Zibra Labs
We build distributed compute clusters with the cheapest CPUs and GPUs across Hyperscalers and Neoclouds for AI. Our mission is to bring frontier-grade infrastructure to everyone. We're starting by building large scale high performance computing (HPC) clusters for quantitative trading firms to run parallel simulation workloads such as backtesting. Our technology generalizes to critical AI workloads such as post-training with reinforcement learning, fine-tuning, long-horizon agents with high tool
Y Combinator
yc-company-directory #465We build distributed compute clusters with the cheapest CPUs and GPUs across Hyperscalers and Neoclouds for AI. Our mission is to bring frontier-grade infrastructure to everyone. We're starting by building large scale high performance computing (HPC) clusters for quantitative trading firms to run parallel simulation workloads such as backtesting. Our technology generalizes to critical AI workloads such as post-training with reinforcement learning, fine-tuning, long-horizon agents with high tool
sourceZibra Labs is classified as AI agents from source descriptions and fund-directory context. Similar companies below are ranked from offline product facets and meaningful description overlap; category and source affiliation can only strengthen an existing product match. For lookup-created rows, the profile starts as pending enrichment until deeper source collection runs.
Local clusters rerank extracted product concepts and description terms. The generated graph remains the fallback for sparse descriptions: offline reciprocal product-similarity graph: extracted concepts + description terms + bounded category/affiliation boosts. Minimum fallback score: 0; max competitors: 6.
Companies similar to Zibra Labs
A deterministic local cluster built from offline product facets and meaningful description terms. Category and selected-institution affiliation provide bounded tie-breaks. Open any peer to continue exploring its cluster.
Cedana
match 65Cedana (YC S23) brings hyperscaler and frontier-lab orchestration capabilities for AI workflows. Our core capability is live migration for CPUs and GPUs workloads. This increases cost savings up to 80%, accelerates time to first token 2-10x, and enables stateful reliability of training jobs even through catastrophic GPU failures. We've integrated our solution into K8s, and support Kueue and Slurm for training distributed jobs, and Kserve for serving inference. OpenAI, Meta and Microsoft have fla
shared product terms: bring, hyperscaler, frontier, cpus; shared affiliation: Y Combinator
Zoa Research
match 62Historically, quantitative models are domain specific. Brilliant people spend their best years testing features, tuning hyperparameters, and iterating architectures within a narrow domain. But scale is the panacea: large models will find patterns people, and specialized models, could not. Forecasting generalizes. Zoa trains cross-domain event forecasting engines. *Automating Iteration* LLMs—embedded in multi-agent optimization loops and evaluated against fixed policies—can automate the build-tes
shared extracted concept terms: quantitative; shared product terms: large, scale, quantitative, generalize; same category: AI agents; shared affiliation: Y Combinator
Polymath
match 60We’re heading towards a future where AI agents will be able to perform useful work over long horizons, with little or no human supervision. To increase the reliability, performance, and safety of autonomous agents, they must be trained in simulation environments that reflect the real world. Polymath builds simulated worlds for agents to practice and learn through experience. We're a team of researchers and engineers from UC Berkeley, Hume AI, Plaid, and Amazon. We have years of experience post-t
shared product terms: re, long, horizon, performance; same category: AI agents; shared affiliation: Y Combinator
Synthetic Sciences
match 54We're building the infrastructure for the era of autonomous science. Our core thesis: scientific foundation models with real research "taste" require two things, built in parallel. A product that captures high-quality organic research-process data at scale, and the training and research to turn that data into models with genuine scientific "taste". We're building both.
shared product terms: re, infrastructure, parallel, high; shared product theme: developer infrastructure; shared affiliation: Y Combinator
Synth
match 48Choose a coding agent harness, model, and task dataset and optimize context and prompts to get the best performance for long-horizon tasks
shared extracted concept terms: long, horizon; shared product terms: performance, long, horizon; same category: AI agents; shared affiliation: Y Combinator
RedBrick AI
match 46RedBrick AI's mission is to accelerate the adoption of artificial intelligence in radiology by building world-class software infrastructure. Currently, we are focused on helping radiology AI teams prepare high-quality datasets to train their algorithms. Radiology data, such as CT and X-ray scans, is an incredibly important source of truth in healthcare delivery. In fact, over 90% of all healthcare data is medical imagery! However, the global radiology workforce is overburdened. In the UK, for ex
shared product terms: mission, infrastructure, high, train; shared product theme: developer infrastructure; shared affiliation: Y Combinator