Applied research lab curating data solutions to accelerate foundation model development.
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AfterQuery retweeted
@AfterQuery is hosting a panel with @GoldmanSachs during SF Tech Week to discuss the frontier of finance!
RSVP in the replies👇
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This is a historic moment for Korean sovereign AI and the open weights ecosystem writ large. We look forward to supporting Motif and the Korean AI ecosystem as they push the frontier of open intelligence.
Motif 3 leads its comparison set on τ³-Banking, and posts 94.7 on τ²-Bench Telecom, 74.9 on Terminal-Bench 2.1, and 76.2 on SWE-bench Verified. It’s also great to see @nvidia's open foundation put to use here - Motif 3's post-training was done via NeMo-RL.
In a world where it’s easy to assume that the AI race is already won, the team at Motif is proof that the field is open, and we’re still at the starting line.
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Congratulations to the @motif_tech team on the release of Motif 3!
AfterQuery is proud to have served as the sole data partner on this model.
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Congrats to the @WeAreLegora team on the release of the Legora BAR, a benchmark for agentic legal work built on 5,161 real law firm cases across 28 practice areas!
@AfterQuery helped QA the benchmark with Legora. Proud to work with their team on measuring the frontier of agentic legal work.
Link to the full blog post in the replies.
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Join our team.
The best operators at @AfterQuery chose us over Wall Street, and we’re hiring more of them.
They’re business or CS majors who left Goldman, JPMorgan, and Jane Street because they believe this is the most consequential work they could be doing right now.
If this is you, apply at the link in the comments or leave your email below.
Know someone cracked? Tag them - we pay $10,000 for successful referrals.
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Congrats to the @PoolsideAI team on Laguna S 2.1!
With thinking enabled, Poolside reports scoring 70.2% on Terminal-Bench 2.1.
Today we're releasing Laguna S 2.1, our most capable model to date.
It's a 118B total parameter Mixture-of-Experts model with 8B activated per token, a context window of up to 1M tokens, and thinking and no-thinking modes.
Capable enough to hold its own against models many times its size. Small enough to run on a single @NVIDIAAI DGX Spark.
Laguna S 2.1 is fully open under OpenMDW-1.1, with weights available today on @huggingface
poolside.ai/blog/introducing…
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Last Thursday, we hosted Poker Night at our SF office.
Researchers, founders, and builders joined the @AfterQuery team for 3 things:
1. Poker.
2. Drinks.
3. Lively conversation.
We're hosting more events soon.
Comment if you want to join the next one ↓
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See what your codebase could be worth: atrium.afterquery.com/
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See what your codebase could be worth.
Introducing Atrium by @AfterQuery.
We’re paying companies for their private codebases. Connect a repo, get an automated quality analysis in minutes, and receive a quote in days.
Link in the replies.
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Kimi K3: kimi.com/blog/kimi-k3
SpreadsheetBench 2, which AfterQuery helped build: arxiv.org/pdf/2606.29955
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Kimi K3 ranks #1 on @AfterQuery's SpreadsheetBench 2, surpassing Claude Fable 5.
An open weight model now outperforms all closed-sourced models. Read more in the Kimi K3 blog and SpreadsheetBench 2 paper linked below.
Congrats to the @kimi_moonshot team on the incredible model!
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Congrats to the @thinkymachines team on the release of Inkling!
Inkling is a fully open-weight (Apache 2.0) 975B-A41B MoE model with native support for text, image, and audio inputs, featuring a context window of up to 1M tokens. It leads all U.S. open-weight models on @ArtificialAnlys’s Intelligence Index.
We’ve tested it internally and seen strong performance across reasoning, coding, and agentic tasks.
Inkling has day-one support on Tinker, and we're excited to start running experiments with it.
AfterQuery is proud to have supplied data that helped train the model.
Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
thinkingmachines.ai/news/int…
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
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Excited to see ReactBench launch!
@Parth4apple from @AfterQuery worked with the ReactBench team on the task approach, quality assurance, and blog review.
Introducing ReactBench
A benchmark for coding agents on real React work
Models write bad React code - useEffect, slow performance, memory leaks
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The @AfterQuery team is in Korea this week for ICML, but the best conversations didn’t happen in the conference halls. They happened around a chef’s table.
This week, we partnered with @altosvc to bring together researchers, founders, and investors for an evening of great food, drinks, and meaningful conversation.
More than 100 people joined us for the evening, with a special menu prepared by Culinary Class Wars Lim hee-won and cocktails from Zest, named Asia’s Best Bar in 2025.
Huge thank you to @altosvc for partnering with us, and to all the attendees who made the event special. We look forward to hosting more events in the future that bring the AI research community together.
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