anand iyer ✓
@ai · San Francisco, CA · joined 25 Feb 2008
Founder Canonical · We back, build, incubate early-stage companies · Venture Partner Lightspeed · Few-shotting open source AI
54 247Followers
752Following
18 028Posts total
2.9MViews on collected posts
Derniers posts
Recherche de nouveaux posts…
There is a multiplayer AI opportunity lurking that lets you monetize your private AI context in a secure, verifiable, provable way. It's like GLG/expert networks rebuilt as a protocol.
1.6K views · 0 likes
· 0 reposts · 0 replies
Open on X →
To everyone visiting SF for the first time this week:
- This heavy police presence is not normal.
- It doesn't usually take an hour to cross Market
- We have no idea what it's going to take for Monaco to stop. https://t.co/gKuzxEPSOQ
16.8K views · 162 likes
· 4 reposts · 20 replies
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US: pacing the frontier
China: passing on the right, tyvm
2.1K views · 8 likes
· 0 reposts · 1 replies
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China’s AI labs must accelerate development, says Huawei chair https://t.co/pvjwnpM8bd
37.3K views · 75 likes
· 23 reposts · 8 replies
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Iron dome but for data centers https://t.co/Rz3v7sfHLU
2.6K views · 9 likes
· 0 reposts · 3 replies
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The @X algorithm is working exceptionally well. Well done @benfielding and team on OPEN-1B! https://t.co/vqg5teuWjH
2.9K views · 33 likes
· 7 reposts · 2 replies
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@ai We help enterprises with this kind of stuff at @OsmanticAI in case anyone is wondering how it’s done btw
1.8K views · 37 likes
· 1 reposts · 0 replies
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@ai Ive told my network this for a long time, have echoed it here and that spammy professional social network as well.
Local models, trained, augmented for specific use cases are the future, not god-like LLMs that know everything about everything, you included.
4.2K views · 69 likes
· 3 reposts · 0 replies
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@ai Literally how it should be done ..
Why give a centralized company that much access to your data ..
Once you are reliant on them it will be extremely hard to leave
6.2K views · 98 likes
· 0 reposts · 0 replies
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Our new Trusted Person network is now available to all Instinct users! You can get started by asking your Instinct to add a few friends to your trusted network.
For the next two days, you’ll receive an additional 15 invites - bring the most important people in your life into
66.8K views · 442 likes
· 18 reposts · 45 replies
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Latham & Watkins, the 2nd largest US law firm, is buying Nvidia hardware to fine-tune open weights in-house.
Open weights + proprietary data + local compute = enterprise sovereign AI stack.
1.6M views · 3.6K likes
· 298 reposts · 85 replies
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Latham & Watkins buys Nvidia servers to set up in-house AI systems https://t.co/JHJiKQJ57b
1.1M views · 1.1K likes
· 192 reposts · 29 replies
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This reads like a Ludlum novel. Every CISO's nightmare scenario.
1.4K views · 1 likes
· 0 reposts · 2 replies
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SpaceX spent 1000 days engineering giant metal chopsticks to snatch a massive rocket out of the sky. Meanwhile, I have been using @duolingo for 1000 straight days to master saying things like "my horse is sad" in French. https://t.co/BfWxuACQHo
1.6K views · 21 likes
· 0 reposts · 2 replies
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Everyone focuses on GPUs for token generation, but scheduling, memory movement, and tool calls are where an agent actually spends its life. That’s all CPU work.
2.9K views · 22 likes
· 1 reposts · 5 replies
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A modern CEO has three jobs:
- set the vision,
- hoard enough cash and compute,
- orchestrate top talent paired with agents.
17.5K views · 438 likes
· 38 reposts · 19 replies
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Face aux comptes de taille comparable
17 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.
Vues médianes2 918ce compte924médiane pour 10K–100K
Portée, %5.38%ce compte3.62%médiane pour 10K–100K
Engagement, %0.79%ce compte1.52%médiane pour 10K–100K
| Indicateur | Ce compte | Médiane pour 10K–100K | Rapport |
|---|---|---|---|
| Vues médianes par post | 2 918 | 924 | 3.16× |
| Portée (vues ÷ abonnés) | 5.38% | 3.62% | 1.49× |
| Taux d'engagement | 0.79% | 1.52% | 0.52× |
Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →
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
1.6K4 Sep
1.4K
1.1M10 Sep
1.6M13 Sep
66.8K14 Sep
6.2K
4.2K
1.8K
2.9K15 Sep
2.6K
37.3K17 Sep
2.1K
16.8K
1.6K18 Sep
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
1.43%4 Sep
0.21%
0.14%10 Sep
0.25%13 Sep
0.78%14 Sep
1.58%
1.72%
2.15%
1.44%15 Sep
0.45%
0.29%17 Sep
0.42%
1.12%
0.06%18 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes66.8%6 170 in total
Reposts6.3%585 in total
Replies2.4%221 in total
Quotes3.2%298 in total
Bookmarks21.2%1 961 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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