Warp ✓
@warpdotdev · joined 01 Jun 2021
The open platform for automating development. Infrastructure to build, measure, and interact with agents across the SDLC
58 715Followers
3Following
3 232Posts total
204.2KViews on collected posts
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@warpdotdev Love this custom benchmarks from your own agent runs is such a powerful idea
36 views · 1 likes
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You can get early access at https://t.co/Y3HNH4Wn1K 🔖
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Factory benchmarks let you build your own model bench using your past coding agent runs.
- Mirrors your environment, secrets, and MCPs for each run
- Scores output on judging criteria you define
- Generates a report with model recs using cost vs. quality
Here's how it works: ht
70.9K views · 55 likes
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Public benchmarks are easy to game. The best solution: benchmark on your own data
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You can try benchmarks as part of Warp Factories. For select folks, we are offering $10k of factory usage to help get you started.
https://t.co/1dVW2ke7xj 🔖
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We used a factory benchmark to reduce our own cost-per-PR by 63%, from $80 per PR down to $30.
Here's a full walkthrough of the benchmark: https://t.co/84OeSmqwMk 👀 https://t.co/OLEVq6tkKq
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You can use results to inform your default models, or define custom model routers to let your agent pick the right model for each task.
For example, you may benchmark frontend UI changes in your codebase, and define a routing rule to always send those tasks to Grok 4.6 medium ht
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Then, review your benchmark report. This includes the overall model recommendation + a performance breakdown across tasks.
You can see a pareto graph along each scored dimension (ex: cost vs. quality), and how models performed on each task across your scoring criteria https://t.
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With your benchmark defined, you can customize the models and scoring criteria you want to test.
Use built-in scorers like correctness, code quality, and efficiency, or define custom rubrics for metrics your team cares about (Figma mockup alignment, e2e test quality, etc) https:
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Benchmarks are built from your team's past agent runs.
All agent runs are tracked in Warp Factories, letting you build a representative sample to test against. You can select tasks and define judging criteria yourself, or build a sample agentically using the Warp Factories MCP h
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Introducing Factory Benchmarks: The first model bench generated from your own coding tasks.
Measure, test and improve coding agents by replaying past agent runs, and cut cost-per-PR by 63%+
Here’s how it works 🧵 https://t.co/uZXVdRt8LZ
67K views · 181 likes
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Agents need a skill to improve their skills. Here's what that looks like:
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Rispetto ad account della stessa dimensione
16 post degli ultimi 90 giorni, accanto alla fascia di 10K–100K follower. sotto i pari sia per copertura sia per interazione.
Visualizzazioni mediane1 271questo account924mediana per 10K–100K
Copertura, %2.16%questo account3.62%mediana per 10K–100K
Interazione, %0.53%questo account1.52%mediana per 10K–100K
| Metrica | Questo account | Mediana per 10K–100K | Rapporto |
|---|---|---|---|
| Visualizzazioni mediane per post | 1 271 | 924 | 1.38× |
| Copertura (visualizzazioni ÷ follower) | 2.16% | 3.62% | 0.60× |
| Tasso di interazione | 0.53% | 1.52% | 0.35× |
Altri account di questa fascia → Confronta con un altro account → Come sono costruiti questi parametri →
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
67K3 Sep
1.3K
1.1K
654
630
1.2K
1.1K
229
9.2K
2.9K
70.9K
966
364 Sep
1.8K
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
0.35%3 Sep
0.90%
0.67%
0.92%
0.95%
0.67%
0.45%
0.87%
0.50%
0.55%
0.10%
0.10%
2.78%4 Sep
0.39%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes61.8%458 in total
Reposts4.0%30 in total
Replies6.2%46 in total
Quotes3.2%24 in total
Bookmarks24.7%183 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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