{ "@type": "Dataset", "name": "@marvin_tong on X", "description": "Followers, posts and engagement for @marvin_tong.", "url": "https://tweetindex.com/account/marvin_tong", "isAccessibleForFree": true, "creator": { "@id": "https://tweetindex.com/#organization" }, "variableMeasured": [ { "@type": "PropertyValue", "name": "Followers", "value": 14613 }, { "@type": "PropertyValue", "name": "Following", "value": 4415 }, { "@type": "PropertyValue", "name": "Posts", "value": 4779 }, { "@type": "PropertyValue", "name": "Collected posts", "value": 6 }, { "@type": "PropertyValue", "name": "Views on collected posts", "value": 20571 }, { "@type": "PropertyValue", "name": "Likes on collected posts", "value": 128 } ], "distribution": [ { "@type": "DataDownload", "contentUrl": "https://tweetindex.com/api/accounts/marvin_tong", "encodingFormat": "application/json" } ], "@id": "https://tweetindex.com/account/marvin_tong#dataset" }

Marvin Tong ✓

@marvin_tong · San Francisco, CA · joined 30 Sep 2015

do things for AI privacy : Trust Machines @PhalaNetwork / Privacy LLM @redpill_gpt / 🦞 icloud for AI agent: https://t.co/4MpkGHHOhb /

14 613Followers

4 415Following

4 779Posts total

20.6KViews on collected posts

Son gönderiler

Marvin Tong ✓@marvin_tong4 Sep

modern inference’s quality-efficiency frontier increasingly comes down to cache hit rate. Infra should be built around maximizing it. Encryption makes cache reuse extremely hard in private inference—but we’re breaking through that barrier. https://t.co/EuYTZ1ZL3L 836 views · 15 likes · 1 reposts · 0 replies Open on X →

Marvin Tong ✓@marvin_tong2 Sep

Congrats! 1.4K views · 9 likes · 0 reposts · 4 replies Open on X →

Marvin Tong ✓@marvin_tong31 Aug

If you are using H200 to inference GLM 5.3, try this model version! 1.6K views · 11 likes · 0 reposts · 2 replies Open on X →

Marvin Tong ✓@marvin_tong31 Aug

Benchmark checks: 91.92 GPQA-Diamond (182/198), 82.2 on a 45-item BFCL subset, and 3/3 NIAH retrieval at ~930k-token prompts. Weights, serving config, and per-item evaluation artifacts: https://t.co/F8oBJf7zkp 2K views · 12 likes · 0 reposts · 0 replies Open on X →

Marvin Tong ✓@marvin_tong28 Aug

Common misunderstanding. They are not subsidizing, it’s just very good infrastructure work from top inference teams. If you can make cache hit rate 99.5% with over 500m TPM with 99% online status, you could pricing low too. 1.2K views · 6 likes · 0 reposts · 0 replies Open on X →

Marvin Tong ✓@marvin_tong25 Apr

I run 2 companies with 5 AI agents every day: Claude Code, IDE+CC, Codex, OpenClaw, Hermes. And I fucking hate it. https://t.co/eW0bJAJJtS 13.7K views · 75 likes · 6 reposts · 15 replies Open on X →

Aynı büyüklükteki hesaplara karşı

Son 90 güne ait 5 gönderi, 10K–100K takipçi aralığıyla yan yana. geniş kitleye gösteriliyor, ama izleyenlerin azı tepki veriyor.

Medyan görüntülenme1 372bu hesap92410K–100K için medyan

Erişim, %9.39%bu hesap3.62%10K–100K için medyan

Etkileşim, %0.83%bu hesap1.52%10K–100K için medyan

Ölçüt	Bu hesap	10K–100K için medyan	Oran
Gönderi başına medyan görüntülenme	1 372	924	1.48×
Erişim (görüntülenme ÷ takipçi)	9.39%	3.62%	2.59×
Etkileşim oranı	0.83%	1.52%	0.55×

Bu aralıktaki diğer hesaplar → Başka bir hesapla karşılaştır → Bu kıyas değerleri nasıl kuruluyor →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.