Magna Ding ✓
@MagnaDing · Los Angeles (Remote) · joined 10 May 2022
Founding https://t.co/T7hSYhdwC4 (Top AI Newsletter with 1.5M Subs), https://t.co/MUuYT8XRT7 (The #1 Agent Cloud)
15 252Followers
4 006Following
7 493Posts total
682.9KViews on collected posts
Últimas publicaciones
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@MagnaDing The 361-day timeline is definitely the part I’d want to verify for myself.
54 views · 1 likes
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@MagnaDing The 24-day turnaround after Tobi spoke up is honestly the part that caught my attention.
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@MagnaDing The timeline says more than the headlines. 361 days of silence, then 24 days of action—that contrast is hard to ignore. 👀
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Anthropic is a coward with a price list.
405 devs asked @AnthropicAI for AGENTS.md. 361 days of nothing.
Then Tobi Lütke (@tobi) said 39 words about leaving. Anthropic folded in 24 days.
I kept every receipt. Read all 361 days: https://t.co/LdapLLj4Ol https://t.co/NXg8qunGSQ
126.4K views · 227 likes
· 102 reposts · 46 replies
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@MagnaDing muse spark 1.3 max scoring higher than gpt-6 astra max on artificial analysis, what other evidence we want https://t.co/btB3SxGhPH
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@MagnaDing The “internship, not exam” analogy is absolutely on point.
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@MagnaDing The internship analogy is spot on.
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ChatGPT went down. Claude went down. Grok went down.
However my agent is just sitting here like: "Here's your morning brief."
DeepSeek V4 Pro x https://t.co/ebgCISqLrq - Keep Living.
492 views · 11 likes
· 1 reposts · 4 replies
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The wearable recording category has spent years optimizing for one thing: smaller. Almost nobody asks whether smaller just means easier to hide. Vocci flips that — small for comfort, not for concealment. That distinction has been missing from this conversation for too long.
498 views · 4 likes
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Privacy in AI wearables starts before data reaches the cloud. It starts with whether the people around you know the device is there.
With Vocci, transparency is built in from the beginning—not added later as a disclaimer.
Watch our CEO JY shares how we think about privacy by ht
496.4K views · 460 likes
· 31 reposts · 33 replies
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Most RL training uses a fixed scoring system to judge a model's work after the fact. TEMPO does something different: the same model switches roles mid-task, stops, reasons over its own progress, and estimates how close it actually is, before continuing.
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do you understand what just got open-sourced?
a model that scored perfect gold at the International Math Olympiad, by learning to be its own critic mid-solution.
meet dots3-note Preview, developed by rednote’s dots studio @dotsstudioai.
280B total / 16B active → 512K context h
40.4K views · 29 likes
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AI memory gets much more interesting when the model learns what deserves to be remembered instead of simply storing everything. Explore → hypothesize → verify → update memory feels much closer to how genuinely adaptive agents should operate.
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rednote just released an open-weight AI model built for tasks that take hours, not seconds.
dots3-note preview. 280B parameters. Text, vision, and audio.
Here is what makes it different: 👇 https://t.co/Boyiw7vzvK
17.3K views · 78 likes
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Frente a cuentas del mismo tamaño
14 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. justo en la mediana de su rango de seguidores.
Visualizaciones medianas495esta cuenta924mediana de 10K–100K
Alcance, %3.25%esta cuenta3.62%mediana de 10K–100K
Interacción, %1.16%esta cuenta1.52%mediana de 10K–100K
| Métrica | Esta cuenta | Mediana de 10K–100K | Proporción |
|---|---|---|---|
| Visualizaciones medianas por publicación | 495 | 924 | 0.54× |
| Alcance (visualizaciones ÷ seguidores) | 3.25% | 3.62% | 0.90× |
| Tasa de interacción | 1.16% | 1.52% | 0.76× |
Otras cuentas de este rango → Comparar con otra cuenta → Cómo se construyen estas referencias →
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.
Views per post
17.3K31 Aug
512
40.4K
5061 Sep
496.4K2 Sep
4983 Sep
492
1184 Sep
50
107
126.4K21 Sep
47
44
54
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.63%31 Aug
0.59%
0.13%
1.38%1 Sep
0.11%2 Sep
1.41%3 Sep
3.25%
0.85%4 Sep
2.00%
0.93%
0.31%21 Sep
2.13%
2.27%
1.85%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes63.0%822 in total
Reposts12.0%157 in total
Replies8.4%109 in total
Quotes5.4%70 in total
Bookmarks11.2%146 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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