Promptel
Prompt DSLDeclarative prompt engineering: a small DSL (and equivalent YAML) for LLM prompts, with typed params, technique blocks, and a provider abstraction over OpenAI, Anthropic, and Groq.
JavaScript LLM & Agents MIT
The Portfolio
Every product lives at its own subdomain and ships its own docs. Grouped by the problem it solves.
Prompts, agent protocols, routing, and client-side LLM plumbing.
Declarative prompt engineering: a small DSL (and equivalent YAML) for LLM prompts, with typed params, technique blocks, and a provider abstraction over OpenAI, Anthropic, and Groq.
A Python CLI and library that extracts prompts from your codebase, versions them in .prompt files, and pins each with a sha256 hash in a prompts.lock file — "package.lock for AI prompts".
The Meaning Protocol Layer: contracts, quality measurement (QoM), and BLAKE3-hashed audit trails for AI agent communication over MCP, A2A, and HTTP.
An OpenAI-compatible LLM gateway that routes across OpenAI, Anthropic, Google, Ollama, Cohere, and Mistral, tracks per-prompt analytics in DuckDB, and reruns MIPROv2 optimisation against captured traces.
A lightweight framework for building AI-powered browser extensions — plugs into any OpenAI-compatible provider and ships a CLI scaffold, response cache, per-provider rate-limit queue, and settings panel.
A Python library and CLI that turns a text file into a podcast-style video via a six-stage resumable pipeline: script (GPT-4), narration (BARK), transcription (Distil-Whisper), image prompts, stills (FLUX), and FFmpeg composition.
Embeddings, vector search, answer engines, and deliberative search.
A Rust library and REST API that generates text embeddings locally with FastEmbed (22+ models: BGE, MiniLM, Nomic, E5) and caches them in SQLite — no external embedding API calls, no per-token billing, no rate limits.
A lightweight vector search library for Rust that pairs SQLite metadata storage with USearch (HNSW) similarity search, exposing a small Actix-web HTTP API. Experimental (v0.1.x).
A Rust web service that turns search into an answer engine: it sits in front of SearxNG, fetches top result URLs, chunks and embeds them, and returns a JSON map of chunk_id → (source_url, text). Infrastructure, not a turnkey RAG product.
An open-source AI search engine that drafts an answer and a knowledge graph before it queries the web — a fixed six-step pipeline, no agent loop, with every intermediate artefact exposed.
Sandboxing, NUMA-aware runtimes, and correctness oracles.
An OCI-compatible Zig container runtime that takes a selective-denial approach to isolation: namespaces, all 41 capabilities dropped, Landlock, seccomp-BPF, cgroups v2 — a single static binary, no daemon.
A NUMA-first runtime for latency-critical Rust applications: explicit control over memory placement, thread pinning, and per-node work scheduling, with topology discovery and locality observability.
A GPU-less correctness oracle for deep-learning kernels: catches silently-wrong CUDA/Triton kernels with an fp64 reference, op-schema-aware adversarial fuzzing, per-op calibrated tolerances, and static PTX/SASS lint.
Constraint solving, ranking, trading signals, and simulation.
Natural-language to constraint solver: describe optimisation problems in English, an LLM translates to Answer Set Programming, and Clingo searches exhaustively for a valid solution — with a typed self-repair loop.
A Python package and FastAPI service for pairwise comparison ranking: a UCB1 multi-armed bandit picks pairs adaptively and Elo produces the ranking output.
The Quant’s Compiler: a type-safe DSL and Rust-native runtime for quantitative trading strategies — compile alpha ideas to deterministic backtests and ship the same binary to production.
An open, deterministic discrete-event simulator and RL benchmark for task allocation in Robotic Mobile Fulfillment Systems — Kiva-style fleets of AMRs moving pods to pick stations.
Metadata-private messaging, key hygiene, and SQL data protection.
Ship AI features without shipping your API keys: a self-hosted Node proxy plus browser SDK that lets client-side apps call OpenAI-compatible APIs behind fingerprinting, short-lived JWT sessions, and per-fingerprint rate limits.
A developer toolkit that adds AI-powered validation, PII detection, and continuous pattern learning to SQL workflows — an AI co-pilot for analytics engineers that keeps warehouse data local.
A Python privacy-protocol library for authenticated, metadata-private one-to-one messaging: Schnorr zero-knowledge proofs, per-recipient blinded pseudonyms, AES-GCM delivery, and (ε,δ)-differentially-private cover traffic.
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We welcome academic collaborators, research partners, and funders who believe the hardest problems in AI deserve open, rigorous, reproducible investigation.