strategic projects @AfterQuery prev threw a leather ball around and tackled people @calpolyfootball
Joined April 2026
- Tweets224
- Following155
- Followers41
- Likes238
ian retweeted
@AfterQuery is partnering with @NVIDIA to help enterprises post-train Nemotron 3.5 Super VL.
The future of enterprise agents is open. 💚
Incredible work from our research team, truly high caliber talent at AQ.
Our research team post-trained an open model with @hebbia to approach Astra performance in finance, at 1/7th the cost.
Enterprise-specific frontier intelligence is becoming increasingly affordable.
ian retweeted
Our research team post-trained an open model with @hebbia to approach Astra performance in finance, at 1/7th the cost.
Enterprise-specific frontier intelligence is becoming increasingly affordable.
@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.
ian retweeted
@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.
ian retweeted
Proud of the team pushing what open models can do in finance.
@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.
Ownership trend continues
Every serious enterprise will soon own and control its own intelligence.
Huge congratulations to the @datadoghq team on Scout.
ian retweeted
Every serious enterprise will soon own and control its own intelligence.
Huge congratulations to the @datadoghq team on Scout.
Meet Scout 1.0, our first AI model built for investigating production incidents.
Scout matches frontier-model investigation quality at a fraction of the cost, which makes it practical to investigate more issues, earlier.
We trained Scout on realistic simulations built from real-world incidents and production telemetry, using reinforcement learning to reward accurate root causes backed by evidence.
Learn more and sign up for the preview: bit.ly/3TJknGO
NVIDIA 🤝 AQ
@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.
ian retweeted
@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.
Best tech week happy hour
!!!
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.
🐐 mike
One of the best decisions we’ve made early at @AfterQuery was building an in-house research team.
Knowing what great data looks like is table stakes. Raising the bar means showing labs their model’s losses, then providing data to close those gaps.
ian retweeted
One of the best decisions we’ve made early at @AfterQuery was building an in-house research team.
Knowing what great data looks like is table stakes. Raising the bar means showing labs their model’s losses, then providing data to close those gaps.
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.
ian retweeted
Progress is moving much faster than most people appreciate. So incredible to watch this happen in real time.
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
👀
US AI leadership should include strong open models that other teams can train, adapt, and deploy themselves.
Congrats @reflection_ai on the release of Beam! Excited to see what the AQ team builds on it.
ian retweeted
US AI leadership should include strong open models that other teams can train, adapt, and deploy themselves.
Congrats @reflection_ai on the release of Beam! Excited to see what the AQ team builds on it.
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
ian retweeted
Join to meet some insanely cracked people 👀
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.