Arcee.ai ✓
@arcee_ai · San Francisco · joined 05 Sep 2023
Open models from an American research lab. https://t.co/6gcSwU5nk3
15 490Followers
450Following
904Posts total
1.5MViews on collected posts
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The Arcee API now includes GLM-5.3 and GLM-5.3-Flash.
Two weeks ago we opened the catalog beyond Trinity so you can run your favorite open models from one endpoint: Trinity, DeepSeek, Kimi, Inkling, and now GLM.
➡️ Use them in nac for long-running, hands-off work. https://t.co/
6K views · 59 likes
· 5 reposts · 4 replies
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nac v0.1.4 is out.
What’s new:
• Conversation forks
• Stop waits for cleanup
• Project sessions stay put
• Bug fixes
➡️ Upgrade now with: nac-web upgrade
https://t.co/LcNIHjvISZ
2.1K views · 24 likes
· 3 reposts · 1 replies
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nac v0.1.3 is out.
What’s new:
• Project-backed session organization
• Skill prompt expansion & composer autocomplete
• Sandboxed Git worktrees per session
• Vision-aware image reading
• Shiki syntax highlighting for code & diffs
➡️ Upgrade now with: nac-web upgrade
2.1K views · 25 likes
· 6 reposts · 1 replies
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Congratulations to the @radixark team on the launch of Miles v0.1!
We've enjoyed working with the framework and are glad to see another strong contribution to the open post-training ecosystem.
5.9K views · 62 likes
· 2 reposts · 6 replies
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@arcee_ai They said they wanted options and you gave them options. Amazing job, Arcee team.
We're biased towards K3, of course, but you know. Try 'em all.
569 views · 8 likes
· 0 reposts · 0 replies
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Credit to our team for shipping both today, and shoutout to @deepseek_ai, @Zai_org, @Kimi_Moonshot, and @thinkymachines for building great open models.
We build harness runtimes and open model infrastructure so you can own your AI stack.
1.1K views · 21 likes
· 0 reposts · 0 replies
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Today, this is a mix of internally hosted endpoints and external partners like @FireworksAI_HQ for Kimi-K3 (via their agreement with Kimi), allowing us to serve beyond our current internal capacity.
We do not monitor or train on API outputs, nor do our partners as a part of the
1.2K views · 30 likes
· 0 reposts · 2 replies
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Try it today with $5 in credits.
Sign up for the Arcee API and add a payment method to get $5 in API credits to test nac against the new catalog.
• Platform: https://t.co/sMH8yJHWbI
• Open models beta blog: https://t.co/t1pvz96loQ
• Nac repo: https://t.co/c1KqzWrHWr
• Nac
1.2K views · 28 likes
· 0 reposts · 2 replies
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To support these workloads, we are expanding our API beyond Trinity with the Arcee open models API beta.
Long-horizon tasks require different model strengths at different steps. Hosting a broader catalog gives you choice, while helping us learn how frontier open models perform
1.2K views · 21 likes
· 0 reposts · 1 replies
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Launch catalog & pricing per 1M tokens (input/output):
• deepseek-v4-flash-latest ($0.14 / $0.28)
• trinity-large-thinking ($0.25 / $0.80)
• thinkingmachines/inkling-small ($0.50 / $1.20)
• deepseek-v4-pro ($1.74 / $3.48)
• zai-org/glm-5.2 ($1.40 / $4.40)
•
1.2K views · 22 likes
· 0 reposts · 1 replies
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A central orchestrator plans work and dispatches it to isolated worker threads. Workers execute in clean contexts, make their edits, and return a concise summary—an episode.
The worker process exits and its raw context is discarded, while the episode persists. https://t.co/PWVp
3.1K views · 29 likes
· 0 reposts · 3 replies
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Threads can weave episodes between each other to share context without carrying historical noise. Nac also ships with an MCP server.
Interactive coding agents like Claude Code or Codex can use nac as a tool, delegating long-running background execution while keeping your https:/
1.6K views · 27 likes
· 0 reposts · 1 replies
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Today we're open-sourcing nac, an agent harness for long-running tasks, and launching the Arcee open models API beta.
Nac is available now under Apache 2.0 on GitHub, built for developers running complex, multi-step engineering workloads. https://t.co/Dl17GJdg8d
1.5M views · 603 likes
· 52 reposts · 46 replies
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Against accounts of the same size
13 posts from the last 90 days, next to the 10K–100K follower range. shown to more people than peers of the same size.
Median views1 589this account955median for 10K–100K
Reach, %10.26%this account3.52%median for 10K–100K
Engagement, %1.50%this account1.56%median for 10K–100K
| Metric | This account | Median for 10K–100K | Ratio |
|---|---|---|---|
| Median views per post | 1 589 | 955 | 1.66× |
| Reach (views ÷ followers) | 10.26% | 3.52% | 2.91× |
| Engagement rate | 1.50% | 1.56% | 0.96× |
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
1.5M13 Aug
1.6K
3.1K
1.2K
1.2K
1.2K
1.2K
1.1K
569
5.9K18 Aug
2.1K21 Aug
2.1K27 Aug
6K2 Sep
Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.05%13 Aug
1.76%
1.04%
1.84%
1.87%
2.50%
2.58%
1.94%
1.41%
1.19%18 Aug
1.50%21 Aug
1.34%27 Aug
1.15%2 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.56%.
What the audience does
Likes59.6%959 in total
Reposts4.2%68 in total
Replies4.2%68 in total
Quotes1.0%16 in total
Bookmarks30.9%497 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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