Results for “tf-binding”

Trains, evaluates, exports, and runs inference for FastFoundationStereo (FFS) stereo depth estimation models, a distilled variant of FoundationStereo with lower latency.

Reviews and repairs risky or unclear code with minimal validated changes, running a fixed-point loop until the diff is clean across two consecutive review cycles.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

Query BindingDB for measured protein-ligand binding affinities (Ki, Kd, IC50, EC50) via its keyless REST API or the full TSV download, searching by target (UniProt ID), compound (SMILES), or pathogen. Use when looking up experimental binding constants, profiling inhibitors of a protein target, doing lead optimization, polypharmacology analysis, or structure-activity relationship (SAR) studies; for curated bioactivity mining or drug-like compound library screening at scale prefer alterlab-chembl instead. Part of the AlterLab Academic Skills suite.

Track code changes with structured JSON records, an enforced state machine, and a session handoff format for AI continuity.

Recovers transform chains, hidden media payloads, mobile signing paths, and local trust boundaries in CTF challenges involving crypto, encoding, steganography, APK, IPA, and mobile trust-boundary analysis.

Fetches real-time London transit data from the TfL API, including Tube and bus arrivals, line status, disruptions, journey planning, and route information.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

Provides patterns and strategies for migrating imperative TypeScript code to fp-ts functional programming patterns, covering error handling, null checks, callbacks, dependency injection, and loops.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

Strict test-first gate only when explicitly requested or required by repository policy. Do not use for ordinary practical testing.

Guides minimal-diff code changes by defining contracts and invariants, choosing a stable seam, and proving the result with an executed check.

Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs), searching by TF name, species, or class, scanning DNA sequences for binding sites, and comparing matrices. Use when doing motif analysis, regulatory genomics, transcription factor binding prediction, or interpreting regulatory/non-coding GWAS variants. Part of the AlterLab Academic Skills suite.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Compile deep repository evidence into artifact-bound correctness doctrine, including authority/law/proof maps, owned invariants, failure archaeology, and knowledge routing, with an optional minimal repository-specific skill portfolio.

Trace mailbox abuse chains including OAuth consent, forwarding rules, delegate access, and message rerouting in a CTF sandbox environment.

TypeScript Type Guards

Orchestrate token-heavy research, coding, and testing by delegating bounded tasks to cheaper subagents while reserving Claude Fable for architecture, synthesis, and final review.

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Enforces a disciplined Red-Green-Refactor (TDD) workflow in TypeScript/Node.js. Use this whenever creating new features, fixing bugs, or migrating logic to ensure high-quality, verifiable implementations.

Multimodal Few-Shot Learning with Frozen Language Models

Hooks Android APKs to inspect signer logic, trace Java and native boundaries, bypass SSL pinning or root checks, and replay accepted mobile requests.

Infrastructure-as-code with Terraform/OpenTofu — resources, state, modules, plan/apply. Use when writing `.tf` files, managing cloud infrastructure with Terraform or OpenTofu, handling state and backends, or reviewing IaC code.

Monitor bonding curves, graduation progress, and trade analytics on Pump.fun

Delivers measurement-driven performance optimization for latency, throughput, memory, and tail behavior, with correctness preservation and regression guards.

Specialized CTF workflow for reverse engineering, malware analysis, DFIR, firmware, pwnable, and native exploit challenges under sandbox assumptions.

Fine-tune and align language models using reinforcement learning with TRL, including SFT, DPO, PPO, GRPO, and reward model training.

Replace exception-heavy TypeScript code with fp-ts Either and TaskEither for type-safe error handling as values.

Convert single-channel TIF directories to pyramidal OME-TIFF with SubIFDs

Monitors euthyroid adults with growth hormone deficiency (GHD) who are initiating GH replacement therapy for the development of central hypothyroidism (CH). Initiates levothyroxine (L-T4) if free thyroxine (fT4) falls below the laboratory reference range; triggers include GHD patient starting GH replacement.

heath-no-fluff

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

Write reliable, maintainable Go tests using table-driven tests, subtests, benchmarks, fuzzing, and golden files following TDD methodology.

Provides practical patterns for using fp-ts with React, including hooks, state management, form validation, and data fetching.

Harness TDD