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۳۷ مقاله
Google researchers open sourced EnvHarness, a framework that dynamically adapts agent training environments to an agent's weaknesses without modifying the agent, environment, or evaluation system. According to the researchers, this approach can improve both learning effectiveness and efficiency while helping agents…
We put NVIDIA's new Vera CPU through its paces using the full TPC H workload on scale factor 1,000.
Brendan O'Leary discusses how real world AI adoption depends on building trust rather than chasing model benchmarks. Drawing from 40 trillion tokens of usage data, he explains the four rungs of the AI adoption ladder and analyzes key failure points in context construction, multi model routing, and feedback loops to…
By Emma Yanyang Kong , JJ Tan , Ishan Gupta , Lars Olds , Claire Campbell , David Fagnan , Ratna Kavuri , Rohan Gosain , Veli Balin , Minsu Jang , Louis Garcia Introduction Any team generating text with an LLM at scale hits the same problem. You cannot evaluate everything manually. You don't know which outputs are…
Clera migrated its 500 GB production database and 500+ tables to ClickHouse Managed Postgres overnight, cutting CPU usage from 100% to 10–20%.
TIN v1.0.6 supports stemming in 18 languages and is 2 3x as fast as the GA version
We’re launching ReviewBench, a benchmark for code review agents built on representative GitHub pull requests, multi source ground truth, calibrated evaluation, and production aligned metrics. The post ReviewBench: An open benchmark for AI code review appeared first on The GitHub Blog .
The app your agent built in minutes stays on the same Postgres from prototype to petabyte.
Hugging Face hosts hundreds of thousands of datasets, and DuckDB can read them directly, exactly where they are and without downloading anything, over the DuckDB protocol. This post looks at how the integration works and the scenarios where it works best.
Teams can now browse HashiCorp managed pre written policies, add them to a policy set, and apply common compliance guardrails directly in HCP Terraform.
Posted by Matthew McCullough, VP, Product Management, Android Developer When we first launched Android Bench, we built a rigorous foundation for evaluating how large language models (LLMs) assist developers with real world Android tasks. As AI models and agents rapidly evolve, we’ve been updating our methodology,…
Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task. The post How we make AI coding more cost efficient without sacrificing task quality appeared first on The GitHub Blog .
We just published a deep dive on how we keep AI coding costs under control at Uber, even as usage keeps climbing. Since February, weekly active employees across all functions on our agentic offerings grew 7x, and weekly agent requests grew 9.4x. Over that same stretch, total spend has relatively stabilized. We broke…
Ora on Vercel Front end, back end, and agent runtime on one platform Every major agent tested side by side on live sites Hundreds of commits a day from a 16 person engineering team Ora sends agents onto live websites with instructions to sign up for a product, integrate with it, and pay for it. Agents often fail, and…
Auto Intelligence now delivers above Fable level satisfaction at 68% lower cost. Here's how Compass and the task router choose models from real developer traffic.
Our open source benchmark for how well AI coding agents build with Supabase.
Part 3 of a series where we take public & 8220;token saver& 8221; add ons for coding agents and run the same paired A/B benchmark against each of them. Part 1 was the caveman skill (advertised −65%, measured −8.5%). Part 2 was rtk (advertised −60–90%, measured +7.6%). We ran 80 paired tasks to test the ponytail skill…
Does & 8220;rtk& 8221; reduce Claude Code token usage? Part 2 of a series where we take public “token saving” add ons for coding agents and run the same paired A/B benchmark against each of them. Part 1 was the caveman skill (advertised −65%, measured −8.5%). TL;DR: rtk advertised saving: 60–90%. Measured on real…
Agentic coding benchmarks are getting closer to real world software development. For Kotlin teams, the most important question is how reliably AI agents can complete end to end Kotlin tasks, from reading an issue to producing a solution that passes validation. We’re taking the first step in addressing that gap by…
