Stay updated with the latest developments in Artificial Intelligence
An open-source, local, read-only MCP server that checks your request before Google quietly hands back nothing Ask the Google Search Console API for clicks filtered by country us and you get zero rows. No error, no warning, just nothing. It's not the only one.
Noisy evaluators have limited value for production decisions that hinge on judging a single output (e.g. guardrails).
Most engineers know they need to evaluate models on their specific task because performance varies. But so does cost: the same input can cost several times more on one provider than another, even when their list prices look similar.
We’re building an automated AI engineer that dramatically improves performance of LLM agents on every single benchmark we’ve tried.
Developing LLM applications involves numerous design decisions: which model to use, how to structure prompts, what temperature and sampling parameters to set, etc.
We benchmarked OpenAI’s supervised fine-tuning (SFT) and reinforcement fine-tuning (RFT) workflows on three tasks:Data Extraction: RFT improved performance with limited data, though SFT on a larger dataset achieved better results at a still much lower cost.Agentic Coding: RFT improved performance…
We’re excited to announce a $7.3M seed round to support the development of 11.5KTensorZero, an open-source stack for industrial-grade LLM applications.
Every LLM application builder faces a fundamental tradeoff: large models deliver superior performance at higher costs, while small models are affordable but often underperform. This choice becomes critical when scaling to production workloads where inference costs can quickly explode.
AI coding assistants are performing an ever-increasing share of software engineering work. Generic LLM coding benchmarks fail to capture the effectiveness of a coding assistant for specific use cases and workflows.
We wired 11.5KTensorZero between Cursor and the LLMs to see every token fly by… and bend those API calls to our own will.
Crafting good system prompts is one of the trickiest parts of working with language models — especially when you’re dealing with complex, multi-step tasks. Doing it manually doesn’t scale, and the results are often hit-or-miss.
One of the largest European banks used 11.5KTensorZero to build an LLM-powered changelog generator for GitLab merge requests. They optimized model quality, deployed fully on-premise, and iterated quickly — without needing a team of ML engineers.
LLM engineering is a young field. Practitioners suffer from a lack of clarity about their problem space, resulting in tools and applications that are hard to reason about, manage, and iterate on — slop.
Showing 12 of 12 stories
You're all caught up.
Get the top AI news and tools straight to your inbox every week.
Weekly · AI news from TensorZero Blog
Join 15,000+ AI enthusiasts
