Higgs
Summary
Higgs is a productivity tool with no disclosed features, approach, or positioning.
No information exists on what Higgs actually does or the workflow problems it targets. It sits in the productivity space yet offers no headline differentiator or concrete pricing figure. The listing confirms it is not open source but provides nothing further on costs or mechanics. The largest catch is the complete absence of usable details, leaving any assessment dependent on outside sources that are not referenced here.
Bottom line: Skip until documentation appears; no basis exists for further evaluation.
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Most email AI tools assume cloud inference is acceptable. For Proton Mail users, it isn’t — that’s the entire premise of choosing Proton. higgs is a local-first CLI that sits between Proton Mail Bridge (running on 127.0.0.1) and a self-hosted model, exposing classify, search, archive, and Q&A commands. Mail flows from Bridge to the model and back without crossing a network boundary. The vendor states every byte stays on 127.0.0.1, with no cloud inference, no cloud API keys, and no telemetry of any kind.
The differentiating feature is not privacy alone — it’s the agent contract. higgs is engineered so that agents driving it don’t have to parse –help prose, regex unstructured output, or branch on English error strings. The docs describe a `higgs schema` command that returns a JSON manifest of every subcommand: flags, arguments, stdout format, and an enum of exit codes. NDJSON output streams one object per line and terminates with a `{“type”:”summary”}` object, so an agent knows precisely when a stream ends without heuristics or timeouts. Errors carry typed keys, not freeform strings.
This fits tightly scoped, privacy-sensitive workflows: classifying an inbox with local labels, piping search results into archive commands, running Q&A against local mail data. The wall appears when the local model you host isn’t capable enough — classification confidence drops, and higgs has no fallback to a stronger model without you changing the backend. Teams who reach that ceiling either upgrade their hardware, swap to a larger self-hosted model, or accept that the privacy constraint rules out the accuracy a cloud model would give them. There is no hosted tier and no cloud option; that is a deliberate architectural choice, not a missing feature.
Installation is via Homebrew, `go install`, or signed release tarballs with cosign signatures and an SBOM. Bridge credentials live in the OS keyring (macOS Keychain, Windows Credential Manager, libsecret) with an AES-256-GCM encrypted-file fallback, so nothing sensitive enters the model’s context window. The default model is Gemma 4 via Ollama; any OpenAI-compatible server works via the `PM_LLM_BACKEND=openai` environment variable. The project is Apache-2.0 licensed with zero telemetry — no analytics, no crash reporting, no update pings.