformers inference + SGLang serving with OpenAI-compatible streaming requests Built to push DeepSeek-OCR-style document parsing further.

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⚠️CKenJa⚠️ @CKenJaC

2 days ago

コーディングAIエージェントCLI各位！ホームディレクトリに隠しフォルダを作るな！ WindowsにはAppDataが、Linuxには~/.configフォルダがあるだろ行儀が悪い！

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⚠️CKenJa⚠️ @CKenJaC

2 days ago

@ynishi2015 いや～きっとGで始まるLLMでしょうね！

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Cline @cline

2 days ago

We've kept hearing how GLM-5.2 beats Opus 4.8, and are skeptical of benchmarks - so we tested them on a real bug from the Cline repo. While both models fixed the issue, GLM was the winner in terms of cost and code quality: - GLM used twice as many tokens (GLM 1.1m vs Opus 660K) but cost half as much (GLM $0.41 vs Opus $0.81) - Opus finished quicker - 1.6 min and 12 tool calls vs GLM 4.7 min and 28 tool calls - GLM cleaned up dead code and verified the build compiled before completing. Opus didn't - it left type errors that passed tests but broke the production build. Both runs used the same Cline harness prompting and tools, so it seems GLM is RL trained to spend more tokens verifying its work before completing. Impressive work by the @Zai_org team!

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somunia @she_is_wired

3 days ago

先日、LanPageの制作チームで会議をしました。ライブ演出や方向性など、みんなで真剣に話し合うことができて、とてもよかったです。それはそれとして、自分デスゲームのルールを丁寧に説明している人みたいでシュールだな。

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⚠️CKenJa⚠️ @CKenJaC

5 days ago

無駄に編集のクオリティが高いのにコマンド勢にしかﾂﾀﾜﾗﾅｲ...

Mononobe / モノノベ @mikadomikado863

5 days ago

カス構文を打つコマンド勢は爆破します youtu.be/QAo_Kqx97dA

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深津貴之 / THE GUILD, note @fladdict

a week ago

こういう「AI動画で最初に楽しそうなゲーム画面」を作って、それから「これは実際作れるのか？」を議論する開発方式がこれから増えてくと思われる。

NOBU @nbykos

a week ago

ai voxel style Seedance2 (TapNow)

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西村/learningBOX/競プロｱｶ @ynishi2015

a week ago

ひたすら世界のラボで潤沢にお金注ぎ込みまくって実験しまくったら技術開発が加速すると言うスケーリング則が実証されてしまった。ロケットに投資しまくったらスペースXが伸びたし、核融合はほぼ確実に行けるし、量子コンピュータや医療も多分そう。

Torishima / INTP @izutorishima

2 weeks ago

LLM の性能向上は「スケーリング則でひたすら大金溶かしてデータとグラボを集めて規模をデカくするだけで順当に性能を上げられそう」＋「それに実際に物凄い実需がある」という気づきを市場が得られたからなんだろうな実際には順当なスケーリング則は頭打ちでテクニック的な改善が多そうだけど

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キュレェ @kyremoe

2 weeks ago

コーディングエージェントを並列で動かすテクニックが全く培えていないのでぼちぼちやっていきたい

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Susan Zhang @suchenzang

2 weeks ago

current LLMs fundamentally consist of four main components: - input layer: where input "words" (prompt) get mapped to "latents" aka some-model-representation-you-don't-understand-unless-you-start-reading-tea-leaves-of-spurious-correlations (some quite compelling à la word2vec style; latents is also unnecessary lingo so i will refer to these as "inputs" with quotes from now on) - mixing layers: where you jumble all your "inputs" together to see if any correlations between "inputs" can become useful (commonly used to compress or expand dims; predicting a single classification target == compress to a single dim, etc) - attention layers: where you learn how "inputs" relate to each other (aka discern what's important to remember vs fluff) - residuals: where you short-circuit a mixing/attention layer because it's probably adding too much confusion (aka avoid overthinking for simple things) ----- a "big" LLM simply scales two things: - width == how many dimensions you give to your "inputs" (the more dims, in theory the more unique/discerning/precise/complex your knowledge can become) - depth == how many mixing/attention/residual layers you can stack/loop between (aka "reason" over, where more of these ~= more "reasoning" abilities) "capabilities" that seem impressive to humans usually arise from taking advantage of both depth & width: where a model seemingly makes connections between disparate ideas, beyond what an average human can hold in working memory. this requires models to "completely light up" when responding to a "hard prompt", where effectively no param/layer goes unused. ----- the anatomy of a "model capability" is precisely the same mechanism that can be co-opted for a jailbreaking exploit: your goal is simply to "light up" as much of the model as possible, dodging any shallow input-classifiers at the beginning by triggering as many disparate "input ideologies" as possible, and subsequently have these "inputs" relate to each other in seemingly unrelated-yet-related ways that ideally have similar "complexity" as your jailbreak goal (to make it past enough layers of the model). think of the attack-vector as bundling your goal in a series of schizo-nerd-snipes: a sufficiently capable model will try to reason through everything all at once, eliminate the dead-ends, and successfully deliver the one jailbreak use-case you bubble-wrapped for. of course, there's an art to the above, and some are already extraordinarily proficient at the trojan-horse-packaging, but at some point there's no difference between "a capability" and "a jailbreak", though i'll be happy to be proven otherwise. ----- tl;dr ant flew too close to the sun, better kiss the ring or get buried.

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してはる @popico_pi

a week ago

#おはようVtuber

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今井翔太 / Shota Imai@えるエル @ImAI_Eruel

2 weeks ago

政府によるAnthropicのFable/Mythos停止騒動は、単体ではこの記事がおそらく一番詳しそう（あまりにも生々しいのと、自分も一応開発側なので、見ていて胃がキリキリする）・まずAmazon CEOのジャシーが、政権の意見要請に応じてジェイルブレイクの問題を伝えた・金曜日の朝の時点までに、ホワイトハウスの中で最高レベルの問題扱いになった・ベッセント長官等の政権幹部で会合があり、アモデイに直接連絡。アモデイは誤解を解こうとしたが受け入れられず・アモデイはもう少し時間が惜しいと訴えたが、Fable/Mythosの削除の約束はせず、ベッセントが「悪い決定をしている」と断言・この電話会談の直後に輸出規制発動・Anthropicの関係者曰く「削除の根拠を一切示さず、90分以内にモデル削除をするよう要請された」（怖すぎる・・・） politico.com/news/2026/06/1…

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Sakana AI @SakanaAILabs

a week ago

🐠 Sakana Marlin リリース 🐠 本日、Sakana AIは、初の商用プロダクトとして、ビジネス向けの自律型リサーチアシスタント「Sakana Marlin」を提供開始しました。詳細はこちら：sakana.ai/marlin ブログ：sakana.ai/marlin-release

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⚠️CKenJa⚠️ @CKenJaC

2 weeks ago

SplitgateのメーカーのTtitanfall系FPSの早期アクセスが6/15から1週間無料らしい

FPS G33KS＠FPS最新ニュース @FPS_G33KS

2 weeks ago

【Titanfall好き注目の新作FPS】・高速移動FPS『EMPULSE』のゲームプレイ公開・Steam Next Festに6月15日から参加・アーリーアクセスは6月24日開始・6vs6の対戦型ムーブメントシューター・壁走りを前後方向に活用可能・グラップルによる高速移動に対応・ホロジャンプで独自ルートを構築