Mono

[OpenAI @OpenAI]

This is GPT-6 Astra.

Anything you can do on a computer, Astra can do for you. Fast.

[Mono @Mono_chww]

Nice experience with @AutoClawAIer

[jietang @jietang]

Thoughts About Scaling Law

Scaling, but not only of parameters. Every model release now ends with the same question: how many parameters? It isn't a question that can be answered on its own. Parameter count is only meaningful alongside three others — how much data you have, where you intend to spend your compute, and who will run the model, under what conditions.

The field learned this the hard way. Kaplan et al. (2020) fit an exponent that told everyone to grow parameters faster than data — roughly 2.7:1 — and the industry complied: GPT-3, Gopher, MT-NLG. Hoffmann et al. (2022) redid the experiment across four hundred models and found the compute-optimal split is closer to 20 tokens per parameter, and that with sufficient compute the two should grow at the same rate rather than drifting apart. The error in the earlier fit compounded with every order of magnitude of compute, which is why the largest models of that generation were the most misallocated. The trillion-parameter round was, in retrospect, a detour the whole field took together and then reversed.

Chinchilla wasn't the end either. It optimized training compute for models that would be trained once and evaluated. Today a model is called billions of times a day and inference dominates lifetime cost. Put inference into the objective and the optimum moves toward smaller models trained far longer — deliberate over-training, which is what Llama-2-7B and Gemma-2-9B were doing at roughly 290 and 889 tokens per parameter.

Sparsity moved the target again. In a MoE model two quantities have to be kept apart: total parameters govern roughly how much the model can hold — knowledge, facts, the long tail — while activated parameters and effective depth govern roughly how far it can think, how many steps of a causal chain it can carry before it comes apart. A dense 20:1 ratio does not transfer. And the ratio isn't a single number at all: Roberts et al. (2025) find the optimal tokens-per-parameter is task-dependent, with memorization favoring more parameters and reasoning favoring more data. Follow-up work on MoE observes that at fixed TPP, pushing total parameters higher actually degrades reasoning, while activating more experts reliably helps it.

This matters for what we are building toward. Finding a vulnerability is not a retrieval problem. It doesn't come from having memorized more CVEs; it comes from carrying a twenty-step chain of inference to the end without losing the thread. That capability does not live in total parameter count.

Which brings us to this release. Total parameters appear to matter up to a threshold — enough to hold the world — after which additional capability comes from scaling elsewhere: effective depth per forward pass, and above all post-training. GLM-5.3 is our controlled experiment on that claim. Same base, same architecture, same total and activated parameters as GLM-5.2. One month of scaling long-horizon environments and RL. The gains are not marginal. Well, scaling has more than one dial. We turned the post-training one this time because it had the most slack left in it — not because the others are finished. Base model size, pretraining data, compute spent per forward pass: all of them are still on the table, and we will come back to each. What this experiment taught us is that the dials do not have to be turned together, and that the one worth turning next is rarely the one that was worth turning last. We are not done scaling. Next time, maybe mid-training, pre-training, and even more.

[向阳乔木 @vista8]

https://t.co/6aFGZsDCzQ

[Elon Musk @elonmusk]

Let’s see if @Grok 5 can beat the best human team @LeagueOfLegends in 2026 with these important constraints:

1. Can only look at the monitor with a camera, seeing no more than what a person with 20/20 vision would see.

2. Reaction latency and click rate no faster than human.

Join @xAI if you are interested in solving this element of AGI.

Note, Grok 5 is designed to be able to play any game just by reading the instructions and experimenting.

[柴郡🔔｜Crypto+AI Plus @0xCheshire]

这位亿万富翁靠着证明所有人都错了而致富。

他是脸书、比特币和太空探索技术公司的早期投资人。

他刚刚说：“AI 的热潮有 90% 是噪音。只有逆向思考者才能获胜。”

彼得·蒂尔下一步押上的 5 个预测（以及你为什么必须重视）：🧵

[柴郡🔔｜Crypto+AI Plus @0xCheshire]

21 世纪最伟大的思想家：纳瓦尔·拉威康特。

他是 Uber、Twitter 和 Notion 的早期投资者。

他曾说过一句著名的话：“靠出租时间你是不会致富的。”

下面是他关于财富的十条教诲，改变了我的人生：🧵

[OmniCrypto @OmniCryptoAI]

Welcome to Omni Catch Alpha 🩵
Not just a tool. Not just a dApp.

It’s how you cut through the noise and see crypto clearly, ahead of the curve.
👉Try it now https://t.co/5ZWL9XYCWm

[OmniCrypto @OmniCryptoAI]

What’s Hot on Omni Today?

1️⃣ Trends
🔥@trendsdotfun is a Solana-based tweet tokenization protocol where users can instantly turn any X post into a tradable asset.

✅Supported by a coalition of Solana’s top institutions and founders — such as @toly — spanning key verticals including L1s, DEXs, and NFTs.

💧 No pre-funded liquidity pools
100% market-driven pricing (🚫 fake likes!)
💰 First-Mover Gold: Creator gets 20% supply
The founder @mablejiang 's project introduction tweet token surged over 10x within 24 hours.

🫡 Is this the start of a new trench war on Solana?

2️⃣BNB Attestation Service (BAS)
@BASCAN_io is a native verification infrastructure built on BNB Chain that generates attestations to verify information.

Generate attestations for —
✅ On-chain KYC / Identity
✅ Asset proof
✅ RWA / DeFi compliance & scaling
🔥 Launched on Binance Alpha ion July 17
💡 Deep integration with @Polyflow_PayFi — Building cross-chain credit systems Privacy-preserving credentials.

[Mono @Mono_chww]

@Leologinweb3 @OmniCryptoAI 好牛！想用！

[MemeRadar @MemeRadar_sol]

MemeRadar 0.7.0 【小红书】+【大众点评】地址标签上线！
现在不仅有AI帮你对地址进行总结评价，更有专业玩家对标签进行打标，让地址的评价更加精准有效！
首期我们邀请到了行业内十位地址专家参与到了地址打标，他们分别是 @MasonCanoe @LZYH88 @DiYi_Community @onefly @Hovz0217 @Web3Tashan @FanShen_eth @in7ergalactic @Web3_Mian_ZH @in7ergalactic

地址专家入驻持续进行中，如果你也是链上分析的专业地址打标玩家，想要让你的标签被更多人看见，TG联系 @chaininsight_7 入驻平台！

[Mono @Mono_chww]

@MemeRadar_sol @MasonCanoe @LZYH88 @DiYi_Community @onefly @Hovz0217 @Web3Tashan 太棒了！期待看到更多精准有效的地址评价！加油，MemeRadar团队！