KDnuggets ✓

@kdnuggets · San Juan, PR · joined 05 Feb 2009

Mining insights in data science, machine learning, analytics, and AI • Edited by @mattmayo13 • KD stands for "knowledge discovery"

219 483Followers

355Following

86 230Posts total

13.6KViews on collected posts

โพสต์ล่าสุด

KDnuggets ✓@kdnuggets9 Sep

A six-stage pipeline that checks its numbers before calling anything an answer. https://t.co/C5EqJWeO3N 2.6K views · 1 likes · 1 reposts · 0 replies Open on X →

KDnuggets ✓@kdnuggets9 Sep

Learn seven engineering techniques to train large language models on consumer GPUs without running out of memory. https://t.co/H9FLgsEqvc 3.1K views · 4 likes · 0 reposts · 0 replies Open on X →

KDnuggets ✓@kdnuggets9 Sep

@kdnuggets The interesting bit isn't the framework, it's that agents don't need the abstractions we built for human memory. Most of a modern stack exists so people can hold less in their head. An agent reads the whole file every time. 9 views · 1 likes · 0 reposts · 1 replies Open on X →

KDnuggets ✓@kdnuggets9 Sep

Over the past several years, I have worked through three successive generations of intelligent retrieval systems, each solving problems the previous generation could not. Here is what I have learned. https://t.co/kUv3lJYBeD 2.3K views · 4 likes · 0 reposts · 0 replies Open on X →

KDnuggets ✓@kdnuggets9 Sep

@kdnuggets @kdnuggets The recent Astra momentum has brought a fresh audience to 3D, from stylized scenes to compact game concepts. Given your focus on open-source discovery, this felt especially relevant. I made a public repository that documents interesting Astra creations and 35 views · 0 likes · 0 reposts · 0 replies Open on X →

KDnuggets ✓@kdnuggets8 Sep

The way we build interfaces is changing. As AI agents write more of our code, the tools we use to render that code may need to change too. https://t.co/hGyJVbB80V 2.9K views · 3 likes · 1 reposts · 2 replies Open on X →

KDnuggets ✓@kdnuggets8 Sep

Explore five free ways to access AI coding agents, proprietary coding models, and open-weight models without paying for expensive subscriptions or GPUs. https://t.co/TEmJLTd8Hp 2.6K views · 2 likes · 0 reposts · 0 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

7 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 100K–1M ผู้ติดตาม ต่ำกว่าบัญชีรุ่นเดียวกันทั้งการเข้าถึงและการมีส่วนร่วม.

ยอดดูมัธยฐาน2 599บัญชีนี้4 317ค่ามัธยฐานของ 100K–1M

การเข้าถึง, %1.18%บัญชีนี้1.68%ค่ามัธยฐานของ 100K–1M

การมีส่วนร่วม, %0.13%บัญชีนี้1.35%ค่ามัธยฐานของ 100K–1M

ตัวชี้วัด	บัญชีนี้	ค่ามัธยฐานของ 100K–1M	อัตราส่วน
ยอดดูมัธยฐานต่อโพสต์	2 599	4 317	0.60×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)	1.18%	1.68%	0.70×
อัตราการมีส่วนร่วม	0.13%	1.35%	0.10×

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

2.6K8 Sep

2.9K

359 Sep

2.3K

3.1K

2.6K

Last 7 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.08%8 Sep

0.21%

0.00%9 Sep

0.17%

22.22%

0.13%

0.08%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.35%.

What the audience does

Likes34.1%15 in total

Reposts4.5%2 in total

Replies6.8%3 in total

Bookmarks54.5%24 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.

โพสต์ล่าสุด

เทียบกับบัญชีขนาดเดียวกัน

Growth & engagement

Views per post

Engagement rate per post

What the audience does

บัญชีที่คล้ายกัน