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Gradient

@Gradient_HQ · joined 22 May 2024

Open infrastructure for open intelligence. Lattica · Parallax · Echo

700 303Followers
77Following
499Posts total
554.7KViews on collected posts

Latest posts

Can web agents learn through imagination? DynaWeb: Model-Based Reinforcement Learning of Web Agents has been accepted to EMNLP 2026 Main Conference. Training web agents with online RL means costly, risky interaction with the live web. DynaWeb learns a Web World Model instead, 26.3K views · 200 likes · 40 reposts · 57 replies Open on X →
Proud to share MERA: joint work from a group of brilliant researchers including our very own. It evolves small models with SkillBook + routing + adapters so they take on more of the agent workload over time. Paper + code 👇 38.1K views · 222 likes · 106 reposts · 71 replies Open on X →
🚀 MERA: Model Evolution and Routing with Skill Adaptation for Agentic Systems at Scale How can we make small models stronger for on-device agent deployment? MERA uses stronger models to guide an iterative loop of RL/GRPO, skill learning, and router optimization. Student https:/
33.4K views · 16 likes · 3 reposts · 3 replies Open on X →
Agents are impressive. Multi-agent systems are where things get interesting. As AI systems evolve from single agents into networks of specialists, a new question follows: How do agents decide who does what? That’s a problem we’ve been exploring with Symphony. Symphony brought 48.6K views · 334 likes · 51 reposts · 109 replies Open on X →
@Gradient_HQ @MiniMax_AI @tryParallax @GA_agent_ai Wait… what is this magic? 👀 1.1K views · 3 likes · 0 reposts · 0 replies Open on X →
@Gradient_HQ @MiniMax_AI @tryParallax @GA_agent_ai https://t.co/x0s89GUFA6
GIF
729 views · 4 likes · 0 reposts · 1 replies Open on X →
This is why we built Parallax @tryParallax As open models get stronger and agents get more capable, local-first AI becomes much more than a privacy story. It becomes a new way to build with open intelligence that stays close to your data, your tools, and your machines. 51.5K views · 277 likes · 36 reposts · 64 replies Open on X →
@Gradient_HQ @MiniMax_AI @tryParallax @GA_agent_ai Woww. Zero to hero. Local AI feel real! 1.4K views · 2 likes · 0 reposts · 0 replies Open on X →
This is a glimpse of where local AI is heading and we are glad to be part of it. Really impressive work by all the teams involved @Gradient_HQ, @tryParallax, and @GA_agent_ai 130.5K views · 993 likes · 56 reposts · 27 replies Open on X →
A self-evolving agent + a 428B model + 3 Macs = ? Your own AI lab. We ran @MiniMax_AI M3 locally with @tryParallax, right on our desk. Then @GA_agent_ai took over to create a 5-stock portfolio and write it to disk. No cloud. No API bills. Nothing left the machine. Wild to ht
1:06
153K views · 471 likes · 177 reposts · 125 replies Open on X →
https://t.co/bDtBJaSdQr
69.9K views · 545 likes · 55 reposts · 157 replies Open on X →

Against accounts of the same size

4 posts from the last 90 days, next to the 100K–1M follower range. shown to more people than peers of the same size.

Median views35 768this account5 983median for 100K–1M
Reach, %5.11%this account1.49%median for 100K–1M
Engagement, %1.07%this account0.93%median for 100K–1M
MetricThis accountMedian for 100K–1MRatio
Median views per post35 7685 9835.98×
Reach (views ÷ followers)5.11%1.49%3.43×
Engagement rate1.07%0.93%1.16×

Others in this range →   Compare with another account →   How these benchmarks are built →

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

69.9K1 May
153K29 Jun
130.5K
1.4K
51.5K
729
1.1K
48.6K7 Aug
33.4K12 Aug
38.1K13 Aug
26.3K10 Sep

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.12%1 May
0.53%29 Jun
0.83%
0.14%
0.76%
0.69%
0.27%
1.07%7 Aug
0.07%12 Aug
1.08%13 Aug
1.17%10 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.

What the audience does

Likes63.8%3 067 in total
Reposts10.9%524 in total
Replies12.8%614 in total
Quotes2.8%134 in total
Bookmarks9.8%470 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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