Alternatives
Products that do what Agenomics does
Evaluate AI Agents. Build Real-World Evidence.
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17AA
2025 · ashenfad.github.io
- 18BA
Hey HN, For the last couple of months, we have been building an AI agent for continuous statistical analysis, and we're looking for feedback while it's still early in development. We call it BIGWIG - an autonomous agent that is specialised, and very good at, performing advanced statistical analysis, through long traces of iteration and reasoning. As it builds statistical models it also "emits" outputs back to the user that you can then interact with, iterate on and schedule for follow up analysis. While we're still in BETA, we've launched a public analysis site that showcases some of the…
2025 · askbigwig.com
- 19FA
Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…
Jul 2026 · github.com
- 20FF
I built Hermes, an open-source Python framework for multi-agent financial research. Most AI “equity research” demos stop at generating text. In practice, real workflows require pulling structured XBRL financials from SEC filings, extracting labeled sections like MD&A and Risk Factors, merging macro and market data, building actual Excel models with formulas, and generating investment memos in Word or PDF. Hermes is designed to handle that full pipeline end to end. It includes 35 financial data tools covering SEC EDGAR (via edgartools), FRED, Yahoo Finance market data, and RSS-based financial…
Feb 2026 · github.com
- 21AR
Feb 2026 · trypillar.com
- 22MD
We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…
2025 · github.com
- 23AO
Mar 2026 · nickakre.github.io
- 24AD
Hey HN, as a former data analyst, I’ve been tooling around trying to get agents to do my old job. The result is this system that gets you maybe 80% of the way there. I think this is a good data point for what the current frontier models are capable of and where they are still lacking (in this case — hypothesis generation and general data intuition). Some initial learnings: - Generating web app-based reports goes much better if there are explicit templates/pre-defined components for the model to use. - Claude can “heal” broken charts if you give it access to chart images and run a…
Mar 2026 · rubenflamshepherd.com
Ranked by how close each launch is in meaning, then by votes. Refine with a description →