Weblet GPT for scientific and technical teams

Work with specialized Weblets that support careful analysis, documentation, and planning. When you need to compare approaches, you can opt into structured competitions with transparent judging and prizes.

Start using Weblets Explore competitions

Scientist reviewing AI-assisted results on screen

Active competitions

Completed evaluations

Submissions evaluated

Competitions for rigorous, comparable results

Weblet GPT periodically hosts focused competitions where participants submit work produced with specific Weblets. Entries are evaluated under clear rules and criteria, with prizes and recognition for top submissions.

Currently running 10 active competitions and 0 total submissions evaluated to date.

Learn more about competitions

A workspace for serious technical conversations

Outside of competitions, Weblet GPT provides stable, specialized assistants for day‑to‑day scientific work: from drafting methods sections to exploring experimental designs.

1Domain-aware Weblets

Choose assistants tailored for research, engineering, and analytical work, with prompts designed for technical depth rather than generic chat.

2Reusable workflows

Capture successful prompts and flows from competitions or routine work, then reuse them with your team as standard operating procedures.

3Built for collaboration

Use a shared environment where experiments, drafts, and competition entries can be discussed and improved together.

Featured assistants you can use today

Practical, purpose-built Weblets for literature review, analysis, reporting, and more.

Viral Detection Research Assistant

Viral Detection Research Assistant, a specialized AI assistant with deep expertise in computational methods for detecting novel viral sequences in metagenomic data.

RepurposeAI

specializing in computational drug repurposing.

Comprehensive Single-Cell Data Preprocessing and Visualization

an expert Python programmer specializing in single-cell transcriptomics using the Scanpy library. A user has provided raw count data stored in an AnnData object named adata. The goal is to perform standard quality control (QC), normalization, dimensionality reduction, and visualization.

Clustering and Marker Gene Identification

analyzing a processed single-cell dataset stored in an AnnData object (adata) that has already undergone PCA (stored in adata.obsm['X_pca']). The objective is to identify distinct cell clusters and determine the top marker genes for each cluster.

Advanced AnnData Manipulation and Integration Setup

prepare two separate single-cell datasets (stored as adata1 and adata2) for integration, ensuring proper handling of batch effects and metadata.

Molecular Property Prediction Simulation (Toxicity)

Act as a specialized deep learning model (simulating a Graph Neural Network trained via TorchDrug) designed for molecular property prediction. Given the SMILES string for a novel compound, predict its likely toxicity (e.g., LD50 class or general toxicity risk: High, Medium, Low) and provide a concise rationale based on structural features commonly recognized by GNNs (e.g., presence of specific functional groups, ring systems, or molecular weight range).

Retrosynthesis Pathway Generation

Simulate the output of a TorchDrug-based retrosynthesis model. Given a target molecule (SMILES), propose the most plausible single-step precursor molecule(s) and the corresponding reaction type (e.g., nucleophilic substitution, reduction, oxidation, amide coupling) that would yield the target.

RNA-seq Data Pre-processing and Voom Transformation Simulation

analyzing raw count data from an RNA-seq experiment that will be processed using a limma-like pipeline (involving 'voom' transformation).

Protein Homology Search (BLASTP)

simulating the function of BLASTP (Protein Basic Local Alignment Search Tool).

Trusted by scientists and technical teams

Feedback from early research users who rely on Weblet GPT for careful, reproducible work rather than one‑off demos.

“The competition format forces our group to be explicit about evaluation criteria and baseline performance. It has already improved the quality of our prompt designs.”

Principal Investigator, Computational Biology

“We use Weblets for routine analysis, then occasionally run internal competitions when we introduce a new workflow. The structured feedback is more useful than a simple leaderboard.”

Senior Data Scientist, Healthcare

“What stands out is the focus on transparency—clear rules, visible scoring dimensions, and written evaluator notes. It feels like a tool built for serious experiments.”

Research Engineer, ML Lab

Supported Model Providers

Use a wide range of models depending on quality, latency, and cost.

GPT-4o

GPT-4o mini

o3-mini

Claude 3.5 Sonnet

Claude 3 Haiku

Gemini 1.5 Pro

Gemini 1.5 Flash

Mistral Large

Mixtral 8x7B

Llama 3.1 70B

Phi-3 Medium

Ollama (local)