FAR Labs ✓

@FARLabsAI · Be First to Try FAR AI 👉🏼 · joined 06 Jun 2022

Building FAR AI | Cheaper, faster and scalable AI inference | Based on distributed compute | Powered by @Dizzaract https://t.co/0w5nrjnFyJ

172 254Followers

235Following

11 195Posts total

211.2KViews on collected posts

โพสต์ล่าสุด

FAR Labs ✓@FARLabsAI2 Sep

What will matter most for scaling AI infrastructure by 2030? 4.4K views · 6 likes · 1 reposts · 0 replies Open on X →

FAR Labs ✓@FARLabsAI31 Aug

A study published in Joule estimates that a typical frontier-model query consumes a median 0.31 Wh of energy. When queries are 15× longer in a test-time scaling scenario, estimated consumption rises to 3.91 Wh. This shows why inference workloads cannot be understood through one

3.9K views · 9 likes · 1 reposts · 1 replies Open on X →

FAR Labs ✓@FARLabsAI24 Aug

Strong hardware alone does not make a node reliable. Uptime, successful job completion, end-to-end latency and previous incidents all affect how well the network performs. FAR AI’s Reliability Score converts verified behavior into a rolling performance record. The orchestrator h

3.8K views · 25 likes · 3 reposts · 10 replies Open on X →

FAR Labs ✓@FARLabsAI17 Aug

Gartner expects global AI inference spending to reach $23.3 billion, ahead of the $19 billion allocated to training. Training develops model capabilities. Inference puts those capabilities to work in live applications, where every request creates an operational workload. As htt

3.6K views · 17 likes · 1 reposts · 8 replies Open on X →

FAR Labs ✓@FARLabsAI24 Jun

@FARLabsAI Idle GPU markets are always interesting, real test is demand vs actual sustained compute usage. 48 views · 1 likes · 0 reposts · 0 replies Open on X →

FAR Labs ✓@FARLabsAI4 Apr

@FARLabsAI Let’s collab contact me 🚀 199 views · 0 likes · 0 reposts · 0 replies Open on X →

FAR Labs ✓@FARLabsAI3 Apr

@FARLabsAI Love seeing networks put idle GPUs to work. This is exactly the kind of distributed compute infrastructure that powers next-level AI agents. Excited to see FAR Labs in action! 540 views · 5 likes · 0 reposts · 0 replies Open on X →

FAR Labs ✓@FARLabsAI3 Apr

Your GPU could do more than sit idle. Node registrations for FAR AI are now open. See your estimated output, submit your details, and secure your place early in the network. Register here: https://t.co/q4yYRBjFVe https://t.co/1M7zuGEdXH

0:41

194.8K views · 286 likes · 90 reposts · 14 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

5 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 100K–1M ผู้ติดตาม แสดงในวงกว้าง แต่มีผู้ชมตอบสนองน้อย.

ยอดดูมัธยฐาน3 802บัญชีนี้4 560ค่ามัธยฐานของ 100K–1M

การเข้าถึง, %2.21%บัญชีนี้1.63%ค่ามัธยฐานของ 100K–1M

การมีส่วนร่วม, %0.73%บัญชีนี้1.26%ค่ามัธยฐานของ 100K–1M

ตัวชี้วัด	บัญชีนี้	ค่ามัธยฐานของ 100K–1M	อัตราส่วน
ยอดดูมัธยฐานต่อโพสต์	3 802	4 560	0.83×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)	2.21%	1.63%	1.35×
อัตราการมีส่วนร่วม	0.73%	1.26%	0.58×

บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →

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

194.8K3 Apr

540

1994 Apr

4824 Jun

3.6K17 Aug

3.8K24 Aug

3.9K31 Aug

4.4K2 Sep

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.20%3 Apr

0.93%

0.00%4 Apr

2.08%24 Jun

0.73%17 Aug

1.00%24 Aug

0.28%31 Aug

0.16%2 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.26%.

What the audience does

Likes68.6%349 in total

Reposts18.9%96 in total

Replies6.5%33 in total

Quotes0.4%2 in total

Bookmarks5.7%29 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.