RadixArk ✓
@radixark
SHIP AI FOR ALL.
6 625Followers
14Following
158Posts total
1.1MViews on collected posts
Son gönderiler
Yeni gönderiler aranıyor…
Website: https://t.co/hU7zgVGMBo
Repo: https://t.co/OO5iPHAI30
Blog: https://t.co/uriqLe6Hr1
2.4K views · 31 likes
· 2 reposts · 0 replies
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Miles trains coding and computer-use agents on environments from Harbor (@harborframework), HUD (@hud_evals), NeMo Gym (@NVIDIAAI), OpenEnv (@huggingface) and Verifiers (@PrimeIntellect), each with a connector at the rollout layer that fits it. Sandboxes run on AgentENV
2.3K views · 29 likes
· 1 reposts · 2 replies
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Miles combines TITO and R3 to make long-running agent training easier to verify and debug. https://t.co/pMg5704irV uses its trajectory management and rollout infrastructure to train agents across models, tasks, and environments.
“Miles removes much of the engineering complexity
3K views · 26 likes
· 0 reposts · 2 replies
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@DecagonAI is using Miles to post-train its AI agents on instruction-following and tool-calling, delivering the low-latency, high-precision interactions behind a concierge customer experience.
“Miles gives us the best of both worlds for RL: a simple abstraction for the common
2.5K views · 22 likes
· 1 reposts · 1 replies
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“Miles has been a powerful framework for our agentic RL work. Its efficiency, robustness, and advanced features for large-scale models allowed us to iterate faster and scale experiments with ease.”
Rameswar Panda, IBM
2.3K views · 14 likes
· 0 reposts · 1 replies
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@humansand uses Miles as core infrastructure for production-scale, long-horizon, multi-agent asynchronous RL research, adapting it to its own orchestration and extending it with MXFP8 and NVFP4 low-precision RL recipes.
“Miles combines a high-performance foundation for RL
3.8K views · 25 likes
· 2 reposts · 1 replies
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Miles supports LoRA training on the largest models such as GLM and Kimi, with fast, numerically stable adapter-only weight sync through the tight SGLang integration. @modal uses Miles both on its own and inside Training Gym, its RL stack, and builds Stitch on top to disaggregate
4.5K views · 34 likes
· 3 reposts · 1 replies
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Today we're launching Miles v0.1, an open-source RL framework for LLMs and multimodal models.
RL training is easy to start and hard to debug. Miles helps you ensure your run is correct, use hardware efficiently, and keep RL running at scale.
Over the past 9 months, 72 https://t
620.9K views · 926 likes
· 140 reposts · 57 replies
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@periodiclabs builds on top of Miles to train trillion-parameter models across thousands of GPUs with extremely long-horizon rollouts for scientific RL. Together, we improved rollout throughput by 3×, weight synchronization by 10×, and weight conversion by 30×, with everything
10.7K views · 36 likes
· 3 reposts · 2 replies
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@Accel @sparkcapital More angels we're deeply grateful for 🙏
@jhong @gharik @shreyshahi @H0wie_Xu @adityagrover_ @tydsh @BeidiChen @haozhangml
2.8K views · 21 likes
· 1 reposts · 0 replies
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@radixark @Accel @sparkcapital Huge congrats!!
3.9K views · 41 likes
· 2 reposts · 1 replies
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Read the full story: https://t.co/vpi7QjvyzL
Read the full blog: https://t.co/iYCOVe4ugR
Join us: https://t.co/7HYexAyqDI
Get started at: https://t.co/zxtEi6yoyb
Every star motivates our team:
SGLang Github: https://t.co/jopp2SwQZt Miles Github: https://t.co/OO5iPHAI30
10K views · 64 likes
· 4 reposts · 1 replies
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Today, we are thrilled to officially launch RadixArk with $100M in Seed funding at a $400M valuation. The round was led by @Accel and co-led by @sparkcapital.
RadixArk exists to make frontier AI infrastructure open and accessible to everyone. Today, the systems behind the most h
396.8K views · 647 likes
· 101 reposts · 86 replies
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Aynı büyüklükteki hesaplara karşı
Son 90 güne ait 10 gönderi, under 10K takipçi aralığıyla yan yana. hem erişimde hem etkileşimde benzerlerinin altında.
Medyan görüntülenme3 382bu hesap4 175under 10K için medyan
Erişim, %51.05%bu hesap250.82%under 10K için medyan
Etkileşim, %0.79%bu hesap1.41%under 10K için medyan
| Ölçüt | Bu hesap | under 10K için medyan | Oran |
|---|---|---|---|
| Gönderi başına medyan görüntülenme | 3 382 | 4 175 | 0.81× |
| Erişim (görüntülenme ÷ takipçi) | 51.05% | 2.5× audience | 0.20× |
| Etkileşim oranı | 0.79% | 1.41% | 0.56× |
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.
Views per post
396.8K5 May
10K
3.9K
2.8K
10.7K18 Aug
620.9K
4.5K
3.8K
2.3K
2.5K
3K
2.3K
2.4K
4.2K
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
0.21%5 May
0.69%
1.14%
0.78%
0.39%18 Aug
0.19%
0.84%
0.75%
0.66%
0.97%
0.93%
1.41%
1.39%
0.50%
Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.41%.
What the audience does
Likes60.0%1 936 in total
Reposts8.1%260 in total
Replies4.8%156 in total
Quotes2.3%73 in total
Bookmarks24.9%803 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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