AfterQuery

Today, humanity is shackled by scarcity of expertise. When expertise becomes infinitely scalable, humans will be freed to tackle problems we can't even conceive of today. Introducing @AfterQuery. We’re building a world where expertise is abundant. Domain by domain, profession by profession, AfterQuery is crafting datasets that encode excellence into forms that machines can learn. Data is the final frontier.

Paper: arxiv.org/abs/2601.20886

Introducing IDE-Bench! A multi-language, full-stack benchmark evaluating LLMs acting as autonomous IDE agents IDE-Bench assesses agents' ability to navigate, reason, and modify complex repositories using the same tools available in modern AI-native IDEs like Cursor Models tested from @AnthropicAI, @OpenAI, @Alibaba_Qwen, @GoogleDeepMind, @xai, @deepseek_ai, @Meta, and @cohere Check out the full results at ide-bench.com!

RT @spencermateega: Congrats to @ValsAI team for launching Finance Agent Benchmark v1.1! Proud that @AfterQuery could contribute finance e…

Our findings show that current models lack the ability to perform even the most basic tasks in high-impact, real-world domains like quantitative trading. We hope Market-Bench can serve as a shared framework to evaluate models’ understanding of trading strategies and code generation for quantitative finance. Excited to track how these capabilities evolve!

Leaderboard: marketbench.ai Paper: arxiv.org/abs/2512.12264

Introducing Market-Bench by @AfterQuery! The first-of-its-kind benchmark on LLMs for quantitative finance. We challenged models to attempt a frequent introductory quantitative trading task: coding an executable backtester from a natural-language strategy description and market assumptions. > 13 models build backtesting systems for directional, pair trading, and delta hedging strategies > evaluated on reliability (executable passes) and accuracy (MAE) across 5 attempts per strategy > real order book data with exchange delays and liquidity constraints > @xAI’s Grok 4 achieved the overall lowest mean MAE (deviation from the golden backtest), followed closely by @OpenAI’s GPT 5.2 > @AnthropicAI's Sonnet 4.5 and @AlibabaGroup's Qwen 3 Max at perfect executability but high MAE > Models from @Meta, @Amazon, @NVIDIA, and @Cohere continued to fail to produce executable backtesters Leaderboard & full paper below!

How far can vibe coding actually go? Introducing App-Bench by @AfterQuery, a benchmark for end-to-end web app development. We tested 6 production web apps on 10 coding agents from @OpenAI, @GoogleDeepMind, @AnthropicAI, @cursor_ai, @orchidsapp, @v0, @boltdotnew, @Replit, and

@cigdemoztabak_ 👀

what a grand opening! watching @AfterQuery

Really excited to have contributed to this sick creative vision and brought the @AfterQuery website to life 😎😎

@shrawberryy @sashabirukoff shrawberry 🙌

website and brand identity i did for @AfterQuery !! site design, visuals, copy, animations all by me - logo in collaboration with @sashabirukoff more details soon yippeeee

The frontier begets the frontier. I highly recommend reading @jaminball's latest Clouded Judgement article which spells out the AfterQuery thesis (thread)

Excited for UI-Bench to be the leading benchmark for UI/web design! Congrats to the @figma make team for claiming the #2 ranking

Introducing UI-Bench by @afterquery. The first and only rigorous eval of vibe coding tools. > 4,000+ blinded pairwise judgments > @orchidsapp, @figma make, and @lovable take the lead > @v0 and @replit ranked dead last > performance gaps = differences in LLM orchestration, prompting, design templates, and post-processing > link to our paper in the comments!

finally got the @afterquery team to touch grass

🇺🇸 249 years ago, America declared that innovation belongs to the bold. Today, we're writing the next chapter—one dataset at a time. At @AfterQuery, we believe AI's future isn't just about algorithms. It's about the human ingenuity that teaches machines to think, reason, and

Today, we’re pulling back the curtains. After collecting thousands of original, human-written coding problems, @AfterQuery created internal, contamination-free evals to test LLM code generation. No leaderboard tricks. No test-set leakage. Just raw task execution. Thread 🧵

Excited to share VADER, AfterQuery's new, human-evaluated benchmark for evaluating LLMs on real-world vulnerability handling! Paper: lnkd.in/g7EfAi2cAll data, evaluation tools & results are open-sourced at: lnkd.in/gYPUKwub [1/4]

Hey devs! Trae’s back in SF! We’re proud to be a lead partner of AGENTHACKS hackathon 📍 Join us in San Francisco and meet our amazing hosts and candidate. 🗓️ May 23–24 @ AGI House SF 💰 $10K+ in prizes, free AI credits, bounties & more! 🌐 Join now: agenthacks.org 🚀 Bring friends to build your awesome project together. 👉 BTW, check out the standout tech companies of Silicon Valley: 🎯 Hosted by: @AfterQuery (YC W25) @dexterity_ai (YC W25) @AGIHouseSF 🤝 Supported by: @Trae_ai – IDE powered by autonomous agents. @OpenAI – Creators of GPT, DALL·E, and Codex. @AnthropicAI – AI research company building safe systems like Claude. @MistralAI – Open frontier models built in Europe. @vercel – The frontend cloud for developers. @boltdotnew – Build stunning apps & websites, just by prompting @Shopify – Leading global e-commerce platform. @novita_labs – Unified LLM API platform for developers. @TensorPool – Serverless GPU access for AI workloads. @weHRTyou – HRT AI Labs driving AI for trading and infrastructure. @Dalus_io – AI-first hardware system design for aerospace & defense. @Guse_AI – Open-source infrastructure for agent systems. 🌐 Learn more: agenthacks.org #Trae #build_on_trae #AgentHacks #AIHackathon #SanFrancisco #AIagents #OpenSourceAI #YC