AfterQuery

Applied research lab curating data solutions to accelerate foundation model development.

[AfterQuery @AfterQuery]

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!

[AfterQuery @AfterQuery]

Congrats to @spectorb @amspector100 @aidanmantine and the @flappyairplanes team on the $1B Series A!

Excited to see what their work on GPU systems and learning algorithms unlocks next ✈️

[Flapping Airplanes @flappyairplanes]

We’ve spent the past year developing new primitives for GPUs. We believe this enables new algorithms to change how machines learn, open new markets, and dramatically reshape AI’s cost structure.

We have raised a $1 billion Series A, co-led by Index and Lux.

[Spencer Mateega @spencermateega]

@AfterQuery is partnering with @NVIDIA to help enterprises post-train Nemotron 3.5 Super VL.

The future of enterprise agents is open. 💚

[AfterQuery @AfterQuery]

@NVIDIAAI @hebbia Learn more: https://t.co/fcGB9BiVnN

[AfterQuery @AfterQuery]

@AfterQuery just moved an open model past the cost-accuracy frontier in finance.

We post-trained @NVIDIAAI Nemotron 3.5 Super VL on expert credit tasks with @hebbia.

Perfect solved: 47% to 74%. That beats Sol 6, and lands within 3 points of Astra 6, at about 1/7 the cost.

Link to the full case study in replies.

[AfterQuery @AfterQuery]

@spencermateega @NVIDIAAI Join us.

[Spencer Mateega @spencermateega]

@AfterQuery’s throwing a sftw happy hour with @NVIDIAAI tonight at 6:30pm.

reply for an invite.

[AfterQuery @AfterQuery]

Read more about our experiment using SkyRL here: https://t.co/LOVN81MrDE

To learn more about our tasks and reward design, reach out to us at [email protected]

[AfterQuery @AfterQuery]

Not all coding data is created equal.

Using only 500 tasks from @AfterQuery's SWE agent dataset, our internal post-training team was able to improve Qwen3.8-27B-Medium by 11.3 points in only 15 steps of GRPO.

Read more about our training experiment in the blog post linked in the comments.

[Spencer Mateega @spencermateega]

Working on AI research, post-training, or deployment? Come meet the @AfterQuery Team during SF Tech Week.

Wed 10/7: Frontier of Finance with Goldman Sachs
Fri 10/9: Rooftop happy hour

We'd love to see you there. Details + RSVPs below.

[Michelle Choe @_michellechoe]

@AfterQuery is hosting a panel with @GoldmanSachs during SF Tech Week to discuss the frontier of finance!

RSVP in the replies👇

[Spencer Mateega @spencermateega]

People still underestimate how large the data market will become.

Better models do not reduce demand for data. They increase the number of useful things models can learn.

The frontier keeps moving, so the data has to keep getting harder.

[Michelle Choe @_michellechoe]

last nights spread for @AfterQuery‘s 3rd poker night

now hosting these biweekly♠️

[Spencer Mateega @spencermateega]

fluffy work trialers at the @AfterQuery office today

[Michelle Choe @_michellechoe]

If you're free tomorrow night, @AfterQuery is hosting a researcher poker night tmr @ 8:30pm

rsvp for deets👇

[Spencer Mateega @spencermateega]

The @AfterQuery post-training team helped patch a critical LoRA weight-loading bug in SkyRL today.

Check out our PR in the replies.

[Spencer Mateega @spencermateega]

Gemini 3.8 set SOTA on DeepSWE at 9am. Muse Spark 1.3 took it back at 12:30pm.

Incredible work from the MSL coding team.

[AfterQuery @AfterQuery]

@spencermateega ⚡️

[Spencer Mateega @spencermateega]

Legal and finance are two of the highest-impact domains to crack.

3.8 Flash now leads both Harvey’s Legal Agent Benchmark and Vals Finance Agent v2. Huge congrats to GDM ⚡️

[Spencer Mateega @spencermateega]

New @nvidia DGX Spark just came in.

Hyped to put it to work and eliminate inference costs on a few of my workflows.

[AfterQuery @AfterQuery]

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.

[AfterQuery @AfterQuery]

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.