Blog
Research, comparisons, and updates on finetuning and inference — plus product and company news from the Maniac team.
Autonomously Beating GPT-5.2 and Gemini 3 Pro in Prediction Accuracy, with 30x Cheaper Inference for Commerce AI
Our autonomous pipeline took production traffic hooks as input and output frontier-beating Small Language Models — no ML team required. Here's how it works, and why it generalizes to any predictive task.
Limitations of Together and Fireworks finetuning (and why autonomous finetuning can win)
Managed finetuning reduces setup time, but it can bottleneck iteration and portability. Here’s what breaks in practice—and how autonomous finetuning can lower total cost, including inference.
Inference stacks compared: vLLM, TGI, TensorRT-LLM, llama.cpp, and SGLang
A practical guide to choosing an inference stack based on latency targets, model size, and operational tradeoffs.
The finetuning platform landscape: how teams compare providers
A neutral map of the finetuning ecosystem, with decision criteria that apply across managed providers and open-source stacks.
Maniac raises $2M pre-seed round from PearX and a16z / speedrun
Maniac raised $2M pre-seed from PearX and a16z Speedrun to build a self-optimizing AI dev platform for finetuning, optimization, and automatic routing.
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